Smallholder farmers’ intention to adopt Climate-Smart Agricultural (CAS) practices: Insights into the socio-psychological dimensions of their pro-environmental behaviours

doi:10.1016/j.regsus.2026.100332

Abstract

Abstract:

Although various Climate-Smart Agricultural (CSA) practices are promoted to safeguard agricultural production and food security in the Global South, their adoption remains limited. This study conducted a cross-sectional survey using a semi-structured questionnaire between March and April in 2023, with a sample of 231 smallholder farmers covering 5 villages (Purbo Karpara, Rampasa, Monohor Pur, Boro Isubpur, and Char Alapur) in Haor wetlands of Bangladesh. Then, this study examined smallholder farmers’ adoption intention of floating farming practice in Haor wetlands of Bangladesh based on an extended Theory of Planned Behaviour (TPB) model. The result demonstrated moderate predictive power of the proposed model, where farmers’ attitude, subjective norm, knowledge of floating farming practice, perceived behavioural control, and institutional accessibility jointly explained 11.50% of the total variance in adoption intention, with subjective norm and perceived behavioural control showing significant impact on smallholder farmers’ adoption intention. However, key barriers reported by smallholder farmers included unavailability of resources, limited access to modern farming practices, input and credit unavailability, insufficient access to training, and limited access to market system. These findings indicated that improving smallholder farmers’ access to extension services and credit facilities, raising awareness, providing training, and guaranteeing access to accurate and timely climate information and early warning systems could significantly improve their adaptive capacity. This study contributes to socio-psychological understanding of smallholder farmers’ pro-environmental behaviour and emphasises the need for context-specific interventions to support sustainable livelihoods and resilient agri-food systems in climate-vulnerable regions.

Key words: Climate-Smart, Agricultural (CSA) practices, Theory of Planned Behaviour (TPB), Partial Least, Square Structural Equation, Modeling (PLS-SEM), Pro-environmental behaviour, Food security, Haor wetlands

Dash Baishakhy SMITA, Speelman STIJN. Smallholder farmers’ intention to adopt Climate-Smart Agricultural (CAS) practices: Insights into the socio-psychological dimensions of their pro-environmental behaviours[J]. Regional Sustainability, 2026, 7(2): 100332.

Figures/Tables 11

Fig. 1.

Table 1

Measurement of smallholder farmers’ socio-psychological factors to predict their pro-environmental behavioural intention to climate change in Haor wetlands."

Factor	Statement/item	Item code	Expected outcome	Scale of measurement	Reference
Attitude (AT)	Adoption of floating agriculture practice capacitates me to cope and adapt to the impact of climate change.	AT₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Tama et al. (2021)
	Adoption of floating agriculture practice gives me a more stable agricultural production.	AT₂
	Adoption of floating agriculture practice will make agriculture systems in Haor wetlands sustainable against climate change impact.	AT₃
	Adoption of floating agriculture practice is a promising strategy for developing smallholder farmers’ climate change resilience.	AT₄
Subjective norm (SN)	Agriculture extension officers of my Upazilla Department of Agricultural Extension (DAE) office think that I should adopt floating agriculture practice.	SN₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Tama et al. (2021)
	Smallholder farmers in Haor wetlands think that I should adopt floating agriculture practice.	SN₂
	When it comes to climate change adaptation, I am expected to be like my peers (neighboring smallholder farmers) in adopting floating agriculture practice.	SN₃
	Agriculture extension officers will motivate me to adopt floating agriculture practice.	SN₄
	Other smallholder farmers in Haor wetlands use floating agriculture practice.	SN₅
	Most other smallholder farmers in Haor wetlands like me value the floating agriculture practice to face the impact of climate change.	SN₆
Perceived behavioural control (PBC)	I am confident that I have the ability to adopt floating agriculture practice to mitigate climate change impacts.	PBC₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Faisal et al. (2020); Tama et al. (2021)
	My knowledge and expertise in engaging in floating agriculture practice are quite adequate.	PBC₂
	It is mostly up to me whether I engage in adopting floating agriculture practice.	PBC₃
	If I face obstacles like lack of institutional support, limited knowledge, and the lack of resources, I will feel confident to adopt floating agriculture practice.	PBC₄
Institutional accessibility (IA)	I get agricultural extension services/support to adopt a new practice.	IA₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Ofoegbu and Ifejika Speranza (2017)
	I have access to credit facilities, i.e., Bank and Small and Medium-sized Enterprises.	IA₂
	I have access to nearby markets to sell my agricultural products.	IA₃
	I have access to and use improved production inputs and technologies, i.e., seeds and fertilizer.	IA₄
	I have access to different organizations to get training about new practices and mitigate the impact of climate change.	IA₅
	I have membership in smallholder farmers’ networks, associations or cooperatives.	IA₆
Knowledge of floating farming practice (KW)	I know about the principles and methods of floating agriculture practice.	KW₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Tama et al. (2021)
	I know about the benefit and suitability of floating agriculture practice.	KW₂
	Floating agriculture practice is less vulnerable to floods and flash floods.	KW₃
	Floating agriculture practice needs less resources and the resources are locally available.	KW₄
Adoption intention (AI)	I expect to adopt floating agriculture practice in the coming year.	AI₁	Positive	1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=neither agree nor disagree, 5=somewhat agree, 6=agree, and 7=strongly agree	Yazdanpanah et al. (2014); Tama et al. (2021)
	I want to adopt floating agriculture practice in the coming year.	AI₂
	I intend to adopt floating agriculture practice in the coming year.	AI₃
	I will adopt floating agriculture practice in the coming year.	AI₄

