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Faecal near-IR spectroscopy to determine the nutritional value of diets consumed by beef cattle in east Mediterranean rangelands

Published online by Cambridge University Press:  01 September 2015

S. Y. Landau ,
L. Dvash ,
M. Roudman ,
H. Muklada ,
D. Barkai ,
Y. Yehuda  and
E. D. Ungar
S. Y. Landau*
Affiliation:
Department of Natural Resources, Institute of Plant Sciences, Agricultural Research Organization, the Volcani Center, Bet Dagan 50250, Israel
L. Dvash
Affiliation:
Department of Natural Resources, Institute of Plant Sciences, Agricultural Research Organization, the Volcani Center, Bet Dagan 50250, Israel
M. Roudman
Affiliation:
Department of Natural Resources, Institute of Plant Sciences, Agricultural Research Organization, the Volcani Center, Bet Dagan 50250, Israel
H. Muklada
Affiliation:
Department of Natural Resources, Institute of Plant Sciences, Agricultural Research Organization, the Volcani Center, Bet Dagan 50250, Israel
D. Barkai
Affiliation:
Department of Natural Resources, Gilat Experimental Station, M.P. HaNegev 2, Israel
Y. Yehuda
Affiliation:
Northern R&D, P.O. Box 831, Kiryat Shmona 11016, Israel
E. D. Ungar
Affiliation:
Department of Natural Resources, Institute of Plant Sciences, Agricultural Research Organization, the Volcani Center, Bet Dagan 50250, Israel
*
†E-mail: [email protected]
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Abstract

Rapid assessment of the nutritional quality of diets ingested by grazing animals is pivotal for successful cow–calf management in east Mediterranean rangelands, which receive unpredictable rainfall and are subject to hot-spells. Clipped vegetation samples are seldom representative of diets consumed, as cows locate and graze selectively. In contrast, faeces are easily sampled and their near-IR spectra contain information about nutrients and their utilization. However, a pre-requisite for successful faecal near-infrared reflectance spectroscopy (FNIRS) is that the calibration database encompass the spectral variability of samples to be analyzed. Using confined beef cows in Northern and Southern Israel, we calibrated prediction equations based on individual pairs of known dietary attributes and the NIR spectra of associated faeces (n=125). Diets were composed of fresh-cut green fodder of monocots (wheat and barley), dicots (safflower and garden pea) and natural pasture collected at various phenological states over 2 consecutive years, and, optionally, supplements of barley grain and dried poultry litter. A total of 48 additional pairs of faeces and diets sourced from cows fed six complete mixed rations covering a wide range of energy and CP concentrations. Precision (linearity of calibration, R2cal, and of cross-validation, R2cv) and accuracy (standard error of cross-validation, SEcv) were criteria for calibration quality. The calibrations for dietary ash, CP, NDF and in vitro dry matter digestibility yielded R2cal values >0.87, R2cv of 0.81 to 0.89 and SEcv values of 16, 13, 39 and 31 g/kg dry matter, respectively. Equations for nutrient intake were of low quality, with the exception of CP. Evaluation of FNIRS predictions was carried out with grazing animals supplemented or not with poultry litter, and implementation of the method in one herd over 2 years is presented. The potential usefulness of equations was also established by calculating the Mahalanobis (H) distance to the spectral centroid of a calibration population of 796 faecal samples collected throughout 2 years in four herds. Seasonal trends in pasture quality and responses to management practices were identified adequately and H<3.0 for 98% of faecal samples collected. We conclude that the development of FNIRS equations with confined animals is not only unexpensive and ethically acceptable, but their predictions are also sufficiently accurate to monitor dietary composition (but not intake) of beef cattle in east Mediterranean rangelands.

Keywords

nutritionbeef cattleNIRSmonitoringdecision-making
Type
Research Article
Information
animal , Volume 10 , Issue 2 , February 2016 , pp. 192 - 202
Copyright
© The Animal Consortium 2015 

