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

References

• Berauer BJ, Wilfahrt PA, Reu B, Schuchardt MA, Garcia-Franco N, Zistl-Schlingmann M, Dannenmann M, Kiese R, Kühnel A, Jentsch A. 2020. Predicting forage quality of species-rich pasture grasslands using vis-NIRS to reveal effects of management intensity and climate change. Agric Ecosyst Environ. 296:106929.
   Web of Science ®Google Scholar
• Boval M, Coates DB, Lecomte P, Decruyenaere V, Archimède H. 2004. Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle. Anim Feed Sci Technol. 114(1–4):19–29.
   Web of Science ®Google Scholar
• Brogna N, Palmonari A, Canestrari G, Mammi L, Dal Prà A, Formigoni A. 2018. Technical note: near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows. J Dairy Sci. 101(2):1234–1239.
   PubMed Web of Science ®Google Scholar
• Chandler JA, Jewell WJ, Gossett JM. 1980. Predicting methane fermentation biodegradability. Biotechnol Bioeng Symp NO. 10(10):93–107..
   Google Scholar
• Clivot H, Mary B, Valé M, Cohan JP, Champolivier L, Piraux F, Laurent F, Justes E. 2017. Quantifying in situ and modeling net nitrogen mineralization from soil organic matter in arable cropping systems. Soil Biol Biochem. 111:44–59. .
   Web of Science ®Google Scholar
• Cochran RC, Adams DC, Wallace JD, Galyean ML. 1986. Predicting digestibility of different diets with internal markers: evaluation of four potential markers. J Anim Sci. 63(5):1476–1483.
   Web of Science ®Google Scholar
• Cotanch KW, Grant RJ, Van Amburgh ME, Zontini A, Fustini M, Palmonari A, Formigoni A. 2014. Applications of uNDF in ration modeling and formulation. Cornell nutrition conference for feed manufacturers. Cornell University, Ithaca, NY
   Google Scholar
• Cozzolino D, La Manna A, Vaz Martins D. 2002. Use of near infrared reflectance spectroscopy to analyse bovine faecal samples. J Near Infrared Spectrosc. 10(4):309–314.
   Web of Science ®Google Scholar
• De Boever JL, Cottyn BG, De Brabander DL, Vanacker JM, Boucqué CV. 1996. Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near-infrared reflectance spectroscopy. Anim Feed Sci Technol. 60(1–2):103–115.
   Web of Science ®Google Scholar
• De La Torre A, Andueza D, Renand G, Baumont R, Cantalapiedra-Hijar G, Nozière P. 2019. Digestibility contributes to between-animal variation in feed efficiency in beef cows. Animal. 13(12):2821–2829.
   PubMed Web of Science ®Google Scholar
• De Marchi M. 2013. On-line prediction of beef quality traits using near infrared spectroscopy. Meat Sci. 94(4):455–460.
   PubMed Web of Science ®Google Scholar
• De Marchi M, Berzaghi P, Boukha A, Mirisola M, Galol L. 2007. Use of near infrared spectroscopy for assessment of beef quality traits. Ital J Anim Sci. 6(1):421–423.
   Google Scholar
• De Marchi M, Costa A, Goi A, Penasa M, Manuelian CL. 2019. Novel applications of infrared technologies in dairy industry. Adv Technol. 8(2):92–98.
   Google Scholar
• De Marchi M, Riovanto R, Penasa M, Cassandro M. 2012. At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy. Meat Sci. 90(3):653–657.
   PubMed Web of Science ®Google Scholar
• Decruyenaere V, Froidmont E, Bartiaux-Thill N, Buldgen A, Stilmant D. 2012. Faecal near-infrared reflectance spectroscopy (NIRS) compared with other techniques for estimating the in vivo digestibility and dry matter intake of lactating grazing dairy cows. Anim Feed Sci Technol. 173(3–4):220–234.
   Web of Science ®Google Scholar
• Decruyenaere V, Lecomte P, Demarquilly C, Aufrere 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 calibration. Anim Feed Sci Technol. 148(2–4):138–156.
