IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v286y2023ics0378377423002512.html
   My bibliography  Save this article

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

Author

Listed:
  • Wang, Yongqiang
  • Huang, Donghua
  • Sun, Kexin
  • Shen, Hongzheng
  • Xing, Xuguang
  • Liu, Xiao
  • Ma, Xiaoyi

Abstract

Using crop models to develop reasonable irrigation and nitrogen schedules (INSs) is critical for achieving high yields and maximizing water use efficiency (WUE) and nitrogen use efficiency. However, the INSs developed based on crop parameters determined through field experiments have low robustness at the regional scale. Remote sensing data indicate the growth conditions of regional crops, and representative regional crop parameters can be determined using remote sensing data. The objective of the present study was to develop a regional INS by adopting a crop model with crop parameters calibrated using the remotely sensed leaf area index (LAI). First, the crop parameters of a field-scale crop model were calibrated on the basis of field-scale trial data for four summer maize growing seasons. Sensitive crop parameters at the regional scale were then identified using an assimilation algorithm based on the remotely sensed LAI. The rationality of using the remotely sensed LAI for determining regional crop parameters was demonstrated by simulating regional maize growth. Finally, a regional-scale crop model was developed and coupled with a multiobjective genetic algorithm to optimize the regional INS for different typical-rainfall years. The results indicated that the regional-scale crop model was more accurate than was the field-scale crop model in simulating the regional LAI, plant height, soil water content, and yield and the corresponding RMSE decreased by 0.50 m2/m2, 0.15 m, 0.02 cm3/cm3, 344.11 kg/ha. Compared with the INSs currently used by farmers, the optimized INS was associated with a marginally higher irrigation quantity (average increase of 2.66%) but a significantly lower nitrogen application rate (average decrease of 31.6%). The crop yield, WUE, and partial factor productivity for nitrogen were 6.35–12.51%, 2.64–10.41%, and 61.8–66.2% higher, respectively, in different typical-rainfall years when using the optimized INS than when using an unoptimized INS. The results of this study indicate that a regional INS established on the basis of regional crop model parameters estimated using remote sensing data can aid irrigation and nitrogen application scheduling.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:agiwat:v:286:y:2023:i:c:s0378377423002512
    DOI: 10.1016/j.agwat.2023.108386
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423002512
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2023.108386?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. Brown, Peter D. & Cochrane, Thomas A. & Krom, Thomas D., 2010. "Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing," Agricultural Water Management, Elsevier, vol. 97(6), pages 892-900, June.
    3. Shaista Nosheen & Iqra Ajmal & Yuanda Song, 2021. "Microbes as Biofertilizers, a Potential Approach for Sustainable Crop Production," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    4. Xin Zhang & Eric A. Davidson & Denise L. Mauzerall & Timothy D. Searchinger & Patrice Dumas & Ye Shen, 2015. "Managing nitrogen for sustainable development," Nature, Nature, vol. 528(7580), pages 51-59, December.
    5. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Lu, Junsheng & Hu, Tiantian & Zhang, Baocheng & Wang, Li & Yang, Shuohuan & Fan, Junliang & Yan, Shicheng & Zhang, Fucang, 2021. "Nitrogen fertilizer management effects on soil nitrate leaching, grain yield and economic benefit of summer maize in Northwest China," Agricultural Water Management, Elsevier, vol. 247(C).
    7. Wen, Yeqiang & Shang, Songhao & Yang, Jian, 2017. "Optimization of irrigation scheduling for spring wheat with mulching and limited irrigation water in an arid climate," Agricultural Water Management, Elsevier, vol. 192(C), pages 33-44.
    8. 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).
    9. Malik, Wafa & Isla, Ramon & Dechmi, Farida, 2019. "DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 213(C), pages 298-308.
    10. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
    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 & 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).
