Feed supply-demand databases as decision making tools for prioritizing livestock interventions to close yield gaps and reduce negative environmental foot prints
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Blϋmmel, M., Haileslassie, A., Samireddypalle, A., Herrero, M., Ramana Reddy, Y. and Mayberry, D. 2016. Feed supply-demand databases as decision making tools for prioritizing livestock interventions to close yield gaps and reduce negative environmental foot prints. IN: Neelam Kewalramani, N. et al. 2016. Innovative approaches for animal feeding and nutritional research. Invited Papers of XVI Biennial Animal Nutrition Conference, Karnal, India, 6-8 February 2016. Karnal: NDRI: 70-81.
Permanent link to cite or share this item: http://hdl.handle.net/10568/78445
Feed data bases describing feed supply - demand scenarios are important tools for researchers, development practitioners and private sector for example to gauge opportunities and limitations for increasing livestock production and to obtain information about potential feed surplus and deficit areas. The two pillars of such feed data bases are assessment of quantitative and qualitative feed availability to calculate present feed supply and livestock census data (livestock population, species composition, herd structure, productivity levels) to estimate feed demand. The present paper proposes, and demonstrates, that such feed supply-demand data bases can be further developed into decision making tools to prioritize and compare various interventions for increasing livestock production and productivity. For example feed-based interventions can be compared with herd-based interventions around animal species, breed and reproduction and the possible interdependence of interventions can be explored and modeled. In addition the implications of choices of interventions on environmental foot prints particularly water requirements and greenhouse gas emissions can ultimately be estimated by such tools. The paper presents example of how feed supply can be linked to water requirements based on the variables: 1) reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity and rainfall, 2) crop specific coefficient derived from crop phenology (Kc); and 3) length of growing period (LGP). Huge differences were observed in the water use efficiencies of classes of feeds but also among the same feeds when sourced from different districts.