Table 1

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Fig. 2.

Table 7

Table 8

Fig. 3.

References 113

[1]	Ahsan M.N., Islam M.S., Halim S.F., et al., 2025. Examining farmers’ behavioral drivers to adopt floating farming: A study in the wetlands of Southwestern Coastal Bangladesh. Earth Systems and Environment. 9(1), 215-239. doi: 10.1007/s41748-024-00433-w
[2]	Ajzen I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T
[3]	Ajzen I., 2005. Attitudes, Personality, and Behavior. New York: McGraw-Hill.
[4]	Akter A., Jahan M.S., Geng X.H., et al., 2023. Building smallholder farmers’ capacity to adopt climate‐smart agricultural practices in flood prone areas: Lessons from Bangladesh. Review of Development Economics. 27(4), 2301-2330. doi: 10.1111/rode.v27.4
[5]	Al Mamun A., Naznen F., Gao J.Z., et al., 2023. Predicting the intention and adoption of hydroponic farming among Chinese urbanites. Heliyon. 9(3), e14420, doi: 10.1016/j.heliyon.2023.e14420.
[6]	Al-Maruf A., 2020. Floating gardening in coastal Bangladesh: Evidence of sustainable farming for food security under climate change. Journal of Agriculture, Food and Environment. 1(4), 161-168. doi: 10.47440/JAFE
[7]	Arunrat N., Wang C., Pumijumnong N., et al., 2017. Farmers’ intention and decision to adapt to climate change: A case study in the Yom and Nan basins, Phichit Province of Thailand. Journal of Cleaner Production. 143, 672-685.
[8]	Atta-Aidoo J., Antwi-Agyei P., Dougill A.J., et al., 2022. Adoption of climate-smart agricultural practices by smallholder farmers in rural Ghana: An application of the theory of planned behavior. PLOS Climate. 1(10), e0000082, doi: 10.1371/journal.pclm.0000082.
[9]	Azadi Y., Yazdanpanah M., Mahmoudi H., 2019. Understanding smallholder farmers’ adaptation behaviors through climate change beliefs, risk perception, trust, and psychological distance: Evidence from wheat growers in Iran. Journal of Environmental Management. 250, 109456, doi: 10.1016/j.jenvman.2019.109456.
[10]	Bagheri A., Bondori A., Allahyari M.S., et al., 2019. Modeling farmers’ intention to use pesticides: An expanded version of the theory of planned behavior. Journal of Environmental Management. 248, 109291, doi: 10.1016/j.jenvman.2019.109291.
[11]	Bala H., Ghosh A.K., Kazal M.M.H., et al., 2020. Floating gardening in Bangladesh: A sustainable income generating activity in wetland areas. International Journal of Agricultural Research, Innovation and Technology. 10(1), 87-93. doi: 10.3329/ijarit.v10i1.48098
[12]	Bandura A., 1986. Social Foundations of Thought and Action. New Jersey: Prentice Hall, 23-28.
[13]	Bazzana D., Foltz J., Zhang Y., 2022. Impact of climate smart agriculture on food security: An agent-based analysis. Food Policy. 111, 102304, doi: 10.1016/j.foodpol.2022.102304.
[14]	Belay A., Oludhe C., Mirzabaev A., et al., 2022. Knowledge of climate change and adaptation by smallholder farmers: Evidence from southern Ethiopia. Heliyon. 8(12), e12089, doi: 10.1016/j.heliyon.2022.e12089.
[15]	Boateng G.O., Neilands T.B., Frongillo E.A., et al., 2018. Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health. 6, 149, doi: 10.3389/fpubh.2018.00149.
[16]	Burton R.J.F., 2004. Reconceptualising the ‘behavioural approach’ in agricultural studies: A socio-psychological perspective. Journal of Rural Studies. 20(3), 359-371. doi: 10.1016/j.jrurstud.2003.12.001
[17]	Child D., 2006. The Essentials of Factor Analysis. New York: Continuum Intl. Pub Group.
[18]	Cohen J., 2013. Statistical Power Analysis for the Behavioral Sciences. New York: Routledge.
[19]	Collins A., 2018. Saying all the right things? Gendered discourse in climate-smart agriculture. The Journal of Peasant Studies. 45(1), 175-191. doi: 10.1080/03066150.2017.1377187
[20]	Cronbach L.J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika. 16(3), 297-334. doi: 10.1007/BF02310555
[21]	Cronbach L.J., Gleser G.C., 1957. Psychological Tests and Personnel Decisions. New York: University of Illinois Press.
[22]	Dang H.L., Li E., Nuberg L., et al., 2018. Vulnerability to climate change and the variations in factors affecting farmers’ adaptation: A multi-group structural equation modelling study. Climate and Development. 10(6), 509-519. doi: 10.1080/17565529.2017.1304885
[23]	Das S., 2024. Women’s experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas. Climatic Change. 177(4), 59, doi: 10.1007/s10584-024-03716-3.
[24]	DeVellis R.F., Thorpe C.T., 2021. Scale Development: Theory and Applications. California: Sage Publications.
