Application of NIR Spectroscopy to the Analysis of Forages

Minet, Olivier; Decruyenaere, Virginie; Godin, Bruno; Baeten, V.

doi:10.1201/b22513-32

Cited by 1 publication

(2 citation statements)

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“…In the current context of the price volatility of feedstuffs and concentrates, it is essential to characterize forage quality to minimize production costs. Knowledge of the forage's quality is also essential to meet animal requirements while controlling the environmental impact (Minet et al, 2021). Since the seventies, near-infrared spectroscopy (NIRS) has been recognized as a tool for predicting the quality of forage (Norris et al, 1976;Deaville & Flinn, 2000;Baeten et al, 2016;Debode et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, handheld NIR spectrometers were developed for rapidly predicting the DM content and the chemical composition of forages (Bell et al, 2018). These miniaturized and mobile instruments allow frequent analyses, with real-time results, that can help the farmer to feed their cattle more efficiently (Fernández Pierna et al, 2010;Bell et al, 2018;Carreira et al, 2021;Minet et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Performance of three handheld NIR spectrometers for predicting grass silage quality

Pierna¹,

Vermeulen²,

Chamberland³

et al. 2022

Biotechnol. Agron. Soc. Environ.

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Description of the subject. Feed is the main variable cost in dairy farming. More efficient use of forage resources is one way to reduce production costs. Improving forage resource efficiency can start with a better assessment of the dry matter content and nutritional value of forages. Currently, analytical process time is often long and analyses are not repeatable while the quality of the fodder changes over time. Being able to analyze forages directly on-farm would make it possible to adapt the animal diet according to forage variability, in order to improve the profitability of the farm. Objectives. To propose in situ rapid analysis solutions to better characterize dry matter content and the chemical composition of fodder for assessing its feeding value. Method. The performance of three recently developed spectroscopic handheld devices, namely the Viavi’s MicroNIR 1700, the Ocean Insight’s FlameNIR and the Malvern Panalytical’s ASD FieldSpec 4, are evaluated to predict dry matter content and the chemical composition of fresh and unground grass silage in the framework of precision feeding and compared to the reference benchtop Foss’s XDS instrument. The conventional global PLS and local PLS are used as multivariate calibration methods. Results. The assessed handheld devices allow the dairy farmer to obtain a relatively precise quantitative prediction of the dry matter and crude fiber content (2.5% and 1.8% respectively on average, in terms of ratios between the local PLS error on fresh forage and the reference method error) in order to adapt the livestock diet. Crude protein, even if the prediction accuracy is lower (6.4%), is still well predicted. Higher errors are obtained for ash (9.2%), crude neutral (6.8%) and acid detergent fiber (6.9%). Conclusions. The studied devices should allow the dairy farmer to obtain a relatively precise quantitative prediction of those quality parameters in order to directly adapt the quantity of forage distributed to the animals. Performances could probably be improved by including more samples/spectra into the databases.

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