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Artificial night lighting in the Mediterranean: Management priorities and constraints for sea turtle conservation
No full textInternational audienc
Fine roots and N uptake efficiency shape genotypic variability in early vigor of rapeseed under low nitrogen conditions
No full textEarly vigor is a determining factor for successful winter oilseed rape cultivation in low-input systems, as efficient establishment enhances weed competitiveness and tolerance to early pest pressure. Yet the physiological and genetic bases of early vigor remain poorly understood, particularly regarding root traits. Here, we combined high-throughput phenotyping and genetic analysis to dissect early vigor determinants under low nitrogen conditions.A population of 100 recombinant inbred lines and four commercial cultivars were grown under low nitrogen supply up to BBCH 16 in RhizoTubes, allowing non-destructive imaging of root and shoot development. Forty traits were measured or derived, including traits related to carbon and nitrogen metabolism and detailed root system architecture descriptors. Random forest models and QTL mapping were used to identify ecophysiological and genetic determinants of early vigor.Early vigor -assessed by total dry weight, shoot dry weight, leaf area, and projected leaf area -showed strong positive correlations with fine-root dry weight and nitrogen uptake efficiency (NUpE), but weak relationships with fine-scale root system architecture traits. Random forest analyses confirmed fine-root dry weight and NUpE as the main contributors to genotypic variation in early vigor. QTL mapping identified 17 QTLs across 10 chromosomes, including a locus on A01 co-localizing for total, root, and fine-root dry weight, highlighting shared genetic control of shoot and fine-root growth.Altogether, under low nitrogen supply, early vigor is primarily governed by traits related to nitrogen capture and fine-root growth, highlighting fine-root development and nitrogen uptake as promising breeding targets for sustainable winter oilseed rape
Itaconic Acid as a Platform Chemical for Bio-Based Polymers: From Green Polymerization Strategies to Structure-Driven Applications
No full textInternational audienceAs a promising bio-based platform chemical, itaconic acid (IA) combines two carboxyl groups with an activated vinyl group, enabling extensive derivatization and multiple polymerization pathways. This review provides a comprehensive overview of IA-derived polymers, focusing on their green polymerization strategies, structure-performance relationships, and especially structure-driven applications. Whenever possible, we also evaluate the sustainability of reported polymerization routes by calculating E-factor values to compare waste generation across different processes. We first discuss two main classes: poly(itaconate)s and itaconate polyesters. Poly(itaconate)s produced by chain-growth polymerization exhibit tunable flexibility and polarity, supporting applications in green tires, high-temperature oil-resistant gaskets, pressuresensitive adhesives, organic glass, dielectric elastomer actuators, and 3D printing, thereby serving as practical alternatives to certain petroleum-based poly(meth)acrylates. In parallel, itaconate polyesters produced by step-growth polycondensation, which are potentially hydrolytically degradable, enable applications in fully biodegradable shoes, bio-based engineering elastomers, UV-curable materials, and biomedical fields. Additionally, we discuss other IA-derived polymer families obtained through diverse polymerization strategies, including polyurethanes, itaconic anhydride-containing polymers, epoxy thermosets, and polyamides. These polymers highlight the chemical versatility of IA as a bio-based platform and extend its utility to superabsorbent resins, heat-resistant materials, and functional coatings. Finally, we offer our perspective on challenges and opportunities in shifting toward application-driven structural design and industrial-scale production of IA-derived polymers to reduce reliance on fossil resources, decrease costs, and enhance performance.</div
Low-Complexity and Consistent Graphon Estimation from Multiple Networks
