Evaluating farmers’ strategies and socioeconomic drivers for adaptation to climate uncertainty: A case study in the Himalayan region of West Bengal, India

doi:10.1016/j.regsus.2026.100334

Abstract

Abstract:

Climate change poses a profound threat to mountain agro-ecosystems, particularly in the Himalayan region of West Bengal, India, by disrupting precipitation patterns, increasing temperature variability, and intensifying extreme weather events. Despite growing evidence of climate change impacts, there remains a critical research gap in understanding how socioeconomic factors drive farmers’ adaptation strategies to climate change in this vulnerable region. This study examines how farmers in the Himalayan region of West Bengal, India, perceived and responded to the growing impacts of climate change on mountain agro-ecosystems. Drawing on cross-sectional data from 370 farm households selected through multistage sampling, the research employs a combination of analytical tools, including the severity index (SI) to assess farmers’ perceptions to climate change, the adaptation index (AI) to evaluate adaptive responses, the Garrett’s ranking technique to prioritize constraints, and the ordered logistic regression to identify key socioeconomic drivers of adaptation. Findings reveal a high level of climate awareness among farmers, particularly regarding the increase in weather extremes (SI=74.87%), increase in temperature (SI=72.31%), and irregular rainfall patterns and highly erratic rainfall (SI=62.52%). The most commonly adopted strategies include adopting intercropping and mixed cropping systems (AI=0.613), adoption of the integrated farming system model (AI=0.600), and shift towards non-farm employment (AI=0.608), while the adoption of climate-resilient crop varieties and improved irrigation remains limited. Regression analysis highlights that education (regression coefficient=0.38), average landholding size (regression coefficient=1.21), and access to daily weather forecast information (regression coefficient=1.92) significantly promote adaptive behaviour, whereas age (regression coefficient= -0.09) and gender (regression coefficient= -0.76) are negatively associated. Institutional constraints, particularly unavailability of institutional credit, emerge as primary barriers. The study underscores the urgent need for region-specific, inclusive policy frameworks that enhance climate advisory services, support technology dissemination, and empower marginalized groups in the Himalayan region of West Bengal. By fostering informed, equitable, and resilient agricultural systems, these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.

Key words: Adaptation index, Severity index, Climate change, Adaptation strategies, Garrett’s ranking, Himalayan region

Sourakanti SARKAR, Aishi MUKHERJEE, Auindrila BISWAS, Subrata GORAIN, Bimal BERA, Anmol GIRI, Malini ROY CHOUDHURY, Suman DUTTA, Sumanta DAS. Evaluating farmers’ strategies and socioeconomic drivers for adaptation to climate uncertainty: A case study in the Himalayan region of West Bengal, India[J]. Regional Sustainability, 2026, 7(2): 100334.

Figures/Tables 10

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Characteristic		Frequency	Percentage of respondents (%)
Age	Young (20-40 years old)	144	38.92
	Middle-aged (41-60 years old)	158	42.70
	Elderly (>60 years old)	68	18.37
Education	Illiterate	7	1.90
	Primary	137	37.02
	Secondary	154	41.62
	Post-secondary	72	19.45
Household size	Small (≤3 persons)	140	37.83
	Middle (4-6 persons)	162	43.78
	Large (7-9 persons)	68	18.37
Gender	Male	215	58.10
Gender	Female	155	41.89
Caste category	Un-reserved	161	43.51
Caste category	Reserved	209	56.49
Average landholding size	Marginal (≤1.00 hm²)	204	55.13
	Small (1.00-2.00 hm²)	136	36.76
	Semi-medium (2.00-4.00 hm²)	30	8.10

Variable	Description	Anticipated relationship with adaptation strategies	Source
Age	Continuous (in years)	+	Abid et al. (2016)
Gender	Dummy (male: 1 and female: 0)	+	Pello et al. (2021)
Marital status	Dummy (married: 1 and otherwise: 0)	+	Atube et al. (2021)
Education	Dummy (illiterate: 0, literate: 1, primary: 2, secondary: 3, and post-secondary: 4)	+	Ali and Erenstein (2017); Biswas et al. (2023)
Caste category	Dummy (unreserved: 0 and reserved: 1)	NA	No reference available
Average landholding size	Continuous (in hm²)	+	Ravera et al. (2016); Choden et al. (2020)
Household size	Continuous (in numbers)	+	Patel et al. (2023)
Dependency ratio	Continuous (in %)	-	Pandey and Jha (2012)
Access to credit	Dummy (yes: 1 and no: 0)	+	Chepkoech et al. (2020); Pello et al. (2021)
Access to the nearby market	Dummy (yes: 1 and no: 0)	+	Pello et al. (2021)
Access to daily weather forecast information	Dummy (yes: 1 and no: 0)	+	No reference available

