Probabilistic Decision Tools for Determining Impacts of Agricultural Development Policy on Household Nutrition. Issue 3 (2nd March 2018)

• Record Type: Journal Article
• Authors: Whitney, Cory W.; Lanzanova, Denis; Muchiri, Caroline; Shepherd, Keith D.; Rosenstock, Todd S.; Krawinkel, Michael; Tabuti, John R. S.; Luedeling, Eike
• Is Part Of: Earth's future. Volume 6:Issue 3(2018)
• Page Start: 359
• Page End: 372
• Publication Date: 2018-03-02
• Subjects: Bayesian Networks -- Probabilistic modeling -- Decision Analysis -- Nutrition -- Micronutrient deficiency -- Hunger; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1002/2017EF000765
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract : 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. Plain language summary: Governments around the world have agreed to end hunger and food insecurity and improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach for determining the nutritional impacts resulting from agricultural development policy. The approach uses expert knowledge for model development and analysis. 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 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 the strategy 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. Decision analysis tools can make these trade‐offs explicit and support evidence‐based agricultural development decisions. Key Points: Probabilistic decision modeling can be used to integrate expert knowledge, considering system complexity and uncertainty Decision modeling with Bayesian Networks can be applied to quantify the pathways from agricultural policy to impact on household nutrition Decision analysis of Uganda's agricultural development strategy illustrates the use of the approach to identify nutrition outcomes