###### Abstract
Indirect pasture sampling methods minimize the physical removal of vegetation and were mainly developed with the aim of obtaining rapid methods that can be used over large areas [18]. In this sense, new non-destructive technologies are increasingly used, in order to better understand the variability of production in large areas and implement new production strategies [19]. Recent developments in the technological field, in terms of electronic sensors, are already an effective response today, allowing fast, reliable and large-scale measurements [20]. As an alternative to traditional methods, a method emerged that uses near-infrared (NIR) equipment, which through reflected infrared radiation estimate the chemical constituents of pastures [10]. NIR spectroscopy (NIRS) is based on the absorptions of C-H, N-H, O-H, C-N and C-C groups present in organic constituents [13], as well as S-H groups [21]. This technique measures the spectrum of infrared energy reflected from a sample illuminated by white light [11]. NIR radiation has an amplitude from about 780 to 2500 nm in the electromagnetic spectrum [17; 22; 23]. According to [PERSON] et al. [24] this radiation amplitude varies between 700 and 2500 nm. [PERSON] and [PERSON] [25] refer that this spectral region varies between 730 and 2500 nm. NIR measurements can occur by transmittance, reflectance or transfection; transmittance allows information to be obtained from the entire volume of the sample that is traversed by light, while reflectance only allows information to be obtained from the surface of the sample [17]. With NIRS it is possible to determine the chemical composition of animal feed quickly, non-destructively [10; 17], with less sample pre-processing and with high precision [14; 25]. According to these authors, this technique contrasts with traditional chemical analyses, as it does not require reagents and does not produce residues. In addition, this method allows for faster and cheaper determinations [10; 26; 27; 28]. [PERSON] et al. [29] report that NIRS has been widely used because it is a fast, reliable and capable method of evaluating the quality of pastures. The technique is rapid, non-destructive [21; 25; 26; 30; 31], precise, and cost-effective, compared with other laboratory techniques [32]. However, for it to be used in different conditions and different pastures, it requires calibration, using reference data [10]. The variability associated with natural pastures compromises the use of NIRS somewhat, since calibration and validation are more challenging when environmental variation is high [33]. On the other hand, [PERSON] al. [27] report that this technique allows a reliable evaluation of pasture quality, especially in terms of CP (crude protein) and NDF (neutral detergent fibre). The use of near-infrared spectroscopy (NIRS) for green pasture analysis has been available for several years in many European laboratories, which determine the components of animal feed [12]. However, the use of this laboratory equipment requires the cutting of pasture samples, which, as already mentioned, is a costly method in terms of time, money and labour, as well as being inaccurate, as it is not possible to sample the entire field. According to [PERSON] et al. [12] it also requires pre-processing the sample to be homogeneous, before performing the spectroscopic analysis. This pre-processing involves the dehydration of pasture samples and their grinding. When the pre-processing of the samples is not carried out, the precision decreases, due to the heterogeneity of the samples, especially with regard to the size of the particles [33]. The use of the NIRS technique for the evaluation of fresh samples has several obstacles, including irregular particle size and sample heterogeneity [34]. With the advancement of technology, smaller and lighter equipment emerged, which gave rise to portable equipment [17]. These sensors have several advantages over laboratory models, especially in terms of size, weight and manufacturing process, as well as less initial investment [35].