Utility of MARS Algorithm for Describing Non-Genetic Factors Affecting Pasture Revenue of Morkaraman Breed and Romanov x Morkaraman F1 Crossbred Sheep under Semi Intensive Conditions

This study was conducted on indigenous Morkaraman breed and Romanov x Morkaraman F1 Crossbreed sheep reared under semi-intensive conditions in order to determine the influence of dam age, genotype, birth weight, year, sex and birth type at lambing, weaning weight, weaning age, grazing period and weight at the end of the grazing period on pasture revenue, which is defined as the obtained revenue per lamb from weaning period to the end of the grazing period. Multivariate adaptive regression splines (MARS) data mining algorithms in addition to least squares method were scrutinized comparatively in the prediction of weight at the end of grazing period. The best statistical approach was selected based on goodness of fit criteria viz. determination coefficient (R-2), adjusted coefficient of determination (R-2 (ADJ)), and Pearson correlation coefficient between the actual and the predicted values in the response trait handled. The greatest importance order was obtained for age at the end of grazing period (100%), followed by weight at the end of grazing period, weaning age, birth weight and grazing period. To obtain the pasture revenue, weight at the end of grazing period was required to be heavier than 26.2 kg, and age at the end of the grazing period was needed to be longer than 173 days. Average weaning weight of the lambs (15.7 kg) contributed the pasture revenue of 106.08 TL. When the weaning weights increased from 17.0 to 21.5 kg, the pasture revenue reduced from 79.56 to 7.34 TL. As a result, MARS algorithm may be a good approach to predict pasture revenue and to capture ideal cut-off values of significant factors affecting the revenue for increasing profitability of lamb meat production in the sheep.