Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya
The rise of artificial intelligence (AI) has heightened interest in digital models to strengthen agricultural extension. Such tools could help provide personalized advisories tailored to a farmer's unique conditions at scale and at a low cost. This study evaluates the fundamental assumption that per...
| Main Authors: | , , |
|---|---|
| Format: | Artículo preliminar |
| Language: | Inglés |
| Published: |
International Food Policy Research Institute
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/169348 |
| _version_ | 1855541321446457344 |
|---|---|
| author | Ceballos, Francisco Chugh, Aditi Kramer, Berber |
| author_browse | Ceballos, Francisco Chugh, Aditi Kramer, Berber |
| author_facet | Ceballos, Francisco Chugh, Aditi Kramer, Berber |
| author_sort | Ceballos, Francisco |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The rise of artificial intelligence (AI) has heightened interest in digital models to strengthen agricultural extension. Such tools could help provide personalized advisories tailored to a farmer's unique conditions at scale and at a low cost. This study evaluates the fundamental assumption that personalized crop advisories are more effective than generic ones. By means of a large-scale randomized controlled trial (RCT), we assess the impact of personalized picture-based advisories on farmers’ perceptions, knowledge and adoption of recommended inputs and practices, and other downstream outcomes. We find that personalizing advisories does not significantly improve agricultural outcomes compared to generic ones. While farmers who engage relatively more with advisories (i.e., those who receive and read a substantial number of messages based on self-reports) tend to achieve better outcomes, this is irrespective of whether the advisories they receive are tailored to their specific situation or not. We conclude that investments in digital extension tools should aim to enhance engagement with advisories rather than focusing solely on personalization. |
| format | Artículo preliminar |
| id | CGSpace169348 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1693482025-12-02T21:02:52Z Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya Ceballos, Francisco Chugh, Aditi Kramer, Berber agricultural extension artificial intelligence farmers inputs The rise of artificial intelligence (AI) has heightened interest in digital models to strengthen agricultural extension. Such tools could help provide personalized advisories tailored to a farmer's unique conditions at scale and at a low cost. This study evaluates the fundamental assumption that personalized crop advisories are more effective than generic ones. By means of a large-scale randomized controlled trial (RCT), we assess the impact of personalized picture-based advisories on farmers’ perceptions, knowledge and adoption of recommended inputs and practices, and other downstream outcomes. We find that personalizing advisories does not significantly improve agricultural outcomes compared to generic ones. While farmers who engage relatively more with advisories (i.e., those who receive and read a substantial number of messages based on self-reports) tend to achieve better outcomes, this is irrespective of whether the advisories they receive are tailored to their specific situation or not. We conclude that investments in digital extension tools should aim to enhance engagement with advisories rather than focusing solely on personalization. 2024-12-31 2025-01-17T16:08:45Z 2025-01-17T16:08:45Z Working Paper https://hdl.handle.net/10568/169348 en Open Access application/pdf International Food Policy Research Institute Ceballos, Francisco; Chugh, Aditi; and Kramer, Berber. 2024. Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya. IFPRI Discussion Paper 2322. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/169348 |
| spellingShingle | agricultural extension artificial intelligence farmers inputs Ceballos, Francisco Chugh, Aditi Kramer, Berber Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title | Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title_full | Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title_fullStr | Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title_full_unstemmed | Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title_short | Impacts of personalized picture-based crop advisories: Experimental evidence from India and Kenya |
| title_sort | impacts of personalized picture based crop advisories experimental evidence from india and kenya |
| topic | agricultural extension artificial intelligence farmers inputs |
| url | https://hdl.handle.net/10568/169348 |
| work_keys_str_mv | AT ceballosfrancisco impactsofpersonalizedpicturebasedcropadvisoriesexperimentalevidencefromindiaandkenya AT chughaditi impactsofpersonalizedpicturebasedcropadvisoriesexperimentalevidencefromindiaandkenya AT kramerberber impactsofpersonalizedpicturebasedcropadvisoriesexperimentalevidencefromindiaandkenya |