How can machine learning help in understanding the impact of climate change on crop yields?

Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models

(2022) Xiaomeng Yin et al.   Environmental Research Letters

On the relative importance of climatic and non-climatic factors in crop yield models

(2022) Balsher Singh Sidhu et al.   CLIMATIC CHANGE

The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning

(2020) Alexis Hoffman et al.   Environmental Research Letters

The effects of climate extremes on global agricultural yields

(2019) Elisabeth Vogel et al.   Environmental Research Letters

Sensitivity of grain yields to historical climate variability in India

(2019) Kyle Frankel Davis et al.   Environmental Research Letters

Unpacking the climatic drivers of US agricultural yields

(2019) Ariel Ortiz-Bobea et al.   Environmental Research Letters

Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields

(2019) Xiao Zhu et al.   Environmental Research Letters

The role of irrigation in changing wheat yields and heat sensitivity in India

(2019) Esha Zaveri et al.   Nature Communications

Points of Significance: Statistics versus machine learning

(2018) Danilo Bzdok et al.   NATURE METHODS

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production

(2018) Meetpal S. Kukal et al.   Scientific Reports

Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

(2018) Andrew Crane-Droesch   Environmental Research Letters

Comparing estimates of climate change impacts from process-based and statistical crop models

(2017) David B Lobell et al.   Environmental Research Letters

More uneven distributions overturn benefits of higher precipitation for crop yields

(2016) Ram Fishman   Environmental Research Letters

Random Forests for Global and Regional Crop Yield Predictions

(2016) Jig Han Jeong et al.   PLoS One

Placing bounds on extreme temperature response of maize

(2015) Christopher J Anderson et al.   Environmental Research Letters

Assessing the relative importance of climate variables to rice yield variation using support vector machines

(2015) Hui Chen et al.   THEORETICAL AND APPLIED CLIMATOLOGY

Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest

(2014) D. B. Lobell et al.   SCIENCE

Predictive ability of machine learning methods for massive crop yield prediction

(2014) Alberto Gonzalez-Sanchez et al.   SPANISH JOURNAL OF AGRICULTURAL RESEARCH

Adaptation of US maize to temperature variations

(2012) Ethan E. Butler et al.   Nature Climate Change

Climate Trends and Global Crop Production Since 1980

(2011) D. B. Lobell et al.   SCIENCE

Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change

(2009) W. Schlenker et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A working guide to boosted regression trees

(2008) J. Elith et al.   JOURNAL OF ANIMAL ECOLOGY