   Web of Science ®Google Scholar
• Dixon R, Coates D. 2009. Review: near infrared spectroscopy of faeces to evaluate the nutrition and physiology of herbivores. J Near Infrared Spectrosc. 17(1):1–31.
   Web of Science ®Google Scholar
• Fondevila M, Castrillo C, Gasa J, Guada JA. 1995. Rumen-undegradable dry matter and neutral detergent fibre as ratio indicators of digestibility in sheep given cereal straw-based diets. J Agric Sci. 125(1):145–151.
   Google Scholar
• Fustini M, Palmonari A, Canestrari G, Bonfante E, Mammi L, Pacchioli MT, Sniffen GCJ, Grant RJ, Cotanch KW, Formigoni A. 2017. Effect of undigested neutral detergent fiber content of alfalfa hay on lactating dairy cows: Feeding behavior, fiber digestibility, and lactation performance. J Dairy Sci. 100(6):4475–4483.
   PubMed Web of Science ®Google Scholar
• Garnsworthy PC, Unal Y. 2004. Estimation of dry-matter intake and digestibility in group-fed dairy cows using near infrared reflectance spectroscopy. Anim Sci. 79(2):327–334.
   Google Scholar
• Goi A, Manuelian CL, Currò S, De Marchi M. 2019. Prediction of mineral composition in commercial extruded dry dog food by near-infrared reflectance spectroscopy. Animals 9: 640.
   Web of Science ®Google Scholar
• Goi A, Manuelian CL, Righi F, De Marchi M. 2020. At-line prediction of gelatinized starch and fiber fractions in extruded dry dog food using different near-infrared spectroscopy technologies. Animals. 10(5):1–11.
   Web of Science ®Google Scholar
• Goi A, Simoni M, Righi F, Visentin G, De Marchi M. 2020. Application of a handheld near-infrared spectrometer to predict gelatinized starch, fiber fractions, and mineral content of ground and intact extruded dry dog food. Animals. 10(9):1660.
   Web of Science ®Google Scholar
• Goulart RS, Vieira RAM, Daniel JLP, Amaral RC, Santos VP, Toledo Filho SG, Cabezas-Garcia EH, Tedeschi LO, Nussio LG. 2020. Effects of source and concentration of neutral detergent fiber from roughage in beef cattle diets on feed intake, ingestive behavior, and ruminal kinetics. J Anim Sci. 98(5):1–15.
   Web of Science ®Google Scholar
• Higgs RJ, Chase LE, Ross DA, Van Amburgh ME. 2015. Updating the Cornell net carbohydrate and protein system feed library and analyzing model sensitivity to feed inputs. J Dairy Sci. 98(9):6340–6360.
   PubMed Web of Science ®Google Scholar
• Huhtanen P, Detmann E, Krizsan SJ. 2016. Prediction of rumen fiber pool in cattle from dietary, fecal, and animal variables. J Dairy Sci. 99(7):5345–5357.
   PubMed Web of Science ®Google Scholar
• Jancewicz LJ, Penner GB, Swift ML, McKinnon JJ, Waldner CL, McAllister TA. 2016. Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy. Can J Anim Sci. 96(4):532–549.
   Web of Science ®Google Scholar
• Jancewicz LJ, Penner GB, Swift ML, Waldner CL, Koenig KM, Beauchemin KA, McAllister TA. 2017. Predicting fecal nutrient concentrations and digestibilities and growth performance in feedlot cattle by near-infrared spectroscopy. J Anim Sci. 95(1):455.
   PubMed Web of Science ®Google Scholar
• Jancewicz LJ, Swift ML, Penner GB, Beauchemin KA, Koenig KM, Chibisa GE, He ML, McKinnon JJ, Yang WZ, McAllister TA. 2017. Development of near-infrared spectroscopy calibrations to estimate fecal composition and nutrient digestibility in beef cattle. Can J Anim Sci. 97(1):51–64.
