Integrated modeling framework for sustainable agricultural intensification

Agricultural scientists are pursuing sustainable intensification strategies to increase global food availability, but integration from research to impact at the local-level requires knowledge of demographic and human-environment to enhance the adaptive capacity of farmers cultivating <10 ha. Enhancing close collaboration among transdisciplinary teams and these smallholders is critical to co-elaborate policy solutions to ongoing food security crises that are likely to be attuned with local conditions. Human and socio-cultural aspects need to be considered to facilitate both adoption and dissemination of adapted management practices. Despite this well-known need to co-produce knowledge in human systems, we demonstrate the inequality of current agricultural research in smallholder farming systems with heavy focus on a few domains of the sustainable intensification agricultural framework (SIAF), ultimately reducing the overall impact of interventions due to the lack compatibility with prevailing social contexts. Here we propose to integrate agriculture and agronomic models with social and demographic modeling approaches to increase agricultural productivity and food system resilience, while addressing persistent issues in food security. Researchers should consider the scale of interventions, ensure attention is paid to equality and political processes, explore local change interactions, and improve connection of agriculture with nutrition and health outcomes, via nutrition-sensitive agricultural investments.

Automated summary

Agricultural scientists are pursuing sustainable intensification strategies to increase food availability. Collaboration with smallholders is essential for co-elaborating policy solutions to food security crises, taking into account local conditions and socio-cultural aspects. The current inequality of agricultural research in smallholder farming systems reduces the overall impact of interventions, highlighting the need for integration of agriculture, social and demographic modeling to address persistent food security issues.