Embed Size (px)
Citation preview
ISSN 0970-6429 2015, Vol. 28 Issue 1
Indian Journal of Hill Farming
The Official Publication of Indian Association of Hill Farming
Printed at : print21, R.G. Baruah Road, Ambikagiri Nagar, Guwahati-781024, e-mail :[email protected]
INDIAN ASSOCIATION OF HILL FARMING (Registered under Societies Act XII of 1983) Registration
No. SR/IAHF 439/87 of 1987
COUNCIL: Chief Patron Dr. S Ayyappan Patrons Dr. AK Sikka
Dr. KM Bujarbaruah Dr. M Premjit Singh
President Dr. SV Ngachan Vice President Dr. AK Tripathi
Dr. Arnab Sen Secretary Dr. Anup Das Joint Secretary Dr. Ramkrushna GI
Dr. Manoj Kumar Treasurer Dr. BC Verma
Councillors: Dr. IM Singh (Manipur) Dr. S Mukharjee (Nagaland) Dr. K Barman (Assam) Dr. JP Singh (Uttarkhand) Dr. AN Shylesha (Karnataka) Dr. Gulab Singh (Tripura) Dr. AK Handa (Uttar Pradesh) Dr. Doni Jini (Arunachal Pradesh) Dr. H Rymbhai (Meghalaya) Dr. Raghavendra Singh (Sikkim) Dr. AR Singh (Mizoram) Dr. JMS Tomar (Uttarkhand)
CHIEF EDITOR:
Dr. A Arunachalam
Associate Chief Editors: Dr. RK Singh, Dr. JK Bisht, Dr. Mayank Rai, Dr. I Shakutala.
Technical Editors:
Dr. C Aochin, Dr. Amrita Banerjee, Dr. J Layek
Editors: Dr. Debojit Sharma (Fisheries) Dr. AK Mishra (PGR) Dr. TK Bag (Pathology) Dr. KP Mohapatra (Forestry) Dr. AK Jha (Hort) Dr. NK Sharma (SWC) Dr. AK Singha (Extension) Dr. AK Mohanty (Extension) Dr. SS Roy (Hort) Dr. BU Choudhury (Soil Science) Dr. GT Behere (Entomology) Dr. D Thakuria (Soil Biology) Dr. K Puro (Animal Health) Dr. Amod Sharma (Economics) Dr. N Haque (Animal Science) Dr. UK Behera (Agronomy) Dr. Med Ram Verma (Statistics) Dr. Arvind Kumar (Agril. Engg) Dr. BC Verma (Soil Science) Dr. Ashish Yadav (Hort) Dr. Suresh Kumar DS (Animal Science) Dr. Ram Singh (Economics) Dr. P Baiswar (Pathology) Dr. Premila Devi (Bio-chemistry) Dr. R Laha (Animal Science) Dr. D MandaI (SWC)
National Advisory Committee:
Dr. BP Bhatt (Patna) Dr. AS Panwar (Umiam)
Dr. A Pattanayak (Almora) Dr. RK Saha (Agartala)
Dr. NS Azad Thahkur (Umiam) Dr. SB Singh (Mizoram)
Dr. SM Deb (Dirang) Dr. RK Avasthe (Sikkim)
Dr. DR Singh (Gangtok) Dr. S Chandra (Umiam)
Dr. Abhijit Mitra (Nagaland) Dr. DJ Rajkhowa (Umiam)
Dr. DK Sarma (Guwahati) Dr. JM Laishram (Imphal)
Dr. HS Gupta (Delhi) Dr. SK Das (Umiam)
Dr. PK Mishra (Deharadhun) Dr. GN Hazarika (Jorhat)
Dr. A Singh (Bhimtal) Dr. SK Dhyani (Delhi)
Dr. Ch. Srinivasa Rao (Hydrabad) Dr. BC Deka (Nagaland)
Dr. NP Singh (Goa) Dr. M Datta (Tripura)
Dr. R Bhagawati (Arunachal Pradesh) Dr. N Prakash (Manipur)
Dr. PK Ghosh (Jhansi) Dr. Alemla Ao (Nagaland)
International Advisory Committee:
Dr. Rattan Lal (USA) Dr. Sohela Aktar (Bangladesh)
Dr. R Darai (Nepal) Dr. D Gaydon (Australia)
Dr. H Pathak (India) Dr. HS Gupta (India)
Dr. PK Joshi (IFPRI) Dr PO Adebola (South Africa)
The Indian Journal of Hill Farming (Indian Journal Hill Farming) is the official organ of
the Indian Association of Hill Farming, Barapani (Umiam), Meghalaya, India and is devoted to
original research on all aspects of agriculture, Animal Husbandry, Fisheries, Forestry, Ecology
and other related fields pertaining to Hill Farming. The Journal published twice a year, includes
communications in form of research papers, review papers, short notes and book reviews. For
Submitting paper online please visit - http://epubs.icar.org.in/ejournal/index.php/IJHF
Subscription may be sent by Cheque/Draft/M.O./Online transfer in favour of “Indian
Association of Hill Farming” (if sent by post to Treasurer, Indian Association of Hill Farming,
C/o ICAR Research Complex for NEH Region, Umiam (Barapani) -793103 (Meghalaya),
India), with extra collection charge on outstation cheques.
Annual Subscription
Rs. India
500/- Abroad $ 100
Institution Rs. 10000/- $ 200
Life member Rs. 3000/- $ 1500
Student member Rs. 1500/- $ 1000
Donor member Rs. 20,000/- $ 5000
Registration fee Rs. 100/-
Indian Journal of Hill Farming Reg. No. SR/IAHF 439/87 of 1987 ISSN 0970-6429
Vol. 28 June 2015 No.1 Contents
SL No Title Page
1. Collection and Documentation of Vegetables and Fruits in Kurseong Region of Darjeeling of West Bengal Bani Sharma, A.N. Dey, Anjali Kumari and Nazir A. Pala
1-6
2. Effect of Equilibrium pH on Phosphate Potential and Equilibrium Phosphate Potential in Acid Soils of Karnataka T. Ramesh , R. Ananthanarayana, S. Hazarika, B.U. Choudhury, Manoj Kumar, B.C. Verma, P. Moirangthem and S.V. Ngachan
7-11
3. Gastrointestinal Parasitism in Turkeys and Quails of Umiam, Meghalaya M. Das, R. Laha, A. Goswami and S. Doley
12-13
4. Host Plant Resistance and Yield Loss Due to Anthracnose Caused by Colletotrichum linemuthianum in French bean (Phaseolus vulgaris) Nirmala Maibam, Satish Chandra, Pankaj Baiswar, D. Majumde and Kanchan Saikia
14-18
5. Level of Adoption and Perceived Constraints in Scientific Rabbit Farming Practices in Darjeeling Himalaya Rakesh Roy
19-22
6. Rainfall Characteristics , Pattern and Distribution at Cherapunjee, Meghalaya Lala I.P. Ray, P.K. Bora, V. Ram, A.K. Singh, R. Singh and S.M. Feroze
23-26
7. Response of Levels of Inorganic Fertilizer with Organic Manure on Potato in Aquic Hapludoll of Himalayan Foothills Dibyendu Chatterjee and Jaya Srivastava
27-34
8. Socio-economic Analysis of Ginger Crop in Himachal Pradesh Sukhjinder Singh and Sharanjit Singh Dhillon
35-42
9. Studies of Enzyme Glutamine Synthetase (GS) in Sesuvium portulacastrum (L.), an Associate Halophyte Anil Avhad and Himanshu Dawda
43-49
10. Traditional Agriculture Tools and Implements Used in Wokha, Nagaland L. Kanta Singh, S. Roma Devi and Meitram Hemerjit Singh
50-55
11. Variability Studies for Seed and Seedling Traits in Calophyllum inophyllum (1.) at South India Palani Kumaran
56-62
12. Comparative Performance of Puddlers in Low lands of Hilly Areas Arvind Kumar, S. Mandal, R.K. Singh and M.B. Tamhankar
63-68
13. Effect of Weather Parameters on Population Build-up of Different Insect Pests of Rice and their Natural Enemies H. Kalita, R.K. Avasthe and K. Rameash
69-72
14. Influence of Canopy Pruning on Orange and Rhizome Yield of Intercrop Ginger under Agri-Horticulture System Tasso Tabin, D. Balasubramanian and A. Arunachalam
73-76
15. Extension of Shelf Life to Tomato Using KMnO4 as Ethylene Absorbent
A. Nath, Bandita Bagchi, V.K. Verma, H. Rymbai, A. K. Jha and Bidyut C. Deka 77-80
1
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 1-6
Collection and Documentation of Vegetables and Fruits in Kurseong Region of Darjelling District of West Bengal
Bani Sharma • A.N. Dey* • Anjali Kumari • Nazir Pala Department of Forestry, Uttar Banga Krishi Viswavidyalaya, Cooch Behar 736165, West Bengal.
ARTICLE INFO ABSTRACT Article history: Received 26 December 2014 Received Revised 15 April 2015 Accepted 17 April 2015 ----------------------------------------------- Key words: Homegarden Vegetable Protein Brassica -----------------------------------------------
The study was carried out in five gram Panchayats of Kureseong block viz. Ghayabari, Tindharia, Singel, Mahanandi and Rongtong of Darjeeling district of West Bengal. A total 41 species of vegetables and 10 species of fruits utilized by local communities were documented as cultivated or planted in their homegardens of this surveyed region. These species are belonging to 31 genera and 19 families respectively. The most cultivated vegetable was found to be Sechium edule followed by Brassica juncea, Colocassia esculenta, Utica parviflora. The dominant genus was represented by Brassica followed by Capsicum. Rosaceae with five species was the dominant family among the utilized fruit species. Though not extensively but Citrus grandis, Musa spp., Psidium guajava, Pyrus pyrifolia, peaches and plums are grown here for the sustenance of the livelihood. The reported vegetables and fruit plants are very nutritious vitamins, proteins, polypeptides and flavonoids. Therefore, sustainable management of these resources for the wellbeing of the local communities as well as to conserve biodiversity is needed.
1. Introduction
The people of the Kurseong region are mostly dependent on the homestead gardens and tea estates or traditional farming. This area is endowed with unique physiographic and enormous plant genetic resources and diversity because of the wide variation in climate and ecological diversity. It is considered to be native of many leafy green vegetables and fruits which remain underutilized and even if unexplored. People of small land holdings and mainly cultivate Sechium edule, Zea mays, Zingiber officinale and Brassica juncea. Uses of edible plants and locally available vegetable have played an important role in human life since time immemorial particularly in this hilly region. These vegetables are grown in wild or semi-wild conditions and need less care and attention. In remote rural areas, local inhabitants depend on indigenous vegetable either grown in their kitchen gardens or collected from wild for enriching the diversity of food (Sundriyal and Sundriyal 2001: Mishra et al. 2008) than several known common vegetables.
__________________ *Corresponding author: [email protected]
These wild, green, leafy vegetables and fruits play a vital contribution to the diet in the life of rural people as they are a rich source of various nutritive macro and micro elements including pro-vitamin which can compensate for the dietary deficiencies of vitamins and minerals for human diet. Moreover, their consumption gives diversity to daily food intake, adding flavours to the diet (Asfaw 1997). The phyto-chemicals in vegetable also protect human beings from various ailments, as a result vegetable are considered as protective food (Rai et al. 2004).
Due to various natural and anthropogenic reasons natural
resources of wild vegetables, fruits and their habitats are depleting rapidly (Bhogaonkar et al. 2010). Besides this, modern agricultural systems have succeeded in providing calories, but in the process, they have increased `hidden hunger’ (micronutrient malnutrition) by displacing edible local plants (Ross and Graham 1997). So, cultivation of these vegetables and fruits will not only provide balanced nutrition, food security, health security but also helps to reduce poverty alleviation through the sale of the surplus of these vegetables and fruits
2
Which ultimately serves as an alternative to the usual
agriculture crops. Due to the paucity of sufficient information of these vegetable and fruits, an attempt has been made to enlist the available vegetables and fruit plants mostly used by local communities to assess their potential in the nutritional security point of view in Kurseong subdivision of Darjeeling in the present study.
Questionnaire was prepared to collect the information
regarding the vegetable and fruits used by local people. Discussions were held with the elders of the local regarding the use of the plant parts. The nutritive value of the plants was referred from secondary literature including different articles and websites. Identification of the plants was done with the help of the local people and the unidentified plants were identified from the local floras and experts from Department of Forestry, Uttar Banga Krishi Bhawan Viswavidalaya, Pundibari, Cooch Behar, West Bengal.
Figure 1. Location of Kurseong area in West Bengal
2. Materials and methods The study was carried out in five gram panchayats namely Gayabari, Rongtong, Singhel, Mahanadi and Tindharia of Kurseong block of Darjeeling district of West Bengal (Figure 1). The study area lies in the lower hill of Himalaya with an elevation of 6,710 ft (2,045.2 m). The soil is chiefly composed of sandstone and conglomerate formations, which are the solidified and up heaved detritus of the great range of Himalaya. However, the soil is often poorly consolidated and is not considered suitable for agriculture. The study site has a temperature climate with wet summers caused by monsoon rains. The mean annual maximum and minimum temperatures are recorded as 160C and 90C, respectively. On an average, the average annual precipitation is 309.2 cm, with an average of 126 days of rain in a year (Malley 1999). The heavy and concentrated rainfall is experienced in the region, aggravated by deforestry and haphazard planning, often causes devastating landslides, leading to loss of life and property (Sarkar 1999). Data was collected through a combination of tools and technique of questionnaire, PRA techniques. The information thus gathered was compared with available literature sources as cited by Dey et al. (2007).
3. Results and Discussion
A total 41 species of vegetables utilized by local communities were documented from the surveyed region. These species are belonging to 31 genera and 19 families, are presented in Table 1. Mekonnen et al. (2014) also reported 69 species belonging to 40 families from homegardens of Ethiopia. The number of reported species is less as only vegetables and fruits into account. Solanum betaccum was only single tree species is less as only single tree species whose fruits were used by local communities as vegetables while rest of the species comprise of herbs and shrubs. The dominant genus was represented by Brassica followed by Capsicum, Solanaceae was the dominant family represented by six species followed by Cucurbitaceae with five species. Fruit (12 species) was the dominant plant part used as vegetable followed by leaves (10 species), shoots (7 species), and whereas least utilized plant part was held, fronds, inflorescence and corn of one species each. Ten species of fruits belonging to eight genera and six families available in the locality utilized by the communities were also documented (Table 3). Rosacea with five species was the dominant family among utilized fruit species. The plant species like Zea mays and Prunus species have been also reported in other homegarden studies (Larato 2012).
3
Table 1. Vegetables species found in the study area and their pattern of use
Scientific Name Local Name Family Part Use Amaranthus caudatus L. Laal Saag Amaranthacea Leaves and shoot Amatanthus tricolor L. Laal Saag Amaranthacea Leaves
Bambusa tulda Roxb. Tama Baas Poacea Tender shoot Beta vulgaris L. Beet Chenopodiaceae Root
Brassica campestris L. Tori Saag Brassicaceae Leaves Brassica junecea L. Rayo Saag Brassicaceae Leaves
Brassica oleracea L. Banda Gobi Brassicaceae Head Brassica oleracia L. Fulgobi Brassicaceae Curd
Brassica oleracia L. var botrytis Brocauli Brassicaceae Curd Capsicum frutescences L. Khorsani Solanaceae Fruit
Capsicum frutescens var. Conoides (Mill.) L.H. Bailey
Jire Khorsani Solanaceae Fruit
Capsicum sp L. Dalle Khorsani Solanaceae Fruit
Colocasia esculenta (L.) Schott Karkalo Araceae Tuber, leaves, shoot Coriandrum sativum L. Dhaniya Umbelliferae Leaves and Shoots
Cusumis sativus L. Kakro Cucurbitaceae Fruit, Leaves, tender Cucurbita pepo L. Pharsi Cucurbitaceae Fruit, leaves, tender shoot
Curcuma domestica Val. Hardi Zingiberaceae Root Daucus carota L. var. sativa DC. Gajar Umbelliferae Root
Dioscorea alata L. Ghar tarul Dioscoreaceae Root Dioscorea spp L. Bun tarul Dioscoreaceae Root
Diplazium esculentum (Reta.) Sw. Niuro Athyricaeae Fronds Ipomoea batatus (L.) Lamk. Sakkar khanda Convovulaceae Root
Luffa cylindrical auct. Pl. Non M.J. Roem. (Syn. L. aegyptiaca Mill.)
Ghiraunla Cucurbitaceae Fruit
Lycopersicon esculentum (L.) Karst. Tamatar Solanaceae Fruit Manihot esculenta Crantz Simal tarul Euphorbiaceae Root Momordica charantia L. Tite Karela Cucurbitaceae Fruit
Momordica cochinchinenesis Spreng. Chatela Cucurbitaceae Fruit Moringa oleifera Lamk. Sajna Moringaceae Pods
Musa paradisiaca L. Bunga Musaceae Inflorescence Nasturtium officinale R. Br. Simraya Brassicaceae Leaves and shoot
Phaselous vulgaris L. Simi Leguminosae Pods and seeds Pisum sativum L. var. arvense (L.) Poir. Matar Leguminosae Pods and seeds
Raphanus sativus L. Mula Brassicaceae Root Sechium edule (Jacq.) Sw. Ishkush Cucurbitaceae Fruit, leaves, tender shoot
and root Solanum betaceum Cav. Rukh Tamatar Solanaceae Fruit
Solanum sp Bee Solanaceae Fruit
Solanum tuberosum L. Alu Solanaceae Root
Urtica ardens Link Sisnu Urticaceae Leaves and shoot Vigna sinensis L. Bodi Leguminosae Pods and seeds
Zea mays L. Makai Poaceae Corn Zingiber officinale Rose. Aduwa Zingiberaceae Root
4
Table 2. Vegetables species with their nutritive values
Scientific name Nutritional value
Amaranthus caudatus L. Rich in energy, proteins, vitamins and minerals Amaranthus tricolour L. Rich in Vitamin - A and minerals like iron and calcium.
Bambusa tulda Roxb. Rcih in dietary fibres and Vitamin B- complex Brassica campestris L. Low in fats and cholesterol levels and rich in dietary fibres, vitamins, electrolytes and
mineral. Brassica junecea L. Rich in protein, vitamins , dietary fibres and electrolytes
Brassica oleracea L. Low in fat and calories, torehouse of phyto-chemicals like thiocyanates, indole-3-carbinol, lutein, zea – xanthin, sulforaphane, and isothiocyanates.
Brassica oleracia L. Rich in phyto-nutrients such as vitamins, indole-3-carbinol, sulforaphane Brassica oleracia L. var botrytis Rich in dietary fiber, minerals , vitamins , and anti-oxidants
Capsicum frutescences L. Rich in energy, vitamins specially Vitamin B9 and minerals
Colocasia esculenta (L.) Schott Good source of energy, carbohydrates and vitamin –B6 Coriandrum sativum L. Very good source of Vitamin A, C, E, K and folates.
Cusumis sativus L. One of the very low calorie vegetables, good source of dietary fibre, potassium, and electrolyte
Cucurbita pepo L. Good source of energy, carbohydrates ,fats, proteins, vitamins, electrolytes and minerals Curcuma domestica Val. Curcumin, a poly-phenolic compound present have anti – inflammatory, anti – tumour
and antioxidant properties. It is also rich in Vitamins and Minerals. Daucus carota L. var. sativa DC. Rich in beta carotene, dietary fibres, electrolytes and Vitamin-A
Dioscorea spp L. Good source of energy, carbohydrates, Vitamin –B complex, Minerals like calcium, iron and copper.
Diplazium esculentum (Reta.) Sw. Fresh frond sare very high in antioxidant vitamin –A and carotenes. Ipomoea batatus (L.) Lamk. The tuber is an excellent source of flavonoid phenolic compounds such as beta-carotene
and vitamin –A Luffa cylindrical auct. Pl. Non M.J. Roem. (Syn. L. aegyptiaca Mill.)
Rich in amino acid and fatty acid
Lycopersicon esculentum (L.) Karst. Have low fat and zero cholesterol levels and are excellent sources of antioxidants, dietary fibre, minerals, and vitamins.
Manihot esculenta Crantz Good source of energy, carbohydrates, phosphorus, electrolytes, Vitamin-C and fatty acids.
Momordica charantia L. Rich in polypeptide-P: a plant insulin known to lower blood sugar levels.
Momordica cochinchinenesis Spreng.
Rich in carotenoids especially beta- carotene and lycopene which is a cancer cell inhibitor.
Moringa oleifera Lamk. Fresh pods and seeds are a good source of oleic acid, a health benefiting monounsaturated fat.
Musa paradisiaca L Rich in energy, dietary fibres, potassium and Vitamin B-6
Nasturtium officinale R. Br. Rich source of Vitamin –C , Vitamin-K and Calcium
Phaselous vulgaris L. Low in calories contain no saturated fat, good source of vitamins, minerals, and plant derived micronutrients, dietary fibre vitamin A contain healthy amounts of minerals like iron, calcium, magnesium, manganese, and potassium.
Pisum sativum L. var. arvense (L.) Poir.
Rich source of protein, vitamins like thiamine and folate and iron.
Raphanus sativus L. Contains isothiocyanate anti-oxidant compound called sulforaphane which helps in cancer-cell growth inhibition
Sechium edule (Jacq.) Sw. Rich in Vitamin B9 (Folates), Vitamin- C and Vitamin-K , dietary fibres and electrolytes Solanum betaceum Cav. Rich in Vitamin –C, minerals specially magnesium and water content.
Solanum tuberosum L. Good sources of starch, vitamins, minerals and sietary fiber.
Urtica ardens Link Good source of energy, potassium, Vitamin-A and calcium Vigna sinensis L. Rich source of proteins and calories, as well as minerals and vitamins.
Zea mays L. Good source of energy, carbohydrates, fats, proteins, vitamins, electrolytes and minerals Zingiber officinale Rose. Rich in electrolytes particularly potassium, Vitamin B-6 and magnesium.
5
Table 3. Documented fruit trees utilized and their values/uses
Scientific name
Local name
Family Documented uses and other reported uses
Citrus grandis (L.) Osbeck
Bhogate Rutaceae It is a good source of energy and Vitamin – C
Emblica officinalis Gaertn.
Amala Euphorbiaceae It is highly nutritious and is an important dietary source of Vitamin - C, minerals and amino acids.
Musa paradisiaca L. Kera Musaceae It is a high calorie fruit, it contains good amounts of health benefiting anti-oxidants, minerals, and vitamins specially Vitamin- B9
Passiflora edulis Sims. Garandal Passifloraceae It is a rich source of antioxidants, minerals, vitamin A and C and fiber.
Prunus domestica L. Alubhakara Rosaceae It is rich in many vital vitamins and minerals such as potassium, fluoride and iron.
Prunus persica (L) Batsch Aru Rosaceae It is packed with numerous health promoting anti-oxidants, plant nutrients, minerals and vitamins.
Psidium guajava L. Ambak Myrtaceae It is low in calories and fats but contain several vital vitamins, minerals, and antioxidant poly-phenolic and flavonoid compounds
Pyrus communis L. Nashpati Rosaceae It is packed with health benefiting nutrients such as dietary fibre, anti-oxidants, minerals and vitamins.
Rubus ellipticus Smith Ainselu Rosaceae It is rich in carbohydrates, proteins and minerals. Rubus folilosus Hal csy
Kalo Ainselu
Rosaceae It is rich in carbohydrates, proteins and minerals.
The reported vegetables and fruit plants are very nutrients having contents like vitamins, minerals, proteins, polypeptides, flavonoids as presented in Tables 2 and 3. Wild vegetables and fruits may be of great importance as they remain the cheapest source of protein, vitamins, minerals, essential amino acids, bioactive compounds and also as source of dietary supplements or functional foods of many people (Lyimo et al. 2003; Sanchez-Mata et al. 2011). Over all, the people of Kurseong have rich Knowledge on use of edible plant species which provide seasonal, staple and nutraceutical foods. These plants are important alternative to the agriculturally cultivated crops. It shows that vegetable and fruit use is influenced by traditional knowledge, culture, and socio-economic conditions. Several vegetables and fruits can benefit local people not only as food, but also with their medicinal properties. These vegetables and fruits could also augment income generation, if managed sustainably. Government policies should be focussed on wild vegetables and fruits in rural areas for economic growth and food security. Therefore, sustainable management of these resources for the wellbeing of the local communities as well as to conservation biodiversity is needed as well as to preserve cultural value and to maintain eco fragile conditions of the hill region.
References Asfaw Z (1997). Conservation and use of traditional
vegetables in Ethiopia. In: L Guarino (ed) Traditional African Vegetables, Proceedings of the IPGRI International Workshop on Genetic Resources of Traditional Vegetables in Africa: Conservation and Use, IPGRI, Nairobi,pp 57.65
Bhogaonkar PY, Marathe Vishal R and PP Kshirsagar (2010). Documentation of Wild Edible plants of Melghat Forest, Ethnobot Leaflets 14:751-758
Dey AN, Datta S and S Maitra (2007). Traditional knowledge on medicinal plants for remedy of common ailment in northern parts of West Bengal. Ind For 133(11): 1535-1544
Larato Y (2012). An assessment of useful plant diversity in homegardens and communal lands of Tihakgameng, M.Sc the is North-west University.
Lyimo M, Temu RPC and JK Mugula (2003). Identification and nutrient composition of Indigenous vegetables of Tanzania. Plant Foods Hum Nutr 58:82-92
Malley LSSO (1999). Bengal District Gazetteer: Darjelling. Concept Publishing Company. P.15-16
6
Mishra S, Maikhuri RK, Kala CP, Rao KS and KG Saxena
(2008). Wild leafy vegetables: A study of their subsistence dietetic support to the inhabitants of Nanda Devi Biosphere Reserve. Ind J Ethnobio and Ethomed 4:15
Mekonnen EL, Asfaw Z and S Zewudie (2014). Plant species diversity of homegarden agroforestry in jabithenan district, North Western Ethiopia, International Journal of Biodiversity and Conservation 694): 301-307
Rai M, Singh J and AK Pandey (2004). Vegetables: A source of nutritional security. Ind Hort 48(4): 14-17
Ross M and RK Graham (1999). A new paradigm for world agriculture: Meeting human needs, productive, sustainable, and nutritious. Field Crops Res 60: 1-10
Sarkar S (1999). Landslides in Darjeeling Himalayas, India. Transactions of the Japanese Geomorphological Union 2013): 299-315
Sanchez-Mata MC, Cabrera Loera RD, Morales P, Fernandez-Ruiz V, Camera M, Diez Marques C, Pardo-de-Santayana M and J Tardio (2011). Wild vegetables of the Mediterranean area as valuable sources of bioactive compounds. Genet Resour Crop Evol 59:431-443
Sundriyal M and RC Sundriyal (2001). Wild edible plants of the Sikkim Himalaya: Nutritive values of selected species. Econ Bot 55:377-390
7
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 7-11
Effect of Equilibrum pH on Phosphate Potential and Equlibrium Phosphate Potential in Acid Soils of Karnataka T. Ramesh1* • R. Ananthanarayana • S. Hazarika1 • B.U. Choudhary1 • Manoj Kumar1 • B.C. Verma1 • P. Moirangthem1 • S.V. Ngachan1 Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences , Bangalore, 560065, Karnataka 1Division of Natural Resources Management, ICAR Research Complex for NEH Region, Umaim, Meghalaya-793103
ARTICLE INFO ABSTRACT Article history: Received 17 June 2015 Received Revised 24 June 2015 Accepted 25 June 2015 ----------------------------------------------- Key words: Available phosphorus, Phosphate potential, Soil pH -----------------------------------------------
A laboratory experiment was conducted to study the effect of pH on available phosphorus (P) (Bray and Kurtz No.1 extractable), phosphate potential (PP) and equilibrium phosphate potential (EPP) in four acid soils collected from different districts of Karnataka representing various agro-climatic zones. Significant changes in soil available P was observed with a unit increment in soil pH in all the soils. At pH 4.0, available P decreased in all the soils except soils from Bangalore compared to the initial P. On the other hand, increase in pH up to 7.0 increased the available P by 83, 38, 14 and 120% in soils from Bangalore, Shimoga, Mudigere and Uttar Kannada, respectively compared to initial soil P. The PP and EPP which measure negative logarithm of inorganic phosphate (H2PO4
-) ions concentration in soil solution, decreased with increase in pH from 4.0 to 6.0 irrespective of the soils. Further increase in pH up to 7.0 decreased the PP and EPP values in all the soils except in Mudigere soil. The changes in both PP and EPP values due to the changes in pH values were found to be significant. A negative and highly significant correlation was found to exist between available P and EPP.
1. Introduction
The available P in soil is influenced by several characteristics of the soil such as amount and types of clay, amounts of iron and aluminium oxides, organic matter, calcium carbonate, soluble silica, concentration of solution cations and anions, temperature and pH (Bolan et al. 1985; Kuo 1990). Among these characteristics, soil pH primarily influences the available P concentration in soil. However, soil pH does not affect the P availability directly. Instead, soil pH levels indicate how certain minerals (iron, aluminium and calcium minerals) interact with P in the soils, and it is the interaction that affects the P availability. In acid soils iron and aluminium concentrations are high because the minerals are soluble, while calcium concentration is low because the mineral has been dissolved and leached out of the soil (Curtin et al. 1993). When P reacts with these minerals, the products formed are not very soluble and the P in the insoluble compounds has a direct impact on the availability of P for crop growth. In Karnataka about 40 per cent of the soils are acidic and therefore __________________ Corresponding author: [email protected]
In Karnataka about 40 per cent of the soils are acidic and therefore appropriate management of phosphate fertilizer is a major concern for these soils (Ramesh and Ananthanarayana 2012). Soil testing for soil pH and the P level determines the amount of P needed for the crop to be grown. Several soil test methods have been developed, and Bray’s and Olsen’s soil test methods are commonly used in India to determine the available P content in the acid and neutral and alkaline soils, respectively. The soil solution is the key to plant nutrition because all P that is taken by plants comes from P dissolved in soil solution. Not like common soil P testing methods (Bray, Olsen, etc.), Schofield’s (1955) phosphate potential estimates the available P in soil solution. With some modification of Schofield’s phosphate potential, White and Beckett (1964) introduced the concept of EPP to measure the available P concentration in soil solution. The information on the use of these methods to predict the P availability with different pH in acid soils is very little India, particularly in Karnataka. Hence in this study, an attempt was made to investigate the influence of pH on soil phosphorus availability with these soil-testing methods.
8
2. Materials and methods
Four soils were collected from different agro-climatic zones belonging to different soil groups. The initial properties of the soils used for this experiment are given in table 1. The collected soil samples were air-dried and passed through 2 mm sieve for further analysis. In a laboratory experiment, 500 g of each soil was incubated with different pH of 4.0, 5.0, 6.0 and 7.0 by using 0.01 N HCl and 0.01 N NaOH and, the treatments were replicated thrice. The soils were incubated for one month under aerated condition. Moisture was maintained at field capacity throughout the incubation period by adding water at the rate of 35% field capacity. After one month, the soils were air-dried and used for the estimation of Bray and Kurtz No.1 solution extractable P, PP and EPP.
The PP of soil was determined by the procedure
suggested by Aslyng (1954). Twenty grams of soil was taken in a 100 mL polyethylene test tube. Fifty-milli litre of 0.01 M CaCl2 solution was added and shaken for one minute. The pH of the suspension was measured immediately with a glass electrode assembly. After taking reading for pH, the suspension was filtered and P concentration in the filtrate was determined by sulfomolybdenum blue colour method at 660 nm as described by Jackson (1967). Calcium concentration in the filtrate was determined by complexometric titration method (Schwartzenbach et al. 1946).
Phosphate potential (PP) = ½ p Ca + pH2PO4 Where, ½ pCa = -1/2 (log10 Conc. Ca + log10 fi) Where, Conc. Ca = Molar concentration of Ca ions in filtrate Activity co-efficient (fi) was calculated by Debye and Huckel’s (1923) equation, where fi = Activity coefficient
-log fi = Z2 A √μ
where, Ionic strength, μ = 0.5 Ci Zi2 A = 0.5 (constant) Zi = Valency of the particular ion Ci = Molar concentration of particular ion in solution H+ pH2PO4 was calculated by pP + p K" + H+
pP = log10 (P), where (P) = Total concentration of inorganic
phosphorus in solution, p = Correlation factor worked out by Aslyng (1954) and it is the K" + H+ proportion of H2PO4/P at different pH, where, H+ is the Hydrogen ion concentration; K" is the second dissociation constant of phosphoric acid and was calculated to be 7.0 when 0.01M CaCl2 was used.
Equilibrium phosphate potential was estimated by the procedure given by White and Beckett (1964) with some modification of various higher equilibrium P concentrations and longer shaking period of two hours. When relatively large amount of P was added, longer period of shaking was suggested by Jension (1971).
3. Results and Discussion
The soil available P content extracted by Bray and Kurtz No.1 solution, PP and EPP values observed after the incubation period are presented in table 2. It is clearly evident from the table 2 that initially available P content in all the soils increased with increase in pH up to 6.0 and, further increase up to pH 7.0 also increased the available P in all soils except in Mudigere soil. Bangalore soil recorded the maximum available P content of 15.8 mg kg-l followed by Uttar Kannada (11.9) and Mudigere (7.2) soils. Shimoga soil recorded the lowest available P content of 6.6 mg kg-l at pH 7.0. Compared to initial soil P, at pH 7.0 the maximum increase in available P content was recorded in soils from Uttar Kannada soil (120%) followed by Bangalore (83%) and Shimoga (38%) while Mudigere soil recorded the lowest increase of 14%. The changes in available P concentration with a unit increase or decrease of pH may simply be related to (i) the possibility that phosphate minerals are equilibrating with iron and aluminium phosphates controlling the low pH and calcium phosphates controlling the high pH end of the range (Sato and Camerford 2005) or (ii) P sorption reactions which are affected by pH may have contributed to these changes in available P concentration (Rodenburg et al. 2003). Since these changes have been found to be similar in native soils and with P addition, it seems that at high pH calcium phosphate is dominant, but at the lower pH range may be that predominantly sorption is increased by the oxides of iron and
aluminium in the soil (Sato and Camerford 2005). It is known that fixation of P is less at higher pH (White 1980). Perassi and Borgnino (2014) reported that P adsorption was consistently higher at high pH and negative values of P adsorption were found at low pH. This has been attributed to the perception of calcium with phosphate ion.
