title: Prediction of metabolisable energy concentrations of fresh-cut grass using digestibility data measured with non-pregnant non-lactating cows.
abstract: 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.
authors: Sustainable Agri-Food Sciences Division, Agriculture Branch, Agri-Food and Biosciences Institute,Large Park, Hillsborough,County DownBT26 6DR,UK.
authors: Sokratis
authors: S
authors: Stergiadis
authors: Finance and Corporate Affairs Division, Biometrics and Information Systems Branch, Agri-Food and Biosciences Institute,18a Newforge Lane, Belfast,County AntrimBT9 5PX,UK.
authors: Michelle
authors: M
authors: Allen
authors: Sustainable Agri-Food Sciences Division, Agriculture Branch, Agri-Food and Biosciences Institute,Large Park, Hillsborough,County DownBT26 6DR,UK.
authors: Xianjiang
authors: X
authors: Chen
authors: Sustainable Agri-Food Sciences Division, Agriculture Branch, Agri-Food and Biosciences Institute,Large Park, Hillsborough,County DownBT26 6DR,UK.
authors: David
authors: D
authors: Wills
authors: Sustainable Agri-Food Sciences Division, Agriculture Branch, Agri-Food and Biosciences Institute,Large Park, Hillsborough,County DownBT26 6DR,UK.
authors: Tianhai
authors: T
authors: Yan
mesh_terms: D000465:Algorithms; D000821:Animal Feed / Q000032:analysis*; D000818:Animals; D002417:Cattle; D016208:Databases, Factual; D004063:Digestion; D002149:Energy Intake; D004734:Energy Metabolism; D005260:Female; D008697:Methane / Q000378:metabolism; D008954:Models, Biological; D009660:Northern Ireland; D009753:Nutritive Value; D035261:Plant Components, Aerial / Q000737:chemistry* / Q000378:metabolism; D006109:Poaceae / Q000737:chemistry* / Q000378:metabolism; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic
chemical_list: D008697:Methane
keywords: Digestibility; Grass; Grazing: Maintenance feeding; Metabolisable energy; Pasture-based ruminant production; Prediction