A multi-site study to classify semi-natural grassland types

International audienceCalibration and validation of simulation models describing herbage growth or feed quality of seminatural grasslands is a complex task for agronomists without investing effort into botanical surveys. To facilitate such modelling efforts, a limited number of grassland types were identified using a functional classification of species. These grassland types were characterized by three descriptors required to model herbage growth or feed quality: the abundance-weighted mean leaf dry matter content across grass species, the relative abundance of grasses, and an estimate of species richness. We conducted a multi-site analysis over 749 grasslands from eight temperate regions in France and northern Spain. Using Restricted Maximum Likelihood models, explanatory variables having a significant impact on the descriptors of grassland vegetation were identified. These were management type and nutrients availability described either with an ecological approach based on Ellenberg nutrient indices, or an agronomic approach based on plant nutrition indices. By fixing expert-assigned boundaries along the gradients of management type and nutrients availability, we identified a classification of 3 x 3 seminatural grassland types. When compared pairwise with a Kruskal-Wallis test, the identified seminatural grassland types showed significant differences for vegetation descriptors. The model inputs corresponding to different semi-natural grassland types led to simulated differences in herbage growth and feed quality comparable with the uncertainty on the model output. This confirmed the relevance of the classification based only on nine species-rich semi-natural grassland types easily identifiable on the field to model herbage growth or feed quality. (C) 2008 Elsevier B.V. All rights reserved