Commonwealth Agricultural Bureaux International (CABI)www.cabi.org and www.angrau.net
ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITYRajendranagar, Hyderabad - 500 030.
J. Res. ANGRAU Vol. XXXIX No.4 pp 1-134, October -December, 2011
ISSN 0970-0226
J. Res. ANGRAU Vol. XL No. 3 pp 1-146, July- September 2012
The Journal of Research ANGRAU(Published quarterly in March, June, September and December)
Dr. T. PradeepPrincipal Scientist(Breeding),Maize Research Station,ARI Campus, Rajendranagar,Hyderabad.
Dr. R. SudhakarSenior Scientist (Plant Pathology),Seed Research & Technology Centre,ANGRAU, Rajendranagar, Hyderabad.
Dr. M. V. RamanaSenior Scientist (Agronomy),AICRP on Integrated Farming Systems,Water Technology Centre,College of Agriculture, Rajendranagar,Hyderabad.
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Dr. T. RameshAssociate ProfessorDept. of Plant PhysiologyCollege of Agriculture, Rajendranagar,Hyderabad.
Dr. I. Sreenivas RaoProfessor and Head, Dept. of Extension Education,ANGRAU, Rajendranagar, Hyderabad.
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CONTENTS
PART I : PLANT SCIENCE
Effect of invigoration treatments on seed germination and seedling vigour in 1carry-over onion seed (Allium cepa L.)B. SOMRAJ, K. RAVINDER REDDY, K.V. RADHA KRISHNA and D. SRIHARI
Evaluation of different organic nutrient sources and varieties for organic rice 6(Oryza sativa L.) productionR. JAGADEESHWAR, N. RAMA GOPALA VARMA, B. GOPAL REDDY,P. NARSIMHA REDDY, CH. SURENDER RAJU and S. VANISREE
Study on the incidence of insect pests and natural enemies as influenced by 9organic treatments in Rice ecosystem
A. VENKAT REDDY, R. SUNITHA DEVI, G. ANITHA and D. VISHNU VARDHAN REDDY
Fertility status of rice growing soils of Nalgonda district in Andhra Pradesh - a GIS approach 14Y. SUDHA RANI and G. JAYASREE
Population of meloidogyne sp and rhizoctonia sp in tomato in different agroclimatic 18zones of Andhra PradeshB. VIDYA SAGAR, V. KRISHNA RAO and K. S. VARA PRASAD
PART II : SOCIAL SCIENCE
An empirical study of Groundnut in Ananthapur district of Andhra Pradesh 21K. SUPRIYA, A.K.GOEL, M. H. V. BHAVE and V. V. NARENDRANATH
Study of sales and services provided by the farm input retailers of West Bengal 25 A. DAS and D. BASUCritical analysis of e-Sagu- an information and communication technology project in Andhra Pradesh 29P. GANESH KUMAR, R. RATNAKAR and P. GIDDA REDDY
PART III : HOME SCIENCE
Non-woven drapery lining with Ultra Violet resistance 33D. HARINI, A. SHARADA DEVI and D. ANITHA
PART IV : RESEARCH NOTES
Mapping of QTLs for grain Iron and Zinc in samba mahsuri and wild rice 37(Oryza rufipogon) using RM and gene specific markersV. ROJA, N. SARLA, K. MANORAMA and K. RADHIKA
Identification of nutrient deficiencies by critical nutrient concentration (CNC) and 41dris methods and determination of sufficiency ranges of nutrients by dris technique in maizeCH. RAMULU and G. BHUPAL RAJ
Development of a functional food for diabetes 45S. SPANDANA and M. PENCHALARAJU
Evaluation of wheat (Triticum aestivum L.) genotypes for different sowing dates 48under irrigated conditionsJ. VIJAY, K. MADHAVI, A. RAMACHANDRA RAO and A. SAIRAM
Heavy metal removal efficiency of zeolite from sewage 51RESHMA BHADANGE, AKULA BABY, P.PRABHU PRASADINI, M.UMA DEVI andD. JAGDISHWAR REDDY
Production potential of multicut fodder bajra genotypes under varied dates of sowing 54V. CHANDRIKA, T. SHASHIKALA, M. SHANTI and K. LOKA REDDY
Per se performance and correlation studies in F1 generation of tomato (Solanum lycopersicum Mill.) 58
K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R.V.S.K. REDDY
Pesticide residues in selected fruits/vegetables of Nalgonda district of Andhra Pradesh 64SHAIK FARHATH and S. SHOBHA
Development of lycopene enriched mixed fruit bar 67M. PENCHALA RAJU , B. SHIREESHA , B. SRAVANTHI and KUNA APARNA
Study on the effect of irradiation on sensory qualities of preserved tomato products 69M. KIRTHY REDDY and V. VIJAYALAKSHMI
Combining ability studies in tomato (Solanum lycopersicum Mill.) 74K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R. V. S. K. REDDY
Attitude of the rural women towards telugu television programme -Mee Arogyam Mee Chetullo 77GRANDHI MANASA , R. GEETHA REDDY and M. PREETHI
Response of tomato (Solanumly copersicum L.) to potassium fertilization along with foliar 79application of potassium humate and triacontanolPALAKSHI BORAH, V. SAILAJA, P.CHANDRASEKHAR RAO and A. PRATAP KUMAR REDDY
Recommended specific production practices followed by farmers in supply chain management of 82horticultural productsM. BHAVYAMANJARI, M.SURYAMANI and C.PADMAVENI
Effect of heating of the gel at different temperatures on physico-chemical characteristics 86and microbial content in different accessions of aloe (Aloe barbadensis Miller).B. AMARESWARI, M.PADMA, M.RAJKUMAR and A. SIVA SHANKAR
Effect of different levels of nitrogen and sulphur on growth and yield of 90sunflower (Helianthus annuus L.)S. PAVANI, K. BHANU REKHA, S. N. SUDHAKARA BABU and G. PADMAJA
Food habits, preferences and meal pattern of the professionals in information technology sector 94PRIYA SHARMA, M. USHA RANI, K. UMA MAHESWARI and K. SUPRIYA
Nutrient content of selected herbal crops 97ELMUONZO, K. UMA MAHESWARI, ANURAG CHATURVEDI and T. SUSILA
Pesticide residues in spices of Guntur district of Andhra Pradesh 99SHAIK. SANHERA and S. SHOBHA
Response of wheat (Triticum aestivum L.) cultivars to varying levels of nitrogen 101MATHURA YADAV, V. PRAVEEN RAO, M. YAKADRI and G. JAYASREE
Association of grain iron content with grain yield and other traits in 105sorghum (Sorghum bicolor L. Moench)S. V. P. L. GAYATHRI, K. RADHIKA, A. ASHOK KUMAR and P. JANILA
Determinants of goal directed behaviour of adolescents 108NEHA JOSHI and M. SARADA DEVI
Identification of important qtls for grain number in popular rice varieties of Andhra Pradesh 110SONALI DATTA MUHURI, M. SHESHU MADHAV, CH. SURENDER RAJU and CH. V. DURGA RANI
Morphological characterization of cotton hybrids and their parental lines 113P. ARUNA, P. S. RAO, G. ANURADHA and K. KESHAVULU
Effect of age of seedlings on high yielding rice varieties 116D. NARESH, M. MALLA REDDY, D. VISHNU VARDHAN REDDY and T.V. SRIDHAR
Weed management studies in kharif Maize 121M. A. ALEEM AHMED and R. SUSHEELA
Effect of inoculum levels of rhizoctonia solani and meloidogyne incognita on root 124rot and root knot incidence on tomato CV. pusa rubyB. VIDYA SAGAR, V. KRISHNA RAO, K. S. VARA PRASAD and D. R. R. REDDY
Soil fertility mapping of pillaipally anacut command area, musi river in andhra pradesh 127Y. KRISHNAVENI, K. AVIL KUMAR, M. UMA DEVI and M. D. REDDY
Response of Cauliflower (Brassica Oleracea var. Botrytis) to potassium fertilization 132on light textured soils of Rangareddy districtK. KALYANI, V. SAILAJA, A. PRATAP KUMAR REDDY and P. CHANDRASEKHAR RAO
J.Res. ANGRAU 40(3) 1-5, 2012
Date of Receipt : 10.05.2012 Date of Acceptance : 19.06.2012
email: [email protected]
EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION ANDSEEDLING VIGOUR IN CARRY-OVER ONION SEED (Allium cepa L.)
B. SOMRAJ, K. RAVINDER REDDY, K.V. RADHA KRISHNA and D. SRIHARIDepartment of Horticulture, College of Horticulture,
Dr.Y.S.R. Horticultural University, Rajendranagar, Hyderabad-500030.
ABSTRACT
An investigation was carried out to find out the appropriate invigoration treatment on seed germination andseedling vigour in carry-over onion seed during 2010. Six different aged seed lots with one month interval fromexpiry date along with fresh seed lot were subjected to invigoration with growth regulators and chemical nutrients.The seed treatments comprised growth regulators GA3 (100, 200, 400 ppm), NAA (50, 100, 150 ppm) and chemicalnutrients KNO3 (0.25%, 0.50%, 1.0%), Na2HPO4 (10-2M, 10-4M, 10-8M) and FeSO4 (0.25%, 0.50%, 1.0%) each withthree concentrations. Among the treatments GA3 @ 100ppm followed by NAA @ 100ppm and KNO3 @1% followedby Na2HPO4 @10-4 and FeSO4 @1% were found effective in registering higher values for seed germination andseedling vigour in all the germination and vigour tests. Irrespective of the invigoration treatments, the germinationpercentage and seedling vigour gradually declined as age of seed lot advanced.
Onion (Allium cepa L.) is one of the most
important commercial vegetable crop grown in India
and world. Of the 15 vegetable crops listed by the
FAO, onion falls 2nd only after tomato. It is a bulbous,
herbaceous, monocot cultivated as annual. But for
seed production it is biennial. Onion seeds have poor
longevity and storability which lose its viability very
rapidly (Mumtaz Khan et al., 2004). Generally, the
demand for seed fluctuates very often and sometimes
there may be a surplus of seeds which need to be
stored upto 2-3 sowing seasons. These carry-over
seeds exhibit poor germinability and less vigour.
Since onion seeds found to be poor storers,
maintenance of seed viability of carry-over seed lot
is of great importance in the sowing seasons following
the periods of low production. Hence, proper seed
treatments are needed during storage to maintain the
seed quality. Plant growth regulators and chemical
nutrients have been reported to play a major role in
controlling the biochemical changes accompanying
deterioration of seeds. Seed treatments with growth
regulators and nutrients can invigorate seeds for
better germination and seedling emergence. Keeping
this in view, present investigation was taken up in
order to find out appropriate invigoration treatments
for seed germination and seedling vigour in carry-
over onion seed.
MATERIALS AND METHODS
The present investigation was conductedduring 2010 at Department of Horticulture, College ofHorticulture, Rajendranagar, Hyderabad. Six differentaged seed lots with one month interval from the dateof expiry along with one fresh seed lot of onion having70 per cent germination were procured from privateseed dealers during 2009-10. The seed lots wereinvigorated by soaking them in double the quantityof distilled water and aqueous solutions of growthregulators and chemical nutrients over-night.Treatments consisted of growth regulators Gibberillicacid (GA
3) and Naphthalene Acetic Acid (NAA) each
with three concentrations i.e., GA3@100 ppm, 200
ppm, 400 ppm and NAA @ 50 ppm, 100 ppm, 150ppm and chemical nutrients viz., KNO
3 @0.25%,
0.5%, 1.0%; FeSO4
@0.25%, 0.5%, 1.0% andNa
2HPO
4 @10-2M, 10-4M, 10-8M as well as control with
distilled water. The experiment was laid out in afactorial completely randomized design adoptingFisher’s analysis of variance technique (Sundararaj,et. al., 1972) with three replications.
Germination tests were conducted byadopting top paper, between paper, soil media andsand media as described by ISTA (1985). Ten normalseedlings grown in moist towel paper kept at optimumtemperature were selected for measuring length incentimeters on the day of final count (12th day). Ten
normal seedlings selected at random from germinationtests on 10th day were dried in hot air oven at 80±1oCfor two days for measuring mean dry weight ofseedlings and expressed in milligrams. The seedlingvigour index was calculated by adopting the methodsuggested by Abdul-Baki and Anderson (1973). Thefield emergence was recorded on alternate days from4 to 8 DAS (Days after sowing) in all treatments foreach replication adopting the method suggested byBaskin (1969). Speed of germination was computedby using the formula given by Heydecker (1972)germination index= no. of seeds germinated/daystaken for germination (GI= n/d). Paper exertion testwas calculated by sowing the seeds on 1.25 cm ofmoist sand. It was covered with specially selecteddry filter paper (with 0.4 mm thickness). This filterpaper was again covered with 3 cm of moist sand.This was kept at 20-25oc for the days required forfinal count. The seedlings which were able topenetrate the paper were considered vigorous. Brickgravel test was calculated by selecting hundred seedsat random for each replication which were sown on2 cm thick layer of 2-3 mm size porous brick gravel.Again a 2 cm layer of moist brick gravel was laidabove the seed and wetted upto 60% water holdingcapacity. The count of normal emerged seedlingswas taken 12days after planting and expressed asgermination percentage. Electrical conductivity (E.C.)was measured by digital E.C. meter and expressedin μ mhos /cm (Dadlani and Agarwal, 1983). Coldtest was calculated by sowing seeds on 2 cm thickleveled moist soil. The same quantity of soil wasthen placed on the top of the seed. Enough cold water(10oC) was added to the soil to bring the medium to70 percent of its water holding capacity and thenincubated at 10oC for 7 days. After 7 days, it wastransferred to required temperature for germination.The lot showing minimum variation in germinationpercentage in comparison to check was consideredas vigorous.
RESULTS AND DISCUSSION
Germination percentage:
The interaction effect of invigorationtreatment in relation to the age of the onion seed lotson top of the paper (92.75% to 75.81%), between thepaper (95.20% to 81.06%), soil media method(84.62% to 64.54%) and sand media method (75.33%to 57.81%) decreased progressively from fresh seedlot to the six months old aged seed lot. Between the
paper method recorded highest germinationpercentage than other methods but the germinationpercentage gradually decreased with the age of theseed lots in all the methods. The decrease ingermination was very slow in case of invigorated seed,especially with GA
3 @100 ppm (83.98%), followed
by NAA @100 ppm (82.73%), KNO3 @ 1% (81.39%),
Na2HPO
4 @ 10-4M (80.69%) and FeSO
4 @ 1%
(78.96%) compared to the rapid decrease in untreatedor control (74.87%) seed lots. It may be due to repairor re-organization of the cell organelles, enzymaticreactions, activation of metabolic process of the foodreserves, repair and replacement of cell membraneand rejuvenation of embryo viability (Sudershan,2004).
Seedling Vigour Index
The Seedling Vigour index declinedprogressively and significantly with the age of theonion seed lots from 1503 to 974 based on theseedling length (SV I) and 1374 to 905 based on theseedling dry weight (SV II). It was high in fresh seedlot and decreased with the age of the seed lots fromone month old seed lot till six months old seed lot.Similar findings were reported by Sudershan (2004).Treatment with GA
3 @100 ppm recorded highest
SVI and SVII while FeSO4 @ 0.25% registered lower
seedling vigour indices but these indices were higherthan those in control. Irrespective of invigorationtreatments fresh seed lot recorded maximumseedlings vigour indices based on length and massof the seedling. The increased seedling length andseedling dry weight may be due to the retention ofmembrane activity and high dehydrogenase activityin the seeds (Doijode, 1989).
Seed Germination Percentage in Stress Tests
The advancing age of the seed resulted insignificantly reduced seed germination under stresstests namely, Brick gravel test (82.14% to 62.43%),Paper piercing test (73.43 to 54.43%), cold test(74.1% to 53.87%) and field emergence test (16.89to 12.14). Irrespective of the invigoration treatments,fresh seed lot recorded higher germinationpercentage, and six months old aged seed lotrecorded lower germination percentage in all thestress tests. Invigoration treatment activates themetabolic activity in the first phase of germinationbefore sowing and hence provides added advantageof better emergence, growth and establishment ofseedlings in the field (Vanangamudi and
SOMRAJ et al
EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION
Tab
le 1
. E
ffe
ct
of
Invig
ora
tio
n t
reatm
en
ts w
ith
gro
wth
reg
ula
tors
an
d c
hem
ical
nu
trie
nts
usin
g d
iffe
ren
t m
eth
od
s i
n c
arr
y-o
ver
on
ion
s
eed
in
rela
tio
n t
o t
he a
ge o
f th
e s
eed
lot
F
resh
seed
lo
t O
ne m
on
th
old
seed
lo
t T
wo
mo
nth
s
old
seed
lo
t
Th
ree
mo
nth
s o
ld
seed
lo
t
Fo
ur
mo
nth
s
old
seed
lo
t F
ive m
on
ths
old
seed
lo
t
Six
mo
nth
s
old
seed
lo
t
Germ
inati
on
(%
)
Top
of t
he p
aper
92
.70
91.2
9 89
.14
86.2
5 83
.39
79.9
1 76
.87
Bet
wee
n th
e pa
per
95.2
0 93
.79
92.2
7 88
.95
87.5
4 84
.43
81.8
9 S
oil m
etho
d 84
.62
82.2
9 79
.27
75.2
2 72
.20
68.1
6 64
.91
San
d m
etho
d 75
.33
73.1
6 70
.83
69.1
2 66
.47
61.6
0 58
.37
Vig
ou
r in
dex
See
dlin
g vi
gour
I 13
44
1290
12
15
1125
10
72
998
905
See
dlin
g vi
gour
II
1503
14
24
1328
12
31
1167
10
60
974
Ele
ctr
ical
co
nd
ucti
vit
y (
μs/p
pt)
Ele
ctric
al c
ondu
ctiv
ity
211
231
246
266
274
292
310
Seed
germ
inati
on
in
str
ess t
ests
(%
)
Bric
k gr
avel
test
82
.14
79.4
7 76
.16
72.3
1 69
.52
66.5
2 62
.43
Fie
ld e
mer
genc
e in
dex
16.8
9 16
.22
15.2
6 14
.42
13.8
6 13
.12
12.1
4 P
aper
pie
rcin
g te
st
73.4
3 71
.47
68.4
1 65
.62
62.1
0 57
.97
53.0
4 C
old
test
74
.10
71.2
7 66
.95
63.5
6 60
.91
57.1
0 52
.83
Sp
eed
of
germ
inati
on
Spe
ed o
f ger
min
atio
n 13
.46
13.1
9 12
.75
12.1
0 11
.54
10.9
4 10
.28
SOMRAJ et al
Tab
le 2
. E
ffect
of
Invig
ora
tio
n w
ith
gro
wth
reg
ula
tors
an
d c
hem
ical
nu
trie
nts
usin
g d
iffe
ren
t m
eth
od
s i
n c
arr
y-o
ver
on
ion
seed
in
rela
tio
n t
o t
he
invig
ora
tio
n t
reatm
en
ts
G
A3
@
10
0
pp
m
GA
3
@ 2
00
pp
m
GA
3
@ 4
00
pp
m
NA
A @
50
pp
m
NA
A @
10
0
pp
m
NA
A
@ 1
50
pp
m
KN
O3
@
0.2
5%
KN
O3
@
0.5
0%
KN
O3
@
1.0
0%
FeS
O4
@
0.2
5%
FeS
O4
@
0.5
0%
FeS
O4 @
1.0
0%
N
a2H
PO
4
@ 1
0-2
M
Na
2H
PO
4
@ 1
0-4
M
Na
2H
PO
4
@ 1
0-8
M
Co
ntr
ol
Germ
inati
on
(%
)
Top
of t
he p
aper
89
.00
87.7
6 88
.38
86.6
1 87
.40
87.2
8 84
.95
85.2
3 84
.76
84.8
1 84
.00
83.6
1 84
.52
84.7
1 84
.61
83.2
3
Bet
wee
n th
e pa
per
92.6
6 89
.47
90.4
7 90
.23
92.9
5 90
.38
86.3
3 87
.85
91.5
7 85
.76
87.2
3 90
.52
85.4
7 90
.95
87.3
3 84
.66
Soi
l met
hod
82.2
3 78
.23
79.9
0 75
.19
79.1
4 76
.52
71.0
4 73
.61
77.4
2 70
.33
72.2
8 75
.61
70.9
0 76
.90
73.6
6 68
.57
San
d m
etho
d 72
.04
66.9
0 68
.19
66.4
7 71
.42
67.5
7 65
.42
67.3
8 71
.81
64.4
2 66
.09
66.0
9 65
.42
70.1
9 65
.61
63.0
0
Mea
n 83
.98
80.5
9 81
.74
79.6
3 82
.73
80.4
4 76
.94
78.5
2 81
.39
76.3
3 77
.40
78.9
6 76
.58
80.6
9 77
.80
74.8
7
Viig
our i
ndex
See
dlin
g vi
gour
I 13
.24
1204
12
47
1197
13
77
1215
99
3 10
57
1168
94
0 98
6 10
82
1051
12
48
1152
91
3
See
dlin
g vi
gour
II 14
60
1331
13
58
1195
13
86
1287
11
23
1233
13
78
1043
11
10
1219
11
61
1319
11
97
1007
Ele
ctr
ical
co
nd
ucti
vit
y (
μs/p
pt)
Ele
ctric
al
cond
uctiv
ity
183
196
191
265
239
250
296
276
267
298
286
268
296
266
284
316
Seed
germ
inati
on
in
str
ess t
ests
(%
)
Bric
k gr
avel
test
78
.00
72.6
1 75
.66
70.6
1 78
.38
73.1
9 69
.85
72.1
4 75
.95
67.6
1 70
.47
74.2
3 68
.33
75.2
3 71
.00
65.8
1
Fie
ld e
mer
genc
e in
dex
16.2
3 15
.24
15.5
6 14
.79
15.8
4 15
.11
14.0
5 14
.44
15.3
1 13
.38
13.7
5 14
.59
13.6
3 14
.69
13.9
4 12
.40
Pap
er p
ierc
ing
test
67
.66
62.6
1 64
.47
63.1
4 68
.04
64.9
5 63
.38
65.9
5 71
.00
59.9
0 62
.90
66.8
1 66
.81
68.8
1 64
.42
57.4
2
Col
d te
st
68.3
3 62
.19
64.0
0 63
.04
67.4
7 63
.09
61.5
2 63
.85
68.6
1 59
.95
62.1
9 66
.61
61.4
7 66
.85
63.1
4 58
.76
Sp
eed
of
germ
inati
on
Spe
ed o
f ge
rmin
atio
n 13
.30
12.6
9 12
.84
12.3
5 12
.84
12.3
0 11
.28
11.9
8 12
.43
10.7
0 11
.47
12.1
2 11
.29
12.3
4 12
.18
9.78
Kulandaivelu, 1989). Invigoration treatment helpedto record higher seed germination percentage in allthe stress tests with GA
3 @ 100ppm, NAA @
100ppm, KNO3 @ 1%, Na
2 HPO
4 10-4M and FeSO
4
@ 1%. Improved performance of seeds hydratedbefore sowing might be due to increased DNA andRNA and protein synthesis, higher mitochondrialactivity and allied sequential changes in the elasticityand viscosity of protoplasm (Henckel, 1964).
Speed of Germination
All the Invigoration treatments helped toregister higher speed of germination over control oruntreated seed lots, especially with GA
3@100ppm
which recorded highest speed of germination (13.46to 10.28) followed by NAA@100ppm, KNO
3 @ 1%,
Na2 HPO
4 @ 10-4M and FeSO
4 @1%.
Electrical Conductivity
There was a significant increase in theelectrical conductivity of seed leachate in relation tothe age of the Onion seed lots which increased fromfresh lot (211μs/ppt) to the six months old aged seedlot (310μs/ppt). Irrespective of the invigorationtreatment, fresh seed lot recorded lower electricalconductivity of seed leachate while the six monthsold aged seed lot recorded the higher values. It mightbe due to destructive changes in cellular membranesystem resulting in more leakage of organic solutes(free sugars, Fatty acids and Amino acids). Damageto the membrane system could be repaired andprotected against such changes by invigorationtreatment, indicated by low Electrical Conductivityof seed leachate. Protective action of invigorationchemicals could presumably have extended theviability of seeds. Seed soaking in water effectivelycontrolled the leakage of electrolytes, sugars andamino acids from the seeds (Dias et al., 2004). TheE.C of seed leachate was lowest in seed lots treatedwith GA
3 @100ppm followed by NAA@100ppm,
KNO3@1%, Na
2HPO
4 @ 10-4M and FeSO
4 @ 1.0%.
All the Invigoration treatments helped to record lowerE.C. of the seed leachate compared to the control.
In the present investigation, irrespective ofthe invigoration treatments, the seed qualityparameters declined progressively with the increasein the age of the seed lot. However, invigorationtreatments helped to register higher values for seedgermination and seedling vigour compared to control.
REFERENCES:
Abdul- Baki, A. A and Anderson, J. D 1973. Vigourdetermination in soybean seed by multiplecriteria. Crop Science 13: 630-633.
Baskin, C. C 1969. GADA and seedlingmeasurements as tests for seed quality.Proceedings of Seedsman Short Course,Mississippi State University, pp.59-64.
Dadlani, M and Agarwal, P. K. 1983. Factorsinfluencing leaching of sugars and electrolytesfrom carrot and okra seeds. ScientiaHorticulture 19: 39-44.
Dias, D. C. F. S., Freitas, R. A., Dias, L. A. S andOlivera, M. G. A 2004. Storage potential ofcotton seeds predicted by vigour tests and bio-chemical assays. Abstracts, 27th ISTACongress Seed Symposium, Budapest,Hungary, May 17-19, p.70.
Doijode, S. D 1989. Some biochemical changes inrelation to lose of seed viability in cauliflowergermplasm. Indian Journal of Plant GeneticResources 2:136-139.
Henckel, P. A 1964. Physiology of plants underdrought. A Review on Plant Physiology15: 363 -386.
Heydecker, 1972. Vigour In Roberts E H (ed.) “Viabilityof seeds” Syracuse University press SyracuseNew York. pp.209-252.
ISTA International Seed Testing Association 1985.International rules for seed testing. SeedScience and Technology 13: 307-513.
Mumtaz khan M., Javed Iqbal M., Abbas M., RazaH., Waseem, R and Arshad Ali 2004. Loss ofvigour and viability in aged onion (Allium cepaL.) seeds. International Journal of Agricultureand Biology 6 (4) : 708-71.
Sudershan, G 2004. Effect of seed vigour andinvigoration treatments on storability, seedlingvigour, growth, development and yield incotton hybrids. M.Sc. (Ag.) Thesis, AndhraPradesh Agricultural University, Hyderabad.
Sundararaj, N., Nagaraj, S., Venkataram, M. N andJagannath, M. K 1972. Design and Analysisof Field Experiments, University of AgriculturalSciencess. Bangalore.
Vanangamudi, K and Kulandaivelu, R 1989. Pre-sowing seed treatment for dryland farming.Seeds and Farms 15(9): 33-34.
EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION
J.Res. ANGRAU 40(3) 6-8, 2012
Date of Receipt : 26.05.2012 Date of Acceptance : 10.06.2012
e-mail: [email protected]
EVALUATION OF DIFFERENT ORGANIC NUTRIENT SOURCESAND VARIETIES FOR ORGANIC RICE (Oryza sativa L) PRODUCTION
R. JAGADEESHWAR, N. RAMA GOPALA VARMA, B. GOPAL REDDY,P. NARSIMHA REDDY, CH. SURENDER RAJU and S. VANISREE
Rice Section, Agricultural Research Institute,Acharya N. G. Ranga Agricultural University, Rajendranagar, Hyderabad - 500 030
ABSTRACT
Field experiment was conducted at Rice Section, ARI, Rajendranagar, Ranga Reddy district during kharif2008 and rabi 2008-09 seasons to evaluate the influence of organic nutrient sources and varieties on productivityand on major insect pests and diseases. The results revealed that among the varieties Sugandha Samba respondedwell and recorded higher grain yield (4.4t/ha) during kharif and Tellahamsa (5.1t/ha) during rabi. Higher productivitywas realized from karanj cake (4.4 and 5.2t/ha in both seasons) and neem cake treated plots (5.1t/ha during rabi).Reduced incidence of thrips with application of vermicompost and reduced incidence of blast and sheath rot withapplication of karanj cake and neem cake was observed.
Organic farming is gaining much importancein the world with more than 100 countries alreadypracticing it. The trade in world organic market hasnow touched 26 billion US$ and is expected toincrease to 102 billion US$ by 2020. According toAPEDA (2011), about 9,76,646 MT of different organicproducts worth 498 crore rupees are being exportedfrom India. One of the constraints identified forpromoting organic rice cultivation in India is lack ofsufficient FYM and also efficient production andprotection technologies that can be easily adaptedby the farmers for cultivating rice organically. Further,varieties with inherent potential to perform well evenunder low input management and can best suit toorganic farming systems have to be identified toevolve profitable and productive organic farmingpackage.
MATERIALS AND METHODS
A field experiment was conducted duringkharif 2008 and rabi 2008-09 seasons at Rice Section,ARI, Rajendranagar with 5 rice varieties (SugandhaSamba, Sumati, Early Samba, Rajendra,Tellahamsa) and 4 organic nutrient sources (FYM,Green manure, Neem cake and Karanj cake @ 5.0 t/ha) and an untreated control, in randomized blockdesign replicating thrice, to compare the influence oforganic nutrient sources and varieties on productivity
and document the incidence of insect pests /diseases of major concern.
The trial was conducted in an exclusivelymaintained organic plot for last three years. The cropwas transplanted at a spacing of 15x15cm. All theorganic nutrient sources were applied only basally.The mean data of insect pests, diseases and grainyield were subjected to statistical analysis usingINDOSTAT package.
RESULTS AND DISCUSSION
Among the varieties tested, Sugandha Samba(RNR 2465) responded very well with grain yield of4.4 t/ha (Table 1) in comparison to Rajendra (2.3t/ha). Whereas, during rabi 2008-09 Tellahamsa, acold tolerant variety produced highest grain yield of5.1 t/ha as against the same check Rajendra(3.8 t/ha). With respect to organic nutrient sources,application of karanj cake @5.0 t/ha resulted inhigher grain yield (Table 2) in both the seasons (4.4and 5.2t/ha, respectively) and next best source forrabi was neem cake (5.1 t/ha).
Surekha et al., (2010) observed that grain yieldsin the inorganic fertilizer plots were superior toorganics during first two years by 15-20%, while grainyields gradually improved with organics in the lateryears to parity with inorganics. However, in this
Table 1. Grain yield (t/ha) as influenced by varieties under organic farming at Rajendranagar during kharif 2008 and rabi 2008-09
Grain yield (t/ha) Variety
kharif 2008 rabi 2008-09 Sugandha Samba 4.4c 4.8bc
Sumati 4.0bc 4.5b
Early Samba 4.2bc 4.9c
Rajendra 2.3a 3.8a
Tellahamsa 3.9a 5.1c
Figures in a row within a season with different letters differ significantly (p=0.05)
Table 2. Grain yield (t/ha) as influenced by different organic sources at Rajendranagar,kharif 2008 and rabi 2008-09
Grain yield (t/ha) Treatments
kharif 2008 rabi 2008-09 Untreated control 3.3a 3.9a
FYM @5t/ha 3.5ab 4.3a
Vermicompost @ 5t/ha 3.9b 4.7b
Neem cake @ 5t/ha 3.9b 5.1c Karanj cake @ 5t/ha 4.4c 5.2c
Figures in a row within a season with different letters differ significantly (p=0.05)
Table 3. Incidence of insect pests as influenced by organic sources at Rajendranagar during kharif2008 and rabi 2008-09
% THDL %WMDL %DH %WE Treatments
Kh 08 Ra 08-09 Kh 08 Ra 08-09 Kh 08 Untreated control 16.69d 14.27b 5.26a 3.98a 8.08a 2.64a
FYM @5t/ha 13.45b 14.08b 5.28a 4.47a 9.52a 2.14a
Vermicompost @ 5t/ha 11.09a 11.31a 5.30a 4.03a 9.26a 2.49a
Neem cake @ 5t/ha 15.07c 12.17b 5.57a 3.87a 8.50a 1.23a
Karanj cake @ 5t/ha 15.17c 14.29b 5.36a 3.98a 8.26a 1.27a
Figures in a row within a season with different letters differ significantly (p=0.05)THDL: thrips damaged leaves, WMDL: Whorl maggot damaged leaves, DH: dead hearts, WE: white ears
Table 4. Incidence of blast and sheath rot as influenced by organic sources at Rajendranagarduring kharif 2008
Treatments % leaf blast % neck blast % sheath rot Untreated control 30.19d 35.06d 47.8c
FYM @5t/ha 16.80c 19.90c 26.2b
Vermicompost @ 5t/ha 16.67c 19.80c 26.0b
Neem cake @ 5t/ha 14.26b 17.01b 22.1a
Pongamia cake @ 5t/ha 13.09a 15.45a 20.1a
Figures in a row within a season with different letters differ significantly (p=0.05)
EVALUATION OF DIFFERENT ORGANIC NUTRIENT SOURCES
continuous study, the yields during rabi reached parityby 5th year after build up of sufficient soil fertilitythrough improved soil biological activity.
During these two seasons there was reducedincidence of thrips (11.09 and 11.31%) withvermicompost application (Table 3) compared tountreated control (16.67 and 14.27%). However, therewas no significant difference in incidence of whorlmaggot or stem borer vis a vis organic nutrientsources.
Kajimura et al., (1995) and Chandramani etal., (2009) reported reduced incidence of plant andleafhoppers when neem cake was added as organicnutrient source. Studies indicated that Karanjin (analkaloid present in Karanj cake) has a dramaticantifeedant/repellent effect, with many insectsavoiding treated crops. It suppresses ecdysonehormone and, thereby, it acts as an insect growthregulator (IGR) and antifeedant. There were claimsthat it inhibits cytochrome P450 in susceptibleinsects and mites (Copping, 2009).
Incidence of leaf blast, neck blast andsheath rot was low (Table 4) in the treatment whereKaranj cake was applied (kharif 2008). Similarobservations were made by Barnwal et al., (2007).The organic sources like karanj cake and neem cakecontain certain alakaloids which inhibit the fungalgrowth and sporulation. As there is meagerinformation on these aspects, further study wasenvisaged.
Based on the studies it can be inferred that,varieties like Sungandha Samba and Tellahamsa aresuitable for organic rice production for kharif and rabi,respectively. Addition of nutrient sources likevermicompost and karanj cake could not only helprealize higher grain yield but also to minimize problemdue to biotic stresses under organic rice cultivation.
Integration of these components in organic farmingwould pave way for managing insect pests anddiseases in rice, apart from their role as nutrientsources.
REFERENCES:
APEDA, 2011. Agricultural and Processed FoodProducts Export Development (APEDA):http://www.apeda.gov.in/apedawebsite/organic/Organic_Products.htm
Barnwal, M. K., Agarwal, B. K., Prasad, S. M. 2007.Effect of different levels of nitrogen and karanjcake in relation to occurrence of diseases andyield of rice. Journal of Plant Protection andEnvironment 2007 Vol. 4 (2): 122-125.
Chandramani P., Rajendran, R.P., Sivasubramanianand Muthiah, C. 2009. Management of hoppersin rice through host nutrition – A novelApproach. Journal of Biopesticides 2(1): 99-106.
Copping, L.G [ed.] 2009. The Manual of BiocontrolAgents. a World Compendium - 4th ed. / ed.L.G. Copping Alton. Published by BCPCcop.XLIV, pp.851
Kajimura, T., Fujisaki, K and Nakasuji, F. 1995. Effectof organic rice farming on leafhoppers andplanthoppers 2. Amino acid content in the ricephloem sap and survival rate of planthoppers.Applied Entomology and Zoology 30(1): 17-22.
Surekha K., Jhansilakshmi, V., Somasekhar, N.,Latha, P.C., Kumar, R. M., Shobha Rani, N.,Rao, K.V and Viraktamath, B.C. 2010. Statusof Organic Farming and Research Experiencesin Rice. Journal of Rice Research Vol.3 (1).23-35.
JAGADEESHWAR et al
STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES ASINFLUENCED BY ORGANIC TREATMENTS IN RICE ECOSYSTEM
A. VENKAT REDDY, R. SUNITHA DEVI, G. ANITHA and D. VISHNU VARDHAN REDDYRegional Agricultural Research Station,
Acharya N.G. Ranga Agricultural University, Warangal - 506 007
J.Res. ANGRAU 40(3) 9-13, 2012
Date of Receipt : 30.04.2012 Date of Acceptance : 11.06.2012
ABSTRACT
A field experiment was conducted in rice (Oryza sativa) at Regional Agricultural Research Station, AcharyaN.G. Ranga Agricultural University, Warangal, AndhraPradesh for four years i.e., during Kharif 2007-08, 2008-09,2009-10 and 2010-11 to study the effect of organic manures with and with out plant protection on the incidence ofinsect pests, natural enemies and yield. The treatments include: 100% Organic manures + Need based plantprotection, 100% Organic Manures + No plant protection, 50% Organic Manures + 50% Recommended Doses ofFertilisers + Need based plant protection, 50% Organic Manures + 50% Recommended Doses of Fertilisers + Noplant protection, 100% Recommended Doses of Fertilisers + Need based plant protection and 100% RecommendedDoses of Fertilisers + No plant protection . With regards to insect pests, the lowest percent dead hearts, silver shootsand hoppers were recorded in 100% Organic manures + Need based plant protection, while highest pest incidencewas recorded in 100% Recommended Doses of Fertilisers + No plant protection. The population of spiders, miridbugs and coccinellids was recorded to be highest in 100% Organic Manures + No plant protection and lowest in100% Organic manures + Need based plant protection. The grain yield was found to be significantly highest in 50%Organic Manures + 50% Recommended Doses of Fertilisers + Need based plant protection (5460.8 kg/ha) followedby 100% Recommended Doses of Fertilisers + Need based plant protection( 5312.5 kg/ha) and lowest in 100%Recommended Doses of Fertilisers + No plant protection (4589.5 kg/ha). Ihe benefit cost ratio was highest in 100%Recommended Doses of Fertilisers + Need based plant protection (1.85) and lowest in 100% Organic Manures + Noplant protection (1.25)
Increased and indiscriminate use of chemicalfertilizers and pesticides during green revolutionperiod resulted in several harmful effects on soil,water and air. This has reduced the productivity ofthe soil by deteriorating soil health in terms of soilfertility and biological activity. The excess /indiscriminate use of pesticides has led to the entryof harmful compounds into food chain, death ofnatural enemies and development of resurgence /resistance to pesticides. Out breaks of insect pestshave occurred after insecticides were over used andoutbreak of brown plant hopper (BPH), Nilaparvathalugens in rice is an example of this over use Wang etal (1994). Rice is the major crop that receivesmaximum quantity of fertilizers (40%) and pesticides(17-18%). Hence, enhancement and maintenanceof system productivity and resource quality isessential for sustainable agriculture. Increasinginsect pest and disease pressure in agro eco systemis due to changes that have occurred in agriculturalpractices since World War II. Usage of fertilizersand pesticides has increased rapidly during this period
email: [email protected]
and evidence suggests that such excessive use ofagro chemicals in conjunction with expandingmonocultures has exacerbated pest problems(Conway and Presty; 1991). On the other hand,proponents of alternative agricultural methods contendthat crop losses to insect and diseases are reducedwith organic farming (Merrill; 1983 ; Oelhaf; 1978).Hence, studies comparing insect pest and naturalenemies populations on plants treated with syntheticfertilizers versus organic sources of fertilizers areneeded.
MATERIALS AND METHODS
A field experiment was conducted duringkharif season for four years (2007-08 to 2010-11) ona deep black soil at Regional Agricultural ResearchStation, Warangal, Andhra Pradesh, to compare theinfluence of manures and conventional fertilizertreatments on the incidence of insect pests, naturalenemies and yield.
A randomized complete block design wasset up with four replications. Each treatment has a
plot size of 200 m2. Variety BPT-5204 was selectedfor the experiment. There were six treatments (table).
The recommended dose of fertilizers were given at100-60-40 kg N, P
2O
5, K
2O/ha through Urea, Single
Super Phosphate, Muriate of potash, respectively,in addition to Recommended quantity of Farm YardManure @ 25 t/ha. Nitrogen was given in three equalsplits at basal, maximum tillering and panicle initiationstages, P as basal, while K was given at basal andpanicle initiation. Through organics, N dose wasadjusted to recommended level based on their ‘N’content on dry weight basis. Organic manures wereincorporated one day before transplanting rice. Theorganic manures used were: Farm Yard Manure,Neem Cake and Vermi compost. In organictreatments need based plant protection sprays weregiven with 5 percent Neem Seed Kernel Extract(NSKE) and in fertiliser treatments, need basedsprayings were given with recommended pesticideswhen the pest reached above Economic ThresholdLevels (ETL). The number of sprays ranged from2-4 depending on the incidence in different seasons.
For recording the incidence of insect pestsand natural enemies, field investigations were carriedout at tillering stage, elongation stage, panicleinitiation stage and milky grain stage and grainhardening stage. The data were recorded for theincidence of insect pests - stem borer, gall midge,plant hoppers and predator populations of spiders,mirid bugs and coccinellids.
RESULTS AND DISCUSSION
I. INSECT PESTS
A. STEM BORER
The incidence of stem borer in terms of deadhearts ranged from 5.7% to 10.0%, while that of whiteear heads ranged from 8.1% to 9.0 %. Lowest meanpercent dead hearts was recorded when need basedplant protection was taken up either with 100%Organics (5.7%) or by adopting RDF with chemicalprotection (5.9%) . The highest percent of dead heartswas noticed when no plant protection measures weretaken with 100% RDF (10.0%) or with 50% OrganicsManures + 50% RDF (9.6%). With regards to percentwhite ears heads, the differences among thetreatments were found to be non significant. Lowerincidence of stem borer in organics compared to
inorganics was reported by several workers.
Surekha et al (2010) reported that, the white ears
and dead hearts in rice were slightly more in inorganic
treatments compared to organics. European corn
borer (Ostria nubilalis) females laid more eggs withchemical fertilization compared to organics (Phelan
et al, 1995.). Outbreak of stem borer in rice crop
wasreported to be low with organics compared to
synthetic inorganic fertil izers (Luong and
Heong.,2005).
B. GALLMIDGE
The percent silver shoots ranged from 15.9%to 26.5%. Significantly lowest percent of silver shoots
was reported when nutrients were met through
organics irrespective of plant protection measures
(15.9 and 18.0%). The highest incidence of gallmidge
was found with the use of fertilizers without any plant
protection (26.5%) with 100% RDF and 22.4% with50% RDF.
C. HOPPERS
The hopper population included brown plant
hoppers and white backed plant hoppers. Lowest
population of hoppers was recorded when rice was
grown solely with organics, either with organics for
plant protection (17.3%) or without plant protection(22.7%) . Their incidence was also lower (22.5%) ,
when 50% fertilizer N was substituted through
organics coupled with need based chemical plant
protection. Chemical plant protection was less
effective on hoppers when rice was grown with
fertilizers (28.3% with protection and 31.1% withoutprotection). Low incidence of insect pests such as
plant hoppers and leaf hoppers was observed in
organically farmed rice fields compared to traditional
fields (Hidaka; 1997 ; Kajimura et al., 1993, and
Kajimura .,1995).
The density of immigrants of the rice planthopper species Sogotella furcifera was significantly
lower and the settling rate of female adults and
survival rate of immature stages of ensuing
generations were generally lower in organic compared
to conventional rice field. Luong, and Heong, (2005)
shared that the organics or manures affected riceplant growth and minimized the outbreak of brownplant hopper.
VENKAT et al
In case of cotton crop, Adkinson (1985)reported nearly three times as many boll weevil larvaeon crop receiving heavy application of fertilizerscompared to unfertilized checks. Lower abundanceof herbivores in low-input system can be partlyattributed to the lower N content in organically farmedcrops (Lampkin, 1990). Increased soluble N levelsin plant tissue following N fertilization were found togenerally decrease pest resistance as reported byPhelan et al (1995).
Chemical fertil izers can dramaticallyinfluence the balance of nutritional elements in plantsand it is likely that their excessive use will createnutrient imbalances, which in turn, reduce resistanceto insect pests. In contrast, organic practicespromote an increase of soil organic matter andmicrobial activity and gradual release of plantnutrients which does not lead to enhanced N levelsin plant tissues. Organic practices can also providesupplies of secondary and trace elements thusproviding a proper balance of elements, which canstimulate resistance to insect attack (Luna; 1988).Ramesh et al (2005) stated that organic crops haveshown more tolerance to insect attacks and organicrice is reported to have thicker cell wall and lowerlevels of free amino acids than conventional rice.
II. NATURAL ENEMIES
The population of natural enemies viz.,spiders, mirid bugs, coccinellids ranged from 14.5 to25.4, 19.6 to 34.2, 11.0 to 20.3 per hill, respectively.The treatmental effects were not significant on spiderpopulation. The population of mired bugs andcoccinellids was highest when rice was grown with100% Organics with out any plant protection ( 34.2,20.3) or with need based organic protection (21.8,15.6) or when 50% fertilizer N was substituted throughorganics without any plant protection (25.8, 15.5).Use of chemicals for plant protection has broughtdown the population of mired bugs and coccinellidswhen fertilizers were used either solely or inconjunction with organics.
Lawanprasert et al (2006) reported higherpopulation of natural enemies particularly spiders,lady bird beetles and dragon flies in the naturallygrown rice than conventional rice. More number ofnatural enemies such as spiders, mirid bugs and
insect pathogenic nematodes were reported in
organically framed rice by Hidaka (1997) and Kajimura
et al (1993). Surekha et al (2010) also reported more
populations of natural enemies such as Platygasteroryzae, mirid bugs and spiders in organic treatmentscompared to inorganics. Hesler et al (1993) and Louis
et al (1993) reported significantly abundant
populations of predators like giant water bugs, back
swimmers and diving beetles in organic fields.
III. YIELD AND BENEFIT COST RATIO
Grain yield of rice was higher when need
based plant protection (5460 kg/ha) was adoptedeither with INM or with 100% RDF (5312 kg/ha).
Chemical fertilization without chemical plant
protection seemed to be less effective to realised
good yields of rice crop. Organic rice gave lower yields
with or without organic plant protection.
The highest benefit cost ratio of 1.85 was
achieved in 100% RDF + Need Based PlantProtection and the next best was INM + Need Based
Plant Protection (1.47) . The returns from organic
rice were comparatively less.
Similar reports of lower yields in organics
compared to inorganics were also reported by other
researchers. A 20-30% less yield of crops in organicfarming was reported by Rajendra Prasad (2006).
Yield loss of organically grown rice was reported to
the tune of 24% (Mader et al: 2002) and Atkison and
Betsy Woods (1999) reported organic rice yield at
50-60% of conventional yields.
The lower benefit cost ration of 1.09 wasrecorded in organic rice compared to 1.37 in in-
organic rice (Surekha et al, 2010). Lowanprasert etal; (2006) reported lower benefit cost ratio in organic
method and attributed this to highest production cost
The experimental results indicated that the
incidence of insect pests was low in organically
grown rice and the population of natural enemies /predators was highest in this system, more so when
plant protection measures were not taken. But ,
chemical farming resulted in higher grain yield and
higher benefit cost ratio. Organic rice production is
more advantageous than conventional farming when
environmental concerns become important.
STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES
VENKAT et al
Tab
le. E
ffect
of
org
an
ic a
nd
in
org
an
ic t
reatm
en
ts o
n t
he i
ncid
en
ce o
f in
sect
pests
, n
atu
ral
en
em
ies a
nd
yie
ld o
f ri
ce (
mean
of
4 y
ears
)
Insect
Pests
N
atu
ral
en
em
ies
Tre
atm
en
ts
% D
ead
h
eart
s
% S
ilver
sh
oo
ts
No
. o
f h
op
pers
/hill
% W
hit
e
ears
N
o.
of
sp
iders
/
hil
l
No
. o
f m
irid
b
ug
s /
hil
l N
o.
of
co
ccin
ell
ids /
h
ill
Gra
in
yie
ld
(kg
/ha)
Ben
efi
t-
co
st
rati
o
T 1-1
00%
Org
anic
s +
N
eed
base
d pl
ant
prot
ectio
n 5.
