## 4 Discussion
### NIRS Models Accuracy: Calibration and Validation  
The most accurate models are those that have a high R\({}^{2}\) and RPD, a low RMSE and Bias [12; 28; 37; 46]. The coefficients of determination obtained in this study can be considered good for NDF (0.73 for the calibration model and 0.69 for the validation model, with RPD of 1.75) and moderate for CP (0.51 for calibration and 0.36 for internal validation, with RPD of 1.26). The values obtained from RPD are considered low [47]; however, these are expected and acceptable in experimental work involving field measurements [9]. In this discussion we have focused our attention particularly on the coefficient of determination (R\({}^{2}\)). The NIRS technique can be used in two ways: (i) with laboratory bench NIR equipment (LBNE); (ii) in field with portable spectrometer (micro-NIR). The first requires pasture sample pre-preparation (consisting of dehydration followed by crushing and grinding, or at least crushing/homogenization), the second is non-invasive, carried out in the field without any disturbance to the pasture. There are many works published with results of the use of LBNE to estimate the quality of plant material, whether pasture or other plant types. For example, [PERSON] et al. [10] in a study to calibrate and validate a LBNE to estimate the quality of grasses with pre-processing samples, obtained R\({}^{2}\) values of 0.97-0.98 for both calibration and validation models of CP and NDF. Also, [PERSON] et al. [14], in a study in which they intended to calibrate a LBNE to predict the chemical composition of feed for ostrich, found very high calibration correlations with pre-processing samples (R\({}^{2}\) = 0.96 and 0.94, respectively for CP and NDF). In relation to biodverse pastures, the study of [PERSON] et al. [37] showed significant correlation between calibration models obtained with LBNE and reference methods for quantifying pasture quality parameters, with greater accuracy from dry and pre-processing samples (R\({}^{2}\) = 0.936 for CP and R\({}^{2}\) = 0.914 for NDF) than fresh samples (without sample pre-preparation; R\({}^{2}\) = 0.702 for CP and R\({}^{2}\) = 0.720 for NDF). Also, with LBNE, [PERSON] et al. [33] obtained very strong correlations in analysis of green pasture (fresh samples) for both, CP and NDF (R\({}^{2}\) values of 0.96 and 0.84, respectively). [PERSON] et al. [45], in a study carried out in heterogeneous pastures in Chile, used a LBNE to predict various parameters of the fresh pasture, including CP (R\({}^{2}\) = 0.84) and NDF (R\({}^{2}\) = 0.78). With regard to the use of portable spectrometers (micro NIR), no published works were found referencing pastures in the Monado ecosystem, which makes it difficult to compare the results obtained in this study. However, there are works with other plants, such as in durum wheat grain CP content (R\({}^{2}\) of 0.98 to calibration and validation with samples previously homogenized; [31]). In field measurements in pastures, without pre-processing, accuracy is normally low. For example, to estimate pasture CP, [PERSON] et al. [48] obtained calibration R\({}^{2}\) of 0.63, while [PERSON] et al. [43] obtained R\({}^{2}\) values of 0.58 and 0.56, for calibration and for cross validation, respectively. The best results were published by [PERSON] et al. [28], two estimated various parameters of pasture quality in New Zealand, including CP (R\({}^{2}\) of 0.82 and 0.78 for calibration and validation models, respectively), and NDF (R\({}^{2}\) of 0.77 and 0.75 for calibration and validation models, respectively). One factor that can help explain these values obtained with the micro-NIR spectrometer is that the readings can be influenced by the water that exists on the surface of the pasture/soil [29]. NIR is more accurate with pre-dried samples than with fresh samples [27]. The obscuration effect, caused by the moisture's absorption of NIR light, is a factor that negatively influences the quality of the calibration and validation models with samples not previously dehydrated [24; 49].