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

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.

Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland

“…It has a track record of successful applications using real-world data in a wide variety of domains. BNs are used for modeling beliefs in social sciences (11), decision support (12), biology (13), and finance and marketing (14). More recently, this approach has been applied in veterinary epidemiology (15,16), anti-microbial resistance (17)(18)(19), and animal welfare (2).…”

Improving development efficiency through decision analysis: Reservoir protection in Burkina Faso

“…Environmental Modelling and Software 115 (2019) 164-175 To elicit more details, we followed the four-stage procedure (Fig. 2) outlined by Whitney et al (2018). In the first stage of the procedure the decision about sedimentation control was broken down by the entire group into specific sub-questions.…”

Decision analysis methods guide; Agricultural policy for nutrition

“…Clemen and Winkler (1999) and Bolger and Wright (2017) offer more examples of these tools in practice. Figure 2 has been adapted from Whitney et al (2018). It illustrates a process that can be used to elicit graphical representations of decisions from expert groups, to be used in developing a BN.…”

“…Figure 2. The process used for eliciting graphical representations of decisions from expert groups to be used in developing a Bayesian Network (Source: Whitney et al, 2018) The iterative process of building a model in the workshop is illustrated in Figure 2. The steps are repeated until all experts have worked on each question and are satisfied that all specific relationships have been identified.…”

“…Figure 3). Figure 3 presents an example of a tool for translating expert knowledge into a Conditional Probability table (CPT) for use in a Bayesian Network (see Whitney et al, 2018). Table (CPT) for use in a Bayesian Network (Source Whitney et al, 2018) This approach helps analysts to gather expert knowledge for all model parameters.…”