Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T04:27:41.191Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of the metabolizable energy value of whole-crop wheat from laboratory-based measurements

Published online by Cambridge University Press:  18 August 2016

A.T. Adesogan ,
E. Owen  and
D.I. Givens
A.T. Adesogan
Affiliation:
Department of Agriculture, University of Reading, Earley Gate, Reading RG6 6AT Feed Evaluation and Nutritional Sciences, ADAS Drayton, Alcester Road, Stratford-upon-Avon CV37 9RQ
E. Owen
Affiliation:
Department of Agriculture, University of Reading, Earley Gate, Reading RG6 6AT
D.I. Givens
Affiliation:
Feed Evaluation and Nutritional Sciences, ADAS Drayton, Alcester Road, Stratford-upon-Avon CV37 9RQ
Get access

Abstract

The accuracy with which several laboratory-based measurements predict the metabolizable energy (ME) value of whole-crop wheat (WCW) was determined. Twenty-six WCW forages differing in variety (cv. Slepjner, Hussar and Cadenza), maturity at harvest (milk, cheese and dough stages) and treatment applied (urea-treated, untreated or acid-based additive treated) were harvested in 2 years and conserved anaerobically in 200·1 barrels. The forages were then scanned using near infrared reflectance spectroscopy (NIRS) and analysed for chemical composition, in vitro rumen fluid-pepsin digestibility, in vitro neutral detergent-cellulase plus gamannase digestibility, in vitro fermentation gas production and in situ rumen degradability. ME was calculated using measured energy losses in faeces and urine and predicted energy losses as methane. The relationships between ME and the laboratory-based measurements were determined by regression. Gross energy was consistently the best predictor of ME (R2 = 0.53 and 0.86 in years 1 and 2 respectively). However the autocorrelation involved, militates against the prediction of ME from gross energy. None of the chemical constituents or biological techniques gave a good, robust prediction of ME. However, an NIRS calibration developed using the WCW samples was highly correlated (R2 = 0.68) with ME. This work therefore suggests that traditional laboratory-based, food evaluation techniques are unsuitable for predicting the ME content of WCW but that NIRS holds promise.

Keywords

in vitro digestibilitymetabolizable energysilagewhole-crop silage
Type
Research Article
Information
Animal Science , Volume 69 , Issue 2 , October 1999 , pp. 427 - 439
Copyright
Copyright © British Society of Animal Science 1999

