Identifying key determinants of pulse crop productivity in Jammu region using principal component analysis

This study examines the factors influencing agricultural productivity, with a specific focus on pulse cultivation, in the purposively selected districts of Kathua, Doda, Kishtwar and Udhampur in the Jammu region. Primary data were collected from 240 farmers using a multistage random sampling technique. To identify the underlying determinants of productivity, Principal Component Analysis (PCA) was employed. The PCA results extracted four principal components in each district, collectively accounting for 71 % to 81 % of the total variance in the dataset. The analysis identified that land characteristics, income diversification and farmer education were the most significant contributors to agricultural performance. Land fragmentation and ownership emerged as critical factors in Doda and Kishtwar, whereas income sources and educational qualifications of farmers were more influential in Kathua and Udhampur. These findings highlight the importance of region-specific policy interventions, particularly those aimed at improving land management, promoting income diversification and enhancing farmer education, to effectively boost pulse productivity and overall agricultural sustainability in the region.

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Publication Details

Journal
Plant Science Today
Published
2025-08-14
DOI
https://doi.org/10.14719/pst.6238
Primary Topic
Agricultural Economics and Practices
Type
article
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article

Identifying key determinants of pulse crop productivity in Jammu region using principal component analysis

R Pooja, Shibani
Plant Science Today
Agricultural Economics and Practices
article

Identifying key determinants of pulse crop productivity in Jammu region using principal component analysis

R Pooja, Shibani
article en

Abstract

This study examines the factors influencing agricultural productivity, with a specific focus on pulse cultivation, in the purposively selected districts of Kathua, Doda, Kishtwar and Udhampur in the Jammu region. Primary data were collected from 240 farmers using a multistage random sampling technique. To identify the underlying determinants of productivity, Principal Component Analysis (PCA) was employed. The PCA results extracted four principal components in each district, collectively accounting for 71 % to 81 % of the total variance in the dataset. The analysis identified that land characteristics, income diversification and farmer education were the most significant contributors to agricultural performance. Land fragmentation and ownership emerged as critical factors in Doda and Kishtwar, whereas income sources and educational qualifications of farmers were more influential in Kathua and Udhampur. These findings highlight the importance of region-specific policy interventions, particularly those aimed at improving land management, promoting income diversification and enhancing farmer education, to effectively boost pulse productivity and overall agricultural sustainability in the region.

Plant Science Today
Chandigarh University (IN), Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu (IN)
Openalex Percentile: Top 64%
Agricultural Economics and Practices
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