Hostname: page-component-7dd5485656-kp629 Total loading time: 0 Render date: 2025-10-29T12:51:39.927Z Has data issue: false hasContentIssue false
  • English
  • Français

NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies

Published online by Cambridge University Press:  02 February 2011

D. Andueza ,
F. Picard ,
M. Jestin ,
J. Andrieu  and
R. Baumont
D. Andueza*
Affiliation:
INRA, UR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
F. Picard
Affiliation:
INRA, UR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
M. Jestin
Affiliation:
INRA, UR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
J. Andrieu
Affiliation:
INRA, UR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
R. Baumont
Affiliation:
INRA, UR1213 Herbivores, 63122 Saint-Genès-Champanelle, France
*
†E-mail: donato.andueza@clermont.inra.fr
Get access

Abstract

Near infrared reflectance spectroscopy (NIRS) of 924 fresh temperate forages were used to develop calibration models for chemical composition – crude ash (CA) and crude protein (CP) – organic matter digestibility (OMD) and voluntary intake (VI). We used 110 samples to assess the models. Four calibration strategies for determining forage quality were compared: (i) species-specific calibration, (ii) family-specific calibration, (iii) a global procedure and (iv) a local approach. Forage calibration data sets displayed CA values ranging from 52 to 205 g/kg of dry matter (DM), CP values from 50 to 280 g/kg DM, OMD values from 0.48 to 0.85 g/g and VI values from 22.5 to 115.2 g DM/kg metabolic body weight (BW0.75). The calibration models performed well for all the variables except for VI. For CA, local procedure showed lower standard error of prediction (SEP) than species-specific, family-specific or global models. For CP, the calibration models all showed similar SEP values (11.13, 11.08, 11.38 and 11.34 g/kg DM for species-specific, family-specific, global and local approaches). For OMD, the local procedure gave a similar SEP (0.024 g/g) to specific species and global procedures (0.027 g/g) and a lower SEP than the family-specific approach (0.028 g/g). For VI, the local approach and species-specific calibration showed lower SEP (7.08 and 7.16 g/kg BW0.75) than the broad-based calibrations (8.09 and 8.34 g/kg BW0.75 for family-specific model and global procedure, respectively). Local calibration may thus offer a practical way to develop robust universal equations for animal response determinations.

Keywords

chemical compositionin vivo organic matter digestibilityvoluntary intakenear infrared reflectance spectroscopy