[25]	Dey B.L., Balmer J.M.T., Pandit A., et al., 2018. Selfie appropriation by young British South Asian adults: Reifying, endorsing and reinforcing dual cultural identity in social media. Information Technology & People. 31(2), 482-506.
[26]	Dey S., Singh P.K., 2023. Estimating farmers’ intention towards institutional credit adoption by using extended theory of planned behavior. Journal of Applied Social Psychology. 53(7), 684-703. doi: 10.1111/jasp.v53.7
[27]	Faisal M., Chunping X., Akhtar S., et al., 2020. Modeling smallholder livestock herders’ intentions to adopt climate smart practices: An extended theory of planned behavior. Environmental Science and Pollution Research. 27, 39105-39122.
[28]	FAO (Food and Agriculture Organization of the United Nations), 2022. World Food and Agriculture—Statistical Year Book 2022. Rome: FAO.
[29]	Farstad M., Melås A.M., Klerkx L., 2022. Climate considerations aside: What really matters for farmers in their implementation of climate mitigation measures. Journal of Rural Studies. 96, 259-269.
[30]	Fishbein M., Ajzen I., 2011. Predicting and Changing Behavior:The Reasoned Action Approach. New York: Psychology Press, 538.
[31]	Fornell C., Larcker D.F., 1981. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research. 18(3), 382-388. doi: 10.1177/002224378101800313
[32]	Frick J., Kaiser F.G., Wilson M., 2004. Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample. Personality and Individual Differences. 37(8), 1597-1613. doi: 10.1016/j.paid.2004.02.015
[33]	Fusco G., Melgiovanni M., Porrini D., et al., 2020. How to improve the diffusion of climate-smart agriculture: What the literature tells us. Sustainability. 12(12), 5168, doi: 10.3390/su12125168.
[34]	Ge Y.H., Fan L.X., Li Y.B., et al., 2023. Gender differences in smallholder farmers’ adoption of crop diversification: Evidence from Shaanxi Plain, China. Climate Risk Management. 39, 100482, doi: 10.1016/j.crm.2023.100482.
[35]	Gebre G.G., Isoda H., Rahut D.B., et al., 2019. Gender differences in the adoption of agricultural technology: The case of improved maize varieties in southern Ethiopia. Women’s Studies International Forum. 76, 102264, doi: 10.1016/j.wsif.2019.102264.
[36]	Ghazali S., Azadi H., Kurban A., et al., 2021. Determinants of farmers’ adaptation decisions under changing climate: the case of Fars Province in Iran. Climatic Change. 166(1-2), 6, doi: 10.1007/s10584-021-03088-y.
[37]	Gomez-Zavaglia A., Mejuto J.C., Simal-Gandara J., 2020. Mitigation of emerging implications of climate change on food production systems. Food Research International. 134, 109256, doi: 10.1016/j.foodres.2020.109256.
[38]	Hair J., Alamer A., 2022. Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics. 1(3), 100027, doi: 10.1016/j.rmal.2022.100027.
[39]	Hair J.F., Ringle C.M., Sarstedt M., 2013. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning. 46(1-2), 1-12. doi: 10.1016/j.lrp.2013.01.001
[40]	Hair J.F., Risher J.J., Sarstedt M., et al., 2019a. When to use and how to report the results of PLS-SEM. European Business Review. 31(1), 2-24.
[41]	Hair J.F., Sarstedt M., Ringle C.M., 2019b. Rethinking some of the rethinking of partial least squares. European Journal of Marketing. 53(4), 566-584. doi: 10.1108/EJM-10-2018-0665
[42]	Hair J.F., Sarstedt M., 2021. Explanation plus prediction—The logical focus of project management research. Project Management Journal. 52(4), 319-322. doi: 10.1177/8756972821999945
[43]	Hair Jr J.F., Hult G.T.M., Ringle C.M., et al., 2021. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R:A Workbook. Cham: Springer Nature, 197.
[44]	Hajjar S.T., 2018. Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods. 6(1), 27-38.
[45]	Haque M.N., Siddika S., Ahmed Sresto M., et al., 2021. Geo-spatial analysis for flash flood susceptibility mapping in the North-East Haor (Wetland) Region in Bangladesh. Earth Systems and Environment. 5(2), 365-384. doi: 10.1007/s41748-021-00221-w
[46]	Hayden M.T., Mattimoe R., Jack L., 2021. Sensemaking and the influencing factors on farmer decision-making. Journal of Rural Studies. 84, 31-44.
[47]	Hu L., Bentler P.M., 1998. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods. 3(4), 424-453. doi: 10.1037/1082-989X.3.4.424
[48]	Hu L., Bentler P.M., 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 6(1), 1-55. doi: 10.1080/10705519909540118
[49]	Hundera H., Mpandeli S., Bantider A., 2019. Smallholder farmers’ awareness and perceptions of climate change in Adama district, central rift valley of Ethiopia. Weather and Climate Extremes. 26, 100230, doi: 10.1016/j.wace.2019.100230.
