What does this research mean for the field?

Farm mechanization adoption in the Betul district is significantly hindered for female-headed households and marginalized caste groups, while higher education and larger farm sizes promote higher levels of mechanization. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To assess the extent of farm mechanization and identify key socio-demographic and farm-level determinants influencing its adoption among tribal farming communities.

September 10, 2026Journal of Development Policy and Practice

An Empirical Investigation of Farm Mechanisation Adoption in Betul District of Madhya Pradesh

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Authors

ASAnkit Kumar SuryawanshiGovernment of Uttar Pradesh PTPranay ThakurChameli Devi Group of Institutions

Discussion

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Implication

Survey reveals socio-demographic disparities in farm mechanization adoption among rural households, indicating that targeted interventions are vital for marginalized and female farmers.

Key Points

To assess the extent of farm mechanization and identify key socio-demographic and farm-level determinants influencing its adoption among tribal farming communities.
Surveyed 150 farmers across five villages in Chicholi tehsil, Betul district, Madhya Pradesh, India, using stratified random sampling (30 farmers per village).
Constructed a composite farm mechanisation index (CFMI) to categorize farmers into low, moderate, and high levels of mechanization.
Evaluated determinants of mechanization adoption using multiple linear regression on CFMI cut-off scores and multinomial logistic regression across mechanization tiers.
Categorization by CFMI score showed that 40.00% of farmers had low mechanization, 42.67% had moderate mechanization, and 17.33% had high mechanization.
Multiple regression revealed that mechanization scores were significantly and positively driven by higher education levels and larger farm size.
Female household leadership and Scheduled Caste or Scheduled Tribe classifications negatively impacted mechanization, with female-managed farms significantly more likely to fall into the low-mechanization category.

Cite This Study

Suryawanshi et al. (2026) studied this question.

synapsesocial.com/papers/6aa27b3558559d80afc74544 https://doi.org/10.1177/24551333261476567

View Full Paper

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September 10, 2026Journal of Development Policy and Practice

An Empirical Investigation of Farm Mechanisation Adoption in Betul District of Madhya Pradesh

View Full Paper

Ask AI

Bookmark

Authors

ASAnkit Kumar SuryawanshiGovernment of Uttar Pradesh PTPranay ThakurChameli Devi Group of Institutions

Discussion

Member takes

Implication

Survey reveals socio-demographic disparities in farm mechanization adoption among rural households, indicating that targeted interventions are vital for marginalized and female farmers.

Key Points

To assess the extent of farm mechanization and identify key socio-demographic and farm-level determinants influencing its adoption among tribal farming communities.
Surveyed 150 farmers across five villages in Chicholi tehsil, Betul district, Madhya Pradesh, India, using stratified random sampling (30 farmers per village).
Constructed a composite farm mechanisation index (CFMI) to categorize farmers into low, moderate, and high levels of mechanization.
Evaluated determinants of mechanization adoption using multiple linear regression on CFMI cut-off scores and multinomial logistic regression across mechanization tiers.
Categorization by CFMI score showed that 40.00% of farmers had low mechanization, 42.67% had moderate mechanization, and 17.33% had high mechanization.
Multiple regression revealed that mechanization scores were significantly and positively driven by higher education levels and larger farm size.
Female household leadership and Scheduled Caste or Scheduled Tribe classifications negatively impacted mechanization, with female-managed farms significantly more likely to fall into the low-mechanization category.

Cite This Study

Suryawanshi et al. (2026) studied this question.

synapsesocial.com/papers/6aa27b3558559d80afc74544 https://doi.org/10.1177/24551333261476567

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