Abstract
The web application webGRAS (https://webgras.civis.bz.it) provides an on-line estimation of the potential forage quality of permanent meadows at the first 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. The information whether the first regrowth can be used as a representative for all other subsequent regrowths represents, therefore, pivotal information for the preparation of an efficient investigation plan. At nine environments a sequential sampling at the first 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. The statistical analysis, including also growing degree days (GDD) as a covariate, showed a differentiation between all the regrowths. This shows that the models based on GDD alone are not sufficient 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.