About

David Lobell is the Benjamin M. Page Professor at Stanford University in the Department of Earth System Science and the Gloria and Richard Kushel Director of the Center on Food Security and the Environment. He holds additional positions as the William Wrigley Senior Fellow at the Stanford Woods Institute for the Environment, and as a senior fellow at the Freeman Spogli Institute for International Studies (FSI) and the Stanford Institute for Economic Policy Research (SIEPR). His research focuses on agriculture and food security, specifically on generating and utilizing unique datasets to study rural areas worldwide. His early work concentrated on climate change risks and adaptations in cropping systems, and he served as a lead author for the food chapter of the IPCC Fifth Assessment Report and as a core writing team member for the Summary for Policymakers. More recent work involves developing new techniques to measure progress on sustainable development goals and studying the impacts of climate-smart practices in agriculture. Lobell's contributions have been recognized through numerous awards, including the Macelwane Medal from the American Geophysical Union, a Macarthur Fellowship, the National Academy of Sciences Prize in Food and Agriculture Sciences, and election to the National Academy of Sciences. He holds a PhD in Geological and Environmental Sciences from Stanford University and a Sc.B. in Applied Mathematics from Brown University.

Research topics

• Computer Science
• Geography
• Artificial Intelligence
• Environmental resource management
• Remote sensing
• Natural resource economics
• Ecology
• Economics
• Engineering
• Business
• Economic growth
• Machine Learning
• Data science
• Environmental science
• Biology
• Environmental planning
• Development economics
• Oceanography
• Geology
• Climatology
• Physical geography

Selected publications

• Anthropogenic climate change has slowed global agricultural productivity growth

  Nature Climate Change · 764 citations

  Senior authorCorresponding
  • Environmental science
  • Natural resource economics
  • Climatology

  Abstract Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change (ACC) on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that ACC has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 7 years of productivity growth. The effect is substantially more severe (a reduction of ~26–34%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.

  DOI

• Informing water and nutrient management for sustainable agriculture

  Stanford Digital Repository · 2026-03-13

  dissertationOpen access
  PublisherDOI

• A satellite foundation model for improved wealth monitoring

  ArXiv.org · 2026-04-25

  articleOpen accessSenior author

  Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone. Satellite imagery offers global coverage and the possibility of predicting economic livelihoods at scale, yet existing approaches to predicting livelihoods with imagery or other non-traditional data often fail to reliably identify local-level variation and, as we show, degrade under temporal shift. Here we introduce Tempov, a satellite foundation model pretrained by self-supervision on three million bi-temporal Landsat pairs and adapted with parameter-efficient fine-tuning to sparse survey labels. The model enables large-scale, high-resolution wealth mapping and dynamic measurement, including zero-shot nowcasting up to a decade after observed labels, retrospective hindcasting, and decadal change tracking, while outperforming existing neural network and geospatial foundation-model baselines. In low-label regimes, Tempov achieves competitive accuracy with only 10% of survey samples, indicating substantially reduced dependence on expensive label collection. The model further generalizes across populous countries within and outside Africa, and scales to a unified Africa-wide model with strong continent-level performance ($R^2=0.63$, $r^2=0.68$), from which we generate high-resolution decadal maps of wealth and wealth changes for the African continent. Analysis of these maps shows large variation in recent economic performance both within and across countries. Our open-source approach provides a pathway to timely, scalable, low-cost monitoring of wealth and poverty from routinely collected satellite data.

  PublisherOA PDF

• Scalable Vision-Guided Crop Yield Estimation

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen accessSenior author

  Precise estimation and uncertainty quantification for average crop yields are critical for agricultural monitoring and decision making. Existing data collection methods, such as crop cuts in randomly sampled fields at harvest time, are relatively time-consuming. Thus, we propose an approach based on prediction-powered inference (PPI) to supplement these crop cuts with less time-consuming field photos. After training a computer vision model to predict the ground truth crop cut yields from the photos, we learn a "control function" that recalibrates these predictions with the spatial coordinates of each field. This enables fields with photos but not crop cuts to be leveraged to improve the precision of zone-wide average yield estimates. Our control function is learned by training on a dataset of nearly 20,000 real crop cuts and photos of rice and maize fields in sub-Saharan Africa. To improve precision, we pool training observations across different zones within the same first-level subdivision of each country. Our final PPI-based point estimates of the average yield are provably asymptotically unbiased and cannot increase the asymptotic variance beyond that of the natural baseline estimator --- the sample average of the crop cuts --- as the number of fields grows. We also propose a novel bias-corrected and accelerated (BCa) bootstrap to construct accompanying confidence intervals. Even in zones with as few as 20 fields, the point estimates show significant empirical improvement over the baseline, increasing the effective sample size by as much as 73% for rice and by 12-23% for maize. The confidence intervals are accordingly shorter at minimal cost to empirical finite-sample coverage. This demonstrates the potential for relatively low-cost images to make area-based crop insurance more affordable and thus spur investment into sustainable agricultural practices.

