Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia
Agricultural decision making in Ethiopia is highly influenced by climate variability, however the effective use of agricultural data remains limited across meteorological, soil, and crop management domains. Differences in formats, spatial and temporal scales, and metadata standards reduce the usabil...
| Main Authors: | , , , , , , , |
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| Format: | Artículo preliminar |
| Language: | Inglés |
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International Crops Research Institute for the Semi-Arid Tropics
2025
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| Online Access: | https://hdl.handle.net/10568/180317 |
| _version_ | 1855531407995043840 |
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| author | Kumar, Kishore G. Dessalegn, Olika Folorunso, Akinseye Maila, Nagaraju Ugendar, K. Mohan, Divya Gopi, T. Kumar, Shalander |
| author_browse | Dessalegn, Olika Folorunso, Akinseye Gopi, T. Kumar, Kishore G. Kumar, Shalander Maila, Nagaraju Mohan, Divya Ugendar, K. |
| author_facet | Kumar, Kishore G. Dessalegn, Olika Folorunso, Akinseye Maila, Nagaraju Ugendar, K. Mohan, Divya Gopi, T. Kumar, Shalander |
| author_sort | Kumar, Kishore G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agricultural decision making in Ethiopia is highly influenced by climate variability, however the effective use of agricultural data remains limited across meteorological, soil, and crop management domains. Differences in formats, spatial and temporal scales, and metadata standards reduce the usability of these datasets for digital and AI-based agro-advisory systems. This study presents a practical framework for standardizing and Harmonizing multi-source agricultural data to support the Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia. Building on pilot implementations in India and parts of Africa, this study examines the transition of ISAT toward an AI-enabled advisory system. The analysis emphasizes data corpus standardization for AI readiness and assesses platform flexibility and suitability across diverse agro-ecological contexts. Climate, soil, and crop management datasets were standardized using common units, aligned spatial and temporal scales, and crop-stage-based structuring, following FAIR data principles. The harmonized datasets were integrated into the ISAT framework by linking climate, soil, and crop information with advisory generation processes. This integration enables location-specific advisories, including recommendations on sowing, nutrient and water management, and climate risk mitigation. Overall, the study demonstrates how systematic data harmonization and integration can enhance data readiness and provide a scalable foundation for AI-driven agro-advisory services in data-constrained contexts. |
| format | Artículo preliminar |
| id | CGSpace180317 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Crops Research Institute for the Semi-Arid Tropics |
| publisherStr | International Crops Research Institute for the Semi-Arid Tropics |
| record_format | dspace |
| spelling | CGSpace1803172026-01-22T02:10:28Z Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia Kumar, Kishore G. Dessalegn, Olika Folorunso, Akinseye Maila, Nagaraju Ugendar, K. Mohan, Divya Gopi, T. Kumar, Shalander data collection artificial intelligence climate change adaptation extension activities Agricultural decision making in Ethiopia is highly influenced by climate variability, however the effective use of agricultural data remains limited across meteorological, soil, and crop management domains. Differences in formats, spatial and temporal scales, and metadata standards reduce the usability of these datasets for digital and AI-based agro-advisory systems. This study presents a practical framework for standardizing and Harmonizing multi-source agricultural data to support the Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia. Building on pilot implementations in India and parts of Africa, this study examines the transition of ISAT toward an AI-enabled advisory system. The analysis emphasizes data corpus standardization for AI readiness and assesses platform flexibility and suitability across diverse agro-ecological contexts. Climate, soil, and crop management datasets were standardized using common units, aligned spatial and temporal scales, and crop-stage-based structuring, following FAIR data principles. The harmonized datasets were integrated into the ISAT framework by linking climate, soil, and crop information with advisory generation processes. This integration enables location-specific advisories, including recommendations on sowing, nutrient and water management, and climate risk mitigation. Overall, the study demonstrates how systematic data harmonization and integration can enhance data readiness and provide a scalable foundation for AI-driven agro-advisory services in data-constrained contexts. 2025-12 2026-01-21T17:14:13Z 2026-01-21T17:14:13Z Working Paper https://hdl.handle.net/10568/180317 en Open Access application/pdf International Crops Research Institute for the Semi-Arid Tropics Kumar, Kishore G.; Dessalegn, Olika; Folorunso, Akinseye; Maila, Nagaraju; Ugendar, K.; Mohan, Divya; Gopi, T.; Kumar, Shalander. 2025. Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| spellingShingle | data collection artificial intelligence climate change adaptation extension activities Kumar, Kishore G. Dessalegn, Olika Folorunso, Akinseye Maila, Nagaraju Ugendar, K. Mohan, Divya Gopi, T. Kumar, Shalander Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title | Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title_full | Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title_fullStr | Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title_full_unstemmed | Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title_short | Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia |
| title_sort | harmonizing agricultural data for ai powered intelligent agricultural systems advisory tool isat in ethiopia |
| topic | data collection artificial intelligence climate change adaptation extension activities |
| url | https://hdl.handle.net/10568/180317 |
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