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Recently, biochar has shown to be an alternative to waste disposal and a source of nutrients, acting as a soil amendment. The effects of two types of biochar on soil properties and sugar beet production as well as potential for carbon (C) sequestration were evaluated: biochar produced from sewage sludge (SB) and biochar produced from a 1:1 mixture of sewage sludge and sugarcane bagasse (MB). A greenhouse pot experiment was conducted using a sandy loam soil from the Brazilian savanna under treatments of MB applications at 2.5%, 5.0%, 7.5%, and 10.0%, SB application at 5.0%, and a conventional fertilization (CF) using lime and mineral fertilizers, with no fertilization as a control. After incubation for 45 d, seedlings were transplanted into each pot and cultivated for 55 d. Biochar characterization showed that pyrolysis reduced the biomass volume drastically, but concentrated the trace elements per unit of biochar weight. The MB treatments increased soil total C (by 27.8%) and pH (by 0.6), reduced the concentrations of nutrients, except for potassium (K), and chromium (Cr), and did not significantly alter lead (Pb) and cadmium (Cd) concentrations. Results of stable isotopes showed that all biochar treatments increased the total soil C stock and stability, suggesting a potential for application in C sequestration, and improved overall soil fertility. However, the biochar treatments also increased the concentrations of trace elements in the soil and plants. The sugar beet yields at 10.0% MB and 5.0% SB corresponded to 55% and 29% of the yield obtained in the CF treatment, respectively. These results may be due to biochar nutrients not being bioavailable when required by plants or to biochar nutrient adsorption.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE; Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (Vls) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these Vls may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level. Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various Vls computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected Vls included some classical Vls from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red edge and near-infrared domains. Overall, mND(blue)[728,850] defined as (R-440-R-728)/(R-440+R-850) and computed over the brightest green pixels obtains the best correlations with C-ab for spatial resolutions finer than 8.8 cm with a root mean square error of prediction better than 2.6 mu g/cm(2). Conversely, mND(blue)[728,8501 poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between C-ab and these canopy structure variables. As mND(blue)[728,8501 can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve C-ab from millimeter- to centimeter-scale reflectance imagery. (C) 2017 Elsevier Inc. All rights reserved.

Background: Pseudomonas syringae is a very heterogeneous bacterial species, inhabiting various ecological niches, affecting numerous host plants and representing great phylogenetic diversity. In 2013, a leaf spot disease of sugar beet caused by P. syringae was detected for a first time in Serbia.Objectives: We aimed to determine the phylogenetic diversity of isolates from sugar beet and the potential host range in order to define status of isolates within P. syringae species complex and estimate the pathogenic diversity.Methods: Twenty representatives among 104 collected P. syringae isolates from commercial fields of sugar beet were tested. Sequencing of cts gene, sequence analysis by the Neighbor-joining method and comparison with 59 P. syringae strains from 13 phylogenetic groups was performed for all tested isolates. The host range was determined by leaf inoculation of 16 different plant species in controlled greenhouse conditions. The virulence and aggressiveness of tested isolates were measured.Results: Phylogenetic analysis showed that all tested P. syringae isolates from sugar beet belong to the 02b phylogroup within the P. syringae species complex, representing one of the most abundant P. syringae phylogroups in the environment. Host range tests revealed extremely aggressive isolates causing symptoms on every tested plant (70% of tested isolates) as well as weakly aggressive isolates (30% of tested isolates). These findings provide the first insight to status of collected isolates from Serbian sugar beet fields within P. syringae metapopulation and provide information about potential risk to other crops.

International audience

UMR BFP - Equipe Métabolisme; International audience; Yield formation in regions with intermittent drought periods depends on the plant's ability to recover after cessation of the stress. The present work assessed differences in metabolic recovery of leaves and roots of drought-stressed sugar beets with high temporal resolution. Plants were subjected to drought for 13 days, and rewatered for 12 days. At one to two-day intervals, plant material was harvested for untargeted 1H-NMR metabolomic profiling, targeted analyses of hexose-phosphates, starch, amino acids, nitrate and proteins, and physiological measurements including relative water content, osmotic potential, electrolyte leakage and malondialdehyde concentrations. Drought triggered changes in primary metabolism, especially increases in amino acids in both organs, but leaves and roots responded with different dynamics to rewatering. After a transient normalization of most metabolites within 8 days, a second accumulation of amino acids in leaves might indicate a stress imprint beneficial in upcoming drought events. Repair mechanisms seemed important during initial recovery and occurred at the expense of growth for at least 12 days. These results indicate that organ specific metabolic recovery responses might be related to distinct functions and concomitant disparate stress levels in above- and belowground organs. With respect to metabolism, recovery was not simply a reversal of the stress responses.

