Effect of Biofertilizer on Growth, Productivity, Quality and Economics of Rainfed Organic Ginger ( Zingiber officinale Rosc.) Bhaisey cv. in North-Eastern Region of India

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Agricultural Science

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Volume 3, Number 2, February 2013 (Serial Number 22)

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D

Journal of Agricultural Science

and Technology A

Volume 3, Number 2, February 2013 (Serial Number 22)

Contents

Research Papers

83 Effect of Biofertilizer on Growth, Productivity, Quality and Economics of Rainfed Organic Ginger (Zingiber officinale Rosc.) Bhaisey cv. in North-Eastern Region of India

Nongmaithem Jyotsna, Mainak Ghosh, Dulal Chandra Ghosh, Wahengbam Ingo Meitei and Jagadish

Timsina

99 Research on the Soil Carbon Storage of Alpine Grassland under Different Land Uses in Qinghai-Tibet Plateau

Tao Li, Lei Ji, Tao Liu, Zhongqi Song, Shujing Yang and Youmin Gan

105 Chemical and Sensory Properties of Olive Oil as Influenced by Different Sources of Irrigation Water

Salam Ayoub, Saleh Al-Shdiefat, Hamzeh Rawashdeh and Ibrahim Bashabsheh

113 Ethical Trading: The Implicatio ns of the Human Rights Watch Report on South African Fruit Exports

Portia Ndou and Ajuruchukwu Obi

126 Physiological and Phytosanitary Potentials of Coriander and Radish Seeds

Jucilayne Fernandes Vieira, Francisco Amaral Villela, Orlando Antonio Lucca Filho and Raifer Simões

Campelo

131 Response of Amaranth to Irrigation and Organic Matter

Jimmy Akinfemi Osunbitan

140 Quantitative Changes in Protein and Cholesterol in Haemolymph of the Red Palm Weevil Rhynchophorus ferrugineus after Treatment LeucokininII

Mona Mohammed Saleh Al-Dawsary

146 The Role of Cellulase and Pectinase in Apricot Canker Caused by Hendersonula torulidi and Phiaoacremonium aleophillium

Nidhal Y. M. Al-Morad

151 Innovation and Technical Efficiency in the Smallholder Dairy Production System in Ethiopia

Amlaku Asres, Johann Sölkner and Maria Wurzinger

165 Richness and Diversity of Ants and Beetles in Genetically Modified Cotton Field in Brazil

Carla Cristina Dutra, Marcos Gino Fernandes, Josué Raizer and Camila Meotti

Journal of Agricultural Science and Technology A 3 (2013) 83-98

Earlier title: Journal of Agricultural Science and Technology, ISSN 1939-1250

Effect of Biofertilizer on Growth, Productivity, Quality

and Economics of Rainfed Organic Ginger (Zingiber

officinale Rosc.) Bhaisey cv. in North-Eastern Region of

India

Nongmaithem Jyotsna1, Mainak Ghosh2, Dulal Chandra Ghosh3, Wahengbam Ingo Meitei1 and Jagadish Timsina4

1. College of Agriculture, Central Agricultural University Imphal, Manipur 795001, India

2. Department of Agronomy, Bihar Agricultural University, Bihar 813210, India

3. Institute of Agriculture, Visva-Bharati, Sriniketan-731236, West Bengal, India

4. IRRI-Bangladesh Office, Banani DOHS, Dhaka-1206, Bangladesh

Received: November 13, 2012 / Published: February 20, 2013.

Abstract: Ginger yields in the NorthEastern region of India are low because the extremely poor farmers of the region can not afford

to apply any chemical fertilizers and hence apply only the locally-available farmyard manures to ginger fields. Biofertilizers may be a

cheap source of fertilizers for ginger cultivation as they can increase nutrient availability and improve rhizome quality and are

required in small quantity. An investigation was thus undertaken to study the effect of different biofertilizers on growth, productivity,

quality and economics of organic ginger grown under rainfed condition in NorthEastern region of India. Seed treatment with

biofertilizers enhanced growth, increased rhizome yield by 19.0% and resulted in 32.4% higher net profit over control. Among the

seed treatments, Azotobacter 5.0 kg ha-1, Azospirillum 3.75 kg ha-1 and Phosphotica 3.75 kg ha-1 were found optimum in improving

most of the growth attributes, increasing yield components and yield of rhizome by 5.6%-13.5%. They also improved rhizome

quality by increasing specific gravity, oleoresin and dry matter content and by decreasing crude fibre in rhizome. They resulted in

higher net return by 4.0%-12.0% as compared to their other levels. Combined use of Azotobacter 5.0 kg ha-1 along with Phosphotica

3.75 kg ha-1 was found to be the best treatment combination which greatly improved growth and yield attributes of ginger and

ultimately recorded markedly higher productivity (2.0%-23.5%) over other combinations. This treatment combination improved the

quality of the produce and resulted in the highest gross return ($4,905 ha-1), net return ($3,525 ha-1) and return per dollar (3.55)

invested in ginger cultivation. It appears that growing organic ginger by treating the seed rhizome with Azotobacter 5.0 kg ha-1 along

with Phosphotica 3.75 kg ha-1 can result in good growth and high productivity of improved quality rhizome and ultimately result in

maximum net profit and thus can be recommended for the NorthEastern region of India.

Key words: Biofertilizers, growth, productivity, quality, economics, ginger (Zingiber officinale Rosc.).

1. Introduction Ginger (Zingiber officinale Rosc.) is a tropical

rhizomatous high value spice crop adapted for

cultivation in tropical and subtropical climate. The

NorthEastern region of India with the subtropical

climate, where it is the main cash crop, has

Corresponding author: Jagadish Timsina, Ph.D., research field: agronomy. E-mail: [email protected].

tremendous potential for ginger production and hence

can support the livelihoods and improve the economic

level of many ginger growers. The average yield (5.8 t

ha-1) of ginger in this region, however, is considered

to be low [1]. The soil, climate and other ecological

factors in the NorthEastern region favour the growth

and development of this crop and there is a

tremendous scope to increase its yield and total

D DAVID PUBLISHING

Effect of Biofertilizer on Growth, Productivity, Quality and Economics of Rainfed Organic Ginger (Zingiber officinale Rosc.) Bhaisey cv. in North-Eastern Region of India

84

production. One possible reason for its low yield in

NorthEastern region could be due to the poor nutrient

management practices adopted for this crop. The

ginger production in the NorthEastern region is

organic by default because the farmers of the region

apply only the locally available farmyard manures

(e.g., cow dung manure, pig manure, poultry manure

and rabbit manure) and do not apply any chemical

fertilizers or pesticides [2]. In contrast, ginger being a

heavy feeder and an exhaustive crop requires large

quantities of manures and fertilizers. Considering the

increasing demand for organic products all over the

world, the ginger farmers can receive higher returns

from their produce if grown organically. Organic

farming has attracted increasing attention for

environmental protection, improved quality and better

market demands [3]. However, ginger cultivated with

only organic manures will produce low yields and

would require chemical fertilizers for increased

nutrition or biofertizers for increased availability.

Ginger requires a tropical or sub-tropical humid

climate for its commercial production. The crop is

sensitive to water logging, frost and salinity and

tolerant to wind and drought. It thrives on a wide

variety of soils; but for high yield, it prefers light,

loose, friable and well drained soil rich in humus and

slightly acidic (pH 6.0-6.5) in reaction [1].

Biofertilizers have now emerged as a promising

component of nutrient supply [4-6]. The role of

different biofertilizers like Azospirillum and

Azotobacter cultures in fixing atmospheric nitrogen

has been well established by several workers [7-9].

The microorganisms can build up organic matter of

the soil which can increase the availability of other

nutrients [10, 11] and secrete growth promoting

substances [12]. The use of phosphate solubilizing

microorganisms has shown positive responses in

many demonstrations and field trials [13, 14]. Use of

biofertilizers in organic ginger production may further

enhance its growth and productivity by producing

growth promoting substances and enhancing plant

nutrients supply through greater mineralization due to

higher microbial activities [12, 15]. However,

information about the use of different biofertilizers in

ginger production is very limited. The present study

was thus carried out to investigate the effect of

biofertilizer on growth, productivity, quality and

economics of rainfed ginger production in

NorthEastern region of India.

2. Materials and Methods

2.1 Experimental Site

The field experiment was conducted during 2007

and 2008 at the Horticulture Experimental Farm,

College of Agriculture, Central Agricultural

University, Imphal, Manipur in NorthEastern India.

The place is located at 24°45′N latitude, 93°56′E

longitude with an altitude of 790 m above mean sea

level. The experimental soil was clayey in texture

(15.5% sand, 21.2% silt and 61.1% clay), medium in

fertility status (230, 13.3 and 267 kg ha-1 available N,

P and K, respectively), well-drained with gentle slope.

The experimental site comes under warm humid moist

region where monsoon normally starts from April and

extends up to September. Unpredictable pre-monsoon

shower during March is not uncommon in this region.

The crop was grown on rainfed condition but received

1,341 and 1,207 mm rainfall during the growing

period of 2007 and 2008, respectively (Table 1). The

maximum temperature ranged from 24.1 °C to 29.6 °C

while the minimum temperature varied from 9.6 °C to

22.5 °C during the cropping seasons. The relative

humidity varied from 58.5% to 84.7% in 2007 and

58.9% to 88.5% in 2008. Both temperature and

relative humidity remained very conducive for growth

and rhizome productivity of ginger.

2.2 Experimental Details

The experiment was laid out in a completely

randomized block design with three biofertilizers

each at three levels along with a common control (no

biofertilizer) in three replications in 3.6 m 3.0 m


85

Table 1 Mean maximum and minimum air temperature, relative humidity and precipitation and during growing season 2007 and 2008.

Month

T max (°C)

T min (°C)

Relative humidity (%)

Precipitation (mm)

2007 2008 2007 2008 2007 2008 2007 2008

Mar. 25.1 24.9 10.7 12.5 58.5 70.2 17.6 39.6

Apr. 26.2 28.8 14.3 15.6 70.7 58.9 136.8 97.8

May 28.3 29.6 19.9 19.3 74.5 69.7 233.4 94.6

Jun. 28.7 28.6 21.9 21.3 81.6 80.3 201.0 260.2

Jul. 28.1 28.1 22.2 22.2 84.7 84.0 218.6 210.2

Aug. 28.8 28.5 22.3 22.5 82.7 83.0 112.2 244.5

Sept. 27.8 29.4 20.9 21.3 79.7 84.3 191.0 151.1

Oct. 26.9 27.6 18.3 18.2 81.5 88.5 178.0 87.6

Nov. 24.1 25.3 13.6 9.6 74.1 83.8 52.6 21.7

plots. The three levels for each of three biofertilizers

are: (a) Azotobacter (a1 = 2.5, a2 = 3.75 and a3 = 5.0

kg ha-1), (b) Azospirillum (b1 = 2.5, b2 = 3.75 and b3 =

5.0 kg ha-1) and (c) Phosphotica (c1 = 2.5, c2 = 3.75

and c3 = 5.0 kg ha-1). Thus there were 28 treatment

combinations. Culture solutions of different

biofertilizers were prepared by dissolving 10 g, 15 g

and 20 g of each biofertilizer (for their respective

doses) and their combinations in 500 mL of water

separately for each biofertilizer treatment, with total of

27 biofertilizer culture solutions (Table 2). Each

biofertilizer treatment (culture solution) was mixed

thoroughly with 8 kg ginger sets (required for each

treatment) of variety “Bhaisey” and dried in shade

before planting. The rhizomes (20 g set) were planted

on March 16, 2007 and March 18, 2008 with a

spacing of 30 cm 30 cm in 3.6 3.0 m plots.

A general dose of 20 t farm yard manure (FYM) ha-1

containing 0.50% N, 0.13% P and 0.55% K was

Table 2 Treatment details along with composition of 500 mL biofertilizer culture solution.

Treatments Combinations Biofertilizer composition in 500 mL culture solution

T1 a1b1c1 Azotobacter 10 g + Azospirillum 10 g + Phosphotica 10 g



























T28 a0b0c0 Control (No use of biofertilizer)


86

applied during land preparation. The crop received

hand weeding twice along with light earthing up on

May 25 and July 14 in 2007 and May 27 and July 16

in 2008. No chemical fertilizer, pesticide and

irrigation water was applied.

2.3 Observations Recorded

The canopy spread was measured in centimeter

with the help of a linear scale from five randomly

selected clumps of each plot at 90, 150 and 210 days

after planting (DAP). The canopy spread was

calculated by multiplying the length with width of

each canopy in each plot and average canopy spread

(cm-2 clump-1) was estimated for each plot. Four

clumps from each plot were collected at 90, 150 and

210 DAP for recording biomass production. The plant

samples were cleaned and washed in water to remove

surface contamination and separated into stem (dry

leaves + stem), green leaves (lamina) and rhizome.

Fresh weight of rhizome was recorded in g m-2. A

piece of rhizome of each plot was taken; its fresh

weight was noted before chopping. All plant parts

including chopped rhizome were kept in separate

paper packets which in turn placed in an oven for

drying at 65-70 °C till constant weights were obtained.

The dry weight of leaves, stems and rhizome was then

recorded in g m-2. The sum of dry weights of these

plant parts were taken as the total dry matter

accumulation (DMA). The area of 10 leaves of each

treatment was measured with a leaf area meter (AM

300, USA), and the leaves were put in an oven for

recording dry weight. The area/dry weight of these

leaves was used for determining leaf area index (LAI)

as suggested by Watson [16]. The crop growth rate

(CGR) during the period of 90-150 and 150-210 DAP

were estimated as: CGR = (W2 W1)/(t2 t1),

expressed in g m-2 day-1, where W2 and W1 were the

final and initial dry weights of the crop per unit land

area at times t2 and t1, respectively [16]. The fresh

weight of rhizome was used for determining rhizome

bulking rate (RBR). The RBR during the period of

90-150 and 150-210 DAP were estimated using the

following formula: RBR = (R2 R1)/(t2 t1) expressed

in g m-2 day-1, where, R2 and R1 were the final and

initial fresh weights of rhizome per unit land area at

times t2 and t1 respectively. The crop was harvested on

November 12 in 2007 and November 14 in 2008 when

the leaves turned yellow and started drying up. Fifty

clumps of each plot were lifted carefully with the help

of a spade and the rhizomes were separated and kept

in shade for 2 days. The fresh weights of rhizome

were recorded in t ha-1. Harvest index (HI) was

estimated as: HI = Rhizome dry weight (kg ha-1)/total

dry biomass (kg ha-1) 100, and expressed in

percentage (%).

2.4 Quality Parameters

The quality parameters like specific gravity, dry

matter content, oleoresin content and crude fibre

content in rhizome were estimated in the laboratory.

The specific gravity of rhizome was measured by water

displacement method. After removing the mud, roots

and shoots, rhizomes were weighed in a balance, and

expressed in gram. A 250 mL measuring cylinder was

filled with distilled water and rhizomes were

submerged into the water. The level of water in the

measuring cylinder was noted before and after the

submergence of rhizomes. The displaced water was

measured in milliliter and specific gravity was

determined by the following equation: Specific gravity

= Weight of rhizomes (g)/amount of water displaced

(mL) and expressed in g mL-1. Equal quantity of fresh

ginger from each treatment was oven dried uniformly

after chopping into thin slices and the dry weights were

recorded. The dry matter content in rhizome was

estimated by the equation: Dry matter content = Dry

weight of the sample/fresh weight of the sample 100,

and expressed in percent. The oleoresin content was

determined by using acetone as a solvent. 10 g of the

dried sample was weighed and transferred to a glass

column (18 mm 450 mm) with stopcock, then 50 mL

of acetone was added and allowed to stand overnight


87

for 16 h at 25 ± 2 °C. The filtrate extracted through non

absorbent cotton was collected in a pre-weighed 100

mL beaker. Column was washed with 20 mL of acetone.

The extracts were pooled and evaporated to dry at

80 °C over a water bath. The amount of oleoresin was

estimated gravimetrically as per the “Official

Analytical Methods of the American Spices Trade

Association” [17], and expressed in percentage. The

crude fibre was determined following the acid digestion

method [17]. Equal quantity of fresh ginger from each

treatment was oven dried uniformly after chopping into

thin slices. The dried sample was allowed to digest with

distilled sulphuric acid and sodium hydroxide. The

crude fibre was estimated by weighing the organic

matter in the dried residue remaining after digesting the

sample with distilled sulphuric acid and sodium

hydroxide, and expressed in percent.

2.5 Economics

The cost of inputs such as FYM, seed rhizome,

biofertilizer and labour and output (rhizomes) were

estimated as per prevailing market price. The gross

return, net return and return per dollar invested in

different biofertilizer treatments were assessed by

computing the cost of the inputs and price of the

produce (output) to study the economics of rainfed

ginger production.

2.6 Data Analysis

All data were analyzed statistically following the

standard procedures as described by Gomez and

Gomez [18]. The data were tested for analysis of

variance and least significant difference (P = 0.05) to

compare the effect of biofertilizer treatments on

growth, productivity, quality and economics of rainfed

ginger production. The interaction effects were

presented wherever they were significant. Multiple

regression analysis was done to examine the

relationships between the rhizome yield and the

growth attributes like canopy spread and LAI at

different stages. The relationships among the quality

parameters such as specific gravity, dry matter,

oleoresin and crude fibre content in rhizome were also

studied. The average values of three replications for

all the treatments of two years data were used for this

purpose.

3. Results

3.1 Growth Parameters

Use of biofertilizer exhibited significant effect on

growth attributes of rainfed organic ginger. The

canopy spread increased by 14.2% to 38.9%, LAI by

39.1% to 41.4% (Table 3), CGR by 23.9% to 25.2%

and DMA by 21.2% to 27.9% (Table 4) at different

stages due to biofertilizer treatments over control.

Table 3 Effect of biofertilizer on canopy spread and leaf area index of ginger at different stages (average data of two years).

ParticularsCanopy spread (cm-2 clump-1)

Leaf area index

90 DAP

150 DAP

210 DAP

90 DAP

150 DAP

210 DAP

Control 504 1,597 1,965 0.29 2.33 2.55

Treatment 700 1,823 2,306 0.41 3.24 3.56

S. Em (±) 40.3 89.8 107.3 0.03 0.15 0.21

LSD 0.05 121.8 271.3 323.9 0.08 0.46 0.62

Azotobacter level

a1 540 1,626 2,010 0.31 2.41 2.72

a2 585 1,680 2,100 0.34 2.63 2.96

a3 682 1,825 2,298 0.40 3.32 3.49

S. Em (±) 13.4 29.9 35.8 0.01 0.05 0.07

LSD 0.05 38.0 84.8 101.2 0.02 0.14 0.19

Azospirillum level

b1 561 1,653 2054 0.32 2.53 2.84

b2 658 1,792 2,250 0.39 3.17 3.40

b3 587 1,686 2,103 0.33 2.66 2.94

S. Em (±) 13.4 29.9 35.8 0.01 0.05 0.07

LSD 0.05 38.0 84.8 101.2 0.02 0.14 0.19

Phosphotica level

c1 571 1,666 2,077 0.33 2.59 2.90

c2 650 1,781 2,234 0.39 3.11 3.33

c3 585 1,684 2,097 0.34 2.66 2.94

S. Em (±) 13.4 29.9 35.8 0.01 0.05 0.07

LSD 0.05 38.0 84.8 101.2 0.02 0.14 0.19

*a = Azotobacter; b = Azospirillum; c = Phosphotica; doses of

biofertilizer 1, 2 and 3 are 2.5, 3.75 and 5.0 kg ha-1, respectively.


88

Among the different biofertilizer treatments, seed

treatment with high dose (5.00 kg ha-1) of Azotobacter

(a3) showed significant improvement of all the growth

attributes over their lower levels. Similarly, seed

treatment with medium dose (3.75 kg ha-1) of both

Azospirillum (b2) and Phosphotica (c2) caused marked

increase in all the growth attributes at most of the

growth stages over those of their higher and lower

levels. Application of low level (2.50 kg ha-1) of

biofertilizers became less effective in improving the

growth parameters as compared to their higher levels.

Combined use of Azotobacter and Phosphotica

showed significant interaction effect on influencing

most of the growth attributes of this crop. The highest

values of canopy spread (Fig. 1A), LAI (Fig. 1B),

CGR (Fig. 1C) and DMA (Fig. 1D) at most of the

stages were recorded with the use of high level (5.00

kg ha-1) of Azotobacter in combination with medium

level (3.75 kg ha-1) of Phosphotica (a3c2). These

values were significantly higher than those obtained

with other treatment combinations except the

combinations of high level of Azotobacter with high

or low levels of Phosphotica (a3c1 or a3c3) in most of

the cases. This treatment combination (a3c2) was

found to be optimum in improving the growth

parameters of ginger under the study.

3.2 Yield and Yield Attributes

The seed treatment with biofertilizer showed

positive and significant effect on influencing yield

components like rhizome growth and rhizome bulking

rate at different growth periods that ultimately

influenced the rhizome yield over control.

Biofertilizer increased rhizome yield over control by

15.9% in 2007 and 22.4% in 2008. The highest

rhizome growth and rhizome bulking rate were found

with the highest level (5.0 kg ha-1) of Azotobacter.

The medium level (3.75 kg ha-1) of both Azospirillum

and Phosphotica also showed markedly greater yield

components as compared to its high and low levels at

Table 4 Effect of biofertilizer on crop growth rate (CGR) and dry matter accumulation (DMA) in ginger (average data of two years).

ParticularsCrop growth rate

(g m-2 day-1) Dry matter accumulation

(g m-2)

90-150 DAP

150-210 DAP

90 DAP 150 DAP

210 DAP

Control 6.54 1.51 124.2 507.0 614.2

Treatment 8.10 1.89 153.2 648.4 744.4

S. Em (±) 0.47 0.12 8.6 29.7 36.9

LSD 0.05 1.43 0.38 26.1 89.6 111.3

Azotobacter level

a1 6.76 1.64 132.0 538.0 643.0

a2 7.16 1.70 137.0 565.0 667.0

a3 8.03 1.77 147.0 630.0 728.0

S. Em (±) 0.16 0.04 2.9 9.9 12.3

LSD 0.05 0.45 0.12 8.2 28.0 34.8

Azospirillum level

b1 6.95 1.67 135.0 551.0 654.0

b2 7.85 1.74 144.0 615.0 716.0

b3 7.17 1.70 137.0 567.0 668.0

S. Em (±) 0.16 0.04 2.9 9.9 12.3

LSD 0.05 0.45 0.12 8.2 28.0 34.8

Phosphotica level

c1 7.06 1.65 136.0 559.0 661.0

c2 7.74 1.75 143.0 608.0 709.0

c3 7.17 1.70 137.0 566.0 668.0

S. Em (±) 0.16 0.04 2.9 9.9 12.3

LSD 0.05 0.45 0.12 8.2 28.0 34.8

a = Azotobacter; b = Azospirillum; c = Phosphotica; doses of

biofertilizer 1, 2 and 3 are 2.5, 3.75 and 5.0 kg ha-1, respectively.

all the growth stages (Table 5). The increased yield

components at high to medium level of biofertilizer

enhanced rhizome yield of rainfed ginger and

accordingly the high level of Azotobacter increased

rhizome yield by 13.5% over its lower level and 9.2%

over its medium level as evidenced by pooled analysis

of two years data (Table 6). Similarly, medium level

of both Azospirillum and Phosphotica enhanced

rhizome yield by 8.9% and 8.5% respectively over

their lower level and 5.6% and 4.6% over their higher

level (Table 6). The lower levels of biofertilizer were

found less effective in improving yield components

and rhizome yield of rainfed ginger. The harvest index,

however, did not vary much among the different

biofertilizer treatments during both years.


89

Fig. 1 Interaction effect of Azotobacter and Phosphotica on growth attributes of ginger. (A) canopy spread, (B) leaf area index, (C) crop growth rate and (D) dry matter accumulation; vertical lines show the error bars. a1, a2 and a3 represent Azobactor levels of 2.50 kg ha-1, 3.75 kg ha-1 and 5.00 kg ha-1, respectively; c1, c2 and c3 represent Phosphotica

levels of 2.50 kg ha-1, 3.75 kg ha-1 and 5.00 kg ha-1, respectively.



the yield attributes and yield of this crop. The highest

values of rhizome growth (Fig. 2A) and rhizome

bulking rate (Fig. 2B) were recorded with the use of

high level of Azotobacter in combination with

medium level of Phosphotica (a3c2). This treatment

combination ultimately produced the higher rhizome

yield (Fig. 2C) in both years (22.06 t ha-1 in 2007 and

22.09 t ha-1 in 2008) except the combinations of high

level of Azotobacter with higher and lower levels of

Phosphotica. Use of lower levels of both Azotobacter


90

Table 5 Effect of biofertilizer on rhizome growth and rhizome bulking rate of ginger (average data of two years).

Particulars Rhizome growth

(fresh weight in g m-2) Rhizome bulking rate

(g m-2 day-1)

90 DAP

150 DAP

210 DAP

90-150 DAP

150-210 DAP

Control 388 1,760 2,293 22.39 8.34

Treatment 438 2,061 2,622 27.57 9.90

S. Em (±) 21 86 105 1.14 0.45

LSD 0.05 62 260 317 3.44 1.37

Azotobacter level

a1 394 1,777 2,332 22.99 8.65

a2 413 1,877 2,422 24.54 9.20

a3 433 2,079 2,620 27.41 9.50

S. Em (±) 6.8 28.7 35.0 0.38 0.15

LSD 0.05 19.3 81.2 99.0 1.07 0.43

Azospirillum level

b1 402 1,824 2,373 23.68 8.76

b2 428 2,032 2,579 26.73 9.51

b3 408 1,878 2,423 24.53 9.08

S. Em (±) 6.8 28.7 35.0 0.38 0.15

LSD 0.05 19.3 81.2 99.0 1.07 0.43

Phosphotica level

c1 404 1,853 2,398 24.15 8.77

c2 427 2,003 2,558 26.29 9.47

c3 409 1,878 2,419 24.50 9.11

S. Em (±) 6.8 28.7 35.0 0.38 0.15

LSD 0.05 19.3 81.2 99.0 1.07 0.43


biofertilizer 1, 2 and 3 are 2.5 kg ha-1, 3.75 kg ha-1 and 5.0 kg

ha-1, respectively.

and Phosphotica recorded the lowest values of the

yield parameters as compared to those of the

other treatment combinations at all the growth stages.

Accordingly, seed treatment with lower levels of both

the biofertilizers produced the lowest rhizome yield

(18.12 t ha-1 in 2007 and 17.62 t ha-1 in 2008)

resulting in 21.7% and 25.4% less yield when

compared with the highest yield in respective years.

Seed treatment with Azotobacter 5.0 kg ha-1 along

with Phosphotica 3.75 kg ha-1 (a3c2) was found the

best for improving yield components and producing

high rhizome yield of rainfed organic ginger.

3.3 Rhizome Quality

The seed treatment with different biofertilizers

exerted favourable effect on quality of ginger rhizome.

Table 6 Effect of biofertilizer on productivity and efficiency of ginger.

Particulars Rhizome yield (t ha-1) Harvest index (%)

2007 2008 pooled 2007 2008

Control 18.97 17.88 18.43 53.8 54.5

Treatment 21.98 21.89 21.94 54.4 54.4

S. Em (±) 0.80 0.83 0.76 1.2 1.4

LSD 0.05 2.43 2.50 2.29 NS NS

Azotobacter level

a1 19.42 18.74 19.08 54.2 54.6

a2 20.16 19.50 19.83 54.2 54.5

a3 21.87 21.43 21.65 53.9 54.2

S. Em (±) 0.27 0.28 0.25 0.4 0.5

LSD 0.05 0.76 0.78 0.71 NS NS

Azospirillum level

b1 19.76 19.06 19.41 54.3 54.6

b2 21.40 20.87 21.14 53.8 54.2

b3 20.29 19.73 20.01 54.2 54.5

S. Em (±) 0.27 0.28 0.25 0.4 0.5

LSD 0.05 0.76 0.78 0.71 NS NS

Phosphotica level

c1 19.74 19.06 19.40 54.2 54.5

c2 21.21 20.86 21.04 54.0 54.3

c3 20.50 19.74 20.12 54.1 54.5

S. Em (±) 0.27 0.28 0.25 0.4 0.5

LSD 0.05 0.76 0.78 0.71 NS NS



ha-1, respectively.

The biofertilizers treated crop increased the dry matter

content, specific gravity and oleoresin content but

decreased the crude fibre content in rhizome and

thereby improved its quality over control (Table 7).

Application of high and medium doses of Azotobacter

increased the dry matter, specific gravity and oleoresin

content over its low level but decreased the crude fibre

content. Use of medium doses of Azospirillum and

Phosphotica increased the specific gravity and

oleoresin content over those of their higher and lower

levels. The dry matter content in rhizome was also

increased by the application of medium level of

Phosphotica over its higher and lower levels. Medium

level of Phosphotica resulted in the highest dry matter

content (22.1%) in rhizome. The crude fibre content in

rhizome, however, did not vary much among the

different levels of Azospirillum and Phosphotica.


91

Fig. 2 Interaction effect of Azotobacter and Phosphotica on (A) rhizome growth, (B) rhizome bulking rate, (C) rhizome yield and (D) economics of rainfed ginger; vertical lines indicate the show the error bars. a1, a2 and a3 represent Azobactor levels of 2.50 kg ha-1, 3.75 kg ha-1 and 5.00 kg ha-1, respectively; c1, c2 and c3 represent Phosphotica

levels of 2.50 kg ha-1, 3.75 kg ha-1 and 5.00 kg ha-1, respectively.

3.4 Economics of Rainfed Ginger

The results showed significant effect of biofertilizer

treatments on increasing gross return, net return and

return per dollar invested in ginger cultivation, but it

had no effect on cost of cultivation of ginger. Seed

treatment with biofertilizers considerably increased

the gross return ($4,886 ha-1), net return ($3,499 ha-1)

and return per dollar invested (3.5) as compared to

those of the control plots ($4,086 ha-1, $2,805 ha-1 and


92

Table 7 Effect of biofertilizer on quality attributes of ginger (average data of two years).

Particulars Specific gravity (g cc-1)*

Dry matter content (%)

Oleoresin content (%)

Crude fibre content (%)

Control 1.19 20.41 5.20 6.33

Treatment 1.30 22.02 6.41 5.65

S. Em (±) 0.03 0.52 0.16 0.19

LSD 0.05 0.08 1.59 0.48 0.57

Azotobacter level

a1 1.22 20.69 5.44 6.17

a2 1.26 21.49 5.98 6.06

a3 1.27 21.49 6.01 5.75

S. Em (±) 0.01 0.17 0.05 0.06

LSD 0.05 0.02 0.49 0.15 0.18

Azospirillum level

b1 1.24 21.09 5.74 6.08

b2 1.27 21.29 5.93 5.94

b3 1.24 21.29 5.76 5.97

S. Em (±) 0.01 0.17 0.05 0.06

LSD 0.05 0.02 NS 0.15 NS

Phosphotica level

c1 1.24 20.69 5.51 5.98

c2 1.27 22.09 6.32 6.05

c3 1.24 20.89 5.60 5.96

S. Em (±) 0.01 0.17 0.05 0.06

LSD 0.05 0.02 0.49 0.15 NS



ha-1, respectively.

