A universal equation to predict methane production of forage-fed cattle in Australia

Abstract

Abstract. The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (AE0.28) · DMI (kg/day) (R 2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (AE0.008) · GEI (MJ/day) (R 2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia's national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO 2 -e reduction in calculated annual emissions from Australian cattle.

Global beef cattle methane emissions: yield prediction by cluster and meta-analyses

“…The prediction of MY value for Australian beef cattle on non-grain diets measured in chambers in Eqn 4, derived from an analysis of global MY data, was 21.85 + 0.77 + d_breed (0, 1, 2, average of 0.35) + 3.76 -3.37 = 23.4. This is 2.7 g/kg DMI higher than the MY value of Charmley et al (2016). Charmley et al (2016) noted that MP and DMI variances increase as MP and DMI levels increase while their coefficient of variance remain steady.…”

“…However, they found that the best methane-prediction model for North American lactating dairy-cow studies (modified from Nielsen et al 2013) was different than the best model for European, Australian and New Zealand studies (Yan et al 2000). Charmley et al (2016) analysed a total of 1034 individual animal records of MP to re-assess the relationship between MP and DMI. Data were restricted to trials conducted in the past 10 years using respiration chambers, with cattle fed forage-based diets (forage >70%).…”

“…This has the implicit assumption that the MY value is constant for all DMI values if there is no intercept in the MP/DMI equation (e.g. Bannink et al 2011;Charmley et al 2016). If there is an intercept in the MP/DMI equation (e.g.…”

“…One example of using predictive Eqn 4, which can be compared with the prediction equation of Charmley et al (2016), is for British cattle breeds, measured in chambers, on a roughage diet in Australia. In this case, the MY value solves to 21.85 + 0.77 -0.61 + 3.76 -3.37 = 22.40 AE 1.20 g methane/ kg DMI.…”

“…Recently Charmley et al (2016) conducted a meta-analysis of MP and DMI data from Australian, forage-fed, beef-cattle methane-chamber studies to develop a universal MP prediction equation for use in Australian GHG accounting. If one relationship could be recommended for all forage-fed beef cattle in Australia, then could a global equation with country or region and dietary correction effects (if needed) be developed?…”