Information

Type
Full Paper
Information
animal , Volume 5 , Issue 7 , 31 May 2011 , pp. 1002 - 1013
Copyright
Copyright © The Animal Consortium 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Andrieu, J, Demarquilly, C, Wegat-Litré, E 1981. Tables de prévision de la valeur alimentaire des fourrages. In Prévision de la valeur nutritive des aliments des ruminants (ed. C Demarquilly), pp. 345–577. INRA Publications, Versailles, France.Google Scholar
Andrieu, J, Demarquilly, C, Sauvant, D 1989. Tables of feeds used in France. In: Ruminant nutrition – recommended allowances and feed tables (ed. R Jarrige), pp. 213–304. John Libbey Eurotext, London, UK; Paris, France.Google Scholar
Andueza, D, Picard, F, Peccatte, JR, Gallard, Y, Hassoun, P, Viudes, G, Egal, D, Pradel, P, Trocquier, O, Thomas, D, Delaby, L, Agabriel, J, Baumont, R 2007. Variability within and among laboratories of in vivo digestibility and voluntary intake of two hays evaluated in sheep. In 14e Rencontres Recherches Ruminants, p. 247. Institut de l'Elevage – INRA, Paris, France.Google Scholar
Association of Official Analytical Chemists (AOAC) 1990. Official methods of analysis, vol. 2, 15th edition. AOAC, Arlington, VA, USA.Google Scholar
Aufrère, J, Demarquilly, C 1989. Predicting organic matter digestibility of forage by two pepsin-cellulase methods. In Proceedings of the XVI International Grassland Congress vol. 2 (ed. R Jarrige), pp. 877–878. Association Française pour la Production Fourragère, Nice, France.Google Scholar
Barnes, RF 1968. Variability within and among experiment stations in the determination of in vivo digestibility and intake of alfalfa. Journal of Animal Science 27, 519–524.CrossRefGoogle Scholar
Barnes, RJ, Dhanoa, MS, Lister, SJ 1989. Standard normal variate transformation and detrending of near infrared diffuse reflectance spectra. Applied Spectroscopy 43, 772–777.CrossRefGoogle Scholar
Berzaghi, P, Shenk, JS, Westerhaus, MO 2000. LOCAL prediction with near infrared multi-product databases. Journal of Near Infrared Spectroscopy 8, 1–9.CrossRefGoogle Scholar
Brown, WF, Moore, JE 1987. Analysis of forage research samples utilizing a combination of wet chemistry and near infrared reflectance spectroscopy. Journal of Animal Science 64, 271–282.CrossRefGoogle Scholar
Brown, WF, Moore, JE, Kunkle, WE, Chambliss, CG, Portier, KM 1990. Forage testing using near infrared reflectance spectroscopy. Journal of Animal Science 68, 1416–1427.Google Scholar
Chai, K, Kennedy, PM, Milligan, LP, Mathison, GW 1985. Effects of cold exposure and plant species on forage intake, chewing behaviour and digesta particle size in sheep. Canadian Journal of Animal Science 65, 69–76.CrossRefGoogle Scholar
Chenost, M, Demarquilly, C 1982. Measurement of herbage intake by housed animals. In Herbage intake handbook (ed. JD Leaver), pp. 95–112. British Grassland Society, Hurley, Maidenhead, Berkshire, UK.Google Scholar
Clark, DH, Mayland, HF, Lamb, RC 1987. Mineral analysis of forages with near infrared reflectance spectroscopy. Agronomy Journal 79, 485–490.CrossRefGoogle Scholar
Cochran, RC, Galyean, ML 1995. Measurement of in vivo forage digestion by ruminants. In Forage quality, evaluation and utilization (ed. GC Fahey, M Collins, DR Mertens and LE Moser), pp. 613–643. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, WI, USA.Google Scholar
Dardenne, P, Sinnaeve, G, Baeten, V 2000. Multivariate calibration and chemometrics for near infrared spectroscopy: which method? Journal of Near Infrared Spectroscopy 8, 229–237.CrossRefGoogle Scholar
Decruyenaere, V, Lecomte, Ph, Demarquilly, C, Aufrère, J, Dardenne, P, Stilmant, D, Buldgen, A 2009. Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): developing a global equation. Animal Feed Science and Technology 148, 138–156.CrossRefGoogle Scholar
Demarquilly, C, Chenost, M, Giger, S 1995. Pertes fécales et digestibilité des aliments et des rations. In Nutrition des ruminants domestiques. Ingestion et digestion (ed. R Jarrige, Y Ruckebusch, C Demarquilly, MH Farce and M Journet), pp. 601–647. INRA éditions, Paris, France.Google Scholar
Dixon, R, Coates, D 2009. Near infrared spectroscopy of faeces to evaluate the nutrition and physiology of herbivores. Journal of Near Infrared Spectroscopy 17, 1–31.CrossRefGoogle Scholar
Dulphy, JP, Baumont, R, L'Hotelier, L, Demarquilly, C 1999. Amélioration de la mesure et de la prévision de l'ingestibilité des fourrages chez le mouton par la prise en compte des variations de la capacité d'ingestion à l'aide d'un fourrage témoin. Annales de Zootechnie 48, 469–476.CrossRefGoogle Scholar