9
Table 1. Physico-chemical characteristics of the soils
Soil property Bangalore Shimoga Mudigere Uttar Kannada
Soil group Kandic Haplustalfs Fluvent Ustropepts Ustic Haplumults Aquic Ustorthents pH (1:2.5) 5.9 4.8 5.1 5.4 EC (1:2.5) dS m-1 0.11 0.12 0.04 0.12 Organic Carbon (g kg-1) 8.2 6.8 5.8 13.7 Available P (mg kg-1) 8.6 4.8 6.3 5.4 Texture SCL SL SL SCL Total Fe2O3 (%) 12.76 6.93 14.50 10.50 Total Al2O3 (%) 4.91 8.58 16.91 11.01 CEC (meq 100g-1) 12.8 18.3 12.3 12.9 PP* 7.40 7.60 7.46 7.31 EPP** 8.01 8.19 8.09 7.98
*PP: Phosphate potential; **EPP: Equilibrium phosphate potential Table 2. Effect of equilibrium pH on phosphate potential and equilibrium phosphate potential
Location pH Bray’s P (mg kg-1) PP* EPP**
Bangalore 4.0 5.0 6.0 7.0
10.8 12.2 14.0 15.8
7.70 7.50 7.31 7.18
7.98 7.69 7.52 7.31
Shimoga 4.0 5.0 6.0 7.0
4.0 5.0 6.2 6.6
7.74 7.31 7.21 7.09
8.25 8.07 7.78 7.63
Mudigere 4.0 5.0 6.0 7.0
5.2 6.4 7.4 7.2
7.40 7.20 7.08 7.10
8.23 7.99 7.85 7.87
Uttar Kannada 4.0 5.0 6.0 7.0
5.0 7.2 11.0 11.9
7.45 7.30 7.18 6.98
8.08 7.83 7.73 7.48
SEm CD
F Test
0.15 0.43 **
0.09 0.26 **
0.12 0.33 **
*PP – Phosphate potential **EPP – Equilibrium phosphate potential
Table 3. Relationships between pH and Brays P, phosphate potential and equilibrium phosphate potential
Location Brays’ P PP EPP
Bangalore 0.998** -0.996** -0.994**
Shimoga 0.983** -0.935** -0.993**
Mudigere 0.905** -0.899** -0.902**
Uttar Kannada 0.977** -0.995** -0.988**
** Significance at 1% level
Table 4. Correlation between soil properties and, phosphate and equilibrium phosphate potential
** Significance at 1% level
Soil properties PP EPP
pH -0.251 -0.787**
pOH 0.251 0.787**
Available P -0.664 -0.789**
Organic carbon -0.831** -0.556
Clay 0.041 0.380
10
The PP and EPP showed an opposite trend to available P as they measure the negative logarithm of inorganic phosphate (H2PO4
-) ion concentration in the soil solution. Shimoga soil recorded the highest values of PP (7.74) and EPP (8.25) followed by Bangalore and Uttar Kannada at pH 4.0. At pH 7.0, Uttar Kannada soil showed lowest PP (6.98) while soils from Bangalore recorded lowest EPP (7.31). However, the per cent decrease in PP was highest in Shimoga soil, while for EPP, it was highest in Bangalore soil in relative to the initial soil PP and EPP values. The relationships between the pH and Brays P, PP and EPP are given diagrammatically (Figure 1). The lowest amount of P in Shimoga soil may be related to its higher content of iron and aluminium oxides, which are responsible for phosphate retention, results in low availability of P (Ramesh and Ananthanarayana 2012). In general, the values of PP and EPP decreased with increasing pH up to 6.0 in all the soils and, further increase up to 7.0 also decreased these values in all the soils except Mudigere soil. At the pH about 5.5 and below, the concentration of Al ions is more (Brady and Weil 2002). Below this pH values, soluble Al, Fe and Mn react with H2PO4
- ions resulting in the formation of insoluble hydroxyl phosphates (Gosh 2015), which might have caused the increase in PP and EPP values. Iron and aluminium phosphates have a minimum solubility around pH 3.0-5.0.
At higher pH values, some of the bound P is released and the fixation capacity is somewhat reduced. As the pH approaches 6.0, P precipitation as calcium compounds occurs that is soluble and at pH 6.5, the formation of slightly soluble calcium minerals is the key factor in reducing P availability. Above pH 6.5, even more insoluble P is formed which might have increased the PP and EPP values in all the four soils. The reaction of P with Fe and Al and their hydrous oxides result in stronger P binding at the lower pH values (Kumar 2015). On the other hand, above pH 7.0, Ca and Mg ions and carbonate result in added P to precipitate and the available P decreases. It has been stated that P concentration in pH range of 5.0 to 8.5 is governed by the calcium phosphate minerals (Kumar 2014). The changes in Bray's P, PP and EPP with a unit increase in pH were found to be significant. Soil pH had positive and significant correlation with available P while showing negative and significant relation with PP and EPP irrespective of the soils (Table 3) suggesting increasing in pH in acid soils enhances the availability of phosphate ions to the plants. In the correlation study, available phosphorus had shown negative and highly significant correlation with EPP (-0.769**) suggesting that EPP may successfully be used to estimate the available P in acid soils (Table 4). In the future, field experiments will be conducted to evaluate the crop response to P based on these three available phosphorous estimation methods.
Figure 1. Relationships between the pH and Brays P, PP and EPP
4
6
8
10
12
14
16
4 5 6 7
Bra
y's P
/PP
/EP
P
pH
Bangalore
Bray’s P PP EPP
4
6
8
10
12
14
16
4 5 6 7
Bra
y's P
/PP
/EP
P
pH
Shimoga
Bray’s P PP EPP
4
6
8
10
12
14
16
4 5 6 7
Bra
y's P
/PP
/EP
P
pH
Mudigere
Bray’s P PP EPP
4
6
8
10
12
14
16
4 5 6 7
Bra
y's P
/PP
/EP
P
pH
Uttar Kannada
Bray’s P PP EPP
11
References Aslyng HC (1954). The lime and phosphate potentials of
soils, the solubility and availability of phosphate. Year book, Royal Veterinary Agricultural College, Copenhagen, pp 1-50
Bolan NS, Barrow NJ and AM Posner (1985). Describing the effect of time of sorption of phosphate by iron and aluminium hydroxides. J Soil Sci 36: 187-197
Brady NC and RR Weil (2002). The Nature and Properties of Soils. 13th Edition. Pub: Pearson Education, New Delhi.
Curtin D, Syers JK and NS Bolan (1993). Phosphate sorption by soil in relation to exchangeable cation composition and pH. Aus J Soil Res 31: 137-149
Debye P and E Huckel (1923). Zhur Theorie der electrolyte. Physik Zs 24: 185-206
Gosh S (2015). Spatial variation of soil pH and soil phosphorous and their interrelationship in the plateau area of West Bengal, India. Int J Rec Sci Res 6(3): 3208-3212
Jackson ML (1967). Soil Chemical Analysis. Advanced course, University of Wisconsin, Madison, USA, pp 134-182
Jension HE (1971). Phosphate solubility in Danish soils equilibrated with solution of differing phosphate concentration. J Soil Sci 22(2): 261-266
Kuo S (1990). Phosphate sorption implications on phosphate soil tests and uptake by corn. Soil Sci Soc Am J 54: 131-135
Manoj Kumar (2014). Influence of Seed Priming with Urine, Phosphorus and Zinc on Maize (Zea mays L.) Yield in an Acid Soil of Northeast India. Ind J Hill Farm 27(1): 78-80
Manoj Kumar (2015). Phosphate Requirement of Acidic Soils in Northeast India: A Reappraisal Based on Phosphate Sorption Isotherms. Natl Acad Sci Lett: DOI 10.1007/s40009-015-0376-2
Perassi I and Borgnino (2014). Adsorption and surface precipitation of phosphate onto CaCO3–montmorillonite: effect of pH, ionic strength and competition with humic acid. Geoderma 232-234: 600-608
Ramesh T and R. Ananthanarayana (2012). Effect of liming on quantity-intensity parameters of phosphorous in acid soils of Karnataka. J Ind Soc Soil Sci 60(2): 163-166
Rodenburg J, Stein A, Noordwijk M and QM Ketterings (2003). Spatial variability of soil pH and phosphorous in relation to soil run-off following slash-and-burn land clearing in Sumatra, Indonesia. Soil Tillage Res 71: 1-14
Sato S and NB Camerford (2005). Influence of soil pH on inorganic soil phosphorous sorption and desorption in a humid Brazilian ultisols. R Bras Ci Solo 29: 685-694
Schofield RK (1955). Can a precise meaning be given to available soil phosphorus? Soils Fertil 18: 373-375
Schwartzenbach G, Biedermann W (1946) Bangerter F (1946). Komplexone VI. Nene cinfache Titriermethoden Zur Bestimmungder Wasserharte. Helv Chim Acta 29: 811-818
White RE and PHD Beckett (1964). Studies on phosphate potential of soils III. The pool of labile inorganic phosphates. Plant Soil 21: 253-282
12
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 12-13
Gastrointestinal Parasitism in Turkeys and Quails of Umiam, Meghalaya M. Das1 • R. Laha1* • A. Goswami1 • S. Doley2
1Division of Animal Health, 2Division of Livestock Production 2ICAR Research Complex for NEH Region Umroi Road, Umiam, Meghalaya-793103
ARTICLE INFO ABSTRACT Article history: Received 26 February 2015 Received Revised Version 1 May 2015 Accepted 5 May 2015 ----------------------------------------------- Key words: Turkey, Quail, Ascaridia galli, Capillaria sp. -----------------------------------------------
Prevalence of gastrointestinal parasitism in turkeys and quails reared in the poultry farm of ICAR Research Complex for North Eastern Hill Region, Umiam, Meghalaya was carried out by examining faecal samples from 25 turkeys and 100 quails of about 12 and 8 weeks of age, respectively. Faecal samples were collected at weekly intervals for a period of four months. Faecal samples have been subjected to floatation technique using saturated sugar solution. Microscopic examination of faecal samples revealed eggs of Ascaridia galli (EPG 50-350) and Capillaria sp. (EPG 50-100) in turkeys and only Ascaridia galli (EPG 50-300) in quails. Both turkeys and quails are reared under deep litter system.
1. Introduction
Turkey (Meleagris gallopavo) is a large gallinaceous bird native of North America, domesticated in Europe. In India turkeys are found in good numbers in Kerala, Tamil Nadu, eastern districts of Uttar Pradesh and some other parts of India. They are reared for meat only and the meat of Turkey is the leanest among other domestic avian species. However, Japanese quail (Coturnix japonica) is a small sized domesticated bird reared for meat and egg. It grows fast and is ready to market for table purpose at 4-5 weeks of age. Gastrointestinal parasitism is very common in both the birds.
Presence of few parasites does not usually cause a
problem. However, large numbers of gastrointestinal parasites can have a devastating effect on growth, egg production and overall health (Butcher and Miles 1992). Natural infections of helminths in the Japanese quail have been reported overseas, mostly from Asia (China, Japan) (Sawada and Funabashi 1972; Wang 1982; Uchida et al. 1984), from the Palaearctic region (Barus and Sonin 1983) and India (Kumar et al. 2003a).
The report of gastrointestinal parasitic infection in turkeys and quails from Meghalaya is not available. So, an attempt has been made to study the prevalence of gastrointestinal parasites in turkeys and quails of hilly region of Meghalaya.
__________________________ *Corresponding author: [email protected]
2. Materials and methods
To study the prevalence of gastrointestinal parasitism in turkeys and quails reared in the poultry farm of ICAR Research Complex for North Eastern Hill Region, Umiam, Meghalaya pooled faecal samples from 25 turkeys and 100 quails of about 12 and 8 weeks of age, respectively were collected at weekly intervals for a period of four months. Both turkeys and quails are reared under deep litter system. Faecal samples have been subjected to floatation technique using saturated sugar and salt solution (MAFF 1986). The eggs of the helminths were identified after observing the size and morphological characteristics of eggs (Soulsby 1986).
3. Results and Discussion Microscopic examination (100X) revealed presence of eggs of Ascaridia galli (EPG 50-350) and Capillaria sp. (EPG 50-100) in faecal samples of turkeys (Figure 1) and only eggs of A. galli (50-300) in faecal samples collected from quails (Figure 2). In Japanese quails helminth species such as trematode Pancreatrema coturnicola (Wang 1982) and kidney trematode Tanaisia inopina (Pinto et al. 2005); cestodes Metroliasthes coturnix (Sawada and Funabachi 1972) and nematodes A. galli and Heterakis gallinarum (Movsessian and Pkhrikian 1994) has been reported. Butcher and Miles (1992) reported that large round worms (A. galli) probably inflict the most damage especially to the young birds by interfering with feed absorption causing poor growth and production. In severe infections there may be intestinal blockage by the worms causing death.
13
Deka and Borah (2008) from Kolkata reported that the
total erythrocytic count (TEC), packed cell volume (PCV) and haemoglobin (Hb) percentage decreased significantly in A. galli infected quails. The total leucocytic count (TLC), heterophils and eosinophils were also increased significantly in the infected quails. Moreover, Matta and Ahluwalia (1982) and Kumar et al. (2003b) opined that lowered haemoglobin concentration in infected birds was correlated with the activities of early larval stage of A. galli in the process of penetration with resultant destruction of mucosa of small intestine and rupture of small blood vessels. Kumar et al. (2003b) also cited that fall of Hb content might be due to metabolic disturbance caused by worms rather than a direct blood loss.
While small round worms (Capillaria sp.) infect the
intestines causing haemorrhage and thickening of the intestinal walls, leading to poor feed absorption and poor growth Capillaria sp. is also associated with squamous cell carcinoma in the oesophagus and crop of birds. Rosa and Shivaprasad (1999) reported presence of Capillaria contorta from crop and oesophagus of a 5 year old male vulture guinea fowl. Present study revealed prevalence of A. galli and Capillaria sp. in the turkeys and Japanese quails of the hilly region of Meghalaya. Thus, as precautionary measures it is necessary to regularly screen the faecal samples of birds to prevent contamination and spread of infection to healthy birds. It could therefore be concluded from this study that Turkeys and Quails of Umiam are suffering from gastrointestinal parasitic infections which will spread the infections to the healthy birds if they are not treated.
Acknowledgement
Authors are thankful to the Director, ICAR Research Complex for NEH Region, Umiam, Meghalaya for providing facilities to carry out this research work.
References Barus V and MD Sonin (1983). Survey of nematodes
parasitizing the genus Coturnix (Galliformes) in the Palaearctic region. Helminthol 20: 175-186
Butcher GD and RD Miles (1992). Intestinal Parasites in Backyard Chicken Flocks. M76,Veterinary Medicine-Large Animal Clinical Sciences Department, Florida Cooperative Extension Service,Institute of Food and Agricultural Sciences, University of Florida
Deka K and J Borah (2008). Haematological and Biochemical Changes in Japanese Quails (Coturnix
coturnix Japonica) and Chickens due to Ascaridia galli infection. International J Poul Sci 7(7):704-710
Figure 1. Egg of Capillaria sp. (Turkey)
Figure 2. Egg of Ascaridia galli (Quail)
Kumar R, Sinha SRP and MN Sahay (2003a). Seasonal and agewise prevalence of helminthic infection in Japanese
quail (Coturnix coturnix japonica) in Patna, Bihar. Indian J Poul Sci 38: 32-36
Matta SC and SS Ahluwalia (1982). Haematological indices as influenced by Ascaridia galli infection in fowl. Effect on the haemoglobin concentration, packed cell volume and erythrocytes sedimentation rate. Indian J Poul Sci 17: 46-51
MAFF (1986). Manual of Veterinary Parasitological Laboratory Techniques, HMSO, London
Movsessian SO and LV Pkhrikian (1994). Reciprocal infection of quails and hens with the nematodes Ascaridia galli (Schrank, 1788) and Heterakis gallinae (Gmelin, 1790): single and mixed infections. Parasitol Hungarica 27: 83-85
Pinto RM, Menezes RC, Tortelly R and D Noronha (2005). First report of a natural helminth infection in the Japanese quail Coturnix japonica Temminck & Schlegel (Aves, Phasianidae, Galliformes) in the neotropical region. Revista Brasileira de Zoologia 22(4): 836-838
Rosa MD and HL Shivaprasad (1999). Case report – Capillariasis in a vulture Guinea Fowl. Avian Dis 43:131-135
Sawada I and F Funabashi (1972). A new avian cestode, Metroliasthes coturnix sp. From the intestine of a Japanese quail, with an avian cestode from a macaw. Japanese J Parasitol 21: 395-399
Soulsby EJL (1986). Helminths, Arthropods and Protozoa of Domesticated Animals. 7th Edn. The English
Language Book Society and Bailliere Tindal, London Uchida A, Uchida K and Sagawa T (1984). The first record of the cestode, Choanotaenia infundibulum (Dilepididae) in Japanese quails from Japan. Bulletin Azabu Univ Vet Med 5: 29- 32
Wang PQ (1982). Notes on some digenetic trematodes of birds in Fujian Province. Wuyi Sci J 2: 75-90
14
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 14-18
Host Plant Resistance and Yield Loss due to Anthracnose caused by Colletotrichum lindemuthianum in French Bean (Phaseolus vulgaris)
Nirmala Maibam1 • Satish Chandra1*• Pankaj Baiswar1• D. Majumder2 • Kanchan Saikia1
1ICAR Research Complex for NEH Region, Umiam-793103, Meghalaya 2College of Post Graduate Studies, Central Agricultural University, Umiam-793103, Meghalaya
ARTICLE INFO ABSTRACT Article history: Received 9 January 2015 Received Revised 21 May 2015 Accepted 22 May 2015 ----------------------------------------------- Key words: Colletotrichum lindemuthianum, Phaseolus vulgaris, resistance, yield loss -----------------------------------------------
Screening against anthracnose (Colletotrichum lindemuthianum) was conducted using 20 genotypes of French bean (Phaseolus vulgaris) for identifying the resistant genotypes. Three genotypes viz., Rajma Gold, ML-D and ML-F were found moderately resistant, whereas ten and seven genotypes were categorized as moderately susceptible and susceptible, respectively. None of the genotypes was found highly resistant or highly susceptible. Estimation of yield loss due to anthracnose under protected and non-protected conditions using a susceptible variety Manipuri local -J revealed that reduction in pod wt. varied from 9.1 to 11.2%, pod length from 4.3 to 6.6%, breadth 4.5 to 8.8%, and thickness 16.6 to 30.5% according to disease severity rating. This study provided conclusive evidence that pod length, breadth and thickness are also affected according to various infection categories.
1. Introduction
French bean (Phaseolus vulgaris L.) is a leguminous vegetable also known as Rajmash or Rajma (Hindi) or haricot bean or kidney bean or common bean or snap bean. It is rich in protein content (23%) and it also contains calcium, phosphorus and iron. It is used as pulse as well as green vegetable (tender pods). In India, the green bean is cultivated in an area of 218352 ha with a production of 617869 MT and yield of 28297 hectogram/ha (FAOSTAT 2011). It is largely grown in Himachal Pradesh, Jammu and Kashmir, Uttar Pradesh, North Eastern Hills, Darjeeling, South plateau Hills (Nilgiri and Palni hills) Mahabaleshwar, Ratnagiri (Maharastra) and Chickmanglore (Karnataka) having mild climate with humid environmental conditions.
Anthracnose of French bean caused by fungus
Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara is a major problem throughout the world but it is present in more severe form in the temperate regions than in the tropics. In north India, the disease appears in the second or the third week of June and reaches the maximum damaging stage from the beginning of August to mid-September. Since this pathogen overwinters inside bean seeds hence the losses can be 100% when badly contaminated seed is planted under conditions favourable for disease development (cool and wet weather) (Sharma et al. 1994).
Plants at all growth stages are susceptible and susceptibility increases with age Infection of a susceptible cultivar under favourable conditions leading to an epidemic may result in 100% yield loss (Fernandez et al. 2000). Symptoms can be seen on the stems, leaves and fruits of the French bean (Perfect et al. 1999). Purple to red, elongated angular lesions are more prominent on lower side of the leaves and veins, becoming dark red as the disease progresses. Elongated, sunken and reddish brown lesions are present on the pods. The fungus can invade the pod surface and infect the seed coat and cotyledon of the developing seeds. Under favourable conditions during the growing season, infected seeds become discoloured, shrivelled and dark acervuli are prominent on the lesions (Gonzalez et al. 1998). Under very humid conditions pink spore mass can also be seen oozing out from the lesions. Bean production is considerably reduced due to bean anthracnose because of poor seed germination and seedling vigor, more plant death and low yields. Marketing losses are attributed to seed spots and blemishes, which lower their quality rating and salability (Dillard 1988). In Himachal Pradesh, India, the incidence of this disease has been reported to range from 5.0 to 65.0 per cent in different localities leading to considerable yield losses in certain years (Sharma et al. 1994).
_________________ Corresponding author: [email protected]
15
This disease is also common in the French bean fields of North-Eastern Hill (NEH) states. Although the disease is endemic in the NEH region, no systematic investigation with respect to varietal reaction (resistant genotypes) and yield loss has been done on the anthracnose-French bean pathosystem. These kind of investigations are needed since agro climatic conditions in NEH region differ from rest of the country and profitable French bean cultivation for green pod has potential to improve the economic conditions of tribal farmers in this region. Therefore, the present investigation was done for identification of different resistant genotypes of French bean and estimation of yield loss due to anthracnose.
2. Materials and methods Host Plant Resistance
Screening of 20 French bean genotypes including popular local land races and released varieties was done in the experimental field of Plant Pathology, ICAR Research Complex for NEH Region, Umiam, Meghalaya. The sowing was done on 18th March 2013 in randomized complete block design (RCBD) with two replications. Each test genotype was planted in 3 m row with spacing of 30 cm between the row and 15 cm within the row. Fertilizer dose of 40-60-40 (NPK) was given as basal application. No disease protection measures were adopted. Three weeks old plants were inoculated with spore suspension (106conidia/ml) of the pathogen C. lindemuthianum by using pin prick method to check for resistance or susceptibility against C. lindemuthianum
Ten pods from each genotype were randomly selected from
each replication for assessing disease severity. The severity of the disease was recorded on the basis of 1 to 9 scales (CIAT 1987). The description of the scale is given below in Table 1. Per cent Disease Index (PDI) was calculated on the basis of rating scale using the following formula,
PDI =Sum of all numerical rating
Number of pods x maximum score in scale x 100
On the basis of PDI, genotypes were classified into different categories (Table 2). Yield loss assessment
A susceptible variety Manipuri local- J was used for estimating yield loss due to French bean anthracnose. The experiment was laid out in Replicated Measurement’s’ Test (paired plot). Experimental setting consisted of two
treatments i.e. fungicide protected and a control i.e. nonprotected with 12 replicates and a spacing of 40x30 cm with plot size 3x2 m². In protected plots, carbendazim @ 2 g/kg seeds was used for seed treatment before sowing to control seed borne infection, if any. The disease severity (PDI) was recorded at maturity stage (R8) (CIAT 1987).
Ten pods from protected and unprotected plots were randomly selected, harvested separately and data were recorded separately for different infection grades based on (1-9 scale) for yield loss assessment. Per cent reduction in pod weight, length, breadth and thickness due to different severity grade of infection was also calculated. The data pertaining to yield loss was calculated by employing the formula,
Per cent yield loss =YCP−YDP
YCP x 100
where, YCP is the yield of 10 pods randomly selected from control (protected) plots and YDP is the yield of 10 pods randomly selected from diseased (non-protected) plots
3. Results and Discussion Host Plant Resistance
French bean genotypes were screened during 2012, for identifying the degree of resistance against the anthracnose under mid hill conditions of Meghalaya. Out of 20 different genotypes tested, three genotypes viz., Rajma Gold, ML-D and ML-F were moderately resistant with PDI of 24.44, 25.55 and 31.11, respectively. Ten genotypes exhibited moderate susceptibility against the anthracnose disease viz., ML-A, Rajma Purple, ML-C, ML-B, ML-H, ML-G, ML-I, Arka Anoop, Naga local-A and ML-K. Seven genotypes viz., Annapurna, ML-J, Selection 9, Darjeeling White, Anupama, Naga local-B and selection-3 showed susceptible disease reaction. Resistant reaction was not recorded in any of the tested genotypes. Reactions recorded against anthracnose are presented in Table 3.
Screening is one of the important processes involved in
breeding programmes and it ensures that cultivars chosen exhibits increased resistance to a wide range of diseases and insects, better tolerance to environmental stress, better seed quality and improved efficiency in the utilization of limited soil nutrients. Many workers have conducted screening and reported varying degree of resistance to anthracnose in local land races and exotic French bean genotypes (Pathania et al. 2006, Kour et al. 2012). More than 10 different anthracnose resistance genes have been identified in a number of bean varieties (Kelly and Vallejo 2004). According to Mahuku and Riascos (2004), the best strategy to manage this disease is planting resistant cultivars, which is most effective, least expensive and easiest for farmers to adopt.
Yield loss assessment
The yield loss experiment in the present study was conducted based on per cent reduction in pod weight, length, breadth and thickness. Pods from protected and non-protected plots exhibited various categories of infection grades. Hence infected pods were collected from protected and non-protected plots and sorted into various infection grades (1-9). The ten pod wt. of healthy pods (score-1) varied from 66.00 to 65.58 g (Table 4).
16
The pods showing infection grade- 9 and 7 recorded
significant reduction in pod length. The reduction in pod breadth (mm) was found highest (8.84%) with disease score-9 and the difference between protected and non-protected was significant (Table 4). The mean pod breadth for healthy pods varied from 10.73-11.43. There was no significant difference between healthy and diseased pod breadth with disease score-3, 5 and 7 (Table 4). The pod thickness was significantly reduced with disease score-3, 5, 7 and 9. The reduction was found highest in disease score-7 (Table 4, Figure 1). The differences between protected and non-protected at all four disease scores were significant (Table. 4). Variation in percent reduction in respect of pod thickness was highest ranging from 16.57 to 30.47% (Table 5, Figure 1). Least variation in percent reduction was in pod weight ranging from 9.08 to 11.18%.
The data pertaining to effect of pod infection in terms of
number of seeds/pods and seed weight/pods was earlier recorded by Sharma et al. (2008). They found reduction of 57.76 per cent in number of seed/pod and in pods graded 9 followed by 52.59 per cent in pods grade 7. In the present study the highest reduction in pod weight was found in grade 7 (11.18%) followed by grade 9 (11.02%). The reduction in pod length, breadth and thickness was not studied by other workers. This study provides first conclusive evidence that pod length, breadth and thickness are also affected according to various infection categories.
Table 1. Description scale for rating against anthracnose
Rating Scale Per cent Infection 1 no infection
3 up to 1% of pod surface area
5 up to 5% of surface area
7 Up to 10% of surface area
9 more than 25% pod surface area
Table 2. Reaction of different genotypes of French bean against Colletotrichum lindemuthianum
PDI Categories
0 Absolutely resistant (AR)
0.01 Highly resistant (HR)
12.22-33.33 Moderately resistant (MR)
34.44-55.55 Moderately susceptible (MS)
56.66-77.77 Susceptible (S)
78-88-100.00 Highly susceptible (HS)
Figure 1. Percent reduction in various parameters related to pod for four infection grades
17
Table 3. Reaction of different genotypes of French bean against anthracnose
Table 4. Different pod parameters as affected by disease severity scores under protected and non-protected conditions
Disease
score
Pod weight (g) Pod length (cm) Pod breadth (mm) Pod thickness (mm)
P NP D P NP D P NP D P NP D
9
64.25
57.17
7.08*
5.84
14.86
0.98*
11.43
10.42
1.01* 8.57 6.41 2.16*
7
65.58
58.25
7.33*
15.99
14.94
1.05*
10.92
10.42
0.50ns
9.22
6.41
2.81*
5
66
58.75
7.25*
6.06
15.37
0.69ns
10.94
10.44
0.50ns
9.47
7.24
2.23*
3
66.17
60.16
6.01*
15.98
14.97
1.01ns
10.73
10.85
0.12ns
8.93
7.45
1.48*
*significant (p = 0.05), ns- non significant; P-Protected; NP – non-protected; D-Difference
French bean genotypes Percent Disease index Category
Rajma Gold 24.44 MR
ML-D 25.55 MR
ML-F 31.11 MR
ML-A 34.44 MS
Rajma Purple 34.44 MS
ML-C 36.11 MS
ML-B 37.77 MS
ML-H 39.99 MS
ML-G 43.33 MS
ML-G 47.77 MS
Arka Anoop 47.77 MS
Naga local-A 49.44 MS
ML-K 53.33 MS
Annapurna 57.22 S
ML-J 59.99 S
Selection 9 61.10 S
Darjeeling white 67.21 S
Anupama 68.88 S
Naga local-B 69.99 S
Selection 3 74.44 S
18
4. Conclusion
Out of 20 genotypes screened, three genotypes viz., Rajma Gold, ML-D and ML-F were found moderately resistant, whereas other ten and seven genotypes were categorized as moderately susceptible and susceptible, respectively. None of the genotypes was found highly resistant or highly susceptible. Estimation of yield loss due to anthracnose disease under field experiment revealed reduction in pod weight, length, breadth and thickness according to disease severity rating. These moderately resistant varieties can be used by tribal farmers for maximizing profit.
Acknowledgements
Authors wish to thank the Director, ICAR Research Complex for NEH region, Umiam, Meghalaya for providing all the facilities and support for this research work. The Dean and faculty members of CPGS, CAU are also thanked for help and support.
References CIAT (1987). Standard evaluation system for the bean
germplasm. Cali, Colombia Dillard HR (1988). Bean anthracnose. Vegetable MD Online.
vegetablemdonline.ppath.cornell.edu/factsheets/Beans_Anthracnose.htm
FAOSTAT (2011). Statistical Database of the Food and Agriculture Organization of the United Nations. http://faostat.fao.org/site/567
Fernandez MT, Casares A, Rodriguez R, and M Fueyo (2000). Bean germplasm evaluation for anthracnose resistance and characterization of agronomic traits. A new Physiological strain of Colletotrichum lindemuthianum infecting Phaseolus vulgaris L. in Spain. Euphytica 114: 143-149
Gonzalez M, Rodriguez R, Zavala M, Jacobo J, Hernandez F, Acosta J, Martinez and J Simpson (1998). Characterization of Mexican isolates of Colletotrichum lindemuthianum by using differential cultivars and molecular markers. Phytopathology 88: 292-299
Kelly JD and VA Vallejo (2004). A comprehensive review of the major genes conditioning resistance to anthracnose in common bean. Hort Sci 39: 1196-207
Kour B, Kour G, Kaul S and MK Dhar (2012). Screening of Phaseolus vulgaris cultivars growing in various areas of Jammu and Kashmir for anthracnose. International J Sci Res Pub 2: 1-8
Mahuku GS and JJ Riascos (2004). Virulence and molecular diversity within Colletotrichum lindemuthianum isolates from Andean and Messoamerican bean varieties and regions. Eur J Plant Pathol 110: 253-263
Pathania A, Sharma PN, Sharma OP, Chahota RK, Bilal A
and P Sharma (2006). Evaluation of resistance sources and inheritance of resistance in kidney bean to Indian isolates of Colletotrichum lindemuthianum. Euphytica 149: 97-103
Perfect SH, Hughes HB and RJ O’Connel (1999). Colletotrichum; A model genus for studies on Pathology and fungal-plant interactions. Fungal Genet Biol 27: 186-198
Sharma PN, Sharma OP and PD Tyagi (1994). Status and distribution of bean anthracnose in Himachal Pradesh. Himachal J Agric Res 20: 91-96
Sharma PN, Sharma OP, Padder BA and R Kapil (2008). Yield loss assessment in kidney bean due to bean anthracnose (Colletotrichum lindemuthianum) under sub temperate conditions of north-western Himalayas. Indian Phytopath 61: 323-330
19
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 19-22
Level of Adoption and Perceived Constraints in Scientific Rabbit Farming Practices in Darjeeling Himalayas Rakesh Roy Darjeeling Krishi Vigyan Kendra, UBKV, Kalimpong- 734301, Darjeeling, West Bengal
ARTICLE INFO ABSTRACT Article history: Received 27 May 2015 Received Revised 15 June 2015 Accepted 16 June 2015 ----------------------------------------------- Key words: Adoption, Constraints, Darjeeling Himalayas, Garretranking, Rabbit farmers -----------------------------------------------
The study deals with the adoption level and perceived constraints associated with scientific rabbit farming practices in Darjeeling Himalayas. In all, 50 respondents were randomly selected for the study. The study shows that majority of the respondents had partially adopted scientific breeding, feeding and management practices but were non adopter in healthcare practices. Overall level of adoption was also partial in scientific rabbit farming. The study also shows that highest ranked production constraints as perceived by rabbit farmers were veterinary aid not available when required, medicine not available at right time and lack of technical knowledge. The study further shows that highest ranked marketing constraints as perceived by rabbit farmers were low price of the live animals, lack of regular markets for farm product and negative attitude to consume rabbit meat.
