Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems
Agricultural production sustains a large part of the population in the Great South area (sub-Saharan Africa, South America and South Asia), relying heavily on rainfed agriculture and partly on reservoir-based irrigation schemes. This study evaluates the effects of citizen science approaches in shapi...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ada1ac |
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| author | Vasilios Plakandaras Fahad Khan Khadim Vassiliki Kazana Emmanouil Anagnostou Amvrossios C Bagtzoglou |
| author_facet | Vasilios Plakandaras Fahad Khan Khadim Vassiliki Kazana Emmanouil Anagnostou Amvrossios C Bagtzoglou |
| author_sort | Vasilios Plakandaras |
| collection | DOAJ |
| description | Agricultural production sustains a large part of the population in the Great South area (sub-Saharan Africa, South America and South Asia), relying heavily on rainfed agriculture and partly on reservoir-based irrigation schemes. This study evaluates the effects of citizen science approaches in shaping the farmers’ perceptions towards adequacy of the quantity of the provided water and the fairness of the irrigation distribution system in the area, using as a case study a project implemented in the Upper Blue Nile (UBN) region of Ethiopia in two irrigated communities. Harnessing the analytical power of machine learning models in extracting patterns from data, the informational content of social surveys coupled with hydrological data for the survey region from a calibrated MODFLOW-NWT groundwater (GW) model, we draw inferences on the importance of socioeconomic rather than hydrological variables as drivers in agricultural decisions about crop selection and planting period, underscoring those factors as potential criteria in drawing successful agricultural policies for crop yield optimization in the Great South area. This study extends the existing literature towards understanding the interplay between people and water under a qualitative framework with distinct policy implications. |
| format | Article |
| id | doaj-art-2b6bf530e03f483baa64fd7e0ff46d88 |
| institution | Kabale University |
| issn | 2515-7620 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-2b6bf530e03f483baa64fd7e0ff46d882025-02-06T17:01:59ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017202500810.1088/2515-7620/ada1acUnveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systemsVasilios Plakandaras0https://orcid.org/0000-0001-9351-9546Fahad Khan Khadim1Vassiliki Kazana2Emmanouil Anagnostou3Amvrossios C Bagtzoglou4Department of Economics, Democritus University of Thrace , Komotini, 69100, GreeceNASA Goddard Space Flight Center, Greenbelt, MD 20770, United States of AmericaDepartment of Natural Environment and Climate Resilience, Democritus University of Thrace , Drama, 66100, GreeceDepartment of Civil and Environmental Engineering, University of Connecticut , Storrs, CT 06269, United States of AmericaDepartment of Civil and Environmental Engineering, University of Connecticut , Storrs, CT 06269, United States of AmericaAgricultural production sustains a large part of the population in the Great South area (sub-Saharan Africa, South America and South Asia), relying heavily on rainfed agriculture and partly on reservoir-based irrigation schemes. This study evaluates the effects of citizen science approaches in shaping the farmers’ perceptions towards adequacy of the quantity of the provided water and the fairness of the irrigation distribution system in the area, using as a case study a project implemented in the Upper Blue Nile (UBN) region of Ethiopia in two irrigated communities. Harnessing the analytical power of machine learning models in extracting patterns from data, the informational content of social surveys coupled with hydrological data for the survey region from a calibrated MODFLOW-NWT groundwater (GW) model, we draw inferences on the importance of socioeconomic rather than hydrological variables as drivers in agricultural decisions about crop selection and planting period, underscoring those factors as potential criteria in drawing successful agricultural policies for crop yield optimization in the Great South area. This study extends the existing literature towards understanding the interplay between people and water under a qualitative framework with distinct policy implications.https://doi.org/10.1088/2515-7620/ada1acmachine learningsocio-hydrologyfairness of water managementfarmers' perceptions |
| spellingShingle | Vasilios Plakandaras Fahad Khan Khadim Vassiliki Kazana Emmanouil Anagnostou Amvrossios C Bagtzoglou Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems Environmental Research Communications machine learning socio-hydrology fairness of water management farmers' perceptions |
| title | Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| title_full | Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| title_fullStr | Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| title_full_unstemmed | Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| title_short | Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| title_sort | unveiling farmers perceptions a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems |
| topic | machine learning socio-hydrology fairness of water management farmers' perceptions |
| url | https://doi.org/10.1088/2515-7620/ada1ac |
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