A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia

Context: Characterizing crop production environments is essential for targeted interventions, resource allocation, scaling localized findings, and agricultural decision-making. However, existing methods lack the spatial and temporal rigor required to capture spatial and temporal variability in crop...

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Autores principales: Gelagay, Habtamu S., Leroux, Louise, Tamene, Lulseged, Chernet, Meklit T., Blasch, Gerald, Tibebe, Degefie, Wuletawu Abera, Sida, Tesfaye S., Fantaye, Kindie Tesfaye, Corbeels, Marc, Silva, João Vasco
Formato: Journal Article
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Acceso en línea:https://hdl.handle.net/10568/175115
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author Gelagay, Habtamu S.
Leroux, Louise
Tamene, Lulseged
Chernet, Meklit T.
Blasch, Gerald
Tibebe, Degefie
Wuletawu Abera
Sida, Tesfaye S.
Fantaye, Kindie Tesfaye
Corbeels, Marc
Silva, João Vasco
author_browse Blasch, Gerald
Chernet, Meklit T.
Corbeels, Marc
Fantaye, Kindie Tesfaye
Gelagay, Habtamu S.
Leroux, Louise
Sida, Tesfaye S.
Silva, João Vasco
Tamene, Lulseged
Tibebe, Degefie
Wuletawu Abera
author_facet Gelagay, Habtamu S.
Leroux, Louise
Tamene, Lulseged
Chernet, Meklit T.
Blasch, Gerald
Tibebe, Degefie
Wuletawu Abera
Sida, Tesfaye S.
Fantaye, Kindie Tesfaye
Corbeels, Marc
Silva, João Vasco
author_sort Gelagay, Habtamu S.
collection Repository of Agricultural Research Outputs (CGSpace)
description Context: Characterizing crop production environments is essential for targeted interventions, resource allocation, scaling localized findings, and agricultural decision-making. However, existing methods lack the spatial and temporal rigor required to capture spatial and temporal variability in crop production environments. Objective: This study aimed to introduce a data-driven and dynamic spatial framework that integrates crop area mapping with the delineation of agro-ecological spatial units (ASUs) to characterize Ethiopia's rainfed wheat crop production environments. Methods: Annual rainfed wheat areas for the 2021 and 2022 Meher growing seasons were mapped using an ensemble machine-learning approach, leveraging time-series satellite images and environmental data. Dynamic ASUs were delineated using pixel- and object-based clustering methods, considering short-term changes (annual ASUs for 2021 and 2022) and longer-term trends (ASUs developed using data aggregated over the period 2016-2022). Clustering was based on key biophysical variables, including climatic, soil, topographic, and vegetation indices derived from satellite images that capture crop growth and development over space and time. Results and conclusions: The framework captured the spatial and temporal variability of wheat production environments, demonstrating its scalability across space and time. Rainfed wheat area mapping across two growing seasons revealed an expansion in rainfed wheat areas, highlighting the evolving nature of rainfed wheat cultivation in Ethiopia. The integration of rainfed wheat area mapping with dynamic ASU delineation identified five main production environments for wheat in Ethiopia, allowing to better target future research and development activities toward increasing wheat productivity in the country. Significance: The developed framework can facilitate agronomic assessments and inform the targeting of agricultural interventions, with potential applications that extend beyond this case study of rainfed wheat in Ethiopia.
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spelling CGSpace1751152025-12-08T09:54:28Z A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia Gelagay, Habtamu S. Leroux, Louise Tamene, Lulseged Chernet, Meklit T. Blasch, Gerald Tibebe, Degefie Wuletawu Abera Sida, Tesfaye S. Fantaye, Kindie Tesfaye Corbeels, Marc Silva, João Vasco crops crop production remote sensing wheat spatial units Context: Characterizing crop production environments is essential for targeted interventions, resource allocation, scaling localized findings, and agricultural decision-making. However, existing methods lack the spatial and temporal rigor required to capture spatial and temporal variability in crop production environments. Objective: This study aimed to introduce a data-driven and dynamic spatial framework that integrates crop area mapping with the delineation of agro-ecological spatial units (ASUs) to characterize Ethiopia's rainfed wheat crop production environments. Methods: Annual rainfed wheat areas for the 2021 and 2022 Meher growing seasons were mapped using an ensemble machine-learning approach, leveraging time-series satellite images and environmental data. Dynamic ASUs were delineated using pixel- and object-based clustering methods, considering short-term changes (annual ASUs for 2021 and 2022) and longer-term trends (ASUs developed using data aggregated over the period 2016-2022). Clustering was based on key biophysical variables, including climatic, soil, topographic, and vegetation indices derived from satellite images that capture crop growth and development over space and time. Results and conclusions: The framework captured the spatial and temporal variability of wheat production environments, demonstrating its scalability across space and time. Rainfed wheat area mapping across two growing seasons revealed an expansion in rainfed wheat areas, highlighting the evolving nature of rainfed wheat cultivation in Ethiopia. The integration of rainfed wheat area mapping with dynamic ASU delineation identified five main production environments for wheat in Ethiopia, allowing to better target future research and development activities toward increasing wheat productivity in the country. Significance: The developed framework can facilitate agronomic assessments and inform the targeting of agricultural interventions, with potential applications that extend beyond this case study of rainfed wheat in Ethiopia. 2025-06 2025-06-16T21:52:53Z 2025-06-16T21:52:53Z Journal Article https://hdl.handle.net/10568/175115 en Open Access application/pdf Elsevier Gelagay, H. S., Leroux, L., Tamene, L., Chernet, M., Blasch, G., Tibebe, D., Abera, W., Sida, T., Tesfaye, K., Corbeels, M., & Silva, J. V. (2025). A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia. Agricultural Systems, 227, 104360. https://doi.org/10.1016/j.agsy.2025.104360
spellingShingle crops
crop production
remote sensing
wheat
spatial units
Gelagay, Habtamu S.
Leroux, Louise
Tamene, Lulseged
Chernet, Meklit T.
Blasch, Gerald
Tibebe, Degefie
Wuletawu Abera
Sida, Tesfaye S.
Fantaye, Kindie Tesfaye
Corbeels, Marc
Silva, João Vasco
A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title_full A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title_fullStr A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title_full_unstemmed A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title_short A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
title_sort crop specific and time variant spatial framework for characterizing rainfed wheat production environments in ethiopia
topic crops
crop production
remote sensing
wheat
spatial units
url https://hdl.handle.net/10568/175115
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