The development of a body weight prediction method for Ongole Crossbred cattle using a meta-analysis and field experiment approach - Frediansyah Firdaus; Bayu Andri Atmoko; Panjono .; Yudi Adinata; Tanda Sahat Panjaitan; Noor Hudhia Krishna; Retno Widiyawati; Malik Makmur; Fajar Ariadi (Veterinary Integrative Sciences)
This study aims to develop and validate a model for predicting the body weight (BW) of Ongole Crossbred (OC) cattle using body measurements. To achieve this, a combination of meta-analysis and field experiments was employed. The meta-analysis involved identifying relevant keywords and databases, reviewing titles and abstracts, extracting data, and subsequently tabulating and analyzing the data. A total of 1,141 animal records were included in the quantitative synthesis process. Following the meta-analysis, a BW prediction model for OC cattle was developed. The model incorporated recommendations obtained from the meta-analysis, considering body measurement, age, and sex. Data from 507 animals were utilised to construct the model. Finally, a field experiment was conducted on 35 animals to assess the accuracy of the model. The meta-analysis revealed that body volume (BV) (r=0.96) and heart girth (HG) (r=0.89) exhibited stronger correlations with BW compared to body length (BL) (r=0.68). Linear regression modeling of OC cattle BW, demonstrated that HG yielded high correlation coefficients for both male (r=0.98) and female (r=0.94) cattle. Similarly, BV showed strong correlations for male (r=0.99) and female (r=0.95) cattle. Furthermore, the analysis revealed that both HG and BV were effective predictors across different age groups, with high correlation coefficients observed for cattle aged 1-12 months and over 24 months. The field experiment confirmed the high reliability of the model, achieving an accuracy of 90.8% for HG and 91% for BV. In conclusion, HG and BV are strong predictors of OC cattle BW, with categorization by breed further improving prediction accuracy.