No full textInternational audienceRecovering the random graph model from an observed collection of networks is known to present significant challenges in the setting, where the networks do not share a common node set and have different sizes. More specifically, the goal is the estimation of the graphon function that parametrizes the nonparametric exchangeable random graph model. Recovering the graphon from a collection of networks is known to present significant challenges in the setting, where the observed networks do not share a common node set and have different sizes. Existing methods typically suffer from either limited accuracy or high computational complexity. We introduce a new histogram-based estimator with low algorithmic complexity that achieves high accuracy by jointly aligning the nodes of all graphs, in contrast to most conventional methods that order nodes graph by graph. Consistency results of the proposed graphon estimator are established. A numerical study shows that the proposed estimator outperforms existing methods in terms of accuracy, especially when the dataset comprises only small and variable-size networks. Moreover, the computing time of the new method is considerably shorter than that of other consistent methodologies. Additionally, when applied to a graph neural network classification task, the proposed estimator enables more effective data augmentation, yielding improved performance across diverse real-world datasets
Tas d’ordures, poubelle moderne et ville-cloaque: Les ambivalences du déchet dans les littératures africaines
No full textInternational audienc
Mapping soil property classes over a large territory with multiple soilscapes by digital extrapolations of legacy detailed soil maps: A case study in Karnataka -South India
No full textInternational audienceUsing detailed soil maps to calibrate DSM models could be an alternative to point observations, as they would account for local soil patterns more accurately than the sparse sets of soil profiles classically used in broad-scale DSM applications. However, the detailed soil surveys are most often scarce on large territories, which generates clustered calibration sets that may not represent the whole unmapped area. It is therefore important to delineate extrapolation areas that have soil-landscape relationships sufficiently similar to those of the soil map perimeter.We developed a DSM approach for mapping soil property classes (depth, texture and stoniness) over a large part (156,499 km2) of Karnataka state, South India. We used a sparse set of 91 soil maps of micro-watersheds (464 km2 of soil mapped areas) collected from recent land inventory programmes. Soilscape distances between soil maps were first defined by measuring the differences between soil property class distributions for each couple of soil-mapped micro-watersheds. A predictive model (random forest) that can estimate these ’ground-truth’ soilscape distances was then calibrated by using as covariates the differences of distributions and variograms of soil covariates (e.g. relief, climate, remote sensing data and small-scale soil maps), as well as the geographical distance between micro-watersheds. Soilscape distances were then used to select the appropriate DSM model for predicting soil property classes at each location (i.e. the model calibrated with the map of the closest micro-watershed). Soilscape distances served also to delineate extrapolation areas around existing soil maps in which soil property classes can be predicted with the highest accuracy and lowest predicted uncertainty.Using a leave-one-micro-watershed-out evaluation approach, We found that a single model calibrated onto the entire set of soil maps successfully predicted the texture and stoniness classes of soils over an extrapolation area covering 7% of the entire study area. Accuracies of 94% and 90% were obtained for texture, and stoniness, with respective predicted uncertainties of 6% and 7%. However, lower accuracy (57%) and higher uncertainty (31%) were obtained for predicted soil depth classes. Using multiple DSM models, each selected from soilscape distances, did not improve upon these results.This exploratory study paves the way for a possible hybrid approach to mapping soils across large territories. This approach would combine conventional soil surveys for detailed mapping of soil properties with digital soil mapping to extrapolate detailed soil maps. Digital soil mapping sampling techniques should also be employed in the future to select the locations of further detailed soil maps for mapping the target territory in an optimal way, thereby extending the extrapolation area while reducing survey costs
Managing trade-offs between economic, environmental, and animal welfare performance in French suckler cattle farms through feeding practices
