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1 Tomasella, J.; Martins, M. A.; Shrestha, Nirman. 2023. An open-source tool for improving on-farm yield forecasting systems. Frontiers in Sustainable Food Systems, 7:1084728. [doi: https://doi.org/10.3389/fsufs.2023.1084728]
Yield forecasting ; Crop forecasting ; Soil fertility ; Irrigation management ; Yield gap ; Crop modelling ; Optimization ; On-farm research ; Wheat ; Maize ; Soil water content ; Water productivity ; Biomass ; Canopy ; Climate change ; Assessment ; Computer software / Tunisia / Nepal / Brazil / Tunis / Chitwan / Araripina
(Location: IWMI HQ Call no: e-copy only Record No: H052083)
https://www.frontiersin.org/articles/10.3389/fsufs.2023.1084728/pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
(6.59 MB) (6.59 MB)
Introduction: The increased frequency of extreme climate events, many of them of rapid onset, observed in many world regions, demands the development of a crop forecasting system for hazard preparedness based on both intraseasonal and extended climate prediction. This paper presents a Fortran version of the Crop Productivity Model AquaCrop that assesses climate and soil fertility effects on yield gap, which is crucial in crop forecasting systems
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
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