[50]	Islam M.M., 2024. Unravelling the complexities of wetland agriculture, climate change, and coping mechanisms: An integrative review using economics and satellite approaches. Environment, Development and Sustainability. 1-33.
[51]	Islam M.S., Okubo K., Saiful Islam A.H.M., et al., 2022a. Investigating the effect of climate change on food loss and food security in Bangladesh. SN Business & Economics. 2, 4, doi: 10.1007/s43546-021-00177-z.
[52]	Islam Z., Sabiha N.E., Salim R., 2022b. Integrated environment-smart agricultural practices: A strategy towards climate-resilient agriculture. Economic Analysis and Policy. 76, 59-72.
[53]	Jakariya M., Islam M.N., 2017. Evaluation of climate change induced vulnerability and adaptation strategies at Haor areas in Bangladesh by integrating GIS and DIVA model. Modeling Earth Systems and Environment. 3(4), 1303-1321. doi: 10.1007/s40808-017-0378-9
[54]	Jellason N.P., Baines R.N., Conway J.S., et al., 2019. Climate change perceptions and attitudes to smallholder adaptation in Northwestern Nigerian Drylands. Social Sciences. 8(2), 31, doi: 10.3390/socsci8020031.
[55]	Jhariya M.K., Meena R.S., Banerjee A., 2021. Ecological intensification of natural resources towards sustainable productive system. Ecological Intensification of Natural Resources for Sustainable Agriculture, 1-28.
[56]	Jordan P., 2020. The International Conference on Climate Services 6:Advancing the Knowledge and Practice of Climate Services for Climate Resilience. In: The International Conference on Climate Service. Pune, India.
[57]	Jost C., Kyazze F., Naab J., et al., 2016. Understanding gender dimensions of agriculture and climate change in smallholder farming communities. Climate and Development. 8(2), 133-144. doi: 10.1080/17565529.2015.1050978
[58]	Kabir K.H., Sarker S., Nasir Uddin M., et al., 2022. Furthering climate-smart farming with the introduction of floating agriculture in Bangladeshi wetlands: Successes and limitations of an innovation transfer. Journal of Environmental Management. 323, 116258, doi: 10.1016/j.jenvman.2022.116258.
[59]	Kaiser H.F., Caffrey J., 1965. Alpha factor analysis. Psychometrika. 30(1), 1-14. doi: 10.1007/BF02289743
[60]	Karimi H., Ataei P., 2024. An analysis of the perceived effectiveness and upscaling potential of climate-smart agriculture interventions in the Sistan Plain, Iran. Journal of Cleaner Production. 460, 142582, doi: 10.1016/j.jclepro.2024.142582.
[61]	Karimi S., Mohammadimehr S., 2022. Socio-psychological antecedents of pro-environmental intentions and behaviors among Iranian rural women: An integrative framework. Frontiers in Environmental Science. 10, 979728, doi: 10.3389/fenvs.2022.979728.
[62]	Knekta E., Runyon C., Eddy S., 2019. One size doesn’t fit all: Using factor analysis to gather validity evidence when using surveys in your research. CBE—Life Sciences Education. 18(1), rm1, doi: 10.1187/cbe.18-04-0064.
[63]	Knight G.P., 2018. A survey of some important techniques and issues in multiple regression. In: New Methods In Reading Comprehension Research. Oxfordshire: Routledge, 13-30.
[64]	Lan L., Sain G., Czaplicki S., et al., 2018. Farm-level and community aggregate economic impacts of adopting climate smart agricultural practices in three mega environments. PLoS ONE. 13(11), e0207700, doi: 10.1371/journal.pone.0207700.
[65]	Lang Z., Rabotyagov S., 2022. Socio-psychological factors influencing intent to adopt conservation practices in the Minnesota River Basin. Journal of Environmental Management. 307, 114466, doi: 10.1016/j.jenvman.2022.114466.
[66]	Lipper L., McCarthy N., Zilberman D., et al., 2017. Climate Smart Agriculture:Building Resilience to Climate Change. Cham: Springer Nature, 630.
[67]	MacGillivray B.H., 2018. Beyond social capital: The norms, belief systems, and agency embedded in social networks shape resilience to climatic and geophysical hazards. Environmental Science & Policy. 89, 116-125.
[68]	Mankad A., 2016. Psychological influences on biosecurity control and farmer decision-making. A review. Agronomy for Sustainable Development. 36(2), 40, doi: 10.1007/s13593-016-0375-9.
[69]	Martin M., Billah M., Siddiqui T., et al., 2014. Climate-related migration in rural Bangladesh: A behavioural model. Population and Environment. 36(1), 85-110. doi: 10.1007/s11111-014-0207-2
[70]	Meijer S.S., Catacutan D., Ajayi O.C., et al., 2015. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. International Journal of Agricultural Sustainability. 13(1), 40-54. doi: 10.1080/14735903.2014.912493
