"Predicting the Body Weight of Indigenous Sheep from Linear Body Measurement Traits Using Classification and Regression Tree Data Mining Algorithm"

Abstract

Objectives: This study aimed to investigate the association between body weight (BW) and linear body measurements (LBMs) in indigenous sheep. Additionally, the study sought to establish a predictive model for BW using the Classification and regression tree (CART) data mining algorithm. Materials and Methods: A total of 347 ewes were randomly selected for physical measurements. LBMs, i.e., height at wither (HW), body length (BL), chest depth (CD), chest girth (CG), rump length (RL), rump height (RH), pelvic width (PW), shoulder width (SW), head width (HW), head length (HL), cannon bone length (CBL), cannon bone circumference (CBC), ear length (EL), horn length (HL), tail length (TL), and tail circumference (TC), were recorded following the recommended FAO descriptors for sheep genetic resources. Statistical analyses, including descriptive statistics, correlation coefficients, and CART algorithm were employed to assess the impact of LBMs on sheep BW. Results and Discussions: The correlation coefficients between LBMs and BW ranged from 0.11 (between RL and PW) to 0.97 (between HG and BL and between BW and HG). For the training dataset, the model explained 93% of the variance in BW acounted for by the LBMs. The root average squared error was found to be 1.27, suggesting that, on average, the model's predictions deviated from the actual BWs by approximately 1.27 units. The CART analysis identified distinct nodes and partitions based on LBMs, specifically heart girth (HG), body length (BL), rump height (RH), and shoulder width (SW). The study reveals that BW can be effectively predicted using different combinations of LBMs. Conclusion: The findings provide valuable insights for researchers seeking to understand the relationships between LBMs and BW in sheep. The developed predictive model can aid in estimating BW accurately, facilitating decision-making in livestock management. Further research should focus on validating these results using larger datasets and diverse sheep breeds. Additionally, future studies should consider factors such as age, sex, and breed effects to gain a comprehensive understanding of BW determinants in sheep.