Probabilistic decision tools for determining impacts of agricultural development policy on household nutrition

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dc.contributor.authorWhitney, Cory W.
dc.contributor.authorLanzanova, Denis
dc.contributor.authorMuchiri, Caroline
dc.contributor.authorShepherd, Keith D.
dc.contributor.authorRosenstock, Todd S.
dc.contributor.authorKrawinkel, Michael
dc.contributor.authorTabuti, John R. S.
dc.contributor.authorLuedeling, Eike
dc.date.accessioned2022-06-11T16:51:27Z
dc.date.available2022-06-11T16:51:27Z
dc.date.issued2018
dc.description.abstractGovernments 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.en_US
dc.identifier.citationWhitney, C. W., Lanzanova, D., Muchiri, C., Shepherd, K. D., Rosenstock, T. S., Krawinkel, M., ... & Luedeling, E. (2018). Probabilistic decision tools for determining impacts of agricultural development policy on household nutrition. Earth's Future, 6(3), 359-372. doi: 10.1002/2017EF000765en_US
dc.identifier.other10.1002/2017EF000765
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/3887
dc.language.isoenen_US
dc.publisherEarth's Futureen_US
dc.subjectBayesian Networksen_US
dc.subjectProbabilistic modelingen_US
dc.subjectDecision Analysisen_US
dc.subjectNutritionen_US
dc.subjectHungeren_US
dc.subjectMicronutrient deficiencyen_US
dc.titleProbabilistic decision tools for determining impacts of agricultural development policy on household nutritionen_US
dc.typeArticleen_US
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