Soil quality, properties, and functions in life cycle assessment: an evaluation of models

Soils provide essential ecosystem services for supporting both human and ecosystem needs and has been under pressures resulting from the intensification and expansion of human activities. In the last 15 years, substantial efforts have been made to quantify the impacts on soils derived from production systems and their related supply chains. In this study, a systematic, qualitative evaluation of up-to-date models connecting land occupation and land transformation to soil impact indicators (e.g., soil properties, functions, and threats) is performed. The focus is on models that may be applied for assessing supply chains, namely in the context of life cycle assessment (LCA). A range of eleven soil-related models was selected and evaluated against different criteria, including scientific soundness, stakeholders' acceptance, reproducibility, and the applicability of models from the perspective of LCA practitioners. Additionally, this study proposes a new land use cause-effect chain to qualify the impacts of land use on soils. None of the models is fulfilling all the criteria and includes comprehensively the cause-effect impact pathways. Notably, trade-offs were most frequent between the relevance of the modeled impact processes and the models' applicability. On the one hand, models proposing multi-indicators cover several drivers of impacts and have a broader scope. On the other hand, several models just focus on one driver of impact, but may provide more relevant impact characterization. Our results provide common ground for the development and identification of models that provide a comprehensive and robust assessment of land use change and land use impacts on soils. Indeed, to ensure both a comprehensive and relevant characterization of impacts, the study identifies several research needs for further models' developments, namely: 1) adopting a common land use cause-effect chain and land use classification; 2) accounting for different land management and land use intensities; 3) expanding the inventory data beyond the accounting of the area related to a certain land use; 4) assessing the added value of multi-indicators compared to single indicators, including the reduction of possible redundancies in the impact evaluation; 5) improving consistency from midpoint to endpoint characterization, especially the link with biodiversity; 6) guiding the calculation of normalization factors; and 7) assessing systematically model's uncertaintyinfo:eu-repo/semantics/publishedVersio

Agroecological measures and circular economy strategies to ensure sufficient nitrogen for sustainable farming

Sustainable food systems face trade-offs between demands of low environmental pressures per unit area and requirements of increasing production. Organic farming has lower yields than conventional agriculture and requires the introduction of nitrogen (N) fixing legumes in crop rotations. Here we perform an integrated assessment of the feasibility of future food systems in terms of land and N availability and the potential for reducing greenhouse gas (GHG) emissions. Results show that switching to 100% organic farming without additional measures results in N deficiency. Dietary change towards a reduced share of animal products can aggravate N limitations, which can be overcome through the implementation of a combination of agroecological, circular economy and decarbonization strategies. These measures help to recycle and transfer N from grassland. A vegan diet from fully decarbonized conventional production performs similarly as the optimized organic scenario. Sustainable food systems hence require measures beyond the agricultural sector

Satellite-based estimation of soil organic carbon in Portuguese grasslands

Introduction: Soil organic carbon (SOC) sequestration is one of the main ecosystem services provided by well-managed grasslands. In the Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a nature-based, innovative, and economically competitive livestock production system. As a co-benefit of increased yield, they also contribute to carbon sequestration through SOC accumulation. However, SOC monitoring in SBP require time-consuming and costly field work. Methods: In this study, we propose an expedited and cost-effective indirect method to estimate SOC content. In this study, we developed models for estimating SOC concentration by combining remote sensing (RS) and machine learning (ML) approaches. We used field-measured data collected from nine different farms during four production years (between 2017 and 2021). We utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance bands and vegetation indices. We also used other covariates such as climatic, soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity problems between the different variables, we performed feature selection using the sequential feature selection approach. We then estimated SOC content using both the complete dataset and the selected features. Multiple ML methods were tested and compared, including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We used a random cross-validation approach (with 10 folds). To find the hyperparameters that led to the best performance, we used a Bayesian optimization approach. Results: Results showed that the XGB method led to higher estimation accuracy than the other methods, and the estimation performance was not significantly influenced by the feature selection approach. For XGB, the average root mean square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1 (r2 equal to 0.68) without feature selection, and 2.77 g kg−1 (r2 equal to 0.68) with feature selection (average SOC content is 13 g kg−1 ). The models were applied to obtain SOC content maps for all farms.Discussion: This work demonstrated that combining RS and ML can help obtain quick estimations of SOC content to assist with SBP management

Grazing for carbon

International audienceThe potential of grasslands as a carbon (C) sink in Europe is large despite the number of uncertainties related to the effect of grazing systems on C sequestration. The EIP-AGRI Focus Group (FG) ‘Grazing for Carbon’, a temporary group of 20 selected European experts from research and practice, shared knowledge and experience from different disciplines on the relationship between grazing and soil C. The FG explored grazing management strategies, drivers and barriers for different grazing systems, as well as tools and business models to support them successfully. The overall aim was to identify how to increase the soil C content in grazing systems. Six priorities were addressed: the effects and trade-offs associated with approaches to sequestering C in different grazing systems, the effect of grazing on C and soil nutrients, the role of plant mixtures and native species, general guidelines for optimal grazing, effective monitoring of soil C as a tool for soil quality evaluation and incentives to promote the adoption of grazing systems to optimise soil C content