Prediction Accuracy Improvement of Indonesian Dairy Cattle Fiber Feed Compositions Using Near-Infrared Reflectance Spectroscopy Local Database

Despal Despal, L. A. Sari, R. Chandra, R. Zahera, I. G. Permana, L. Abdullah


The accuracy of near infrared reflectance spectroscopy (NIRS) depends on the database generated from the conventional wet chemistry (CWC). Currently, the local database of fiber-source feeds for tropical dairy cattle are still limited. The study aimed to compare CWC and NIRS initial database (NIRSID) results, to predict CWC from NIRSID, and to improve the accuracy of NIRS prediction using local database (NIRSLD). Five feeds as sources of fiber (Napier grass, natural grass, corn leaves, corn husk, and rice straw) from 4 areas of dairy cattle farming were used (4 farms from each area). For external calibration, 20 independent Napier grass samples were tested. Samples were analyzed using NIRS and CWC to measure dry matter (DM), ash, crude protein (CP), ether extract (EE), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and silica (Si) to calculate hemicellulose, cellulose, and lignin contents. The results obtained by NIRSID were compared to those obtained by CWC using T-test. Predictions of CWC from the results obtained by NIRSID were attempted using regressions. The NIRSLD was developed by inputting the CWC value to NIRS spectrums. Internal calibration and validation as well as external calibration, were run. The results showed that NIRSID has low capacity in determining CWC (R2<0.683). Calibration using local database (NIRSLD) improved CWC prediction accuracy (residual predictive deviation (RPD) > 2 except for DM, EE, CF, ADL, and lignin). External validation showed that CWC and NIRSLD were similar in all parameters (p<0.05). The ratios of the standard error of prediction (SEP) to the standard error of laboratory (SEL) were > 2 for CP, CF, and ADF. It is concluded that the local database of NIRS of fiber-source feeds is necessary to improve the prediction accuracy of local dairy fiber-source feeds values using NIRS.


AOAC. 2015. Official Methods of Analysis of AOAC International. 20th ed. Arlington: Assoc. Off. Anal. Chem.

AOCS. 2005. Official Methods and Recommended Practices of the AOCS. 7th ed. Urbana: The American Oil Chemists’ Society.

Baillères, H., F. Davrieux, & F. Ham-Pichavant. 2002. Near infrared analysis as a tool for rapid screening of some major wood characteristics in a Eucalyptus breeding program. Annals of Forest Sci. 59: 479–90.

Cooke, R. F., C.L. Daigle, P. Moriel, S.B. Smith, L.O. Tedeschi, & J.M. Vendramini. 2020. Cattle adapted to tropical and subtropical environments (I): Social, Nutritional, and carcass quality considerations. Anim. Sci. J. 98 (2).

Despal, I.G. Permana, T. Toharmat, & D.E. Amirroennas. 2017. Pemberian Pakan Sapi Perah. 1st ed. IPB Press, Bogor.

Dykes, L., L. Hoffmann, O. Portillo-Rodriguez, W. L. Rooney, & L. W. Rooney. 2014. Prediction of total phenols, condensed tannins, and 3-deoxyanthocyanidins in sorghum grain using near-infrared (NIR) spectroscopy. J. Cereal Sci. 60: 138–42.

Fairbrother, T. E. & G. E. Brink. 1990. Determination of cell wall carbohydrates in forages by near infrared reflectance spectroscopy. Anim. Feed Sci. Technol. 28: 293–302.

Hall, M. B. 2014. Feed analyses and their interpretation. Vet. Clin. North Am. - Food Anim. Practice. 30: 487–505.

Hammond, K. J., A. K. Jones, D. J. Humphries, L. A. Crompton, & C. K. Reynolds. 2016. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using greenfeed and respiration chamber techniques. J. Dairy Sci. 99: 7904–17.

Hasanah, U., I. G. Permana, & Despal. 2017. Introduction of complete ration silage to substitute the conventional ration at traditional dairy farms in Lembang. Pakistan J. Nutr. 16: 577–87.

Hoffman, P. C., N. M. Brehm, L. M. Bauman, J. B. Peters, & D. J. Undersander. 1999. Prediction of laboratory and in situ protein fractions in legume and grass silages using near-infrared reflectance spectroscopy. J. Dairy Sci. 82: 764–70.

Krämer-Schmid, M., P. Lund, & M. R. Weisbjerg. 2016. Importance of NDF digestibility of whole crop maize silage for dry matter intake and milk production in dairy cows. Anim. Feed Sci.Technol. 219: 68–76.

Lestari, D.A., L. Abdullah, & Despal. 2015. Comparative study of milk production and feed efficiency based on farmers best practices and National Research Council. Med. Pet. 38: 110-117.

Lobos, I., P. Gou, S. Hube, R. Saldaña, & M. Alfaro. 2013. Evaluation of potential NIRS to predict pastures nutritive value. J. Soil Sci. Plant Nutr.13: 463–68.

Lozano, R.R. 2015. Grass Nutrition. Palibrio, Bloomington.

