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A deterministic evaluation of heat stress mitigation and feed cost under climate change within the smallholder dairy sector

Published online by Cambridge University Press:  28 December 2016

L. York*
Affiliation:
Livestock Development Group (LDG), Faculty of Life Sciences, University of Reading, Reading RG6 6AR, UK
C. Heffernan
Affiliation:
Livestock Development Group (LDG), Faculty of Life Sciences, University of Reading, Reading RG6 6AR, UK
C. Rymer
Affiliation:
Food Production and Quality Division, Faculty of Life Sciences, University of Reading, Reading RG6 6AR, UK
N. Panda
Affiliation:
Department of Animal Nutrition, Faculty of Veterinary Science and Animal Husbandry, Orissa University of Agriculture and Technology, Bhubaneswar 751003, India
*
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Abstract

In the global South, dairying is often promoted as a means of poverty alleviation. Yet, under conditions of climate warming, little is known regarding the ability of small-scale dairy producers to maintain production and/or the robustness of possible adaptation options in meeting the challenges presented, particularly heat stress. The authors created a simple, deterministic model to explore the influence of breed and heat stress relief options on smallholder dairy farmers in Odisha, India. Breeds included indigenous Indian (non-descript), low-grade Jersey crossbreed and high-grade Jersey crossbreed. Relief strategies included providing shade, fanning and bathing. The impact of predicted critical global climate parameters, a 2°C and 4°C temperature rise were explored. A feed price scenario was modelled to illustrate the importance of feed in impact estimation. Feed costs were increased by 10% to 30%. Across the simulations, high-grade Jersey crossbreeds maintained higher milk yields, despite being the most sensitive to the negative effects of temperature. Low-capital relief strategies were the most effective at reducing heat stress impacts on household income. However, as feed costs increased the lower-grade Jersey crossbreed became the most profitable breed. The high-grade Jersey crossbreed was only marginally (4.64%) more profitable than the indigenous breed. The results demonstrate the importance of understanding the factors and practical trade-offs that underpin adaptation. The model also highlights the need for hot-climate dairying projects and programmes to consider animal genetic resources alongside environmentally sustainable adaptation measures for greatest poverty impact.

Type
Research Article
Copyright
© The Animal Consortium 2016 

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Footnotes

a

Present address: School of Veterinary Sciences, University of Bristol, Langford House, Langford, Bristol BS40 5DU, UK.

