Publications

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

A wide range of long-term experiments (LTEs) exist within Europe, and these LTEs provide unique and valuable information relevant for the farming systems in which they occur. These LTEs can be used to derive factors of soil carbon change under different management and climate so that the models can be used to confidently upscale to regional, national and continental scales. We have tested the DNDC and DayCent models at four long term experiments covering 9 treatments and 4 management practices. The simulated grain yields matched the measurement in the majority of treatments and management practices. Overall, both DNDC and DayCent simulated grain yields satisfactorily. Both DNDC and DayCent simulated top soil (20 cm depth) soil organic carbon (SOC) in crops under different tillage management well, but both models overestimated SOC in plots where crop residues were incorporated, suggesting that further improvements are required in the crop residue and decomposition modules of these models. The performance of both DNDC and DayCent are comparable with other studies in the literature. Several limitations of DNDC and DayCent models were identified. These limitations can be overcome by incorporating new process and increasing quality and quantity of input data. DNDC and DayCent are useful tools for land managers and policymakers when recommending long-term practices to enhance SOC gains in agriculture. However, it is recommended that both models should be further developed to better simulate the impact of management practices such as residue management, since both models showed poor fit for observed yields and soil carbon dynamics for these practices..

Abstract Understanding and predicting the effects of land‐use change to short rotation forestry ( SRF ) on soil carbon (C) is an important requirement for fully assessing the C mitigation potential of SRF as a bioenergy crop. There is little current knowledge of SRF in the UK and in particular a lack of consistent measured data sets on the direct impacts of land use change on soil C stocks. The ECOSSE model was developed to simulate soil C dynamics and greenhouse gas ( GHG ) emissions in mineral and organic soils. The ECOSSE model has already been applied spatially to simulate land‐use change impacts on soil C and GHG emissions. However, it has not been extensively evaluated under SRF . Eleven sites comprising 29 transitions in Britain, representing land‐use change from nonwoodland land uses to SRF , were selected to evaluate the performance of ECOSSE in predicting soil C and soil C change in SRF plantations. The modelled C under SRF showed a strong correlation with the soil C measurements at both 0–30 cm ( R = 0.93) and 0–100 cm soil depth ( R = 0.82). As for the SRF plots, the soil C at the reference sites have been accurately simulated by the model. The extremely high correlation for the reference fields ( R ≥ 0.99) shows a good performance of the model spin‐up. The statistical analysis of the model performance to simulate soil C and soil C changes after land‐use change to SRF highlighted the absence of significant error between modelled and measured values as well as the absence of significant bias in the model. Overall, this evaluation reinforces previous studies on the ability of ECOSSE to simulate soil C and emphasize its accuracy to simulate soil C under SRF plantations.

Plastic mulching is widely used in traditional maize production system, but its excessive application has led to diminishing returns in yield improvement and water productivity. No tillage with plastic re-mulching offers a sustainable alternative by reducing plastic input and regulating soil hydrothermal conditions, potentially ensuring high and stable yields along with improved water productivity. However, the underlying mechanisms linking this approach to yield sustainability and water use efficiency through water eco-physiological and root traits remains unclear. This study aims to investigate the effect of different plastic mulching strategies on water eco-physiology, root traits, grain yield, and water productivity in maize production. In 2013, a field experiment took place in the northwestern area of China, utilizing data collected between 2021 and 2023, comparing three plastic mulching treatments: (1) no tillage with plastic re-mulching (NTP), (2) no tillage in autumn and new plastic mulching in spring (RTP), and (3) conventional tillage with annual new plastic mulching (CTP, the control). Compared to CTP, NTP increased net photosynthetic rate, transpiration rate, and leaf water use efficiency by 26.1 %, 8.7 %, and 16.2 %, respectively, at 90–120 d after sowing. NTP also expanded canopy cover by 5.3 % at 75 d after sowing. Soil evaporation was reduced by 36.2 % and 24.5 % at 90–105 and 105–135 d after sowing, respectively, while soil water storage increased by 12.0 % and 7.1 %. Meanwhile, transpiration rose by 5.2 % and 17.3 %, leading to an 8.8 % increase in the transpiration-to-soil evaporation ratio. During the filling stage, NTP enhanced root development in the 0–30 cm soil layer, with increases of 35.6 % in root length density, 32.8 % in root surface area density, 32.2 % in root dry weight density, and 25.2 % in root biomass. In the 30–60 cm soil layer, root parameters also improved, with root activity and root zone water uptake rate increasing by 24.4 % and 6.5 %, respectively. Consequently, NTP increased grain yield by 5.5 %, with higher yield stability driven by a 6.8 % increase in harvested ears and a 9.1 % rise in1000-kernel weight, while maintaining water productivity comparable to CTP. These findings provide theoretical support for reducing plastic use while maintaining maize yield stability and water productivity in arid irrigated regions.

