Modelling the soil C impacts of cover crops in temperate regions

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 var-iable regression model based on cover crop biomass (dry matter) production was the best combination of sta-tistical 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.