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Advances in regional-scale crop growth and associated process modeling.

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Title: Advances in regional-scale crop growth and associated process modeling.
Authors: Liu, Wenfeng1,2,3 (AUTHOR) wenfeng.liu@cau.edu.cn, Bai, Yawei1,2,3 (AUTHOR), Du, Taisheng1,2,3 (AUTHOR), Li, Mengxue1,2,3 (AUTHOR), Yang, Hong4 (AUTHOR), Chen, Shichao1,2,3 (AUTHOR), Liang, Chuanbin1,2,3 (AUTHOR), Kang, Shaozhong1,2,3 (AUTHOR)
Source: SCIENCE CHINA Earth Sciences. Mar2025, Vol. 68 Issue 3, p653-668. 16p.
Subjects: Sustainable agriculture, Greenhouse gases, Nonpoint source pollution, Crop physiology, Crop growth, Human activity recognition
Abstract: In the context of global change, ensuring national food security and achieving sustainable development of agricultural production systems have become major challenges worldwide. To address these issues, regional-scale crop growth and associated process (CROP-AP) models, with their robust simulation and predictive capabilities, have emerged as important tools for studying a wide range of issues relating to agricultural production at river basin, national, and even global scales. Here, we provide a systematic review of the advances of regional-scale CROP-AP models. First, regional-scale CROP-AP models are categorized based on model characteristics: statistical models, crop growth models, hydrology-crop coupling models, and ecosystem models. The origin, development, principle, structure, and application of each model type are introduced. Then, the main functions of regional-scale CROP-AP models are critically reviewed from five aspects: crop yield prediction, crop water consumption, agricultural non-point source pollution, greenhouse gas emissions, and climate change impact and responses. Finally, the future development trends and research priorities of regional-scale CROP-AP models are explored from six key perspectives: model validation and calibration, the ability to simulate the coupling of crop physiology and human activities, enhancing model scalability, multi-model ensembles, data and code sharing, and the integration of artificial intelligence. This review aims to provide comprehensive references and insights for the further development and application of large-scale, high-precision CROP-AP models. [ABSTRACT FROM AUTHOR]
Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Advances in regional-scale crop growth and associated process modeling.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Wenfeng%22">Liu, Wenfeng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> wenfeng.liu@cau.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bai%2C+Yawei%22">Bai, Yawei</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Du%2C+Taisheng%22">Du, Taisheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Mengxue%22">Li, Mengxue</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Hong%22">Yang, Hong</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Shichao%22">Chen, Shichao</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Chuanbin%22">Liang, Chuanbin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kang%2C+Shaozhong%22">Kang, Shaozhong</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22SCIENCE+CHINA+Earth+Sciences%22">SCIENCE CHINA Earth Sciences</searchLink>. Mar2025, Vol. 68 Issue 3, p653-668. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Sustainable+agriculture%22">Sustainable agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Greenhouse+gases%22">Greenhouse gases</searchLink><br /><searchLink fieldCode="DE" term="%22Nonpoint+source+pollution%22">Nonpoint source pollution</searchLink><br /><searchLink fieldCode="DE" term="%22Crop+physiology%22">Crop physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Crop+growth%22">Crop growth</searchLink><br /><searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink>
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  Label: Abstract
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  Data: In the context of global change, ensuring national food security and achieving sustainable development of agricultural production systems have become major challenges worldwide. To address these issues, regional-scale crop growth and associated process (CROP-AP) models, with their robust simulation and predictive capabilities, have emerged as important tools for studying a wide range of issues relating to agricultural production at river basin, national, and even global scales. Here, we provide a systematic review of the advances of regional-scale CROP-AP models. First, regional-scale CROP-AP models are categorized based on model characteristics: statistical models, crop growth models, hydrology-crop coupling models, and ecosystem models. The origin, development, principle, structure, and application of each model type are introduced. Then, the main functions of regional-scale CROP-AP models are critically reviewed from five aspects: crop yield prediction, crop water consumption, agricultural non-point source pollution, greenhouse gas emissions, and climate change impact and responses. Finally, the future development trends and research priorities of regional-scale CROP-AP models are explored from six key perspectives: model validation and calibration, the ability to simulate the coupling of crop physiology and human activities, enhancing model scalability, multi-model ensembles, data and code sharing, and the integration of artificial intelligence. This review aims to provide comprehensive references and insights for the further development and application of large-scale, high-precision CROP-AP models. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11430-024-1477-2
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        Text: English
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        Type: general
      – SubjectFull: Greenhouse gases
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      – SubjectFull: Nonpoint source pollution
        Type: general
      – SubjectFull: Crop physiology
        Type: general
      – SubjectFull: Crop growth
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      – SubjectFull: Human activity recognition
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              Text: Mar2025
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              Y: 2025
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