International audience; Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content (RMSE'10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE=10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE=17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.

Efsa Panel On Genetically Modified Organisms (gmo)Scientific opinionRequestor: European Commission (DG SANTE)Question number: EFSA-Q-2017-00026; Following the submission of application EFSA-GMO-RX-006 under Regulation (EC) No 1829/2003 fromKWS SAAT SE and Monsanto Company, the Panel on Genetically Modified Organisms of the EuropeanFood Safety Authority (GMO Panel) was asked to deliver a scientific risk assessment on the datasubmitted in the context of the renewal of authorisation application for the herbicide-tolerantgenetically modified sugar beet H7-1. The data received in the context of this renewal applicationcontained a systematic search and evaluation of literature, updated bioinformatics analyses andadditional documents or studies performed by or on behalf of the applicant. The GMO Panel assessedthese data for possible new hazards, modified exposure or new scientific uncertainties identified duringthe authorisation period and not previously assessed in the context of the original application. Underthe assumption that the DNA sequence of the event in sugar beet H7-1 considered for renewal isidentical to the originally assessed event, the GMO Panel concludes that there is no evidence in thecontext of this renewal application for new hazards, modified exposure or scientific uncertainties thatwould change the conclusions of the original risk assessment on sugar beet H7-1.

La betterave sucrière est la deuxième plante en termes de production de sucre avec 19,6% du sucre produit dans le monde entre 2015 et 2016, derrière la canne à sucre. Cultivée dans les zones tempérées comme le nord de la France et la Belgique, elle est sensible au stress thermique modéré. En effet, une augmentation de 1°C de la température moyenne entraîne une perte de rendement de 29%. Dans le cadre du réchauffement climatique et afin de fournir aux agriculteurs des variétés ayant une meilleure résistance à la chaleur, la compréhension des réponses de la betterave sucrière au stress hyperthermique est nécessaire. Pour cela, une étude des réponses physiologiques et métaboliques a été effectuée avec trois hybrides : un hybride de type sucré avec 18% de sucre dans sa racine, un hybride de type lourd avec un meilleur rendement que la moyenne mais avec seulement 17% de sucre dans sa racine, et un hybride dit « résistant » au stress hyperthermique, ayant le meilleur rendement aux champs avec des températures plus élevées. Alors que la photosynthèse nette n’était pas modifiée en condition de stress, une croissance plus rapide de la rosette a été observée aux stades jeunes pour les trois hybrides. Une approche de modélisation suggère qu’une croissance de la rosette plus rapide contrecarre les effets du stress hyperthermique sur la production racinaire. Cette hypothèse est confirmée par le fait que l’hybride le plus performant a la croissance foliaire la plus rapide. Le stress hyperthermique provoque une forte diminution du stockage transitoire du carbone dans les feuilles matures. Dans les feuilles en cours de développement, le stockage transitoire du carbone, dont la moitié sous forme d’amidon, était seulement maintenu chez l’hybride le plus performant. Un calcul intégrant les besoins en carbone de la croissance foliaire suggère que ce maintien contribue significativement au maintien du rendement chez cet hybride. Par ailleurs, un marqueur putatif de la sensibilité au stress hyperthermique a été identifié lors de cette étude. Au final ce travail a permis de mieux comprendre comment la betterave sucrière répond à l’élévation de la température et ainsi proposer de nouvelles stratégies pour l’amélioration de cette espèce

