Probabilistic Decision Tools for Determining Impacts of Agricultural Development Policy on Household Nutrition

Governments around the world have agreed to end hunger and food insecurity and to improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, since any agricultural changes will influence social and biophysical systems, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach with Bayesian Network (BN) models for nutritional impacts resulting from agricultural development policy. The approach includes the elicitation of expert knowledge for impact model development, including sensitivity analysis and value of information calculations. It aims at a generalizable methodology that can be applied in a wide range of contexts. To showcase this approach, we develop an impact model of Vision 2040, Uganda's development strategy, which, among other objectives, seeks to transform the country's agricultural landscape from traditional systems to large-scale commercial agriculture. Model results suggest that Vision 2040 is likely to have negative outcomes for the rural livelihoods it intends to support; it may have no appreciable influence on household hunger but, by influencing preferences for and access to quality nutritional foods, may increase the prevalence of micronutrient deficiency. The results highlight the trade-offs that must be negotiated when making decisions regarding agriculture for nutrition, and the capacity of BNs to make these trade-offs explicit. The work illustrates the value of BNs for supporting evidence-based agricultural development decisions

Land use change, fuel use and respiratory health in Uganda

This paper examines how biomass supply and consumption are affected by land use change in Uganda. We find that between 2007 and 2012 there was a 22% reduction in fuelwood sourced from proximate forests, and an 18% increase in fuelwood sourced from fallows and other areas with lower biomass availability and quality. We estimate a series of panel regression models and find that deforestation has a negative effect on total fuel consumed. We also find that access to forests, whether through ownership or proximity, plays a large role in determining fuel use. We then explore whether patterns of biomass fuel consumption are related to the incidence of acute respiratory infection using a cross-sectional data set of 1209 women and 598 children. We find a positive and significant relationship between ARI and the quantity of fuelwood from non-forest areas; a 100 kilogram increase in fuelwood sourced from a non-forest area results in a 2.4% increase in the incidence of ARI for children. We find the inverse effect of increased reliance on crop residues. As deforestation reduces the availability of high quality fuelwood, rural households may experience higher incidence of health problems associated with exposure to biomass burning