A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions - Estrada-Carmona, Natalia; Raneri, Jessica Evelyn; Álvarez, S.; Timler, Carl J.; Abe Chatterjee, S.; Ditzler, L.; Kennedy, G.; Remans, R.; Brouwer, Inge D.; Borgonjen-van den Berg, Karin J.; Talsma, Elise F.; Groot, Jeroen C.J. (Springer, 2020)
Assessing progress towards healthier people, farms and landscapes through nutrition-sensitive agriculture (NSA) requires transdisciplinary methods with robust models and metrics. Farm-household models could facilitate disentangling the complex agriculture-nutrition nexus, by jointly assessing performance indicators on different farm system components such as farm productivity, farm environmental performance, household nutrition, and livelihoods. We, therefore, applied a farm-household model, FarmDESIGN, expanded to more comprehensively capture household nutrition and production diversity, diet diversity, and nutrient adequacy metrics. We estimated the potential contribution of an NSA intervention targeting the diversification of home gardens, aimed at reducing nutritional gaps and improving livelihoods in rural Vietnam. We addressed three central questions: (1) Do ‘Selected Crops’ (i.e. crops identified in a participatory process) in the intervention contribute to satisfying household dietary requirements?; (2) Does the adoption of Selected Crops contribute to improving household livelihoods (i.e. does it increase leisure time for non-earning activities as well as the dispensable budget)?; and (3) Do the proposed nutrition-related metrics estimate the contribution of home-garden diversification towards satisfying household dietary requirements? Results indicate trade-offs between nutrition and dispensable budget, with limited farm-household configurations leading to jointly improved nutrition and livelihoods. FarmDESIGN facilitated testing the robustness and limitations of commonly used metrics to monitor progress towards NSA. Results indicate that most of the production diversity metrics performed poorly at predicting desirable nutritional outcomes in this modelling study. This study demonstrates that farm-household models can facilitate anticipating the effect (positive or negative) of agricultural interventions on nutrition and the environment, identifying complementary interventions for significant and positive results and helping to foresee the trade-offs that farm-households could face. Furthermore, FarmDESIGN could contribute to identifying agreed-upon and robust metrics for measuring nutritional outcomes at the farm-household level, to allow comparability between contexts and NSA interventions.