Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-22T18:32:39.789Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of rumen degradability parameters of a wide range of forages and non-forages by NIRS

Published online by Cambridge University Press:  18 February 2015

A. Foskolos ,
S. Calsamiglia ,
M. Chrenková ,
M. R. Weisbjerg  and
E. Albanell
A. Foskolos
Affiliation:
Animal Nutrition, Management and Welfare Research Group, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
S. Calsamiglia
Affiliation:
Animal Nutrition, Management and Welfare Research Group, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
M. Chrenková
Affiliation:
National Agricultural and Food Centre, Research Institute for Animal Production, Nitra, 951 41 Lužianky, Slovakia
M. R. Weisbjerg
Affiliation:
Department of Animal Science, AU Foulum, Aarhus University, 8830 Tjele, Denmark
E. Albanell*
Affiliation:
Group of Ruminant Research, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
*
†E-mail: [email protected]
Get access

Abstract

Kinetics of nutrient degradation in the rumen is an important component of feed evaluation systems for ruminants. The in situ technique is commonly used to obtain such dynamic parameters, but it requires cannulated animals and incubations last several days limiting its application in practice. On the other hand, feed industry relies strongly on NIRS to predict chemical composition of feeds and it has been used to predict nutrient degradability parameters. However, most of these studies were feedstuff specific, predicting degradability parameters of a particular feedstuff or category of feedstuffs, mainly forages or compound feeds and not grains and byproducts. Our objective was to evaluate the potential of NIRS to predict degradability parameters and effective degradation utilizing a wide range of feedstuffs commonly used in ruminant nutrition. A database of 809 feedstuffs was created. Feedstuffs were grouped as forages (FF; n=256), non-forages (NF; n=539) and of animal origin (n=14). In situ degradability data for dry matter (DM; n=665), CP (n=682) and NDF (n=100) were collected. Degradability was described in terms of washable fraction (a), slowly degradable fraction (b) and its rate of degradation (c). All samples were scanned from 1100 to 2500 nm using an NIRSystems 5000 scanning in reflectance mode. Calibrations were developed for all samples (ALL), FF and NF. Equations were validated with an external validation set of 20% of total samples. NIRS equations to predict the effective degradability and fractions a and b of DM, CP and NDF could be evaluated from being adequate for screening (r2>0.77; ratio of performance to deviation (RPD)=2.0 to 2.9) to suitable for quantitative purposes (r2>0.84; RPD=3.1 to 4.7), and some predictions were improved by group separation reducing the standard error of prediction. Similarly, the rate of degradation of CP (CPc) and DM (DMc) was predicted for screening purposes (RPD⩾2 and 2.5 for CPc and DMc, respectively). However, the rate of degradation of NDF was not predicted accurately (NDFc: r2<0.75; RDP<2).

Keywords

NIRSin situeffective degradationdegradability parameters
Type
Research Article
Information
animal , Volume 9 , Issue 7 , July 2015 , pp. 1163 - 1171
Copyright
© The Animal Consortium 2015 

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.)