7 15
.9
17.3
8.
9 22
.2
21.8
15
.6
4678
1.
35
T 2-1
00%
Org
anic
s +
No
plan
t pro
tect
ion
8.5
18.0
22
.7
8.7
25.4
34
.2
20.3
47
09
1.25
T 3-5
0% O
rgan
ics
+ 50
%
RD
F +
Nee
d B
ased
pl
ant p
rote
ctio
n 6.
8 19
.6
22.5
9.
0 17
.1
23.7
13
.1
5460
1.
40
T 4-5
0% O
rgan
ics
+ 50
%
RD
F +
No
plan
t pr
otec
tion
9.6
22.4
25
.9
8.3
20.6
25
.8
15.5
50
01
1.47
T 5-1
00%
RD
F +
Nee
d B
ased
Pla
nt P
rote
ctio
n 5.
9 18
.2
28.3
8.
1 14
.5
19.6
11
.0
5312
1.
85
T 6-1
00%
RD
F +
No
plan
t pro
tect
ion
10.0
26
.5
31.1
8.
8 19
.9
25.0
14
.9
4589
1.
35
SE
m ±
3.
03
2.2
5.6
NS
N
S
2.8
2.4
310
CD
at 5
%
0.99
0.
96
2.5
1.2
1.
2 1.
1 13
9
RD
F =
Rec
omm
ende
d D
ose
of F
ertil
izer
s ( 1
00:6
0:40
kg/
ha o
f NP
K)
REFERENCES
Adkisson, P.L., 1985. The influence of fertilizerapplication on population of Heliothis zea andcertain insect predators. Journal of EconomicEntomology. 51:144-149
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Conway, G.R and Presty, J. 1991. Unwelcomeharvest. Agricultural pollution. Earthscan,London.
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Kajimura, T., Maeoka, Y., Widirta, I.N., Sudo, T.,Hidaka, K., Naksuji, F and Nagai, K. 1993.Effects of organic farming of rice plants onpopulation densities of leaf hoppers and planthoppers. I. Population density and reproductiverate. Japanase journal of Applied Entomologyand Zoology. 37 (3): 137-144
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Lampkin, N., 1990. Organic farming. Forming pressBooks, IPswitch, U.K.
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Louis, S.H., Albert, A.G., Michael, I.O and Andrew,
T.P. 1993. Arthro pod founa of conventional
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Luna, J.M., 1988. Influence of Soil fertility practices
on agricultural pests. In:Proceedings of the
Sixth International Science Conference of
IFUAM on Global Perspectives on Agroecology
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Luong Minh Char and Heong, K.L., 2005. Effects of
organic fertilizers on insect pest and diseases
of rice. Omon rice. 13:26-33
Mader, P., Fliessbach, A., Dubois, D., Gunst L.,
Fried, P and Niggli, U. 2002. Soil fertility and
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1694-1697
Merrill, M.C. 1983. Eco – Agriculture ; A review of
its history and philosophy. Biological
Agriculture and Horticulture. 1:181-210
Oelhaf, R.C. 1978. Organic Agriculture. Halstead
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Phelan, P.L., Mason, S.F and Stinner, B.R. 1995.
Sum fertility management and host preference
by European Corn borer, Ostria nubilalis, on
Zea mays: a comparison of organic and
conventional chemical farming. Agricultural
Ecosystem and Environment. 56 :1-8
Rajendraprasad. 2006. Organic farming. Indian
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Ramesh, P., Singh, M and Subba Rao, A. 2005.Organic farming : Its relevance to the Indiancontext. Current Science. 88(4):561-568
STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES
J.Res. ANGRAU 40(3) 14-17, 2012
FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT INANDHRA PRADESH - A GIS APPROACH
Y. SUDHA RANI and G. JAYASREEDepartment of Soil Science and Agricultural Chemistry
College of Agriculture, Acharya N. G. Ranga Agricultural University,Rajendranagar, Hyderabad-500 030.
Date of Receipt : 14.03.2012 Date of Acceptance : 04.05.2012
ABSTRACT
A study was undertaken to map the nutrient status of Nalgonda district of Andhra Pradesh. The soil analysisshowed the deficiency of N, P, K and organic carbon in the study area. From the soil fertility maps it was observed thatall rice growing soils in the district are low in organic carbon content and available nitrogen. An area of 9462 ha (6.4per cent) was in the low category and 138390 ha (93.6%) was in the medium category in available phosphorus. Anarea of 14046 ha (9.5%) was in low category and 133806 ha (90.5 %) was in medium category in availablepotassium.
email: [email protected]
Rice requires application of heavy doses of
nutrients, particularly nitrogen (N) to achieve full yield
potential. Application of fertilizers by the farmers in
the fields without prior knowledge of soil fertility status
might result in adverse effects on soils as well as
crops both in terms of nutrient deficiency and toxicity
either by adequate or overuse of fertilizers (Sharma,
2004). Scan attention was paid to collect the geo-
referenced samples taking whole district as a single
mapping unit. With the invent of modern technologies
of remote sensing, GIS and GPS, it is now possible
to monitor the changes in fertility status of the studied
area with site-specific nutrient requirement of the
crop. The present study was conducted in Nalgonda
district of Andhra Pradesh to know the spatial
distribution of soil nutrients during 2009.
MATERIALS AND METHODS
Rice occupied an area of 147 thousand ha,
with production of 420 thousand tonnes and
productivity of 2.84 t ha-1 in Nalgonda district.
Surface soil samples from 0-15 cm and sub surface
soil samples from 15-30 cm depth were drawn from
the ground truth sites from intensively rice growing
areas. Sixty-one geo-referenced soil samples were
collected and location of soil sampling sites (X, Y
coordinates) of the district was worked out with the
help of global positioning system (GPS) used during
collection of soil samples. The soil samples were
ground and passed through 2 mm sieve. Important
soil physical and chemical properties were estimated
by standard procedures. The points having same
category of soil property were grouped into class as
a polygon and the maps for individual nutrients were
generated in Arc GIS.
RESULTS AND DISCUSSION
The soils were slightly acidic to alkaline with
pH ranging from 5.5 to 8.5. Nearly 41 % of soils were
slightly acidic in nature, while 25 % samples were
neutral and 34 % of soils were in alkaline range. The
soil reaction of subsurface soils varied from 6.1 to
8.8 with a mean value of 7.6. The pH of subsurface
soils varied from 6.1 to 8.8 with a mean value of 7.6.
The reaction of applied fertilizer material with soils
colloids, results in the retention of basic cations on
the exchangeable complex of the soil (Nayak et al.,2002 and Madhuvani et al., 2000), thus influencing
the soil pH.
The electrical conductivity (EC) varied from
0.13 to 1.37 dS m-1. The normal EC may be ascribed
to leaching of salts to lower horizons. The electrical
conductivity was more in sub surface soil and ranged
from 0.56 to 1.90 with a mean value of 0.97.
The organic carbon content of the surface
soils varied from 1.1 to 4.8 g kg-1 with a mean value
of 3.7 g kg-1 and all the samples were under low
category. The organic carbon content in subsurface
soils was low and varied from 0.8 to 3.5 g kg-1 with a
mean value of 2.0 g kg-1. All sub surface soils were
also under low category. The low level was further
categorized into 2 levels for map presentation
(Fig 1).
The texture of the soils varied from sandy
loam to sandy clay loamy in nature. The clay content
in these soils, varied from 8 to 26 %, with a mean
value of 15.1 %, while the sand fraction varied from
65 to 82 % with a mean value of 73.2 %. The silt
content of these soils varied from 5 to 21 % with a
mean value of 11.7 %. The texture of the subsurface
soils varied from sandy loam to sandy clay loam.
With increase in the depth of soil, the percent of clay
and silt increased, while the sand content decreased.
The clay content of subsurface soils varied from 13
to 36 % with a mean value of 22 and standard
deviation of 4.96. The sand fraction varied from 52
to 69 % with a mean value of 61 and standard
deviation of 3.82. The silt content of these soils varied
from 10 to 25 with a mean value of 17 and standard
deviation of 3.38.
The cation exchange capacity of surface
soils under the study was in a range of 6.4 to 19.5 c
mol (p+) kg-1soil, with a mean value of 9.8 c mol (p+)
kg-1soil. Of the total samples 80 % of the soils were
low and 20 % of the soils were moderate in CEC.
The CEC content of the sub surface soils varied from
7.6 to 21.1 c mol (p+) kg-1soil, with a mean value of
11.7 c mol (p+) kg-1soil. The CEC of the soils was
low to high and was related to clay and organic carbon
content of the soils (Gangopadhyay et al.1998).
The available nitrogen of the soils varied from
125 to 321 kg ha-1, with a mean value of 212 kg ha-1
and standard deviation of 60.5. Seventy seven per
cent of soils were low in available nitrogen, while 31
% of soils were medium in available nitrogen. The
available nitrogen content of subsurface soils was
low and varied from 67 to 240 kg ha-1, with a mean
value of 125 kg ha-1 with a standard deviation of 38.5.
Organic carbon content in soils was low and thus the
soils also were low in available nitrogen status
(Suribabu et al.,2002). Low level of nitrogen was
further categorized into 3 levels for map presentation
(Fig 2). It is observed that all soils were under low
category (147853 ha).
Phosphorus status of soils under study
ranged from 14.5 to 48.5 kg ha-1, with a mean value
of 25.6 kg ha-1 with a standard deviation of 7.6. Of
the total samples, 46 % were low in available
phosphorus content and 54 % soils were medium in
available phosphorus. In sub-surface soil samples,
it ranged from 7.8 to 31.5 kg ha-1, with a mean value
of 16.1 kg ha-1. Majority of subsurface soils were low
in available P. The variations in phosphorus status
were mapped under GIS environment. (Fig 3). An
area of 9462 ha (6.4 per cent) was in the low category
and 138390 ha (93.6%) was in the medium category.
The available potassium content of soils
varied from 78 to 361 kg ha-1, with a mean value of
174 kg ha-1. Out of the total 61 soils, 41 % of the
soils were low in available potassium. Majority of
soils (56%) were medium in available potassium. The
available potassium content in subsurface soils
ranged from 47 to 280 kg ha-1, with a mean value of
102 kg ha-1. The variations in potassium status were
mapped under GIS environment (Fig 4). Potassium
content in all sub surface soils sample was low. It
could be due to high leaching losses of native and
applied potassium (Ramesh et al., 2003). It was
observed that 14046 ha (9.5%) were low in available
potassium and 133806 ha (90.5 %) were medium in
available potassium.
Based on soil analysis and nutrient maps it
was observed that the fertility status of rice growing
soils of Nalgonda district was low. The organic carbon
and available nitrogen in rice growing soils was low
and available phosphorus and potassium was low to
medium.
FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT
Fig 1. Variations in organic carbon (g kg-1) content inNalgonda district Fig 2. Spatial distribution of available nitrogen (kg ha-1)
in Nalgonda district
Fig 3. Spatial distribution of available phosphorus(P2O5) in Nalgonda district (kg ha-1)
Fig 4. Spatial distribution of available potassium (K2O)in Nalgonda district (kg ha-1)
SUDHA and JAYA
REFERENCES
Gangopadhyay, S. K., Walia, C. S., Chamuah, G. Sand Baruah, U. 1998. Rice growing soils ofupper Brahmaputra valley of Assam-theircharacteristics and suitability. Journal of theIndian Society of Soil Science. 46(1):103-109.
Madhuvani, P., Bhanu Prasad, V., Seshagiri Rao, Mand Prasuna Rani, P. 2000. Physical, physic-chemical and chemical properties of soilsdeveloped on granite-gnesis and sand stone.The Andhra Agricultural Journal. 47: 301-305.
Nayak, R., Sahu, G. C and Nanda, S. S. K. 2002.Characterization and classification of the soilsof Central Research Station, Bhubaneswar.Agropedology, 12 : 1-8.
Ramesh, K., Bhanu Prasad, V and Seshagiri Rao,
M. 2003. Physical nature and fertility status
of soils of Singarayakonda mandal in prakasam
district of Andhra Pradesh. The AndhraAgricultural Journal. 50:54-57.
Sharma, P. K. 2004. Emerging technologies of remote
sensing and GIS for the development of spatial
infrastructure. Journal of the Indian Society ofSoil Science 52: 384-406.
Suribabu, K., Bhanu Prasad, V., Seshagiri Rao, M
and Prasuna Rani, P. 2002. Nutrient status of
soils of A.Konduru mandal in Krishna district
of Andhra Pradesh. The Andhra AgriculturalJournal. 49: 155-158.
FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT
J.Res. ANGRAU 40(3) 18-20, 2012
Date of Receipt : 02.06.2012 Date of Acceptance : 25.08.2012
ABSTRACT
POPULATION OF MELOIDOGYNE SP AND RHIZOCTONIA SP IN TOMATO INDIFFERENT AGRO CLIMATIC ZONES OF ANDHRA PRADESH
B. VIDYA SAGAR, V. KRISHNA RAO and K. S. VARA PRASADDepartment of Plant Pathology, College of Agriculture, Acharya NG Ranga Agricultural University,
Rajendranagar, Hyderabad-500030
A survey was conducted during 2007- 2008 to study the on the population of root knot and other plantparasitic nematodes and Rhizoctonia sp, in deferent agro climatic zones and results revealed that a high density of171.11/250 cc soil and 94.88/250 cc soil of Meloidogyne and other nematodes, respectively were recorded inSouthern zone, while a low density of 28.08/250 cc soil and 7.49/250 cc soil were recorded in North TelanganaZone. The occurrence of Rhizoctonia was also high in Southern zone with 5.19X 103 cfu/ g soil. The variation in thepopulation of both pathogens in different zones could be due to variation in cropping sequence, interaction andenvironmental factors prevailed in the respective zones.
Tomato (Lycopersicon esculentum Mill.) isan important vegetable crop grown widely throughoutthe world under various agro-climatic conditions andacquired the status of one of the world’s most popularvegetables. Plant parasitic nematodes are estimatedto cause monetary loss to the extent approximately21.0 million rupees in different crops in the country(Jain, et.al, 2007). Among these, root knot nematode,Meloidogyne incognita (Kofoid and White) Chitwood,is worldwide in occurrence in most of the vegetablecrops resulting in crop losses ranging from 15 to 60per cent and monetary loss of about 2204 millionrupees in tomato and has become a major constraintto successful cultivation of tomato in India. Similarly,Rhizoctonia solani Kuhn is also an important soil bornepathogen and is widely distributed in nature. Thepathogen causes several complex symptoms viz.,damping off, root rot and wilt symptoms etc, resultingin about 69.25 per cent yield losses in tomato crop(Mehta and Gupta, 1992). Several workers havereported that the association of root knot nematodewith Rhizoctonia solani aggravates the diseaseincidence resulting in huge yield loss (Chahal andChhabra, 1984, Khan and Reddy, 1993, Kumhar,2001). Further, the populations of the nematode andpathogen complex may vary with agroclimaticsituation. Hence, keeping the above in view, thepresent investigation was carried out to know theeffect of agroclimatic zones on population ofMeloidogyne incognita and Rhizoctonia solani ontomato.
email: [email protected]
MATERIALS AND METHODS
A survey of major tomato growing areas ofAndhra Pradesh was carried out to determine theextent of root knot and root rot disease complex duringkharif, 2009. The soil and root samples werecollected from sufficiently wet field from 10-15random locations, from the rhizhosphere of tomatocrop. Each sample was filled in a polythene bag andtied with a rubber band and labelled immediately.Information pertaining to the locality, crop history etc.,was also obtained along with samples as given inthe data sheet. A composite sample of 250g of soiland 5g of root was used for further studies. The soiland root samples were analysed on the day ofcollection or after keeping for a few days underrefrigerated conditions.
The propagules of fungi were estimated byplating the rhizosphere soil following serial dilutionplate technique. Nematode Population in soil wasestimated by combined gravity sieving and Baermannfunnel technique (Christie and Perry, 1951).
Root samples of known quantity were directlyobserved under stereo binocular microscope forcounting adult females of sedentary nematodes andthe same was processed using blending andBaermann funnel method (Ayoub, 1977) for theextraction of active forms of sedentary as well asmigratory nematodes. The nematodes were identifiedas per the keys provided by Mai and Lyon (1975),Taylor and Sasser (1978) and Jepson, (1987).
POPULATION OF MELOIDOGYNE SP AND RHIZOCTONIA SP IN TOMATO
Tab
le 1
. P
opul
atio
n of
roo
t kn
ot a
nd o
ther
pla
nt p
aras
itic
nem
atod
es a
nd R
hizo
cton
ia s
p. a
ssoc
iate
d w
ith t
omat
o in
diff
eren
t ag
ro c
limat
ic z
ones
A
ndhr
a P
rade
sh
* H
elic
otyl
ench
us s
p., H
oplo
laim
us s
p., P
raty
lenc
hus
sp. R
otyl
ench
ulus
sp.
, Tyl
ench
oryn
chus
sp
Ave
rage
den
sity
(D
) fr
eque
ncy
(F)
and
Per
cent
age
(P)
of n
emat
odes
in 2
50
cc g
soi
l/ 5
g of
roo
t M
eloi
dogy
ne s
p.
Oth
ers*
T
otal
Fre
quen
cy o
f oc
curr
ence
(%
) A
gro
clim
atic
zo
ne
Tot
al
sam
ples
S
oil
Roo
t T
otal
S
oil
Roo
t T
otal
Soi
l R
oot
Gra
nd
Tot
al
Rhi
zoct
onia
pr
opag
ules
cf
u/g
soil
x 10
3 S
oil
Roo
tN
orth
coa
stal
zo
ne
10
D
F
P
67.2
80
.0
45.4
0
34.1
80
.0
71.3
3
101.
3 80
.8
80.0
13.7
40
.0
94.5
14
8.0
47.8
19
5.8
0.0
0 0
God
avar
i zo
ne
4
D
F
P
56.7
5 10
0.0
33.0
4
45.5
10
0.0
85.4
4
102.
2511
5.0
75.0
7.
75
50.0
12
2.75
17
1.75
53
.25
225.
0 0.
0 0
0
Kris
hna
zone
7
D
F
P
20.5
7 57
.14
29.5
0
14.8
5 57
.14
60.4
6
35.4
2 49
.14
71.4
9.
71
42.8
5 58
.85
69.7
1 24
.56
94.2
7 0.
0 0
0
Sou
ther
n zo
ne
34
D
F
P
103.
67
94.1
1 50
.47
67.4
4 94
.11
71.0
7
171.
1110
1.70
10
0.0
27.4
4 61
.76
129.
14
205.
37
94.8
8 30
0.25
5.
19
11.7
6 14
.70
Nor
th
Tel
anga
na
zone
12
D
F
P
22.0
41
.66
64.5
5
6.08
41
.66
81.1
7
28.0
8 12
.08
41.6
6 1.
41
24.9
13
.49
34.0
8 7.
49
41.5
7 1.
76
8.3
8.3
Sou
th
Tel
anga
na
zone
12
D
F
P
69.3
3 74
.99
47.3
5
42.9
1 74
.99
54.0
3
112.
2477
.08
91.6
6 36
.5
74.9
9 11
3.58
14
6.41
79
.41
225.
82
0.0
0 0
Cen
tral
T
elan
gana
zo
ne
10
D
F
P
36.2
60
.0
62.4
1
17.3
60
.0
81.6
0
53.5
21
.8
70
3.9
20
25.7
58
.0
21.2
79
.2
0.0
0 0
Sca
rce
Rai
nfal
l zon
e 9
D
F
P
52.8
8 66
.66
67.1
4
23.3
3 55
.55
80.1
7
76.2
1 25
.88
66.6
6 5.
7 33
.33
31.6
5 78
.76
29.1
10
7.86
0.
0 0
0
To
tal
98
4
28
.6
25
1.5
16
80
.11
48
3.4
81
06
.11
58
9.6
69
12
.08
35
7.6
9
12
69
.77
6
.95
RESULTS AND DISCUSSION
In case of soil samples, highest density ofMeloidogyne (103.67/250cc) was observed inSouthern Zone, and lowest (22.0) in North TelanganaZone (Table 1). The occurrence was noticed in allthe samples in Godavari zone while it was noticed in41.66 % in North Telangana Zone. The percentagepopulation was more (67.14) in Scarce rainfall zoneand less (29.50) in Krishna zone.
In case of root samples, the maximumdensity of Meloidogyne sp. (67.44/5g) was observedin Southern Zone while the frequency and percentagewas high in Godavari zone with 100.0 and 85.44respectively. The density of Meloidogyne sp. wasminimum (6.08) in North Telangana Zone with a lowfrequency (41.66) and percentage (81.17). The totalMeloidogyne sp. population (soil and root) werehighest (171.11) in Southern Zone while lowest inKrishna zone. The other nematodes were also high(129.14) in Southern zone and low (13.49) in NorthTelangana Zone.
The population of Rhizoctonia sp. in soil washighest (5.19x 103) in Southern Zone (Table 1),followed by North Telangana Zone (1.76 x103). TheSouthern Zone recorded highest frequency ofoccurrence of fungus both in soil (11.76) and root(14.70).
In the present study, a high frequency ofRhizoctonia sp. and Meloidogyne sp. was observedin Southern zone. Naidu et al. (2007) and Sudheer etal. (2008) reported a high prevalence of M. incognitain Chittoor, Ananthapur and Kurnool districts. In thepresent investigation the samples from these districtspositively yielded Meloidogyne population.
The variation in the population of bothpathogens in different zones might be due to variationin cropping sequence, interaction and environmentalfactors prevailed in the respective zones.
REFERENCES
Ayoub, S. M. 1977. Plant Nematology, An AgriculturalTraining Aid. Division of Plant IndustryLaboratory Services, Nematology Sacremento.156 pp.
Chahal, P. P. K and Chhabra, K. 1984. Effect ofMeloidogyne incognita and Rhizoctonia solanion the emergence and damping off of tomatoseedlings. Journal of Research of the PunjabAgricultural University. 21 (4): 642-644
Christie, J. R and Perry V. G. 1951. Removingnematodes from the soil. Proceedings ofHelminthological Society of Washington 18;106-108
Jain, R.K., Mathrr, K.N and Singh. R.V. 2007Estimation of losses due to plant parasiticnematodes on different crops in India. IndianJournal of Nematology 37(2): 219-221.
Jain, R.K., Mathur, K.N and Singh R.V. 2007.Estimation of losses due to plant parasiticnematodes on different crops in India. IndianJournal of Nematology. 37(2): 219-221.
Jepson, S. B. 1987. Identification of Root-knotNematodes (Meloidogyne species). CABInternational, Wallingford, UK. 265 pp.
Khan,R.M Reddy and Parvatha, P. 1993.Management of disease complex. In :Nematode interactions. (ed. M.W. Khan)Chapman and Hall, London, pp. 345-365.
Kumhar, K.C. 2001. Studies on integratedmanagement of root rot of tomato caused byRhizoctonia solani Kuhn. PhD thesis. CCSHaryana Agricultural University Hisar- India.
Mai, W. F and Lyon, H. H. 1975. Pictorial key toGenera of Plant Parasitic Nematodes.Comstock Pub. Association, Cornell UniversityPress. 219 pp.
Mehta, N and Gupta, D.C. 1992. Influence of culturemedia on the pathogenic behaviour ofRhizoctonia solani and R. bataticola on varioushost plants. Plant Disease Research. 7:245-247.
Naidu, P., Harinath., Haritha, V and John sudheer,M. 2007.Community analysis of plant parasiticNematodes in Chittoor district of AndhraPradesh. Indian Journal of Nematology. 37:(2) 2007-211.
Sudheer John, M., Kalairasan, P., Senthamarai, M.,Prabhu, S. S., Prasad Rao, G.M.V., Priya, P.,Harinatha Naidu, P., Haritha,V., Reddy Kumar,M and Shailaja Rani. 2008. Diversity andcommunity structure of major plant parasiticnematodes in selected districts of AndhraPradesh, India. Indian Journal of Nematology.38 (1) 68-74.
Taylor, A.L and Sasser, J.N. 1978. Biology,identification and control of root-knotnematodes. NCSU Raleigh. 111pp.
VIDYA SAGAR et al
J.Res. ANGRAU 40(3) 21-24, 2012
Date of Receipt : 10.05.2012 Date of Acceptance : 01.06.2012
AN EMPIRICAL STUDY OF GROUNDNUT IN ANANTHAPUR DISTRICTOF ANDHRA PRADESH
K. SUPRIYA, A.K.GOEL, M. H. V. BHAVE and V. V. NARENDRANATHDepartment of Statistics and Mathematics,
College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad- 500 030
ABSTRACT
Groundnut crop is grown extensively in Ananthapur district of Andhra Pradesh. An attempt was made tostudy the growth of area under cultivation, production and productivity of groundnut for 47 years i.e., 1961 to 2007in the district. The compound growth rates recorded for area, production and productivity were 3.84%, 3.75% and -0.19% respectively. It was also attempted to identify the rainfall patterns and their impact on groundnut crop yield inthe district. A period of 23 years was the most probable cluster for the rainfall pattern in south west monsoon duringkharif season. The average rainfall was estimated as 280 mm for the southwest monsoon. The association betweenrainfall and the crop yields for the most probable cluster was 78% with an average yield of 737 kg ha-1.
email: [email protected]
In India, during the year 2009, groundnutoccupied an area of 6700 thousand hectares withproduction of 7338 thousand tonnes and productivityof 1095 kg ha-1. Andhra Pradesh ranked second nextto Gujarat with a share of 26.31% of area (1763thousand hectares) and 13.04% of production (957thousand tonnes) with a productivity of 543 kg ha-1.
In Andhra Pradesh, Ananthapur districtranked first with 50% of total area and 30% of totalgroundnut production of the state. The present studywas taken up to examine the growth of area undercultivation, production and productivity of groundnutcrop as well as analysis of rainfall patterns and theirimpact on crop yields in the Ananthapur district onthe basis of time series data covering 47 years (1961-2007).
MATERIALS AND METHODS
The crop yield and rainfall data were collectedfrom www.indiastat.com, CMIE and StatisticalAbstracts of Andhra Pradesh State. The shifts in thedata disturb the normal trend of the data and thusthe continuity. These points (years) of discontinuityreferred as ‘knots’ were identified by applying theprocedure of graphical analysis (Kulkarni and Pandit,1988). The growth in the data were measured for theperiods formed with points (years) of discontinuity orknots i.e., 1992 (1961-1991; 1992-2007), instead ofthe overall data.
In the context of agricultural data recorded
over a period of years, the Compound Growth Rate
(CGR) was derived as an exponential equation,
Y = abt
Where, Y = Crop area/production/productivity ,
t = Time (years) and (a, b) = Parameters
The Average Linkage approach proposed by
Kulkarni and Narendranath (2007) was applied to
identify the rainfall patterns. The monthly rainfall data
of 47 years was subjected to cluster analysis to obtain
clusters consisting of years with similar rainfall. The
cluster averages indicated the pattern of rainfall. The
cluster analysis also provided the patterns of rainfall
at different levels. Assured availability of rainfall,
was identified from the cluster, which is relatively
more frequent. The average rainfall corresponding to
this relatively more frequent cluster was regarded as
the most probable “Multivariate Estimate of Rainfall”.
Further, Cluster analysis approach was also
applied to the time series data on crop yield to classify
the years into different clusters representing low yield
years, high yield years and unusual/abnormal yield
years. In the present study the relation of rainfall
pattern with the groundnut yields was quantified by
applying the approach of Kulkarni et al., (2004).
RESULTS AND DISCUSSION
The study indicated that there was aconsiderable increase in the average area ofgroundnut during 1992 to 2007 over the earlier years(Table 1). These groups (1961-91; 1992-2007) werebased on the year of discontinuity or knot. Theincrease recorded was about 90 per cent.
The production data indicated a discontinuityfrom the normal trend during the year 1995. Due tothis point of discontinuity, the data were grouped intotwo sub-periods. The growth rate was thereforemeasured separately within the periods formed bythe year of discontinuity.
Table 1. Growth rates for area, production and productivity of groundnut (1961-2007)
Area (ha)
Period Year Average CV (%) CGR (%)
1 1961-1991 414384 49.62 4.61
2 1992-2007 786513 10.54 1.51
Overall 1961-2007 538427 48.45 3.84
Production (tonnes)
Period Year Average CV (%) CGR (%)
1 1961-1994 310219 68.73 6.17
2 1995-2007 473715 52.66 -3.71
Overall 1961-2007 351962 65.82 3.75
Productivity (kg ha-1)
Period Year Average CV (%) CGR (%)
1 1961-1994 709 28.24 1.19
2 1995-2007 683 56.17 -7.74
Overall 1961-2007 702 36.96 -0.19
As evident by the data, the groundnut croprecorded a considerable increase in the averageproduction during the second period, 1995 to 2007(4,73,715 tonnes) over the first period (3,10,219tonnes). The production data of groundnut recordeda considerable growth of 6.17% during the first periodas compared to a negative growth (-3.71%) in thesecond period. Though, the production level during
the second period was at higher level, the rate ofgrowth, however had exhibited a declining trend ofgrowth. The increase in average production per year(52.7%) was due to increase in area under groundnutcultivation (89.8%). Ninety percent increase in areawith an average increase of 53% in production wasan indicator of loss in productivity per unit area. Singhand Sirohi (1974), Reddy (1978), Dandekar (1980),Mathur (2005), Upadhaya and Saxena (2009) alsoused similar approach to study the compound growthrates for various crops.
The year 1995 was identified as the year ofdiscontinuity in the productivity data. Hence, thegrowth rate was measured separately within the two
periods 1961 to 1994 and 1995 to 2007. The averageproductivity during the first period was 709 kg ha-1
and during the second period, it was 683 kg ha-1. Areduction of 3.61% in the average level of productivitywas recorded during the second period. Betweenthese two periods, the average level of productivityin the first period was relatively more consistent with28.24% coefficient of variation (cv) as compared to
SUPRIYA et al
cv of 56.17% during the second period. It wasobserved that the growth in productivity reduced from1.19% during the first period to -7.74% during thesecond period, indicating the loss in level ofproductivity over the years.Analysis of Rainfall and its Association with CropYields
The cluster analysis resulted in identifyingclusters with years of similar rainfall. The year wiserainfall of Ananthapur was classified into 7 clusters(Table 2). Among these, Clusters 3, 4, 5, 6 and 7consisting of only one to two years. The characteristicof these clusters was unusual rainfall in all the fourmonths of Kharif season(jun-sep). Cluster 2 ofrainfall patterns was formed with highest number ofyears i.e., twenty-three years, which accounts for 0.49probability of occurrence. Hence, it was regarded asmost probable ‘representative’ cluster for studyingthe pattern of rainfall. The rainfall pattern of
Table 2. Association between rainfall pattern and groundnut yield (1961-2007)
Levels of Crop Yield (Kg ha-1)
Rainfall Pattern
Rainfall
(June-
Sept)
mm
Cluster 1 Cluster 2 Cluster 3
Cluster 1 (18) 356
337 (4) 737 (9) 1147 (5)
Cluster 2 (23) 280
359 (3) 737 (18) 1136 (2)
Cluster 3 (1)
231 - 967 (1) -
Cluster 4 (1)
397 444 (1) - -
Cluster 5 (1) 251 - 689 (1) -
Cluster 6 (2) 670 324 (1) 560 (1) -
Cluster 7 (1) 559 - 680 (1) -
Average (kg/ ha-1) 355 (9) 735 (31) 1143 (7)
(Figures in parenthesis are the number of common years corresponding to thecombination of crop yield and rainfall pattern cluster)
cluster-1, consisting of eighteen years, had 0.38probability of occurrence. It was regarded as the nextbest ‘representative’ pattern of rainfall. During the fourmonths (June-Sep) of kharif season, the
characteristic of rainfall pattern was highest rainfallin September (about135 mm) and lowest in June(about 55 mm).
The 47 years of groundnut yield data (Fig.1)represented 3 different levels of average yields incluster 1, 2 and 3. Cluster 1 represented ‘low yield’years with average yield of 355 kg ha-1 based on nineyears. Cluster 2, which was the largest cluster withthirty-one years, represented average yield of 735kg ha-1; where as cluster 3 was formed with onlyseven years with a very high average yield of 1143kg ha-1. Thus, the average yield of 735 kg ha-1 of thelargest cluster i.e., cluster-2 represented the potentialyield in Ananthapur district.
The Two-way association table (Table 2)indicated the twenty three years in the cluster ofrepresentative rainfall pattern (cluster 2) wasrepresented by three years in cluster 1, eighteen
years in cluster 2 and two years in cluster 3 of thecrop yields. This clearly indicated that the averageproductivity level of cluster 2 (i.e.737 kg ha-1, basedon eighteen years) could be regarded as most
AN EMPIRICAL STUDY OF GROUNDNUT IN ANANTHAPUR DISTRICT
‘assured’ yield resulting out of the representativerainfall pattern.
Similarly, the association between the nextrainfall pattern (cluster 1) and the crop yields indicatedthat this rainfall pattern also contributed to a majorextent, in influencing the crop yields of cluster 2having nine years common with the rainfall pattern.The remaining rainfall patterns have however no major
SUPRIYA et al
influence on the crop yields, as seen from the numberof common years in the respective clustercombinations. The results of the association betweenrainfall patterns and the groundnut crop yields under‘normal’ i.e., representative rainfall pattern of cluster2 revealed the potential of 737 kg ha-1 with theassociation of 78 per cent crop yields in theAnanthapur district.
REFERENCES
Dandekar, V. M. 1980. Introduction to seminar ondatabase and methodology for the study ofgrowth rates in agriculture. Indian Journal ofAgricultural Economics. 35: 1-12.
Kulkarni, B.S and Pandit, S.N.N. 1988. A discretestep in the technology trend for sorghum yieldin Parbhani, India. Agriculture and ForestMeteorology. 42: 157-165.
Kulkarni, B.S., Sreenivasa Rao, T and Krishna Kanth
G. 2004. A study on association of combined
effect of rainfalls on crop yields. Journal of
the Indian Society of Agricultural Statistics.
58: 344-351.
Kulkarni, B.S and Narendranath, V.V. 2007. A
multivariate approach for studying the rainfall
pattern. Journal of Research ANGRAU. 35:
39-45.
Mathur, K. N. 2005. Trend analysis of area, production
and productivity of rice in India. Journal of the
Indian Society of Agriculture Statistics. 59: 39.
Reddy, V. M. 1978. Growth Rates. Economic and
Political Weekly. 132: 806-812.
Singh, C. B and Sirohi, A. S. 1974. Disparities in
agricultural growth and equality in India. Indian
Journal of Agricultural Economics. 29: 234-
247.
Upadhaya, S and Sexana, R. C. 2009. Trends in area,
production and productivity of rainfed cereal
crops in central Rajasthan. Current Agriculture.
33(1): 99-106.
J.Res. ANGRAU 40(3) 25-28, 2012
Date of Receipt : 22.11.2010 Date of Acceptance : 09.08.2012
email: [email protected]
Farmers buy agricultural inputs in theirlocality more often from private traders like inputretailers than government agencies (Mitra, 1999). Inspite of the business attitude, the input retailersprovide various services to the farmers (Jana, 2005).Agro input retailers are the most visible person ofagricultural extension at the grass root with their easyaccess to the farmers. Their round the clock servicesand contributions in agriculture development has beenrelatively less recognized and cultured till date. Astudy was undertaken to understand the sales aswell as services provided by different categories ofthe farm input retailers towards development of thefarming community.
MATERIALS AND METHODS
The present study was conducted in sixblocks of South 24 Parganas district of West Bengal,selected through multistage random sampling fromeighteen districts of the State during 2009. The farminput retailers who were dealing with pesticide alongwith fertilizers, seed and farm implements wereconsidered in this study and the sample was drawnfrom pesticide retailers specifically so as to getinformation regarding different categories of inputretailers in best possible way. Probability proportional
STUDY OF SALES AND SERVICES PROVIDED BY THE FARM INPUTRETAILERS OF WEST BENGAL
A. DAS and D. BASUState Agricultural Management and Extension Training Institute (SAMETI),
R. K. Mission Ashrama, Narendrapur, West Bengal-700 103
ABSTRACT
A survey of 118 farm input retailers selected from six blocks of South 24 Parganas district of West Bengalwas conducted in 2009 to study type the sales and services of agri-input retailers in promotion of agriculturalextension and development. It was observed that all the respondents sold pesticides like insecticides, fungicides,nematicides and bactericides, and fertilizers. Eighty five per cent of the amount of sale value emerged from chemicalfertilizer, chemical pesticide and seed, and the rest were contributed by other inputs. In respect of total sale value theretailers were categorized into three groups; retailers with high sale (RHS), retailers with medium sale (RMS) andretailers with low sale (RLS). Twenty six customer services offered by the retailers were ranked according to theirperformance and priority. Some of the important services which were prioritized by all the categories of retailerswere, ‘taking care all types of farmers in technology dissemination irrespective of caste and class’, displayingmagazines, booklets, leaflets, posters etc. for farmers’ awareness’, ‘selling various agricultural products in rightprice’, ‘promoting judicious use of agricultural inputs’, ‘supplying right inputs and information at right time’ etc.
to size sampling technique was followed to selectsample from the pesticide retailers from each block.One hundred eighteen retailers from the total pesticideretailers of 233 from the selected blocks wereconsidered as final sample. The sample retailers werecategorized as retailers with high sale (RHS), retailerswith medium sale (RMS) and retailers with low sale(RLS) on the basis of total sale value (mean splitwith standard deviation). The respondents were askedto respond to services provided by them in a 4 pointcontinuum of ‘always’, ‘sometimes’, ‘seldom’ and‘never’ with weights 4, 3, 2, 1 respectively. The datawere collected through personal interview.Spearman’s rank correlation coefficient was used tofind out the association among the three types ofretailers in respect of their services.
RESULTS AND DISCUSSION
Agricultural inputs sold by the farm input retailer:Pattern of sales of agricultural inputs followed by allcategories of input retailers was similar. So, thepooled data were taken to study the sales providedby all categories of farm input retailers. There arefive kinds of agricultural inputs viz. pesticide (chemicalinsecticide, chemical fungicide, bio-insecticide andbio-fungicide, herbicide, rodenticide, nematicide and
bactericide), plant growth hormone, seed, fertilizer(chemical fertilizer, micronutrient and bio-fertilizer),and agricultural implement and machinery (paddythresher, knap-sack sprayer etc.) which are usuallysold by the farm input retailers. It has been observedthat, all 118 respondents (100 per cent) sold pesticidelike insecticide, fungicide, nematicide, bactericideand fertilizer. Ninety nine respondents (83.9 per cent)sold rodenticide and 72 respondents (61.0 per cent)sold herbicide. Fifty eight respondents (49.2 per cent)sold seed especially paddy seed of improved andhigh yielding varieties and vegetable seed in smallquantities. Ninety four respondents (79.7 per cent)sold plant growth hormone. Study also reveals that85 respondents (72.0 per cent) sold micronutrient,70 respondents (59.2 per cent) sold bio-fertilizer and60 respondents (50.8 per cent) sold bio-pesticide.Farm implement and machinery especially paddythresher and sprayer were sold by 61 respondents(51.7 per cent) in the study area. The present findingsregarding distribution of agricultural inputs sold bythe farm input retailer are in agreement with theobservations of Mitra (1999) and Jana (2005).
Share of various agricultural inputs in sale valueof the farm input retailer: The share of fertilizer wasthe highest (50.91 per cent) in terms of sale valuefollowed by pesticide (19.78 per cent) and seed (14.04per cent) in the study area. The contributions fromother inputs (bio-fertilizer, bio-pesticide, micronutrientand growth hormone, farm implement, farm machineryand plant protection equipment) were only 15 per cent.
Services provided by the farm input retailer: Thethree categories of retailers, RHS, RMS and RLS,identified twenty six services provided by them.Those services were ranked according to the priorityas perceived by the retailers. RHS ranked ‘Displayingmagazines, leaflets, booklets, posters etc. forfarmers’ awareness’ and ‘taking care all types offarmers in technology dissemination irrespective ofcaste and class’; ‘providing idea about plant protectionmeasures’ and ‘promoting judicious use of agriculturalinputs’; and, supplying right inputs at right time’ and‘selling various agricultural products in right price’ asfirst, second and third position respectively. BothRMS and RLS ranked ‘taking care all types of farmersin technology dissemination irrespective of caste and
class’, and ‘displaying magazines, leaflets, booklets,posters etc. for farmers’ awareness’ as first andsecond position respectively. ‘Promoting judicioususe of agricultural inputs’ was ranked as third positionby RMS whereas, RLS prioritized ‘selling variousagricultural products in right price’ as third position.In general, the services which were more or lessprioritized by all the categories of the retailers were‘taking care all types of farmers in technologydissemination irrespective of caste and class’,‘displaying magazines, booklets, posters etc. forfarmers’ awareness’, ‘selling various agriculturalproducts in right price’, ‘promoting judicious use ofagricultural inputs’, ‘providing idea about plantprotection measures’, ‘supplying right inputs at righttime’, ‘organizing farmers’ training programme onbehalf of agricultural department/company’ etc.(Table 1). Services like ‘paying active roles to carryout various activities of farmers’ organization’ and‘highlighting the advantages of organic farming’ wereprioritized by RHS possibly due to the fact that suchtype of retailers had enough fund and manpower tohelp the farmers’ organization effectively, and thetechnical persons who worked under them couldpromote and made the farmers aware about the
advantage of farming using organic manures and bio-
pesticides. Though there are some differences in
placing the priority on various services, a strong
significant association was observed among RHS and
RMS and, RMS and RLS. Significant association was
not observed between RHS and RLS indicating nature
and extent of services provided by RHS and RLS
were independent . Though not categorized, various
services of the input retailers were reported by many
researchers. (Jana, 2005; Parikh et al., 2007)
Therefore, it may be concluded that the farm
input retailers along with sales of various agricultural
inputs provides various services to the farmers
facilitating the farm production process both directly
and indirectly through their multidimensional services.
In order to undertake extension intervention and
developmental strategies retailers, particularly
retailers with low investment should be addressed
separately. However, this finding has far reaching
implications beyond the study area.
DAS and BASU
Services Provided
RHS RMS RLS
Mean score
Rank Mean score
Rank Mean score
Rank
Displaying magazines, leaflets, booklets,
posters etc. for farmers’ awareness
3.90 1.5 3.91 2 3.9 2
Taking care all types of farmers in
technology dissemination irrespective of
caste and class
3.90 1.5 4.00 1 4.00 1
Providing valuable suggestions to solve
different problems of cultivation
3.41 4.5 3.23 9.5 2.90 12
Selling various agril. products in right price 3.65 3.5 3.71 4 3.81 3
Providing idea about plant protection
measures
3.82 2.5 3.34 8.5 3.44 7
Organizing farmers’ training programme on
behalf of agril. department/company
2.73 9 3.34 8.5 3.50 6
Helping in linking the farmers with the
extension agencies (public and private) for
demonstration and other extension work
2.44 12 3.00 11 3.32 8
Conveying likes and dislikes of the farmers
to the wholesalers and producers and
bridges the gap between them
2.20 14 2.64 15 2.98 11
Promoting judicious use of agril. inputs 3.82 2.5 3.84 3 3.66 4
Selling products in credit specially when
farmers are affected by natural calamities
2.31 13.5 2.44 17 2.40 17
Providing credit at the time of selling inputs 2.31 13.5 3.14 10 1.88 22
Providing right information at right time 2.53 11 3.53 6 3.64 5
Supplying right inputs at right time 3.65 3.5 3.63 5 3.18 9
Giving referral services to the farmers 2.64 10 2.92 12 3.10 10
Convincing farmers about the importance of
soil testing
2.80 8 2.68 14 2.00 21
Providing guarantee for the inputs sold by
him to the farmers and assures the risk for
bad quality
1.13 15 1.93 22 2.33 18
STUDY OF SALES AND SERVICES PROVIDED BY THE FARM INPUT
Table 1. Services provided by the farm input retailer
Provi
disse
produ
Provi
which
Creat
about
Verify
Payin
activi
Provi
repai
Highl
Provi
farme
Rank
RHS = Retailers with high sale; RMS = Retailers with medium sale;
RLS = Retailers with low sale
** Significant at 0.01 level of significance; N.S.= Non-significant
REFERENCES
Jana, H. 2005. Agricultural input retailers and theirrole in extension. Ph. D. Thesis (Unpublished)submitted to Dept. of Agricultural Extension,Bidhan Chandra Krishi Viswavidyalaya, Nadia,West Bengal.
Mitra, S. 1999. Study on pesticide market asperceived by retailers. M.Sc. (Agri.) Thesis
(Unpublished) submitted to Dept. of AgriculturalExtension, Bidhan Chandra KrishiViswavidyalaya, Nadia, W. Bengal.
Parikh, T. S., Berkeley, U. C., Patel, N andSchwartzman, Y. 2007. A survey of informationsystems reaching small producers in Globalagricultural value chains. Retrieved on 15.2.09from http://www.stanford.edu /~neilp/pubs/ictd2007.pdf.
DAS and BASU
ABSTRACT
e-Sagu is an IT based personalized agro-advisory system commissioned in Warangal district of AndhraPradesh in kharif 2004. The study was under taken to find out benefits of this project. Exploratory research designwas adopted for conducting the study. The most important services that was accessible to majority of farmers werepest/disease warning and control and fertilizer management. Marketing information was not accessible to all thefarmers. Pest/Disease warning and control service was most utilized by 64 per cent farmers and fertilizer managementservice was utilized by 28 per cent farmers. Majority of farmers had low(40%) and very low (38%) level of satisfaction.
J.Res. ANGRAU 40(3) 29-32, 2012
CRITICAL ANALYSIS OF e-SAGU- AN INFORMATION AND COMMUNICATIONTECHNOLOGY PROJECT IN ANDHRA PRADESH
P. GANESH KUMAR, R. RATNAKAR and P. GIDDA REDDYExtension Education Institute, College of Agriculture
Rajendranagar, Hyderabad- 500030
The Information and CommunicationTechnology (ICT) provides the flexibility in providinginformation on various modes of farming practicesfor all the crops, specific commodities and real timeprice information and all other information related totechnological advances. Thus the ICT play anincreasingly important role in linking the research-extension-market continuum towards developingprofessional competencies and entrepreneurialcapabilit ies among specialists and farmingcommunities. There is huge potential to harnessICT for agricultural development. Only a few isolatedprojects have been initiated in different parts of India(Meera, 2002). One of the prerequisites of harnessingICTs in the field of agriculture development is to buildstrong interfaces between the farming communityand ICT. e-Sagu (Sagu means cultivation in Telugulanguage) is a tool for IT based personalized agro-advisory system commissioned in Warangal districtof Andhra Pradesh state in India by IIIT in kharif 2004.It aims to improve farm productivity by delivering highquality personalized (farm specific) agro expert advicein a timely manner to each farm at the farmers’ doorsteps. How far the e-Sagu project is successful inproviding the information services to the farmersbecomes an important issue to be explored. Thestudy was therefore undertaken to analyze thebenefits accrued to the farmers of this project area.
MATERIALS AND METHODS
Exploratory research design was adopted forconducting the study. The study was conducted in
Warangal district of Andhra Pradesh. The district wasselected because e-Sagu project is operating in thedistrict for the last 3 years. Two villages were selectedrandomly for the study. The respondents who hadenrolled their names to avail the services of e-Saguwere called “users” for the purpose of study. Twentyfive farmers from each village were selected. A totalof 50 users constituted the sample for the study.
The perceived benefit was measured in termsof their access to services and their utility and overallsatisfaction of the farmers towards this initiative. Theaccess was measured based on the scheduledeveloped on six parameters for e-Sagu. Therespondents were asked to mention their access toservices based on the five-point scale developed asalways, most often, often, less often, and not at alland the scores assigned were 4, 3, 2, 1 and 0respectively.