1. Introduction
Rabbits have high reproductive potentials and fast growth rate (Hassan et al., 2012), utilize low grain and high roughage diets and breed all year-round (Irlbeck, 2001). Other attributes are short gestation period, early sexual maturity, ability to rebreed shortly after kindling and short generation interval (Hassan et al., 2012). These qualities confer on rabbits a potential to bridge the shortage of animal protein in developing countries, where grain can only be justified for human use (Irlbeck, 2001; Hassan et al., 2012). The rabbit when raised with appropriate technologies can contribute virtually to improve the diet of large numbers of both rural and urban families, particularly landless and low-income ones, eventually providing such families with employment and a source of regular income (Onuekwus and Okezie, 2007). The adoption of available technologies has been a problem although they have been introduced to farmers (Onuekwus and Okezie, 2007; Madubuike, 2004). The farmers face with lots of problems hindering their desire to adopt these technologies. It is established that many farmers are still exposed to the traditional ways of raising rabbits resulting in low performance and profitability (Frimpong, 2009). Therefore, this study has been taken up with the objectives to access the level of adoption in scientific rabbit farming practices and the constraints perceived in production and marketing of rabbit.
__________________ Corresponding author: [email protected]
2. Methodology
The study was purposively conducted in Darjeeling Himalayas of West Bengal. A total of 50 respondents were considered for the study. Data were collected through structured interview schedule. The extent of adoption of improved technology i.e., breeding, feeding, healthcare and management were measure by score assign in three continuum such as high adopter = 2, partial adopter =1 and non-adopter= 0. The adoption index was measured using the following formula, Adoption index = (Respondent’s total score / Total possible score) X 100.
Constraints perceived in scientific rabbit farming were
assessed by Garret ranking technique (Garret, 1981). The respondents were asked to rank the factors given. The order of merit, assigned by the respondents was converted into ranks by using the following formula, Percent position of each rank = 100 (Rij-0.05)/Nj, where Rij = Rank given for the ith factors for the jth respondent. Nj= Number of factors ranked by the jth respondent
The percentage position of each rank obtained is converted
into scores by referring to the table given by Henry Garret. Then for each factors the scores of individual respondents were added together and divided by the total number of the respondents for whom the score were added. These mean scores (MS) for all the factors were arranged in order of merit and inference drawn.
20
3. Results and Discussion
Table 1 shows that majority (54%) of the respondents had partially adopted scientific breeding practices followed by non-adopter (30%) and high adopter (16) in scientific breeding practices. Scientific feeding practices were partially adopted by majority (44%) of the respondents, followed by high adopter (40%) and non-adopter (16). But, majority (58%) of the respondents was non adopter to scientific healthcare practices followed by partial adopter (24%) and high adopter (18). Scientific management practices were partially adopted by majority (64%) of the respondents followed by non-adopter (28%) and high adopter (8). Das (2012) had nearly similar findings in his study.
Table 1. Distribution of the respondents according to the extent of adoption (N=50)
Sl. No.
Level of adoption Frequency
A. Breeding 1. Non adopter 15 (30)
2. Partial adopter 27 (54) 3. High adopter 8(16)
B. Feeding 1. Non adopter 8(16)
2. Partial adopter 22(44) 3. High adopter 20(40)
C. Healthcare
1. Non adopter 29(58)
2. Partial adopter 12(24) 3. High adopter 9(18)
D. Management 1. Non adopter 14(28)
2. Partial adopter 32(64) 3. High adopter 4(8)
Figures in parentheses indicate percentage
Table 2. Overall adoption level of rabbit farmers (N =50)
Sl. No.
Level of adoption
Score index
Frequency
1. Non adopter Up to 33% 19 (38) 2. Partial adopter 34-66% 24 (48)
3. High adopter 67-100% 7(14) Figures in parenthesis indicate percentage
The study reveals that majority (48%) of the respondents had partially adopted overall scientific rabbit farming practices whereas 38 percent had not adopted and 14 percent of the respondents had highly adopted scientific rabbit farming practices (Table 2). Das (2012) reported that farmers adopted rabbit production technology at high level followed by partial and low level.
Veterinary aid not available when required was the
highest ranked constraints (MS= 59.86) as perceived by rabbit farmers followed by medicine not available at right time (MS= 56.32), lack of technical knowledge (MS= 54.52), inadequate training facilities (MS= 53.54) and high incidence of diseases (MS= 53.48) among production problems in rabbit farming (Table 3). Hungu et al. (2013) had reported that the major constraints of rabbit farming those dealing with production were high incidence of disease (83%), predators like rats (29%), death of rabbits (69%) and unavailability of rabbit feed (19%). Lukefahr (2008) reported although rabbits are often observed to be healthy and productive but there are exceptions: in least developed countries, rabbits are particularly vulnerable. Oseni et al. (2008) reported that lack of access to information on rabbit management under smallholder units is one of the major challenges in rabbit production. Ramodisa (2007) reported that lack of technical knowledge in rabbit production by farmers and advisors is a challenge in many countries.
Low price of live animals was the highest ranked (MS=
60.98) marketing constraints as perceived by rabbit farmers followed by lack of regular markets for farm product (MS= 57.14) and negative attitude to consume rabbit meat (MS= 52.16) (Table 4). Kumar et al. (2013) also reported similar finding in his study in Himachal Pradesh, India. The industry still lagged for several reasons which might include the lack of viable and well established markets, insufficient promotion, erratic product supply, unreasonable prices, and competition from other meats (Mailu et al., 2012).
4. Conclusion
The study has shown that adoption of scientific rabbit farming practices in the study area was partial. Further, constraints perceived in production of rabbit was mainly inadequate healthcare and technical knowledge facilities in rabbit farming. This shows that there is an utmost need to provide them with veterinary inputs and the technical knowledge on scientific rabbit farming practices using different extension methods. The policy makers also need to take proper policies so that rabbit farming can be encourage from just pet animals to a meat industries. The extension agencies working in the study area also need to campaign about the advantages of rabbit farming in relation to other enterprise.
21
Table 3. Perceived production constraints in scientific rabbit farming practices
Sl. No. Problems Score Mean Score Ranking 1. Inadequate supply of breeding stock 2563 51.26 VII
2. Quality feed not available at appropriate time 2667 53.34 VI 3. Feed prices not reasonable 2523 50.46 VIII
4. Shortage of fodder 2459 49.18 IX 5. Inadequate supply of equipment’s 2232 44.64 XV
6. High price of equipment’s 2271 45.42 XIII 7. High incidence of diseases 2674 53.48 V
8. Veterinary aid not available when required 2993 59.86 I 9. Medicines not available at right time 2816 56.32 II
10. Lack of technical knowledge 2726 54.52 III 11. Lack of access to credit 2349 46.98 X
12. Lack of government support 2307 46.14 XII 13. Inadequate training facilities 2677 53.54 IV 14. Lack of extension facilities 2348 46.96 XI
15. Lack of package of practices 2245 44.9 XIV
Table 4. Perceived marketing constraints in scientific rabbit farming practices
Problems Score Mean Score
Ranking
Low price of live animals 3049 60.98
I
Low price of meat 2506 50.12
IV
Negative attitude to consume rabbit meat 2608 52.16
III
High marketing costs 2320 46.4
V
Lack of regular markets for farm product 2857 57.14
II
Involvement of middleman 2055 41.1
VII
Inadequate transportation facilities 2105 42.1
VI
References Das SK (2012). Adoption behavior of rabbit production
technology in Meghalaya of India. Indian Research Journal of Extension Education. Special Issue. 1: 75-79
Frimpong J (2009). A guide to domestic rabbit breeding in Ghana, The farmer’s husbandry manual, Nungua Livestock Breeding Station. Johl SS, Kapur TR (2001). Fundamentals of Farm Business Management, Kalyani Publishers, pp. 253-259.
Garrett HE (1981). Statistics in Psychology and Education. Published by Vakils, Feffer and Simons Ltd., Mumbai.
Hassan HE, Elamin KM, Yousif IA, Musa AM and Elkhairey MA (2012). Evaluation of body weight and some morphometric traits at various ages in local rabbits of Sudan. Journal of Animal Science Advances. 2(4): 407-415
Hungu CW, Gathumbi PK, Maingi N and Ng’ang’a CJ (2013). Production characteristics and constraints of rabbit farming in Central, Nairobi and Rift-valley provinces in Kenya. Livestock Research for Rural Development. 25(3) Retrieved January 8, 2015, from http://www.lrrd.org/lrrd25/1/ hung25003.htm
Irlbeck NA (2001). How to feed the rabbit (Oryctolagus cuniculus) gastrointestinal tract. Journal of Animal Science. 79: 343–346.
Kumar A, Dogra A and Guleria JS (2010). Problems and constraints of rabbitry in India: A study of Himachal Pradesh. Global Journal of Science Frontier Research. 10(8): 40-46
Lukefahr SD (2007). Strategies for the development of small- and medium-scale rabbit farming in South-East Asia. Livestock Research for Rural Development 19(9). Retrieved 12 January 2015 from http://www.lrrd.org/lrrd19/9/luke19138.htm
Madubuike FN (2004). Arresting animal protein insufficiency in Nigeria: A multi-sectional approach. J. Agric. Food Sci. 2: 141-149
22
Mailu SK, Muhammad L,Wanyoike M and R Mwanza (2012). Rabbit meat consumption in Kenya. MPRA Paper No. 41517. Retrieved January 8, 2015, from http://mpra.ub.uni-muenchen.de/41517/1/Rabbit_meat_ consumption24092012.pdf
Onuekwus GC and CA Okezie (2007). Youths’ adoption of improved rabbitry technology in Umuahia, Medwell. J. Res. J. Appl. Sci. 2(1):65-69
Oseni S, Ajayi B, Komolafe S, Siyanbola O, Ishola M and G Madamidola (2008). ‘Smallholder rabbit production in south western Nigeria: current status, emerging issues and ways forward’ Management and Economy presented at the 9th World Rabbit Congress Verona Italy. Pages 1597-1601
Ramodisa J (2007). Rabbit production. Agrinews Magazine. 38(2): 11
23
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 23--26
Rainfall Characteristics, Pattern and Distribution at
Cherapunjee, Meghalaya
Lala I.P. Ray† • P.K. Boraa • V. Rama . • A.K. Singhb • R. Singha • S.M. Ferozeb
College of post Graduate Studies, Central Agricultural University, Umiam, Barapani 793103, Meghalaya
ARTICLE INFO ABSTRACT Article history: Received 7 January 2015 Received Revised 9 April 2015 Accepted 10 April 2015 ----------------------------------------------- Key words: Crop planning; normal rainfall; probability of occurrence; monsoon ----------------------------------------------
Rainfall plays a major role not only in agriculture but also in allied day to day activities. The knowledge amount of rainfall, number of rainy days and its distribution over the cropping season are important for timely preparation of seed bed, selection of crop varieties, choice of cropping pattern. Rainfall analysis with advanced statistics methods using computer programming and software bring out many features which can be directly used for crop planning, land-water management, aquaculture and floriculture planning etc. The analysis of 37years (1971-2007) daily rainfall data of Cherapunjee, Meghalaya has been done for determining the characteristics of rainfall and probability of occurrence of normal weekly rainfall.
1. Introduction
The amount of rainfall at a particular place is important, an equally important factors is its temporal distribution. The importance of this distribution is realised in agricultural and allied sectors. In most part of our country, rainfall is uneven, uncertain and erratic. The knowledge of distribution of dry spells and amount of rainfall during wet spells is very much essential for successful management of agriculture. The information of amount of rainfall during wet spell is useful for storage purpose based on the magnitude of dry spells and drought severity. Also the crop development is severely affected if dry spells coincide with the sensitive phonological stage of the crop and it is sometimes beneficial, if it coincide with ripening stage. An attempt has been made in this paper to analyse the rainfall in respect of standard week wise as well as monthly rainfall distribution at different probability levels for Cherapunjee, Meghalaya by using suitable techniques. Cherapunjee, in Meghalaya is happened to the wettest place on the world. The distribution of rainfall, weekly, monthly and seasonally is discussed in this paper.
_________________ Corresponding author: [email protected]
The probability of occurrence of a quantum of normal weekly rainfall is also analysed. A lot of work has been carried out in the past by various investigations on rainfall analysis (Chakraborty et al., 2008.; Jakhar et al., 2011; Mohanty et al., 2001; Satapathy et al., 1998.; Sharda, and Bhushan, 1985.; Verma, and Sharma. 1989). The criteria set by Raman (1979) for rainfall of 1 mm for defining a rainy day are not suitable for agriculture purpose. Ashokraj (1979) used the criteria fixed by IMD for defining the rainy day i.e. the day i.e. the day with at least 2.5 mm rain is called rain day. When probability of occurrence of dry spell different length in a week bounded by wet weeks is know; adequate steps may be taken by shifting the sowing time or arranging minimal irrigation to get optimum yield.
2. Materials and methods
Cherapunjee, located at an elevation of 1,300 m above
sea level is coming under East Khasi Hill district of Meghalaya. The amount of rainfall and number of rainy days in a week at Cherapunjee, Meghalaya from historic daily rainfall records (1971-2007) collected from India Meteorological Department (IMD) Pune are calculated using probabilistic approach.
24
Table 1. Number of years under different magnitude of rainy days
Annual rainy day Number of years Percentage of rainy day (%) <100 0 0
100-150 14 37.84 >150 23 62.16
Weibull’s Method of Probability Analysis
The weekly rainfall data have been analysis at different levels of probability by using Weibull’s method. In this method the weekly rainfall are arranged in descending order of magnitude was given rank, 1 next magnitude was given rank 2 and so on. The probability ‘p’ of the week having rainfall exceeding or equalling normal value was calculated
by using Weibull’s formula:1
n
mP … (1) where ,
P = probability of occurrence m = rank number n = number of years of data used
Table 2. Average monthly rain and rainy days at
Cherapunjee, Meghalaya
Month Rainfall, mm Rainy days
January 10.67 1
February 40.11 2
March 237.63 7
April 830.07 17
May 1164.79 19
June 2290.80 23
July 3273.93 28
August 1544.18 21
September 1167.54 17
October 409.67 7
November 68.62 2
December 22.43 1
Table 3. Weekly observed minimum, maximum and normal rainfall and the probability of the weekly normal rainfall equaling or exceeding the normal in a year at Cherapunjee station.
Standard week
Minimum (mm)
Maximum (mm)
Normal (mm)
Probability (%)
1 0.00 18.00 1.68 15.58 2 0.00 18.20 1.67 22.24 3 0.00 17.50 2.68 35.56 4 0.00 20.40 2.56 21.55 5 0.00 15.00 3.05 34.25 6 0.00 81.70 7.05 23.56 7 0.00 64.20 7.90 26.72 8 0.00 112.10 15.91 26.84 9 0.00 121.00 15.88 30.33 10 0.00 720.80 57.40 23.86 11 0.00 553.00 61.08 22.65 12 0.00 306.30 39.28 26.58 13 0.00 382.60 84.33 37.28 14 0.40 687.00 159.23 34.85 15 1.80 725.70 161.63 23.84 16 1.00 1341.00 188.93 26.68 17 0.00 830.80 231.83 31.57
18 0.00 1237.80 260.88 30.84 19 0.00 579.50 153.32 35.96
20 0.00 1728.30 310.91 33.48
21 0.00 2078.00 276.05 27.86 22 9.20 2217.50 398.96 26.88
23 0.00 2048.00 388.13 34.68
24 0.00 1707.30 563.45 36.27 25 0.00 1975.50 587.87 50.25
26 44.60 2363.50 635.53 40.22
27 30.40 1484.60 630.71 49.86 28 103.80 2482.30 681.81 30.24
29 58.00 2163.20 725.26 42.68
30 52.00 3524.10 874.54 41.68 31 0.00 1616.80 495.12 39.86
32 0.00 1237.40 247.33 34.65
33 0.00 1095.00 382.30 36.88 34 0.00 1019.20 300.47 33.58
35 0.00 1631.00 254.64 24.68
36 0.00 1601.80 290.53 35.82 37 0.00 4038.20 502.43 31.66
38 0.00 755.80 156.93 30.62
39 0.00 834.40 114.51 35.68 40 0.00 1425.00 146.72 24.58
41 0.00 1026.40 118.58 26.74
42 0.00 366.20 54.83 23.28 43 0.00 486.60 30.46 18.88
44 0.00 775.40 43.64 12.35
45 0.00 294.00 33.35 15.54 46 0.00 218.40 16.15 20.15
47 0.00 268.70 11.96 10.22
48 0.00 91.40 5.27 21.22 49 0.00 119.10 5.11 8.84
50 0.00 123.40 10.89 14.26
51 0.00 10.60 0.44 6.62
52 0.00 42.10 2.44 12.24
25
Figure 1. Number of rainy days in a standard week at Cherapunjee
Figure 3. Probability distribution of number of rainy days in a standard week at Cherapunjee
Figure 5. Average number of rainy days in a month at Cherapunjee station
Figure 7. Yearly distribution of rainfall at Cherapunjee
station
Figure 2. Depth of rainfall in mm at Cherapunjee on standard week basis
Figure 4 .Probability distribution of amount of rainfall in a standard week at Cherapunjee
Figure 6. Yearly distribution of rainy day at Cherapunjee station
3. Results and Discussion
The number of rainy days and amount of rainfall in a standard week throughout a water year was calculated by simple average of the 37 years of daily rainfall. The average number of rainy days and amount of rainfall in a standard week at Cherapunjee is presented in Figures 1 and 2 respectively. It is found that the average number of rainy day is more than four (4) from 16th to 37th week in a year. Annual rainy day is always more than 100 for the analysed period. It is found that more than 62% of the analysed years is having rainy days more than 150 days and there is no year where the number of rainy days is less than 100 days (Table 1).
Num
ber
of
rain
yd
ays
Month
26
Average weekly rainfall exceeds 100 mm for standard week 12th to 41st. Probability level prediction from 50% to 90% was made to find out the approximate number of rainy days and amount of rainfall in a standard week at Cherapunjee is shown in Figures 3 and 4 respectively. The weekly observed minimum, maximum and normal rainfall and the probability of the weekly normal rainfall equalling or exceeding the normal in a year is presented in Table 3. The weekly quantum of rainfall is more than 35 mm from 15th week to 32nd week of the year for probability of 50% to 90%. The monthly distribution of rainy days and amount of rainfall is presented in Figures 5 and 6 respectively. It is found that around 86% of rainfall is confined to five months of the year (i.e. May to September). During these five months the number of rainy days exceeds more than fifteen (15). Normally at the first week of October the seasonal rainfall analysis for rainy days and amount of rainfall is presented in Fig-7 and 8, respectively. Monsoon rainfall accounts for 75% of the whole rainfall, with pre-monsoon and post-monsoon shower of 21% and 4%, respectively. The monsoon rainy days limits to 62% of the total rainy day in a year.
The average monthly rainfall and number of rainy days is
presented in Table 2. It is found that the chances of occurrence of normal weekly rainfall are more than 35% for the standard week 23rd to 39th. However, for the 37th and 38th week the probability of getting normal rainfall is around 31%. On 25th week there is a chance of getting a huge shower of 587.87 mm with a probability level of more than 50%. It indicates that 25th week in Cherapunjee is the wettest week. This indicates there is a chance for critical dry spell in this week. Minimum value of 0 mm rainfall is recorded for the weeks 1st to 14th; 17th to 21st; 23th; 25th and 31st to 52nd week. The normal weekly rainfall is more than 100 mm for standard week 14th to 41st. The average annual rainfall of Cherapunjee is worked out to be 10,753.61 mm with a maximum of 23,442.4 mm corresponding to the year 1974 and a minimum of 5967.7 mm corresponding to the year 1971. The average monthly rainfall of the place is 237.63, 830.07, 1,164.79, 2,290.80, 3,273.93, 1,544.18, 1,167.54 and 409.67 mm for the months of March, April, May, June, July, August, September and October respectively. The maximum average rainfall is received during the month of July of a tune of 3,273.93 mm and the minimum average rainfall is received during the month of January of a tune of 10.67 mm.
4. Conclusion Rainfall is the most component in agriculture production and its spatial and temporal distribution is uneven, uncertain and erratic in nature. Cherapunjee, Meghalaya has an average annual rainfall of 10,753.61 mm and its trend is alternatively increasing and decreasing.
The average annual rainy days are 145 days and number of rainy days in a month varies from 1 to 20 days. More than 80 percent rainfall occurs during 17th to 42nd week. There is a good amount of rainfall (more than 800 mm) both in the months of April and May, which is considered as pre-monsoon showers, help in seed bed preparation. Hence the length of monsoon is about 145 days which helps in growing paddy and other cereal crops in valley and foot hills of Meghalaya. Since winter season gets only four percent of total rainfall, it is necessary to construct water harvesting systems, to store excess water during rainy season, which will be utilized as lifesaving irrigation for fruit crops, vegetables and other cash crops during winter season. A good amount of rainfall during monsoon season helps the farmer to go for fish cum paddy culture and pisciculture in the water harvesting ponds. In Meghalaya the temperature is low, humidity is high and almost all weeks get some rainfall, whatever small the amount may be; which helps this place as a centre for Orchids and other floriculture activities.
References Asho PC and Ashokraj (1979). Onset of effective monsoon
and critical dry spell. IARI Research Bulletin No, 11, WTC New Delhi, pp: 6-18.
Chakraborty PB and APN Mandal (2008). Rainfall characteristics of Sagar island in Sunderban, West Bengal. Indian J. Soil Cons., 36(3): 125-128.
Jakhar P, Hombe Gowda HC, Naik BS and D Barman (2011). Probability analysis of rainfall characteristics of Semiliguda in Koraput, Orissa. Indian J. Soil Cons., 39(1): 9-13.
Mohanty S, Marathe RA and S Singh (2001). Rainfall Characteristics of Vidarbha Region. Indian Journal of Soil Cons., 29 (1): 18-21.
Raman CRV (1979). Analysis of commencement of monsoon rains over Maharastrha state for agricultural planning. Scientific Report No -216, IMD, Pune.
Satapathy KK, Jena SK and D Das Choudhury (1998). Characteristics of monsoon and rainfall pattern at Umiam, Meghalaya. Journal of Soil and Water Cons., 42:155-161.
Sharda VN and LS Bhushan (1985). Probability analysis of annual maximum daily rainfall for Agra. Indian J. Soil Cons., 13(1): 16-20.
Verma HN and PBS Sharma (1989). Critical dry spells and supplemental irrigation to rainfed crops. J. of Indian Society of Water Res., 9(4): 12-16.
27
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 27--34
Response of Levels of Inorganic Fertilizer with Organic Manure on Potato in Aquic Hapludoll of Himalayan Foothills Dibyendu Chatterjee1* • Jaya Srivastava2
1Indian Council of Agricultural Research, Research Complex for North Eastern Hill Region, Nagaland Centre, Jharnapani, Medziphema 797106, Nagaland
2Department of Soil Science, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar 263145, Uttarakhand
ARTICLE INFO ABSTRACT Article history: Received 24 January 2015 Received Revised 20 April 2015 Accepted 21 April 2015 ----------------------------------------------- Key words: Economic yield, Fertilizer response, Multiple regressions, Potato, Yield -----------------------------------------------
Response of nutrient addition from combined sources like fertilizers and farmyard manure on potato (Solanum tuberosum L.) was studied in aquic Hapludoll in Uttarakhand. In the beginning, fertility gradient was established by growing an exhaustive crop, maize cv. Shweta. A test crop, potato cv. Kufri Jyoti was grown on the same site, where four levels of fertilizer nitrogen, phosphorus, potassium and three levels of farmyard manure were randomly distributed in 24 plots and 3 fertility strips with a total of 72 plots. Multiple regression equations were made using quadratic model. Response type (+ - -) for the nutrients phosphorus and potassium followed law of diminishing return, while the response type (- + +) observed for nitrogen. Application of 100-150 kg of nitrogen, 0-50 kg of phosphorus, and 0-50 kg of potassium showed maximum response at middle doses of other nutrients. Tuber yield was positively correlated with applied fertilizer nutrient (0.702** with fertilizer nitrogen, 0.481** with fertilizer phosphorus and 0.476** with fertilizer potassium) and with soil test value of potassium (0.202*). These findings can successfully be used in the larger parts of Mollisol dominating areas as an effective guide of fertilizer application, because this is economically viable and suitable for the situation of resource constraint situation like in India and other countries having similar soil type.
1. Introduction Potato (Solanum tuberosum L.), world’s fourth important food crop after wheat, rice and maize (Pandey et al. 2005; Reshi et al. 2013), is a heavy feeder of plant nutrients having very high requirement of nitrogen, phosphorus, potassium and other nutrients. Potato provides a source of low cost energy to the human diet and it is the rich source of starch, vitamin C and B and minerals (Kumar et al. 2013; Lokendrajit et al. 2013). The combined use of inorganic and organic sources of nutrients in potato produce maximum yield (Singh and Kushwah 2006).
Application of nitrogen @ 240 kg ha-1 increased tuber number (38-293%) and yield (59-369%) of large grade tubers in all the cultivars (Trehan 2003). Phosphorus carriers had a significant effect on total dry matter of potato, though leaf litter tended to give the maximum dry matter of potato tuber over control Phosphorus carriers had a significant effect on total dry matter of potato, though leaf litter tended to give the maximum dry matter of potato tuber over control (Sud and Sharma 2001). Potassium plays a role in sugar translocation and starch synthesis in plants. Due to the high starch of the potato tuber, K is an important nutrient in tuber development (Rhue et al. 1986). In an experiment on potato cv. Kufri Ashoka, the maximum plant height and tuber yield were recorded at 120 kg K ha-1 and 160 kg N ha-1 (Singh and Raghav 2000).
__________________
*Corresponding author: [email protected]
28
Sasani et al. (2003) reported, remunerative higher yield of potato cv. Kufri Badsah can be attained with 25% extra dose of NPK fertilization than recommended dose of inorganic fertilizer along with 25 tonnes farmyard manure. Concept of fertilizer prescriptions for desired targeted yields based on available nutrient status was pioneered by Truog (1960).
Ramamoorthy et al. (1967) established theoretical basis and experimental verification for the principle of fertilizer application for targeted yield of field crops. It was also emphasized that this concept should make a balance between ‘fertilizing the crop’ and ‘fertilizing the soil’. The fertilizer applied on the basis of yield targets provided higher benefit to cost ratio, indicating superiority over other methods of fertilizer application. The application of fertilizer on the basis of yield targets is meaningful, precise and eco-friendly (Saxena et al. 2008, Chatterjee et al. 2010). In the field experiment, the yield variations due to management practices and soil factors other than the nutrient under study are avoided by creating the desired fertility gradient artificially by growing an exhaust crop followed by test crop. Presently there is no information regarding the fertilizer response on potato, the major tuber crop in daily diet. Keeping above factors in view, an experiment was conducted to find out the response of levels of inorganic fertilizer with organic manure on potato with
2. Materials and methods Experimental details Field experiment was conducted at the foot hills of Shivalik range of Himalayas in Pantnagar, Uttarakhand at 290 N latitude, 79029’ E longitude and an altitude of 243.8 m above the mean sea level. Before conducting the experiment, fertility gradient stabilizing experiment was conducted by sowing maize cv. Shweta as an exhaust crop to minimize interference of other soil and management factors affecting crop yield. Experimental site was divided into three equal strips with the dimension of 38.5 m × 9.5 m and three levels of nutrient viz. 0-0-0, 120-60-40, 240-120-80 kg ha-1 N, P2O5 and K2O, respectively were applied. At the second phase, a test crop, potato cv. Kufri Jyoti was grown on same site of fertility gradient experiment. Layout was made according to approved plan of All India Coordinated Research Project on investigation for Soil Test Crop Response (STCR) Correlation. Each strip (made in the fertility gradient stabilizing experiment in the previous crop) was divided into 24 plots (21 treatments + 3 controls) resulting in 72 (24×3) plots. 3 blocks comprising of 8 treatments, were made within a strip randomized with farmyard manure level.
These treatments comprised of various selected combinations levels of nitrogen (N0, N1, N2, N3 i.e. 0, 100, 150, 200 kg N ha-1), phosphorus (P0, P1, P2, P3 i.e. 0, 50, 100, 150 kg P2O5 ha-1), potassium (K0, K1, K2, K3 i.e. 0, 50, 100, 150 kg K2O ha-1) and farm yard manure (F0, F1, F2 i.e. 0, 10, 20 tonnes farmyard manure ha-1) were randomized in each of the 3 strips. Soil analysis Soil samples at 0-15 cm depth were collected from each plot before and after sowing of test crop and analyzed for pH in soil water suspension (1:2.5) was measured using combined electrode
in digital pH meter (Jackson 1958), electrical conductivity (μSm-
1) in supernatant liquid of soil water suspension (1: 2.5) with the help of conductivity bridge at 25ºC (Bower et al. 1965), per cent oxidizable organic carbon by wet digestion method of Walkley and Black (1934), mineralizable nitrogen (SN) by alkaline- KMnO4 method (Subbiah and Asija 1956), available phosphorus (SP) by 0.5 M NaHCO3 (pH 8.5) extraction method (Olsen et al. 1954) followed by colour development by ascorbic acid method (Murphy and Riley 1964), available potassium (SK) in soil was extracted by 1 N neutral NH4OAc (Hanway and Hiedal 1964). Bouyoucos hydrometer method (Black 1965) was used for separation of sand, silt and clay and expressed in per cent. Statistical analysis
Multiple regression approach is used to calculate the dose of nutrient to get the maximum yield of crops under given set of experimental conditions. This can further be used to calculate economic dose of fertilizer nutrients by incorporating a constant factor, i.e. per unit cost of produce divided by per unit cost of fertilizer. In this approach, yield is regressed with soil nutrients, fertilizer nutrients, their quadratic terms and the interaction term of soil and fertilizer nutrients as given below:
Y = A ± b1 SN ± b2 SN2 ± b3 SP ± b4 + SP2 ± b5 SK ± b6 SK2 ± b7 FN ± b8 FN2± b9 FP ± b10 FP2 ± b11 FK ± b12 FK2 ± b13 FNSN ± b14 FPSP ± b15 FKSK ………………. (1) where, Y = crop yield (kg ha-1); A= Intercept (kg ha-1); bi= Regression coefficients (kg ha-1); SN, SP, SK= Available soil nitrogen, phosphorus and potassium (kg ha-1) respectively; FN, FP, FK =Fertilizer nitrogen, phosphorus and potassium (kg ha-1) respectively. Nutrient requirement for maximum and economic yield The ideal equation for the partial function of fertilizer nutrient is as follows- y = a + bx - cx2 – dxz ………………. (2) where, a = constant independent of x and z; b, c & d = regression coefficient of linear and quadratic terms of x; z = soil test values of nutrient in question; x = fertilizer doses (kg ha-1).
29
On differentiating the equation (1), the following mathematical expression appears,
dz - xc2 - b dx
dy ………………… (3)
or, dz - xc2 - b0 (Since, 0 dx
dy under condition of
maximum yield) or, 2c
dz - b x (max) ……………… (4)
Where, x (max) = dose of fertilizer for maximum yield at soil test values z. Substituting the values b, c and d of regression equation, where response type ‘+ – –’ was obtained on the need of fertilizer for maximum yield was calculated. For economic dose, consequence of ‘law of diminishing return’ is considered. Under such conditions where marginal return just equals the last rupee invested on fertilizer nutrient, that is output/input ratio become unity. Mathematically, it may be expressed as:
pY = qX or, p
q
dx
dy ………………………. (5)
Where, p = price of 1 kg of tuber in rupee, q = price of 1 kg of nutrient in rupee, y = yield in kg ha-1. From the equations (3) and (5) it can be inferred as: b – 2cx – dz = q/p
or, 2c
dz - (q/p) - b(eco)x ………..(6)
By putting the value of b, c and d from the regression equation with (+ – –) response type, the economic dose was calculated at a particular level of ratio (q/p) and soil test value (z).
