## 3 Results  
### Evaluation of Near Infrared Spectroscopy (NIRS)  
Table 2 shows PMC, CP, and NDF values of pasture samples at each location and each sampling date, determined with the reference method and used in the calibration and external validation models. The first requirement to obtain a good calibration model is to have a wide variation of the chemical composition of a set. The average values of these parameters (PMC between 62.5% and 89.1%; CP between 10.4% and 24.3%; and NDF between 29.4% and 60.2%) are characteristic for dryland pastures during the growing season (autumn, winter, and spring). The variation ranges of these parameters showed, on the other hand, that the samples used in this study are representative of the inherent variability of biodiverse pastures of different fields in different phases of the vegetative cycle. Table 3 shows statistics for calibration and external validation of prediction models developed using PLSR to correlate NIRS absorbance spectra with the invasive quality measurements (CP, NDF, and the PQI). Four regression models were developed using the listed mathematical procedure (raw spectra, normalization, SNV, and normalization and SNV). This table shows that the PLSR models selected for CP, NDF, and PQI quantification in pasture samples exhibited a small difference between RMSE from the calibration and RMSE from the external validation models. Figure 4 shows the optimized spectra of NIRS, considering several pre-processing methods, for CP, NDF, and the PQI. These are typical absorbance pasture spectrums. Considering that the main goal of this study was to obtain predictive models to quantify CP, NDF, and indirectly, pasture quality (PQI), the pasture raw spectra region selected in this study was defined as that within the wavenumber region from 4000 to 9000 cm\({}^{-1}\) (2500-1111 nm). Considering that an accurate model should have a high RPD, a high R\({}^{2}\), a low RMSE, a low average difference between predicted and actual values (bias) [29], and a small difference between RMSE from calibration and external validation models [30], when evaluating all pre-processing methods used to evaluate CP, NDF, and the PQI in pasture samples, the best results were obtained using:  
1. The \"raw spectra\" procedure for CP prediction model due to the highest RPD (4.0) and R\({}^{2}\) (0.844) and the lowest RMSE (1.622) and bias (0.057) of the external validation model (Table 3). 2. The \"normalization and SNV\" pre-processing for the NDF prediction model due to the highest RPD (2.4) and R\({}^{2}\) (0.826) and lowest RMSE (4.200) of the external validation model (Table 3). 3. The \"raw spectra\" procedure for the PQI prediction model due to the highest RPD (3.2) and R\({}^{2}\) (0.808) and lowest RMSE (0.066) and bias (0.009) of the external validation model (Table 3). Figure 5 shows measured vs. predicted values for CP, NDF, and the PQI, in calibration and validation phases. It is visible that the range of the calibration and validation sets was similar for all parameters, which contributed to a good representativeness of the whole group of samples. According to the coefficients of determination and the predicted vs. reference values, the CP model had the higher prediction capability and the NDF model had the lowest, which is in accordance with other studies [31]; nevertheless, these results showed that NIRS calibration models provided significantly identical data to reference methods to quantify CP, NDF, and the PQI.