IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v291y2024ics0378377423005103.html
   My bibliography  Save this article

Improving crop model accuracy in the development of regional irrigation and nitrogen schedules by using data assimilation and spatial clustering algorithms

Author

Listed:
  • Wang, Yongqiang
  • Sun, Kexin
  • Gao, Yunhe
  • Liu, Ruizhe
  • Shen, Hongzheng
  • Xing, Xuguang
  • Ma, Xiaoyi

Abstract

Crop growth models have been used to develop irrigation and nitrogen schedules (INSs). However, differences in crop cultivar coefficients are often ignored in the development of regional INSs. This study aimed to formulate suitable INSs under spatial heterogeneities in crop cultivar coefficients. Therefore, we propose two strategies for retrieving maize cultivar coefficients by using a data assimilation algorithm and spatial clustering algorithm. The first strategy involves determining the cultivar coefficients for each simulation unit in the examined region and then assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm, with the cultivar coefficients employed as clustering characteristics (CAs). The second strategy involves assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm on the basis of cultivar coefficients and geographical characteristics (CAGCs). By using observational data, the accuracy of cultivar coefficients CAs and CAGCs was compared with that of commonly used regional representative coefficients (RRs) and sub-region representative coefficients (SRRs). Furthermore, we formulated INSs with these four coefficients by using yield maximization as the objective and water use efficiency (WUE) and nitrogen use efficiency (NUE) as constraints. We examined the differences between the INSs, yields, WUE values, and NUE values obtained using each of the aforementioned four coefficients and those obtained using a point optimization approach. The results revealed that the highest accuracy in the simulation of the regional leaf area index, yield, and phenological stage was exhibited by the CAGCs-based strategy, followed by the CAs-based strategy. The RRs-based and SRRs-based strategies produced considerable errors. Crucially, the INSs obtained using the CAGCs-based strategy were more similar to those obtained through point optimization and more reasonable than were the INSs obtained using the other three strategies. In addition, more accurate yield, WUE, and NUE values were obtained with the CAGCs-based strategy than with the other three coefficients-based strategies. The results of this study indicate that the combination of a data assimilation algorithm and spatial clustering algorithm can improve the application potential of crop models in agricultural systems.

Suggested Citation

  • Wang, Yongqiang & Sun, Kexin & Gao, Yunhe & Liu, Ruizhe & Shen, Hongzheng & Xing, Xuguang & Ma, Xiaoyi, 2024. "Improving crop model accuracy in the development of regional irrigation and nitrogen schedules by using data assimilation and spatial clustering algorithms," Agricultural Water Management, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:agiwat:v:291:y:2024:i:c:s0378377423005103
    DOI: 10.1016/j.agwat.2023.108645
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423005103
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2023.108645?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kropp, Ian & Nejadhashemi, A. Pouyan & Deb, Kalyanmoy & Abouali, Mohammad & Roy, Proteek C. & Adhikari, Umesh & Hoogenboom, Gerrit, 2019. "A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification," Agricultural Systems, Elsevier, vol. 173(C), pages 289-302.
    2. Xu, Jiatun & Cai, Huanjie & Wang, Xiaoyun & Ma, Chenguang & Lu, Yajun & Ding, Yibo & Wang, Xiaowen & Chen, Hui & Wang, Yunfei & Saddique, Qaisar, 2020. "Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    4. Guo, Daxin & Olesen, Jørgen Eivind & Manevski, Kiril & Ma, Xiaoyi, 2021. "Optimizing irrigation schedule in a large agricultural region under different hydrologic scenarios," Agricultural Water Management, Elsevier, vol. 245(C).
    5. Guo, Ruiping & Lin, Zhonghui & Mo, Xingguo & Yang, Chunlin, 2010. "Responses of crop yield and water use efficiency to climate change in the North China Plain," Agricultural Water Management, Elsevier, vol. 97(8), pages 1185-1194, August.
    6. Sun, Shuang & Yang, Xiaoguang & Lin, Xiaomao & Sassenrath, Gretchen F. & Li, Kenan, 2018. "Climate-smart management can further improve winter wheat yield in China," Agricultural Systems, Elsevier, vol. 162(C), pages 10-18.
    7. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yongqiang & Huang, Donghua & Sun, Kexin & Shen, Hongzheng & Xing, Xuguang & Liu, Xiao & Ma, Xiaoyi, 2023. "Multiobjective optimization of regional irrigation and nitrogen schedules by using the CERES-Maize model with crop parameters determined from the remotely sensed leaf area index," Agricultural Water Management, Elsevier, vol. 286(C).
    2. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Zhang, Chao & Xie, Ziang & Wang, Qiaojuan & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2022. "AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity," Agricultural Water Management, Elsevier, vol. 266(C).
    4. Luca Zanni, 2006. "An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines," Computational Management Science, Springer, vol. 3(2), pages 131-145, April.
    5. Li, Sien & Kang, Shaozhong & Zhang, Lu & Du, Taisheng & Tong, Ling & Ding, Risheng & Guo, Weihua & Zhao, Peng & Chen, Xia & Xiao, Huan, 2015. "Ecosystem water use efficiency for a sparse vineyard in arid northwest China," Agricultural Water Management, Elsevier, vol. 148(C), pages 24-33.
    6. Peng Han & Xinyue Yang & Yifei Zhao & Xiangmin Guan & Shengjie Wang, 2022. "Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network," Sustainability, MDPI, vol. 14(9), pages 1-13, May.
    7. Rashid, Muhammad Adil & Jabloun, Mohamed & Andersen, Mathias Neumann & Zhang, Xiying & Olesen, Jørgen Eivind, 2019. "Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 222(C), pages 193-203.
    8. Karner, Katrin & Schmid, Erwin & Schneider, Uwe A. & Mitter, Hermine, 2021. "Computing stochastic Pareto frontiers between economic and environmental goals for a semi-arid agricultural production region in Austria," Ecological Economics, Elsevier, vol. 185(C).
    9. Li, Cheng & Luo, Xiaoqi & Li, Yue & Wang, Naijiang & Zhang, Tibin & Dong, Qin’ge & Feng, Hao & Zhang, Wenxin & Siddique, Kadambot H.M., 2023. "Ridge planting with transparent plastic mulching improves maize productivity by regulating the distribution and utilization of soil water, heat, and canopy radiation in arid irrigation area," Agricultural Water Management, Elsevier, vol. 280(C).
    10. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    11. Guo, Qinghua & Wu, Wenliang, 2024. "Dynamics of soil water and nitrate within the vadose zone simulated by the WHCNS model calibrated based on deep learning," Agricultural Water Management, Elsevier, vol. 292(C).
    12. Fanqi Zou & Tinghui Li, 2022. "The Impact of Agricultural Ecological Capital Investment on the Development of Green Circular Economy," Agriculture, MDPI, vol. 12(4), pages 1-21, March.
    13. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    14. Tianrui Yin & Wei Chen & Bo Liu & Changzhen Li & Luyao Du, 2023. "Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    15. Parisa Paymard & Mohammad Bannayan & Reza Sadrabadi Haghighi, 2018. "Analysis of the climate change effect on wheat production systems and investigate the potential of management strategies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 1237-1255, April.
    16. El Chami, D. & Daccache, A., 2015. "Assessing sustainability of winter wheat production under climate change scenarios in a humid climate — An integrated modelling framework," Agricultural Systems, Elsevier, vol. 140(C), pages 19-25.
    17. Mohammad Bannayan & Ehsan Eyshi Rezaei, 2014. "Future production of rainfed wheat in Iran (Khorasan province): climate change scenario analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 19(2), pages 211-227, February.
    18. Jialing Yu & Jian Wu, 2018. "The Sustainability of Agricultural Development in China: The Agriculture–Environment Nexus," Sustainability, MDPI, vol. 10(6), pages 1-17, May.
    19. Ding, Dianyuan & Yang, Zijie & Wu, Lihong & Zhao, Ying & Zhang, Xi & Chen, Xiaoping & Feng, Hao & Zhang, Chao & Wendroth, Ole, 2024. "Optimizing nitrogen-fertilizer management by using RZWQM2 with consideration of precipitation can enhance nitrogen utilization on the Loess Plateau," Agricultural Water Management, Elsevier, vol. 299(C).
    20. Wang, Weiguang & Yu, Zhongbo & Zhang, Wei & Shao, Quanxi & Zhang, Yiwei & Luo, Yufeng & Jiao, Xiyun & Xu, Junzeng, 2014. "Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: Historical simulation and future projections," Agricultural Water Management, Elsevier, vol. 146(C), pages 249-261.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:291:y:2024:i:c:s0378377423005103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.