References

Berman, A 2011. Invited review: are adaptations present to support dairy cattle productivity in warm climates? Journal of Dairy Science 94, 21472158.CrossRefGoogle ScholarPubMed
Berman, A, Folman, Y, Kaim, M, Mamen, M, Herz, Z, Wolfenson, D, Arieli, A and Graber, Y 1985. Upper critical temperatures and forced ventilation effects for high-yielding dairy cows in a subtropical climate. Journal of Dairy Science 68, 14881495.CrossRefGoogle Scholar
Bouraoui, R, Lahmar, M, Abdessalem, M, Djemali, M and Belyea, R 2002. The relationship of temperature-humidity index with milk production of dairy cows in a Mediterranean climate. Animal Research 51, 479491.CrossRefGoogle Scholar
Bryant, JR, Lopez-Villalobos, N, Pryce, JE, Holmes, CW and Johnson, DL 2007. Quantifying the effect of thermal environment on production traits in three breeds of dairy cattle in New Zealand. New Zealand Journal of Agricultural Research 50, 327338.CrossRefGoogle Scholar
Cunningham, K 2009. Connecting the milk grid: smallholder dairy in India. In Millions fed: proven successes in agricultural development (ed. DJ Spielman and R Pandya-Lorch), pp. 117125. International Food Policy Research Institute, Washington, DC, USA.Google Scholar
Datta, TN, Shrestha, and Chokkalingam, G 2015. Agricultural land and bovine population in India: a critical review of agricultural census data. Journal of Rural Development 34, 167186.Google Scholar
Government of India 2006. Basic husbandry statistics. Government of India, New Delhi, India.Google Scholar
Government of India 2013. National Livestock Policy 2013. Government of India, New Delhi, India.Google Scholar
Government of India 2014. Basic animal husbandry and fisheries statistics. Government of India, New Delhi, India.Google Scholar
Government of Odisha 2012. Labour and ESI Department: notification. Government of Orissa, Cuttack, India.Google Scholar
Government of Odisha 2013. Odisha agricultural statistics 2011-2012. Directorate of Agriculture and Food Production, Bhubaneswar, India.Google Scholar
Heffernan, C, Salman, M and York, L 2012. Livestock infectious disease and climate change: a review of selected literature. CAB Reviews 7, 126.CrossRefGoogle Scholar
Hoekstra, AY 2012. The hidden water resource use behind meat and dairy. Animal Frontiers 2, 38.CrossRefGoogle Scholar
Igono, MO, Bjotvedt, G and Sanford-Crane, HT 1992. Environmental profile and critical temperature effects on milk production of Holstein cows in desert climate. International Journal of Biometeorology 36, 7787.CrossRefGoogle ScholarPubMed
Indian Grassland and Fodder Research Institute 2013. Vision 2050. Indian Grassland and Fodder Research Institute – Indian Council of Agricultural Research, New Delhi, India.Google Scholar
International Farm Comparison Network 2015. Calculator. Retrieved on 29 June 2015 from http://www.ifcnnetwork.org/en/calculator.php.Google Scholar
Keister, ZO, Moss, KD, Zhang, HM, Teegerstrom, T, Edling, RA, Collier, RJ and Ax, RL 2002 . Physiological responses in thermal stressed Jersey cows subjected to different management strategies. Journal of Dairy Science 85, 32173224.CrossRefGoogle ScholarPubMed
Kendall, PE, Verkerk, GA, Webster, JR and Tucker, CB 2007. Sprinklers and shade cool cows and reduce insect-avoidance behavior in pasture-based dairy systems. Journal of Dairy Science 90, 36713680.CrossRefGoogle ScholarPubMed
Key, N and Sneeringer, S 2014. Potential effects of climate change on the productivity of U.S. dairies. American Journal of Agricultural Economics 96, 11361156.CrossRefGoogle Scholar
Little, S and Campbell, J 2010. Cool cows: shade, sprinklers and fans on dairy farms. Dairy Australia, Southbank, VIC, Australia.Google Scholar
Mohapatra, AK, Swain, SK, Dash, AK and Behera, D 2015. Microenvironment of a model bullock shed in different seasons compared to conventional shed and open shelter in tropical humid climatic condition of coastal Odisha. Animal Science Reporter 9, 2227.Google Scholar
Moran, J 2005. Diet and milk production. In Tropical dairy farming: feeding management for small holder dairy farmers in the humid tropics (ed. J Moran), pp. 159170. Landlinks Press, Kyabram, VIC, Australia.CrossRefGoogle Scholar
O’Brien, K, Leichenko, R, Kelkar, U, Venema, H, Aandahl, G, Tompkins, H, Javed, A, Bhadwal, S, Barg, S, Nygaard, L and West, J 2004. Mapping vulnerability to multiple stressors: climate change and globalization in India. Global Environmental Change 14, 303313.CrossRefGoogle Scholar
Pathak, PS and Devakumar, C 2011. Carrying capacity of Indian agriculture. National Academy of Agricultural Sciences, New Delhi, India.Google Scholar
Ravagnolo, O, Misztal, I and Hoogenboom, G 2000. Genetic component of heat stress in dairy cattle, development of heat index function. Journal of Dairy Science 83, 21202125.CrossRefGoogle ScholarPubMed
Reena, K, Triveni, D, Patel, BHM, Amitava, D, Chandran, PC, Barari, SK, Asit, C and Bharat, B 2014. Effect of shade materials on microclimate of crossbred calves during summer. Veterinary World 7, 776783.Google Scholar
Reserve Bank of India 2013. Database on Indian economy: monthly wholesale price index – inflation. Retrieved on 22 September 2013 from http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home.Google Scholar
Santana, MLJ, Pereira, RJ, Bignardi, AB, Filho, AEV, Menéndez-Buxadera, A and El Faro, L 2015. Detrimental effect of selection for milk yield on genetic tolerance to heat stress in purebred Zebu cattle: genetic parameters and trends. Journal of Dairy Science 98, 90359043.CrossRefGoogle ScholarPubMed
Sharma, A, Tyagi, S, Singh, U and Mandal, DK 2011. Vision 2030: Project Directorate on Cattle. Indian Council of Agricultural Research, Meerut Cantt, India.Google Scholar
Sirohi, S and Michaelowa, A 2007. Sufferer and cause: Indian livestock and climate change. Climatic Change 85, 285298.CrossRefGoogle Scholar
Smith, DL, Smith, T, Rude, BJ and Ward, SH 2013. Short communication: comparison of the effects of heat stress on milk and component yields and somatic cell score in Holstein and Jersey cows. Journal of Dairy Science 96, 30283033.CrossRefGoogle ScholarPubMed
St-Pierre, NR, Cobanov, B and Schnitkey, G 2003. Economic losses from heat stress by US livestock industries. Journal of Dairy Science 86 (Suppl), E52E77.CrossRefGoogle Scholar
Thornton, PK, van de Steeg, J, Notenbaert, A and Herrero, M 2009. The impacts of climate change on livestock and livestock systems in developing countries: a review of what we know and what we need to know. Agricultural Systems 101, 113127.CrossRefGoogle Scholar
Upadhyay, RC, Mohini, M, Kansal, VK, Singh, SV, Ashutosh, A, Sirohi, SK and Kumar, S 2007. Final report of the Network Project on Climate Change. National Dairy Research Institute, Karnal, India.Google Scholar
Zimbleman, RB, Rhoads, RP, Rhoads, ML, Duff, GC, Baumgard, LH and Collier, RJ 2009. A re-valuation of the impact of temperature humidity index (THI) and black globe humidity index (BGHI) on milk production in high producing dairy cows. In the 24th Annual Southwest Nutrition and Management Conference, 9–11 March 2009, Reno, NV, USA, pp. 158–168.Google Scholar
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