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 drought, we collected real-time survey data across 11 districts of Bihar. We then developed a causal machine learning model to quantify the impacts of this dro...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Language: | Inglés |
| Published: |
SSRN
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/176664 |
| _version_ | 1855527324538109952 |
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| author | Mkondiwa, Maxwell Kishore, Avinash Veettil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram K. McDonald, Andrew J. |
| author_browse | Ajay, Anurag Craufurd, Peter Kishore, Avinash Malik, Ram K. McDonald, Andrew J. Mkondiwa, Maxwell Pinjarla, Bhavani Poonia, Shishpal Saxena, Satyam Sherpa, Sonam Urfels, Anton Veettil, Prakashan Chellattan |
| author_facet | Mkondiwa, Maxwell Kishore, Avinash Veettil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram K. McDonald, Andrew J. |
| 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 drought, we collected real-time survey data across 11 districts of Bihar. We then developed a causal machine learning model to quantify the impacts of this drought and how access to affordable irrigation (through electric pumps) affected agronomic behavioural responses to the drought and ultimately determined rice yield losses. Our model addresses the empirical challenge of identifying a credible control group and conducting a counterfactual causal analysis when a factor like drought is widespread and affects nearly all sampled farmers. We find that droughts led to rice acreage reduction, transplanting delays, nursery losses, and more irrigation. For fields that were planted, we also document substantial yield losses from water stress averaging 0.94 t/ha (about 23% yield loss) with partial adaptation (0.3 t/ha) achieved through owned electric tubewell irrigation. Complementary behavioural agronomic management responses to a drought like early transplanting would have improved adaptation effectiveness of the affordable irrigation. To be effective against droughts, the huge investments in electric irrigation infrastructure appear to require complementary agricultural extension support to encourage farmers to make economically rational use of available water resources to maintain yield and profitability. |
| format | Preprint |
| id | CGSpace176664 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | SSRN |
| publisherStr | SSRN |
| record_format | dspace |
| spelling | CGSpace1766642025-09-24T19:52:50Z Farmers agronomic management responses to extreme drought and rice yields in Bihar, India Mkondiwa, Maxwell Kishore, Avinash Veettil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram K. McDonald, Andrew J. crop yields drought farmers irrigation machine learning rice In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to drought, we collected real-time survey data across 11 districts of Bihar. We then developed a causal machine learning model to quantify the impacts of this drought and how access to affordable irrigation (through electric pumps) affected agronomic behavioural responses to the drought and ultimately determined rice yield losses. Our model addresses the empirical challenge of identifying a credible control group and conducting a counterfactual causal analysis when a factor like drought is widespread and affects nearly all sampled farmers. We find that droughts led to rice acreage reduction, transplanting delays, nursery losses, and more irrigation. For fields that were planted, we also document substantial yield losses from water stress averaging 0.94 t/ha (about 23% yield loss) with partial adaptation (0.3 t/ha) achieved through owned electric tubewell irrigation. Complementary behavioural agronomic management responses to a drought like early transplanting would have improved adaptation effectiveness of the affordable irrigation. To be effective against droughts, the huge investments in electric irrigation infrastructure appear to require complementary agricultural extension support to encourage farmers to make economically rational use of available water resources to maintain yield and profitability. 2024 2025-09-24T19:52:48Z 2025-09-24T19:52:48Z Preprint https://hdl.handle.net/10568/176664 en Open Access SSRN Mkondiwa, Maxwell; Kishore, Avinash; Veettil, Prakashan Chellattan; Sherpa, Sonam; Saxena, Satyam; Pinjarla, Bhavani; et al. 2024. Farmers agronomic management responses to extreme drought and rice yields in Bihar, India. SSRN Preprint available online December 31, 2024. https://ssrn.com/abstract=5077316 |
| spellingShingle | crop yields drought farmers irrigation machine learning rice Mkondiwa, Maxwell Kishore, Avinash Veettil, Prakashan Chellattan Sherpa, Sonam Saxena, Satyam Pinjarla, Bhavani Urfels, Anton Poonia, Shishpal Ajay, Anurag Craufurd, Peter Malik, Ram K. McDonald, Andrew J. 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 | crop yields drought farmers irrigation machine learning rice |
| url | https://hdl.handle.net/10568/176664 |
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