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Application of the Integrated Decision Support Systems to Improve Livestock Systems and Household Nutrition in Rwanda for Research and Capacity Development

  • Research Project Location: Rwanda
  • Timeframe: May 2022 - February 2024
  • Funding: USAID


Rwanda aims to transform the agriculture sector from subsistence to a market-oriented agriculture including the livestock sub-sector, particularly through Rwanda’s Vision 2050. Rwanda’s agriculture sector contributes about 27% to the national gross domestic product (GDP), including 12% from the livestock sub-sector. Livestock are a secure source of income, nutrition and insurance that help households cope with shocks through wealth accumulation. However, household food insecurity and undernutrition remain a barrier to the overall development and well-being, partly due to an under-developed livestock sub-sector. Food products from animal sources provide key micronutrients such as vitamin A, iron, riboflavin, calcium, zinc and vitamin B12, but per capita consumption levels of milk and meat are below the minimum recommended by FAO and WHO. In Rwanda, 33% of children under 5 years suffer from chronic malnutrition (stunting) and 37% suffer from anemia. The level of food insecurity is specifically high in western and northern areas. This project contributes to Rwanda’s policy, programs and capacity to address constraints related to livestock production and productivity to ensure access and consumption of animal-source foods for better nutrition and diversified diets within environmental boundaries.

Project Plan

To improve livestock fodder production in sustainable ways, an understanding of the integrated nature of humans, the environment, and livestock is needed. Using modeling systems to understand these dynamics will aid livestock farmers and decision-makers in targeting ways to sustainability intensify livestock fodder production in Rwanda.

Project Goal and Objectives

The project aims to contribute to the identification of the best combinations of feed production technologies within environmental boundaries and their potential impacts on livestock production and human nutrition in Rwanda, and to strengthen capacity for policy, programs, planning and monitoring.

  1. Identify and map areas suitable to intensify production of fodder in Rwanda
  2. Identify best combinations of fodder production practices and technologies based on quantification of impacts on animal nutrition, milk production, and household income and nutrition
  3. Strengthen local capacity on analytical methods and co-generate and share knowledge from research results

Research Approach

The proposed project combines participatory engagement of stakeholders and computational models. Participatory stakeholder engagement events – both in person and virtual – are used to identify the livestock systems and fodder for analysis, the study sites within the Feed the Future Zones of Influence for in-depth analysis, and the multiple scenarios to be modeled. The analytical research utilizes the Integrated Decision Support System (IDSS) tool to assess the integrated impacts of improved livestock systems, especially fodder, on production, the environment, animal nutrition, and household income and nutrition in Rwanda’s Western Province. The IDSS tool comprises two biophysical (SWAT and APEX) and one socio-economic (FARMSIM) models. Capacity development is embedded in both the engagement and the analysis processes.


Principal Investigator (PI) and Lead Institution

Raghavan Srinivasan, Texas A&M University System, TAMU

Additional Collaborators

  • University of Rwanda
  • Rwanda Agriculture and Animal Resources Development Board, RAB
  • North Carolina A&T State University


Project Poster


Feed the Future Innovation Lab for Livestock Systems is part of Feed the Future

This work was funded in whole or part by the United States Agency for International Development (USAID) Bureau for Resilience, Environment and Food Security under Agreement # AID-OAA-L-15-00003 as part of Feed the Future Innovation Lab for Livestock Systems. Additional funding was received from Bill & Melinda Gates Foundation OPP#060115.  Any opinions, findings, conclusions, or recommendations expressed here are those of the authors alone.