3.2, respectively). Seed treatment with high dose of

Azotobacter resulted in higher gross return ($4,811 ha-1)

over its medium ($4,407 ha-1) and low ($4,239 ha-1)

levels (Table 8). The net return and return per dollar

invested also followed a similar trend. High dose of

Azotobacter paid maximum profit ($3,449 ha-1) and

return per dollar invested (3.53) as compared to medium

and low doses. The lower dose of Azotobacter paid the

lowest gross return ($4,239 ha-1), net return ($2,928 ha-1)

and return per dollar invested (3.23) indicating its less

efficiency in ginger productivity. Azospirillum and

Phosphotica also exerted significant effect on gross

return, net return and return per dollar invested in ginger

cultivation. Use of medium dose of Azospirillum and

Phosphotica paid higher gross return ($4,696 and 4,674

ha-1) over their high ($4,447 and 4,472 ha-1) and low

($4,314 and 4,311 ha-1) levels. Both the treatments paid

Table 8 Effect of biofertilizer on economics of ginger cultivation (average data of two years).

ParticularsCost of cultivation ($ ha-1)*

Gross return ($ ha-1)

Net return ($ ha-1)

Return per$ invested

Control 1,281.02 4,085.75 2,804.73 3.19

Treatment 1,387.02 4,885.78 3,498.76 3.52

S. Em (±) 65.47 191.65 149.21 0.10

LSD 0.05 NS 578.78 450.62 0.32

Azotobacter level

a1 1,311.02 4,239.09 2,928.07 3.23

a2 1,329.03 4,406.87 3,077.84 3.32

a3 1,362.02 4,811.36 3,449.34 3.53

S. Em (±) 21.82 63.88 49.74 0.03

LSD 0.05 NS 180.79 140.76 0.10

Azospirillum level

b1 1,318.49 4,313.74 2,995.25 3.27

b2 1,358.08 4,696.38 3,338.30 3.46

b3 1,334.50 4,447.20 3,112.70 3.33

S. Em (±) 21.82 63.88 49.74 0.03

LSD 0.05 NS 180.79 140.76 0.10

Phosphotica level

c1 1,318.20 4,311.27 2,993.07 3.27

c2 1,355.92 4,674.49 3,318.57 3.45

c3 1,336.93 4,471.56 3,134.63 3.34

S. Em (±) 21.82 63.88 49.74 0.03

LSD 0.05 NS 180.79 140.76 0.10



ha-1, respectively; Ginger seed rhizome $330/ton, FYM

$5.5/ton, Biofertilizers $5/kg, Labour charges $1.4/man-day

and price of ginger rhizomes $220/ton.

markedly higher net return ($3,338 and 3,319 ha-1) and

return per dollar invested (3.46 and 3.45) in ginger

cultivation over their high and low levels.

The combined use of Azotobacter and Phosphotica

showed significant interaction effect on gross return,

net return and return per dollar invested in ginger

cultivation. The highest gross return ($4,905 ha-1), net

return ($3,525 ha-1) and return per dollar invested

(3.55) in ginger cultivation were recorded with the

application of high level of Azotobacter along with

medium level of Phosphotica. This treatment

combination paid significantly higher gross return, net

return and return per dollar invested than those

obtained with most of the other treatment

combinations (Fig. 2D). The lowest gross return


93

($3,971 ha-1), net return ($2,688 ha-1) and return per

dollar invested (3.1) were obtained with combined use

of low level of both Azotobacter and Phosphotica.

4. Discussion

4.1 Growth Parameters

Biofertilizers exhibited significant and positive

effect on growth attributes like canopy spread, LAI,

CGR and DMA in organic rainfed ginger over those

of the control plots. Seed treatment with high dose

(5.0 kg ha-1) of Azotobacter (a3) and medium dose

(3.75 kg ha-1) of both Azospirillum (b2) and

Phosphotica (c2) caused marked increase of all of

these growth attributes at most of the growth stages

over those of their higher and lower levels. The higher

canopy spread and greater LAI of biofertilizer treated

plots might be due to better development of shoots by

greater availability of plant nutrients and growth

promoting hormones released by higher microbial

activities [4]. Similar beneficial effect of biofertilizer

inoculation on growth attributes of different crops was

observed by many workers [9, 11, 19].

The leaf area index is a factor closely related to its

productivity because of the fact that the total leaf area

affects the amount of photosynthate available. Increase

in LAI enables the plant to enhance photosynthetic rate

and results in higher growth and yield. The higher

CGR and DMA in biofertilizers inoculated plants

might be correlated with the increased LAI and canopy

spread. The increased LAI and canopy spread

ultimately enhanced CGR and DMA. Such increase

was attributed to effective N fixation by Azotobacter

and Azospirillum, solubilization of soil available and

native P through production of organic acids by

phosphate solubilizing bacteria (PSB) and release of

growth regulators [4, 10].



most of the growth attributes like canopy spread, LAI,

CGR and DMA. Combined application of high level

(5.0 kg ha-1) of Azotobacter with medium level (3.75

kg ha-1) of Phosphotica seemed to be optimum on

influencing the above growth parameters in most of

the cases (Fig. 1). The different types of biofertilizers

in association improved the growth of the crop than

their application in isolation. This is because of the

fact that range of organic acid and plant growth

promoting substances produced by combined use of

biofertilizers increased substantially over their single

application [20]. Different types of acid and growth

promoting substances secreted by the microorganisms

in association are additive in action and synergistic in

effect [10, 21]. The increased growth attributes

obtained at combined use of Azotobacter and

Phosphotica in this investigation was due to their

additive effect. However, the low level (2.5 kg ha-1) of

Azotobacter and Phosphotica application recorded

low values of the growth attributes which was

significantly lower than those obtained with higher

level of Azotobacter and Phosphotica at most of the

growth stages.

4.2 Yield and Yield Attributes

The yield of ginger is a function of rhizome growth

and rhizome bulking rate. The development of

rhizome starts after initial period of establishment and

early shoot growth. Seed treatment with biofertilizer

showed positive effect on rhizome growth and

rhizome bulking rate that ultimately increased the

rhizome yield over control. The high level (5.0 kg ha-1)

of Azotobacter exerted greater effect on increasing

rhizome growth, rhizome bulking rate and rhizome

yield as compared to its lower levels. The medium

levels (3.75 kg ha-1) of both Azospirillum and

Phosphotica also showed markedly greater yield

components and greater rhizome yield than their

higher and lower levels. Increased yield by

biofertilizer inoculation was due to the high canopy

spread and LAI enabling production of relatively large

amount of assimilates for high yield. The rhizome

yield showed very strong and positive correlation with

canopy spread (Figs. 3A, 3B and 3C) and LAI (Figs.


94

Fig. 3 Subfigures [A-C] represent the relationships of rhizome yield with canopy spread at [A] 90, [B] 150 and [C] 210 days after planting; subfigures [D-F] represent the relationships of rhizome yield with leaf area index at [D] 90, [E] 150 and [F] 210 days after planting. DAP represents days after planting and LAI indicates leaf area index.

3D, 3E and 3F) at all the growth stages. This showed

very vital role of these growth parameters in the yield

improvement of ginger. The above growth attributes

enhanced by the application of biofertilizers might be

owing to higher availability and efficient use of the

nutrients through out the growing period as a result of

greater microbial activities. This in turn increased the

rhizome growth and rhizome bulking rate at different

periods. Increase in yield could be attributed to

increase in growth and yield attributing characters


95

resulting from higher fixation of atmospheric N,

dissolution of insoluble phosphates in soil to soluble

forms and production of plant growth hormones and

vitamins by microorganisms [22]. High rhizome yield

of ginger was mainly due to high DMA in crop

resulting from high rhizome growth by greater RBR.

High DMA in biofertilizer treated crop during its

growth period was, thus, responsible for determining

high rhizome yield of ginger. This is in conformity

with the findings of Gupta and Awasthi [23], Stoop et

al. [24] and Wijebandara et al. [25].


showed significant interaction effect on yield

attributes and yield of this crop. Use of high level (5.0

kg ha-1) of Azotobacter together with medium level

(3.75 kg ha-1) of Phosphotica seemed to be the best

treatment combination for improving yield and yield

attributes (Fig. 2). This treatment combination

ultimately produced the highest rhizome yield (22.08 t

ha-1). The increased growth parameters were mainly

responsible for enhancing yield attributes that

ultimately produced high rhizome yield [11, 26].

4.3 Rhizome Quality

The biofertilizer treatments improved the rhizome

quality by increasing specific gravity, dry matter

content and oleoresin content in rhizome and

decreasing the crude fibre content in it. Application of

higher levels of Azotobacter (5.0 kg ha-1 and 3.75 kg

ha-1) increased the specific gravity, dry matter content

and oleoresin content in rhizome over its lower level

(2.50 kg ha-1) while decreased the crude fibre content

with increasing its dose. Similarly, use of medium

levels of Azospirillum and Phosphotica (3.75 kg ha-1)

increased specific gravity and oleoresin content in

rhizome over their higher and lower levels.

Phosphotica was also responsible for increasing dry

matter content but both Azospirillum and Phosphotica

did not affect the crude fibre content in rhizome.

Azotobacter and Azospirillum, apart from their ability

to fix N, produce anti-fungal antibiotics that inhibit

the growth of several pathogenic fungi in the root

region and hence improving root growth and crop

nutrition that ultimately improves the quality of the

product [27]. They also produce growth regulating

substances like phytohormones, vitamins etc. that help

in improving the quality of the produce by balancing

the nutrition of the crop [28]. Similarly, Phosphotica

helps in solubilization of soil available and native P

through production of organic acids and improves

crop quality by promoting physiological activities

through better P nutrition [29]. It is interesting to note

that these quality parameters of rhizome were strongly

related to each other either positively or negatively.

The dry matter content was positively correlated with

specific gravity (Fig. 4A) and oleoresin content (Fig.

4B) indicating their compatibility in improving

rhizome quality. It was also noticed that crude fibre

content in rhizome was strongly but negatively

correlated with dry matter (Fig. 4C) and oleoresin

contents (Fig. 4D). This has a good impact on quality

control. Increase in dry matter and oleoresin contents

in rhizome decreases the crude fibre content and thus

improves the rhizome quality. The beneficial effect of

biofertilizer treatment on rhizome quality might be

due to better nourishment of the crop by increasing

nutrient availability in the root zone through greater

microbial activities [30].

4.4 Economics of Rainfed Ginger Cultivation

The seed treatment with different biofertilizers

increased the gross return, net return and return per

dollar invested on ginger cultivation. This was mainly

because biofertilizer application improved growth and

increased rhizome productivity and quality through

better crop nutrition and thus resulted in higher profit.

Azotobacter showed marked effect on gross return, net

return and return per dollar invested in ginger

cultivation and application of high level of Azotobacter

resulted in higher gross return ($4,811 ha-1), net profit

($3,449 ha-1) and return per dollar invested (3.54) over

its medium ($4,407 ha-1, $3,078 ha-1 and 3.31,


96

Fig. 4 Relationships among different quality parameters of rhizome: [A] dry matter content (%) with specific gravity (g cc-1), [B] dry matter content (%) with oleoresin content (%), [C] dry matter content (%) with crude fibre (%), and [D] crude fibre content (%) with oleoresin content (%).

respectively) and low ($4,239 ha-1, Rs 2,928 ha-1 and

3.23, respectively) levels. Azospirillum and

Phosphotica also favourably influenced the economics

of ginger cultivation and use of medium level of both

the biofertilizers increased gross return, net return and

return per dollar invested over those obtained at their

high and low levels. Greater availability of plant

nutrients together with increased activities of plant

growth promoting substances in biofertilizers treated

crop might be responsible for higher productivity and

greater income [8, 12]. The high economic return was

attributed to high rhizome productivity resulting from

enhanced nutrient supply through greater microbial

activities induced by biofertilizer treatments [28, 29].

The significant interaction effect of seed treatment

with Azotobacter and Phosphotica on economics of

ginger cultivation showed their compatibility in

microbial activities. The highest gross return ($4,905

ha-1), net return ($3,525 ha-1) and return per dollar

invested (3.55) in ginger resulting from the combined

use of high level of Azotobacter along with medium

level of Phosphotica (a3c2) proved to be the best

treatment combination out of all treatment

combinations (Fig. 2D). This treatment combination

was found to be optimum in exerting maximum

additive and synergistic microbial effects on

enhancing growth, productivity and economic return

of rainfed organic ginger in the north-eastern region of

India.

5. Conclusions

The beneficial effect of biofertilizer on influencing

the growth, productivity, quality and economics of

organic ginger grown under rainfed condition has

been established. The seed treatment with Azotobacter

5.0 kg ha-1 (a3), Azospirillum 3.75 kg ha-1 (b2) and


97

Phosphotica 3.75 kg ha-1 (c2) improved most of the

growth attributes, increased yield components and

yield of rhizome, improved crop quality like specific

gravity, oleoresin, and increased dry matter content in

rhizome. These treatments also resulted in higher

return as compared to their other levels. Combined use

of high level of Azotobacter along with medium level

of Phosphotica (a3c2) was found to be the best

treatment combination which improved growth and

yield attributes of ginger and ultimately resulted in

markedly higher productivity (22.08 t ha-1) of the crop.

This treatment combination greatly improved the

quality of the produce and resulted the highest gross

return ($4,905 ha-1), net return ($3,525 ha-1) and return

per dollar (3.55) invested in ginger cultivation. The

results suggest growing of organic ginger by treating

the seed rhizome with Azotobacter 5.0 kg ha-1 along

with Phosphotica 3.75 kg ha-1 (a3c2) for obtaining

good growth, high yield of good quality rhizome and

maximum profit in the NorthEastern region of India.

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[29] S.S. Mahdi, G.I. Hassan, S.A. Samoon, Bio-fertilizers in

organic agriculture, Journal of Phytology 2 (2010) 42-54.

[30] A.K. Bijaya, L. Ado, Effect of fertilizers and biofertilizer

on physiological parameters of multiplier onion (Allium

cepa var. aggregatum), Indian Journal of Agricultural

Sciences 75 (2005) 352-354.

Journal of Agricultural Science and Technology A 3 (2013) 99-104 Earlier title: Journal of Agricultural Science and Technology, ISSN 1939-1250

Research on the Soil Carbon Storage of Alpine

Grassland under Different Land Uses in Qinghai-Tibet

Plateau

Tao Li, Lei Ji, Tao Liu, Zhongqi Song, Shujing Yang and Youmin Gan

Department of Grassland Science, College of Animal Science and Technology, Sichuan Agricultural University, Sichuan, Ya’an

625014, China

Received: November 19, 2012 / Published: February 20, 2013. Abstract: In this article, we mainly analysis the soil carbon storage of the alpine grassland under different land uses in Qinghai-Tibet Plateau. The samples of this investigation include six experimental fields which are fenced mowing grassland, artificial grassland, winter and spring grazing meadowland, summer and autumn mild grazing land, summer and autumn moderate grazing pasture and summer and autumn severe grazing land and seven soil layers included 0 cm-5 cm, 5 cm-10 cm, 10 cm-20 cm, 20 cm-30 cm, 30 cm-50 cm, 50 cm-70 cm and 70 cm-100 cm. The results show that the soil carbon storage in different soil layers will gradually reduce and the difference was remarkable (P < 0.05). What is more, the soil carbon storage of alpine grassland under different land uses has following sequence: winter and spring grazing grassland > summer and autumn mild grazing land > artificial grassland > summer and autumn moderate grazing meadowland > summer and autumn severe grazing pasture > fenced mowing meadow, and the significant difference between them is remarkable (P < 0.05).

Key words: Qinghai-Tibet Plateau, alpine grassland, soil, carbon storage.

1. Introduction The grassland ecosystem is one of the most

important and widely distributed ecosystem types in

terrestrial ecosystems and playing an important role in

the global carbon cycle and climate regulation [1, 2].

The Qinghai-Tibet Plateau, as the highest and largest

Eurasia geomorphology unit, is an essential

component of the grassland ecosystem and the world’s

concentrated distribution area of low-latitude

permafrost. It is not only sensitive to global climate

change, but also playing an important role in Asia

even global climate change [3]. Due to its unique

geographical location, the Qinghai-Tibet Plateau

widely distributed with such typical alpine grassland

vegetation as the alpine meadow, alpine steppe and

Corresponding author: Youmin Gan, professor, research

fields: grassland resources and ecology. E-mail: [email protected].

alpine meadow grassland. These grasslands occupy

more than 60% of the plateau area [4]. The consequent

development of alpine meadow soil, sub-alpine

meadow soil, alpine grassland meadow soil and other

alpine soil are rich in organic matter. Soil carbon

density is significantly higher than that of other

regions [5].

In recent years, with the temperature rising of the

Qinghai-Tibet Plateau, studies [5, 6] have shown that

the plateau permafrost has great potential for

emissions of greenhouse gases such as carbon and

nitrogen, because of the remarkable permafrost

thermal sensitivity. Because of this special

geographical and ecological unit of the Qinghai-Tibet

Plateau and its important role in global change, so the

study of carbon storage of grassland ecosystem in the

Qinghai-Tibet Plateau and its distribution

characteristics for the evaluation of grassland

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Research on the Soil Carbon Storage of Alpine Grassland under Different Land Uses in Qinghai-Tibet Plateau

100

ecosystems, even the carbon cycle response and

feedback effects of the whole terrestrial ecosystems

have important scientific value to the global carbon

cycle and global climate change.

Different use of grassland is one of the main human

factors of the grassland ecosystem carbon storage [7].

Domestic and foreign scholars have undertaken some

researches on the soil carbon storage under different

grassland uses: Staben et al. [8] reported that after

becoming a perennial artificial grassland, the soil

organic carbon storage of cultivated land would

increase; Mensah et al. [9] said pasture could increase

surface soil’s organic carbon storage, while reclaimed

farmland would result in reduction of soil organic

carbon storage; Andeoon et al. [10] showed that

grassland during reclamation could lead to large losses

of soil organic storage, which accounted for 30% to

50%; Risse et al. [11] demonstrated that overgrazing

could promote soil respiration, accelerating the carbon

release from soil to the atmosphere; Studies about the

typical temperate grassland in the Xilin River basin of

Inner Mongolia [12] have shown that reclamation and

grazing can reduce the soil carbon and nitrogen

content, but fence rotational grazing can increase the

content of the soil carbon and nitrogen; Studies about

the different grassland soil properties and the density

of the soil carbon, nitrogen, phosphorus in Eastern

Qilian Mountains of the Qinghai-Tibet Plateau [13]

have shown that plant communities and characteristics

of root distribution influenced the density of soil

carbon, nitrogen and phosphorus. The difference is

obvious between the plots and the soil layers. The

above studies have shown that different grassland uses

have an influence on carbon cycle of the grassland

ecosystem. At present, the problem of the carbon

source and sink of the grassland ecosystems has not

been determined. Therefore, the study of carbon

storage has more important theoretical value and

practical ecological significance.

In this article, through the analysis of the alpine

grassland soil carbon storage under different land uses

in Science and Technology Park of Sichuan Academy

of Grassland Science, we intend to explore the main

factors, providing data support for accurately

estimating the carbon storage of grassland ecosystem

in Qinghai-Tibet Plateau in China, analyzing its

variation, as well as researching on the function of

grassland carbon source and sink and the carbon cycle

mechanism in the system.


2.1 General Situation of the Study Area

The Science and Technology Park of Sichuan

Academy of Grassland Science located at the

Hongyuan County, Aba autonomous prefecture,

Sichuan province. The region belongs to the

Qinghai-Tibet Plateau, which average elevation is

3,560 m, the topography is high and smooth, the

annual precipitation is 500 mm-800 mm and the

average annual temperature is -2 °C-5 °C. It has a

large temperature difference between day and night

and the region belongs to the alpine semi-humid and

humid areas. Grassland vegetation and livestock, in

the region, have many species. The type of the soil

belongs to meadow soil and marsh soil. And the soil

has large amount of organic matter, good texture and

non-saline. Grassland vegetation is meadow and

marsh vegetation mainly formed by Gramineae and

Cyperaceae plant [14, 15].

The Science and Technology Park of Sichuan

Academy of Grassland Science have total land area is

328.14 hm2 and the grassland area of which can make

use is 314.76 hm2. The type of the grassland is mainly

alpine meadow. In recent years, to reasonable using

grassland resources, the park has been fenced

construction, which created favorable conditions for

scientific and rational use and management of

grassland. The park has summer and autumn pastures

about 165.71 hm2, winner and spring pastures about

147.64 hm2, fenced mowing meadow 3.54 hm2 and

artificial grassland. In the research, we select six plots

as objects of the research for observation, sampling


101

and analysis of soil carbon storage. The plots come

from fenced mowing meadow, artificial grassland,

winter and spring grazing grassland, summer and

autumn grazing grassland (according to the degree of

the use, summer and autumn grazing grassland is

divided into summer and autumn mild grazing

grassland, summer and autumn moderate grazing

grassland and summer and autumn severe grazing

grassland).

2.2 Sampling

We randomly selected ten sample plots which size

is one square meters in the selected plots in August.

And then, we investigated the species and their

coverage within each sample and recorded the

elevation, slope and aspect of the plot. In the sample,

we cut the vegetation that grown above the ground,

and next cleared the residue and impurities leaved on

the soil surface. The method of taking sample that we

adopted is the soil auger [16]. We used a five-diameter

soil auger to get seven soil layers that are

0-5-10-20-30-50-70-100 cm. The upper 4-6 drill

mixed and the lower 3-4 drill mixed. We put the

samples in the ziplock bags by layers and then

removed the plant residues over 2 mm sieve and last

put the samples on the brown paper for shade drying

and analyzing; we used cutting ring to collect soil

samples for determination of soil bulk density [17].

2.3 Laboratory Analysis

Soil samples were dried to constant weight at 65 °C.

And we used gravimetric method to determine the soil

bulk density [18]. For determining the soil organic

carbon content [19], we used K2Cr2O7-H2SO4

oxidation and volumetric method.

2.4 Statistical Analysis

We adopted software SPSS (version 17.0) for the

mathematical statistics. In the process, we selected the

LSD test method (0.05 significant levels) for the

multiple comparisons. The formula calculated the soil

organic carbon storage:

Soil carbon storage CSsoil = iisoil ADBDsoil

In this formula, BDsoil indicates soil bulk density;

Dsoil shows soil organic carbon content; Ai represents

area of each grassland type; i (1, 2…) means the

number of grassland types.

3. Results and Discussion

3.1 Changes of the Soil Carbon Storage in Different

Soil Layers

The soil carbon storage under different grassland

used in the region shows the certain regularity in the

vertical direction and the soil carbon storage of

grassland consistently shows a decreasing tendency

from the surface to the bottom with the gradient. The

significant difference between the layers is P < 0.05

(Table 1). Soil carbon storage in 0 cm-5 cm layer is

2.8388-5.3013 kg m-2; Soil carbon storage in 5-10 cm

layer has 2.2796-5.1381 kg m-2; Soil carbon storage in

10-20 cm layer contains 3.9607-9.7223 kg m-2; Soil

carbon storage in 20-30 cm layer is 3.0538-8.2274 kg

m-2; Soil carbon storage in 30-50 cm has 4.274-15.414

kg m-2; Soil carbon storage in 50-70 cm contains

3.1939-13.1211 kg m-2; Soil carbon storage in 70-100

cm is 4.2221-15.3571 kg m-2.

3.2 Changes of the Soil Carbon Storage under the

Different Uses of Grassland

Under the different uses of the grassland, soil

carbon storage in each soil layer and the total soil

carbon storage show the certain regularity in the

horizontal direction (Table 2). And the soil carbon

storage has significant difference (P < 0.05). In 0

cm-5 cm layer with relatively high carbon storage in

soil, the sequence of carbon storage is winter and

spring grazing grassland > summer and autumn severe

grazing grassland > artificial grassland > summer and

autumn moderate grazing grassland > summer and

autumn mild grazing grassland > fenced mowing

meadow; In 5 cm-10 cm soil layer, the situation of

carbon storage is similar to the layer in 0 cm-5 cm; Of

the carbon storage in 10 cm-20 cm soil layer, winter and


102

Table 1 Soil carbon storage of each soil layers of the different use of grassland kg m-2.

Sampling depth

Fenced mowing meadow

Artificial grassland

Winter and spring grazing grassland

Summer and autumn mild grazing grassland

Summer and autumn moderate grazing grassland

Summer and autumn severe grazing grassland

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

0-5cm 2.8388±0.1083bc 4.2563±0.0236e 5.3013±0.032e 2.9987±0.1062c 3.1333±0.4526d 5.2789±0.013d

5-10cm 2.2796±0.0671c 4.0667±0.0917e 5.1381±0.0207e 2.0814±0.0602c 3.34±0.0925cd 4.6695±0.0518e

10-20cm 3.9607±0.0907a 7.6916±0.0987a 9.7223±0.0528c 5.0521±0.0458ab 4.5842±0.234bc 7.4828±0.0458c

20-30cm 3.0538±0.0457b 5.6219±0.0835d 8.2274±0.053d 3.2696±0.1343c 4.8803±0.324b 4.9867±0.0638de

30-50cm 4.274±0.2409a 7.2643±0.0842b 15.414±0.7047a 5.7963±0.1633a 8.1111±0.49a 8.1363±0.0294b

50-70cm 3.1939±0.0398b 3.7170±0.0481f 13.121±0.4235b 4.4705±0.2464b 7.8368±0.8098a 7.1507±0.101c

70-100cm 4.2221±0.4632a 6.3422±0.1291c 15.357±0.0999a 5.4117±0.7212a 6.9234±0.4132a 9.4946±0.5121a

Total 3.4033±0.1709 5.5657±0.3327 10.3259±0.9155 4.2572±0.2718 5.5442±0.4525 6.7428±0.3810

Lowercase in the same treatment of each column in the table show significant difference (P < 0.05).

Table 2 Analysis of variance about the soil carbon storage of the different use of grassland.

Grassland types 0 cm-5 cm 5 cm-10 cm 10 cm-20 cm 20 cm-30 cm 30 cm-50 cm 50 cm-70 cm 70 cm-100 cm

Fenced mowing meadow

2.8388±0.1083c 2.2796±0.0671f 3.9607±0.0907e 3.0538±0.0457d 4.274±0.2409d 3.1939±0.0398d 4.2221±0.4632e

Artificial grassland 4.2563±0.0236b 4.0667±0.0917c 7.6916±0.0987b 5.6219±0.0835b 7.2643±0.0842b 3.7170±0.0481cd 6.3422±0.1291cd

Winter and spring grazing grassland

5.3013±0.032a 5.1381±0.0207a 9.7223±0.0528a 8.2274±0.053a 15.414±0.7047a 13.121±0.4235a 15.357±0.0999a

Summer and autumn mild grazing grassland

2.9987±0.1062c 2.0814±0.0602e 5.0521±0.0458c 3.2696±0.1343d 5.7963±0.1633c 4.4705±0.2464c 5.4117±0.7212de

Summer and autumn moderate grazing grassland

3.1333±0.4526c 3.34±0.0925d 4.5842±0.234d 4.8803±0.324c 8.1111±0.49b 7.8368±0.8098b 6.9234±0.4132c

Summer and autumn severe grazing grassland

5.2789±0.013a 4.6695±0.0518b 7.4828±0.0458b 4.9867±0.0638c 8.1363±0.0294b 7.1507±0.101b 9.4946±0.5121b

Lowercase in the same treatment of each column in the table show significant difference (P < 0.05).

spring grazing grassland > artificial grassland >

summer and autumn severe grazing grassland >

summer and autumn mild grazing grassland > summer

and autumn moderate grazing grassland > fenced

mowing meadow; Of the carbon storage in 20 cm-30

cm, winter and spring grazing grassland > artificial

grassland > summer and autumn severe grazing

grassland > summer and autumn moderate grazing

grassland > summer and autumn mild grazing

grassland > fenced mowing meadow; Of the carbon

storage in 30 cm -50 cm, winter and spring grazing

grassland > summer and autumn severe grazing


grassland > artificial grassland > summer and autumn

mild grazing grassland > fenced mowing meadow; Of

the carbon storage in 50 cm-70 cm, winter and spring

grazing grassland > summer and autumn moderate

grazing grassland > summer and autumn severe

grazing grassland > summer and autumn mild grazing

grassland > artificial grassland > fenced mowing

meadow; In 70 cm-100 cm, the situation of carbon

storage is similar to it in 30 cm-50 cm soil. Total soil

carbon storage of grassland under different use

patterns, winter and spring grazing grassland >

summer and autumn severe grazing grassland >

artificial grassland > summer and autumn moderate

grazing grassland > summer and autumn mild grazing

grassland > fenced mowing meadow.

Carbon storage of fenced mowing meadow is the

lowest and the main reason is that the structure of the

vegetation is simple, thus the carbon fixed and

transformed to the soil are relatively small, whereas

the biomass is larger and the intensity of soil

respiration is bigger in growing season. Ploughing and


103

planting have caused loses of soil organic carbon of

artificial grassland, therefore, the carbon storage gets

lower than mild grazing grassland. The otherness of

the soil carbon storage under different uses of the land

shows in the Table 2. Grasslands of different grazing

degree would demonstrate a certain differences in the

characteristics of its vegetation and the biomass, so it

is inevitable to result in the differences in carbon

storage and efficiency of fixing and transforming

organic carbon. The vegetation cover and richness of

species in wild grazing grassland, as well as the

biomass, is relatively large, so the efficiency of fixing

and transforming carbon to the soil is higher and the

fixed amount of carbon will be relatively large. The

condition in severe grazing grassland is just the

reverse. Therefore, the soil carbon storage will

increase as the grazing decreases.

4. Conclusions

One of the characteristics of the grassland

ecosystem significantly different from the other

terrestrial ecosystems is that its carbon storage most

concentrated in the soil [20]. Grazing decline the

ability on fixed carbon of grassland vegetation,

promoting soil respiration and accelerating carbon

release from the soil to the atmosphere, which reduce

the soil carbon storage [21]. The experimental results

show that carbon storage of grazing grassland is more

than artificial grassland and fenced mowing grassland

during the vegetation growing season in August in

alpine grassland. The research results are similar to

Liu and Zhang [22], namely soil organic carbon

storage show that summer and autumn severe grazing


grassland > summer and autumn mild grazing

grassland > fenced mowing meadow.

In the soil carbon storage, artificial grassland is less

than mild grazing grassland, which may be closely

relate to vegetation characteristics and humid

environmental conditions. Soil carbon storage shows a

significant aggregation. The larger difference between

the results may be due to the following reasons: first,

research grassland ranges are different; second,

research methods are different; third, the sampling

process and determination process has error.

Although China now has many scholars study the

characteristics of grassland carbon storage, but for the

research on carbon storage in alpine grassland is not

enough [23, 24]. Grassland carbon stocks affected by

many factors, in the different geographical regions,

climate conditions and in different seasons, grassland

carbon storage may make different changes; in the

same geographical region, climate conditions and in

the same season, different types of vegetation and

grassland under different land uses can also cause

different soil carbon storage. Therefore, the study of

the characteristics about carbon storage of grassland

under different land uses in alpine grassland is

necessary, it provide data reference for study the

carbon storage of grassland in Qinghai-Tibet Plateau

and it has important significance to establish the

reasonable development policies of animal husbandry

in pastoral areas and ensure the sustainable

development of grassland animal husbandry.

