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AquaCrop-OS: An open source version of FAO's crop water productivity model

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  • Foster, T.
  • Brozović, N.
  • Butler, A.P.
  • Neale, C.M.U.
  • Raes, D.
  • Steduto, P.
  • Fereres, E.
  • Hsiao, T.C.

Abstract

Crop simulation models are valuable tools for quantifying crop yield response to water, and for devising strategies to improve agricultural water management. However, applicability of the majority of crop models is limited greatly by a failure to provide open-access to model source code. In this study, we present an open-source version of the FAO AquaCrop model, which simulates efficiently water-limited crop production across diverse environmental and agronomic conditions. Our model, called AquaCrop-OpenSource (AquaCrop-OS), can be run in multiple programming languages and operating systems. Support for parallel execution reduces significantly simulation times when applying the model in large geospatial frameworks, for long-run policy analysis, or for uncertainty assessment. Furthermore, AquaCrop-OS is compliant with the Open Modelling Interface standard facilitating linkage to other disciplinary models, for example to guide integrated water resources planning.

Suggested Citation

  • Foster, T. & Brozović, N. & Butler, A.P. & Neale, C.M.U. & Raes, D. & Steduto, P. & Fereres, E. & Hsiao, T.C., 2017. "AquaCrop-OS: An open source version of FAO's crop water productivity model," Agricultural Water Management, Elsevier, vol. 181(C), pages 18-22.
    • Handle: RePEc:eee:agiwat:v:181:y:2017:i:c:p:18-22
    DOI: 10.1016/j.agwat.2016.11.015
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    References listed on IDEAS

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    1. Corentin Girard & Jean-Daniel Rinaudo & Manuel Pulido-Velazquez & Yvan Caballero, 2015. "An interdisciplinary modelling framework for selecting adaptation measures at the river basin scale in a global change scenario," Post-Print hal-01183833, HAL.
    2. Kim, Daeha & Kaluarachchi, Jagath, 2015. "Validating FAO AquaCrop using Landsat images and regional crop information," Agricultural Water Management, Elsevier, vol. 149(C), pages 143-155.
    3. Richard Taylor, 2014. "When wells run dry," Nature, Nature, vol. 516(7530), pages 179-180, December.
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    11. Martins, Minella Alves & Tomasella, Javier & Dias, Cássia Gabriele, 2019. "Maize yield under a changing climate in the Brazilian Northeast: Impacts and adaptation," Agricultural Water Management, Elsevier, vol. 216(C), pages 339-350.
    12. Knowling, Matthew J. & Walker, Rob R. & Pellegrino, Anne & Edwards, Everard J. & Westra, Seth & Collins, Cassandra & Ostendorf, Bertram & Bennett, Bree, 2023. "Generalized water production relations through process-based modeling: A viticulture example," Agricultural Water Management, Elsevier, vol. 280(C).
    13. Taguta, C. & Senzanje, A. & Kiala, Z. & Malota, M. & Mabhaudhi, Tafadzwanashe, 2022. "Water-energy-food nexus tools in theory and practice: a systematic review," Papers published in Journals (Open Access), International Water Management Institute, pages 1-4:837316..
    14. Emmanuel Lekakis & Athanasios Zaikos & Alexios Polychronidis & Christos Efthimiou & Ioannis Pourikas & Theano Mamouka, 2022. "Evaluation of Different Modelling Techniques with Fusion of Satellite, Soil and Agro-Meteorological Data for the Assessment of Durum Wheat Yield under a Large Scale Application," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
    15. Feng, Dingrui & Li, Guangyong & Wang, Dan & Wulazibieke, Mierguli & Cai, Mingkun & Kang, Jing & Yuan, Zicheng & Xu, Houcheng, 2022. "Evaluation of AquaCrop model performance under mulched drip irrigation for maize in Northeast China," Agricultural Water Management, Elsevier, vol. 261(C).
    16. Nyathi, M.K. & van Halsema, G.E. & Annandale, J.G. & Struik, P.C., 2018. "Calibration and validation of the AquaCrop model for repeatedly harvested leafy vegetables grown under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 208(C), pages 107-119.
    17. 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).
    18. Oviroh, Peter Ozaveshe & Austin-Breneman, Jesse & Chien, Cheng-Chun & Chakravarthula, Praneet Nallan & Harikumar, Vaishnavi & Shiva, Pranjal & Kimbowa, Alvin Bagetuuma & Luntz, Jonathan & Miyingo, Emm, 2023. "Micro Water-Energy-Food (MicroWEF) Nexus: A system design optimization framework for Integrated Natural Resource Conservation and Development (INRCD) projects at community scale," Applied Energy, Elsevier, vol. 333(C).
    19. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).

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