3. Results Initial soil properties Soil samples collected from several spots of the experimental field before sowing of potato were used to make composite soil samples, which were analyzed for various physico-chemical properties (Table 1). The experimental soil was sandy loam in texture having high organic carbon (0.80%) and available phosphorus (60.5 kg ha-1), medium available potassium (170.2 kg ha-1), while low in available nitrogen (138.7 kg ha-1). Soil profile study at the experimental site was also conducted (Table 2). In the experimental site, slope was 1-3 per cent, drainage was moderately well, profile colour varied from yellowish brown to dark yellowish brown; soil texture was loam in Ap and E horizon, slightly gravely loam in B1 and gravely sandy loam in B2 and CB horizons where loamy sand in C horizon. Consistency varied from friable to loose in moist condition; soil structure was sub-angular blocky throughout the profile
Statistical verification for proper creation of fertility gradient Statistical analysis was carried out by using the soil nutrients (SN, SP and SK) separately as dependent Statistical analysis was carried out by using the soil nutrients (SN, SP and SK) separately as dependent variable for each level of FYM as well as for whole plots (Table 3). The effect of the strips was found to be highly significant in all the cases by taking SN, SP and SK separately as dependent variable. This indicated that fertility gradient was created in respect of N, P and K, respectively at FYM at 0 t ha-1, 10 t ha-1, 20 t ha-1 and whole plots. Alkaline KMnO4-N, Olsen’s-P and neutral normal NH4OAc-K content of soil increased in the order of strip I < strip II < strip III of the experimental site which represents that there was proper creation of fertility gradient. Multiple regression of soil test values and fertilizer dose with potato tuber yield Relationship between tuber yield as dependent variable and the soil test values, fertilizer doses, FYM doses, interactions between soil test values, fertilizer doses and among fertilizer nutrients as independent variables was established through multiple regression equations using the quadratic model. The following regression equations have been worked out by using the quadratic equation function on soil test values and fertilizer doses with potato tuber yield. Multiple regression for all the treated plots Y = 28.853 - 0.343 FN + 0.524 FP + 0.513 FK – 0.474
SN + 0.523 SP + 0.435 SK + 0.0007531 FN2 – 0.000193 FP2 – 0.002293 FK2* + 0.003861 FNSN – 0.007046 FPSP – 0.0005357 FKSK (R2 = 0.614**).................................... (7)
Multiple regression for FYM at 0 t ha-1 Y = - 58.255 - 0.146 FN - 1.035 FP + 2.578 FK - 0.60177 SN - 0.456 SP + 1.437 SK + 0.00124 FN2 + 0.00310 FP2 – 0.00624 FK2 + 0.00309 FNFP - 0.00523 FNFK + 0.00485 FPFK + 0.00314 FNSN – 0.00776 FPSP – 0.00602 FKSK (R2 =0.611).................(8) Multiple regression for FYM at 10 t ha-1 Y = 38.754 - 0.0326 FN + 0.8374 FP + 0.06430 FK -
0.07661 SN -0.09963 SP + 0.27754 SK - 0.00069 FN2 - 0.00336 FP2 -0.00328 FK2 + 0.00213 FNFP + 0.00464 FNFK - 0.00402 FPFK - 0.00150 FNSN + 0.00135 FPSP + 0.00164 FKSK (R2 = 0.872*).........................................................(9)
Multiple regression for FYM at 20 t ha-1 Y = 173.96082 - 0.53829 FN + 0.08150 FP - 0.25930 FK -
0.80180 SN - 0.40233 SP + 0.13866 SK + 0.000569 FN2 + 0.000148 FP2 - 0.00000576 FK2 - 0.00101 FNFP
+ 0.00164 FNFK - 0.00332 FPFK + 0.00580 FNSN + 0.00563 FPSP + 0.00199 FKSK (R2 = 0.896*)............................................ (10)
30
Table 1. Physicochemical properties of the soil of experimental site (0-15 cm. soil depth)
Property Value obtained
Textural analysis Sand (%) 51.80 Silt (%) 33.75 Clay (%) 16.25 Textural class Sandy loam pH (1:2.5 soil water suspension) 6.55
Electrical conductivity (μSm-1) 79.7
Organic carbon (%) 0.80 Available nitrogen (kg N ha-1) 138.7 Available phosphorus (kg P ha-1) 60.5 Available potassium (kg K ha-1) 170.2
Table 2. Characteristics of the horizons at the experimental site
Horizon Depth(cm) Characteristics Ap 0-27 Yellowish brown 10YR4/3 dry; loam; medium, moderate, subangular blocky structure; mottles
nil; mollic epipedon, medium porosity, slightly hard, dry consistency, medium porosity, cutans, nodule and effervescence nil, abrupt-smooth boundary.
E 27-59 Brown 10YR5/3; dry; loam; medium, moderate, subangular blocky structure; mottles nil; friable consistency, medium porosity, medium roots, cutans, nodule and effervescence nil, clear-smooth boundary.
B1 59-85 Light yellow brown 10YR6/4 dry; slightly gravely loam; medium, week, subangular blocky structure; mottles nil; friable consistency, coarse porosity, organic cutans, coarse roots, effervescence nil, abrupt-smooth boundary.
B2 85-116 Yellowish brown 10YR5/6 dry; gravely sandy loam; medium, week, subangular blocky structure; friable consistency, coarse porosity, organic cutans, coarse roots, effervescence nil, clear -smooth boundary.
CB 116-131 Pale brown 10YR6/3 dry; gravely sandy loam; medium, week, subangular blocky structure; very friable consistency, coarse porosity, organic cutans, slight effervescence, clear -smooth boundary; mottles nil.
C 131+ Dark yellow brown 10YR4/4 dry; loamy sand; fine, week, subangular blocky structure; few, medium, distinct mottles; loose moist consistency, coarse porosity, organic cutans, strong effervescence, abrupt-smooth boundary.
Table 3. Statistics of soil test values of FYM level 0 t ha-1, 10 t ha-1, 20 t ha-1 and whole plots FYM level
Dependent Variable Prob. > F R Square Co-eff. of Variation
Mean
0 t ha-1 SN 0.0223** 0.30 9.78 116.55 SP 0.0002** 0.56 8.71 68.45
SK 0.0002** 0.56 7.24 182.52 10 t ha-1 SN 0.0013** 0.47 9.80 116.03
SP <0.0002** 0.55 15.17 62.52 SK 0.0002** 0.45 9.06 170.86
20 t ha-1 SN 0.0260** 0.29 12.16 114.47 SP <0.0001** 0.67 11.15 66.97 SK <0.0327* 0.28 10.56 164.13
Whole plots SN 0.0008** 0.19 11.43 115.68
SP <0.0001** 0.55 12.23 65.98 SK <0.0001** 0.33 9.94 172.50
SN= Alkaline KMnO4-N, SP= Olsen’s-P, and SK= neutral normal NH4OAc-K
31
Soil test value of potassium as ammonium acetate-K (kg ha-1); FN= Fertilizer nitrogen (kg ha-1); FP = Fertilizer phosphorus (kg ha-1); FK = Fertilizer Potassium (kg ha-1). From R2 value described the extent of variations in potato tuber yield which can be explained by the variation in soil test values and fertilizer doses. Multiple regression equation enables fertilizer recommendation based on soil tests to calculate the maximum yield per hectare and maximum profit per hectare at varying input and output prices. Fertilizer response type
The response types (Table 4) were observed from regression equation (Eqn. 7). The response type (+ - -) characterizes at a given soil test value, the yield increases upto a limit with increasing doses of fertilizer but above which there will be no increase but decrease in yield. These response types (+ - -) for the nutrients phosphorus and potassium followed law of diminishing return (Table 4). While the response type (- + +) observed for nitrogen, characterizes that there is positive and increasing response to applied fertilizer and negative correlation between soil and fertilizer nutrients (Table 4). If the soil test values are below
74.37 kg ha 1 as Olsen’s P and 957.63 kg ha-1 as NH4OAc-K for potassium, then positive response with increasing fertilizer doses may be expected. At and above the critical soil test values for given fertilizer dose, no response to fertilizer dose may be expected with respect to variety Kufri Jyoti of potato in Mollisol.
Potassium (kg ha-1); PR (Price ratio) = q/p = price of 1 kg nutrient in question/price of 1 kg potato tuber.
Response to a nutrient at middle doses of other nutrients
had been worked out (Table 5, 6, 7). Yield of potato tuber (q ha-1) at different doses of nitrogen and FYM with constant level of P and K (middle or recommended) were taken and then averaged (Table 5). The response of 100, 150 and 200 kg of nitrogen application over control were found to be 0.396, 0.395, and 0.387 q ha-1 in terms of potato tuber, respectively. These results showed constancy in tuber yield over increasing application of nitrogen. Considering over successive doses, for 0 to 100 kg i.e. increment of 100 kg N resulted in increasing of yield to the extent of 36.5 q ha-1. So the increment was 0.365 q ha-1 potato tubers per kg of N application in this range. Similarly, for 100 to 150 kg i.e. increment of 50 kg N resulted in increasing of yield to the amount of 22.8 q ha-1. So, the increment was 0.456 q ha-1 potato tubers per kg of N application in this range. Increment of potato tuber yield was 0.362 q ha-1 potato tubers per kg of N application for 150 to 200 kg range. Increment of N application from 100 to 150 kg, hence, showed highest response at the middle doses of P and K. Yield of potato tuber (q ha-1) at different doses of phosphorus and FYM with constant level of N and K (middle or recommended) were taken and then averaged (Table 7).
Table 4. Response type and critical limit for soil test value obtained by regression equation of treated plots
R2 value Nutrient Response type Critical soil test value
0.614** Nitrogen - + + 88.84 kg ha-1 (Min.) Phosphorus + - - 74.37 kg ha-1 (Max.)
Potassium + - - 957.63 kg ha-1 (Max.) Table 5. Response to N at middle doses of P and K (kg ha-1) (N100 P100 K100 )
OM0 OM 10 OM 20 Average Over N0 Over Successive Doses
N 0 106.5 72.4 74.9 84.6 - - N 100 99.3 139.8 124.1 121.1 0.365 0.365 N 150 152.1 144.3 135.3 143.9 0.395 0.456 N 200 153.1 154.2 178.7 162.0 0.387 0.362 Average 127.8 127.7 128.2 127.9 - -
Table 6. Response to P at middle doses of N and K (kg ha-1) (N150 P50 K100 )
OM0 OM 10 OM 20 Average Over P0
Over Successive Doses
P0 106.5 72.4 74.9 84.6 - - P 50 147.1 149.4 142.5 146.4 1.235 1.235 P 100 152.1 144.3 135.3 143.9 0.593 -0.049 P150 166.4 165.0 156.5 162.6 0.520 0.375 Average 143.0 132.8 127.3 134.4 - -
32
Fertilizer nutrients (i.e. phosphorus and potassium) which showed (+ - -) type of response were further used for maximum yield ha-1 and maximum profit ha-1 or economic yield calculation. As the three co-efficient of liner, quadratic and interaction terms are also required to be significant at 5 per cent level of significance, for deriving optimum fertilizer dose, which was not found in the present study. Optimum fertilizer dose, therefore, was not worked out further. The usual response type (+ – –) for linear, quadratic and interaction term was found with phosphorus and potassium. Adjustment equations for maximum and economic yield have been worked out for the same as follows: FP(max) = 1357.5 – 18.254 SP ………………... (12) FP(eco) = 1357.5 – 2590.67 PR - 18.254 SP…….. (13) FK(max) = 112.00 – 0.1169 SK …………………(14) FK(eco) = 112.00 – 218.34 PR - 0.1169 SK ………(15) where, SP = Soil test value of phosphorus as Olsen’s P (kg ha-1); SK = Soil test value of potassium as Am. Ac.-K (kg ha-
1); FP = Fertilizer phosphorus (kg ha-1); FK = Fertilizer The response of 50, 100 and 150 kg of phosphorus application over control was found to be 1.235, 0.593, and 0.520 q ha-1 in terms of potato tuber, respectively.
First it increased at 0 to 50 kg P application and then decreased, fitting well with the law of diminishing return. Considering over successive doses, for 0 to 50 kg i.e. increment of 50 kg P resulted in increasing of yield to the amount of 61.8 q ha-1. So the increment was 1.235 q ha-1 potato tubers per kg of P application in this range. Similarly for 50 to 100 kg i.e. increment of 50 kg P resulted in increasing of yield to the amount of -2.5 q ha-1. So the increment was -0.049 q ha-1 potato tubers per kg of P application in this range. Increment of potato tuber yield was 0.375 q ha-1 potato tubers per kg of P application for 100 to 150 kg range. Hence increment of P application from 0 to 50 kg showed highest response at middle doses of N and K. Yield of potato tuber (q ha-1) at different doses of potassium and FYM with constant level of N and P (middle or recommended) were taken and then averaged (Table 8). The response of 50, 100 and 150 kg of potassium application over control was found to be 0.804, 0.593, and 0.301 q ha-1, respectively. First it was increasing at 0 to 50 kg K application and then decreasing. Considering over the successive doses, for 0 to 50 kg i.e. increment of 50 kg K resulted in increasing of yield to the amount of 40.2 q ha-1. So the increment was 0.804 q ha-1 potato tubers per kg of K application in this range. Similarly for
Table 6. Response to P at middle doses of N and K (kg ha-1) (N150 P50 K100 )
OM0 OM 10 OM 20 Average Over P0 Over Successive Doses
P0 106.5 72.4 74.9 84.6 - - P 50 147.1 149.4 142.5 146.4 1.235 1.235 P 100 152.1 144.3 135.3 143.9 0.593 -0.049 P150 166.4 165.0 156.5 162.6 0.520 0.375 Average 143.0 132.8 127.3 134.4 - -
Table 7. Response to K at middle doses of N and P (kg ha-1) (N150 P100 K50)
OM0 OM 10 OM 20 Average Over K0 Over Successive Doses
K 0 106.5 72.4 74.9 84.6 - - K 50 132.1 110.3 131.9 124.8 0.804 0.804 K 100 152.1 144.3 135.3 143.9 0.593 0.382 K 150 103.1 133.2 153.1 129.8 0.301 -0.282 Average 123.4 115.1 123.8 120.8 - -
Table 8. Correlation between potato tuber yield and nutrients treatments
TY FN FP FK FYM SN SP SK OC
TY 1.000 0.702** 0.481** 0.476** NS NS NS 0.202* NS
FN 1.000 0.556** 0.556** 0.000 -0.197* NS NS NS
FP 1.000 0.529** 0.000 NS NS NS NS
FK 1.000 0.000 NS NS NS NS FYM 1.000 NS NS -0.361** -0.282*
SN 1.000 0.312** 0.422** 0.250* SP 1.000 0.395** 0.387**
SK 1.000 0.343** OC 1.000
Sig. (1-tailed); *Significant at 5% level; **Significant at 1% level
33
Similarly for 50 to 100 kg i.e. increment of 50 kg K resulted in increasing of yield to the amount of 19.1 q ha-1. So the increment was 0.382 q ha-1 potato tubers per kg of K application in this range. Increment of potato tuber yield was -0.282 q ha-1 potato tuber per kg of K application for 100 to 150 kg range. Increment of K application from 0 to 50 kg, therefore, showed highest response at middle doses of N and P.
Correlation studies
Correlation between tuber yield and nutrients applied through fertilizers, FYM and soil was studied (Table 8).
In the present investigation, strip wise creation of
fertility gradient was proper and this is in line of the finding by Mahajan et al. (2013) in wheat grown in alluvial soil. Generally at a given soil test value the yield will increase up to a limit with increasing dose of fertilizer and beyond which the yield will not increase but decrease, following the ‘Law of diminishing return’. The fertilizer dose, at which maximum yield increase occurs, decreases with increasing soil test value of the nutrient in question. In a quadratic type response curve, this happens only when the linear term is positive for any fertilizer nutrient and quadratic and interaction terms are negative (+ - -). Eight different types of responses are possible that is there are eight ways in which the algebraic symbols (+) and (-) of the linear, quadratic and interaction terms of regression co-efficient could be arranged (ICAR 1970). Only in (+ - -) type of response situation, site specific optimum fertilizer dose of nutrient can be derived by differentiation provided that the three coefficient are significant at least at 5 per cent level of significance. In case of nitrogen, the ideal response type (+ – –) has not been obtained, therefore, adjustment equations for maximum and economic yield could not be worked out. The critical dose of P and K was observed as 74.37 kg ha-1 as Olsen’s P and 957.63 kg ha-1 as NH4OAc-K. Very high response in case of potassium indicates the requirement of this nutrient in potato tuber development. High utilization efficiency of absorbed K From native soil source indicated the desirability for maintenance of fertilization for soil K status (Singh and Marwaha 1996). Likewise in foothills, the luxurious vegetative growth of the crop during tuber bulking phase on account of onset of monsoon put more demand on K supply from fertilizer and soil pool. Moreover, the response to K also depends upon the soil organic matter. The response to applied K had been found to increase with increase in organic matter status of the soils. Since the soil contained high amount (0.8%) of organic carbon, more response can be expected. Deka and Dutta (1999) also found that tuber yield increased with increasing N and K rate, and the fertilizers also increased the net profit and net production value. .
Increment of N application from 100 to 150 kg ha-1, hence, showed highest response at the middle doses of P and K. Application of nitrogen increased the tuber number and size of large grade tubers in all the cultivars and thus increased yield (Trehan 2003). Sarkar et al. (2007) showed that application of 180:150:150 kg N, P2O5, K2O ha-1 (150% recommended dose of fertilizers) in India significantly increased the tuber yield of potato when it was used along with organic manure.Tuber yield was positively and significantly correlated with applied fertilizer nutrient (0.702** with FN, 0.481** with FP and 0.476** with FK) and with soil test value of potassium (0.202*).
4. Discussion
On the basis of soil profile study, the soil of the experimental site was classified as Mollisol. More precisely, the soil was classified as aquic Hapludoll in subgroup level (Despande et al. 1971). Increment of P application from 0 to 50 kg showed highest response at middle doses of N and K. On contrary, research finding showed that potato and high value vegetable crops have shown responses to higher P levels (Kellig and Speth 1997). Increment of K application from 0 to 50 kg, therefore, showed highest response at middle doses of N and P. In a study, Kumar et al. (2001) found that three cultivar viz. Kufri Jyoti, Kufri Ashoka, Kufri Sindhuri responded to 150% of currently recommended dose of fertilizers and hence required 240 kg N, 90 kg P2O5, and 180 kg K2O ha-1. Tuber yield was positively and significantly correlated with applied fertilizer nutrient and with soil test value of potassium. Similarly, Chettri et al. (2002) observed, potassium content in potato plant was positively correlated with tuber yield and application of potassium increased potassium content of potato plant.
Conclusion The tendency of the farmers in developing countries is to
use imbalance and inappropriate dose of fertilizer by overlooking the return per unit investment of fertilizer. These findings can successfully be used in the larger parts of Mollisol dominating areas as an effective guide of fertilizer application, because this is economically viable and suitable for the situation of resource constraint situation like in India and other countries having similar soil type.
References Black CA (1965). Methods of soil chemical analysis. Part-
1 and 2. Agronomy series. American Society of Agronomy, Madison, Wisconsin, USA.
Bower CA and LA Wilcox (1965). Soluble salts. In: Black CA (Ed.), Methods of Soil Analysis, Part-1, chemical and microbiological properties, American Society of
Agronomy, Madison, Wisconsin 62: 933-951
34
Chatterjee D, Srivastava A and RK Singh (2010). Fertilizer recommendations based on targeted yield concept involving integrated nutrient management for potato (Solanum tuberosum) in tarai belt of Uttarakhand. Indian J Agric Sci 80: 1048-1053.
Chettri M, Mondal SS and B Roy (2002). Influence of potassium and sulphur with or without FYM on growth, productivity and disease index of potato in soils of West Bengal. J. Indian Potato Assoc 29: 61-65.
Deka NC and TC Dutta (1999) Response of potato (Solanum tuberosum L.) to nitrogen and potassium in acidic soil of Assam. Adv Plant Sci 12: 515-520.
Despande SB, Ferenbacher JB, Beavers AH, and BW Ray (1971). Mollisols of tarai region of Uttar Pradesh, Northern India. II Genesis and Classification. Geoderma 6: 195-201.
Hanway JJ and H Hiedal (1952). Soil analysis method used in Iowa State Soil Testing Laboratory. Iowa Agric. 57, 1-31. In: Black CA (Ed.), Methods of Soil Analysis, part 2, American Society of Agronomy, Madison, Wisconsin: 1025-1027.
ICAR (1970). Coordinator’s Progress report. All India Scheme for Investigation on Soil Test Crop Response Correlation. Jackson ML (1958). Soil chemical analysis. Prentice Hall.
Inc. Englehood cliffs, N.J., USA Reshi M, Bhat A, Kaul RK and M Gupta (2013). Evaluation
of various potato cultivars of Jammu region for processing attributes. Bioscan 8: 1203-1205.
Rhue RD, Hensel DR and G Kidder (1986). Effect of K fertilization on yield and leaf nutrient concentrations of potatoes grown on a sandy soil. American Potato J 63: 665-681.
Sarker B, Mondal SS, Nayek SS and MS Saha (2007). Integrated nutrient management for the productivity and quality improvement of potato under irrigated condition. Potato J 34: 99-100.
Sasani GV, Patel CK, Patel RN and NH Patel (2003). Effect of levels of inorganic fertilizer with and without organic manure on yield of potato in North Gujarat. J Indian Potato Assoc 30: 77–78.
Saxena AK, Singh S, Srivastava A and P Gautam (2008). Yield target approach under integrated nutrient management for assessing fertilizer requirements of onion in Mollisols of Uttarakhand. Indian J Hort 65: 302–306
Singh JP, Marwaha RS and JS Grewal (1996). Effect of sources and levels of K on potato yield, quality and storage behavior. J Indian Potato Assoc 23: 153-156.
Singh NP and M Raghav (2000). Response of potato to nitrogen and potassium fertilization under U.P. tarai conditions. J Indian Potato Assoc 27: 47-48.
Singh SP and VS Kushwah (2006). Effect of integrated use of organic and inorganic sources of nutrients on potato (Solanum tuberosum) production. Indian J Agron 51: 236–238.
Subbiah BV and GL Asija (1956). A rapid procedure for estimating of available nitrogen in soil. Curr Sci 31: 196-200.
Kelling KA and PE Speth (1997). Influence of phosphorus rate and timing on Wisconsin potatoes. Proc Annual Wisconsin Potato Meetings 10: 68-79.
Kumar D, Praharaj CS, Sharma RC and SMP Khurana (2001). Response of potato varieties in Indo-Gangetic plains of Bihar. J Indian Potato Assoc 28: 56-57.
Kumar M, Baishya LK, Ghosh DC, Ghosh M, Gupta VK and MR Verma (2013). Effects of organic manures, chemical fertilizers and biofertilizers on growth and productivity of rainfed potato in the eastern Himalayas. J Plant Nutr 36: 1065–1082.
Lokendrajit N, Singh CB, Swapana N and MS Singh (2013). Evaluation of nutritional value of two local potato cultivars (Aberchaibi and Amubi) of Manipur, Northeast India. Bioscan 8: 589-593.
Mahajan GR, Pandey RN, Datta SC, Kumar D, Sahoo RN and Parsad R 2013. Soil test based fertilizer recommendation of nitrogen, phosphorus and sulphur in wheat (Triticum aestivum L.) in an alluvial soil. Int J Agric Environ Biotechnol 6: 271-281.
Murphy J and JP Riley (1962). A modified single method for determination of phosphates in natural waters. Anal Chem Acta 27: 31-36. Olsen SR, Cole CV, Watanabe FS and LA Dean (1954).
Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Circ.939, Washington DC, USA.
Pandey SK, Singh SV and D Sarkar (2005). Potato (Solanum tuberosum) for sustaining food and nutrition security in developing world. Indian J Agric Sci 75: 3-18.
Ramamoorthy B, Narasimham RL and RS Dinesh (1967). Fertilizer application for specific yield targets of Sonora 64. Indian Farming 17: 43-44.
Sud KC and RC Shama RC (2001). Effect of crop residue on phosphorus availability to potato in acid soil. J Indian Potato Assoc 28: 46-47
Trehan SP (2003). Evaluation of N efficiency of different potato cultivars. J Indian Potato Assoc 30: 63-64. Truog E (1960). Fifty years of soil testing. Transactions of
Seventh Int Congress Soil Sci, 3, Commission IV, Paper No. 7: 46–53.
Walkley A and IA Black (1934). An examination of degtjareff method for determining soil organic and a proved modification of chromic acid titration method. Soil Sci 37: 29-38.
35
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 35--42
Socio- Economic Analysis of Ginger Crop in Himachal Pradesh Sukhjinder Singh • Sharanjit Singh Dhillon CSIR-Institute of Himalayan Bioresource Technology, Palampur 176061, Himachal Pradesh
ARTICLE INFO ABSTRACT Article history: Received 26 May 2015 Received Revised 19 June 2015 Accepted 20 June 2015 ----------------------------------------------- Key words: Socio-economics, Benefit cost ratio, Income measures, Regression coefficient, Farming Land holding ----------------------------------------------
The study was conducted with the objective to evaluate the socio-economics of ginger growers in Sirmour district of Himachal Pradesh, India. The data collected from 50 ginger growers of two blocks of the district by personal interview using multi-stage purpose sampling technique. In ginger farms average male members as well as their percentage were found to be more than their female counterparts. Majority of the farmers were literate. Farming followed by daily paid labourers, service and business was the main occupation of adult family members. Owned land holding for ginger farms were 1.76 hectares. Cropping intensity during the study period was 190.34% on ginger farms. Returns over variable cost for ginger crop was worked out as Rs. 113324/ha. BCR (over total variable cost) as well as BCR (over total cost) was worked out as 2.617 and 1.167 respectively for ginger crop. All income measures per hectare were found to be positive for ginger crop. 86% and 74% of selected ginger farmers’ responded positively regarding availability of good quality seed/ seedlings and disease problem in seed/ seedlings in the initial stage of sowing of ginger crop respectively. 52% responded that there is lack of extension training facilities and 92% responded that there is a problem of weed infestation. 28% responded there is an un-remunerative price of produce, 98% responded that cost of marketing of produce was high, all the respondents were hiring transport to market the produce. 66% responded for favour of Govt. policies for ginger farming. 60% responded that there is lack of cheap credit from banks for ginger farming. Strongly suggested the strengthening of R&D work, extension services, training of farmers, establishment of semi-processing and cold storage facilities, and co-operatives farming societies for better ginger farming.
1. Introduction Ginger belongs to Zingiberaceae family and is
originated from South-East Asia. Tropical areas having high rainfall and hot and humid weather conditions are favourable for Ginger. The name ‘ginger’ is derived from the Sanskrit word ‘Srngaveram’ which means ‘horn root’. In South East Asia the most popular form of ginger is raw ginger. It is revered as one of the most important and valued spices of the world. For over 5000 years ginger has been recognized as the “universal medicine” by the ancient orientals of China and India. Today ginger remains a component of more than 50 percent of the traditional herbal remedies and has been used to treat nausea, indigestion, fever and infection and to promote vitality and longevity. Ginger contains 2-3 per cent protein, 0.9 per cent fat, 1.2 per cent minerals, 2.4 percent fiber, 12.3 per cent carbohydrate and a good source of calcium, phosphorous, iron and vitamins.
_________________ Corresponding author: [email protected]
Ginger is one of mainstay in Indian spice account and has been used for flavoring and medicinal purposes. Ginger occupies fourth position among spices produced in India, fifth position in terms of quality and sixth position in export earning among spices. India has also imported significant quantities of ginger in various forms, viz. ginger fresh, ginger unbleached, ginger bleached, ginger powder (not elsewhere specified) including dried ginger to the tune of 12,807 tons valued at Rs. 1, 925 lakh in 2009-10. Nepal has been our main source of import (http://etd.uasd.edu/ft/th10189.pdf).
India is the major producer of ginger having production of 655000 MT of ginger from 132000 ha area under its cultivation (National Horticulture Board, 2014). Productivity of ginger in India is more (3,417 kg/ha) than the average productivity (2,546 kg/ha) in the world. USA is having the highest (51,925 kg/ha) productivity of ginger in the world.
36
Though grown all over India, the finest quality ginger
comes from Kerala due to its congenial climate and a rich earthy soil. 'Cochin Ginger' (NUGC) and 'Calicut Ginger' (NUGK) varieties are famous Indian dry ginger in the world market. Kerala, Karnataka, Orissa, Meghalaya, West Bengal, Sikkim and Mizoram are the major ginger producing states in India (Zala, 2009). In Himachal Pradesh, ginger is a cash crop of mid and low hills of the state and is being cultivated in an area of 3,230 ha with a production of 7640 MT (National Horticulture Board, 2014). In this state, ginger is mostly grown in Sirmour district having more than 3/4 of the area and production followed by Solan, Mandi, Shimla, Kangra, Bilaspur, Hamirpur and Chamba districts. Most of the fresh ginger produced in the state is sold to the nearby states like Punjab, Haryana, Delhi, Uttar Pradesh etc. (Dohroo et. al., 2012).
2. Methodology
For evaluating the objectives of the study, primary data were collected through personal interview method with the help of a well-structured and pre-tested schedule for the year 2012-13. The primary data with respect to household composition, educational profile, land ownership, cropping pattern, costs of inputs, returns and the problems involved in cultivation of ginger was collected from selected ginger growers from Himachal Pradesh. A field survey was undertaken to work out the socio-economics of ginger, factors affecting its productivity and the constraints in the production of this crop in the state of Himachal Pradesh. In case of ginger, Sirmour district is predominant in ginger farming having both area as well as number of farmers (State Deptt. of Agriculture, Himachal Pradesh). Thus in the first stage, Sirmour district was selected. Secondly, two blocks (Paonta Sahib and Shillai from Sirmour district) having concentration of ginger growing farmers and area were selected. Depending on the number of growers and area under ginger; Masu, Sharli and Sataun villages from Paonta Sahib block and Bela, Dads and Kando villages from Shillai block were selected purposively. Further, 30 farmers from Paonta Sahib block and 20 farmers from Shillai block were selected purposively. The complete list of selected districts, blocks, villages and number of respondents is presented in the Table 1.
Economics of Ginger Crop
The data pertaining to input use pattern in ginger farming of Himachal Pradesh was collected from the sample farmers for the year 2012-13.
For valuation of various inputs, market price or cost were used
in the analysis. For various machine related farm operations, rental value of farm operation prevailing in the selected villages was used for calculating total variable cost. Ginger in the study area is having rainfed irrigation. Farm labour used in various farm operations was imputed at the prevailing wage rate. Interest on the working capital was calculated @ 7 per cent for the life period of the ginger crop as per its season. Besides, for bringing out the gross returns, price realized by the respondent farmers by selling the produce was used. Average output obtained was recorded on the basis of respondent’s perception. Benefit cost ratios were calculated for ginger crop to make the results of the study more specific.
Cost concepts Costs were computed as per the guidelines of CACP (Commission for Agricultural Costs and Prices) and are discussed below: (a) Cost A1: Includes following costs
(i) Value of hired human labour, (ii) Value of hired bullock labour, (iii) Value of owned bullock labour, (iv) Value of owned machinery, (v) Hired machinery charges, (vi) Value of seed/ seedlings , (vii) Value of manures , (viii) Value of fertilizers, (ix) Value of plant protection chemicals , (x) Irrigation charges, (xi) Depreciation on farm buildings and implements, (xii) Interest on working capital, (xiii) Insurance premium (xiv) Land revenue, and (xv) Miscellaneous expenses (b) Cost A2: Cost A1 + rent paid for leased-in land (c) Cost B1: Cost A1+ interest on fixed capital (excluding land) (d) Cost B2: Cost B1+ rental value of owned land + rent paid for leased-in land (e) Cost C1: Cost B1 + imputed value of family labour (f) Cost C2: Cost B2 + imputed value of family labour (g) Cost C3: Cost C2 +10 per cent of cost C2 as management cost Income measures For working out profitability of ginger cultivation in the study areas following income measures were worked out: (a) Family labour income (FLI) It is the return to family labour (including management). F.L.I. = Gross income – Cost B2 (b) Net income (NI) It is the net profit after deducting all cost items i.e., variable and fixed costs from gross income. NI = Gross income – Total cost (Cost C2) (c) Farm business income (FBI) It is the disposal income out of the enterprise and is defined as: FBI = Gross income – Cost A1 (cost A2 in case of tenant operated land)
37
Table 1. District-wise Ginger respondents selected from cluster villages, Himachal Pradesh, 2012-13 District Block Villages Number of Respondents Sirmour Paonta Sahib Masu
Sharli Sataun
14 13 3
Shillai Bela Dads Kando
10 5 5
Total 2 6 50
(d) Return per rupee (RPR) Gross Income /ha RPR = ----------------------------- Total Cost (Cost C2)/ha Functional Analysis
To examine the factors affecting value productivity of ginger crop of Himachal Pradesh, both linear and log-linear production function were fitted and numerous equations were tried by taking different explanatory variables. Best fit function was determined on the basis of level of significance of the explanatory variables, the value of coefficient of multiple determinations (R2) and the logical signs of the explanatory variables included in the model. Cobb-Douglas function of the following form was considered the most appropriate for the present investigation:
n
Y = A ∑ Xi bi eu
i=1
Where, Y represented the value productivity per hectare of ginger crop under study. X i the selected explanatory variables (per hectares); A, the technical efficiency parameter and b i the coefficient of production elasticity of the respective variable X i at the mean level of input used and output obtained. The 'e' is an error term. The estimated form of the equation becomes:
Log Y = Log A +
n
i 1
bi log xi + u
Log Y= Log A + b1 log x1 + b2 log x2+……………..+bn log xn + u Function fitted for Ginger crop was: Log Y= Log A + b1 log x1 + b2 log x2+……………..+b8 log x8 + u Where, Y = Value productivity per hectare of ginger crop (Rs./ha) X1 = Value of seed (Rs./ha)
X2 = Fertilizers (Rs./ha) X3 = Plant protection chemical (PPC) measures (Rs./ha) X4 = Bullock labour (Rs./ha) X5 = Irrigations (Rs./ ha) X6 = Human labour charges (Rs./ha) X7 = Machine labour charges (Rs./ha) X8 = Area under crop (hectares) Statistical significance of the estimates:
To test the statistical significance of these estimates, t -value of the estimates was worked out at (n-k) degrees of freedom. The t-value of the regression coefficients (b i) were worked out as under:
t(n-k) = ).(. biES
bi
Where S.E. is the standard error of the variable X i Coefficient of multiple determinations (R2) The coefficient of multiple determination was worked out to estimate the proportion of variations in total output/gross returns per hectare explained by the different explanatory variables, taken together in the analysis. Statistical significance of R2, which examines the goodness of fit of the function, was tested by working out F-ratio as follows: R2 / k F= ----------------- (1- R2) / n-k Where,
R2 is the value of the coefficient of multiple determinations, n is the number of observations and k is the number of parameters included in the study. Constraints Analysis
The respondent farmers were asked about the various constraints affecting the productivity of ginger crop. Simple tabular analysis using averages and percentages was also carried out to fulfil the objectives of the study.
38
3. Results and Discussion
Socio-Economic Characteristics of Sample Ginger Respondents:
It is necessary to look into the various socio-economic characteristics of sample farmers before proceeding for analysing a particular enterprise undertaken on the farm. This section deals with various socio- economic characteristics of sample respondents which includes their household composition, educational status, occupational status, land details, and cropping pattern followed on their farms. (a) Household composition
The family composition of the sample households is displayed in Table 2. The average number of male members, female members, children (of 12-18 years age) and children (below 12 years age) per farm were found to be 5.14, 3.64, 2.14 and 1.72 respectively. Further, the percentage of male members, female members, children (of 12-18 years age) and children (below 12 years age) was found to be 40.66%, 28.80%, 16.93% and 13.61% respectively for ginger farms. Thus, in ginger farms average male members as well as their percentage were found to be more than their female counterparts. Table 2. Household composition of ginger farms
Family composition Number per household Male 5.14(40.66)
Female 3.64 (28.80) Children (12- 18 yr) 2.14 (16.93)
Children (below 12 yr) 1.72 (13.61) Total 12.64
Figures in parentheses are percentages of total.
