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A simplified method to determine the abundance of grass functional groups in natural grasslands

Delphine Fallour, Jean Pierre Theau, K Corler, Laure Hossard, Guillaume Martin, Claire Jouany, Michel Duru, Pablo Cruz

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Abstract

A functional classification of grass species into four groups has been proposed, based on their leaf dry matter content. These plant functional groups (PFTs) show well differentiated properties for vegetation use (e.g. digestibility). The aim of this work was to assess the reliability of an easy methodology for identification of the dominant grass group in a given grassland community to facilitate technical advising. Three methods of botanical relevés were tested on grasslands of beef and dairy production systems of the Aubrac region (Central Massif) in France. Methods display differences in complexity of field work: a complete botanical description (Braun-Blanquet), the method of de Vries and de Boer, and a simplified non-destructive adaptation of the latter. Data analyses were performed using Spearman rank correlation tests on grassland classification according to different variables, mainly species richness and proportion of grass functional groups. Results show differences in the number of species listed. No differences were observed between grassland classifications based on PFTs dominance. We conclude that the simplest of them could be used to assess the grass dominant group necessary to implement grassland management tools.

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What this paper is about

A functional classification of grass species into four groups has been proposed, based on their leaf dry matter content. These plant functional groups (PFTs) show well differentiated properties for vegetation use (e.g. digestibility). The aim of this work was to assess the reliability of an easy methodology for identification of the dominant grass group in a given grassland community to facilitate technical advising. Three methods of botanical relevés were tested on grasslands of beef and dairy production systems of the Aubrac region (Central Massif) in France. Methods display differences in complexity of field work: a complete botanical description (Braun-Blanquet), the method of de Vries and de Boer, and a simplified non-destructive adaptation of the latter. Data analyses were performed using Spearman rank correlation tests on grassland classification according to different variables, mainly species richness and proportion of grass functional groups. Results show differences in the number of species listed. No differences were observed between grassland classifications based on PFTs dominance. We conclude that the simplest of them could be used to assess the grass dominant group necessary to implement grassland management tools.

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Available abstract

A functional classification of grass species into four groups has been proposed, based on their leaf dry matter content. These plant functional groups (PFTs) show well differentiated properties for vegetation use (e.g. digestibility). The aim of this work was to assess the reliability of an easy methodology for identification of the dominant grass group in a given grassland community to facilitate technical advising. Three methods of botanical relevés were tested on grasslands of beef and dairy production systems of the Aubrac region (Central Massif) in France. Methods display differences in complexity of field work: a complete botanical description (Braun-Blanquet), the method of de Vries and de Boer, and a simplified non-destructive adaptation of the latter. Data analyses were performed using Spearman rank correlation tests on grassland classification according to different variables, mainly species richness and proportion of grass functional groups. Results show differences in the number of species listed. No differences were observed between grassland classifications based on PFTs dominance. We conclude that the simplest of them could be used to assess the grass dominant group necessary to implement grassland management tools.

Key concepts: Abundance (ecology), Natural (archaeology), Grassland, Computer science, Environmental science, Ecology, Geography, Biology

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