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Journal Articles

Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions

David Lobell
Remote Sensing of Environment , 2021
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.
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Journal Articles

Cleaner air has contributed one-fifth of US maize and soybean yield gains since 1999

David Lobell, Jennifer Burney
Environmental Research Letters , 2021
Crop productivity is potentially affected by several air pollutants, although these are usually studied in isolation. A significant challenge to understanding the effects of multiple pollutants in many regions is the dearth of air quality data near agricultural fields. Here we empirically estimate the effect of four key pollutants (ozone (O3), particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2)) on maize and soybean yields in the United States using a combination of administrative data and satellite-derived yield estimates. We identify clear negative effects of exposure to O3, PM, and SO2 in both crops, using yields measured in the vicinity of monitoring stations. We also show that while stations measuring NO2 are too sparse to reliably estimate a yield effect, the strong gradient of NO2 concentrations near power plants allows us to more precisely estimate NO2 effects using satellite measured yield gradients. The presence of some powerplants that turn on and others that shut down during the study period are particularly useful for attributing yield gradients to pollution. We estimate that total yield losses from these pollutants averaged roughly 5% for both maize and soybean over the past two decades. While all four pollutants have statistically significant effects, PM and NO2 appear more damaging to crops at current levels than O3 and SO2. Finally, we find that the significant improvement in air quality since 1999 has halved the impact of poor air quality on major crops and contributed to yield increases that represent roughly 20% of overall yield gains over that period.
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Journal Articles

Historical warming has increased U.S. crop insurance losses

Marshall Burke
Environmental Research Letters , 2021
Quantification of the sector-specific financial impacts of historical global warming represents a critical gap in climate change impacts assessment. The multiple decades of county-level data available from the U.S. crop insurance program – which collectively represent aggregate damages to the agricultural sector largely borne by U.S. taxpayers – present a unique opportunity to close this gap. Using econometric analysis in combination with observed and simulated changes in county-level temperature, we show that global warming has already contributed substantially to rising crop insurance losses in the U.S. For example, we estimate that county-level temperature trends have contributed $US2017 23.9 billion – or 17% – of the national-level crop insurance losses over the 1991-2017 period. Further, we estimate that observed warming contributed approximately one third of total losses in the most costly single year (2012). In addition, analyses of a large suite of global climate model simulations yield very high confidence that anthropogenic climate forcing has increased U.S. crop insurance losses. These sector-specific estimates provide important quantitative information about the financial costs of the global warming that has already occurred (including the costs of individual extreme events), as well as the economic value of mitigation and/or adaptation options.
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Journal Articles

Using satellite imagery to understand and promote sustainable development

Marshall Burke, David Lobell
Science , 2021
Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence methods. Drawing on examples mostly from Africa, they conclude that satellite-based methods enhance rather than replace ground-based data collection, and progress depends on a combined approach.
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Journal Articles

A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt

Jillian Deines, David Lobell
Remote Sensing of Environment , 2021
Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.
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Journal Articles

Contribution of historical precipitation change to US flood damages

Marshall Burke, Noah Diffenbaugh
PNAS , 2021

Precipitation extremes have increased in many regions of the United States, suggesting that climate change may be exacerbating the cost of flooding. However, the impact of historical precipitation change on the cost of US flood damages remains poorly quantified. Applying empirical analysis to historical precipitation and flood damages, we estimate that approximately one-third (36%) of the cost of flood damages over 1988 to 2017 is a result of historical precipitation changes.

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Journal Articles

Uniting remote sensing, crop modelling and economics for agricultural risk management

David Lobell
Nature Reviews Earth & Environment , 2021
The increasing availability of satellite data at higher spatial, temporal and spectral resolutions is enabling new applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of their influence on the value of risk-reduction programmes. In this Review, we discuss how approaches to estimate agricultural losses for index insurance have evolved from costly field-sampling-based campaigns towards lower-cost techniques using weather and satellite data. We identify advances in remote sensing and crop modelling for assessing agricultural conditions, but reliably and cheaply assessing production losses remains challenging in complex landscapes. We illustrate how an economic framework can be used to gauge and enhance the value of insurance based on earth-observation data, emphasizing that even as yield-estimation techniques improve, the value of an index insurance contract for the insured depends largely on how well it captures the losses when people suffer most. Strategically improving the collection and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable misestimation complement efforts to detect and protect against large losses.
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Journal Articles

The changing risk and burden of wildfire in the United States

Marshall Burke, Anne Driscoll, Sam Heft-Neal, Jiani Xue
PNAS , 2021

Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from wildfire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the United States. We estimate that nearly 50 million homes are currently in the wildland–urban interface in the United States, a number increasing by 1 million houses every 3 y.

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Journal Articles

Changes in the drought sensitivity of US maize yields

David Lobell, Jillian Deines, Stefania Di Tomasso
Nature Food , 2020

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties.

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Journal Articles

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

Walter Dado, Jillian Deines, David Lobell
Remote Sensing , 2020

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts.

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Journal Articles

Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

David Lobell, Sherrie Wang, Stefania Di Tomasso
Remote Sensing , 2020

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India.

