## 1 Introduction  
Montado is a highly complex agro-forestry-pastoral ecosystem due to the particular climate and soil conditions and the synergies between animals, trees, and pastures. In addition, the Mediterranean climate presents two very distinct dry and wet seasons accentuated by an increasing inter-annual irregularity [1]. Consequently, dryland pasture quality and productivity fluctuate greatly over time as a result of the seasonal distribution of rainfall [2]. Therefore, it is important to highlight the fact that decisions (e.g., about soil amendment or fertilization, dynamic grazing, and livestock feed supplementation) have to be made in a context of great unpredictability [1]. Understanding seasonal changes in pasture nutritive value can enhance ruminant production systems and management [3]. The value of pasture is a combination of not only pasture production but also its nutritional quality [3]. Decisions on supplementation are based on assessments of pasture quantity and quality. The quantification of pasture quality can be done by using measurable parameters, such as crude protein (CP) content and neutral detergent fiber (NDF) content [4]. Animal growth and development are favored by a high CP content and a low NDF content, which are usually associated with early stages of pasture development. During the spring, pastures tend to lose quality. The inter-annual variability of rainfall distribution has an important impact on the dryland pasture vegetative cycle, to the point that supplementation needs, which are normal in the critical summer period, can be anticipated by one to two months (in late spring) in years of reduced spring precipitation [5]. CP levels below maintenance requirements (9.4% of dry matter in adult sheep [6]) require the use of feed supplements, hence the interest of regular monitoring of evolution of these pasture parameters. As a result, farm managers heavily rely on the monitoring of the pasture quality for making decisions related to animal management. The conventional method for assessing CP and NDF consists of collecting representative samples and carrying out laboratory analysis. However, this methodology is not practical because it requires demanding field work and access to a specialized laboratory (cutting, collection, and analytical procedures). This results in a lengthy and often expensive process that is not practical for a busy farm manager [7]. Consequently, there is a demand for fast procedures that can monitor pasture variables and provide farmers with timely information. Proximal sensing (PS) and remote sensing (RS) are relatively recent technologies that measure certain plant and other indices, with particular interest in the use of the normalized difference vegetation index (NDVI), and they have gained widespread acceptance in agriculture [1; 8]. In recent years, near-infrared spectroscopy (NIRS) technology, based on the absorption of the electromagnetic spectrum (radiation at wavelengths between 780 and 2500 nm), has been used in the pharmaceutical, petrochemical, agricultural, and food processing industries, among others [9]. Particularly, it has had a wide range of applications in agriculture, such as real-time pasture management [3], the prediction of the chemical composition of feeds [10], the detection of plant protein content [11], and the prediction of grape and wine quality [12]. NIR spectroscopy requires little or no sample preparation [9], thereby offering a simple, rapid, and reliable way to substitute some routine laboratory procedures and providing a fingerprint of sample composition. Briefly, energy in the NIR range is directed at the sample, and the reflected energy is measured by the instrument. However, this non-destructive technique requires a calibration procedure using some reference methods. The combination of NIR spectroscopy and multivariate data analysis (chemometrics or computational chemistry) provides calibration models that correlate the spectral response of a sample with its compositional profile [12; 13].