Estimation of relative feed value, relative forage quality and net energy lactation values of some roughage samples by using near infrared reflectance spectroscopy

Yıl 2020, Cilt: 4 Sayı: 3, 109 - 118, 30.12.2020

Hasan Atalay Fatih Kahrıman

https://doi.org/10.30704/http-www-jivs-net.791669

Cited By: 3

Öz

The aim of this study is to determine the relative feed value (RFV), relative forage quality (RFQ) and net energy lactation (NEL) values of some roughage samples, based on the measurements taken from two different near infrared reflectance (NIR) devices. Corn silage (n = 18), alfalfa (n = 9), oat grass hay (n = 15), wheat straw (n = 10) were used as roughage samples and NIR measurements were taken from these samples in two ways. In the study, two different NIR (NIR1, NIR2) devices were used and nutrient values of roughage samples which were used were determined. The reference chemical analyzes of the roughage samples used in the study were made in the laboratory as dry matter. After the determination of the nutritional values, by adding these nutritional values (Dry matter, ether extract, crude protein, ash, neutral detergent fiber, acid detergent fiber) to the RFV, RFQ, NEL equations, RFV, RFQ and NEL values of each roughage sample were calculated. Meanwhile, the roughage samples used in the study were also analyzed in NIR devices and after the nutritional values were determined, these nutritional values were written in the RFV, RFQ, NEL equations thus RFV, RFQ, NEL values of each roughage samples were calculated separately for each device. Relationships between predictions obtained from NIR1 and NIR2 devices and reference chemical analysis values were determined by statistical tests. It was determined that the type of device and sample used had an important effect on the relationships between the results obtained from the reference analyzes and the predictions based on NIR measurements. Regression coefficients between RVF, RFQ and NEL values obtained from NIR1 and NIR2 devices and RVF, RFQ and NEL values obtained from reference (REF) analysis were determined to be 0.37 and 0.50. Among the roughage types, the highest similarity between NIR results and reference analysis results was found in alfalfa samples. The study results showed that indirect estimation by NIR relating to RVF, RFQ and NEL parameters is possible depending on the sample type and that there is a need for calibration improvement studies to determine these parameters directly with NIR.

Anahtar Kelimeler

Roughages, relative feed value, relative forage quality, net energy lactation, NIR spectroscopy

