Prioritizing climate-smart livestock technologies in rural Tanzania: A minimum data approach uri icon

abstract

  • Crop-livestock production systems play an important role in the livelihoods of many rural communities in sub-Saharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is critical information for decisionmaking. While there is continued investment in testing adaptation options using on-farm experiments, simulation models remain important tools for ?ex-ante? assessments of the impacts of proposed climate-smart agricultural technologies (CSA). This study used the Ruminant model and the Trade-offs Analysis model for Multi-Dimensional Impact Assessment (TOA-MD) to assess how improved livestock management options affect the three pillars of CSA: increased productivity, improved food security, and reduced greenhouse gas (GHG) emissions. Our sample was stratified into: 1) households with local cow breeds (n = 28); 2) households with improved dairy cow breeds (n = 70); and 3) households without dairy cows (n = 66). Results showed that the predicted adoption rates for improved livestock feeding among households with improved dairy cows (stratum 2) were likely to be higher compared to householdswith only local cows (stratum1). Both householdswith local cows and thosewith improved cows had increased incomeand food security.However, overall poverty reduction was only modest for households with local cows. Expected methane emissions intensity declined with adoption of improved livestock feeding strategies both in stratum 1 and stratum 2, and greater impacts were observed when households in stratum 2 received an additional improved cow breed. Providing a cow to households that were not keeping cows showed substantial economic gains. Additional research is, however, needed to understand why those farms currently do not have cows, which may determine if the predicted adoption rates are feasible
  • Crop-livestock production systems play an important role in the livelihoods of many rural communities in subSaharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is critical information for decision making. While there is continued investment in testing adaptation options using on-farm experiments, simulation models remain important tools for 'ex-ante' assessments of the impacts of proposed climate-smart agricultural technologies (CSA). This study used the Ruminant model and the Trade-offs Analysis model for Multi Dimensional Impact Assessment (TOA-MD) to assess how improved livestock management options affect the three pillars of CSA: increased productivity, improved food security, and reduced greenhouse gas (GHG) emissions. Our sample was stratified into: 1) households with local cow breeds (n = 28); 2) households with improved dairy cow breeds (n = 70); and 3) households without dairy cows (n = 66). Results showed that the predicted adoption rates for improved livestock feeding among households with improved dairy cows (stratum 2) were likely to be higher compared to households with only local cows (stratum 1). Both households with local cows and those with improved cows had increased income and food security. However, overall poverty reduction was only modest for households with local cows. Expected methane emissions intensity declined with adoption of improved livestock feeding strategies both in stratum 1 and stratum 2, and greater impacts were observed when households in stratum 2 received an additional improved cow breed. Providing a cow to households that were not keeping cows showed substantial economic gains. Additional research is, however, needed to understand why those farms currently do not have cows, which may determine if the predicted adoption rates are feasible. (C) 2016 The Authors. Published by Elsevier Ltd.

publication date

  • 2017
  • 2017
  • 2017