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Saint-Sauveur-en-Rue, France

Sausse C.,Terres Inovia | Sausse C.,Agro ParisTech | Sausse C.,French National Institute for Agricultural Research | Barbottin A.,Agro ParisTech | And 4 more authors.
PeerJ | Year: 2015

The promotion of biodiversity in agricultural areas involves actions at the landscape scale, and the management of cropping patterns is considered an important means of achieving this goal. However, most of the available knowledge about the impact of crops on biodiversity has been obtained at the field scale, and is generally grouped together under the umbrella term "crop suitability." Can field-scale knowledge be used to predict the impact on populations across landscapes? We studied the impact of maize and rapeseed on the abundance of skylark (Alauda arvensis). Fieldscale studies inWestern Europe have reported diverse impacts on habitat selection and demography. We assessed the consistency between field-scale knowledge and landscape-scale observations, using high-resolution databases describing crops and other habitats for the 4 km2 grid scales analyzed in the French Breeding Bird Survey. We used generalized linear models to estimate the impact of each studied crop at the landscape scale.We stratified the squares according to the local and geographical contexts, to ensure that the conclusions drawn were valid in a wide range of contexts. Our results were not consistent with field knowledge for rapeseed, and were consistent for maize only in grassland contexts.However, the effect sizes weremuch smaller than those of structural landscape features. These results suggest that upscaling from the field scale to the landscape scale leads to an integration of new agronomic and ecological processes, making the objects studied more complex than simple "crop * species" pairs.We conclude that the carrying capacity of agricultural landscapes cannot be deduced fromthe suitability of their components. © 2015 Sausse et al. Source

Ducrot D.,CNRS Center for the Study of the Biosphere from Space | Duthoit S.,University Paul Sabatier | D'Abzac A.,CNRS Center for the Study of the Biosphere from Space | Marais-Sicre C.,CNRS Center for the Study of the Biosphere from Space | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

Agro-Ecological Infrastructures (AEIs) include many semi-natural habitats (hedgerows, grass strips, grasslands, thickets...) and play a key role in biodiversity preservation, water quality and erosion control. Indirect biodiversity indicators based on AEISs are used in many national and European public policies to analyze ecological processes. The identification of these landscape features is difficult and expensive and limits their use. Remote sensing has a great potential to solve this problem. In this study, we propose an operational tool for the identification and characterization of AEISs. The method is based on segmentation, contextual classification and fusion of temporal classifications. Experiments were carried out on various temporal and spatial resolution satellite data (20-m, 10-m, 5-m, 2.5-m, 50-cm), on three French regions southwest landscape (hilly, plain, wooded, cultivated), north (open-field) and Brittany (farmland closed by hedges). The results give a good idea of the potential of remote sensing image processing methods to map fine agro-ecological objects. At 20-m spatial resolution, only larger hedgerows and riparian forests are apparent. Classification results show that 10-m resolution is well suited for agricultural and AEIs applications, most hedges, forest edges, thickets can be detected. Results highlight the multi-Temporal data importance. The future Sentinel satellites with a very high temporal resolution and a 10-m spatial resolution should be an answer to AEIs detection. 2.50-m resolution is more precise with more details. But treatments are more complicated. At 50-cm resolution, accuracy level of details is even higher; this amplifies the difficulties previously reported. The results obtained allow calculation of statistics and metrics describing landscape structures. © 2015 SPIE. Source

Mosenthin R.,University of Hohenheim | Messerschmidt U.,University of Hohenheim | Sauer N.,University of Hohenheim | Carre P.,OLEAD | And 2 more authors.
Journal of Animal Science and Biotechnology | Year: 2016

