Prediction of metabolisable energy concentrations of fresh cut grass using digestibility data measured with non-pregnant non-lactating cows

Stergiadis, S. ORCID: https://orcid.org/0000-0002-7293-182X, Allen, M., Chen, X., Wills, D. and Yan, T. (2015) Prediction of metabolisable energy concentrations of fresh cut grass using digestibility data measured with non-pregnant non-lactating cows. British Journal of Nutrition, 113 (10). pp. 1571-1584. ISSN 0007-1145 doi: 10.1017/S0007114515000896

Abstract/Summary

Pasture-based ruminant production systems are common in certain areas of the world, but energy evaluation in grazing cattle is performed with equations developed, in their majority, with sheep or cattle fed total mixed rations. The aim of the current study was to develop predictions of metabolisable energy (ME) concentrations in fresh-cut grass offered to non-pregnant non-lactating cows at maintenance energy level, which may be more suitable for grazing cattle. Data were collected from three digestibility trials performed over consecutive grazing seasons. In order to cover a range of commercial conditions and data availability in pasture-based systems, thirty-eight equations for the prediction of energy concentrations and ratios were developed. An internal validation was performed for all equations and also for existing predictions of grass ME. Prediction error for ME using nutrient digestibility was lowest when gross energy (GE) or organic matter digestibilities were used as sole predictors, while the addition of grass nutrient contents reduced the difference between predicted and actual values, and explained more variation. Addition of N, GE and diethyl ether extract (EE) contents improved accuracy when digestible organic matter in DM was the primary predictor. When digestible energy was the primary explanatory variable, prediction error was relatively low, but addition of water-soluble carbohydrates, EE and acid-detergent fibre contents of grass decreased prediction error. Equations developed in the current study showed lower prediction errors when compared with those of existing equations, and may thus allow for an improved prediction of ME in practice, which is critical for the sustainability of pasture-based systems.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/42192
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Agriculture, Policy and Development > Department of Animal Sciences > Animal, Dairy and Food Chain Sciences (ADFCS)- DO NOT USE
Publisher	Cambridge University Press
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