Acknowledgments

This study was supported by Strategic and Pilot

Project of Chinese Academy of Science

(XDA05050404-1-2).

References

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[2] O.J.M. Scurlock, Estimating net primary productivity from grassland biomass dynamics measurements, Global Change Biology 8 (2002) 736-753.

[3] G.D. Cheng, P.J. Li, X.S. Zhang, Influences of Climatic Changes on Snow Cover, Glaciers and Frozen Soils in China. Gansu Cultural Press, Lanzhou, 1997. (in Chinese)

[4] H.L. Sun, The formation and evolution of the Qinghai-Tibet Plateau, Shanghai Science and Technology Press, Shanghai, 1996. (in Chinese)

[5] S.Q. Wang, C.H. Zhou, Estimating soil carbon reservior of terrestrial ecosystem in China, Geographical Research


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18 (4) (1999) 349-356. (in Chinese) [6] Y.C. Qi, Y.S. Dong, Y.B. Geng, X.H. Yang, H.L.Geng,

The progress in the carbon cycle research in grassland ecosystem in China, Progress in Geography 23 (4) (2003) 342-352. (in Chinese)

[7] R.A. Houghton, D.L. Skole, C.A. Nobre, Annual fluxes or carbon from deforestation and regrowth in Brazelian Amazon, Nature 403 (2000) 301-304.

[8] M.L. Staben, D.F. Bezdicek, J.L. Smith, Assessment of soil quality in conservation reserve program and wheat-fallow soils, Soil Science Society of America Journal 61 (1997) 124-130.

[9] F. Mensah, J.J. Schoenau, S.S. Malhi, Soil carbon changes in cultivated and excavated land converted to grasses in east-central Saskatchewan, Biogeochemistry 63 (2003) 85-92.

[10] D.W. Andeoon, D.C. Coleman, The dynamics of organic matter in grassland soils, Journal of Soil and Water Conservation 40 (1985) 211-216.

[11] D.G. Risse, E.E. Biney, H.D. Blocker, The True Prairie Ecosystem, Hutchinson Ross Publ. Comp, Stroudsburg Pennsylvania, 1981, pp. 244-246.

[12] M.F. Li, Y.S. Dong, Y.C. Qi, Y.B. Geng, Effect of land-use change on the contents of C & N in temperate grassland soils, Grassland of China 27 (1) (2005) 1-6. (in Chinese)

[13] C.D. Yang, R.J. Long, X.R. Chen, C.L. Xu, J.M. Wang, Characteristics of carbon, nitrogen and phosphorus density in top soil under different alpine grasslands on the Eastern Qilian Mountains, Chinese Journal of Grassland 30 (1) (2008) 1-5. (in Chinese)

[14] Y.P. Liao, J.H. Wang, R. Fu, An analysis on dynamic changes and driving factors of the sandy land of Aba

District in Northwest Sichuan Province, Research of Soil and Water Conservation 18 (3) (2011) 51-54. (in Chinese)

[15] P.H. Deng, The desk study of grassland animal husbandry in Aba pastoral areas and grassland ecological construction, Pratacultural Science 20 (1) (2003) 36-38. (in Chinese)

[16] Soil Observation Standard of Terrestrial Ecosystems, Scientific Committee of the Chinese Ecosystem Research Network, 2007. (in Chinese)

[17] B.S. Chen, The Grassland Learning and Pasture Practice Experimental Instructions, Gansu Science and Technology Press, Lanzhou, 1991. (in Chinese)

[18] Y.K. Li, Conventional Analysis Methods of Soil Agricultural Chemistry, Science Press, Beijing, 1983. (in Chinese)

[19] Nanjing Agricultural University, Analysis of Soil Agrochemical, Agricultural Press, Beijing, 1990. (in Chinese)

[20] H.P. Zhong, J.W. Fan, G.R. Yu, B. Han, The research progress of carbon storage in grassland ecosystem, Pratacultural Science 22 (1) (2005) 4-11. (in Chinese)

[21] Q.J. Ren, G.L. Wu, G.H. Ren, Effect of grazing intensity on characteristics of alpine meadow communities in the eastern Qinghai-Tibetan Plateau, Acta Prataculturae Sinica 18 (5) (2009) 256-261. (in Chinese)

[22] N. Liu, Y.J. Zhang, Effect of grazing on soil organic carbon and total nitrogen in typical steppe, Pratacultural Science 27 (4) (2010) 4-11. (in Chinese)

[23] S.L. Piao, J.Y. Fang, J.S. He, Y. Xiao, Spatial distribution of grassland biomass in china, Acta Phytoecologica Sinica 28 (2004) 491-498. (in Chinese)

[24] J. Ni, Carbon storage in grasslands of China, Journal of Arid Environments 5 (2002) 205-218.



Chemical and Sensory Properties of Olive Oil as

Influenced by Different Sources of Irrigation Water

Salam Ayoub1, Saleh Al-Shdiefat1, Hamzeh Rawashdeh2 and Ibrahim Bashabsheh2

1. Olive Research Department, National Center for Agricultural Research and Extension, P.O. Box 639, Baqa 19381, Jordan

2. Water Management and Environment Research Department, National Center for Agricultural Research and Extension, P.O. Box

639, Baqa 19381, Jordan

Received: October 8, 2012 / Published: February 20, 2013.

Abstract: This research was conducted throughout the years 2008-2010 to study the influence of irrigation water quality on oil

quality of “Nabali Muhassan” olive trees. Reclaimed municipal wastewater and fresh water were used twice a week using drip

irrigation system. Rainfed olive trees of the same farm were taken as control. No significant differences were observed between

rainfed, fresh water and reclaimed wastewater treatments in terms of acidity, peroxide values and UV absorbance of the extracted

olive oil. Heavy metals and microbiological pathogens were not detected in all tested olive oil samples. Oleic acid was significantly

higher in olive oil obtained from rainfed trees than irrigated trees. Linoleic acid content was significantly higher in reclaimed

wastewater irrigated trees than the rainfed trees. Total polyphenol contents were significantly higher in oil obtained from rainfed

olive trees than oil obtained from the irrigated olive trees. Results of organoleptic analysis showed no significant differences in the

fruity attribute within treatments, while the bitter and pungent attributes were higher in olive oil obtained from rainfed trees as

compared to that obtained from irrigated trees. No negative attributes were observed in oil obtained from the irrigated or rainfed trees

and they were all classified as extra virgin grade.

Key words: Olive oil, irrigation, reclaimed wastewater, fresh water, rainfed trees, total phenols.

1. Introduction The olive (Olea europaea L.) is one of the most

important trees in the Middle-East region. Olive tree

has been traditionally grown under rainfed conditions

and is considered as one of the best adapted species to

the semi-arid environment [1]. Area under olive

cultivation in Jordan is about 129,000 ha with an

annual production of 222,000 tons of olive fruits [2].

Jordan is facing a drastic water shortage problem

and is classified among the poorest countries with

regard to water availability [3]. Therefore, one of the

strategies to be adopted to alleviate the water shortage

problem in the country is to use treated municipal

wastewater for irrigation purposes. Olive is considered

moderately tolerant to salinity [4-6] and therefore

Corresponding author: Salam Ayoub, Ph.D., research

fields: olive tree physiology and oil quality. E-mail: [email protected].

treated wastewater may be a useful option [7].

In a series of studies, it has been demonstrated that

introduction of irrigation to rainfed olive orchards

dramatically increases yields [8, 9]. However, fresh

water in these rainfed areas is scarce and only

low-quality sources of water (saline and reclaimed

wastewater) is available for olive irrigation.

It is well established that the quantity of water used

in irrigating of olive trees affects the chemical and

sensory quality of olive oil [10]. The phenolic

compounds of olive oil are mostly influenced by

irrigation, since their levels decreased as the amount

of applied water increased [11]. Sales et al. [12]

reported that the content of monounsaturated and

polyunsaturated fatty acids was higher in the oils of

the dry-farming olive, while the relative contents of

the saturated fatty acids and phenolic compounds were

higher in the irrigated one.

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106

It was reported that treated wastewater available for

irrigation is commonly characterized by high salinity,

excess levels of B and significant but non-uniform

concentrations of both potential plant nutrients (N, P,

K and micronutrients) and environmental

contaminants including heavy metals, COD and BOD5

[13]. Little information is available regarding the

effect of the reclaimed wastewater on olive oil quality.

Palese et al. [14] reported that the irrigation of olive

trees using treated wastewater during six crop seasons

in Southern Italy did not affect significantly the

quality parameters of the obtained olive oil.

A Tunisian study demonstrated that the use of

treated wastewater increased vegetative growth and

olive yield in comparison to non-irrigation regime

[15]. A study carried out in Southern Italy indicated

that applying treated sewage water for olive trees does

not result in significant microbial contamination of

fruit harvested from the wastewater-irrigated trees [7].

Wiesman et al. [16] found no effect of irrigation

with saline water as compared to fresh water on olive

oil basic quality parameters, such as free acidity,

peroxide value and fatty acids composition. However,

saline water increased the level of certain antioxidant

components (polyphenols and Vitamin E).

Furthermore, Cresti et al. [17] found no effect of

irrigation with saline water on peroxide value and

fatty acids profile, while the oleic-linoleic ratio

decreased in olive oil obtained from trees irrigated

with saline water.

The aim of this research was to study the influence

of using secondary treated wastewater for irrigation of

“Nabali Muhassan” olive trees on the quality of the

produced olive oil in comparison to irrigation with

fresh water and rainfed conditions.


The experiment was conducted for three successive

seasons 2008, 2009 and 2010 at a private olive

orchard located in Ramtha area in the north of Jordan

(altitude 484 m, latitude 32°35′N and longitude

35°59′E), with an average annual rainfall of about 275

mm in a silty clay soil texture.

Fourteen-year-old “Nabali Muhassan” olive trees

spaced 6 m × 6 m were selected for the experiment. A

randomized complete block design with five

replications and three treatments was used. Each

experimental plot consisted of nine trees for each

treatment providing one “inner” tree for monitoring

and for data collection and surrounded by border trees

receiving the same treatment. Experimental treatments

were carried out to compare between rainfed trees (not

irrigated), irrigation with fresh water and finally

irrigation with reclaimed wastewater.

Reclaimed wastewater was supplied from Ramtha

wastewater secondary treatment plant located near the

experiment orchard. It was pumped from the treatment

plant through a main pipeline to the drip irrigation

system at the olive orchard. Underground well water

was brought by tanker vehicle and reserved in tanks at

the experiment location, then it was pumped through

the drip irrigation system. Rainfed olive trees at the

same orchard were taken as the control treatment.

Quantity of irrigation water was determined

according to the maximum crop evapotranspiration

(ETc), using the FAO method [18, 19] based on

reference crop evapotranspiration (ETo) multiplied by

crop coefficient (Kc) and a plant cover factor

measured as percent surface cover by trees in the

orchard and determined by measuring shaded area at

solar noon. Irrigation water (fresh and reclaimed) was

provided two days per week by drip irrigation method

(seven emitters per tree; 8 L/h). Distances between

emitters were 0.4 m and were equally spaced around

the tree 0.5 m from the main trunk. Irrigation

scheduling and monitoring was controlled manually

through opened valves according to a time schedule.

Irrigation was started on April and finished on

October for each growing season.

Total quantity of fresh water and reclaimed

wastewater applied were similar during the irrigation

period. Based on daily calculation of ETc, the total


107

quantity of applied water for each type of irrigation

water was 382 mm, 308 mm and 295 mm for the 2008,

2009 and 2010 seasons, respectively. Samples of fresh

water and reclaimed wastewater were taken during the

irrigation period and analyzed for physical, chemical

and biological characteristics.

Olive fruits were harvested by hand during

November of each growing season at a maturity index

around four. Each inner tree from the experimental

plot was harvested separately. Oil extraction was

performed within two days using small scale olive

mill (two phase centrifugal system, Model BuonOlio

Campagnola, Italy).

Oil samples were analyzed for free acidity, peroxide

value, UV absorbance (K270, K232, ΔK), heavy metals,

total polyphenol contents and fatty acid composition

using official methodologies of the International

Standard Organization (ISO) and the International

Olive Council standards [20].

Sensory evaluation of the olive oil samples was

performed by eight trained olive oil tasters from the

accredited Jordanian olive oil tasting panel according

to the official methodology of the International Olive

Council [21]. The mean of each tasting session for

each olive oil sample for the three treatments (two

samples/replicate) was calculated.

Data were analyzed using two-way analysis of

variance (ANOVA) using the statistical package

MSTAT-C [22]. Means were compared using

Duncan’s multiple range tests. Significance was set at

P ≤ 0.05. The means for each sensory olive oil

attribute was also calculated using the IOC statistical

computer program and accordingly the classification

of the oil was determined.


Analysis of irrigation water showed higher electric

conductivity (EC) value for reclaimed wastewater (2.6

dS/m) as compared to fresh water (1.22 dS/m). The

mean values of pH, TSS, cations, anions, N, NO3, B,

heavy metals, BOD5, COD and fecal coliform in

reclaimed wastewater were within the Jordanian

standard for treated wastewater used in irrigation of

fruit trees. However, values of EC, Sodium adsorption

ratio (SAR), Cl and Na were higher than the Jordanian

standard limits (Table 1). Although the EC value of

the reclaimed wastewater was relatively high, olive

trees can tolerate irrigation water salinity of up to 5

dS/m with a SAR of 18 and can produce new growth

at leaf Na levels of 0.4%-0.5% dry weight (d.w.) [23,

24].

Results of oil quality analysis showed no significant

differences among the three treatments throughout the

three seasons in terms of acidity, peroxide values, K270,

K232 and ΔK of the extracted olive oil with the

exception of fresh water treatment for the year 2008

which gave significantly lower free acidity value as

Table 1 Characteristics of water used in irrigation of olive trees as compared to Jordanian standard for treated wastewater used for irrigation of fruit trees. Each value is the mean of six samples ± standard deviation.

Maximum limit (Jordanian standard, No. 893/2006)*

Type of irrigation water

Parameter Reclaimed wastewater

Fresh water

6.0-9.0 7.97 ± 0.31 8.20 ± 0.10 pH

< 2.5 2.60 ± 0.29 1.22 ± 0.11 EC (dS/m)

150 47.00 ± 19.92 19.67 ± 0.58TSS (ppm)

9 11.05 ± 3.02 2.93 ± 0.42 SAR

11.5 3.43 ± 0.25 3.81 ± 0.54 Ca (meq/L)

8.0 3.52 ± 0.16 3.01 ± 0.42 Mg (meq/L)

10.0 20.59 ± 5.35 5.38 ± 0.69 Na (meq/L)

11.0 18.75 ± 3.31 5.92 ± 0.80 Cl (meq/L)

- 1.41 ± 0.12 0.28 ± 0.04 K (meq/L)

30 0.67 ± 0.75 0.00 T-PO4 (ppm)

10.0 6.78 ± 3.03 4.91 ± 0.66 SO4 (meq/L)

- 70.41 ± 5.34 43.15 ± 4.38Na (%)

70 49.67 ± 20.55 28.67 ± 11.55T-N (ppm)

45 16.73 ± 11.45 8.27 ± 5.24 NO3 (ppm)

1.0 0.50 ± 0.27 0.24 ± 0.13 B (ppm)

100 < 0.002 < 0.002 As (ppb)

0.01 < 0.002 < 0.002 Cd (ppm)

5 < 0.01 < 0.01 Pb (ppm)

500 36.67 ± 12.34 9.00 ± 1.73 COD (ppm)

200 14.20 ± 2.31 5.33 ± 1.53 BOD5 (ppm)

1,000 58.00 ± 15.87 < 2 Fecal coliform(100 mL-1)

*Jordanian standard for reclaimed waste water, Ministry of

Water and Irrigation, 893/2006.


108

compared to rainfed and reclaimed wastewater

treatments. The free acidity values for the year 2008

were higher than the years 2009 and 2010. These

results may indicate that the enzymatic lipolysis in the

olive fruits or the attack of parasites was greater in

2008 compared to the years 2009 and 2010. The

peroxide value was significantly higher for the rainfed

treatment for the year 2010 as compared to reclaimed

wastewater treatment (Table 2). The low level of

acidity, peroxide values, K270, K232 and ΔK in this

experiment may be due to the handling process since

the olives were hand-picked and processed within 1-2

days. The K270, K232 and ΔK values measure some of

the oxidation products of fatty acids and reflect the

degree of stability of olive oil [25]. These results

indicated that irrigation of olive trees with reclaimed

wastewater characterized by high water salinity gave

high quality olive oil similar to olive oil obtained from

rainfed or fresh water irrigated trees. Our results agree

with the finding of Palese et al. [14] who reported that

irrigation of olive trees with treated wastewater during

six crop seasons in Southern Italy did not affect

significantly the quality parameters of the obtained

olive oil. Furthermore, Wiesman et al. [16] reported

that olive trees of the cv. Barnea irrigated with

moderately saline water (4.2 dS/m EC) produced oils

of relatively high quality parameters in terms of free

fatty acids, peroxide value and fatty acid profile.

There was no detection of heavy metals in all olive

oil tested samples for the three seasons (Table 2). This

finding is in agreement with Al-Shdiefat et al. [26]

who reported no detection of heavy metals in the

extracted olive oil from trees irrigated with treated

wastewater.

Fatty acid composition values were within the

limits of the International Olive Council standard over

the three seasons (Table 3). Oleic acid content was

significantly higher in oil obtained from rainfed trees

as compared to oil obtained from fresh water irrigated

trees for the three seasons. While linoleic acid content

was significantly higher in oil obtained from fresh

water irrigated trees than rainfed trees for 2008 and

2009 seasons. Stearic acid content was significantly

higher in olive oil obtained from trees irrigated with

reclaimed wastewater or fresh water irrigated trees for

2009 season (Table 3). With respect to 2010 season,

no significant differences were observed among

rainfed, fresh water and reclaimed wastewater

treatments for all measured fatty acids except for oleic

acid and linoleic acid. Oleic acid was significantly

higher in oil obtained from rainfed trees than

that obtained from fresh water or reclaimed wastewater

Table 2 Quality characteristics of olive oil extracted from “Nabali Muhassan” olive fruits grown under rainfed and irrigated conditions during the three experimental years (2008, 2009 and 2010).

Parameter 2008 2009 2010

Limit value**Rainfed

Fresh water

Reclaimed wastewater

RainfedFresh water


RainfedFresh water


Free acidity as oleic acid (%) 0.60 a* 0.47 b 0.67 a 0.39 a 0.29 a 0.35 a 0.28 a 0.30 a 0.30 a Max 3.3

Peroxide value (meq O2 Kg-1 oil) 7.18 a 7.20 a 6.00 a 5.54 a 4.18 a 4.16 a 6.24 a 5.62 ab 4.18 b Max 20

K270 0.15 a 0.15 a 0.15 a 0.10 a 0.11 a 0.14 a 0.10 a 0.10 a 0.12 a Max 0.22

K232 1.81 a 1.78 a 1.80 a 2.12 a 2.06 a 2.14 a 2.07 a 1.90 a 1.81 a Max 2.5

ΔK 0.001 a 0.001 a 0.001 a 0.001 a 0.001 a 0.002 a 0.002 a 0.001 a 0.001 a Max 0.01

Iron (Fe) (mg kg-1) < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 0.5 < 0.1 < 0.1 < 0.1 Max 3.0

Copper (Cu) (mg kg-1) < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 Max 0.1

Lead (Pb) (mg kg-1) < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 Max 0.1

Cademium (Cd) (mg kg-1) < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 < 0.1 Max 0.1

*Means within rows for each parameter and year having the same letters are not significantly different at 5% probability level

according to Duncan’s multiple-range test;

**Source: IOC, 2008, trade standard applying to olive oils and olive-pomace oils, International Olive Council, COI/T.15/NC

no.3/Rev. 2, Madrid, Spain.


109

Table 3 Fatty acid composition (%) of olive oil extracted from “Nabali Muhassan” olive fr uits grown under rainfed and irrigated conditions during the three experimental years (2008, 2009 and 2010).

Fatty acids 2008 2009 2010

Limit value** Rainfed

Fresh water


RainfedFresh water


RainfedFresh water


Myristic acid C14:0 0.02 a* 0.02 a 0.02 a 0.02 a 0.02 a 0.02 a 0.02 a 0.02 a 0.02 a Max 0.05

Palmitic acid C16:0 15.88 b 17.78 a 17.28 ab 15.86 a 16.44 a 15.64 a 16.13 a 15.97 a 17.32 a 7.5-20.0

Palmitoleic acid C16:1 1.18 b 1.44 a 1.32 ab 1.03 a 1.14 a 1.08 a 1.12 a 1.29 a 1.29 a 0.3-3.5

Heptadecanoic acid C17:0 0.04 a 0.04 a 0.04 a 0.04 a 0.04 a 0.04 a 0.05 a 0.05 a 0.05 a Max 0.3

Heptadecenoic acid C17:1 0.06 a 0.06 a 0.06 a 0.06 a 0.06 a 0.06 a 0.06 a 0.06 a 0.07 a Max 0.3

Stearic acid C18:0 2.14 a 2.10 a 2.03 b 2.27 b 2.26 b 2.46 a 2.50 a 2.69 a 2.32 a 0.5-5.0

Oleic acid C18:1 64.23 a 60.62 b 62.77 ab 65.87 a 62.94 b 64.43 ab 66.20 a 61.79 b 62.16 b 55.0-83.0

Linoleic acid C18:2 15.01 b 16.50 a 15.06 b 13.70 b 15.81 a 14.71 ab 14.17 b 15.14 ab 16.59 a 3.5-21.0

Linolenic acid C18:3 0.74 a 0.74 a 0.73 a 0.74 a 0.72 a 0.75 a 0.75 a 0.72 a 0.72 a Max 1

Arachidic acid C20:0 0.34 b 0.36 a 0.36 a 0.42 a 0.41 a 0.40 a 0.40 a 0.44 a 0.40 a Max 0.6

Gadoleic acid C22:1 0.22 b 0.20 a 0.22 b 0.22 a 0.22 a 0.23 a 0.23 a 0.24 a 0.22 a Max 0.4

Behenic acid C22:0 0.09 b 0.10 a 0.09 b 0.11 a 0.10 a 0.11 a 0.10 a 0.12 a 0.11 a Max 0.2

Lignoceric acid C24:0 0.04 b 0.06 a 0.06 a 0.05 a 0.06 a 0.06 a 0.06 a 0.06 a 0.06 a Max 0.2


according to Duncan’s multiple-range test;

**Source: IOC, 2008, trade standard applying to olive oils and olive-pomace oils, International Olive Council, COI/T.15/NC

no.3/Rev. 2, Madrid, Spain.

irrigated trees. Linoleic acid content was significantly

higher in oil obtained from reclaimed wastewater

irrigated trees than that from the rainfed trees (Table

3). These finding agree with Al-Ismail et al. [25] who

reported a decrease in oleic acid and an increase in

linoleic acid content in olive oil samples obtained

from olive trees irrigated with fresh or treated

wastewater when compared to rainfed trees. Our

results indicated that the quality of irrigation water

had no significant effect on fatty acid composition

over the three seasons. Olive oil obtained from trees

irrigated with reclaimed wastewater of high salinity

was not different in fatty acid composition from trees

irrigated with fresh water. This finding agrees with

El-Agaimy et al. [27] who reported that irrigation of

olive trees with saline water up to 6,000 ppm had no

adverse effect on fatty acid composition.

Total polyphenol contents for 2008 and 2009

seasons was higher in olive oil obtained from rainfed

trees than olive oil obtained from trees irrigated with

fresh water or reclaimed wastewater, however, the

difference was not significant. The same trend for

polyphenol contents was observed in the 2010 season

but with significant differences between rainfed

treatment and fresh or reclaimed wastewater

treatments (Table 4). Results of our experiment

indicated that total polyphenol contents were not

affected by the quality of irrigation water. These

results agree with the finding of Al-Ismail et al. [25]

who reported that total phenols decreased in olive oil

samples obtained from trees irrigated with fresh water

or treated wastewater when compared to rainfed

samples, but disagree with their finding that total

phenols content were affected by the quality of

irrigation water, being lower in oil obtained from trees

irrigated with treated wastewater characterized by

high salinity compared with samples obtained from

trees irrigated with fresh water. Furthermore, our

results agree with the finding of Patumi et al. [9] who

reported that, as the amount of water applied to the

olive tree increased, the total polyphenol content in

the oil obtained decreased. This might be due to the

change in the activity of the enzymes responsible for

the phenolics synthesis such as L-phenylanalinine

ammonia type-lyase whose activity is greater under

higher water stress conditions [28].


110

Table 4 Total polyphenol content in olive oil extracted from “Nabali Muhassan” olive trees grown under rainfed and irrigated conditions during the three experimental years (2008, 2009 and 2010).

Parameter 2008 2009 2010

Rainfed Fresh water


Rainfed Fresh water


Rainfed Fresh water


Total polyphenol contents (mg L-1 caffeic acid)

213.56 a 174.41 a 185.20 a 224.91 a 178.17 a 155.24 a 255.99 a 182.5 b 175.23 b

Table 5 Means of positive and negative attributes of olive oil from trees grown under rainfed and irrigated conditions during the three experimental years (2008, 2009 and 2010).

Parameter 2008 2009 2010

Rainfed Fresh water Reclaimed wastewater

RainfedFresh water


Rainfed Fresh water Reclaimed wastewater

Fruity 4.1 a* 3.9 a 3.7 a 3.0 a 2.8 a 2.6 a 3.2 a 2.9 a 2.8 a

Bitter 2.8 a 1.7 b 1.5 b 1.8 a 1.2 b 1.3 b 1.9 a 1.4 b 1.2 b

Pungent 3.1 a 1.9 b 2.1 b 3.2 a 1.9 b 2.3 b 3.4 a 2.2 b 2.5 b

Fusty 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a

Musty 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a

Winy 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a

Metallic 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a

Rancid 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a Olive oil Grade

Extra virgin

Extra virgin

Extra virgin

Extra virgin

Extra virgin

Extra virgin

Extra virgin

Extra virgin

Extra virgin


according to Duncan’s multiple-range test.

Results of sensory analysis of the olive oil obtained

over the three seasons showed no significant

differences in the fruity attribute, however, the bitter

and pungent attributes were significantly higher in

olive oil obtained from rainfed trees as compared to

olive oil obtained from trees irrigated with fresh or

reclaimed wastewater. This finding can be explained

since the quantity of water in irrigated treatments was

higher than rainfed treatment. In addition, rainfed

trees received water during winter months which

expose the trees to stress conditions during summer

months compared to irrigated trees that receive water

during the whole season. This finding agrees with

Vossen et al. [29] who reported that the intensity

levels of bitterness and pungency declined in olive oil

made from trees receiving more water. The lowest

irrigation levels produced oils that were characterized

by excessive bitterness and high pungency. In addition,

the high bitterness and pungency levels of olive oil

obtained from rainfed trees might be due to high

polyphenol contents reported in these trees. Water

stress has been proven to decrease phenol content in

olive fruits [30]. No negative attributes were observed

in olive oil obtained from fresh water, reclaimed

wastewater irrigated trees or rainfed trees over the

three seasons and they were classified as extra virgin

olive oils (Table 5). These results indicated that

irrigation water quality had no effect on sensory

attributes of the obtained olive oil. The achieved

sensory results for the three treatments were almost

similar to those obtained by Patumi et al. [7] and

Gomez-Rico et al. [8] who reported that olive oil

under irrigated and rainfed conditions were classified

as extra virgin and oil from irrigated regime were less

bitter and less pungent than those obtained from

rainfed trees.

4. Conclusions

Olive oil quality parameters (acidity, peroxide

values, K270, K232, and ΔK) were not affected

significantly among the treatments. Oleic acid and

total polyphenol contents were significantly higher in


111

olive oil obtained from rainfed trees than irrigated

trees. Olive oil obtained from rainfed trees or from

trees received fresh or reclaimed wastewater over the

three seasons was free from sensory defects and

accordingly classified as extra virgin olive oil. The achieved results showed that olive oil produced

from trees irrigated with reclaimed wastewater was

not inferior in terms of chemical and sensory

properties to oil produced from rainfed or fresh water

irrigated trees.

Acknowledgments

This research was supported by the US Agency for

International Development, Middle East Regional

Cooperation (MERC) Program, Grant No.

TA-MOU-06-M26-062. The authors wish to thank the

Director General of the National Center for

Agricultural Research and Extension for his support

during the implementation of this study.

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Ethical Trading: The Implications of the Human Rights

Watch Report on South African Fruit Exports

Portia Ndou and Ajuruchukwu Obi

Department of Agricultural Economics and Extension, University of Fort Hare, P. Bag X1314, Alice 5700, South Africa

Received: October 12, 2012 / Published: February 20, 2013. Abstract: This paper presents an ex-ante assessment of the potential implications of the 2011 Human Rights Watch Report on the South African fruit industry. The report explicitly mentions the lack of compliance with ethical codes within the fruit industry, the prevalence of exploitative conditions for farm workers and diverse human rights abuses in farms. The report recommends import ban on culprits as well as engaging third party auditing to ensure compliance. The high vulnerability of the smallholder farmers justified the special interest in the implications on their reputation and hence their ability to access profitable export markets. Although large-scale commercial farmers are potentially at risk of reputational damage due to the Human Rights Watch Report, they are better able to cope owing to their stronger capital base. The opinions of knowledgeable industry insiders were therefore elicited through qualitative research that used a five-point Likert scale to assess perceptions about market access. On the basis of the results, it is probable that the report will lead to improved working conditions for farm workers, improved concern for consumers’ health, and enforcement of legislation by the government. The negative implications involve increased competition, possibility of retailers’ rationalising their supply base and increased evaluation that ends at the farm gate. There is also high probability of increased marginalisation of the already disadvantaged smallholder suppliers, and possible increase in costs of auditing and accreditation for the entire fruit industry. Thus, active collaboration among all stakeholders to ensure the competitiveness of the fruit industry is inevitable. Key words: Ethical trading, ethical codes, South Africa, South African fruit exports, Human Rights Watch.

1. Introduction The Human Rights Watch Report about the

negative ethical conditions in the South African fruit

farms and wine industry made strong

recommendations to international retail markets. The

recommendations may have very significant

implications and impact on the nation’s fruit and wine

market shares in international markets. The

implications can add to existing market access

challenges that include food health and safety

regulations as well as trade-related private standards.

An ex-ante establishment of the stakeholder views and

opinions on the implications of the may result in the

establishment of possible preventive actions and

multi-stakeholder engagements to regain a good

Corresponding author: Portia Ndou, Ph.D., research field:

agricultural economics. E-mail: [email protected].

public image in the export market.

Ethical trade is concerned with the responsibility

taken by companies and retailers especially in Europe,

to introduce codes of conduct to cover minimum

working conditions among their suppliers (agriculture

value chains in this instance-export oriented) in

developing countries [1]. Ethical trade has its origin,

labour practices and the rights of employees of supplier

companies around the world, many of whom are based

in developing countries where laws designed to protect

workers’ rights are inadequate or not enforced.

However, it is commonly used to refer to other

business practices such as treating customers and

vendors fairly, providing transparency of financial

practices, environmental [2] and more social

responsibility.