Table 3. Educational status of family
Table 4. Occupational status of adults in the family
Particulars Occupation/ farm
Farming 3.26 (63.42)
Business 0.18 (3.50)
Service sector 0.68 (13.23)
Daily Paid Labourer 1.02 (19.85)
Figures in parentheses are percentages of total.
Particulars % Illiterate 8.00
Read & Write 12.00 Elementary (1 to 5 class) 28.00
Middle (6 to 7 class) 24.00 Secondary (8 to 12 class) 22.00
Graduate 6.00
(b) Educational status The educational level of a person plays an important role in
adoption of latest farm technology. Therefore, the educational status of head of the family who acted as decision maker was enquired from the sample farms. The educational status of head of the family members is depicted in Table 3. It was found that 8% were illiterate, 12% were who can read & write, 28% having elementary education, 24% having middle school education, 22% were having education up to secondary school and 6% were graduate. Thus majority of the farmers were literate. (c) Occupational status
The occupational status of adult family members is displayed in Table 4. It is clear from the table that farming is the main occupation for 63.42% of the ginger farmers, 19.85% as daily paid workers and 13.23% in some service/jobs whereas only 3.50% were engaged in some petty business.
(d) Cropping pattern and cropping intensity The cropping pattern of the sample farms has been analysed
in order to work out the relative share of various crops grown as kharif and rabi crops on the sample farms. Ginger is mainly grown as rainfed and as an annual crop. The cropping intensity was worked out to see the number of crops grown on the farms. Cropping pattern and cropping intensity of sample ginger farms is presented in Table 6. It shows that relative share of maize (53.73%) was much higher than ginger (19.59%), vegetables (17.45%), pulses (5.91%), oilseeds (4.00%) and fodder crops (2.41%) on ginger farms. Maize, ginger and vegetables were found to be major kharif crops on ginger farms.
It is also evident from the table that wheat, ginger and
vegetables are the major rabi crops of ginger growing farms. The relative share of wheat (44.45%), ginger (18.86%), and vegetables (8.95%) was much higher than pulses (6.18%) and fodder crops (1.14%) on ginger farms. Cropping intensity during the study period worked out to be 190.34% on ginger farms. The table 5 shows that the average operational holding was 1.76 hectares for ginger farms of Himachal Pradesh. Owned land holding for ginger farms were 1.76 hectares.
Table 5. Land holding details on sample ginger farms
Particulars Hectares per farm
Owned 1.76 (100.00)
Leased in Nil Leased out Nil
Average operational holding 1.76 Figures in parentheses are percentages of total.
39
Benefit cost ratio (BCR)
Benefit cost ratio (BCR) was undertaken to examine the
profitability from ginger crop on sample farm and has been shown in
Table 7.
Table 7. Benefit cost analysis of different medicinal crops on
sample ginger farms
Particulars Per hectare
Human labour (Rs.) 38247
Machine labour (Rs.) 1776
Seed/ seedlings (Rs.) 19857
Fertilizer use (Rs.) 5975
Plant protection chemicals i.e. PPC (Rs.) 1859
Irrigations (Rs.) -
Interest on variable cost @ 7% p.a. (Rs.) 2370
Total variable cost (Rs.) 70084
Rental value of owned land (Rs.) 67634
Depreciation (Rs.) 10165
Interest on fixed capital @ 12% p.a. (Rs.) 9315
Total cost 157198
Yield (kg/ha)-main product 1467
Gross returns (Rs.) 183408
Returns over variable cost (Rs.) 113324
BCR (over total variable cost) 2.617
BCR (over total cost) 1.167
Table 8. Cost concepts and Income measures of ginger farms
Particulars Ginger
Cost Concepts
Cost A1 67320
Cost A2 67320
Cost B1 76636
Cost B2 144270
Cost C1 89564
Cost C2 157198
Cost C3 172918 Income Measures
Family labour income (Rs.) 39138 Farm business income (Rs.) 116087
Net income (Rs.) 26209 Return per rupee (RPR) 1.166
Figures in parentheses are standard errors of regression coefficients **, *, indicate significance at 1 per cent and 5 per cent level of significance
Table 6. Cropping pattern and cropping intensity on sample ginger farms
Crops Ha per farm
Kharif Crops
Maize 0.95 (53.73)
Pulses 0.10 (5.91)
Oilseeds 0.07 (4.00)
Vegetables 0.31 (17.45)
Fodder Crops 0.04 (2.41)
Ginger 0.34 (19.59)
Other medicinal aromatic crops 0.004 (0.27)
Rabi Crops
Wheat HYV 0.78 (44.45)
Oats 0.01 (0.64)
Other oilseed crops 0.01 (0.45)
Pulses 0.11 (6.18)
Potato 0.00 (0.00)
Pea 0.00 (0.00)
Other Vegetables 0.16 (8.95)
Fodder Crops 0.02 (1.14)
Mentha 0.00 (0.00)
Ginger 0.33 (18.86)
Other medicinal/aromatic crops 0.01 (0.55)
Perennial crops
Fruit trees 0.11 (6.00)
Gross cropped area 3.35
Cropping intensity 190.34
Figures in parentheses are percentages of the total
40
Cost concepts and Income measures
Table8 reveals that total variable cost of growing ginger
worked out to be Rs. 70084 per hectare. The major constituents of total variable cost were human labour (Rs. 38247), planting material/ seed (Rs. 19857.00), fertilizers (Rs. 5975), plant protection chemicals (Rs. 1859.00), and machine labour (Rs. 1776). Yield of ginger on an average worked out to be 1467 kg/ha and gross returns were Rs. 183408/ha. Returns over variable cost for ginger crop worked out at Rs. 113324/ha. BCR (over total variable cost) as well as BCR (over total cost) was 2.617 and 1.167 respectively for ginger crop. Returns over variable cost, BCR (over total variable cost), and BCR (over total cost) were found to be positive and more than one respectively for ginger crop, which reveals that farmers cultivating this crop were recovering variable costs as well as getting returns over variable costs and total cost incurred.
Cost concepts (A1, A2, B1, B2, C1 and C2) and income
measures (family labour income, farm business income, net income and return per rupee) for ginger crop has been presented in Table 8. Overall costs A1, A2, B1, B2, C1 and C2 were found to be Rs.67320, Rs. 67320, Rs.76636, Rs. 144270, Rs. 89564 and Rs. 157198 respectively. Family labour income, farm business income, net income and returns per rupee were Rs. 39138, Rs. 116087, Rs. 26209 and 1.166 respectively. The analysis reveals that all income measures were positive for ginger crop. However, farmers of this region grow these kinds of cash crops only in some proportion on their farms and not on all land holdings because crops like ginger are risky and there is high price valtality, during some years it gives high profits and may lead to losses in other years as price falls. Majority of other crops being grown are those having minimum support price (MSP) like wheat and maize which gives them assured income.
Factors affecting productivity of ginger crop
The discussion in previous section was focused on studying the various parameters related to economics of ginger crop. Various factors affecting productivity of ginger are discussed in this section. This section will bring out the strategies needed to augment the value productivity of these crops. The regression coefficients of various explanatory variables included in the model for ginger crop have been depicted in Table 8. The table reveals that the value of adjusted coefficient of multiple determinations (R2) came out to be 0.418 for ginger crop which shows that only 41.80 per cent of the variation in the model has been explained by the explanatory variables included in the model. The coefficient of expenditure on fertilizer and machine labour were found to be positive and significant at five per cent level of significance showing thereby that with increase in expenditure on fertilizer and machine labour by one per cent the resultant value productivity
of ginger increases by 0.063 per cent and 0.165 percent respectively. The coefficients of expenses incurred on bullock labour were negatively related to value productivity of ginger at one per cent level of significance. It shows the excessive use of bullock labour on the ginger crop. Hence, with increase in expenses on bullock labour by one per cent, the resultant value productivity decreases by 0.091 per cent. The regression coefficients of other explanatory variables such as expenditure on PPC, human labour and area under ginger crop were found to be positive but non-significant; whereas regression coefficient of planting material/ seed was found to be negative but non-significant.
Therefore, on ginger, the explanatory variables affecting the value productivity of ginger crop positively and sufficiently were found to be; expenses on fertilizer and machine labour. Also there is excessive use of bullock labour on ginger farms. From the results it is suggested that bullock labour should be replaced by machine labour for having more efficiency on ginger farms.
Issues/ Problems related to Ginger Farming
Since cultivation of ginger farming has both direct and indirect effect on the establishment and working of herbal industry related to value addition of ginger, so it becomes important to study the problems/ issues related to the ginger farming. The problems/ issues highlighted by farmers during survey are presented in Table 9.
(a) Seed/ Seedling issues
Certain issues related to seedlings were studied for ginger crop. When asked about the availability of sufficient quantity of planting material, all the farmers of ginger farms responded for the availability. 86%, 88% and 74% of selected ginger farmers’ responded regarding availability of good seed/ seedlings, availability of seed/ seedlings at reasonable price, and disease problem in seed/ seedlings in the initial stage of sowing of ginger crop respectively.
(b) Input issues
Regarding know-how support from any organization, 68% of ginger farmers responded positively. All the ginger growers responded for availability of inputs (fertilizers/ chemicals), whereas regarding availability of labour, 72% responded positively.
41
Table 9. Problems/ issues highlighted by farmers during the survey
c) Agronomic issues When asked about certain agronomic issues, 60% of
ginger farmers reported that there is availability of package of practices in local language, 52% respondents were of the view that there is lack of extension training facilities, problem of weed infestation was reported by 92% respondents and 44% responded that there is insect/pest infestation. About 66% responded for favour of Govt. policies for ginger farming.
d) Marketing issues Marketing issues were also studied for ginger crop. 82% of ginger growers adopted grading system, 82% were getting prices according to grades and 32% ginger growers were marketing their produce through middlemen. All the ginger growers responded positively for having nearby regulated market for ginger produce at Dehradun. Only 28% responded there is an un-remunerative price of produce. 98% responded that cost of marketing of produce was high as all the respondents were marketing their produce through hired means of transport.
Issues/Problems
Response
Yes No
A. Seed/ Seedlings Issues:
Getting seed/ seedlings in sufficient quantity 50 (100) 0 (0)
Getting good quality seed/ seedlings 43 (86) 7 (14)
Reasonable price of seed/ seedlings 44 (88) 6 (12)
Any special subsidy on seed/ seedlings 0 (0) 50 (100)
Disease problem 37 (74) 13 (26)
B. Input Issues
Know- how support from any organization 34 (68) 16 (32)
Availability of inputs (fertilizers/ chemicals) 50 (100) 0 (0)
Labour availability 36 (72) 14 (28)
C. Agronomic Issues
Availability of package of practices 30 (60) 20 (40)
Lack of extension training facilities 26 (52) 24 (48)
Weed problem 46 (92) 4 (8)
Insect/pest problem 22 (44) 28 (56)
Favorable Government Policies 33 (66) 17 (34)
D. Marketing Issues
Adopting grading system 41 (82) 9 (18)
Getting prices according to grades 41 (82) 9 (18)
Marketing through middleman 17 (34) 33 (66)
Availability of regulated market 50 (100) 0 (0)
Unremunerative prices 14 (28) 36 (72)
High cost of marketing of produce 49 (98) 1 (2)
Own Means of transport 0 (0) 50 (100)
E. Credit Issues
Acquired loan from bank 22 (44) 28 (56)
Lack of credit facility 21 (42) 29 (58)
Lack of cheap credit 30 (60) 20 (40)
Figures in parentheses are percentages of the total.
42
(e) Credit issues When asked about the availability of credit, 44 % respondents said that they avail the facility of crop loan from bank, 42% responded that there is lack of credit facility and 60% responded that there is lack of cheap credit from banks for ginger farming. Suggestions to Overcome the Constraints
More emphasis should be given on R&D to release new verities/ seed for better yield and more resistance from diseases especially rotting problem of ginger.
Govt. institutes should provide more extension services for getting good quality and high yield Farmers should also be trained for grading practices of ginger produce for having better market prices.
Govt. should make provision for establishment of storage facility so that produce could be sold out during favorable market price of the produce.
Establishment of cleaning, grading and semi-processing facility for fresh ginger raw material at the village level will ensure better returns to the ginger growers.
Co-operative farming societies for ginger should be promoted to reduce high costs incurred on transport for marketing of produce.
Govt. should also make provision for cheap financial services to promote ginger farming.
References
National Horticulture Board (2014). Area, production statistics, retrieved from http://nhb.gov.in/area%20_production.html
Dohroo NP, Kansal Sandeep and Ahluwalia Neha (2012). Status of Soft Rot of Ginger. Booklet, Department of Vegetable Science, Dr.Y.S. Parmar University of Horticulture & Forestry Nauni, Solan, Himachal Pradesh
Zala YC (2009). Ginger cultivation: Capital intensive but profitable. Commodityonline, 22 Sep 2009, retrieved from http://www.commodityonline.com/news/ginger-cultivation-capital-intensive-but-profitable-21330-1-21331.html
http://etd.uasd.edu/ft/th10189.pdf http://nhb.gov.in/report_files/ginger/GINGER.htm
43
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 43-49
Studies of Enzyme Glutamine Synthetase (GS) in Sesuvium Portulacastrum (L.), an Associate Halophyte
Anil Avhad* • Himanshu Dawda Department of Botany, Ramniranjan Jhunjhunwala College, Ghatkopar, W, Mumbai – 400 086
ARTICLE INFO ABSTRACT Article history: Received 9 January 2015 Received Revised 15 March 2015 Accepted 16 March 2015 ----------------------------------------------- Key words: Associate halophyte, Nitrogen metabolism Enzymes ---------------------------------------------
Sesuvium portulacastrum L. (Aizoaceae) is a pioneer, psammophytic associate halophyte of subtropical, Mediterranean regions. It dominates in coastal and warmer zones of the world. Apart from being utilized as a vegetable by local people and forage for domestic animals in the coastal area, environmentally too it is utilized for the bio-reclamation of saline soil in the arid and semiarid regions. Coastal soils as well as sea water, which permeate the soil characteristically, have a poor content of available nitrogen. In contrast, halophytes which inhabit these areas have high protein content. This is because halophytes have the ability to conserve nitrogen and recycle it through their body metabolism. Efficient enzyme mechanism for Nitrogen metabolism in halophytes has been thoroughly studied and communicated. In present investigation, Sesuvium portulacastrum (L.) is used as a model system representing an associate halophyte with efficacy in Nitrogen utilization in saline conditions. To begin with kinetics of enzyme Glutamine synthetase (GS) (EC: 6.3.1.2) is studied in terms of effect of varying temperature, pH and concentration of enzyme and substrate. The same study would be extended to other important enzymes of Nitrogen metabolism to get an insight in efficacy of such halophytes to conserve available Nitrogen from saline soils and help in phytoremediation of saline soils.
1. Introduction
Associate halophytes grow in the fringe area of mangrove swamps, get inundated 1-5 times per fortnight during spring tide and are also found growing in mesophytic habitat. Sesuvium portulacastrum L. (Seapurslane) is one such fast growing, herbaceous, dichotomous, perennial, pioneer, psammophytic halophyte naturally growing in the subtropical, mediterranean, coastal and warmer zones of the world. Sesuvium portulacastrum is found occurring on the coastlines of five continents and widely distributed as a pioneer strand species on tropical and subtropical shores (Lonard and Judd, 1997). It grows naturally in the subtropical, mediterranean, coastal and warmer areas around the world (Balasubramanian et al., 2006). Sesuvium frequently grows in the backshore topographic zone on sandy beaches as the initial pioneer species just above the high tide line on barrier islands. It is also a common species on the margins of hurricane washover channels, disturbed roadsides, and tidal flats. _______________ Corresponding author: [email protected]
In the tropics, the species occurs on estuarine mudflats adjacent to mangrove swamps (Joshi and Bhosale, 1982), in salt marshes and on calcareous shorelines, on the margins of lagoons, on coral sand and rubble shorelines. It is also found along coasts and river mouths and in lower mountains (Hammer, 2001). In India, it grows among the eastern and western coastal regions as inland or seashore species including areas where mangrove plants are found. This includes coastal regions of Gujarat, Maharashtra, Goa, Kerala, Tamilnadu, Andhra Pradesh and Orissa. The present study on Sesuvium portulacastrum L. was done keeping following objectives in mind S. portulacastrum (L.), as a model system representing an associate halophyte with efficacy in nitrogen utilization in saline conditions.
44
To study kinetics of enzyme Glutamine synthetase (GS)
(EC: 6.3.1.2) in terms of effect of varying temperature, pH and concentration of enzyme and substrate. The same study would be extended to other important enzymes of Nitrogen metabolism to get an insight in efficacy of such halophytes to conserve available Nitrogen from saline soils and help in phytoremediation of saline soils. To get an insight in the world of associate halophyte helping in natural conservation of mangrove locations. Glutamine synthetase, a key enzyme of ammonia assimilation, catalyses the ATP dependent production of glutamine from glutamate and ammonia. The native molecular weight of GS from different plant tissues is in the range of 320,000 to 400,000 (Stewart et al, 1980; McCully and Hirel, 1983). The enzyme consists of 8 identical subunits of 39,000 to 45,000 (McCormack et al., 1982; Cullimore et al., 1983).
The reaction mechanism of GS involves the binding of substrate in an ordered sequence (Meister, 1974). First, the complex of ATP and divalent cation (Mg++, Mn++ or Co++) binds to the enzyme followed by glutamate which reacts to form an
enzyme bound γ glutamyl-phosphate. Ammonia, thus binds to the enzyme, attacks the phosphoryl group, resulting in the formation of tetrahedral intermediate before the products are released. GS is important as it is involved in the assimilation, storage and translocation of ammonia in higher plants (Yemm and Folkes, 1958; Lingnowski et al., 1971). The end product glutamine is an important metabolite as it serves a building block of protein and as nitrogen donor in various biosynthetic pathways (Kanamori and Matsumoto, 1972).
Mn2+
L- Glutamate + ATP + NH3 Glutamine + ADP + Pi
In addition to catalysing the synthesis of glutamine, GS catalyses the formation of glutamylhydroxamate when ammonia is
substituted by hydroxylamine
Mn2+
NH2OH + L-glutamate +ATP γ glutamylhydroxamate + ADP + Pi
GS also catalyses the transferases reaction
Mn2+
NH2OH + glutamine +ADP+Pi γ glutamylhydroxamate + NH3
Materials and methods GS activity was studied by the method of Elliot (1955) with some modifications. Reagents: Extraction buffer-0.1M Tris-HCl containing 1mM EDTA, 1mM Cysteine and 0.1% v/v Mercaptoethanol (pH 7.0)
Assay buffer-0.1M Tris-HCl, (pH 7.0) MgSO4.7H2O 0.1M (pH adjusted to 7.0 With NaOH)
The reaction was initiated by the addition of sodium glutamate, which was replaced in the blank by buffer. After incubation at 30oC for 30 minutes, 1.0 ml of ferric chloride reagent was added to each tube and the absorbance was read at 540 nm in EQIP-Tronics digital spectrophotometer (EQ 820).The protein content of the enzyme was estimated by the method of Lowry et al. (1951). The specific activity of the
enzyme is expressed as ΔOD/mg protein/30 minutes.
45
Hydroxylamine-0.1M (pH adjusted to 7.0 with NaOH) ATP-0.06M, Na Glutamate- 0.6M. Ferric Chloride Reagent- Prepared from equal volumes of 10% FeCl3.6H2O in 0.2 N HCl, 24% Trichloroacetic acid and 50% HCl. Principle
GS catalyses the formation of glutamine from glutamate with the simultaneous cleavage of ATP to ADP.Glutamate serves as the primary substrate and hydroxylamine supplies
the amino group. The product γ- glutamylhydroxymate develops yellowish brown colour with ferric chloride reagent, which is estimated Spectrophotometrically.
Enzyme Extraction
One g fresh leaves were ground using 10ml chilled extraction buffer. The homogenate was filtered through 4 layers of muslin and the filtrate was centrifuged at 10,000 rpm for 20 minutes. The supernatant thus obtained was used as the source of enzyme. Throughout the procedure, the temperature was maintained near 00C±20C.
Enzyme Assay Initial 3ml of assay mixture consisted of: Assay buffer 0.1M MgSO4.7H2O 7.5 mM Hydroxylamine 2.5 mM ATP 7.5 mM Na Glutamate 75 mM Enzyme source as per the requirement
GS extracted from the leaves of Sesuvium portulacastrum was assayed at different pH ranging from 6 to 8.5. Effect of variation of substrate concentrations were also studied, wherein ATP concentration was varied between 1.5 mM and 10.5 mM, Na-glutamate was varied between 0.625 mM and 5 mM and hydroxylamine concentration was varied between 75 mM and 375 mM in the assay mixture. Enzyme concentration in terms of protein was varied between 0.081mg and 0.486mg in the assay mixture to understand GS activity in terms of active enzyme protein participation.
3. Results Effect of hydrogen ion variation on glutamine synthetase activity:
The response of Glutamine synthetase (GS) enzyme extracted from the leaves of Sesuvium portulacastrum to different hydrogen ion concentration in the range of pH 6 to 8.5 is recorded in Figure 1.
The enzyme activity was observed to be maximum at pH 7. At the extremes of pH range selected, the enzyme activity remained low.
Effect of ATP variation on glutamine synthetase activity
Figure 3.11 represent the rate of activity of the GS extracted from the leaves of Sesuvium portulacastrum as a function of ATP concentration in the assay medium. The enzyme activity had a linear relationshipwith ATP till a concentration of 7.5 mM, beyond which, the enzyme activity recorded low value than its maximum at 7.5 mM. The Vmax obtained from the graph for 10.3 mM ATP
concentration in the assay mixture was 0.12 ∆OD/mg protein/30 minutes with a corresponding Km of 4.2mM ATP.
Effect of Na-glutamate variation
The response of enzyme GS obtained from leaves of Sesuvium portulacastrum to varying concentration of substrate, Na glutamate is depicted in Figure 2.The plot of Na-glutamate variation showed a linear relationship upto 3.5 mM Na-glutamate concentration, beyond which there is slowing of enzyme activity. Beyond 4.5 mM concentration of substrate there was a sharp decline in enzyme activity. The Vmax for 3.75 mM Na-glutamate concentration in the
assay mixture is 0.17∆OD with a corresponding Km1.8 mM Na-glutamate
Effect of hydroxylamine variation The results of GS enzyme extracted from the leaves of
Sesuvium portulacastrum when studied as a function of hydroxylamine concentration are represented in Figure 3. The enzyme recorded a rapid increase in the activity in the range of 75 mM to 300 mM hydroxylamine concentration. At concentrations higher than 300mM, the enzyme activity recorded a sharp decline. The Vmax obtained at complete saturation of enzyme by substrate Hydroxylamine was
0.15∆OD with a corresponding Km of 170 mM Hydroxylamine.
Effect of enzyme variation
The concentration of enzyme protein was varied in the assay mixture to study the response of ratios of enzyme and substrate available in the assay mixture. Figure 3.14 depicts the correlation between the rates of enzyme activity as a function of enzyme protein concentrations in the assay medium. From the figure 5, it can be observed that the enzyme extracted from the leaves of Sesuvium portulacastrum exhibits a direct proportionality to the changes in protein concentration from 0.089mg protein to 0.712 mg of protein in the assay mixture.
46
Figure 1. Effect of pH variation on the in vitro activity of GS from the leaves of Sesuvium portulacastrum
Figure 2. Effect of ATP variation on the in vitro activity of GS from the leaves of Sesuvium portulacastrum
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
6 6.5 7 7.5 8 8.5
∆O
D/m
g p
rote
in/3
0 m
in
pH
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0 2 4 6 8 10 12
∆O
D/m
g p
rote
in/3
0 m
in
ATP mM
47
Figure 3. Effect of Na-Glutamate variation on the in vitro activity of GS from the leaves of Sesuvium portulacastrum
Figure 4. Effect of Hydroxylamine variation on the activity of GS from the leaves of Sesuvium portulacastrum
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0 1 2 3 4 5 6
∆O
D/m
g p
rote
in/3
0 m
in
Na Glutamate mM
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 50 100 150 200 250 300 350 400
∆O
D/m
g p
rote
in/3
0 m
in
Hydroxylamine mM
48
Figure 5 Effect of enzyme variation on the in vitro activity of GS in leaves of Sesuvium portulacastrum
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.089 0.178 0.356 0.534 0.712
∆O
D/3
0 m
in
Enzyme (mg of proteins)
4. Discussion
In higher plants, GS has been reported to occur in seeds (Elliot, 1953), seedlings (Webster, 1964), roots (Kanamori and Matsumoto, 1972), root nodules (Dunn and Klucas, 1973) and shoots (O’Neal and Joy, 1973). In leaves apart from its primary function of assimilation of ammonia, GS is also responsible for reassimilation and detoxification of large amounts of ammonia released during photorespiration (Tingey and Coruzii, 1987).
In halophytes, high GS activity has been reported
earlier. According to Steward and Rhodes (1978) and Boucard and Billard (1979), GS in shoot play a greater role in nitrogen assimilation under saline conditions than roots. In opinion of Bottasin et al., (1985), salt resistance depends on the capacity of halophytic plants to withstand the inhibition of GS by NaCl, as salt adaptation in halophyte is a shift of nitrogen metabolism towards glutamate route. High affinity of GS for ammonia (Km 100-200 mM) (Steward and Rhodes, 1977) as against low affinity of GDH for ammonia (Stewart et al., 1980), further supports functioning of glutamate route to GDH system for nitrogen metabolism in halophytes.The pH optima and the kinetic properties of the enzyme GS depend upon the cation present (O’Neal and Joy, 1974). With Mg+2 as the cofactor the pH optimum is in the range of pH 8.0 and with Mn+2 as the cofactor, it is in the range of pH 5.0.The Km values for glutamate (1-13 mM) and ATP (0.1 -1.5 mM) for GS greatly vary and are proportional to the concentration of each other. The results obtained in present investigation are in accordance with the findings of these
scientists.
References Balasubramanian R, Thilo R, Ahmed D, Ralf S, Bernhard H,
Ahlert S and P Jutta (2006) Aster tripolium L. and Sesuvium portulacastrum L.: two halophytes, two strategies to survivein saline habitats. Plant Physiol Biochem44:395–408
Bottasin A, Cacco G and M Saccomani (1985) Nitrogen absorption and NaCl resistant and NaCl susceptible millet genotypes (Pennisetum americanum). Can. J. Bot.63: 517-520.
Boucaud J and JP Billard (1979) Etude comparee des activities glutamate dehydrogenasique et de glutamine synthetasique dans les recines et les parties aerinnes d’un halophyte, obligatory la Suaeda maritima var. macrocarpa. Physiol. Plant.44: 31-37.
Cullimore JV and M J Bennet (1988) The molecular biology and biochemistry of plant glutamine synthetase from root nodules of Phaseolus vulgaris L. and other legumes. J. Plant Physiol. 132: 387-393.
Dunn SD and RV Klucas (1973) Studies on possible routes of ammonium assimilation in Soybean root nodule baltereoids. Can. J. Microbiol., 19: 1493-1499.
Stewart GR, Mann AF and PA Fentem (1980) Enzymes of glutamate formation: GDH, GS and GOGAT In: The biochemistry of plants, Vol. 5, Ed. B.J. Miflin, pp. 271-327, Academic Press, New York, London, Toranto, Sydney, San Francisco.
Tingey SV and GM Coruzzi (1987) Glutamine synthetase in Nicotiana plumbaginifolia cloning and in vivo expression. Plant Physiol., 84: 366-373.
49
Elliot WH (1953) Isolation of glutamine synthetase and glutamotransferase from green peas. J. Biol. Chem., 201: 661-672.
Elliot WH (1955) Methods in enzymology7 vol. II.Eds. SPColowick and NO Kaplan, pp.337-342, Academic Press Inc. Pub. New York.
Hammer K (2001) Aizoaceae. In: Hanelt P, Institute of Plant Genetics and Crop Plant Research (eds) Mansfeld’s encyclopedia on agricultural and horticultural crops, vol1. Springer Verlag, Berlin, Heidelberg, New York, 1986,pp 223–227
Joshi GV and LJ Bhosale (1982) Estuarine ecology system of India. In: “Contribution to the ecology of Halophytes”. Tasks for vegetation science, vol. 2 ed. By DN sen and KS Rajpurohit. Pp. 21-31. Dr. Junk Publishers, The Hague, Boston, London, 1982.
Kanamori T and H Matsumoto (1972) Glutamine synthetase from rice plant roots. Arch. Biochem. Biophys., 152: 404-412.
Lingnowski EM, Splittstoesser WE and K Chou (1971) Glutamine Synthesis in germinating seeds of Cucurbita mosch ata. Plant Cell Physiol., 12: 733-738.
Lonard RI and FW Judd (1997) The biological flora of coastal dunes and wetlands. Sesuvium portulacastrum (L.) J Coast Res 13(1):96–104
Lowry OH, Rosenbrough NJ, Farr AL and RJ Randall (1951) Protein measurement with the Folin phenol reagent. J. Biol. Chem. 49: 64-71.
McCormack DK, Farnden KJF and MJ Boland (1982) Purification and properties of glutamine synthetase from the plant cytosol fraction of lupin nodules. Arch. Biochem. Biophys. 218: 561-571.
Meister A (1974) Glutamine synthetase of mammals. In: The Enzymes, Ed. P. D. Boyer, Vol 10: pp. 699-754. Academic Press, New York.
O’Neal D and KW Joy (1973) Glutamine synthetase of pea leaves. 1. Purification, Stabilization and pH optima. Arch. Biochem. Biophys. 159: 113-122.
O’Neal D and KW Joy (1974) Glutamine synthetase of pea leaves-divalent cation effects, substrate specificity and other properties. Plant Physiol., 54: 773-779.
Stewart CR and D hodes (1978) Nitrogen metabolism of halophytes III Enzymes of ammonia assimilation.New Phytol. 80: 307-316.
Webster G (1964) In: Modern methods of plant analysis. Eds. H. F. Linskens, B. D. Sanwal and M. W. Tracey, Vol. 7, pp. 392-420, Springer-Verlag, Berlin.
Yemm E W and BF Folkes (1958) The metabolism of amino acids and proteins in plants. Ann. Rev. Plant Physiol., 9: 245-280.
50
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 50-55
Traditional agricultural tools and implements used in Wokha, Nagaland
L Kanta Singh* • S. Roma DeviϮ • Meitram Hemerjit Singhǂ
* KVK Imphal West, ICAR Research Complex for NEH Region, Manipur Centre -795004 Ϯ KVK Churachandpur, ICAR Research Complex for NEH Region, Manipur Centre Division of Agricultural Engineering, ICAR Research Complex for NEH Region, Umroi Road, Umiam, Meghalaya
ARTICLE INFO ABSTRACT Article history: Received 13 April 2015 Received Revised 24 July 2015 Accepted 25 July 2015 ----------------------------------------------- Key words: Farming system, Agricultural operation, Jhum cultivation, Traditional agricultural tools and implements, Agricultural mechanization ----------------------------------------------
Wokha district has population of 166,343 with geographical area of 1628 sq. km and main occupation of the people in the district is cultivation. People in the district mainly depend on shifting cultivation or Jhum but horticulture plantation and other non-agricultural resources are also being practiced at minor scale. Jhum cultivation has been devised over generations through the innate experience and knowledge gained by the rural people over the land, labour, environment resources available and the cropping requirements. Average annual area under Jhum cultivation of Wokha district is 13900 ha and total area under agricultural crops in the district ranges from 38680 ha to 48150 ha. Traditional tools and implements dominated over the modern equipments in all agricultural activities in Wokha district. About 93842 agricultural farmers out of total population involved in all the agricultural activities in the district. Because of the geographical condition like steep slope, small terrace size, undulating terrain, etc. area is not suitable for modern power and machinery and the only alternatives left to solve such problems are through traditional tools and implements. Traditional tools and implements are locally developed and cost of manufacturing is less due to use of locally available raw materials and these implements and tools may be further improved through local artesian and famers for achieving self-reliant in the district.