Event	Description	SA	A	MA	D	SD	SI (%)
Acquainted with the term ‘climate change’	Frequency	108	169	76	10	7	80.42
Acquainted with the term ‘climate change’	Percentage (%)	29.19	45.68	20.54	2.70	1.89	80.42
Do you feel climate change happening in real?	Frequency	63	178	72	27	30	69.91
Do you feel climate change happening in real?	Percentage (%)	17.03	48.11	19.46	7.30	8.11	69.91
Irregular rainfall patterns and highly erratic rainfall	Frequency	96	88	72	64	50	62.52
Irregular rainfall patterns and highly erratic rainfall	Percentage (%)	25.94	23.78	19.45	17.29	13.51	62.52
A decline in the number of rainy days	Frequency	88	108	86	68	20	66.91
A decline in the number of rainy days	Percentage (%)	23.78	29.19	23.24	18.38	5.40	66.91
Late in the onset of the monsoon	Frequency	78	66	139	36	51	60.18
Late in the onset of the monsoon	Percentage (%)	21.08	17.84	37.57	9.73	13.78	60.18
Decrease in the duration of the winter season	Frequency	41	70	183	74	29	59.45
Decrease in the duration of the winter season	Percentage (%)	11.08	18.92	49.46	20	7.84	59.45
Increase in temperature	Frequency	124	82	110	28	26	72.31
Increase in temperature	Percentage (%)	33.51	22.16	29.73	7.57	7.03	72.31
Increase in weather extremes	Frequency	141	75	105	26	23	74.87
Increase in weather extremes	Percentage (%)	38.11	20.27	28.38	7.03	6.22	74.87

Statement	Description	SA	A	MA	D	SD	SI (%)
Reducing agricultural yield and low farm income	Frequency	98	135	107	18	12	75.16
Reducing agricultural yield and low farm income	Percentage (%)	26.48	36.49	28.92	4.87	3.24	75.16
Drying up of surface water bodies	Frequency	31	77	188	42	32	56.46
Drying up of surface water bodies	Percentage (%)	8.37	20.81	50.81	11.35	8.64	56.46
Increasing incidence of pest and disease attacks	Frequency	101	104	110	30	25	70.56
Increasing incidence of pest and disease attacks	Percentage (%)	27.29	28.10	29.72	8.11	6.76	70.56
Forest degradation	Frequency	14	38	198	62	58	45.87
Forest degradation	Percentage (%)	3.78	10.27	53.51	16.76	15.67	45.87
Food shortage	Frequency	18	30	127	143	52	40.83
Food shortage	Percentage (%)	4.86	8.12	34.32	38.66	14.05	40.83
Increasing the incidence of farmers’ indebtedness	Frequency	18	17	202	74	59	43.90
Increasing the incidence of farmers’ indebtedness	Percentage (%)	4.86	4.59	54.59	20	15.95	43.90
Increasing migration to the city	Frequency	41	88	107	98	36	54.05
Increasing migration to the city	Percentage (%)	11.08	23.78	28.92	26.48	9.73	54.05
Incidence of farmers’ suicide in the distress year	Frequency	0	0	158	149	63	33.96
Incidence of farmers’ suicide in the distress year	Percentage (%)	0.00	0.00	42.71	40.27	17.03	33.96

Strategy	AI value	Ranking
Adoption of improved varieties	0.388	13
Changing the timing of agricultural operations	0.518	7
Shift in total cultivation	0.371	14
Adopting intercropping and mixed cropping systems	0.613	1
Shift towards non-farm employment	0.608	2
Adoption of the integrated farming system model	0.600	3
Using rainwater harvesting techniques	0.442	11
Using water-efficient irrigation techniques	0.408	12
Partial sale of livestock in a distress year	0.529	6
Using soil moisture conservation techniques	0.492	9
Using organic and compost fertilizers	0.533	5
Rescheduling loan repayment	0.508	8
Availing crop insurance services	0.488	10
Participating in rural employment schemes	0.558	4