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Livestock production in India, expressed in kg/km2/year (a) Milk production by cattle, (b) Meat production by small ruminant, (c) Meat production by poultry, (d) Egg production by poultry

We look for pathways to maximise yield in livestock systems to address global food security.

Donors and development agencies need to target investments in the livestock sector that maximize their impacts for broad numbers of producers and consumers. To do this, estimates of livestock yield gaps are needed. A yield gap is the difference the yield the farmer could achieve and the actual yield achieved. Productivity and yield gap analyses help define the most appropriate technology entry points for different livestock species in terms of: health, nutrition, genetics, or policy levers.

For LiveGAPS1, we estimated the productivity of livestock (see the figure above) and assessed different entry points for closing the yield gaps of livestock in Ethiopia and India. We then developed projections of productivity and production at a continental level. This information contributed to making informed investment decisions and target technologies in the livestock sectors of developing countries.

For LiveGAPS2, we are going to expand on LiveGAPS1 and look at closing the livestock yield gaps in Nigeria and Tanzania.

  1. We will estimate livestock productivity in Nigeria and Tanzania and will update the productivity data for the LiveGAPS1 target countries, Ethiopia and India. We will use new information from surveys and livestock monitoring systems to develop this work. Additionally we will update the distributions of animal numbers and the share of smallholder production for regional level assessments. At the farm level we will use household-level analysis for estimating the productivity of dairy, small ruminants and poultry. This will enable us to determine baseline hotspots of production, contributions of varied production systems, feed demand and supply, and the contribution of livestock to livelihoods.
  2. Using livestock and household simulation models applied in LiveGaps1 (such as Integrated Assessment tool (IAT), RUMINANT, and other models from partners), we will estimate the impacts of best-bet intervention packages provided by key informants (genetics, nutrition, health) on livestock productivity, and farmers’ incomes and nutrition in different production systems in Nigeria and Tanzania.
  3. Assess the projected impacts of these practices if they were upscaled broadly throughout the countries, using a range of scenarios to 2030. This information enables doners and development agencies to assess the feasibility and investment needs of selected practices, enabling better informed investment decisions.