Diverging climate response of corn yield and carbon use efficiency across the U.S
Bibliographic Data
| ID | 15550300 |
|---|---|
| Authors | Shuo Yu (0009-0008-3346-7527, University of California, Berkeley, corresponding author), Nicola Falco (0000-0003-3307-6098, Lawrence Berkeley National Laboratory), Nivedita Patel (New York University), Yuxin Wu (0000-0003-3681-1473, Lawrence Berkeley National Laboratory), Haruko Wainwright (0000-0002-2140-6072, Lawrence Berkeley National Laboratory) |
| Year | 2023 |
| Volume | 18 |
| Issue | 6 |
| Pages | 064049-064049 |
| Publication date | 2023-05-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/acd5e4 |
| OpenAlex | W4376636516 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing
Agriculture · Agronomy · Atmospheric sciences · Climate change · Climatology · Crop yield · Ecosystem · Geography · Growing degree-day · Meteorology · Moderate-resolution imaging spectroradiometer · Phenology · Physical geography · Precipitation · Primary production · Productivity · Satellite · Yield (engineering · Agricultural risk and resilience · Agriculture Sustainability and Environmental Impact · Climate change impacts on agriculture · Environmental Science · Ecology
Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison
A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture
Random Forests
The Impact of Global Warming on U.S. Agriculture
Shifting agricultural practices to produce sustainable, low carbon intensity feedstocks for biofuel production
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |