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Green fodder cultivation improves technical efficiency of dairy farmers in semi-arid tropics of central India: a micro-analysis

Published online by Cambridge University Press:  01 December 2022

Bishwa Bhaskar Choudhary ,
Purushottam Sharma ,
Priyanka Singh ,
Sunil Kumar ,
Gaurendra Gupta ,
S. R. Kantwa ,
Deepak Upadhyay ,
Vinod Kumar Wasnik ,
Mahendra Prasad  and
R. K. Sharma
Bishwa Bhaskar Choudhary
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Purushottam Sharma*
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Priyanka Singh
Affiliation:
ICAR-Central Agroforestry Research Institute, Jhansi 284003, India
Sunil Kumar
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Gaurendra Gupta
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
S. R. Kantwa
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Deepak Upadhyay
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Vinod Kumar Wasnik
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Mahendra Prasad
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
R. K. Sharma
Affiliation:
ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284003, India
*
Author for correspondence: Purushottam Sharma, Email: [email protected]
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Abstract

This study assessed the impact of improved green fodder production activities on technical efficiency (TE) of dairy farmers in climate vulnerable landscapes of central India. We estimated stochastic production frontiers, considering potential self-selection bias stemming from both observable and unobservable factors in adoption of fodder interventions at farm level. The empirical results show that TE for treated group ranges from 0.55 to 0.59 and that for control ranges from 0.41 to 0.48, depending on how biases are controlled. Additionally, the efficiency levels of both adopters and non-adopters would be underestimated if the selectivity bias is not appropriately accounted. As the average TE is consistently higher for adopter farmers than the control group, promoting improved fodder cultivation would increase input use efficiency, especially in resource-deprived small holder dairy farmers in the semi-arid tropics.

Keywords

Central Indiagreen fodderstochastic production frontiertechnical efficiency
Type
Research Article
Information
Journal of Dairy Research , Volume 89 , Issue 4 , November 2022 , pp. 367 - 374
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