References

Åkerlind, M, Weisbjerg, MR, Eriksson, T, Thøgersen, R, Uden, P, Olafsson, BL, Harstad, OM and Volden, H 2011. Feed analysis and digestion methods. In NorFor – the Nordic feed evaluation system (ed. H Volden), pp. 41–54. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Andrés, S, Murray, I, Calleja, A and Giráldez, F 2005a. Review: nutritive evaluation of forages by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy 13, 301–311.Google Scholar
Andrés, S, Giráldez, FJ, González, JS, Peláez, R, Prieto, N and Calleja, A 2005b. Prediction of aspects of neutral detergent fibre digestion of forages by chemical composition and near infrared reflectance spectroscopy. Australian Journal of Agricultural Research 56, 187–193.Google Scholar
Association of Official Analytical Chemists 1990. Official methods of analysis, 15th edition. AOAC, Arlington, VA, USA.Google Scholar
Baldwin, RL 1995. Modeling ruminant digestion and metabolism. Chapman & Hall, London, UK.Google Scholar
Belanche, A, Weisbjerg, MR, Allison, GG, Newbold, CJ and Moorby, JM 2013. Estimation of feed crude protein concentration and rumen degradability by Fourier-transform infrared spectroscopy. Journal of Dairy Science 96, 7867–7880.Google Scholar
Belanche, A, Weisbjerg, MR, Allison, GG, Newbold, CJ and Moorby, JM 2014. Measurement of rumen dry matter and neutral detergent fiber degradability of feeds by Fourier-transform infrared spectroscopy. Journal of Dairy Science 97, 2361–2375.Google Scholar
De Boever, JL, Vanacker, JM and De Brabander, DL 2003. Rumen degradation characteristics of nutrients in compound feeds and the evaluation of tables, laboratory methods and NIRS as predictors. Animal Feed Science and Technology 107, 29–43.Google Scholar
de La Roza, B, Martínez, A, Santos, B, González, J and Gómez, G 1998. The estimation of crude protein and dry matter degradability of maize and grass silages by near infrared spectroscopy. Journal of Near Infrared Spectroscopy 6, 145–151.Google Scholar
Dyer, DJ 2004. Analysis of oilseeds and coarse grains. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, JJ Workman and JB Reeves), pp. 321–344. American Society of Agronomy Inc., Madison, WI, USA.Google Scholar
Ferreira, DS, Galão, OF, Pallone, JAL and Poppi, RJ 2014. Comparison and application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for determination of quality parameters in soybean samples. Food Control 35, 227–232.Google Scholar
Hackmann, TJ, Sampson, JD and Spain, JN 2010. Variability in in situ ruminal degradation parameters causes imprecision in estimated ruminal digestibility. Journal of Dairy Science 93, 1074–1085.Google Scholar
Harstad, OM and Prestløkken, E 2000. Effective rumen degradability and intestinal indigestibility of individual amino acids in solvent-extracted soybean meal (SBM) and xylose-treated SBM (SoyPass®) determined in situ. Animal Feed Science and Technology 83, 31–47.CrossRefGoogle Scholar
Herrero, M, Jessop, NS, Fawcett, RH, Murray, I and Dent, JB 1997. Prediction of the in vitro gas production dynamics of kikuyu grass by near-infrared reflectance spectroscopy using spectrally-structured sample populations. Animal Feed Science and Technology 69, 281–287.Google Scholar
Hsu, H, McNeil, A, Okine, E, Mathison, G and Soofi-Siawash, R 1998. Near infrared spectroscopy for measuring in situ degradability in barley forages. Journal of Near Infrared Spectroscopy 6, 129–143.Google Scholar
Huhtanen, P, Seppälä, A, Ots, M, Ahvenjärvi, S and Rinne, M 2008. In vitro gas production profiles to estimate extent and effective first-order rate of neutral detergent fiber digestion in the rumen. Journal of Animal Science 86, 651–659.CrossRefGoogle ScholarPubMed
Huhtanen, S, Ahvenjärvi, S, Weisbjerg, MR and Norgaard, P 2006. Digestion and passage of fibre in ruminants. In Ruminant physiology: digestion, metabolism and impact of nutrition on gene expression, immunology and stress (ed. K Sejrsen, T Hvelplund and MO Nielsen), pp. 87–135. Wageningen Academic Publishers, Wageningen, The Netherlands.CrossRefGoogle Scholar
Hvelplund, T and Weisbjerg, MR 2000. In situ techniques for the estimation of protein degradability and postrumen availability. In Forage evaluation in ruminant nutrition (ed. DI Givens), pp. 233–258. CABI, Wallingford, UK.Google Scholar
