Şengül, ÖmerÇelik, Şenolİbrahim, A. K.2024-11-202024-11-202022-05-121300-6045https://doi.org/10.9775/kvfd.2022.27149https://vetdergikafkas.org/uploads/pdf/pdf_KVFD_2909.pdfhttps://hdl.handle.net/11452/48241This study was carried out to determine the effect of silage type, silage consumption, birth type (single or twin) and birth weight on live weight at the end of fattening in Kivircik lambs. In the experiment, 40 male Kivircik lambs aged 2.5-3 months were used and the animals were fattened for 56 days. During the fattening period, the lambs fed with 5 different types of silage (100% sunflower silage, 75% sunflower + 25% corn silage, 50% sunflower + 50% corn silage, 25% sunflower + 75% corn silage, 100% corn silage) pure and mixed in different proportions and concentrate feed. Data on fattening results were analyzed with MARS and Bagging MARS algorithms. The main objective of this research is to predict fattening final live weight (FFLW) of lambs using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a nonparametric regression technique. Live weight value was modeled based on factors such as birth type, birth weight, silage type and silage consumption. Correlation coefficient (r), determination coefficient (R2), Adjust R2, Root-meansquare error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error (MAPE), mean absolute deviation (MAD), and Akaike Information Criteria (AIC) values of MARS algorithm predicting live weight were as follows: 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88 respectively. Like statistics for Bagging MARS algorithm were 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47 and 115 respectively. It was observed that MARS and Bagging MARS algorithms have revealed correct results according to goodness of fit statistics. In this study it has been determined that the MARS algorithm gives better results in live weight modeling.eninfo:eu-repo/semantics/openAccessRegressionGrowthModelsKivircik lambSilage typeBirth weightBirth typeData miningVeterinary sciencesArticle00079901990000137938928310.9775/kvfd.2022.27149