Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations

References

• Alexander RH, McGowan M. 1966. The routine determination of in vitro digestibility of organic matter in forages. J Bri Grass Soc. 21:140–147.
   Web of Science ®Google Scholar
• AOAC. 2003. Official Method 2003.06 Crude fat in feeds, cereal grains, and forages. J. AOAC Int. 86:888–899.
   PubMedGoogle Scholar
• Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and Detrending of near infrared diffuse reflectance spectra. Appl Spectrosc. 43:772–777.
   Web of Science ®Google Scholar
• Bastianelli D, Bonnal L, Barre P, Nabeneza S, Salgado P, Andueza B. 2018. La spectrométrie dans le proche infrarouge pour la caractérisation des ressources alimentaires [Near infrared spectrometry for the characterization of feed resources]. Dossier: Ressources alimentaires pour les animaux d'élevage. 31(3):237–254.
   Google Scholar
• BOE. 1995. Métodos oficiales del MAPA BOE n° 52, del 2 de marzo de 1995, pp. 7161-7237. ESPAÑA [Official Methods of the Ministry of Agriculture, Fisheries and Food (Ministerio de Agricultura, Pesca y Alimentación – MAPA), Official State Gazette (Boletín Oficial del Estado – BOE) no. 52 of March 2, 1995, pp. 7161-7237, SPAIN]. Real Decreto 2257/1994 del 25 noviembre 1995, por el que se aprueba los métodos oficiales de análisis de piensos o alimentos para animales y sus primeras materias [Royal Decree 2257/1994 of November 25, 1995, whereby the official methods of analysis of animal feed and their raw materials are approved]. Boletín Oficial del Estado, 2 de marzo de 1995, 52, 7161-7237.
   Google Scholar
• De Boever JL, Cottyn BG, De Brabander DL, Vanacker JM, ChV B. 1999. Equations to predict digestibility and energy value of grass silages, maize silages, grass hays, compound feeds and raw materials for cattle. Nutr Abstr Rev Ser B. 69(11):835–850.
   Google Scholar
• De Boever JL, Cottyn BG, Vanacker JM, ChV B. 1995. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Anim Feed Sci Technol. 51:243–253.
   Web of Science ®Google Scholar
• Donker JD, Naik DG. 1979. Predicting total digestible nutrients and estimated net energy of dairy cows rations from chemical components. J Dairy Sci. 62:424–432.
   Web of Science ®Google Scholar
• Eurostat. 2019. Available at: http://ec.europa.eu/eurostat/statisticsexplained/index.php/Milk_and_milk_product_statistics
   Google Scholar
• Gierus M, Eickler B, Resch R, Loges R, Taube F, Poetsch EM. 2016. Using in vitro methods to estimate metabolizable energy content of five forage legumes harvested under different defoliation systems. Die Bodenkultuur: J. Land Manag. Food Environ. 67(3):121–131.
   Google Scholar
• Giger S, Sauvant D, Hervieu J, Dorleans M. 1986. Étude de la prévision de la digestibilité des rations mixtes distribuées à des chèvres laitières par ses caractéristiques analytiques [Predicting ability of different analytical criteria for digestibility of mixed diets given to dairy goats]. Ann zootech. 35(2):137–160.
   Google Scholar
• Goering HK, Van Soest PJ. 1970. Forage fiber analysis (apparatus, reagents, procedures and some applications). In: United States Department of Agriculture, editor. Agriculture handbook no. 379. Washington (DC): USDA; 20 p.
   Google Scholar
• Gosselink JMJ, Dulphy JP, Poncet C, Jailler M, Tamminga S, Cone JW. 2004. Prediction of forage digestibility in ruminants using in situ and in vitro techniques. Anim Feed Sci Technol. 115:227–246.
   Web of Science ®Google Scholar
• Huhtanen P, Rinne M, Nousiainen J. 2009. A meta-analysis of feed digestion in dairy cows. 2. The effects of feeding level and diet composition on digestibility. J Dairy Sci. 92(10):5031–5042.
   PubMed Web of Science ®Google Scholar
• Khaleduzzaman ABM, Mamun MAA, Salim HM. 2017. Development of local calibrations for the nutritional evaluation of commercial poultry diets by using near infrared reflectance spectroscopy. J Appl Anim Res. 45(1):8–14.
   Web of Science ®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 Kor Grassl Forage Sci. 29(1):31–36.
   Google Scholar
• Linn J, Hutjens M, Shaver R, Otterby D, Howard WT, Kilmer L. 2018. Formulating dairy cow rations. University of Minnesota Extension. Available at: https://extension.umn.edu/dairy-milking-cows/formulating-dairy-cow-rations
   Google Scholar
• Mark H, Workman J. 2003. Statistics in Spectroscopy. Amsterdam: Elsevier – Academic Press.
   Google Scholar
• Martens H, Næs T. 1987. Multivariate calibration by data compression. In: Williams P., Norris K., editor. Near-infrared technology in the agricultural and food industries. Minnesota, USA: American Association of Cereal Chemists, Inc. St. Paul; p. 57–88.
   Google Scholar
• Massart DL, Vandeginste BGM, Buydens LMC, De Jong S, Lewi PJ, Smeyers-Verbeke J. 1997. Handbook of Chemometrics and Qualimetrics, Part A. Data Handling in Science and Technology, volume 20. Amsterdam: part A Elsevier.