The utility was measured based on theschedule developed for measuring access on threepoint continuum viz., mostly utilized, utilized and notat all utilized and the scores assigned were 2, 1, and0 respectively.
The farmers’ satisfaction towards servicesof ICT project was measured following five pointcontinuum with the scores of 0, 1, 2, 3 and 4 forstrongly disagree, disagree, undecided, agree andstrongly agree respectively. An index was developedfor all the above three parameters as the ratio ofscore obtained to the maximum score expressed inpercentage.
email: [email protected]
Date of Receipt : 29.02.2012 Date of Acceptance : 01.06.2012
GANESH et al
In case of other dimensions, some open endedquestions were posed to the farmers, to get deeperinsight into the situations prevailed and responseswere sought. Besides this, the results of this initiativeare examined by non- participant observation and bydirect questioning of the farmers in the project area.
RESULTS AND DISCUSSION
In case of e-sagu, the advice is provided on a regularbasis (typically once in a week) from sowing toharvesting with an aim to reduce the cost ofcultivation and increase the farm productivity as wellas quality of agri-commodities. In e-Sagu, rather thanvisiting the crop in person, the agricultural scientistdelivers the expert advice by getting the crop statusin the form of digital photographs and otherinformation. The farmers are the end users of thesystem.
The operation of e-Sagu is as follows, a teamof agricultural experts work at the e-Sagu (main lab)supported by agricultural information system. Onee-Sagu local centre (few computers and computeroperator) is established for a group of about tenvillages. Educated and experienced farmers (who arefrom the villages) work as coordinators. Dependingon the crop, the coordinator is assigned with fixednumber of farms. The coordinator collects theregistration details of the farms under line, includingsoil data, water resources and capital availability andsends the information to main e-Sagu system.Everyday, the coordinator visits a fixed number offarms (10-15) and takes 4 to 5 photographs for eachfarm. A CD is prepared with the photographs andthen information is transported to the main systemby a regular courier service. The agricultural expertswith diverse back ground (Entomology, Pathology,Agronomy, Soil Science and other subjects) at thee-Sagu lab analyse the crop situation with respect tosoil, weather and other agronomic practices andprepare a farm specific advice. At the local e-Sagucentre, the advice is down loaded electronicallythrough internet. The coordinator collects the adviceprint outs and delivers them to the concerned farmer.In this way each farm gets the proactive advice atregular intervals starting from pre sowing operationsto post-harvest precautions.
The project focuses on the followingservices. 1) pest/disease warning and control 2)
fertilizer use in terms of amount and timing 3) choiceof crops based on soil and other information 4)scheduling of crop activities 5) weather informationand 6) marketing information.
Pest/disease warning and control was themost important service that was accessible tomajority (90%) of the farmers and ranked first(Table1).Fertilizer use and management was ranked second(88%) in terms of accessibility. scheduling of cropactivities (44%) was ranked third in terms ofaccessibility. Choice of crops based on soil and otherinformation service was accessible to 38 per cent,but it was less often. Weather information was alsoless often accessible to 20 per cent farmers.Marketing information was not at all accessible tocent per cent farmers, as it was not covered by projectstaff till now.
A glance at the results, indicate that majorityof the farmers having accessibility to pest/diseasemanagement and fertilize management. This mightbe because of project functionaries may be focusingon these two activities, because they were importantto reduce cost of cultivation, minimize crop loss andincrease profitability. For the remaining services, dueconsideration was not given by project.
Pest/disease warning and control servicewas utilized by 64 per cent farmers(mostly +occasionally). Remaining 36 per cent did not utilizeany such service. Fertilizer management service wasutilized by 28 per cent farmers, and this service wasnot utilized by 72 per cent. Weather information wasutilized by 16 per cent, scheduling of crop activitiesby 10 per cent and remaining services were not at allutilised by the farmers. This is due to the fact thatthe frequency of providing remaining services wasoccasional by the functionaries.
Only 24 per cent of the farmers opined that e-Saguwas contributing in decision making of farm activitieswith regard to pest/disease management and fertilizermanagement.
With the timely information on pest/diseaseoccurrence they were able to identify the problem inearly stages and could manage the pest/disease withsuitable plant protection measures. Farmers were ableto apply recommended dose of fertilizers, whichhelped in reduced cost of cultivation.
Farmers felt that services offered at kiosk, takes 24-36 hrs for advices and they were spending Rs. 200-300/- per season for getting e-Sagu services.70 percent of the farmers opined that they were not in aposition to afford the services.
Farmers felt that the project should provideservices related to input supply, credit facilities,marketing services, weather forecasting, training onnew technologies and package of practices on cropsgrown in that area.
Overall satisfaction of farmers towards services ofe-Sagu was rated as low by 40 per cent and as verylow by another 38 per cent.No farmer stated the overall satisfaction. Twelve per cent stated thesatisfaction to be medium and ten per cent as high.Majority farmers had low level of satisfaction withrespect to the technical information, coordinatorscompetence and timeliness of services.
For more effective selection of coordinatorsopinion leadership can be identified from personsthrough socio metric technique. This techniqueensures wide acceptability of coordinators byvillagers.
In the study area some farmers noticedreduction in cost of cultivation, with reduction in costof pesticides and fertilizers in cotton crop. This shows
early identification of pest and diseases wouldminimises input cost in crops like cotton and chillies.Hence conducting diagnostic visits by the experts inthe project area would improve the knowledge offarmers and gross root extension workers(coordinators) which results in reduced cost ofcultivation.
e-Sagu extension system should be gearedthrough regular assessment of needs of farmers,improved participation of farmers in e-Sagu extensionmechanism. Project officials should conduct massivecampaigns in the service villages through extensionworkers about the utility of ICT led extensionservices.
Creating awareness is another importantfactor in the successful operation. The servicesoperators should reach out to every village andexplain the benefits of availing services through theinformation kiosk.
Successful implementation of any program / projectwould depend on human resources. They act asinterface between ICT tools and farming community.By giving incentives to the grass root level extensionworkers and extension personnel by fixing targets,access to information services could be improved.
Level of access (in %) S. No.
Service
Farmers having
access (in %)
Always Most often Often Less
often Not at
all Mean score
Rank
1. Pest/disease warning and control
90 64 10 16 - 10 3.58 I
2. Fertiliser use -interms of amount and timing
88 4 8 36 40 12 1.7 II
3. Choice of crops based on soil and other information
38 - - - 38 62 0.41 IV
4. Scheduling of crop activities
44 - - - 44 56 0.44 III
5. Weather information
20 - - - 20 80 0.4 V
6. Marketing information
0 - - - - 100 0 VI
Table 1. Access to various services of e-sagu
CRITICAL ANALYSIS OF e-SAGU- AN INFORMATION AND COMMUNICATION PROJECT
N=50
Table 2. Utility of various services of e-sagu by farmers N=50
Level of utility (%) S.NO Service Mostly
utilized Occasionally
utilized Not at all utilized
1 Pest/disease warning and control 40 24 36 2 Fertilizer use in terms of amount
and timing 4 24 72
3 Choice of crops based on soil and other information
- - 100
4 Scheduling of crop activities - 10 90
5 Weather information - 16 84
6 Marketing information - - 100
REFERENCES
Meera, N. Shaik. 2002. A Critical Analysis ofInformation Technology in AgriculturalDevelopment: Impact and Implications. Ph.D.Thesis submitted to IARI, New Delhi.
Meera, S N., Jhamtani A and Rao, D.U.M. 2004.Information and Communication Technologyon Agricultural Development: A ComparativeAnalysis of Three Projects from India: AgRENNetwork Paper no.135
GANESH et al
J.Res. ANGRAU 40(3) 33- 36, 2012
email: [email protected]
NON-WOVEN DRAPERY LINING WITH ULTRA VIOLET RESISTANCED. HARINI, A. SHARADA DEVI and D. ANITHA
Department of Apparel and Textiles, College of Home Science,Acharya N.G. Ranga Agricultural University, Saifabad, Hyderabad - 500 004
Date of Receipt : 28.06.2011 Date of Acceptance : 28.05.2012
ABSTRACT
To overcome the negative effects of UV radiation in households, a study had been undertaken to formulatea suitable non-woven drapery lining fabric with UV protective finish. Non-woven fabrics were manufactured throughneedle punching technique with different combinations of cotton, virgin polyester and recycled polyester fibres.Inorganic UV blocking agent ZnO nano particles were selected for UV resistance finish. Finishing process wascarried out through pad-dry-cure technique. This study revealed that finished fabrics composed of 50 % cotton-50%recycled polyester, 30 % cotton- 70 % recycled polyester and 30 % cotton- 70 % virgin polyester scored maximumUPF of 50+ and were found more suitable for drapery lining fabrics.
Approximately 10 per cent of Sun’s radiationis the Ultra violet radiation of which only 5 per centreaches the surface of the earth. The gradualdepletion of ozone layer around the earth has alarmedmany environmentalists through out the globe.
Exposure to ultraviolet radiation is a risk factorin for people spending in outdoor activities. Long-term exposure to UV radiation can result in tanning,ageing of skin, photodermatosis, skin reddening, andsunburn, increased risk of skin cancer, eye damageand DNA damage. To combat the problems encountedwith the increased UV radiation, scientists have beensearching for different ways. In textiles UV protectivefinish is a latest technological advancement.
Nature of the fibre influences the UVtransmittance. Natural fibres like cotton, silk and woolhave lower degree UVR absorption than syntheticfibres such as polyester, because they have hasaromatic group in the molecular structure (Hussainand Jahan, 2010). Cotton fabric in a grey stateprovides a higher Ultraviolet Protection Factorbecause of natural pigment pectin and waxes. The
polyester fibres absorb more in the UV-A and UV-Bregions than aliphatic polyamides. Among the naturalfibres, protein fibres such as silk and wool have betterresistance against UV radiation. The other factorsthat influence the UV resistance include weavedensity, cover factor, porosity, weight and thickness.
UV-A rays are the least powerful of UV rays.They penetrate more deeply into the skin andcontribute to premature ageing of the skin and skincancers, when not significantly filtered by theatmosphere. UV-B rays are the most powerful andpotentially harmful form of radiation. It is the mostcommon cause of sunburn, ageing, wrinkling and skincancer. UVB is particularly strong at the equator, athigh elevations or during the summer months.(Hussain and Jahan, 2010).
The protection extended by the textilematerials is denoted by Ultraviolet Protection Factor(UPF). UPF measures both UV-A & UV-B radiationblocked. A fabric with a UPF 15 allows only 1/15th(6.66 per cent) of the UV-radiation to penetrate theskin as compared to uncovered skin. This wasmeasured as follows:
Table 1. UVR rating scheme
UVR Protection Category UV Protection Factor Mean % UVR Transmission
Moderate UPF 10 to 19 10 to 5.1
High UPF 20 to 29 5.0 to 3.4
Very high UPF 30 to 49 3.3 to 2.0
Maximum UPF ? 50 ? 2.0
(Source: Menezes, 2009).
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The fabric construction factors are the mostimportant determinants of UV- radiation. The closerthe weave, the higher the UV radiation protectionbecause the fibres of tightly woven fabrics are closertogether, less UV radiation is able to pass through tothe skin. But later, after the introduction of nonwovens,it was found that these non-woven fabrics have goodUV resistance and flame retardent property than thewoven fabrics. Therefore, nonwovens are replacingthe woven fabrics in the present scenario of hometextiles. (Srinivasan, 2001) Generally curtains are
provided lining material to protect from sunlight and
UV rays. This requires another backing fabric whichalso adds to the weight and cost of the drapery. If a
non-woven lining is applied, it provides light weight
curtain with reduced cost besides providing UV
resistance.
Nano technology is a recent innovation that
covers a wide range of technologies concerned with
structures and procedures on the nanometer scale.Nano technology has a real commercial potential for
the textile industry and considered as latest finishing
technology. Inorganic UV blockers like ZnO, TiO2,
large surface area-to-volume ratio and high surfaceenergy. (Shanmugasundaram,2010)
MATERIAL AND METHODS
An attempt has been made to formulatesuitable non-woven drapery lining fabrics with acombination of cotton, virgin and recycled polyesterand finished with ZnO nano particles. These particlesare advantageous over other inorganic UV blockers,as being chemically stable, environmental friendly,good transparency, better UV-blocking properties,ability to absorb electromagnetic waves, easysynthesis and also commercial availability. Thefinishing process was carried out at differentconcentration levels of 0.2 %, 0.6 % and 1 % usingpad-dry-cure technique. The ZnO nano particles weretaken based on weight of fabric. Material to liquorratio was considered as 1:40. Nano finishing solutionwas prepared by dispersing the soluble starch intowater to make sure of complete exhaustion of nanoparticles in water and maintained in acidic conditionat 60°C. These fabrics were exposed to sunlight for24 hours to assess the efficiency of the finish. Thefabrics are coded as furnished in table 2.
Table 2. Code number for different fabrics
Non-woven Curtain Lining Farbric Control 0.2% UV 0.6% UV 1% UVNano Nano Nano
Finish Finish Finish
50% Cotton + 50% Virgin Polyester A A1
A2
A3
Samples exposed for 24 hrs A4
A5
A6
30% Cotton + 70% Virgin Polyester B B1
B2
B3
Samples exposed for 24 hrs B4
B5
B6
50% Cotton + 50% Recycled Polyester C C1
C2
C3
Samples exposed for 24 hrs C4
C5
C6
30% Cotton + 70% Recycled Polyster D D1
D2
D3
Samples exposed for 24 hrs D4
D5
D6
SiO2 are more stable and prefered than the organicUV blockers as they are non-toxic and chemicallystable under high temperature through nano coatingof the fabrics with UV blocking agents. Nanofinishingis a cost effective technology that provides moreeffective and durable textiles as nano particles have
RESULTS AND DISCUSSION
The fabrics were assessed for theirfunctionality through objective evaluation. The meanweight and tear strength of control and finished fabricsare presented in table 3.
HARINI et al
Table 3. Fabric weight and tear strength of nonwoven fabric
S.No Non-woven Fabrics Exposed samples (24 hrs)
Fabriccode
Fabricweightg/m2
Tearstrength(kgf) kilo-
gram force
Increase%
Fabriccode
Fabricweightg/m2
Tearstrength
kgf)kilogram
force
Increase%
1 A 1.20 64.3 A 1.20 64.3
2 A 1
1.18 66.6 3.5 A4
1.17 67.2 4.5
3 A2
1.15 72.6 12.9 A5
1.14 73.1 13.6
4 A3
1.13 78 21.3 A6
1.13 78.9 22.7
5 B 1.42 66.3 B 1.42 66.3
6 B 1
1.39 70.3 6.0 B 4
1.38 71 7.0
7 B2
1.34 73.3 10.5 B5
1.33 74.1 11.7
8 B3 1.27 78.6 18.5 B6 1.26 79.4 19.7
9 C 1.44 66.6 C 1.44 66.6
10 C 1
1.43 70 5.1 C 4
1.41 71 6.6
11 C 2
1.41 73.6 10.5 C 5
1.40 75 12.6
12 C 3
1.39 76.6 15.0 C 6
1.39 78 17.1
13 D 1.27 72 D 1.27 72
14 D 1
1.18 76.6 6.3 D 4
1.17 76 5.5
15 D 2
1.14 80.3 11.5 D 5
1.14 81.5 13.1
16 D 3
1.11 85.3 18.4 D 6
1.10 86.07 19.5
The weight of non-woven control fabrics orthe GSM ranged from 1.20 in case of sample A to1.44 in case of sample C. Among the finished fabricssample C
1 was heavy and sample A
6 was light weight.
It was observed that all finished fabrics decreased inweight after finishing. It was also obvious that weightloss increased with the increase in the concentrationof the nano ZnO. However, the change in the weightwas not significant statistically.
The tear strength of the fabric is an averageforce required to tear a fabric which was determinedby measuring the work done in tearing a fabric at afixed distance. It is an indication of the efficiency offabrics against UV protection (Booth, 1983).
The tear strength of the control samplesranged from 64.3 kgf in case of sample A and 72 kgfin case of sample D. All treated samples increasedin tear strength. The increase in tear strength was
progressive with increase in concentration of the
finish. At 0.2 per cent level concentration, sample D1
(6.3 per cent) showed maximum increase; at 0.6 per
cent, sample A2 showed maximum of 12.9 per cent
and at 1 per cent level, A3 sample showed maximum
increase of 21.3 per cent. It is interesting to note
that all exposed samples increased in tear strength
when compared to unexposed samples.
The increase in tear strength in all treated
samples might be attributed to criss cross layering
of fibre arrays during the formation of web of non-
wovens, the padding and UV finish. This clearly
indicated the efficiency of the finish as exposure to
sunlight would always lower the strength of textile as
indicated through many studies. (Katangur, 2009).
The finished fabrics were tested for UV
transmittance through UPF rating.
NON-WOVEN DRAPERY LINING WITH UV RESISTANCE
Table 4. UPF values of finished non-woven fabrics
S.No. Fabric UV (A) % UV (B) % UPF rating
1 A 9.79 0.82 30
2 A 1
13.14 1.42 35
3 A2
14.05 1.44 40
4 A3
13.18 1.54 45
5 B 11.12 0.53 40
6 B 1 11.75 0.53 45
7 B2
9.59 0.42 50+
8 B3
10.78 0.46 50+
9 C 1.93 0.18 40
10 C 1
2.51 0.20 50+
11 C 2
3.00 0.24 50+
12 C 3
4.55 0.43 50+
13 D 2.87 0.26 40
14 D 1
3.50 0.31 50+
15 D 2
3.64 0.32 50+
16 D 3
2.68 0.24 50+
Among the control samples, sample B hadhigher UV –A rating than other samples. Sample B
2
had lower UV-A than other treated samples. SampleC and D had lesser UV-A values than other controlsamples. The UV-A values were increased withincrease in finishing concentrations. There was notmuch difference between the control and finishedsamples of fabrics C and D. But the UV-B value hadprogressively increased after each treatment for allthe control samples. The highest UV-B value wasobserved in sample A
3 and least was found in sample
C. The UV-B was high in sample A than other controlfabrics.
The finished fabrics scored maximum UPFrating 50+ than control fabrics with 30 and 40. Thisindicated that the finished non-woven fabrics weremore suitable for drapery lining to protect from UVrays.
By taking UV-A, UV-B and UPF values alongwith tear strength results into consideration, thecontrol and finished samples of B-30% cotton+ 70%virgin polyester, C-50% cotton+50%recycledpolyester and D-30% cotton+70% recycled polyesterfabrics showed high resistance. However, all finishedfabrics were found to be suitable for durable drapery
lining which could provide necessary UV protectionfor the households.
REFERENCES
Booth, J. E, 1983. Principles of Textile Testing – Anintroduction to physical methods of testingtextile fibers, yarns and fabrics. Butter worth’spublications, London:209
Hussain, A and Jahan, S. 2010. Protective Textiles:Protection against UV Radiation. The IndianTextile Journal, 129 (9):20-32.http://www.arpansa.gov.au/uvindex/daily/ausuvindex.htm
Katangur, 2009. http://www.scribd.com/doc/19791954/Nonwoven-Fabrics
Menezes, E. 2009. UV protective finish. Man-madeTextiles Journal. February; 22(2): 59-62.
Srinivasan, R, 2001. Nonwoven fabric having bothUV stability and flame retardancy, Patentno:US 6,309,97 B1. October 30:1-16
Shanmugasundaram, O. L. 2010. Application ofnanotech in textile finishing: A review, TheIndian Textile Journal, November 121(2):16-22
HARINI et al
Research NoteJ.Res. ANGRAU 40(3) 37-40, 2012
MAPPING OF QTLS FOR GRAIN IRON AND ZINC IN SAMBA MAHSURI AND WILDRICE (Oryza rufipogon) USING RM AND GENE SPECIFIC MARKERS
V. ROJA, N. SARLA, K. MANORAMA and K. RADHIKADepartment of Agricultural Biotechnology, College of Agriculture,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030
email: [email protected]
Date of Receipt : 04.05.2012 Date of Acceptance : 17.06.2012
Rice is one of the cheapest sources of foodenergy and protein. However, it is deficient inessential micronutrients such as iron (Fe) and zinc(Zn). It has limited contents of Fe (2.0-5.0 mg kg-1)and Zn (1.3 -5.0 mg kg-1) after milling (IRRI,1991).The loss of minerals, particularly of Fe, during ricemill ing is high because most of the iron isaccumulated in the aleurone layer. Therefore,micronutrient enrichment i.e. biofortification of foodcrops is the best approach for alleviatingmicronutrient deficiencies. Wild rice (Oryza rufipogon)contributed favorable alleles for Fe, Zn, Mn, Cu, Ca,Mg, P and K. Three QTLs for Zn content wereidentified on chromosomes 5, 8 and 12. The favorableallele for iron content on chromosome 2 wascontributed by Oryza rufipogon. (Garcia-Oliveira etal., 2009).
In this study the parents selected for parentalpolymorphism were Samba Mahsuri (BPT 5204) andO.rufipogon screened with RM primers and genespecific primers. 188 RM markers located on all the12 rice chromosomes and 36 gene specific primersdesigned from genes
YS (Yellow Stripe), FRO (Fe3+-chelatereductase oxidase), ZIP (Zinc regulated transporter /Iron regulated transporter Protein), NRAMP (NaturalResistance - Associated Macrophage Protein),reported to be associated with Fe and Zn homeostasisin plants were used for screening the parents. Totalgenomic DNA was isolated from the leaf samples ofparents using the method described by Zheng et al.,(1991) with some modifications. A mini-preparationprocedure adopted for extraction of total genomicDNA from the leaf samples of 15-30 days old riceseedlings grown in the nurseries of Directorate of RiceResearch, Hyderabad.The purity and concentration
of the isolated genomic DNA samples were estimatedby Nano-drop (Nanodrop Technologies (U.S.A).
PCR was carried out using a programmablethermocycler (Eppendorf, Germany). The PCR plateswere taken and 50ng of template DNA was pipettedout into each of the PCR tubes after proper labelingand kept the PCR plate at 40C. Master mix wasprepared by taking 10 picomolar of each primer (bothforward and reverse primer), 2.5 mM deoxyribonucleotides (dNTPS), Genei 10 X assay buffer(10 mM Tris-Cl (pH 8.3,50 mM KCl,1.5mMMgCl
2,0.01% gelatin) and 1U/ml Taq DNA polymerase
(Bangalore Genei private Limited, Bangalore) andsterile distilled water was added to make up thereaction volume to 10 ml. The quantity of mastermix to be prepared depends on the number of samplesthat are set for PCR. The master mix was centrifugedfor short duration of about 10 sec for thorough mixingof the components and added to each of PCR tubehaving template DNA. The PCR plate was coveredand kept in a thermo cycler for the reaction to takeplace, which was set to thermal profile for Initialdenaturation at 94oC for 5 minutes, Primer annealingat 55oC for 30 seconds and final elongation at 72oCfor 7 minutes. After completion of the PCR, the stripwas stored at 4oC and the amplified products werelater resolved on 3% agarose gel.
Parental polymorphism survey betweenindica cultivar, Samba Mahsuri and wild rice,O.rufipogon was studied using 188 rice micro satellitemarkers chosen based on their distribution throughthe genome and 36 gene specific markers designedfrom the genes to be associated with iron and zinchomeostasis in plants. Among 188 RM primers, 51(27%) were polymorphic and among 36 gene specificmarkers only 3 (8%) were polymorphic. (Table1).
Polymorphic markers identified in the present
investigation were spread over all the chromosomes
except on 5, 10 and 11 used in the present study for
parental polymorphism survey. Out of 54 polymorphic
loci most of the loci showed lighter band in SambaMahsuri and heavier band in O.rufipogon, indicating
that the repeat length of these SSR loci is more in
the wild accession (Fig-1). Polymorphism is the
measure of genetic diversity and varies with the
parents. Earlier reports of mapping yield QTLs using
single accession of O. rufipogon (IRGC105491) asdonor parent and different O. sativa accessions
revealed 60 to 90% polymorphism (Xiao et al., 1998;
Septiningsih et al., 2003; Thomson et al., 2003; Marriet al., 2005). The polymorphism reported in thepresent study is lower compared to the earlier reports.
The lower level of polymorphism may be because ofthe agarose gels used to resolve the bands as minorbase changes may not be detected, or the randomSSR markers used were probably from conservedregions of chromosomes. The polymorphism betweencultivar and O. rufipogon accessions also dependson their genetic similarity (Londo et al., 2006).
Screening of markers for parentalpolymorphism among the parents forms the basisfor tagging and subsequent Marker Assisted Selection(MAS) programmes. The polymorphic RM markerscan be used in studying the segregation pattern inmapping population derived from the cross betweenthese two parents and also identifying the QTLs/genes for high iron and zinc concentration in brownrice.
Table1. List of polymorphic markers between O. rufipogon and Samba Mahsuri (BPT 5204)
S. SSR Chromosome S. SSR ChromosomeNo. markers No. No. markers No.
1 2 3 4 5 6
1 RM 237 1 28 RM 13656 2
2 RM 490 1 29 RM 3688 2
3 RM 1 1 30 RM 3762 2
4 RM 272 1 31 RM 110 2
5 RM 283 1 32 RM 6881 3
6 RM 579 1 33 RM 85 3
ROJA et al
Contd...
S. SSR Chromosome S. SSR ChromosomeNo. markers No. No. markers No.
1 2 3 4 5 6
7 RM 423 1 34 RM 517 3
8 RM 11968 1 35 RM 16 3
9 RM 11952 1 36 NAS 1 3
10 RM 11969 1 37 RM 156 3
11 RM 11962 1 38 RM 448 3
12 RM 11997 1 39 RM 15448 3
13 RM 226 1 40 OSZIP 11 3
14 RM 3515 2 41 ZIPRM5511 4
15 RM 6379 2 42 RM 518 4
16 RM 5651 2 43 RM 470 4
17 RM 5699 2 44 RM 19263 6
18 RM 535 2 45 RM 18 7
19 RM 12486 2 46 RM 21589 7
20 RM 13530 2 47 RM 21596 721 RM 12487 2 48 RM 223 8
22 RM 279 2 49 RM 5485 8
23 RM 13599 2 50 RM 264 8
24 RM 263 2 51 RM 28361 9
25 RM 13514 2 52 RM 205 9
26 RM 12496 2 53 RM 24829 9
27 RM 13630 2 54 RM 235 12
REFERENCES
Garcia-Oliveira, A.L., Lubin, T., Yongcai, F andChuanqing, S. 2009. Genetic Identification ofQuantitative Trait Loci for Contents of MineralNutrients in Rice Grain. Journal of IntegrativePlant Biology. 51 (1): 84–92.
IRRI. 1991. Rice grain marketing and quality issues.Manila: International Rice Research Institute.
Londo, J.P., Chiang, Y., Hung, K., Chiang, T andSchaal, B.A. 2006. Phylogeography of Asianwild rice, Oryza rufipogon, reveals multipleindependent domestications of cultivated rice,Oryza sativa. Proceedings of Nationalof Academy of Sciences. 103: 9578-9583.
Marri, P.R., Sarla, N., Reddy, V.L.N and Siddiq, E.A.2005. Identification and mapping of yield andyield related QTLs from an Indian accessionof Oryza rufipogon. BMC Genetics. 6: 33.
Septiningsih, E.M., Prasetiyono, J., Lubis, E., Tai,
T.H., Tjubaryat, T., Moeljopawiro, S and Mc
Couch, S.R. 2003. Identification of quantitative
trait loci for yield and yield components in an
advanced backcross population derived from
the Oryza sativa variety IR64 and the wild
relative O. rufipogon. Theoretical and Applied
Genetics. 107: 1419-1432.
Thomson, M.J., Tai, T.H., Mc Clung, A.M., Lai,
X.H., Hinga, M.E., Lobos, K.B., Xu, Y.,
Martinez, C.P and Mc Couch, S.R. 2003.
Mapping quantitative trait loci for yield, yield
components and morphological traits in an
advanced backcross population between Oryzarufipogon and the Oryza sativa cultivar
Jefferson .Theoretical and Applied Genetics.
107:479-493.
MAPPING OF QTLS FOR GRAIN IRON AND ZINC IN SAMBA MAHSURI
Xiao, J., Li, J., Grandillo, S., Ahn, S.N., Yuan, L.,Steven, D and Mc Couch, S.R. 1998.Identification of trait-improving quantitative traitloci alleles from a wild rice relative. Oryzarufipogon. Genetics. 150:899-909.
Zheng, K.L., Shen, B and Qian, H. R. 1991. DNApolymorphism generated by arbitrary primedPCR in rice. Rice Genetics Newsletter. 8: 134-136.
ACKNOWLEGEMENTS
We thank our Professor and Head, Dept ofBiotechnology, ANGRAU and the ProjectDirector, DRR for encouragement and facilities.Financial support from ICAR-NTPC Functionalgenomics project on Iron and Zinc in rice grainsto NS is gratefully acknowledged.
ROJA et al
IDENTIFICATION OF NUTRIENT DEFICIENCIES BY CRITICAL NUTRIENTCONCENTRATION (CNC) AND DRIS METHODS AND DETERMINATION OFSUFFICIENCY RANGES OF NUTRIENTS BY DRIS TECHNIQUE IN MAIZE
CH. RAMULU and G. BHUPAL RAJDepartment of Soil Science and Agricultural Chemistry,College of Agriculture,Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500 030
Research NoteJ.Res. ANGRAU 40(3) 41-44, 2012
Maize is an important crop grown in AndhraPradesh in an area of 7.83 lakh hectares with theproduction of 27.61 lakh tonnes (DES, 2010). Thirtyone percent of the total maize area and twenty ninepercent of the total production in this state is locatedin three districts viz. Karimnagar, Nizamabad andWarangal.
Foliar analysis is usually considered to bean important method for monitoring the nutrient statusof the plants and to identify the nutrient deficiencies.Sufficiency range approach and the DRIS techniqueare most common methods for interpretation of leaftissue nutrient status of field crops. The interpretationis done based on comparing the level of nutrient (s)in the index tissue with critical or optimum range ofthe concerned nutrient. In case of maize, the indicatedtissue to be sampled has been identified as ear leaf(leaf opposite to cob at tasseling or silking stage) ofthe plant (Escano et al., 1981 and Walworth et al.,1988).
In the conventional approach, the diagnosisof nutrient deficiencies was done on the basis ofactual tissue concentrations comparing with CriticalNutrient Concentrations (CNC) or Sufficiency Range(SR) of the nutrients. The interpretation of the yieldlimiting nutrients based on this CNC of one nutrientdoes not take into consideration the level of othernutrients. Moreover this conventional approach hasseveral weaknesses. These CNC vary with localclimatic and soil conditions, as well as with cultivartype, age and portion of the plant sampled (Escanoet al., 1981).
Both the methods CNC and DRIS in maizewere compared in the present study.
Date of Receipt : 07.02.2012 Date of Acceptance : 19.03.2012
Index leaves (ear leaves opposite to cob atsilking or tasseling stage) were collected at randomfrom the 150 selected fields covering 50 locations ineach district in 10 mandals covering 3-5 locations ineach mandal for the analysis of nutrients. 15-20 indexleaves at random were collected in each field andcomposite sample was prepared. Leaf samplescollected were immediately washed first with tap waterfollowed by 0.1 N HCl and then followed by repeatedwashings with running tap water. The samples werethen rinsed in distilled water and finally with doubledistilled water. They were first dried under shade andthen in hot air oven at 700C. Oven dried plant sampleswere powdered in a stainless steel grinder to afineness of 40 meshes and stored in butter papercovers. Powdered plant samples were analyzed forN, P, K, Zn, Cu, Fe and Mn following standardprocedures.
Nitrogen content of samples was calculated(AOAC, 1980) by microkjeldhal method.
Phosphorus was estimated in the diacidextract by vanadomolybdate phosphoric yellow colourmethod as described by Jackson (1973) andpotassium was estimated with flame photometermethod (Muhr et al., 1965) and expressed in per cent.
The Zinc content in diacid extract was measuredwith Atomic Absorption Spectrophotometer of modelVarian 240 FS (AOAC, 1980) and expressed in mgkg-1.
There was a total divergence of interpretationof nutrient requirements by conventional CNC andDRIS methods. Based on CNC, the deficiencies ofN were in 110 fields, P in 18 fields, K in no fields andZn in 26 fields, but based on DRIS, the deficiency of
email: [email protected]
N, P, K and Zn were in 74, 52, 45 and 71 fieldsrespectively (Table 1).
While based on CNC, the deficiency of Nwas identified in 26 mandals, P and Zn were deficientin two mandals each and K was adequate in all 30mandals, but based on DRIS, N, P, K and Zn wereyield limiting in 15, 10, 8 and 13 mandalsrespectively.
Based on both CNC and DRIS, the deficiencyof N was identified in all the three districts understudy. P, K and Zn were identified as adequate basedon conventional CNC whereas only P and K wereidentified as yield limiting based on DRIS inNizamabad district. In case of Zn, it was adequatebased on both CNC and DRIS in all the three districtsunder study.
Based on CNC, no deficiency of K wasobserved in all the selected farmers fields under study,but DRIS diagnosed this as yield limiting to an extentof 30 percent. Nitrogen was yield limiting based onDRIS indices, to an extent of 49 per cent, but itsdeficiency was more to an extent of 73 per cent basedon CNC method. Phosphorus was deficient to anextent of 12 percent samples through CNC, but itwas yield limiting to an extent of 35 per cent basedon DRIS. Zinc was identified as a yield limiting in 48per cent samples through DRIS, but its deficiencywas only to an extent of only 17 per cent based onCNC method. These deficiencies of nutrients basedon CNC and DRIS indices are quite divergent.
Majority of the nutrients indicated asdeficient by CNC method in this study, also find a
place among the nutrients diagnosed as limiting by
DRIS indices, but many of the nutrients diagnosed
as limiting by DRIS method do not find place among
the nutrients indicated as deficient by CNC method.
Bhupal Raj (2006), also reported similar findings inmango. Singh and Agarwal (2007), also found that
some extra nutrients were identified as yield limiting
by DRIS other than those found to be deficient by
CNC in rice crop. Similar observations were alsomade by Dharmender (2002) and Ravi (2010), insweet orange and rice crops, respectively in AndhraPradesh. The poor correlation between leaf
composition of individual nutrients and yield was alsoone of the reasons for not getting the correct diagnosisof nutrients by CNC method. The variance ratiobetween the low and high yielding populations fordifferent forms of expressing the nutrients (say fornutrients N and P, five possible forms are N/P, P/N,N*P, N and P) clearly shows that for none of thenutrients tested, the variance ratio for differentnutrients content (from N and P for the nutrients Nand P, in the above examples) is the highest. It isonly for the ratio of these two nutrients and theirproducts, the variance ratio is high. Based on thisalone, such forms of expressions find a place in DRISindices developed. In other words it implies that, theadequacy of any nutrient is to be judged in terms ofits level in relation to that of all other nutrients, whichis the basic philosophy of DRIS method, Walworthand Sumner (1987), in their extensive review on DRISindices, concluded the superiority of the DRISdiagnosis over the conventional Critical NutrientConcentration (CNC) or sufficiency range (SR)method.
Therefore, the diagnosis of nutritionaldisorders by DRIS indices was found superior ascompared to that of CNC method. DRIS not onlydiagnoses the limiting nutrients may be of evenhidden hunger but also predict the nutrients likely tobe deficient in priority wise.
The sufficient ranges of nutrients derived byDRIS technique are given in Table 2. It was foundthat the optimum ranges for N, P, K and Zn were2.27 - 2.72, 0.23 -0.39, 2.43 - 2.87 per cent and 26.6- 66.2 mg kg–1 respectively. It was also determinedthat the nutrients N, P, K and Zn can be said deficient,when the concentration of these nutrients were lessthan 2.27%, 0.23 %, 2.43 %, and 26.62 mg kg–1
respectively. These values derived in this study forN and P were lower / near to the values suggestedby Melsted et al.(1969) and Neubert et al. (1969),respectively, but the derived value of K was higherthan suggested by Melsted et al.(1969), Neubert etal. (1969) and Jones et al. (1990). In case of Zn, thederived value in this study was higher than thatsuggested by Melsted et al. (1969) and Jones et al.(1990), but lower than that by Neubert et al. (1969).
RAMULU and BHUPAL
Table 1. Extent of deficiencies indicated by CNC and DRIS(Total fieds 150, mandals 30 and districts 3)
CNC DRIS Nutrient
No. of fields deficient
No. of mandals deficient
No. of districts
deficient
No. of fields deficient
No. of mandals deficient
No. of districts
deficient N 110 26 3 74 15 3
P 18 2 0 52 10 1
K 0 0 0 45 8 1
Zn 26 2 0 71 13 0
Table 2. Sufficiency ranges of nutrients derived by DRIS technique from nutrientindexing survey of maize in three districts under study
Nutrient Mean S.D Low Optimum High Excessive
N (%) 2.47 0.19 <2.27 2.27-2.72 2.72-2.97 >2.97
P (%) 0.31 0.06 <0.23 0.23-0.39 0.39-0.47 >0.47
K (%) 2.65 0.16 <2.43 2.43-2.87 2.87 -3.01 >3.01
Zn (mg kg–1) 39.56 19.96 <26.62 26.62-66.17 66.17-92.79 >92.79
REFERENCES
AOAC,1980. Official and Tentative Methods ofAnalysis. Association of Official AgriculturalChemistry. Washington DC.
Bhupal Raj, G. 2006. Identification of yield-limitingnutrients in mango through DRIS indices.Communications in Soil Science and PlantAnalysis. 37:1761-1774.
DES, 2010. Statistical Abstracts. Directorate ofEconomics and Statistics. Government ofAndhra Pradesh.
Dharmendar, R. G. 2002. Identification of yield limitingnutrients in sweet orange (Citrus sinensis L.)through DRIS. M. Sc. Thesis. Acharya N GRanga Agricultural University, Hyderabad,India.
Escano, C. R., Jones, C. A and Uehara, 1981.Nutrient diagnosis in corn grown on hydricdystrandepts: Optimum tissue nutrientconcentrations. Soil Science Society ofAmerica Journal. 45: 1135-1139.
Jackson, M. L. 1973. Soil Chemical Analysis,Prentice Hall of India Pvt. Ltd., New Delhi.
Jones, J. B., Eck, H. V and Voss, R. 1990. Plant
analysis as an aid in fertilizing corn and grain
sorghum. Soil testing and plant analysis. 3rd
ed. SSSA Book Ser. 3. SSSA, Madison, WI.
Melsted, S. W., Motto, H. L and Peck, T. R. 1969.Critical plant nutrition composition values
useful in interpreting plant analysis data.
Agronomy Journal.61:17-20.
Muhr, R., Gilbert., Dutta, N and Sankara Subramoney,
H., Dever, R. F., Laley, V. K and Donahue, R.
L. 1965. Soil testing in India.United StatesAgency for International Development Mission
to India, New Delhi.
Neubert, P. W., Wrazidlo, N. P., Vielmeyer I Hundt,
F., Gullmick and Bergman, W. 1969. Tabellen
zur Pflanzenanelze- ersteorientierrende
Ubersicht.Institut fur Pflanzenerah rung Jena,
Berlin.
Ravi, P. 2010. Diagnosis of yield limiting nutrients in
rice (Oryza sativa L.) through DRIS in
Karimnagar district. M. Sc. Thesis. Acharya
N G Ranga Agricultural University, Hyderabad,
India.
IDENTIFICATION OF NUTRIENT DEFICIENCIES BY CRITICAL NUTRIENT
Singh, V. K and Agarwal, H. P. 2007. Developmentof DRIS norms for evaluating N, P, K and Srequirements of rice crop. Journal of the IndianSociety of Soil Science. 55(3): 294-303.
Walworth, J. L.,Woodard, H. J and Sumner, M. E.1988. Generation of corn tissue norms from asmall, high-yield data base. Communicationsin soil science and plant analysis.19(5):563-577.
Walworth, J. L and Sumner, M. E. 1987. Diagnosisand Recommendation Integrated S y s t e m .Advances in Soil Science. 6: 149-188.
RAMULU and BHUPAL
Research NoteJ.Res. ANGRAU 40(3) 45-47, 2012
DEVELOPMENT OF A FUNCTIONAL FOOD FOR DIABETES S. SPANDANA and M. PENCHALARAJU Department of Food Technology, Post Graduate and Research Centre, Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500030.
Date of Receipt : 16.4.2012 Date of Acceptance : 13.06.2012
Over 220 million people worldwide havediabetes. In 2005, an estimated 1.1 million peopledied from diabetes (the actual number is much higherbecause people may live for years with diabetes andtheir cause of death is often recorded as heart diseaseor kidney failure – brought on by their diabetes.)Nearly 80% of diabetes deaths occur in low- andmiddle income countries. About half of diabetesdeaths occur in people under the age of 70 years;55% of diabetes deaths are in women. The WHOprojects that diabetes deaths will double between 2005and 2030 (Taylor, 2010).
Diabetes is being treated with drugs andinsulin with emphasis on dietary modifications.Utilization of the functional properties like thehypoglycemic effect of different foods will be of usein the diets of diabetic patients. Sorghum, soybeanand Ocimum sanctum have been shown to havefunctional and medicinal properties.
Certain varieties of sorghum bran mayaffect critical biological processes that are importantin diabetes and insulin resistance (Farrar et al., 2008).Grain sorghum contains beneficial components thatcould be used as food ingredients or dietarysupplements to manage cholesterol levels in humans(Carr et al., 2005).
Legumes such as soybeans contain complexcarbohydrates, minerals, phytoestrogens, vegetableprotein, soluble fiber, oligosaccharides, particularlythe isoflavones genistein and daidzein that may bebeneficial in the management of diabetes (NSRL,2012).
Diets containing soy protein rich inisoflavones have been shown to improve insulinresistance in ovariectomized cynomolgus monkeysand to reduce insulin levels in healthy postmenopausalwomen (Goodman and Kritz, 2001).
Ocimum sanctum (holy basil) called Tulsi inIndia, is ubiquitous in Hindu tradition. Ocimum is
email: [email protected]
explored as medicinal plant and it occupies anenviable position in the holistic system of Indianmedicine ‘Ayurveda’ which has its root in antiquityand has been practiced for centuries. Ocimum is anerect, herbaceous, much branched, soft hairy, annualwith purple or crimson flowers. Leaves of Ocimumsanctum were found to be rich in Vitamin C, VitaminE and phytochemicals, possessing antioxidantproperties beneficial to health (Prakash and Gupta,2005).The juice of the leaves is given to the childrenfor cold and bronchitis. The leaves are also used forsauces, soups and salads (Sethi, et al., 2004).
Keeping the properties of the abovefunctional foods in controlling diabetes, it was feltthat a food product which could incorporate all threeof them, if developed and accepted, would bebeneficial to diabetic patients. Therefore this studywas designed and conducted in the department ofFood Technology, Post Graduate and ResearchCentre, Acharya N.G. Ranga Agricultural University,Rajendra Nagar, Hyderabad.
For the present investigation, soybean flourand sorghum flour were procured from a localsupermarket in Hyderabad as a single lot. Tulasi(Ocimum sanctum) leaves were procured from theHerbal Garden, Acharya N.G. Ranga AgriculturalUniversity, Rajendra Nagar, Hyderabad. The Ocimumsanctum leaves were washed under running water toremove any adhering particles of dirt. The leaves werethen dried in a hot air oven at low temperatures (50-60oC), powdered, sieved and stored in airtightcontainers in a refrigerator till standardization ofproducts and analysis for various parameters wascarried out at a later date.
Two products namely a baked and anextruded product were standardized incorporatingOcimum sanctum leaf powder at various levels.
Table 1. Different combinations carried out for product development
Numcombi
The two products developed were subjected
to Organoleptic evaluation (appearance, colour,
flavour, texture and overall acceptability) by fifteen
(15) trained taste panel members. A score card with
five point hedonic scale was used for the purpose of
evaluation. After product development trials and
acceptability studies, it was observed that the
extruded product was inferior in all aspects as
compared to the baked product. Whichever
combination of sorghum and soy were used (75:20,
80:12.5) the product became hard with the strands
mingling into a single mass which tend to break easily
into small pieces. Incorporation of maida at 40%
level also did not produce a good product. Hence the
extruded product was excluded and the baked product
was selected for further study.
Dry Ocimum sanctum leaf powder and the
baked product were analyzed for their proximate viz.
moisture, protein, fat, vitamin C, total carotenes, iron,
calcium and zinc, adopting standard procedures.
Since both sorghum and soy have been
analyzed in a number of studies, the table values
have been taken. Soy contained the highest amount
of protein i.e. 43.2 g, followed by sorghum and
ocimum leaf powder (table 2). Ocimum leaf powder
contained 2.98 g of fat and it was high, whereas in
sorghum it was found to be 1.9 g. There was little
amount of fat in defatted soy flour. Vitamin C was
absent in both sorghum and soy whereas it was 81.96
mg in Ocimum leaf powder so also the highest
amount of total carotenes was present in Ocimum
leaf powder (653.6 mg). Higher amount of iron was
present in defatted soy flour (10.4 mg). The calcium
content of defatted soy flour was found to be 240 mg
where as in sorghum it was 25 mg and in Ocimum
leaf powder it was found to be only 1.25 mg. The
foods were also analysed for zinc and it was found
that least quantity was observed in Ocimum leaf
powder (1.34 mg).
The product prepared from the above three
ingredients and selected for experimental procedure
was a baked product – Biscuit. The product was
analysed in the lab for different nutrients.
The moisture content of the biscuit was
5.53g. The protein percentage was 4.5 g. The fat
content was found to be 12.5 g. The vitamin C content
was found to be 0.34/100g. The iron and calcium
content was 0.03 mg and 0.05 mg whereas zinc
content was found to be 0.18 mg. The carbohydrate
content was 69.4g and those of total carotenes
was153.1 mcg. Thus each biscuit weighing 9 grams
contained about 0.4g of protein, 1.1 g of fat 0.03 mg
of vitamin C, 13.7 mg of total carotenes, 0.0027 mg
of iron, 0.0045 mg of calcium and zinc was 0.0162
mg.
Organoleptic evaluation of the biscuit was
carried out by a trained panel and the mean scores
obtained for standard (baked product) and selected
experimental product for various parameters were
presented in Table 3.
SPANDANA and RAJU
Table 2. The nutrient content of dried Ocimum leaf powder, sorghum and defatted soy flour
S. No Nutrient per 100g Sorghum * Defatted soy flour *
Ocimum leaf powder
1.
2.
3.
4.
5.
6.
7.
8.
Moisture (g)
Protein (g)
Fat (g)
Vitamin – C (mg)
Total carotenes (mcg)
Iron (mg)
Calcium (mg)
Zinc(mg)
11.9
10.4
1.9
-
47
4.1
25
1.6
8.1
43.2
1.2
-
426
10.4
240
3.4
11.58
3.5
2.98
81.96
654
0.086
1.25
1.31
* Calculated as per data - Nutritive value of Indian foods ICMR (2002)
Table 3. Mean scores obtained by standard and experimental product to different variables
S.No. Variable Extruded Bakedproduct (Experimental
product)
1. Colour 3.6 + 0.63 3.5 + 0.61
2. Flavour 3.9 + 0.35 4.0 + 0.53
3. Texture 4.0 + 0.53 4.0 + 0.53
4. Taste 4.1 + 0.63 4.0 + 0.59
5. Overall acceptability 3.7 + 0.49 3.9 + 0.35
The differences between standard productand experimental product for all the organolepticscores were meagre (Table 3) indicating that theexperimental product was comparable with thestandard product.
REFERENCES
Carr, T.P., Weller, C.L., Schlegel, V.L., Cuppett,S.L., Guderian, D.M. Jr and Johnson,K.R.2005. Grain sorghum lipid extract reducescholesterol absorption and plasma non-HDLcholesterol concentration in hamsters. Journalof nutrition. (9): 2236-40.
Farrar,J.L., Hartle,D.K., Hargrove, J.L and Greenspan, P. 2008. A novel nutraceuticalproperty of select sorghum (Sorghum bicolor)brans: inhibition of protein glycation.NRL.22(8):1052-6.