1. Introduction
Wokha district is one of the districts, out of 11 districts of Nagaland, it became separate district in December 19731 and earlier to it was one of the sub-division under Mokokchung District. Wokha district has population of 166,3432 with geographical area of 1628 sq. km. Wokha town, the district head quarter is situated 80 km east of Kohima at an altitude of 1313.69 MSL; the district shares its borders with Zunheboto on the East, Kohima on the South, Assam on the West and Mokokchung on the North. Out of the total population, 78.96% of the populations live in rural areas consisting of 135 villages and rest 21.04 % live in towns. The district has literacy rate of 87.69%. The district is divided into five blocks namely, Wokha Sadar, Chukitong, Sanis, Wozhuro-Ralan and Bhandari blocks. Brief information on demographic and geographical area of Wokha are given in Table 1a and Table 1b. ___________________________ Corresponding author: [email protected]
Table 1a. Profile of Wokha district
Description Numbers/area/percent/ ratio/rate
Actual Population 166,343 Male 84,505 Female 81,838 Area Sq. Km 1,628 Density/km2 102 Proportion to Nagaland Population
8.41%
Sex Ratio (Per 1000) 968 Literacy rate 87.69% Male Literates 67,385 Female Literates 60,823 Total Geographical area (sq. km.) 1628 Irrigated area (Ha)3 9960 Jhum area (Ha)3 13900 Forest Area (Ha)3 25372
51
Table 1b. Profile of rural and urban areas of Wokha District
Description Rural Urban Population (%) 78.96 % 21.04 %
Total Population 131,339 35,004
Male Population 66,435 18,070 Female Population 64,904 16,934
Sex Ratio 977 937
Average Literacy 85.48 % 95.79 %
Farming system of the Wokha district
The main occupation of the people in the district is cultivation. People in the district mainly depend on shifting cultivation or Jhum but horticulture plantation and other non-agricultural resources are also being practiced at minor scale. Jhum cultivation has been devised over generations through the innate experience and knowledge gained by the rural people over the land, labour, environment resources available and the cropping requirements. The main crop is rice and various other crops like maize, millets and pulses are grown in the same field with the rice. Vegetables like cabbage, chilies, okra, etc. are grown along with rice. The people practice backyard poultry farming and some of the people practice piggery and dairy farming in a small scale.Peach, plums, pineapple and citrus also do well in the district and their productions are sold to local market. The other vegetables such as chow-chow, colocasia, tapioca, radish, leafy vegetables are commonly planted near homestead and ridge boundary of the Jhum field. Till date agriculture continues to be the main source of livelihood however, the district is not self-sufficient in production of food grains. Year wise agricultural crop cover area in Wokha district is given in Table 2. Table 2. Year wise agricultural crop cover area of Wokha district
Year Area under agricultural crops (Hectares)
2000-2001 38680 2001-2002 48150 2002-2003 42750 2006-2007 40420 2007-2008 43810
Traditional tools and implements in Wokha District
Traditional tools and implements dominated over the modern equipments in all agricultural activities in Wokha district. Still power source available from animal and mechanical in the district is very low and most of farm works are depend on human labour. About 938424 agricultural farmers out of total population involved in all the agricultural activities in the district. Modern agricultural tools and implements are still lagging in the district. The reason behind the lagged of modern agricultural mechanization is due to its geographical condition like steep slope, small terrace size, undulating terrain, etc. which make unfavourable for running the modern implements and machinery. The only alternatives left to solve such problems are through traditional tools and implements and machinery. The only alternatives left to solve such problems are through traditional tools and implements. Traditional tools and implements are locally developed and cost of manufacturing is less due to use of locally available raw materials.
Most of the traditional tools and implements are manufacture by famers themselves, so it also solves the problems of maintenance and repairing, which generally major issues to the modern equipment’s. These traditional tools and implements are use in various agricultural operation rights from the cutting of bushes to land preparation to post harvest management. Traditional tools and implements used in various farm operations are shown in the Table 3.
Table 3. Traditional tools and implements used in various farm operation
Operation Activities Traditional tools and implements used in various operations
Land clearing Cutting of trees, bushes and grass
Naga Dao-Lepok (Small, Medium, Large), Vekhüro (Sickle), Kheya (Bamboo and Wooden), Choktchü (Large Spade), Lirhon choktchü (Medium Spade), Choktchü (Small Spade)
Land development
Burning and clearing of trees, bushes and grasses
Kheya (Bamboo and Wooden), Choktchü (Large Spade), Lirhon Choktchü (Medium Spade)
Land preparation
Ploughing, Seed bed preparation, etc.
Choktchü (Large Spade), Choktchü (Large Spade), Lirhon choktchü (Medium Spade), Choktchü (Small Spade), Litaphen, Kholo
Planting or seeding
Seed distribution, dibbling, drilling, etc.
Choktchü (Small Spade), Loksa, Sopuk
Transplanting Establishment of seedlings, uprooting and moving the plant to a new location
Choktchü (Small Spade), Bharü, Okhyak,
Crop husbandry Irrigation, Weeding, spraying of manure etc.
Ehe (Hand weeder), Choktchü (Small Spade), Bharü and Okhyak for carrying water can and manure
Harvesting Gathering of a ripened crop
Vekhüro (Sickle), Naga Dao-Lepok
Threshing Separation of grain or seeds from the husks and straw
Kholo (Wooden Stick), Litaphen, Ophuk, Sopuk
Winnowing Separation grain from chaff and other unwanted particles
Moro, Okhyak, Ophuk
Handling Carrying farm produce
Bharü, Sopuk, Loksa, Okhyak
Storage Depository for grains
Chaba, Oson
Milling Removal of husk from paddy
Jenkok and Mhenki (Hand operated rice ponder)
52
Traditional tools, implements and other equipments uses in various agricultural activities in Wokha district, Nagaland Naga Dao-Lepok Lepok are the common Naga Dao used in Wokha district, Nagaland. The size ranges from small, medium to large. They are generally made of mild steel. The length of large Lepok generally ranges from 25 to 30 cm without handle portion. The length of medium dao ranges from 22 to 25 cm and small Lepok ranges from 15 to 20 cm. Handle of the Lepoks are made of bamboo, winded with steel wire or mild steel sheet. Thicknesses of Lepoks are generally ranges from 5 to 7 mm. Lepoks are multipurpose tools generally used for wood cutting, clearing of jungle, bushes, butchering of meat, etc. Lepoks are generally use by male farmer member and field capacity of Lepok generally ranges from 8 m2 to 12 m2 per hour for clearing bushes in Jhum field.
Figure 1. Naga Dao-Lepok (Small, Medium, Large)
Choktchü It is resembled to modern spade; there are generally three categories of Choktchü, i.e. small, medium and large Choktchü. Choktchüs are made mild steel sheet. Average weight of large Choktchü generally ranges from 1.4 to 1.6 kg, medium weight ranges from 0.8 to 1.0 kg and small weight ranges from 0.3 to 0.5 kg. Large and medium Choktchü are used in field for land preparation like clearing of jungle and digging of land or seed bed preparation while small Choktchüs are used for planting of seeds and other seedling in the field. Field capacity of Choktchü measured for land preparation ranges from 10 m2 to 15 m2 per hour.
Figure 2. (a) Choktchü (Large Spade) (b) Lirhon Choktchü (Medium Spade) (c) Choktchü (Small Spade)
Litaphen Litaphen is made of wood. The length of handle ranges from 80 to 100 cm and length of head portion ranges from 25 to 30 cm. The diameter of Litaphen ranges from 4 to 6 cm. It is multipurpose tool uses in various operations like breaking of soil blocks during land preparation and it also used as threshing device for removing paddy from the straw.
Figure 3. Litaphen
Vekhüro (Sickle) Sickle is locally known as Vekhüro and it is made of mild steel. It is available through local blacksmith and design locally. The length of sickle ranges from 25 to 30 cm including the wooden handle and thickness ranges from 1 to 2 mm. It is a harvesting device used for cutting of paddy straw during harvesting and other crops as well and also used for cutting of fodder for animals. The field capacity of Vekhüro measured in hourly basis ranges from 20 m2 to 30 m2 depending upon the experience and healthiness of the farmer.
Figure 4. Vekhüro (Sickle)
Ehe (Hand weeder) Blade of Ehe is made of mild steel and handle is made of bamboo or made of wood. The average length of the Ehe is 25 to 30 cm. Ehe is generally used for hand weeding and all type weeding in field. The weight of the Ehe ranges from 0.3 kg to 0.5 kg and the field capacity ranges from 10 m2 to 12 m2 per hour depending upon the types of weeding. Ehe sometime used for cleaning poultry and pig droplets.
Figure 5. Ehe (Hand weeder)
53
Kheya Kheya are generally made of wooden or bamboo. The length of handle of Kheya ranges from 80 to 100 cm and diameter of handle generally ranges from 4 to 5 cm. It is generally light in weight ranges from 0.5 to 1 kg. During the jungle clearing Kheya are used for removing the weeds and other unwanted particles in the field and is also used as weeder in Jhum field. Because of its light in weight it can be use both by male as well as female member. Field capacity of Kheya generally ranges from 15 m2 to 20 m2 per hour depending upon the types of weeding.
Figure 6. Kheya (Bamboo and Wooden)
Kholo (Wooden Stick) Kholo is wooden stick, made from locally available wood. The length of Kholo ranges from 80 to 100 cm and diameter ranges from 4 to 6 cm. It is used as threshing device for removing paddy from the straw. Kholos are generally made by farmers themselves and materials for making Kholo can be easily available in the farmers’ field. A farmer can threshed from 15 to 20 bundles of paddy straw in an hour.
Figure 7. Kholo (Wooden Stick)
Jenkok and Mhenki (Hand operated rice ponder) It is post-harvest device for removal of husk from paddy. It is hand operated rice ponder. Ponder (Jenkok) is made of wood and handle (Mhenki) is also made of wood and cover with mild steel sheet at its head portion. Such rice ponders are very common in Wokha and post-harvest machinery for processing of rice and other crops are still lag in the district.
Figure 8. Jenkok and Mhenki (Hand operated rice ponder)
Okhyak It is made of bamboo and cane, used for carrying of paddy, and also used as winnowing device for pouring the paddy during winnowing, capacity ranges from 20 to 30 kg. It is use both by male as well as female member. Capacity Okhyak uses by male member are generally more than the Okhyak uses by female member. Okhyak sometime use as temporary grainery for storage of grain when is not in use in other purposes. Generally Okhyak are also use for carrying food item to the field and carrying back firewood while returning from the field to home.
Figure 9. Okhyak
Chaba Chaba is used for storing of dehusk rice and has storage capacity ranges from 30 to 40 kg. It is made bamboo and cane. It is generally kept at kitchen place. Chaba has a cap on the top in order to protect from insects and rodents.
Figure 10. Chaba
Ophuk Ophuk is a mat made of bamboo. It is multipurpose mat used in threshing, drying of grains etc. The size of Ophuk ranges from 3 to 4 m in length and width ranges from 1.5 to 2 m. Most of the household in the Wokha district generally own an Ophuk. It is an essential post-harvest material for every household. Ophuk generally weight from 7 kg to 12 kg and can be easily carry by a person to the farm or any other places depend on requirements. About 40 to 60 kg of rice can be dried over the Ophuk at a time and it is also use drying of other materials like vegetables, fruits, medicinal herbs, etc.
Figure 11. Ophuk
54
Bharü Bharü is made of bamboo or cane or both. It is generally used for carrying various agricultural commodities or even carries water by putting container inside the Bharü. It can carry weight ranges from 20 to 30 kg depending upon its size.
Figure 12. Bharü Oson (Granary) Oson is the granary for storing of paddy. It is made of wooden plank or bamboo and in some cases granary are plaster with mud clay on the floor. The size generally varies from one Oson to other, but on an average Osons are constructed at capacity ranging from 30 jute bags (70 kg) to 70 jute bags. Earlier it used to be roofed with thatch grasses, but now days many of roofs are used of tin sheets.
Figure 13. Oson (Granary)
Moro Moro is made of bamboo and cane. It is a winnowing device for cleaning the grain. Generally Moro are used by the female member of the family. The weight of Moro generally ranges from 0.7 to 1.2 kg, so it is very light in weight and work can be easily carried for long without much tiredness.
Figure 14. Moro
Sopuk Sopuk is a basket made of bamboo; it is generally used for carrying seed materials in field and also used for other domestic purposes. The capacity ranges from 15 to 20 kg.
Figure 15. Sopuk
Loksa Loksa is also another form of basket made of bamboo and cane. It also used for carrying seed and other materials in the field and also used for other domestic purposes. The capacity ranges from 10 to 15 kg.
Figure 16. Loksa
2. Conclusion
The productivity of farms depends greatly on the availability and judicious use of farm power by the farmers. Agricultural tools, implements and machines enable the farmers to employ the power judiciously for production purposes. Agricultural development aiming at higher production would require mechanization and supply of sufficient energy is a prerequisite for mechanized agriculture. The geographical constraints of Wokha, Nagaland prevents from using modern power and machinery. Traditional implements and tools will play the lead role in developing the agricultural sector of Wokha and these implements and tools may be further improved through local artesian and famers for achieving self-reliant in the district.
Acknowledgements
Authors are thankful to Mr. Mhabemo Lotha, Mr. Kilumo Ezung and all staff of KVK, Wokha for their help in collection of data and translation of local Lotha dialect.
55
References Anonymous (2008). Statistical Hand Book of Nagaland.
Directorate of Economics and Statistics, Government of Nagaland, Kohima.
Anonymous (2004). Statistical Hand Book of Nagaland. Directorate of Economics and Statistics, Government of Nagaland, Kohima.
Anonymous (2008). Department of Agriculture, Government of Nagaland.
http://www.census2011.co.in/census/district/611-wokha.html (browsed on 01-10-2014)
56
Contents available at http://epubs.icar.org.in; www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 56-62
Variability Studies for Seed and Seedling Traits in Calophyllum Inophyllum (L.) at South India
Palani Kumaran Forest College and Research Institute, Mettupalayam 641301, Tamil Nadu
ARTICLE INFO ABSTRACT Article history: Received 21 May 2015 Received Revised 15 June 2015 Accepted 16 June 2015 ----------------------------------------------- Key words: Seed source, Seed length, Seed width, Pod length, Germination percent,
Germination value. -----------------------------------------------
The present investigation was carried out at Forest College and Research Institute, Mettupalayam, Tamilnadu to identify the best half sibs of Calophyllum inophyllum across its natural distribution of south India for further collection of seeds for afforestation or breeding purpose. The seeds were collected from different climatic zones of south India. Seeds were measured for its length, width, Pod length, Width and then sown in nursery to study the variation in germination and initial growth parameters viz., germination percent, germination value, peak value, mean daily germination of seedlings. Seeds collected from Western Ghats of Karnataka were superior compared to seeds from other parts in all the traits considered for the study. These seed sources can be further screened for tree improvement traits considering their immense value in yielding bio diesel.
1. Introduction
Calophyllum inophyllum is a multipurpose tree belonging to the family Clusiaceae, commonly known as mangosteen family .This plant has multiple origins including East Africa, India, South East Asia, Australia, and the South Pacific. Calophyllum inophyllum is known by various names around the world. Shows different vernacular names of Calophyllum inophyllum in some selected countries of the world. Shows the distribution map of Calophyllum inophyllum around the world. As can be seen this tree is widely available in India, South East Asia and Australia. It grows in areas with an annual rain of 1000–5000 mm at altitudes from 0 to 200 m. Calophyllum inophyllum is allow-branching and slow-growing tree with two distinct flowering periods of late spring and late autumn. But sometimes its flowering may occur throughout the year. Calophyllum inophyllum grows best in sandy, well drained soils. However it tolerates clays, calcareous, and rocky soils. The tree supports a dense canopy of glossy, elliptical, shiny and tough leaves, fragrant white flowers, and large round nuts. Its size typically ranges between 8 and 20 m (25–65 ft) tall at maturity, sometimes reaching up to 35 m (115ft). The growth rate of the tree is1m (3.3ft) in height per year on good sites.
Its leaves are heavy and glossy, 10–20 cm (4–8 inch) long and 6–9 cm (2.4–3.6 inch) wide, light green when young and dark green when older. Fruits are spherical drupes and arranged in clusters. The fruit is reported to be pinkish-green at first. However, it turns later to be bright green and when ripe, it turns dark grey- brown and wrinkled. The tree yield is100–200fruits/kg. In each fruit, one large brown seed 2– 4 cm (0.8–1.6in.) India meter is found. The trees yield 3000 –10,000 seeds /tree/ season. The seed is surrounded by a shell and a thin layer of pulp of 3–5 mm. Calophyllum inophyllum Oil is non-edible and dark green. Traditionally, its oil has been used as a medicine, soap, lamp oil, hair grease and cosmetic in different parts of the world. Recently, Calophyllum inophyllum has been proposed as a source of biodiesel.
2. Materials and methods
The present study was under taken during the year 2014-15 at Forest College and Research Institute (Mettupalayam), Coimbatore. Which is situated at 11°19’N latitude and 77°56’E longitude and an altitude of 350 m above MSL. The average annual rainfall is 945 mm, most of which is received between June to September. The temperature varies from 15 to 34.9 0C.
57
The extensive survey was under taken across three different state and one union territory of India. A distance of at least 200 mts was maintained between two trees and at least 25 kms between two seed sources. The individual tree was identified based on their phenotypical characteristics and the individual tree identity was also maintained. Seeds were extracted from 100 pods after sun drying for ten days for assessment of seed characteristics. Further same seeds were used for assessing germination and seedling characters. Seed parameters such as seed length, seed width, seed thickness, seed volume and 100 seed weight were recorded for each seed source. The experiment was laid out in completely randomized design with five replications of 100 seeds each. The seeds were sown in the standard nursery bed and regular watering was done. Observations on daily germination were recorded up to 31 days from date of sowing. Germination percentage, peak value, mean daily germination, germination rate and germination value were recorded for each seed source. Data collected was analysed statistically using Mstatc program. Number of seeds germinated
Germination per cent = ……… X 100 Number of seeds sown Germination value (GV) = PV X MDG, where, PV- Peak Value of germination. MDG- Mean Daily Germination
Total germination per cent Peak Value = ----------------------------------
Total number of days Final germination per cent
Mean Daily Germination = -------------------------------- Number of days that took to reach Peak Germination
Estimation of oil content using Soxhlet method
For estimating oil, the seeds were depulped, the kernels dried at 50°C for 16 hrs and allowed to cool in a desiccator. Five grams of seeds were pulverized to a fine powder in a porcelain mortar. Ground samples were placed in a filter paper and fastened in such a way to prevent escape of the meal and then carefully transferred to an extraction thimble. The thimble was then placed in a Soxhlet extractor to which sufficient quantity of solvent petroleum ether (40 - 60°C) was added and heated until eleven siphonings were completed. The oil content was recorded by evaporating the petroleum ether at 60°C. The entire extraction process was carried out in
Soxhlet extractor according to AOAC (1970). The percentage of oil content was then calculated by using the formula.
Oil weight (g) Oil per cent = ----------------------------- x 100 Sample weight (g) Table.1 Calophyllum inophyllum seed source collection from different places in South India
Source Latitude Longitude Altitude
Tamilnadu
FCRICI 1 (Vedaranyam) 10o22’N 79o51’E 14
FCRICI 2 (Nagapattinam) 10o45’N 79o49’E 5
FCRICI 3 (Velankanni) 10o41’N 79o50’E 21
FCRICI 4 (Thiruvarur) 10o45’N 79o37’E 38
FCRICI 5 (Pudhucherry) 11o54’N 79o47’E 25
FCRICI 6 (Tindivanam) 12o13’N 79o39’E 140
FCRICI 7 (Cuddalore) 11o44’N 79o42’E 35
FCRICI 8 (Nagercoil) 08o09’N 77o22’E 148
FCRICI 9 (Pechiparai) 08o26’N 77o18’E 326
FCRICI10(Mettupalayam) 11o19’N 76o58’E 1036
FCRICI 11 (Thiruchencode)
11o22’N 77o53’E 1405
FCRICI 12 (Coimbatore I) 10o59’N 76o54’E 1307
FCRICI 13 (Coimbatore II)
10o57’N 76o55’E 1373
Karnataka
FCRICI 14 (Honnavara) 14o15’N 74o26’E 43
FCRICI 15 (Hubli) 15o22’N 75o04’E 2127
FCRICI 16 (Sirsi) 14o39’N 74o52’E 2049
FCRICI 17 (Bhatkal) 13o59’N 74o31’E 51
FCRICI 18 (Kumta) 14o26’N 74o23’E 114
FCRICI 19 (Udupi) 13o20’N 74o43’E 49
FCRICI 20 (Mangalore) 12o54’N 74o51’E 79
FCRICI 21 (Talugoppa) 14o12’N 74o54’E 1961
FCRICI 22 (Shimoga) 13o53’N 75o33’E 1922
FCRICI 23 (Sagar) 14o09’N 75o00’E 1982
FCRICI 24 (Tumkur) 13o20’N 76o09’E 2974
FCRICI 25 (Mandiya) 12o29’N 76o54’E 2210
FCRICI 26 (Mysore) 12o22’N 76o40’E 2299
Kerala
FCRICI 27 (Tiruvandrum) 8o29’N 76o59’E 81
FCRICI 28 (Thirissur) 10o29’N 76o17’E 161
FCRICI 29 (Kottayam) 9o33’N 76o32’E 21
FCRICI 30 (Upala) 12o41’N 79o54’E 35
58
Table 2. Seed characteristics as influenced by various place in South India
SEd = 1.895 1.516 1.478 1.624 0.125 CD (0.05) = 3.794 3.035 2.960 3.252 0.250
Seed source Pod length
(mm)
Pod width
(mm)
Seed length
(mm)
Seed width
(mm)
100 Seed weight
(g)
FCRICI 1 (Vedaranyam) 29.13 22.38 19.50 18.76 102.13
FCRICI 2 (Nagapattinam) 34.39* 25.18 21.34 20.66* 146.10*
FCRICI 3 (Velankanni) 30.64 26.92 19.10 17.54 107.09
FCRICI 4 (Thiruvarur) 24.96 25.61 21.34 20.38* 124.19*
FCRICI 5 (Pudhucherry) 30.27 24.06 21.90 19.68* 100.17
FCRICI 6 (Tindivanam) 29.86 25.67 20.99 14.77 108.27
FCRICI 7 (Cuddalore) 29.75 24.51 21.34 15.11 102.02
FCRICI 8 (Nagercoil) 29.39 25.03 20.81 13.89 111.24
FCRICI 9 (Pechiparai) 33.77* 23.38 20.42 17.91 133.30*
FCRICI10(Mettupalayam) 29.56 22.06 21.56 14.99 96.30
FCRICI 11 (Thiruchencode) 24.30 24.31 20.05 14.53 89.20
FCRICI 12 (Coimbatore I) 28.97 23.88 20.36 13.26 128.30*
FCRICI 13 (Coimbatore II) 27.56 25.97 20.06 14.26 68.08
FCRICI 14 (Honnavara) 39.57* 34.44* 27.15* 23.50* 164.27*
FCRICI 15 (Hubli) 25.07 25.65 18.77 13.31 134.07*
FCRICI 16 (Sirsi) 26.51 24.08 18.99 14.38 125.32*
FCRICI 17 (Bhatkal) 28.61 24.07 17.71 12.44 96.13
FCRICI 18 (Kumta) 30.75 25.25 21.98 13.49 134.04*
FCRICI 19 (Udupi) 28.32 23.15 21.74 15.05 127.17*
FCRICI 20 (Mangalore) 26.20 29.10* 21.48 12.85 125.26*
FCRICI 21 (Talugoppa) 27.68 25.08 20.07 12.81 117.15
FCRICI 22 (Shimoga) 28.42 24.04 20.99 14.12 131.09*
FCRICI 23 (Sagar) 34.65* 23.02 21.31 12.89 120.12*
FCRICI 24 (Tumkur) 33.35* 28.14 22.27 21.44* 115.33
FCRICI 25 (Mandiya) 29.94 26.81 25.29* 21.92* 120.27*
FCRICI 26 (Mysore) 27.73 27.00 23.13 15.09 126.16*
FCRICI 27 (Tiruvandrum) 26.93 25.64 21.60 14.49 119.15*
FCRICI 28 (Thirissur) 34.15* 24.69 20.72 14.88 121.16*
FCRICI 29 (Kottayam) 29.01 25.76 18.87 14.21 126.15*
FCRICI 30 (Upala) 25.38 25.88 22.13 15.51 134.25*
Mean 29.49 25.36 21.10 15.94 118.45
59
Table 3. Seed germination attributes in different place in South India
Seed source Germination percent
Germination value
Peak value Mean daily germination
Oil %
FCRICI 1 (Vedaranyam) 63.00 4.41 2.30 1.92 44.9
FCRICI 2 (Nagapattinam) 68.00 4.71* 2.85* 1.69 58.3
FCRICI 3 (Velankanni) 61.67 3.68 1.92 1.93 47.8
FCRICI 4 (Thiruvarur) 56.67 3.87 2.18 1.79 42.6
FCRICI 5 (Pudhucherry) 58.00 3.32 2.09 1.59 49.6
FCRICI 6 (Tindivanam) 58.00 3.32 1.98 1.67 41.0
FCRICI 7 (Cuddalore) 59.33 3.07 1.93 1.59 38.2
FCRICI 8 (Nagercoil) 68.33 3.80 2.17 1.74 52.4
FCRICI 9 (Pechiparai) 64.00 4.45 2.55 1.56 57.4
FCRICI10(Mettupalayam) 59.67 3.10 1.97 1.57 45.8
FCRICI 11 (Thiruchencode) 54.00 3.64 2.06 1.77 44.4
FCRICI 12 (Coimbatore I) 64.67 3.50 2.04 1.70 43.7
FCRICI 13 (Coimbatore II) 58.33 3.48 2.20 1.57 50.6
FCRICI 14 (Honnavara) 78.00* 5.11* 3.06* 1.79 64.6*
FCRICI 15 (Hubli) 56.00 3.64 2.31 1.56 39.3
FCRICI 16 (Sirsi) 53.00 3.63 1.99 1.83 44.8
FCRICI 17 (Bhatkal) 54.67 3.25 1.92 1.68 51.4
FCRICI 18 (Kumta) 59.00 3.48 2.02 1.73 45.5
FCRICI 19 (Udupi) 55.67 2.88 1.98 1.43 43.5
FCRICI 20 (Mangalore) 46.67 2.54 1.77 1.44 43.0
FCRICI 21 (Talugoppa) 54.67 2.53 1.68 1.51 43.6
FCRICI 22 (Shimoga) 52.33 3.26 1.89 1.72 46.3
FCRICI 23 (Sagar) 56.00 3.62 1.98 1.82 48.2
FCRICI 24 (Tumkur) 56.00 2.63 1.68 1.73 37.9
FCRICI 25 (Mandiya) 52.00 2.96 1.87 1.66 48.3
FCRICI 26 (Mysore) 55.67 2.90 2.09 1.40 35.7
FCRICI 27 (Tiruvandrum) 57.33 2.24 1.70 1.33 47.7
FCRICI 28 (Thirissur) 54.67 2.72 1.98 1.52 44.4
FCRICI 29 (Kottayam) 57.33 2.63 1.74 1.53 44.7
FCRICI 30 (Upala) 54.33 3.29 2.06 1.58 46.3
Mean 58.23 3.39 2.06 1.64 46.4
SEd = 8.880 0.596 0.305 0.170 6.133 CD (0.05) = 17.77 1.193 0.610 0.341 12.27
60
Table 4. Genotypic correlation of seed and seedling attributes of Calophyllum inophyllum in South India (** Significant at 1 % level; * Significant at 5 % level)
Table 5. Phenotypic correlation of seed and seedling attributes of Calophyllum inophyllum in South India (** Significant at 1 % level; * Significant at 5 % level)
Characters Pod length
Pod width
Seed length
Seed width
Seed weight
Germination percent
Germination value
Peak value
MDG Oil %
Pod length 1.000 0.396* 0.566** 0.547** 0.405* 2.403** 0.663** 0.803** 0.533** 0.853**
Pod width 1.000 0.822** 0.525** 0.480** 1.274** 0.196 0.450** 0.067 0.389*
Seed length
1.000 0.753** 0.530** 1.520** 0.283 0.576** -0.124 0.408*
Seed width 1.000 0.295 1.639** 0.557** 0.704** 0.498** 0.639**
Seed wt 1.000 0.928** 0.365* 0.575** -0.076 0.373*
G.percent -1.000 1.758** 2.179** -0.700 2.793**
G.value 1.000 0.991** 0.607** 1.217**
Peak value 1.000 0.346 1.336**
MDG Oil %
1.000 0.639** 1.000
Characters Pod length
Pod width
Seed length
Seed width
Seed weight
Germination percent
Germination value
Peak value
MDG Oil %
Pod length 1.000 0.230 0.343* 0.406* 0.325* 0.226 0.198 0.236 0.074 0.370*
Pod width 1.000 0.389* 0.326* 0.357* 0.048 0.065 0.165 -0.053 0.150
Seed length 1.000 0.438** 0.344* 0.027 0.005 0.137 -0.090 0.171
Seed width 1.000 0.243 0.173 0.375* 0.342* 0.229 0.195
Seed wt 1.000 0.164 0.210 0.296 -0.030 0.187
G.percent 1.000 0.552** 0.511** 0.459** 0.269
G.value 1.000 0.848** 0.638** 0.318*
Peak value 1.000 0.232 0.339*
MDG Oil %
1.000 0.060
1.000
61
3. Results and Discussion
Data from the Table. 2 revealed that seed traits for all seed
sources showed significant differences. The seeds collected from Honnavara region were longest, thickest and had higher mass as well as seed volume compared to all other seed sources. Seed length amongst various seed sources varied from 17.71 mm to 27.15 mm, seed width from 12.44 mm to 23.50 mm. Pod length and pod width varied from 25.07 mm to 39.57 mm and 22.06 mm to 34.44 mm respectively. The 100 seed weight ranged between 68.08 g to 133.30 g. These variations may be due to the fact that, this species grows over a wide range of climatic conditions as well as soil types and altitudes. Similar findings were revealed by Sudhir Kumar (2003) in Jatropha curcas and Vasanth Reddy et al. (2007) in Pongamia pinnata. Analysis of variance (ANOVA) revealed that the results were statistically significant for all the germination attributes (Table. 3). Overall germination per cent was on/or above the 50 per cent. Maximum germination per cent was found in Honnavara region (78.00 %), owing to higher mean daily germination (1.79), Germination value (5.11) and peak value of germination (3.06). It was followed by Nagapattinam region (68.00 %) and Pechiparai region (81.67 %) seed sources. Whereas, minimum germination per cent was recorded on Dharapuram region (64.00 %). The size and shape of seeds is variable depending on the structure and form of the ovary and environmental conditions under which plant is growing. It is evident from the result that seeds from Honnavara region was found to be superior with respect to germination percentage. This is in line with study made by Dwivedi (1993) in Azadirachta indica and Devagiri et al. (1998) in Dalbergia sissoo. They found that the variation observed in the seed characters may be attributed to adverse environment and differences in their distribution range this in turn affect the germination of seeds. Seedlings of Honnavara region higher oil content (64.6 %). It was followed by Nagapattinam (58.30%) and Pechiparai region (57.30 %). Genotypic correlation
Pod length (0.853), Pod width (0.389), Seed length (0.408),, seed width(0.639) Seed weight (0.373), Germination percent (2.793), Germination value (1.217),
Peak value (1.336) and Mean daily germination (0.639)
showed positive significant correlation with oil content (Tables 4). Phenotypic correlation
Pod length (0.370), Germination value (0.318), Peak value (0.339) showed positive but significant correlation with oil content. Pod width (0.150), seed length (0.171), seed width (0.195), Seed weight (0.187), Germination percent (0.269), Mean daily germination (0.060), showed positive but non-significant correlation with oil content (Tables 5 and 6).
A highly significant and positive correlation existed between Pod length (0.370), Germination value (0.318), Peak value (0.339). Significant correlation among various seed germination and seedling traits suggests that test weight may prove to be important criteria in selection of geographic seed sources for raising stock for bulk commercial plantations. This study identifies two best sources for Calophyllum inophyllum L based upon seed and seedling traits for those place of Honnavara and Nagergoil which were sampled. On a short term basis, breeding zones may be set up in these environmentally homogeneous areas. However, this may be preliminary as only seedling traits have been considered. Hence, seed source screening provides a great opportunity to the tree breeder to screen and capture natural variation for success of afforestation, besides providing information on the raw material for breeding and evolving improved planting stock within a seed source.
Acknowledgements
Acknowledgments are due to department of tree
breeding, forest college and research institute (FC&RI) and UGC – Government of India funded by the whole project. Gratitude is expressed towards my guide and all the scientist for their kind and support. Thanks are also due UGC- RGNF for the project for further execution.
62
References Devagiri, GM, Dhiman, RC Thapiyal, R. C. and S Nautiyal
(1998). Seed source variation in pod and seed traits of Dalbergia sissoo. Ann. For., 6: 148 -155.
Dwivedi, AP (1993). National level of Neem Seed source trials at Jodhpur. Syst. Ecol. Contrib. 5(7):20-34. Geethanjali, K., Balasubramanian, A. and Paramathma, M., 2003, Seed technological studies in Jatropha Curcus. Nation. Workshop Jatropha Other Perennial Oil Seed Species, 5th to 8th August 2003, Bharathiya Agro-Industries Federation of India (BAIF), Pune, pp.31-33.
George Jenne, M, Dasthgiri, Prathiban, K and Judesudhagar (2003). Variability studies in seed and seedling attributes in Mahauva (Madhuca latifolia). Indian For., 129 (4): 509-516.
Sniezko, RA and HTL Stewart (1989). Range wise seed sources variation in growth and nutrition of Acacia albida seedlings propagated in Zimbabwe. For. Ecol. Mgmt., 27: 179 -197. Sudhir Kumar, 2003, Effect of seed size on germination and seedling traits of Jatropha curcas. Nation. Workshop Jatropha Other Perennial Oil Seed Species, 5th to 8th Aug. 2003, Bharathiya Agro-Industries Federation of India (BAIF), Pune, pp. 5-7. Vasanth Reddy, K.N., Pradeep Kumar, H., Siddraju, C.M., Rajesh P. Gunga, Madiwalar, S.L. and Patil, S.K., 2007, Seed source variation for seed and seedling traits in Pongamia pinnata (L.) Pierre; An important biofuel yielding tree species. My For., 43(1):61-68.
Zobel, B and JJ Talbert (1984). Applied Forest Tree Improvement. John Wiley and Sons, New York, pp. 75 -116.
63
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 63-68
Comparative Performance of Puddlers in Low Lands of Hilly Areas
Arvind Kumar1* • S. Mandal1 • R.K. Singh1 • M.B. Tamhankar2 1Division of Agril. Engineering, ICAR Research Complex for NEH Region, Umiam, Meghalaya 2Central Institute of Agril. Engineering, Nabi Bagh, Berasia Road, Bhopal, Madhya Pradesh
ARTICLE INFO ABSTRACT Article history: Received 27 December 2014 Received Revised 4 April 2015 Accepted 12 April 2015 ----------------------------------------------- Key words: Puddler, Puddling index, Weeding efficiency, Eenergy ----------------------------------------------
A comparative study of the quality of puddled soil and energy requirement was carried out with animal drawn traditional country plough (T1), rectangular blade puddler (T2), disc harrow (T3) and power tiller operated rotavator (T4). Weeding efficiency, puddling depth, percentage increase in bulk density, puddling index, percolation rate and energy requirement were studied for the above treatments. Puddling performance by different implements in comparison to the traditional animal drawn country plough (T1) shows that there is a definite reduction in time requirement for field preparation. Increase in weeding efficiency, bulk density and puddling index were also observed. The highest values of weeding efficiency and puddling index were found 69.3% and 51.5, respectively, for power tiller rotavator (T4). The total time requirement for preparation of puddled field for treatment T4 was found to be the lowest (27.4 h/ha). Energy requirement for preparation of puddled field was found highest (2844.91MJ/ha) for power tiller operated rotavator (T4) followed by T1, T3 and T2 treatments.