Fearn, T 1996. Comparing standard deviations. NIR News 7 (5), 5–6.CrossRefGoogle Scholar
Gauch, HG, Hwang, GJT, Fick, GW 2003. Model evaluation by comparison of model-based predictions and measured values. Agronomy Journal 95, 1442–1446.CrossRefGoogle Scholar
Givens, DI, Baker, CW, Adamson, AH, Moss, AR 1992. Influence of growth type and season on the prediction of the metabolisable energy content of herbage by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 37, 281–295.CrossRefGoogle Scholar
Menke, KH, Raab, L, Salewski, A, Steingass, H, Fritz, D, Schneider, W 1979. The estimation of the digestibility and metabolizable energy content of ruminant feeding stuffs from the gas production when they are incubated with rumen liquor in vitro. Journal of Agricultural Science Cambridge 93, 217–222.CrossRefGoogle Scholar
Michalet-Doreau, B, Gatel, F 1983. Evolution au cours d'une année des quantités de foin ingérées par des béliers castrés. Annales de Zootechnie 32, 459–464.CrossRefGoogle Scholar
Norris, KH, Barnes, RF, Moore, JE, Shenk, JS 1976. Predicting forage quality by infrared reflectance spectroscopy. Journal of Animal Science 43, 889–897.CrossRefGoogle Scholar
Osborne, BG, Fearn, T 1986. Near infrared spectroscopy in food analysis. Longman Scientific and Technical, Essex, UK.Google Scholar
Park, RS, Agnew, RE, Gordon, FJ, Steen, RWJ 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Animal Feed Science and Technology 72, 155–167.CrossRefGoogle Scholar
Robert, P, Bertrand, D, Demarquilly, C 1986. Prediction of forage digestibility by principal component analysis of near infrared reflectance spectra. Animal Feed Science and Technology 16, 215–224.CrossRefGoogle Scholar
Ruano-Ramos, A, Garcia-Ciudad, A, Garcia-Criado, B 1999. Determination of nitrogen and ash contents in total herbage and botanical components of grasslands systems with near infrared spectroscopy. Journal of the Science of Food and Agriculture 79, 137–143.3.0.CO;2-F>CrossRefGoogle Scholar
Sekulic, S, Seasholtz, MB, Wang, Z, Kowalski, BR, Lee, SE, Holt, BR 1993. Nonlinear multivariate calibration methods in analytical chemistry. Analytical Chemistry 65, 835A–845A.CrossRefGoogle Scholar
Shenk, JS, Westerhaus, MO 1995. Analysis of agriculture and food products by Near Infrared Reflectance Spectroscopy. Monograph, NIR Systems Inc. Pennsylvania State University and Owners of Infrasoft International, Port Matilda, PA, USA.Google Scholar
Shenk, JS, Westerhaus, MOBerzaghi, P 1997. Investigation of a LOCAL calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy 5, 223–232.CrossRefGoogle Scholar
Shenk, JS, Landa, I, Hoover, MR, Westerhaus, MO 1981. Description and evaluation of a near infrared reflectance spectro-computer for forage and grain analysis. Crop Science 21, 355–358.CrossRefGoogle Scholar
Shenk, JS, Workman, JJ, Westerhaus, MO 1992. Application of NIR spectroscopy to agricultural products. In Handbook of near infrared analysis (ed. DA Burns and EW Ciurczak), pp. 383–431. Marcel Dekker, New York, USA.Google Scholar
Sinnaeve, G, Dardenne, P, Agneessens, R, Biston, R 1994a. The use of near infrared spectroscopy for the analysis of fresh grass silage. Journal of Near Infrared Spectroscopy 2, 79–84.CrossRefGoogle Scholar
Sinnaeve, G, Dardenne, P, Agneessens, R 1994b. Global or local? A choice for NIR calibrations in analyses of forage quality. Journal of Near Infrared Spectroscopy 2, 163–175.CrossRefGoogle Scholar
Snedecor, GW, Cochran, WG 1980. Statistical methods, 7th edition. Iowa State University Press, Ames, IA, USA.Google Scholar
Stuth, J, Jama, A, Tolleson, D 2003. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Research 84, 45–56.CrossRefGoogle Scholar
Tilley, JM, Terry, RA 1963. A two-stage technique for the in vitro digestion of forage crops. Journal of the British Grassland Society 18, 104–111.CrossRefGoogle Scholar
Williams, PC, Sobering, DC 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy 1, 25–32.CrossRefGoogle Scholar
Williams, PC, Sobering, DC 1996. How we do it: a brief summary of the methods we use in developing near infrared calibrations. In Near infrared spectroscopy: the future waves (ed. AMC Davies and PC Williams), pp. 185–188. NIR Publications, Chichester, UK.Google Scholar
Windham, WR, Hill, NS, Stuedemann, JA 1991. Ash in forage, esophageal and fecal samples analyzed using near-infrared reflectance spectroscopy. Crop Science 31, 1345–1349.CrossRefGoogle Scholar