   Web of Science ®Google Scholar
• Johnson JR, Carstens GE, Prince SD, Ominski KH, Wittenberg KM, Undi M, Forbes TDA, Hafla AN, Tolleson DR, Basarab JA. 2017. Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle. J Anim Sci. 95(1):447–454.
   PubMed Web of Science ®Google Scholar
• Johnson JA, Sutherland BD, McKinnon JJ, McAllister TA, Penner GB. 2020. Use of barley or corn silage when fed with barley, corn, or a blend of barley and corn on growth performance, nutrient utilization, and carcass characteristics of finishing beef cattle. Trans Anim Sci. 4(1):129–140.
   PubMed Web of Science ®Google Scholar
• Karoui R, Mouazen AM, Dufour E, Pillonel L, Picque D, Bosset JO, De Baerdemaeker J. 2006. Mid-infrared spectrometry: A tool for the determination of chemical parameters in Emmental cheeses produced during winter. Lait. 86(1):83–97.
   Google Scholar
• Ki KS, Kim SB, Lee HJ, Yang SH, Lee JS, Jin ZL, Kim HS, Jeo JM, Koo JY, Cho JK. 2009. Prediction on the quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy. J Korean Soc Grassland Forage Sci. 29(3):253–262.
   Google Scholar
• Kononoff P, Heinrichs J, Varga G. 2002. Using manure evaluation to enhance dairy cattle nutrition. p. 1–5.
   Google Scholar
• Kustantinah K, Suhartanto B, Indarto E, Zulfa IH, Atmojo FA. 2020. Degradation of nitrogen fraction in kacang goats feed supplementation Calliandra calothyrsus substituted soybean meal. Key Eng Mater. 840:118–123.
   Google Scholar
• Leiber F, Ivemeyer S, Perler E, Krenmayr I, Mayer P, Walkenhorst M. 2015. Determination of faeces particle proportions as a tool for the evaluation of the influence of feeding strategies on fibre digestion in dairy cows. J Anim Plant Sci. 25(1):153–159.
   Web of Science ®Google Scholar
• Lucas A, Andueza D, Rock E, Martin B. 2008. Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy. J Agric Food Chem. 56(16):6801–6808.
   PubMed Web of Science ®Google Scholar
• Lyons G, Sharma S, Aubry A, Carmichael E, Annett R. 2016. A preliminary evaluation of the use of mid infrared spectroscopy to develop calibration equations for determining faecal composition, intake and digestibility in sheep. Anim Feed Sci Technol. 221:44–53.
   Web of Science ®Google Scholar
• Magrin L, Gottardo F, Fiore E, Gianesella M, Martin B, Chevaux E, Cozzi G. 2018. Use of a live yeast strain of Saccharomyces cerevisiae in a high-concentrate diet fed to finishing Charolais bulls: effects on growth, slaughter performance, behavior, and rumen environment. Anim Feed Sci Technol. 241:84–93.
   Web of Science ®Google Scholar
• Manley M. 2014. Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chem Soc Rev. 43(24):8200–8214.
   PubMed Web of Science ®Google Scholar
• Mihaljev ŽA, Jakšić SM, Prica NB, Ćupić ŽN, Živkov-Baloš MM. 2015. Comparison of the Kjeldahl method, Dumas method and NIR method for total nitrogen determination in meat and meat products. J Agroaliment Proc Technol. 21(4):365–370.
   Google Scholar
• Miller CE. 2001. Chemistry principles of near infrared technology. In: Williams PC, Norris KH, editors. Near infrared technology in the agricultural and food industries, American Association of cereal chemist p. 19–37.
   Google Scholar
• Moir BKW, Swain AJ. 1972. The metabolic excretion and true digestibilities of nitrogen and fat by cattle and sheep with particular reference to forage-faeces relationships. Aust J Agric Res. 23(5):879–884.
   Google Scholar
• NRC. 2016. Nutrient Requirements of Beef Cattle. National Academies of Sciences Engineering and Medicine.National Academies Press..