1. Introduction
Rice is the most important cereal food crop of India occupying about 24 % of gross cropped area of the country. It contributes 42 % of total food grain production and 45 % of total cereal production of the country. It is also the main food grain of the North-Eastern region occupying 3.51 million hectares which accounts for more than 80% of the total cultivated area of the region and 7.8 per cent of the total rice cultivated area of the country. The common method of land preparation for wetland rice in North-East is puddling. Puddling primarily helps in water saving by decreasing percolation losses and generally refers to breaking down of soil aggregates into smaller soil particles. The quality of puddling effects the crop growth and depends mainly on type of tillage implement and intensity of puddling. The two common methods for planting of rice in the region are broadcasting and manual transplanting. In some parts of the North-East region, two crops of rice are taken annually. _________________ *Corresponding author, [email protected]
During puddling operation the soil gets manipulated, soil structure is preparation for puddling starts in May-June, when soil moisture content is suitable for ploughing. The puddling operation is thoroughly disturbed and air voids are drastically reduced. Land performed with standing water in the field. Puddling is performed to reduce deep percolation of water, destroy weeds and to facilitate transplanting of rice seedling by making the soil softer. Puddling leads to compaction of soils and increases bulk density and soil penetration resistance in sub soil, which in turn reduces water losses (Rautaray et al. 1997). Farmers generally maintain standing water in the field for better growth which triggers higher percolation losses. Soil manipulation through puddling decreases infiltration, increases water holding capacity, facilitates easy transplanting and controls weed especially in heavy textured soil (Verma et al. 2006)
64
Behera et al. (2009) concluded that peg type puddler with two passes produced highest depth of puddling (109.3 mm) and puddling index (30.13%) compared to rotary puddler with one pass (100.9 mm and 24.60 %) and peg type puddler with one pass (89.1 mm and 19.40 %). The bulk density of soil increased and hydraulic conductivity decreased 30 and 60 days after puddling but puddler and level of puddling had significant (p<0.5) effect on hydraulic conductivity only. The buried and floating hill percentage was high at 24 h sedimentation and gradually decreased with Increase in sedimentation period. Grain yield was influenced by sedimentation period rather than puddler and level of puddling. Verma and Dewangan (2006) found that puddling by different implements in comparison to the traditional animal drawn country plough had a definite reduction in time requirement for field preparation. Increase in weeding efficiency, bulk density, grain yield and puddling index were also observed. The highest values of weeding efficiency and puddling index were found 98.6 and 79.3%, respectively, for rotavator. The total time requirement for preparation of puddled field with tractor operated cultivator was found to be the lowest (9.4 h/ha) with 67% weeding efficiency and 62.7 puddling index as compared to other alternatives tested. Energy requirement for preparation of puddled field was found highest (2390 MJ/ha) for tractor operated rotavator. Mousavi et al. (2009) found that under laboratory conditions, water content of the puddled layers decreased with an increase in settling time. During drying period, no puddling condition dried faster than low, medium and high intensity puddled soil. Puddling with low intensity in laboratory and field conditions caused bulk density of 0–150 mm soil layer to decrease by 24.07 and 25.45%, respectively. Bulk density increased with time as particles settled after halting the puddling. Bulk density increased with depth as well. Under laboratory conditions, increasing puddling intensity from low to medium reduced percolation rate significantly. For all puddling intensities, soil moisture characteristic curves of both field and laboratory samples showed that puddling increased the amount of water retained over the whole range of suctions. More water was needed for high puddled field as compared to low and medium. Under the laboratory and field conditions, the high puddled field required 27.72 and 28.58% more water as compared to medium, respectively. Bulk density, soil moisture content and water percolation rate decreased faster in the puddled soil under field and laboratory conditions. Verma (1996) tested the effectiveness of puddling with different implements in relation to water use and grain yield of rice in clay loam soil.
It was observed that 100, 250 and 300 mm less water was used, respectively, after puddling by bullock-operated cultivator, angular puddler and disc harrow, compared with the local comb harrow.The tractor operated puddler reduced water was 350 mm. The major part of the region has subtropical climate.The annual rainfall received in the region comes largely from south-west monsoon and received during middle of May to end of October. The average annual minimum and maximum rainfall in the region is estimated to be 1637 mm and 6317 mm respectively. Due to the intense rainfalls, the time available for seedbed preparation is short. Puddling can be started soon after water gets accumulated in the paddy field. It involves a lot of drudgery to human and animals. Puddling is also energy intensive due to heavy churning of soil and water together. Therefore, the study was undertaken to compare the different methods of puddling with regards to effectiveness of puddling, impact on soil properties and energy used in puddling.
2. Materials and methods
The study was conducted at the research farm of ICAR Research Complex for NEH Region, Umiam, Meghalaya, during the years 2006–2010. The soils of this region are rich in organic matter and fertile. These are mostly under thick forest and where jhum cultivation is practiced the soils loose the organic matter and suffer from erosion hazards. Due to considerable amount of loss of bases from the soil under the influence of high rainfall the soils become acidic. The soils in the valley are mainly composed of sand, silt, clay and gravel developed due to the washing down on alluvial materials from the surrounding hills. Soil of the experimental site was sandy in texture (sand 48.2%, silt 32.8% and clay 19.0%) with a bulk density ranging from 1.2 to 1.4 mg/m3 at 18% moisture content (db). The basic infiltration rate was found to be 0.91 and 1.4 cm/h in June and October, respectively. The experiment consisted of four puddling treatments with three replications in
randomised block design having plot size of 8 m 5 m as summarized in Table 1. Implements used for puddling were animal drawn local plough (T1), rectangular blade puddler (T2), disc harrow (T3) and power tiller operated rotavator (T4). The data collected over 4 years were statistically analysed to test the significance at 5% level for each parameters. Initially the experimental plots were ploughed once at friable moisture condition (13.5–18.3% db) with power tiller rotavator and the ploughed field was flooded to saturation (24 h). Water to a depth of 45–50 mm was maintained in the field prior to puddling operation. Figure. 1 (a-d) shows the different treatments and the sets of implements used for puddling the field.
65
Any standing water present in the field after 24 h of puddling operation was drained out allowing the disturbed soil to settle. Different soil physical properties such as puddling index, bulk density, permeability were measured to see the effect of puddling on soil profile. Measurements of puddled soil conditions
The puddling performance of different implements was compared on the basis of depth of puddling, percolation rate, bulk density, puddling index and weeding efficiency. For recording the depth of puddling a flat tipped gauge was used. The percolation rate was measured with a straight edged cylinder of 100 mm diameter and 200 mm length. The cylinder was immersed in to the soil to a depth of 100 mm and a constant head of water was maintained in the cylinder. The fall in the water level was recorded daily to find out the percolation rate. The core sampler method was used for measuring the bulk density of the soil. Soil samples were collected by core sampler after segregating the area and then removing water. A mild steel tube of 63 mm inside diameter and 50 mm long having 300 bevelled edges at the one end was used to take out the soil samples. The tube was vertically pushed into the soil slurry at a slow rate and a uniform pressure was applied by hammering it to avoid compaction of the soil due to its contact with the surface of the sampler tube. The sample was then withdrawn from the tube. The bulk density of puddled soil at depth 50–100 mm was determined. Standard oven drying method was used to find out the moisture content of the soil. Just after final puddling operation, puddled soil sample of 200 ml was collected in a graduated glass cylinder to determine the puddling index. The volume of soil sample was noted after allowing it to settle for 48 h. Puddling index was then calculated by using the following formula (Baboo, 1976).
Puddling index (%), PI = Vs
V× 100 ……(1)
Where, Vs = Volume of soil after settlement, ml V = Total volume of sample before settlement, ml Weeding efficiency was measured by quadrant of 1 m2 size selected randomly in each plot and counting the number of weeds present before and after puddling operations. Weeding efficiency was calculated by:
Weeding efficiency (%) =N1−N2
N1× 100 ... (2) where,
N1 = Number of weeds before the puddling operation N2 = Number of weeds after the puddling operation The theoretical field capacity of an implement is the rate of field coverage that would be obtained if the machine were performing its function 100% of its rated width (Kepner et al., 1987).
Theoretical field capacity (ha/h) = W×S
10 ... (3)
where, w = Actual width of the implement, m S = Speed of operation, km/h The theoretical field capacity, effective field capacity, field efficiency of puddling implements were calculated by recording the time consumed for actual work and the time lost for other miscellaneous activities such as turning at head land, adjustments under field operating conditions, etc. The effective field capacity, field efficiency of puddling implements was determined by the following formula (BIS 9818-Part II, 1981): Effective field capacity (ha/h) =
Actual area covered
Time required to cover the area ...(4)
% Field-efficiency = Eeffective field capacity
Theoretical field capacity × 100 (5)
After final puddling, each experimental plot was levelled by planking with a thick rectangular wooden plank. Computation of energy requirement for puddling The energy equivalence as suggested by Binning et al. (1984) was used for the calculation of energy requirement in puddling operation. It is based on the number of hours required by the different sources of power and then converting the same to energy terms with the help of energy constants as given in Table 2.The fuel input to power tiller with 12 hp engine was calculated considering the puddling operation as heavy work (load coefficient 0.6) and specific fuel consumption 330 ml/kWh (Mittal et al. 1985). The machinery mass contributing towards the activity was estimated based on total mass of the machinery, total useful hours, and the hours used to complete the activity.
Table 1. Different puddling treatments
Sl. No.
Treatment Power source
Puddling equipment used
Tillage operation
1. T1 Pair of bullock
Local plough One summer ploughing, flooding the field, two ploughing with local plough (traditional puddling), planking
2. T2 Pair of bullock
Rectangular blade puddler
One summer ploughing, flooding the field, two puddling with rectangular blade puddler, planking
3. T3 Pair of bullock
Disc harrow One summer ploughing, flooding the field, two puddling with disc harrow, planking
4. T4 Power tiller 12 hp
Rotavator One summer ploughing, flooding the field, two puddling with rotavator, planking
66
Table 2. Energy equivalence used for computing energy for puddling
Particulars Unit Equivalent energy (MJ)
Remarks
Adult man Man-h 1.96 Normal health Adult woman Wman-h 1.60 Normal health Medium size bullock Pair-h 10.10 Weight of bullock 350-450 kg Agricultural machinery kg 62.70 Distributing the manufacturing energy uniformly over
the life, based on weight
Source: Binning et al. (1984)
3. Results and Discussion Time requirement and quality of puddling
The results of four puddling treatments over 4 years are given in Table 3. The effective field capacity (EFC) of the local plough was 0.0225 ha/h which was less than the theoretical field capacity (TFC) because of unploughed space left between two passes. The tilling width of the bullock drawn rectangular blade puddler and disc harrow were 630 and 720 mm respectively. The EFC of the animal drawn disc harrow was more than rectangular blade puddler because of higher width of coverage. The average working speeds for bullocks and power tiller were found to be 1.8 and 2.7 km/h respectively. Power tiller rotavator had the highest EFC of 0.173 ha/h followed by the animal drawn disc harrow (0.134 ha/h). This was because the speed of operation of power tiller (2.7 km/h) was more than that of animals (1.8 km/h). The time required for T1 was highest (108.2 h/ha) followed by treatments T2, T3, and T4. The power tiller operated rotavator required lesser time (27.4 h/ha) than other puddlers. A planking operation to level the field was required for all the treatments. The comparison of difference in treatments and total time required for puddled field (Table 3) indicates that all the treatments have significant difference at 5% level of significance. Different treatments were compared on the basis of weeding efficiency, puddling depth, increase in bulk density, percolation rate and puddling index. Results showed that the weeding efficiency of the power tiller rotavator was higher than the other methods of puddling (Table 3). The reason may be the rotary motion of the implement, which due to shear stress physically destroy weeds more effectively. Weeding efficiency was the lowest for traditional plough. The analysis of variance of weeding efficiency indicates that treatments T2, T3, and T4 are significantly different. The depth of puddling with power tiller rotavator (T4) was highest (105.2 mm) followed by T1, T3, and T2.
The increase in bulk density was highest for treatment T4 (2.08%) followed by T3, T1 and T2. Puddling index was highest (51.5) for power tiller rotavator (T4) and lowest (31.6) for animal drawn local plough (T1). The lowest percolation rate (0.39 mm/h) was observed in case of treatment T4 and highest (0.42 mm/h) for treatment T2. The difference between percolation rates of non-puddled and puddled soil ranges between 0.05 to 0.08 mm/h.
Energy requirement for preparation of puddle field Table 4 shows the energy requirement for puddling fields in different treatments. The energy requirement for preparing the puddled field for transplanting of rice seedling in four puddling treatments varied from 443.50 to 2844.91 MJ/ha. It was highest for treatment T4 followed by T1, T3 and T2. The animal drawn rectangular blade puddler of 630 mm working width (T2) required 32.5% less energy than traditional plough. The energy required for T3 was higher than T2; this may be due to the higher weight (68 kg) of the disc harrow (T3) as compared to rectangular blade puddler T2 (33 kg). Energy requirement was highest for treatment T4 but it has yielded better puddling quality with highest puddling index (51.5) and weeding efficiency (69.6%). Also the time required for preparing puddled field was lowest (27.4 h/ha) for treatment T4. Energy requirement for T2 (443.50 MJ) was less than T3 (461.97 MJ) but the puddling quality of T2 was poor with less weeding efficiency (50.7%) as compared with T3 giving 53.8% weeding efficiency. The study indicates that power tiller operated implement can be adopted for timeliness of operation and better quality of puddling in hills.
67
Figure 1. Puddling operation by a) Traditional country plough, b) animal drawn rectangular blade puddler, c) animal drawn disc harrow and d) power tiller operated rotavator Table 3. Comparison of four puddling operations measured over 4 years
Performance indicators Treatments
T1 T2 T3 T4 Time required (h/ha) 108.2 33.1 31.7 27.4
A) A) Saturated non-puddled soil Bulk density at 100 mm depth from surface (Mg/m3) 0.97 0.98 0.97 0.96 Moisture content (%) db 34 35 35 33 Percolation rate (mm/h) 0.46 0.48 0.45 0.47
B) B) Puddled soil Puddling depth (mm) 79.4 58.6 61.3 105.2 Bulk density at 100 mm depth from surface (Mg/m3) 0.98 0.99 0.99 0.98 Moisture content (%) db 36 37 36 35 Percolation rate (mm/h) 0.41 0.42 0.40 0.39
Weeding efficiency (%) 47.4 50.7 53.8 69.3 Puddling index 31.6 35.2 41.9 51.5
Table 4. Energy used for preparation of puddle field
Treatment Energy used for puddling (MJ/ha)
Human Animal Machinery/ Implement
Fuel Total
T1 211.68 1090.80 56.87 - 1359.35 T2 64.68 333.30 45.52 - 443.50 T3 60.76 313.10 88.11 - 461.97 T4 52.92 - 145.89 2646.10 2844.91
68
4. Conclusion Based on the above study following conclusions were drawn:
1. The highest increase in bulk density with highest puddling index and lowest percolation rate was observed when puddling operation was performed by the power tiller drawn rotavator.
2. Percolation rate decreased as energy input to puddling increased.
3. Lowest percolation can be achieved with puddling by a rotavator.
4. The power tiller operated rotavator has given better quality of puddling in least time of operation.
Acknowledgments Authors are highly thankful to the Director, ICAR Research Complex for NEH Region, Umiam for enabling research work which constitute a part of the institute project “Identification and evaluation of puddlers suitable for North-eastern Region”. The authors are also thankful to the local farmers for providing draft animals used for operating the implements during study.
References Behera BK, Varshney BP and AK Goel (2009). Effect of
Puddling on Puddled Soil Characteristics and Performance of Self-propelled Transplanter in Rice Crop. Agricultural Engineering International: the CIGR Ejournal. Vol. X. Manuscript PM 08 020.
Verma AK and ML Dewangan (2006). Efficiency and energy use in puddling of lowland rice grown on Vertisols in Central India. Soil & Tillage Research 90: 100–107.
Mousavi SF, Yousefi-Moghadam S, Mostafazadeh-Fard B, Hemmat A and MR Yazdani (2009). Effect of puddling intensity on physical properties of a silty clay soil under laboratory and field conditions. Paddy Water Environ 7:45–54.
Rautaray SK, Watts CW and AR Dexter (1997). Puddling effects on soil physical properties. AMA 28 (3): 37–40.
Verma VP (1996). Water use in relation to soil manipulation for wet land paddy cultivation on clay-loam. J. Indian Water Resour. Soc. 2: 62–64.
Binning AS, Pathak BS and BS Panesar (1984). Energy audit of crop production system. Research Bulletin, School of Energy Studies for Agriculture, PAU, Ludhiana
Mittal, VK, Mittal, JP and KC Dhawan, (1985). Digest on Energy Requirement in Agricultural Sector. All India Co-ordinates Research Project on Energy Requirements in Argicultural Sector, Punjab Agricultural University, Ludhiana, India.
Kepner, RA, Bainer, R and EL Barger (1987). Principles of Farm Machinery, third ed. CSB Publishers and Distributor, Delhi, India. Ngachan SV, Mohanty AK and A Pattanayak (2012). Status
Paper on Rice in North East India. Rice Knowledge Management Portal, http://www.rkmp.co.in.
Baboo B (1976). Effect of lug angle of cage wheel on traction and puddling performance of dual wheels. M. Tech. diss., Dept. of Farm Machinery and Power Engineering, G. B. Pant Univ. of Agriculture and Technology. Pantnagar, India.
BIS 9818-(Part II) (1981). Glossary of terms relating to tillage and inter cultivation equipments. Bureau of Indian Standards, Mank Bhawan, New Delhi, India.
69
Contents available at http://epubs.icar.org.in, www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 69-72
Effect of Weather Parameters on Population Buildup of Different Insect Pests of Rice and Their Natural Enemies
H. Kalita • R. K. Avasthe • K. Ramesh ICAR Research Complex for NEH Region, Sikkim Centre, Tadong 737102.
ARTICLE INFO ABSTRACT
Article history: Received 14 February 2015 Received Revised 16 May 2015 Accepted 20 June 2015 --------------------------------------------- Key words: Rice Insect Pests Population Weather ---------------------------------------------
The effect of weather parameters on population buildup of some major rice pests viz., stem borer, whorl maggot, leaf folder, and gundhi bug and their natural enemies viz., spiders, dragonfly/damselfly, lady bird beetle and Apanteles was studied by taking observations at fortnightly interval starting from second fortnight of July to first fortnight of November through fixed plot survey during kharif, 2007 and 2008 at ICAR Research Complex for NEH Region, Sikkim Centre, Tadong. It was observed from the study that the whorl maggot started the infestation at the initial stage of the crop and damaged maximum (22.25 -24.25 damaged leaves/10 hills) in the second fortnight of July. Stem borer infestation was found maximum during August-September (6.82–7.62% dead heart). They also attacked the rice crop at reproductive stage which resulted in white ear head. The maximum white ear head% was recorded in the second fortnight of October (7.56 % in 2007 and 8.14% in 2008). The leaf folder population was found maximum in the last part of August and first part of September (14.50 -16.75 damaged leaves/10 hills). Gundhi bug population was found maximum when the crop attained the milky stage in the first fortnight of October (14.80-16.40 gundhi bug/10 hills). Among the natural enemies the population of spiders, Apanteles and dragon flies was recorded during August and September but the lady bird beetle population was found maximum during the last fortnight of September. The correlation study revealed that the population build-up of different pests and their natural enemies was influenced by the weather parameters in both the years.
1. Introduction
The rice crop in Sikkim is prone to stress throughout the crop growth period due to onslaught from different insect pests such as stem borer, Scirpophaga incertulas; whorl maggot, Hydrellia philippina; leaf folder, Cnephalocrocis medinalis; and Gundhi bug, Leptocorisa orientalis. On an average, the yield losses in the country due to insect pests in every year are around 30% (Prakash & Rao, 1998). In this context, the development of a suitable management strategy is utmost necessity for combating the menace of the rising insect pest populations. _________________ Corresponding author; [email protected]
Study on the population buildup of insect pests and their natural enemies and their relationship with weather parameters is an essential component of pest management as it generates information which can be utilized to improve cultural, mechanical, behavioural and chemical methods of insect control. With this knowledge even the time of crop sowing/planting can also be adjusted so as to avoid the coincidence of the peak insect population with the most susceptible stage(s) of the crop.
70
Besides, the weather parameters are known to have
profound influence on the occurrence, growth and development and population build-up of insect pests in crop ecosystem and ultimately on the extent of damage to the crop and yield loss thereof. Information on the relationship of the prevalence and build-up of different insect pests and their natural enemies with the weather parameters is a prerequisite before formulating a location specific IPM module for management of these pests. Therefore, the present investigation was undertaken to study the population build-up of some major rice pests and their natural enemies and relationship with weather parameters.
2. Materials and methods
Around 600 sq m area was taken to study the population buildup of major insect pests of rice and their natural enemies. The total area was divided into four equal plots. The observations were taken at fortnightly interval. In case of stem borer 20 plants were selected randomly for recording of dead hearts along with total tillers from each plot. For leaf feeders like whorl maggot and leaf folder 10 plants were taken for counting of damaged leaves by these insects. The gundhi bug damaged grain was recorded from randomly collected 10 panicles from each plot.
Forty hills were observed randomly by walking
diagonally to record the number of adult insects and predators’ visually. The larval and pupal parasitoids were recorded by rearing larvae and pupae in the laboratory collecting from field (50 nos.). In case of stem borer 20 plants were selected randomly for recording of white ear head along with total panicles from each plot before harvesting.
3. Results and Discussion
The data on infestation of different insects, population of insects and natural enemies and their relationship with weather parameters are presented in Table 1 and 2. It was observed from the study that the whorl maggot started the infestation at the initial stage of the crop and damaged maximum (22.25 -24.25 damaged leaves/10 hills) in the second fortnight of July. The data showed that the stem borers a key group of insect pests damaging rice crop were abundant from July to November and they cause maximum damage to rice crop starting from August to October resulting in dead heart (6.82 – 7.62% in August-September) and white ear head (7.56 – 8.14 % in October) in both the years. It might be due to the favourable weather parameters (Table 2) and availability of rice plants in their preferred stages. This result corroborates the findings of Bora et al. (1995).
Table 1. Population buildup of insect pests of rice and their natural enemies during 2007 and 2008 Fortnights
Dead heart/WE (%)
Damaged leaves/10 hills
No. of insects/parasitized insects/predators/ 10 hills
Leaf folder
Whorl maggot
Stem borers Gundhi bug Spiders Coccinellids Apanteles Dragon fly
2007 2008
2007 2008 2007 2008 2007 2008 2007 2008 2007
2008 2007 2008 2007 2008 2007 2008
July-ll
2.08 2.48 5.5 3.5 24.3 22.3 0.80 0.60 0.00 0.00 1.0 1.2 0.0 0.0 0.0 0.0 0.4 0.6
Aug-l
4.54 4.16 12.8 12.3 20.5 17.75 1.80 1.60 0.00 0.00 1.6 2.0 0.0 0.0 0.2 0.4 0.6 0.8
Aug-ll
7.62 6.56 14.5 16.8 14.8 12.50 1.60 1.40 0.00 0.00 2.0 2.6 2.0 2.2 1.0 1.2 1.0 1.4
Sept-l
7.14 6.82 11.3 14.5 8.3 6.50 1.20 1.20 4.20 3.60 2.2 2.8 5.4 6.6 0.8 0.8 1.4 1.2
Sept-ll
5.86 6.12 8.0 8.0 3.8 2.75 0.80 0.80 13.60 10.40 1.6 1.8 10.6 12.4 0.4 0.6 1.2 .80
Oct-l
5.24 5.36 4.3 4.3 0.0 0.00 0.40 0.60 16.40 14.80 0.6 0.8 7.4 8.6 0.2 0.4 0.4 0.6
Oct-ll
7.56 8.14 2.0 2.0 0.0 0.00 0.00 0.00 10.50 8.20 0.0 0.0 2.2 2.8 0.0 0.0 0.0 0.2
Nov-l
6.32 6.74 0.0 0.0 0.0 0.00 0.00 0.00 2.00 2.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Damaged leaves from 4 plots; No. of insects from 40 hills.
71
Table 2. Correlation between population of insect pests and natural enemies with weather parameters Weather parameters/ Insect population and natural enemies
Temperature0C Relative humidity (%) Total rainfall(mm) BSSH
Maximum Minimum Maximum Minimum
2007 2008 2007 2008 2007 2008 2007 2008 2007 2008 2007 2008
Stem borers
0.742* R2=0.55
0.664* R2=0.44
0.049 R2=0.00
0.015 R2=0.00
0.023 R2=0.00
0.007 R2=0.00
0.012 R2=0.00
0.013 R2=0.00
0.131 R2=0.017
0.296 R2=0.087
-0.104 R2=0.01
-0.028 R2=0.00
Whorl maggot
0.642* R2=0.41
0.688* R2=0.47
0.671* R2=0.45
0.768** R2=0.59
0.561 R2=0.31
0.328 R2=0.11
0.601 R2=0.36
0.521 R2=0.27
0.649* R2=0.42
0.696* R2=0.48
-0.601 R2=0.36
-0.561 R2=0.31
Leaf folder 0.268 R2=0.07
0.237 R2=0.06
0.744* R2=0.55
0.753* R2=0.57
0.659* R2=0.43
0.744* R2=0.55
0.723* R2=0.52
0.793** R2=0.63
0.651* R2=0.42
0.685* R2=0.47
-0.460 R2=0.21
-0.569 R2=0.32
Gundhi bug 0.069 R2=0.00
-0.133 R2=0.017
-0.149 R2=0.02
-0.421 R2=0.177
-0.381 R2=0.145
0.117 R2=0.01
-0.348 R2=0.12
-0.115 R2=0.01
-0.018 R2=0.00
-0.308 R2=0.094
0.353 R2=0.12
-0.007 R2=0.00
Spiders 0.287 R2=0.087
0.257 R2=0.07
0.725* R2=0.53
0.825** R2=0.68
0.634* R2=0.40
0.799** R2=0.64
0.690* R2=0.48
0.873** R2=0.76
0.658* R2=0.43
0.854** R2=0.729
-0.637* R2=0.405
-0.763** R2=0.582
Dragonfly/damselfly 0.278 R2=0.08
0.326 R2=0.11
0.641* R2=0.41
0.790** R2=0.62
0.331 R2=0.11
0.766** R2=0.59
0.532 R2=0.28
0.571 R2=0.33
0.671* R2=0.45
0.790** R2=0.63
-0.290 R2=0.09
-0.595 R2=0.35
Lady bird beetle 0.138 R2=0.02
-0.101 R2=0.01
0.139 R2=0.02
0.000 R2=0.00
-0.117 R2=0.01
0.473 R2=0.22
0.013 R2=0.000
0.273 R2=0.07
0.414 R2=0.17
0.163 R2=0.03
0.083 R2=0.006
-0.320 R2=0.10
Apanteles
0.503 R2=0.03
0.566 R2=0.31
0.114 R2=0.01
0.185 R2=0.03
0.562 R2=0.31
0.087 R2=0.01
0.573 R2=0.32
0.086 R2=.01
0.557 R2=0.31
0.190 R2=0.04
0.586 R2=0.34
0.555 R2=0.31
* Significant at p=0.05, ** Significant at p=0.01
The leaf folder population was found maximum in the last part of August and first part of September (14.50 -16.75 damaged leaves/10 hills). This might be attributed to the influence of weather parameters and coincidence of late tillering/reproductive stage of the crop. The similar observation was reported by the earlier workers like Kushwaha (1998), Mishras et al., (1999) and Khan et al., (2004). Gundhi bug population was found maximum in the first fortnight of October (14.80-16.40 gundhi bug/10 hills). The per cent of damaged grain of Gundhi bug was recorded maximum in the second fortnight of October (12.86%). This might be attributed to the coincidence of milky stage of the rice crop in this period.
Among the natural enemies the population of different species of spiders viz., wolf spiders (Lycosa sp), lynx spiders (Oxyopes sp), dwarf spiders (Atypena sp), orb spiders (Argiope sp) and long- jawed spider (Tetragnatha sp) (2.0 – 2.8 spiders/10 hills), dragonfly (Anax sp) and damselfly (1.0 – 1.4 /10 hills) are most common and potential natural enemies in the months August and September but the lady beetles (Micraspis sp) (7.4 – 12.4/ 10 hills) population is found maximum during the last fortnight of September. The natural parasitism of Apanteles flavipes was found very less. The correlation study revealed that the population build-up of different pests and their natural enemies was influenced by the weather parameters in both the years.
72
Relationship of insect population with temperature (maximum & minimum) The correlation study revealed that the maximum temperature has significant effect on population of whorl maggot and stem borers but the minimum temperature showed significant effect on the population build-up of pests like whorl maggot and leaf folder and natural enemies like spiders and dragonfly/damselfly (Table 2).
Relationship with relative humidity (maximum & minimum) Except gundhi bug and lady bird beetle the population of all insects and natural enemies was influenced positively by the relative humidity (maximum & minimum) in both the years but it has significant effect on the population of leaf folder and spiders (Table 2). Relationship with total rainfall The total rainfall also exhibited marked effect on the multiplication of insect populations and population of natural enemies. The results revealed that in both the years the population of insect pests’ viz., whorl maggot and leaf folder and natural enemies like spiders and dragonfly/damselfly showed the significant positive relationship with total rainfall.
Over all, the Bright Sun Shine Hours has significant effect on the population buildup of spiders only.
References Prakash, A and J Rao (1998). Insect Pests of Cereals and
Their Management, Applied Zoologists Research Association IAZRAI, Division of Entomology, CRRI, Cuttack, 1: 1.
Bora, DK, Saharia, D and S Hussain (1995). Seasonal incidence of stem borers in ahu and Sali rice. Journal of Agricultural Science Society, 8(1):10-13.
Khan, Z.H., Gupta, S. L. and VV Ramamurthy (2004). Population dynamics of rice leaf folder (Cnephalocrocis medinalis) on Pusa Basmati-1 cultivar in relation weather factors in Delhi. Indian Journal of Entomology, 66(4):361-363.\
Kushwaha, KS (1988). Leaffolder epidemic in Haryana, India. Intn. Rice Res. Newsletter. 13(3):16-17.
Mishra, BK, Senapati, B and SK Behera (1999).Rice leaf folders, caseworm and cutworms. (In) Insect Pests of Cereals and Their Management, pp 59-75. Prakash, A & Rao, J. (Eds.). Applied Zoologists Research Association IAZRAI, Division of Entomology, CRRI, Cuttack.
Sain, M, Zainul-Abedin, S and S.K. Singh (1999). Rice green leafhoppers. (In) Insect Pests of Cereals and Their Management, pp 77-87.Prakash, A & Rao, J.(Eds.). Applied Zoologists Research Association IAZRAI, Division of Entomology, CRRI, Cuttack
.
73
Contents available at http://epubs.icar.org.in,www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 73-76
Influence of Canopy Pruning on Orange Growth and Rhizome Yield of Intercrop Ginger under Agri-Horticulture System.
Tasso Tabin1,a* • D. Balasubramanian1,b • A. Arunachalam1,b
1Restoration Ecology Lab, Department of Forestry, North Eastern Regional Institute of Science & Technology, Nirjuli 791109, Arunachal Pradesh, India aPresent address: Krishi Vigyan Kendra, Geku (Korak) 791002, Upper Siang district, Arunachal Pradesh bPresent address: Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110001.
ARTICLE INFO ABSTRACT Article history: Received 16 November 2014 Received Revised 5 May 2015 Accepted 6 May 2015 ----------------------------------------------- Key words: Canopy Pruning, Tree cover, Intercropping
----------------------------------
A field experiment was conducted during 2010-2011 at farmer orchard to determine the influence of canopy pruning on productivity of orange tree and intercrop (ginger) under rainfed conditions at Geku, Arunachal Pradesh. The growth parameter (dhb, height and canopy diameter) of orange tree was significantly (P < 0.05) higher in control than different canopy pruning intensities (50 % and 70 %) in both the year. However, the rate of growth, number of fruit/tree and kg/tree was significantly higher in pruned trees than control (unpruned). The growth and yield of intercrop was also significantly higher in trees pruned at different canopy intensities than control. Among canopy pruning intensities, 50 % canopy pruning intensity had higher rate of growth (5.7 %), number of fruit/tree (50 numbers) and kg/tree (1.13 kg) of orange while control (unpruned) had decline result with 1.7 %, 0.06 numbers of fruit/tree and 0.05 kg/tree respectively. However, the growth and rhizome yield of ginger was increase significantly at 70 % canopy pruning higher than 50 % canopy pruning and control. However, there was decline in growth and yield at 70 % canopy pruning and control (unpruned) in the next cropping season except, for 50 % canopy pruning which increase significantly with 38.66 cm, 3.78 numbers and 72.20 g/plant.
