title: Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics.
abstract: The prediction of methane outputs from ruminant livestock data at farm, national, and global scales is a vital part of greenhouse gas calculations. The objectives of this work were to quantify the effect of physiological stage (lactating or nonlactating) on predicting methane (CH4) outputs and to illustrate the potential improvement for a beef farming system of using more specific mathematical models to predict CH4 from cattle at different physiological stages and fed different diet types. A meta-analysis was performed on 211 treatment means from 38 studies where CH4, intake, animal, and feed characteristics had been recorded. Additional information such as type of enterprise, diet type, physiological stage, CH4 measurement technique, intake restriction, and CH4 reduction treatment application from these studies were used as classificatory factors. A series of equations for different physiological stages and diet types based on DMI or GE intake explained 96% of the variation in observed CH4 outputs (P<0.001). Resulting models were validated with an independent dataset of 172 treatment means from 20 studies. To illustrate the scale of improvement on predicted CH4 outputs from the current whole-farm prediction approach (Intergovernmental Panel on Climate Change [IPCC]), equations developed in the present study (NewEqs) were compared with the IPCC equation {CH4 (g/d)=[(GEI×Ym)×1,000]/55.65}, in which GEI is GE intake and Ym is the CH4 emission factor, in calculating CH4 outputs from 4 diverse beef systems. Observed BW and BW change data from cows with calves at side grazing either hill or lowland grassland, cows and overwintering calves and finishing steers fed contrasting diets were used to predict energy requirements, intake, and CH4 outputs. Compared with using this IPCC equation, NewEqs predicted up to 26% lower CH4 on average from individual lactating grazing cows. At the herd level, differences between equation estimates from 10 to 17% were observed in total annual accumulated CH4 when applied to the 4 diverse beef production systems. Overall, despite the small number of animals used it was demonstrated that there is a biological impact of using more specific CH4 prediction equations. Based on this approach, farm and national carbon budgets will be more accurate, contributing to reduced uncertainty in assessing mitigation options at farm and national level.
authors.affiliation: Future Farming Systems Group, SRUC, West Mains Road, Edinburgh, EH9 3JG, UK.
authors.forename: P
authors.initials: P
authors.lastname: Ricci
authors.forename: J A
authors.initials: JA
authors.lastname: Rooke
authors.forename: I
authors.initials: I
authors.lastname: Nevison
authors.forename: A
authors.initials: A
authors.lastname: Waterhouse
mesh_terms: D000393:Air Pollutants / Q000737:chemistry / Q000378:metabolism*; D000821:Animal Feed / Q000032:analysis; D000818:Animals; D002417:Cattle / Q000378:metabolism*; D003198:Computer Simulation; D004032:Diet / Q000662:veterinary*; D005260:Female; D008297:Male; D008697:Methane / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D015203:Reproducibility of Results
chemical_list: D000393:Air Pollutants; D008697:Methane