Goodman-Gruen, D and Kritz-Silverstein, D. 2001.Usual dietary isoflavour intake in association
with cardio vascular disease risk factors inpostmenopausal women. Journal of Nutrition.131:1202-1206.
National Soybean Research Laboratory(NSRL). SoyBenefits. Retrieved February 16, 2012.
Prakash, P and Gupta, N. (April 2005). Therapeuticuses of Ocimum sanctum Linn (Tulsi) with anote on eugenol and its pharmacologicalactions: A short review. Indian Journal ofPhysiology and Pharmacology 49 (2): 125–131.
Sethi., Jyoti, Sood., Sushma, Seth., Shashi, Talwarand Anjana. 2004. ”Evaluation ofhypoglycemic and antioxidant effectof Ocimum sanctum”. Indian Journal ofClinical Biochemistry. 19(2): 152–155.
Taylor, D.W. 2010.The Burden of Non-CommunicableDiseases in India, Hamilton ON: The CameronInstitute.
DEVELOPMENT OF A FUNCTIONAL FOOD FOR DIABETES
Research NoteJ.Res. ANGRAU 40(3) 48-50, 2012
Date of Receipt : 25.02.2012 Date of Acceptance : 02.04.2012
EVALUATION OF WHEAT (Triticum aestivum L.) GENOTYPES FOR DIFFERENTSOWING DATES UNDER IRRIGATED CONDITIONS
J. VIJAY, K. MADHAVI, A. RAMACHANDRA RAO and A. SAIRAMDepartment of Agronomy, College of Agriculture,
Acharya. N.G Ranga Agricultural University, Rajendranagar, Hyderabad-5000 30
email: [email protected]
Wheat (Triticum aestivum L.) is the secondmost important food crop after rice in India whichcontributes about 35 percent to total food grain basketof our country from about 28.37 million hectares witha production of 80.80 million tonnes and productivityof 2.87 tonnes ha-1 (CMIE, 2010). In order to meetthe projected wheat demand of 109 million tonnesfor the growing population of about 1.3 billion by 2020AD, it is essential to sustain the productivity at muchhigher level. At global level, India ranks as secondlargest wheat producing nation and contributesapproximately 11.9 percent to the world’s wheatproduction from about 12 percent of global areameeting 20% of the total food requirement of the Worldpopulation (USDA, 2010).
The late sown wheat crop gets exposed toboth extremes of temperature; i.e. low temperatureduring early growth period that restricts vegetativephase and high temperature during grain filling andripening phases and this is one of the major causesof poor wheat growth and productivity. Temperaturecannot be manipulated easily under field conditionsbut seeding time can be so adjusted that the variousphysiological stages can meet their specifictemperature requirements during cycles (Tiwari andSingh 1993). The adverse effect of delayed sowingcan be minimized by selecting suitable cultivars asthe magnitude of yield reduction varies with varieties.
A field experiment was conducted during rabi2010 – 2011 on sandy loam soil at AgriculturalResearch Institute (ARI), Rajendranagar, Hyderabad.The experiment was laid out in split plot design andreplicated thrice. Four sowing dates were maintreatments viz., D
1 (15th October), D
2 (1st November),
D3 (15th November) and D4 (1st December) and three
genotypes were the sub treatments viz., V1
(RAJ
4037), V2 (HP 4080) and V
3 (PDW 315). The soil was
low in available nitrogen (195.0 kg ha -1), high inphosphorus (62.5 kg ha -1) and potassium (352.5 kgha -1).
Plant height at harvest was found to beunaffected by different sowing dates. Among thegenotypes PDW 315 was significantly taller than therest. Dry matter production was significantly higherin 15th November sowing compared to 1st Decemberand 15th October sowings but on par with 1st Novembersowing. All the genotypes were on par with each otherin dry matter production. Number of tillers m -2 weresignificantly higher with 15th November sowing over1st December sowing but were comparable with 15th
October and 1st November sowings. All the genotypeswere comparable with each other for tillers at harvest.These results are in conformity with the findings ofTahir et al (2009) and Pandey et al. (2010).
More ears m -2 and longer ear length werewith 15th November sowing compared to 1st Decembersowing but were on par with 1st November and 15th
October sowings. The wheat crop sown on 15th
November had more grains per ear. Test weight wasnumerically high in 15th October sown crop but wascomparable with that of late sowings till 15th
November. The test weight decreased sharply in caseof December 1st sown crop. Regarding the genotypestested, all of them were comparable to each otherwith respect to number of ears m-2 and test weight.The ears of HP 4080 were longer compared to others,especially PDW 315, and had significantly moregrains ear-1.
Among the sowing dates 1st November and15th November sowings recorded comparable grainand straw yields but significantly higher than 15th
October and December 1st sowings. Among the
EVALUATION OF WHEAT (Triticum aestivum L.) GENOTYPES
Tab
le 1
. E
ffect
of
dif
fere
nt
so
win
g d
ate
s a
nd
wh
eat
gen
oty
pes o
n g
row
th p
ara
mete
rs,
yie
ld a
ttri
bu
tes,
harv
est
ind
ex,
gra
in a
nd
str
aw
yie
ld
So
win
g d
ate
s
Pla
nt
heig
ht
(cm
)
Dry
matt
er
(g
pla
nt-1
) T
ille
rs
m2
Ears
m
-2
Ear
len
gth
(c
m)
Gra
ins
ear-
1
Tes
t w
eig
ht
(g)
Gra
in
yie
ld
(k
g h
a1)
Str
aw
yie
ld
(kg
ha
-1)
Harv
est
ind
ex (
%)
D1
(15th
Oct
ober
) 68
26
.3
347
336
7.0
35
47.2
27
30
2940
48
.1
D2
(1st N
ovem
ber)
69
31
.5
355
335
7.0
41
45.5
42
68
4851
46
.8
D3 (1
5th N
ovem
ber)
69
33
.8
362
341
7.5
45
44.4
43
99
5071
46
.5
D4
(1st D
ecem
ber)
66
21
.8
316
283
6.4
39
28.3
21
01
2354
47
.2
SE
m ±
2
.6
2.7
11
9.9
0.2
2.4
3.8
122
203
2.8
CD
at 5
%
NS
5.
3 22
19
.4
0.5
4.8
9.4
299
497
NS
Gen
oty
pe
s
V1
(Raj
403
7)
65
25.5
34
8 32
0 7.
2 35
42
.1
3162
35
94
46.8
V2
(HP
408
0)
66
30.4
34
1 32
1 7.
8 47
39
.6
3404
38
44
47.0
V3 (P
DW
315
) 74
29
.1
345
330
6.0
36
42.5
35
58
3979
47
.2
SE
m ±
2.
4 2.
5 7
12.0
0.
3 2.
2 3.
5 11
1 91
1.
7
CD
at 5
%
4.6
NS
N
S
NS
0.
7 4.
3 N
S
236
229
NS
Inte
racti
on
(D
×V
)
Su
b a
t s
am
e l
ev
el
main
SE
m ±
4.
7 5.
0 14
24
.0
0.7
4.4
7.0
223
182
3.4
CD
at 5
%
NS
N
S
NS
N
S
1.4
8.7
NS
47
2 38
6 N
S
Main
at
sam
e o
r d
iffe
ren
t le
vels
of
su
b
SE
m ±
4.
6 4.
8 18
.2
19.7
0.
5 4.
3 6.
8 21
5 30
6 4.
4
CD
at 5
%
NS
N
S
NS
N
S
1.0
8.4
NS
47
8 71
4 N
S
genotypes PDW 315 and HP 4080 recordedcomparable yields but significantly higher than Raj4037. Singh and Ahmad (1997) expressed that higherpost anthesis temperature contributed to decline yieldattributing parameters and yield of grain and straw.Kulhari et al. (2003), Shah et al. (2006), Pardeshi etal. (2009), Muduli et al. (2010) and Pandey et al. (2010)viewed that grain and straw yields were significantlyhigher with the crop sown on normal (15th November)compared to delayed sowings.
REFERENCES
CMIE, 2010. Centre for monitoring Indian Economy(CMIE). Apple heritage, Mumbai. URL: http://www.CMIE.com/. Mumbai. 96-97.
Kulhari, S.C., Sharma, S.L and Kantwa, S.R. 2003.Effect of Varieties, sowing dates and nitrogenlevels on yield nutrient uptake and quality ofdurum wheat (Triticum deurum DESF). Annalsof Agriculture Research. 24 (2): 332-336.
Muduli, K.C., Kar, A.K., Sahu, K.C., Mishra, B.K.,Swain, S. K and Nayak, R. N. 2010. Influenceof sowing dates of seed crops on seed yieldand quality parameters in wheat cv Sonalika.Environment and Ecology. 28 (1A): 284-287.
Pandey, I.B., Pandey, R.K., Dwivedi, D.K and Singh,R.S. 2010. Phenology, heat unit requirementand yield of wheat (Triticum aestivum) varietiesunder different crop- growing environment.Indian Journal of Agricultural Sciences.80 (2): 136-140.
Pardeshi, H.P., Jadav, K.V and Bagul, R.S. 2009.
Effect of sowing dates and fertilizer levels on
yield attributes and grain yield of wheat
(Triticum aestivum L.). International Journal
of Agricultural Sciences. 5 (1): 148-150.
Shah, W.A., Bakht, J., Tehreen Hullah, Khan, A.W.,
Zubair, M.D and Khakwani, A.A. 2006. Effect
of sowing on the yield and yield components
of different wheat varieties. Journal of
Agronomy. 5 (1): 106-110.
Singh, N.B and Ahmed, Z. 1997. Response of wheat
(Triticum aestivum) varieties to different dates
of sowing. Indian Journal of Agricultural
Sciences. 67 (5): 208-211.
Tahir, M.D., ALI, A., Nadeem, M.A., Hussain, A and
Farhan Khalid. 2009. Effect of different sowing
dates on growth and yield of wheat (Triticumaestivum L.) varieties in district Jhang.
Pakistan Journal of Life and Social Sciences.
7 (1): 68-69.
Tiwari, S.K and Singh, M. 1993. Yielding ability of
wheat (Triticum aestivum) at different dates
of sowing-a temperature – dependent
performance. Indian Journal of Agronomy.
38 (2): 204-209.
USDA, 2010. Grain: World Market and Trade Jult,
www.fas.usda.gov/psdonline, 45 & 51.
VIJAY et al
Research NoteJ.Res. ANGRAU 40(3) 51-53, 2012
HEAVY METAL REMOVAL EFFICIENCY OF ZEOLITE FROM SEWAGERESHMA BHADANGE, AKULA BABY , P.PRABHU PRASADINI, M.UMA DEVI and
D. JAGDISHWAR REDDYDepartment of Environmental Science and Technology
College of Agriculture, Acharya N.G. Ranga Agricultural University,Rajendranagar, Hyderabad-500 030.
Date of Receipt : 09.08.2011 Date of Acceptance : 04.05.2012
e-mail: [email protected]
Noor Mohammed Kunta (NMK) situated nearANGRAU campus and providing irrigation water tothe university forms has been contaminated withvarious pollutants which come from two sources viz.,industries situated in Katedan, and pollutants fromthe domestic untreated waste water of the surroundingperi urban areas. Mir Alam Tank water was also usedfor the study as second source. Waste water of NMKcould be put to use for irrigation provided the pollutantsdischarge could be checked and the lake waterdecontaminated using appropriate technologies.
Zeolite (an aluminosilicate clay mineral) hasshown a great capacity for metal adsorption of Cu,Cd, Pb and Zn (Kim et al., 2009). Hence, zeolite (Naform) has been used in the present study to checkits efficiency as a nanofilter by virtue of its veryregular pore structure of molecular dimensions.
A column experiment was carried out, usingpolyvinyl chloride (PVC) tubes of 50 cm height and10.5 cm of diameter. The filter paper was placed atthe bottom of the column; over it nylon cloth wassecured tightly. The column was divided into threelayers viz., 0-15 cm, 15-30 cm and 30-45 cm. Thesieved red sandy loam soil (< 2mm) was filled in thelower 30 cm of the column maintaining 1.54 g cm-3 ofbulk density. The soil has 7.2 pH and EC of 0.34dsm-1. The organic carbon content was 0.6%,available N, P and K contents were 50.17, 12.67 and360 kg ha-1. The upper 15 cm layer of soil columnwas left as such and treated as control beside fillingmixing of Zeolite in soil on weight basis @ 0.25 %,0.5 %, 0.75 %, 1.0 %, 1.25 % and 1.50 %. The upper5 cm of space in the column was kept for applicationof sewage. The column was saturated for 24 hoursby adding 1440 ml of sewage (pore volume basis),collected from inlet of two sources viz., NoorMohammed Kunta sewage treatment plant (NMKSTP) and Mir Alam sewage treatment plant (Mir Alam
STP). Leachates were collected by adding 480 ml ofwater, at an interval of 24 hours, up to nine leachingevents from L
1 to L
9. The collected leachates were
analysed for heavy metal content, using atomicabsorption spectrometer and the metal contentpresented.
The removal efficiency of zeolite for heavymetals was worked out by using following formula,
C Ion initial
- C Ion final
—————————— X100
C Ion initial
Where, R Ion,
% - Ion removal efficiency, C
Ion
initial - Initial concentration of ion
and C
Ion final - Final
concentration of ion. The experimental results wereanalyzed in Completely Randomized Design (CRD),with factorial concept
The sewage brought from NMK STPcontained heavy metals like cobalt (0.487 mg l-1) andcadmium (0.21 mg l-1) which were found to be abovethe permissible limits, while lead (1.70 mg l-1) wasfound within the permissible limits. Similarly, theheavy metals from the Mir Alam STP also containedcobalt (0.507 mg l-1) and cadmium (0.25 mg l-1), abovethe permissible limits and lead (1.73 mg l-1) withinthe permissible limits, as per the guidelines of ISI.
Cobalt content obtained after nine leachingevents in NMK recorded a decrease with theincreased addition of Zeolite in the column studies(0.459 to 0.427). Leachate from Zeolite @1.50%column recorded maximum cobalt removal (0.427mgL-1) as compared to untreated control (0.478). Theefficiency of removal was 11.73%. In Miralam tank,the Cobalt content obtained after nine leaching eventsrecorded a decrease with the increased addition ofZeolite in the column studies (0.472 to 0.431).Leachate from Zeolite @1.50% column recordedmaximum cobalt removal (0.431 mgL-1) as compared
Ion removal efficiency (R Ion,
%) =
RESHMA et al
Tab
le .
1. E
ffic
ien
cy o
f Z
eo
lite
on
rem
oval o
f C
ob
alt
, L
ead
an
d C
ad
miu
m
co
nte
nt
(mg
L-1
) fr
om
sew
ag
e e
fflu
en
t o
f N
MK
, M
irala
m S
TP
aft
er
nin
e
leach
ing
eve
nts
.
Co
balt
co
nte
nt
Lead
co
nte
nt
Cad
miu
m
Zeo
lite
% in
co
lum
n
NM
K
Mir
Ala
m
NM
K
Mir
Ala
m
MK
M
ir A
lam
T1-
0.2
5
0.4
59
0.4
72
1.4
6
1.4
7
0.1
71
0.2
21
T2 -
0.5
0
0.4
56
0.4
65
1.3
4
1.3
8
0.1
66
0.2
14
T3 -
0.7
5
0.4
46
0.4
59
1.2
0
1.2
9
0.1
60
0.2
04
T4 -
1.0
0.4
33
0.4
52
1.1
8
1.2
1
0.1
44
0.1
96
T5 -
1.2
5
0.4
26
0.4
41
1.1
0
1.1
2
0.1
32
0.1
90
T6 -
1.5
0
0.4
27
0.4
31
0.9
7
1.0
7
0.1
32
0.1
89
T7-
co
ntr
ol
0.4
78
0.4
92
1.5
6
1.5
9
0.1
99
0.2
37
Eff
icie
ncy*
11.7
3
12.3
6
34.7
7
32.7
6
34.2
9
20.6
7
Sem
+
0.0
05
0.0
05
0.0
48
0.0
43
0.0
04
0.0
03
CD
at
5%
N
S
NS
N
S
NS
N
S
NS
*Eff
icie
ncy o
f T
6 o
ver
co
ntr
ol
to untreated control (0.492). The efficiency of removalwas 12.36 % (Table 1).
Lead content obtained after nine leachingevents in NMK recorded a decrease with theincreased addition of Zeolite in the column studies(1.46 to 0.97). Leachate from Zeolite @1.50% columnrecorded maximum cobalt removal (0.97 mgL-1 ) ascompared to untreated control (1.56). The efficiencyof removal was 34.77%. Where as in Miralam tankthe Lead content obtained after nine leaching eventsrecorded a decrease with the increased addition ofZeolite in the column studies (1.47 to 1.07). Leachatefrom Zeolite @1.50% column recorded maximumLead removal (1.07 mgL-1 ) as compared to untreatedcontrol (1.59). The efficiency of removal was 32.76%(Table 1)
Cadmium content obtained after nineleaching events in NMK recorded a decrease withthe increased addition of Zeolite in the column studies(0.171 to 0.132). Leachate from Zeolite @1.50%column recorded maximum cadmium removal (0.132mgL-1 ) as compared to untreated control (0.199). Theefficiency of removal was 34.29%. Where as inMiralam tank the Cadmium content obtained afternine leaching events recorded a decrease with theincreased addition of Zeolite in the column studies(0.221 to 0.189). Leachate from Zeolite @1.50%column recorded maximum cadmium removal(0.189mgL-1 ) as compared to untreated control(0.237). The efficiency of removal was 20.67%. (Table1)
It was observed that increase in amount ofzeolite resulted in increase in efficiency of cadmiumremoval from leachates of both the sources (Table)This decrease may be due to binding of Zeolite withCd2+ ions and thus, forming ion-exchanged zeolitecomplex James and Sampath, 1999, Lin and Hsi.(1995) reported decrease in cadmium concentrationin leachates with increase in zeolite concentration.These may be due to exchangeable cationsdistributed on surface as well as in the pores of zeoliteand adsorption of the ions on its charged surface.Baker et al. (2008) studied the adsorption behaviorof zeolite for heavy metals such as cadmium bycolumn experiment and opined similarly.
At lower concentration the exchange processis limited to the exchange on sites of the zeolite,and hence, huge concentration of cadmium wasnoticed in the leachates. In contrast, at higher
concentration diffusion also occurs within the poresof zeolite which results in less heavy metal contentin leachates. Thus, the results revealed that zeolitewas an efficient ion exchanger for removing heavymetals from sewage.
Several studies emphasized the differentremoval efficiency and selectivity of Zeolite towardsheavy metals (Querol et al., 2002).
Conclusively, if sewage is to be used for theirrigation by the farmers in periurban areas ofHyderabad, the application of zeolite can be used asan nanofilter to reduce the load of pollutants viz.,heavy metals.
Zeolite at the rate of 1.50 % w/w of soil,though showed the better removal efficiency of heavymetals but not up to permissible limits, emphasizesthe necessity to carry further research at elevatedlevels of concentrations to ascertain its role in wastewater management.
REFFERENCES
Baker, H.M., Massadeh, A.M and Younes, H.A. 2008.Natural Jordanian zeolite: removal of heavymetal ions from water samples using columnand batch methods. Environmental Monitoringand Assessment. 157(1-4):319-30.
James, R and Sampath, K. 1999. Effect of Zeoliteon the Reduction of Cadmium Toxicity inWater and a Freshwater Fish, Oreochromismossambicus. Environmental Contaminationand Toxicology. 62:222-229.
Kim, T.H., Nam, Y.K and Lee, M. 2009. Nitrogenand Phosphorus removal from livestock wastewater by zeolite ion exchange and ionizingradiation. World Academy of Science,Engineering and Technology. (54) pp 9-13.
Lin, C.F and Hsi, H.C. 1995. Resource recovery ofwaste fly-ash synthesis of zeolite-likematerials, Environmental Science andTechnology. 29: 1109–1117.
Querol, X., Moreno, N., Umana, J.C., Juan, R.,Hernandez S., Fernandez-Pereira C., AyoraC., Janssen M., Garcia-Martinez J., Linares-Solano, A and Cazorla-Amoros D. 2002.Application of zeolitic material synthesisedfrom fly ash to the decontamination of wastewater and flue gas. Journal of ChemicalTechnology and Biotechnology. 77 292–298.
HEAVY METAL REMOVAL EFFICIENCY OF ZEOLITE FROM SEWAGE
Research NoteJ.Res. ANGRAU 40(3) 54-57, 2012
PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES UNDERVARIED DATES OF SOWING
V. CHANDRIKA, T. SHASHIKALA, M. SHANTI and K. LOKA REDDYAICRP on Forage Crops, Acharya N.G. Ranga Agricultural University,Agricultural Research Institurte, Rajendranagar, Hyderabad - 500 030.
Date of Receipt : 16.04.2012 Date of Acceptance : 03.06.2012
email: [email protected]
Bajra is a fast growing short duration cerealcrop. It has high biomass production potential andserves as an ideal crop under low rainfall, with hightillering ability, high dry matter production, high proteincontent (10-12% CP) and ratoonability, thus makingit as an outstanding summer growing fodder crop forthe dry farming areas or rainfed situations (Patel etal., 2008). The green fodder of bajra is leafy, palatableand very nutritious feed stock for cattle ensuring goodmilk yield. It has no HCN content as compared tosorghum and can be fed to cattle at any stage of thecrop. Multicut bajra types have a potential ofproducing about 70-80 t of green fodder / ha in 3-4cuts. The varieties ICMV 08111 and Rijko bajrarecorded 812 and 791 q/ha of green fodder yield,respectively in three cuts during summer season atRajendranagar, Hyderabad (Shashikala et al., 2011).Green fodder scarcity is the most common problemfrom November to June due to various reasons.Therefore a study was conducted at AICRP on ForageCrops, Hyderabad centre to identify a suitablegenotype of multicut bajra and to find out an optimumdate of sowing to achieve higher green fodder yieldduring summer season /lean period to over come thescarcity.
A field experiment was conducted during laterabi / early summer, 2011 at AICRP on Forage Crops,ARI, Rajendranagar, Hyderabad, Acarya N.G. RangaAgricultural University. The soil was sandy loam intexture with pH of 8.13, low in available nitrogen andmedium in available phosphorus and available K2O.The experiment was laid out in randomized blockdesign with factorial concept replicated thrice. Twovarieties of multicut bajra viz., V1-Gaint Bajra andV2- BAIF Bajra were sown at four different datesstarting from January I fortnight at fortnightly intervals.The crop was supplied with recommended dose of
fertilizer 100- 50- 40 kg N, P2O5 and K2O /ha. Nitrogenwas given in two splits, half as basal and theremaining half at 30 days after sowing. After everycut, 30 kg N/ha was given as top dressing. Crop wassown at a row distance of 30 cm. First cut was takenat 50% flowering stage (65 days after sowing). From2nd cut onwards, harvesting was done at 40 daysinterval after previous cut. Irrigations were givenimmediately after each cut and whenever necessary.
The crop sown during I fortnight of Februaryrecorded taller plants (Table-1), higher green fodderyield, dry matter yield and crude protein yield(Table-2). In first cut, higher green fodder yield wasrecorded with D2 (30.1 t/ha) and it was on par withD3 (27.6 t/ha) and both were significantly superiorover D1 (23.0 t/ha) and D4 (19.6 t/ha). Similar trendwas observed with dry matter yield (5.73 and 5.34 t/ha, respectively) and crude protein yield (406 and379 kg/ha, respectively). In second cut, irrespectiveof genotypes green fodder yield was higher over firstcut (Table-2). Higher green fodder yield was noticedwith D2 (31.1 t/ha) in second cut and it was on parwith D1 (29.6 t/ha) and D3 (29.1 t/ha). Similar trendwas noticed with 3rd and 4th cuts also (Table-2).Significant differences were not observed with leaf:stem ratio with respect to genotypes and dates ofsowing (Table-1). In all the cuts D4 recordedsignificantly lower green fodder yield, dry fodder yieldand crude protein yield. Delayed sowing might havereduced the yields due to increase in temperatures.When the data were pooled over four cuts, significantlyhigher green fodder yield (118 t/ha) was recorded withD2 over other dates of sowing (Table-2). Where as,D3 (107 t/ha) was on par with D1 (103 t/ha) with regardto the green fodder yield. Similar trend was noticedwith dry fodder yield and crude protein yield also.Similar results were obtained by Patel et al., (2008)with the summer sown bajra crop.
PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES
Tab
le 1
. Y
ield
pa
ram
ete
rs o
f m
ult
icu
t fo
dd
er
bajr
a g
en
oty
pe
s
Pla
nt
heig
ht
(cm
) L
eaf
: ste
m r
ati
o
Tre
atm
en
ts
1s
t cu
t 2
nd c
ut
3rd
cu
t 4
th c
ut
1s
t cu
t 2
nd c
ut
3rd
cu
t 4
th c
ut
Da
tes o
f so
win
g
D1
– Ja
n II
FN
14
1.06
13
2.50
13
2.50
13
2.80
0.
22
0.20
0.
20
0.17
D2
– F
eb I
FN
18
8.55
16
7.27
16
5.16
16
4.43
0.
22
0.23
0.
21
0.18
D3
– F
eb II
FN
17
2.06
13
5.66
15
0.26
15
0.70
0.
20
0.23
0.
21
0.18
D4–
Mar
ch I
FN
13
2.05
11
2.36
14
1.73
13
2.53
0.
20
0.24
0.
20
0.16
S E
m+
5.
4 6.
4 6.
6 6.
2 0.
009
0.12
0.
009
0.00
9
CD
at 5
%
16.5
19
.3
20.0
18
.8
NS
N
S
NS
N
S
En
trie
s
V1-
Gai
nt B
ajra
14
8.05
12
7.17
15
1.93
14
4.55
0.
20
0.24
0.
21
0.18
V2-
BA
IF B
ajra
16
1.80
12
6.74
14
2.90
14
5.67
0.
20
0.23
0.
21
0.18
SE
m +
3.
9 4.
5 4.
7 4.
4 0.
006
0.00
8 0.
007
0.00
6
CD
at 5
%
11.7
N
S
NS
N
S
NS
N
S
NS
N
S
D
x V
SE
m+
7.
7 9.
0 9.
3 8.
8 0.
012
0.01
7 0.
013
0.01
3
CD
at 5
%
34.6
N
S
NS
N
S
NS
N
S
NS
N
S
CHANDRIKA et al
Tab
le 2
. G
reen
fo
dd
er
yie
ld,
dry
matt
er
yie
ld a
nd
cru
de p
rote
in y
ield
of
mu
ltic
ut
fod
der
bajr
a g
en
oty
pe
s
G
FY
(t/h
a)
DM
Y(t
/ha)
CP
Y(k
g/h
a)
Tre
atm
en
ts
1s
t cu
t 2
nd
cu
t 3
rdcu
t 4
th
cu
t p
oo
led
1s
t cu
t 2
nd
cu
t 3
rd
cu
t 4
th
cu
t p
oo
led
1s
t
cu
t 2
nd
cu
t 3
rd
cu
t 4
th
cu
t p
oo
led
Da
tes o
f so
win
g
D1
– Ja
n II
FN
23
.0
29.6
22
.2
27.9
103
4.42
5.
884.
074.
86
19.3
314
406
281
319
1318
D2
– F
eb
I F
N
30.1
31
.1
24.0
32
.511
8 5.
73
6.19
4.46
5.70
22
.140
6 42
7 30
837
1 15
12
D3
– F
eb I
I F
N
27.6
29
.1
22.5
28
.110
7 5.
34
5.77
4.17
4.93
20
.237
9 39
8 28
832
1 13
86
D4–
Mar
ch
I F
N
19.6
19
.5
20.1
19
.078
3.
72
3.88
3.73
3.34
14
.726
5 26
8 25
821
7 10
06
S E
m+
0.
0009
1.
01
1.29
1.
482.
49
0.27
0.
200.
230.
26
0.45
19
14
1617
31
CD
at 5
%
NS
3.
06
3.91
4.
487.
4 0.
8 0.
60.
710.
71
1.4
57
42
4951
95
En
trie
s
V1-
Gai
nt
Baj
ra
23.7
27
.8
21.3
26
.299
4.
43
5.52
3.93
4.56
18
.531
5 38
0 27
129
8 12
66
V2-
BA
IF
Baj
ra
26.5
26
.8
23.1
27
.610
4 5.
17
5.34
4.29
4.82
19
.636
7 36
8 29
631
5 13
46
S E
m+
1.
00
0.71
0.
91
1.05
1.69
0.
19
0.14
0.17
0.19
0.
3214
10
11
12
22
CD
at 5
%
3.0
NS
N
S
NS
5.3
0.58
N
SN
SN
S
0.97
41
30
NS
NS
67
D x
V
S E
m+
2.
0 1.
43
1.83
2.
093.
5 0.
38
0.28
0.33
0.37
0.
6427
19
23
24
44
CD
at 5
%
NS
N
S
NS
N
S10
.5
NS
N
SN
SN
S
2.88
NS
N
S
NS
NS
19
9
Both the varieties performed similarly withrespect to plant height, L: S ratio, green fodder yield,dry fodder yield and crude protein yield. In first cut,V2 performance was superior over V1 (Table 1 and2). Where as in second cut V1 performed better withrespect to green fodder yield, dry matter yield andcrude protein yield (Table2). No significant differenceswere observed during third and fourth cut. Thoughgreen fodder yield, dry matter yield and crude proteinyield were higher with BAIF Bajra, it was on par with
Giant Bajra when pooled over four cuts. Genotypic
variations in forage bajra were reported by Pathan
and Bhilare, (2009). Desale et al., (2000) reported
that Giant Bajra recorded significantly higher green
fodder yield (918.2 q/ha) and dry matter yield (205.7q/ha) over other varieties.
It can be inferred from the above study that
sowing of either giant bajra or BAIF multicut fodder
bajra during I fortnight of February will be beneficialto the dairy farmers to over come the green fodderscarcity during the lean period by providing sufficientgreen fodder.
REFERENCES
Desale, J.S., Bhilare, R.L., Pathan, S.H and Babar,R.M. 2000. Response of multicut fodder bajravarieties to nitrogen fertilizer levels., Journalof Maharastra Agricultural Universities. 25(1):74-75.
Patel, M.R., Sadhu, A.C., Patel, R.M., Parmar, H.Pand Kher, H.R., 2008. Cutting management indifferent genotypes of forage bajra duringsummer season. Research on Crops. 9(2) :325-327.
Pathan, S.H and Bhilare, R.L. 2009. Growthparameters and seed yield of forage pearl milletvarieties as influenced by nitrogen levels.Journal of Maharastra Agricultural Universities.34(1): 101-102.
Shashikala, T., Rai, K.N., Chandrika, V., Shanti, Mand Loka Reddy. K. 2011. Fodder yieldpotential of multicut pearl millet genotypesduring summer under irrigation. Proceedingsof National Symposium on “Forage resourceand livestock for livelihood, environment andnutritional security”. Jhansi, 10-11 September,2011. pp76
PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES
Research NoteJ.Res. ANGRAU 40(3) 58-63, 2012
Date of Receipt : 08.02.2012 Date of Acceptance : 09.06.2012
PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION OFTOMATO (Solanum lycopersicum Mill.)
K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R.V.S.K. REDDYCollege of Horticulture, Andhra Pradesh Horticultural University,
Rajendranagar, Hyderabad - 500 030
The fruit yield in tomato as in most other
field crops is a very complex character and is
dependent on a number of yield components. A
knowledge of the association of quantitative
characters especially the yield and its attributes will
be of immense practical interest in the field of plant
breeding.
An experiment was conducted at NBPGR,
Regional Station, Rajendranagar, Hyderabad during
rabi and summer 2008-09. In randomized block design
with three replications. The treatments included eight
diverse genotypes of tomato used as lines (female
parents) and four popular varieties used as Testers
(male parents). The selfed seeds of twelve parents
were raised in the green house of NBPGR, Regional
Station, Rajendranagar during rabi 2008 and were
crossed in a Line x Tester mating design. The riped
fruits were harvested, seeds from each crossed fruit
were collected by using fermentation method for seed
extraction. Twenty six days old tomato seedlings were
transplanted on 13th April 2009 with a spacing of 60 x
45 cm The experimental unit consisted of parents
followed by F1s in all the three blocks. All the
treatments were assigned randomly to the
experimental units. The data were recorded on 13
characters in F1s and parents and correlations were
worked out using the method given by Johnson etal., (1955).
email: [email protected]
The mean performance of the crosses EC
163663 x Pusa Ruby, EC 257489 x Pusa Ruby, EC
257489 x Arka Saurabh for fruit yield per plant was
better , but none of the hybrids exhibited better yield
over the standard check (Arka Vikas). The crosses
EC 145057 x Pusa Ruby, EC 338717 x PED, EC
338717 x Marutham exhibited high rind thickness
over standard check. Higher ascorbic acid content
over standard check was recorded in crosses EC
257489 X Arka Saurabh, EC 238308 x Marutham,
EC145057 x Marutham, EC145057 x PED
respectively. TSS was high in crosses EC145057 x
Arka Saurabh, EC145057 x Pusa Ruby and EC
163663 x Pusa Ruby over the standard check. In
general, the genotypic correlations were found to be
higher than phenotypic correlations. The study of
correlations revealed that fruit yield per plant was
positive and significantly correlated with fruit weight,
fruit length, fruit diameter, fruit volume, rind thickness,
TSS and fruit juice content. Similar results have been
reported earlier in tomato for the association of fruit
yield with fruit weight (Harer et al., 2002; Pradeep etal., 2002, Brar and Hari Singh, 1998), Fruit length
(Tiwari, 2002 and Arun Joshi et al., 2004)., Plant
height, number of branches per plant, days to 50 %
flowering (Shifaraw Nesgea et al., 2002), rind
thickness (Kumar et al., 2003). The crosses that
proved to be better than the elite parent are useful
for further exploitation.
PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION
Tab
le 1
. M
ean
Perf
orm
an
ce o
f p
are
nts
, cro
sses a
nd
sta
nd
ard
ch
eck f
or
yie
ld a
nd
yie
ld c
on
trib
uti
ng
ch
ara
cte
rs i
n T
om
ato
Tre
atm
en
ts
Pla
nt
h
eig
ht
(cm
)
Nu
mb
er
of
bra
nc
he
s
Da
ys
to
5
0%
flo
we
rin
g
Fru
it
we
igh
t (g
)
Fru
it
len
gth
(cm
)
Fru
it
dia
mete
r
(cm
)
Fru
it
vo
lum
e
(cc
)
Yie
ld p
er
pla
nt
(g)
Rin
d
thic
kn
es
s
(mm
)
Asc
orb
ic
ac
id
(mg
/100
g
pu
lp)
Fru
it ju
ice
co
nte
nt
(%)
See
d
co
nte
nt
(%)
Lin
es
EC
145
057
118.
23
9.00
61
.00
46.3
2.
67
2.67
40
.33
1153
3.
27
23.7
5 48
.30
0.37
EC
163
663
65.7
0 14
.66
52.6
7 42
.3
2.50
2.
57
41.5
3 11
26
3.33
25
.03
50.1
4 0.
32
EC
238
308
78.0
3 12
.33
56.0
0 38
.0
2.53
2.
37
26.5
3 12
23
3.40
23
.20
50.0
3 1.
24
EC
257
489
89.0
0 11
.33
54.0
0 46
.0
2.50
2.
73
41.8
7 11
66
3.47
25
.23
50.6
9 0.
66
EC
320
574-
1 11
3.83
9.
66
61.6
7 51
.6
2.77
3.
00
45.8
3 12
76
3.53
25
.60
42.7
3 1.
20
EC
338
714
81.6
0 10
.66
60.0
0 59
.0
3.13
3.
53
55.9
7 14
50
3.37
26
.63
44.1
0 0.
64
EC
338
717
98.0
3 13
.33
56.3
3 46
.6
2.47
2.
73
41.6
7 12
36
3.23
25
.35
41.9
7 0.
93
EC
338
735
102.
87
11.3
3 61
.33
45.0
2.
70
2.90
39
.60
1226
3.
00
24.8
0 46
.30
0.86
Lin
es
Me
an
9
3.4
1
11.5
3
57.8
8
46.8
2.6
6
2.8
1
41.6
7
123
2
3.3
3
24
.95
4
6.7
8
0.7
8
Tes
ters
Pus
a R
uby
90.2
0 11
.66
63.6
7 26
.6
2.27
2.
27
23.5
7 97
6 3.
13
26.2
5 38
.47
1.63
PE
D
93.1
0 12
.00
67.3
3 33
.0
2.70
3.
37
52.5
3 10
53
3.47
20
.27
42.1
7 1.
27
Ark
a S
oura
bh
111.
43
13.0
0 64
.00
27.6
2.
13
2.43
25
.17
1188
2.
27
23.5
5 16
.63
1.78
Mar
utha
m
102.
83
12.6
6 63
.67
32.6
2.
30
2.70
38
.33
1085
2.
33
23.8
0 26
.34
1.07
Te
ste
rs M
ea
n
99
.39
1
2.3
3
64.6
7
30.0
2.3
5
2.6
9
34.9
0
107
5
2.8
0
23
.47
3
0.9
0
1.4
4
Cro
sse
s
EC
145
057
× P
usa
Rub
y 10
8.10
14
.66
62.0
0 65
.3
3.60
3.
87
66.1
0 12
86
5.73
24
.15
35.0
7 0.
54
EC
145
057
× P
ED
98
.30
13.0
0 65
.67
56.3
3.
27
3.70
54
.00
1303
4.
27
26.6
3 42
.00
0.81
EC
145
057
× A
rka
Sou
rabh
98
.93
13.3
3 64
.67
47.6
2.
97
2.63
45
.30
1293
3.
23
24.9
5 53
.23
0.69
EC
145
057
× M
arut
ham
10
3.00
12
.66
65.3
3 48
.0
2.80
2.
73
39.0
7 12
60
3.23
26
.70
37.6
0 0.
66
VINAY et al
Tre
atm
en
ts
Pla
nt
h
eig
ht
(cm
)
Nu
mb
er
of
bra
nc
he
s
Da
ys
to
5
0%
flo
we
rin
g
Fru
it
we
igh
t (g
)
Fru
it
len
gth
(cm
)
Fru
it
dia
mete
r
(cm
)
Fru
it
vo
lum
e
(cc
)
Yie
ld p
er
pla
nt
(g)
Rin
d
thic
kn
es
s
(mm
)
Asc
orb
ic
ac
id
(mg
/100
g
pu
lp)
Fru
it ju
ice
co
nte
nt
(%)
See
d
co
nte
nt
(%)
EC
163
663
× P
usa
Rub
y 12
1.97
11
.66
61.6
7 52
.0
3.13
3.
33
49.3
7 14
36
3.93
20
.43
38.0
7 0.
77
EC
163
663
× P
ED
86
.10
14.0
0 62
.00
52.3
3.
10
3.40
56
.13
1300
3.
27
26.1
1 52
.30
0.71
EC
163
663
× A
rka
Sou
ra
96.6
0 13
.00
61.3
3 44
.0
2.73
2.
90
42.5
0 12
36
3.77
21
.23
42.2
3 0.
93
EC
163
663
× M
arut
ham
10
2.73
11
.33
63.6
7 48
.3
2.33
2.
83
47.0
0 12
83
4.03
24
.43
48.2
0 1.
13
EC
238
308
× P
usa
Rub
y 11
9.67
10
.66
55.6
7 37
.6
2.37
2.
67
29.3
3 11
93
3.37
23
.23
36.9
0 1.
63
EC
238
308
× P
ED
99
.50
13.3
3 62
.33
26.0
1.
83
2.20
23
.70
1043
3.
13
25.5
1 42
.07
1.90
EC
238
308
× A
rka
Sou
ra
105.
93
11.0
0 62
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32.6
2.
33
2.33
28
.33
1033
3.
43
23.8
5 40
.15
1.65
EC
238
308
× M
arut
ham
85
.77
12.6
6 62
.67
27.0
1.
90
2.23
26
.27
1060
3.
33
27.2
3 36
.11
1.82
EC
257
489
× P
usa
Rub
y 98
.43
13.3
3 64
.00
56.6
3.
23
3.37
53
.37
1393
4.
03
20.1
1 40
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0.70
EC
257
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× P
ED
82
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13.3
3 65
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51.0
2.
60
2.90
49
.27
1226
3.
13
22.7
3 41
.83
0.73
EC
257
489
× A
rka
Sou
ra
82.9
7 12
.66
62.6
7 55
.0
3.13
3.
37
58.1
3 13
45
3.03
27
.67
38.0
7 0.
81
EC
257
489
× M
arut
ham
79
.17
13.0
0 64
.67
38.6
2.
23
2.50
44
.20
1176
3.
37
23.4
0 40
.00
0.81
EC
320
574-
1 ×
Pus
a R
uby
123.
10
13.3
3 53
.67
35.0
2.
30
2.20
27
.67
1216
2.
23
24.2
3 15
.70
1.79
EC
320
574
-1×
PE
D
82.7
3 8.
33
62.3
3 28
.3
2.13
2.
30
30.1
0 11
70
2.23
21
.53
17.7
3 1.
84
EC
320
574-
1 ×
Ark
a S
oura
10
2.20
12
.00
54.0
0 25
.3
2.00
2.
13
25.1
3 10
66
2.20
22
.71
16.4
7 1.
76
EC
320
574-
1 ×
Mar
utha
m
96.5
7 10
.00
58.0
0 26
.0
2.00
2.
13
24.2
0 10
80
2.20
24
.32
16.7
7 1.
73
EC
338
714
× P
usa
Rub
y 12
5.97
13
.00
58.6
7 45
.0
2.40
2.
37
37.0
7 11
73
2.77
26
.28
28.6
7 0.
65
EC
338
714
× P
ED
73
.00
12.3
3 55
.00
47.6
3.
10
3.27
52
.47
1200
2.
90
19.6
3 42
.27
0.67
EC
338
714
× A
rka
Sou
ra
80.0
7 13
.33
57.0
0 49
.0
2.73
2.
87
41.2
7 11
76
3.00
22
.12
40.1
3 0.
62
PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION
Tre
atm
en
ts
Pla
nt
h
eig
ht
(cm
)
Nu
mb
er
of
bra
nc
he
s
Da
ys
to
5
0%
flo
we
rin
g
Fru
it
we
igh
t (g
)
Fru
it
len
gth
(cm
)
Fru
it
dia
mete
r
(cm
)
Fru
it
vo
lum
e
(cc
)
Yie
ld p
er
pla
nt
(g)
Rin
d
thic
kn
es
s
(mm
)
Asc
orb
ic
ac
id
(mg
/100
g
pu
lp)
Fru
it ju
ice
co
nte
nt
(%)
See
d
co
nte
nt
(%)
EC
338
714
× M
arut
ham
82
.97
13.3
3 58
.33
40.0
2.
23
2.60
39
.97
1113
2.
77
20.5
7 39
.47
0.68
EC
338
717
× P
usa
Rub
y 12
8.03
12
.66
54.6
7 55
.0
2.97
3.
20
50.6
3 12
76
5.17
23
.20
35.2
7 0.
46
EC
338
717
× P
ED
77
.53
10.3
3 55
.33
50.3
2.
97
3.23
43
.97
1303
5.
47
24.2
7 42
.20
0.84
EC
338
717
× A
rka
Sou
ra
104.
00
11.0
0 52
.00
48.0
2.
50
2.70
44
.60
1233
5.
17
23.8
5 38
.17
0.74
EC
338
717
× M
arut
ham
10
6.80
8.
33
55.0
0 47
.6
2.77
2.
47
39.5
0 12
13
5.47
26
.06
39.5
3 0.
82
EC
338
735
× P
usa
Rub
y 11
5.20
13
.66
62.0
0 46
.6
2.87
2.
97
39.9
3 11
53
3.70
25
.53
38.7
3 0.
37
EC
338
735
× P
ED
69
.33
12.3
3 62
.67
48.0
2.
90
2.97
44
.00
1158
3.
63
24.5
0 42
.70
0.41
EC
338
735
× A
rka
Sou
ra
98.8
0 11
.66
60.0
0 49
.3
2.73
2.
97
46.1
7 11
40
3.30
23
.33
41.8
7 0.
45
EC
338
735
× M
arut
ham
10
3.63
12
.66
53.0
0 57
.6
3.37
3.
43
54.3
0 12
10
3.40
22
.17
41.5
6 0.
34
Cro
ss
Me
an
98
.13
12
.24
59
.95
44
.9
2.6
7
2.8
4
42
.28
1
211
3
.56
23
.83
37
.54
0.9
5
Ark
a vi
kas
(che
ck)
72.5
7 12
.66
58.6
7 64
.0
3.73
3.
40
60.2
0 17
90
3.16
26
.24
51.7
3 0.
79
Gen
era
l m
ea
n
96
.83
12
.11
59
.97
44
.3
2.6
7
2.8
3
41
.91
1
215
3
.44
24
.05
38
.91
0.9
6
S.E
. 1.
60
0.58
0.
94
2.0
0.08
0.
07
2.26
32
0.
10
0.06
0.
50
0.05
C.D
at 5
%
4.51
1.
65
2.64
5.
7 0.
22
0.19
6.
34
90
0.27
0.
17
1.40
0.
13
VINAY et al
Tab
le 2
. P
hen
oty
pic
(P)
an
d G
en
oty
pic
(G)
co
rrela
tio
n c
oeff
icie
nts
betw
een
yie
ld c
on
trib
uti
ng
ch
ara
cte
rs i
n T
om
ato
Tre
atm
en
ts
Pla
nt
he
igh
t (c
m)
Nu
mb
er
of
bra
nc
hes
/ p
lan
t
Day
s t
o
50%
fl
ow
eri
ng
Fru
it
weig
ht
(g)
Fru
it
len
gth
(c
m)
Fru
it
dia
me
ter
(cm
)
Fru
it
vo
lum
e
(cc
)
Rin
d
thic
kn
es
s (
mm
)
TS
S
(0b
rix
)
As
co
rbic
a
cid
(m
g/1
00
g p
ulp
)
% o
f fr
uit
ju
ice
% o
f se
ed
c
on
ten
t
Yie
ld/
pla
nt
(gm
P
1.0
00
0
0.19
25*
-0.0
155
-0.0
246
-0.0
805
-0.0
91
-0.1
513
0.12
04
0.03
93
0.00
61
-0.3
497**
0
.121
4 -0
.077
2 P
lant
hei
ght
(cm
) G
1
.00
00
0.72
83**
-0.0
202
-0.0
301
-0.0
834
-0.0
969
-0.1
660
0.12
49
0.05
70
0.00
45
-0.3
570**
0.
1198
-0
.080
3
P
1.0
000
0.
1239
-0
.026
6 -0
.018
8 0.
0615
0.
0224
-0
.166
3 -0
.073
3 -0
.186
1* -0
.074
0 0.
0999
0.
0110
N
umbe
r of
br
anch
es/ p
lant
G
1.0
000
1.
0964
**
-0.2
422**
-0
.035
6 0.
1898
* 0.
1067
-1
.147
6**
-0.5
606**
-1
.214
2**
-0.4
683**
0.
4526
**
-0.0
493
P
1.0
000
-0
.058
3 0.
0244
0.
1514
0.
1080
-0
.118
4 -0
.053
3 0.
0838
0.
0675
0.
1092
-0
.006
2 D
ays
to 5
0%
flow
erin
g G
1.0
000
-0
.065
2 0.
0259
0.
1543
0.
1203
-0
.132
5 -0
.040
4 0.
0902
0.
0706
0.
1186
-0
.020
3
P
1
.00
00
0.
8653
**
0.80
77**
0.87
87**
0.52
46**
0.30
99**
0.09
25
0.50
89**
-0.7
601**
0.
7306
**
Fru
it w
eigh
t (gm
)
G
1
.00
00
0.
9213
**
0.84
94**
0.89
42**
0.56
82**
0.37
72**
0.09
63
0.53
92**
-0.8
089**
0.
7616
**
P
1.0
00
0
0.87
26**
0.82
38**
0.46
07**
0.27
73**
0.00
27
0.45
21**
-0.6
496**
0.
