Modelos de predicción del valor nutricional de henos de hierba

In the present work it is studied the predictive ability of NIRS for the estimation of chemical composition (n = 81) and organic matter digestibility (n = 52) of permanent and temporary pasture hays, being developed empirical equations based on chemical parameters to estimate the organic matter digestibility (OMD) values and compared the predictive ability of empirical models vs. NIRS equations. The collections of sam-Pereira-Crespo with standards of known in vivo digestibility (n = 38 samples). The predictive ability of the NIRS models for estimating the OMD and chemical composition ranged from excellent to good, according with the observed coefficient of determination in cross-validation (1 – VR) , higher than 0.90, except for the organic matter content (1 – VR = 0.87), whilst the ratio of the standard deviation of the original data to standard error of cross-validation ( RPD ) values were higher than 3.0 for all the parameters studied. Appl-ying NIRS models to the prediction of OMD of hay led to the reduction by half of the standard error of cross-validation ( SECV ) of the best empirical models, from ± 3.9 % to ± 1.9 %. It is concluded that the NIRS models developed in the present work as a tool for the rapid and precise nutritional evaluation of hay used in Galician dairy farms and can be satisfactorily used in routine analysis.