The labour and supply chains are challenging in

D DAVID PUBLISHING

Ethical Trading: The Implications of the Human Rights Watch Report on South African Fruit Exports

114

nature. The modern global supply chain makes the

aspects more complex and can not be addressed by

individual companies singlehandedly. The Ethical

Trading Initiative (ETI) brings together

Non-Governmental Organisations (NGOs), trade

unions, and corporations in a unique alliance to

collectively tackle these issues [2]. ETI base codes

were developed to address the exploitation of workers

through such conducts as physical abuse, extreme

form of intimidation and forced, bonded or

involuntary prison labour and reflects the most

relevant international standards on labour practices.

The ETI promotes, improves implementation and

encourages adoption of corporate codes of practice

covering supply chain working conditions. The labour

practice codes, which include wages that are enough

to meet basic needs, working hours that are not

excessive, health and safety and the right to join free

trade unions, have been increasing since their

introduction in the early 1990s [3]. Other issues

included in ETI are the ban on child labour,

non-discriminating hiring, compensation, access to

training, promotion, termination or retirement based

on race, caste, national origin, religion, age, disability,

gender, marital status, sexual orientation, union

membership or political affiliation and avoidance of

harsh and inhumane treatment. The codes are adopted

by companies committed to ethical trade.

The implementation of the ethical codes has

numerous challenges in addition to the complexity of

the global supply chain. Many companies expend

large amounts of money in the monitoring and

auditing of the codes of labour [3]. Cost of

compliance with ethical codes is borne by the

producers and not passed to consumers while the

retailers reluctantly squeeze their margins [4]. Though

the interdependencies created by globalisation have

the potential to generate greater global solidarity, the

rules of the new global economy are only partially

written and are themselves the subject of contention

[5]. There are discrepancies of code implementation

activities and the need for bridging performance

between different companies [6]. Very few

organisations have the capacity to effectively work

with companies on code implementation. Differences

in approaches to ethical trade owing to a growing

number of codes and code initiatives have created

significant confusion and duplication of effort on the

ground. The global production systems manifest

complexities of the commercial networks and the

wider social and institutional environment in which

the codes operate [3]. Child labour is more common in

the less developed countries (LDCs) as a function of

poverty and the values esteemed by developed

countries (DCs) to protect children may be unrealistic.

This is manifest in the setting especially of the small

scale farmers that uses all members of the family to

perform the work in the fields [7]. The increase in the

number of working hours for minors under the age of

18 in the USA from 44 h to 48 h a week may mean the

acceptance of the use of child labour to help family

earnings. It is now left to the discretion of the child to

determine if they are willing and able to work for that

long [7]. Actually, retrenching children in some

instances may worsen their livelihoods.

Integration of the codes into the complex systems

of supply chain management and other internal

systems that are deeply integrated within business

strategies is a big challenge [4]. While the codes are

supposedly considered as acceptable international

norms, they represent to a larger degree the retailer

markets of the developed countries [4]. Thus, the ETI

is thought to save the transnational corporations and

retailer agendas. Since the emphasis on conditions of

labour is more toward the supplier companies, the

retailers themselves are spared from the responsibility

of creating an environment of non-exploitation. The

challenge of unjust trade power between the rich and

poor nations is ignored by the ethical trade issues [4].

In addition to ethical challenges, access to

international markets is currently faced with many

challenges among which are, food safety and health


115

regulations, private standards, need for traceability,

packaging, carbon footprints, green marketing and the

changes in the agro-business environment.

Compliance with many recommendations set from the

consumer side attracts additional costs for most

suppliers especially from the developing countries.

The farmers in developing countries and especially the

smallholder producers are continually contending with

the international Human Rights Watchdogs regarding

the conditions and practices in farms which, however,

present additional challenges to already existing

barriers to market entry. Commercial producers are

equally affected by the negative publicity by the

Human Rights Watchdogs but they are able to meet

the challenges. Investing in recommended practices

increases costs which their robust capital base can

handle.

The South African fruit industry especially the table

grape and citrus industries are export oriented. For

instance, about 54% of citrus produced in South

Africa is exported [8]. The major destinations for

South African citrus fruit for the past five seasons

were Central Europe (28% of total exports), Middle

East and the Mediterranean (27%), Far East and Asia

(25%), UK (13%) and the Americas (6%) [8]. Ethical

trade became a challenge for all South African citrus

suppliers from 2008/2009 season in response to

pressures from the UK-based retailers [9]. The initial

cost implications were between R15,000 and R20,000

per audit, but have declined to between R6,000 and

R9,000 per audit. Besides the cost associated with the

audit, the self-assessment questionnaire makes use of

too much time, on average 2-8 h. There is also

over-emphasis on the audit rather than on a

continuous improvement approach [9]. The industry

was challenged to create a capacity within fruit SA to

drive the Ethical Trading (ET) issues for its members.

The ET is presently reported to be better suited to the

South African agricultural context and better

organised to promote Ethical Trade principles

throughout the supply chain [9].

There was unlikely to anticipate threat related to

ruinous cost that might be caused by Ethical Trading

[4]. The implementation of Base Codes was, however,

linked to the possibility of exacerbating dynamics that

widen the inequality gap in the South African

agriculture which concentrates power in the hands of

those who are already wealthy, have ready access to

capital resources and lines of credit [4]. This reduces

the government’s efforts on integrating land reform

with the development of commercial agriculture.

Tight regulation of access to premium markets and

emphasis on the ability to demonstrate compliance

with the host array of standards is very critical.

In June 2001, the South African Human Rights

Commission (SAHRC) [10] conducted an inquiry into

the Human Rights Violation in farming communities

of South Africa. The inquiry focused mainly on the

general widespread lack of compliance with labour

legislation, the vulnerability of women and seasonal

workers on farms, unacceptable levels of crime and

violence experienced in farming communities, limited

access to services and housing among many others.

SAHRC [11] made a follow-up investigation to

establish the progress since 2003. Its general findings

were that there was lack of awareness of human rights,

lack of mechanisms to enforce rights, lack of access to

farms and skewed power dynamics between dwellers

and owners. SAHRC [11] also found out that there

was an increasing tendency for farm workers to live

off farm and be employed via labour brokers,

widespread poor conditions of employment in the

industry as well as abuse of non nationals who are

illegally employed in farms. Few farm workers were

members of trade unions and though child labour

existed in farms, there was limited information on

how prevalent it was.

In August 2011, the Human Rights Watch came up

with a report on ethical situations in South African

fruit and wine industries. The Human Rights Watch

carried out an industry survey that is more inclined

towards compliance with the ethical trading codes.


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This report is very new and its contents are not yet

fully disseminated among the scientific community

and its implications have not been assessed with

regard to the extent to which it can add to market

access challenges, consumer decision making and

South African fruit flows into the high value markets.

The impact can only be known several years from

now by means of ex-poste studies. It is thus

worthwhile to analyse the report and assess the

opinions and views of the stakeholders with regard to

the implications of the recommendations made by the

report on the demand and supply of the South African

fruit into the high value markets.

The main objective of this study is to establish the

stakeholder views and opinions on the implications of

the Human Rights Watch Report about the South

African fruit industries. Representatives of labour and

government are obviously important this study is

primarily concerned with the impact on producers.

The other stakeholders involved in regulation and

support have a role and do respond to such opinions

and that is taken in a separate study. Specifically, the

study seeks to establish the possible implications and

impact of the report on the extent to which it can add

to market access challenges, general economic

situation of South Africa, rural development, poverty

alleviation, welfare of farm workers, international

consumer decision making and the South African fruit

and wine flows into the high value markets. The study

also seeks possible precautionary measures and

interventions that the industry and other stakeholders

may take to regain a good public image especially in

the export market.

2. Methods

This article focuses specifically on the implications

of the Human Rights Watch Report on the South

African fruit industry ethical codes, the corporate

strategies for the management of labour codes and the

responsibility of the producers, exporters and

government. It is an investigation of the implications of

the recommendations made by the Human Rights

Watch upon the South African fruit and wine exports.

First, the paper sets the scene with regard to the Human

Rights Watch Report and recommendations to different

stakeholders based on the findings of its investigations

of labour relations on South African farms. Second, the

article closely investigates the implications of the

recommendations on the fruit exports to the most high

value international market. Third, the study considers

possible strategies and responsibilities for the South

African fruit producers, exporters, other value chain

players and the government.

The aims of the research required a methodology

that would uncover the perceptions of exporters and

producers on the probable implications of the

recommendations made by the Human Rights Watch

Report on labour conditions in South African fruit

farms. The methodology adopted was therefore

qualitative, utilising the expertise of key informants

by means of questionnaires. This study takes

cognisance of the element of multiple stakeholders

involved in the export of fruit and also in the human

rights issues including the government, labour

representatives (Trade Unions), ETI, farm labourers,

retailers and consumers. However, as a preliminary

study, the study sought the perceptions of two groups

of stakeholders. The target groups included fruit

producers and exporters. Fruit producers were chosen

because the immediate changes in fruit trade flows in

response to the recommendations made by the Human

Rights Watch are better viewed from the production

side and export side. Also, the exporters are the

recipients of the strong recommendations to cut on

sourcing fruit from South African fruit producing

farms. Exporters provide the link and may not source

fruit that they will not be able to sell. A survey was

designed through structured questionnaires to

determine the views of fruit exporters and farmers on

the implications of ethical codes of conduct as

enforced by the Human Rights Watch. The

questionnaire focused on information on the


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challenges and benefits of compliance, and the

probable strategies that will promote competitiveness

of the South African fruit industry in the export

market. Of the 80 questionnaires distributed, 36 were

returned. The participants comprised of fruit exporters

(24%), table grape producers (37%) and citrus

producers (39%). The study sought to establish the

threats that may rise as a result of the implementation

of the Human Rights Watch Report for the South

African fruit and wine industries. It also sought to

establish strategies to curb the likely losses of

consumer faithfulness, perception and trust.

Respondents were asked to evaluate the implications

of the report on predetermined possible benefits,

conditions and circumstances associated with

compliance. Each aspect was measured using a

five-point Likert scale anchored by one for “strongly

decreasing” to five for “strongly increasing”. The

closer to 5 the index is, the stronger the impact and

vice versa for the lower index. The respondents also

provided the possible strategies that the stakeholders

can put into action for the sustainability of the South

African fruit and wine exports.


The results begin with a presentation of the results

of the document analysis with respect to the Human

Rights Watch Report. The opinion survey of

knowledgeable insiders is then presented. The

researchers are aware that the actual impact can only

be assessed ex-poste, but this ex-ante assessment

saves as a basis for identifying the key issues to focus

on in more systematic study.

Farm workers in the South African fruit and wine

industries have been found to be subject to

exploitative conditions and human rights abuses

without sufficient protection of their rights and thus

benefit very little from the success of country’s

valuable fruit and wine industries [12]. The Human

Rights Watch Report for ethical conditions in South

African fruit farms highlights very sensitive issues

such as the lack of hand washing facilities, toilets and

lack of access to drinking water and descent housing.

Difficulty for seasonal farm workers to demand wages

and benefits owing to lack of contracts that stipulate

work conditions have been cited as prevalent cases.

According to the report, the lack of government to

promote health and farm worker rights in the Western

Cape Province in fact represents better conditions

compared to other provinces. The violation of the

human rights and lack of compliance with ethical

codes has been cited as against the South African

constitution and other international covenants which

South Africa has ratified [12]. These findings have a

strong bearing on any claims by the fruit and wine

industries to deliver. This is particularly important

because the health issues associated with international

trade prompted the need for such issues as ethical

trade, packaging and traceability among others.

The labour inspectors’ failure to comply with health

and safety regulations contravenes ethical trade as the

best practices in agriculture are not promoted [12].

The report may have far-reaching implications

especially considering the recommendations to

international consumers which include the inquiry into

the human rights and labour rights of conditions on

farms that grow the products they purchase and the

need to push retailers to only purchase from farms

with working conditions that meet international

standards [12]. Another forceful recommendation to

international consumers is the need to ask for ethical

trading initiatives including strong assurance measures,

as well as independent third-party audits down the

supply chain, so that consumers can be confident that

the “ethical trade” products they purchase are in fact

made without the exploitation of workers. The report

advises consumers to inquire into the human rights

and labour rights conditions on farms that grow the

products they purchase.

The retailers are urged to put pressure on suppliers

to comply with the law and to improve labour, health,

and housing conditions [12]. It is of great interest that


118

powers are given to the retailers who already have an

unfair dominance in the supply chain, overbearing

influence in the sourcing, setting of private standards

that are difficult to harmonise as well as having

powers to extract favourable pricing terms and

switching suppliers [13], and yet these are not touched

by the ETI Base Code. Also, the retailers that adhere

to the ETI Base Code are urged to ensure that the

standards contained therein, including the freedom of

association, are respected on supplying farms. They

also should establish a safe and transparent complaints

mechanism that allows workers to launch queries,

including intimidation or discrimination based on

union membership. The report advises retailers to

ensure that, following any third-party audits, all

recommendations are followed. In fact, the title of the

report itself is forceful to any reader: “Ripe with

abuse!” Considering the sensitivity of the report

coupled with the power of the consumers and retailers

in today’s global trade environment, serious

consequences may befall the industry if stern

measures are not put in place. Serious losses might be

incurred especially considering the share of the

produce destined for the export market.

While it is good to advocate for the welfare of the

farm workers, the publicity of the lack of compliance

for the South African producers may lead to much

scrutiny and challenges by the importers of South

African fruit. There is evidently bound to be an

increased pressure for the South African fruit

exporters to compete with the heavily subsidised

Northern rivals. The report instils a sense of insecurity

among the South African suppliers. The uncertainties

surrounding the extent to which retailers may treat the

South African suppliers have a bearing on the future

efficiencies to meet the Base Codes and also for the

retention of suppliers within the retailers’ supply base.

The complexity of monitoring standards, the problem

of traceability coupled with ethical trade creates a

complex chain. The responsibility itself is more

complex. The involvement of retailers in the

monitoring process may make the whole process not

only complex but difficult.

The Human Rights Watch Report has extensive

implications for the South African fruit industry,

economy and policy. The report has the potential of

impacting both negative and positive outcomes. On a

short-term basis, three significant supply risks can be

identified, namely, immediate withdrawal by the

importers, extreme cuts on labour force and increased

scrutiny by the international retailers. On the positive

side, the awareness of non-compliance can raise a

possibility for public policy support as well as

government financial intervention to the aid of the

producers. This is possible only through the

realisation of the fact that the producers already are

faced with high transaction costs especially relating to

transport to export markets [14]. This is crucial

considering the fact that the Northern hemisphere

rivals are heavily subsidised by their governments.

3.1 Implications of the Report on the South African

Fruit Industry

Tables 1 and 2 sum up the possible implications of

the Human Rights Watch Report if the

recommendations put across will be implemented by

the relevant authorities.

The compliance with ethical codes of conduct has

the potential to beset obstacles to entry into high value

fruit markets. This will have negative long term

consequences on rural development, economic growth

and the agrarian reform. The export driven

horticulture has had significant contributions towards

poverty alleviation, export earnings, employment and

growth as well as gross domestic product [15].

Compliance with ethical codes of conduct can

especially promote the marginalisation of the

resource-poor farmers. The failure by the

resource-poor producers to demonstrate compliance

with base codes can further widen the inequality gaps

associated with South African agriculture which

has seen wealth distribution, access to credit, capital


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Table 1 The positive implications of the Human Rights Watch recommendations on South African fruit.

Positive implications Score

Ethical trade can also help to raise support for public policies 3.9

Increased human resource capacity 3.5

Networking to develop ethical codes of conduct 3.7

Environmental responsibility 4.8

Ethical premium 3.2

Voluntary codes can encourage ministries of labour to carry out their proper role 3.6

Savings through increased efficiency 2.3

Improved market access 3.6 Voluntary codes and monitoring systems can potentially be used to reinforce existing legislation and encourage governments to enforce the legislation

4.2

Price competitiveness in international markets 1.8

Long-term co-operative and transparent relationships 3.7

Descent wages and fair working conditions 4.3

Improves government services in farms 3.8

Respect for workers’ rights 4.7 Increased and urgent need for basic competence training for farm workers and for smallholder farmers and their extension officers

4.8

Concern for health and fairness to consumers 3.9

Better working conditions for employees 4.2

Investing in labour saving technology 2.6

Codes standardise practices and assure consumers of the highest quality, safe and hygienic produce 3.1

Producers gain credibility with the European markets 3.7

Compliance with codes becomes very critical determinant for competitiveness in the export markets 4.5

Voluntary codes and monitoring systems impact on local labour legislation and its enforcement 3.6

1 = strongly decrease; 5 = strongly increase.

resources and access to high value agriculture being

prevalent in the white farming community [4]. The

procedures associated with adherence to the codes and

their implementation by the main export markets may

serve as a barrier to the smallholder market

participation. The problem of limited information

transfer associated with smallholder farmers may

hamper the implementation of the ethical codes in the

smallholder production units. The riskiness of work

and potentially unsustainable livelihoods are some of

the likely consequences of implementing the

recommendations set by the Human Rights Watch

despite the possible positive impact for workers that

this development might bring along.

The enforcement of the labour laws that might have

remained dormant for some time may be ensured

through such interventions and through the

enforcement of ethical codes of conduct. The

economic significance of the fruit industry may

encourage the government to enforce not only the

national labour laws but also promote the compliance

with international labour standards. Standardisation of

practices and the assurance of consumers of the

highest quality, safety and hygienic produce can be

facilitated through codes of conduct. All reasonable

due diligence to protect consumers, workers and the

environment will be adhered to and the ethical codes

will become a very critical aspect to remain

competitive in the export markets. Compliance with

the standards will gain the producers credibility with

the international markets.

Negatively, codes of conduct can be used to

substitute and destabilise collective bargaining. The

producers are forced to strive for compliance and

competitive advantage in the international market.

They strive also for the improvement and maintenance


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Table 2 The negative implications of the Human Rights Watch recommendations on South African fruit.

Negative implications ScoreIncreased impact evaluation which generally stops at the farm gate and does not reach far enough down the supply chain 4.3

Trade flows 2.7

Revenues 2.7

Increased prices to consumers 3.2

Elimination of small producers 3.9

Increased pressure in competing with heavily subsidized northern hemisphere rivals 4.8

Marginalisation of the already disadvantaged producers and marketing chains 3.7

Increased independent monitoring and evaluation 3.6

Increasing costs of auditing and certification for compliance with the code of practice 3.8

Impact on rural livelihoods 3.5

The code of conduct has a potential to result in many suppliers being cut by retailers 3.2

Lack of capacity among the South African exporters to engage at a technical, strategic and governance level 2.5 Limited amount of information transfer in smallholder production units may hamper the implementation of ethical codes of conduct

2.4

Procedure imposed on exporters by their main markets may serve as a barrier to the inclusion of the smallholders 3.9 Unfair advantage given to retailers to rationalise their supply base in view of the greater efficiencies to be gained through monitoring only a small number of larger suppliers

4.2

Contracts cancelled due to boycotts 2.3

Voluntary processes used to undermine public regulation 1.7

Increased need for more coordination in order to meet the requirements 3.6 Increased complexity of monitoring standards against the Base Code in the context of the kinds of vast networks of subcontracting relationships

3.6

Relocation of multinational exporting companies to other countries if improved conditions lead to cost increases locally 2.3 Exporters assume the role of setting up strict supervisory system and assuming responsibility for rigid enforcement of standards

3.7

Codes used to replace and thereby undermine collective bargaining 4.2 Increased complexity of traceability as the retailers in the export market have to lay down specifications and details of the traded product at the point of production

3.3

Failure of membership of the ETI and acceptance of Base Code to resolve supplier insecurity 3.2 The demands for ethical trade may strengthen the economic power of multinational corporations thus squeezing out medium and small sized enterprises

3.1

1 = strongly decrease; 5 =strongly increase.

of their market share, sustained loyalty and good

reputation. Thus, the stringent market-side demands

encourage individual effort and solitary positioning in

the over-supplied global market. There also might be

shifts, changes and relocations by export-linked

companies if the improved conditions bear cost

implications locally.

The socio-economic situations of different nations

present a challenge for the consideration of smooth

operation of generic code of conduct. The

implementation of codes may be situational biased.

For instance, South Africa has undergone rapid

socio-economic and legal changes since 1994 [1].

Applying the codes of conduct uniformly across

borders may fail to take into account the inherent

socio-economic situations that may hinder the

implementation under certain circumstances. The

encouragement of retailers to purchase only from

compliant suppliers may lead to discrimination in

sourcing which in turn impact on the returns for the

affected group of producers. This is imperative

realising that the South African fruit produce is export

oriented. This will also impact on the South African

economy since the fruit industry is a high foreign

currency earner with a significant contribution

towards the GDP.

The South African agricultural industry is dynamic,

composed of a diverse array of producers ranging


121

from the resource-poor smallholders to the

well-established large-scale producers. The demands

of ethical trade may be the most easily and profitably

met by large-scale producers. Compliance and

implementation of the ethical trade strengthens the

economic power of the well-established commercial

farmers while squeezing out the resource-poor

smallholder producers. This may seriously undermine

the efforts by the government of South Africa to

ensure equity and fair distribution of wealth and land.

The smallholders as well as the medium sized

producers lack capacity to engage at a technical,

strategic and governance levels.

3.2 Strategies for the Competitiveness of the South

African Fruit Industry

Fig. 1 summarises the institutions or stakeholders

that should be actively involved in the implementation

of the ethical codes of conduct as well as ensuring the

compliance with basic codes by the South African

fruit producers. These key stakeholders include the

ETI, fruit exporters, trade unions, the South African

government and fruit producers. The responsibilities

of each are further stated in Table 3.

Fruit exporters, the ETI, trade unions, the South

African government, the international retailers and the

fruit producers need to actively collaborate in ensuring

the competitiveness of the South African fruit industry

in the export market without compromising the labour

conditions. External intervention is especially

important because the implementation of ethical codes

has cost implications which the producers can not

manage single-handedly without aid and support in

the face of the pressures of prices. Leaving the

responsibility of complying with ethical codes to the

producers will severely squeeze out their profit

margins in the face of an oversupplied international

market where prices are dictated by forces of demand

and supply. Table 3 lists some of the strategies that

these bodies can engage in to maintain or improve

compliance with ethical codes and thus improve the

competitiveness of the fruit exports.

Fig. 1 Supporting stakeholders to sustain export competitiveness (own recommendations based on results and approach used for this study).


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Table 3 Strategies towards a sustainable competitive position.

Authority Responsibility Fruit exporting companies

Provision of information Assist producers with support in implementing the codes

ETI

Capacity building Consolidation of international codes of conduct Provide incentives for those suppliers who comply with codes Establishing efficient systems of conformity and enforcement assessment

Trade unions Educating farm workers on ethical codes

The South African government

Promotion of skills development Strengthening the legislations on: basic employment conditions, employment insurance, labour relations and the right to strike action, ensuring adequate health and safety condition on farms Capacity building Financial support e.g. in the form of soft loans Provide incentives for those suppliers who comply with codes Enforce compliance with ethical codes and labour laws Enforcing the provision of descent housing to workers Ensure protection of immigrant workers

Fruit producers Engagement of independent accreditation bodies Engage in self-assessment of compliance with codes

Retailers

Support producer compliance with codes Improved communication with suppliers Explaining codes to suppliers and workers Active in monitoring of audits rather than rely on third parties

The government needs to introduce, implement and

adopt legislative measures to ensure compliance with

labour laws and access to adequate and descent

housing which are already entrenched in the South

African Bill of Rights. The government should

provide financial support for industrial level

investments that are prerequisites to effective and

demonstrable implementation of ethical trade codes.

Gaps and variations leading to greater implication

costs should be eliminated through the establishment

of efficient systems of conformity and enforcement

assessment of the ethical codes. Centralised systems

of implementation and assessment and compliance

with codes of conduct can help in the closing of gaps,

overlaps and any inefficiency associated with a

decentralised system.

While the Human Rights Watch Report rules out

in-house audits through the suggestion of engaging

third party audits, this study still recommends the

incorporation of self-assessment. Despite the lack of

the generation of “objective” results attributed to

in-house audits, it, however, lessens the problem of

social accounting to the organisational complexity

associated with the global supply chain [13]. Third

party audits attract additional costs if these audit costs

are borne by the producers. The engagement of

independent accreditation bodies, however, can

eliminate bias and ensure fairness in assessment.

4. Conclusions

By way of conclusion, the implications of lack of

compliance with the ethical codes of conduct may ruin

the industry’s export sector. There is need for urgent

action to address the perceptions of exporters and

consumers, redress the bad image that may emanate

from the report on the South African farm labour

conditions and equipping exporters for compliance.

On the other hand, strategies for a sustainable

competitive advantage, compliance with the ethical

trading codes and government intervention are

urgently needful.

Recognition is not made for any possible options

available to existing suppliers who may be facing an

initial failure to adhere to the ethical codes. Immediate

removal from the supply base seems to be the only

option available to the retailers as recommended by

the Human Rights Watch. The main challenge is that

it is easier to retain a market than to regain it


123

especially in the over-supplied and highly competitive

global market which is also characterised by supplier

integrity, consistency and loyalty. The report should

have left the decision to the retailers on how they will

implement the codes to their South African suppliers.

This study, however, does not condone

non-compliance but weighs the balance of possible

available options, especially to long-established

relationships.

The terms of trade between retailers and suppliers

are themselves not explicitly incorporated into the

code [13]. It is ambiguous to have the report

proposing trading terms on a trading platform that still

gives additional privileges to the powerful over the

weak rather than moderating the situation by

suggesting means that will enable continued

relationships, while working on possibilities of

complying with the minimum standards. The honours

should have been given to the supermarkets to

establish whether to abruptly stop purchases from

South Africa or to continue while helping the exporter

step-up their compliance levels. It is apparently clear

that the lack of compliance with the ETI Base Code

will be borne by the producers and not passed on to

consumers, and such partial recommendation that

ignores the organisational dynamics of the supply

chain relationships in turn poses some significant

organisational challenges for the South African export

producers and retailers. This is contrary to the ETI’s

unique alliance to collectively bring together NGOs

and exporters to tackle ethical issues.

While the producers will be faced with difficulties

of offsetting their production costs, the international

retailers will be reluctantly squeezing their margins.

This distorts the whole purpose of fair trade through

the promotion of unjust power relations between rich

consumer countries and poor supplier countries.

Corporate responsibility is evaded while retailers

distance themselves from their own role in creating an

environment for exploitation [4]. Retailer control and

power can dampen competition and at the same time

raise barriers to entry through such instances as

resorting to dealing with a limited number of highly

favoured and reliable suppliers, i.e., exclusive supply.

There is bound to be a one-sided determination of

terms of trade. This worsens the traditional

characteristics of the perishable products producers

who are often small, unconcentrated and

uncoordinated relative to the buyers. Their bargaining

power is thus further reduced by the price inelasticity

of their highly perishable products. Viability and

efficiency can be seriously affected, leaving

detrimental effects upon the fruit sector.

The growth in the market-side requirements and the

need for ethical code compliance may act as a means

to indirectly regulate the activity of the exporters in

the global market. Imposing retailer control as a

means to serve the interests of the producers in the

developing countries for the purposes of

implementation and auditing of the codes are contrary

to the ethical trade. In a highly competitive global

market, ethical trade proves to be another compliance

cost and disguised market entry barrier. The benefits

of fair trade are thus not well-distributed among

players when producers are not only faced with

stringent food safety standards but also the retailers

are directed on how to treat the non-complying. The

rise in costs for the producers may negatively affect

the revenues as oversupplied markets may turn out not

to favour price competitiveness. If the audit costs are

borne by the producers, then the retailers will be

enjoying over dominance in the trade. This justifies

[16] attribution of the strategies, codes and auditing

methods to agents in the developed world.

This study makes several recommendations based

on the findings. In addition to ensuring information

transfer especially to the smallholders, there is need

for coordination and setting up of a strict supervisory

system by both the producers, exporters and the

government. Nationwide compliance with the ethical

trade codes should be ensured with the exporters

assuming responsibility for the setting of standards.


124

There is need for education through education on

ethical codes especially for the smallholders. There is

need for an urgent unified industry approach to the

adoption of codes of practice to defend the industry in

the face of negative publicity. Continual

familiarisation with the Code of Practice is needful to

curb any non-compliance with some clauses that may

be subject to updates and revisions.

Contracting producers and suppliers by retailers

may help eliminate insecurity and uncertainty. This

may also be an opportunity for the inclusion of

smallholders in main supply chains. Contracting the

producers may also ensure continued production and

supply to the international market while continually

working towards adhering to the ethical codes of

conduct. This may in turn secure the employment of

the labourers in these farms which may otherwise be

lost when the producers get out of business and are

unable to meet salary and wage payouts.

Interventions should ensure other socio-economic

issues such as rural development, poverty alleviation

and the general housing conditions in the nation as a

whole. Otherwise, the situation may not yield

improved results if not holistically addressed. The

possibility of resorting to machinery may be an

available option to producers and exporters, which

may worsen the welfare of the dismissed farm workers

and thus adding on to the poverty problem.

Since this study did not exhaust all the key and

important stakeholders involved in the South African

fruit and wine industries, a recommendation is made

for a ex post evaluation of the impact of the report

which will include the opinions of all stakeholders.

The ex-post study is worthwhile for establishing the

impact of the report on real product flows across

borders and the changes in market shares in certain

export markets of choice after several seasons of fruits

exports post the publishing of the report. Thus this

study has set a stage with regards to the most

important issues to evaluate when carrying out an

ex-post study of the implications of the Human Rights

Watch Report.

References

[1] S. Barrientos, S. McCleneghan, L. Orton, Ethical trade

and South African deciduous fruit exportAddressing

gender sensitivity, The European Journal of Development Research 12 (1) (2000) 140-158.

[2] L.F. Golodner, Understanding the difference: Ethical trade, fair trade, sustainable consumption, social responsibility, National Consumers League, ANSI, USA COPOLCO Workshop, May 23, 2007, Salvador de Bahia, Brazil, 2007, pp. 13-23.

[3] S. Barrientos, S. Smith, Do workers benefit from ethical trade? Assessing codes of labour practice in global production systems, Third World Quarterly 28 (4) (2007) 713-729.

[4] A. Du Toit, Ethical tradingA force for improvement, or corporate whitewash? Natural Resources perspectives Number 71, Oct. 2001, ODI Programmes for Land and Agrarian Studies (PLAAS), 2001, pp. 1-4.

[5] Ethical issues in food and agriculture [Online], Food and Agriculture Organization of the United Nations Rome, 2001, http://www.fao.org/DOCREP/003/X9601E/X9601E00.HTM.

[6] Ethical Trading Initiative, Key challenges in ethical trade. Report on the ETI Biennial Conference 2003, Ethical Trading Initiative, Cromwell House, London UK, 2003, p. 10.

[7] A. Rushton, Child labour is the global recession squeezing childhood [Online], Aug. 8, 2011, http://www.ethicaltrade.org/news-and-events/blog/abi-rushton/chld-labour-is-the-global-recession-squeezing-childhood%3F.

[8] Citrus Growers Association [Online], 2008, www.cga.co.za/site/awdep.asp?depnum=4289.

[9] CGA 2009 Annual Report, Citrus Growers Association of Southern Africa [Online], 2009, www.cga.co.za.

