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022			_a0378-3774
022			_a1873-2283 (Online)
024	8		_ahttps://doi.org/10.1016/j.agwat.2025.109830
040			_aMX-TxCIM
041			_aeng
100	1		_8001712974 _aMkondiwa, M. _gSustainable Agrifood Systems _926831
245	1	0	_aFarmers agronomic management responses to extreme drought and rice yields in Bihar, India
260			_aAmsterdam (Netherlands) : _bElsevier B.V., _c2025.
500			_aPeer review
500			_aOpen Access
520			_aIn 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.
546			_aText in English
591			_aSaxena, S. : Not in IRS staff list but CIMMYT Affiliation
591			_aMcDonald, A. : No CIMMYT Affiliation
597			_bExcellence in Agronomy _aClimate adaptation & mitigation _aPoverty reduction, livelihoods & jobs _cResilient Agrifood Systems _dBill & Melinda Gates Foundation (BMGF) _uhttps://hdl.handle.net/10568/176664
650		7	_aIrrigation _2AGROVOC _91164
650		0	_aMachine learning _2AGROVOC _911127
650		7	_aCrops _2AGROVOC _91069
650		7	_aDrought _2AGROVOC _91080
650		7	_aRice _2AGROVOC _91243
651		7	_aIndia _2AGROVOC _93726
700	1		_aKishore, A. _910920
700	0		_aPrakashan Chellattan Veetil _940181
700	1		_aSherpa, S.R. _8001712516 _gSustainable Agrifood Systems _928790
700	1		_aSaxena, S. _940182
700	1		_aPinjarla, B. _940183
700	1		_aUrfels, A. _8001711637 _gFormerly Sustainable Agrifood Systems _94925
700	1		_aPoonia, S.P. _gSustainable Intensification Program _96359
700	0		_aAnurag Ajay _92767
700	1		_aCraufurd, P. _gSustainable Agrifood Systems _8I1705950 _9792
700	1		_aMalik, R. _gSustainable Intensification Program _8R1705430 _9972
700	1		_aMcDonald, A. _gSustainable Intensification Program _8INT3034 _9883
773	0		_tAgricultural Water Management _gv. 320, art. 109830 _dAmsterdam (Netherlands) : Elsevier B.V., 2025. _x0378-3774 _w444468
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35898
942			_cJA _n0 _2ddc
999			_c69375 _d69367