6925
**
Fru
it le
ngth
(cm
)
G
1.0
00
0
0.90
48**
0.88
92**
0.48
86**
0.35
85**
0.00
43
0.47
56**
-0.6
973**
0.
7558
**
P
1
.00
00
0.
8793
**
0.46
22**
0.20
81*
-0.0
823
0.41
28**
-0.5
940**
0.
5947
**
Fru
it di
amet
er
(cm
) G
1.0
000
0.
9419
**
0.48
94**
0.25
08**
-0.0
853
0.42
80**
-0.6
193**
0.
6227
**
P
1.0
00
0
0.44
28**
0.22
18*
-0.0
477
0.46
85**
-0.6
964**
0.
6227
**
Fru
it vo
lum
e (c
c)
G
1.0
00
0
0.48
00*
0.26
82**
-0.0
557
0.50
07**
-0.7
438**
0.
6612
**
P
1
.00
00
0.37
75**
0.06
61
0.37
37**
-0.4
057**
0.
2499
**
Rin
d th
ickn
ess
(mm
) G
1.0
00
0
0.46
19**
0.06
74
0.38
19**
-0.4
238**
0.
2838
**
P
1.0
000
0.
1102
0.
5949
**
-0.2
925**
0.
1711
* T
SS
(0 br
ix)
G
1.0
000
0.
1359
0.
7078
**
-0.3
582**
0.
2358
**
P
1.0
00
0
0.14
70
0.02
16
0.11
06
Asc
orbi
c ac
id
(mg/
100g
pul
p)
G
1.0
00
0
0.14
79
0.02
10
0.12
27
P
1.0
000
-0
.555
2**
0.30
38**
% o
f fru
it ju
ice
G
1.0
000
-0
.565
7**
0.32
87**
P
1.0
00
0
-0.3
521**
%
of s
eed
cont
ent
G
1.0
00
0
-0.3
782**
P
1.0
000
Y
ield
/ pl
ant (
g)
G
1.0
000
* , *
* S
ignific
ant
at
5%
and 1
% levels
resp
ect
ivel
REFERENCES
Arun, Joshi, Amit, Vikram and Thakur, M. C. 2004Studies on genetic variability, correlation andpath analysis for yield and physico-chemicaltraits in tomato (Lycopersicon esculentumMill.). Progressive Horticulture 36 (1) : 51-58.
Brar, P. S and Hari Singh. 1998. Variability andcorrelation studies in different varieties oftomato (Lycopersicon esculentum Mill.).Punjab Vegetable Grower 33: 23-26.
Harer, P. N., Lad D. B and Bhor, T .J 2002.Correlation and path analysis studies intomato. Journal of Maharashtra AgriculturalUniversities 27 (3) : 302-303.
Johnson, H. W., Robinson, H. F and Comstock, R.E. 1955. Estimates of genetic andenvironmental variability of Soybeans.Agronomy Journal 47: 314-318.
Kumar, V. R. A., Thakur M.C and Hedau, N. K. 2003.Correlation and path coefficient analysis intomato (Lycopersicon esculentum Mill.).Annalsof Agricultural Research 24(1) :175-177.
Pradheep, K.,Veeraragavathatham, D and Auxcilia,J. 2002. Correlation analysis in tomato(Lycopersicon esculentum Mill.) with anemphasis to virus resistance. South IndianHorticulture 55 (1-6) : 12-19.
Shiferaw Nesgea, Krishnappa, K. S and Raju, T. B.P. 2002. Correlation coefficient analysis intomato. Current Research - University ofAgricultural Sciences (Bangalore) 31(7/8)127-130.
Tiwari, J. K. 2002. Correlation studies in tomato.Haryana Journal of Horticultural Sciences.31 (1/2) 146-147.
PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION
Research NoteJ.Res. ANGRAU 40(3) 64-66, 2012
Date of Receipt : 12.03.2012 Date of Acceptance : 11.06.2012
PESTICIDE RESIDUES IN SELECTED FRUITS/VEGETABLES OF NALGONDADISTRICT OF ANDHRA PRADESH
SHAIK FARHATH and S. SHOBHA Department of Foods and Nutrition, Post Graduate and Research Centre,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030.
Regarding the consumption of pesticide for
different crops, cotton consumes 55 per cent of total
pesticides in India while the total area under cotton
is only 5 per cent. Similarly in the case of paddy, 17
per cent of the pesticide was consumed, while the
total area under paddy is 24 per cent. In fruits and
vegetables, the usage is 13 per cent and the area is
only 3 per cent of the total cultivated area (Dikshit,
2008).
This study was planned to monitor pesticide
residues in sweet lime, tomato and bitter gourd
collected from different locations of Madhariguda,
Ranga reddy guda, Ganapavaram and Pedda
Madhapur adisherlapally villages of Nalgoda districtof Andhra Pradesh.
Samples of sweet lime, tomato and bitter
gourd were procured from farmers in the selected
villages of Nalgonda district of Southern Telangana
Zone of Andhra Pradesh. The selected samples were
procured after interviewing the farmers growing them.
The samples were obtained after proper sampling was
done using standard sampling tecthniques.
email: [email protected]
Selected whole and processed fruits and
vegetables were analyzed for the pesticides such as
monocrotophos, quinalphos, endosulfan,
chloripyriphos, acephate and carbofuran. Most non-
ionic residues were extracted with acetone and the
residues were portioned from aqueous acetone to
dichloromethane/ hexane phase. After removing the
traces of dichloromethane, final extract was made
with acetone. Organophosphate residues were
determined directly by gas liquid chromatography with
FPD-P/NPD. For determination of organochlorine and
synthetic pyrethroid residues, the extract was
subjected to cleanup using florisil column.(Sharma,
2007).
The data revealed that pesticide residues in
processed as well as unprocessed samples were
below detectable limits (BDL) in all the samples.
Therefore, statistical analysis was not carriedout. The
chromatograms of the standards and samples are
given Table no 1, 2 and 3 and figure 1,2 and 3.
Table 1. Standard chromatographs-I
S.No
1.
2.
3.
4.
Table 2. Standard chromatograph-II
S.No Name of the
Pesticide standard Retention time
(in minutes) 1 Monocrotophos 16.034
2 Quinalphos 23.332
Table 3. Standard chromatographs-III
S.No Name of the
Pesticide standard Retention time
(in minutes) 1. Carbofuran 19.297
2. Quinalphos 23.312
Chromatograms of pesticides obtained from theselected crops
As the samples selected had enough safetylimiting period, the pesticide metabolites weredegraded. Pesticide residues of samples were innegligible quantities that were below detectable limits(BDL). That might be because of pre-processingtechniques contributed to the declinement of pesticideresidues.
Rao et al.(2009) studied pesticide residues
in vegetables (brinjal, cucumber, okra, ridge gourd
and tomato) and water samples collected from
Kothapally Adarsha watershed in Rangareddy district,
Andhra Pradesh, India during 2007.The resultsrevealed the presence of monocrotophos (range
0.001-0.044 mg kg-1), chlorpyrifos (0.001 to 5.154 mg
kg-1), cypermethrin (0.001 to 0.352 mg kg-1) and
endosulfan (0.001 to 0.784 mg kg-1). The residues of
monocrotophos and endosulfan were below maximum
residue limit (MRL) in all the 59 vegetable samples.The water samples also revealed the presence of
pesticide residues, but below MRLs.
Similar study was conducted by Srivastava
et al. (2011). on 20 vegetables including leafy, root,
modified stem, and fruity vegetables like bitter gourd,
jack fruit, french-bean, onion, colocassia, pointedgourd, capsicum, spinach, potato, fenugreek seeds,
carrot, radish, cucumber, beetroot, brinjal, cauliflower,
cabbage, tomato, okra, and bottle gourd. Forty-eight
pesticides including 13 organochlorines (OCs), 17
organophosphates (OPs), 10 synthetic pyrethriods
(SPs), and eight herbicides (H) pesticides were
analyzed. A total number of 60 samples, each in
triplicates, However the limit of detection ranged from
0.001-0.009 mg kg-1 for OCs, SPs, OPs, and H,
respectively. Twenty-three pesticides were detected
from total 48 analyzed pesticides in the samples with
the range of 0.005-12.35 mg kg-1. The detected
pesticides were: Sigma -HCH, Dicofol, Sigma -
Endosulfan, Fenpropathrin, Permethrin-II, beta -
cyfluthrin-II, Fenvalerate-I, Dichlorvos, Dimethoate,
Diazinon, Malathion, Chlorofenvinfos, Anilophos, and
Dimethachlor. In some vegetables like radish,
cucumber, cauliflower, cabbage, and okra, the
detected pesticides ( Sigma -HCH, Permethrin-II,
Dichlorvos, and Chlorofenvinfos) were above
maximum residues limit (MRL) (PFA 1954). However,
in other vegetables the level of pesticide residues
was either below detection limit or MRL.
Unit operations normally employed in
processing food crops reduce or remove residues of
insecticides and other pesticides that are present in
them. These operations such as washing, peeling,
blanching and cooking play a role in the reduction of
residues
Studies with tomatoes fortified with (14 C)
ETU (0.006 ppm) showed that 70% of the radioactivity
was lost during washing of the tomatoes in water
(Knio et al., 2000).
The initial Diazinon residue level (0.822 ppm)
on cucumbers was decreased by 22.3% by washing
for 15sec by rubbing under running water (Cengiz etal., 2006).
PESTICIDE RESIDUES IN SELECTED FRUITS/VEGETABLES
The residues of Azoxystrobin on grapes were0.49–1.84 ppm and washing removed 75% of theresidues (Lentza et al., 2006).
The initial procymidone residue level (0.86ppm) on tomatoes was decreased 68% by washingfor 15sec by rubbing under running water (Cengiz etal., 2007).
Captan residues in apples washed for 10–15sec with continuous hand rubbing were 50% lowerthan in those apples that received no post-harvestwashing (25.5–5100 ng/g) (Rawn et al., 2008).
Washing is generally the first step in varioustypes of treatments which are given to foodcommodities in combination (like washing followedby cooking, washing and drying, washing and peelingand washing, peeling and juicing to allow for effectivedecontamination from pesticides.
REFERENCES
Cengiz, M. F., Certel, M., Karakas, B and Gocmen,H. 2006. Residue contents of DDVP(Dichlorvos) and diazinon applied oncucumbers grown in greenhouses and theirreduction by duration of a pre-harvest intervaland post-harvest culinary applications. FoodChemistry, 98, 127–135.
Cengiz, M. F., Certel, M., Karakas, B and Gocmen,H. 2007. Residue contents of captan andprocymidone applied on tomatoes grown ingreenhouses and their reduction by durationof a pre-harvest interval and post-harvestculinaryapplications. Food Chemistry, 100,1611–1619.
Dikshit, A. K. 2008. Pesticide protecting crops andenvironment: The Journey has Just Began.Corp Care. 34 (3): 37-41.
Knio, K. M., Saad, A and Dagher, S. 2000. The fateand persistence of zineb, maneb andethylenethiourea on fresh and processedtomatoes. Food Additives and Contaminants,17(5), 393–398.
Lentza-Rizos, C., Avramides, E. J and Kokkinaki,K. 2006. Residues of azoxystrobin from grapesto raisins. Journal of Agricultural and FoodChemistry, 54(1),138–141.
Rao, G. V. R., Sahrawat, K. L., Rao, C. S., BinithaDas Reddy, K. K., Bharath, B. S., Rao, V. R.,Murthy, K. V. S and Wani, S. P. 2009.Insecticide residues in vegetable crops grownin Kothapalli watershed, Andhra Pradesh,India: a case study. Indian Journal of DrylandAgricultural Research and Development.24(2):21-27.
Rawn, D. F. K., Quade, S. C., Sun, W., Fouguet, A.,Belanger, A and Smith, M. 2008.
Captan residue reduction in apples as a result ofrinsing and peeling. Food Chemistry, 109, 790–796.
Srivastava, A. K Purushottam Trivedi Srivastava, M.K. Lohani, M and Srivastava, L. P. 2011.Monitoring of pesticide residues in marketbasket samples of vegetable from LucknowCity, India: QuEChERS method. Environ-mental Monitoring and Assessment. 176(1/4):465-472.
Sharma, K. K. 2007. Cereals, pulses and grains.Pesticide Residue Analysis Manual. All IndiaNetwork Project on Pesticide Residues.Directorate of Information and Publication ofAgriculture. New Delhi.
FARHATH and SHOBHA
DEVELOPMENT OF LYCOPENE ENRICHED MIXED FRUIT BARM. PENCHALA RAJU , B. SHIREESHA , B. SRAVANTHI and KUNA APARNA
Department of Food Technology, Post Graduate and Research Centre,Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030.
Research NoteJ.Res. ANGRAU 40(3) 67-68, 2012
Lycopene is a phytochemical nutrient thatis found in many fruits and vegetables but abundantlyin tomatoes and tomato products, contributing about80 to 90 percent of total pigments. The lycopeneamount in tomato juice varies from 4.0 to 11.6mgper 100g depending on tomato cultivars. Other sourcesof lycopene are watermelon (2.3 – 7.4 mg), papaya(2.0 – 5.3 mg), grape fruit (0.2 – 3.4 mg.) and guava(4.5 – 5.5 mg) per 100 gm on gross weight basis(Pohar et al., 2003)
Lycopene provides the mechanism thatinhibits the oxidation of low density lipoproteins (LDL)and in reducing the circulating LDL cholesterol.Reducing the LDL oxidation in a long term could inhibitatherosclerosis that may lower coronary risk (Balch,2001).
The present study was undertaken with theobjective to standardize the method for preparationof lycopene enriched mixed fruit bar and to evaluatethe best acceptable product.
Firm ripe red coloured mature tomatoes(Lycopersican esculentum), green coloured uniformlyripened banana (Musa paradasica), fully matured ripepapaya (Carica papya), sugar and liquid glucose wereused for the development of bars. All the ingredientsrequired were obtained from local markets ofHyderabad. Fruits were washed thoroughly to removedust from surface. Tomatoes were blanched in hotwater at 75 to 80 0C for 10 min to loosen the skin andsoften the pulp.
Blanched tomatoes were cut into halves andpassed through the pulper and filtered through muslincloth. Residue was returned to the pulper twice toextract maximum amount of pulp.
This extracted pulp was boiled at 85 0C for25 min to concentrate the pulp and to remove theraw tomato flavour.
Date of Receipt : 12.03.2012 Date of Acceptance : 11.06.2012
email: [email protected]
Banana and papaya were peeled and cut into½ inch and 5x3 inch slices respectively. The pulpwas made by grinding and it was passed through a30 mm stainless steel sieve to obtain a homogenouspulp free from skin and seeds. Extracted pulp wassubjected to boiling separately. Three products weredeveloped with variations in fruit pulp as given inTable 1.
All the three (Tomato, banana & papaya)pulps were weighed according to formulations; 15%sugar, 15% liquid glucose were added to the pulpand mixed properly to get a homogenized pulp. Thepulp was subjected to boiling for 25min at 80 0C. Thehomogenized and boiled pulp was then spread ongreased trays at a rate of 250g/sq.ft. The trays wereallowed to dry at 60 to 65 0C in a tray drier for 6hours. After drying, the dried pulp layer was removedfrom the tray and six layers were piled and cut into 3x 9 cm and packed in flexible packaging covers andstored in air tight container. The developed mixedfruit bars were subjected to organoleptic evaluation.For this purpose ten semi-trained panel membersalready involved in other studies were selected and5-point hedonic scale score card was designed forevaluation. The results of the study were subjectedto statistical analysis for Means and Standarddeviation.
The colour of T1 fruit bar was more liked bythe panelists .This could be due to the incorporationof 30% banana pulp and 30% papaya pulp in T1 fruitbar.
The flavour of T2 was best as per the sensorypanel. This could be due to a balance of fruit pulpadded in the formulation ie., 20% tomato , 25%banana & 25% papaya where as the otherformulations had either 30% tomato or 30% papayadue to which either of the flavour dominated the otherflavour.
The taste of T2 was ranked higher than T1and T3 which could be due to the flavour componentas discussed earlier. The texture of T2 was rankedhigher which could be due to balance of ingredients.There was an after taste reported in T1 as comparedto T2 and T3 which could be due to 30% papayaincorporation in T1 formulation which gave a strongpapaya after taste as per the sensory panelobservations.
The overall acceptability of T2 was bestfollowed by T3 and T1. The individual attributes suchas colour, flavour, taste, texture added to the overallacceptability of the T2 formulation. Further studiescan be carried out on the T2 formulation to study theshelf life and packaging parameters which can provideinputs for commercialition of the product.Lycopenecontent in developed products was estimated. It was1.4mg in the product T1, 2.8mg in T2 and 4.0mg inT3.
Formulations of mixed fruit bars developed.
Mean score of sensory evaluation of lycopene enriched mixed fruit bar
Ingredients
(%) Treatments Characteristic Treatments
T1 T2 T3 T1 T2 T3 Tomato 10 20 30 Colour 4.4 ± 0.8
4.4 ± 0.5
4.3 ± 0.6
Banana 30 25 20 Flavour 3.7 ± 1.0
4.0 ± 0.6
3.9 ± 0.9
Papaya 30 25 20 Taste 3.7 ± 1.0
4.5 ± 0.5
3.9 ± 0.8
Sugar 15 15 15 Texture 3.7 ± 1.0
4.0 ± 0.6
3.4 ± 0.9
Liquid Glucose
15 15 15 After taste 4.2 ± 0.7
4.0 ± 0.4
3.9 ± 0.7
Overall Acceptability 3.9 ± 0.9 4.2 ± 0.4 3.9 ± 0.8
Note: Values are expressed as mean ± SD
Table 1. Mean score of sensory evaluation and formulation of mixed fruit bars developed
References
Balch, J. 2001. Tomato Phytonutrients: Contributorsin the Battle Against DegenerativeDiseases.Total health. 21(4): 54-55.
Pohar, K.S., Gong, M.C., Bahnson, R., Miller, E.Cand Clinton, S.K.2003.Tomatoes, lycopeneand prostate cancer: a clinicians guide forcounselling those at risk for prostate cancer.World Journal of Urology. 21: 9-14.
RAJU et al
STUDY ON THE EFFECT OF IRRADIATION ON SENSORY QUALITIES OFPRESERVED TOMATO PRODUCTSM. KIRTHY REDDY and V. VIJAYALAKSHMI
College of Home Science, Acharya N.G. Ranga Agricultural UniversitySaifabad, Hyderabad-500004
Research NoteJ.Res. ANGRAU 40(3) 69-73, 2012
Date of Receipt : 12.03.2012 Date of Acceptance : 11.06.2012
Increasing levels of dietary lycopene through
the consumption of fresh tomatoes and tomato
products has been recommended by many health
experts (Tonucci et al. 1995; Giovannucci, 1999).
Fruits and vegetables are living entities and undergo
several physiological changes (Haard and Salunkhe,
1980). Moreover most of them contain more than 80
percent water and some even upto 90-95 per cent.
Even 5 per cent loss of water causes many
vegetables to appear wilted or shriveled and
eventually unmarketable. These characters
significantly limit the storage life of fruits and
vegetables (Madan et al, 1993). Tomato has limited
shelf life under ambient conditions and is highly
perishable. It creates glut during peak season of
production and becomes scanty during off-season.
Short shelf life coupled with inadequate processing
facilities results in heavy revenue loss to the country.
Food irradiation has been identified as a safe
and alternative technology of food preservation to
reduce the risk of food borne illness as a part of high
quality food production, processing, handling and
preparation. The process has been approved by more
than 40 countries around the world. The technology
utilizes a source of ionizing energy that passes
through food to destroy harmful bacteria and other
organisms. Often it is referred to as “cold
pasteurization”.
An attempt was made to process and
preserve tomato products viz. puree and crush study
the effect of irradiation on sensory qualities on storage
for 60 days.
Tomato puree: Tomatoes were thoroughly washed
and blanched at 85oC for 2-3 min. Blanching helps in
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loosening the skin, then after washing hands
thoroughly skin was removed Tomatoes were passed
through pulper to get the pulp and strained to remove
seeds. The strained pulp was concentrated to 12
percent total soluble solids (heavy tomato puree).In
the control product 0.5g of sodium benzoate per kg
was added as a preservative in the final stage of
preparation, cooled packed in polyethylene
bags.(Srivastava, 2004)
Tomato crush: Cut pieces of tomatoes were
concentrated to a brix of 15 by boiling to which glacial
acetic acid (5ml/kg) was added in the final stage of
preparation. In the control product, the preservatives
(0.4g of potassium metabisulphite+0.2g of sodium
benzoate per kg) were added in the final stage of
preparation and after cooling packed in polythene
bags.(Anand and Vijay, 1977).
Irradiation: Both the products were subjected to
gamma irradiations at dosages of 0.5kGy, 1.00kGy
and 2.00kGy. Control product in which chemical
preservatives were added. In puree (0.5g of sodium
benzoate per kg) and crush (0.4g of potassium
metabisulphite+0.2g of sodium benzoate per kg,
glacial acetic acid 5ml/kg) and no preservatives were
added in remaining treatments. Other three products
were subjected to 0.50 kilo Gray for duration of
11mins and 12seconds, 1.00 kilo Gray for duration
0f 22mins and 35 seconds, 2.00 kilo Gray for duration
0f 45mins and 7 seconds.
Sensory evaluation and storage: The evaluation
was done by a 10 trained panelists at Post Graduate
and Research Centre and College of Home Science,
Hyderabad. Judgments for the products were made
through rating products on a 9 point Hedonic scale
with corresponding descriptive terms ranging from
9 ‘like extremely’ to 1 ‘dislike extremely’. Parameters
like color, flavor, taste, texture and overall
acceptability were evaluated on 0 day, 30th day and
60th day. (Marek Sikaro et al. 2007).
The data obtained were subjected to statistical
analysis as per procedure outlined by Snedecor and
Cochran, (1983).
There were no significant changes observed
during storage period in the color of puree. Lu et al.(1986) reported that doses of gamma irradiation up
to 2000 Gy showed no effect on color of sweet potato.
Non significant interactions were observed between
treatments and storage periods in case of the colour
of tomato crush. Anuradha et al. (2002) also found
similar results in diced tomatoes by irradiation and
on storage. (Table 1).
Flavor development in tomato is
accompanied by the maturation process, which
involves many changes such as metabolic reactions
of synthesis and degradation of many compounds,
besides the complex energy transfer associated with
various phases of tomato development. (Hayasa etal. 1984)
Significant changes (P <0.05) were noticed
during the storage period, decrease in the flavor was
observed from 0 day (8.00%) to 60th day (7.60%) in
flavor of puree. The interaction between treatments
and storage periods were not significant. The flavor
of the tomato crush was not influenced by any of the
irradiation treatments, storage period or their
interaction. Fetter et al. (1969) found that doses up
to 5 kGy had no effect on flavor of commercial
tomato, orange, guava, red currant, black currant,
apricot, peach, pear and grape juices. (Table 2).
Significant changes (P<0.05) were observed
among treatments in tomato puree with irradiation
treatment of 1.00kGy (7.93%) scoring highest for taste
followed by irradiation treatment of 2.00kGy (7.73%)
and lowest score was recorded in irradiation treatment
of 0.50kGy (7.50%). There were no significant
changes noticed during storage period and
interactions were not significant. The decrease could
be due to the loss of volatile aromatic substances
responsible for taste during storage (Reddy, 2004).
Effect of gamma irradiation on taste of crush was
significant during the storage period. There was
decrease in the taste of crush from 0 day (8.02%) to
60th day (7.68%). The effects of treatments and their
interaction period of storage were found to be non
significant in case of the taste of crush. (Table 3).
The texture of both the products – puree and
crush was not influenced by treatments, periods of
storage on their interactions (Table 4). Higher
dosages of gamma irradiation had an impact on
aroma, flavor and textural properties of diced
tomatoes Anuradha et al. (2002). With increasing
levels of irradiation, panelists noted a decrease in
fresh tomato aroma and flavor and an increase in
ripe tomato aroma. Treatment with 1.24 and 3.70 kGy
significantly decreased firmness.
The consistency of tomato puree decreased
significantly by storing 60 days. There was no effect
of irradiation on the consistency of puree and crush,
the effects of storage were also not seen in case of
crush. (Table 5).
The overall acceptability of tomato puree was
not affected till 30 days, but decreased significantly
by storing for 60 days. The treatments of irradiation
did not show any influence on the overall acceptability
of puree as well as crush (Table 6).The effects of
storage were not seen on crush. The interaction
effects were also not significant. Bregvadze, (1963)
also found that no significant changes were observed
in overall acceptability of sterilized apple juice at 0.5
kGy.
The results of sensory evaluation indicated
that due to irradiation at 1.00kGy level the tomato
crush can be stored safely upto 60 days as there
was no change observed in various attributes like
taste, texture, color, flavor and overall acceptability.
The tomato puree can be stored safely for 30 days.
REDDY and LAKSHMI
Table 1. Color of preserved tomato products
Treatments 0 day 30th day 60th day Mean Tomato
puree Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
T1 (Control) 8.5 8 8.5 7.7 8.2 7.6 8.40 7.77 T2 (0.50kGy) 8.5 8 8.4 8 8.2 7.8 8.36 7.93 T3 (1.00kGy) 8.5 8 8.4 8 8.3 7.9 8.40 7.97 T4 (2.00kGy) 8.5 8.1 8.4 8 8.2 7.8 8.37 7.96 Mean 8.50 8.02 8.45 7.98 8.30 7.82
Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%
ForTreatments 0.134 NS 0.134 NS For periods 0.116 NS 0.116 NS For T*P 0.233 NS 0.233 NS
NS –Non Significant
Table 2. Flavor of preserved tomato products
Treatments 0 day 30th day 60th day Mean Tomato
puree Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
T1 (Control) 8 7.8 7.7 7.8 7.3 7.7 7.67 7.77 T2 (0.50kGy) 8 7.9 7.8 7.7 7.6 7.6 7.80 7.73 T3 (1.00kGy) 8 7.9 8 7.8 7.6 7.7 7.87 7.80 T4 (2.00kGy) 8 7.8 7.9 7.7 7.8 7.7 7.90 7.73 Mean 8.00 7.85 7.85 7.75 7.60 7.68
Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%
ForTreatments 0.151 NS 0.134 NS For periods 0.131 0.256* 0.116 NS For T*P 0.262 NS 0.232 NS
NS –Non Significant
Table 3. Taste of preserved tomato products
Treatments 0 day 30th day 60th day Mean Tomato
puree Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
T1 (Control) 7.6 8 7.6 7.8 7.5 7.7 7.66 7.83 T2 (0.50kGy) 7.6 8.2 7.5 7.9 7.4 7.8 7.50 7.97 T3 (1.00kGy) 8 7.9 7.9 7.7 7.9 7.6 7.93 7.73 T4 (2.00kGy) 7.9 8 7.7 7.8 7.6 7.6 7.73 7.8 Mean 7.77 8.02 7.67 7.80 7.60 7.68
Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%
ForTreatments 0.131 0.256* 0.134 NS For periods 0.113 NS 0.116 0.227* For T*P 0.226 NS 0.232 NS
NS –Non Significant
STUDY ON THE EFFECT OF IRRADIATION
Table 4. Textural properties of preserved tomato products
Trea
T1 (Co
T2 (0.5
T3 (1.0
T4 (2.0
Mean
Fortrea
For pe
For T*
Table 5. Consistency of preserved tomato products
Treatments 0th day 30th day 60th day Mean Tomato
puree Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
T1 (Control) 8.2 7.8 7.8 7.9 7.7 7.8 7.90 7.83
T2 (0.50kGy) 8 7.8 7.9 7.8 7.8 7.7 7.90 7.67 T3 (1.00kGy) 8.3 8 8.3 7.8 7.9 7.8 8.20 7.83 T4 (2.00kGy) 8.1 7.9 7.9 7.8 7.7 7.7 7.90 7.00 Mean 8.15 7.87 7.98 7.82 7.78 7.72
Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%
ForTreatments 0.123 0.245 0.130 0.256
For periods 0.107 0.210* 0.113 0.222 For T*P 0.214 0.420 0.226 0.443
NS –Non Significant
NS –Non Significant
Table 6. Overall acceptability of preserved tomato products
Treatments 0 day 30th day 60th day Mean Tomato
puree Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
Tomato puree
Tomato crush
T1 (Control) 8.1 7.9 8 7.6 7.7 7.5 7.93 7.67 T2 (0.50kGy) 8 7.9 8 7.8 7.8 7.7 7.93 7.83 T3 (1.00kGy) 8.2 8 8 7.9 7.8 7.8 8.06 7.86 T4 (2.00kGy) 8 7.9 7.9 7.9 7.8 7.7 7.90 7.83 Mean 8.08 7.92 7.98 7.80 7.78 7.67
Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%
ForTreatments 0.100 NS 0.142 NS For periods 0.086 0.170* 0.123 NS For T*P 0.173 NS 0.246 NS
NS –Non Significant
REDDY and LAKSHMI
REFERENCES
Anand, J. C and vijay sethi 1977. Whole tomatoconcentrate. Indian Horticulture. 22(2) :14.
Anuradha Prakasha, Jacqueline Manleya, SureshDeCostaa, Fred Caporasoa and Denise Foleyb.2002. The effects of gamma irradiation on themicrobiological, physical and sensory qualitiesof diced tomatoes. Radiation Physics andChemistry. 63: 387–390.
Brevadge, U. D. 1963. Preserving fruit juices by thecombined effects of gamma irradiation andascorbic acid. Navy Fiz. Metody ObrabotkiPisch. Produktov Kiev . sb. 2: 199-205.
Fetter, F., Stehlik, G K., ovascs, J and Weiss, S.1969. The Flavour halten einigergammabetrahlter Fruchtsaefte. Mitteilung-enirebe, Wein , Obstbau and Fruechtt-everwertung 19 :140-151.
Giovannucci, E. 1999. Tomatoes, tomato-basedproducts, lycopene, and cancer: review of theepidemiologic literature. Journal NationalisedCancer Institute. 91(4):317–331.
Haard, N. F and Salunkhe D.K. 1980. Perspectivesof post harvest physiology. In symposium:post harvest biology and handling of fruits andvegetables. AVI publishing Company,USA.pp:1-4.
Hayase, F., Chung, T,Y and Kato, H. 1984. Changesof volatile components of tomato fruits duringripening. Food Chemistry. 14: 113–124.
Lu, J.Y., S .White, P., Yakubu and Loretan, P.A.1986. Effects of gamma irradiation on nutritiveand sensory qualities of sweet potato storageroots. Journal Food Quality. 9: 425-435.
Madan, M S, Ullasa, B. A and Gopalakrishna, RaoK., P. 1993. Post harvest loses in vegetables.In advances in Horticulture vol 6-vegetablecrops part 2 (ed Chanda KL and Kalloo G)Malhotra Publishing House, New Delhi pp:1059.
Marek Sikaro., Stainslaw Kowalski., Piotr Tomasikand Marek Sedy. 2007. Rheological andsensory properties of dessert saucesthickened by starch-xanthan gum. Journal ofFood Engineering. 79: 1144-1151.
Reddy, G.N.V.V. 2004. Studies on methods onextraction of sapota juice for optimum yieldand quality. MSc. Thesis submitted toAcharya N.G. Ranga Agricultural University,Hyderabad, India.
Snedecor, G.W and Cochran, W.G.1983.StatisticalMethods Oxford and IBH publishingcompany New Delhi.
Srivastava, R.P. 2004. Fruits and Vegetablepreservation. Principles and practices. Tomatoprocessing. 265-266.
Tonucci, L.H., Holden, J.M., Beecher, G.R., Khachik,F., Davis, C.S and Mulokozi. G. 1995.Carotenoid content of thermally processedtomato-based food products. Journal ofAgricultural Food Chemistry. 43:579–586.
STUDY ON THE EFFECT OF IRRADIATION
Research NoteJ.Res. ANGRAU 40(3) 74-76, 2012
Date of Receipt : 07.02.2012 Date of Acceptance : 09.06.2012
COMBINING ABILITY STUDIES IN TOMATO (Solanum lycopersicum Mill.)
K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R. V. S. K. REDDY College of Horticulture, Andhra Pradesh Horticultural University,
Rajendranagar, Hyderabad-500 030.
This experiment was conducted at NBPGR,Regional Station, Rajendranagar, Hyderabad duringrabi and summer 2008-09. The treatments includedeight diverse genotypes of tomato used as lines(female parents) and four popular varieties used astesters (male parents). The selfed seeds of twelveparents were raised in the green house of NBPGR,Regional Station, Rajendranagar during rabi 2008 andwere crossed in a Line x Tester mating design. Theriped fruits were harvested, seeds from each crossedfruit were collected by using fermentation method forseed extraction. Twenty six days old tomatoseedlings were transplanted on 13th April 2009 with aspacing of 60 x 45 cm in a randomized block designwith three replications. The experimental unitconsisted of parents followed by F
1s in all the three
blocks. All the treatments were assigned randomlyto the experimental units. The data were recorded on13 characters in F1s and parents and analyzed foranalysis of variance and combining ability studies(Kempthorne, 1957).
Significant differences were noticed amongthe genotypes for all the traits studied. The Line xTester was found significant for all the charactersunder study except for ascorbic acid content . Thegca effects of the parents (Table 1) revealed that theline EC 145057 recorded significant positive gcaeffects for fruit weight, fruit length, fruit diameter andascorbic acid content. EC 163663 recorded significantpositive gca effects for TSS, fruit juice and yield perplant. Another line EC 338717 showed positivelysignificant gca effects for plant height, rind thicknessand negative gca effects for days to 50% flowering.
Among the testers, Pusa Ruby and PED werefound to be good general combiners for plant height,days to 50% flowering, fruit weight ,fruit length, fruitdiameter, fruit volume, rind thickness, TSS, fruit juice,yield and seed content. None of the parents was found
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to be good general combiner for all the characters.These results are inconformity with findings ofPremalakshmi et al (2006) and Rao et al (2007).
The sca effects of the crosses (Table 2)revealed that three crosses had significant andpositive sca effects for fruit yield per plant.EC 163663 x Pusa Ruby, EC 257489 x Arka Saurabh,EC 338735 x Marutham were found to be bestcombiners for fruit yield per plant. Kamal Veer et al.(2006), Pradeep Kumar Singh et al. (2006),Premalakshmi et al. (2006) and Rao et al. (2007) alsoreported positive significant affects of sca for fruityield per plant.
Based on the magnitude of sca effects indesirable direction for various characters, the crossEC 538735 x Marutham was found to be the bestperforming cross for five characters namely days to50 % flowering, fruit weight, fruit length, fruit diameterand fruit yield per plant. The next best hybrids wereEC 14507 x Pusa Ruby for fruit volume and rindthickness, EC 145057 x Arka Sourabh for TSS, fruitjuice (%), EC 163663 x PED for less seed content(%), EC 338714 x Pusa Ruby for more plant heightand ascorbic acid content. Hence, these crosseswere identified as good specific combiners for theabove characters. Similar views have also beenreported by Arun Joshi and Kohli (2006) and PujaRattan et al. (2008).
In general, the hybrids with significant scaeffects in the desirable direction involved parents withhigh x high, low x high and low x low gca effects,which indicated that both additive and non additivegene actions were involved in the inheritance of thesetraits.
Fruit yield is the ultimate result, which isdependent on its components. Griffing (1956)suggested that there would be no separate genesystem for yield per se and it is an end product of
COMBINING ABILITY STUDIES IN TOMATO (Solanum lycopersicum Mill.)
Tab
le 1
. Esti
mate
s o
f g
en
era
l co
mb
inin
g a
bilit
y f
or
lin
es a
nd
teste
rs f
or
thir
teen
ch
ara
cte
rs i
n T
om
ato
Lin
es
Pla
nt
h
eig
ht
(cm
)
Nu
mb
er
of
bra
nc
he
s
Da
ys t
o
50%
flo
we
rin
g
Fru
it w
eig
ht
(g)
Fru
it
len
gth
(c
m)
Fru
it
dia
mete
r (c
m)
Fru
it
vo
lum
e
(cc)
Yie
ld p
er
pla
nt
(g)
Rin
d
thic
kn
es
s
(mm
)
TS
S
(0b
rix)
Asc
orb
ic
ac
id(m
g/
100
g
pu
lp)
Fru
it
juic
e(%
)
see
d
co
nte
nt
(%)
EC
145
057
3.96
**
1.20
**
4.47
**9.
41**
0.
49**
0.40
**8.
83**
74
.69*
*0.
56**
0.
29**
1.78
**
4.44
**
-0.2
8**
EC
163
663
3.72
**
0.28
2.
22**
4.24
**
0.15
**0.
28**
6.47
**
103.
02**
0.19
**
0.33
**-0
.78*
* 7.
66**
-0
.07*
*
EC
238
308
4.59
**
-0.5
5 0.
89-1
4.09
**
-0.5
6**
-0.4
8**
-15.
37**
-1
28.6
5**
-0.2
4**
0.22
**1.
12**
1.
27**
0.
80**
EC
257
489
-12.
24**
0.
86**
4.
30**
5.41
**
0.13
**0.
20**
8.96
**
74.2
7**
-0.1
7**
-0.2
2**
-0.3
6**
2.48
**
-0.1
9**
EC
320
574-
1 3.
02**
-1
.30*
* -2
.95*
*-1
6.26
**
-0.5
6**
-0.6
4**
-15.
51**
-7
7.81
**-1
.34*
* -0
.48*
*-0
.63*
* -2
0.87
**
0.83
**
EC
338
714
-7.6
3**
0.78
**
-2.7
0**
0.49
-0
.06
-0.0
6*0.
41
-45.
31**
-0.7
0**
-0.1
9**
-1.6
8**
0.10
-0
.30*
*
EC
338
717
5.96
**
-1.6
4**
-5.7
0**
5.32
**
0.13
**0.
06*
2.39
* 45
.52*
*1.
76**
0.
080.
51**
1.
25**
-0
.24*
*
EC
338
735
-1.3
9 0.
36
-0.5
3**
5.49
**
0.29
**0.
25**
3.82
**
-45.
73**
-0.0
5 -0
.04
0.05
* 3.
68**
-0
.56*
*
SE
li
0.80
0.
29
0.47
1.03
0.
040.
031.
13
15.7
50.
04
0.06
0.03
0.
24
0.02
SE
li-
lj
1.13
0.
41
0.66
1.46
0.
050.
041.
60
22.2
80.
06
0.09
0.04
0.
34
0.03
Teste
rs
Pus
a R
uby
19.4
3**
0.66
**
-0.9
1**
4.24
**
0.19
**0.
16**
1.90
* 55
.10*
*0.
31**
0.
12**
-0.4
4**
-3.9
7**
-0.0
9**
PE
D
-14.
44**
-0
.22
1.43
**0.
07
0.06
**0.
16**
1.92
* 1.
98-0
.06*
-0
.01
0.03
* 2.
85**
0.
04**
Ark
a S
oura
bh
-1.9
4**
0.03
-0
.66*
-1.0
5*
-0.0
3*-0
.10*
*-0
.85
-20.
52-0
.17*
* 0.
01-0
.12*
* 1.
25**
0.
01*
Mar
utha
m
-3.0
5**
-0.4
7*
0.14
-3.2
6**
-0.2
2**
-0.2
2**
-2.9
7**
-36.
56**
-0.0
8**
-0.1
3**
0.53
**
-0.1
3 0.
05**
SE
ti
0.56
0.
20
0.33
0.
73
0.02
0.
02
0.80
11
.14
0.03
0.
04
0.02
0.
17
0.01
SE
ti-
tj
0.80
0.
29
0.47
1.
03
0.04
0.
03
1.13
15
.75
0.04
0.
06
0.03
0.
24
0.02
** S
igni
fican
t at 1
% le
vel;
*
Sig
nific
ant a
t 5 %
leve
l;
multiplicative interaction between yield and its
components. In the present investigation the cross
EC 338735 x Marutham with high per se performance
and sca effects for fruit yield per plant also exhibited
high sca effects for other important yield components
like fruit weight, fruit length and diameter.
REFERENCES
Arun Joshi and Kohli, U. K. 2006. Combining ability
and gene action studies for processing quality
attributes in tomato ( Lycopersicon esculentumMill.). Indian Journal of Horticulture
63(3): 289-293.
Griffing, B. 1956. Concept of general and specific
combining ability in relation to diallel crossing
system. Australian Journal of Biological
Sciences 9: 463-493.
Kamal Veer., Sharma, V. K and Uniyal S. P. 2006.
Combining ability studies in tomato [Solanumlycopersicon (Mill.) Wettsd.]. VegetableScience 33(1): 76-78.
Kempthorne, O. 1957. An introduction to GeneticStatistics.
Pradeep Kumar Singh., Singh B Singh, J. P andSantosh Singh., 2006. Combining ability intomato (Solanum lycopersicon Mill.).Vegetable science 33(1) : 85-87.
Premalakshmi V. ,Thangaraj, T Veeraragavathatham,D and Arumugam, T 2006. Heterosis andcombining ability analysis in tomato (Solanumlycopersicon Mill.) for yield and yieldcontributing traits. Vegetable Science33( 1): 5-9.
Puja Rattan,, Vidyasagar and Sanjeev Kumar., 2008.Line x tester analysis for combining abilitystudies involving bacterial wilt resistantgenotypes across environments in tomato.Indian Journal of Horticulture 65(2) 239-242.
Rao, E. S., Munshi, A. D Balraj Singh and Raj Kumar2007 Studies on heterosis and combiningability for yield and resistance to early blightin tomato. Indian Journal of Horticulture64 (3): 331-334.
VINAY et al
ATTITUDE OF THE RURAL WOMEN TOWARDS TELUGU TELEVISIONPROGRAMME -MEE AROGYAM MEE CHETULLOGRANDHI MANASA , R. GEETHA REDDY and M. PREETHI
Department of Home Science Extension and Communication Management, College of Home Science,Acharya N.G. Ranga Agricultural University, Saifabad, Hyderabad- 500 030
Research NoteJ.Res. ANGRAU 40(3) 77-78, 2012
Date of Receipt : 30.05.2012 Date of Acceptance : 21.06.2012
Television has been acclaimed to be themost effective media for diffusing information in torural areas and particularly to rural women than anyother mass media sources. (Meena and RekhaBhagat, 2011). Many health and nutritionalprogrammes are being produced in different channelsto improve the knowledge levels of the women infamily. “Mee Arogyam Mee Chetullo” is one of theprogrammes which gained lot of popularity andcredibility among the audience of Andhra Pradeshwith high Television rating points (TRP). Taking abovefacts into consideration the present study wasattempted to find out the attitude of rural womentowards ‘Mee Arogyam Mee Chetullo’ Programme.
Ex post facto research design was adoptedand Ranga Reddy District was purposively selectedfor the study. Out of the 37 Mandals of the RangaReddy district, one Mandal- Shamshabad wasselected by using random sampling Method. The listof villages under the selected mandal was collectedand four villages viz., Mutchintal, Kothwalguda,
email: [email protected]
Peddatopra, and Kavvaguda were selected randomly.120 rural women televiewers were selected asrespondents with the help of Sarpanch, anganwadiworkers and village level workers by usingproportionate random sampling technique from theselected villages. Personal interview method wasapplied to collect data by adopting the attitudinal scaledeveloped by Ganesh Kumar (2005) with suitablemodifications.
Attitude in the present study wasoperationalised as the degree of positive or negativeeffect associated with the programme Mee ArogyamChetullo with specific reference to the content,presentation and utility of the programme among thewomen respondents of the selected sample.
Based on the attitude of the respondentstowards the aspects like content, presentation andutility of the ‘Mee Arogyam Mee Chetullo’ Programmethey were classified into three categories by takingrange into the consideration – less favourable,favourable and more favourable attitude.
Table 1. Distribution of respondents according to their attitude towards the TV Programme
(n=120)
Mutchintal (n=30)
Kothwalguda (n=30)
Peddathopra (n=30)
Kavvaguda (n=30)
Total (n=120)
SI. No
Attitude
% % % % (%)
1. Less favourable ( below 51)
30.00
20.00 6.66 20.00 19.16
2. Favourable (52-57)
53.30 73.33 63.33 40.00 57.50
3. More favourable ( above 57)
16.66 6.66 30.00 40.00 23.33
Total 100 100 100 100 100
Majority of the respondents in Mutchintalvillage had favourable attitude towards theprogramme. A little less than three fourths of therespondents in Kothwalguda village had favourableattitude. In Peddathopra village two thirds of therespondents had favourable attitude. Forty per centof the women in Kavvaguda village had favourableattitude and another 40.00 per cent had morefavourable attitude towards the programme. On thewhole 57.50\ per cent of the respondents had afavourable attitude
Right from the inception of the programme ithad been covering wide range of topics on nutritionand health irrespective of gender, age, along with
remedies that can be practiced at home level withavailable resources.
This may be the reason for the womenteleviewers to develop favourable attitude towardsthe programme.
Reddy (2002) in his study on AnnadataVelugubata a farm telecast programme reported that60.8 per cent of the viewers had medium useful levelof opinion about the programme.
Profile characteristics of the rural womenwere studied and correlation analysis was carried outin order to study the nature of relationship betweenthe independent variables and attitude of therespondents towards the programme.
Table 2. Correlation analysis of independent variables with dependent variable - Attitude
(n= 120)
S.No Independent Correlation coefficient (r)Variables values
1. Age -0.0026 NS
2. Education 0.2059 *
3. Family type 0.0507 NS
4. SES 0.2054 *
5. Extension contact 0.1650 NS
6. Mass media exposure 0.1238 NS
7. Social participation 0.2798 **
8. Urban contact 0.0863 NS
9. Nutrition and health orientation 0.2590 **
10. Frequency of viewing 0.4579 **
11. Length of viewing 0.2693 **
*: Significant at 0.05 level of probability, ** : Significant at 0.01 level of probability
NS= non significant
Social participation, nutrition and healthorientation, frequency of viewing and length ofviewing had positive and significant relationship withthe attitude at 1% level of significance where asEducation and SES, had positive and significantrelationship at 5% level of significance with theattitude of the respondents towards the programme.
REFERENCES
Ganesh Kumar, P. 2005. A study on Rythumitratelevision programme for farm televiewers inChitttoor District of Andhra Pradesh.
M.Sc(Ag).Thesis. Acharya N. G RangaAgricultural University. Hyderabad.
Meena, K.C and Rekha Bhagat. 2011. Socio profilecharacteristics and psychological factorsinfluencing the farm telecast viewing. Journalof Communication Studies. 29(2): 47-54.
Reddy, K. 2002. A critical analysis of AnnadataVelugubata - Farm telecast programme inAndhra Pradesh. M.sc. (Ag) Thesis. AcharyaN.G. Ranga Agricultural University,Hyderabad.
MANASA et al.
Research NoteJ.Res. ANGRAU 40(3) 79-81, 2012
Fertilizers play an important role in increasingthe production and improving the quality of vegetablesand among them Potassium is considered to be primenutrient because of its role in various physiologicalprocesses. Besides potassium, organic products likeTriacontanol (a neem based product with the formulaCH3(CH2)28CH2OH) and K- humate also play animportant role in manipulating the yield potential andaugmenting the uptake of nutrients in many vegetablespecies including tomato. Hence, keeping all this inview an experiment was conducted.
A field experiment was conducted on a sandyclay soil at College Farm, College of Agriculture,Rajendranagar, Hyderabad during kharif season of2011. It was laid out in Randomized Block Designwith 3 replications and 16 treatments. The soil understudy was sandy clay in texture, slightly alkaline (7.64pH) and normal in salt content (0.19 dS m-1). Thecation exchange capacity of the soil was 35.8 c mol(p+) kg-1. The soil was medium in organic carbon(0.67%), low in available nitrogen (156.7 kg N ha-1),high in available phosphorus (57.0 kg P ha-1) andpotassium (449.0 kg K ha-1).Tomato fruits wereharvested at five days interval and a total of 14pickings were taken and yield was recorded, the drymatter yield at flowering and harvest were alsorecorded and expressed in q ha-1.
The dry matter put forth by tomato atflowering and harvest was significantly influenced bythe combined application of inorganic K, potassiumhumate and triacontanol.