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Journal Articles

Water-food-energy challenges in India: political economy of the sugar industry

Rosamond L. Naylor
Environmental Research Letters , 2020

Sugar is the second largest agro-based industry in India and has a major influence on the country's water, food, and energy security. In this paper, we use a nexus approach to assess India's interconnected water-food-energy challenges, with a specific focus on the political economy of the sugar industry in Maharashtra, one of the country's largest sugar producing states. Our work underscores three points. First, the governmental support of the sugar industry is likely to persist because policymakers are intricately tied to that industry.

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Journal Articles

Fight fire with finance: a randomize field experiment to curtail land-clearing fire in Indonesia

Rosamond L. Naylor
American Economic Association Registry , 2020

This paper presents one of the rst randomized evaluations of collective pay-for-performance payments for ecosystem services. We test whether community-level scal incentives can curtail the use of land-clearing re, a major source of emissions and negative health externalities.

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Working Papers

Leaving the Enclave: Historical Evidence on Immigrant Mobility from the Industrial Removal Office

Ran Abramitzky, Leah Platt Boustan, Dylan Connor
NBER , 2020

We study a program that funded 39,000 Jewish households in New York City to leave enclave neighborhoods circa 1910. Compared to their neighbors with the same occupation and income score at baseline, program participants earned 4 percent more ten years after removal, and these gains persisted to the next generation. Men who left enclaves also married spouses with less Jewish names, but they did not choose less Jewish names for their children. Gains were largest for men who spent more years outside of an enclave.

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Working Papers

The Changing Risk and Burden of Wildfire in the US

Marshall Burke, Anne Driscoll, Jennifer Burney, Sam Heft-Neal, Jenny Xue, Michael Wara
The National Bureau of Economic Research , 2020

Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from fire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the US. We estimate that nearly 50 million homes are currently in the wildland-urban interface in the US, a number increasing by 1 million houses every 3 years.

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Journal Articles

Dust pollution from the Sahara and African infant mortality

Marshall Burke, Sam Heft-Neal
Nature Sustainability , 2020

Estimation of pollution impacts on health is critical for guiding policy to improve health outcomes. Estimation is challenging, however, because economic activity can worsen pollution but also independently improve health outcomes, confounding pollution–health estimates. We leverage variation in exposure to local particulate matter of diameter <2.5 μm (PM2.5) across Sub-Saharan Africa driven by distant dust export from the Sahara, a source uncorrelated with local economic activity.

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Journal Articles

Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity

Eric Lambin
Nature Sustainability , 2020

In response to the important benefits forests provide, there is a growing effort to reforest the world. Past policies and current commitments indicate that many of these forests will be plantations. Since plantations often replace more carbon-rich or biodiverse land covers, this approach to forest expansion may undermine objectives of increased carbon storage and biodiversity. We use an econometric land use change model to simulate the carbon and biodiversity impacts of subsidy driven plantation expansion in Chile between 1986 and 2011.

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Journal Articles

Handgun Ownership and Suicide in California

David M. Studdert, Yifan Zhang, Sonja A. Swanson, Lea Prince, Jonathan Rodden, Erin E. Holsinger, Matthew J. Spittal, Garen J. Wintemute, Matthew Miller
The New England Journal of Medicine , 2020

Research has consistently identified firearm availability as a risk factor for suicide. However, existing studies are relatively small in scale, estimates vary widely, and no study appears to have tracked risks from commencement of firearm ownership.

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Journal Articles

Habitat fragmentation, livelihood behaviors, and contact between people and nonhuman primates in Africa

Eric Lambin
Landscape Ecology , 2020

Deforestation and landscape fragmentation have been identified as processes enabling direct transmission of zoonotic infections. Certain human behaviors provide opportunities for direct contact between humans and wild nonhuman primates (NHPs), but are often missing from studies linking landscape level factors and observed infectious diseases.

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Working Papers

Productivity Dispersion and Persistence Among the World's Most Numerous Firms

Marshall Burke, Casey C. Maue, Kyle J. Emerick
National Bureau of Economic Research , 2020

Marshall Burke and fellow researchers study productivity in smallholder farms to understand variation across the adbundant but understudied firms. They use a novel framework, satellite data, and machine learning to understand such variation, and they find that output measurement error contributes significantly to this discrepancy in productivity.

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Journal Articles

Climate change is increasing the risk of extreme autumn wildfire conditions across California

Noah Diffenbaugh
Environmental Research Letters , 2020

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change.

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Journal Articles

Verification of extreme event attribution: Using out-of-sample observations to assess changes in probabilities of unprecedented events

Noah Diffenbaugh
Science Advances , 2020

Independent verification of anthropogenic influence on specific extreme climate events remains elusive. This study presents a framework for such verification. This framework reveals that previously published results based on a 1961–2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006–2017 period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events.

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Working Papers

Does Information About Climate Risk Affect Property Values?

Marshall Burke
The National Bureau of Economic Research , 2020

Floods and other climate hazards pose a widespread and growing threat to housing and infrastructure around the world. By incorporating climate risk into asset prices, markets can discourage excessive development in hazardous areas. However, the extent to which markets actually price these risks remains poorly understood. Here we measure the effect of information about flood risk on residential property values in the United States.

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Journal Articles

On the role of anthropogenic climate change in the emerging food crisis in southern Africa in the 2019–2020 growing season

David Lobell
Global Change Biology , 2020

Researchers including David Lobell analyze how human-caused climate change has impacted a water deficit in Southern Africa and might contribute to a rising food security crisis in the region.

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