[10] The South African Human Right Commission (SAHRC), Final Report on the Inquiry into Human Rights Violations in Farming Communities, SAHRC, 2003, pp. 1-56.

[11] The South African Human Rights Commission (SAHRC), Progress made in terms of Land Tenure Security, Safety and Labour relations in the Farming Communities since 2003, SAHRC, 2008, pp. 14-46.

[12] Human Rights Watch, Ripe with abuse: Human rights conditions in South Africa’s Fruit Wine Industries, Human Rights Watch, New York, USA, 2011, pp. 1-103.

[13] A. Hughes, Multi-stakeholder approaches to ethical trade: Towards a reorganisation of the UK retailers’ global supply? Journal of Economic Geography 1 (2001)


125

421-437. [14] P. Ndou, A. Obi, The business environment and

international competitiveness of the South African citrus industry, Paper presented at the International Food and Agribusiness Association (IFAMA)’s 21st World Annual Forum and Symposium, June 20-23, 2011, Frankfurt, Germany, 2011, p. 15.

[15] K. Ellies, J. Keane, A review of ethical standards and labels: Is there a gap in the market for a new ‘Good for Development’ label, Overseas Development Institute (ODI), 11 Westiminster Bridge Road, London, UK. Working paper 297 (2008) 1-35.

[16] M. Blowfiled, Ethical trade: A review of developments and issues, Third World Quarterly 20 (1999) 753-770.


Physiological and Phytosanitary Potentials of Coriander

and Radish Seeds

Jucilayne Fernandes Vieira, Francisco Amaral Villela, Orlando Antonio Lucca Filho and Raifer Simões Campelo

Department of Plant Science-Sector of Science and Seed Technology, Federal University of Pelotas (UFPel), Pelotas, RS 96001-970,

Brazil

Received: November 15, 2012 / Published: February 20, 2013. Abstract: Information on seed vigor of the vegetable crops is always important and necessary due to the increasing demand of high quality seeds for sowing and high-value commercial. The cultivation of these species, conducted intensively, should be established with seed high potential physiological and health for the development of a more productive and sustainable agriculture. The present study was conducted to evaluate the potential physiological of seed lots of radish and coriander. The experiment was conducted at the laboratory of seed analysis and greenhouse of the plant science department of the Federal University of Pelotas in South Brazil. Four radish seed lots, cultivar “Saxa”, and four coriander seed lots, cultivar “Verdão” were used. Germination seed test, first count of germination, accelerated aging test, electrical conductivity and seedling emergence were used to evaluate the physiological quality of the lots. The experimental design was completely randomized design with five replications. Means were compared by Tukey test. The accelerated aging test was the most efficient test in assessing the physiological quality for both lots of radish seeds and coriander and this test provide coherent results with seedlings emergence. Key words: Radish (Raphanus sativus L.), coriander (Coriandrum sativum L.), vegetable crops, seed quality, seed vigor.

1. Introduction Vegetable seeds have a high commercial value, thus

a lot of information about seed vigor are always

important. The cultivation of these species, conducted

intensively, should be established with seed of high

physiological potential and seed health [1].

Researches talking about vegetable seeds have shown

significant development in Brazil since the beginning

of the 1990s, but papers are still less frequent than

those conducted with species of other crops. It is true

that many vegetable crops are grown, but each one has

its own peculiarity, and the volume of available

knowledge is not enough to the importance of this

subject [2]. There are increasing demands on high

quality seeds for the establishment of more productive

and sustainable agriculture, and it is growing also the

Corresponding author: Jucilayne Fernandes Vieira, Ph.D.,

research fields: seed science and technology. E-mail: [email protected].

importance of monitoring each stage of the seed

production process of the seed industry [3].

The radish crop (Raphanus sativus L.) is an annual

vegetable crop grown mainly on small farming and in

areas of green belts of the country [4]. It has shorter

cycle among vegetables; and because of this, radish

becomes an excellent choice for the small producer [5].

It has considerable economic importance, but it is still

produced and consumed in volumes less significant in

the country, though, the seeds have great commercial

value, which reinforces the importance of assessing

seed physiological quality. Coriander (Coriandrum

sativum L.) is a crop consumed in various regions of

Brazil, especially in the North and Northeast.

Vegetable widely consumed in Brazil as a condiment

featuring great socio-economic importance. However,

problems related to low seed vigor and crop

establishment are reported in this specie [6].

There are only few studies about the use of vigor

test to estimate the seeds physiological potential of

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Physiological and Phytosanitary Potentials of Coriander and Radish Seeds

127

these species [2, 6, 7]. In this context, the objective of

this study was to evaluate the seed physiological

quality of lots of radish and coriander.


2.1 Materials

The work was conducted at the Seed Analysis

Laboratory and in the greenhouse of the Federal

University of Pelotas. Seeds from four lots of radish

seeds cultivar “Saxa” and from four lots of coriander

seeds cultivar “Verdão” were subjected to following

tests and assessments described below.

2.2 Determination of Moisture Content

The experiment was conducted in an oven with

forced air circulation at 105 ± 3 °C for 24 h, in

accordance to the Rules for Seed Analysis RAS [8],

using two samples of 4 g seed for each lot. Results were

expressed as mean percentage weight per lot. The

moisture content of the seed was assessed before and

after the accelerated aging test.

2.3 Germination Test

The experiment was conducted at 20 °C with 200

seeds per seed lot (four replicates of 50 seeds). The seeds

were distributed on two sheets of paper, previously

moistened with distilled water in an amount equivalent

to 2.5 times the mass of paper. Evaluations were

conducted for radish at 4 and 10 days and for coriander

at 7th day and 21th day after sowing and the results

expressed in percentage of normal seedlings [8].

2.4 First Count of Germination

The experiment was made in conjunction with the

germination test, by computing the percentage of

normal seedlings on the fourth day after sowing for

radish and seventh day for coriander [8].

2.5 Electrical Conductivity

The experiment was assessed with four replications

of 50 seeds per seed lot. The seeds were soaked in 200

mL plastic cups containing 50 mL of deionized water.

The readings were taken at 2 h, 4 h, 6 h, 8 h and 24 h.

The conductivity value provided by the device was

expressed in mS cm-1 g-1.

2.6 Accelerated Aging with NaCl (Sodium chloride)

Solution

A total of 200 seeds (four replicates of 50 seeds) per

seed lot were distributed over an aluminum screen

placed inside a plastic box (gerbox) containing 40 mL

of NaCl solution, maintained at 41 °C for 48 h. This

solution was prepared by adding 40 g of NaCl in 100

mL of water, establishing an environment with relative

humidity of 76%, according to the procedure proposed

by ref. [9]. After this period, the seeds were put to

germinate following the methodology used in the

germination test described above. The percentage of

normal seedlings was assessed on the fourth (radish)

and seventh (coriander) days after sowing.

2.7 Seedling Emergence in Substrate

The sowing of each lot was manual, in beds, in a

greenhouse, with four replicates of 100 seeds in rows

2.0 m long with spacing of 0.20 m at a depth of 0.01

m to coriander seeds. For the radish seeds were sown

four replications of 50 seeds in furrows spaced at 0.20

m and 0.01 m depth. Evaluations were performed at

12 days after sowing for radish seeds and 30 days for

coriander seeds, computing normal seedlings emerged

and the results expressed as a percentage.

2.8 Statistics

The data expressed in percentages were subjected to

normality tests that indicated they did not need any

transformation. The experimental design was

completely randomized layout with five repetitions. In

the statistical procedure, analysis of variance was

performed separately for each test and the means of the

lots were compared by Tukey test at 5% probability.

The conductivity test was analyzed in a factorial of 4

lots 5 seed imbibition periods.


128


The water content of the seeds was similar between

lots, which allow obtaining more consistent results.

The germination rate of all radish seed lots used was

above 95%, which is above the minimum standard

required for commercialization of most seed vegetable

crops (80%). These germination values indicate high

physiological seed quality and seedling emergence in

the greenhouse ranged from 80% to 88%, depending

on the lot (Table 1). Radish seed lots did not differ on

the percentage of germination (Table 1), showing low

sensitivity of germination tests performed at the

laboratory, to identify differences in performance of

the seeds, often highlighted in the literature, justifying

the use of vigor testing (Table 1). Using the laboratory

germination test was also not possible to differentiate

seed lots of lentil [10] and wheat [11], because the

germination test is conducted under favorable

conditions, maximizing the potential of physiological

seed lots.

However, the first count of germination test

classified the seed lots into two different vigor levels

(low and high). The data obtained by the author [12] in

the first count of germination test in tomato seeds

permitted the separation of the lots in two vigor levels,

showing similar results as they were observed in this

work (Table 1).

The accelerated aging test with saturated solution of

NaCl detected vigor differences between radish seeds

lots, and a similar response was also observed in

seedling emergence (Table 1). These results showed

consistency with those obtained in the seedling

emergence. The seed water content after aging period

showed maximum change of 0.4% points among seed

lots, not interfering, a priori in the results.

Among the available tests, accelerated aging is one

of the most studied and recommended for various crops.

Initially developed for the purpose of estimating the

longevity of stored seeds, it has been extensively

studied with a view to its standardization [13] and its

ability to provide information with high consistency

Table 1 Initial moisture content of the lots (IMC), moisture content after accelerated aging (MCA), percentages for germination test (G), first count of germination (FCG), accelerated aging with NaCl-76% (AA), emergence of seedlings in substrate (ESS) of the different lots of radish seeds.

Lots IMC(%) MCA(%) G(%) FCG(%) AA(%) ESS(%)

1 8.8 10.7 95 a 88 b 83 ab 84 ab

2 8.9 10.9 95 a 87 b 86 a 88 a

3 8.6 10.5 97 a 93 a 81 b 80 b

4 8.3 10.7 96 a 94 a 83 ab 86 ab

CV (%) - - 3.22 5.25 4.93 5.31

Means followed by the same letter in a column do not differ in level of 5% probability by the Tukey test.

degree [1]. This method was also efficient for

evaluating seed vigor of different vegetable seeds such

as carrot [13], cucumber [14], tomato [15], melon [16]

and rocket [17]. For the electrical conductivity (Table

2), it was found that there was no significant

interaction between the periods of soaking and lots of

radish seeds, although this test detected differences in

vigor levels into four lots, with superiority for lots 1

and 2 and inferiority for lot 3. With increasing

imbibition time was greater leaching of exudates from

seeds.

However, for black oat seeds, the electrical

conductivity test at the different conditions detected

difference based on seed vigor levels. Furthermore,

there were significant effects only on main factors:

lots of seeds, number of seeds and imbibing periods

[17].

Among vegetable seeds, coriander has a national

standard germination of (60%) and it is considered

low for seed commercialization. Lots tested in this

present work, seed lot germination exceeding the

minimum value and there was a variation between lots

from 71% to 81% (Table 3). In the germination test

was observed that lot 2 showed lower seed

germination than other seed Lots (Table 3). This result

as also reported [18], they found that the germination

test was not sensitive enough to distinguish

differences physiological quality of onion seed lots,

being only able to show the inferiority of one of the

tested lots.


129

Table 2 Electrical conductivity test with soaking 50 seeds in 50 mL of distilled water for periods of 2, 4, 6, 8 and 24 h from four radish seed lots.

Lots Periods of imbibing (h)

2 4 6 8 24 Média

1 32.90 42.30 46.80 51.40 62.40 47.44 B

2 32.00 41.00 43.70 47.60 58.70 44.60 A

3 36.80 46.10 51.60 57.40 65.40 51.46 C

4 34.50 43.60 47.20 52.10 64.80 48.44 B

Média 34.05 a 43.23 b 47.32 c 52.12 d 62.82 e

CV (%) 9.00

Means followed by same letter in lower case in the line and capital letters on the column do not differ by Tukey test at 5% probability.

Table 3 Initial moisture content of the lots (IMC), moisture content after accelerated aging (MCA), percentages for germination test (G), first count of germination (FCG), accelerated aging with NaCl-76% (AA), emergence of seedlings in substrate (ESS) of the different lots of coriander seeds.

Lots IMC(%) MCA(%) G(%) FCG(%) AA(%) ESS(%)

1 8.8 10.7 81 a 72 a 35 a 50 b

2 9.1 11.9 71 b 63 bc 15 c 35 c

3 8.8 10.7 82 a 59 c 26 b 53 b

4 8.9 11.7 77 a 66 b 35 a 61 a

CV(%) - - 7.74 8.26 10.0 10.3

Means followed by the same letter in a column do not differ in level of 5% probability by the Tukey test.

Table 4 Electrical conductivity test with soaking 50 seeds in 50 ml of distilled water for periods of 2, 4, 6, 8 and 24 h from four coriander seed lots.

Lots Periods of imbibing (h)

2 4 6 8 24 Average

1 330 362 388 398 468 389 B

2 476 512 521 535 615 532 C

3 317 342 358 365 432 363 A

4 556 600 625 635 724 628 D

Average 420 a 454 b 473 c 483 c 560 d 478

CV(%) 6.04

The first count germination test separated the seed

lots in different vigor classes: high vigor (lot 1), low

vigor (lot 3), and intermediate vigor (lots 2 and 4).

Results similar to those obtained in the present study

were reported [19], working with corn, found that test

first countal lowed to classify lots into three vigor

levels (high, low and intermediate).

The accelerated aging test, performed for 48 h at

41 °C was excessively drastic because of reducing seed

germination by more than 50% points in relation to

non-aging seeds; this situation can compromise the test

result. However, this test found differences between

seed lots and allowed to separate the three seed lots into

vigor levels (Table 3). Similar results were obtained

[20], with cotton seed, because in less severe aging

conditions, 41 °C for 48 h, most of the seed lots had

germination decreased by about 50% points compared

to initial values. Despite drastic effects, aging at 41 °C

for 48 hallowed to classify lots into four and three vigor

levels.

For all lots, in general, seedling emergence was low,

ranging from 35% to 61%. The physiological seed

quality is routinely assessed by laboratory germination

test; however, it is believed that this test conducted

under appropriate conditions can super estimate the

seed vigor [21]. In this case, if the ambient conditions

after sowing are not optimal, the percentage of

seedling emergence may be less than determined in

laboratory [22]; and this occurrence was found in this

work. Seedling emergence separated the seed lots into

three vigor levels, highlighting lot 4 as greater vigor,

lot 2 as a lower physiological potential and the other

as intermediaries. The worst performance presented

by the seeds of lot 2 in the test seedling emergence

was also observed in germination and accelerated

aging.

In the electrical conductivity test there was a

significant effect on the interaction seed and imbibing

periods (Table 4). However, it was possible to stratify

the lots based on the vigor. Lot 3 showed higher vigor

and lower lot 4, contradicting the vigor levels

separation obtained in accelerated aging tests and

seedling emergence.

The first count test, accelerated aging and seedling

emergence showed efficient differentiation of

physiological quality of seed lots of coriander,

however, the tests do not separated lots exactly the

same way. The accelerated aging test despite being


130

very dramatic was allowed to rank the lots a manner

similar to the results observed in seedling emergence.

4. Conclusions

The accelerated aging test using saturated saline

solution showed efficiency in assessing the

physiological quality of both lots of radish seeds and

coriander. This test at 41 °C for 48 h is recommended

to be used for both species.

Acknowledgments

The authors would like to thank the supporting

agency, CAPES (Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior), for granting a scholarship

to the first author and CNPq (ConselhoNacional de

Desenvolvimento Científico e Tecnológico) for

financial support that was indispensable for the

accomplishment of this work.

References

[1] J. Marcos Filho, Vigor tests: Importance and utilization, in: F.C. Kryzanowski, R.D. Vieira, J.B. França Neto (Eds.), Seed Vigor: concepts and tests, Londrina, Paraná ABRATES, 1999, pp. 1-21.

[2] J. Marcos Filho, A.L.P. Kikuti, Radish seed vigor and plant field performance, Rev. Bras. de Sementes 28 (2006) 44-51.

[3] E.U. Alves, A.P. Oliveira, R.L.A. Bruno, R. Sader, A.U. Alves, Yield and physiological quality of coriander seeds cultivated with manure and mineral fertilizer, Rev. Bras. Sementes 27 (2005) 132-137.

[4] A.I.I. Cardoso, H. Hiraki, Evaluation of calcium nitrate level and timing of top-dressingin radish, Hort. Bras. 19 (2001) 328-331.

[5] P.C.F. Linares, M.L. Silva, M.F.S. Pereira, A.K.H. Becerra, A.C.C. Pavia, Amounts and times of decomposition of rooster tree on agronomic performance of radish, Rev. Verde 6 (2011) 168-173.

[6] R.S. Pereira, M.F.B. Muniz, W.M. Nascimento, Aspects related to coriander seed quality, Hort. Bras. 23 (2005) 703-706.

[7] N.L. Menezes, C.M.R. Santos, E.P. Nunes, Germination and vigor of radish seeds influenced by osmotic potential

induced by sodium chloride and pH, Informativo Abrates 2 (2001) 189.

[8] Ministry of Agriculture, Livestock and Supply ‘MAPA’, Rulesfor seed testing, Agriculture Defense Department, MAPA Brasília, DF, Brazil, 2009, p. 395.

[9] Z. Jianhua, M.B. Mcdonald, The saturated salt accelerated aging test for small-seeded crops, Seed. Sci. and Tech. 25 (1997) 123-131.

[10] R.A. Freitas, W.M. Nascimento, Accelerated aging test on lentil seeds, Rev. Bras. de Sementes 28 (2006) 59-63.

[11] L.M. Mertz, S.R. Segalin, C. Huth, T.D. Rosa, Electrical conductivity test in evaluating the physiological potential of wheat seeds, Infor. Abrates 22 (2012) 35-39.

[12] S.B. Torres, A.R. Peixoto, I.M.S. Carvalho, Sanitary and physiological quality of tomato seeds from the “submedio São Francisco” Region, Ciência e Agrotec 23 (1999) 825-829.

[13] A.B. Rodo, M. Panobianco, J. Marcos Filho, Alternative methodology for the accelerated aging test for carrot seeds, Scientia. Agric. 57 (2000) 289-292.

[14] M.C. Bhering, D.C.F.S. Dias, J.M. Gomes, D.I. Barros, Vigor evaluation methods of cucumber seeds, Rev. Bras. de Sementes 22 (2000) 171-175.

[15] M. Panobianco, J. Marcos Filho, Accelerated aging and controlled deterioration of tomato seeds, Scientia. Agric. 58 (2001) 525-531.

[16] S.B. Torres, J. Marcos Filho, Accelerated aging of melon seeds, Scie. Agric. 70 (2003) 77-82.

[17] N.P. Ramos, E.P.O. Flor, E.A.F. Mendonça, K. Minami, Accelerated aging of Eruca sativa L. seeds, Rev. Bras. de Sementes 26 (2004) 98-103.

[18] N.L. Menezes, D.C. Garcia, C.A. Bahry, N.M. Mattioni, Electrical conductivity test in black oat seed, Rev. Bras. de Sementes 29 (2007) 138-142.

[19] Z. Piana, M.A.A. Tillmann, D. Minami, Physiological quality evaluation of onion seeds (Allium cepa L.) and its relationship with vigorous seedlings production, Rev. Bras. de Sementes 12 (1995) 149-153.

[20] M.V.C. Miguel, J. Marcos Filho, Potassium leakage and maize seed physiological potential, Scie. Agrícola 59 (2002) 315-319.

[21] E.A.F. Mendonça, S.C. Azevedo, S.C. Guimarães, M.C.F. Albuquerque, Vigor tests in upland cotton seeds, Rev. Bras. de Sementes 30 (2008) 1-9.

[22] M.F. Caliari, J. Marcos Filho, The physiological quality of pea (Pisumsativum L.) seed lots as evaluated by different procedures, Rev. Bras. de Sementes 12 (1990) 52-75.


Response of Amaranth to Irrigation and Organic Matter

Jimmy Akinfemi Osunbitan

Department of Agricultural & Environmental Engineering, Obafemi Awolowo University, Ile-Ife, Osun State 220005, Nigeria

Received: September 25, 2012 / Published: February 20, 2013. Abstract: The study investigated the response of Amaranth to irrigation depth and irrigation interval as well as poultry waste incorporation. A replicated 4 × 3 × 3 factorial arrangement with treatments consisting of percent organic matter incorporation (0%, 2%, 4% and 6% by weight), irrigation interval (1, 3 and 5 days) and irrigation depth (4, 6 and 8 mm) was used for the experiment which was conducted in pots in a green house. The result of the experiment showed that percent organic matter incorporation, irrigation interval, and irrigation depth significantly (P < 0.05) affected the dry matter yield of the vegetable. However, only the percent organic matter incorporation and irrigation interval were significant (P < 0.05) in their effects on the plant height. The highest plant height of 12.03 cm occurred when 2% organic matter was incorporated into the soil while the lowest plant height of 9.04 cm occurred with 6% organic matter incorporation. The maximum plant yield (47.44 kg/ha) occurred when 2% poultry litter was incorporated into the soil while the lowest plant yield (31.57 kg/ha) occurred with the control experiment. Irrigation interval of three days as well as irrigation depth of 6 mm resulted in the highest plant height and maximum dry matter yield of the vegetable. From the result of the experiment, the factor combination of 2% organic matter incorporation with 6 mm irrigation depth applied at three days irrigation interval resulted in the highest plant height of 16.7 cm above ground surface and maximum yield of 67.8 kg/ha. Key words: Amaranth, irrigation depth, irrigation interval, organic matter, dry matter yield.

1. Introduction The rainfall distribution in the SouthWestern

Nigeria is bimodal with peaks in June and September.

The dry season extends from November to March.

During the rainy season, the rainfall is high and

relatively adequate for the production of most crops.

However, towards the end of the rainy season, the

rainfall becomes erratic and reduces to about 500 mm.

In the dry season, there is always an increasing

competition for water by agriculture and domestic

uses and irrigation is the only option for providing

water to crops. Soils with low water holding capacities,

combined with high evaporation rates, result in low

water use efficiency [1]. Water use efficiency is useful

because of its role in sustainable development.

Improving the efficiency of resource use is one of the

means of meeting sustainable development goals.

Corresponding author: Jimmy Akinfemi Osunbitan, Ph.D., research fields: soil and water conservation, soil and water management for improved soil conditions and increased crop yield, irrigation, organic farming. E-mail: [email protected], [email protected].

Improved water use efficiency will result in lowering

the water needs to achieve a unit of crop production.

The problem of low water use efficiency may be

minimized by the use of drought-resistant crop variety

mulching and proper irrigation scheduling [2, 3].

Amaranth species are a group of highly popular

vegetables, belonging to many different species. They

are the most commonly grown leafy vegetables of the

lowland tropics in Asia and Africa. Amaranths as a

plant are crop with ancient history and have been

grown for centuries for vegetables in different part of

the world [4]. It is consumed as vegetable in Africa,

Caribbean, China, Greece and Southern Pacific islands

[5]. The species were most commonly utilized as

vegetables have short plant with leaves and small

inflorescence [6]. The leaves are comparable to

spinach (Spinacia oleracea L.) in taste [7]. However,

unlike spinach, an ideal season for producing

Amaranth crop in the temperate climate is during the

hot month of the year. It is highly adapted under

lowland condition, grows well at day temperature

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above 25 °C and night temperature not lower than

15 °C. They are quantitative short day plants and

consumes high amount of water and prefers fertile,

well drained soil with a loose structure.

Organic matter content (OMC) incorporation into

the soil has been found to improve soil structure,

increase water holding capacity, soil porosity and low

bulk density which in itself indicates that the soil is

well aerated, easy to till and facilitates root growth.

Osunbitan et al. [8] reported that the moisture

retention of three SouthWestern Nigerian soils

increased with increase in livestock manure

incorporation into the soil. Adekalu and Osunbitan [9]

also observed that incorporation of goat dung into

soils reduced the dry density and increased the

hydraulic conductivity at different moisture contents.

It was observed by Millar and Turk [10] that

incorporation of organic matter into the soil generally

increases the water retaining power of the soil, and

decreases the water runoff. Osunbitan and Adekalu

[11] reported that the total depth of water for the

growing period of Jute Mallow decreased with

increase in the poultry waste incorporation into the

soil. According to Brady [12], the incorporation of

organic matter (OM) into the soil is one of the most

effective soil management measures aimed at

improving soil fertility and structure, hence, organic

matter curbs soil erosion. Likewise, organic matter has

been found to enhance the stability of soil aggregate

[12] and its effect is being generated in soil having

low clay content [13] as well as unstable soils [14]. As

such, most well known soil scientists agreed that

organic matter plays a very important role in tropical

soils. This is even true of production systems using

high external inputs.

Also, the availability of water is a very essential

condition for growth in plants to derive their nutrients

uptake in soluble form from the soil. In other words,

the amount of water available to plant through the soil

provides the nutrients required for germination,

growth and yield of plants. Thus, the amount of water

(transpiration, evaporation from the soil, drainage loss)

required to produce a unit amount of dry weight

material (e.g., 1 kg corn) is a measure of efficiency

[15]. And also, how efficiently water can be used by

plant is a function of the transpiration ratio.

Furthermore, the level of irrigation depth is also an

essential factor that should be considered in crop

production. Irrigation is the artificial application of

water to the soil usually for assisting in growing crops.

In crop production, it is mainly used in dry areas and

in periods of rainfall shortfalls, but also protects plants

against frost [16]. It also helps to suppress weed

growth on fields. Irrigation is very essential for plant

survival before permanent wilting point is reached and

also, it cools the soil and atmosphere, making the

environment favorable for plant growth. Small scale

irrigation system is gaining grounds in the country

because of the cost of owning and management of the

system. It was observed by Adekalu et al. [2] that

majority of vegetable farmers in the country depend

on hand watering during the dry season when crop

value is high to produce vegetable crops because of

the high cost of fuel. There is scarcity of information

on optimum irrigation scheduling or the effects of

poultry litters on the yield of Amaranth vegetable. The

objective of this study was therefore to investigate the

response of Amaranth to irrigation scheduling

(irrigation depth and interval) and organic matter.


2.1 Study Area and Soil Sampling

Pot experiments were conducted in a green house of

the Faculty of Agriculture, Obafemi Awolowo

University, Ile-Ife, Nigeria, on sandy loam soil

classified as Alfisol. Total annual rainfall of the study

area was about 1,350 mm. While the average daily

minimum temperature ranged between 20 °C and

22 °C and the average maximum temperature between

27 °C and 35 °C. The soil is classified at series level

as two series [17]. It was derived from granite and

gneiss parent material. The texture of the plough layer


133

(0-15 cm) is sandy loam having 80% sand, 5% silt and

15% clay with organic matter content of 1.56%. The

soil samples were collected from the top 15 cm layer,

packed inside sacks and transferred to the laboratory

for the determination of its physical and chemical

compositions. Plant residues and gravels in the soil

sample were removed by passing the soil through a 2

mm sieve so as to obtain a more homogeneous soil

sample.

2.2 Experimental Procedure

The experimentation was a 4 3 3 factorial

arrangement with three replicates. The factors are

organic matter incorporation, irrigation interval and

irrigation depth. The organic matter treatment

consisted of four levels of poultry litters applied at the

rate of 0, 16, 32 and 48 t/ha, respectively, in form of

0%, 2%, 4% and 6% by weight of poultry litter

incorporation into the soil. The three levels of

irrigation interval used in the study were 1, 3 and 5

days while the three levels of irrigation depth were 4,

6 and 8 mm of water. The mixture of soil and poultry

litter at different levels of poultry litter incorporation

was compacted to bulk density of 1.5 g/cm3 inside

perforated plastic pots with surface area of 314.2 cm2

and depth of 45 cm. The soil/poultry litters mixture

was then left for 14 days to allow for some

decomposition of the manure during which water was

sprinkled into the pots. Ten seeds of Amaranth plant

were planted on September 29, 2009 inside each of the

pots. Two weeks after planting, on October 12

thinning of germinated plants was done to five stands

per plot after which irrigation water at the above

mentioned depths and intervals were applied using

small watering cans as is done by the majority of

small-scale farmers in the country.

Before every application of irrigation water,

moisture contents of soils in the pots were determined

using calibrated soil moisture meter about 1 h prior to

irrigation and drainage water through the perforation

of the pots was collected by small plastic bowls placed

under the soil pots after each irrigation for the

determination of the evapotranspiration by the crop.

Evapotranspiration (E) was calculated by the water

balance method:

E = I + R + SWD + D + RO

where, I is irrigation (mm), R is rainfall (mm), SWD is

soil water depletion from the beginning to the end of

the season, D is drainage below the root zone (mm)

and RO is run-off. Rainfall and runoff were taken to

be zero since the experiment was conducted in pots

inside green house. Plant height and leaf area were

measured once a week from the 5th week after

planting. The leaves of the vegetable are sometimes

plucked from five weeks after planting for

consumption and marketing by some farmers while

some wait till eight weeks after planting before

harvesting. Plant height was measured from the

ground level to the top most leaf while the leaf area

was measured by tracing the leaf on a while paper and

measuring the traced area using a planimeter. The

crop was finally harvested on November 16, 2009.

After the final harvest, the plants were cut at the

ground level and placed in an oven for 70 °C for 24 h

to determine dry matter [18]. Analysis of variance [19]

was performed on the data and factor analyses were

run to determine the effects of treatments and their

interactions. Treatment means and significant

differences were evaluated using Duncan Multiple

Range (DMR) test.


Organic matter, irrigation interval, and irrigation

depth affected dry matter yield, depth of drainage and

total water used by the plant (Table 1). Only the

organic matter and irrigation interval were significant

for plant height. Leaf area was not significantly

affected by the three factors considered in this study.

3.1 Total Water Used

Depth of water used by Amaranths at different

irrigation depths in response to irrigation interval for


134

Table 1 Analysis of variance for growth and yield of Amaranth grown under different organic waste incorporation.

Source of variation DF F value

Leaf area (cm2) Plant height (cm) Dry matter (Kg/ha) Total water use (cm) Drainage (cm)

Organic matter (OM) Irrigation interval (II) Depth (ID) OM II OM ID II ID OM II ID

3 2 2 6 6 4

12

0.70 NS 2.15 NS 0.14 NS 0.06 NS 0.02 NS 0.21 NS 0.07 NS

5.45** 13.96** 0.23 NS 0.49 NS 0.25 NS 0.19 NS 0.24 NS

> 5,000** 155** 4,077** 114 ** 990**

7.23**

21.91**

26.48**

759.84**

38.96** 11.55** 1.47NS

12.35** 12.22**

6.74**

> 5,000**

3,696** 2.33NS 1.47NS

958** 1.28NS

NS, ** represent not significant and significant at P < 0.05 respectively.

(a)

(b)

(c)

Fig. 1 Total depth of water used at different irrigation depths, (a) 4 mm, (b) 6 mm, (c) 8 mm irrigation depths and irrigation interval in response to organic matter.


135

the four levels of organic matter incorporation is

shown in Fig. 1. Total water used by the crop varied

significantly (P < 0.05) with the three factors

considered in the study. Total depth of water used by

the crop increased with decrease in the irrigation

interval. On the average, the lowest depth of 29.02

mm of water was used by the vegetable when the

irrigation interval was five days while the highest

depth of 44.06 mm of water was used by the plant

when irrigation water was applied daily. According to

Duncan Multiple Range (DMR) test, the three levels

of irrigation interval were significantly different in

their effects on the total water used by the plant.