[71]	Mitter H., Larcher M., Schönhart M., et al., 2019. Exploring farmers’ climate change perceptions and adaptation intentions: Empirical evidence from Austria. Environmental Management. 63(6), 804-821. doi: 10.1007/s00267-019-01158-7
[72]	Mizik T., 2021. Climate-smart agriculture on small-scale farms: A systematic literature review. Agronomy. 11(6), 1096, doi: 10.3390/agronomy11061096.
[73]	Moerkerken A., Blasch J., van Beukering P., et al., 2020. A new approach to explain farmers’ adoption of climate change mitigation measures. Climatic Change. 159, 141-161.
[74]	Mortreux C., Barnett J., 2017. Adaptive capacity: Exploring the research frontier. Wiley Interdisciplinary Reviews: Climate Change. 8(4), e467, doi: 10.1002/wcc.467.
[75]	Mutenje M.J., Farnworth C.R., Stirling C., et al., 2019. A cost-benefit analysis of climate-smart agriculture options in Southern Africa: Balancing gender and technology. Ecological Economics. 163, 126-137.
[76]	Mutyasira V., Hoag D., Pendell D., 2018. The adoption of sustainable agricultural practices by smallholder farmers in Ethiopian highlands: An integrative approach. Cogent Food & Agriculture. 4(1), 1552439, doi: 10.1080/23311932.2018.1552439.
[77]	Ngigi M.W., Mueller U., Birner R., 2017. Gender differences in climate change adaptation strategies and participation in group-based approaches: An intra-household analysis from rural Kenya. Ecological Economics. 138, 99-108.
[78]	Nordhagen S., Pascual U., Drucker A.G., 2021. Gendered differences in crop diversity choices: A case study from Papua New Guinea. World Development. 137, 105134, doi: 10.1016/j.worlddev.2020.105134.
[79]	Nyang’au J.O., Mohamed J.H., Mango N., et al., 2021. Smallholder farmers’ perception of climate change and adoption of climate smart agriculture practices in Masaba South Sub-county, Kisii, Kenya. Heliyon. 7(4), e06789, doi: 10.1016/j.heliyon.2021.e06789.
[80]	Ofoegbu C., Ifejika Speranza C., 2017. Assessing rural peoples’ intention to adopt sustainable forest use and management practices in South Africa. Journal of Sustainable Forestry. 36(7), 729-746. doi: 10.1080/10549811.2017.1365612
[81]	Ogunyiola A., Gardezi M., Vij S., 2022. Smallholder farmers’ engagement with climate smart agriculture in Africa: Role of local knowledge and upscaling. Climate Policy. 22(4), 411-426. doi: 10.1080/14693062.2021.2023451
[82]	Parvez M., Islam M.R., Dey N.C., 2022. Household food insecurity after the early monsoon flash flood of 2017 among wetland (Haor) communities of northeastern Bangladesh: a cross‐sectional study. Food and Energy Security. 11(1), e326, doi: 10.1002/fes3.326.
[83]	Pörtner H.O., Roberts D.C., Tignor M.M.B., et al., 2022. Climate Change 2022:Impacts, Adaptation and Vulnerability. In: IPCCSixth Assessment Report. Brussels, Belgium.
[84]	Rana M.M., Kiminami L., Furuzawa S., 2025. An analysis of factors affecting farmers’ capacity building for sustainable rural and agricultural development in Bangladesh. Regional Science Policy & Practice. 17(8), 100202, doi: 10.1016/j.rspp.2025.100202.
[85]	Raza M.H., Abid M., Yan T.W., et al., 2019. Understanding farmers’ intentions to adopt sustainable crop residue management practices: A structural equation modeling approach. Journal of Cleaner Production. 227, 613-623.
[86]	Rezaei R., Mianaji S., Ganjloo A., 2018. Factors affecting farmers’ intention to engage in on-farm food safety practices in Iran: Extending the theory of planned behavior. Journal of Rural Studies. 60, 152-166.
[87]	Robert M., Thomas A., Bergez J.E., 2016. Processes of adaptation in farm decision-making models. A review. Agronomy for Sustainable Development. 36(4), 64, doi: 10.1007/s13593-016-0402-x.
[88]	Rola‐Rubzen M.F., Paris T., Hawkins J., et al., 2020. Improving gender participation in agricultural technology adoption in Asia: From rhetoric to practical action. Applied Economic Perspectives and Policy. 42(1), 113-125. doi: 10.1002/aepp.v42.1
[89]	Sarkar A., Wang H., Rahman A., et al., 2022. Structural equation model of young farmers’ intention to adopt sustainable agriculture: a case study in Bangladesh. Renewable Agriculture and Food Systems. 37(2), 142-154. doi: 10.1017/S1742170521000429
[90]	Sarstedt M., Ringle C.M., Hair J.F., 2021. Partial least squares structural equation modeling. In: HomburgC., KlarmannM., VombergA., (Handbookof Market Research.eds). Cham: Springer Nature, 587-632.
[91]	Schanbacher W.D., 2019. Food as a Human Right:Combatting Global Hunger and Forging a Path to Food Sovereignty. London: Bloomsbury Publishing PLC.
