Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X

Comparison of NIR and wet chemistry methods for evaluation of chemical composition and nutrient digestibility in forage legumes

Document Type : Research Paper

Authors

jaber khani yousef reza 1
Ali Asadi Alamouti 2
Mojtaba Yari 3

1 Department of Animal and Poultry Sciences, Aburayhan Faculty of Agricultural Technology, University of Tehran, Tehran, Iran. E-mail: j_khani@ut.ac.ir

2 Corresponding Author, Department of Animal and Poultry Science, Aburayhan Faculty of Agricultural Technology, University of Tehran, Tehran, Iran. E-mail: a.alamouti@ut.ac.ir

3 Department of Animal Science, Faculty of Agriculture, Malayer University, Malayer, Iran. E-mail: myari@malayeru.ac.ir

https://doi.org/10.22059/jap.2026.398903.623859
Abstract
Objective: Understanding chemical composition and nutritional quality of feedstuffs, especially forage crops, are important components of ration formulation, livestock performance, and production costs. Near-infrared reflectance (NIR) spectroscopy is becoming popular as a rapid, non-destructive, and cost-effective alternative to traditional wet chemistry methods for determining chemical composition and nutritional quality of feedstuffs. The objective of this study was to compare the accuracy of NIR with standard laboratory procedures in determining chemical constituents, protein and carbohydrate fractions according to the Cornell Net Carbohydrate and Protein System (CNCPS), and nutritional attributes of four legume forages.
Method: Organic matter (OM), ash, acid detergent lignin (ADL), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), starch, and CNCPS-based fractionation of protein and carbohydrates, and nutritional attributes such as potential dry matter intake (DMI), total digestible nutrients (TDN), digestible energy (DE), metabolizable energy (ME), and quality index (QI) were measured in forage samples from four species including two cultivars of common vetch (Vicia sativa) and hairy vetch (Vicia villosa), one cultivar of forage pea (Pisum arvense), and second-year alfalfa (Medicago sativa, used as the control crop). All analyses were conducted in parallel using NIR and the reference wet chemistry methods and statistical agreement and precision between the two methods were assessed using mean bias, root mean square error (RMSE), concordance correlation coefficient (CCC), and Bland–Altman limits of agreement (LOA).
Results: The NIR results were highly accurate and highly correlated (CCC> 0.85, P> 0.05) with wet chemistry methods for key components (CP, OM, starch, total carbohydrates, and fraction B1 (B1)), but acceptable precision was observed for predicting energy-related parameters (TDN, DE, and ME) which are critical for ration formulation. However, the accuracy and concordance declined, and statistically significant differences were observed for structural constituents (ADL, NDF,  protein fractions (ADIP, NADIP) and carbohydrates (B2, B3, and C). This indicates that NIR has limited spectral sensitivity when evaluating slowly degradable or indigestible fractions of carbohydrate and protein, which are the parameters of dynamic nutritional models such as CNCPS.
Conclusions: Owing to special advantages, particularly speed, ease of operation, and applicability to field analyses, NIR can replace routine proximate analysis in feed laboratories; but conventional chemical methods provide more benefits for evaluation of CNCPS model components, especially those that resist digestion. The NIR integrated with classical approaches may represent a rational cost-effective strategy for extensive feed analyses.

