Prediction of Egg Weight from External Egg Traits of Guinea Fowl Using Multiple Linear Regression and Regression Tree Methods

ABSTRACT The study was done to predict egg weight from the external traits of the Guinea fowl egg using the statistical methods of multiple linear regression (MLR) and regression tree analysis (RTA). A total of 110 eggs from a flock of 23-week-old Guinea fowl were evaluated. Egg weight (EW) and external traits: eggshell weight (ESW), egg polar diameter (EPD), egg equatorial diameter (EED), egg shape index (ESI), and egg surface area (ESA) were measured. Descriptive statistics, Pearson correlation coefficients, and regression equations using the MLR were obtained; additionally, a RTA was done using the CHAID algorithm with the SPSS software (IBM ver. 22). EW presented positive correlations (p<0.0001) with ESA (r = 0.72), EPD (r = 0.65), and EED (r = 0.49). EW can be predicted through MLR using ESA as a predictor variable (R2 = 72%). Predictive accuracy improves when adding EPD and EED traits to the model (R2 = 75%). The RTA built a diagram using ESA, EED, and EPD as significant independent variables; of these, the most important variable was ESA (F = 50,295, df1 = 4, and df2 = 105; Adj. p<0.000) and the variation explained for EW was 74%. Likewise, the RTA showed that the highest egg weight (41.818 g) is obtained from eggs with a surface area > 59.03 cm2 and a polar diameter > 5.10 cm. The proposed statistical methods can be used to reliably predict the egg weight of Guinea fowl.