Analysing Farmer Indebtedness: Determinants and Repayment Constraints in Bikaner District, India

Farmer indebtedness in water-scarce regions poses a critical challenge to rural livelihoods and credit sustainability. This study examines 90 farm households in Bikaner district, Rajasthan—where 67.8% of households carry outstanding debt averaging ₹181.8 thousand each—to identify the key determinants of borrowing levels, default risk, and repayment constraints. Multiple linear regression explains 54.1% of variation in total loans, revealing that access to irrigated land (b = 0.495, p < 0.01), higher education (b = 0.234, p < 0.05), and greater reliance on non-institutional credit (b = 0.252, p < 0.01) significantly increase borrowing, while demographic and socio-economic factors such as age and non-farm income exert weaker effects. Logistic regression shows that older farmers (β = 0.779, p < 0.05) and those borrowing informally (β = 0.803, p < 0.05) face higher default risk, whereas larger landholdings (β = –0.140, p < 0.05) and off-farm income (β = –1.331, p < 0.10) enhance repayment capacity. The foremost barriers to loan servicing are low farmgate prices (64.8%), inadequate income (64.1%), and natural calamities (64.0%), compounded by crop failures, loan diversion, and excessive farm expenditure.

Authors

Institutions

Publication Details

Journal
Asian Journal of Agricultural Extension Economics & Sociology
Published
2025-10-14
DOI
https://doi.org/10.9734/ajaees/2025/v43i102835
Primary Topic
Agricultural Economics and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysing Farmer Indebtedness: Determinants and Repayment Constraints in Bikaner District, India

Vikram Yogi, Bhupender Dighliya, Shahnawaz Malik, Kiran Choudhary et al.
Asian Journal of Agricultural Extension Economics & Sociology
Agricultural Economics and Practices
article

Analysing Farmer Indebtedness: Determinants and Repayment Constraints in Bikaner District, India

Vikram Yogi, Bhupender Dighliya, Shahnawaz Malik, Kiran Choudhary, Jaipal, Shubham Shubham, Kavita Kavita
article en

Abstract

Farmer indebtedness in water-scarce regions poses a critical challenge to rural livelihoods and credit sustainability. This study examines 90 farm households in Bikaner district, Rajasthan—where 67.8% of households carry outstanding debt averaging ₹181.8 thousand each—to identify the key determinants of borrowing levels, default risk, and repayment constraints. Multiple linear regression explains 54.1% of variation in total loans, revealing that access to irrigated land (b = 0.495, p < 0.01), higher education (b = 0.234, p < 0.05), and greater reliance on non-institutional credit (b = 0.252, p < 0.01) significantly increase borrowing, while demographic and socio-economic factors such as age and non-farm income exert weaker effects. Logistic regression shows that older farmers (β = 0.779, p < 0.05) and those borrowing informally (β = 0.803, p < 0.05) face higher default risk, whereas larger landholdings (β = –0.140, p < 0.05) and off-farm income (β = –1.331, p < 0.10) enhance repayment capacity. The foremost barriers to loan servicing are low farmgate prices (64.8%), inadequate income (64.1%), and natural calamities (64.0%), compounded by crop failures, loan diversion, and excessive farm expenditure.

Asian Journal of Agricultural Extension Economics & SociologyVol. 43(10)
The University of Queensland (AU), Chaudhary Charan Singh Haryana Agricultural University (IN), Swami Keshwanand Rajasthan Agricultural University (IN)
Openalex Percentile: Top 56%
Agricultural Economics 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.