Efficient Beef Production from Temperate Grasslands in North-Western Europe

Ireland’s cool temperate maritime climate is conducive to grass growth and, as a result, ruminant livestock systems have evolved that maximise both grazed pastures and conserved grassland forage as winter feed. Most Irish pastures are permanent, capable of achieving high herbage production (Keating and O’Kiely 2000) and, accordingly, supporting intensive livestock production systems. Most male progeny from the 1.1 million Irish dairy herd are reared as steers, typically slaughtered at 24-26 months of age. Approximately 85% of dairy calves available for beef production are spring-born, usually in February/March (AIMS 2011). The progeny of Holstein-Friesian (Ho/Fr) sires account for 0.5-0.6 of the calf crop, with 0.6 and 0.4 of the remainder being sired by early-maturing (EM; e.g. Aberdeen Angus, Hereford) or late-maturing (LM; e.g. Limousin, Belgian Blue, Charolais) sires, respectively. This paper summarises some of the main grassland-based steer beef production systems applicable to Ireland

Intake and Growth of Steers Offered Different Allowances of Autumn Grass and Concentrates

The aim of this experiment was to quantify the relationship between autumn grass supply and concentrate supplementation level on grass intake and animal performance. One hundred and ten continental steers (567kg) were assigned to ten treatments in a three grass allowances: (6, 12 and 18kg dry matter (DM) per head daily) by three concentrate levels: (0, 2.5 and 5kg/head/daily) factorial design with a positive control group offered concentrates ad-libitum. Grass allowance was offered daily and concentrates were fed individually. The experiment began on August 22 and all animals were slaughtered after a mean experimental period of 95 days. Grass allowance increased (P\u3c 0.001) complete diet digestibility only in the absence of concentrates and supplementary concentrates increased (P\u3c 0.001) complete diet digestibility only at the low grass allowance. Both offering animals supplementary concentrates (P\u3c 0.001) and increasing daily grass allowance (P\u3c 0.001) increased their carcass growth rate. Grazed grass supported only one third the carcass growth rate of supplementary concentrates per kg of DM eaten. As a strategy for increasing the performance of cattle grazing autumn grass, offering supplementary concentrates offers more scope than altering grass allowance

Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

peer-reviewedIn vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro

Cluster analysis of fasciolosis in dairy cow herds in Munster province of Ireland and detection of major climatic and environmental predictors of the exposure risk

Fasciolosis caused by Fasciola hepatica is a widespread parasitic disease in cattle farms. The aim of this study was to detect clusters of fasciolosis in dairy cow herds in Munster Province, Ireland and to identify significant climatic and environmental predictors of the exposure risk. In total, 1,292 dairy herds across Munster was sampled in September 2012 providing a single bulk tank milk (BTM) sample. The analysis of samples by an in-house antibody-detection enzyme-linked immunosorbent assay (ELISA), showed that 65% of the dairy herds (n = 842) had been exposed to F. hepatica. Using the Getis-Ord Gi* statistic, 16 high-risk and 24 low-risk (P <0.01) clusters of fasciolosis were identified. The spatial distribution of high-risk clusters was more dispersed and mainly located in the northern and western regions of Munster compared to the low-risk clusters that were mostly concentrated in the southern and eastern regions. The most significant classes of variables that could reflect the difference between high-risk and low-risk clusters were the total number of wet-days and rain-days, rainfall, the normalized difference vegetation index (NDVI), temperature and soil type. There was a bigger proportion of well-drained soils among the low-risk clusters, whereas poorly drained soils were more common among the high-risk clusters. These results stress the role of precipitation, grazing, temperature and drainage on the life cycle of F. hepatica in the temperate Irish climate. The findings of this study highlight the importance of cluster analysis for identifying significant differences in climatic and environmental variables between high-risk and low-risk clusters of fasciolosis in Irish dairy herds

Spatial analysis and risk mapping of Fasciola hepatica infection in dairy herds in Ireland

Fasciolosis is generally a subclinical infection of dairy cows and can cause marked economic losses. This study investigated the prevalence and spatial distribution of fasciolosis in dairy cow herds in Ireland using an in-house antibodydetection enzyme-linked immunosorbent assay applied to bulk tank milk (BTM) samples collected during the autumn of 2012. A total of 5,116 BTM samples were collected from 4,602 different herds, with 514 farmers submitting BTM samples in two consecutive months. Analysis of the BTM samples showed that 82% (n = 3,764) of the dairy herds had been exposed to Fasciola hepatica. A total of 108 variables, including averaged climatic data for the period 1981-2010 and contemporary meteorological data for the year 2012, such as soil, subsoil, land cover and habitat maps, were investigated for a possible role as predictor of fasciolosis. Using mainly climatic variables as the major predictors, a model of the predicted risk of fasciolosis was created by Random Forest modelling that had 95% sensitivity and 100% specificity. The most important predictors in descending order of importance were: average of annual total number of rain-days for the period 1981-2010, total rainfall during September, winter and autumn of 2012, average of annual total number of wet-days for the period 1981- 2010 and annual mean temperature of 2012. The findings of this study confirm the high prevalence of fasciolosis in Irish dairy herds and suggest that specific weather and environmental risk factors support a robust and precise distribution model

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation