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. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. 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).
    6. 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.
    7. 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.
    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. Dono, Gabriele & Cortignani, Raffaele & Doro, Luca & Giraldo, Luca & Ledda, Luigi & Pasqui, Massimiliano & Roggero, Pier Paolo, 2013. "Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems," Agricultural Systems, Elsevier, vol. 117(C), pages 1-12.
    5. 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.
    6. 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.
    7. Azam Lashkari & Amin Alizadeh & Ehsan Rezaei & Mohammad Bannayan, 2012. "Mitigation of climate change impacts on maize productivity in northeast of Iran: a simulation study," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 17(1), pages 1-16, January.
    8. 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.
    9. 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.
    10. 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).
    11. Ma, L. & Ahuja, L.R. & Islam, A. & Trout, T.J. & Saseendran, S.A. & Malone, R.W., 2017. "Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation," Agricultural Water Management, Elsevier, vol. 180(PA), pages 88-98.
    12. Lu, Junsheng & Geng, Chenming & Cui, Xiaolu & Li, Mengyue & Chen, Shuaihong & Hu, Tiantian, 2021. "Response of drip fertigated wheat-maize rotation system on grain yield, water productivity and economic benefits using different water and nitrogen amounts," Agricultural Water Management, Elsevier, vol. 258(C).
    13. 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).
    14. 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.
    15. Chen, Mengting & Linker, Raphael & Wu, Conglin & Xie, Hua & Cui, Yuanlai & Luo, Yufeng & Lv, Xinwei & Zheng, Shizong, 2022. "Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data," Agricultural Water Management, Elsevier, vol. 272(C).
    16. Zhu, Hongyan & Zheng, Bingyan & Nie, Weibo & Fei, Liangjun & Shan, Yuyang & Li, Ge & Liang, Fei, 2024. "Optimization of maize irrigation strategy in Xinjiang, China by AquaCrop based on a four-year study," Agricultural Water Management, Elsevier, vol. 297(C).
    17. Alary, Véronique & Messad, Samir & Aboul-Naga, Adel & Osman, Mona A. & H. Abdelsabour, Taha & Salah, Abdel-Aal E. & Juanes, Xavier, 2020. "Multi-criteria assessment of the sustainability of farming systems in the reclaimed desert lands of Egypt," Agricultural Systems, Elsevier, vol. 183(C).
    18. Hoi-Ming Chi & Okan K. Ersoy & Herbert Moskowitz & Kemal Altinkemer, 2007. "Toward Automated Intelligent Manufacturing Systems (AIMS)," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 302-312, May.
    19. Haowei Sun & Jinghan Ma & Li Wang, 2023. "Changes in per capita wheat production in China in the context of climate change and population growth," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(3), pages 597-612, June.
    20. Yan, Fulai & Zhang, Fucang & Fan, Xingke & Fan, Junliang & Wang, Ying & Zou, Haiyang & Wang, Haidong & Li, Guodong, 2021. "Determining irrigation amount and fertilization rate to simultaneously optimize grain yield, grain nitrogen accumulation and economic benefit of drip-fertigated spring maize in northwest China," Agricultural Water Management, Elsevier, vol. 243(C).

    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.