Global modeling of SDG indicators related to small-scale farmers: testing in a changing climate

Some indicators used to track the progress of the Sustainable Development Goals (SDGs) suffer from a lack of reported data, and therefore need estimates to fill the data gaps. Using crop model outputs and global cropping system datasets, we present a modeling of small-scale farmer productivity and agricultural output (conceptually similar to the formal SDG 2.3.1 and 2.3.2 indicator, respectively). We analyze the responses of the indicators for 106 low- and middle-income countries for the periods 2051-2060 and 2091-2100, relative to 2001-2010, to various scenarios of climate, socioeconomic development, cost-free adaptation, and irrigation expansion. The results show the potentials of modeling in gap-filling of reported national data, and that the agricultural output indicator indicates the positive effect of climate mitigation to small-scale farmers. The contributions of adaptation are evident when agricultural output indicator is used but are no longer visible, or even wrongly interpreted, when productivity indicator is used, underling the importance of selecting robust indicators to track SDG goals in a changing climate. Also discussed are the caveats identified in the SDG 2.3 indicators that enable the design of indicators more aligned with the other development goals, such as poverty eradication.

Automated summary

This research uses modeling to predict the future trends of agricultural output and productivity indicators. The results show that modeling can help fill data gaps and that climate adaptation has a greater impact on agricultural output than on small-scale farmer productivity. It emphasizes the importance of selecting appropriate indicators to track the Sustainable Development Goals (SDGs).