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References

AOAC 1990. Official methods of analysis of the Association of Official Analytical Chemists, 15th edition. Association of Official Analytical Chemists, Arlington, VA, USA.Google Scholar
Barnes, RJ, Dhanoa, MS and Lister, SJ 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43, 772–777.CrossRefGoogle Scholar
Bhattacharya, AN and Fontenot, JP 1966. Protein and energy value of peanut hull and wood shaving poultry litters. Journal of Animal Science 25, 367–371.CrossRefGoogle Scholar
Boval, M, Coates, DB, Lecomte, P, Decruyenaere, V and Archimede, H 2004. Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle. Animal Feed Science and Technology 114, 19–29.CrossRefGoogle Scholar
Briske, DD, Derner, JD, Brown, JR, Fuhlendorf, S, Teague, WR, Havstad, KM, Gillen, RL, Ash, AJ and Williams, WD 2008. Rotational grazing on rangelands: reconciliation of perception and experimental evidence. Rangeland Ecology and Management 61, 3–17.Google Scholar
Brooks, J III 1984. Infrared reflectance analysis of forage quality for elk. Journal of Wildlife Management 48, 254–258.Google Scholar
Brosh, A, Aharoni, Y, Shargal, E, Choshniak, I, Sharir, B and Gutman, M 2004. Energy balance of grazing beef cows in Mediterranean pasture, the effects of stocking rate and season: 2. Energy expenditure estimation from heart rate and oxygen consumption, and the energy balance. Livestock Production Science 90, 101–115.Google Scholar
Brosh, A, Henkin, Z, Rothman, SJ, Aharoni, Y and Arieli, A 2003. Effects of faecal n-alkane recovery in estimates of diet composition. Journal of Agriculture Science 140, 93–100.Google Scholar
Coates, DB 1998. Predicting diet digestibility and crude protein from the faeces of grazing cattle. Final report of project CS.253. Meat Research Corporation, Sydney, Australia.Google Scholar
Coates, DB 2004. Faecal NIRS – technology for improving nutritional management of grazing cattle. Final Report of Project NAP3.121. Meat and Livestock Australia, Sydney.Google Scholar
Coates, DB and Dixon, RM 2010. Fecal NIRS calibration for predicting protein and digestibility in the diet of cattle: fistulate and pen feeding procedures for generating diet-fecal pairs. In Shining light on manure improves livestock and land management (ed. JW Walker and D Tolleson), pp. 23–41. Texas AgriLife Research and Society for Range Management. Technical Bulletin SANG-2010-0250, Jefferson City, MO, USA.Google Scholar
David, DB, Poli, CHEC, Savian, JM, Amaral, GA, Azevedo, EB, Carvalho, PCF and McManus, CM 2014. Faecal index to estimate intake and digestibility in grazing sheep. Journal of Agricultural Science 152, 667–674.Google Scholar
Decandia, M, Giovanetti, V, Boe, F, Scanu, G, Cabiddu, A, Molle, G, Cannas, A and Landau, S 2007. Faecal NIRS to assess the chemical composition and nutritive value of dairy sheep diet. In Proceeedings of 12th FAO-CIHEAM Meeting on Sheep Nutrition, 11–13 October, Thessaloniki, Greece, pp. 135–139.Google Scholar
Decruyenaere, V, Froidmont, E, Bartiaux-Thill, N, Buldgen, A and 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. Animal Feed Science and Technology 173, 220–234.Google Scholar
Dixon, R and Coates, DB 2009. Review: near infrared spectroscopy of faeces to evaluate the nutrition and physiology of herbivores. Journal of Near Infrared Spectroscopy 17, 1–31.Google Scholar
Dove, H and Mayes, RW 1991. The use of plant wax alkanes as marker substances in studies of the nutrition of herbivores: a review. Australian Journal of Agricultural Research 42, 913–952.Google Scholar
Fanchone, A, Boval, M, Lecomte, PH and Archimede, H 2007. Faecal indices based on near infrared spectroscopy to assess intake, in vivo digestibility and chemical composition of the herbage ingested by sheep (crude protein, fibres and lignin content). Journal of Near Infrared Spectroscopy 15, 107–113.CrossRefGoogle Scholar
Gibbs, SJ, Coates, DB, Poppi, DP, McLennan, SR and Dixon, R 2002. The use of faecal near infrared spectroscopy to predict dietary digestibility and crude protein content for cattle fed supplements. Animal Production Australia 24, 299.Google Scholar
Glasser, T, Landau, S, Ungar, ED, Muklada, H, Perevolotsky, A, Dvash, L, Muklada, H, Kababya, D and Walker, JW 2008. A fecal NIRS-aided methodology to determine goat dietary composition in a Mediterranean shrubland. Journal of Animal Science 86, 1345–1356.Google Scholar
Goering, HK and Van Soest, PJ 1970. Forage fiber analyses (apparatus, reagents, procedures, and some applications). Agriculture handbook no. 379. ARS-USDA, Washington, DC.Google Scholar
Goley, PB 1961. Energy of ecological materials. Ecology 42, 581–584.Google Scholar
