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INRA, Bordeaux Aquitaine Res Ctr, Agron Unit, F-33883 Villenave Dornon, France. Limburgs Univ Ctr, Dept SBG, B-3590 Diepenbeek, Belgium. Univ Kuopio, Dept Ecol & Environm Sci, FIN-70211 Kuopio, Finland. Univ Tras os Montes & Alto Douro, P-5001 Villa Real, Portugal.Boisson, J, INRA, Bordeaux Aquitaine Res Ctr, Agron Unit, BP 81, F-33883 Villenave Dornon, France.

International audience; Milk from domestic cows has been a valuable food source for over 8,000 years, especially in lactose-tolerant human societies that exploit dairy breeds. We studied geographic patterns of variation in genes encoding the six most important milk proteins in 70 native European cattle breeds. We found substantial geographic coincidence between high diversity in cattle milk genes, locations of the European Neolithic cattle farming sites (>5,000 years ago) and present-day lactose tolerance in Europeans. This suggests a gene-culture coevolution between cattle and humans.

Cover crops, as either a living plant or mulch, can suppress weeds by reducing weed germination, emergence and growth, either through direct competition for resources, allelopathy, or by providing a physical barrier to emergence. Farmers implementing conservation agriculture, organic farming, or agroecological principles are increasingly adopting cover crops as part of their farming strategy. However, cover crop adoption remains limited by poor and/or unstable establishment in dry conditions, the weediness of cover crop volunteers as subsequent cash crops, and seed costs. This study is the first to review the scientific literature on seed traits of cover crops to identify the key biotic and abiotic factors influencing germination and early establishment (density, biomass, cover). Knowledge about seed traits would be helpful in choosing suitable cover crop species and/or mixtures adapted to specific environments. Such information is crucial to improve cover crops’ establishment and growth and the provision of ecosystem services, while allowing farmers to save seeds and therefore money. We discuss how to improve cover crop establishment by seed priming and coating, and appropriate seed sowing patterns and depth. Here, three cover crop families, namely, Poaceae, Brassicaceae, and Fabaceae, were examined in terms of seed traits and response to environmental conditions. The review showed that seed traits related to germination are crucial as they affect the germination timing and establishment of the cover crop, and consequently soil coverage uniformity, factors that directly relate to their suppressive effect on weeds. Poaceae and Brassicaceae exhibit a higher germination percentage than Fabaceae under water deficit conditions. The seed dormancy of some Fabaceae species/cultivars limits their agricultural use as cover crops because the domestication of some wild ecotypes is not complete. Understanding the genetic and environmental regulation of seed dormancy is necessary. The appropriate selection of cover crop cultivars is crucial to improve cover crop establishment and provide multiple ecosystem services, including weed suppression, particularly in a climate change context.

Abstract The carbon and land footprint of 26 certified food products – geographical indications and organic products and their conventional references are assessed. This assessment goes beyond existing literature by (1) designing a calculation method fit for the comparison between certified food and conventional production, (2) using the same calculation method and parameters for 52 products – 26 Food Quality Schemes and their reference products – to allow for a meaningful comparison, (3) transparently documenting this calculation method and opening access to the detailed results and the underlying data, and (4) providing the first assessment of the carbon and land footprint of geographical indications. The method used is Life Cycle Assessment, largely relying on the Cool Farm Tool for the impact assessment. The most common indicator of climate impact, the carbon footprint expressed per ton of product, is not significantly different between certified foods and their reference products. The only exception to this pattern are vegetal organic products, whose carbon footprint is 16% lower. This is because the decrease in greenhouse gas emissions from the absence of mineral fertilizers is never fully offset by the associated lower yield. The climate impact of certified food per hectare is however 26% than their reference and their land footprint is logically 24% higher. Technical specifications directly or indirectly inducing a lower use of mineral fertilizers are a key driver of this pattern. So is yield, which depends both on terroir and farming practices. Overall, this assessment reinforces the quality policy of the European Union: promoting certified food is not inconsistent with mitigating climate change.

International audience; Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in different directions simultaneously.This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions.In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed.The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2° for the motion direction.

