Global breeding programs

Integrated behavioral and machine learning approaches to predict farmer adoption of improved semen technologies and breed improvement program in cattle

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Abstract

Understanding behavioral and structural drivers of farmer adoption is essential for improving the effectiveness of cattle genetic-improvement programs in low- and middle-income livestock systems. This study applied an integrated behavioral and machine-learning framework to predict farmers' willingness to adopt improved semen technologies in Punjab, Pakistan. Survey data from 900 cattle farmers across major agro-ecological zones were analyzed using behavioral segmentation, multivariable logistic regression, and supervised machine-learning models. Three distinct farmer typologies were identified using Multiple Correspondence Analysis and clustering techniques and validated through Latent Class Analysis. Inferential analysis showed that higher education (OR = 2.35), prior use of artificial insemination (OR = 3.67), and preference for exotic semen (OR = 1.92) were the strongest predictors of adoption willingness. Machine-learning models outperformed traditional regression, with Extreme Gradient Boosting achieving the highest predictive accuracy (AUC = 0.87). Predicted probabilities were integrated into a Target Farmer Index to support precision targeting of extension and breeding interventions. Farmers characterized by higher education and preference for artificial insemination exhibited adoption probabilities exceeding 85%. The proposed framework offers a scalable, cost-effective approach for improving adoption and accelerating genetic gains in resource-constrained livestock systems.

Keywords: 2026

How to Cite:

Nasir, K., Khan, S. & Ahmad, S., (2026) “Integrated behavioral and machine learning approaches to predict farmer adoption of improved semen technologies and breed improvement program in cattle”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2295723. doi: https://doi.org/10.31274/wcgalp.24364

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