1. Introduction
The orange and ginger is the most important cash crop in Geku, Upper Siang district of Arunachal Pradesh, India. Ginger is intercropped under orange tree in agri-horticulture system. It is a sustainable land management system in which fruit tree is intercropped with spice crop. They compete inevitably for light, nutrients and other resources that affect the growth performance of the crop. The effect of tree on intercrop is not consistent and this effect may complimentary or competitive. The canopy tree provides shade and create conducive atmosphere for underneath crop to grow. Pruning of tree component is also a powerful approach to regulate this competition (Frank and Eduaro 2003). __________________ *Corresponding author: [email protected]
However, as the functional balance of the tree is altered through pruning, it reacts both morphologically and physiologically in response to the change consequently, the growth and development of shoots and foliage may be altered. If sufficient recovery time is provided after pruning, such as reduction in growth gradually decreases and pruned tree resume their normal growth status. In agri-horticulture systems, presence of well-developed tree canopy and resultant shade make light an important factor in determining the potential of understory crops (Osman et al. 2007). Shoot pruning alleviates shading of crop an appeared as an effective means of increasing the light permeability and yield of intercrop (Newaj et al. 2007).
74
Pruning also reduces the competitive ability of trees,
which allow the top to take advantage of the higher nutrient availability under tree cropping system. Biomass yield and productivity of crop have also been reported higher under pruned tree (Dar and Newaj 2008). Keeping these points in view the present study was undertaken to find out the effect of different pruning intensity on tree growth and rhizome yield of intercrop.
2. Materials and methods
The experiment was conducted during 2010 to 2011 in farmer’s orange orchard field at Geku, Arunachal Pradesh under rainfed conditions. The soil is well drained lateritic and red loamy type with several rocky patches. The orchard contained well established 10 year old mandarin trees planted with 4.5 m x 4.5 m. There were 3 levels of pruning intensity i.e. unpruned plant as control, 50 % and 70 % canopy pruning. Levels of canopy pruning were based on a percentage of green crown length and calculated on the basis of the method described by Samaddar & Chakrabarti (1988) and Pilania et al. (2010). The experiment was laid out in complete randomize block designed with 3 replications. The field spice crop viz., Ginger was grown sequentially as intercropping under tree. Pruning was done during off season atleast a month before planting of ginger. In the next year, intercropping was done under previous pruning season growth canopy. Growth parameters of mandarin orange, diameter at breast height (dbh), height, canopy diameter, fruit number/tree and kg/tree were measured in January whereas ginger height, leaf diameter and rhizome yield/plant were determined in February in both the year. The man power required for the pruning was determined on the basis of cost treatment and yield/plant. The net income was obtained by subtracting the treatment cost from gross income. It was expressed on net excess income over control.
3. Results and Discussion Effect of pruning intensity on the growth of tree
The canopy pruning and intercropping significantly influenced the growth of an orange tree. The growth parameter (DBH, height and canopy diameter) of the tree in Table 1 reveals that the pruned tree had better growth parameter than the control.
The development and growth of tree during 2011 was better than the 2010. The tree The DBH did not exhibit significant variation, however height and canopy diameter was significantly (P=0.05) affected with pruning intensity and intercropping. The 50 % pruning of previous season (2011) growth canopy had significantly higher rate of DBH, height and canopy diameter growth than 50 % canopy pruning season (2010). The higher tree growth and canopy diameter may be probably due to higher organic sources and moisture retention with intercropping under tree (Newaj et al. 2005). Among the pruning intensities, the trees in control had higher DBH, height and canopy diameter than 50 % and 70 % canopy pruning. However, the rate of growth in the tree at 50 % and 70 % canopy pruning had higher than control Generally, it has been found that the impact of pruning on tree growth increased with the amount of the pruning with 5.7 % and 3.7 % as compare to 1.7 %. This may be due to the rejuvenation process which encourages nutritional status and physiological activities of the tree. intensities (Chandrashekhar 2007 and Newaj et al. 2010) which may be due to change in development activities of the tree after pruning as it removes old leaves, unproductive and infected branches. The Pruning of Albizia procera tree and cropping sequence with leguminous crop have been reported to benefit higher tree growth and canopy diameter by Newaj et al. 2010.
The average yield data of 2010 and 2011 reveals that the number of fruit/tree and kg/tree under different pruning intensity significantly increase as compare to control. The maximum increase in number of fruit/tree and kg/tree from 2010 to 2011 was recorded at 50 % canopy pruning with 50 No’s of fruit/tree and 1.13 kg/tree followed by 70 % canopy with 23 No’s of fruit/tree and 0.44 kg/tree while control (unpruned) had decline result with 0.06 No’s of fruit/tree and 0.05 kg/tree respectively. These findings were in close conformity with findings of Pilania et al. (2010) maximum fruit yield in Guava at 50 % canopy pruning. The significant interactive effect as a consequence of organic sources, light and intercrop alley attributed a favorable nutritional status and moisture content in the soil resulting into increased biomass production in the tree. B: C ratio was significantly affected by pruning intensity and cropping sequence. The pruning intensity obtained maximum B: C at 50 % canopy with 4.32 as compared to 70 % canopy and control during both the season.
75
Rhizome yield of intercrop The growth and yield of ginger as the intercrop under orange tree shows significant variation at different pruning intensities. The plant height, number of leaves and rhizome/plant increased with increase in the pruning intensity. However, the growth and yield of intercrop during pruning year 2010 was considerably higher than the next cropping season 2011. The growth and rhizome yield was significantly higher at 70 % and 50 % canopy pruning than control. The plant height, leaves number and rhizome yield/plant of intercrop was 40.71 cm, 3.92 Nos & 77.32 g at 70 % canopy pruning higher than 50 % canopy pruning and control during 2010. However, there were decline in growth and yield in the next crop season except, for 50 % canopy pruning which increase significantly with 38.66 cm, 3.78 No’s & 72.20 g/plant.
This may be due to regulation of temperature and light
intensity under tree canopy indicating more compatibility for crop growth. In agri-horticulture system, canopy pruning of fruit tree facilitates penetration of light and alleviates shading of understorey crop. Newaj et al. (2010) reported higher grain yield of greengram and wheat under leguminous and non-leguminous cropping sequence intercropped under white siris tree which was pruned at different level. In another study, Newaj et al. (2007) also reported high yield of blackgram and mustard at 50 % canopy pruning
Table 1. Effect of pruning intensity and cropping sequence on the growth of orange tree.
Pruning intensity
2010 2011
Growth rate DBH (cm)
Height (m)
Canopy dia (m)
DBH (cm)
Height (m)
Canopy dia (m)
70 % canopy 13.72 4.61 2.71 13.74 4.72 2.82 3.7 % 50 % canopy 13.75 4.14 2.64 13.78 4.25 2.78 5.7 % Control (unpruned)
14.09 5.02 2.92 14.08 5.08 2.98 1.7 %
CD (P=0.05) 0.86 0.28 1.18 0.93 0.74 1.47 2.30
Table 2. Effect of pruning intensity on yield and B: C ratio of orange.
Treatment No. of fruit/tree Yield (kg/tree)
B:C ratio 2010 2011 2010 2011
70 % canopy 470 493 20.81 21.25 4.03 50 % canopy 351 401 17.78 18.91 4.32 Control (unpruned) 685 679 27.13 27.08 3.87
CD (P=0.05) 6.32 6.41 1.93 1.18 0.87
Table 3. Effect of pruning per cent on the growth and yield of intercropped ginger.
Pruning intensity
2010 2011
Height (cm)
No. of leaves/ plant
Weight/plant (g)
Height (cm)
No. of leaves/ plant
Weight/plant (g)
70 % canopy 40.71 3.92 77.32 40.20 3.90 76.66 50 % canopy 37.32 3.15 70.71 38.66 3.78 72.20 Control (unpruned) 33.43 3.08 58.43 32.57 2.88 55.47 CD (P=0.05) 0.35 0.07 1.50 1.50 1.29 1.29
76
References Chandrashekhar, U.M. (2007). Effect of pruning on radial
growth and biomass increment of trees growing in home gardens of Kerala, India. Agroforestry Systems 69: 231-37.
Dar, S.A. and R. Newaj (2008). Effect of canopy pruning in Albezia procera on green gram (Vigna radiate)-wheat (Triticium aestivum) crop sequence. Indian Journal of Agricultural Sciences 78 (11): 978-980.
Frank, E. and S. Eduardo (2003). Biomass dynamics of Erythrina lanceolata as influenced by shoot pruning intensity in Costa Rica. Agroforestry Systems 57: 19-28.
Osman, M., Emminhgam, W. H. and S.H. Sharrow (1998). Growth and yield of sorghum or cowpea in an agri-silviculture system in semi-arid India. Agroforestry Systems 42: 91-105.
Newaj, R., Dar, S.A. and S.K. Dhyani (2010) Influence of canopy pruning on tree growth and grain yield of intercrops in white siris (Albezia procera)-based agrisilviculture system. Indian Journal of Agricultural Sciences 80 (5): 377-79.
Newaj, R., Dar, S.A., Bhargava, M.K., Yadav, R.S., and Ajit
(2007). Effect of management practices on growth of white siris (Albezia procera), grain yield of intercrops, weed population and soil fertility change in agrisilviculture system in semi-arid India. Indian Journal of Agricultural Sciences 77 (7): 403-407.
Newaj, R., Yadav, R.S., Dar, S.A., Ajit and A.K. Shankar, (2005). Response of management practices on rooting pattern of Albezia procera and their effect on grain yield of soyabean and wheat in agrisilviculture system. Indian Journal of Agroforestry 7 (2): 1-9.
Pilania, S., Shukla, A.K., Mahaver, L.N., Sharma, R., and H.L. (2010). Standardization of pruning intensity and integrated nutrient management in meadow orcharding of Guava. Indian Journal of Agriculture Sciences 80(5): 673- 678.
Samaddar, H.N. and U. Chakrabarti (1988). Effect of different root stocks on Himsagar and Langra mango. Acta Horticulture 231: 220-224.
77
Contents available at http://epubs.icar.org.in,www.kiran.nic.in; ISSN: 0970-6429
Indian Journal of Hill Farming
June 2015, Volume 28, Issue 1, Page 77-80
Extension of Shelf Life of Tomato Using KMnO4 as Ethylene Absorbent 1 A. Nath • Bandita Bagchi • V. K. Verma • H. Rymbai • A. K. Jha • Bidyut C. Deka Division of Horticulture, ICAR Research Complex for NEH Region, Umiam, Meghalaya, India PIN-793103.
ARTICLE INFO ABSTRACT Article history: Received 13 July 2015 Received Revised 11 August 2015 Accepted 12 August 2015 ---------------------------------- Key words: Tomato, Shelf life, Ethylene absorbent, KMnO4, Polypropylene
--------------------------------
Ethylene absorbents can increase the shelf life of fruits and vegetables since they scavenge away ethylene hormone, responsible for fruit ripening. Such a technique is used in increasing the shelf life of ripe tomato. Ripe tomatoes, after washing and removal of surface moisture were packed in polypropylene (200 gauge) packets with 0.002% perforation and without perforation. Chalks treated with different concentrations of KMnO4 (1500 ppm-2500ppm) were kept inside the polypropylene packets, along with the tomatoes. Tomatoes packed in perforated (0.002%) polypropylene (200 gauge) packets with 2500 ppm KMnO4
treated chalks had the shelf life of 28 days in cold storage (04±5◦C and 85% RH) and 14
days in room temperature storage (24±5◦C and 70% RH). Analysis of the samples were carried out and it was found that KMnO4 (2500ppm) + perforation showed minimum decay percentage, and slow rate of changed in TSS, colour ‘a’ value and lycopene content, while maximum value was recorded for fruit firmness, acidity, ascorbic acid by the treatment.
1. Introduction
Tomato (Solanum esculentum L.) is one of the most importance vegetables, which has high demand in the market. In India, this crop occupies about 0.87 m ha of the area with production of 16.5 million MT, of which 68, 183.7 MT were exported with annual export earnings of 11,480.6 lakhs Rupees (NHB, 2011). It is being used as an extender in Indian dishes, as well as used to make a number of value added products like tomato sauce, ketchup, puree, etc. Tomato is rich in lycopene content (1.82-11.19 mg/g) (Markovic et al. 2000), Ascorbic acid (8 - 120 mg/100g) and Organic acid (0.4%) of fresh fruits (Cantwell 2000). These bioactive compounds, especially lycopene and ascorbic acid are a good natural antioxidant, particularly effective at quenching the destructive potential of singlet oxygen (Rymbai et al. 2011). Intake of this antioxidants has been linked to lower incidence of prostate cancer, cholesterol, atherosclerosis, coronary heart disease and protect the skin against harmful UV rays (O’Hare et al. 2004; Rymbai et al. 2011).
________________ Corresponding author: [email protected]
However, tomato being the climacteric fruit, marked increase of respiration rate and ethylene production during ripening process occurs, which reduce it shelf life at ambient temperature. Extending its shelf life is very important for utilizing the fruit to its fullest extent. Numerous attempts have been made in this aspect. Potassium permanganate was found to extend the shelf-life of climacteric fruit (Nwufo et al. 1994). Bhagwan et al. (2000) had reported the extension of shelf life of tomato by post-harvest antioxidant application such as ascorbic acid, sodium benzoate and benzyladenine. The technique of modified atmosphere packaging has also been used for its shelf life extension as reported by Isabel et al. (2008). Ethylene absorbents like monocyclopropene (MCP) could also be used to increase the storage life of the whole fruit. In the present investigation different concentrations of KMnO4, as ethylene absorbents with (or without) polypropylene as packing material were used to conduct a study on the shelf life extension of ripe tomatoes.
78
2. Materials and methods Plant material
Fully ripe tomatoes (Hybrid: Avinash-2) were collected from the Experimental field, Division of Horticulture, ICAR-RC for NEH Region, Umiam, Meghalaya (India) in the month of May, 2010. The tomatoes were washed properly and surface moisture was removed with tissue papers.
Treatments
These tomatoes were then packed in individual polypropylene (200 gauge) bags with 0.002% perforations or without perforations. To use KMnO4 as ethylene absorbent in the bags, initially KMnO4 solutions of different concentrations (1500-2500 ppm) were made and pieces of chalks were dipped in for 2 hours. The chalks were dried in
oven at 100◦C for 1 hour. These chalks were then put into each bag of tomatoes and sealed. The packets were kept at room temperature (24±50C and 70% RH) and cold storage (04±20C and 85% RH).
Observations
The treated tomatoes were evaluated at 7 days interval for various quality parameters, viz., total soluble solids, ascorbic acid, acidity and lycopene. Decay loss percentage, changes in colour and texture of the tomatoes during the storage period was also evaluated. Analysis
Total Soluble Solids (TSS) content was determined using digital refractometer (Erma refractometer) and reading was expressed in degree brix (ºB). Titratable acidity (TA) was determined according to the method described by Ranganna (1994). Ascorbic acid was estimated by using 2, 6 dichlorophenol indophenol dye visual titration method (Ranganna 1994). Lycopene content was estimated as per the method suggested by Ranganna (1994). The texture of the tomatoes were studied using a Stable Micro System TA-XT2 texture analyzer (Texture Technologies Corp. UK) and colour by using Hunter L, a, b colour measuring system (Colour Quest XE model) and estimated as Hunter value L, a and b where ‘ a ’ (‘ + ’ value indicated redness and ‘−’ value indicated greenness), ‘ b ’ (‘ + ’ value indicated yellowness and ‘−’ value indicated blueness) and ‘ L ’ (varies from 0 to 100 where ‘ 100 ’ indicated white and ‘ 0 ’ indicated black)
Texture Analyzer assessed the samples on the basis of
the force required for deformation of the tissues of the tomatoes (Ranganna 1993). The replicated data of all the observations were analyzed by using of Complete Randomized Design (CRD) with the help of statistical OP-STAT software.
3. Results and Discussion Decay loss
By texture analysis, it was found that slow softening of the tissues of tomatoes occurred during the storage period (Table 1). The maximum shelf life extension was found in case of perforated (0.002%) polypropylene (PP) bags with 2500 ppm KMnO4. This treatment had the decay loss of 0 % for 28 days in cold storage (CS) and 14 days in room temperature storage (RT). The minimum decay loss might be due to reduction in ethylene evolution, leading to slow the rate of softening of the fruits (Pangaribuan et al. 2003).
Fruit firmness
The maximum fruit firmness was recorded with the treatment KMnO4 (2500ppm) + perforated polypropylene bag in the entire storage interval, i.e., at 14 (2.52 kg) and at 28 days (2.43 kg) under room and cold storage condition respectively (Table 2). This might be due to slow rate of softening of the fruits (Pangaribuan et al. 2003).
Total soluble solid
A gradual increase in TSS due to treatments was observed during the storage period of the tomatoes (Table 3). Among the treatments, KMnO4 (2500ppm) + perforation had a slower evolution rate of TSS content, i.e., 4.7 and 4.8 under RT and CS respectively at 7 days, 4.9 and 5.1 under RT and CS respectively at 14 days and 5.6 at 28 days under CS. These results are in accordance with the finding of Silva et al. (2009). The increase in TSS content was mainly due to conversion of starch into sugars, thereby resulting in the increase of total soluble solids.
Titratable acidity
Conversely to TSS, the acidity was found to be declining with storage days. Among the treatments, KMnO4
(2500ppm) + perforation showed minimum rate of declining of acidity with storage interval, i.e., 0.244 at 17 days and 0.25 at 28 days under RT and CS respectively.
79
Decrease in acid content was perhaps due to utilization of organic acid during respiration or their conversion to sugars (Sudheer et al. 2007).
Ascorbic acid The ascorbic acid content also showed a declining trend
during storage (Table 5). Among treatments, KMnO4
(2500ppm) + perforation showed the maximum value at 14
This could be further minimized in order to extend the shelf-life of tomatoes to one month by using 0.002 % perforated polypropylene (200 gauge) packets for packaging treated ripe tomatoes with 2500 ppm KMnO4 at storage conditions of about 40C and 85% relative humidity. Applying of this finding will be helpful for the storage and transport of the produce without the occurrence of substantial loss in marketability and freshness quality
days (32.1 mg/100gm) and at 28 days (34.2 mg/100gm) under RT and CS respectively. This indicated that treatment KMnO4 (2500ppm) + perforation gave minimum reduction in the ascorbic content of tomato in all the storage days. However, the declining rate is minimum under cold storage. The reason for decreasing trend of ascorbic acid could be attributed to the existence of a balance between Vitamin C synthesis and aging of fruit (Miller 1945).
Colour ‘a’ value
There was a progressive increase in the colour with all the treatments, as indicated by value ‘a’ (Table 6). However, among treatments, KMnO4 (2500ppm) + perforation exhibited slow rate of colour development both under RT and CS. Sammi and Masud (2008) found that plastic films incorporated with potassium permagnate were effective in delaying tomato ripening and colour development. This might be due to its ethylene absorbent capacity (Briceno et al. 1999).
Lycopene
Similarly, lycopene content followed the same pattern as colour ‘a’ value (Table 7) which exhibited slow rate of lycopene development both under RT and CS. KMnO4
(2500ppm) + perforation gave maximum lycopene content (2.03 mg/100 g) under room temperature, while KMnO4
(2000ppm) + perforation (2.11 mg/100 g) under cold storage. This was also confirmed by Silva et al. (2009) who reported the slower evolving of peel colour in papaya due to KMnO4 treatment. This might be due to decrease in respiratory rate, inhibiting ethylene activity consequently reducing metabolism of the fruit (Hao and Hao 1993).
Conclusion Based on this finding, it was observed that the rate of change of each of the mentioned parameters varied for different concentrations of KMnO4. For increasing concentrations of KMnO4, the rate of change of TSS, acidity, ascorbic acid, lycopene, firmness and colour slowed down, which was minimum in case of 2500 ppm KMnO4 treated tomatoes.
Acknowledgement
Authors are sincerely acknowledges the Director, ICAR Research Complex for NEH Region Umiam for providing all kind of support needed for completion of the work.
References
Bhagwan A, Reddy YN, Rao PV and KC Mohankumar (2000). Shelf life extension of tomato fruits by postharvest antioxidant application. J Applied Hortic 2 (2): 88-91
Briceno S, Zambrono J and E Castellanos E (1999). Retardo En La Maduracion De Frutos De Mango Cv. Keitt Y Palmer Mediante Las Mezclas VermiculitaKMnO4 Y Silicagel- KMnO4.
Agron. Tropic 49(1), 41-49. Cantwell M (2000). Optimum procedures for ripening
tomatoes. In: Management of Fruit Ripening. University of California Post Harvest Horticultural Series No. 9: 80-88.
Hao HP and L Hao (1993). Study on storing strawberry at a temperature near the freezing point of water. J Fruit Sci 10(1): 21-24
Isabel OS, Robert SF and MB Olg (2008). Antioxidant properties and shelf life extension of fresh cut tomatoes stored at different temperatures. J Sci Food Agric 88 (15): 2606-2614
Markovic K, Hruskar M and N Vancic (2000). Lycopene content of tomato products and their contribution to the lycopene intake of Croatians. Nutr Res 26 (11): 556-560
Miller CD (1945). Fruits of Hawaii. Hawaii Agricultural Experiment Statistics Bulletin, 96 pp.
NHB (2011). National Horticulture Board, Indian Horticulture database. Ministry of Agriculture, New Delhi, India, pp 4-254
Nwufo ML, Okonkwo ML and JC Obiefune (1994). Effect of post-harvest treatments on the storage life of avocado pear (Persea Americana, Mill). Trop Sci 34: 364-370
80
O’Hare TJ, Wong LS and DJ Mc Grath (2004). Increasing
visual appeal and health benefit of fresh-cut tomato slices using high-lycopene varieties. Acta Hortic 687: 395-398
Pangaribuan DH, Irving DE and TJO O’Hare (2003). Effect of an ethylene absorbent on quality of tomato slices. Poster presentations. Australian Postharvest Horticulture Conference, p 252
Ranganna S (1994). Handbook of analysis and quality control for fruit and vegetable products. Tata Mc Graw Hills Publishing Company Limited, New York
Rymbai H, Sharma RR and M Srivastav (2011). Biocolorants and its implications in health and food industry - a review. Int J PharmTech Res 3(4): 2228-2244
Sammi and T Masud (2008). Effect of different packaging systems on the quality of tomato (Lycopersicon esculentum var. Rio Grande) fruits during storage. Inter. J. Food Sci. Technol. 44(5): 918–926, DOI: 10.1111/j.1365-2621.2007.01649.x
Silva DFP, Salomão LCC, Lopes de Siqueira D, Cecon PR, and A Rocha (2009). Potassium permanganate effects in postharvest conservation of the papaya cultivar Sunrise Golden. Pesq Agropec Bras Brasília 44(7): 669-675
Sudheer KP and V Indira (2007). Post-harvest technology of horticultural crops, New India Publishing Agency, New Delhi, pp 87-90
Indian Journal of Hill Farming
July 2015 Volume 28 Issue 1
Author Guidelines
The journal accepts manuscripts from members only.
Both the corresponding author and the first author (if
not the same) should be members of the society. Authors
are required to indicate their membership number at the
time of submission of the manuscript. Membership form
available in this issue can be used for application.
1. Submission of a manuscript
Submission of a manuscript automatically implies
that the submitted work has not been published earlier;
that it is not under consideration for publication anywhere
else; that its publication has been approved by all co-
authors, if any, as well as by the appropriate authorities
at the institute where the work has been carried out. The
publisher will not be responsible for any conflict of
interest, violation of rules or norms or any compensation
claim. Authors wishing to include portions (text, fig.
etc.) from already published material are required to
obtain permission from the copyright owner(s) for both
the print and online format and to submit evidence that
such permission has been granted when submitting their
papers. Any material received without such evidence will
be assumed to originate from the authors.
Original manuscripts should not exceed 15 typed
pages (including tables and references but excluding
figures and legends) while short notes should not exceed
10 typed pages including all. Submission of electronic
copy to [email protected] is mandatory.
Authors will be informed electronically about the
reviewer ’s decision. The manuscript should contain
sections ‘Introduction’, Materials and methods’ ‘Results’,
‘Discussion’ and ‘References’. The author(s) may
combine the ‘Results’ and ‘Discussion’ sections together.
Discussion should contain a paragraph summarizing the
major findings of the article, drawing some conclusion
and indicating way forward. An ‘Abstract’ and a list of
‘Key words’ should be provided. Abstract and key words
are not required for short communications and results
and discussions should be combined. Articles selected
for short communications should not contain more than
twelve references, one table, one figure, and should not
exceed three typed pages including tables and figures.
Prospective review authors may provide a short
outline (one or two pages) of the proposed review. The
general instructions for authors will apply to the reviewers
for all technical aspects of manuscript preparation. The
“Materials and methods” and “Results” sections are not
needed, but an introduction to the subject and informative
headings for all sections are required.
Manuscripts that are accepted for publication will be
checked by the editors for spelling and formal style.
However, this may not be enough. We encourage authors
to get their manuscript checked by some of their colleague
or a professional language editor prior to submission. A
clear and concise manuscript helps editors and reviewers
to properly evaluate manuscript. A manuscript with too
many errors in language may be rejected without review.
2a. Title Page
The title page should include:
The name(s) of the author(s)
A concise but informative title
The affiliation(s) and address (es) of the author (s)
The e-mail address, telephone and fax numbers of
the corresponding author
2b. Abstract
Please provide an abstract of not more than 250
words. The abstract should be self contained and must
provide a clear picture of the manuscript including
conclusion and recommendation, if any. It should not
contain any undefined abbreviations or unspecified
references. For short notes please provide a summary.
2c. Keywords
Please provide 4 to 6 keywords which can be used
for indexing purposes. If possible avoid the words used
in the title.
2d. Text Formatting
Manuscripts should be submitted in Word.
Use a normal, plain font (e.g., 12-point Times Roman)
for text.
Use italics for emphasis.
Use the automatic page numbering function to
number the pages.
Use line numbering except in the title page.
Do not use field functions.
Use tab stops for indents, not the space bar.
Use the table function, not spreadsheets, to make
tables.
Use the equation editor for equations.
Genus and species names should be in italics.
2e. Headings
A maximum of three levels of headings can be used
2f. Abbreviations
Abbreviations should be defined at the first instance
and used consistently thereafter.
2g. Footnotes
Footnotes can be used to give additional information,
which may include the citation of a reference included
in the reference list. They should not consist solely of a
reference citation, and they should never include the
bibliographic details of a reference. They should also
not contain any figures or tables.
Indian Journal of Hill Farming
July 2015 Volume 28 Issue 1
Footnotes to the text are numbered consecutively;
those to tables should be indicated by superscript lower-
case letters (or asterisks for significance values and other
statistical data). Footnotes to the title or the authors of
the article are not given reference symbols.
Always use footnotes instead of endnotes.
2h. Acknowledgments
Acknowledgments of people, grants, funds, etc.
should be placed in a separate section before the reference
list. The names of funding organizations should be written
in full. The author(s) is/ are responsible for any inclusion
or omission.
3. Citation
Cite references in the text by name and year in
parentheses. Some examples are:
TDZ is a potential hormone for shoot induction
(Robert 1990).
This finding has been further supported by Janes and
Kin (2001).
Path analysis has been reported by several researchers
(Dutta 1995; Sarma et al. 1999; Sarma and Abhram 1993;
Singh 2005, 2007; Chowdhury 2000a; Peterson 200a,b).
3a. List of references
The list of references should only include published
or accepted works that are cited in the text. Personal
communications and unpublished works should only be
mentioned in the text. Do not use footnotes or endnotes
as a substitute for a reference list. Reference list entries
should be alphabetized by the last names of the first
author of each work.
Examples :
Journal article
Sarma BK, Das G, Sahay G (2005). Effect of low
temperature on growth of ricebean. Theor Appl Genet
35 : 405-412 (or only the doi - 105:731-738. doi:
10.1007/s00421-008-0955-8)
Please give names of all authors. Use of et al. in the
list is also accepted when the number of authors are more
than three.
Article by DOI
Das JG, Singh ML (2000). Application of TDZ as a
herbicide. J Agric Sci. doi:10.1007/s001090000086
Book
Sarma BK, Pattanayak A (2009). Rice in North_East
India. Pragati, Guwahati
Book chapter
Ogura H (1990). Chromosome variation in plant
culture. In: YPS Bajaj (ed) Biotechnology in Agriculture
and Forestry, Vol 2: Somaclonal variation in crop
improvement I, Springer, Berlin, Heidelberg, New York,
pp 49-84
Taylor PWJ, Fraser TA, Ko HL, Henry RJ (1995).
RAPD analysis of sugarcane during tissue culture. In:
Terzi M, Cella R, Falarigna A (eds) Current issues in
plant molecular and cellular biology. Kluwer Academic,
Dordreeht, The Netherlands, pp 241-245
Online document
Gupta NL (2008). Pig breeding for sustainable
inco me. ICAR Co mplex Kno wled ge Repositor y.
www.icarneh.ernet.in/kiran/16/pig.html , Accessed 15
January 2010
Dissertation
Phukeri K (2011). Path analysis in rice. Dissertation,
Central Agricultural University, Imphal, India
Always use the standard abbreviation of a journal’s
name according to the ISSN List o f Title Word
Abbreviations, see www.issn.org/2-22661-LTWA-
online.php
4. Tables :
Please keep the number of tables to minimum.
A single table should not exceed one full page (A4).
All tables are to be numbered using Arabic numerals.
Tables should invariably be cited in text in numerical
order.
For each table, please provide a table title explaining
the content of the table.
Source of Tables from a previously published material
should be mentioned at the footnote.
Footnotes to tables should be indicated by superscript
lower-case letters (or asterisks for significance values
and other statistical data) and included beneath the table
body.
5. Photograph and artwork :
Individual figures (plates or artwork) should not
exceed one page. Please submit originals of all of your
artwork – photographs, line drawings, etc. wherever
possible. Electronic copy of all figures and drawings is a
must.
All figures are to be numbered using Arabic numerals.
Figures should invariably be cited in text in numerical
order.
Figure parts should be denoted by lowercase letters
(a, b, c, etc.).
6. Figure legends
Each figure should have a concise legend describing
accurately what the figure depicts. Include the captions
in the manuscript file and not in the figure file.
Figure captions begin with the term Fig. in bold type,
followed by the figure number, also in bold type.
Use full stop after the number and at the end of the
caption.
Legend of graph/chart should not be less than 10 pt.
Identify already published material by giving the
original source at the end of the figure caption. The source
should be cited in the same way as in the list of references.
7. Acknowledgements / conflict of interest
All benefits in any form from a commercial party
related directly or indirectly to the subject of the
Indian Journal of Hill Farming
July 2015 Volume 28 Issue 1
submitted manuscript or any of the authors must be
acknowledged. For each source of funds, both the name
of the funding agency and the grant number should be
given. This should be included in the acknowledgement.
If no conflict exists, authors should state: The authors
declare that they have no conflict of interest within
themselves and others including the funding agency and
the agency where the research was carried out.
8. After acceptance :
Upon acceptance, acceptance intimation will be sent
to the corresponding author.
9a. Copyright transfer
Submission of a manuscript implies that when the
manuscript is accepted for publication, the authors agree
to automatic transfer of the copyright to the publisher
(or grant the Publisher exclusive publication and
dissemination rights). The Indian Association of Hill
Farming (IAHF), as the publisher, has the right to enter
into any agreement with any organization in India or
abroad engaged in reprography, photocopying, storage
and dissemination of information contained in these
journals. The IAHF has no objection in using the
material, provided the information is being utilized for
academic purpose but not for commercial use. Due credit
line should be given to IAHF where information will be
utilized.
9b. Offprints
Offprints can be ordered by the corresponding author.
Free offprints are not provided.
9c. Color illustrations
Online publication of color illustrations is free of
charge. In the print version, colour printing is charged to
the authors.
9d. Proof reading
Proof (electronic) will be provided for checking
typesetting or conversion errors and the completeness
and accuracy of the text, tables and figures. Major / long
changes in the text or figures are not accep ted.
Corrections may be indicated as comments on the body
of the proof or may be sent as a separate document file
indicating page, section and line number where the
correction should be introduced. After online publication,
further changes can only be made in the form of an
Erratum, which will be hyperlinked to the article.
9e.Online First
The article will be published online after receipt of
the corrected proofs. This is the official first publication.
After release of the volume (either electronic or print),
the paper can also be cited by issue and page numbers.
Membership Application Form
Name: In capital letter only, leave one box after name, surname etc
Date of Birth: Sex: Nationality:
Date Month Year
Qualification/ Specialization : _________________________________________________________
Designation : _______________________________________________________________________
Permanent Address: _________________________________________________
_________________________________________________
_________________________________________________
_________________________________________________
_________________________________________________
Mailing Address: _________________________________________________
_________________________________________________
_________________________________________________
_________________________________________________
_________________________________________________
Telephone/ Mobile number:
Email ID:
Membership sought for:
Fee Details: Rs : ______________________ Cheque/ DD No. ______________________ Date:
Date Month Year
9 1
Life
Annual
Student
Donor
Institutional
Duly filled application form along with the DD,Cheque in favour of the Secretary “Indian Association of Hill Farming” payable
at SBI,ICAR Complex Branch, Barapani may be submitted to the following address.
The Director
ICAR Research Complex for NRH Region
Umroi Road, Umiam (Barapani)- 793103
Meghalaya.
Bank details for online payment: Account Name : Indian Association of Hill Farming
SB A/c No: 10881245794
State Bank of India, ICAR Complex Branch, Barapani
IFSC: SBIN0012464.
Membership Fees:
Registration (compulsory) : Rs 100 /-
Annual Member : Rs 400 /-
Life Member : Rs 3000 /-
Student Member : Rs 200 /-
Donor Member : Rs 10000 /-
Institutional Member : Rs 5000 /-
Signature & Date____________________________
Indian Association of Hill Farming (Registered under Societies Act XII of 1983)
Registration No. SR/AHF 43/87 of 1987 Office: ICAR Research Complex for NEH Region
Umroi Road, Umiam – 793103 (Meghalaya) _______________________________________________________________
Note: Please submit one page note biodata containing information on Qualification, Area of expertise, numbers of publications,
Country visited, Award and recognition etc with a passport size photograph.
Male
Female
Indian
Others, Please specify _______________________________
The Application form and Biodata may be sent through
email to [email protected]
