Agricultural Systems Journal_Dataset.xlsx

Greenhouse gas (GHG) emissions from livestock grazing systems are a major contributor to agricultural emissions in Australia, yet their quantification remains challenging due to large interannual variability, diverse agroecological contexts, and differences in management practices. This study, Accounting for temporal and agroecological variability in GHG emissions from Australian livestock grazing systems, addresses these challenges by examining how temporal resolution in accounting frameworks influences estimates of total emissions and emissions intensity (EI).The research applied a partial life cycle assessment (LCA) from pre-farm to farm gate, consistent with IPCC 2019 and ISO 14040/14067 standards, across two contrasting case study farms over a ten-year period. The first, in Northeastern Victoria (NEV), represents a temperate, high-rainfall system characterised by mixed beef and wool production, as well as an integrated feedlot. The second, in Central-Western Queensland (CWQ), is a large-scale, semi-arid grazing enterprise based on Wagyu beef, later diversified with rangeland goats. Emissions were estimated using a customised Greenhouse Accounting Framework (GAF) tool, covering major sources such as enteric methane, manure management, soils, fertiliser use, energy, and pre-farm inputs, including feed and purchased livestock. Temporal analysis compared annual estimates with three- and five-year running averages to test the extent to which smoothing improved the representativeness of results.Findings revealed that the NEV farm produced substantially higher annual emissions (11,039 t CO₂-eq) than CWQ (2,096 t CO₂-eq), but with far lower variability (coefficient of variation, CV, 7% vs. 37%). Emissions intensity for beef averaged 13.0 kg CO₂-eq/kg liveweight at NEV and 10.8 kg CO₂-eq/kg at CWQ, but interannual variability was much greater in the northern rangeland system. For sheep meat and greasy wool at NEV, EI values averaged 8.5 kg CO₂-eq/kg and 32.2 kg CO₂-eq/kg, respectively, while early goat production at CWQ showed extremely high variability (mean 44.7 kg CO₂-eq/kg, CV 115%). Applying three- and five-year rolling averages reduced fluctuations, particularly at CWQ, where the CV fell from 37% annually to 16% with a five-year average. By contrast, the already stable NEV system showed little change, with CV declining only from 7% to 5%.These results demonstrate that while enteric methane remains the largest single source of emissions, accounting for 77–85% of totals, it is not the primary driver of variability. Instead, short-term fluctuations stemmed primarily from management responses to climate and market conditions, including supplementary feeding, fertiliser application, destocking, and restocking. The analysis highlights the risk of misinterpretation when using single-year estimates in highly variable systems, as anomalous years may be mistaken for mitigation gains or setbacks. To address this, the study proposes a 10% CV threshold as a practical benchmark for determining when emissions estimates are statistically representative. Under this criterion, NEV achieved stability with annual or three-year averages, while CWQ required longer averaging windows and still exceeded the threshold, underlining the challenge of benchmarking emissions in extensive, climate-sensitive systems.Overall, this research demonstrates that farm-scale accounting frameworks must consider temporal and agroecological variability when evaluating emissions performance. Annual reporting remains important for capturing management responsiveness, but multi-year averages provide a more reliable basis for benchmarking and policy reporting. Stable temperate systems may be adequately represented with shorter timeframes, while extensive rangeland systems require longer windows to generate representative results. These findings provide critical insights for the design of farm- and industry-scale emissions reporting frameworks, ensuring that observed changes reflect genuine mitigation rather than natural variability. Future research should extend temporal records beyond ten years and expand across agroecological zones to strengthen the robustness of baselines and to assess whether mitigation strategies—such as feed additives, improved reproductive efficiency, or grazing management—can consistently reduce emissions intensity beyond the bounds of natural system variability.