At flowering, the dry matter produced withthe individual application of recommended doseinorganic K @ 60 kg K
2O ha-1, foliar applications of
triacontanol and potassium humate were 23.79, 21.57and 20.30 q ha-1 respectively.
RESPONSE OF TOMATO (Solanum lycopersicum L.) TO POTASSIUMFERTILIZATION ALONG WITH FOLIAR APPLICATION OF POTASSIUM
HUMATE AND TRIACONTANOLPALAKSHI BORAH, V. SAILAJA, P.CHANDRASEKHAR RAO and A. PRATAP KUMAR REDDY
Department of Soil Science and Agricultural ChemistryCollege of Agriculture, Acharya N.G. Ranga Agricultural University,
Rajendranagar, Hyderabad -500 030
Date of Receipt : 26.05.2012 Date of Acceptance : 19.06.2012
A higher dose of 90 kg K2O ha-1 also was
found to be inferior with a dry matter production of25.17 q ha-1. Individual application of eithertriacontanol or potassium humate or their combinedapplication without inorganic K produced significantlylower dry matter yields of tomato.
Among the treatments, T4 receiving 60 kgK
2O ha-1 along with foliar application of potassium
humate and triacontanol showed significantly higherdry matter production of 30.79 q ha-1 which was 29% more when compared to control receivingrecommended dose of 60 kg K
2O ha-1. However, this
was on par with the dry matter produced by integratedapplication of potassium humate and triacontanolalong with a lower dose of 45 kg K
2Oha-1.
The mean dry matter yield at harvest showeda significant increase from 21.17 q ha-1 with 30 kgK
2O ha-1 alone to 34.19 q ha-1 obtained from the
treatment receiving 60 kg K2O ha-1along with the foliar
applications of triacontanol and potassium humatewhich surpassed the dry matter yield obtained withinorganic K alone at the same level of application by35 %.
These findings are in agreement with thereports of Besford and Maw (1975). Potassium playsa vital role in growth and plant productivity,metabolism, ionic balance, activation of severalenzymes and plant defense systems (Marschner,2002). Plant growth regulator triacontanol plays animportant role in manipulating the yield potential andaugmenting the uptake of nutrients in many vegetablespecies including tomato (Muthuvelet al., 2001). TRIAtreated plants had a significantly higher rate ofphotosynthesis and hence increased the dry weightby 30 per cent as was documented by Eriksen et al.(1981). The positive influence of TRIA on tomato isphotosynthesis stimulation and enhanced water
email: [email protected]
uptake, which eventually results in increase in thedry weight.
The yield of tomato varied from 102.7 q ha-1
in the treatment receiving 30 kg K2O ha-1 alone to a
significantly higher yield of 135.0 q ha-1 when 60 kgK
2O ha-1 was integrated with triacontanol and
potassium humate, which was 18 per cent more whencompared to control (T1) receiving recommended doseof K.
However, the fruit yields obtained from thetreatments integrating the triacontanol and potassiumhumate with either 45 or 60 kg K
2O ha-1were all at a
par. Individual applications of either inorganic K atany level or triacontanol or potassium humate werefound to be significantly inferior to the best treatment.
Conjunctive use of chemical fertilizer withfoliar application of potassium humate and triacontanol
produced higher yields as compared to their individualapplications due to increase in nutrient uptake fromsoil and effective utilization of foliar applied nutrients.This might be due to soil application of inorganicpotassium resulting in increased uptake of nutrientsand improved translocation of photosynthates andother metabolites to the reproductive parts and foliarapplication of potassium humate and triacontanolresulted in accumulation of increased assimilates inthe sink and increase in chlorophyll content andphotosynthetic area of the plant, which is responsiblefor the increased photosynthetic efficiency (Muthuvelet al., 2001 and Marschner, 2002). The results are inagreement with the findings of Duraisamy and Mani(2002) in tomato. Schnitzer (1978) reported that thefavorable effect of humic substances in stimulatinggrowth, yield and yield attributes could be attributedto the presence of auxin like properties in humic acid.
T1 : 60 kg K2O ha-1 (Recommended K) as basal application 23.79 25.24 114.2
T2 : T1 + Foliar application of Triacontanol 28.61 32.47 125.8
T3 : T1 + Foliar application of K- humate 26.84 30.79 128.8
T4 : T1 + Foliar application of Triacontanol and K- humate 30.79 34.19 135.0
T5 : 45 kg K2O ha-1 as basal application 21.80 23.44 106.7
T6 : T5 + Foliar application of Triacontanol 25.45 27.89 127.2
T7 : T5 + Foliar application of K- humate 26.29 24.05 126.1
T8 : T5 + Foliar application of Triacontanol and K- humate 29.91 29.47 124.7
T9 : 30 kg K2O ha-1 as basal application 21.15 21.17 102.7
T10 : T9+ Foliar application of Triacontanol 23.13 25.68 124.7
T11 : T9 + Foliar application of K- humate 23.91 22.40 123.9
T12 : T9+ Foliar application of Triacontanol and K- humate 25.91 28.41 125.1
T13 : Foliar application of Triacontanol 21.57 24.23 115.2
T14 : Foliar application of K- humate 20.30 23.22 114.0
T15 : Foliar application of Triacontanol and K- humate 22.19 26.66 119.0
T16 : Inorganic K @ 90 kg K2O ha-1 as basal application 25.17 27.13 117.4
CD at 5% 4.93 5.37 11.26
SE(d) ± 2.40 2.62 5.48
Table1. Effect of individual and integrated application of inorganic potassium, potassium humateand triacontanol on dry matter production and fruit yield of tomato
Treatments Dry matter Fruitproduction (q ha-1) yield
Flowering Harvest
BORAH et al
REFERENCES
Besford, R. T and Maw, G. A. 1975.Effect ofpotassium nutrition on tomato plant growth andfruit development. Plant and soil. 42: 395-412.
Duraisami, V. P and Mani, A. K. 2002. Effect of majornutrients on yield and fruit quality of tomatounder rainfed condition in Entisol. South IndianHorticulture. 50(1-3): 56- 64.
Eriksen, A. B., Sellden, G., Skogen, D and Nilsen,S. 1981. Comparative analyses of the effectof triacontanol on photosynthesis,photorespirstion and growth of tomato(C3-plant) and maize (C4-plant). Planta.152: 44-49.
Marschner, H. 2002. Mineral nutrition of higher plants.
Academic Press, London.
Muthuvel, P., Muralidharan, R and Saravanan,
A. 2001. Effect of bio-regulators on soil
available nutrients, yield and uptake of nutrients
by Tomato (Lycopersicon esculentum, Mill.).
Madras Agricultural Journal. 115-117.
Schnitzer, M. 1978. Humic substances- Chemistry
and reactions. In: Schnitzer, M and Khan,
S. V (Eds.) Soil Organic Matter (pp. 1-64).
Elsevier Scientific Publishing Company,
Amsterdam.
RESPONSE OF TOMATO TO POTASSIUM FERTILIZATION
RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS INSUPPLY CHAIN MANAGEMENT OF HORTICULTURAL PRODUCTS
M.BHAVYAMANJARI, M.SURYAMANI and C.PADMAVENIExtension Education Institute, Acharya N.G.Ranga Agricultural University
Rajendranagar, Hyderabad -500030
Date of Receipt : 26.05.2012 Date of Acceptance : 01.06.2012
Supply Chain Management refers to the
management of the entire set of production,
distribution, and marketing processes by which a
consumer is supplied with a desired product. (Gadre
and Bomandes, 2004). The present study was taken
up with the objective to find out recommended
specific production practices followed by farmers in
SCM of horticultural products.
An exploratory research design was followed
to conduct the present study. Three districts namely,
East Godavari, Ranga Reddy and Cuddapah of
Andhra Pradesh were selected based on the highest
area and production of horticultural crops. 40 banana
growing farmers, 30 onion farmers, 40 tomato farmers
and 40 mango farmers were selected for the study.
Thus a total of 150 farmers were selected from
existing models of SCM at Mandal level by following
proportionate random sampling. The interview
schedule consisted of six major components viz.,
land preparation, seed and sowing, manure and
fertilizer application, weeding, irrigation and plant
protection measures. Schedule was administered to
farmers. Responses were obtained and frequency
and percentage were calculated for each practice.
The details of important production practices
as per the recommendation and the frequency of
adoption by different categories of horticultural
farmers are furnished in Table1.
Over all distribution of the farmers into three
categories (low, medium and high) as per the adoption
of specific production practices is given in Table 2.
Majority of the banana farmers adopted most
of the specific practices. This might be due to the
fact that, the percentage of rejection of produce was
high during the initial stages of establishment because
farmers were not accustomed to producing good
quality produce in a scientific manner. Hence the
farmers changed their practices to provide required
good quality products to the retail outlets.
In case of tomato farmers, majority of the
SCM farmers adopted specific production practices.
Here again, farmers changed their cultivation
practices due to larger scale rejection of produce
during initial period for want of quality specifications.
Due to change in cultivation practices the rate of
rejection was reduced.
Majority of Onion farmers preferred to supply
their produce to the Consolidation Center, as it
provided them stable prices and assured market,
compared to the highly volatile prices at the wholesale
market. To supply the required quality, farmers
adopted specific practices required by the
Consolidation Center.
Regarding mango farmers, majority of them
fell under high category. Large land holding, medium
level of risk taking behavior, medium income level
and medium economic orientation were the attributes
for changing the farmers attitude to adopt the
recommended practices. It was aimed to provide the
quality produce to retail outlets.
email : [email protected]
Research NoteJ.Res. ANGRAU 40(3) 82-85, 2012
RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS
Table1. Distribution of SCM farmers according to their recommended specific production practices
Banana (N=40)
Tomato (N=40)
Onion (N=30)
Mango (N=40)
S.No Specific Production practices Practices/
F & (%) Practices/
F & (%) Practices/
F & (%) Practices F & (%)
1. Soil and field preparation
a. Soil testing
Conducted 24
(60.00)
Conducted 24
(60.00)
Conducted 17
(56.67)
Conducted 24
(60.00) b.
Ploughings for field preparation
3 ploughings
25 (62.50)
3 ploughings
25 (62.50)
3 ploughings
20 (66.67)
3 ploughings
31 (77.50)
2. Seed and Sowing
a. Recommended varieties
Chakrakeli, Amruthapani,
Dwarf Cavendish and
Robusta 25
(62.50)
Hybrids 25
(62.50)
Agri found dark red/ light
red, Arka pragati,
Agrifound rose and
Early Grano 21
(70.00)
Totapuri, Neelam and
Badami 25
(62.50)
b. Method of propagation
Tissue culture 25
(62.50) - - -
c. Time of planting February – April
26 (65.00)
June – July 31
(77.50)
August – September
22 (73.33)
-
d Size of pits 60x60x60
27 (67.50)
- - 1mx1mx1m
25 (62.50)
e Recommended spacing
Dwarf- 1.8mx1.8m Robusta – 2.1mx2.1m
27 (67.50)
60cmx 45cm 28
(70.00)
Bulbs dibbled at 15 cm on
the side of 45 cm wide ridges
23 (76.67)
10mx 10m 28
(70.00)
f Method of seed treatment -
Seeds are treated with
Carbendazim Seedlings are treated with
0.2% Dithane M-45 and 0.1%
Nuvacron 26
(65.00)
Seeds are treated with fungicides
20 (66.67)
Seeds are treated with fungicides
29 (72.50)
g Recommender seed rate -
75-100g seeds per hectare
32 (80.00)
10-12 kg seeds per
acre 22
(73.33)
-
BHAVYA et al
h Recommended method of sowing
-
Raising seedlings and Transplanting
29 (72.50)
Direct sowing 21
(70.00) -
i Recommended
dose of FYM before sowing
-
5-8 kg of manure
before sowing 28
(70.00)
20-25 tonnes/ ha 24
(80.00)
-
3. Manure and fertilizer application
a. Use of manures/ Vermi-compost
5-8 tonnes vermi compost
10-12 tonnes FYM/acre
26 (65.00)
5-10 tonnes of FYM 28
(70.00)
14-16 tonnes of FYM
20 (66.67)
10-15 kg FYM with top soil
used as filling material
27 (67.50)
b. Use of fertilizers
1-2 kg complex fertilizers
27 (67.50)
100kg/ha complex fertilizers
32 (80.00)
50 kg/ ha DAP and
potassium 22
(73.33)
1-3 kg/ tree complex fertilizer
24 (60.00)
4. Inter cropping
a. Inter crops
Leguminous crops
25 (62.50)
- -
Maize and red gram
29 (72.50)
5. Weeding and inter cultivation
a. Time of weeding
Once in a month after planting
27 (67.50)
40-45 days after transplanting
28 (70.00)
After 45 days 18
(60.00) -
b. Use of weedicides
0.4% Glycel 25
(62.50)
Pendimethalin @ 1kg ai/ha
28 (70.00)
Basalin @ 1 lit/ha 20
(66.67)
-
c. After care De suckering
25 (62.50)
- - -
6. Irrigation
a. Recommended no. of Irrigations
3-5 days after planting
25 (62.50)
8-12 days after sowing
29 (72.50)
8-20 days after sowing
21 (70.00)
15 days after planting
26 (65.00)
7. Plant protection measures
a. Disease control measures
25 (62.50)
30 (75.00)
- 25
(62.50)
b. Pest control measures
25 (62.50)
25 (62.50)
22 (73.33)
26 (65.00)
Figures in parentheses indicate percentage
REFERENCES
Gadre, H and Bomandes, J. 2004. An Introductoryof supply chain management.The International Journal of LogisticsManagement. 8 (1): 15–24.
RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS
Table2. Overall distribution of SCM farmers towards adoption of specific practices
.
Figures in parentheses indicate percentage
Banana (N=40)
Tomato (N=40)
Onion (N=30)
Mango (N=40)
Category
F & (%) F & (%) F & (%) F & (%)
Low 13 (32.50)
8 (20.00)
4 (13.33)
10 (25.00)
Medium 1 (2.50)
6 (15.00)
7 (23.33)
4 (10.00)
High 26 (65.00)
26 (65.00)
19 (63.33)
26 (65.00)
The herb Aloe is as old as human civilization.It belongs to the family “Liliaceae”. Aloe was knownto Indians for its medicinal value since timeimmemorial in the name of Ghrit kumaree orKanyasara. The Aloe is known as “Mussambar” inIndian market (Saroj et al., 2004). The inner mostpart of the leaf is a clean, soft, moist and slipperytissue where water is held in the form of viscousmucilage called gel (Newton, 2004). The gel is therich source of polysaccharides, antioxidants,enzymes, minerals and vitamins (Chauhan et al.,2007). For centuries, this plant has been used for itsmedicinal and therapeutic properties. It has a historyof use in folk medicine for treating skin and otherdisorders. Today, the Aloe industry is flourishing andthe gel is being used in many products, such asfresh gel juice and other formulations for health,medical and cosmetic purposes. The leaves are tobe harvested at the right age and cut exactly at rightplace on the plant to ensure the best gel (Chauhanet al., 2007). Heating of gel is an effective method ofpasteurization and add better flavour (He et al.,2005). Gel heating may change the compositionwhich also has effect on storage. Aloe gel can bestored for more number of days (up to 30 days) at5°C without any deterioration in quality (Hemalathaet al., 2008). Keeping in view the composition,importance and usage of Aloe gel, the presentinvestigation was carried out.
The experiment was conducted during 2010at Herbal garden, College of Horticulture,Rajendranagar, Hyderabad to study the effect oftemperatures on physico- chemical characteristicslike pH, acidity and gel microbial content in differentaccessions of Aloe during storage. Three accessionsof Aloe viz., yellow flowering accession-1, yellow
EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES ONPHYSICO-CHEMICAL CHARACTERISTICS AND MICROBIAL CONTENT IN
DIFFERENT ACCESSIONS OF ALOE (Aloe barbadensis Miller)
B. AMARESWARI, M.PADMA, M.RAJKUMAR and A. SIVA SHANKARHerbal garden, College of Horticulture, Andhra Pradesh Horticultural University,
Rajendranagar, Hyderabad-500 030.
Date of Receipt : 19.01.2012 Date of Acceptance : 15.06.2012
flowering accession-2 and orange floweringaccession-3 were studied at three temperatures viz.,50°C, 75°C and 100°C. Nine treatments wereevaluated in Completely Randomized Design withfactorial concept in three replications.
Healthy and matured leaves of differentaccessions were harvested manually. Afterharvesting, Aloe leaves were washed thoroughly. Thealoetic juice was separated from the leaves by cuttingthem transversely at the base and keeping the cutportion touching the ground. The leaf was allowed tostand in slanting position for half an hour to removeyellow latex from leaf. The leaves were again washedthoroughly and cut into pieces by stainless steel knifeunder hygienic conditions. Then the outer peels wereseparated. Then the extracted gel from the leaveswas thoroughly homogenized in a mixer and gel ofeach accession was heated as per treatments for 15minutes and stored at room temperature. Standardpreservative (Sodium benzoate 1000 ppm + Citricacid 1%) was added to aloe gel. Then the juice wasanalyzed for pH, acidity and microbial content at every10 days interval up to 30th day of storage.
The characteristics of three accessions wereas given under.
Accession-1 leaves were dark green in colorwith purple tinge. On an average leaf size, weightand thickness were more than the accession-2 andaccession-3. Spines on leaf margin were set closerwhen compared to accession-2 and accession-3. Gelcolor was yellow.
Accession-2 leaves were green in color.Distance between the spines on leaf margin wasmedium when compared to accession-1 andaccession-3. Gel color was yellow.
email: [email protected]
Research NoteJ.Res. ANGRAU 40(3) 86-89, 2012
Accession-3 leaves were green in color asaccession-2. On an average leaf size, weight andthickness were less than accession-1 and accession-2. Small white spots appeared on lower surface ofthe leaf. Spines on leaf margin were set widely whencompared to accession-1 and accession-2. Gel colorwas pale yellow and very sticky when compared toyellow flowering accessions.
Among the three accessions, the highest pH(5.17, 5.30, 5.63 and 5.89) was recorded by orangeflowering accession-3 followed by yellow floweringaccession-2 (4.76, 4.83, 5.11 and 5.41) and yellow
flowering accession-1 (4.41, 4.48, 4.64 and 4.77) atday1, 10th, 20th and 30th day of storage respectively.Among the three temperatures, the highest pH wasrecorded by heating at 50°C (4.79, 4.90, 5.20 and5.46) and 100°C temperature (4.79, 4.89, 5.19 and5.46) followed by 75°C (4.76, 4.82, 5.0 and 5.16).
There was a significant increase in pH withincrease in storage period in all the accessions. Itmight be due to the fact that, acidity of Aloe geldecreases with the extension of storage periodirrespective of the temperature. Similar observationswere reported by Hemalatha et al. (2005) in Aloe and
( pH) (ACIDITY) Days after storage Days after storage
Fresh 10 20 30 Fresh 10 20 30
A1 4.41 4.48 4.64 4.77 0.24 0.24 0.22 0.22 A2 4.76 4.83 5.11 5.41 0.16 0.16 0.15 0.14 A3 5.17 5.30 5.63 5.89 0.07 0.07 0.07 0.06
SEm ± 0.0471 0.0497 0.0537 0.0595 0.0057 0.0057 0.0057 0.0056
CD at 5% 0.0991 0.1044 0.1128 0.1250 0.0120 0.0120 0.0120 0.0117
t1 4.79 4.90 5.20 5.46 0.15 0.15 0.14 0.14
t2 4.76 4.82 5.00 5.16 0.16 0.16 0.15 0.15
t3 4.79 4.89 5.19 5.46 0.15 0.15 0.14 0.14
SEm ± 0.0471 0.0497 0.0537 0.0595 0.0057 0.0057 0.0057 0.0056
CD at 5% 0.0991 0.1044 0.1128 0.1250 0.0120 0.0120 0.0120 0.0117
Accessions × Temperatures
A1 t1 4.43 4.50 4.77 4.83 0.24 0.23 0.22 0.22
A1 t2 4.40 4.43 4.53 4.67 0.24 0.24 0.23 0.23
A1 t3 4.40 4.50 4.63 4.80 0.24 0.23 0.22 0.22
A2 t1 4.77 4.87 5.17 5.53 0.16 0.16 0.15 0.14
A2 t2 4.73 4.77 5.03 5.20 0.16 0.16 0.15 0.14
A2 t3 4.77 4.87 5.13 5.50 0.15 0.15 0.15 0.14
A3 t1 5.17 5.33 5.67 6.00 0.07 0.07 0.06 0.06
A3 t2 5.13 5.27 5.43 5.60 0.08 0.08 0.07 0.07
A3 t3 5.20 5.30 5.80 6.07 0.07 0.07 0.06 0.06
SEm ± 0.0817 0.0861 0.0930 0.1030 0.0099 0.0099 0.0099 0.0097
CD t 5% 0 1716 0 1808 0 1953 0 2165 0 0208 0 0208 0 0208 0 0203
TREAT-MENTS
Table 1. Effect of heating at different temperatures on gel pH and acidity indifferentaccessions of Aloe.
EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES
Gajanana (2002) in amla juice. A combination of
orange flowering accession-3 heated at 100°C has
recorded the maximum pH (5.20, 5.30, 5.80 and 6.07)
at all storage intervals. But the optimum pH of Aloe
was maintained by yellow flowering accession-1heated at 75°C (4.40, 4.43, 4.53 and 4.67) at day1,
10th, 20th and 30th day of storage respectively.
The highest gel acidity (0.24, 0.24, 0.22 and
0.22%) was recorded by yellow flowering accession-
1 followed by yellow flowering accession-2 (0.16,
0.16, 0.15 and 0.14%) and orange flowering
accession-3 (0.07, 0.07, 0.07 and 0.06%) at day1,10th, 20th and 30th day of storage respectively. Among
the temperatures, 75°C has recorded the maximum
gel acidity (0.16, 0.16, 0.15 and 0.15%) at all storage
intervals.
The results indicated that there was acorresponding decrease in acidity during the storageperiod. Similar observations were reported in Aloeby Hemalatha et al. (2005). The result showed thatthere was an inverse relationship between the pHand acidity in Aloe gel. The maximum gel acidity(0.24, 0.24, 0.23 and 0.23%) was recorded by yellowflowering accession-1 heated at 75°C at all storageintervals.
Similarly, Miranda et al. (2009) reported thata drying temperature of 80°C and 90°C resulted insignificant variation and/ loss of the physico-chemicaland nutritional properties of the gel. But, there was aminor alteration in the physico-chemical andnutritional properties of Aloe gel produced at dryingtemperature of 60-70°C, which resulted in theproduction of highquality gel.
Table 2 . Effect of heating at different temperatures on microbial count indifferent accessions of Aloe.
Microbial count
1st day 10th day 20th day 30th day
Treatments
B Y/M B Y/M B Y/M B Y/M A1 t1 - - 28 15 63 38 >100 >100
A1 t2 - - 10 5 35 27 87 75
A1 t3 - - 22 12 50 36 >100 93
A2 t1 - - 37 21 75 54 >100 >100
A2 t2 - - 15 9 44 32 92 79
A2 t3 - - 29 15 50 41 >100 >100
A3 t1 - - 40 30 92 65 >100 >100
A3 t2 - - 17 10 60 35 >100 85
A3 t3 - - 35 17 73 47 >100 >100
The gel microbial count increased graduallywith increase in storage period up to 30th day in allthe treatments (Table 2). On day 1, no microbial countwas observed in all the treatments. On 10th day ofstorage highest microbial count was observed inorange flowering accession-3 heated at 50°C (bacterialcount-40, mould count-30) which increased further(bacterial count- >100, mould count- >100) at 30th
day of storage. The lowest microbial count wasrecorded with yellow flowering accession-1 heated
B - Bacterial count, Y/M - Yeast / Mould count
at 75°C temperature on 10th day of storage (bacterialcount-10, mould count- 5) which increasedconsiderably (bacterial count- 87, mould count- 75)at 30th day of storage.
Aloe gel and leaf itself has an antimicrobialactivity. The results obtained showed that there wasslight increase in microbial load in all the treatmentsat all heating temperatures during the storage period.The Aloe gel can be stored for 20 days at roomtemperature without deterioration in quality. Similar
AMARESWARI et al
observations were reported by Hemalatha et al.(2005) in Aloe.
At 30th day of storage of Aloe gel, significantmicrobial load was observed in all the treatments.But, Aloe gel heated at 75°C had more storagestability. He and Changhang (2006) reported that thepolysaccharide from Aloe exhibited a maximal
stability at temperature of 70°C where as the stabilitydecreased either at higher or lower temperatures. Theincreased microbial count was attributed to decreasedacidity and increased sugars during the storageperiod. The experimental results indicated that,Yellow flowering accession-1 heated at 75°C wasgood in keeping quality.
REFERENCES
Chauhan, O. P., Raju, P. S., Farhat Khanum andBawa, A. S. 2007 Aloe vera - Therapeuticand Food applications Indian Food Industry.26(3):43-51.
Hemalatha, P., Vadivel, E., Saraswathi, T andRajamani, K. 2005 Role of preservatives andstorage temperature on the post harvestquality of Aloe vera gel. Academy of PlantSciences. 21:2, 471-473. 5 ref.
He, Q., Changhong, L., Koje, E and Tian, Z. 2005Quality and safety assurance in the processingof Aloe vera gel juice. Food Control. 16:95-104.
Miranda, M., Maureira, H., Rodriguez, K and Vega-Galvez, A. 2009 Influence of temperature onthe drying kinetics, physicochemicalproperties, and antioxidant capacity of Aloevera gel.(Aloe barbadensis Miller). Journal ofFood Engineering. 91:2, 297-304.
Newton, L. E. 2004 Aloes in habitat at In aloes thegenus Aloe; Reynolds T Ed CRS press BocaRaton pp:3- 36.
Saroj, P. L., Dhandar, D.G and Singh, R.S. 2004Indian Aloe. Central Institute for AridHorticulture, Bikaner, Rajastha, pp-3-6.
EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES
Sunflower (Helianthus annuus L.) holds greatpromise as an edible oilseed crop because of its shortduration, photo-insensitivity, drought tolerance andwide adaptability to different agro-climatic regions andsoil types. Sunflower can play a key role in meetingthe shortage of edible oils in the country. Lowproductivity of sunflower (625 kg ha-1) is mainly dueto its cultivation with low and imbalanced nutrition.
Nitrogen is crucial for growth anddevelopment while, sulphur fertilization is most criticalfor oil and protein synthesis besides seed yieldenhancement. After N, P and K, S is the fourthnutrient, whose deficiency is widespread in India(Sakal et al., 2001). N and S deficiencies are commonas the crop is grown mostly on energy starvedconditions with poor organic matter content. A suitablecombination of major and secondary nutrients is byand large the most important factor that affects theyield and quality of sunflower oil. This warants thatnutrient schedule should be worked out for differentgrowing conditions and to sustain soil health and cropproduction. Keeping this in view the present studywas undertaken to find out the response of sunflowerto different levels of nitrogen and sulphur during kharif2011 in Alfisols.
The experiment was conducted at Collegefarm, College of Agriculture, Rajendranagar. The soilwas sandy loam in texture, slightly alkaline in reaction(pH 7.5), low in organic carbon (0.38 %), low inavailable nitrogen (226.4 kg ha-1), medium in availablephosphorous (38.7 kg ha-1) and potassium (258.0 kgha-1) and low in available sulphur (17.9 kg ha-1).
The experiment was laid out in a randomizedblock design with factorial concept, replicated thrice,having nine treatment combinations comprising threenitrogen levels (60, 90 and 120 kg ha-1) and threesulphur levels (0, 15 and 30 kg ha-1). A uniform doseof 60 kg P
2O
5 ha-1 and 30 kg K
2O ha-1 was applied
Research NoteJ.Res. ANGRAU 40(3) 90-93, 2012
EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTHAND YIELD OF SUNFLOWER (Helianthus annuus L.)
S. PAVANI, K. BHANU REKHA, S. N. SUDHAKARA BABU and G. PADMAJADepartment of Agronomy, College of Agriculture
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030
email : [email protected]
through di-ammonium phosphate and muriate ofpotash respectively as basal dose to all the plots.Nitrogen was applied through urea as per thetreatments in three splits i.e. half at sowing, one fourthat bud initiation stage and the remaining one fourthat flowering stage. Sulphur through elemental sulphur(Gromor S - 90 % S) was applied as per treatments15 days before sowing, in presence of moisture andincorporated into the soil. The crop (DRSH-1 hybrid)was sown on 16th July adopting a spacing of 60 cm x30 cm.
Irrigation was given as and when requiredconsidering the rainfall. During the crop period a totalof 466.1 mm rainfall was received in 29 rainy days.No specific pest and disease incidence was noticed.Pre-emergence herbicide pendimethalin 30% EC @240 ml ha-1 was sprayed one day after sowing inoptimum soil moisture followed by two hand weedingsat 20 and 40 days after sowing. The crop washarvested on 19th October, threshed, dried and seedyield was recorded.
Application of nitrogen and sulphursignificantly influenced the growth parameters, yieldattributes and yield of sunflower. The increase in theplant height, dry matter accumulation, stem girth, headdiameter, number of filled seeds head-1, seed yieldand stalk yield were significant with each incrementlevel of nitrogen up to 120 kg N ha-1 and sulphur upto 30 kg S ha-1.
Significant N x S interaction was observed onplant height, dry matter accumulation, number of filledseeds head-1, seed yield and stalk yield.
Application of 120 kg N ha-1 registeredsignificantly higher seed yield (2108 kg ha-1) overcorresponding lower levels of 60 and 90 kg N ha-1.Among the sulphur treatments application of 30 kg Sha-1 recorded significantly higher seed yield (2048 kgha-1) over 0 and 15 kg S ha-1. The results of Maity
Date of Receipt : 30.05.2012 Date of Acceptance : 19.06.2012
EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTH
Ta
ble
1.
Eff
ec
t o
f d
iffe
ren
t le
vels
of
nit
rog
en
an
d s
ulp
hu
r o
n g
row
th,
yie
ld a
ttri
bu
tes
an
d y
ield
of
su
nfl
ow
er
Pla
nt
heig
ht
(cm
)
Dry
matt
er
ac
cu
mu
lati
on
(g p
lan
t-1)
Ste
m g
irth
(c
m)
Head
dia
mete
r (c
m)
Nu
mb
er
of
fill
ed
seed
s
head
-1
Seed
yie
ld
(kg
ha
-1)
Sta
lk y
ield
(kg
ha
-1)
Tre
atm
en
t
At
harv
est
Nitr
ogen
(kg
ha-1
)
60
160.
8 97
.9
9.63
16
.83
654.
0 16
63
3638
90
171.
9 10
7.3
10.3
9 19
.13
738.
7 19
30
3897
120
181.
1 11
3.3
10.8
5 20
.67
783.
0 21
08
4072
SE
m±
0.
9 0.
4 0.
14
0.22
1.
1 10
7
CD
at 5
%
2.9
1.3
0.43
0.
66
3.2
29
21
Sul
phur
(kg
ha-1
)
0 16
3.1
100.
5 9.
87
17.4
3 68
0.3
1732
36
96
15
172.
2 10
6.7
10.3
3 18
.93
728.
3 19
21
3882
30
178.
5 11
1.4
10.6
7 20
.27
767.
0 20
48
4028
SE
m±
0.
9 0.
4 0.
14
0.22
1.
1 10
7
CD
at 5
%
2.9
1.3
0.43
0.
66
3.2
29
21
N x
S in
tera
ctio
n
SE
m±
1.
7 0.
7 0.
24
0.38
1.
8 17
12
CD
at 5
%
5.0
2.2
NS
N
S
5.5
50
36
PAVANI et al
Ta
ble
1.1
. P
lan
t h
eig
ht
(cm
) a
nd
dry
ma
tter
ac
cu
mu
lati
on
(g
pla
nt-1
) at
harv
est
of
su
nfl
ow
er
as i
nfl
uen
ced
by N
x S
in
tera
cti
on
Tre
atm
en
t P
lan
t h
eig
ht
Dry
matt
er
accu
mu
lati
on
Nit
rog
en
(kg
ha
-1)
S0
S1
5
S3
0
S0
S1
5
S3
0
N60
15
4.2
158.
0 17
0.1
92.6
96
.7
104.
5
N90
16
0.0
176.
0 17
9.8
100.
8 10
8.8
112.
4
N12
0 17
5.2
182.
6 18
5.6
108.
0 11
4.8
117.
2
SE
m±
1.
7 0.
7
CD
at 5
%
5.0
2.2
Tab
le 1
.2.
Nu
mb
er
of
filled
seed
s h
ead
-1,
seed
yie
ld (
kg
ha
-1)
an
d s
talk
yie
ld (
kg
ha
-1)
of
su
nfl
ow
er
as i
nfl
uen
ced
by N
x S
in
tera
cti
on
Tre
atm
en
t N
um
ber
of
filled
seed
s
head
-1
See
d y
ield
S
talk
yie
ld
Nitr
ogen
(kg
ha-1
) S
0
S1
5
S3
0
S0
S1
5
S3
0
S0
S1
5
S3
0
N60
62
1 63
5 70
6 15
36
1629
18
24
3451
35
96
3867
N90
68
0 75
2 78
4 17
10
1977
21
04
3734
39
40
4017
N12
0 74
0 79
8 81
1 19
51
2157
22
16
3904
41
12
4200
SE
m±
1.
8 17
12
CD
at 5
%
5.5
50
36
and Gajendra Giri (2003) and Satish Kumar and Singh(2005) corroborate with the above findings. Applicationof 120 kg N ha-1 recorded significantly higher stalkyield (4072 kg ha-1) in comparison to 60 and 90 kg Nha-1. Application of 30 kg S ha-1 recorded more stalkyield (4028 kg ha-1) when compared to 15 kg S ha-1
and the plots with no sulphur application. Theseresults are in agreement with the observations madeby Sumathi and Rao (2007).
N x S interaction effect on seed yield and stalkyield was significant. Nitrogen @ 120 kg ha-1 alongwith sulphur application @ 30 kg S ha-1 recordedsignificantly higher seed and stalk yield and this wasfollowed by 120 kg N ha-1 along with 15 kg S ha-1.Similar results of increased seed yield due to sulphurand nitrogen application were reported by Reddi Ramuand Maheshwara Reddy (2003) and Sarkar andMallick (2009).
Among three different levels of nitrogen and
sulphur studied, crop applied with 120 kg N ha-1 and
30 kg S ha-1 performed better in terms of growth
characters, yield attributes and yield.
REFERENCES
Maity, S.K and Gajendra Giri, 2003. Influence of
phosphorus and sulphur fertilization onproductivity and oil yield of ground nut and
sunflower (Helianthus annuus) in intercroppingwith simultaneous and staggered planting.Indian Journal of Agronomy. 48 (4): 267-270.
Reddi Ramu, Y and Maheswara Reddy, P. 2003.Growth and yield of sunflower as influencedby nitrogen and sulphur nutrition. Indian Journalof Dry Land Agriculture Research &Development. 18 (2): 192-195.
Sakal, R., Singh, A.P., Choudhary, B.C and Shahi,B. 2001. Sulphur status of Usifluvents andresponse of crops to sulphur application.Fertilizer News. 46 (10): 61-65.
Sarkar, R.K and Mallick, R.B. 2009. Effect ofnitrogen, sulphur and foliar spray of nitratesalts on performance of spring sunflower(Helianthus annuus). Indian Journal ofAgricultural Sciences. 79 (12): 986-990.
Satish Kumar and Singh, S.S. 2005. Effect of differentlevels of phosphorous and sulphur on thegrowth, yield and oil content of sunflower(Helianthus annuus L.). Journal of OilseedsResearch. 22 (2): 404-409.
Sumathi, V and Rao, D.S.K. 2007. Effect of organicsources of nitrogen with different irrigationschedules on growth and yield of sunflower.Indian Journal of Agronomy. 52 (1): 77-79.
EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTH
Research NoteJ.Res. ANGRAU 40(3) 94-96, 2012
Globalization, mall culture and urbanizationhave brought about radical changes in the mealpattern of the people across the nation, resulting intothe escalating intake of unhealthy food. Meal patternrepresent a picture of the type of food consumptionand may thus be more predictive of the food choicesand eating habits of the population. Correct foodchoices and healthy eating practices related tovarious meals are believed to be a cause of presentconcern. Thus, the aim of this study was to examinethe food habits, preferences and meal pattern of theprofessionals in information technology (IT) sector.
The selected sample consisted of 40 ITprofessionals (29 males and 11 females). Entiresample was drawn from four IT companies ofHyderabad city using stratified random samplingtechnique. A structured questionnaire was used forcollection of the data. The schedule assessed thebasic information, meal pattern, food habits and foodpreferences of the subjects.
Age wise distribution of IT professionalsshowed that 70% of the respondents were between20-29 years and 30% of them were of 30-39 years.87.5% of the respondents were from Andhra Pradeshand the rest were from other Southern states (12.5%).
email: [email protected]
Date of Receipt : 11.06.2012 Date of Acceptance : 27.06.2012
FOOD HABITS, PREFERENCES AND MEAL PATTERN OF THE PROFESSIONALSIN INFORMATION TECHNOLOGY SECTOR
PRIYA SHARMA, M. USHA RANI, K. UMA MAHESWARI and K. SUPRIYADepartment of Food and Nutrition, College of Home Science, Saifabad,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500030
All the respondents were literate with 72.5%of postgraduates and 27.5% graduates. Among allthe subjects, 67.5% were unmarried and the rest32.5% were married. It was observed that 65% ofthe subjects (72.4% males and 45.5% females) hadmonthly income ranging from Rs. 21,000 to Rs.30,000 while 22.5 percent earned Rs. 31,000 to Rs.40,000 and the rest had income of Rs. 10,000 to20,000 per month.
Data reveals that the major meals i.e., lunchand dinner were regularly consumed by all thesubjects (100%). The general meal pattern of the ITprofessionals is presented in the Table 1.
It was reported that 82.5% subjects (82.8%males and 81.8% females) consumed breakfastregularly; 70% (69% males and 72.7% females) ofthe subjects consuming evening snacks whereas;57.5 percent and 42.5 percent of the subjects werehaving mid-morning and bedtime meal, respectivelyin the general meal pattern.
Majority of the subjects were following asimilar four meal pattern i.e., breakfast, lunch,snacks and dinner. This could be due to culturaldietary habits.
Table 1. General meal pattern of IT professionals
N=40
Males (n=29) Females (n=11) Total (N=40) S. No. Meal % % %
1 Breakfast 82.8 81.8 82.5 2 Mid-morning 55.2 63.6 57.5 3 Lunch 100 100 100 4 Evening snacks 69 72.7 70 5 Dinner 100 100 100 6 Bedtime 41.4 45.5 42.5
Food habits and preferences of ITprofessionals include their liking towards fried foods,frequency of eating junk foods, eating outside thehome and habit of snacking at different occassions.It was found that majority (70%) of the subjects werenon-vegetarian (79.4% males and 45% females) while20% were ova-vegetarian (17.2% males and 27%females) and 10% were vegetarians.
The preference for fried foods like frenchfries, potato chips, samosas, chips, puffs, pattiesetc. was reported high by the males as compared tothe females. Among the females, 18.2% preferredfried foods and 27.3% preferred and liked moderatelyand 54.5% responded that they do not like, whereas20.7% of the males preferred fried foods and 62.1%preferred and liked moderately and 17.2%respondents did not like. Polednak (1997) reportedthat the mean estimated fat intake from the 13 fattyitems was significantly greater for men than thewomen especially for french fries.
Junk foods like pizzas, burgers, noodles,chocolate, icecream, soft drinks etc. which areconsidered as favorite snacks of the professionalswere consumed by 35% of the subjects daily and
Table 2. Food preferences of IT professionals
N=40
Males (n=29) Females (n=11) Total (N=40) Food preferences towards % % %
Fried/fatty foods
a) Like very much 20.7 18.2 20
b) Like moderately 62.1 27.3 52.5
c) Don’t like 17.2 54.5 27.5
Junk foods
a) Daily 37.9 27.3 35
b) 2-3 times/week 51.7 27.3 45
c) Occasionally 10.3 45.5 20 Consumption of food outside home
a) Daily 17.2 - 12.5
b) 2-3 times/week 75.9 36.4 65
c) Occasionally 6.9 63.6 22.5
Consumption of snacks during Working on computers
a) Yes 82.8 72.7 80
b) No 17.2 27.3 20
45% of them consume atleast 2-3 times a week andthe remaining 20% consuming occasionally. Similarresults have also been reported by Frary et al. (2001),who indicated that 77.5% of participants ate junk fooddaily and the majority consumed junk food severaltimes a day. The daily consumption of junk food waspreferred by 37.9% of the males and 27.3% of thefemale subjects. 51.7% of the male subjects and27.3% of the females reported to consume junk food2-3 times a week. Junk food pervades virtually allsegment of the society including local communities,privates institutions, industries, companies. Thesetrends seem to have been driven by massiveadvertising and marketing compaigns aimed at thesedentary lifestyle.
According to Demory et al. (2004), changesin eating behaviors, diet quality and physical activitymay result in an increased risk of obesity. It wasfound that young adulthood consumption was greaterfor sweetened beverages, salty snacks compared tochildhood.
The consumption of food outside home incase of males was high and 75.9% of them consumethese atleast 2-3 times a week, 17.2% of them ondaily basis and rest consume occasionally. Whereas,
FOOD HABITS, PREFERENCES AND MEAL PATTERN OF THE PROFESSIONALS
36.4% of females consume the food outside home2-3 times a week and rest of 63.6% eat outsideoccasionally.
It was observed that about 80% of the totalsubjects (82.8% males and 72.7% females) used toconsume snacks during working on computers/laptops. It was seen that the habit of eating andsnacking during TV viewing and working oncomputers/laptops was more in case of males as
compared to females. Kourlaba et al. (2008) reported
that the time spent watching television was positively
associated with the ‘junk food’ pattern and inversely
associated with the ‘healthy’ pattern.
The results of the study revealed that the
male subjects were to prefer more of fried food, junk
food, food outside home and snacks while working
on computers as compared to the female subjects.
REFERENCES
Demory-Luce, D., Morales, M., Nicklas, T.,Baranowski, T., Zakeri, I and Berenson, G.2004. Changes in food group consumptionpatterns from childhood to young adulthood:The Bogalusa Heart Study. Journal of theAmerican Dietetic Association. 104 (11): 1684-1691.
Frary, C.D., John Son, R.K and Wang, M.Q. 2001.Children and adolescents choices of food andbeverages high in added sugars areassociated with intakes of key nutrients andfood groups. Journal of Adolescent Health. 28:16-25.
Kourlaba, G., Panagiotakos, D.B., Mihas, K.,Alevizos, A., Marayiannis, K., Mariolis, A andTountas, Y. 2008. Dietary patterns in relationto socio-economic and lifestyle characteristicsamong Greek adolescents: a multivariateanalysis. Public Health Nutrition. 12 (9): 1366-1372.
Polednak, A.P. 1997. Use of selected high fat foodsby Hispanic adults in the North eastern US.Ethnicity & Health. 2 (1 & 2): 71-76.
PRIYA et al
It is very important to establish a system of
evaluation for every plant medicine in the market,
since the scope for variation in different batches of
medicine is enormous. Nutrition is the provision, to
cells and organisms, of the materials necessary (in
the form of food) to support life. A poor diet can have
an injurious impact on health, causing deficiency
diseases. Herbal nutritional supplements provide
essential nutrients that are not present or present in
less amount in diet (Soni et al., 2010).
The samples {Basella rubra, Centellaasiatica, Withania somnifera (Ashwagandha) and
Leucas aspera} were procured from Herbal garden,
Dr. Y. S. R. Horticulture University, Hyderabad. The
plant material used were in dried form except for
moisture analysis. The fresh leaves of Basella rubra,Centella asiatica, Leucas aspera and root of Withaniasomnifera were cleaned and dried in air drier, ground
to powder and used for analysis in triplicate. The
recommended methods of the Association of Official
Analytical Chemists (AOAC, 1990 and 2005) were
used for the determination of moisture, fat, ash, crude
fiber and protein.
Moisture content of the analyzed samples
ranged between 65.63 % and 90.99 %, highest being
in Basella rubra (90.99 %). Withania somnifera had
the lowest moisture content of 65.63 %. The variations
in moisture content were found to be statistically
significant.
The protein content ranged between 1.38g –
2.12 g/100g. Of the four herbs analyzed, Basellarubra had the highest protein content (2.12g) followed
NUTRIENT CONTENT OF SELECTED HERBAL CROPSELMUONZO, K. UMA MAHESWARI, ANURAG CHATURVEDI and T. SUSILA
Department of Foods and Nutrition, Post Graduate and Research CentreANGRAU, Rajendranagar, Hyderabad -500 030.
Research NoteJ.Res. ANGRAU 40(3) 97-98, 2012
Date of Receipt : 29.05.2012 Date of Acceptance : 13.06.2012
email: [email protected]
by Cantella asiatica (1.86 g/100g) which is similar to
the finding reported by Gupta et al. (2005). Withaniasomnifera had the lowest protein content (1.38 g/
100g). Similar value was reported by Krishnamurthy
and Sarala (2011).
All the samples were found to be low in fat
content, which ranged between 1.38 and 2.12 g/100g.
The maximum amount of fat was observed in Basellarubra and Withania somnifera was found to have a
low fat content of 0.82 g/100g.
The ash content ranged from 0.88 g/100g to
2.21 g/100g. The ash content of the samples was
high except for Leucas aspera (0.88 g/100g). Variation
in ash content were statistically significant. Crude
fibre content in the sample varied from 1.15 g/100 to
18.21g/100g. Withania somnifera showed the highest
value of 18.21g/100g while Basella rubra showed the
lowest crude fibre value 1.15 g/100 among the four
samples but Mensah et al. (2008) reported only 0.6g
/100 g crude fibre in Basella rubra. Significant
difference was observed in crude fibre content among
the samples.
The analyzed herbal samples were high in
moisture. They also had appreciable amount of fibre
content except Basella rubra and protein and fat
content was found to be low.
The analyzed herbal samples were high in
moisture. They also had appreciable amount of fibre
content except Basella rubra and protein and fat
content was found to be low.
Sample Moisture
(%)
Protein
(g/100g)
Fat
(g/100g)
Crude fibre
(g/100g)
Ash
(g/100g)
Withania somnifera 65.63 ± 1.39 1.38 ± 0.38 1.38 ± 0.38 18.21 ±0.08 2.21 ± 0.02
Centella asiatica 73.90 ± 0.12 1.86 ± 0.21 1.86 ± 0.21 15.04 ±0.02 1.93 ± 0.03
Leucas aspera 79.39 ± 1.47 1.51 ± 0.31 1.51 ± 0.31 4.56 ± 0.03 0.88 ± 0.12
Basella rubra 90.99 ± 0.91 2.12 ± 0.26 2.12 ± 0.26 1.15 ± 0.03 1.03 ± 0.04
Sem + 0.64 0.17 0.18 2.59 3.88
CD at 5% 2.29* 0.47 0.50 0.07* 0.11*
Table 1. Nutritive value of herbal crops
REFERENCES
AOAC, 1990. Official methods of Analysis for protein,fat, and ash 15th edition. Association ofAnalytical Chemist, Washington, D.C.
AOAC, 2005. Official methods of Analysis formoisture and crude fibre 18th edition.Association of Analytical Chemist,Washington, D.C.
Gupta, S., Lakshmi, A. J., Manjunath, M. N andPrakash J. 2005. Analysis of nutrient andantinutrient content of underutilized green leafyvegetables. LWT Food Science andTechnology. 38( 4): 339-345.
Krishnamurthy, S. R. and Sarala, P. 2010. ProximateNutritive Values and Mineral Components of
Withania Somnifera (Linn.) Dunal. E-Journal
of Chemistry. 7(3): 985-996.
* Significant at 5 % level, Values expressed are mean of three replicates ± SD
Mensah, J. K., Okoli, R. I., Ohaju-Obodo, J. O and
Eifediyi, K. 2008. Phytochemical, nutritional
and medical properties of some leafy
vegetables consumed by Edo people of
Nigeria. African Journal of Biotechnology. 7
(14): 2304-2309.