[92]	Senger I., Borges J.A.R., Machado J.A.D., 2017. Using the theory of planned behavior to understand the intention of small farmers in diversifying their agricultural production. Journal of Rural Studies. 49, 32-40.
[93]	Shahangian S.A., Tabesh M., Yazdanpanah M., 2021. How can socio-psychological factors be related to water-efficiency intention and behaviors among Iranian residential water consumers? Journal of Environmental Management. 288, 112466, doi: 10.1016/j.jenvman.2021.112466.
[94]	Shi D.X., Maydeu-Olivares A., DiStefano C., 2018. The relationship between the standardized root mean square residual and model misspecification in factor analysis models. Multivariate Behavioral Research. 53(5), 676-694. doi: 10.1080/00273171.2018.1476221 pmid: 30596259
[95]	Shmueli G., Sarstedt M., Hair J.F., et al., 2019. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 53(11), 2322-2347. doi: 10.1108/EJM-02-2019-0189
[96]	Singh C., Dorward P., Osbahr H., 2016. Developing a holistic approach to the analysis of farmer decision-making: Implications for adaptation policy and practice in developing countries. Land Use Policy. 59, 329-343.
[97]	Stephens E.C., Jones A.D., Parsons D., 2018. Agricultural systems research and global food security in the 21^st century: An overview and roadmap for future opportunities. Agricultural Systems. 163, 1-6.
[98]	Šūmane S., Kunda I., Knickel I., et al., 2018. Local and farmers’ knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. Journal of Rural Studies. 59, 232-241.
[99]	Swart R., Levers C., Davis J.T.M., et al., 2023. Meta-analyses reveal the importance of socio-psychological factors for farmers’ adoption of sustainable agricultural practices. One Earth. 6(12), 1771-1783. doi: 10.1016/j.oneear.2023.10.028
[100]	Tama R.A.Z., Ying L., Mark M.Y., et al., 2021. Assessing farmers’ intention towards conservation agriculture by using the Extended Theory of Planned Behavior. Journal of Environmental Management. 280, 111654, doi: 10.1016/j.jenvman.2020.111654.
[101]	Taramuel-Taramuel J.P., Montoya-Restrepo I.A., Barrios D., 2023. Drivers linking farmers’ decision-making with farm performance: A systematic review and future research agenda. Heliyon. 9(10), e20820, doi: 10.1016/j.heliyon.2023.e20820.
[102]	Thakkar J.J., 2020. Structural Equation Modelling. Application for Research and Practice. Singapore: Springer Nature, 124.
[103]	Thompson C.G., Kim R.S., Aloe A.M., et al., 2017. Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology. 39(2), 81-90. doi: 10.1080/01973533.2016.1277529
[104]	Tofu D.A., Woldeamanuel T., Haile F., 2022. Smallholder farmers’ vulnerability and adaptation to climate change induced shocks: The case of Northern Ethiopia highlands. Journal of Agriculture and Food Research. 8, 100312, doi: 10.1016/j.jafr.2022.100312.
[105]	van Winsen F., de Mey Y., Lauwers L., et al., 2016. Determinants of risk behaviour: effects of perceived risks and risk attitude on farmer’s adoption of risk management strategies. Journal of Risk Research. 19(1), 56-78. doi: 10.1080/13669877.2014.940597
[106]	Webb T.L., Sheeran P., 2006. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin. 132(2), 249-268. doi: 10.1037/0033-2909.132.2.249 pmid: 16536643
[107]	World Bank, 2017. Climate-Smart Agriculture in Bangladesh. Washington, D.C.: World Bank, 28.
[108]	World Bank, 2021. Climate Risk Country Profile: Bangladesh. Geneva: World Bank Group.
[109]	Wuepper D., Roleff N., Finger R., 2021. Does it matter who advises farmers? Pest management choices with public and private extension. Food Policy. 99, 101995, doi: 10.1016/j.foodpol.2020.101995.
[110]	Yazdanpanah M., Hayati D., Hochrainer-Stigler S., et al., 2014. Understanding farmers’ intention and behavior regarding water conservation in the Middle-East and North Africa: A case study in Iran. Journal of Environmental Management. 135, 63-72.
[111]	Zeweld W., Van Huylenbroeck G., Tesfay G., et al., 2017. Smallholder farmers’ behavioural intentions towards sustainable agricultural practices. Journal of Environmental Management. 187, 71-81.
[112]	Zhang K., 2018. Theory of planned behavior: Origins, development and future direction. International Journal of Humanities and Social Science Invention. 7(5), 76-83.
[113]	Zobeidi T., Yaghoubi J., Yazdanpanah M., 2022. Exploring the motivational roots of farmers’ adaptation to climate change-induced water stress through incentives or norms. Scientific Reports. 12(1), 15208, doi: 10.1038/s41598-022-19384-1.

Variable	Category	Frequency	Percentage (%)	Mean	Standard deviation
Gender	Male	180	77.92	-	-
Gender	Female	51	22.08	-	-
Age	Young people (≤34 years old)	141	61.00	33.79	5.76
	Middle people (35-50 years old)	89	38.53
	Old people (>50 years old)	1	0.40
Education level	Below primary education	0	0.00	5.58	1.82
	Primary education (≤5 classes)	0	0.00
	Secondary education (6-10 classes)	145	62.77
	Higher secondary education (11-12 classes)	86	38.53
	Post-secondary education (>12 classes)	0	0.00
Family size	Small (<5 persons)	106	45.89	-	-
	Medium (5-6 persons)	65	28.14
	Large (>6 persons)	60	26.00
Farm size	Small (>1.0 hm²)	38	16.45	2.11	0.79
	Medium (1.0-3.0 hm²)	128	55.41
	Large (>3.0 hm²)	66	28.14
Experience as a farmer	Less experienced (<12 years)	115	49.78	14.47	5.06
	Moderate experienced (12-15 years)	58	25.11
	Highly experienced (>15 years)	58	25.11
Source of income	Crop production	231	100.00	-	-
Source of income	Non-crop activities	-	-	-	-
Annual income	Low income (≤410 USD)	121	52.38	52,316.02	6911.22
	Moderate income (411-450 USD)	61	26.41
	High income (>451 USD)	49	21.21