Kaynakça

• AOAC (1997). Association of Official Analytical Chemists, 16th ed., Washington, D.C. USA.
• Ball, D. M., Hoveland, C. S., & Lacefield, G. D. (2007). Adapted from Southern Forages, 4 th Ed. International Plant Nutrition Institute. U.S.A.
• Gerhardt (2010). Analytical systems documents. Gerhardt GmbH & Co.KG Cäsariusstraße 97, D-53639, Königswinter.
• Goldman, A., Genizi, A., Yulzari, A., & Seligman, N. G. (1987). Improving the reliability of the two stage in vitro assay for ruminant feed digestibility by calibrasyon against intwo data from a wide range of sources. Animal Feed Science and Technology, 18, 233-245.
• Hayırlı, A. (2016). Evaluation of roughages. Atatürk Üniversitesi, Rasyonel Hayvancılık ARGE Ata Teknokent, Erzurum.
• Henn, R., Schwab, A., & Huck, C. W. (2016). Evaluation of bench-top versus portable near- infrared spectroscopic method combined with multivariate approaches for the fast and simultaneous quantitative analysis of main sugars in syrup formulations. Food Control, 68, 97-104.
• Jaranyama, P., & Garcia, A. D. (2004). Understanding relative feed value (RFV) and relative forage quality (RFQ). College of Agric and Biological Sci, South Dakota State University, USDA.
• Lovetta, D. K., Deavilleb, E. R., Moulda, R., Givensb, D. I., & Owena, E. (2004). Using near infrared reflectance spectroscopy (NIRS) to predict the biological parameters of maize silage. Animal Feed Science and Technology, 115(1-2), 179-187.
• Lovetta, D. K., Deavilleb, E. R., Givensb, D. I., Finlayc, M., & Owena, E. (2005). Near infrared reflectance spectroscopy (NIRS) to predict biological parameters of maize silage: effects of particle commination, oven drying temperature and the presence of residual moisture. Animal Feed Science and Technology, 120(3-4), 323-332.
• Marten, G. C., Shenk, J. S., & Barton, F. E. (1989). Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. U.S. Department of agriculture, agriculture handbook No. 643 (revised with supplements), 110 p.
• Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews, (43), 8200-8214.
• NRC (2001). Nutrient requirements of dairy cattle. 7th rev. ed. , Washington DC, USA: National Academy Press.
• Parrini, S., Acciaioli, A., Franci, O., Pugliese, C., & Bozzi, R. (2019). Near infrared spectroscopy technology for prediction of chemical composition of natural fresh pastures, Journal of Applied Animal Research, 47(1), 514-520.
• Pasquini, C. (2003). Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society, 14(2), 198-219.
• Pehlevan, F., & Özdoğan, M. (2015). Comparison between chemical and near Infrared reflectance spectroscopy methods for determining of nutrient content of some alternative feeds. Journal of Tekirdag Agricultural Faculty, 12(2), 1-10.
• Kellems, R. O., & Church, D. C. (2010). Livestock feeds and feeding (Alp, M., Kocabağlı, N. Ed.) Istanbul, Turkey, Nobel.
• Romero, J. J., Castillo, M. S., Burns, J. C., Moriel, P., & Davidson, S. (2014). Forage quality: concepts and practices, NC State University College of Agriculture and Life Sciences, Published by North Carolina Cooperative Extension.
• Rushing, J. B., Saha, U. K., Lemus, R., Sonon, L., & Baldwin, B. S. (2016). Analysis of some important forage quality attributes of southeastern wildrye (Elymus glabriflorus) using near infrared reflectance spectroscopy. American Journal of Analytical Chemistry (7), 642-662.
• Samiei, A., Liang, J. B., Ghorbani, G. R., Hirooka, H., Mahyari, S. A., Sadri, H., & Tufarelli, V. (2015). Relationship between dietary energy level, silage butyric acid and body condition score with subclinical ketosis incidence in dairy cows. Advances in Animal and Veterinary Sciences, 3(6), 354-361.
• SAS (1999). Institute SAS V8 user manual, SAS Cary, NC, USA.
• Sheaffer, C. C., Peterson, M. A., McCaslin, M., Volenec, J. J., Cherney, J. H., Johnson, K. D., Woodward, W. T., & Viands, D. R. (1995). Acid detergent fiber, neutral detergent fiber concentration and relative feed value. North American Alfalfa Improvement Conference, Minneapolis.
• Ünal Y. (2005). Near intrared reflektans spektroskopinin hayvan besleme bilim alanında kullanım imkânları. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi, 45(1) 33-39.
• Van Soest, P. J., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber and non starch polysaccharides in relation to animal nutrition. Journal Dairy Science, (74), 3583-3597.
• Van Dyke, N. J., & Anderson, P. M. (2000). Interpreting a forage analysis. Alabama Cooperative Extension, Circular ANR-890.
• Vander Schaaf, D. (2013). Near infrared reflectance spectroscopy comparison of dairy one and agrinir forage analyzer, California Polytechnic State University, San Luis Obispo.
• Volkers, K. C., Wachendorf, M., Loges, R., Jovanovic, N., & Taube, F. (2003). Prediction of the quality of forage maize by near-infrared reflectance spectroscopy. Animal Feed Science and Technology, 109(3), 183-194.
• 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 for age quality parametersin Lolium multiflorum. Peer Journal 2017;5 :e3867.
• Yu, H., Liu, H., Wang, Q., & Van, Ruth S. (2020). Evaluation of portable and benchtop NIR for classification of high oleic acid peanuts and fatty acid quantitation. LWT, 128, [109398].