However, the total depth of water used by the plant

did not vary linearly with the remaining two factors

considered in this study. On the average, the highest

water used by the vegetable occurred when the

irrigation depth was 8 mm while the lowest average

water used occurred when the irrigation depth was 6

mm. The three levels of irrigation depth were also

significantly different (DMR test) in their effects on

the total water used by the plant. Moreover, when

organic matter was considered as a factor, the highest

water used by the plant (37.11 mm) was recorded

when 4% of poultry litter was incorporated into the

soil while the lowest water used by the plant (33.59

mm) was recorded when the percent incorporation of

organic matter was 6%. The control experiment (0%

organic matter incorporation) and 4% organic matter

incorporation were not significantly different in their

effects on total water used. However, these two levels

of poultry litter incorporation were significantly

different from the remaining two levels of organic

matter incorporation in their effects on total water

used.

3.2 Plant Height

The effect of the organic matter incorporation into

the soil, depth of irrigation and irrigation interval on

the plant height is shown in Fig. 2. Plant height varied

significantly (P < 0.05) with organic matter and

irrigation interval. The highest plant height of 16.7 cm

was recorded when 2% organic matter was

incorporated and 6 mm of water applied every three

days. This is similar to what was observed by Adekalu

et al. [2] that growth and yield were higher when

irrigation was scheduled at 30% soil water depletion.

On the average, the lowest plant height of 9.19 cm

was observed when the irrigation interval was five

days while the highest plant height of 12.30 cm was

recorded when the irrigation interval was three days.

According to Duncan Multiple Range (DMR) test, the

irrigation intervals of one day and five days were not

significantly different in their effects on the plant

height while three days irrigation interval was

significantly different from the remaining two levels

of irrigation intervals. Moreover, 6 mm irrigation

depth resulted in the highest (10.51 cm) plant height

on the average while 8 mm irrigation depth gave the

lowest (10.06 cm) plant height. However, the three

levels of irrigation depth considered in this study were

not significantly (DMR test) different in their effects

on the plant height of the vegetable. Organic matter as

a factor significantly affected the plant height. The

highest plant height (12.03 cm) occurred when 2%

organic matter was incorporated into the soil while the

lowest plant height (9.04 cm) occurred with 6%

organic matter incorporation. Manure exerts favorable

effects upon granulation and aeration in the soil for

good moisture status to ensure free flow of nutrients

and improves plant growth [9]. According to DMR

test, the 2% organic matter incorporation was

significantly different in its effect on the plant height

from the remaining three levels of organic matter

levels. The remaining three levels were not

significantly different in their effects on the plant

height.

3.3 Dry Mater Yield

The effect of the organic matter incorporation into

the soil, depth of irrigation and irrigation interval on

the plant yield is shown in Fig. 3. Plant yield varied


136

(a)

(b)

(c)

Fig. 2 Plant height as affected by the different irrigation depths, (a) 4 mm, (b) 6 mm, (c) 8 mm irrigation depths and irrigation interval in response to organic matter incorporation.

significantly (P < 0.05) with organic matter, depth of

irrigation and irrigation interval. On the overall, when

all the factors were considered together, the maximum

dry matter yield of 67.8 kg/ha occurred with 2%

organic matter incorporation when 6 mm irrigation

depth was applied every three days while the

minimum dry matter yield of 30.0 kg/ha was observed

when no poultry litter was added with 4 mm of

irrigation water applied daily. However, on the

average, the minimum plant yield of 36.88 kg/ha was

observed when the plant was irrigated everyday while

the maximum yield of 38.89 kg/ha was recorded when


137

(a)

(b)

(c)

Fig. 3 Plant yield as affected by the different irrigation depths, (a) 4 mm, (b) 6 mm, (c) 8 mm irrigation depths and irrigation interval in response to organic matter incorporation.

the irrigation interval was three days. Efficient

irrigation interval decreased deep percolation and

evaporation [20] in other crops. According to Duncan

Multiple Range (DMR) test, all the irrigation intervals

were significantly different in their effects on the dry

matter yield. Likewise, the three levels of irrigation

depths considered in this study were significantly

different in their effects on the dry matter yield of the

plant. On the average, irrigation depth of 6 mm

resulted in the maximum (43.78 kg/ha) dry matter


138

Table 2 Relationship between percent organic matter content (OM) and dry matter yield (DMY).

Irrigation depth (mm) Irrigation interval (day) Equation*

4 1 3 5

DMY = 0.43 OM3 – 5.55 OM2 +19.11 OM + 16.3 DMY = 2.55 OM3 – 21.15 OM2 +52.6 OM – 1.4 DMY = 1.65 OM3 – 14.65 OM2 +39.4 OM + 4.3

6

1 3 5

DMY = 7.65 OM3 – 64.05 OM2 +165.2 OM – 76.7 DMY = 14.88 OM3 – 120.20 OM2 +291.30 OM – 153 DMY = 10.15 OM3 – 83.65 OM2 +208 OM + 103

8

1 3 5

DMY = 2.7 OM3 – 24.15 OM2 +64.35 OM – 12.4 DMY = 5.27 OM3 – 43.10 OM2 +103.60 OM – 31.3 DMY = 4.10 OM3 – 34.70 OM2 +87.20 OM – 24.9

* Equations are significant at 0.05 level of significance; DMY and OM are dry matter yield and organic matter content respectively.

yield followed by 8 mm irrigation depth (35.41 kg/ha)

while the minimum yield (34.15 kg/ha) occurred when

the irrigation depth was 4 mm. Moreover, the four

levels of organic matter incorporation were

significantly different (P < 0.05) in their effects on the

dry matter yield. The maximum plant yield (47.44

kg/ha) occurred when 2% poultry litter was

incorporated into the soil while the lowest plant yield

(31.57 kg/ha) occurred with the control experiment, i.e.,

when no organic matter was incorporation. Yield of

crops is related to water use efficiency, and this

probably explains the higher yield obtained at 2%

organic matter incorporation with higher water use

efficiency [11]. The remaining two levels of organic

matter incorporation, 4% and 6%, resulted in dry

matter yield of 37.41 kg/ha and 34.63 kg/ha,

respectively. Table 2 gives the relationship between

the percent organic matter incorporation (OM) and

observed dry matter yield (DMY). For all the irrigation

depths and intervals, the relationships between DMY

and OM were polynomial of the third order. There was

also high correlation between DMY and OM with R2

values of close to 1.00 for the correlation of DMY

against OM for the three irrigation depths and

irrigation intervals. Using these relationships, the

optimum level of poultry waste for the highest yield is

between 1.8% and 2.2%. This is in agreement with the

suggested value by researchers [11, 21, 22].

4. Conclusions

The study investigated the effects of irrigation

depth and irrigation interval as well as poultry waste

incorporation into loamy sand soil on the yield of

Amaranth. Total depths of water used by the crop

increased with decrease in the irrigation interval. Plant

height varied significantly (P < 0.05) with organic

matter and irrigation interval. The highest plant height

occurred with 2% organic matter incorporation while

the lowest plant height occurred with 6% organic

matter incorporation. Plant yield varied significantly

with organic matter, depth of irrigation and irrigation

interval. The maximum dry matter yield occurred with

2% organic matter incorporation when 6 mm

irrigation depth was applied every three days while the

minimum dry matter yield was observed when no

poultry litter was added with 4 mm of irrigation water

applied daily. It is therefore imperative that the

nutrient uptake from the different levels of organic

matter incorporated into the soil should be estimated.

References

[1] K.O. Adekalu, D.A. Okunade, Effect of irrigation amount

and tillage system on yield and water use efficiency of

cowpea, Communication in Soil Sci. and Plant Analysis

37 (2006) 225-238.

[2] K.O. Adekalu, L.A.O. Ogunjimi, F.O. Olaosebikan, S.O. Afolayan, Response of Okra to irrigation and mulching, Int. Journal of Vegetable Science 14 (4) (2008) 339-350.

[3] A.A. Al-Masoum, I.M. El-Gharib, Plastic mulches for winter okra production, Egyptian Journal of Hort. 23 (2) (1996) 211-219.

[4] National Research Council (NRC), Amaranth: Modern Prospects for an Ancient Crop, National Academy Press, Washington, DC, United State, 1984.

[5] G.F. Stallknecht, J.R. Schulz-Schaeffer, Amaranth rediscovered, in: J. Janick, J.E. Simon (Eds.), New Crops, Wiley, New York United State, 1993, pp. 211-218.


139

[6] P.C. Haung, A study of taxonomy of edible amaranth, an investigation of amaranth both of botanical and horticultural characteristics, in: Proceeding of the 2nd Amaranth Conference, Rodel Press. Emmaus, P.A., 1980, pp. 142-150

[7] J.A. Abbott, T.A. Campbell. Sensory evaluation of vegetable amaranth (Amaranthus spp.), Hortscience 17 (1982) 409-410.

[8] J.A. Osunbitan, K.O. Adekalu, O.B. Aluko, Effect of incorporating organic wastes on the moisture retention of three southwestern Nigeria soils, Agric. Mech in Asia, Africa and Latin America 33 (3) (2002) 11-15.

[9] K.O. Adekalu, J.A. Osunbitan, The influence of goat dung on the compatibility and the hydraulic properties of some soils in Southwestern Nigeria, Journal of Agric. Engineering and Technology 3 (1995) 97-105.

[10] T.S. Millar, J.K. Turk, Fundamental of Soil Science, Khuwer Academics Publishers, London, 1957, pp. 72-79.

[11] J.A. Osunbitan, K.O. Adekalu, Yield and water use efficiency of Jute Mallow (Corchorus: Olitorius) as influenced by poultry waste, in: Proceedings of Minia International Conference for Agriculture and Irrigation in the Nile Basin Countries, 2012, pp. 1163-1169.

[12] N.C. Brady, Decision and Operation of the Sprinkler Infiltrometer, Bulletin 723, Agricultural Experiment Station, Purdue University, 1984.

[13] L.D. Baver, W.H. Gardner, W.R. Gardner, Soil Physics,

John Wiley and Sons Inc, New York, 1972, p. 155. [14] M.E. Johnson, Programming irrigation for greater

efficiency in, optimizing the soil physical environment towards greater crop yields, in: D. Hillel (Ed.), Academic Press, New York, 1982, pp. 133-161.

[15] H. Daniel, Optimizing the Soil Physical and Environmental Factor towards Greater Crop Yields, Academic press inc., New York, 1972, p. 90.

[16] R.L. Snyder, J.P. Melo-Abreu, Frost protection: Fundamentals, practice and economics volume 1, Food and Agriculture Organizations of United Nation, 2005.

[17] A.G. Ojanuga, Morphology, Physical and chemical characteristics of Ife and Ondo areas, Nigerian Journal of Soil Sci. 9 (1975) 225-269.

[18] Association of Official Analytical Chemists, Official Methods of Analysis, Association of Official Analytical Chemists, Arlington, Va, 1990.

[19] SAS/STAT Guide for personnel computer, v. 6. SAS Institute Inc., Car, NC, 1998.

[20] S. Camposeo, P. Rubino, Effect of irrigation frequency on root water uptake in sugar beet, Plant Soil 253 (2003) 301-309.

[21] R.R. Schippers, African indigenous vegetables, an overview of the cultivated species, Chatham, UK: Natural Resources Institute/ACP-EU, 2000, p. 13.

[22] H.O. Fapohunda, K.O. Adekalu, Cowpea yield response to water and fertilizer, Discovery and Innovation 7 (1) (1995) 61-67.



Quantitative Changes in Protein and Cholesterol in

Haemolymph of the Red Palm Weevil Rhynchophorus

ferrugineus after Treatment LeucokininII

Mona Mohammed Saleh Al-Dawsary

College Science and Humanities, Salman Bin Abdul Aziz University, Al-Kharj- Kingdom Saudi Arabia

Received: November 22, 2012 / Published: February 20, 2013.

Abstract: We estimated quantitative changes to the content of protein and cholesterol in the Haemolymph of adult Red Palm Weevil

after being fed on sugar cane treatment with different concentrations of LeucokininII. In males, 0.05% has recorded significant

increase in total protein, then 0.25% concentration compeer control, while the maximum high of females 4.846 mg at 0.05% compeer

control. The effect of leucokininII on content of haemolymph cholesterol has shown result that 0.05% concentration and have a clear

impact on cholesterol concentration for both sexes with an average reduction of 37.989 mg in males compeer with 120.123 for

control, and 57.263 in females compeer with 96.087 mg for control.

Key words: LeucokininII, red palm weevil, protein, cholesterol, reduction.

1. Introduction The red palm weevil (RPW) Rhychophorus

ferrugineus (Olivier) (Coleoptera: Curculionidae) is

considered as one of the most damaging invasive insect

species [1, 2]. The geographic origin of RPW was

claimed to be South East Asia and Melanesia [3-6].

Multiple introductions of RPW to the Middle East, the

Mediterranean Basin and US have occurred since mid

1980s through movement of infested offshoots [2].

Recently, the RPW is being considered as a major pest

of date palms (Phoenix dactylifera) and different

ornamental palm species in the Middle East and the

Mediterranean basin, Caribbean (Island of Curacao,

Netherland Antilles), Lebanon and USA [2, 6-9].

Insect neuropeptides are involved in almost all

physiological aspects in insects, such as diereses,

ecdysis, pheromone biosynthesis and control of

muscle activity. Thus, these peptide hormones and

Corresponding author: Mona Mohammed Saleh

Al-Dawsary, Ph.D., research fields: insects physiology, sense organs and pharmacology. E-mail: [email protected].

their receptors are promising targets for a novel

generation of selective and non-neurotoxic

insecticides. However, due to poor bioavailability,

pharmacokinetics and short half-life, the peptides

themselves can not be used as insect control agents.

The past two decades have witnessed an increase in

research into the discovery of nonpeptide small

molecules that function as mimics for neuropeptides.

As early as the Polish scientist Kopec [10] proposed

that substances in the brain control of the moulting

and metamorphosis processes. Today, we know that

numerous physiological processes in insects are

controlled by small, bioactive peptides. Because these

regulatory peptides are synthesized in modified

neurons, they are called neuropeptides. Neuropeptides

are chemical messengers which are released from the

neurons into the Haemolymph of the insect to reach

their distant target organs. Neuropeptides are

ubiquitos in the nervous system of insects and they are

by far the most diverse signaling substances, both

structurally and functionally [11]. Neurohormone

D DAVID PUBLISHING

Quantitative Changes in Protein and Cholesterol in Haemolymph of the Red Palm Weevil Rhynchophorus ferrugineus after Treatment LeucokininII

141

regulated processes, can, however, be grouped into

four major functional categories: 1) growth and

development, 2) behavior and reproduction, 3)

metabolism and homeostasis, and 4) muscle

movement [12]. The leucokinins are considered

cephalomyotropic peptides, to indicate their isolation

from the head of the cockroach Leucophaea maderae

and their ability to increase muscular contractions in

the cockroach hindgut [13]. Curious agents which

stimulate hindgut contractions might also enhance

excretory activity in Malpighian tubules upstream, we

have discovered that leucokinin has diuretic effects in

Malpighian tubules of the yellow fever mosquito

Aedes aegypti [14]. Given the diverse roles of insect

leucokinins, elucidation of the mode of action of these

peptides via their cognate G protein-coupled receptors

(GPCRs) are of importance. Furthermore, as

leucokinins have only found in invertebrates, it is

likely that careful design of leucokinin antagonist or

agonist analogues will avoid interactions with

mammalian species. While identification of

leucokinins and their cognate receptors have been

successfully undertaken in some insects [15-17],

including the genetically tractable Dipteran,

Drosophila melanogaster [18, 19], less progress has

been made in studies of leucokinin signaling in

biomedical relevant insects. As importance of

physiological peptides and their roles on insects

metabolism, have focused this study on quantitative

changes of total protein and lipids in haemolymph

adults red palm weevil after treatment leucokininII,

which may support the vital role of peptide.


2.1 Weevils

Adult insects, which were free from previous

pesticide exposure, were obtained from infected date

palms with the help of the Ministry of Agriculture and

Water located in Al-Kharj, Kingdom of Saudi Arabia.

Breeding of larvae was conducted using different

varieties in accordance with modern established

methods [20, 21] in laboratory College Science and

Humanities, Salman Bin Abdul Aziz University,

Kingdom of Saudi Arabia.

2.2 LeucokininII

LeucokininII was obtained from American peptide

company, INC. 777E. Evelyn Ave. Synnyvale, CA

94086 USA. The peptide was dissolved in 3 mL of

acetone solution and preparation different

concentrations: 0.05%, 0.25%, 0.4% respectively for

treatment of newly adults from cocoon in accordance

with the author [22].

2.3 Treatments

2.3.1 Treatment of Sugar Cane

Pieces of sugar cane were treated high with the

weight of the different known concentrations of

LeucokininII, and distributed each concentration of

each LeucokininII alone to pieces sugar cane using a

thin needle to make sure the distribution of solution

over the entire piece. We have left pieces of sugar

cane in the laboratory atmosphere for a period of time

to make sure the smell of the solvent evaporate

completely.

Sections of sugar cane were treated with various

concentrations of the LeucokininII and carefully

distributed using a thin needle. The sections of sugar

cane were then allowed to remain in the laboratory

atmosphere to permit the solvent to completely

evaporate.

2.3.2 Treatment of Insects

After the treatment of sugar cane pieces of various

concentrations of LeucokininII, they were transferred

to the boxes for rearing. A packet was placed inside a

piece treatment of sugar cane and a pair of insects. We

have conducted 15 replicates for each concentration

separately, as well as a control group treated only by

solvent. Adults were fed for one week on sugar cane

treatment, then, they were transferred into a diet of

untreated sugar cane. We have collected haemolymph

from insects in preparation for physiological studies.


142

2.3.3 Samples for Biochemical Studies

After treatment of insects with different

concentrations leucokininII collected haemolymph

from males and females severally, with these samples

accordance the requirements of the procedures for

each test.

2.3.3.1 Total Proteins Determination

Total proteins determination in haemolymph of

insects treatment and untreatment via proteins kit from

Jermany Human Company, it depended on biuret

method where estimation is as follow:

-Prepare samples for determination take 20 µL from

haemolymph (haemolymph collected from fore leg via

capillary tube, added in eppendorf tube contain phenyl

thiourea crystals) in clear tube and added for it 1,000

µL from color reagent are ready;

-Prepare standard solution added 20 µL from

standard solution are ready in kit to 1,000 µL from

color reagent the same samples;

-Use 1,000 µL from color reagent blank for

spectrophotometer uv-120-02 shimadzu at 546 nm;

-Mix, incubate for 10 min at 20 °C to 25 °C.

Measure the absorbance of the sample and standard

against the reagent blank within 30 min;

-Reading is recorded for each sample and calculate

quantitative protein according to the equation:

8 Δ A sampleC=

Δ A standard

(1)

2.3.3.2 Cholesterol Determination

Total cholesterol determination in haemolymph of

insects treatment and untreatment via Cholesterol-kit

from Jermany Human Company where estimation is

as follow:

-Prepare samples for determination take 10 µL from

haemolymph as previously in clear test tube and added

for it 1,000 µL from color reagent are ready for use;

-Prepare standard solution added 10 µL from

standard solution are ready for use to 1,000 µL from

color reagent the same samples;

-Use 1,000 µL from color reagent blank for

spectrophotometer uv-120-02 shimadzu at 500 nm;

-Mix, incubate for 10 min at 20 °C to 25�°C.

Measure the absorbance of the sample and standard

against the reagent blank within 60 min;

-Reading is recorded for each sample and calculate

quantitative protein according to the equation:

200 sampleC=

ΔA standard

(2)


Results were analyzed by F test in one way using

the program MSTAT [23].

3. Results

3.1 Effect of LeucokininII on Total Proteins and

Cholesterol Concentration in Haemolymph of R.

ferrugineus

3.1.1 Total Proteins

Table 1 shows significant difference between total

protein concentration in Haemolymph when treating

males and females with different concentrations of

leucokininII. These treatments have significant effects

on protein content in Haemolymph for adult females

compared with control, where concentration recorded

significant increase, 0.05% in the protein content

followed by 0.4% compared control. Also, the

leucokininII have significant increase when treating

females with 0.25% that decreased recorded in protein

content 0.110 µg mg-1. These results show

leucokininII have an active effect in creating protein

in heamolymph. It is clear that the changes in protein

content in insects tissues fall under hormonal system

control of insect treatment which is the juvenile

hormone and ecdysteroid hormone. With exception to

the females treated with 0.25% concentration which

recorded a decrease in the protein content 0.110 µg

mg-1, it indicates suppression in creating protein

[24-26].

3.1.2 Cholesterol

Table 2 shows effect of leucokininII on cholesterol

concentration in haemolymph for adults of red palm

weevil. That results showed a significant decrease in


143

Table 1 Effect of treatment of LeucokininII on total protein concentration in haemolymph of adult red palm weevil R. ferrugineus.

Concentration Males Mean µg mg-1 ± SD

Females Mean µg mg-1 ± SD

0.05% 3.872* ± 1.00 4.846 ± 0.13

0.25% 0.500* ± 0.18 0.110 ± 0.00

0.4% 1.679*± 0.04 1.296 ± 0.00

Cont. 0.232* ± 0.00 0.134* ± 0.00

Significant of 0.001.

Table 2 Effect treatment of LeucokininII on cholesterol concentration in haemolymph adult red palm weevil R. ferrugineus.

Concentration Males Mean µg mg-1 ± SD

Females Mean µg mg-1 ± SD

0.05% 37.989* ± 1.12 57.263* ± 0.48

0.25% 114.243* ± 0.29 63.967* ± 0.00

0.4% 82.960* ± 0.56 82.681* ± 2.79

Cont. 120.123* ± 22.91 96.087* ± 5.58

Significant of 0.001.

cholesterol concentration in haemolymph for both

sexes compared to the control group. Decreased rate

in cholesterol concentration of males treatment 37.989

µg mg-1, 82.970 µg mg-1, 114.24 µg mg-1 at

concentrations 0.05%, 0.4%, 0.25%, while in females

57.263 µg mg-1, 63.967 µg mg-1, 82.681 µg mg-1 at

concentrations 0.05%, 0.25%, 0.4% respectively. Also,

these results show that 0.05% concentration recorded

very low cholesterol concentration in both sexes

compared with the control group. This low rate

resulted of the leucokininII effect on secretion

juvenile hormone from CA and defected metabolism

of lipids [27].

4. Discussion

Neuropeptides are chemical messengers which are

released from neurons into the haemolymph of the

insect to reach their distal target organs. Insect

neuropeptides are involved in almost all physiological

process in insects, such as dieresis, pheromone

biosynthesis, and control of muscle activity. In

previous study, we have studied leucokininII effect on

some biological aspects of red palm weevil, and

observed leucokininII, and some deformation on

larvae [22]. In this study, we have focused on the

effect of leucokininII on total proteins and cholesterol

concentration in haemolymph of adults R. ferrugineus.

These results showed in Table 1 that defect happened

with leucokininII on total protein in haemolymph

because these concentration worked high of protein

content in haemolymph both sexes, so leucokininII

effected on neurosecretory glands hormone that

controlling proteins metabolism, or effect leucokininII

attributable to effected enzymes of neuropeptides on

metabolism for neuropeptides in insects [28] that

enzymes of neuropeptides are keys of indicators

systems. It was the changes in the protein content of

the tissue insects under control of the hormonal

system of the insect [10, 24, 25]. This defect in the

protein content of decreased or increased due to

irregular of these hormones for the control group.

Also, these results coincided with indicated by

previous studies of link metabolism proteins with

hormonal regulation for secretory nerve glands

specially CA, CC and Prothoracicgland. Also, this

study showed results in Table 2 significant reduction

of cholesterol concentrations in haemolymph in both

sexes after treatment with different concentrations

from leucokininII. These reductions are attributable to

effected leucokininII on secretion juvenile hormone

from CA and defected metabolism for lipids according

to references [27, 29, 30]. And, these effected for

lipids may be leucokininII effect on Adipokinetic

hormone that regulated lipids metabolism which

secretion from CC.

5. Conclusions

LeucokininII effected significantly on total proteins

and cholesterol concentration in haemolymph for Red

Palm Weevil, it has effect on nerve gland secretion

hormones that regulated metabolism of these

compounds.

Acknowledgments

The authors extend their sincere thanks to the


144

deanship of Scientific Research, the University for its

support to this research and is part of the project No.

37/H/1432H, which was supported by the Deanship.

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The Role of Cellulase and Pectinase in Apricot Canker

Caused by Hendersonula toruloidea and

Phiaoacremonium aleophillium

Nidhal Y. M. Al-Morad

Department of Plant Protection, College of Agriculture and Forestry, University of Mosul, Iraq

Received: October 22, 2012 / Published: February 20, 2013. Abstract: Significant increasing in cellulase and pectinase activity were observed in response to infection with Hendersonula

toruloidea and Phiaoacremonium aleophillium in apricot trees comparing with uninfected trees. Pectinase highest activity was recorded at the first week after inoculation with H. toruloidea (1.76 unit/g f.w.), while cellulase highest activity was recorded after two weeks of inoculation with P. aleophillium (6.49 unit/g f.w.). Peroxidase and polypolyphenol oxidase activity were significantly increased after inoculation with H. toruloidea and P. aleophillium, Peroxidase highest activity was recorded after 48 h of inoculation with H. toruloidea (1.77 unit/g f.w.), while polypolyphenol oxidase highest activity was recorded after two weeks of inoculation with P. aleophillium (3.33 unit/g f.w.). The result also showed that total phenol contents was significantly increased as a result to inoculation with H. toruloidea and P. aleophillium, highest total phenol contents was recorded after 48 h of inoculation with H.

toruloidea (1.61 mg/g f.w.). Key words: Cellulase, pectinase, peroxidase, polypolyphenol oxidase, apricot, Hendersonula toruloidea, Phiaoacremonium

aleophillium.

1. Introduction Phytopathogenic fungi that attempt to colonize higher

plants must contend with physical barriers of the host as

surface waxes and cell wall [1], phytopathogenic fungi

produce several pectolytic enzymes, which are capable

to degrade plant cell wall components during plant

pathogenesis. These enzymes are called pathogenic

enzymes mostly cellulolytic and pectinolytic enzymes

by the activity of these enzymes, which may facilitate

fungal growth and provide the fungus with nutrients [2,

3]. In a number of systems, a strong correlation has been

found between the presence of pectinolytic enzymes and

disease symptoms and disease virulence [4, 5]. Many

phytopathogenic fungi possess the enzymes destroying

cellulose, hemicelluloses and pectin. Wood-destructive

Corresponding author: Nidhal Y. M. Al-Morad, assistant

professor, research field: plant pathology. E-mail: [email protected].

fungi possess active cellulases, while the most active

enzymes in grassy plant parasites are pectinases [6-8].

Apricot is one of the most popular fruit in Iraq, apricot is

greatly hindered due to a number of diseases. Majority

of the diseases is fungal origin, among of the fungal

diseases branch wilt and decline diseases caused by

Phiaoacremonium aleophillium and Hendersonula

toruloidea, which attack branches forming typical

necrotic canker of brown to black color [9-11]. Hence,

this work deals with the enzymatic profile of isolates of

P. aleophillium and H. toruloidea and its activity in the

infected apricot plants addition to activity of some

defense related enzymes oxidative enzymes peroxidase

(PO) polypolyphenol oxidase (PPO).


2.1 Plant Material, Pathogen Fungi

Apricot trees were used as plant material. P.

D DAVID PUBLISHING

The role of cellulase and pectinase in apricot canker caused by Hendersonula toruloidea and Phiaoacremonium aleophillium

147

aleophillium and H. toruloidea isolated from naturally

infected apricot trees were maintained on PDA at 25 ±

2 °C.

2.2 Preparation of Enzyme Extract from the Pathogen

The isolates were cultivated in Czapeck Dox (CD)

medium by replacing the indigenous source of the

medium and were harvested at 15th day. In order to

elicit the secretion of specific enzymes, related

substrates like 5% of cellulose and pectin were

replaced for carbon source in the (CD) medium for

inducing cellulase and pectinase. The mycelium was

harvested twice using Whahnam No. 1 filtration, the

supernatant was then dried and weighted, the filtered

extract was then used as the respective enzyme source.

2.3 Effect of pH on Enzyme Activity

The effect of pH on enzyme activity was

determined by performing the enzyme assay with

reaction mixtures of which the pH ranged 4-6-7.

2.4 Preparation of Enzyme Extract from the Healthy

and Infected Plants

Samples from each apricot treatment, healthy or

infected, were collected 0, 24, 48, 72, one week, two

weeks after inoculation with P. aleophillium and H.

toruloidea.

2.5 Detection of Pectinase Enzyme Activity

Pectinase activity was expressed as changes in

absorbance/min at 235 nm according to the method

[12].

2.6 Detection of Cellulases Enzyme Activity

The reaction mixture contained 0.1 g cellulose

powder; 1 mL 0.05 M citrate buffer (pH 4.8) and 0.5

mL enzyme source was incubated for 1 h at 50 °C.

One unit of cellulose is defined as the amount of

enzyme which liberates 1 mg reducing sugar per 1

h/0.1 g fresh weight [13].

2.7 Determination of PO and PPO Enzymes Activity

Enzyme extract was obtained by grinding plant

tissues in 0.1 M sodium phosphate buffer at pH (7.1)

(2 mL/ tissues) in a porcelain mortar. The extracted

tissues were strained through four layers of

cheesecloth. Filtrates were centrifuged at 3,000 rpm

for 20 min. PO activity was expressed as changes in

absorbance/min at 420 nm according to the method

[14]. PPO activity was expressed as changes in

absorbance/minat 420 nm according to the method

[15].

2.8 Determination of Total Phenol

Total phenol was obtained from healthy and infected

tissues by homogenizing the tissues (1 g) with 80% (v/v)

methanol and agitated for 15 min at 70 °C, 1 mL of the

methanolic extract was added to 5 mL of distilled water

and 250 µL of Folin-Ciocalteau reagent (1N), the

solution was kept at 25 °C. The abortion of developed

blue colure was measured at 725 nm catechol was used

as the standard as µg catechol mg-1 FW [16].


All the grouped data were statistically evaluated

using SAS 9. Hypothesis testing methods included are

one way analysis of variance (ANO VA) followed by

Duncan with P < 0.05 were considered to be

statistically significant. All the results were expressed

as the mean of six replicated values.


3.1 Effect of pH on Pectinase and Cellulase Activity

The optimum pH for pectinase and cellulase

Fig. 1 Effect of pH on pectinase and cellulase activity.


148

activity was 4 and 6, respectively (Fig. 1).

3.2 Hydrolytic Enzymes

The results presented in Table 1 show that the

relative activities of pectinase and cellulase were

significantly increased in response to infection with H.

toruloidea, P. aleophillium compared to healthy plants

(control). The highest activity of pectinase in infected

apricot trees was recorded at the first week after the

inoculation with H. toruloidea, P. aleophillium and

with both fungi (1.76, 1.88, 1.98 unit/g f.w.)

respectively. While the highest activity of cellulase in

infected apricot trees was recorded at the first week

after the inoculation with H. toruloidea (5.80 unit/g

f.w.) and recorded at the second week after the

inoculation with P. aleophillium and with both fungi

H. toruloidea and P. aleophillium (6.49 and 7.27

unit/g f.w.), respectively.