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdalla, A.L., Sutton, J.D., Humphries, D.J. and Phipps, R.H. 1996. Digestibility of diets of grass silage and whole-crop wheat in the rumen of lactating dairy cows. Animal Feed Science and Technology 63: 631.Google Scholar
Adesogan, A.T. 1996. Prediction of the nutritive value of fermented and urea-treated whole crop wheat. Ph.D. thesis, The University of Reading.Google Scholar
Adesogan, A.T., Owen, E. and Givens, D.I. 1998a. Prediction of the in vivo digestibility of whole crop wheat from in vitro digestibility, chemical composition, in situ degradability, in vitro fermentation and near infrared reflectance spectroscopy. Animal Feed Science and Technology 74: 259–272.CrossRefGoogle Scholar
Adesogan, A.T., Owen, E. and Givens, D.I. 1998b. The chemical composition, digestibility and energy value of fermented and urea-treated whole crop wheat harvested at three stages of maturity. Grass and Forage Science 53: 66–75.Google Scholar
Agricultural and Food Research Council. 1993. Energy and protein requirements of ruminants. An advisory manual prepared by the AFRC Technical Committee on Responses to Nutrients. CAB International, Wallingford.Google Scholar
Alderman, G. 1985. Prediction of the energy value of compound feeds. In Recent advances in animal nutrition (ed. Haresign, W. and Cole, D.J.A.), pp. 3–52. Butterworths, London.Google Scholar
Baker, C.W., Givens, D.I. and Deaville, E.R. 1994. Prediction of organic matter digestibility in vivo of grass silage by near infrared reflectance spectroscopy: effect of calibration method, residual moisture and particle size. Animal Feed Science and Technology 50: 17–26.CrossRefGoogle Scholar
Barber, G.D., Givens, D.I., Kridis, M.S., Offer, N.W. and Murray, I. 1990. Prediction of the organic matter digestibility of grass silage. Animal Feed Science and Technology 28: 115–128.Google Scholar
Barber, G.D., Offer, N.W. and Givens, D.I. 1989. Predicting the nutritive value of silage. In Recent advances in animal nutrition — 1989 (ed. Haresign, W. and Cole, D.J.A.), pp. 141–158. Butterworths, London.CrossRefGoogle Scholar
Barber, W.P., Adamson, A.H. and Altman, J.F.B. 1984. New methods of forage evaluation. In Recent advances in animal nutrition (ed. Haresign, W. and Cole, D.J.A.), pp. 161–176. Butterworths, London.Google Scholar
Barnes, R.J., Dhanoa, M.S. and Lister, S.J. 1989. Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Applied Spectroscopy 43: 772–777.Google Scholar
Beuvink, J.M.W. and Kogut, J. 1993. Modelling gas production kinetics of grass silages incubated with buffered rumen fluid. Journal of Animal Science 71: 1041–1046.Google Scholar
Blaxter, K.L. and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. British Journal of Nutrition 19: 511–521.Google Scholar
Boever, J.L. de, Cottyn, B.G., Andries, J.I., Buysse, F.X. and Vanacker, J.M. 1988. The use of a cellulase technique to predict digestibility, metabolisable and net energy of forages. Animal Feed Science and Technology 19: 247–260.Google Scholar
Deinum, B., Es, A.J.H. van and Van Soest, P.J. 1968. Climate, nitrogen and grass. II. The influence of light intensity, temperature and nitrogen on in vivo digestibility of grass and the prediction of these effects from some chemical procedures. Netherlands Journal of Agricultural Science 16: 217–223.CrossRefGoogle Scholar
Dowman, M.G. 1993. Modifications to the neutral detergent cellulase digestibility method for the prediction of the metabolisable energy of compound feedstuffs containing palm kernel meal. Journal of the Science of Food and Agriculture 61: 327–331.CrossRefGoogle Scholar
Dowman, M.G. and Collins, F.C. 1982. The use of enzymes to predict the digestibility of animal feeds. Journal of the Science of Food and Agriculture 33: 689–696.Google Scholar
Dulphy, J.P. and Demarquilly, C. 1981. Problemes particuliers aux ensilages. In Prevision de la Valeur Nutritive des Ruminants [Nutrient requirements of ruminants] (ed. Demarquilly, C.), pp. 81–104. Institut National de la Recherche Agronomique, Versailles.Google Scholar
Giger-Reverdin, S., Aufrère, J., Sauvant, D., Demarquilly, C. and Vermorel, M. 1994. Prediction of the energy values of compound feeds. Animal Feed Science and Technology 48: 73–98.Google Scholar
Givens, D.I., Baker, C.W., Adamson, A.H. and Moss, A.R. 1992. Influence of growth type and season on the prediction of the metabolisable energy content of herbage by near infrared reflectance spectroscopy. Animal Feed Science and Technology 37: 281–295.Google Scholar
Givens, D.I. and Brunnen, J.M. 1987. Prediction of the metabolisable energy content of grass silage. Proceedings of the eighth silage conference, Hurley.Google Scholar
Givens, D.I., Everington, J.M. and Adamson, A.H. 1989. The digestibility and metabolisable energy content of grass silage and their prediction from laboratory measurements. Animal Feed Science and Technology 24: 27–43.Google Scholar
