Abstract
Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.
Highlights
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
The variation ranges of these parameters showed, on the other hand, that the samples used in this study are representative of the inherent variability of biodiverse pastures of different fields in different phases of the vegetative cycle
According to the coefficients of determination and the predicted vs. reference values, the crude protein (CP) model had the higher prediction capability and the neutral detergent fiber (NDF) model had the lowest, which is in accordance with other studies [31]; these results showed that Near Infrared Spectroscopy (NIRS) calibration models provided significantly identical data to reference methods to quantify CP, NDF, and the pasture quality index (PQI)
Summary
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. Dryland pasture quality and productivity fluctuate greatly over time as a result of the seasonal distribution of rainfall [2]. 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 pasture production and its nutritional quality [3]. Decisions on supplementation are based on assessments of pasture quantity and quality
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