TY - JOUR
T1 - Understanding the impact of sub-seasonal meteorological variability on corn yield in the U.S. Corn Belt
AU - Jiang, Hao
AU - Hu, Hao
AU - Wang, Shaowen
AU - Ying, Yibin
AU - Lin, Tao
N1 - Funding Information:
This work was partially funded by National Natural Science Foundation of China under Grant Number 31701316 and China National Key Research and Development Plan under Grant Number 2017YFD0700605 .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Rain-fed corn system has varied optimal environmental requirements by growth phases and regions. Understanding spatiotemporal characteristics of such requirements are important to ensure food security. To capture the stage-variant growing requirements, we develop and compare statistical models with various spatial and temporal resolutions to quantify the relationships between corn yield and meteorological factors. Multilinear regression models are trained using cross-sectional datasets pooled at three magnitudes (state, district, county) with temperature and precipitation related predictors according to three temporal resolutions (growing season, fixed month, growing phase). The models are applied to the U.S. Corn Belt for the time period of 1981–2016. The results show that average corn yield variation explained by meteorological factors can be improved to 50.2% at the agricultural district scale with growth phase resolution from ~30% at the state-level with growing season resolution. The results reveal that corn yield is most sensitive to extreme heat stress during the grain filling phase. From a spatial perspective, the northern counties in the U.S. Corn Belt are less limited by precipitation resources but are more vulnerable to extreme heat. The spatiotemporal explicit statistic modeling approach quantifies the impact and adaptation potential of changing the planting date for production. Appropriate adaptions by changing plant dates can increase the potential of corn production by 0.87 million Mg year−1 in the Corn Belt.
AB - Rain-fed corn system has varied optimal environmental requirements by growth phases and regions. Understanding spatiotemporal characteristics of such requirements are important to ensure food security. To capture the stage-variant growing requirements, we develop and compare statistical models with various spatial and temporal resolutions to quantify the relationships between corn yield and meteorological factors. Multilinear regression models are trained using cross-sectional datasets pooled at three magnitudes (state, district, county) with temperature and precipitation related predictors according to three temporal resolutions (growing season, fixed month, growing phase). The models are applied to the U.S. Corn Belt for the time period of 1981–2016. The results show that average corn yield variation explained by meteorological factors can be improved to 50.2% at the agricultural district scale with growth phase resolution from ~30% at the state-level with growing season resolution. The results reveal that corn yield is most sensitive to extreme heat stress during the grain filling phase. From a spatial perspective, the northern counties in the U.S. Corn Belt are less limited by precipitation resources but are more vulnerable to extreme heat. The spatiotemporal explicit statistic modeling approach quantifies the impact and adaptation potential of changing the planting date for production. Appropriate adaptions by changing plant dates can increase the potential of corn production by 0.87 million Mg year−1 in the Corn Belt.
KW - Corn yield
KW - Growth phase
KW - Meteorology
KW - Spatiotemporal resolution
KW - Statistical analysis
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U2 - 10.1016/j.scitotenv.2020.138235
DO - 10.1016/j.scitotenv.2020.138235
M3 - Article
C2 - 32268290
AN - SCOPUS:85082674505
VL - 724
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 138235
ER -