The EU Soil Strategy 2030 aims to increase soil organic carbon (SOC) in agricultural land to enhance soil health and support biodiversity as well as to offset greenhouse gas emissions through soil carbon sequestration. Therefore, the quantification of current SOC stocks and the spatial identification of the main drivers of SOC changes is paramount in the preparation of agricultural policies aimed at enhancing the resilience of agricultural systems in the EU. In this context, changes of SOC stocks (Δ SOCs) for the EU + UK between 2009 and 2018 were estimated by fitting a quantile generalized additive model (qGAM) on data obtained from the revisited points of the Land Use/Land Cover Area Frame Survey (LUCAS) performed in 2009, 2015 and 2018. The analysis of the partial effects derived from the fitted qGAM model shows that land use and land use change observed in the 2009, 2015 and 2018 LUCAS campaigns (i.e. continuous grassland [GGG] or cropland [CCC], conversion grassland to cropland (GGC or GCC) and vice versa [CGG or CCG]) was one of the main drivers of SOC changes. The CCC was the factor that contributed to the lowest negative change on Δ SOC with an estimated partial effect of −0.04 ± 0.01 g C kg⁻¹ year⁻¹, while the GGG the highest positive change with an estimated partial effect of 0.49 ± 0.02 g C kg⁻¹ year⁻¹. This confirms the C sequestration potential of converting cropland to grassland. However, it is important to consider that local soil and environmental conditions may either diminish or enhance the grassland's positive effect on soil C storage. In the EU + UK, the estimated current (2018) topsoil (0–20 cm) SOC stock in agricultural land below 1000 m a.s.l was 9.3 Gt, with a Δ SOC of −0.75% in the period 2009–2018. The highest estimated SOC losses were concentrated in central‐northern countries, while marginal losses were observed in the southeast.

Nitrous oxide (N₂O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N₂O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N₂O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap‐filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap‐filling soil N₂O emissions. To facilitate the use of improved gap‐filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap‐filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.

Nitrification inhibitors (NIs) have been shown to reduce emissions of the greenhouse gas nitrous oxide (N₂O) from agricultural soils. However, their N₂O reduction efficacy varies widely across different agro-ecosystems, and underlying mechanisms remain poorly understood. To investigate effects of the NI 3,4-dimethylpyrazole-phosphate (DMPP) on N-turnover from a pasture and a horticultural soil, we combined the quantification of N₂ and N₂O emissions with ¹⁵N tracing analysis and the quantification of the N₂O-reductase gene (nosZ) in a soil microcosm study. Nitrogen fertilization suppressed nosZ abundance in both soils, showing that high nitrate availability and the preferential reduction of nitrate over N₂O is responsible for large pulses of N₂O after the fertilization of agricultural soils. DMPP attenuated this effect only in the horticultural soil, reducing nitrification while increasing nosZ abundance. DMPP reduced N₂O emissions from the horticultural soil by >50% but did not affect overall N₂ + N₂O losses, demonstrating the shift in the N₂O:N₂ ratio towards N₂ as a key mechanism of N₂O mitigation by NIs. Under non-limiting NO₃⁻ availability, the efficacy of NIs to mitigate N₂O emissions therefore depends on their ability to reduce the suppression of the N₂O reductase by high NO₃⁻ concentrations in the soil, enabling complete denitrification to N₂.