López, S, Carro, MD, González, JS and Ovejero, FJ 1998. Comparison of different in vitro and in situ methods to estimate the extent and rate of degradation of hays in the rumen. Animal Feed Science and Technology 73, 99–113.Google Scholar
Madsen, J, Hvelplund, T, Weisbjerg, MR, Bertilsson, J, Olsson, I, Spörndly, R, Harstad, OM, Volden, H, Tuori, M, Varvikko, T, Huhtanen, P and Olafsson, BL 1995. The AAT/PBV protein evaluation system for ruminants. Norwegian Journal of Agricultural Sciences (suppl. 19), 1–37.Google Scholar
Mathison, GW, Hsu, H, Soofi-Siawash, R, Recinos-Diaz, G, Okine, EK, Helm, J and Juskiw, P 1999. Prediction of composition and ruminal degradability characteristics of barley straw by near infrared reflectance spectroscopy. Canadian Journal of Animal Science 79, 519–523.Google Scholar
Mertens, DR 2002. Gravimetric determination of amylase-treated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. Journal of AOAC International 85, 1217–1240.Google Scholar
Murray, I and Cowe, I 2004. Sample preparation. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, JJ Workman and JB Reeves), pp. 75–112. American Society of Agronomy Inc., Madison, WI, USA.Google Scholar
National Research Council 2001. Nutrient requirements of dairy cattle, 7th edition. NRC, Washington, DC, USA.Google Scholar
Nocek, JE 1988. In situ and other methods to estimate ruminal protein and energy digestibility: a review. Journal of Dairy Science 71, 2051–2069.Google Scholar
Nordheim, H, Volden, H, Fystro, G and Lunnan, T 2007. Prediction of in situ degradation characteristics of neutral detergent fibre (aNDF) in temperate grasses and red clover using near-infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology 139, 92–108.Google Scholar
Ohlsson, C, Houmøller, LP, Weisbjerg, MR, Lund, P and Hvelplund, T 2007. Effective rumen degradation of dry matter, crude protein and neutral detergent fibre in forage determined by near infrared reflectance spectroscopy. Journal of Animal Physiology and Animal Nutrition 91, 498–507.Google Scholar
Ørskov, ER and McDonald, I 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage. The Journal of Agricultural Science 92, 499–503.Google Scholar
Roberts, CA, Stuth, J and Flinn, P 2004. Analysis of forages and feedstuffs. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, JJ Workman and JB Reeves), pp. 231–267. American Society of Agronomy Inc., Madison, WI, USA.Google Scholar
Sadeghi, AA, Nikkhah, A, Shawrang, P and Shahrebabak, MM 2006. Protein degradation kinetics of untreated and treated soybean meal using SDS-PAGE. Animal Feed Science and Technology 126, 121–133.Google Scholar
Shenk, J, Workman, J and Westerhaus, M 1992. Application of NIR spectroscopy to agricultural products. In Handbook of near-infrared analysis (ed. D Burns and E Ciurczak), pp. 383–431. Marcel Dekker Inc., New York, NY, USA.Google Scholar
Sniffen, CJ, O’Connor, JD, Van Soest, PJ, Fox, DG and Russell, JB 1992. A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science 70, 3562–3577.Google Scholar
Todorov, N, Atanassova, S, Pavlov, D and Grigorova, R 1994. Prediction of dry matter and protein degradability of forages by near infrared spectroscopy. Livestock Production Science 39, 89–91.Google Scholar
Vanzant, ES, Cochran, RC and Titgemeyer, EC 1998. Standardization of in situ techniques for ruminant feedstuff evaluation. Journal of Animal Science 76, 2717–2729.CrossRefGoogle ScholarPubMed
Volden, H 2011. NorFor – the Nordic feed evaluation system. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
von Keyserlingk, MAG, Swift, ML, Puchala, R and Shelford, JA 1996. Degradability characteristics of dry matter and crude protein of forages in ruminants. Animal Feed Science and Technology 57, 291–311.Google Scholar
Williams, P 2014. Tutorial: the RPD statistic: a tutorial note. NIR News 25, 22–26.Google Scholar
Williams, P and Norris, K 1987. Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists Inc., St. Paul, MN, USA.Google Scholar
Workman, JJ and Shenk, J 2004. Understanding and using near-infrared spectrum as an analytical method. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, JJ Workman and JB Reeves), pp. 3–10. American Society of Agronomy Inc., Madison, WI, USA.Google Scholar