Use of robust regression algorithms for better prediction of body weight (BW)
is receiving increased attention. The present study therefore aimed at
predicting BW from chest circumference, breed and sex of a total of 1,012
goats. The animals comprised 332 matured West African Dwarf (WAD) (197 bucks
and 135 does), 374 Red Sokoto (RS) (216 bucks and 158 does) and 306 Sahel
(SH) (172 bucks and 134 does) randomly selected in Nasarawa State, north
central Nigeria. BW prediction was made using automatic linear modeling
(ALM), multivariate adaptive regression splines (MARS), classification and
regression tree (CART), chi-square automatic interaction detection (CHAID)
and exhaustive CHAID. The predictive ability of each statistical approach
was measured using goodness of fit criteria i.e. Pearson?s correlation
coefficient (r), Coefficient of determination (R2), Adjusted coefficient of
determination (Adj. R2), Root-mean-square error (RMSE), Mean absolute
percentage error (MAPE), Mean absolute deviation (MAD), Global relative
approximation error (RAE), Standard deviation ratio (SD ratio), Akaike?s
information criterion (AIC) and Akaike?s information criterion corrected
(AICc). Male RS and SH goats had significantly (P<0.05) higher BW and CC
compared to their female counterparts while in WAD, male goats had
significantly (P<0.05) higher CC (57.88?0.51 vs. 55.45?0.55). CC was
determined to be the trait of paramount importance in BW prediction, as
expected. Among the five models, MARS algorithm gave the best fit in BW
prediction with r, R2, Adj. R2, SDratio, RMSE, RAE, MAPE, MAD, AIC and AICc
values of 0.966, 0.933, 0.932, 0.26, 1.078, 0.045, 3.245, 0.743, 186.0 and
187.0, respectively. The present information may guide the choice of model
which may be exploited in the selection and genetic improvement of animals
including feed and health management and marketing purposes, and especially
in the identification of the studied breed?s standards.
