The economic costs of Indonesia’s 2015 forest fires are estimated to exceed US $16 billion, with more than 100,000 premature deaths. On several days the fires emitted more carbon dioxide than the entire United States economy. Here, we combine detailed geospatial data on fire and local climatic conditions with rich administrative data to assess the underlying causes of Indonesia’s forest fires at district and village scales. We find that El Niño events explain most of the year-on-year variation in fire. The creation of new districts increases fire and exacerbates the El Niño impacts on fire. We also find that regional economic growth has gone hand-in-hand with the use of fire in rural districts. We proceed with a 30,000-village case study of the 2015 fire season on Sumatra and Kalimantan and ask which villages, for a given level of spatial fire risk, are more likely to have fire. Villages more likely to burn tend to be more remote, to be considerably less developed, and to have a history of using fire for agriculture. Although central and district level policies and regional economic development have generally contributed to voracious environmental degradation, the close link between poverty and fire at the village level suggests that the current policy push for village development might offer opportunities to reverse this trend.

As the global population and people’s incomes rise, the demand for ocean-derived food will continue to grow. At the same time, hunger and malnutrition continues to be a challenge in many countries, particularly in rural or developing areas. Looking to the ocean as a source of protein produced using low-carbon methodologies will be critical for food security, nutrition and economic stability, especially in coastal countries where hunger and malnutrition are a challenge. Yet these advances in ocean production can only be achieved with a concurrent focus on addressing threats to ocean health, such as climate change and overfishing.

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low- and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer’s plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study evaluates ground- and satellite-based approaches to estimating crop yields and yield responsiveness to inputs, using data on maize from Eastern Uganda. Using unique, simultaneous ground data on yields based on farmer reporting, sub-plot crop cutting, and full-plot harvests across hundreds of smallholder plots, we document large discrepancies among the ground-based measures, particularly among yields based on farmer-reporting versus sub-plot or full-plot crop cutting. Compared to yield measures based on either farmer-reporting or sub-plot crop cutting, satellite-based yield measures explain as much or more variation in yields based on (gold-standard) full-plot crop cuts. Further, estimates of the association between maize yield and various production factors (e.g., fertilizer, soil quality) are similar across crop cut- and satellite-based yield measures, with the use of the latter at times leading to more significant results due to larger sample sizes. Overall, the results suggest a substantial role for satellite-based yield estimation in measuring and understanding agricultural productivity in the developing world.