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Concurrent data assimilation and model-based optimization of irrigation scheduling

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  • 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
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    References listed on IDEAS

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    1. 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).
    2. 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).
    3. Linker, Raphael, 2020. "Unified framework for model-based optimal allocation of crop areas and water," Agricultural Water Management, Elsevier, vol. 228(C).
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    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.
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