  PublisherDOI

• STaPL: Scale Transfer with Pseudo-Labelling for satellite-based mapping of agricultural practices

  Remote Sensing of Environment · 2026-05-23

  articleSenior author
  PublisherDOI

• A satellite foundation model for improved wealth monitoring

  arXiv (Cornell University) · 2026-04-25

  preprintOpen accessSenior author

  Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone. Satellite imagery offers global coverage and the possibility of predicting economic livelihoods at scale, yet existing approaches to predicting livelihoods with imagery or other non-traditional data often fail to reliably identify local-level variation and, as we show, degrade under temporal shift. Here we introduce Tempov, a satellite foundation model pretrained by self-supervision on three million bi-temporal Landsat pairs and adapted with parameter-efficient fine-tuning to sparse survey labels. The model enables large-scale, high-resolution wealth mapping and dynamic measurement, including zero-shot nowcasting up to a decade after observed labels, retrospective hindcasting, and decadal change tracking, while outperforming existing neural network and geospatial foundation-model baselines. In low-label regimes, Tempov achieves competitive accuracy with only 10% of survey samples, indicating substantially reduced dependence on expensive label collection. The model further generalizes across populous countries within and outside Africa, and scales to a unified Africa-wide model with strong continent-level performance ($R^2=0.63$, $r^2=0.68$), from which we generate high-resolution decadal maps of wealth and wealth changes for the African continent. Analysis of these maps shows large variation in recent economic performance both within and across countries. Our open-source approach provides a pathway to timely, scalable, low-cost monitoring of wealth and poverty from routinely collected satellite data.

  PublisherDOI

• Harvesting AlphaEarth: Benchmarking the geospatial foundation model for agricultural downstream tasks

  International Journal of Applied Earth Observation and Geoinformation · 2026-03-27 · 1 citations

  articleOpen accessSenior author

  • How geospatial foundation models (GFMs) perform in agriculture remains unclear. • We propose a workflow to benchmark Google’s AlphaEarth GFM for agriculture. • AlphaEarth is tested for yield prediction, tillage mapping, and cover crop mapping. • AlphaEarth rivals local models but lacks transferability, interpretability, and stability. • The benchmarking workflow and datasets can readily support future GFM evaluation. Geospatial foundation models (GFMs), pretrained on massive Earth observations (EO), have emerged as a promising approach to overcoming the limitations in existing featurization methods. Although most studies on GFMs have released the source codes and pre-trained weights, their deployment still demands extensive configuration, environment setup, inference EO preparation, and model fine-tuning. More recently, Google DeepMind has introduced AlphaEarth Foundation (AEF), a GFM pre-trained using multi-source EOs across continuous time. An annual and global embedding dataset is produced using AEF that is ready for analysis and modeling. The internal experiments show that AEF embeddings have outperformed operational models in 15 EO tasks without re-training. However, those experiments are mostly about land cover and land use classification. Applying AEF and other GFMs to agricultural monitoring requires an in-depth evaluation in critical agricultural downstream tasks. There is also a lack of comprehensive comparison between the AEF-based models and traditional remote sensing (RS)-based models under different scenarios, which could offer valuable guidance for researchers and practitioners. This study addresses some of these gaps by evaluating AEF embeddings in three agricultural downstream tasks in the U.S., including crop yield prediction, tillage mapping, and cover crop mapping. Datasets are compiled from both public and private sources to comprehensively evaluate AEF embeddings across tasks at different scales and locations, and RS-based models are trained as comparison models. AEF-based models generally exhibit strong performance on all tasks and are competitive with purpose-built RS-based models in yield prediction and county-level tillage mapping when trained on local data. However, we also find several limitations in current AEF embeddings, such as limited spatial transferability compared to RS-based models, low interpretability, and limited time sensitivity. These limitations suggest exercising caution when applying AEF embeddings in agriculture, where time sensitivity, generalizability, and interpretability is important. To our knowledge, this is the first study that systematically implements and evaluates embeddings from GFMs in agricultural downstream tasks across space, time, and spatial resolutions. The evaluation results and analyses can inform the design of future AEF versions and other GFMs and support their applications in agriculture and Earth science domains. Moreover, the proposed benchmarking workflow and datasets can be readily applied to evaluate future GFMs and facilitate their use in agricultural downstream applications.

