Climate change will put millions more people in Africa at risk of food and nutrition insecurity by 2050. Integrated assessments of food systems tend to be limited by either heavy reliance on models or a lack of information on food and nutrition security. Accordingly, we developed a novel integrated assessment framework that combines models with in-country knowledge and expert academic judgement to explore climate-smart and nutrition-secure food system futures: the integrated Future Estimator for Emissions and Diets (iFEED). Here, we describe iFEED and present its application in Malawi, South Africa, Tanzania and Zambia. The iFEED process begins with a participatory scenario workshop. In-country stakeholders identify two key drivers of food system change, and from these, four possible scenarios are defined. These scenarios provide the underlying narratives of change to the food system. Integrated modeling of climate change, food production and greenhouse gas emissions is then used to explore nutrition security and climate-smart agriculture outcomes for each scenario. Model results are summarized using calibrated statements—quantitative statements of model outcomes and our confidence in them. These include statements about the way in which different trade futures interact with climate change and domestic production in determining nutrition security at the national level. To understand what the model results mean for food systems, the calibrated statements are expanded upon using implication statements. The implications rely on input from a wide range of academic experts—including agro-ecologists and social scientists. A series of workshops are used to incorporate in-country expertise, identifying any gaps in knowledge and summarizing information for country-level recommendations. iFEED stakeholder champions help throughout by providing in-country expertise and disseminating knowledge to policy makers. iFEED has numerous novel aspects that can be used and developed in future work. It provides information to support evidence-based decisions for a climate-smart and nutrition-secure future. In particular, iFEED: (i) employs novel and inclusive reporting of model results and associated in-country food system activities, with comprehensive reporting of uncertainty; (ii) includes climate change mitigation alongside adaptation measures; and (iii) quantifies future population-level nutrition security, as opposed to simply assessing future production and food security implications.
IntroductionSoybean farming in Zambia is promoted to increase farm productivity and diversification away from maize, and improve cash income and livelihoods for farmers. However, the impact of soybean farming on women's dietary intake is not clear. This study compares the dietary diversity of women from soybean (S) and non-soybean (NS) farming households as a pathway to understanding policy efficacy.MethodsA cross-sectional survey involving 268 women of reproductive age from 401 rural households was conducted in two soybean-producing districts of Central Province, Zambia. Data from a qualitative 7-day food frequency questionnaire (FFQ) was used to calculate dietary diversity scores (DDS), women's dietary diversity scores (WDDS-10) and assess dietary patterns. Information on household sociodemographic and agricultural characteristics was used to explore determinants of dietary diversity.ResultsResults show there were no significant differences in the mean DDS (S: 10.3 ± 2.4; NS:10.3 ± 2.6) and WDDS-10 (S:6.27 ± 1.55; NS:6.27 ± 1.57) of women from soybean and non-soybean farming households. Both cohorts had similar dietary patterns, plant-based food groups with additional fats and oils. Agricultural diversity was not associated with dietary diversity. Household wealth status was the most important determinant of dietary diversity, as women from wealthier households were more likely to have higher DDS (β = 0.262, 95% CI = 0.26 to 0.70, P < 0.001) and WDDS-10 (β = 0.222, 95% CI = 0.08 to 0.37, P < 0.003) compared to those from poorer households. Women from households that spent more on food had a higher DDS (β = 0.182, 95% CI = 0.002 to 0.07), but not WDDS-10 (β = 0.120, 95% CI = −0.01 to 0.03); for every additional dollar spent on food in the past 7 days, the DDS increased by 0.18. Meanwhile, soyabean farming was not statistically associated with higher wealth.ConclusionsPolicymakers and promoters of agricultural diversification and nutrition-sensitive agriculture need to consider how women can benefit directly or indirectly from soybean farming or other interventions aimed at smallholder farmers.
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