Regression Models for Milk Yield and Intake in Dairy Ration Optimization: An Observational Study at the Chichak-Sud Commercial Dairy Farm

Articles in Press

Document Type : Original Article

Authors
Urfan Turan Turabov
Zakir Nasib Huseynov
Ilgar Farhad Ganjayev
Mahil İsa Məmmədov
Elnura Rafat Cuvarlinskaya
Department of Information Technologies, Azerbaijan State Agricultural University, Ganja, Azerbaijan.
Abstract
Predictive models may help dairy farms allocate feed, but their use requires biological and economic checks. This observational study compared five regression approaches and linked animal-level predictions to a linear programming scenario for ration formulation. The dataset comprised 2,911 cow-month records from 82 Holstein cows at a commercial farm in Azerbaijan during 2023–2025. Predictors described body size, lactation stage, parity, body condition and dietary nutrient composition. Ordinary least squares, ridge, least absolute shrinkage and selection operator, elastic net and second-degree polynomial regression were compared using training and test sets separated by cow. Elastic net had the lowest reported test errors: root mean squared errors of 2.36 liters per day for milk yield and 1.18 kilograms of dry matter per day for intake, with coefficients of determination of 0.84 and 0.80, respectively. The optimization summary reported feed costs of 5.07 and 4.39 Azerbaijani manat per cow per day for standard and candidate rations, with predicted milk volumes of 22.8 and 22.6 liters per day. However, a consistency audit found that the reported ingredient quantities and composition matrix did not reproduce the ration nutrient totals. The economic estimates therefore remain provisional. The framework supports further evaluation of ration decisions; reconciliation of the input matrices, additional nonlinear and seasonal comparisons, milk-composition data, external validation and a controlled feeding trial are required before implementation.
Keywords
Dairy cows
Dry matter intake
Elastic net
Linear programming
Ration optimization
Subjects
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Journal of Agricultural Science and Technology

Articles in Press, Accepted Manuscript
Available Online from 24 October 2026