M. A. Islam P. A. Sofi G. M. Bhat A. A. Wani A. A. Gatoo Amerjeet Singh A. R. Malik


The study investigated the socioeconomic and psychological variables that influence the agroforestry adoption in farming communities of Kashmir. The data were collected from 142 households of 5 villages selected in Baramulla and Kupwara districts utilizing multi-stage random sampling. The results revealed that regarding agroforestry adoption majority (52.11%) of the respondents belonged to medium category followed by low (27.47%) and high (20.42%) categories. The socioeconomic variables specified that the rural people are in underprivileged condition while they are in prosperous condition regarding psychological variables. The correlation analysis (r) indicated that among explanatory variables, education, social participation, family composition, size of land holding, main occupation, housing status, farm power, farm implements, livestock possession, wealth status, gross annual income, knowledge about agrforestry, attitude towards agroforestry and level of aspiration had shown positively significant correlation with the agroforestry adoption, while, the age had a non-significant correlation. All the explanatory variables jointly accounted 90.80 % (R2= 0.908) variation on the agroforestry adoption and among these, nine variables viz., education, size of land holding, main occupation, farm power, livestock possession, wealth status, knowledge about agrforestry, attitude towards agroforestry and level of aspiration were statistically significant (p < 0.05) in influencing the agroforestry adoption. The study recommends that recognition and exploitation of explanatory variables that predict agroforestry adoption, needs due consideration among policy makers, researchers and extension providers as prominent strategy for agroforestry promotion and development.




Adoption, Agroforestry, India, Kashmir, Prediction, Psychological, Socioeconomic

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Research Articles

How to Cite

Prediction of agroforestry adoption among farming communities of Kashmir valley, India: A logistic regression approach. (2016). Journal of Applied and Natural Science, 8(4), 2133-2140. https://doi.org/10.31018/jans.v8i4.1103