The sampling and analysis or visual examination of soil to assess its health status are widely practiced from plot to national scales. However, available data are often not made easily accessible and new data are not always shared with the wider public, creating a partial understanding of soil processes and health status across different geographies, as well as a misuse of resources.To address these challenges, Varda has created an interactive digital soil platform, named SoilHive, which provides an overview of existing soil data across the globe, facilitates the identification of soil data gaps across regions, and supports soil data sharing and interoperability.To facilitate the identification of functional soil data gaps, Varda created a visualization tool to enable users to identify the extent to which the data available in their selected regions of interest allow them to characterize key soil characteristics and functions, starting with soil health. To this end, Varda collaborated with a team at the Aberdeen University to define a minimum set of indicators and associated soil properties, covering diverse soil degradation processes and ecosystem services, to describe soil health at the local level with a focus on agricultural settings. SoilHive users will be able to select a region of interest and verify how many of those indicators are available to characterize soil health: the gap between the available data and the required data will be marked as the "Soil Health Data Gap" and described as the percentage of data over the total number of indicators.This Soil Health Data Gap could facilitate the identification of those regions where data collection efforts should be intensified, informing the prioritization of actions aimed at bridging existing gaps while expanding the availability of soil data. Several proposed activities to address this gap include targeted soil mining activities to collect historical and non-digitized soil data, advocating for data-sharing collaborations among diverse stakeholders, and organizing brand-new soil sampling campaigns. Moreover, additional soil health definitions could be used to provide the user with different representations of soil health data gaps, in line with these definitions.Closing the existing Soil Health Data Gaps bears significant importance, as it empowers us to deepen our comprehension of soil health dynamics. By expanding soil data transparency across all sectors and stakeholders, we would gain the ability to make well-informed decisions regarding optimal soil management practices, efficiently allocate resources, and strive to enhance soil health for the betterment of both present and future generations.

Abstract We estimated the long‐term carbon balance [net biome production (NBP)] of European (EU‐25) croplands and its component fluxes, over the last two decades. Net primary production (NPP) estimates, from different data sources ranged between 490 and 846 gC m −2 yr −1 , and mostly reflect uncertainties in allocation, and in cropland area when using yield statistics. Inventories of soil C change over arable lands may be the most reliable source of information on NBP, but inventories lack full and harmonized coverage of EU‐25. From a compilation of inventories we infer a mean loss of soil C amounting to 17 g m −2 yr −1 . In addition, three process‐based models, driven by historical climate and evolving agricultural technology, estimate a small sink of 15 g C m −2 yr −1 or a small source of 7.6 g C m −2 yr −1 . Neither the soil C inventory data, nor the process model results support the previous European‐scale NBP estimate by Janssens and colleagues of a large soil C loss of 90 ± 50 gC m −2 yr −1 . Discrepancy between measured and modeled NBP is caused by erosion which is not inventoried, and the burning of harvest residues which is not modeled. When correcting the inventory NBP for the erosion flux, and the modeled NBP for agricultural fire losses, the discrepancy is reduced, and cropland NBP ranges between −8.3 ± 13 and −13 ± 33 g C m −2 yr −1 from the mean of the models and inventories, respectively. The mean nitrous oxide (N 2 O) flux estimates ranges between 32 and 37 g C Eq m −2 yr −1 , which nearly doubles the CO 2 losses. European croplands act as small CH 4 sink of 3.3 g C Eq m −2 yr −1 . Considering ecosystem CO 2 , N 2 O and CH 4 fluxes provides for the net greenhouse gas balance a net source of 42–47 g C Eq m −2 yr −1 . Intensifying agriculture in Eastern Europe to the same level Western Europe amounts is expected to result in a near doubling of the N 2 O emissions in Eastern Europe. N 2 O emissions will then become the main source of concern for the impact of European agriculture on climate.

ABSTRACT Nitrogen use efficiency (NUE) serves as a useful performance indicator to benchmark agricultural nitrogen management. It is determined by genotype‐environment‐management interaction, making the quantification of the key drivers and the mechanism behind spatiotemporal trends of NUE challenging. In this study, the soil Water Heat Carbon Nitrogen Simulator (WHCNS) model, integrated with multi‐scale datasets, was applied to clarify the key drivers of the NUE trends for single rice in China from 1978 to 2019. The national NUE for single rice was 0.31 over the study period and showed a slight increase before 2005 and afterwards a rapid increase of 0.048/10 years, primarily controlled by fertilization management and cultivar shifts. The N application rate determined the total N input, influenced the N uptake and utilization efficiency, and therefore the NUE, while the effect of cultivar shifts on NUE is mainly determined by the increase in the proportion of photosynthetic products in the rice grain and thus the harvest index. The benefits of cultivar improvement (0.034/10 years) which were almost entirely offset by the excessive use of N fertilizers (−0.029/10 years) before 2005, became lower (0.021/10 years) and unstable thereafter in many subregions, indicating the challenge of cultivar selection under low N inputs. To reach the ambitious NUE target of 0.6 by 2050 for rice, improving NUE through better management alone will not be enough. Improvements in rice breeding are therefore urgently needed to meet future NUE challenges in rice production under climate change and population growth.