Givens, D.I., Everington, J.M. and Adamson, A.H. 1990. The nutritive value of spring-grown herbage produced on farms throughout England and Wales over four years. III. The prediction of energy values from various laboratory measurements. Animal Feed Science and Technology 27: 185–196.Google Scholar
Goering, H.K. and Van Soest, P.J. 1970. Forage fiber analysis (apparatus, reagents, procedures and some applications). USD A agriculture handbook no. 379. USDA, Washington, DC.Google Scholar
Harvey, J.J. 1992. Assessing whole-crop cereal maturity in the field. In Whole crop cereals (ed. Stark, B.A. and Wilkinson, J.M.), pp. 39–50. Chalcombe Publications, Canterbury.Google Scholar
Ibbotson, C.F., Phillips, M.C., Turner, P.J. and Delaney, M. 1982. The alkali treatment of whole crop cereals. Part II — Farm scale feasibility trial with barley. Experimental Husbandry 38: 154–162.Google Scholar
Jandel Scientific. 1989. Sigmapiot: scientific graphing software. Jandel Scientific, California.Google Scholar
McDonald, P., Edwards, R.A., Greenhalgh, J.F.D. and Morgan, C.A. 1995. Animal nutrition, fifth edition. Longman Scientific and Technical, Harlow.Google Scholar
Ministry of Agriculture, Fisheries and Food. 1986. The analysis of agricultural materials. Reference book no. 427. Her Majesty’s Stationery Office, London.Google Scholar
Morgan, D.J. and Stakelum, G. 1987. The prediction of the digestibility of herbage for dairy cows. Irish Journal of Agricultural Research 26: 23–24.Google Scholar
Newman, G. 1992. Future prospects for whole-crop cereals in the UK. In Whole crop cereals (ed. Stark, B.A. and Wilkinson, J.M.), pp. 165–172. Chalcombe Publications, Canterbury.Google Scholar
Nijkamp, H.J. 1969. Determination of urinary energy — and carbon output in balance trials. Zeitschrift für Tierphysiologie Tierernährung und Futtermittelkunde 25: 1–9.Google Scholar
Norris, K.H., Barnes, R.F., Moore, J.E. and Shenk, J.S. 1976. Predicting forage quality by infrared reflectance spectroscopy. Journal of Animal Science 43: 889–897.Google Scholar
Offer, N. 1993. Background to the new in vivo ME silage prediction equation. Proceedings of the Society of Feed Technologists conference, 14-15 July, 1993, Reading.Google Scholar
Ørskov, E.R. and McDonald, I. 1979. The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage. Journal of Agricultural Science, Cambridge 92: 499–503.Google Scholar
Osbourn, D.F. 1978. Principles governing the use of chemical methods for assessing the nutritive value of forages: a review. Animal Feed Science and Technology 3: 265–275.CrossRefGoogle Scholar
Schwarz, F.J., Heindl, U., Pex, E.J. and Kirchgessner, M. 1996. Estimation of metabolisable and net energy of maize silages for sheep and cattle using the nutrient content and cellulase method. Zeitschrift für Agrarbiologie 49: 157–168.Google Scholar
Shenk, J.S., Landa, I., Hoover, H.R. and Westerhaus, M.O. 1981. Description and evaluation of a near infrared reflectance spectro-computer for forage and grain analysis. Crop Science 21: 355–358.Google Scholar
Shenk, J.S. and Westerhaus, M.O. 1991. Population definition, sample selection and calibration procedures for near infrared reflectance spectroscopy. Crop Science 31: 469–474.Google Scholar
Tetlow, R.M. and Mason, V.C. 1987. Treatment of whole crop cereal with alkali. 1. The influence of sodium hydroxide and ensiling on the chemical composition and in vitro digestibilty of rye barley and wheat crops harvested at increasing maturity and dry matter content. Animal Feed Science and Technology 18: 257–269.CrossRefGoogle Scholar
Theodorou, M.K., Barbara, A.W., Dhanoa, M.S., McAllan, A.B. and France, J. 1994. A simple gas production method using a pressure transducer to determine the fermentation kinetics of ruminant feeds. Animal Feed Science and Technology 48: 185–197.CrossRefGoogle Scholar
Tilley, J.M.A. and Terry, R.A. 1963. A two stage technique for the in vitro digestion of forage crops. Journal of the British Grassland Society 18: 104–111.Google Scholar
Van Soest, P.J. and Mertens, D.R. 1977. Analytical parameters as guides to forage quality. Proceedings of the international meeting on animal production from temperate grassland, Dublin, Ireland.Google Scholar
Weisbjerg, M.R., Bhargava, P.K., Hvelplund, T. and Madsen, J. 1990. Use of degradation curves in feed evaluation. Report no. 679, National Institute of Animal Production, Beretning fra Statens Husdyrbrugsforsøg, Denmark.Google Scholar
Westerhaus, M.O. 1989. Interpretation of regression statistics. In Near infrared reflectance spectroscopy (NIRS): analysis of forage quality (ed. Marten, G.S., Shenk, J.S. and Barton, F.E.), pp. 39–40. Agriculture handbook no. 643 (revised), United States Department of Agriculture.Google Scholar