Background: During processing in a desolventizer/toaster (DT), rapeseed meal (RSM) is heated to evaporate the hexane and to reduce the level of heat-labile anti-nutritional factors such as glucosinolates (GSL). However, excessive heat treatment may reduce amino acid (AA) content in addition to lower AA digestibility and availability in RSM. The objective of the present study was to produce from one batch of a 00-rapeseed variety (17 μmol GSL/g dry matter (DM), seed grade quality) five differently processed RSM under standardized and defined conditions in a pilot plant, and to determine the impact of these different treatments on protein solubility and chemical composition, in particular with regard to contents of AA including reactive Lys (rLys) and levels of total and individual GSL. Methods: Four RSM were exposed to wet toasting conditions (WetTC) with increasing residence time in the DT of 48, 64, 76, and 93 min. A blend of these four RSM was further processed, starting with saturated steam processing (< 100 °C) and followed by exposure to dry toasting conditions (DryTC) to further reduce the GSL content in this RSM. Results: The contents of neutral detergent fiber and neutral detergent fiber bound crude protein (CP) increased linearly (P < 0.05), as residence time of RSM in the DT increased from 48 to 93 min, whereas contents of total and most individual GSL and those of Lys, rLys, Cys, and the calculated ratio of Lys:CP and rLys:CP decreased linearly (P ≤ 0.05). The combination of wet heating and DryTC resulted in the lowest GSL content compared to RSM produced under WetTC, but was associated with lowest protein solubility. Conclusions: It can be concluded that by increasing residence time in the DT or using alternative processing conditions such as wet heating combined with DryTC, contents of total and individual GSL in RSM can be substantially reduced. Further in vivo studies are warranted to elucidate if and to which extent the observed differences in protein quality and GSL content between RSM may affect digestibility and bioavailability of AA in monogastric animals. © 2016 The Author(s). Source

Quinsac A.,Terres Inovia | Labalette F.,Terres Univia | Carre P.,CREOL | Parachini E.,Terres Inovia | Jouffret P.,Terres Inovia
OCL - Oilseeds and fats | Year: 2015

In the context of the development of soybean cultivation in the South-Western France and from a diagnosis of the seed production and feeding industry sectors stakeholders in this region, the establishment of a crushing unit is studied under three scenarios of crushing capacity and location (3000 tons per year (t/year) in the Tarn-Aveyron area, 15 000 t/year in the Gers area and 30 000 t/year in the Garonne valley). The chosen process (flaking-cooking-pressing) allows the production of soymeal whose feed value is close to imported defatted soymeal. A model for calculating the net crushing margin simulated operations and economic results of these crushing units from 2014 to 2007. For 3000, 15 000 and 30 000 t/year scenarios, the results obtained show that increasing the size of the plant greatly reduces the crushing cost (119.6 €/t, 44.9 €/t and 33.6 €/t, respectively) and that the transport costs were logically affected by the density of production and consumption areas. The created economic surplus amounted to -72, 10 and -2 €/t on average respectively, for the 2007-2014 period and improved considerably for the last five quarters (-25, 57 and 45 €/t). The functioning of the sector was simulated by the views of the various economic actors (farmers, elevators, animal breeder) to assess the benefit derived. Prospects are indicated for reducing costs and improving the value of products created. © A. Quinsac et al., published by EDP Sciences, 2015. Source

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: SFS-01a-2014 | Award Amount: 9.93M | Year: 2015

Feed-a-Gene aims to better adapt different components of monogastric livestock production systems (i.e., pigs, poultry and rabbits) to improve the overall efficiency and to reduce the environmental impact. This involves the development of new and alternative feed resources and feed technologies, the identification and selection of robust animals that are better adapted to fluctuating conditions, and the development of feeding techniques that allow optimizing the potential of the feed and the animal. To reach this overall objective, the project will: - Develop new and alternative feeds and feed technologies to make better use of local feed resources, green biomass and by-products of the food and biofuel industry. - Develop methods for the real-time characterization of the nutritional value of feeds to better use and adapt diets to animal requirements. - Develop new traits of feed efficiency and robustness allowing identification of individual variability to select animals that are more adapted to changes in feed and environmental conditions. - Develop biological models of livestock functioning to better understand and predict nutrient and energy utilization of animals along their productive trajectory. - Develop new management systems for precision feeding and precision farming combining data and knowledge from the feed, the animal, and the environment using innovative monitoring systems, feeders, and decision support tools. - Evaluate the overall sustainability of new management systems developed by the project. - Demonstrate the innovative technologies developed by the project in collaboration with partners from the feed industry, breeding companies, equipment manufacturers, and farmers organisations to promote the practical implementation of project results. - Disseminate new technologies that will increase animal production efficiency, whilst maintaining product quality and animal welfare and enhance EU food security to relevant stakeholders.

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