Investigation of Factors Affecting Milk Production in Türkiye Using Principal Component Regression Analysis: Food Engineering Perspective

This study analyzes the factors affecting milk yield using statistical data from Türkiye covering the period 2005–2025. Mean milk yield (MMY) is considered the dependent variable, while mean feed production (MFP), agricultural land (AL), number of milking animals (NMA), and mean annual precipitation (MAP) are evaluated as independent variables.The high correlation among the independent variables reduces the reliability of model coefficients and limits interpretability in multiple linear regression analysis. Therefore, principal component regression analysis (PCR), a dimensionality reduction method, is employed. The PCR results show that the PC1 component alone explains 71% of the total variance and exhibits a strong association with key determinants of milk yield, particularly MFP and NMA.These findings enable the reliable identification of critical control points in terms of raw material efficiency, production planning, and quality standardization in milk production. The study demonstrates that the PCR approach provides a robust, stable, and interpretable method for analyzing MMY and related production parameters.