Development of infrared reflectance spectroscopy databases for efficient livestock managements

There is a strong demand towards livestock for high quality products and services while limiting the impact on the environment. That goes for different management systems as outdoor for which consumer expectation is high. Decision Support Tools (DST) are therefore needed to achieve various food production and other agricultural resources, as manure. This synthesis aim is to highlight the potential of Near and Mid-Infrared Reflectance Spectroscopy (NIRS and MIRS) as tool for enhancing the value of livestock systems. NIRS and MIRS are non-destructive technologies that estimate simultaneously several parameters as chemical composition, nutritive value of various products, feeding characteristics (digestibility, intake), animal physiological status (pregnancy), detection of metabolic disorders (acidosis, mastitis) or enteric greenhouse gas emission (GES). A lot of products can be analysed by IRS: forages and feeds, effluents, faeces, milk, meat. All spectral information can be used in qualitative and quantitative ways to develop DST able to improve livestock system sustainability, herd management in regard to diet and animal welfare, to select efficient animals and this, for various animal species. Many developments are underway to merge databases of several research centres. So, NIRS is used to predict forage chemical composition; to estimate, from faeces analysis, parameters reflecting diet feeding value, in tropical and temperate areas. MIRS analysis of milk is another example of pooling databases for dairy system management. Prediction of new parameters as GES production from milk analysis and performances of livestock grazing on natural areas are studied. These examples illustrate the potential of NIRS and MIRS for the development of effective DST.

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Bibliographic Details
Main Authors: Decruyenaere, V., Boval, Maryline, Dardenne, Pierre, Gengler, Nicolas, Lecomte, Philippe
Format: conference_item biblioteca
Language:eng
Published: Wageningen Academic Publishers
Subjects:L02 - Alimentation animale, U30 - Méthodes de recherche, P01 - Conservation de la nature et ressources foncières,
Online Access:http://agritrop.cirad.fr/571534/
http://agritrop.cirad.fr/571534/1/document_571534.pdf
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Summary:There is a strong demand towards livestock for high quality products and services while limiting the impact on the environment. That goes for different management systems as outdoor for which consumer expectation is high. Decision Support Tools (DST) are therefore needed to achieve various food production and other agricultural resources, as manure. This synthesis aim is to highlight the potential of Near and Mid-Infrared Reflectance Spectroscopy (NIRS and MIRS) as tool for enhancing the value of livestock systems. NIRS and MIRS are non-destructive technologies that estimate simultaneously several parameters as chemical composition, nutritive value of various products, feeding characteristics (digestibility, intake), animal physiological status (pregnancy), detection of metabolic disorders (acidosis, mastitis) or enteric greenhouse gas emission (GES). A lot of products can be analysed by IRS: forages and feeds, effluents, faeces, milk, meat. All spectral information can be used in qualitative and quantitative ways to develop DST able to improve livestock system sustainability, herd management in regard to diet and animal welfare, to select efficient animals and this, for various animal species. Many developments are underway to merge databases of several research centres. So, NIRS is used to predict forage chemical composition; to estimate, from faeces analysis, parameters reflecting diet feeding value, in tropical and temperate areas. MIRS analysis of milk is another example of pooling databases for dairy system management. Prediction of new parameters as GES production from milk analysis and performances of livestock grazing on natural areas are studied. These examples illustrate the potential of NIRS and MIRS for the development of effective DST.