Final Fattening Live Weight Prediction in Anatolian Merinos Lambs from Some Body Characteristics at the Initial of Fattening by Using Some Data Mining Algorithms

Year 2023, , 47 - 53, 01.01.2023

Gizem Coşkun , Özcan Şahin , Yasin Altay , İbrahim Aytekin

https://doi.org/10.47115/bsagriculture.1181444

Cited By: 6

Abstract

This study's objective was to compare the performances of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Bayesian Regularization Neural Network (BRNN) algorithms, which are some data mining algorithms used in final fattening live weight prediction. As the independent variable in the design of the algorithms, some body characteristics taken before fattening of 54 heads of Anatolian Merino lambs, with single birth and male, were withers height (WH), rump height (RH), body length (BL), chest girth (CG), leg girth (LG), and chest depth (CD) was used. The mean±standart errors for the body characteristics of Anatolian Merino lambs were determined to be 63.481±0.538, 63.315±0.501, 78.930±1.140, 60.037±0.549, 47.704±0.543, and 29.926±0.377, respectively. The mean initial live weight (ILW) and the mean final live weight (FLW) were found as 35.89±0.84 and 49.49±0.88 kg, respectively. There was difference of 13.60 kg between ILW and FLW means. The ILW and FLW were shown to positively correlate with body characteristics, and this correlation was statistically significant (P<0.01). While the highest Pearson’s correlation (r=0.95) of FLW was between WH and RH, the lowest Pearson’s correlation (r=0.51) was found between LG and CD. While the largest share of body characteristics in the total variance in the FLW estimation was BL (42.969%) in the XGBoost algorithm, the lowest share was found to be CD (0.00) in the XGBoost algorithm and LG (0.00) in the BRNN algorithm. The model evaluation criterias which were Root mean square error (RMSE), Standard deviation ratio (SDR), Mean absolute percentage error (MAPE), and Adjusted coefficient of determination (R2Adj) performed as 1.492, 0.233, 2.241 and 0.944, in the XGBoost algorithm, as 2.220, 0.347, 3.139 and 0.880 in the BRNN algorithm, as 2.859, 0.446, 4.340 and 0.792 in the RF model, respectively. As a result, it can be said that the data mining algorithms used in prediction FLW taking advantage of body measurements of Anatolian Merino lambs at the beginning of fattening will benefit from their use in fattening due to their high prediction performance.

Keywords

Anatolian Merino, Body characteristics, Data mining algorithms, Fattening, Prediction

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