Near infrared spectrometry to evaluate the feed value of forages

Archive ouverte

Barotin, Charlène | Bonnal, Laurent | Andueza, Donato | Trupin-Maudemain, Séverine | J, Jost, | Caillat, Hugues | Julien, Lionel | Juanes, Xavier | Assouma, Mohamed-Habibou | Lesnoff, Matthieu | Picard, Fabienne | Fumat, Norbert | El Radi, Hadi | Barre, Philippe

Edité par CCSD ; Association Française pour la Production Fourragère -

International audience. Near Infrared Spectrometry (NIRS) is used to evaluate the nutritional value of forages and even their feed value if applied to fresh material. Indeed, it allows to estimate the contents of nitrogenous matter, crude cellulose (or walls or ligno-cellulose), organic matter and dry matter as well as the cellulase digestibility. These data are used to predict the feed value of forages (Forage Units: FU, Digestible Protein in the Intestine: DPI, and Bulking Units: BU) using different regression models. The NIRS estimates are based on the establishment of a relationship between the values obtained by a reference method and the spectral absorbance data. A calibration base is thus built and must correspond to the diversity of the analyzed samples. For this, it is regularly completed by adding "atypical" points. An increasing diversity of forages is used in agriculture to cope with climatic hazards and the desire to reduce inputs. In particular, more and more species mixtures are used in different forms: green, hay or silage. This diversity requires the collection of absorption spectra and the chemical analysis of several hundred or even several thousand samples. Faced with these constraints, CIRAD and INRAE have chosen to pool their respective forage databases and thus benefit collectively from the diversity of each. This was possible thanks to a standardization of chemical reference analyses and absorbance spectra between laboratories. This method, non-destructive and inexpensive, tends towards an evolution of the devices which become miniaturized and allows an analysis in the farms. The advances in this field are discussed in this article.

Consulter en ligne

Suggestions

Du même auteur

Averaging and Stacking Partial Least Squares Regression Models to Predict the Chemical Compositions and the Nutritive Values of Forages from Spectral Near Infrared Data

Archive ouverte | Lesnoff, Mathieu | CCSD

International audience. Partial least square regression (PLSR) is a reference statistical model in chemometrics. In agronomy, it is used to predict components (response variables y) of chemical composition of vegeta...

Near-infrared spectrometry for the characterization of feed resources. La spectrométrie dans le proche infrarouge pour la caractérisation des ressources alimentaires

Archive ouverte | Bastianelli, Denis | CCSD

National audience. The management of livestock systems for technical, economic and environmental optimization requires an increasingly accurate formulation of rations and therefore a detailed knowledge on the feeds ...

A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition

Archive ouverte | Taugourdeau, Simon | CCSD

International audience. Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple f...

Chargement des enrichissements...