## 3 Results
### Approaches for Pasture Quantification
#### 3.4.3 Pasture Management Traits  
The main goal is to assess pasture quality using remote sensing as proxies to quantify its management traits [38, 46, 120, 132, 140, 142, 158, 170, 183, 205, 206, 207]. Nitrogen availability [140, 170, 202], soil water condition [67, 182, 207], irrigation [168], moving [188, 208], livestock distribution [188], soil nutrients [77, 142], and fertilizer treatment [207] are the major management traits that have been examined by authors and expressed as pasture quality indicators. Pasture quality has been linked with the aggregation of livestock (animal units) to areas with a rich concentration of nitrogen as a proxy for the abundance of vegetation greenness in mapping the spatial distribution of grazing animals [140, 208]. Some studies have used vegetation indices by selecting bands (red, red-edge, NIR, SWIR) of interest with linear regression models to relate them with management indicators [140, 170, 176, 202, 208]. Agricultural inputs, such as the irrigation date and LAI, were retrieved from FORMOSAT-2 using spectral indices and integrated into crop models to support water management for grazed pastures in France [176].  
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Other biophysical variables like LAI and fCOVER derived from SPOT imagery have been used with the KNN algorithm to map grazing landscapes to support moving management [188]. LAI shows a higher correlation of 82% with the sampled data compared to fCOVER. Higher-resolution sensors and/or a combination of multiple sensors have improved model accuracy, especially in a complex field for discriminating grasslands treated with fertilizer (i.e., nutrients) than using one sensor. [PERSON] et al. [207] reported an accuracy of 81% for Sentinel-2 and 76% for Landsat (OLI), which were resampled from hyperspectral data in discriminating grasslands treated with fertilizer using sparse partial-least-square regression (SPLSR). The hyperspectral data on its own yielded 92% accuracy. Similarly, HyspIRI data are more accurate (R\({}^{2}\) = 69%) than Sentinel-2 (R\({}^{2}\) = 58%) using wave bands and VIs with SPLSR in predicting burning, mowing, and fertilizer application [179]. However, [140] reported that the accuracy of Sentinel-2 (R\({}^{2}\) = 92%) is higher than RapidEye (R\({}^{2}\) = 53%) in predicting nitrogen concentration levels from simple ratio (SR) and NDVI with RF, due to the three red-edge bands present in Sentinel-2 compared to one red-edge band in RapidEye.  
ML models improve the discrimination of grasslands based on management indicators rather than linear regression. For example, [142] reported that RF achieved the best accuracy (R\({}^{2}\) = 78%) in predicting 77% of the macro and micronutrients derived from hyperspectral UAS (spatial resolution \(\sim\)3.5-11 nm); SVM achieved 86% accuracy for predicting 22% of the nutrients compared to the squares (PLSR) and kernel (PLSR) algorithms. Ancillary data like GPS provide information about livestock distribution and have been found to improve mode accuracy [140; 170; 208].