IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v274y2022ics0378377422004711.html
   My bibliography  Save this article

Concurrent data assimilation and model-based optimization of irrigation scheduling

Author

Listed:
  • Linker, Raphael
  • Kisekka, Isaya

Abstract

Crop models can be combined with optimization algorithms in order to develop management tools. However, such model-based tools are inherently affected by the imperfectness of the model on which they are based. In this paper we describe a procedure in which data assimilation and partial re-parametrization of the model embedded in the optimization procedure used to determine irrigation scheduling are performed before each optimization run. Furthermore, sensitivity analysis is performed before performing data assimilation, which ensures that only influential parameters are adjusted. The procedure was tested via simulation with DSSAT-CROPGRO for a hypothetical processing tomato crop in Davis, CA, in 2010–2019. Several scenarios that differed in terms of the measurements assumed to be available for assimilation (leaf area index, biomass and/or soil water content) were simulated. The results were compared to a benchmark scenario involving a perfect model, as well as a scenario in which data assimilation was not performed. The analysis focused on the overall performance of the irrigation schedule (yield vs. irrigation amount) derived using the model rather on the accuracy of the estimated model parameters. Assimilating weekly measurements of leaf area index led to overall performance that was within 3% of the benchmark performance. Adding weekly measurements of biomass or daily measurements of soil water content did not improve the performance. On the other hand, assimilating only daily soil water content measurements led to poorer results (5% decrease compared to benchmark) and also affected the repeatability of the results. Defining dynamically the subset of parameters for calibration via sensitivity analysis rather than calibrating a fixed subset of parameters or all parameters was beneficial, both in terms of overall performance and repeatability of the results. Overall, concurrent data assimilation and model-based optimization has potential to enhance irrigation scheduling decision making, particularly in water limited environments.

Suggested Citation

  • Linker, Raphael & Kisekka, Isaya, 2022. "Concurrent data assimilation and model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s0378377422004711
    DOI: 10.1016/j.agwat.2022.107924
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422004711
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107924?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. Ioslovich, Ilya & Gutman, Per-Olof & Seginer, Ido, 2004. "Dominant parameter selection in the marginally identifiable case," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 127-136.
    2. Alaa Jamal & Raphael Linker, 2020. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model," Agriculture, MDPI, vol. 10(12), pages 1-22, December.
    3. Linker, Raphael, 2020. "Unified framework for model-based optimal allocation of crop areas and water," Agricultural Water Management, Elsevier, vol. 228(C).
    4. Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    5. Chen, Yuting & Cournède, Paul-Henry, 2014. "Data assimilation to reduce uncertainty of crop model prediction with Convolution Particle Filtering," Ecological Modelling, Elsevier, vol. 290(C), pages 165-177.
    6. Xue, Jingyuan & Bali, Khaled M. & Light, Sarah & Hessels, Tim & Kisekka, Isaya, 2020. "Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize," Agricultural Water Management, Elsevier, vol. 238(C).
    7. Jin, Xiuliang & Li, Zhenhai & Feng, Haikuan & Ren, Zhibin & Li, Shaokun, 2020. "Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model," Agricultural Water Management, Elsevier, vol. 227(C).
    8. Linker, Raphael & Ioslovich, Ilya & Sylaios, Georgios & Plauborg, Finn & Battilani, Adriano, 2016. "Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato," Agricultural Water Management, Elsevier, vol. 163(C), pages 236-243.
    9. 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).
    10. Edmundas Kazimieras Zavadskas & Abbas Mardani & Zenonas Turskis & Ahmad Jusoh & Khalil MD Nor, 2016. "Development of TOPSIS Method to Solve Complicated Decision-Making Problems — An Overview on Developments from 2000 to 2015," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 645-682, May.
    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. Luo, Li & Sun, Shikun & Xue, Jing & Gao, Zihan & Zhao, Jinfeng & Yin, Yali & Gao, Fei & Luan, Xiaobo, 2023. "Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation," Agricultural Systems, Elsevier, vol. 210(C).
    2. 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).
    3. Wang, Weishu & Rong, Yao & Zhang, Chenglong & Wang, Chaozi & Huo, Zailin, 2024. "Data assimilation of soil moisture and leaf area index effectively improves the simulation accuracy of water and carbon fluxes in coupled farmland hydrological model," Agricultural Water Management, Elsevier, vol. 291(C).
    4. 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).
    5. Lu, Yang & Wei, Chunzhu & McCabe, Matthew F. & Sheffield, Justin, 2022. "Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information," Agricultural Water Management, Elsevier, vol. 266(C).
    6. Wu, Hui & Yue, Qiong & Guo, Ping & Xu, Xiaoyu & Huang, Xi, 2022. "Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules," Agricultural Water Management, Elsevier, vol. 266(C).
    7. Alaa Jamal & Raphael Linker, 2020. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model," Agriculture, MDPI, vol. 10(12), pages 1-22, December.
    8. Richwell Mubita Mwiya & Zhanyu Zhang & Chengxin Zheng & Ce Wang, 2020. "Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    9. 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).
    10. Xu, Xianghui & Chen, Yingshan & Zhou, Yan & Liu, Wuyuan & Zhang, Xinrui & Li, Mo, 2023. "Sustainable management of agricultural water rights trading under uncertainty: An optimization-evaluation framework," Agricultural Water Management, Elsevier, vol. 280(C).
    11. Kelly, T.D. & Foster, T. & Schultz, David M., 2023. "Assessing the value of adapting irrigation strategies within the season," Agricultural Water Management, Elsevier, vol. 275(C).
    12. Peddinti, Srinivasa Rao & Kisekka, Isaya, 2022. "Estimation of turbulent fluxes over almond orchards using high-resolution aerial imagery with one and two-source energy balance models," Agricultural Water Management, Elsevier, vol. 269(C).
    13. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    14. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    15. Juan Carlos Martín & Carmen Orden-Cruz & Slimane Zergane, 2020. "Islamic Finance and Halal Tourism: An Unexplored Bridge for Smart Specialization," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    16. Yongli Wang & Xiangyi Zhou & Hao Liu & Xichang Chen & Zixin Yan & Dexin Li & Chang Liu & Jiarui Wang, 2023. "Evaluation of the Maturity of Urban Energy Internet Development Based on AHP-Entropy Weight Method and Improved TOPSIS," Energies, MDPI, vol. 16(13), pages 1-18, July.
    17. Jing Wang & Jian-Qiang Wang & Hong-Yu Zhang & Xiao-Hong Chen, 2017. "Distance-Based Multi-Criteria Group Decision-Making Approaches with Multi-Hesitant Fuzzy Linguistic Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1069-1099, July.
    18. Wanying Zhong & Yue Wang, 2022. "A study on the spatial and temporal variation of urban integrated vulnerability in Southwest China," 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. 114(3), pages 2855-2882, December.
    19. Linker, Raphael & Johnson-Rutzke, Corinne, 2005. "Modeling the effect of abrupt changes in nitrogen availability on lettuce growth, root-shoot partitioning and nitrate concentration," Agricultural Systems, Elsevier, vol. 86(2), pages 166-189, November.
    20. Seyit Ali Erdogan & Jonas Šaparauskas & Zenonas Turskis, 2019. "A Multi-Criteria Decision-Making Model to Choose the Best Option for Sustainable Construction Management," Sustainability, MDPI, vol. 11(8), pages 1-19, April.

    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:274:y:2022:i:c:s0378377422004711. 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.