ABSTRACT
This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.
ABSTRACT
This study determined the effect of feeding Moringa oleifera (MO) leaf extracts to lactating ewes on the physicochemical composition of their milk and yogurt during storage (4 °C for 14 days) and the sensory acceptance of the yogurt. Over 45 days, 24 multiparous lactating Pelibuey and Katahdin ewes (two days in lactation) were randomly assigned to four groups: MO-0, basal diet (BD) + 0 mL MO; MO-20, BD + 20 mL MO; MO-40, BD + 40 mL MO; and MO-60, BD + 60 mL MO. In the milk, an increase of 6% in protein, 26% in leucine, 14% in ash, and 1% in the pH (6.71) was observed with MO-60. The density values decreased by 0.3% at a higher dose of MO compared to MO-0, while the nonfat solids (NFS) in the milk were similar between the treatments. In the yogurt, an increase of 5% in protein, 113% in leucine (MO-20), 9% in NFS, and a reduction of 2% in moisture with MO-60 was observed. The acidity reflected an inverse relationship to the pH, as did the moisture and NFS with MO-60. In conclusion, dietary supplementation with MO in lactating ewes did not have negative effects on the chemical composition of their yogurt during storage (14 days). Overall, feeding sheep with 20 mL of MO positively influenced the physicochemical composition of their milk and yogurt during storage.
ABSTRACT
The present study was designed to evaluate the relationship between the body measurements (BMs) and carcass characteristics of hair sheep lambs. Twenty hours before slaughter, the shrunk body weight (SBW) and BMs were recorded. The BMs involved were height at withers (HW), rib depth (RD), body diagonal length (BDL), body length (BL), pelvic girdle length (PGL), rump depth (RuD), rump height (RH), pin-bone width (PBW), hook-bone width (HBW), abdomen width (AW), girth (GC), and abdomen circumference (AC). After slaughter, the carcasses were weighed and chilled for 24 h at 1 °C, and then were split by the dorsal midline. The left-half was dissected into total soft tissues (muscle + fat; TST) and bone (BON), which were weighed separately. The weights of viscera and organs (VIS), internal fat (IF), and offals (OFF-skin, head, feet, tail, and blood) were also recorded. The equations obtained for predicting SBW, HCW, and CCW had an r2 ranging from 0.89 to 0.99, and those for predicting the TST and BON had an r2 ranging from 0.74 to 0.91, demonstrating satisfactory accuracy. Our results indicated that use of BMs could accurately and precisely be used as a useful tool for predicting carcass characteristics of hair sheep lambs.