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References

Abdulai, AN and Abdulai, A (2017) Examining the impact of conservation agriculture on environmental efficiency among maize farmers in Zambia. Environment and Development Economics 22, 177–201.CrossRefGoogle Scholar
Adane, Z, Shiferaw, K and Gebremedhin, B (2015) Sources of Technical Inefficiency of Smallholder Farmers in Milk Production in Ethiopia. Addis Ababa, Ethiopia: International Livestock Research Institute (ILRI).Google Scholar
Adegbola, TA and Dahl, GE (2020) Milk symposium introduction: dairy production in developing countries. Journal of Dairy Science 103, 9677–9680.Google Scholar
Bardhan, D and Sharma, ML (2013) Technical efficiency in milk production in underdeveloped production environment of India. SpringerPlus 2, 65.CrossRefGoogle ScholarPubMed
Bravo-Ureta, BE, Greene, W and Solís, D (2012) Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project. Empirical Economics 43, 55–72.CrossRefGoogle Scholar
Choudhary, BB and Singh, P (2019) How unequal is rural Punjab? Empirical evidence from spatial income distribution. Current Science 117, 1855–1862.CrossRefGoogle Scholar
Choudhary, BB and Sirohi, S (2022) Economic losses in dairy farms due to heat stress in sub-tropics: evidence from North Indian plains. Journal of Dairy Research 89, 141–147.CrossRefGoogle ScholarPubMed
Diagne, M, Demont, M, Seck, PA and Diaw, A (2013) Self-sufficiency policy and irrigated rice productivity in the Senegal river valley. Food Security 5, 55–68.CrossRefGoogle Scholar
Duncan, AJ, Teufel, N, Mekonnen, K, Singh, VK, Bitew, A and Gebremedhin, B (2013) Dairy intensification in developing countries: effects of market quality on farm-level feeding and breeding practices. Animal: An International Journal of Animal Bioscience 7, 2054–2062.CrossRefGoogle ScholarPubMed
Gelan, A and Muriithi, BW (2012) Measuring and explaining technical efficiency of dairy farms: a case study of smallholder farms in East Africa. Agrekon 51, 53–74.CrossRefGoogle Scholar
Ghosh, PK, Palsaniya, DR and Srinivasan, R (2016) Forage research in India: issues and strategies. Agricultural Research Journal 53, 1–12.CrossRefGoogle Scholar
Girma, H (2019) Estimation of technical efficiency of dairy farms in central zone of Tigray National Regional State. Heliyon 5, e01322.CrossRefGoogle ScholarPubMed
Greene, W (2010) A stochastic frontier model with correction for sample selection. Journal of Productivity Analysis 34, 15–24.CrossRefGoogle Scholar
Imbens, G and Wooldridge, J (2008) Recent developments in the econometrics of program evaluation. Research Working Paper No. 14251, National Bureau of Economic Research.CrossRefGoogle Scholar
Koirala, KH, Mishra, A and Mohanty, S (2016) Impact of land ownership on productivity and efficiency of rice farmers: the case of the Philippines. Land Use Policy 50, 371–378.CrossRefGoogle Scholar
Kumar, S, Sharma, P, Satyapriya, , Govindasamy, P, Singh, M, Kumar, S, Halli, HM, Choudhary, BB and Bagavathiannan, M (2022) Economic impression of on-farm research for sustainable crop production, milk yield, and livelihood options in semi-arid regions of central India. Agronomy Journal 12, 1–13.Google Scholar
Leuven, E and Sianesi, B (2003) PSMATCH2: STATA module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing, Statistical Software Components Series S432001, Department of Economics, Boston College.Google Scholar
Ma, W, Renwick, A, Yuan, P and Ratna, N (2018) Agricultural cooperative membership and technical efficiency of apple farmers in China: an analysis accounting for selectivity bias. Food Policy 81, 122–132.CrossRefGoogle Scholar
Rosenbaum, P and Rubin, D (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55.CrossRefGoogle Scholar
Roy, AK, Agrawal, RK, Bhardwaj, NR, Mishra, AK and Mahanta, SK (2019) Revisiting national forage demand and availability scenario. In Roy, AK, Agrawal, RK and Bhardwaj, NR (eds), Indian Fodder Scenario: Redefining State Wise Status. Jhansi, India: ICAR- AICRP on Forage Crops and Utilization, pp. 1–21.Google Scholar
Satyapriya, , Sharma, P, Pandey, S, Biradar, N and Radotra, S (2012) Technological gap analysis of livestock production system in humid ecosystem of India. Range Management and Agroforestry 33, 89–91.Google Scholar
Saxena, R, Choudhary, BB and Joshi, D (2019) Enhancing farmers’ incomes from livestock in eastern India with special emphasis on dairying: major drivers and strategies. Indian Journal of Dairy Science 72, 659–667.CrossRefGoogle Scholar
Sharma, P, Pandey, S, Meena, BS and Singh, NP (2007) Knowledge and adoption of livestock feeding practices in Jhansi district of Bundelkhand. Indian Journal of Dairy Sciences 60, 63–67.Google Scholar
Sharma, P, Choudhary, BB, Singh, P, Kumar, S, Gupta, G and Dev, I (2021) Can forage technologies transform Indian livestock sector? Evidences from smallholder dairy farmers in Bundelkhand region of central India. Agricultural Economics Research Review 34, 73–82.CrossRefGoogle Scholar
Singh, DK (2020) Technical efficiency of dairy farmers in Bihar, India: status and determinants. Agricultural Economics Research Review 33, 81–86.CrossRefGoogle Scholar
Villano, R, Bravo-Ureta, BE, Solís, D and Fleming, E (2015) Modern rice technologies and productivity in the Philippines: disentangling technology from managerial gaps. Journal of Agricultural Economics 66, 129–154.CrossRefGoogle Scholar
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