CONTEXT

Agricultural land management decisions are based on numerous considerations. Belowground carbon (C) storage for both ecosystem health and greenhouse gas (GHG) management is a growing motivation. Observed heterogeneity in soil C storage in croplands may be driven by various environmental, climatic and management factors. Farm system models can indicate which practices will drive C storage, provided the practice is well parameterised and the land manager can provide necessary input data.

OBJECTIVE

We aimed to predict soil C impacts of temperate cover cropping using simple models suitable for broad farmer use and decision support.

METHODS

The dataset used was initially compiled for a meta-analysis (McClelland et al., 2021) to quantify soil C response to cover crop treatments relative to a non-cover cropped system. It contains 181 data points from 40 existing studies in temperate climates. Environmental, climatic and management indicators were regressed pairwise to predict annual soil C stock change under cover cropping relative to no cover cropping. We also included the IPCC tier 1 methodology and meta-analysis response ratios in our model comparison.

The ease of reliable measurement and monitoring across the modelled indicators was also considered because the best-correlated relationships are squandered if data constraints risk decision-makers being unable to use the model.

RESULTS AND CONCLUSIONS

Using an extended test dataset to consider priorities for model users, several regression models outperformed the IPCC tier 1 methodology. In particular, two regression models reliably predicted negative changes in soil C, which IPCC and meta-analysis factor approaches could not. A single variable regression model based on cover crop biomass (dry matter) production was the best combination of statistical power, biological relevance and parsimony. In temperate climates, we predicted an increase in soil C stocks as long as cover crop biomass production exceeded 1.3 Mg ha⁻¹ yr⁻¹.

SIGNIFICANCE

Our final model can be applied with estimated user input data, and avoids the need for baseline soil C as an input; this makes it relatively accessible for farmers. Parsimonious models for soil C change under land management practices can be effective and are an opportunity to increase access to soil C management information for farmers.

中文翻译：

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模拟温带地区覆盖作物对土壤碳的影响

语境

农业土地管理决策基于多种考虑。用于生态系统健康和温室气体 (GHG) 管理的地下碳 (C) 储存是一个日益增长的动力。观察到的农田土壤碳储存的异质性可能是由各种环境、气候和管理因素驱动的。农场系统模型可以表明哪些实践将驱动 C 存储，前提是实践参数化良好并且土地管理者可以提供必要的输入数据。

客观的

我们旨在使用适合广大农民使用和决策支持的简单模型来预测温带覆盖作物对土壤碳的影响。

方法

使用的数据集最初是为荟萃分析（McClelland 等人，2021 年）编制的，以量化土壤碳对覆盖作物处理相对于非覆盖作物系统的反应。它包含来自 40 项现有温带气候研究的 181 个数据点。对环境、气候和管理指标进行成对回归，以预测覆盖种植相对于无覆盖种植的年度土壤碳库变化。我们还在模型比较中包括了 IPCC 一级方法和荟萃分析响应率。

还考虑了跨建模指标进行可靠测量和监控的难易程度，因为如果数据限制导致决策者无法使用该模型，那么最佳相关关系就会被浪费掉。

结果和结论

使用扩展测试数据集来考虑模型用户的优先级，几个回归模型优于 IPCC 一级方法。特别是，两个回归模型可靠地预测了土壤碳的负变化，这是 IPCC 和荟萃分析因子方法无法做到的。基于覆盖作物生物量（干物质）生产的单变量回归模型是统计功效、生物相关性和简约性的最佳组合。在温带气候下，我们预测只要覆盖作物生物量产量超过 1.3 Mg ha ^-1  yr ^-1 ，土壤碳储量就会增加。

意义

我们的最终模型可以与估计的用户输入数据一起应用，并且无需将基线土壤 C 作为输入；这使得农民相对容易获得。土地管理实践下土壤碳变化的简约模型可能是有效的，并且是增加农民获取土壤碳管理信息的机会。