Farmers agronomic management responses to extreme drought and rice yields in Bihar, India
In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to the meteorological drought, real-time survey data was collected across 11 districts of Bihar. We then developed a causal machine learning model to quantify dro...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/177078 |
| _version_ | 1855513749834694656 |
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| author | Mkondiwa, Maxwell Kishore, Avinash Veetil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram McDonald, Andrew |
| author_browse | Ajay, Anurag Craufurd, Peter Kishore, Avinash Malik, Ram McDonald, Andrew Mkondiwa, Maxwell Pinjarla, Bhavani Poonia, Shishpal Saxena, Satyam Sherpa, Sonam Urfels, Anton Veetil, Prakashan Chellattan |
| author_facet | Mkondiwa, Maxwell Kishore, Avinash Veetil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram McDonald, Andrew |
| author_sort | Mkondiwa, Maxwell |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to the meteorological drought, real-time survey data was collected across 11 districts of Bihar. We then developed a causal machine learning model to quantify drought impacts on rice production and to characterize how access to affordable irrigation from electric pumps mitigated productivity losses. This model addresses the empirical challenge of conducting a counterfactual causal analysis when a factor like drought affects nearly all sampled farmers. In the 2022 event, drought led to rice acreage reduction, transplanting delays, damage to seedling nurseries, and higher use rates of supplemental irrigation. For fields that were planted, average yield losses from water stress were estimated as 0.94 t/ha (∼23 % yield loss) with these losses reduced by 0.3 t/ha in fields with access to electric tubewells. Agronomic management practices such as earlier transplanting were also identified as complementary strategies that increased the adaptation value of investments in irrigation. To reduce the impact of drought in Bihar, additional investments in electric irrigation infrastructure are needed along with focused extension efforts and decision support systems that empower farmers to make economically and sustainably rational use of available water resources to maintain yield and profitability. |
| format | Journal Article |
| id | CGSpace177078 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1770782025-12-08T09:54:28Z Farmers agronomic management responses to extreme drought and rice yields in Bihar, India Mkondiwa, Maxwell Kishore, Avinash Veetil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram McDonald, Andrew farmers drought rice yields machine learning irrigation agronomic practices In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to the meteorological drought, real-time survey data was collected across 11 districts of Bihar. We then developed a causal machine learning model to quantify drought impacts on rice production and to characterize how access to affordable irrigation from electric pumps mitigated productivity losses. This model addresses the empirical challenge of conducting a counterfactual causal analysis when a factor like drought affects nearly all sampled farmers. In the 2022 event, drought led to rice acreage reduction, transplanting delays, damage to seedling nurseries, and higher use rates of supplemental irrigation. For fields that were planted, average yield losses from water stress were estimated as 0.94 t/ha (∼23 % yield loss) with these losses reduced by 0.3 t/ha in fields with access to electric tubewells. Agronomic management practices such as earlier transplanting were also identified as complementary strategies that increased the adaptation value of investments in irrigation. To reduce the impact of drought in Bihar, additional investments in electric irrigation infrastructure are needed along with focused extension efforts and decision support systems that empower farmers to make economically and sustainably rational use of available water resources to maintain yield and profitability. 2025-11 2025-10-14T16:56:54Z 2025-10-14T16:56:54Z Journal Article https://hdl.handle.net/10568/177078 en https://doi.org/10.2139/ssrn.5077316 Open Access application/pdf Elsevier Mkondiwa, Maxwell; Kishore, Avinash; Veetil, Prakashan Chellattan; Sherpa, Sonam; Saxena, Satyam; Pinjarla, Bhavani; et al. Farmers agronomic management responses to extreme drought and rice yields in Bihar, India. 2025. Agricultural Water Management 320: 109830. https://doi.org/10.1016/j.agwat.2025.109830 |
| spellingShingle | farmers drought rice yields machine learning irrigation agronomic practices Mkondiwa, Maxwell Kishore, Avinash Veetil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram McDonald, Andrew Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title | Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title_full | Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title_fullStr | Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title_full_unstemmed | Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title_short | Farmers agronomic management responses to extreme drought and rice yields in Bihar, India |
| title_sort | farmers agronomic management responses to extreme drought and rice yields in bihar india |
| topic | farmers drought rice yields machine learning irrigation agronomic practices |
| url | https://hdl.handle.net/10568/177078 |
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