Soni, H. K., Ribadiya, N. C., Bhatt, S. B and Sheth,
N. R. 2010. Revaluation of herbal formulation
(capsule) containing Ashwagandha as a single
herb with their nutritional value determination.
International Journal of Applied Biology and
Pharmaceutical Technology. 1(3): 960-967.
ELMUONZO et al
Research NoteJ.Res. ANGRAU 40(3) 99-100, 2012
email: [email protected]
Of late, there has been a 30% decline in
exports of spices due to the presence of agriculturalcontaminants and pesticides. The particularinterest
is residues of the chlorinated hydrocarbon.
Pesticides, including organophosphate (OP),
Organochlorine (OC), and Carbamate (CB)
compounds, are widely used in the production of
spices.
Thoughspices are used in less quantity in
the diet which imparts flavor and make the food
palatable. Thus they are important components of
our food. These factors have influenced to take up
the study and investigate the pesticide residues and
nutrients in commonly grown spices in Guntur district.
Spice samples viz.chilli, turmeric and
coriander seeds were procured from farmers
cultivating spices in Guntur district. The pesticide
standards with 98% purity were procured from
Dr.Ehrenstorfer GmbH. (Germany), were used to
analyze the residues in samples. All the solventsused in the study were glass distilled at their boiling
point. All the glassware were soaked in chromic acid
solution and washed thoroughly with water. These
were rinsed with acetone and air-dried before use.
spice samples (500 g) from each replicate were
collected from farmers, from each replicate spiceswere cut into small pieces (whole) and powdered
(processed), representative 50 g sample (in duplicate)
was obtained by quartering method from entire
sample. All the dried samples were processed andsealed in packing material (polyethylene) and thenstored at ambient temperature for analysis.
PESTICIDE RESIDUES IN SPICES OF GUNTUR DISTRICT OFANDHRA PRADESH
SHAIK. SANHERA and S. SHOBHADepartment of Foods and Nutrition, College of Home Science,
Post Graduate & Research Centre, Acharya N. G. Ranga Agricultural University, Hyderabad-500030.
Date of Receipt : 06.06.2012 Date of Acceptance : 21.06.2012
Residues were analyzed adopting methodsuggested bySharma, 2007 on gas chromatograph(GLC) equipped with electron capture detector (ECD)and Col –Elite -1 HT-30M, 0.250mm × 0.1 um filmcolumn. The operating conditions for determinationof Chlorophyriphos, Endosulfan, Acephate,Carbofuran, Monocrotophos and Quinalphoswere:temperatures, injector 225°C and detector275°C,oven temperature at initial of 150°C with holdtime of 1.00min and Ramp 1 programmed as 2°C for1 min, Ramp 2, at 10°Cfor 5 min. Total run time was46.00min. The retention time of standardsareChlorophyriphos (21.294),Quinolphos (23.317),Endosulphan (24.212) and Acephate (25 .996),Monocrotophos (16.034),Quinolphos (23.312) andCarbofuran (19.297) minutes.
The pesticide residue values were belowdetectable levels (BDL) in the whole and processedspice samples analyzed (Table 1). The finding of thisstudy is in concordance with findings of Papiaet al.(2010) According to the study no residues were foundfor two treatments studied in15 days after applicationof pesticides. It was dissipatedup to 83.45% and78.65% in 10 days. Similar inference could be drawnfrom the present investigation that post-harvesttechniques and storage of spices contributed towardsdegradation of residues thus the spices are safe forconsumption.
The MRL values were not determined forspices; however adequate dietary intake (ADI mg/kgbody weight per day) values are available forChlorophyriphos, Endosulfan, Acephate, Carbofuran,Monocrotophos and Quinalphos residues for spices.
Residues were analyzed adopting methodsuggested bySharma, (2007) on gas chromatograph(GLC) equipped with electron capture detector (ECD)and Col –Elite -1 HT-30M, 0.250mm × 0.1 um filmcolumn. The operating conditions for determinationof Chlorophyriphos, Endosulfan, Acephate,Carbofuran, Monocrotophos and Quinalphoswere:temperatures, injector 225°C and detector 275°C,oven temperature at initial of 150 °C with holdtime of 1.00min and Ramp 1 programmed as 2°C for1 min, Ramp 2, at 10°Cfor 5 min. Total run time was46.00min. The retention time of standards areChlorophyriphos (21.294),Quinolphos (23.317),Endosulphan (24.212) and Acephate (25 .996),Monocrotophos (16.034),Quinolphos (23.312) andCarbofuran (19.297) minutes.
The MRL values were not determined for
spices; however adequate dietary intake (ADI mg/kg
body weight per day) values are available for
Chlorophyriphos, Endosulfan, Acephate, Carbofuran,
Monocrotophos and Quinalphos residues for spices.
Processing of spices includes drying, roasting and
grinding and storage, consumed after sufficient
storage. These processing and storage of spices are
factors for degrading pesticide residual content since,
there was a time gap between harvesting and
analyzing the pesticide residues and a well-known
fact that pesticide residues get degraded often.
Thus, these three spices are considered tobe safe for consumption and without any harmful
Source*: Sharma, 2007.BDL-Below Detectable Level
Table 1. Results of Pesticide residues in selected spices
Pesticide Residues ADI mg/kg bodyweight per day*
Residuedetected
Chlorophyriphos 0.01 BDL
Quinalphos 0.01 BDL
Endosulfan 0.006 BDL
Acephate 0.01 BDL
Monocrotophos 0.01 BDL
Carbofuran 0.01 BDL
The pesticide residue values were belowdetectable levels (BDL) in the whole and processedspice samples analyzed (Table 1). The finding of thisstudy is in concordance with findings of Papia et al.(2010) According to the study no residues were foundfor two treatments studied in15 days after applicationof pesticides. It was dissipatedup to 83.45% and78.65% in 10 days. Similar inference could be drawnfrom the present investigation that post-harvesttechniques and storage of spices contributed towards
degradation of residues thus the spices are safe for
consumption.
effects, as they contained no pesticide residues asrevealed from present investigation.
REFERENCES
Papia, Ditya., Das, S. P., Sarkar, P. K and AnjanBhattacharyya. 2010. Degradation Dynamicsof Chlorfenapyr Residue in Chili, Cabbage andSoil. Bull Environ ContamToxicol. 84:602–605.
Sharma, K.K.2007.PesticideResidue AnalysisManual.ICAR.Directorate of information andpublications of Agriculture, New Delhi.
SANHERA and SHOBHA
Wheat (Triticum aestivum L.) is an importantpost-rainy season crop in central and northernTelangana zone of Andhra Pradesh. It occupies anarea of 13000 ha with a total production of 10,000tonnes (CMIE, 2010). The role of improved variety inenhancing the wheat crop yield is well documented(Mukherjee, 2008 and Singh et al., 2010).Replacement of low potential and pest susceptibleold varieties by new high yielding varieties withpromising yield potential is a continuous process andmay be significant to increase the productivity toovercome the present low yield level of 769 kg ha-1inthe state. However, new high yielding varieties differin their optimum nitrogen requirement. Knowing theoptimum yield as affected by nitrogen level is alsovaluable when farmers choose the varieties to plantin the context of commodity prices, production costand environmental conditions.
Keeping the above in view a field experimentwas conducted during rabi season of 2010–11 atAgricultural Research Station, Basantpur, Medakdistrict in Central Telangana Zone of Andhra Pradesh.The soil was loamy sand in texture with a soil pH of6.4 and had S1 salinity class (EC 0.96 dS m-1). Theexperimental soil was low in available nitrogen (265kg N ha-1), phosphorus (19.59 kg P
2O
5 ha-1) and
medium in available potassium (219.4 kg K2O ha-1).
The treatments consisted of five wheat cultivars viz.,Sonalika (V
1 ), NIDW 295 (V
2), UAS 415 (V
3), NIAW
917 (V4) and DWR 162 (V
5) as main plot treatments
and five levels of nitrogen 0, 80, 120, 160 and 200 kgN ha-1 as sub-plot treatments summing up to 25treatment combinations laid out in split-plot designwith three replications. The crop was sown on 1st
November, 2010.
The growth characters except tiller productionwere significantly influenced by different varieties
RESPONSE OF WHEAT (Triticum aestivum L.) CULTIVARSTO VARYING LEVELS OF NITROGEN
MATHURA YADAV, V. PRAVEEN RAO, M. YAKADRI AND G. JAYASREEDepartment of Agronomy, College of Agriculture,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030
Research NoteJ.Res. ANGRAU 40(3) 101-104, 2012
Date of Receipt : 07.12.2011 Date of Acceptance : 19.01.2011
(Table 1). Sonalika was significantly taller. Leaf areaindex was significantly higher in Sonalika and NIDW295; and dry matter production was markedly highestunder Sonalika, NIDW 295 and UAS 415 incomparison to other tested varieties. Several workershave documented variation in plant height, tillerproduction and leaf area index among varietiesdiffering in their genetic makeup (Parihar and Tiwari,2003 and Pandey et al.,1999) under diverseenvironments.Further the yield attributing charactersviz., ears m-2, grains ear-1 and test weight weresignificantly higher in NIDW 295 and UAS 415 wheatvarieties which were on par with each other andsuperior over other tested varieties (Table 1). Furthergrain yield was significantly higher in NIDW 295 andUAS 415 owing to improved growth and yieldattributes. Similarly, both these varieties recordedhigher harvest index than remaining varieties. Theseresults are in conformity with the findings of Singh etal. (2009) and Singh et al. (2010).
Each higher level of nitrogen significantlyimproved growth traits viz., plant height, tillerproduction, leaf area index and dry matter productionand yield attributes viz., ears m-2, grains m-1 and testweight over its lower level up to 160 kg N ha-1. Theseimproved growth and yield attributes in turncontributed to significantly higher yield under 160 kgN ha-1 fertilized crop over its lower levels (Singh etal., 2011 and Mattas et al., 2011). Application of 200kg N ha-1 did not prove to be advantageous over 160kg N ha-1. Harvest index was inversely related tonitrogen levels (Behera and Ghosh, 2009).
Table 2 presents the empirical results of all the linearfits established between grain yield (dependentvariable) of wheat and growth traits, yield componentsand nutrient uptake values (independent variables).All the independent variables showed a significant
email: [email protected]
YADAV et al
T
ab
le 1
. G
row
th a
nd
yie
ld a
ttri
bu
tes o
f w
heat
as i
nfl
uen
ced
by v
ari
eti
es a
nd
nit
rog
en
levels
Tre
atm
en
ts
Pla
nt
heig
ht
(cm
)
Til
lers
(m
-2)
LA
I D
ry m
att
er
(g m
-2)
Ears
(m
-2)
Gra
ins e
ar-
1
Test
weig
ht
(g)
Gra
in y
ield
(kg
ha
-1)
Harv
est
ind
ex
Vari
eti
es (
V)
Son
alik
a
117.
1 29
5 5.
21
875.
2 27
6.3
36
36.3
25
69
0.45
7
NID
W 2
95
77.0
31
7 5.
03
866.
9 28
2.8
42
40.0
31
69
0.48
0
UA
S 4
15
76.5
31
7 4.
88
887.
7 30
0.8
40
41.2
30
91
0.48
4
NIA
W 9
17
76.6
29
4 4.
32
792.
8 27
7.6
35
33.7
23
74
0.47
2
DW
R 1
62
94.2
29
4 4.
23
785.
4 26
7.4
37
37.8
24
47
0.47
7
SE
d
3.5
8
11
0.1
0
30.3
8
9.6
4
0.9
3
0.2
9
14
1
0.0
13
CD
(P
= 0
.05)
8.2
6
NS
0.2
3
70.7
6
22.2
4
2
0.6
7
32
6
0.0
25
Nit
rog
en
levels
(N
)
0 kg
N h
a-1
75.3
22
8 2.
85
496.
7 20
7.0
33
33.8
16
45
0.48
3
80 k
g N
ha-1
88
.1
311
4.65
73
8.7
287.
0 37
38
.0
2734
0.
482
120
kg N
ha-1
89
.5
315
4.93
92
1.0
294.
5 38
38
.7
2930
0.
481
160
kg N
ha-1
93
.6
330
5.54
10
18.4
30
3.6
40
39.2
31
68
0.46
7
200
kg N
ha-1
94
.9
332
5.70
10
33.2
31
2.8
41
39.7
31
72
0.46
6
SE
d
1.3
6
7
0.1
2
18.7
1
4.5
6
0.6
6
0.2
5
76
0.0
08
CD
(P
= 0
.05)
2.7
6
14
0.2
5
37.8
3
9.2
8
1
0.5
2
15
5
0.0
16
Inte
racti
on
(V
N
)
Su
b a
t s
am
e l
ev
el
of
main
tre
atm
en
t
SE
d
3.05
15
0.2
7
41.8
5 10
.24
1 0.
57
171
0.18
CD
(P
= 0
.05)
NS
N
S
NS
84
.59
NS
2
1.16
N
S
NS
Main
tre
atm
en
t at
sam
e o
r d
iffe
ren
t le
vels
of
su
b t
reatm
en
t
SE
d
3.05
17
0.3
3
48.4
0 13
.32
1 0.
59
208
0.27
CD
(P
= 0
.05)
NS
N
S
NS
10
3.37
N
S
3 1.
24
NS
N
S
RESPONSE OF WHEAT CULTIVARS
Table 2. Linear estimates for the relationship between grain yield and other parameters
**= Significant at 1% (P = 0.01)
Relationship a b R2
Yield – Plant height (cm) −4.451168 0.0813098∗∗ 0.986∗∗
Yield – Tillers m-2 −1.634572 0.0139447∗∗ 0.991∗∗
Yield – Leaf area index −2.971010 1.4028101∗∗ 0.984∗∗
Yield – Dry matter (g m-2) 0.515290 0.0026607∗∗ 0.895∗∗
Yield – Days to flowering 38.91386 −0.5124024∗∗ 0.820∗∗
Yield – Ears m-2 −1.449538 0.0148671∗∗ 0.987∗∗
Yield – Grains ear-1 −4.116270 0.1791787∗∗ 0.889∗∗
Yield – Test weight (g) −7.494776 0.2697872∗∗ 0.993∗∗
Yield – Harvest index −42.500520 0.8642943∗∗ 0.693∗∗
Yield – N uptake (kg ha-1) 0.9644030 0.0302001∗∗ 0.986∗∗
Yield – P uptake (kg ha-1) 0.1822273 0.1146001∗∗ 0.997∗∗
Yield – K uptake (kg ha-1) 0.1574281 0.0673001∗∗ 0.999∗∗
positive and linear relationship with grain yield exceptfor days to 50% flowering (Table 2) suggesting thereinforcement of the effects of a given pair of yieldand component relation (Tayyar, 2008). However, themagnitude of this reinforcement varied with the growthtrait, yield component, nutrient uptake values andtheir units. The explained total variation as indicatedby coefficient of determination (R2) in grain yield byvarious growth traits (plant height, tiller production,leaf area index, dry matter production and days to
flowering), yield components (ears m-2, grains ear-1,test weight and harvest index) and nutrient uptake(NPK uptake) chosen as independent variablesindividually ranged from 69.0 to 99.9%. The varianceratio for testing R2 was highly significant (P = 0.01)in all the relations. This suggests that the grain yieldof wheat can be adequately predicted using the testedindependent variables viz., growth traits, yieldcomponents and nutrient uptake.
REFERENCES
Behera, U.K and Ghosh, P.K. 2009. Response ofvery late sown bread wheat cultivars todifferent levels of nitrogen in vertisols. IndianAgriculturist. 53: 21 – 26.
CMIE, 2010. Economic intelligence service,Agriculture June, 2010. Centre for monitoringIndian Economy Pvt. Ltd., Mumbai. pp. 96 –99.
Mattas, K.K., Uppal, R.S and Singh, R.P. 2011. Effectof Varieties and Nitrogen Management on theGrowth, Yield and Nitrogen Uptake of DurumWheat. Research Journal of AgriculturalSciences. 2: 376 – 380.
Mukherjee, D. 2008. Effect of levels of nitrogen ondifferent wheat cultivar under Mid Hill situation.RAU Journal of Research. 18: 37 – 40.
YADAV et al
Pandey, I.B., Thakur, S.S and Singh, S.K. 1999.Response of timely sown wheat (Triticumaestivum) varieties to seed rate and fertilitylevel. Indian Journal of Agronomy. 44: 745 –749.
Parihar S.S and Tiwari, R.B. 2003. Effect of irrigationand nitrogen level on yield, nutrient uptake and
water use of late sown wheat (Triticumaestivum). Indian Journal of Agronomy. 48:103
– 107.
Singh, P., Singh, P., Singh, K.N. Singh, R. Faaga,.
Bahar, F and Waseem Raja. 2010. Evaluation
wheat (Triticum aestivum) genotypes for
productivity and economics under graded
levels of nitrogen in Kashmir. Indian Journalof Agricultural sciences. 80: 380 – 384.
Singh, B. R., Singh, R. V and Rajput,O. P. 2009.
Effect of nitrogen, phosphorous and zinc on
growth, yield and nutrient uptake of wheat.
Current Advances in agricultural Sciences.1(2): 133-134.
Singh, C.M., Sharma, P.K., Kishore, P., Mishra,
P.K., Singh, A.P., Verma, R and Raha, P.
2011. Impact of integrated nutrient
management on growth, yield and nutrient
uptake by wheat. Asian Journal of AgriculturalResearch. 5: 76 – 82.
Tayyar, S. 2008. Grain yield and agronomic
characteristics of Romanian bread wheat
varieties under the conditions of North western
Turkey. African Journal of Biotechnology. 7:
1479 – 1486.
Research NoteJ.Res. ANGRAU 40(3) 105-107, 2012
Date of Receipt : 01.06.2012 Date of Acceptance : 27.06.2012
Sorghum (Sorghum bicolor L. Moench), thefifth most important cereal crop in the world afterwheat, maize, rice and barley, is a major cereal staplefood and forage crop of the semi-arid tropics of Indiansub-continent and several African regions. In AndhraPradesh, it covers an area of 0.28 m ha with an annualproduction of 0.44 m t and productivity of 1420 kgha-1 (CMIE, 2009). Sorghum is the second cheapestsource of energy in the form of starch (63.4 - 72.5 %)and micronutrients such as iron (Fe) and zinc (Zn)after pearl millet.
In order to realize the potential impact of themicronutrient-dense cultivars, the high-yieldingvarieties / hybrids with farmer’s preferred traits suchas early maturity and large seed size coupled withincrease in micronutrient concentration in grain mustbe made available for commercial cultivation (Kumaret al., 2010). Information about the association ofmicronutrients such as grain iron with grain yield andother important traits can help the breeders to devisea suitable breeding strategy for enhancingmicronutrient density in sorghum cultivars. However,limited information is available on the characterassociation of grain iron content with grain yield andother important traits and also the direct and indirecteffects of grain yield and other important traits ongrain iron content. Hence, an attempt was made inthe present investigation to analyse and determinethe traits having greater interrelationship with grainiron utilizing correlation and path analysis.
The present investigation was carried outusing four parental lines (IS 2263, IS 13211, IS 10305and SPV 1359) and 12 hybrids generated by crossingthe parents in a full-diallel fashion during postrainyseason, 2011-12 in Randomized Block Design (RBD)with three replications under high fertility conditions
(80 N : 40 P) on vertisols at ICRISAT farm inPatancheru, located at an altitude of 545 m abovemean sea level, latitude of 17.53° N and longitude of78.27° E. Each genotype was grown in four rows of 2m length with 75 cm spacing between the rows and10 cm between the plants. All the recommendedagronomic practices were followed for raising a goodcrop. The data were recorded on four important traitsi. e., plant height, days to 50 % flowering, 100-grainweight and grain yield. The cleaned seeds of eachgenotype were used to measure the iron content withOxford X-supreme 8000 model X-ray flourescenceanalyzer (XRF). Correlation coefficients werecalculated at phenotypic level using the formulaesuggested by Falconer (1981). The direct and indirecteffects at phenotypic level were estimated by takinggrain iron content as dependent variable, using pathcoefficient analysis as suggested by Dewey and Lu(1959).
In the present investigation, plant height, 100-grain weight and grain (Table 1) exhibited non-significant and lower magnitude of negativeassociation with grain iron, while days to 50 %flowering showed non-significant positive correlation
with grain iron content. Plant aspect score recorded
significant positive correlation with grain iron content.
Reddy et al. (2005) earlier obtained statistically
significant negative but a rather weaker correlation
of grain iron content with grain yield and poorassociation of agronomic traits such as days to 50
% flowering and plant height with grain iron content
and indicated the possibility of developingmicronutrient-dense cultivars in desired maturityduration and plant height background with littlecompromise in grain yield. Reddy et al. (2010) alsoreported weak association of grain iron with agronomic
email: [email protected]
ASSOCIATION OF GRAIN IRON CONTENT WITH GRAIN YIELD AND OTHERTRAITS IN SORGHUM (Sorghum bicolor L. Moench)
S. V. P. L. GAYATHRI, K. RADHIKA, A. ASHOK KUMAR and P. JANILADepartment of Genetics and Plant Breeding, College of Agriculture,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500030.
Table 1. Phenotypic correlation co-efficient matrix of grain iron content withgrain yield and other traits
Character Plant height
Days to 50 % flowering
Plant aspect score
100-grain weight
Grain yield
Correlation with grain
iron Plant height 1.000
0.377**
-0.696**
0.877**
0.811**
-0.133
Days to 50 % flowering
1.000 -0.191
0.370**
0.472**
0.224
Plant aspect score
1.000 -0.757**
-0.767**
0.372**
100-grain weight 1.000
0.901**
-0.097
Grain yield 1.000 -0.177
Grain iron
1.000
* indicates significance at 5 % probability i.e., r=>0.2845
** indicates significance at 1 % probability i.e., r=>0.3683
Table 2. Phenotypic path matrix showing direct and indirect effects of various agronomic traits on grain iron
Character Plant height
Days to 50 %
flowering
Plant aspect score
100-grain weight Grain yield
Correlation with grain
iron Plant height -0.167 0.125 -0.366 0.651 -0.376 -0.133
Days to 50 % flowering
-0.063 0.331 -0.101 0.275 -0.219 0.224
Plant aspect score
0.116 -0.063 0.526 -0.562 0.356 0.372**
100-grain weight
-0.146 0.123 -0.398 0.743 -0.418 -0.097
Grain yield -0.135 0.157 -0.403 0.670 -0.464 -0.177
Residual effect = 0.836
and grain traits indicating possible positive outcomes
from breeding for high grain iron content in varied
agronomic backgrounds. The non-significant
correlation of 100-grain weight with grain iron content
in pearl millet populations was earlier reported by
Gupta et al. (2009), who further indicated that breeding
for higher levels of micronutrients could be achieved
without compromising the improvement for larger
grain size. They also obtained negative correlation
between seed yield and iron content in pearl millet
populations indicating that selection for iron can be
accomplished without compromising on grain yield.
Banziger and Long (2000) found negative correlationof grain iron content with grain yield in maize. The
non-significant association of iron content with grain
yield in rice was earlier reported by Nagesh et al.(2012).
GAYATHRI et al
Out of the five traits taken for path analysis,100-grain weight had maximum positive direct effect(0.743) on the grain iron content followed by plantaspect score (0.526) and days to 50 % flowering(0.331), while, grain yield and plant height showednegative direct effects on the grain iron content (Table2). All the characters studied in the presentinvestigation showed positive indirect effect through100-grain weight and days to 50 % flowering indicatingthat direct selection for grain size might be helpful inimproving the grain iron content. Negative indirecteffects of 100-grain weight through other characterswere more prominent than its positive direct effecton the grain iron content thus resulting in its negativecorrelation with grain iron content and in such asituation direct selection for grain weight should bepracticed to reduce the undesirable indirect effect.Days to 50 % flowering exhibited a positive directeffect and a positive non-significant association withgrain iron content. Hence selection for this trait maybe rewarding during the development of iron richcultivars in sorghum. A very high residual effect(0.836) revealed that some more characters closelyrelated with grain iron content need to be includedapart from the characters studied in this investigation.
REFERENCES
Banziger, M and Long, J. 2000. The potential forincreasing the iron and zinc density of maizethrough plant breeding. Food and NutritionBulletin. 21: 397-400
CMIE. 2009. Area, production and productivity ofsorghum in India and Andhra Pradesh. Centrefor Monitoring Indian Economy (CMIE) Pvt.Ltd. Mumbai
Dewey, D.R and Lu, K.H. 1959. A correlation and
path co-efficient analysis of components of
crested wheat grass seed production.
Agronomy Journal. 51: 515-518
Falconer, D.C. 1981. An introduction to quantitative
genetics. Longman, New York. pp. 67-68
Gupta, S.K., Velu, G., Rai, K.N and Sumalini, K.
2009. Association of grain iron and zinc
content with grain yield and other traits in pearl
millet (Pennisetum glaucum (L.) R. Br.). Crop
Improvement. 36 (2): 4-7
Kumar, A.A., Reddy, B.V.S., Sahrawat, K.L and
Ramaiah, B. 2010. Combating micronutrient
malformation: Identification of commercial
sorghum cultivars with high grain iron and zinc.
SAT e Journal. 8: 1-5
Nagesh, Ravindrababu, V., Usharani, G and Dayakar
Reddy, T. 2012.Grain iron and zinc association
studies in rice (Oryza sativa L.) F1 progenies.
Archives of Applied Science Research. 4 (1):
696-702
Reddy, B.V.S., Ramesh, S and Longvah, T. 2005.
Prospects of breeding for micronutrients and
â-carotene-dense sorghums. Journal of SAT
Agricultural Research. 1 (1): 1-4
Reddy, P.S., Reddy, B.V.S., Kumar, A.A., Ramesh,
S., Sahrawat, K.L and Rao P. V. 2010.
Association of grain Fe and Zn contents with
agronomic traits in sorghum. Indian Journal of
Plant Genetic Resources. 23 (3): 280-284
ASSOCIATION OF GRAIN IRON CONTENT
When a goal is set at a difficult level a personis required to put forth more effort to meet it. Thiseffort is motivation dependent. Goals, therefore, aremotivation-based outcomes leading to personalsatisfaction. Goal content is typically age-graded andreflects the important issues of a particular period ofthe life span (Ogilvie et al. 2001). During the transitionfrom childhood to adulthood, adolescents set andpursue goals unique to this period. Within this life-phase, goal priorities change. Hence to promote theunderstanding of goal directed behaviour ofadolescents, it is very essential to study thedeterminants of goal directed behaviour ofadolescents.
Expost facto research design was used toinvestigate the present study. The total sample sizeof the study was 200, out of which 50 respondentswere from professional and non-professionalinstitution each. The respondents were distributedinto four groups and these were professional girls,professional boys, non-professional girls and non-professional boys. The data were collected throughquestionnaire and modified scales. The data obtainedwere coded, consolidated, tabulated and analyzedby using step down regression analysis.
Four strong predictors of the goal directedbehaviour of the professional adolescents based onthe analysis were self-efficacy, achievementmotivation, independence and organization. Higherthe self beliefs, higher would be the goal directedbehaviour.
Self- efficacy is a central predictor ofintentions and behaviour (Ajzen, 1991 and Kramper,2000). The development of self-efficacy reflects thedevelopment of actual efficacy that is thedevelpoment of the capacity to act autonomouslyand efficiently.
Likewise goals required adolescents’motivation to meet the desired ambitions and for this
DETERMINANTS OF GOAL DIRECTED BEHAVIOUR OF ADOLESCENTSNEHA JOSHI and M. SARADA DEVI
Department of Human Development and Family Studies, College of Home Science, Acharya N.G. Ranga Agricultural University, Hyderabad - 500 004.
Research NoteJ.Res. ANGRAU 40(3) 108-109, 2012
Date of Receipt : 06.06.2012 Date of Acceptance : 15.06.2012
they need to be focused about the course of actions,thus this relates to their goal directed behaviour. Sothis achievement motivation also has a positiverelationship with the goal directed behaviour. Similarlythe rest two variables were equally important inpredicting the goal directed behaviour of professionaladolescents.
The strong determinants of goal directedbehaviour of non-professional adolescents were self-efficacy, achievement motivation, expressiveness,acceptance & caring, control and active-recreationalorientation. Non-professionals have self-efficacy andachievement motivation as strong predictors, whichwas similar to that of professionals. Other than thatexpressiveness was having direct relationshipbecause, adolescents who were expressive feel freeto express their feelings, intentions and goals infrontof parents, peers and teachers which made themmore goal directed in life. Acceptance & caring wouldhelp them to think realistically and because of thatthey easily accept the situations in life. Thus in thisway this aspect of family environment made non-professional adolescents more goal directed towardstheir lives. Similarly active-recreational orientationmight have helped them to live happily with theirfamily. So higher the active-recreational orientation,higher would be the goal directed behaviour.
The strong determinants of goal directedbehaviour of all the adolescents including bothprofessionals and non-professionals were self-efficacy, achievement motivation, expressiveness,acceptance & caring, independence and organization.Out of eleven only six independent variables wereleft out as strong determinants.
Hence self-efficacy and achievementmotivation turned out to be very strong determinantsof goal directed behaviour of adolescents. The betterunderstanding of self-efficacy and achievementmotivation will help the professionals working foradolescents to improve their job performance.
email: [email protected]
Table 1. Determinants of goal directed behaviour of professional adolescents
S.No. Variables Beta value t value
1 Self-efficacy .211 2.527
2 Achievement motivation .586 6.905
3 Independence .206 2.539
4 Organization -.283 -3.543
Coefficient of determination (R²) = .578
Table 2. Determinants of goal directed behaviour of non-professional adolescents
S.No. Variables Beta value t value
1 Self-Efficacy .394 4.243
2 Achievement Motivation .491 5.354
3 Expressiveness .239 2.493
4 Acceptance & Caring -.372 -3.536
5 Control .126 1.677
6 Active-Recreational Orientation .158 2.253
Coefficient of determination (R²) = .683
Table 3. Determinants of goal directed behaviour of professional and non-professional adolescents
S.No. Variables Beta value t value
1 Self-Efficacy .273 4.429
2 Achievement Motivation .554 8.923
3 Expressiveness .120 1.678
4 Acceptance & Caring -.139 -2.078
5 Independence .130 2.367
6 Organization -.141 -2.193
Coefficient of determination (R²) =.626
REFERENCES
Ajzen, I. 1991. The theory of planned behavior.Organizational Behavior and Human DecisionProcesses. 50: 179-211.
Kramper ,G. 2000 . HandlungstheoretischePerssnlichkeitspsychologie [Action-
theoretical psychology of personality] .2nd ed.Gsttingen, FRG: Hogrefe.
Ogilvie, D. M., Rose, K. M and Heppen, J. B. 2001.A comparison of personal project motives inthree age groups. Basic and Applied SocialPsychology. 23: 207–215.
DETERMINANTS OF GOAL DIRECTED BEHAVIOUR
Most agronomically useful traits existing innature have continuous phenotypic distributions,implying that many genes with relatively minor effects,termed quantitative trait loci (QTLs), control thosetraits. The identification of QTLs provides the startingpoint to dissect the molecular basis of natural allelicdiversity, which in turn will be very useful for futurecrop improvement. Rice as the main staple food ofworld’s two-third’s population, it provide 23% ofcalories of daily requirement. Among theagronomically important yield related traits, grainnumber controlled by different QTLs in rice isimportant.
IDENTIFICATION OF IMPORTANT QTLs FOR GRAIN NUMBER IN POPULAR RICEVARIETIES OF ANDHRA PRADESH
SONALI DATTA MUHURI, M. SHESHU MADHAV, CH. SURENDER RAJU and CH. V. DURGA RANI Department of Plant Molecular Biology and Biotechnology
College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030.
Research NoteJ.Res. ANGRAU 40(3) 110-112, 2012
Date of Receipt : 25.05.2012 Date of Acceptance : 13.06.2012
A total of fifty six popular varieties of Andhra
Pradesh were collected and raised in the College
Farm, ANGRAU, Rajendranagar, Hyderabad during
kharif, 2011-2012 and the experiment was carried out
at the Directorate of Rice Research (DRR). Based
on literature survey, three QTL’s for grain number
trait viz., QPGN1a, ng1, gpp2.1 on chromosome 1
and 2 were selected. SSR markers with their physical
location were identified for their QTLs using online
web tool ‘gramene’ (www.gramene.org.) (Table 1).
SL. NO QTL NAME CHROMOSOME NO
SELECTED MARKERS
POSITION OF SELECTED MARKERS
1 QPGN1a 1 HRM 11570 HRM 10167
29.27 Mb 33.93 Mb
4 ng1 1 HRM 11865 36.2 Mb
5 gpp2.1 2 RM 208 37.29 Mb
Table 1. List of selected markers for QTL analysis
The good quality DNA of fifty six varietieswere used for PCR analysis using the abovementioned primers. IR 64 was used as positive control.
The scoring of gels was done based on the presenceand absence of expected bands/alleles as comparedto positive control.
SL.No Steps Temperature Time
1. Initial denaturation 94° C 5 min
2. Denaturation 94° C 30 sec
3. Annealing 59° C 1 min
4. Extension 72° C 1 min 5. Final extension 72° C 10 min
6. Cooling 4° C Long time
email: [email protected]
Table 2. PCR reaction protocol
The reported linked markers of QPGN1a are
RM237- M473A. Physical position of RM237 was
determined on chromosome 1 and in that region two
hypervariable SSR markers were selected namely
HRM11570 and HRM 10167. Using IR 64 as positive
control for both the markers since it was one parent
used in the detection of QPGN1a (Ahamadi et al.,2008), genotyping was done. Upon genotyping with
HRM 10167, out of fifty six varieties, only twenty
eight varieties showed the allele similar to that of
IR64 allele (figure 1) and rest showed the other allele.
In case of HRM 11570, out of fifty six varieties, thirty
five varieties showed the allele similar to IR64 and
rest showed other allele. It indicated that, this QTLoperated in some of the tested varieties, and otherswere affected by other QTLs.
The linked markers of ng1 are RG374- RG394 (Lin etal., 1996). The physical position for both these linkedmarkers on chromosome no 1 was determined andfrom that region, hyper variable SSR marker wasselected i.e. HRM11865. Taking IR64 as the positivecontrol, genotyping was done (figure 2). Out of fiftysix varieties, 46 varieties showed the IR64 allele andrest of the varieties showed other allele. This clearlyshowed that this QTL operated in majority of thetested varieties.
IR64 (Positive control)
Figure 1. QTL detection with primer HRM10167 (QTL QPGN1a)
IR64 (Positive control)
Figure 2. QTL detection with primer HRM11865 (QTL ng1).
Figure 3. QTL detection with primer RM208 (QTL gpp2.1).
IR64 (Positive control)
Similarly, the linked marker i.e. RM208 of
another QTL (present in chromosome 2) gpp2.1
(Septiningsih et al., 2003) was selected. Here also,
genotyping was done by taking IR64 as positive
control (figure 3). Out of fifty six varieties, 39 varieties
showed same IR64 allele. Results indicated that, gpp
2.1 QTL was present in some of the tested varieties.
REFERENCES
Ahamadi, J., Fotokian, M.H and Fabriki-Orang, S.
2008. Detection of QTLs Influencing Panicle
IDENTIFICATION OF IMPORTANT QTLs FOR GRAIN NUMBER IN POPULAR RICE VARIETIES
Length, Panicle Grain Number and Panicle
Grain Sterility in Rice (Oryza sativa L.). Journal
of Crop Science and Biotechnology. 11 (3):
163-170.
Lin, H.X., Qian, H.R., Zhuang, J.Y., Lu, J., Min, S.K.,
Xiong, Z.M., Huang, N and Zheng, K.L. 1996.
RFLP mapping of QTLs for yield and related
characters in rice (Oryza sativa L.). Theoretical
and Applied Genetics. 92: 920-927.
Septiningsih, E.M., Prasetiyono, J., Lubis, E., Tai,
T.H., Tjubaryat, T., Moeljopawiro, S and
McCouch, S.R. 2003. Identification of
quantitative trait loci for yield and yield
components in an advanced backcross
population derived from the Oryza sativa
variety IR64 and the wild relative O. rufipogon.
2003. Theoretical and Applied Genetics.107:
1419–1432.
SONALI et al
Cotton (Gossypium hirsutum L.) is the most
important commercial fibre crop contributing to 75%of total raw material needs of textile industry andalso provides employment to about 60 million people.To sustain high production and productivity of cotton,a large number of hybrids and high yielding varietieshave been developed. So far 50 cotton hybrids havebeen released for commercial cultivation in thecountry through the All India Co-ordinated CottonImprovement Project (Rana et al., 2006). Thevarieties and hybrids attain acceptance when thefarmer gets genetically pure seeds of high standards.For this purpose, each cultivar should be properlydefined with suitable descriptors, so as to maintainits identity during seed production through fieldinspection and certification. In India, Protection ofPlant Varieties and Farmer’s Rights Act (PPV & FRA2001) envisages the registration and protection ofnew and notified/extant plant varieties based
on the criteria of Distinctness, Uniformity andStability (DUS) of morphological characteristics andincreasing attention is being paid towardscomprehensive characterization, identification anddocumentation of plant genetic resources. TheNational Test Guidelines have been developed forcharacterization based on the criteria of DUSmorphological characteristics. Present study wasundertaken to characterize six hybrids and theirparental lines that are in active seed multiplicationchain on the basis of qualitative and quantitativemorphological characters.
The field studies were carried during kharif2011 season. The seed material comprised of sixhybrids (NDLHH 240, WGHH 2, NSPHH 5, CSH 198,CICR 2 and LAHH 4) and their 12 parents(NARASIMHA, MCU 5, NA 1678, NA 4084, NA 1325,
MORPHOLOGICAL CHARACTERIZATION OF COTTON HYBRIDS AND THEIR PARENTAL LINES
P. ARUNA, P. S. RAO, G. ANURADHA and K. KESHAVULUDepartment of Seed Science and Technology, College of Agriculture,
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030
Research NoteJ.Res. ANGRAU 40(3) 113-115 , 2012
Date of Receipt : 25.05.2012 Date of Acceptance : 19.06.2012
L604, CSH 19, CSH8, DS-5, LD 32, AB 6 and M 2
respectively) of cotton. The experiment was laid outin a randomized block design with three replications.
All the recommended agronomic practices were
followed to raise the crop. Morphological characters
based on National DUS test guidelines descriptors
for cotton were used for characterization.
The qualitative and quantitative morphologicalcharacters of genotypes in each treatment and
replication were used to differentiate the cotton
hybrids and their parental lines. Based on leaf size
cotton genotypes were grouped as small (4), medium
(12) and large (2). As per leaf colour, they were
grouped as light green (4), medium green (9) and darkgreen (5). Variation in the leaf size pattern and colour
of the leaf was observed among the genotypes and
it may be due to genetic characters of parents and
may vary according to soil, environmental, cultural
and nutritional factors during crop growth. The shape
of leaves varied as palmate (15), semi digitate (2)and digitate (1) and variation in the shape of the leaves
attributed mainly to the genetic characters. Based
on leaf pubescence genotypes were grouped as
glabrous (4), medium hairy (11) and hairy (3) and leaf
pubescence was largely be controlled by different
genes (Wright et al., 1999). The absence (4) orpresence (14) of stem pigmentation was also helpful
in differentiating the genotypes. The character of
stem was the hairiness which was categorized as
sparsely hairy (10), medium hairy (8). The petal colour
also one of important characters, for characterization
and was classified as cream (12) and yellow colour(6). The presence (4) or absence (14) of petal spot
was also used as the marker for varietal identification
(Ahuja and Dhayal 2006) The variation in the anther
colour is due to herbal characters of genotypes and
email: [email protected]
Gen
otyp
es:
1: A
B 6
, 2:
M 2
, 3:
LA
HH
4,
4: D
S 5
, 5:
LD
327
, 6:
CIC
R 2
, 7:
Nar
asim
ha 8
: M
CU
5,
9: N
DLH
H 2
40,
10:
CS
H 1
9, 1
1: C
SH
8,
12:
CS
H 1
98,
13:
NA
167
8,
14:
NA
408
4, 1
5: W
GH
H 2
, 16
: N
A 1
325,
17:
L 6
04,
18:
NS
PH
H 5
Tabl
e 1.
Des
crip
tion
of m
orph
olog
ical
DU
S d
escr
ipto
rs fo
r eig
htee
n co
tton
gen
otyp
esARUNA et al
Cul
tivar
S
. N
o.
Des
crip
tor
1 2
3 4
5 6
7 8
9 10
11
12
13
14
15
16
17
18
R
emar
ks
1 S
tem
pig
men
tatio
n 9
1 1
9 9
9 9
9 9
9 9
9 1
9 9
9 9
1 1:
abs
ent,
9: p
rese
nt
2 S
tem
pub
esce
nce
5 3
5 3
3 3
5 5
5 3
3 3
3 3
5 5
3 5
1: a
bsen
t, 3:
spa
rse,
5: m
ediu
m, 7
: st
rong
hai
ry
3 Le
af s
hape
1
1 1
2 3*
2
1 1
1 1
1 1
1 1
1 1
1 1
1: p
alm
ate,
2: s
emi-d
igita
te, 3
: di
gita
te, 4
: lan
ceol
ate
4 Le
af s
ize
5 5
5 5
7 5
5 5
5 7
3 5
3 3
3 5
5 5
3: s
mal
l, 5:
med
ium
, 7: l
arge
5 Le
af c
olou
r 1
1 2
3 3
3 2
3 2
1 2
2 2
1 2
2 3
1 1:
ligh
t gre
en, 2
: med
ium
gre
en,
3:da
rk g
reen
6
Leaf
pub
esce
nce
1 1
5 1
1 5
5 5
5 9
9 9
5 5
5 5
5 5
1:ab
sent
/spa
rse,
5: m
ediu
m, 9
: st
rong
7 P
etal
col
or
2 2
2 3
3 3
3 2
2 2
2 2
2 2
2 3
3 2
1:w
hite
, 2:c
ream
, 3: y
ello
w,4
: pin
k, 5
: red
,
8 P
etal
spo
t 1
1 1
9 1
1 1
9 9
1 1
1 1
1 1
1 1
9 1:
abse
nt, 9
: pre
sent
9 P
ositi
on s
tigm
a 1
1 2
2 1
1 2
2 2
1 1
1 2
2 2
2 1
1 1:
em
bded
, 2: e
xert
ed
10
Ant
her
colo
ur
3 1
3 3
3 3
2 3
3 2
2 2
1 1
1 2
1 2
1:w
hite
, 2: c
ream
, 3: y
ello
w, 4
: pur
ple
11
Bol
l sha
pe
2 1
1 2
2 3
2 1
2 2
2 2
3 3
3 2
2 3
1: r
ound
, 2: o
vate
, 3: e
llipt
ic
12
Pla
nt h
eigh
t 9
9 9
7 7
9 7
9 9
7 9
7 7
7 7
7 9
9 1:
ver
y sh
ort,
3: s
hort
, 5: m
ediu
m,
7: ta
ll, 9
: ver
y ta
ll 13
P
lant
type
3
2 2
1 1
1 1
3 3
1 1
1 3
3 2
1 2
2 1:
cyl
indr
ical
, 2; c
onic
al, 3
: glo
bose
14
D
ays
to 5
0%
flow
erin
g 5
5 5
5 7
7 5
5 5
7 3*
5
5 5
5 5
5 5
3:ea
rly, 5
: med
ium
, 7: l
ate
classified as white (5), cream (6) and yellow (7) and
its usefulness to identify genotypes was already
reported by (Ankaiah et al., 2005). Based on shape
of bolls, these were classified as round (3), ovate
(10) and elliptic (5). Embded stigma (9) or exertedstigma (9) position was used in characterization which
was genetically controlled. The plant type varied as
globose (5), cylindrical (8) and remaining 5 genotypes
showed conical shape and presence or absence of
filament colour among the genotypes was also useful
for characterization. The wide variation in plant heightwas noticed and grouped as tall (9) and very tall (9),
thus plant height could be extremely useful for varietal
identification and genetic purity testing. Based on
days to 50% flowering genotypes grouped as early
(1), medium (14) and late duration (3). Manjunath
Reddy et al. (2007) and Ramanadham (2000) using
key morphological characters for cotton hybrids and
their parents, stated that they were least influencedby growing conditions and found distinct, uniform and
stable throughout growing season.
On the basis of morphological characters
unique morphological profiles were obtained for two
cotton genotypes viz., CSH 8, LD 327. LD 327 have
digitate leaf shape unique from other genotypes and
CSH 8 was identified from other genotypes bypossessing early days to 50% flowering.
REFERENCES
Ramanadham, S. 2000. Assessment of genetic
purity, identity and seed vigour in F1 hybrids
and F2 population in cotton. M. Sc Thesis
submitted to Acharya N.G. Ranga Agricultural
University, Rajendranagar, Hyderabad.
Rana, M. K., Singh, S and Bhat, K.V. 2006. RAPD,
STMS and ISSR markers for genetic diversity
and hybrid seed purity testing in cotton. Seed
Science and Technology. 35: 709-721.
Wright, R.J., Thaxton, P.M., El-Zik, K.M and
Paterson, A.H. 1999. Molecular mapping of
genes affecting pubescence of cotton. Journal
of Heredity. 90: 215–219.
Ahuja, S.L and Dhayal, L.S. 2006. Identification ofpetal spotted mutant in upland cotton(Gossypium hirsutum L.) with some desirablemorphological and technological haracters.Indian Journal of Genetics and Plant Breeding.66 (3): 255-256.
Ankaiah, R., Subba Rao, L.V, Kumar, K.V.S.M.,Chaudhary, C.P and Vidya Sagar, C. 2005.Significance of grow out test in assessinggenetic purity of cotton hybrids and theirparental lines. Structural Genomics. 1: 53–54.
Manjunath Reddy, Ravi Hunje, Biradar, D.P andVyakarnahal, B.S. 2007. Characterization ofcotton hybrids and parental lines usingmorphological characters. Karnataka Journalof Agriculture Science. 20 (3): 511-513.
MORPHOLOGICAL CHARACTERIZATION OF COTTON HYBRIDS
Timely planting and use of appropriate aged
seedlings for transplanting are important non cash
inputs for realizing the higher productivity in rice
(Pattar et al., 2001). Tillering is an important
agronomic trait which finally determines the number
of panicles, grains and grain yield per unit land area.
Tillering dynamics of the rice plant greatly depends
on the age of seedling at transplanting (Pasuquin etal., 2008). All the varieties will not perform equally,
when transplanted with aged seedlings. The present
investigation was taken up to know the influence of
age of seedlings on growth and yield of different high
yielding rice varieties during kharif 2011 at RARS,
Warangal. The site is situated at 180 00' 42.1'’ N
latitude and 790 36' 04.5'’ E longitude and 262 m
altitude. The soil was sandy loam in texture, medium
in organic carbon (0.58 %), neutral in reaction (pH
7.4), low in available nitrogen (225.8 kg ha-1), medium
in available phosphorus (21.0 kg ha-1) and low in
available potassium (95.5 kg ha-1). The weekly mean
temperature ranged between 22.6 to 28.6 0C. A total
rainfall of 349.4 mm was received during the crop
growth period spread over 26 rainy days. The
experiment was laid out in a randomized block design
(factorial concept) with 15 treatments replicated
thrice. 20, 30, 40, 50 and 60 days old seedlings and
three varieties i.e., BPT 5204, MTU 1001 and JGL
384 were the treatments. The plot size was 5.4 m X
4.2 m. A uniform dose of 60, 40, 50 kg ha-1 P
2O
5: K
2O:
ZnSO4 was applied basally to all the treatments.
Nitrogen @ 120 kg ha-1 was applied in three equal
split doses each at basal, active tillering and panicle
initiation stages. Need based plant protection
measures were taken up, whenever the incidence
was more than economic threshold level. The other
EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIESD. NARESH, M. MALLA REDDY, D. VISHNU VARDHAN REDDY AND T.V. SRIDHAR
Department of Agronomy, Agricultural College,Aswaraopet, Khammam – 507 301, A.P.
