BWTKGYEQ

2689

5 janvier 2021 17:06

5 janvier 2021 17:06

This study focuses on calibrating and validating models for hyperspectral canopy reflectance data that are useful to predict the nutritive value of ryegrass-white clover mixed herbage available to the grazing cow. Hyperspectral measurements and herbage cuts were collected from 286 sampling plots from a dairy farm from July 2017 to May 2018. Hyperspectral data were pre-treated by applying a Savitzky-Golay filter followed by a Gap-segment derivative algorithm. Herbage samples were analyzed for determination of herbage nutritive value traits, digestible organic matter in dry matter (DOMD), metabolizable energy (ME), crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF). Partial least squares regression was performed to calibrate the spectra against the five nutritive value traits. Results indicate that accuracy was moderately high for the CP model (R2 = 0.78) and moderate for the DOMD, ME, NDF and ADF models (0.54 < R2 < 0.67). The possibility of being able to use proximal sensing for the estimation of herbage nutritive value in the field could potentially contribute to more efficient grazing management with potential economic benefits for the farm business.

None

• AB - Transversal
• FREDO alimentation animale
• FREDO technologie et innovation
• GEO Nouvelle-Zélande
• digestibility

• grazing management
• herbage nutritive value measurement
• partial-least squares regression
• proximal hyperspectral sensing

Agronomy

10

1826

2020/11

24 novembre 2020

10.3390/agronomy10111826 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique