TY - JOUR
T1 - Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States
T2 - Toward a unified modeling framework
AU - You, Yongfa
AU - Tian, Hanqin
AU - Pan, Shufen
AU - Shi, Hao
AU - Bian, Zihao
AU - Gurgel, Angelo
AU - Huang, Yawen
AU - Kicklighter, David
AU - Liang, Xin Zhong
AU - Lu, Chaoqun
AU - Melillo, Jerry
AU - Miao, Ruiqing
AU - Pan, Naiqing
AU - Reilly, John
AU - Ren, Wei
AU - Xu, Rongting
AU - Yang, Jia
AU - Yu, Qiang
AU - Zhang, Jingting
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/10/15
Y1 - 2022/10/15
N2 - Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales.
AB - Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales.
KW - Crop growth
KW - Cross-scale
KW - Dynamic Land Ecosystem Model
KW - Management practice
KW - Yield
UR - http://www.scopus.com/inward/record.url?scp=85138494320&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138494320&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2022.109144
DO - 10.1016/j.agrformet.2022.109144
M3 - Article
AN - SCOPUS:85138494320
SN - 0168-1923
VL - 325
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 109144
ER -