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|Title: ||Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem|
|Authors: ||Serrano, João|
Marques da Silva, J.
Rato, Ana Elisa
pasture quality index
normalized difference vegetation index
normalized difference water index
|Issue Date: ||Jun-2020|
|Citation: ||Serrano, J., Shahidian, S., Marques da Silva, J., Paixão, L., Carreira, E., Carmona-Cabezas, R., Nogales-Bueno, J., Rato, A.E. (2020). Evaluation of near infrared spectroscopy (NIRS) and remote sensing (RS) for estimating pasture quality in Mediterranean Montado ecosystem. Applied Sciences, 10, 4463. doi:10.3390/app10134463|
|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.|
|Appears in Collections:||ERU - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica|
MED - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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