3.3 Oxidative Enzymes

In the present study, the induction of PO activity in

infected apricot trees over their uninfected controls

has been revealed (Table. 2). The highest induction

was recorded at 48 h after inoculation with H.

toruloidea (1.77 unit/g f.w.) and 24 h after inoculation

with P. aleophillium (1.74 unit/g f.w.) and with both

fungi (1.69 unit/g f.w.) where approximately 2-fold

increase over uninfected counterparts. The highest

induction activity of PPO in infected apricot trees was

recorded at second week after inoculation with H.

toruloidea (1.02 unit/g f.w.) and at first week after

inoculation with P. aleophillium (3.33 unit/g f.w.) and

second week after inoculation with both fungi (1.15

unit/g f.w.).

3.4 Total Phenol

As can be seen from the results presented in Table 2,

infection of apricot trees with P. aleophillium or H.

toruloidea induced significant increases in total

phenol contents as compared to the uninfected

(control). The highest increases in total phenol

Table 1 Hydrolytic enzyme activity in Apricot trees infested with H. toruloidea and P. aleophillium under greenhouse condition.

Fungi Period Enzymes activity

(unit/g fresh weight) Pectinase Cellulase

H. toruloidea

24 h 0.92fg 3.9 gi 48 h 1.40de 5.23ef 72 h 1.52cd 5.56cd One week 1.76ac 5.80cd Two weeks 1.72a-d 5.76ce

P. aleophillium

24 h 1.04f 4.44gf 48 h 1.52cd 5.86ce 72 h 1.64a-d 6.23ad One week 1.88ab 6.45ac Two weeks 1.62cd 6.49ac

P. aleophillium and H. toruloidea

24 h 1.18ef 4.97ef 48 h 1.62cd 6.56ac 72 h 1.82 ac 6.98ab One week 1.98a 7.23a Two weeks 1.66ad 7.27a

Control

24 h 0.44h 2.33j

48 h 0.92g 2.56j

72 h 0.64gh 2.82j

One week 0.64gh 3.10j

Two weeks 0.6h 3.41hj Mean within a column followed by the same letter are not significantly different according to Duncan test P < 0.05.

Table 2 Oxidative enzymes activity and phenolic contents in Apricot trees infested with H. toruloidea and P. aleophillium on under greenhouse condition.

Fungi Period Enzyme activity

(unit/g fresh weight)Total phenol mg/g f.w.PO PPO

H. toruloidea

24 h 1.22d 0.58c 0.70f 48 h 1.77a 0.62c 1.61a 72 h 1.39bc 0.71c 1.56b One week 1.61a 0.53c 1.30c Two weeks 1.24cd 1.02c 1.21d

P. aleophillium

24 h 1.74a 1.18c 0.99e 48 h 1.47b 2.01b 1.44bc 72 h 1.12d 1.87b 1.25d One week 1.71a 3.33a 1.34c Two weeks 0.89d 1.15c 0.93e

P.aleophillium and H. toruloidea

24 h 1.69a 0.58c 0.84f 48 h 1.41bc 0.71c 1.56b 72 h 1.1d 0.53c 0.90e One week 1.31 cd 1.02c 1.25d Two weeks 0.90d 1.15c 1.33c

Control

24 h 0.75e 0.38d 0.44g 48 h 0.81e 0.31d 0.74f 72 h 0.85d 0.28d 0.27g One week 0.71e 0.32d 0.39g Two weeks 0.69e 0.35d 0.49g

Mean within a column followed by the same letter are not significantly different according to Duncan test P = 0.05.


149

contents were recorded at first week after inoculation

with H. toruloidea and P. aleophillium (1.30 and 1.34

mg/g f.w.) respectively and second week after

inoculation with both fungi (1.33 mg/g f.w.).

Significant correlation was well demonstrated earlier

between pathogenicity causing severe tissue

maceration and the extracellular pectinase and

cellulases activities. Pectic enzymes that have the

ability to split the 1,4 bonds between the galacturonic

acid moieties in the pectic fraction of the cell wall

remain the only enzyme confirmed to cause plant

tissue maceration [17-19].

Our results indicate that hydrolytic enzymes

pectinase and cellulases induction oxidative enzymes in

apricot trees. We show that combinations of pectic

enzymes and cellulases cooperate in the induction

oxidative enzymes in apricot trees. Time-course activity

of peroxidase in apricot tree infected with P.

aleophillium and H. toruloidea. The induction of PO

activity has been repeatedly reported in several plant

species in response to pathogen infection. The

peroxidase activity, in general increases under different

stress conditions like wounds, fungi infections, salinity,

water stress and nutritional disorders, inducing also the

lignin increment and production of ethylene and induce

the increase of the production of phenols oxidized at the

cell wall [20]. This activity, suggests a cell effort for the

establishment of a physiochemical barrier, able to

isolate the infected area [21]. Similar to the present

report on oxidative enzymes, previous works also

indicated that these oxidative enzymes are involved in

the defense role [22]. With resistance in many plants

[23], an immediate response of plants to injury, in most

cases, is the accelerated accumulation of oxidative

enzymes required for scavenging toxic radicals [24].

Plants were reported to possess efficient antioxidant

defense system due to the presence of pathway for

catalase and peroxidase [25].

4. Conclusions

The present results indicate that pectinase and

cellulases have been inducted peroxidase,

polypolyphenol oxidase in apricot trees. We also find

that combinations of pectic and cellulases enzymes

cooperate in the induction oxidative enzymes in

apricot trees.

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Innovation and Technical Efficiency in the Smallholder

Dairy Production System in Ethiopia

Amlaku Asres, Johann Sölkner and Maria Wurzinger

Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Str. 33,

1180 Vienna, Austria

Received: October 24, 2012 / Published: February 20, 2013. Abstract: This study provides estimates of smallholder household’s production efficiency and its determinants, and separately analyses the technical efficiency of dairy technology adopting and non-adopting farmers using data from Ethiopia. Cobb-Douglas stochastic frontier production function was modeled in the context of local level agricultural innovation systems framework and estimated using 2011 milk production data on 304 dairy farmers. Results show that the mean level of technical efficiency among the sampled farmers was about 26%. This result suggests that there is room for significant increases of production through reallocation of existing resources. Despite significant variation among farmers, these results also indicate only 19% of farmers have mean efficiency

scores ( 50%), implying a need to focus on creating innovation capacity that pushes the production frontier outward in the dairy

production system. It is also revealed that individual farm households’ efficiency varied widely across dairy technology adoption

status, gender and districts. The significant gamma () statistic, of 0.9985 in the analysis indicates that about 99.85% variation in the output of milk production would be attributed to technical inefficiency effects (those under farmer’s control) while only 0.0015% would be due to random effects, i.e., beyond the farmers control and hence calling for a focus on efficiency enhancing investments. Education, farm size, extension visit and off-farm income opportunity were found to be efficiency enhancing. The study recommends that different components of an agricultural innovation system have to interact to improve the innovation capacity of different actors and thereby improve the estimated technical inefficiencies. Key words: Agricultural innovation systems, dairy, Ethiopia, stochastic frontier analysis, technical inefficiency.

1. Introduction Developing-country agriculture is frequently

characterized by low innovation capacity [1], low

productivity [2, 3], demographic pressures [4],

small-scale subsistence farming, and low levels of

market integration and value addition [3]. However,

there is significant variation across developing

countries [2]. This suggests a need for a better

understanding of the factors that influence

productivity and variations in productivity among

countries, development sectors and farm enterprises.

Ethiopia is one of the most populous countries in the

developing world and agriculture is central in its

Corresponding author: Amlaku Asres, M.Sc., research

field: innovation capacity in the livestock systems. E-mail: [email protected].

economy. The agriculture sector is a major source of

livelihood for 80% of the population in the country [5].

The livestock sector in particular is an indispensable

component to sustain the agricultural system, accounts

for about 45% of the agricultural GDP [6] and directly

supports the livelihoods of a large proportion of rural

as well as urban and peri-urban households in Ethiopia.

Nonetheless, there is a great concern that

productivity of livestock, especially the dairy sector,

in Ethiopia is still very low compared to other

neighboring countries. For example, milk productivity

is among the lowest in East Africa. It is estimated to

be 270 L per cow per lactation versus 498 L and 480 L

in neighbouring countries like Kenya and Sudan,

respectively [7]. A wide gap exists between actual

dairy farm production and potential productivity

D DAVID PUBLISHING

Innovation and Technical Efficiency in the Smallholder Dairy Production System in Ethiopia

152

identified in research stations, pointing to potential

technical inefficiency in their current dairy production

practices. There is, however, lack of adequate

empirical evidence regarding the production

efficiency of farmers in Ethiopia.

A number of studies have examined the potential of

the Ethiopian dairy sector to meet the expected growth

in demand as well as to improve the incomes of the

farmers [8-10]. Many of those studies, however, focus

on technological constraints of the sector including poor

genotype of local breeds, animal diseases, availability of

feed, input and output markets, and related policies. The

studies ignore an important source of growth-improving

the technical efficiency of farmers.

There are considerable inefficiency challenges that

have greatly retarded the productivity of the livestock

sector in Ethiopia. Livestock agriculture lacked the

policy level attention it deserves [11]. For example, the

Ethiopian public agricultural research staff allocated to

crop research accounts for 56.8% whereas only 14.2%

researchers focused on livestock [12]. Slow innovation

and technology transfer are observed such as shortage

of genetic material, insufficient supply of forage crop

seeds and feed concentrates. Complementary services

such as extension, credit, breeding, veterinary service,

and input-output marketing are poor [13]. All these

constraints will be considered to evaluate where further

efficiency gains are possible.

This study addresses this issue by performing a

production function analysis within a comprehensive

local level innovation systems approach to dairy

production. The innovation systems approach

examines sets of heterogeneous actors who interact in

the generation, exchange, and use of dairy-related

knowledge in processes of social or economic

relevance, as well as the institutional factors that

condition their actions and interactions [14].

Using variables that characterize a given dairy farm,

we utilize a stochastic frontier production function

analysis to estimate the production possibility frontier

under a given innovation system and a given level of

input use to determine where each farm stands in

relation to this frontier. The objective of this paper,

therefore, is to examine the extent and determinants of

technical efficiencies in the context of smallholder

dairy farmers. In addition to the overall technical

efficiency estimates, we estimate a stochastic frontier

production function to analyze differences in technical

efficiency between dairy technology adopters and

non-adopters, men and women smallholder farmers

and across districts in the study area. Our general

hypothesis in this study is that the different

components of the local level agricultural innovation

system will significantly affect the technical efficiency

of dairy production.

The rest of the paper is organized as follows.

Section two discusses the theoretical and analytical

framework that contributes to technical efficiency

analysis (productivity growth) in the context of

local-level agricultural innovation system. Section

three presents data and the empirical model used in

the econometric estimation. It is a two-step

Cobb-Douglas stochastic production function

estimation model presented to analyze the technical

efficiency of dairy farmers in an innovation systems

perspective. Section four focuses on results and

discussion. Section five concludes the paper and

presented associated recommendations.

2. Theoretical and Analytical Framework

Technical efficiency is a measure of a farm’s

productive performance [15-17]. In the context of this

study, it can be defined as the ability of a smallholder

dairy farmer to obtain maximal output from a given set

of inputs. Technical inefficiency on the other hand is

the deviation of an individual smallholder farm’s

production from the best practice frontier. The level of

technical efficiency of a particular farm is based upon

deviations of observed output from the efficient

production frontier [18]. If the actual production point

lies on the frontier, it is perfectly efficient. If it lies

below the frontier, then it is technically inefficient. The


153

distance between the actual to the achievable optimum

production from given inputs, indicates the level of

production inefficiency of the individual firm [18, 19].

We consider local level (district) agricultural

innovation system perspective as a theoretical

construct that contributes to productivity growth

through four main components: knowledge and

education, business and enterprise, bridging

institutions, and the enabling environment, based

broadly on a construct developed by Arnold and Bell

[20] and adapted to the sphere of agriculture and

agricultural development by World Bank [21]. In this

study, the key elements which proxy the local level

agricultural innovation system are described as

follows. The knowledge and education domain

captures the contribution of education to technological

change and proxies by education of the household

head. The business and enterprise domain captures the

set of input-output market actors and activities that

leverage dairy production inputs to farmers and milk

outputs for markets. Within this component, group

membership of the household head assumed to proxy

the marketing role is included. Bridging institutions

represent the domain in which individuals and

organizations that leverage public extension services

in the innovation process. This component

incorporates two variables: extension visited by

extension agent and technical training given to the

farmer on dairying. Circumscribing these domains are

the enabling or frame conditions that foster or impede

innovation, including public policies on innovation

and dairy enterprise, for example, credit availability,

off-farm income opportunity and land availability.

Generally, two approaches are used to obtain

estimates of farm household efficiency: parametric

and non-parametric. The non-parametric approach is

implemented using data envelopment analysis (DEA)

while the parametric approach uses econometric

techniques. These two methods have a range of

strengths and weaknesses which may influence the

choice of methods in a particular application and the

constraints, advantages and disadvantages of each

approach have been discussed by Coelli [22] and

Coelli and Perelman [23]. However, it is well

documented that the DEA approach works under the

assumption of absence of random shocks in the data

set. Since farmers always operate under uncertainty,

the present study employs a stochastic production

frontier approach introduced by Aigner et al. [16];

Meeusen and Broeck [17] and refined by Battese and

Coelli [24]. Following their specification, the

stochastic production frontier can be written as: ;ii xfy Ni ,........2,1 (1)

where iy is the output of milk for the thj farm,

ix is the thi input used by the thj farm and is

a vector of unknown parameters and is a

composed error term which can be written as:

iii uv (2)

where iv is a systematic random error which

represents random variations outside the control of the

farmer such as disease, weather condition, natural

disaster, luck, fires, and other exogenous random

factors [25] and assumed independently and identically

distributed with zero mean and constant variance 20, v

N . The error term iu is one sided

non-negative term 0iu representing the

deviations from the frontier production function, which

is attributed to controllable factors (technical

inefficiency). This one sided error term can assume

various distributions such as truncated-normal,

half-normal, exponential, or gamma [16, 17]. However,

in this paper, it is assumed iu to be distributed

identically and independently half-normal 20, uN

as typically done in the applied stochastic frontier

literature. Furthermore, the two components iv and

iu are also assumed to be independent of each other.

For a detailed review of the literature on stochastic

production function for developing country agriculture,

see Bravo-Ureta and Pinheiro [26], Coelli [27].

Stochastic production frontier functions have been

widely used to assess the economic efficiency of

agricultural production in recent years [28-38].


154

3. Data and the Empirical Model

3.1 Data and Variable Definition

The data used for our empirical analysis are drawn

from the household survey of 304 rural households in

four districts of Northwest Ethiopia (Amhara region).

Three villages were randomly selected from each of

the four districts and therefore the study has been

conducted in a total of 12 villages. The study focused

on innovations that were introduced by the

Ethio-Austrian Integrated Livestock Development

Project (ILDP) that has been implemented for 10 years

(1998-2008) in three phases at 14 districts. ILDP is an

Austrian government financed program implemented

by the Regional Bureau of Agriculture. The project

supports the government’s endeavor to improve

livestock productivity and income so as to contribute

to the food security conditions of the farmers in

Northwest Ethiopia.

The surveys were conducted using structured

interviews with multistage stratified sampling

technique to collect quantitative household level

information. Within each district, a two-stage

selection process had been followed, selecting first

two villages purposively on the bases of their relative

importance in having more project beneficiaries and

one non-beneficiary village, and finally randomly

selecting households (HHs) within each of the

selected villages. The data collection followed

two-pronged approach. First, sample households were

randomly selected from a list of 922 farmers who

participated in dairy technology interventions

(adopters, n = 80) from the ILDP project beneficiaries.

Second, a wider sub-sample of smallholders, that had

no direct intervention with the project was randomly

selected (non-adopters, n = 224) from the same

districts.

The data collected are cross-sectional data obtained

through the above mentioned procedure. The

questionnaires were administered to dairy farmers and

were designed to elicit information on the

socio-economic characteristics of the respondents and

also on the operational systems adopted. Details of all

variables are presented in Table 1.

Table 1 Description of output, input and technical inefficiency variables.

Variables Description Expected sign

Ln output (Y) Natural log. of household total milk output in Birra

Inputs

Ln roughage Animal feed intake in kg (produced on the farm) +

Ln concentrate Animal feed intake in kg (purchased industrial by-product) +

Ln labor Number of adults working on the farm (ages between 15-64) +

Ln health Veterinary service expense in Birr +

Ln breeding cost Breeding service expense in Birr +

Ln hay Animal feed expenditure in Birr (purchased feed) +

Inefficiency variables

Age Age of the household head in years -

Age2 A proxy for years of farming experience of the household head +/-

Family size Number of family members in the household -

Education Years of formal schooling of the household head -

Farm size Total land owned by the household in hectares -

Credit availability 1 if the farmer gets credit in the production year; 0 otherwise -

Extension visit Number of times the household visited for advice by the extension agents during the production year

-

Training 1 if the household attended any dairy production training sessions; 0 otherwise -

Off-farm income 1 if the household gets income from sources other than farming; 0 otherwise -

Group membership 1 if the household is a member of any kind of farmers’ group; 0 otherwise - aNegative sign in the inefficiency variables indicates a positive effect on efficiency impact.


155

A priori expectations about the relationship of the

variables used in determining the factors influencing

technical efficiency are based on the analytical

framework and from previous empirical results. The

data are described as follows:

In the production function models, the dependent

variable, output (Y), is the natural logarithm of the

annual milk produced per farm measured in the value

of total milk production in Ethiopian Birr. Average

market price of the districts is used to estimate output

values.

The various inputs for milk production are: (1)

produced on farm dairy feed (roughage) measured in

terms of the quantity (kg) of total fed to milking cows

on the farm in a given year; (2) purchased dairy

concentrate feed measured in terms of the quantity (kg)

of total fed to cows on the farm in a given year; (3)

family labor measured in number of persons working

in the farm; (4) animal expenses consisting of

veterinary medicine, breeding services and

supplementary feed cost (hay). The estimated

coefficients on all inputs are hypothesized as positive.

Although accurate data on such milk production

inputs are not easily obtainable in the Ethiopian

traditional agricultural sector in general because of

measurement problems, an endeavor was made to

reduce the error of margin. We first collect the data

(for example, number of heap of roughage produced

on farm) subjectively from the holder and then

recorded after correcting it with the agreed conversion

rate into kilogram with the field experts in each

village.

The inefficiency variables that were used to explain

the character and performance of a given dairy farmer

in the study area were classified into two groups: (1)

household characteristics and (2) factors that can

reflect (proxy) the capacity of the local level

agricultural innovation system (AIS).

They are described as follows: of the household

characteristics, age of the household head proxies for

experience and predisposes farmer to better farming

techniques and is assumed to increase the productivity

of the farm and the higher the farmers’ experience, the

greater the technical efficiency was assumed, such that

the estimated parameter is predicted as positive for

both age and age squared term. Family size was

hypothesized to have positive effect to the technical

efficiency, because bigger household size could mean

a more secure labor source for livestock production.

Of the variables that proxy local level agricultural

innovation system, education was considered as the

number of years of formal schooling and was

supposed to have positive relationship with level of

efficiency. Availability of credit in time would

facilitate farmers to procure inputs timely thereby

increasing productivity and decreasing inefficiency.

Regular visits of an extension agent would spur

farmers to increase the efficiency. Access to technical

trainings was hypothesized to reduce the inefficiency.

It was assumed that the farmer with off-farm income

augments its access to a financial capital to purchase

inputs, which lead to higher efficiency. Group

membership was considered as a mechanism for

information sharing and makes members more

efficient than the non-members. The larger the farm

size (land) was hypothesized to have positive effect to

the technical efficiency because farmers may have

more fodder production to feed their animals and

maintain the productivity of the farm. The estimated

coefficients on all inefficiency variables are, therefore,

hypothesized as positive.

3.2 Characteristics of Sample Farmers

Table 2 presents the descriptive statistics of

dependant and independent variables used in the

stochastic frontier production analysis and on the

determinants for the dairy farm household efficiency

analysis. The data set contains information of 304

sample households (281 men and 23 women) and the

average age of a household head is 48 years. The

average experience of a farmer in dairy farming is 23

years, but farmers have experiences ranging from 5 to


156

Table 2 Summary statistics of the variables for dairy farmers in northwest Ethiopia in 2010/11.

Variables Dairy tech. adopters Dairy tech. non-adopters All respondents

Mean Range Mean Range Mean Range

Milk output (Birr) 4,354 (5,003) 0-30,000 1,595 (2,940) 0-32,000 2,321 0-32,000

1. Inputs

Roughage (kg) 2,759 (2,050) 0-10,000 2,041 (3,104) 0-40,000 2,230 0-40,000

Concentrate (kg) 500 (1,044) 0-7,200 195 (370) 0-2,400 275 0-7,200

Labor (man days) 4 (2) 2-8 4 (2) 1-10 4 1-10

Health expense(Birr) 120 (105) 0-500 108 (106) 0-720 111 0-720

Breeding expense(Birr) 14 (36) 0-300 7 (14) 0-74 9 0-300

Hay purchase(Birr) 1,433 (1,506) 0-7,000 806 (1,587) 0-13,500 971 0-13,500

2. Inefficiency factors

2.1 Household characteristics

Age 47.2 (9.5) 28-75 48.4 (11.9) 22-83 48.11 22-83

Experience in farming (years) 23.17 (9.6) 8-56 24.85 (12.2) 5-60 24.41 5-60

Family size (no.) 7.36 (1.75) 4-11 6.76 (1.8) 2-12 6.92 2-12

2.2 Proxy factors to AIS

Education (years) 4.54 (2.27) 0-11 3.55 (2.66) 0-12 3.81 0-12

Farm size (ha) 1.64 (0.78) 0-3 1.46 (0.82) 0-3.75 1.51 0-3.75

Credit availability (1/0) 0.4 (0.49) 0-1 0.43 (0.5) 0-1 0.42 0-1

Extension visit (no.) 2.93 (1.3) 1-5 2.69 (1.16) 1-5 2.75 1-5

Training (1/0) 0.66 (0.48) 0-1 0.62 (0.49) 0-1 0.63 0-1

Off-farm income (1/0) 0.28 (0.45) 0-1 0.2 (0.4) 0-1 0.22 0-1

Group membership (1/0) 0.96 (0.19) 0-1 0.85 (0.36) 0-1 0.88 0-1

Standard deviations are given in parenthesis.

60 years. On average, dairy farmers have five

household members, four years of formal schooling,

and own 1.51 hectare of land. Of the 304 farmers,

63% have attended technical training on dairying,

42% and 22% have had access to credit and off-farm

income, respectively, and 88% are members at least in

one farmers group. On average, mean milk output

value in the study area is 2,321 Birr, with a very high

variation among farms. Farmers used on average

2,230 kg of roughage, 275 kg of concentrate, and hay

at a value of 971 Birr per farm, but the variation is

quite large.

The descriptive statistics of variables for adopters

and non-adopters show that there is mean output and

input use difference. This is because adopters of dairy

technology have a higher use of land and intermediate

inputs to increase productivity. The mean output of

milk for farmers within the adopters group was about

nearly three-fold than non-adopter farmers. The

roughage, concentrate, health, breeding and hay used

by the farmers within the adopters were about 35%,

156%, 11%, 100%, 78% greater than that used by

non-adopters.

Characteristics of household related variables (age,

farmers’ experience in dairying and household size)

and local level agricultural innovation system

indicators such as education, farm size, number of

extension visit, those participated in technical training,

off-farm income availability and membership in a

farmers group vary slightly across groupings, and

adopters exhibit the highest. More non-adopters (43%)

than adopters (40%) reported having credit access.

3.3 Empirical Model

As in Battese and Coelli [24], this paper follows a

two step estimation model. The first step involves the

specification and estimation of the stochastic frontier

production function and the prediction of the technical


157

inefficiency effects, under the assumption that these

inefficiency effects are identically distributed. The

second step involves the specification of a regression

model for the predicted technical inefficiency effects.

Step 1 Estimating the Stochastic Frontier

We introduce here the Cobb-Douglas form of a

standard stochastic frontier production function model.

The Cobb-Douglas functional form is chosen because

it provides an adequate representation of the

production process, since we are interested in an

efficiency measurement and not an analysis of the

production structure [39]. In addition, the

Cobb-Douglas (CD) functional form (in spite of its

restrictive properties) is used because its coefficients

directly represent the elasticity of production. It is also

widely applied in farm efficiency analysis for both

developing and developed countries [26, 40-42].

The following model is estimated using Frontier

4.1c program [22]:

ii

k

ikkoi UVXY 6

1

lnln (3)

where iY is the value of milk output for observation

i in Ethiopian Birr1; 1X quantity of roughage fed

to cows in kg, 2X quantity of concentrate fed to

cows in kg, 3X family labor man days, 4X veterinary expense in Birr, 5X breeding cost in Birr,

6X value of hay purchased to fed the cows in Birr;

o is intercept and k is an xk1 vector of

parameters to be estimated; iV is 2,0 viidN

random stochastic disturbance term, independently

distributed of the iU ; and iU is a non-negative

random variable associated with the technical

inefficiency of production which is assumed to be

independently distributed.

Step 2 Identifying Sources of Technical Inefficiency

The following multiple regression model was fitted

for explaining technical inefficiency iTE1 for

Cobb-Douglas stochastic frontier production function:

jiii XU 0 (4)

where, iU is technical inefficiency; iX represents 1Ethiopian Birr = US$0.06.

explanatory variables including: age of the household

head (years), farmers’ experiences in farming (years),

family size (no.), education, i.e., years of schooling of

the household head, farm size (ha), availability of credit

(binary), access to extension services (categorical),

availability of technical training opportunity on

dairying (binary), off-farm income (binary), affiliation

to farmers’ group (binary); 0 is the intercept; i is

the unknown parameters to be estimated; and j the

unobservable random disturbance term.

The maximum-likelihood estimates for all the

parameters of the stochastic frontier and inefficiency

model, defined by Eqs. 3 and 4, are simultaneously

obtained by using the program, Frontier Version 4.1c

[22], which estimates the variance parameters in terms

of the parameterization: 222uvs ; (5)

and 2

2

s

u and 10 (6)

where the parameter must lie between 0 and 1.

The technical efficiency of production of the thi farmer in the appropriate data set, given the level

of his inputs, is defined in terms of the observed

output by *iY to the corresponding frontier output iY , that is iiiiiiiii UVxUVxYYTE expexp/exp/ * (7)

Therefore, the technical efficiency of a farmer is

between 0 and 1 10 TE and is inversely

related to the level of the technical inefficiency effect

[32]. The technical efficiencies can be predicted using

the Frontier program which calculates the

maximum-likelihood estimator of the predictor for Eq.

7 that is based on its conditional expectation [43].


The maximum likelihood (ML) estimates of the

parameters of the Cobb-Douglas stochastic frontier

production function and the inefficiency model are

presented in this section. The results are presented for:

(1) pooled data; (2) dairy technology adopters and

non-adopters; (3) men and women dairy farmers; and


158

(4) variation across districts. First, we present the

coefficients for the stochastic production function and

then present the technical inefficiency coefficients and

its determinants.

4.1 Pooled Data

4.1.1 The Stochastic Production Frontier Estimation

of Smallholder Dairy Farmers

The results of the maximum likelihood estimates of

the stochastic frontier production functions for dairy

milk production are presented in Table 3. Findings

reveal that coefficients of concentrate, labor and

breeding are found to be positively significant to the

dependant variable dairy milk output. The positive

coefficient of concentrate, labor and breeding with

respect to milk production implies that the higher the

use of these inputs, the higher the total level of milk

production.

From the nature of the Cobb-Douglas production

function fitted, since the model is a log linear model,

the coefficients represent elasticity of output with

respect to the respective inputs. Production elasticities

indicate the percentage change in output relative to a

percentage change in input if other factors are held

constant. Accordingly, the elasticity of milk output

with respect to concentrate is 0.1078 meaning that

10% change in the total concentrate use will bring

about 1.08% change in the output of milk production

Table 3 ML estimates of the Cobb-Douglas production frontier and inefficiency models. Variable Estimate Standard error t-statistics

Stochastic frontier production

Constant 7.7526 0.4670 16.6016***

Ln roughage -0.0445 0.0425 -1.0477

Ln concentrate 0.1078 0.0321 3.3558***

Ln labor 0.4599 0.2141 2.1483**

Ln health 0.0694 0.0546 1.2707

Ln breeding 0.1826 0.0646 2.8274***

Ln hay -0.0041 0.0242 -0.1682

Inefficiency model

Constant -19.9794 42.9463 -0.4652

Household characteristics

Age -0.7184 1.4639 -0.4908

Age2 0.9751 1.4815 0.6582

Family size 1.3106 1.2822 1.0221

Knowledge and education domain

Education -1.6999 1.6947 -1.0031

Business and enterprise domain

Group membership 1.9282 7.8305 0.2462

Bridging institutions domain

Extension -0.4203 1.7345 -0.2423

Training 0.3434 4.6725 0.0735

Enabling environment domain

Credit availability 1.1133 3.3495 0.3324

Off-farm income -5.1824 7.4664 -0.6941

Farm size -4.7861 5.1519 -0.9290

Variance parameters 2 136.0373 125.1240 1.0872 0.9985 0.0014 736.9893***

Log likelihood function -701.7271

LR test (one-sided error) 153.0634***

*** significance at the 1% level; ** significance at the 5% level.


159

if other factors are held constant. Labor has an

elasticity of 0.4599 meaning that for 10% change in

labor input; output of milk will change by 4.6%. The

same goes for breeding input with an elasticity of

0.1826 meaning that a 10% change in the expenditure

on breeding will bring about a 1.83% change in the

output of milk production in the study area.

4.1.2 Inefficiency Model

The inefficiency effects described above were

estimated against the components of the household

characteristics and the local level innovation systems

approach (Table 3). The result shows that all the

selected variables in the model produced

non-significant coefficients to the inefficiency model.

The inefficiency model, although statistically not

significant, age, education, farm size, extension visit

and off-farm income opportunity are having negative

sign as expected, indicating that these factors led to

decrease in technical inefficiency or are important

factors to increase production efficiency in the dairy

production system. The coefficients in the inefficiency

function are inefficiency effects and therefore a

positive coefficient implies a negative effect on

performance while a negative sign indicates a positive

impact on efficiency.

Among the variables representing efficiency effects

from the AIS framework in Table 3, measures of

education in the knowledge and education domain

shows the expected effects in reducing inefficiency.

The role of education in technology adoption has been

extensively documented. Schooling has been shown to

provide substantial externality benefits by increasing

farm output and shifting the production frontier

outwards [32, 44, 45]. In the business and enterprise

domain, group membership of the household head is

found to be non-significant and negatively related

with dairy production efficiency despite our

expectation that it increases dairy production

efficiency by facilitating input-output marketing. This

could partly be due to innovation is supply-driven by

extension rather than market-driven by product

demand and also may be due to the disaggregated

structure of dairy input-output marketing systems in

Ethiopia. Extension from the bridging institutions

domain is not significant, but it had the expected sign

which indicates that the involvement of extension

agents to visit dairy farmers tends to reduce the

technical inefficiency of milk production. In the

enabling institutions domain, off-farm income and

farm size are not significant, but they had the expected

signs which indicate both variables had positive

contribution to dairy production efficiency.