The varieties choice represents a major issue for sugar beet farmers because their income depends on it. The fast turnover of the commercialized varieties on the market makes this choice difficult. In order to help the farmers, the sugar beet institute (ITB) hopes to develop a varieties choice personalized tool. This choice takes the conventional evaluation criteria and the studied plots specificities into consideration. The aim is to propose a list of varieties that guarantees an optimal productivity under agro-pedo-climatic conditions of every farm. My mission was to define a multicriteria analysis method combining every evaluation criteria available then to propose an optimal solution of varieties; the restrained method enables to take quantitative and qualitative criteria into consideration. Two multicriteria analysis methods have been combined: (i) the DEXi method that permit to evaluate the leaves diseases risk according to agronomic practices and (ii) the Electre III method that gives a list of varieties derived from the combination of the evaluation criteria. Electre III needs a weighting stage. An additional analysis over those weights has, afterwards, permitted to determine the most sensitive criteria. The developed method has been tested upon different practical cases representing some specificities of the French sugar beet areas. We manage to emphasis the interest of such a method that proposes an optimal choice without highlighting the most performing varieties only on one criterion. The method does not take the economic aspect into account; some economic performance index has then been calculated for the yield and the soil tare.; Le choix variétal constitue un enjeu majeur pour les planteurs de betterave car leur revenu en dépend. Le rapide turn-over des variétés commercialisées sur le marché rend ce choix assez complexe. Afin d’aider les planteurs, l’Institut Technique de la Betterave (ITB) souhaite développer un outil personnalisé d’aide au choix des variétés. Ce choix prend en compte les critères classiques d’évaluation des variétés et les spécificités des parcelles étudiées. La finalité est de proposer une liste de variétés garantissant une productivité optimale dans les conditions agro-pédo-climatiques de chaque exploitation. Mon travail a été de définir une méthode d’analyse multicritère combinant tous les critères d’évaluation disponibles puis proposant une solution optimale de variétés ; la méthode retenue permet de prendre en compte des critères qualitatifs et quantitatifs. Deux méthodes d’analyse multicritère ont été combinées : (i) la méthode DEXi, permettant d’évaluer le risque de maladie foliaire en fonction des pratiques agronomiques et (ii) la méthode ELECTRE III, permettant de proposer une liste de variétés issue de l’agrégation des critères d’évaluation. ELECTRE III nécessite une étape de pondération. Suite à une enquête réalisée auprès d’experts de l’ITB, les poids ont été définis. Une analyse supplémentaire sur ces poids a ensuite permis de déterminer les critères les plus sensibles. La méthode développée a été testée sur différents cas pratiques représentant quelques spécificités des régions betteravières françaises. Nous avons pu mettre en avant l’intérêt d’une telle méthode qui propose un choix optimum sans pour autant faire ressortir les variétés performantes sur un seul critère. La méthode ne prenant pas en compte l’aspect économique, des index de performance économique ont ensuite été calculés pour le rendement racine et la tare terre.

Carbon metabolism in sugar beet leaves experiencing heat stress. Metabolomics 2016. 12. Annual Conference of the Metabolomics Society

Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe . International Crop Modelling Symposium

International audience; Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. Great efforts are currently being made to preserve and enhance its competitiveness. Within this context, one of the main bottlenecks identified is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots. In the sugar industry, NIRS has been long used to monitor product quality on sugar processing lines in both sugar beet and cane industrial processes. The main work have been performed on beet brei although some authors studied the feasability of predicting sucrose content of intact and sliced beets. The results are of very good quality, however the beet peel is still a real problem to acquire signals of sufficient quality. Optical fibers are an essential component for remote optical measurements and have been extensively used to deliver and collect light in optical setups. By invasive but non destructive measurement, the fiber-optic probe could be an effective tools for rapid assessment of sugar content of intact beets and deal with the problem caused by the presence of beet peel. The objective of this work is to evaluate the use of a self-designed fiber-optic probe for the estimation of Soluble Solid Content (SSC) in intact sugar beets. Two geometries were tested : a single-fiber probe and a multiple-fiber probe. The impact of different components of the probe were analyzed, such as fiber diameter, distance between illumination and collection fiber in order to optimize optical setup of the probe. Total reflectance over the [400-2500 nm] spectral range, of 180 sugar beet samples, were measured with the two probes. Immediately after spectrum acquisition, Soluble Solids Content measurements were achieved for each sample. Partial Least Square (PLS) algorithm was used to calibrate the SSC estimation of the sugar beets With the single-fiber probes and for the two tested diameters, the results were not satisfactory. The better correlation coefficient and the ratio of sample standard deviation to standard error of prediction are respectively equal to 0.757 and 2.02. For this configuration, the measured signal is mainly composed by the specularly reflected light, which overlaps and therefore masks the information related to chemical absorbance of SSC. On the contrary, Partial Least Squares regression (PLSr) models showed good performance in the estimation of SSC with the multiple-fiber probes. For two different positions of the collecting fiber, results showed good correlation between SSC and reflectance spectra for intact sugar beets, with a correlation coefficient higher than 0.90 and a ratio of sample standard deviation to standard error of prediction higher than 3.30. These first results are very promising. However, further improvement are needed in order to achieve a satisfactory accuracy: •optimization of the distance between emitting and collecting fiber, •reducing the multiple scattering using several preatraitements, •increasing the number of samples in order to increase the robustness of the models.