    2. Ma, Chao & Wang, Jun & Li, Jiusheng, 2023. "Utilization of soil and fertilizer nitrogen supply under mulched drip irrigation with various water qualities in arid regions," Agricultural Water Management, Elsevier, vol. 280(C).
    3. Yucong Geng & Muhammad Amjad Bashir & Ying Zhao & Jianhang Luo & Xiaotong Liu & Feng Li & Hongyuan Wang & Qurat-Ul-Ain Raza & Abdur Rehim & Xuejun Zhang & Hongbin Liu, 2022. "Long-Term Fertilizer Reduction in Greenhouse Tomato-Cucumber Rotation System to Assess N Utilization, Leaching, and Cost Efficiency," Sustainability, MDPI, vol. 14(8), pages 1-15, April.
    4. Wang, Hongzhang & Ren, Hao & Zhang, Lihua & Zhao, Yali & Liu, Yuee & He, Qijin & Li, Geng & Han, Kun & Zhang, Jiwang & Zhao, Bin & Ren, Baizhao & Liu, Peng, 2023. "A sustainable approach to narrowing the summer maize yield gap experienced by smallholders in the North China Plain," Agricultural Systems, Elsevier, vol. 204(C).
    5. Li, Xuemin & Zhang, Jingwen & Cai, Ximing & Huo, Zailin & Zhang, Chenglong, 2023. "Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation," Agricultural Water Management, Elsevier, vol. 276(C).
    6. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    7. Zheng, Jing & Fan, Junliang & Zhou, Minghua & Zhang, Fucang & Liao, Zhenqi & Lai, Zhenlin & Yan, Shicheng & Guo, Jinjin & Li, Zhijun & Xiang, Youzhen, 2022. "Ridge-furrow plastic film mulching enhances grain yield and yield stability of rainfed maize by improving resources capture and use efficiency in a semi-humid drought-prone region," Agricultural Water Management, Elsevier, vol. 269(C).
    8. Mahboobe Ghobadi & Mahdi Gheysari & Mohammad Shayannejad & Hamze Dokoohaki, 2023. "Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    9. 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.
    10. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    11. Paul L. G. Vlek & Asia Khamzina & Hossein Azadi & Anik Bhaduri & Luna Bharati & Ademola Braimoh & Christopher Martius & Terry Sunderland & Fatemeh Taheri, 2017. "Trade-Offs in Multi-Purpose Land Use under Land Degradation," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
    12. Wang, Han & Xiang, Youzhen & Liao, Zhenqi & Wang, Xin & Zhang, Xueyan & Huang, Xiangyang & Zhang, Fucang & Feng, Li, 2024. "Integrated assessment of water-nitrogen management for winter oilseed rape production in Northwest China," Agricultural Water Management, Elsevier, vol. 298(C).
    13. Xin Nie & Jianxian Wu & Han Wang & Weijuan Li & Chengdao Huang & Lihua Li, 2022. "Contributing to carbon peak: Estimating the causal impact of eco‐industrial parks on low‐carbon development in China," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1578-1593, August.
    14. Zhen, Wei & Qin, Quande & Miao, Lu, 2023. "The greenhouse gas rebound effect from increased energy efficiency across China's staple crops," Energy Policy, Elsevier, vol. 173(C).
    15. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    16. Dániel Fróna & János Szenderák & Mónika Harangi-Rákos, 2019. "The Challenge of Feeding the World," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    17. Jiamin Liu & Xiaoyu Ma & Bin Zhao & Qi Cui & Sisi Zhang & Jiaoning Zhang, 2023. "Mandatory Environmental Regulation, Enterprise Labor Demand and Green Innovation Transformation: A Quasi-Experiment from China’s New Environmental Protection Law," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
    18. Otavio Ananias Pereira da Silva & Dayane Bortoloto da Silva & Marcelo Carvalho Minhoto Teixeira-Filho & Tays Batista Silva & Cid Naudi Silva Campos & Fabio Henrique Rojo Baio & Gileno Brito de Azevedo, 2023. "Macro- and Micronutrient Contents and Their Relationship with Growth in Six Eucalyptus Species," Sustainability, MDPI, vol. 15(22), pages 1-12, November.
    19. David I. Stern, 2017. "The environmental Kuznets curve after 25 years," Journal of Bioeconomics, Springer, vol. 19(1), pages 7-28, April.
    20. Anna Lungarska & Thierry Brunelle & Raja Chakir & Pierre‐Alain Jayet & Rémi Prudhomme & Stéphane De Cara & Jean‐Christophe Bureau, 2023. "Halving mineral nitrogen use in European agriculture: Insights from multi‐scale land‐use models," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1529-1550, September.

    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:286:y:2023:i:c:s0378377423002512. 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.