   Google Scholar
• Mentink RL, Hoffman PC, Bauman LM. 2006. Utility of near-infrared reflectance spectroscopy to predict nutrient composition and in vitro digestibility of total mixed rations. J Dairy Sci. 89(6):2320–2326.
   PubMed Web of Science ®Google Scholar
• National Research Council (NRC) . 2001. Nutrient Requirements of Dairy Cattle. Washington (DC): National Academy Press, 381 p.
   Google Scholar
• Nousiainen J. 2004. Development of tools for the nutritional management of dairy cows on silage-based diets [PhD Academic dissertation. University of Helsinki, Department of Animal Science Publications 72, Finland, pp. 61. Available at: http://ethesis.helsinki.fi/julkaisut/maa/kotie/vk/nousiainen
   Google Scholar
• Parrini S, Acciaioli A, Franci O, Pugliese C, Bozzi R. 2019. Near Infrared Spectroscopy technology for prediction of chemical composition of natural fresh pastures. J Appl Anim Res. 47(1):514–520.
   Web of Science ®Google Scholar
• Saeys W, Mouazen AM, Ramon H. 2005. Potential for on site analysis of pig manure using Visible and Near Infrared Reflectance Spectroscopy. Biosyst Eng. 91(4):393–402.
   Web of Science ®Google Scholar
• SAS. 2012. SAS system, version 9.4. Cary (NC): SAS Institute Inc., EEUU.
   Google Scholar
• Shenk JS, Westerhaus MO. 1991. Population structuring of near infrared spectra and modified partial least squares regression. Crop Sci. 31:1548–1555.
   Web of Science ®Google Scholar
• Shenk JS, Westerhaus MO. 1993. Analysis of agriculture and food products by Near Infrared Reflectance Spectroscopy. Monograph. Port Matilda (PA): Department of Agronomy, Pennsylvania State University, Owners of Infrasoft International.
   Google Scholar
• Shenk JS, Westerhaus MO. 1995. The application of near infrared reflectance Spectroscopy (NIRS) to forage analysis. In: G.C. Fahey, editor. Forage Quality, Evaluation, and Utilization. Madison (WI): A.S.A., C.S.S.A., S.S.S.A.; p. 406–449.
   Google Scholar
• Shenk JS, Workman JJ, Westerhaus MO. 2001. Application of NIR spectroscopy to agricultural products. In: Burns DA, Ciurczak EW, editors. Handbook of near infrared analysis. New York: Marcel Dekker; p. 419–474.
   Google Scholar
• Soldado A, De La Roza-Delgado B, Martínez-Fernández A, Vicente F, López L, Díaz-Bueno E, 2009. Predicción de la composición bromatológica en dietas completas: Clonación de instrumentos y transferencia de ecuaciones NIRS en producto intacto [Prediction of bromatologic content of raw total mixed rations: standardization and transference of equations between NIR instruments]. In: F Joy, editor. Actas XIII Jornadas sobre Producción Animal AIDA. Spain: Zaragoza; p. 340–342. https://www.aida-itea.org/aida-itea/files/jornadas/2009/comunicaciones/2009_NyA_44.pdf.
   Google Scholar
• Swart E, Brand TS, Engelbrecht J. 2012. The use of near infrared spectroscopy (NIRS) to predict the chemical composition of feed samples used in ostrich total mixed rations. S Afr J Anim Sci. 42(1):550–554.
   Google Scholar
• Tilley JM, Terry RA. 1963. A two-stage technique for the in vitro digestion of forage crops. J Bri Grass Soc. 18:104–111.
   Google Scholar
• Valiente OL, Andueza D, de Vega A, Olmos G, Muñoz F. 2004. The use of NIRS for prediction of intake, digestibility and diet composition in sheep fed mixed grain: roughage diets. J Anim Feed Sci. 13(1):227–230.
   Google Scholar
• Van Soest PJ, Robertson JB, Lewis BA. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci. 74(10):3583–3597.
   PubMed Web of Science ®Google Scholar
• Williams PC. 2001. Implementation of Near-Infrared technology. In: Williams, Norris, editor. Near-infrared technology in the agricultural and food industries. Minnesota, USA: American Association of Cereal Chemists, Inc. St. Paul; p. 145–169.
   Google Scholar
• Williams PC. 2014. The RPD statistic: a tutorial note. NIR News. 25:22–26.
   Google Scholar
• Williams PC, Sobering DC. 1996. How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In: Davies, Williams, editor. Near Infrared Spectroscopy: The Future Waves. Chichester, Reino Unido: NIR Publications; p. 185–188.
   Google Scholar
• Windham WR, Coleman SW. 1989. Animal response prediction. In: Marten, editor. Near Infrared Reflectance Spectroscopy (NlRS): Analysis of forage quality. Washington (DC): USDA HandBook n° 643; p. 32–35.
   Google Scholar
• Windham WR, Mertens DR, Barton IIFE. 1989. Protocol for NIRS calibration: sample selection and equation development and validation. In: Marten, editor. En: Near Infrared Reflectance Spectroscopy (NlRS): Analysis of forage quality. Washington (DC): USDA Hand Book n° 643; p. 96–103. EEUU
   Google Scholar