| Autor: |
Parrini S; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy., Fabbri MC; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy., Argenti G; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy., Staglianò N; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy., Pugliese C; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy., Bozzi R; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy. |
| Abstrakt: |
This study aims to classify plant communities by applying discriminant analysis based on principal components (DAPC) on near-infrared spectra (FT-NIRS) starting from fresh herbage samples. Grassland samples ( n ~156) belonged to (i) recent alfalfa pure crops (CAA), (ii) recent grass-legume mixtures (GLM), (iii) permanent meadows derived from old alfalfa stands that were re-colonized (PMA), and iv) permanent meadows originated from old grass-legume mixtures (PLM). Samples were scanned using FT-NIRS, and a multivariate exploration of the original spectra was performed using DAPC. The following two scenarios were proposed: (i) cross-validation, where all data were used for model training, and (ii) semi-external validation, where the group assignment was performed without samples of the training set. The first two components explained 98% of the total variability. The DAPC model resulted in an overall assignment success rate of 77%, and, from cross-validation, it emerged that it was possible to assign the CAA and PMA to their group with more than of 80% of success, which were different in botanical and chemical composition. In comparison, GLM and PLM obtained lower success of assignment (~52%). External validation suggested similarity between PLM and GLM groups (93%) and between GLM and PLM (77%). However, a dataset increase could improve group differentiation. |