Large-scale high-resolution satellite observations of plant functional diversity patterns will greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially scalable approach that uses aggregate plant traits estimated from radiative transfer model (RTM) inversion of Sentinel-2 satellite images to calculate community patterns of plant functional diversity. Trait retrieval relied on simulations and Look-up Tables (LUTs) generated by a RTM rather than heavily depending on a priori field data and data-driven statistical learning. This independence from in-situ training data benefits its scalability as relevant field data remains scarce and difficult to acquire. We ran a total of three different inversion algorithms that are representative of commonly applied approaches and we used two different metrics to calculate functional diversity. In tandem with Sentinel-2 image-based estimation of plant traits, we measured Leaf Area Index (LAI), leaf Chlorophyll content (CAB), and Leaf Mass per Area (LMA) in-situ in a (semi-)natural heterogeneous landscape (Montesinho region) located in northern Portugal. Sampling plots were scaled and georeferenced to match the satellite observed pixels and thereby allowed for a direct one-to-one posterior ground truth validation of individual traits and functional diversity. Across approaches, we observe a reasonable correspondence between the satellite-based retrievals and the insitu observations in terms of the relative distribution of individual trait means and plant functional diversity across locations despite the heterogeneity of the landscape and canopies. The functional diversity estimates, based on a combination of canopy and leaf traits, were robust against estimation biases in trait means. Particularly, the convex hull volume estimate of functional diversity showed strong concordance with in-situ observations across all three inversion methods (Spearman's rho: 0.67-0.80). The remotely sensed estimates of functional diversity also related to in-situ taxonomic diversity (Spearman's rho: 0.55-0.63). Our work highlights the potential and challenges of RTM-based functional diversity metrics to study spatial community-level ecological patterns using currently operational and publicly available Sentinel-2 imagery. While further validation and assessment across different ecosystems and larger datasets are needed, the study contributes towards a further maturation of scalable, spatially, and temporally explicit methods for functional diversity assessments from space. The authors would like to acknowledge Christian Rossi, Prof. Geof-frey M. Henebry, and the anonymous reviewers for their valuable comments and suggestions that greatly improved the manuscript. This work was supported financially by the Ecology Fund of the RoyalNetherlands Academy of Arts and Sciences (‘KNAW Fonds Ecologie’; KNAWWF/807/19011). We thank Altino Geraldes, Joao Carlos Aze-vedo, and the local farmers and foresters in the Montesinho-Nogueira Natura 2000 site for their help and collaboration. We thank Emilie Didaskalou for her lab assistance. J.-B. F ́eret acknowledges financial support from Agence Nationale de la Recherche (BioCop project—ANR- 17-32CE-0001).

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; The magnitude of the global terrestrial carbon pool and related fluxes to and from the atmosphere are still poorly known. The European Space Agency P-band radar BIOMASS mission will help to reduce this uncertainty by providing unprecedented information on the distribution of forest above-ground biomass (AGB), particularly in the tropics where the gaps are greatest and knowledge is most needed. Mission selection was made in full knowledge of coverage restrictions over Europe, North and Central America imposed by the US Department of Defense Space Objects Tracking Radar (SOTR) stations. Under these restrictions, only 3% of AGB carbon stock coverage is lost in the tropical forest biome, with this biome representing 66% of global AGB carbon stocks in 2005. The loss is more significant in the temperate (72%), boreal (37%) and subtropical (29%) biomes, with these accounting for approximately 12%, 15% and 7%, respectively, of the global forest AGB carbon stocks. In terms of global carbon cycle modelling, there is minimal impact in areas of high AGB density, since mainly lower biomass forests in cooler climates are affected. In addition, most areas affected by the SOTR stations are located in industrialized countries with well-developed national forest inventories, so that extensive information on AGB is already available. Hence the main scientific objectives of the BIOMASS mission are not seriously compromised. Furthermore, several space sensors that can estimate AGB in lower biomass forests are in orbit or planned for launch between now and the launch of BIOMASS in 2021, which will help to fill the gaps in mission coverage.

In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource). This research was developed in the framework of the project “BeeHope - Honeybee conservation centers in Western Europe: an innovative strategy using sustainable beekeeping to reduce honeybee decline”, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain). info:eu-repo/semantics/publishedVersion