Weighted Milk Yield Feature for Daily Milk Yield Prediction in Dairy Cows

Articles in Press

Document Type : Original Research

Authors
Abdullah Ammar Karcioglu 1
Muhammed Berat Ozgungordu 2
Mete Yağanoğlu 3
1 Department of Software Engineering, Faculty of Engineering, Ataturk University, Erzurum, Türkiye.
2 Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ataturk University, Erzurum, Türkiye.
3 Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, Türkiye.
Abstract
Milk production lies at the intersection of food science, agricultural economics and animal science. Work across these fields concerns milk quality, yield, sustainability and animal welfare. This study aimed to estimate the daily milk yield of cows on dairy farms in the Eastern Anatolia region using routinely collected production and reproductive variables. The final dataset comprised 8,128 individual cows and included breed, lactation number, age, fertility status, days in milk, days pregnant, and insemination count. Seven regression algorithms were applied: Decision Tree Regression (DTR), Linear Regression (LR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Lasso Regression, Ridge Regression (RR), and Support Vector Regression (SVR). Age Category, Pregnancy Status, and Weighted Milk Yield (WMY) were evaluated as engineered features. On the primary test split, GBR produced the lowest errors, with an MAE of 3.985 L/day, an RMSE of 4.992 L/day, a MAPE of 22.741%, and an R² of 0.438. Across 100 additional predefined 80:20 partitions, GBR achieved a mean MAE of 4.100 ± 0.072 L/day and ranked for MAE, RMSE, and R² in all 100 partitions. Although WMY ranked highly in the model-based feature-importance analysis, its incremental contribution was very small: MAE changed from 3.989 L/day without WMY to 3.985 L/day with WMY, and paired-bootstrap confidence intervals for the performance differences included zero. These findings indicate that GBR provided the best predictive performance among the evaluated algorithms, while WMY should be interpreted as a study-specific engineered predictor rather than as a feature with a demonstrated independent improvement in predictive performance.
Keywords
Machine Learning
Milk Yield Depending on Breed
Milk Yield Prediction
Pregnancy and Milk Yield in Cows
Subjects
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Journal of Agricultural Science and Technology

Articles in Press, Accepted Manuscript
Available Online from 13 September 2026