Sustainable Decision-Making: Modeling Adoption Intention of Low-Carbon Agricultural Practices by Farmers

This study explores what motivates farmers in Fars Province, Iran, to consider adopting LCA practices, with a focus on behavioral, moral, and institutional influences. Data were collected from 386 farmers selected through stratified random sampling and analyzed using Structural Equation Modeling (SEM) in Smart Partial Least Squares (PLS) 3. The results confirmed that the measurement model was reliable and valid, and the structural model showed strong explanatory power, explaining 76% of the variance in adoption intention (R2 = 0.766) and 64% in moral norms (R2 = 0.642). Farmers’ intentions were significantly shaped by attitude (β = 0.210, p < 0.001), perceived behavioral control (β = 0.175, p < 0.001), moral norms (β = 0.307, p < 0.001), policy support (β = 0.202, p = 0.003), and perceived climate risk (β = 0.176, p < 0.001). In contrast, subjective norms and trust in institutions did not directly influence intention, although trust strongly strengthened moral norms (β = 0.387, p < 0.001). In general, the findings highlight that farmers’ decisions are shaped not only by practical and economic considerations but also by their sense of responsibility, confidence in their abilities, and perceptions of climate risk and institutional support. The study contributes to sustainability research by integrating moral and institutional perspectives into behavioral models and offers practical insights for policymakers to support the transition toward low-carbon, climate-resilient agriculture in Iran.

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Publication Details

Journal
Sustainability
Published
2026-04-30
DOI
https://doi.org/10.3390/su18094421
Primary Topic
Sustainable Agricultural Systems Analysis
Type
article
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article

Sustainable Decision-Making: Modeling Adoption Intention of Low-Carbon Agricultural Practices by Farmers

Atefeh Ahmadi Dehrashid, Naser Valizadeh, Khadijeh Bazrafkan, Tuyet‐Anh T. Le et al.
Sustainability
Sustainable Agricultural Systems Analysis
article

Sustainable Decision-Making: Modeling Adoption Intention of Low-Carbon Agricultural Practices by Farmers

Atefeh Ahmadi Dehrashid, Naser Valizadeh, Khadijeh Bazrafkan, Tuyet‐Anh T. Le, Imaneh Goli, Ebrahim Rastgar
article en

Abstract

This study explores what motivates farmers in Fars Province, Iran, to consider adopting LCA practices, with a focus on behavioral, moral, and institutional influences. Data were collected from 386 farmers selected through stratified random sampling and analyzed using Structural Equation Modeling (SEM) in Smart Partial Least Squares (PLS) 3. The results confirmed that the measurement model was reliable and valid, and the structural model showed strong explanatory power, explaining 76% of the variance in adoption intention (R2 = 0.766) and 64% in moral norms (R2 = 0.642). Farmers’ intentions were significantly shaped by attitude (β = 0.210, p < 0.001), perceived behavioral control (β = 0.175, p < 0.001), moral norms (β = 0.307, p < 0.001), policy support (β = 0.202, p = 0.003), and perceived climate risk (β = 0.176, p < 0.001). In contrast, subjective norms and trust in institutions did not directly influence intention, although trust strongly strengthened moral norms (β = 0.387, p < 0.001). In general, the findings highlight that farmers’ decisions are shaped not only by practical and economic considerations but also by their sense of responsibility, confidence in their abilities, and perceptions of climate risk and institutional support. The study contributes to sustainability research by integrating moral and institutional perspectives into behavioral models and offers practical insights for policymakers to support the transition toward low-carbon, climate-resilient agriculture in Iran.

SustainabilityVol. 18(9)
Memorial University of Newfoundland (CA), University of Liège (BE), Shiraz University (IR), Gembloux Agro-Bio Tech (BE), University of Kurdistan (IR), Agricultural Research & Education Organization (IR)
Zero hunger
Openalex Percentile: Top 8%
Sustainable Agricultural Systems Analysis
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