It is difficult to identify farm management practices that consistently provide greenhouse gas (GHG) abatement at different locations because effectiveness of practices is greatly influenced by climates and soils. We address this knowledge gap by identifying practices that provide abatement in eight case studies located across diverse conditions in Australian’s grain-producing areas. The case studies focus on soil-based emissions of nitrous oxide (N₂O) and changes in soil organic carbon (SOC), simulated over 100 years for 15 cropping management scenarios. Average changes in the balance of GHG from both N₂O emissions and SOC sequestration (∆GHG balance) and gross margins compared to a high emissions baseline were determined over 25 and 100 simulated years. Because scenarios providing the greatest abatement varied across individual case studies, we aggregated the data over all case studies and analysed them with a random forest data mining approach to build models for predicting ∆GHG balance. Increased cropping intensity, achieved by including cover crops, additional grains crops, or crops with larger biomass in the rotation, was the leading predictor of ∆GHG balance across the scenarios and sites. Abatement from increased cropping intensity averaged 774 CO₂-e ha⁻¹ year⁻¹ (25 years) and 444 kg CO₂-e ha⁻¹ year⁻¹ (100 years) compared to the baseline, with reduced emissions from SOC sequestration offsetting increased N₂O emissions for both time frames. Increased cropping intensity decreased average gross margins, indicating that a carbon price would likely be needed to maximise GHG abatement from this management. To our knowledge, this is the first time that the random forest approach has been applied to assess management practice effectiveness for achieving GHG abatement over diverse environments. Doing so provided us with more general information about practices that provide GHG abatement than would have come from qualitative comparison of the variable results from the case studies.

很难确定在不同地点始终提供温室气体 (GHG) 减排的农场管理做法，因为做法的有效性受气候和土壤的影响很大。我们通过确定在澳大利亚粮食产区不同条件下的八个案例研究中提供减排的做法来解决这一知识差距。案例研究侧重于基于土壤的一氧化二氮 (N ₂ O) 排放和土壤有机碳 (SOC) 的变化，模拟了 100 多年的 15 种种植管理情景。_{来自 N 2}的 GHG 平衡的平均变化O2 排放和 SOC 封存（ΔGHG 平衡）以及与高排放基线相比的毛利率是在 25 年和 100 年的模拟年中确定的。由于提供最大减排的情景因个别案例研究而异，因此我们汇总了所有案例研究的数据，并使用随机森林数据挖掘方法对其进行了分析，以构建用于预测 ΔGHG 平衡的模型。通过在轮作中包括覆盖作物、额外的谷物作物或生物量更大的作物来实现的种植强度增加是情景和地点间 ΔGHG 平衡的主要预测指标。增加种植强度平均减少 774 CO ₂ -e ha ⁻¹年⁻¹（25 年）和 444 kg CO ₂ -e ha ⁻¹年⁻¹（100 年）与基线相比，SOC 封存减少的排放量抵消了两个时间范围内增加的 N _{2 O 排放量。}种植强度的增加降低了平均毛利率，表明可能需要碳价格来最大限度地减少这种管理的温室气体排放。据我们所知，这是首次将随机森林方法应用于评估管理实践的有效性，以实现不同环境中的温室气体减排。这样做为我们提供了比案例研究的可变结果的定性比较更多的关于提供温室气体减排实践的一般信息。