Keywords

CNCPS
Forage quality
Near-infrared reflectance spectroscopy
Reference analytical method
Statistical agreement
References
Abrams, S. M., Shenk, J. S., & Harpster, H. W. (1988). Potential of near infrared reflectance spectroscopy for analysis of silage composition. Journal of Dairy Science, 71(7), 1955-1959.
AOAC (Association of Official Analytical Chemists). (1990). Official Methods of Analysis. 15th ed. AOAC, Washington, DC.
Belanche, A., Weisbjerg, M. R., Allison, G. G., Newbold, C. J., & Moorby, J. M. (2013). Estimation of feed crude protein concentration and rumen degradability by Fourier-transform infrared spectroscopy. Journal of Dairy Science, 96(12), 7867-7880.
Brogna, N., Palmonari, A., Canestrari, G., Mammi, L., Dal Prà, A., & Formigoni, A. (2018). Near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows. Journal of Dairy Science, 101(2), 1234-1239.
Buonaiuto, G., Cavallini, D., Mammi, L. M. E., Ghiaccio, F., Palmonari, A., Formigoni, A., & Visentin, G. (2021). The accuracy of NIRS in predicting chemical composition and fibre digestibility of hay-based total mixed rations. Italian Journal of Animal Science, 20(1), 1730-1739.
Fox, D. G., Tedeschi, L. O., Tylutki, T. P., Russell, J. B., Van Amburgh, M. E., Chase, L. E., & Overton, T. R. (2004). The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology, 112(1-4), 29-78.
Hall, M. B., Hoover, W. H., Jennings, J. P., & Webster, T. K. M. (1999). A method for partitioning neutral detergent‐soluble carbohydrates. Journal of the Science of Food and Agriculture, 79(15), 2079-2086.
Henneberg, W., & Rautenberg, F. (1860). Beiträge zur begründung einer rationellen fütterung der wiederkäuer: Praktisch-landwirthschaftliche und chemischphysiologische untersuchungen. Für landwirthe und physiologen (Vol. 1). CA Schwetschke und sohn.
Hoffman, P. C., Brehm, N. M., Bauman, L. M., Peters, J. B., & Undersander, D. J. (1999). Prediction of laboratory and in situ protein fractions in legume and grass silages using near-infrared reflectance spectroscopy. Journal of Dairy Science, 82(4), 764-770.
Jonker, A., Gruber, M. Y., McCaslin, M., Wang, Y., Coulman, B., McKinnon, J. J., & Yu, P. (2010). Nutrient composition and degradation profiles of anthocyanidin-accumulating L c-alfalfa populations. Canadian Journal of Animal Science, 90(3), 401-412.
Lanzas, C., Sniffen, C. J., Seo, S. A., Tedeschi, L. O., & Fox, D. G. (2007a). A revised CNCPS feed carbohydrate fractionation scheme for formulating rations for ruminants. Animal Feed Science and Technology, 136(3-4), 167-190.
Lanzas, C., Tedeschi, L. O., Seo, S., & Fox, D. G. (2007). Evaluation of protein fractionation systems used in formulating rations for dairy cattle. Journal of dairy science, 90(1), 507-521.
Lin LI-K. )1989(. A concordance correlation coefficient to evaluate reproducibility. Biometrics. Biometrics, 45(1), 255-268.
Licitra, G., Hernandez, T. M., & Van Soest, P. J. (1996). Standardization of procedures for nitrogen fractionation of ruminant feeds. Animal feed science and technology, 57(4), 347-358.
Lundberg, K. M., Hoffman, P. C., Bauman, L. M., & Berzaghi, P. (2004). Prediction of forage energy content by near infrared reflectance spectroscopy and summative equations. The Professional Animal Scientist, 20(3), 262-269.
Mentink, R. L., Hoffman, P. C., & Bauman, L. M. (2006). Utility of near-infrared reflectance spectroscopy to predict nutrient composition and in vitro digestibility of total mixed rations. Journal of Dairy Science, 89(6), 2320-2326.
Moore, J. E., & Undersander, D. J. (2002). Relative forage quality: An alternative to relative feed value and quality index. In:  Proceedings 13th annual Florida ruminant nutrition symposium. 16-29.
Palmonari, A. L. B. E. R. T. O., Gallo, A., Fustini, M. A. T. T. I. A., Canestrari, G. I. O. R. G. I. A., Masoero, F., Sniffen, C. J., & Formigoni, A. (2016). Estimation of the indigestible fiber in different forage types. Journal of Animal Science, 94(1), 248-254.
Righi, F., Simoni, M., Visentin, G., Manuelian, C. L., Currò, S., Quarantelli, A., & De Marchi, M. (2017). The use of near infrared spectroscopy to predict faecal indigestible and digestible fibre fractions in lactating dairy cattle. Livestock Science, 206, 105-108.
Simoni, M., Goi, A., De Marchi, M., & Righi, F. (2021). The use of visible/near-infrared spectroscopy to predict fibre fractions, fibre-bound nitrogen and total-tract apparent nutrients digestibility in beef cattle diets and faeces. Italian Journal of Animal Science, 20(1), 814-825.
Sniffen, C. J., O'connor, J. D., Van Soest, P. J., Fox, D. G., & Russell, J. B. (1992). A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science, 70(11), 3562-3577.
Tilley, J. M. A., & Terry, D. R. (1963). A two‐stage technique for the in vitro digestion of forage crops. Grass and Forage Science, 18(2), 104-111.
Van Soest, P. J. (1964). Symposium on nutrition and forage and pastures: new chemical procedures for evaluating forages. Journal of Animal Science, 23(3), 838-845.
Van Soest, P. V., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74(10), 3583-3597.
Visentin, G., McDermott, A., McParland, S., Berry, D. P., Kenny, O. A., Brodkorb, A., & De Marchi, M. (2015). Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows. Journal of Dairy Science, 98(9), 6620-6629.
Yu, P., McKinnon, J. J., Soita, H. W., Christensen, C. R., & Christensen, D. A. (2005). Use of synchrotron-based FTIR microspectroscopy to determine protein secondary structures of raw and heat-treated brown and golden flaxseeds: A novel approach. Canadian Journal of Animal Science, 85(4), 437-448.
Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X
Volume 28, Issue 1
Winter 2026
Pages 15-26

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us