A Comparative Study of Simple Regression Models to Estimate Fibre Length Growth in Chios Sheep from Common Meteorological Variables

Abstract

Chios sheep is a promising sheep breed, with wool, one of its products, to be of special interest to genetic improvement programs. Recently, it has been reported significant linear correlation between the fibre length growth (FLG) of Chios sheep, an important component of its wool quality, and each of the meteorological variables air temperature (T) and sunshine (SUNS), but nothing is known about the prediction of FLG from T and SUNS. Thus, this work aims to investigate the effectiveness of five simple regression models (linear, quadratic, cubic, logarithmic and inverse), concerning the aforementioned prediction, using visual examination and two widely accepted statistical measures, the adjusted coefficient of determination (R2adj) and the root mean square error (RMSE). Results showed that the applied nonlinear regression models were characterized by higher R2adj and lower RMSE in comparison to the linear one, irrespective of input variable. The inverse model presented the greatest effectiveness to predict FLG from T and SUNS, separately (maximum R2adj and minimum RMSE), followed by the logarithmic and the linear ones, under visual examination and applied statistical measures. Air temperature was superior to SUNS in all cases (higher R2adj and lower RMSE), when comparing the regression models of the same type to check their effectiveness for predicting FLG. The findings of our study could be a decisive step towards a better exploitation of the examined meteorological variables for the sustainable production of Chios sheep.