Growth curve and weight estimates of PCs are significantly valued when determining market price, study says Dec 2018 R4D Highlights Research, Philippine Carabao Center, Department of Agriculture By Charlene Joanino & Jennifer Maramba The selection of buffaloes for breeding and fattening utilizes data on body weights, average daily gain (ADG) and body conformation of the same age group. However, alteration in the estimation of genetic parameters for animal evaluation may occur based on the preliminary analysis of growth trait. Photo by DA-Philippine Carabao Center In relation to this, PCC researchers Jennifer Maramba, Jose Arceo Bautista, Josefina Dizon, Ester Flores and Agapita Salces, studied the “Growth Curve and Weight Estimates of the Philippine Swamp Buffalo (Bubalusbubalis Linn.) in Piat, Cagayan”. It aimed to determine weights and heights for the different age groups of Philippine swamp buffalo or PC in PCC at Cagayan State University (CSU) and identify a growth model that best fit the curve. A total of 10,801 records from 272 animals in the PC nucleus herd of PCC at CSU for growth curve and 352 records from 252 animals for weight prediction were analyzed using JMP version 8 software. Wherein, monthly growth was described in terms of the animal’s body weight (kg), wither height (cm), heart girth (cm) and body length (cm) using animal weighing scale, meter stick as well as tape measure. Four non-linear models (Logistic, Gomperts, Von Bertalanffy and Brody) were used to determine the growth curve of PC using weight and height with different age categories. It was found that Brody model best fit the curve with an average percent difference of 2.4% or 97.6% accuracy. The Brody model rendered the lowest Aikaike Information Criterion (AIC) and Root Mean Square Error (RMSE) values, which serves as determinants in choosing amongst the said models. Among different body measurements in PC, significantly higher correlation of body weight (BW) was found with heart girth followed by body length when analyzed using linear regression. Higher degree of predicting weight was observed in 22 to 33 months of age (1.1% to 2.4%) derived in the model equation. Using the validation data, the percent difference of predicted weight from actual weight ranged from 0.1% to 6.7% or 93.3% to almost 100% accuracy. Thus, it indicated a very good prediction of body weight. Overall, Brody growth curve model best fit the data set of weight and height for all male and female PC using different age category. Prediction of body weight using linear regression can be helpful for the farmer or farm supervisor to monitor the growth for breeding, knowledge on the dosage required in administering medicine, determine the feed requirement when fattening and value of animals when sold to market without the availability of animal weighing scale in the farm or in the field. This can help farmers render better management of rearing buffaloes.
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