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Unified framework for model-based optimal allocation of crop areas and water

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  • Linker, Raphael

Abstract

The paper presents a model-based optimization scheme for allocation of cropping areas and water. The novelty of the scheme is that rather than using highly simplified models of crop response to deficit irrigation, detailed dynamic models of crop/soil/atmosphere interactions are used to determine the crop water productivity function. While the use of such models has traditionally been considered as prohibitive in terms of computation time, the current scheme circumvents this limitation by using each model independently in a simple multi-objective optimization procedure outside of the main optimization procedure. Once the optimization problem dealing with land and water allocation has been solved, the same models are used to compute optimal irrigation schedules for each crop at each location. During the season, a simplified version of the optimization scheme can be used to update water allocation in response to discrepancies between the actual and forecasted weather or factors such as changes in water quota or crop prices. The proposed scheme is illustrated for a hypothetical farm with four fields near Davis, CA. The model AquaCrop is used to determine optimal cropping and water allocation for simultaneous cultivation of maize and sunflower at that farm.

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  • Linker, Raphael, 2020. "Unified framework for model-based optimal allocation of crop areas and water," Agricultural Water Management, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:agiwat:v:228:y:2020:i:c:s0378377419311229
    DOI: 10.1016/j.agwat.2019.105859
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    References listed on IDEAS

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    7. 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.
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    Cited by:

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    2. 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).
    3. Linker, Raphael & Kisekka, Isaya, 2022. "Concurrent data assimilation and model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 274(C).
    4. Ajay Singh, 2022. "Better Water and Land Allocation for Long-term Agricultural Sustainability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3505-3522, August.
    5. Li, Shuoyang & Yang, Guiyu & Wang, Hao & Song, Xiufang & Chang, Cui & Du, Jie & Gao, Danyang, 2023. "A spatial-temporal optimal allocation method of irrigation water resources considering groundwater level," Agricultural Water Management, Elsevier, vol. 275(C).
    6. Li, Mo & Cao, Xiaoxu & Liu, Dong & Fu, Qiang & Li, Tianxiao & Shang, Ruochen, 2022. "Sustainable management of agricultural water and land resources under changing climate and socio-economic conditions: A multi-dimensional optimization approach," Agricultural Water Management, Elsevier, vol. 259(C).
    7. 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.
    8. Mandal, Uday & Dhar, Anirban & Panda, Sudhindra N., 2021. "Enhancement of sustainable agricultural production system by integrated natural resources management framework under climatic and operational uncertainty," Agricultural Water Management, Elsevier, vol. 252(C).
    9. Li, Mo & Sun, Hao & Liu, Dong & Singh, Vijay P. & Fu, Qiang, 2021. "Multi-scale modeling for irrigation water and cropland resources allocation considering uncertainties in water supply and demand," Agricultural Water Management, Elsevier, vol. 246(C).

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