Combining field measurements and process‐based modelling to analyse soil tillage and crop residues management impacts on crop production and carbon balance in temperate areas

peer reviewedCrop residues management is an important issue in the context of climate change. They might be kept on the field and restituted to the soil to enhance its fertility or exported for other uses such as the production of energy through biomethanization. Furthermore, the choices regarding tillage operations impact the potential to incorporate residues, which in turn affects soil physical (e.g. structure, water retention), biological (e.g. organic matter, microorganisms) and chemical (e.g. nutrient release through mineralization) fertility. We combined measurements from a 14‐year field experiment in the Hesbaye loamy region of Belgium and its simulation with the STICS soil‐crop model to investigate the impacts of soil tillage and crop residues management on crop production, soil characteristics and carbon balance. Four treatments were compared, where all combinations of the incorporation versus exportation of crop residues and conventional versus reduced tillage were tested. The comparison of field observations with model simulations proved that the STICS model is adequate to explore the impacts of such contrasted management. The combined analysis of field data and soil‐crop model outputs showed that crop production was positively influenced by conventional tillage but unresponsive to crop residues fate. Reduced tillage led to a clear stratification in observed SOC content in the topsoil (0–30 cm), but also to an increase in simulated SOC stocks (0–26 cm). This SOC gain led to greater water retention under reduced tillage. Moreover, in both tillage treatments, incorporating residues increased soil organic carbon despite the associated augmentation in soil heterotrophic respiration. Finally, the importance of environmental conditions in carbon balance suggests that crop modelling might be very useful to explore the impacts of soil tillage and crop residues management in specific agro‐pedoclimatic contexts, especially when facing climate change

Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing

The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed to be integrated into a decision support system (DSS). Given the area covered (>4000 km2 of pastures of 100 m2 pixels), the consequent challenge of computation time and power requirements was overcome by the development of a platform predicting CSH throughout Wallonia. Four grazing seasons were covered in the current study (between April and October from 2018 to 2021, the mean predicted CSH per parcel per date ranged from 48.6 to 67.2 mm, and the coefficient of variation from 0 to 312%, suggesting a strong heterogeneity of variability of CSH between parcels. Further exploration included the number of predictions expected per grazing season and the search for temporal and spatial patterns and consistency. The second challenge tackled is the poor data availability for concurrent acquisition, which was overcome through the inclusion of up to 4-day-old data to fill data gaps up to the present time point. For this gap filling methodology, relevancy decreased as the time window width increased, although data with 4-day time lag values represented less than 4% of the total data. Overall, two models stood out, and further studies should either be based on the random forest model if they need prediction quality or on the cubist model if they need continuity. Further studies should focus on developing the DSS and on the conversion of CSH to actual forage allowance

Prebiotic effect on mood in obese patients is determined by the initial gut microbiota composition: a randomized, controlled trial

BACKGROUND AND AIMS: Metabolic and behavioural diseases, which are often related to obesity, have been associated to alterations of the gut microbiota considered as an interesting therapeutic target. We have analyzed in a cohort of obese patients treated with prebiotic inulin versus placebo the potential link between gut microbiota changes occurring upon intervention and their effect on psychological parameters (mood and cognition). METHODS: A randomized, single-blinded, multicentric, placebo-controlled trial was conducted in 106 obese patients assigned to two groups: prebiotic versus placebo, who received respectively 16 g/d of native inulin or maltodextrin combined with dietary advice to consume inulin-rich or -poor vegetables for 3 months as well as to restrict caloric intake. Anthropometric measurements, food intake, psychological questionnaires, serum measures, and fecal microbiome sequencing were performed before and after the intervention. RESULTS: Inulin supplementation in obese subjects had moderate beneficial effect on emotional competence and cognitive flexibility. However, an exploratory analysis revealed that some patients exhibiting specific microbial signature -elevated Coprococcus levels at baseline- were more prone to benefit from prebiotic supplementation in terms of mood. Positive responders toward inulin intervention in term of mood also displayed worse metabolic and inflammatory profiles at baseline (increased levels of IL-8, insulin resistance and adiposity). CONCLUSION: This study shows that inulin intake can be helpful to improve mood in obese subjects exhibiting a specific microbial profile. The present work highlights some microbial, metabolic and inflammatory features (IL-8, insulin resistance) which can predict or mediate the beneficial effects of inulin on behaviour in obesity. Food4gut, clinicaltrial.gov: NCT03852069, https://clinicaltrials.gov/ct2/show/NCT03852069