The significant gamma () statistic, which is a

measure of the overall, of 0.9985 indicates that about

99.85% variation in the output of milk production

would be attributed to technical inefficiency effects

(those under farmer’s control) alone while only

0.0015% would be due to random effects, i.e., beyond

the farmers control (Table 3). The high value of

parameter γ highlights the importance of inefficiency

effects in explaining the total variance in the model.

The level of technical efficiency is predicted

simultaneously with the estimated production function

and it was found that the mean technical efficiency is

about 26% (Table 4). Thus in the short run, there is a

scope for increasing dairy milk production by about

74% using the same level of inputs, but improved

management and resource reallocation. One of such

measures is addressing, the issue of negative elasticity

of dairy feed (roughage and hay), other non-significant

input (veterinary service) and improving the innovation

Table 4 TE frequency distribution and deciles range of dairy farmers.

Efficiency level Frequency %

0.80-0.89 4 1.3

0.70-0.79 21 6.9

0.60-0.69 13 4.6

0.50-0.59 20 6.6

< 50 246 80.6

Total 304 100

Mean efficiency 0.2617

Minimum 0.02

Maximum 0.85


160

capacity of the local level agricultural innovation

system actors. The cumulative and frequency

distribution of dairy farmers efficiency scores are

presented in Table 4. Above average (> 50%)

efficiency for 19.4% of the farmers in dairy

production could be the result of ILDP implemented

in the study area to reduce poverty and support the

enhancement of service delivery. Those activities

could have a long-term impact that could have

spillover effects to other non-project farmers.

4.2 Technical Efficiency of Dairy Technology Adopters

and Non-adopter Farmers

4.2.1 The Stochastic Production Frontier Estimation

The stochastic production frontier model estimates

and those for the technical inefficiency model for

adopters and non-adopter farm households are

presented in Table 5.

In the model, a coefficient of breeding is found to

be positively significant to the dependant variable

dairy milk output to both groups. The coefficient of

health and breeding are found to be significantly

positive in case of adopters. Similarly, the coefficients

of concentrate, labor and breeding are significantly

positive in case of non-adopter farmers. The

negatively non-significant coefficient of roughage and

positive but non-significant coefficient of hay inputs

in both groups imply no effect to the output.

The output elasticities of inputs for both adopters

and non-adopter farmers are variable. For the adopters

group, output elasticity of inputs was highest for

breeding (0.1798), followed by health input (0.1105).

In the non-adopters group, output elasticity of inputs

was highest for labor (0.5706), followed by breeding

Table 5 Maximum-likelihood estimates for parameters of the Cobb-Douglas stochastic frontier production functions for dairy technology adopter and non-adopter farmers. Variable Adopters Non-adopters

Stochastic frontier Coefficient Std.dev Coefficient Std.dev

Constant 8.4665 0.0696 7.0735 0.6692

Ln roughage (kg) -0.0296 0.0402 -0.0012 0.0598

Ln concentrate (kg) 0.0470 0.0613 0.1186*** 0.0457

Ln labor (man days) 0.2094 0.3088 0.5706*** 0.3046

Ln health expense(Birr) 0.1105*** 0.0437 0.0316 0.0843

Ln breeding expense(Birr 0.1798*** 0.1741 0.1790** 0.1013

Ln hay purchase(Birr) 0.0072 0.0509 0.0054 0.0400

Inefficiency model

Constant 0.1528 0.9992 -20.5590 29.29

Age 0.1548 0.2274 -0.5387 0.9774

Age2 -0.0996 0.2354 0.6632 0.9295

Family size (number) -0.7839 0.7971 1.8860* 1.2468

Education (years) -1.2337*** 0.4255 -0.2010 0.4045

Farm size (ha) 1.1279 0.9694 -3.4067 2.7478

Credit availability (1/0) -2.1958** 1.1189 2.7738 3.2894

Extension (number ) 0.3813 0.8949 0.1217 1.3739

Training (1/0) -0.9522 1.0179 3.0090 3.3153

Off-farm income (1/0) - 3.3906*** 1.2709 1.0874 4.2103

Group membership (1/0) 1.2093 1.0353 3.8079 5.2752

Variance parameters 2 17.2752*** 0.9451 78.1963* 53.3134 0.9999*** 0.0000 0.9958*** 0.0031

Log likelihood function -146.93 -531.73

LR test (one-sided error) 85.87*** 88.96***

The coefficients in the inefficiency function are inefficiency effects and therefore a positive coefficient implies a negative effect on performance while a negative sign indicates a positive impact on efficiency. Significant at * 10%, ** 5%, *** 1%.


161

(0.1790), and concentrate (0.1186). The overall results

indicate that four inputs, concentrate, labor, health and

breeding have a major influence on milk output of

both adopters and non-adopter farmers.

4.2.2 Inefficiency Model

In the inefficiency model (Table 5), although

statistically not significant, age, farming experience

(age2), family size, education, farm size, credit,

technical training, and off-farm income are negative

indicating important factors to increase production

efficiency in one or the other groups.

The bridging institutions domain (extension visit)

and business and enterprise domain (group

membership) proxies do not seem to be important

factors affecting farm household efficiency in both

groups. The explanation to this could be, extension in

Ethiopia has a supply driven nature and its quality is

very low and group memberships have no direct effect

and are less likely contribute to technology adoption

decisions [45].

Knowledge and education domain (education) and

among the enabling environment domain (credit

access and off-farm income opportunity) variables are

positive and significant factors affecting farm

household efficiency in the adopters group. The role

of education in technology adoption has been

extensively documented. Credit and off-farm access

also contributes to farmer adoption of new

technologies and practices by easing farmers’ liquidity

constraints.

Unlike the adopters group, the positive but

statistically significant coefficient for family size

variable in the non-adopters group indicating possible

excessive use of man power which is a problem of

allocative efficiency. The negative sign on age shows

that younger head of households are productive in the

non-adopters group and hence important factor to

increase production efficiency.

The parameter 2 and results show

significant at 1% level for both groups (except for

non-adopters group 2 that was significant at 10%).

The significance value of the 2 shows the presence

of inefficiency effects in dairy milk production in the

study area while the significant of 0.9999 and

0.9958 indicates that about 99.99% and 99.58%

variation in the output of the dairy milk production

would be attributed to random effects, for adopters

and non-adopter farmers, respectively.

The mean predicted technical efficiency for farmers

within the adopters group was estimated to be 0.40

with 0.25 standard deviations, while for non-adopter

farmers, the mean technical efficiency was 0.21

(Table 6). The fact that both groups have technical

efficiency levels below 50%, suggests a relatively

very low level of innovation capacity in the dairy

production system which resulted substantial technical

inefficiency in dairy production operations in both

adopters and non-adopter farmers given the available

technologies.

4.3 Technical Efficiency of Men and Women Dairy

Farmers

Disaggregating the dairy production data by gender

shows that women headed households were more

technically efficient than male headed households in

the study area. This could be due to the fact that

women spend substantial amounts of time doing

livestock activity [46]. This is denoted in Table 7 with

an efficiency score for the women farmers of 43.89%

compared to 23.54% for men farmers.

Table 6 Technical efficiency frequency distribution and deciles range of dairy farmers by adoption status.

Deciles range of TE

Adopters (N = 80) Non-adopters (N = 224)

Frequency % Frequency %

0.80-0.89 1.2 0.9

0.70-0.79 17.5 3.1

0.60-0.69 7.5 3.1

0.50-0.59 13.8 5.0

50 60 87.9

Total 100 100

Mean TE 0.40 0.21

Std.dev. 0.25 0.20

Minimum 0.03 0.02

Maximum 0.85 0.85


162

Table 7 Summary of farm efficiency descriptive statistics.

Variable Mean StD Min Max

Efficiency combined 0.2617 0.2280 0.02 0.85

Efficiency adopters 0.4000 0.2481 0.03 0.85

Efficiency non-adopters 0.2124 0.1987 0.02 0.85

Efficiency men 0.2354 0.2274 0.02 0.85

Efficiency women 0.4389 0.3757 0.03 0.85

Efficiency by District Gonderzuria 0.2190 0.03 0.85

Efficiency by Lay armachiho 0.2904 0.02 0.85

Efficiency by Wogera 0.3092 0.02 0.85

Efficiency by Debark 0.2061 0.02 0.77

4.4 Technical Efficiency of Dairy Farmers across

Districts

It is also possible to infer something about the

general status of TE in each district. For example,

Table 7 shows the predicted TE values. Values vary

from 20.6% in Debark to 30.9% in Wogera district.

Districts relatively with higher technical efficiencies

above the overall average are those progressing with

breeding, education, access to off-farm income

opportunity and group membership.

5. Conclusions

The paper uses a Cobb-Douglas stochastic frontier

analysis of production functions to estimate the level

of technical efficiency of dairy farmers in four

districts during 2011 production year. The stochastic

production function was modeled in such a way that

local level agricultural innovation systems framework

and indicators of its different domains (the knowledge

and education domain, the business and enterprise

domain, the bridging institutions domain and the

enabling environment domain) serve as an

environment that determines the level of technical

inefficiency. The production function and the

inefficiency effects were estimated simultaneously.

The results showed that the overall mean efficiency

score among sampled dairy farmers is about 26% and

there is room for significant increase of production by

reallocation of the existing resources. Despite

significant variation from the frontier, it is also

revealed that individual farm households’ efficiency

varied widely across dairy technology adoption status,

gender and districts. Accordingly, dairy technology

adopting farmers have relatively high mean efficiency

scores (40%) compared to non-adopters (21%).

Similarly, women farmers have relatively high mean

efficiency scores (44%) compared to men farmers

(24%) and hence in both cases calling for a focus on

efficiency enhancing investments. Education, farm

size, extension visit and off-farm income opportunity

were found to be efficiency enhancing.

We examined separately technical efficiencies of

dairy technology adopting and non-adopting

households. The results indicated significant

difference in productivity changes and technical

inefficiencies between adopters and non-adopter

farmers, which could be explained by various farm

specific and household characteristic variables. The

results show veterinary and breeding services are

important inputs and are strongly associated with total

output. The mean technical efficiencies calculated for

adopters (40%) and non-adopter farmers (21%),

indicating both groups working under inefficient

condition, with high potential for reducing inputs or

increasing outputs in the range of 60%-79%. The

obtained measures of efficiency indicate that

significant differences in productivity changes

between adopter and non-adopter households are

attributed to previous project support by ILDP.

The results suggest that the government might have

to address the technological and institutional

constraints of its extension services. Additionally,

since farmers are yet far from reaching the production

frontier, government may consider reforming the

current agricultural extension system by involving a

large number of service providers, including allowing

more private service delivery actors. This is supported

by the finding that in the adopters and non-adopters,

access to extension and group membership (a proxy to

marketing) was not at all responding to higher

household farm efficiency. We find that quality of the

agricultural extension and marketing services are


163

important if farmers in the dairy sector are to

significantly increase productivity. However, this

should be supported by reforming the current

agricultural extension system to address institutional

and policy issues that constrain effective agricultural

innovation system.

We note that the present study is based on data

from a single production period and specific locality, a

follow-up is recommended to examine national level

technical efficiency in the Ethiopian dairy system.

This would help policy makers in strengthening the

capacity of and investing more in extension and

marketing services.

Acknowledgments

The authors greatly acknowledge OEAD-Gmbh

(Austrian Agency for International Cooperation in

Education and Research), Sustainable Resource

Management Program (SRMP) of North Gondar Zone,

and Sustainable Water Harvesting & Institutional

Strengthening Project (SWHISA), both in Amhara

region of Ethiopia, for financing this study and

facilitating logistics support.

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Richness and Diversity of Ants and Beetles in

Genetically Modified Cotton Field in Brazil

Carla Cristina Dutra, Marcos Gino Fernandes, Josué Raizer and Camila Meotti Department of Entomology, Biological Science Faculty, Federal University of Grande Dourados, Rodovia Dourados-Itahum, Km 12,

Cidade Universitária, PO-Box 533, CEP 79804-970, Dourados, MS, Brazil

Received: October 9, 2012 / Published: February 20, 2013.

Abstract: Genetic engineering has created many genetically modified (GM) crop varieties that express the cry toxin from the

bacterium Bacillus thuringiensis (Bt). The cry toxin, synthesized during plant growth, has insecticidal properties, and can be

expressed anywhere in the plant. This study aimed to ascertain the richness and species diversity of edaphic Formicidae and

Coleoptera in GM cotton fields compared with the conventional non-transformed cotton crop. We analyzed data from commercial

cotton fields located in the municipality of Maracaju, Mato Grosso do Sul, Brazil. The experiment was conducted during the

reproductive period of cotton, employed two treatments: Bt cotton and non-Bt cotton. Samples were collected with pitfall traps.

Formicidae species richness in the Bt area was lower than in the non-Bt area, but species composition did not differ between the two

treatments. Species composition of Coleoptera communities also differed between the treatments because some species were more

abundant in the Bt cotton area. On the other hand, the species richness of this group was similar in both areas.

Key words: Bacillus thuringiensis, bollgard cotton, Bt crops, Coleoptera, Formicidae.

1. Introduction Cotton, Gossypium hirsutum (Malvaceae), has been

genetically modified (GM) by the insertion of the cry

genes, which code for insecticidal proteins cry (type

δ-endotoxin) from a common soil bacterium, Bacillus

thuringiensis (Bt). The cry proteins kill insects that feed

on Bt modified crops by causing the osmotic rupture of

the epithelial layer of the digestive tract, thus acting as

an efficient population control agent [1-3].

Bt cotton is cultivated in many parts of the world,

including Argentina, Australia, China, India, South

Africa and United States [3]. Nowadays, Brazil is the

second largest producer of GM crops, with production

area of 30.3 million hectares, trailing only the United

States (69 million ha) [4]. In Brazil high scale

production are soybeans, maize and GM cotton.

Cotton is the transgenic plant most extensively

cultivated in the mid-west of Brazil, this region is

Corresponding author: Carla Cristina Dutra, Ph.D., research fields: integrated pest management and risk assessment of transgenic plants. E-mail: [email protected].

responsible for 80% of the cotton produced in the

country [5]. The GM cotton Bollgard® (MON531),

legalized in Brazil in 2005 [6], expresses the Cry1Ac

protein, effective against caterpillars such as Heliothis

virescens (Lepidoptera: Noctuidae), Alabama

argillacea (Lepidoptera: Noctuidae) and Pectinophora

gossypiella (Lepidoptera: Gelechiidae).

The little available data on the effects of GM plants

on the biota indicates that transgenic crops have an

impact on the microorganisms that consume the

metabolic wastes they leak into the environment [7].

The main risk associated with Bt cotton, which results

from the accumulation of toxins in the soil, seems to

be associated with the decrease of local biodiversity

and the simplification of the functional dynamics of

soil organisms [8]. It is widely acknowledged that Bt

cotton plants produce the cry protein in the leaves and

flower buds [9], and exude them through the roots

[10]. Several factors modulate the accumulation of

transgenic toxins in the soil, for instance the amount

of toxins in the plant tissues, toxin resistance to

D DAVID PUBLISHING

Richness and Diversity of Ants and Beetles in Genetically Modified Cotton Field in Brazil

166

degradation, as well as the physical and chemical

properties of the soil [11, 12]. Toxin accumulation in

the soil may have an effect on the organisms

responsible for recycling organic matter, i.e.,

decomposers, thus reducing or even preventing

fragmentation of composts like cellulose,

hemicelluloses and lignin, with negative consequences

to plant productivity [10].

The soil encompasses more than 90% of the

biodiversity in agroecosystems [13], and the edaphic

fauna impacts the soil in various ways, as for instance

through excavation, ingestion and transport of organic

matter [14]. Two different groups of insects

representing the edaphic fauna are ants (Formicidae)

and beetles (Coleoptera). Ants, one of the dominant

edaphic groups [15], play an important role not only in

the redistribution of particles, nutrients and organic

matter in the soil, but also in the improvement of soil

permeability to water and air by the construction of

pores and underground ducts [16]. Besides their role in

soil maintenance, some generalists ants help protect

crops [17], for example by preying upon cotton pest

insects [18]. In a manner similar to ants, beetles, the

most diverse group of insects, also modify the physical

structure of the soil, prey upon different groups of

invertebrates [19], and have a role in the decomposition

of organic matter. Thus, ants and beetles, as other

organisms that are not targeted by the Bt toxins, can be

beneficial to agricultural settings [20].

Some field studies show no effect of Bt toxin from

plants on the diversity and abundance of non-target

insects [21-23]. However, on the field, there are

continuous exposures of non-target organisms to cry

protein from Bt plants. Know the diversity of

arthropods associated with crops is essential for

ecological studies and integrated pest management

(IPM).

This research aimed to describe the richness and

diversity of species of ants and soil beetles in two

contiguous cotton areas, one cultivated with GM

cotton crop resistant to insects (Bt cotton) and the

other area cultivated with conventional cotton crop

(non-Bt cotton).


2.1 Description of the Experimental Area

Sampling was conducted in a commercial cotton

field located in the municipality of Maracaju, Mato

Grosso do Sul, Brazil (21°36'52''S, 55°10'06"W,

elevation 384 m). Cotton sowing took place in

November, 2007. Two contiguous areas (20 m 100

m each) were delimited: one of them in a field

containing the Bt cotton (NuOpal®, Bollgard®,

MON531) cultivated in 50 hectares, and the other

cultivated with conventional cotton (DeltaOpal®) in

13 hectares. Two clearings were made manually and

insecticides and herbicides were not applied during

the developmental period of the plants.

2.2 Samplings

Beetles and ants were collected by pitfall traps

distributed between on the two treatment areas. Traps

were made from pet plastic bottles (2 L volume) cut in

half, with the upper part turned upside-down and

inserted on the lower half, making a funnel. The entire

trap was then inserted into the soil, maintaining the

upper border at ground level. The inside of the trap

was filled to 2/3 with sodium hypochlorite (0.1%), a

preservative, and a few drops of detergent (to break

the water tension and allow the insects to submerge).

Each trap was protected from the rain and other

impurities with a piece of wood (15 cm 15 cm)

secured 2.5 cm above the trap with metal rods. The

liquid inside the traps was replaced every three days

between January 8 and February 14, 2008

(corresponding to the reproductive period of the

cotton). There were 20 pitfall traps for each sampling

area, and the distance between the traps was random.

The insects collected were put in 70% ethanol for

posterior triage and initial identification at the

Entomology Laboratory at the Universidade Federal

da Grande Dourados (UFGD).


167

2.3 Species Identification

Species identification was conducted by taxonomists

at the Universidade Federal do Paraná (UFPR).

Morphospecies were assigned to the entities that could

not be identified to species. Voucher specimens from

this study were deposited in the Entomology Museum

of the UFGD and in the entomology collection Pe.

Jesus Santiago Moure, UFPR.

2.4 Statistics

In order to ascertain the main patterns of variation in

community structure, we only took into consideration

species that were presented in at least three traps. To

compare the means of both treatments, we used a

paired t-test per collecting day. Seeking to obtain

gradients of Formicidae and Coleoptera species

composition, we considered five samples from the

cotton genetically modified area (Bt), and five samples

from the conventional cotton area. Each sample

corresponded to four traps taken randomly from the

total of 20 pitfalls available for each area (drawing

without replacement). The frequency of occurrence on

the sample corresponds to the number of traps where

each species occurred (range from 0 to 4).

3. Results

With respect to ants, a total of 18 species in six

subfamilies were collected during the reproductive

period of cotton (Table 1). The nine species present in

at last three traps, used in the statistical analysis, are as

follows: Brachymyrmex sp.1, Camponotus sp.1,

Dorymyrmex sp.1, Labidus sp.1, Pheidole sp.1,

Pheidole sp.2, Pheidole oxyops, Solenopsis sp.1 and

Solenopsis invicta. Labidus sp.1 was found

exclusively in non-Bt cotton and Pheidole sp.1 and

Solenopsis sp.1 were found in all samples from Bt and

non-Bt areas.

Ant species richness was significantly higher in the

non-Bt treatment area (t = 3.05; gl = 8; P = 0.016),

with the exception of one sampling day, when the

reverse trend was observed. On that day, overall species

Table 1 Frequency of species of Formicidae registered in pitfall traps on a field of Bt and another non-Bt cotton, during the reproductive season, Maracaju-MS, Brazil, 2007/2008 crop season.

Taxon Fields

Bt cotton Non-Bt cotton

Formicinae

Brachymyrmex sp.1 66.66% 88.88%

Camponotus crassus 11.11% 0

Camponotus ruphipenis 11.11% 0

Camponotus sp.1 22.22% 11.11%

Dolichoderinae

Dorymyrmex sp.1 100% 88.88%

Dorymyrmex sp.2 11.11% 11.11%

Ecitoninae

Labidus sp.1 0 33.33%

Myrmicinae

Pheidole oxyopsis 22.22% 44.44%

Pheidole sp.1 100% 100%

Pheidole sp.2 22.22% 33.33%

Pheidole sp.3 0 22.22%

Solenopis invicta 11.11% 66.66%

Solenopsis sp.1 100% 100%

Solenopsis sp.2 11.11% 11.11%

Ponerinae

Hypoponera sp.1 11.11% 0

Hypoponera sp.2 0 22.22%

Pachycondyla striata 11.11% 0

Pseudomyrmecinae

Pseudomyrmex sp.1 11.11% 0

The species used in the statistical analysis, present in at least

three traps, are indicated in grey.

richness was the highest (Fig. 1). Most species

occurred with similar frequencies in both areas. In

only two samples, we registered a species that

occurred exclusively in one treatment (Fig. 2).

Therefore, the species composition of ants was very

similar in both treatments.

Regarding beetles, a total of 707 individuals in 54

species and 15 families (Table 2) were collected. Of

these, 27 species were present in at least three samples.

Four species were prevalent in the non-Bt area,

Calosoma granulatum, Tetracha sp. 1, Eriopis

connexa and Canthidium sp.1, and four other species

were ubiquitous in Bt cotton samples (Table 2):

Phloeonemus sp.1, Scirtes sp.1, Conoderus malleatus

and Heteroderes sp.1.


168

Fig. 1 Ant species richness in a field of genetically modified cotton (Bt) and a conventional cotton field (nBt). Each point pairs are corresponding to nine days during the cotton reproductive season, Maracaju-MS, Brazil, 2007/2008 crop season.

Beetle species richness did not significantly differ

between Bt and non-Bt treatment areas (t = 0.496; gl =

8; P = 0.633) (Fig. 3). Most of beetle species occurred

with similar abundance in both areas. However, some

species occurred with higher abundances in samples

of Bt cotton field, while other species were more

abundant in non-Bt cotton field (Fig. 4).

4. Discussion

The following ant genera, regularly encountered in

cotton crops of the Northeast and Midwest Brazil [24,

25], were most frequent in our samples: Solenopsis,

Pheidole, Camponotus, Labidus, Brachymyrmex and

Dorymyrmex. The species richness differences

between Bt and non-Bt area in this research are not in

agreement with the results of a similar study

conducted in the Cauca Valley, Colombia, where ant

species richness was higher in Bt cotton fields [26].

Other similar studies, have found little or no

differences in community composition of arthropods

[27, 28].

The ant genus Labidus is a generalist predator that

was found exclusively in samples from the non-Bt

cotton field, possibly due to the greater abundance of

prey in the conventional cotton field, which had not

Fig. 2 Species composition of ants in 10 samples (occurrences in four pitfalls by samples), five in a Bt cotton field and five in a non-Bt cotton field, Maracaju-MS, Brazil, 2007/2008 crop season.


169

Table 2 Number of specimens of Coleoptera registered in pitfall traps on a field of Bt and another non-Bt cotton, during the reproductive season, Maracaju-MS, Brazil, 2007/2008 crop season.

Taxon Field

Taxon Field

Bt Cotton Non-Bt Cotton Bt Cotton Non-Bt Cotton

Anthicidae Erotylidae

Formicilla sp.1 5 2 Pselaphacus sp. 1 0

Carabidae Histeridae

Calosoma granulatum 3 29 Phelister sp.1 1 0

Galerita sp.1 1 2 Nitidulidae

Scarites sp.1 1 3 Carpophilus sp. 141 143

Tetracha sp.1 1 13 Stelidota sp. 13 10

sp.4 5 1 Scarabaeidae

sp.5 1 2 Ataenius sp.1 2 4

sp.6 0 1 Leucothyreus kirbyanus 1 0

sp.7 0 1 Canthidium sp.1 19 81

sp.8 1 0 Canthon chalybaeus 2 2

Chrysomelidae Canthon histrio 1 0

Brasilaphthona sp. 5 8 Canthon sp.1 2 6

Systena sp.1 1 0 Coprophanaeus cyanecens 1 0

Phaedon consimilis 0 1 Coprophanaeus horus 0 3

Colaspis joliveti 5 1 Pseudocanthon sp. 1 1

Myochrous sp. 0 1 Eutrichillum sp. 0 10

sp.9 3 0 Scirtidae

Coccinelidae Scirtes sp.1 36 14

Cycloneda sanguinea 2 2 Silvanidae

Eriopis connexa 4 14 Ahasverus s.p 4 2

Hippodamia convergens 6 4 Staphylinidae

Hyperaspis festiva 3 4 Pinophilus sp. 1 0

Scymnus (Scymnus) sp. 4 11 sp.12 1 0

Colydiidae sp.13 0 1

Phloeonemus sp.1 7 0 sp.10 1 1

Curculionidae sp.11 2 7

Conotrachelus sp. 0 1 Tenebrionidae

Rhyssomatus sp. 0 1 Ctesia hirta 1 1

Sternechus subsignatus 1 0 Lagria villosa 0 1

Elateridae Blapstinus sp.1 1 1

Aeolus sp.1 2 2 sp.14 1 0

Aeolus sp.2 1 1

Conoderus malleatus 11 4

Heteroderes sp.1 4 0 Total of individuals 310 397

The species used in the statistic analysis, present in at least three traps, are indicated in grey.

been treated with insecticides. This result is consistent

with other studies that have found a gradient in the

abundance of predator species in non-Bt cotton not

treated with insecticides (lower abundance), when

compared with Bt cotton or non-Bt cotton treated with

insecticides (highest abundance) [29, 30]. However,

our results indicate that the exclusive occurrences of

ant species are sporadic in both fields.

The species P. oxyops was recorded in abundance

in both treatments. It is justifiable because it is a

species reported as the most abundant in several

agroecosystems [31].

The predominance of Pheidole and Solenopsis

during the reproductive period of cotton had been

previously noted in Brazil [25]. The two genera are

amongst the most diverse in the neotropic [32] and


170

Fig. 3 Beetle species richness in a field of genetically modified cotton (Bt) and a conventional cotton field (nBt). Each point pairs are corresponding to nine days during the cotton reproductive season, Maracaju-MS, Brazil, 2007/2008 crop season.

their component species are not only highly tolerant to

the physical conditions of the environment [33], but

also are very efficient in colonizing habitats with low

structural complexity, for example, environments have

suffered high anthropic disturbance [34]. Furthermore,

species of these genera play an important role in

agroecosystems, as they prey on larvae of A.

argillacea (Lepidoptera), an important pest of cotton

[35], as well prey other pests, non-target, such as

Helicoverpa armigera (Lepidoptera) [36] and

Anthonomus grandis (Coleoptera) [37].

Assessments of the presence of the endotoxin

Cry1Ab from Bt cotton in seven species of Coleoptera

(Carabidae), namely Agonum placidum, Bembidion

rupicola, Clivina impressefrons, Cyclotrachelus

iowensis, Harpalus pensylvanicus, Poecilus chalcites

and Poecilus lucublandus have shown that all

individuals collected in the field presented signals of

the cry protein, demonstrating that non-target species

can accumulate this compound. The implications of

this contamination are however not yet clear [38].

Most coleopterans collected from the non-Bt

cotton field are predatory species (e.g., C.

granulatum, Tetracha sp.1 and E. connexa). It is

possible that the strong presence of predators on the

conventional cotton field is due to the greater

abundance of prey that can not thrive as well in the

adjacent field because of the deleterious effects of

the cry protein to the target pests. In contrast, the

most species of Coleoptera collected in the Bt field

are herbivores (Phloeonemus sp.1, C. malleatus and

Heteroderes sp.1). This pattern may be determined

by the absence of some predators (as recorded in this

study) in the Bt field. Also in Brazil was conducted

field study that indicate there are no effects of Bt

maize on predators, for example, earwig (Dermaptera:

Forficulidae) and ladybird (Coleoptera:

Coccinellidae) [39].

When attempting to understand the richness of the

fauna on the Bt cotton, it is important to understand

how different organisms are affected by the

cultivation of these plants. Experiments using

organism that are vulnerable to several different toxins

in the soil, as for example earthworms (Annelida),

have prevailed in studies trying to understand how

transgenic plants affect edaphic, non-target species.

The Cry1Ab was not found to affect the total number

of nematodes and protozoan cultures in the digestive

tract of earthworms in soils containing B.

thuringiensis [40]. In addition, another study using

earthworms reared in Bt cotton fields concluded that

this treatment does not affect the growth or

development of Aporrectodea caliginosa (Oligochaeta)

[41]. Experiments using edaphic mite also arrived to

the conclusion that the Bt toxin does not alter the

survival and developmental rates of Scheloribates

praeincisus (Acari: Oribatida: Scheloribatidae) [42].

In regards to predators, the Bt toxin was also not

found to affect the survival or development of the

neuropteran Chrysoperla carnea (Chrysopidae) [20].

In conclusion, similarly to earthworms, Acari, and

neuropterans, ants and beetles are not targets of the Bt

protein, but it is possible that some characteristics of

their populations may be affected by it.


171

Fig. 4 Beetle species composition, in 10 samples (occurrences in four pitfalls trap by sample), five a Bt cotton field and five in a non-Bt cotton field, Maracaju-MS, Brazil, 2007/2008 crop season. We only present the species with more than five beetles, this facilitates the knowledge of the species turnover pattern between the areas. The gradient generated from the association these plots of relative abundance, recovered a pattern where species that occurred with highest abundances in samples of the Bt cotton field are represented in the figure base. Species with highest abundances in samples of the non-Bt cotton field are the other extremity in the figure top.

5. Conclusions

Through this study, we verified the species that

occurred in reproductive period of cotton in one

commercial field cotton (Bt and non-Bt plants) farm

in the Midwest of Brazil. Therefore, additional studies

are necessary through the years and in different

locations, thereby, to make clear enough about the

richness and diversity of ants and beetles. These

insects are very important in maintaining soil quality,

protecting plants from pests among other ecological

interactions.

Acknowledgments

The authors thank the UFPR team members: Dr.

Germano H. Rosado Neto, Paschoal Coelho Grossi,

Geovan Henrique Corrêa, Angelico F. Asenjo Flores

and Fernando Leivas for the identification of

specimens of Coleoptera; Stela de Almeida Soares for

the identification of the ants; Vitor Lemos Landeiro

(Instituto Nacional de Pesquisas da Amazônia) for the

help in the statistical analysis and “Fundação de

Apoio ao Desenvolvimento do Ensino, Ciência e

Tecnologia do Estado de Mato Grosso do Sul

(FUNDECT)” for the Masters scholarship to the

senior author.

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