No full textInternational audienceCONTEXT: Economic, environmental and animal welfare performance objectives are often not achieved simultaneously due to antagonistic relationships related to farming practices.OBJECTIVE: Here, we aim to 1) understand which performance objectives are in conflict on suckler cattle farmsand 2) examine how these conflicts can be managed.METHODS: We first determine the relationships between the different performance indicators via a bioeconomicmodel focusing on variables impacting feeding and analyse the conflicts between performance indicators. Wethen explore how the trade-offs can be managed via a compromise programming approach.RESULTS AND CONCLUSIONS: The obtained compromise solutions show that, compared with feed margindriven optimisation, environmental and animal welfare performance can be substantially enhanced, with foregone feed margins of approximately 15% of the optimum values. These compromises can be achieved byreducing herd size and animal fattening, increasing the share of grassland in farmland and reducing the purchaseof concentrate feed.SIGNIFICANCE: Combining bioeconomic modelling with compromise programming is a promising first step inproviding farmers with guidance on how to ensure good performance in multiple objectives and in providingpublic policy-makers with the means to develop policies along these line
ÉVALUATION DE L'ÉQUILIBRE ÉLECTRIQUE DU SCÉNARIO NÉGAWATT 2022 À L'AIDE DU MODÈLE OPEN SOURCE EOLES
No full textInternational audienceWe present the latest version of the open-source energy system optimisation model Eoles and use it to study whether the energy mix of the négaWatt 2022 scenario manages to meet demand for 2050 in France, for 19 weather-years. We find that even without recourse to interconnections, electricity demand only exceeds production for 3 to 4 hours a year on average, which is only just above the fault criteria of the French Energy Code. To prevent all hours of failure and fulfill reserves requirements, an additional 13.8~GW of dispatchable technologies is required, which corresponds to a 39\% increase compared to the négaWatt scenario. We then study the addition of three disptachable technologies: methane turbines, hydrogen turbines and batteries, that are all close in terms of total system cost. Moreover, electricity balance can be achieved even if the rooftop photovoltaic capacity is reduced compared to the négaWatt scenario. The associated gain (€3.4~bn./year) is higher than the additional cost of the dispatchable capacity mentioned above (around €1~bn./year).Nous présentons la dernière version du modèle d'optimisation du système énergétique open source Eoles et nous l'utilisons pour évaluer dans quelle mesure le mix énergétique du scénario négaWatt 2022 peut satisfaire la demande d'énergie en France à l'horizon 2050, pour 19 années météorologiques. Nous obtenons que même sans recours aux interconnexions, la demande d'électricité n'excède la production que 3 à 4 heures par an en moyenne, ce qui ne dépasse que de très peu les critères de défaillance du Code de l'énergie. Pour éliminer toute heure de défaillance et assurer les besoins de réserves, une puissance supplémentaire de technologies pilotables de 13,8 GW est nécessaire, soit une augmentation de 39 % par rapport au scénario négaWatt. Nous étudions l'ajout de trois technologies pilotables : turbines à gaz (méthane ou hydrogène) et batteries, qui sont toutes proches en termes de coût total du système énergétique. Par ailleurs, l'équilibre électrique peut être atteint même en réduisant la capacité photovoltaïque sur toitures par rapport au scénario négaWatt. Le gain associé (3,4 Md€/an) est plus élevé que le surcoût entraîné par les capacités pilotables mentionnées ci-dessus (environ 1 Md€/an)
Beliefs about EU 2030 climate commitments across public, expert, and policy audiences
No full textInternational audienceThe European Union (EU) has adopted ambitious climate policies to reduce greenhouse gas emissions by 55% by 2030. While technical, economic, and political feasibility have been widely analyzed, far less is known about a critical enabling factor: whether key societal actors perceive these targets as credible and achievable. In this study, we elicit probabilistic beliefs about future EU emission reductions from a sample of citizens designed to be representative across 12 EU member states and compare them with the expectations of climate policymakers and experts. Our findings reveal that all three groups anticipate substantial progress compared to current levels. At the same time, citizens are more skeptical about achieving the target and substantially more uncertain than elites, expecting, on average, a 43% reduction by 2030 compared to the EU’s 55% reference. Moreover, we identify a notable misalignment: elites tend to systematically underestimate public skepticism. This gap underscores the need to improve policy communication, directly address citizens’ concerns, and foster a shared understanding to enhance the perceived credibility and political sustainability of the EU’s climate goals