Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework

You, Yanan; Tian, Hanqin; Pan, Shufen; Shi, Hao; Bian, Zihao; Gurgel, Angelo; Huang, Yitao; Kicklighter, David W.; Liang, Xin‐Zhong; Lü, Chaoqun; Melillo, Jerry M.; Miao, Ruiqing; Pan, Naiqing; Reilly, John; Ren, Wei; Xu, Rongting; Yang, Jia; Yu, Qiang; Zhang, Jingting

doi:10.1016/j.agrformet.2022.109144

Cited by 19 publications

(34 citation statements)

References 129 publications

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“…The annual tillage intensity map from 1960 to 2018 was reconstructed from the county‐level tillage practices survey data obtained from the National Crop Residue Management Survey (CRM) of the Conservation Technology Information Center ( https://www.ctic.org/CRM) following You et al. (2022). Tillage maps for missing years were kept the same as the nearest years with available data.…”

Section: Methodsmentioning

confidence: 99%

“…DLEM v4.0 is a highly integrated TBM that couples major biophysical, biogeochemical, and hydrological processes to quantify daily, spatially explicit carbon, water, and nutrient stocks and fluxes in terrestrial ecosystems and inland water systems at site, regional, and global scales (Pan et al., 2021; Tian, Xu, Canadell, et al., 2010; Tian, Xu, Pan, et al., 2020; You et al., 2022). The simulation of terrestrial carbon, water, and nutrient dynamics is driven by multiple environmental forcings (e.g., climate change, atmospheric CO 2 concentration, and N deposition) and various management factors (e.g., N fertilizer use rate, irrigation, and manure application).…”

Section: Methodsmentioning

confidence: 99%

“…We then identified parameters having a significant impact on simulated SOC sequestration rate and N 2 O and CH 4 fluxes (Figures S1–S5). Next, we used the Monte Carlo sampling scheme to generate an ensemble of 100 sets of these sensitive parameters by randomly varying their values within a 20% range of their calibrated values based on their respective probability distribution functions (Tian et al., 2011; You et al., 2022). Finally, we used the generated parameter sets as inputs for DLEM to simulate regional SOC sequestration rate and N 2 O and CH 4 emissions from U.S. croplands.…”

Section: Methodsmentioning

confidence: 99%

“…However, directly extrapolating site‐specific findings to large spatial areas is difficult due to unique environmental and management conditions of each site (Huang et al., 2022). Process‐based terrestrial biosphere models (TBMs), with well‐represented crop growth processes and agricultural management practices (e.g., N fertilization, tillage, irrigation, and rotation), as well as detailed hydrological, biophysical, and biogeochemical processes, can account for effects of spatial and temporal variations in environmental and management conditions on net soil GHG balance at large scales (Bondeau et al., 2007; McDermid et al., 2017; You et al., 2022). However, the simulation performance of TBMs is largely limited by the availability of high‐quality model forcing datasets (i.e., introducing uncertainties in input data), the lack of sufficient data for model calibration and validation (i.e., introducing uncertainties in model parameters), and the inadequate representation of relevant processes in the model (i.e., introducing uncertainties in model structures; Fisher et al., 2014; Gurung et al., 2020; Ogle et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

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Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

You,

Tian,

Pan

et al. 2023

Global Change Biology

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Agricultural soils play a dual role in regulating the Earth's climate by releasing or sequestering carbon dioxide (CO2) in soil organic carbon (SOC) and emitting non‐CO2 greenhouse gases (GHGs) such as nitrous oxide (N2O) and methane (CH4). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC‐sequestered CO2 and non‐CO2 GHG emissions) and the underlying controls. Herein, we used a model‐data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960–2018. Specifically, we used the dynamic land ecosystem model for regional simulations and used field observations of SOC sequestration rates and N2O and CH4 emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered Tg CO2‐C year−1 in SOC (at a depth of 3.5 m) during 1960–2018 and emitted Tg N2O‐N year−1 and Tg CH4‐C year−1, respectively. Based on the GWP100 metric (global warming potential on a 100‐year time horizon), the estimated national net GHG emission rate from agricultural soils was Tg CO2‐eq year−1, with the largest contribution from N2O emissions. The sequestered SOC offset ~28% of the climate‐warming effects resulting from non‐CO2 GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960–2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO2 levels attenuated net GHG emissions from U.S. croplands. Improving management practices to mitigate N2O emissions represents the biggest opportunity for achieving net‐zero emissions in U.S. croplands. Our study highlights the importance of concurrently quantifying SOC‐sequestered CO2 and non‐CO2 GHG emissions for developing effective agricultural climate change mitigation measures.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

You,

Tian,

Pan

et al. 2023

Global Change Biology

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“…Crop models have recently demonstrated the ability to predict genetic yield potential despite significant difficulties (Guarin et al, 2022). The majority of crop models now in use are created for field-scale modeling and simulate agronomic factors using homogenous (average) field conditions (You et al, 2022). However, simulation at the field scale is no longer sufficient to address precision agriculture concerns (Pasquel et al, 2023), confirming the necessity of spatial simulation, which shifts crop model simulation scale from field scale to finer scale (Pasquel et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Kheir,

Mkuhlani,

Mugo

et al. 2023

Agronomy Journal

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Traditional simulation models are often point based, thus more research is needed to emphasize the spatial simulation, providing decision makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine resolution data from coarse resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the Artificial Neural Network (ANN), creating a hybrid modelling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modelling workflows with the spatial APSIM next generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates, and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg Nha−1 to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89), between spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha−1) relative to the potential yield.This article is protected by copyright. All rights reserved

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Adaptation of agriculture to climate change

Miao,

Malikov

2025

Encyclopedia of Energy, Natural Resource, and Environmental Economics

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Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework

Cited by 19 publications

References 129 publications

Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Adaptation of agriculture to climate change

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