Research NoteJ.Res. ANGRAU 40(3) 116-120, 2012
Date of Receipt : 07.06.12 Date of Acceptance : 13.08.2012
e-mail: [email protected]
recommended cultural practices were followed for
raising the crop.
The growth parameters of rice such as plant
height, LAI and dry matter production were
significantly influenced by the age of seedlings and
varieties (Table 1). Taller plants, higher LAI and dry
matter production were obtained by transplanting 20
days aged seedlings or 30 days old seedlings.
Reduction in these growth parameters was noticed
when 40, 50 and 60 days old seedlings were used.
Bommayasamy et al. (2010) also found the similar
performance of younger seedlings of rice over the
aged seedlings. Plant height, LAI and dry matter
production were significantly higher with MTU 1001
compared to BPT 5204. The number of tillers hill-1
was high when crop was planted with 20 days
seedlings (Fig. 1.a). There was 43 % reduction in
maximum tillering capacity when crop was planted
with 60 days old seedlings. The tillering period was
also extended upto 60 days when young seedlings
(20 or 30 days old seedlings) were transplanted, while
crop reached maximum tillering by 45 DAT when
overaged seedlings were transplanted (40, 50 or 60
days). When a seedling is transplanted carefully at
the initial growth stage, the trauma of root damage
caused during uprooting is minimized following a rapid
growth with short phyllochrons. Mobasser et al.(2007) observed that when seedlings stay for a longer
period of time in the nursery beds, primary tiller buds
on the lower nodes of the main culm become
degenerated leading to reduced tiller production.
Among the three tested varieties tillers hill-1 were
more in BPT 5204 followed by MTU 1001 and JGL
384 at all the stages of observation (Fig. 1.b). The
number of days to 50% flowering and maturity were
increased gradually with successive advancement
in the age of seedlings (Table 1). The crop
transplanted with 20 and 30 days seedlings took less
number of days to flowering and maturity. The
cultivar, MTU 1001 flowered and matured earlier than
JGL 384 and BPT 5204 even though the differences
were not statistically significant.
Yield attributes were also significantly
influenced by the age of seedling and varieties (Table
2). The number of panicles m-2, number of filled grains
panicle-1 were significantly higher in crop planted with
20 days seedlings but on a par with 30 days old
seedlings. Number of chaffy grains panicle-1 was
lower with the usage of 20 days seedlings. The test
weight was not influenced by age of seedlings.
Among the three varieties, the yield attributes like
number of panicles m-2 and test weight were
significantly higher in cultivar MTU 1001 followed by
JGL 384 and BPT 5204. Number of chaffy grains
were also minimum in MTU 1001 followed by JGL
384 and BPT 5204. Whereas, number of filled grains
panicle-1 were more in JGL 384 followed by BPT 5204
and MTU 1001.
The age of seedlings significantly influenced
the grain yield, straw yield and harvest index
(Table 2). Among the different aged seedlings, 20
followed by 30 days seedlings recorded significantly
higher grain yield, straw yield and harvest index.
Delay in transplanting beyond 30 days after sowing
caused 24, 36 and 42 per cent reduction in grain yield
with 40, 50 and 60 days old seedlings, respectively.
The better initial growth coupled with superior yield
attributes (panicles m-2, panicle length and filled
grains panicle-1) besides less chaffy grains panicle-1
might be ascribed the reasons for higher grain and
straw yield under 20 day old seedlings. Pasuquin etal. (2008) and Manjunatha et al. (2010) also found
the superior performance of younger seedlings of rice
over the aged seedlings. The cultivar MTU 1001
recorded significantly higher yield and harvest index
compared with JGL 384 and BPT 5204.
The interaction between the age of seedlings
and varieties for all the growth parameters, yield
attributes and yield was found to be non-significant.
The gross returns, net returns and return per rupee
invested were higher with the use of younger seedlings
(20 or 30 days seedlings). Similarly, higher returns
and return per rupee invested were recorded with MTU
1001. The experimental results revealed that
transplanting of 20 or 30 days old seedlings of MTU
1001 was found to be optimum to realize higher yield
and returns.
0
5
10
15
20
25
15 30 45 60
Days after transplantin g
No.
of t
iller
s h
ill-1 20 days
30 days 40 days
50 days
60 days
Fig. 1.a. Number of tillers hill-1 at different growth stages as influenced by age of seedlings
EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIES
Table 1. Growth parameters of rice as influenced by age of seedlings and varieties
Treatment Plant height
(cm) at PI
Dry matter (g hill-1) at
PI
LAI at PI
Days to 50% flowering
Days to maturity
Age of seedlings (days)
20 103.7 46 6.9 99 133
30 98.0 41 6.4 104 138
40 95.0 35 5.3 110 143
50 89.3 29 4.4 118 150
60 86.0 24 3.6 123 154
SEm± 2.9 2.3 0.3 2 2
CD at 5% 8.6 6.8 0.9 7 7
Variety
BPT 5204 87.8 29 4.4 114 146
MTU 1001 100.6 39 6.4 109 141
JGL 384 94.8 37 5.1 110 143
SEm± 2.3 1.8 0.2 2 2
CD at 5% 6.6 5.2 0.7 NS NS
Interaction
SEm± 5.1 4.0 0.5 4 4
CD at 5% NS NS NS NS NS
0
2
4
6
8
10
12
14
16
18
15 30 45 60
Days after tran splanting
No
. of t
iller
s h
ill-1
BPT 5204
MTU 1001
JGL 384
Fig. 1.b. Number of tillers hill-1 at different growth stages as influenced by varieties
NARESH et al
EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIES T
ab
le 2
. Y
ield
att
rib
ute
s,
yie
ld a
nd
eco
no
mic
s o
f ri
ce a
s i
nfl
uen
ced
by a
ge o
f seed
lin
gs a
nd
vari
eti
es
Tre
atm
en
t
Pan
icle
s
m-2
Fille
d
gra
ins
pan
icle
-1
Ch
aff
y
gra
ins
pan
icle
-1
10
00-g
rain
weig
ht
(g)
Gra
in
yie
ld
(kg
ha
-1)
Str
aw
yie
ld
(kg
ha
-1)
Harv
est
ind
ex (
%)
Gro
ss
retu
rns
(Rs h
a-1
)
Net
retu
rns
(Rs h
a-1
)
Re
turn
per
rup
ee
investe
d
Ag
e o
f se
ed
lin
gs (
days)
20
347
240
19
17.8
61
40
7464
42
.1
(45.
0)
79,2
57
53,5
80
2.1
30
316
218
24
17.3
56
20
7006
41
.7
(44.
3)
72,6
27
46,9
50
1.8
40
286
205
31
17.7
42
60
5913
40
.2
(41.
7)
62,0
54
36,3
77
1.4
50
238
190
40
17.5
35
70
5093
39
.7
(40.
8)
53,6
92
28,0
15
1.1
60
196
145
52
17.0
32
50
4837
39
.1
(39.
8)
48,1
15
26,4
38
0.9
SE
m±
13
13
3
0.3
233
234
0.3
- -
-
CD
at
5%
40
39
9
NS
67
6 67
8 0.
9 -
- -
Vari
ety
BP
T 5
204
253
185
42
13.3
36
64
5137
39
.8
(41.
0)
55,9
50
30,2
73
1.2
MT
U 1
001
300
157
25
24.4
55
76
7107
41
.3
(43.
6)
71,6
90
46,0
13
1.8
JG
L 3
84
276
257
33
14.7
44
64
5945
40
.6
(42.
5)
61,8
07
36,1
30
1.4
SE
m±
10
10
2
0.2
181
181
0.2
- -
-
CD
at
5%
29
30
6
0.7
523
525
0.6
- -
-
Inte
racti
on
SE
m±
22
23
5
0.5
404
405
0.5
- -
-
CD
at
5%
N
S
NS
N
S
NS
N
S
NS
N
S
- -
-
The
val
ues
in th
e pa
rent
hesi
s ar
e or
igin
al v
alue
s [a
ngul
ar tr
ansf
orm
atio
n]
REFERENCES
Bommayasamy, N., Ravisankar, N and Subramani,T. 2010. Influence of non-monetary inputs ongrowth and yield of rice (Oryza sativa L.) undersystem of rice intensification (SRI). IndianJournal of Agronomy. 55(2): 95-99.
Manjunatha, B.N., Basavarajappa, R and Pujari, B.2010. Effect of age of seedlings on growth,yield and water requirement by differentsystem of rice intensification. KarnatakaJournal of Agricultural Sciences. 23(2): 231-234.
Mobasser, H. R., Tari, D.B., Vojdani, M., Abadi, R.Sand Eftekhari, A. 2007. Effect of seedling ageand planting space on yield and yieldcomponents of rice (Neda Variety). AsianJournal of Plant Sciences. 6(2): 438-440.
Pasuquin, E., Lafarge, T and Tubana, B. 2008.Transplanting young seedlings in irrigated ricefields: Early and high tiller productionenhanced grain yield. Field Crops Research.105(1-2): 141-155.
Pattar, P.S., Masthana Reddy and Kuchanur, P.H.2001. Yield and yield parameters of rice (Oryzasativa L.) as influenced by date of plantingand age of seedlings. Indian Journal ofAgricultural Sciences. 71(8): 521-522.
NARESH et al
Research NoteJ.Res. ANGRAU 40(3) 121-123, 2012
In India, maize occupies third position nextto rice and wheat cultivation and plays a pivotal rolein Indian agricultural economy. Yield loss due toweeds in maize (Zea mays L.) varies from 28 to 93%,depending on the type of weed flora and the intensityand duration o crop-weed competition (Sharma andThakur, 1998).
A field experiment entitled “Weedmanagement studies in kharif maize” was conductedon sandy loam soil during kharif 2010.The fieldexperiment was conducted at college farm, Collegeof Agriculture, Rajendranagar, Hyderabad. The farmis located at an altitude of 542.6 m above mean sealevel (MSL) and 780.291 E longitudes and 170 191 Nlatitude. The soil of experimental site was sandy loamin texture, low in available N (193.4 kg ha-1) andmedium in phosphorus (49 kg P
2O
5 ha-1) and high in
potassium (168 kg K2O ha-1) with pH 7.4. The
experiment was laid out in randomized block designwith 10 treatments and three replications.
Medium duration maize hybrid DHM 117was sown on 24 July at a spacing of 60 x 25 cm.The common fertilizer schedule adopted for all thetreatments was 200-60-40 kg N, P
2O
5 and K
2O ha-1 in
the form of urea, single super phosphate and muriateof potash respectively. Entire dose of P
2O
5 and K
2O
and 1/3 rd N were applied as basal, rest of the nitrogenwas applied in two equal splits at knee high andtasseling stage, respectively. The required quantitiesof herbicides were calculated as per the treatmentsand applied as aqueous spray by using the sprayfluid (water @ 500 litres ha -1) using Knapsack sprayerwith flat fan nozzle. Rainfall received during the cropgrowth period was 610.6 mm with 38 rainy daysindicating well distributed rainfall during crop growingseason.
The major weed flora associated with maizecrop in the experimental site consisted of four
WEED MANAGEMENT STUDIES IN KHARIF MAIZEM. A. ALEEM AHMED and R. SUSHEELA
Department of Agronomy, College of Agriculture,Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030
Date of Receipt : 04.06.2012 Date of Acceptance : 13.06.2012
monocots viz., Cyperus rotundus, Cynodon dactylon,Digitaria sanguinalis, Dactyloctenium aegyptium andeight dicots viz., Parthtenium hysterophorus,Commelina benghalensis, Amaranthus viridis,Euphorbia geniculata, Celosia argentia, Digeriaarvensis, Trichodesma indicum, Legasca mollies.Cyperus rotundus and Cynodon dactylon weredominant and problematic weeds observed in theexperimental field. The weeds were better controlledby Intercultivation followed by hand weeding (T-
9) and
atrazine 1.0 kg a.i. ha-1 at 1-2 DAS followed by post-emergence spray topramezone 40ml ha-1 at 25 DAS(T-
5).Thakur and Singh (1989) reported less number
of Cyprus spices with the inclusion of post emergenceherbicide glyphosate while Reddy (2003) observedthat the intensity of this weed spices was greatlysuppressed by the inclusion of post emergenceherbicide 2,4-D Na salt.
The results showed that the farmer’s practiceof eliminating weeds through intercultivation at 20DAS fb HW at 30 DAS (T-
9) reduced the weed density
and weed dry matter production significantly andthereby increased the weed control efficiency (%)Next best was pre emergence spray of atrazine @1.0kg a.i. ha-1at 1-2 DAS followed by intercultivation at20 DAS (T2),The herbicide treatment involving pre-emergence spray of atrazine @1.0 kg a.i.ha-1 fb post-emergence spray of topramezone @ 40 ml ha-1 was
on par with (T-9) and the post-emergence spray of
metribuzin @ 0.75 kg a.i. ha-1 exhibited phyto-toxicity
on maize . Mundra et al. (2003) found that pre
emergence application of atrazine @ 0.50 kg a.i.
ha-1 followed by intercultivation at 35 days after sowinggave the highest weed control efficiency (81.0%).
Plant height, plant dry matter production,number of seed rows cob-1, number of grains cob-1,
100 grain weight (g) were highest in T9 (Intercultivation
at 20 DAS followed by hand weeding at 30 DAS)
email: [email protected]
AHMED and SUSHEEL
Tab
le 1
. A
gro
no
mic
tra
its o
f m
aiz
e in
flu
en
ced
un
der
dif
fere
nt
weed
co
ntr
ol
man
ag
em
en
t p
racti
ces
T
reat
-m
ents
Wee
d de
nsity
at
60
DA
S
(No.
m-
2 )
Wee
d dr
y m
atte
r at
60
DA
S (
g m
-2)
Wee
d co
ntro
l ef
ficie
ncy
at 6
0 D
AS
(%
)
Pla
nt
heig
ht
at 6
0 D
AS
(c
ms)
Pla
nt
dry
mat
ter
at 6
0 D
AS
(g
m-2
)
Leaf
ar
ea
inde
x at
60
DA
S
No
of
seed
ro
ws
cob-1
No
of
seed
s co
b-1
100
seed
w
eigh
t (g
)
See
d yi
eld
kg
ha-
1
Sta
lk
yiel
d
kg h
a-
1
Har
ves
t in
dex
(%
)
Gro
ss
retu
rns
(Rs
ha-1
)
Net
re
turn
s (R
s ha
-
1 )
B:C
R
atio
T1
76
.3
(8.8
) 68
.9
71.3
18
9.9
64.8
2.
92
14.3
35
3.6
19.7
41
32
6876
31
.2
4737
0 30
055
1.73
T2
37
.6
(6.2
) 32
.6
86.4
19
6.6
76.6
3.
51
14.6
40
1.0
20.8
48
52
7383
33
.1
5493
3 35
493
1.82
T3
63
.7
(8.0
) 54
.2
77.5
19
4.2
71.6
3.
15
14.3
36
1.1
20.1
43
15
6949
31
.6
4923
6 31
421
1.76
T4
39
.6
(6.4
) 37
.7
84.3
19
5.8
72.3
3.
28
14.4
37
3.2
20.4
44
88
7261
32
.4
5173
3 33
418
1.82
T5
33
.3
(5.8
) 28
.6
85.3
20
3.6
73.5
3.
43
14.5
38
4.5
20.6
46
46
7304
32
.6
5283
5 34
470
1.88
T6
63
(7
.9)
41.7
82
.6
192.
6 68
.8
3.40
14
.3
347.
4 20
.3
4317
70
45
30.6
49
355
3149
0 1.
76
T7
79
(8
.9)
63.1
73
.8
186.
3 61
.3
2.80
14
.1
311.
8 20
.1
3807
63
70
30.1
43
697
2633
2 1.
51
T8
68
.3
(8.3
) 46
.4
80.7
16
7.3
57.6
2.
48
14.1
15
8.8
20.0
19
17
3893
29
.8
2268
0 39
15
0.20
T9
29
(5
.4)
26.0
89
.2
208.
6 78
.3
3.73
14
.9
424.
1 21
.0
5122
79
34
34.2
58
179
3754
9 1.
77
T10
288
(16.
9)
240.
7 0.
0 15
8.3
52.0
2.
57
13.6
21
3.5
17.9
25
85
5383
24
.8
3071
6 14
401
0.88
SE
m (
+ )
0.
4 3.
2 1.
6 1.
9 2.
2 0.
06
0.11
7.
2 0.
22
99
280
1.1
0 0
0
CD
at
5%
1.
3 9.
5 4.
9 5.
6 6.
6 0.
18
0.32
21
.8
0.65
27
7 83
9 3.
2 0
0 0
T1
A
traz
ine
@ 1
.0 k
g a.
i. ha
-1 P
re-e
m
T6
Atr
azin
e @
0.5
kg
a.i.
ha-1
+ to
pram
ezon
e 40
ml.h
a-1 (
both
at 1
2DA
S)
T2
A
traz
ine
@ 1
.0 k
g a.
i. ha
-1 fb
IC
(20
DA
S)
T
7
Top
ram
ezon
e @
40 m
l ha-1
(
25 D
AS
)
T
3
Atr
azin
e @
1.5
kg
a.i.
ha-1
Pre
-em
T8
Met
ribuz
in @
0.7
5kg
a.i.h
a-1 (
16
DA
S)
T4
A
traz
ine
@ 1
.0 k
g a.
i. ha
-1 f
b at
razi
ne 1
.0 k
g a.
i. ha
-1 (
16 D
AS
)
T
9
IC (
20D
AS
) fb
HW
(30
DA
S)
T5
Atr
azin
e @
1.0
kg
a.i.
ha-1
fb
topr
amez
one4
0 m
l ha-1
(2
5 D
AS
)
T
10
Con
trol
(W
eedy
che
ck)
This was on par with T2 (atrazine @1.0 kg a.i.ha-1 fb
IC and T5 (Pre emergence application of atrazine @
1.0 kg a.i. ha-1 fb topramezone 40 ml ha-1 at 25 DAS).No. of cob plant-1 was not influenced by weed controltreatments. Similar findings were also reported byPandey et al (2001). Thomas et al. (2010) conductedtwo field experiment in northern greece in 2008 and2009 to determine the response of grain maize totopramezone applied as post emergence at 2-4, 4-6and 6-8 maize leaf stages .In both the years grainyields were not differently affected with the use ofherbicide at these growth stages and yields weresimilar with that of hand weeded control.
Higher grain yield was achieved with T9, T2
and T5 were on par with each other. Stover yield
followed the same trend as that of grain yield.
Maximum gross returns and net returns wererealized from (T
9) followed by T
2 and T
5. Benefit cost
WEED MANAGEMENT STUDIES IN KHARIF MAIZE
ratio was higher in T5 (Pre- emergence application of
atrazine @1.0 kg a.i. ha-1 fb topramezone@ 40 ml
ha-1at 25 DAS)
It is clear that weed menace in maize during
kharif season where continuous rains pose a serious
problem, can be managed efficiently through
integration of pre-emergence application of atrazine
@ 1.0 kg a.i. ha-1 fb topramezone @ 40 ml ha-1 as
post-emergence spray to sustain high productivity
and profitability under situations of scarcity and non
availability labour and slushy soil conditions.
Topramezone would be complement to current weed
management program in grain maize. Alternatively
it can be used in sequential application to pre
emergence soil applied treatments or in a total post
emergence program in mixtures with other herbicides
(Porter et al., 2005)
References
Mundra, S. L., Vyas, A. K and Maliwal, P. L. 2003.Effect of weed and nutrient management onweed growth and productivity of maize (Zeamays L.) . Indian Journal of Weed Science.
35(1&2): 57-61
Pandey, A. K ., Singh. P. , Prakash, V., Singh, R. D
and Mani, V. P. 2001. Integrated weed
management in maize(Zea mays) . Indianjournal of Agronomy 46 (2) : 260-265.
Porter, R.M., Vaculin, P. D., Orrr, J. E., Immaraju,
J.A and O Neal, W.B. 2005. Topramezone a
new active for post-emergence weed control
in corn. In: North Central Weed S c i e n c e
Society Proceedings. 60:93
Reddy, U, S, 2003. Integrated weed management in
rainfed maize M Sc (Ag) Thesis submitted
to Acharya N G Ranga Agricultural University,
Hyderabad.
Sharma, V and Thakur, D. R. 1998. Integrated weed
management in maize (Zea mays) under mid-
hill condition of north-western himalayas.
Indian journal of Weed Science 30(3&4): 158-
162.
Thakur, D. R and Singh, K. K. 1989. Effects of weed
management systems on weeds under varying
fertility levels in rainfed maize. Indian Journalof Weed Science 21 (3&4):10-14
Thakur, D. R and Sharma, V.1996. Integrated weed
management in rainfed maize. Indianjournal of Weed Science 28(3&4): 207-208.
Thomas, K., Gitsopoulos., Melidis, V and Evgenidis,
G. 2010. Response of maize (Zea mays L.)
to post-emergence applications of
topramezone. Crop protection 29 :1091- 1093
Tomato (Lycopersicon esculentum Mill.) isan important vegetable crop grown widely throughoutthe world under various agro-climatic conditions. InIndia, the estimated annual production of tomato isabout 11.98 million tonnes with an area of about 3.65lakh ha with an average productivity of 19.32 tonnesper ha. The crop is grown in an area of about 7.41lakh ha with an annual production of 140.8 lakhtonnes in Andhra Pradesh (Indiastat, 2011). The rootrot caused by Rhizoctonia solani is a major problemin monocropping areas and it is reported fromHaryana, Tamilnadu and Andhra Pradesh. Thepathogen generally attacks root region and causespre and post emergence root rot. The root knotnematode is a widespread problem in tomato.Inoculum levels of these pathogens determine theyield losses associated with tomato crop. Thepresent study was carried out to investigate effect ofinoculum levels of Rhizoctonia solani and Meloidogyneincognita on root rot and root knot incidence ontomato cv. Pusa ruby.
The fungus Rhizoctonia solani was isolatedfrom roots of infected disease tomato plants collectedfrom farmers field during survey from Chittoor districtwith a history of maximum disease incidence in thefield during kharif season of 2008. The nematodeM. incognita was extracted from roots of tomatoplants. Based on the morphological characters andperineal pattern the nematode was identified asMeloidogyne incognita following the key provided byHartman and Sasser (1985).
The test pathogen Rhizoctonia solani wasgrown on potato dextrose broth for ten days and usedas inoculum for pathogenicity studies. The pathogenwas tested at different inoculum levels i.e., 0 g., 1.0g., 2.0 g. and 4.0 g / kg soil and the inoculum wasapplied to top 6-7 cm layer of sterilized soil in one kg
EFFECT OF INOCULUM LEVELS OF RHIZOCTONIA SOLANI AND MELOIDOGYNEINCOGNITA ON ROOT ROT AND ROOT KNOT INCIDENCE ON
TOMATO CV. PUSA RUBYB. VIDYA SAGAR, V. KRISHNA RAO, K. S. VARA PRASAD and D. R. R. REDDY
Department of Plant Pathology, College of Agriculture,Acharya NG Ranga Agricultural University, Rajendranagar, Hyderabad-500030.
Research NoteJ.Res. ANGRAU 40(3) 124-126, 2012
email: [email protected]
plastic pots and was mixed thoroughly and moistenedregularly for maintenance of the inoculum. The potswithout pathogen inoculum served as control. Fivereplications were maintained per each treatment andfifteen seeds of tomato cv. Pusa ruby disinfectedwith sodium hypochlorite were sown in the pots.Plants were kept under constant observation forappearance of symptoms. After getting symptoms,the test plants were uprooted and washed thoroughlyand the pathogen was re-isolated from the artificiallyinoculated root rot infested plants. The fungusobtained was compared with the original isolate in allaspects to prove pathogenicity. The data on preemergence rotting was recorded at 12 DAS and postemergence rotting at 50 days after sowing.
The different levels of second stage juveniles(J
2) of M. incognita viz., 0, 1000, 2000 and 4000 J
2
per 1000g of soil were inoculated at root zone depth,of 7 days old tomato seedlings. Uninoculated potsserved as control. The data on shoot and root length,fresh and dry shoot and root weight and root knotindex were recorded at 50 days after inculcation andstatistically analyzed with necessary trans-formations.
An increase of pre and post emergencerotting (Table 1) was observed with increase ininoculum levels of Rhizoctonia solani. Soil inoculationwith 1.0 g mycelium recorded 10.00 and 11.86 percent of pre and post emergence root- rot and the preand post emergence root rot increased to 30.67 and42.54 at a higher inoculum level i.e. 4.0 g ofmycelium. The total rotting was 67.25 and 73.26 percent with 2.0g and 4.0 g mycelium respectively. Theinoculum level of 2.0 g of mycelium per kg soil wasidentified as optimum inoculum threshold level andwas used for further studies.
Date of Receipt : 02.06.2012 Date of Acceptance : 13.08.2012
Table 2. Effect of inoculum levels Meloidogyne incognita on root knot incidence of tomato cv.Pusa ruby (mean of 5 replications)
Table 1. Effect of inoculum levels of Rhizoctonia solani on root rot incidence of tomato cv. Pusaruby (mean of 5 replications)
Per cent disease incidence* Treatment (g mycelium / kg
soil) Pre-emergence
rotting Post-emergence
rotting Total rotting
0 0.00
(4.05)
0.00
(4.05)
0.00
(4.05)
1.0 10.00
(18.24)
11.86
(19.83)
21.86
(27.66)
2.0 30.00
(33.07)
37.25
(37.58)
67.25
(55.30)
4.0 30.67
(33.49)
42.54
(40.70)
73.26
(59.13)
Mean 17.67 22.93 40.59
CD at 5% 4.91 3.73 6.82
* Figures in parenthesis are angular transformed values
Shoot weight/plant
Root weight/Plant Treatment
(J2 /1000 g soil)
**Root knot Index
Shoot length (cm/
plant) Fresh
(g) Dry (mg)
Root length
(cm/ plant)
Fresh (g)
Dry (mg)
0 1.00 (1.23)
28.60 11.24 338 9.06 3.37 145
1000 3.60 (2.00)
22.40 9.97 298 7.72 2.97 127
2000 4.40 (2.21)
20.06 8.65 275 6.84 2.51 117
4000 4.60 (2.25)
19.24 8.54 269 6.57 2.50 115
3.40 22.58 9.60 295 7.55 2.84 126
CD at 5% 0.22 0.87 15.57 9.3 2 0.34 6.65 2.59
Figures in parenthesis are square root transformed values
EFFECT OF INOCULUM LEVELS OF RHIZOCTONIA SOLANI AND MELOIDOGYNE
Similar result was observed by Hadwan andKhara (1992) where in they recorded 19 per centdisease at 1: 24 level of inoculum and 90 per centdisease at 1:3 ratio. Similar results were alsoreported by Jiskani et al. (2007).
The root knot index increased with increasein inoculum levels. The maximum root knot index(4.60) was recorded when plants were inoculated with4000 J
2 /kg soil (Table 2).
The overall plant growth was also decreasedwith the increased inoculum levels of nematode.Shoot length (19.24), fresh (8.54g) and dry shoot(269mg) weight were reduced greatly at high inoculumlevel (4000 J
2/kg soil) when compared to 28.60, 11.24,
338 in control. Similarly, there was maximumreduction in the root length, fresh and dry weight ofroot where plants were inoculated with 4000 J
2 /kg
soil compared to control.
REFERENCES
Hafeezullah Khan., Riaz Ahmad, Akhtar, A.S., Arshad
Mahmood., Tahir Basit and Tariq Niaz. 2000.
Effect of Inoculum Density of Meloidogyneincognita and plant age on the severity of Root
Knot disease in Tomato. International Journal
of Agriculture & Biology. 2(4):360-63
Hadwan, A.H and Khara, H.S. 1992. Studies on the
effect of inoculum level and temperature on
the incidence of damping off and root rot of
tomato by Rhizoctonia solani. Plant Disease
Research. 7 (2) :242-244
Hartman, K. M and Sasser, J.N. 1985. Identification
of Meloidogyne species on the basis of
differential host test and perineal pattern
The root knot index was maximum (4.6) athigh inoculum level (4000 J
2/kg soil) and there was a
progressive decrease in growth parameters of tomatowith increase of inoculum levels. Similar results werealso reported by Hafeezulla Khan (2000) where asignificant reduction of growth parameters of tomato
with increased inoculum levels of nematode wasobserved.
As per the results, the damaging thresholdlevel of R. solani on tomato cv. Pusa ruby was 2.0 gof mycelium and that of M. incognita was2000 J
2/ kg soil.
morphology. In Advanced Treatise on
Meloidogyne. Vol.II Methodology(Eds.
K.R.Barker, C.C. Carter and J. N.Sasser)
NCSU. Raleigh,pp. 69-77
Indiastat. 2011. Crop statistics in India
www.indiastat.com
Jiskani, M.M., Pathan, M.A., Wagan, K.H.L, Imran,
M and Abro, H.2007. Studies on the control of
tomato Damping off disease caused by
Rhizoctonia solani Kuhn. Pakistan Journal of
Botany. 39 (7) 2749-2754.
SAGAR et al
Research NoteJ.Res. ANGRAU 40(3) 127-131, 2012
Date of Receipt : 16.6.2012 Date of Acceptance : 13.8.2012
The present investigation was carried out
during post kharif, 2011 to characterize the soils of
command area of Pillaipally anicut, Musi river,
Nalgonda district of Andhra Pradesh and to map them
by using the GIS tools which can be used for soil
test based, crop specific fertilizer recommendations.
The water from Musi river is source of
irrigation water to different villages of 20 mandals in
Rangareddy and Nalgonda districts, irrigating an area
of 12750 ha through different (23) Anicuts and
Pillaipally anicut is one such anicut among them
selected for the study. Surface (0-15 cm) soil
samples(73) were collected from the farmers’ fields
in Pillaipally anicut command area along with GPS
readings and analyzed for texture, pH, Electrical
conductivity, organic carbon and available
Nitrogren(N), Phosporous (P) and Potassium (K) in
laboratory by using standard procedures suggested
by Piper (1966) and Tandon (1993). The Survey of
India (SOI) topographical maps of 57K7, 57K
11 in
1:50,000 scale covering the command area of
Pillaipally anicut of Musi river, Nalgonda district,
Andhra Pradesh were used as reference maps for
demarcating the study area. The Musi command area
map was vectorised by using Raster to Vector
software (ERDAS 9.3 software), and then it was
exported into Arc GIS 9.3 Software. Database on
soil fertility status of the study area was developed
using Microsoft Excel package.
Then the database was exported to Arc GIS
software, thematic maps (base maps) on spatial
variability of soil fertility were generated by ordinary
krigging method available in the sub mode of
interpolation in the spatial analyst tools of Arc map
9.3 GIS software.
SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA, MUSI RIVER IN ANDHRA PRADESH
Y. KRISHNAVENI, K.AVIL KUMAR, M. UMA DEVI, and M. D. REDDYWater Technology Centre, Achary N.G.Ranaga Agricultural University,
Rajendranagar, Hyderabad-500 030.
email: [email protected]
Soil texture of surface (0-15 cm) samplescollected from command area of Pillaipally anicutranged from sandy clay loam to loam. Among these,70 per cent of soils were sandy clay loam, 25 percent of soils were sandy loam in texture and 5 percent of them were loam in texture. The soilscontained 48 to 72 per cent of sand, 4 to 34 per centof silt and 13 to 32 per cent of clay contents, withoverall means of 62 per cent, 15 per cent and 24 percent of sand, silt and clay contents, respectively.The clay soil particles deposited while irrigating thecrops with Musi water might be responsible for thehigher percentage of soils having relatively finetexture. Rattan et al. (2005) indicated that fieldsirrigated with sewage irrigation water around the peri-urban areas of Keshopur Sewage Treatment Plantwere sandy loam in texture.
Among all the samples in study area ofPillaipally anicut, the pH of the surface soil samplesirrigated with canal water varied from 7.8 to 8.7 withoverall mean value of 8.2 while the pH of the soilsirrigated with ground water varied from 6.9 to 8.8 withoverall mean value of 8.1. Among the soils irrigatedwith canal water, 15.8 per cent of the samples werelow alkaline, 71 per cent were medium alkaline and13 per cent were strong alkaline whereas in case ofsoils irrigated with ground water 6 per cent wereneutral, 35 per cent were low alkaline, 56 per centwere medium alkaline and 3 per cent were strongalkaline (Fig.1). Alkalinity of irrigation water in thecommand area (Musi canal / bore wells / open wells)might be responsible for the higher percentage of soilshaving alkalinity. Tiwari et al. (1996) observed thatsoils irrigated with treated sewage water had relativelyhigher pH (8.0) and alkaline in reaction as comparedwith soils irrigated with tube well water (7.5) atVaranasi, and U.P.
Electrical conductivity of the surface soilsamples irrigated with canal water ranged from 0.1to 1.0 dS m-1 with a mean value of 0.52 dS m-1
whereas surface soil samples irrigated with groundwater ranged from 0.2 to 1.3 dS m-1 with a meanvalue of 0.61 dS m-1(Fig.2). Tiwari et al. (1996) andRamesh (2003) also reported relatively higher totalsoluble contents in sewage water irrigated fields thansoils irrigated with normal water.
Organic carbon content of surface soilsamples irrigated with canal water ranged from 0.23to 2.01 per cent with mean 0.88 per cent while thesoils irrigated with ground water ranged from 0.2 to2.1 per cent with mean 0.8 per cent(Fig.3). Musi canalwater carrying sufficient loads of suspended organicmatter might be responsible for higher organic carboncontents in the study area. The results are inconformity with the findings of Ramesh (2003), whoreported higher organic carbon contents in soilsirrigated with sewage water than with normal water.
Available nitrogen content of surface soilsamples irrigated with canal water ranged from 50.2to 589.6 kg N ha-1 with overall mean value of 189.4kg N ha-1 while available nitrogen content of thesurface soil samples irrigated with ground waterranged from 71.3 to 313.6 kg N ha-1 with overall meanvalue of 149.04 kg N ha-1(Fig.4). These resultscorroborates with findings of Azad et al. (1987) whofound that the normal soils contained very lowamounts of available nitrogen in surface andsubsurface layers, while in case of soils fed withsewage waste water had low to medium availablenitrogen in surface, subsurface layers of the soils ofvillages around ‘Budha Nallah’, Ludhiana.
Available phosphorus content of surface soilsamples irrigated with canal water ranged from 62.8to 549.2 kg P ha-1 with overall mean value of 238.4kg P ha-1 while phosphorus content of the surfacesoil samples irrigated with ground water ranged from73.4 to 514.7 kg P ha-1 with overall mean value of249.4 kg P ha-1(Fig.5). High available phosphoruscontent could be ascribed to enrichment of soils withsewage canal water, heavy application of phosphaticfertilizers by farmers through complex fertilizersapplied as basal as well as top dressing and organicmatter, which favours the solubilisation of fixedphosphorus releasing more quantity to the available
pool. Kharche et al. (2011) also concluded similarresults of higher available P in sewage-irrigated soilsindicating significant addition of P through sewagewater.
Available potassium content of surface soilsamples irrigated with canal water ranged from 90.7to 813.1 kg K ha-1 with overall mean value of 264.5kg K ha-1 while available potassium content of thesurface soil samples irrigated with ground waterranged from 63.8 to 432.3 kg K ha-1 with overall meanvalue of 174.3 kg K ha-1. All the soils are found to behigh to very high in available K status (Fig.3).Additions of potassium by Musi river water could beresponsible for such higher status of K in these soils.The results of high available potassium are inconformity with Priyanieamerasinghe et al. (2008) andKharche et al. (2011).
REFERENCES
Azad, A.S., Arora, B.R., Bijay Singh and Sekhan,G.S. 1987. Effect of sewage waste water onsome soil properties. Indian Journal ofEconomy. 14(1) : 7-13.
Kharche, V.K., Desai, V.N and Pharande, A.L. 2011.Effect of sewage irrigation on soil properties,essential nutrient and pollutant element statusof soils and plants in a vegetable growing areaaround Ahmednagar city in Maharashtra.Journal of the Indian Society of SoilScience.59 (2): 177-184.
Priyanieamerasinghe Philipp Weckenbrock RobertSimmons SreedharAcharya and AxelDrescher. 2008. An atlas of water quality,health and agronomic risks and benefitsassociated with wastewater irrigatedagriculture-A study from the banks of theMusiriver, India.
Piper, C.S. 1966.Soil and Plant Analysis.HansPublishers, Bombay.137-153.
Ramesh, M. 2003. Soil and water resourcecharacteristics in relation to land disposal ofsewage effluents and suitability of sewagewater for irrigation. M.Sc. (Ag.) ThesisAcharyaN.G. Ranga Agricultural University, Hyderabad.
Rattan. R.K., Datta.S.P.,Chhonkar,P.K., Suribabu,Kand Singh, A.K. 2005. Long-term impact of
KRISHNAVENI et al
Fig.1. Spatial variability of soil pH in command area of Pillaipally anicut, Nalognda district, A.P.
Fig.2. Spatial variability of soil EC in command area of Pillaipally anicut, Nalognda district, A.P.
SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA
Fig.3. Spatial variability of soil organic carbon and Potassium in command area of Pillaipally anicut,Nalognda district, A.P.
Fig.4. Spatial variability of available soil nitrogen in Pillaipally anicut command area, Nalognda district, A.P.
KRISHNAVENI et al
irrigation with sewage effluents on heavy metalcontent in soils, crops and groundwater—acase study. Agriculture, Ecosystems andEnvironment 109 (2005): 310–322.
Tandon, H. L .S. 1993.Methods of analysis of soilsplants, water and fertilizers. FertilizersDevelopment and Consultation Organisation,New Delhi, India.
Tiwari, R. C., Arvind Kumar and Mishra, A. K .1996.Influence of treated sewage and tube wellwater irrigation with different fertilizer levelson rice and soil properties. Journal of the IndianSociety of Soil Science. 44(3): 547-549.
Fig.5: Spatial variability of available soil phosphorus in command area of Pillaipally anicut, Nalognda district, A.P.
SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA
Research NoteJ.Res. ANGRAU 40(3) 132-135, 2012
Date of Receipt : 15.06.2011 Date of Acceptance : 03.01.2012
Fertilizers play an important role in increasing
the production and improving the quality of vegetables
specially cauliflower, and among them potassium is
prime because of its role in various physiological
processes. Not much work has been done in light
textured soils of Rangareddy district on the effect of
levels of potassium on cauliflower. Hence, a study
was made in green house of Department of Soil
Science, College of Agriculture, Rajendranagar during
Rabi, 2010-11. Surface soils in bulk were collected
from thirty locations of Rangareddy district.
Greenhouse experiment was conducted with four
different levels of potassium viz., 0, 50, 100 and 150
kg K2O ha-1, each replicated thrice. The soils were
analysed for pH, EC, OC and CEC by standard
procedures (Jackson, 1973). The texture of the soils
ranged from loamy sand to sandy loam except the
soils of Shamshabad, Emamguda and Tukkuguda
which was sandy soil and the soil of Mominpet which
was sandy clay loam. They were slightly acidic to
slightly alkaline with mean pH value 7.9, non-saline
with EC ranging from 0.06 to 0.26 dsm-1, cation
exchange capacity ranging from 14.39 to 20.02 c mol
(p+) kg-1 , organic carbon content ranging from 0.11
to 0.65 per cent. The soils were low to medium (231.1
kg ha-1 to 306.5 kg ha-1) in available N, low to medium
(10.2 kg ha-1 to 48.43 P2O
5 kg ha-1) in available P and
medium to high (121.5 kg ha-1 to 542.9 K2O kg ha-1)
in available K. At harvest, curd yield of cauliflower
was recorded and plant samples were analysed for
K content in diacid (HNO3: HClO
4) extract by adopting
standard procedure (Jackson, 1973).
Curd yield of cauliflower was influenced
significantly by levels of K, soil type and the
email: [email protected]
RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUMFERTILIZATION ON LIGHT TEXTURED SOILS OF RANGAREDDY DISTRICT
K. KALYANI, V. SAILAJA, A. PRATAP KUMAR REDDY and P. CHANDRASEKHAR RAODepartment of Soil Science and Agricultural chemistry
College of Agriculture, ANGRAU, Rajendranagar, Hyderabad -500 030
interaction of both. Significantly higher values were
associated with high native K status. The mean curd
yield was 352.4 g plant-1 in the control which showed
a significant increase to 454.8 g plant-1 with the
application of 150 kg K2O ha-1. The mean curd yield
of 582.6 g plant-1 was obtained from Mominpet soil
(sandy clay loam) which was significantly higher than
the curd yield obtained from all other soils. Curd yield
from Emamguda (sand) was significantly lower, the
mean value being 279.4 g plant-1 which was on par
with the mean yield of 285.3 g plant-1 obtained on
Shamshabad (sand) soils. The interaction effect was
significant. The response to the applied K in terms
of curd yield was 12.2 per cent with Mominpet soils,
where as it was 52.5 per cent with Emamguda soil.
The mean response to the application of K was 25.28
kg cauliflower curd per kg of potassium applied. This
was mainly due to positive response of the yield
contributing characters like equatorial and polar
diameter and average head weight (Table 1).
Sarkar et al., (1994) also obtained 41.46 per
cent increase in the curd yield due to 100 kg K2O per
ha-1. Hariprakasa Rao (1994) proved that the higher
levels of K application significantly reduced the
number of barren plants and number of days taken
for bolting (curd initiation).
The mean K content in the curd of cauliflowerwas significantly influenced by both the levels of K
application and the soils. There was a significant
increase in the mean K content from 1.53 per cent in
the control to 2.13 per cent in the treatment receiving
150 kg K2O ha-1. The mean K content was significantly
higher in the curd of Aglanooru; the value being 2.48
per cent and lower from the soil of Azithnagar with
Table 1. Curd yield (g plant-1) as influenced by K-levels and soil types
Curd Yield (g plant-1)
S.No Village Name Avail. K(kg K2O
ha-1)
Levels of K (kg K2O ha-1) Mean
0 50 100 150
1 Azithnagar 190.8 260.1 301.9 346.1 380.9 322.3
2 Himayathnagar 348.9 374.8 395.8 442.2 497.8 427.7
3 Kammeta 215.5 287.7 321.9 368.2 401.0 344.7
4 Chevella 355.2 362.4 396.0 442.7 471.1 418.1
5 Velverthi 345.3 391.5 428.4 469.8 511.4 450.3
6 Manneguda 237.8 304.8 352.7 412.4 453.7 380.9
7 Yennepalli 412.6 488.4 492.4 535.5 547.0 515.8
8 Parigi 346.7 389.5 391.2 435.8 469.4 421.5
9 Baspally 221.7 271.8 315.5 359.2 390.7 334.3
10 Turkaimzal 204.8 262.4 306.3 375.3 410.1 338.5
11 Navabpet 342.1 383.0 419.3 447.4 482.5 433.1
12 Mominpet 542.9 546.2 576.4 594.7 612.9 582.6
13 Morangapally 448.6 508.9 525.9 556.6 575.5 541.7
14 Siripuram 373.1 409.9 421.3 453.3 487.8 443.1
15 Station marpally 352.2 437.4 441.3 482.3 520.8 470.5
16 Yacharam 154.3 251.8 294.0 329.0 360.8 308.9
17 Nagarambandar 238.2 301.2 349.2 393.5 432.0 369.0
18 Peddamal 323.1 369.8 383.3 425.3 458.8 409.3
19 Thandur 402.1 464.2 482.2 513.3 541.1 500.2
20 Aglanooru 382.6 451.8 472.4 502.1 542.5 492.2
21 Basheerabad 346.2 428.1 445.6 494.8 525.9 473.6
22 College farm (RNGR) 290.1 338.6 362.7 393.0 421.1 378.9
23 Student farm(RNGR) 223.2 268.6 293.7 344.8 374.7 320.5
24 Shamshabad 139.2 232.2 262.5 301.4 344.9 285.3
25 Haithnagar 218.8 291.4 339.9 377.5 407.9 354.2
26 Emamguda 121.5 224.8 251.5 298.1 343.0 279.4
27 Thukkuguda 147.8 242.9 293.8 321.4 369.3 306.9
28 Singaram 252.5 322.9 349.2 392.5 427.3 373.0
29 Aagapalli 311.4 359.6 374.3 421.4 462.0 404.3
30 Seriguda 270.6 345.4 362.6 393.3 420.9 380.6
Mean 292.0 352.4 380.1 420.8 454.8
SEm ± CD at 5%
Factor (A) K-levels 1.18 3.29 Factor (B) Soil types 3.24 9.03Factor (AxB) 6.48 18.0
RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUM
Table. 2. K Concentration (%) in Curd as influenced by K-levels and soil types
Levels of K (kg K2O ha-1)
S.No Village Name Levels of K (kg K2O ha-1) Mean
0 50 100 150
SEm ± CD at 5%
1. Azithnagar 1.16 1.26 1.33 1.42 1.29
2. Himayathnagar 1.34 1.65 1.75 1.78 1.63
3. Kammeta 1.43 1.54 1.74 1.69 1.60
4. Chevella 1.65 1.75 1.80 1.82 1.76
5. Velverthi 1.26 1.28 1.32 2.01 1.47
6. Manneguda 1.33 1.52 1.74 1.90 1.62
7. Yennepalli 1.25 1.33 1.56 1.72 1.47
8. Parigi 1.62 2.29 2.56 2.72 2.30
9. Baspally 1.54 2.19 2.52 2.68 2.23
10. Turkaimzal 1.25 1.33 1.33 1.49 1.35
11. Navabpet 1.34 2.03 1.80 1.96 1.78
12. Mominpet 1.91 2.44 2.53 2.69 2.39
13. Morangapally 1.71 2.47 2.72 2.88 2.45
14. Siripuram 1.39 1.88 1.99 2.15 1.85
15. Station marpally 1.50 2.10 2.42 2.56 2.15
16. Yacharam 1.67 1.86 2.02 2.18 1.93
17. Nagasambandar 1.72 1.87 1.96 2.12 1.92
18. Peddamal 2.15 2.23 2.44 2.60 2.36
19. Thandur 1.79 2.42 2.59 2.75 2.39
20. Aglanooru 2.12 2.34 2.65 2.81 2.48
21. Basheerabad 1.42 1.56 1.69 1.85 1.63
22. College farm (RNGR) 1.59 2.31 2.64 2.81 2.34
23. Student farm(RNGR) 1.59 1.63 1.74 1.90 1.72
24. Shamshabad 1.54 1.76 1.93 2.09 1.83
25. Haithnagar 1.31 1.43 1.57 1.62 1.48
26. Emamguda 1.28 1.54 1.75 1.91 1.62
27. Thukkuguda 1.79 2.11 2.39 2.55 2.21
28. Singaram 1.37 1.48 1.52 1.68 1.51
29. Aagapalli 1.22 1.41 1.76 1.92 1.58
30. Seriguda 1.56 1.61 1.67 1.72 1.64
Mean 1.53 1.82 1.98 2.13
Factor (A) K-levels 0.008 0.02Factor (B) Soil types 0.023 0.06Factor (AxB) 0.046 0.12
KALYANI et al
1.29 per cent. Higher K content of 2.15 per cent in
the control was observed in the Peddamal soil which
increased significantly to 2.60 per cent due to 150
kg K2O ha-1. In Azithnagar soil, the curd had a
significantly lower K content of 1.16 per cent which
increased significantly to 1.42 per cent with the
application of 150 kg K2O ha-1. This increase in K
content of with K application could be due to the
efficient partitioning of K into the curd (Table 2).
From the study it was evident that the light
textured soils of Rangareddy district need 150 kg
K2O ha-1 for getting optimum yields of cauliflower.
REFERENCES
Jackson, M.L. 1973. Soil Chemical Analysis. PrenticeHall of India Pvt. Ltd: New Delhi.
Hariprakasa Rao, M. 1994. Growth, Yield and Qualityof Tomato, Carrot and Cauliflower asInfluenced by Levels and Sources ofPotassium. Journal of Potassium Research.10 (4): 402-406.
Sarkar, S. K., Singh, S.P and Jain, B.P. 1994.Response of Cabbage to Potassium and Limein Bihar Plateau. Journal of PotassiumResearch. 10(4): 398-401.
RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUM
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