   Google Scholar
• Núñez-Sánchez N, Carrion D, Peña Blanco F, Domenech García V, Garzón Sigler A, Martínez-Marín AL. 2016. Evaluation of botanical and chemical composition of sheep diet by using faecal near infrared spectroscopy. Anim Feed Sci Technol. 222:1–6.
   Web of Science ®Google Scholar
• Osborne BG, Fearn T, Hindle PH. 1993. Practical NIR spectroscopy with application in food and beverage analysis. London: Longman Scientific & Technical.
   Google Scholar
• Pagliari PH, Wilson M, Waldrip HM, He Z. 2020. Nitrogen and phosphorus characteristics of beef and dairy manure. In: Waldrip HM, Pagliari PH, He Z, eds. Animal manure: production, characteristics, environmental concerns, and management. Madison (WI): American Society of Agronomy and Soil Science Society of America; pp. 45–62.
   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. Anim Feed Sci Technol. 72(1–2):155–167.
   Web of Science ®Google Scholar
• Powell JM, Barros T, Danes M, Aguerre M, Wattiaux M, Reed K. 2017. Nitrogen use efficiencies to grow, feed, and recycle manure from the major diet components fed to dairy cows in the USA. Agric Ecosyst Environ. 239:274–282.
   Web of Science ®Google Scholar
• Prieto N, Pawluczyk O, Russell Dugan ME, Aalhus JL. 2017. A review of the principles and applications of near-infrared spectroscopy to characterize meat, fat, and meat products. Appl Spectrosc. 71(7):1403–1426.
   PubMed Web of Science ®Google Scholar
• Purnomoadi A, Kurjhara M, Nishida T, Terada F, Abe A, Hamada T. 1997. Two methods of near infrared reflectance spectroscopy for determining the digestibility and energy value of feeds. Anim Sci Technol. 68(4):351–359.
   Google Scholar
• Rahman A, Bayram I, Khanum S, Ullah S. 2015. Use and calibration of near infrared reflectance spectroscopy in feed analysis: a mini review. Pak J Life Soc Sci. 13(1):1–7.
   Google Scholar
• Redshaw ES, Weisenburger RD, Mathison GW, Milligan LP. 1986. Near infrared reflectance spectroscopy for predicting forage composition and voluntary consumption and digestibility in cattle and sheep. Can J Anim Sci. 66(1):103–115.
   Web of Science ®Google Scholar
• Richardson E. C a, Herd RMB. 2004. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection Cooperative Research Centre for Cattle and Beef Quality. Aust J Exp Agric. 44(5):431–440.
   Google Scholar
• Righi F, Quarantelli A, Tonelli L, Renzi M, Gandolfi B. 2007. Use of Penn State particle separator for the evaluation of total mixed rations typical of Parmigiano Reggiano cheese production area. Ital J Anim Sci. 6(1):347–349.
   Google Scholar
• Righi F, Simoni M, Malacarne M, Summer A, Costantini E, Quarantelli A. 2016. Feeding a free choice energetic mineral-vitamin supplement to dry and transition cows: effects on health and early lactation performance. Large Anim Rev. 22(4):161–170.
   Web of Science ®Google Scholar
• Righi F, Simoni M, Visentin G, Manuelian CL, 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 Sci. 206:105–108.
   Web of Science ®Google Scholar
• Ruffo ML, Bollero GA. 2003. Residue decomposition and prediction of carbon and nitrogen release rates based on biochemical fractions using principal-component regression. Agron J. 95(4):1034–1040.
   Web of Science ®Google Scholar
• Sales J, Janssens GPJ. 2003. Acid-insoluble ash as a marker in digestibility studies: a review. J Anim Feed Sci. 12(3):383–401.
   Web of Science ®Google Scholar
• Schuba J, Südekum KH, Pfeffer E, Jayanegara A. 2017. Excretion of faecal, urinary urea and urinary non-urea nitrogen by four ruminant species as influenced by dietary nitrogen intake: a meta-analysis. Livestock Sci. 198:82–88.
   Web of Science ®Google Scholar
• Seo S, Tedeschi LO, Lanzas C, Schwab CG, Fox DG. 2006. Development and evaluation of empirical equations to predict feed passage rate in cattle. Anim Feed Sci Technol. 128(1–2):67–83.
