Predicting methane emissions from beef cattle on different grasslands – does the prediction equation matter?

RICCI P; WATERHOUSE A

Nottingham

Simposio; Advances in Animal Bioscience. British Society of Animal Science Conference; 2011

British Society of Animal Science

Semi-natural grasslands cover significant areas of the world, many of them unsuitable for arable cropping. Ruminants can convert grasslands into food for humans but produce methane (CH4), as an inevitable output. Measurements of CH4 from grazing animals are difficult to carry out; hence mathematical models are useful to predict the outcome of different management and mitigation options. Although several approaches to estimate CH4 emissions have been reported, there is still a lack of information about possible impacts of these different approaches within the context of farming systems. This study aims to compare different equations to estimate CH4 emissions from suckler cows in a simplified scenario with two contrasting grassland characteristics. A simulation model (Armstrong et al., 1997) based on baseline grassland digestibility from the literature was used to estimate monthly digestibility of intake by suckler cows (DIG) of typical lowland (LG) and hill (HG) grasslands in the UK. A 100 years Monte Carlo simulation was then applied to generate grassland DIG replications, using mean values of DIG and standard deviations from the literature. Energy requirements and dry matter intakes (DMI) of spring calving cows on grazed grass as sole diet were predicted based upon AFRC (1993) using equal levels of annual cow performance and body weight, and body weight change, for all years and treatments. Monthly CH4 emissions from cows were predicted by 3 equations 1. Kriss (1930) based on DMI; 2. IPCC Tier 2 (2006) based on energy intake; 3. Yan et al. (2009) based on feed energy and energy requirements. Both annually and monthly CH4 estimations were analysed in a completely randomised design with grassland type, equation and month (when appropriate) as factors. Results were tested with SAS using General Linear Models. Feed energy for LG was higher (P<0.001) than HG (10.9±0.04a vs. 8.3±0.03b ME MJ/kgDM, Mean±SEM with different superscripts being significantly different). DMI had the opposite response being lower (P<0.001) for LG than HG (8.3±0.04b vs. 12.9±0.06a kg/cow/day). There was a significant interaction (P<0.001) between grassland type and equations used to predict annual CH4 (HG: 104.6±0.24a, 111.4±0.26b, 114.2±0.21c; LG: 71.1±0.16d, 74.0±0.18e, 94.6±0.17f kg CH4/cow/annum for IPCC, Kriss and Yan, respectively). On average all estimates of CH4 emissions from HG were higher than LG (P<0.005) due to the lower DIG of HG and higher DMI of these simulated cows with similar level of performance. Differences were smaller during the winter, when DIG of grasslands and animal requirements are both lower. Monthly CH4 estimations were affected by all factor interactions (P<0.001). Within HG simulations, monthly CH4 estimations from Kriss were different (P<0.0001) from the others except in April, July and August. Estimations from Yan were higher than those from IPCC (P<0.0001) except during spring and summer periods. For LG simulations, CH4 estimations from Yan were the highest and different from the other two during the year (P<0.0001). Estimations from Kriss were only higher than IPCC during the winter and autumn periods (P<0.005). The annual pattern of CH4 emissions fluctuated over the year following animal requirements and feed quality variations. Based on the same input values, equations behave differently indicating that the relative importance of each parameter differs between equations. Although it is not typical for cattle in the UK to rely only on grazing during the whole year, this study illustrates that predicted CH4 production not only differs as a result of diverse system inputs, but also as a result of different equations used to perform the estimations.