Animal Production Science最新文献

Wagyu Feeder Check is a genomic-based tool designed to provide genomic estimated breeding values (GEBV) for five feedlot growth and carcase traits. At present, Wagyu Feeder Check is based on a reference population of 8316 genotyped and phenotyped Australian fullblood (FB; N = 2120) Wagyu and Wagyu-crossed (XB; N = 6196) cattle, principally Wagyu × Angus F1 animals.

We provide technical details behind the development of the Wagyu Feeder Check and validate the ability of its GEBV to predict differences in performance of Wagyu cattle in daily weight gain at feedlot, carcase weight, carcase eye muscle area, carcase marbling score and carcase rump fat at the P8 site.

Data supplied from eight commercial supply chains across Australia was used to generate GEBV using mixed-model equations that incorporated a genomic relationship matrix build with 82 504 autosomal markers. The bias, dispersion, and accuracy of the GEBV were evaluated using a four-way cross-validation scheme where, in each turn, the phenotypes from a random 1549 (or 25%) XB cattle were set as missing.

The genomic estimate of the Wagyu content in the FB and XB population averaged 99.12% and 59.55%, respectively, and with most of the non-Wagyu content associated with Angus. The estimates of heritability (± s.e.) were 0.497 ± 0.016, 0.474 ± 0.004, 0.347 ± 0.014, 0.429 ± 0.003 and 0.422 ± 0.003 for daily weight gain at feedlot, carcase weight, eye muscle area, marbling and rump fat, respectively. Averaged across the four XB validation populations, the accuracy of GEBV was 0.624, 0.634, 0.385, 0.620, and 0.526 for the same set of traits.

Genomic predictions generated by Wagyu Feeder Check can predict differences in feedlot and carcase performance of Australian Wagyu cattle. Given the large content of Angus in the XB population, further research is required to determine the predictive ability of GEBV in Wagyu × Bos indicus and Wagyu × dairy animals.

Commercial feedlot operators finishing animals with a strong Wagyu breed component will benefit from using Wagyu Feeder Check for decision making.

The Western Australian (WA) Government has set ambitious emission reduction targets and is developing strategies to reduce emissions across the state economy, including agriculture.

This study determined the product carbon footprint (CF) and total emissions of the WA beef industry, to establish a baseline for emission reduction planning.

A cradle-to-gate attributional life-cycle assessment with a reconciled livestock inventory of herd numbers and turnoff, was used. Emission reduction strategies were examined and included herd management, enteric-methane mitigation, and removals via carbon sequestration in vegetation and soils.

Modelled livestock numbers were found to be 36% higher than reported in the Australian Bureau of Statistics (ABS), resulting in an emission profile of 4.7 million tonnes (Mt) of carbon dioxide equivalent (CO₂-e) (excluding land use (LU) and direct LU change (dLUC)). This profile was 26% higher than emissions reported in state inventories. LU and dLUC were estimated to be a greenhouse-gas removal of −2.6 Mt CO₂-e, although with high uncertainty. The mean CF for WA was 15.3 kg CO₂-e per kg liveweight (LW) (excluding LU and dLUC). State-wide removals from LU and dLUC were estimated to be −8.5 kg CO₂-e/kg LW. The CF was 11.7, 19.2 and 18.2 kg CO₂-e/kg LW for the Agricultural, Kimberley and Arid regions respectively. The implementation of herd-management strategies and anti-methanogenic supplements resulted in a maximum 25% reduction.

Herd productivity and market specifications were key drivers of regional differences in CF. Opportunities exist to reduce the CF in northern herds through diverting cattle to Australian backgrounding and feedlot supply chains to reach slaughter weight at a younger age. Adoption of anti-methanogenic feed supplements were important; however, achieving major reductions in the next decade will rely on removals via carbon sequestration in soil and vegetation.

Considering the magnitude of removals and elevated uncertainty in this result, further research and new datasets are needed to refine this analysis. New datasets are required to accurately report livestock numbers and track and reduce future GHG emissions from this higher baseline. Technical, cost and adoption barriers will need to be addressed by developing actionable pathways to achieve emission reduction in the mid- to long term.