Evidence map›Paper›PMID 41112190›Full record

ArticleAgricultural water management2025

Farmers agronomic management responses to extreme drought and rice yields in Bihar, India.

Maxwell Mkondiwa, Avinash Kishore, Prakashan Chellattan Veetil, Sonam Sherpa, Satyam Saxena, Bhavani Pinjarla, Anton Urfels, Shishpal Poonia, Anurag Ajay, Peter Craufurd and 2 more

Abstract read
In one paragraph

Article in Agricultural water management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Maxwell MkondiwaInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Avinash KishoreInternational Food Policy Research Institute (IFPRI), New Delhi, India.
Prakashan Chellattan VeetilInternational Rice Research Institute (IRRI), New Delhi, India.
Sonam SherpaInternational Maize and Wheat Improvement Center (CIMMYT), Kathmandu, Nepal.
Satyam SaxenaInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Bhavani PinjarlaTechnology Implementation Cell-PMFBY, Sustainable Inclusive Growth, UNDP, New Delhi, India.
Anton UrfelsInternational Rice Research Institute (IRRI), Los Banos, Philippines.
Shishpal PooniaInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Anurag AjayInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Peter CraufurdInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Ram MalikInternational Maize and Wheat Improvement Center (CIMMYT), New Delhi, India.
Andrew McDonaldCornell University, USA.

Funding

Gates Foundation INV-029117
6 · The paper itself

Abstract

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.

Indexed as

Crop abandonmentLate monsoon onsetMachine learningTubewell irrigation

Identifiers

PMID41112190
PMCPMC12534190

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.