55 citations · 110 across the 11 of their papers we have counts for
5 papers · 1 filter
Precrop Payoffs: Causal machine learning reveals large but variable yield benefits of crop rotation in major breadbaskets
Dan M. Kluger, Stefania Di Tommaso, David B. Lobell
Building sustainable food systems that are resilient to climate change will require improved agricultural management and policy. One common practice that is well-known to benefit c…
Regression coefficient estimation from remote sensing maps
Kerri Lu, Dan M. Kluger, Stephen Bates +1
Regressions are commonly used in environmental science and economics to identify causal or associative relationships between variables. In these settings, remote sensing-derived ma…
Biases in estimates of air pollution impacts: the role of omitted variables and measurement errors
Dan M. Kluger, David B. Lobell, Art B. Owen
Observational studies often use linear regression to assess the effect of ambient air pollution on outcomes of interest, such as human health indicators or crop yields. Yet polluti…
Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields
Dan M. Kluger, Art B. Owen, David B. Lobell
With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop…
Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions
Dan M. Kluger, Sherrie Wang, David B. Lobell
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…