  PublisherDOI

• A half-century of climate change in major agricultural regions: Trends, impacts, and surprises

  Proceedings of the National Academy of Sciences · 2025-05-05 · 31 citations

  articleOpen access1st authorCorresponding

  Efforts to anticipate and adapt to future climate can benefit from historical experiences. We examine agroclimatic conditions over the past 50 y for five major crops around the world. Most regions experienced rapid warming relative to interannual variability, with 45% of summer and 32% of winter crop area warming by more than two SD (σ). Vapor pressure deficit (VPD), a key driver of plant water stress, also increased in most temperate regions but not in the tropics. Precipitation trends, while important in some locations, were generally below 1σ. Historical climate model simulations show that observed changes in crops’ climate would have been well predicted by models run with historical forcings, with two main surprises: i) models substantially overestimate the amount of warming and drying experienced by summer crops in North America, and ii) models underestimate the increase in VPD in most temperate cropping regions. Linking agroclimatic data to crop productivity, we estimate that climate trends have caused current global yields of wheat, maize, and barley to be 10, 4, and 13% lower than they would have otherwise been. These losses likely exceeded the benefits of CO 2 increases over the same period, whereas CO 2 benefits likely exceeded climate-related losses for soybean and rice. Aggregate global yield losses are very similar to what models would have predicted, with the two biases above largely offsetting each other. Climate model biases in reproducing VPD trends may partially explain the ineffectiveness of some adaptations predicted by modeling studies, such as farmer shifts to longer maturing varieties.

  PublisherDOI

• Crop productivity in southern Africa is stagnant despite moderate climate trends

  Nature Food · 2025-07-15 · 8 citations

  article1st authorCorresponding
  PublisherDOI

• Evaluating restoration success: long-term impact of sustainable land management practices in Ethiopia using synthetic control with matrix completion method

  Environmental Research Letters · 2025-07-01 · 1 citations

  articleOpen accessSenior author

  Abstract Efforts to combat land degradation globally have led to the widespread promotion of sustainable land management practices (SLMPs) aimed at reducing surface runoff and erosion. Despite their extensive implementation, long-term evaluations of these practices are limited, especially in data-scarce regions. In our study, we assess the long-term impact of large-scale SLMPs in Ethiopia using remotely sensed data from the past 24 years on 122 watersheds. Using a synthetic control method that does not require an explicit control group, we find statistically significant positive effects of SLMPs in both wet and dry seasons. These benefits persist at least eight years beyond the intervention period. Our findings highlight the need for multi-season impact assessments. Focusing only on the wet season may overlook key outcomes in dryland regions, underestimating the effectiveness of large-scale, multi-year projects. We further find that effects were most positive in drought-prone agricultural highlands, and that some administrative zones appear more effective than others at implementation. Efficient and affordable monitoring of sustainable agricultural water and land management and watershed conservation is crucial for understanding which interventions are effective and can provide opportunities for alternative financing mechanisms.

  PublisherDOI

Recent grants

• Collaborative Research: Use of Climate Information in International Negotiation for Adaptation Resources

  NSF · $225k · 2011–2015

• Collaborative Research: ESE: Food Price Spikes in a Warming World: Estimating Risks and Policy Responses

  NSF · $132k · 2010–2014

Frequent coauthors

• Marshall Burke

  Stanford University

  141 shared

• Stefano Ermon

  82 shared

• Christopher B. Field

  Palo Alto Institute

  58 shared

• Gregory P. Asner

  University of Hawaii at Hilo

  50 shared

• Sherrie Wang

  48 shared

• George Azzari

  42 shared

• Claudia Tebaldi

  Joint Global Change Research Institute

  39 shared

• Jillian M. Deines

  Pacific Northwest National Laboratory

  29 shared

Labs

• Lobell LabPI

  Not provided

Awards & honors

• Macelwane Medal from the American Geophysical Union (2010)
• Macarthur Fellowship (2013)
• National Academy of Sciences Prize in Food and Agriculture S…
• election to the National Academy of Sciences (2023)