1 Bingöl Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Biyometri ve Genetik, Bingöl
2 Bingöl Üniversitesi, Gıda, Tarım ve Hayvancılık Meslek Yüksekokulu, Gıda İşleme Bölümü, Bingöl
Abstract
This study evaluates the performance of an Artificial Neural Network (ANN) model on a multi-year time series and presents its future predictions. The research data consists of cow milk production amount data in Türkiye for the period January 2010 to August 2025. Quantitative metrics such as the coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were used in evaluating the model’s performance. The model has high explanatory power (R2=0.877). This indicates that the model successfully explains approximately 88% of the total variance in the dataset and effectively captures the long-term trend and strong annual seasonality of the series. In the application, a network model in the form of the Levenberg-Marquardt backpropagation algorithm (trainlm), consisting of 12 input neurons, 12 hidden neurons, and 1 output neuron, was used. The model provides estimates for cow milk production amount in Türkiye during the January-December 2026 period, ranging from 920046,79 tons to 985781,57 tons. The model also predicted that the seasonally highest (peak) cow milk production would occur in March and May, and the lowest (bottom) cow milk production would occur in September and November. The ANN model was found to provide suitable results in the production modeling of animal products.
ÇELİK, Şenol, & ÇAKIR , Y. (2026). Modeling and Predicting of Cow Milk Production Amount in Türkiye with Artificial Neural Networks. ISPEC Journal of Agricultural Sciences, 10(2), 575–588. https://doi.org/10.5281/zenodo.20261128
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Yusuf ÇAKIR 2