Mutsvangwa, T., K. L. Davies, J. J. McKinnon, & D. A. Christensen. 2016. Effects of dietary crude protein and rumen-degradable protein concentrations on urea recycling, nitrogen balance, omasal nutrient flow, and milk production in dairy cows. J. Dairy Sc. 99: 6298–6310.

Nugroho, H.D., I.G. Permana, & Despal. 2015. Utilization of bioslurry on maize hydroponic fodder as a corn silage supplement on nutrient digestibility and milk production of dairy cows. Med. Pet. 38: 70-76.

Olafadehan, O. A., & S. A. Okunade. 2018. Fodder value of three browse forage species for growing goats. J. Saudi Soc. Agric. Sci. 17: 43–50.

Ozaki, Y., S. Morita, & Y. Du. 2007. Spectral Analysis. In: Near-infrared Spectroscopy in Food Science and Technology. Ozaki, Y., W.F. McClure, & A.A. Christy (Eds.). John Wiley & Sons Inc., New Jersey. pp. 47–52.

Parrini, S., A. Acciaioli, A. Crovetti, & R. Bozzi. 2018. Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture. Italian J Anim. Sci. 17: 87–91.

Phetpan, K., V. Udompetaikul, & P. Sirisomboon. 2019. In-line near infrared spectroscopy for the prediction of moisture content in the tapioca starch drying process.” Powder Technol. 345: 608–15.

Pierna, J. A.Fernández, B. Lecler, J. P. Conzen, A. Niemoeller, V. Baeten, & P. Dardenne. 2011. Comparison of various chemometric approaches for large near infrared spectroscopic data of feed and feed products. Analytica Chimica Acta 705: 30–34.

Puteri, R. E., P. D.M.H. Karti, L. Abdullah, & Supriyanto. 2015. Productivity and nutrient quality of some sorghum mutant lines at different cutting ages. Med. Pet. 38: 132–37.

Rady, A.M., & D.E. Guyer. 2015. Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques. Postharvest Biol. Technol. 103: 17–26.

Riaz, M. Q., K. H. Südekum, M. Clauss, & A. Jayanegara. 2014. Voluntary feed intake and digestibility of four domestic ruminant species as influenced by dietary constituents: A meta-analysis. Livest. Sci. 162: 76–85.

Saha, U., D. Endale, P. G.Tillman, W. C. Johnson, J.Gaskin, L. Sonon, H. Schomberg, & Y. Yang. 2017. Analysis of various quality attributes of sunflower and soybean plants by near infrared reflectance spectroscopy: Development and validation calibration models. Am. J. Anal. Chem. 08: 462–92.

Samadi, S., Wajizah, & A. A. Munawar. 2018. Rapid and simultaneous determination of feed nutritive values by means of near infrared spectroscopy. Trop. Anim. Sci. J. 41: 121–27.

Soldado, A., T. Fearn, A. Martínez-Fernández, & B. De La Roza-Delgado. 2013. The transfer of NIR calibrations for undried grass silage from the laboratory to on-site instruments: Comparison of two approaches. Talanta 105: 8–14.

Sriagtula, R., P. D.M.H. Karti, L. Abdullah, Supriyanto, & D. A. Astuti. 2017. Nutrient changes and in vitro digestibility in generative stage of M10-BMR sorghum mutant lines. Med. Pet. 40: 111–17.

Stergiadis, S., M. Allen, X. J. Chen, D. Wills, & T. Yan. 2015. Prediction of nutrient digestibility and energy concentrations in fresh grass using nutrient composition. J. Dairy Sc. 98: 3257–73.

Tilley, J. M.A., & R. A. Terry. 1963. A two‐stage technique for the in vitro digestion of forage crops. Grass Forage Sci.18: 104–11.

van Soest, P. J., J. B. Robertson, & B. A. Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sc.74: 3583–97.

Williams, P. C., & D.C. Sobering. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectros. 1: 25–32.

Williams, P. 2004. Near-Infrared Technology - Getting the Best Out of Light : A Short Course in The Practical Implementation of Near-Infrared Spectroscopy for the User. PDK Projects, Inc., British Columbia.

Wolfrum, E.J., A. J. Lorenz, & N. deLeon. 2009. Correlating detergent fiber analysis and dietary fiber analysis data for corn stover collected by NIRS. Cellulose. 16: 577–85.

Yang, Z., G. Nie, L. Pan, Y. Zhang, L. Huang, X. Ma, & X. Zhang. 2017. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ 5:e3867.

Yin, Y. 2020. Model-free tests for series correlation in multivariate linear regression. J. Stat. Plan.Infer. 206: 179–95.

Zahera, R., I.G. Permana, & Despal. 2015. Utilization of mungbean’s green house fodder and silage in the ration for lactating dairy cows. Med. Pet. 38: 123-131.


Despal Despal (Primary Contact)
L. A. Sari
R. Chandra
R. Zahera
I. G. Permana
L. Abdullah
DespalD., SariL. A., ChandraR., ZaheraR., PermanaI. G., & AbdullahL. (2020). Prediction Accuracy Improvement of Indonesian Dairy Cattle Fiber Feed Compositions Using Near-Infrared Reflectance Spectroscopy Local Database. Tropical Animal Science Journal, 43(3), 263-269.

Article Details

List of Cited By :

Crossref logo