Hostname: page-component-77c89778f8-n9wrp Total loading time: 0 Render date: 2024-07-19T00:01:01.687Z Has data issue: false hasContentIssue false

Yield gap in milk production is considerable in Indian Himalayan state of Meghalaya

Published online by Cambridge University Press:  17 February 2021

Evans Kemboi*
Affiliation:
School of Social Sciences, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal, Umiam, Meghalaya793103, India
S. M. Feroze
Affiliation:
Department of Agricultural Economics, College of Agriculture, Central Agricultural University, Imphal, Iroisemba, Manipur795004, India
Ram Singh
Affiliation:
School of Social Sciences, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal, Umiam, Meghalaya793103, India
Jabir Ahmed
Affiliation:
School of Social Sciences, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal, Umiam, Meghalaya793103, India
Hehlangki Tyngkan
Affiliation:
School of Social Sciences, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal, Umiam, Meghalaya793103, India
*
Author for correspondence: Evans Kemboi, Email: evanskemboi65@gmail.com

Abstract

Yield gaps in milk production are here defined as the differentials between the actual yield obtained by the dairy farmer and the potential farm yield (production achieved by the top 10% of farmers: Gap 2) as well as the differential between this potential farm yield and the yield registered in the research stations (Gap 1). Assessment of yield gaps provides valuable information on potential production enhancement and drivers behind yield gaps. Milk production can be increased by narrowing the predominant large yield gaps in resource-poor smallholder farming system. Hence, this study assessed the milk yield gap and factors affecting the yield gap in Ri-Bhoi district of Meghalaya, a state located in the north-eastern Himalayan region of India. This research paper provides a scope for exploring the possibilities for improving dairy production in the state as well as contributing to literature through incorporating crucial determinants responsible for milk yield gap. A sample of 81 respondents was drawn purposely from two blocks of the district. The results indicated that the average number of cattle per household was 9.38 in standard animal units. The total yield gap was estimated at 6.20 l (91.06%) per day, composed of 0.80 l (11.76%) per day of yield gap I and 5.40 l (79.30%) per day of yield gap II. This demonstrates that the top performing farms were achieving a production level not dissimilar to that obtained on the research stations, but many were doing far less well. The size of cattle shed, dairy farming experience, concentrate price and human labour were the important determinants of the yield gap. Hence, encouraging the right stocking density of cattle, training on the preparations of home-made concentrates, access to cheap and quality concentrates, incorporating training and experience sharing on proper dairy management practices and use of technology could benefit the dairy farmers of the region.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press of Hannah Dairy Research Foundation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, W, Johansen, C and Siddique, KH (2016) Addressing the yield gap in rainfed crops: a review. Agronomy for Sustainable Development 36, 18.CrossRefGoogle Scholar
Bhalerao, AK, Kumar, B, Singha, AK, Jat, PC, Bordoloi, R and DekaBidyut, C (2015) RiBhoi district inventory of Agriculture, ICAR-Agricultural Technology Application Research Institute, Umiam, Meghalaya, India.Google Scholar
Census (2011) Census of India, Ministry of Home Affairs, New Delhi. Available at www.censusindia.gov.in.Google Scholar
Cortez-Arriola, J, Groot, JC, Massiotti, RDA, Scholberg, JM, Aguayo, DVM, Tittonell, P and Rossing, WA (2014) Resource use efficiency and farm productivity gaps of smallholder dairy farming in North-west Michoacán, Mexico. Agricultural Systems 126, 1524.CrossRefGoogle Scholar
Dalenius, T and Hodges, JL (1959) Minimum variance stratification. Journal of American Statistical Association 54, 88101.CrossRefGoogle Scholar
FAO (2009) How to Feed the World in 2050 Food and Agricultural Organisation of the United Nations. Available at http://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf.Google Scholar
Feroze, SM, Raju, VT, Singh, R and Tripathi, AK (2010) Status of livestock sector: a micro study of North Eastern India. Indian Journal of Hill Farming 23, 4351.Google Scholar
Feroze, SM, Singh, R and Sirohi, S (2016) Profitability and disposal pattern of milk in underdeveloped hill production system of Meghalaya. Indian Journal of Animal Sciences 86, 11981203.Google Scholar
Feroze, SM, Singh, R and Sirohi, S (2019) Economics of milk production and factors affecting milk yield in Meghalaya: estimating the seasonal effect. Indian Journal of Dairy Science 72, 328335.CrossRefGoogle Scholar
