Abstract
e web application webGRAS (https://webgras.civis.bz.it) provides an on-line estimation of the potential forage quality of permanent meadows at the rst cut by means of statistical predictive models. As these models rely on a sequential sampling of forage at a large number of environments, the extension of this application to the regrowths implies time-consuming sampling activities. e information whether the rst regrowth can be used as a representative for all other subsequent regrowths represents, therefore, pivotal information for the preparation of an eecient investigation plan. At nine environments a sequential sampling at the rst cut and of the following two regrowths was performed in a randomized complete block design, and the forage quality of the samples was determined in the laboratory. e statistical analysis, including also growing degree days (GDD) as a covariate, showed a diierentiation between all the regrowths. is shows that the models based on GDD alone are not sucient to describe the forage quality regardless of the regrowth and that a sampling campaign of each regrowth or the use of correction factors would be necessary.