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Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on... Browse is an important source of food for rustic livestock, particularly when herbaceous forage is scarce. The Atlantic Basque Country (Northern Spain) forest landscape is dominated by Pinus radiata D. Don plantations where Rubus sp. and Ulex gallii are understorey dominant species. Knowledge of the nutritive value of these species is needed in the context of silvopastoralism, primarily because do not always meet livestock requirements. The objective of this study was to evaluate the potential of Visible-Near Infrared Spectroscopy to determine the quality attributes of Rubus sp. and U. gallii, using a Sample Turn Table probe to acquire spectra on non-dried samples. VIS–NIRS calibrations were developed for dry matter (DM), crude protein (CP), crude fibre (CF), neutral detergent fibre (NDF), acid detergent fibre (ADF) and ashes. Spectra were pre-treated and Partial Least Squares Regression models were constructed. Calibration models were accurate for most of the considered variables, both for Rubus sp. (DM: Rc2 = 0.95, RPD = 2.53; CP: Rc2 = 0.90, RPD = 2.39; CF: Rc2 = 0.86, RPD = 2.30; NDF: Rc2 = 0.93, RPD = 2.80; ADF: Rc2 = 0.95, RPD = 3.12; Ashes: Rc2 = 0.91, RPD = 2.15) and for U. gallii (DM: Rc2 = 0.98, RPD = 3.67; CP: Rc2 = 0.94, RPD = 1.84; CF: Rc2 = 0.98, RPD = 4.74; NDF: Rc2 = 0.94, RPD = 3.91; ADF: Rc2 = 0.98, RPD = 3.62; Ashes: Rc2 = 0.82, RPD = 1.65). In general, ADF and DM were the most accurately predictable variables and ash content, the least predictable one. The results showed VIS–NIRS potential for the rapid and accurate prediction of quality attributes in non-dried samples and proved as a useful tool for making decisions in silvopastoral systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agroforestry Systems Springer Journals

Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

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References (37)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Life Sciences; Forestry; Agriculture
ISSN
0167-4366
eISSN
1572-9680
DOI
10.1007/s10457-018-0192-z
Publisher site
See Article on Publisher Site

Abstract

Browse is an important source of food for rustic livestock, particularly when herbaceous forage is scarce. The Atlantic Basque Country (Northern Spain) forest landscape is dominated by Pinus radiata D. Don plantations where Rubus sp. and Ulex gallii are understorey dominant species. Knowledge of the nutritive value of these species is needed in the context of silvopastoralism, primarily because do not always meet livestock requirements. The objective of this study was to evaluate the potential of Visible-Near Infrared Spectroscopy to determine the quality attributes of Rubus sp. and U. gallii, using a Sample Turn Table probe to acquire spectra on non-dried samples. VIS–NIRS calibrations were developed for dry matter (DM), crude protein (CP), crude fibre (CF), neutral detergent fibre (NDF), acid detergent fibre (ADF) and ashes. Spectra were pre-treated and Partial Least Squares Regression models were constructed. Calibration models were accurate for most of the considered variables, both for Rubus sp. (DM: Rc2 = 0.95, RPD = 2.53; CP: Rc2 = 0.90, RPD = 2.39; CF: Rc2 = 0.86, RPD = 2.30; NDF: Rc2 = 0.93, RPD = 2.80; ADF: Rc2 = 0.95, RPD = 3.12; Ashes: Rc2 = 0.91, RPD = 2.15) and for U. gallii (DM: Rc2 = 0.98, RPD = 3.67; CP: Rc2 = 0.94, RPD = 1.84; CF: Rc2 = 0.98, RPD = 4.74; NDF: Rc2 = 0.94, RPD = 3.91; ADF: Rc2 = 0.98, RPD = 3.62; Ashes: Rc2 = 0.82, RPD = 1.65). In general, ADF and DM were the most accurately predictable variables and ash content, the least predictable one. The results showed VIS–NIRS potential for the rapid and accurate prediction of quality attributes in non-dried samples and proved as a useful tool for making decisions in silvopastoral systems.

Journal

Agroforestry Systems – Springer Journals

Published: Jan 12, 2018

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