## 4 Discussion  
### Evaluation of Near Infrared Spectroscopy (NIRS)  
Choosing a proper wavenumber range is an essential pre-processing step to find the most representative wavelengths and eliminate uninformative spectra regions. Actually, many authors have found that the diffuse reflection from the wavenumber region of 4000-9000 cm\({}^{-1}\) (2500-1111 nm) carries information associated with organic material and structural fibers that is typical of a NIRS spectra [14; 16]. Considering the selected wavenumber range used in NDF prediction model development (4003-5600 and 7050-7300 cm\({}^{-1}\) or 2498-1786 and 1418-1370 nm), [PERSON] and [PERSON] [15]  
Figure 6: Relationship between (**a**) the PQI and the remote sensing NDVI (NDVI\({}_{\text{IRS}}\)); (**b**) PMC and the remote sensing NDWI (NDWI\({}_{\text{RS}}\)) over sampling times (between January and May 2019) and locations (8 experimental fields). Data are the average values of sampling pixels in each time and site location. also found two regions around 6066-5988 and 4424-4385 cm\({}^{-1}\) (1649-1670 and 2260-2280 nm) that were the most important regions associated with the presence of lignin and cellulose. On the other hand, [PERSON] et al. [11] found that the region around 6798-6535 cm\({}^{-1}\) (1471-1530 nm) corresponded to the absorbance band of protein in molecules. Additionally in the CP calibration model, it was observed in this study that the spectra within 4003-7800 cm\({}^{-1}\) (2498-1282 nm) were the most important regions that contributed to the performance of the models. These results indicated that aforementioned wavenumbers may play important roles in modeling protein content in pasture samples. As mentioned in the Results section, in terms of pre-processing methods used to evaluate CP, NDF, and the PQI in pasture samples, the best results were obtained using, respectively, the \"raw spectra,\" the \"normalization and SNV,\" and the \"raw spectra\" procedures. Additionally [PERSON] and [PERSON] [31] found similar R\({}^{2}\) values for CP quantification, denoting the high capacity of NIRS to predict this parameter in a wide range of forages and pastures. An RPD greater than 3 is an indicator of good quality of calibration model for prediction of CP [25; 26]. On the other hand, successful calibrations have also been made for the prediction of NDF with similar predictive accuracy and using the same spectral region in other plant species (grasses, cereal, and straws) [15]. The lower calibration accuracy of NDF models, relatively to CP models, might reflect some variability in the reference method due to high starch content on the forage and pasture samples [31], as fiber is a more complex component of forages that protein [32]. An RPD greater than 2 is an indicator of a calibration model for the screening purposes of NDF [25; 26] that suggests that more work needs to be done to build a more robust model. Regarding the PQI, as this parameter is a ratio between CP and NDF, an intermediate behavior would be expected. An calibration model with an RPD greater than 3 can be used as routine analysis in the prediction of the PQI [25; 26; 33]. The small difference observed in this study between the RMSE from the calibration and the RMSE from the external validation models selected for CP, NDF, and PQI quantification in pasture samples (Table 3) was similar to that obtained by [PERSON] et al. [30], who indicated robust and accurate calibrations. Models are robust when prediction accuracy is relatively insensitive to unknown changes of external factors. Additionally, according to [PERSON] et al. [34], a model is considered good enough to monitor the quality of individual samples when the R\({}^{2}\) is around 0.90 and the RDP is greater than 3. The R\({}^{2}\) (0.80-0.90) and the RPD (2.4-4.0) obtained in this study indicated that there were very little differences between reference and predicted values of CP, NDF, and the PQI [11].