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.Key words: Grass: Metabolisable energy: Digestibility: Prediction: Grazing: Maintenance feeding: Pasture-based ruminant production Grazed grass is a low-cost feed for cattle and offers an economic advantage to regions capable of long grazing seasons and high productivity of DM per hectare (1,2) . For this reason, pasture-based systems predominate ruminant production in certain countries, such as Ireland, New Zealand and parts of the UK (1 -3) , thus enhancing the competitiveness of dairy and beef industry, preserving rural landscapes and improving the image of milk and meat production for consumers (4) . However, fresh grass-based diets provide lower amounts of glucogenic nutrients (4) , than diets rich in concentrate feeds, and energy supply is challenging in grazing systems, because of the high contribution of pasture in the diets. Efficient assessment of the feeding value of grass is crucial in order to avoid excessive over-supply that would increase production costs, or under-supply that would reduce productivity. Developing tools to predict metabolisable energy (ME) in fresh grass may greatly improve profitability of pasture-based systems, with a 180 000 MJ/ha of ME being recommended as the production target (3) . Parameters such as digestible organic matter in DM (DOMD) and dig...

Effects of crude protein level in concentrate supplements on animal performance and nitrogen utilization of lactating dairy cows fed fresh-cut perennial grass

“…The differences between treatments, genotypes, and interactions were assessed and declared as nonsignificant at P > 0.05 and significant at P < 0.05, P < 0.01, and P < 0.001. A REML analysis was also performed to develop a range of linear and multiple relationships to estimate MUN and urine N outputs using the method previously described by Stergiadis et al (2015). In brief, linear regression relationships were developed where the responses were MUN output, MUN concentration, and urine N output, and the explanatory variables were N intake, dietary CP content, and MUN output, respectively.…”

Equations to predict nitrogen outputs in manure, urine and faeces from beef cattle fed diets with contrasting crude protein concentration

“…Equations for the prediction of FNO including DMI either as sole predictor or in combination with CP, ME and TF, showed similar accuracy among the databases, confirming that DMI is a reliable sole predictor for FNO in low CP diets (Stergiadis et al, 2015a;Angelidis et al, 2019).…”

“…research environment). The method used in the present study to develop the prediction equations has been previously used in several studies (Stergiadis et al, 2015a;Stergiadis et al, 2015b;Stergiadis et al, 2016). In brief, the optimum random model developed for each response variable was built by fitting the same fixed effect model and the prospective models of the random variation.…”

Whole farm modelling the effect of grass silage harvest date and nitrogen fertiliser rate on nitrous oxide emissions from grass-based suckler to beef farming systems

“…In the model, these estimates were not affected by changes in N fertiliser application rates even though there is a strong correlation with N fertiliser rate and the N content of grass (Gately et al, 1972). Grass N content was calculated using equations developed by Stergiadis et al (2015) where metabolisable energy ME content of grass was used as the main predictor. Grass DMD was the main predictor for grass ME content which decreased from 820 g kg DM -1 in February to 730 g kg DM -1 in November.…”

“…However, for grass-based systems, grazed and conserved grass consist of up to 90% of a finishing animal's diet (Crosson et al, 2014). This is a challenge as the composition and quality of grass varies over the grazing season and with different management practices (Keady et al, 2000;Stergiadis et al, 2015), making it difficult to manipulate the diet to reduce N excreted. Therefore as silage quality is largely dictated by management, the efficacy of manipulating silage quality to reduce N excretion in grass-based production systems has been examined.…”