David B. Lobell’s research while affiliated with Stanford University and other places

Increasing agricultural productivity is a gradual process with significant time lags between research and development (R&D) investment and the resulting gains. We estimate the response of US agricultural Total Factor Productivity to both R&D investment and weather and quantify the public R&D spending required to offset the emerging impacts of climate change. We find that offsetting the climate-induced productivity slowdown by 2050 will require R&D spending over 2021 to 2050 to grow at 5.2 to 7.8% per year under a fixed spending growth scenario or by an additional $2.2 to$3.8B per year under a fixed supplement spending scenario (in addition to the current spending of ~$5B per year). This amounts to an additional$208 to $434B or$65 to $113B over the period, respectively, and would be comparable in ambition to the public R&D spending growth that followed the two World Wars.

Air pollution from coal electricity generation is a major driver of poor air quality in India and its effects on human health have been extensively studied. Despite considerable evidence that the same pollution also reduces crop productivity, we lack similar quantitative assessments of coal electricity’s crop damages. Here, we estimate rice and wheat crop losses from coal generation’s nitrogen dioxide (NO 2 ) emissions using a regression model that combines station-level electricity generation and wind direction, satellite-measured NO 2 , and its association with crop productivity. Coal emissions impact yields up to 100 km away from power stations. In parts of West Bengal, Madhya Pradesh, and Uttar Pradesh heavily exposed to coal-linked NO 2 , annual yield losses exceed 10%, equivalent to approximately 6 y worth of average annual yield growth in both rice and wheat in India between 2011 and 2020. While station-specific crop damages (value of lost output) are almost always lower than mortality damages (monetized value of annual premature PM 2.5 -related deaths), crop damage intensity (crop damage per GWh of electricity generated) is frequently higher than mortality damage intensity (mortality damage/GWh). Rice damage intensity exceeds mortality damage intensity at 58, and wheat damage intensity at 35 of the 144 power stations studied. The stations associated with the largest crop losses differ from those associated with the highest mortality. Co-optimizing for crop gains and mortality reduction slightly increases and meaningfully changes the distribution of social benefits from reducing emissions, highlighting the importance of considering crop losses alongside health impacts when regulating coal electricity emissions in India.

Crop rotation has been widely used to enhance crop yields and mitigate adverse climate impacts. The existing research predominantly focuses on the impacts of crop rotation under growing season (GS) climates, neglecting the influences of non‐GS (NGS) climates on agroecosystems. This oversight limits our understanding of the comprehensive climatic impacts on crop rotation and, consequently, our ability to devise effective adaptation strategies in response to climate warming. In this study, we examine the impacts of both GS and NGS climate conditions on the yield effect of the preceding crop in corn‐soybean rotation systems from 1999 to 2018 in the US Midwest. Using causal forest analysis, we estimate that crop rotation increases corn and soybean yields by 0.96 and 0.22 t/ha on average, respectively. We then employ statistical models to indicate that increasing temperatures and rainfall in the NGS reduce corn rotation benefits, while warming GS enhances rotation benefits for soybeans. By 2051–2070, we project that warming climates will reduce corn rotation benefits by 6.74% under Shared Socioeconomic Pathway (SSP) 1‐2.6 and 17.18% under SSP 5‐8.5. For soybeans, warming climates are expected to increase rotation benefits by 8.36% under SSP 1‐2.6 and 13.83% under SSP 5‐8.5. Despite these diverse climate impacts on both crops, increasing crop rotation could still improve county‐average yields, as neither corn nor soybean was fully rotated. If we project that all continuous corn and continuous soybeans are rotated by 2051–2070, county‐average corn yields will increase by 0.265 t/ha under SSP 1‐2.6 and 0.164 t/ha under SSP 5‐8.5, while county‐average soybean yields will gain 0.064 t/ha under SSP 1‐2.6 and 0.076 t/ha under SSP 5‐8.5. These findings highlight the effectiveness of crop rotation in the face of warming NGS and GS in the future and can help evaluate opportunities for adaptation.

Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90 % of global production. Maps were generated for the 2019–2022 period by combining data from Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that, among all non-tree species grown in cropland areas, sugarcane is typically tall for the largest fraction of time. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and high recall of our maps. Agreement with field data at the pixel level exceeded 80 % in most countries, and subnational sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks or due to under-reporting of sugarcane area by governments. The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields. The dataset is available on Zenodo at 10.5281/zenodo.10871164.

Agriculture will play a central role in meeting greenhouse gas (GHG) emission targets, as the sector currently contributes ∼22% of global emissions. Because emissions are directly tied to resources employed in farm production, such as land, fertilizer, and ruminant animals, the productivity of input use tends to be inversely related to emissions intensity. We review evidence on how productivity gains in agriculture have contributed to historical changes in emissions, how they affect land use emissions both locally and globally, and how investments in research and development (R&D) affect productivity and therefore emissions. The world average agricultural emissions intensity fell by more than half since 1990, with a strong correlation between a region's agricultural productivity growth and reduction in emissions intensity. Additional investment in agricultural R&D offers an opportunity for cost-effective (<US$30 per ton carbon dioxide) and large-scale emissions reductions. Innovations that target specific commodities or inputs could even further reduce the cost of climate mitigation in agriculture.

Farmers in the United States have rapidly expanded the use of cover crops (CC), with national CC area nearly doubling since 2012. Despite many benefits that motivate public subsidies, questions remain about potential downsides. Using satellite observations from over 100,000 fields, half of which recently adopted CC, we demonstrate that CC led to: (i) declines in average yields for corn and soybean, by ~3% and ~2%, respectively; (ii) delays in sowing of corn (4 days) and soybean (3 days); (iii) reduced damages in the wet spring of 2019, with CC fields only half as likely to experience prevented planting as non-CC fields. CC appears to reduce important aspects of farmer risk in wet conditions but increase them in dry conditions. Timely planting of the cash crop deserves emphasis moving forward, as we show eliminating sowing delays would reduce yield penalties by roughly 50% for corn and 90% for soybean.