   Web of Science ®Google Scholar
• Sgoifo Rossi CA, Compiani R, Baldi G, Taylor SJ, Righi F, Simoni M, Quarantelli A. 2019. Replacing sodium bicarbonate with half amount of calcareous marine algae in the diet of beef cattle. Rev Bras Zootec. 48:1–12.
   Google Scholar
• Sgoifo Rossi CA, Grossi S, Compiani R, Baldi G, Agovino M, Rossi L. 2020. Programs on beef cattle serum Se, Zn, Cu, Mn concentration, health, growth performance and meat quality. Large Anim Rev. 26:57–64.
   Web of Science ®Google Scholar
• Shenk JS, Westerhaus MO, Abrams S. 1989. Protocol for NIR calibrations: monitoring analysis results and recalibration. In Martens G, Shenk J, Barton F, editors. Near infrared spectroscopy (NIRS): analysis of forage quality. USDA-ARS agriculture handbook, II. Washington (DC): US Government Printing Office; pp. 104–110.
   Google Scholar
• Simoni M, Temmar R, Bignamini DA, Foskolos A, Sabbioni A, Ablondi M, Quarantelli A, Righi F. 2020. Effects of the combination between selected phytochemicals and the carriers silica and Tween 80 on dry matter and neutral detergent fibre digestibility of common feeds. Ital J Anim Sci. 19(1):723–738.
   Web of Science ®Google Scholar
• Simoni M, Tsiplakou E, Pitino R, Quarantelli A, Righi F. 2020. Determination of the optimal priming interval of rumen fluids used as inocula for the in vitro digestibility trials through radial enzyme diffusion method. Anim Prod Sci. 61:723–738.
   Google Scholar
• Tolleson DR, Schafer DW. 2014. Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland. J Anim Sci. 92(1):349–358.
   PubMed Web of Science ®Google Scholar
• Tran H, Salgado P, Tillard E, Dardenne P, Nguyen XT, Lecomte P. 2010. Global” and “local” predictions of dairy diet nutritional quality using near infrared reflectance spectroscopy. J Dairy Sci. 93(10):4961–4975.
   PubMed Web of Science ®Google Scholar
• Van Amburgh ME, Grant RJ, Cotanch KW, Zontini A, Ross DA, Foskolos A. 2015. NDF-making something old, new again. Herd health and nutrition conference. College of Agriculture and Life Sciences, Cornell University, Ithaca, NY
   Google Scholar
• Van Kessel JS, Reeves JB, III, Meisinger JJ. 2000. Nitrogen and carbon mineralization dynamics of manures and composts. HortScience. 29(5):1669–1667.
   Google Scholar
• Van Soest PJ. 1994. Nutritional ecology of the ruminant. Bangkok, Thailand: C. U. Press.
   Google Scholar
• Visentin G, McDermott A, McParland S, Berry DP, Kenny OA, Brodkorb A, Fenelon MA, De Marchi M. 2015. Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows. J Dairy Sci. 98(9):6620–6629.
   PubMed Web of Science ®Google Scholar
• Weiss CP, Gentry WW, Meredith CM, Meyer BE, Cole NA, Tedeschi LO, McCollum FT, Jennings JS. 2017. Effects of roughage inclusion and particle size on digestion and ruminal fermentation characteristics of beef steers. J Anim Sci. 95(4):1707–1714.
   PubMed Web of Science ®Google Scholar
• Williams P. 2014. The RPD statistic: a tutorial note. NIR News. 25(1):22–26.
   Google Scholar
• Windham WR, Lawrence KC, Park B, Buhr RJ. 2003. Visible/NIR spectroscopy for characterizing fecal cantamination of chicken carcasses. Trans Am Soc Agric Eng. 46(3):747–751.
   Google Scholar
• Yang Z, Nie G, Pan L, Zhang Y, Huang L, Ma X, Zhang X. 2017. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ. 2017(10):1–20.
   Google Scholar