An open-source tool for improving on-farm yield forecasting systems
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.
| Main Authors: | , , |
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| Format: | Journal Article biblioteca |
| Language: | English |
| Published: |
Frontiers Media
2023-07-11
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| Subjects: | 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, |
| Online Access: | https://hdl.handle.net/10568/131232 https://www.frontiersin.org/articles/10.3389/fsufs.2023.1084728/pdf https://doi.org/10.3389/fsufs.2023.1084728 |
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