Divergent responses of maize yield to precipitation in the United States
Bibliographic Data
| ID | 15550671 |
|---|---|
| Authors | Ru Xu (0000-0002-5203-5980, Beijing Normal University), Yan Li (0000-0001-6650-7726, Beijing Normal University, corresponding author), Kaiyu Guan (0000-0002-3499-6382, University of Illinois Urbana-Champaign), Lei Zhao (0000-0001-9914-0385, University of Illinois Urbana-Champaign), Bin Peng (0000-0001-5672-2086, University of Illinois Urbana-Champaign), Chiyuan Miao (0000-0001-6413-7020, Beijing Normal University), Bojie Fu (0000-0002-9920-9802, Beijing Normal University) |
| Year | 2021 |
| Volume | 17 |
| Issue | 1 |
| Pages | 014016-014016 |
| Publication date | 2021-11-24 |
| Peer Reviewed | Yes |
| 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/ac3cee |
| OpenAlex | W3216799091 |
| Language | EN |
| References cited | 56 |
How maize yield response to precipitation varies across a large spatial scale is unclear compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. This study provides a spatial-explicit quantification of maize yield response to precipitation in the contiguous United States and investigates how precipitation response is altered by natural and human factors using statistical and crop model data. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39%), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier, cooler, and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ∼90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change
Agronomy · Atmospheric sciences · Biology · Climatology · Crop · Crop yield · Geography · Irrigation · Meteorology · Precipitation · Spatial ecology · Spatial variability · Statistics · Yield (engineering · Agricultural risk and resilience · Climate change impacts on agriculture · Climate variability and models · Environmental Science · Mathematics · Ecology · Geology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |