Farmers’ Perceptions of Policy Support, Ecological Agriculture Adoption, and Green Development in Xinjiang Under China’s Rural Revitalization Strategy: A Sequential Explanatory Mixed-Methods Study

This study examines farmers’ perceptions of how policy support is associated with ecological agriculture adoption and perceived green development outcomes in Xinjiang under China’s Rural Revitalization Strategy. A sequential explanatory mixed-methods design was used, in which the qualitative phase was deliberately connected to the quantitative phase through a shared sampling frame and a construct-aligned interview guide, and the two strands were integrated using a joint display and meta-inferences. In the quantitative phase, survey data from 300 farmers were analyzed using partial least squares structural equation modelling (PLS-SEM) to test the relationships among perceived policy support, ecological agriculture adoption, and green development. In the qualitative phase, semi-structured interviews with 30 participants drawn from the same respondent pool were thematically analyzed to explain, qualify, and contextualize the statistical relationships. The quantitative findings show a strong positive association between perceived policy support and ecological agriculture adoption (β = 0.659, p < 0.001), a strong positive association between ecological agriculture adoption and green development (β = 0.689, p < 0.001), and a smaller but significant direct association between perceived policy support and green development (β = 0.324, p < 0.001). The indirect effect of perceived policy support on green development through ecological agriculture adoption (β = 0.454) indicates partial mediation. The model explains 43.4% of the variance in ecological agriculture adoption and 47.4% of the variance in green development. The integrated joint display shows that technical training, policy clarity, and extension support helped farmers translate policy support into ecological practices, whereas high initial costs, financing constraints, and market uncertainty limited adoption and created uneven outcomes. The integrated findings suggest that policy effectiveness depends not only on the availability of support instruments but also on farmers’ practical capacity, economic security, and confidence in market returns. The study contributes perception-based mixed-method evidence on the policy–adoption–green development nexus in an ecologically vulnerable agricultural region.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-06-17
DOI
https://doi.org/10.3390/su18126254
Primary Topic
Agricultural Innovations and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Farmers’ Perceptions of Policy Support, Ecological Agriculture Adoption, and Green Development in Xinjiang Under China’s Rural Revitalization Strategy: A Sequential Explanatory Mixed-Methods Study

X Li, Yuan Zhang, Guopeng Song
Sustainability
Agricultural Innovations and Practices
article

Farmers’ Perceptions of Policy Support, Ecological Agriculture Adoption, and Green Development in Xinjiang Under China’s Rural Revitalization Strategy: A Sequential Explanatory Mixed-Methods Study

X Li, Yuan Zhang, Guopeng Song
article en

Abstract

This study examines farmers’ perceptions of how policy support is associated with ecological agriculture adoption and perceived green development outcomes in Xinjiang under China’s Rural Revitalization Strategy. A sequential explanatory mixed-methods design was used, in which the qualitative phase was deliberately connected to the quantitative phase through a shared sampling frame and a construct-aligned interview guide, and the two strands were integrated using a joint display and meta-inferences. In the quantitative phase, survey data from 300 farmers were analyzed using partial least squares structural equation modelling (PLS-SEM) to test the relationships among perceived policy support, ecological agriculture adoption, and green development. In the qualitative phase, semi-structured interviews with 30 participants drawn from the same respondent pool were thematically analyzed to explain, qualify, and contextualize the statistical relationships. The quantitative findings show a strong positive association between perceived policy support and ecological agriculture adoption (β = 0.659, p < 0.001), a strong positive association between ecological agriculture adoption and green development (β = 0.689, p < 0.001), and a smaller but significant direct association between perceived policy support and green development (β = 0.324, p < 0.001). The indirect effect of perceived policy support on green development through ecological agriculture adoption (β = 0.454) indicates partial mediation. The model explains 43.4% of the variance in ecological agriculture adoption and 47.4% of the variance in green development. The integrated joint display shows that technical training, policy clarity, and extension support helped farmers translate policy support into ecological practices, whereas high initial costs, financing constraints, and market uncertainty limited adoption and created uneven outcomes. The integrated findings suggest that policy effectiveness depends not only on the availability of support instruments but also on farmers’ practical capacity, economic security, and confidence in market returns. The study contributes perception-based mixed-method evidence on the policy–adoption–green development nexus in an ecologically vulnerable agricultural region.

SustainabilityVol. 18(12)
Nanjing Normal University (CN)
Zero hunger
Openalex Percentile: Top 4%
Agricultural Innovations and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.