GoI (2018) National Action Plan for Dairy Development Vision-2022. Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture and Fisheries Welfare, Government of India.Google Scholar
GoM (2018) Report on Integrated Sample Survey for Estimation of Production of Milk, Egg and Meat for the Year 2017–18. Directorate of Animal Husbandry and Veterinary, Government of Meghalaya.Google Scholar
GoM (2019) Statistical handbook Meghalaya 2019. Directorate of Economics and Statistics, Government of Meghalaya, Shillong.Google Scholar
Gomez, KA (1977) On-farm assessment of yield constraints: methodological problems. In Barker, R, Datta, SKD, Gomez, KA and Herdt, RW (eds), Constraints to High Yields on Asian Rice Farms: An Interim Report. Los Baños, Philippines: International Rice Research Institute, pp. 114.Google Scholar
Graves, RE (1989) Floor plans for cubicle housing of dairy cattle. Proceedings of the 11th International Congress on Agricultural Engineering. September 4–8, 1989. Dublin, Ireland.Google Scholar
Henderson, B, Godde, C, Hidalgo, DM, Wijk, MV, Silvestri, S, Douxchamps, S, Stephenson, E, Power, B, Rigolot, C, Cacho, O and Herrero, M (2016) Closing system-wide yield gaps to increase food production and mitigate GHGs among mixed crop–livestock smallholders in Sub-Saharan Africa. Agricultural systems 143, 106113.CrossRefGoogle ScholarPubMed
Horo, A and Chandel, BS (2019) Economics of milk production and yield differentials among the marginal women farmers of Jharkhand state in India. Asian Journal of Agricultural Extension, Economics and Sociology 30, 111.CrossRefGoogle Scholar
Ittersum, MKV, Cassman, KG, Grassini, K, Wolf, J, Tittonell, P and Hochman, Z (2013) Yield gap analysis with local to global relevance: a review. Field crops Research 143, 417.CrossRefGoogle Scholar
Jafor, AM (2019) Farm level technical efficiency of dairy farms: a study in Barpeta and Morigaon districts of Assam. Journal of Humanities and Social Sciences 24, 0105.Google Scholar
Kumar, A, Staal, S, Elumalai, K and Singh D, K (2007) Livestock sector in North Eastern region of India: an appraisal of performance. Agricultural Economics Research Review 20, 255272.Google Scholar
Kumawat, R, Singh N, K and Meena C, L (2014) Economic analysis of cost and returns of milk production, extent of adoption of recommended management practices on sample dairy farms in Bikaner district of Rajasthan. GJSFR: D Agriculture and Veterinary 14, 4753.Google Scholar
Lalrinsangpuii, Malhotra, R and Sailo, L (2016) Production and consumption pattern of milk and meat in North Eastern region of India. Agricultural Rural Development 3, 1518.Google Scholar
Landes, M, Cessna, J, Kuberka, L and Jones, K (2017) India's Dairy Sector: Structure, Performance, and Prospects. United States Department of Agriculture.Google Scholar
Livestock Census (2019) 20th Livestock Census-2019 All India report, Ministry of fisheries, Animal Husbandry and Dairying, Department of Animal Husbandry and Dairying Animal Husbandry Statistics Division Krishi Bhawan. Government of India, New Delhi.Google Scholar
Lobell, DB, Cassman, KG and Field, CB (2009) Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34, 179204.CrossRefGoogle Scholar
Mayberry, D, Ash, A, Prestwidge, D, Godde, C, Henderson, B, Duncan, A, Blummel, M, Reddy, R Y and Herrero, M (2017) Yield gap analyses to estimate attainable bovine milk yields and evaluate options to increase production in Ethiopia and India. Agricultural Systems 155, 4351.Google ScholarPubMed
Meena, GL, Sharma, L, Mishra, S and Choudhary, S (2019) An economic analysis of milk production of Buffalo and Cow in Rajasthan. Indian Journal of Animal Nutrition 36, 158163.CrossRefGoogle Scholar
Naess, G, Boe, KE and Osteras, O (2011) Layouts for small freestall dairy barns: effects on milk yield for cows in different parities. Journal of Dairy Science 94, 12561264.CrossRefGoogle Scholar
Pathania, MS and Sharma, A (2016) Economic analysis of milch animals in Jaisinghpur tehsil of district Kangra. Himachal Journal of Agricultural Research 42, 3746.Google Scholar
Paul, D and Chandel, BS (2010) Improving milk yield performance of crossbred cattle in North Eastern states of India. Agricultural Economics Research Review 23, 6975.Google Scholar
Sultana, MN, Uddin, MM and Peters, KJ (2016) Socio-economic determinants of milk production in Bangladesh: an implication on on-farm water use. Livestock Research for Rural Development 28, 78.Google Scholar
Suthar, B, Bansal, RK and Gamit, P (2019) An overview of livestock sector in India. Indian Journal of Pure & Applied Biosciences 7, 265271.Google Scholar
Supplementary material: PDF

Kemboi et al. supplementary material

Kemboi et al. supplementary material

Download Kemboi et al. supplementary material(PDF)
PDF 186.1 KB