Factor	Item	Factor loading						Cronbach’s alpha	CR	AVE	VIF
Factor	Item	AT	SN	PBC	IA	KW	AI	Cronbach’s alpha	CR	AVE	VIF
AT	AT₁	0.88						0.80	0.84	0.58	2.43
	AT₂	0.90									3.33
	AT₃	0.58									3.93
	AT₄	0.62									5.23
SN	SN₁		0.87					0.81	0.88	0.72	2.08
	SN₂		Dropped								Dropped
	SN₃		Dropped								Dropped
	SN₄		0.94								2.34
	SN₅		0.72								1.46
	SN₆		Dropped								Dropped
PBC	PBC₁			0.79				0.57	0.73	0.51	1.39
	PBC₂			0.87							2.16
	PBC₃			Dropped							Dropped
	PBC₄			0.37							1.73
IA	IA₁				0.55			0.67	0.65	0.49	1.08
	IA₂				0.83						2.17
	IA₃				0.48						2.09
	IA₄				Dropped						Dropped
	IA₅				Dropped						Dropped
	IA₆				Dropped						Dropped
KW	KW₁					0.59		0.88	0.92	0.75	2.05
	KW₂					0.95					21.80
	KW₃					0.98					21.72
	KW₄					0.88					3.81
AI	AI₁						0.56	0.64	0.79	0.50	1.50
	AI₂						0.41				1.06
	AI₃						0.89				3.03
	AI₄						0.85				2.28

Factor	AI	AT	IA	KW	PBC
AT	0.144
IA	0.173	0.238
KW	0.140	0.070	0.165
PBC	0.315	0.128	0.194	0.122
SN	0.285	0.341	0.241	0.101	0.253

Factor	AI	AT	IA	KW	PBC	SN
AI	0.707
AT	-0.090	0.764
IA	0.084	-0.001	0.702
KW	-0.109	-0.030	0.032	0.864
PBC	0.277	-0.055	0.069	-0.074	0.625
SN	0.163	0.204	0.149	-0.045	0.081	0.750

Path	Sample mean	Standard deviation	t statistic	P-value	Statement
AT→AI	0.11	0.11	1.00	0.157	H₁ was not supported
SN→AI	0.16	0.07	2.19	0.014	H₂ was supported
PBC→AI	0.24	0.07	3.24	0.001	H₃ was supported
KW→AI	0.08	0.09	0.94	0.173	H₄ was not supported
IA→AI	0.09	0.08	0.70	0.241	H₅ was not supported