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1 Kelly, T. D.; Foster, T. 2021. AquaCrop-OSPy: bridging the gap between research and practice in crop-water modeling. Agricultural Water Management, 254:106976. [doi: https://doi.org/10.1016/j.agwat.2021.106976]
Crop modelling ; Crop water use ; Optimization methods ; Irrigation scheduling ; Water demand ; Water management ; Climate change ; Soil moisture ; Simulation / USA
(Location: IWMI HQ Call no: e-copy only Record No: H050484)
https://www.sciencedirect.com/science/article/pii/S0378377421002419/pdfft?md5=f0ca8b964b513f3c54f1aeb9868d5e17&pid=1-s2.0-S0378377421002419-main.pdf
https://vlibrary.iwmi.org/pdf/H050484.pdf
https://vlibrary.iwmi.org/pdf/H050484.pdf
(3.48 MB) (3.48 MB)
Crop-growth models are powerful tools for supporting optimal planning and management of agricultural water use globally. However, use of crop models for this purpose often requires advanced programming expertize and computational resources, limiting the potential uptake in integrated water management research by practitioners such as water managers, policymakers, and irrigation service providers. In this article, we present AquaCrop-OSPy (ACOSP), an open source, Python implementation of the crop-water productivity model AquaCrop. The model provides a user friendly, flexible and computationally efficient solution to support agricultural water management, which can be readily integrated with other Python modules or code bases and run instantly via a web browser using the cloud computing platform Google Colab without the need for local installation. This article describes how to run basic simulations using AquaCrop-OSPy, along with more advanced analyses such as optimizing irrigation schedules and evaluating climate change impacts. Each use case is paired with a Jupyter Notebook, which offer an interactive learning environment for users and can be readily adapted to address a range of common irrigation planning and management challenges faced by researcher, policymakers and businesses in both developed and developing countries (https://github.com/thomasdkelly/aquacrop).
2 Kelly, T. D.; Foster, T.; Schultz, D. M. 2023. Assessing the value of adapting irrigation strategies within the season. Agricultural Water Management, 275:107986. [doi: https://doi.org/10.1016/j.agwat.2022.107986]
Irrigation scheduling ; Optimization methods ; Agricultural water use ; Uncertainty ; Water scarcity ; Weather ; Irrigation management ; Models ; Farmers ; Soil moisture ; Water use ; Water productivity ; Rain ; Case studies / United States of America / Nebraska
(Location: IWMI HQ Call no: e-copy only Record No: H051992)
https://www.sciencedirect.com/science/article/pii/S0378377422005339/pdfft?md5=2aa76d300211335af481f7f44bdc18ae&pid=1-s2.0-S0378377422005339-main.pdf
https://vlibrary.iwmi.org/pdf/H051992.pdf
https://vlibrary.iwmi.org/pdf/H051992.pdf
(5.99 MB) (5.99 MB)
Optimization of irrigation scheduling is a widely proposed solution to enhance agricultural water productivity and mitigate water scarcity. However, there is currently a lack of knowledge about how to most effectively optimize and adapt irrigation decisions under weather and climate uncertainty, or about how the benefits of adaptive irrigation scheduling compare to fixed heuristics commonly used by farmers. In this article, we assess the added value of in-season adaptation of irrigation strategies in comparison to a fixed irrigation strategy that maximizes average profits over a range of plausible weather outcomes, but is not adjusted year-to-year. To perform this assessment, the AquaCrop-OSPy crop-water model is used to simulate a case study of irrigated maize production in a water scarce region in the central United States. Irrigation strategies are defined that maximize mean seasonal profit over a range of historical years. This baseline profit is then compared to the case of adaptive strategies, where the irrigation strategy is re-optimized at multiple stages within each season. Our analysis finds that fixed irrigation heuristics on average achieve over 90 % of potential profits attained with perfect seasonal foresight. In-season adaptation marginally increased agricultural profitability, with greater benefits found when re-optimization occurs more frequently or is accompanied by reliable forecasts of weather for the week ahead. However, the overall magnitude of these additional benefits was small (<5 % further increase in average profits), highlighting that fixed irrigation scheduling rules can be near-optimal when making realistic assumptions about farmers’ potential knowledge of future weather. Since fixed irrigation strategies are easier to design, communicate and implement than data-driven adaptive management strategies, we suggest that implementing these fixed strategies be prioritized over the development of more complex adaptive strategies.
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