ICACG 1994. Israel Council on Animal Care Guidelines: legislation on animal welfare (defending animal rights). Paragraph 14. Knesset Law Pub., Jerusalem, Israel (in Hebrew).Google Scholar
INRA 1989. Energy: the feed unit systems. In Ruminant nutrition: recommended allowances and feed tables (ed. R Jarrige), p. 29. INRA Publications, Paris.Google Scholar
ISI 1999. WinISI, the complete software solution for routine analysis, robust calibrations and networking. Version 1.02A. Infrasoft International, Port Matilda, PA, USA.Google Scholar
Jones, RJ and Lascano, CE 1992. Oesophageal fistulated cattle can give unreliable estimates of the proportion of legume in the diets of resident animals grazing tropical pastures. Grass and Forage Science 47, 128–132.CrossRefGoogle Scholar
Lancaster, RJ 1949. Estimation of digestibility of grazed pasture from faeces nitrogen. Nature 163, 330–331.Google Scholar
Landau, S, Friedman, S, Devash, L and Mabjeesh, SJ 2002. Polyethylene glycol, determined by near-infrared reflectance spectroscopy, as a marker of fecal output in goats. Journal of Agricultural Food Chemistry 50, 1374–1378.Google Scholar
Landau, S, Giger-Reverdin, S, Rapetti, L, Dvash, L, Dorléans, M and Ungar, ED 2008. Data mining old digestibility trials for nutritional monitoring in confined goats with aids of fecal near infra-red spectrometry. Small Ruminant Research 77, 146–158.Google Scholar
Landau, S, Glasser, T and Dvash, L 2006. Monitoring nutrition in small ruminants by aids of near infrared spectroscopy (NIRS) technology: a review. Small Ruminant Research 61, 1–11.Google Scholar
Landau, S, Glasser, T, Muklada, H, Dvash, L, Perevolotsky, A, Ungar, ED and Walker, J 2005. Fecal NIRS prediction of dietary protein percentage and in vitro dry matter digestibility in diets ingested by goats in Mediterranean scrubland. Small Ruminant Research 59, 251–263.Google Scholar
Landau, SY, Muklada, H, Dvash, L, Barkai, D and Yehuda, Y 2011. Evaluation of faecal near-infrared spectrometry as tool for pasture and beef cattle management in herbaceous mid-eastern highlands. Paper presented at the 16th Meeting of the FAO/CIHEAM Mountain Pastures Network, 25–27 May, Krakow, Poland.Google Scholar
Lyons, RK and Stuth, JW 1992. Fecal NIRS equations for predicting diet quality of free-ranging cattle. Journal of Range Management 45, 238–244.Google Scholar
Lyons, RK, Stuth, JW and Angerer, JP 1995. Fecal NIRS equation field validation. Journal of Range Management 48, 380–382.CrossRefGoogle Scholar
Naes, T, Isakson, T, Fearn, T and Davies, T 2002. The idea behind an algorithm for locally weighted regression. In A user-friendly guide to multivariate calibration and classification (ed. T Naes, T Isakson, T Fearen and T Davies), pp 127–137. NIR Publications, Chichester, UK.Google Scholar
NRC 1996. Nutrient requirements of beef cattle, seventh revised edition: update. National Academy Press, Washington, DC.Google Scholar
Osherson, D and Lane, DM 2014. Levels of measurement in online statistics education: a multimedia course of study, Rice University. Retrieved July 3, 2014, from http://onlinestatbook.com/ Google Scholar
Purnomoadi, A, Kurihara, M, Nishida, T, Shibata, M, Abe, A and Kameoka, K 1996. Application of near infrared reflectance spectroscopy to predict faecal composition and its use for digestibility estimation. Animal Science and Technology (Japan) 67, 851–860.Google Scholar
Shenk, JS 1989. Monitoring analysis results. In Near infrared reflectance spectroscopy: analysis of forage quality, Agricultural handbook no. 643, (ed. GC Maren, JS Shenk and FE Marton), pp. 104–110. USDA.Google Scholar
Silanikove, N and Tiomkin, D 1992. Toxicity induced by poultry litter consumption: effect on measurements reflecting liver function in beef cows. Animal Production 54, 203–209.Google Scholar
Sternberg, M, Gutman, M, Perevolotsky, A, Ungar, ED and Kigel, J 2000. Vegetation response to grazing management in a Mediterranean herbaceous community: a functional group approach. Journal of Applied Ecology 37, 224–237.Google Scholar
Tilley, JMA and Terry, RA 1963. A two-stage technique for the in vitro digestion of forage crops. Journal of the British Grassland Society 18, 104–111.Google Scholar
Walker, JW, Clark, DH and McCoy, SD 1998. Fecal NIRS for predicting percent leafy spurge in diets. Journal of Range Management 51, 450–455.Google Scholar
Walker, JW, McCoy, SD, Launchbaugh, KL, Fraker, MJ and Powell, J 2002. Calibrating fecal NIRS equations for predicting botanical composition of diets. Journal of Range Management 55, 374–382.Google Scholar
Williams, PC 2001. Implementation of near-infrared technology. In Near infrared technology in the agricultural and food industries, 2nd edition. p. 145. American Association of Cereal Chemists, St Paul, MI.Google Scholar