Search results for "Simulation."

showing 10 items of 4779 documents

Typology of exogenous organic matters based on chemical and biochemical composition to predict potential nitrogen mineralization

2010

Our aim was to develop a typology predicting potential N availability of exogenous organic matters (EOMs) in soil based on their chemical characteristics. A database of 273 EOMs was constructed including analytical data of biochemical fractionation, organic C and N, and results of N mineralization during incubation of soil–EOM mixtures in controlled conditions. Multiple factor analysis and hierarchical classification were performed to gather EOMs with similar composition and N mineralization behavior. A typology was then defined using composition criteria to predict potential N mineralization. Six classes of EOM potential N mineralization in soil were defined, from high potential N minerali…

[SDV.BIO]Life Sciences [q-bio]/Biotechnologygenetic structures010501 environmental sciences01 natural sciencesMinéralisationBiochemical compositionOrganic ChemicalsWaste Management and DisposalHigh potentialhttp://aims.fao.org/aos/agrovoc/c_35657chemistry.chemical_classificationMineralsChemistry04 agricultural and veterinary sciencesGeneral MedicineComposition chimiqueClassificationhierarchical classificationDisponibilité d'élément nutritifCycle de l'azoteEnvironmental chemistryhttp://aims.fao.org/aos/agrovoc/c_5193http://aims.fao.org/aos/agrovoc/c_1794AlgorithmsP33 - Chimie et physique du solBiochemical fractionationEnvironmental EngineeringNitrogenhttp://aims.fao.org/aos/agrovoc/c_7170Mineralogybiochemical fractionationBioengineeringhttp://aims.fao.org/aos/agrovoc/c_27938FractionationTeneur en azoten mineralizationMatière organique du solhttp://aims.fao.org/aos/agrovoc/c_5268Fertilité du solMultiple factor analysisOrganic matterComputer SimulationNitrogen cycle0105 earth and related environmental sciencesRenewable Energy Sustainability and the EnvironmentP35 - Fertilité du sol[ SDV.BIO ] Life Sciences [q-bio]/BiotechnologyMineralization (soil science)eye diseasesAmendement organiqueModels Chemical040103 agronomy & agriculture0401 agriculture forestry and fisheriessense organsexogenous organic mattertypologyhttp://aims.fao.org/aos/agrovoc/c_12965http://aims.fao.org/aos/agrovoc/c_1653http://aims.fao.org/aos/agrovoc/c_15999F04 - Fertilisation
researchProduct

How optical density measurements on artificially reconstituted soil ecosystems show the validity of the competitive exclusion principle

2010

aeres : C-COM; International audience

[SDV.EE]Life Sciences [q-bio]/Ecology environment[SDV.EE] Life Sciences [q-bio]/Ecology environment[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH][ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH][INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputingMilieux_MISCELLANEOUS
researchProduct

The growth of soil bacteria revisited

2010

aeres : C-COM; International audience

[SDV.EE]Life Sciences [q-bio]/Ecology environment[SDV.EE] Life Sciences [q-bio]/Ecology environment[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH][ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH][INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputingMilieux_MISCELLANEOUS
researchProduct

Modélisation de paysages agricoles pour la simulation et l'analyse de processus

2017

L’objet de ce colloque est de partager des connaissances, expériences et outils autour de la modélisation des paysages agricoles, de leur structure et de leur dynamique en considérant la modélisation de la structure physique du paysage agricole et celle des processus socio-techniques qui gouvernent les usages des éléments le constituant (parcelles, fossés, etc.). Par ailleurs, les paysages agricoles sont le support de processus biotiques et abiotiques spatialisés. Les processus biotiques incluent par exemple les dynamiques d’organismes d’importance en agriculture – ravageurs et auxiliaires – ou contribuant à la biodiversité patrimoniale ou ordinaire. Les processus abiotiques incluent par ex…

[SDV.EE]Life Sciences [q-bio]/Ecology environment[STAT.AP]Statistics [stat]/Applications [stat.AP]partage de connaissance[SDV]Life Sciences [q-bio]outildynamique de paysagemodèle[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[SDE.ES]Environmental Sciences/Environmental and Societyvariable spatiale[SHS]Humanities and Social Sciences[SDE.BE] Environmental Sciences/Biodiversity and Ecology[SDV.EE] Life Sciences [q-bio]/Ecology environment[STAT.AP] Statistics [stat]/Applications [stat.AP][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[INFO]Computer Science [cs][INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[SDE.ES] Environmental Sciences/Environmental and Society[MATH]Mathematics [math][SDE.BE]Environmental Sciences/Biodiversity and Ecologyprocessus socio-techniquepaysage agricolemodélisation
researchProduct

Soilμ3d project: emergent properties of soil microbial functions from 3d modelling and spatial descriptors of pore scale heterogeneity

2021

International audience; The reduction of greenhouse gas emissions by improving the efficiency of agricultural systems through robust ecologically-based management practices represents the most important challenge facing agriculture. Models are needed to evaluate the effects of soil properties, climate, and agricultural management practices on soil carbon and on the nitrogen transformations responsible for GHG emissions. Models of Carbon and nitrogen cycles in soils need improvements so they can provide more accurate and robust predictions. They use empirical functions which account for the different environmental factors that affect microbial functions. However, these types of function have…

[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy[SDE.BE] Environmental Sciences/Biodiversity and Ecology[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[SDE.BE]Environmental Sciences/Biodiversity and Ecology[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
researchProduct

Assessing non-chemical weeding strategies through mechanistic modelling of blackgrass (Alopecurus myosuroides Huds.) dynamics

2010

 ; Because of environmental and health safety issues, it is necessary to develop strategies that do not rely on herbicides to manage weeds. Introducing temporary grassland into annual crop rotations and mechanical weeding are the two main features that are frequently used in integrated and organic cropping systems for this purpose. To evaluate the contribution of these two factors in interaction with other cropping system components and environmental conditions, the present study updated an existing biophysical model (i.e. AlomySys) that quantifies the effects of cropping system on weed dynamics. Based on previous experiments, new sub-models were built to describe the effects on plant survi…

[SDV.SA]Life Sciences [q-bio]/Agricultural sciences0106 biological sciencesgeneric modelbusiness.product_categorynitrogen balancescrop-rotationGRASSLANDMECHANICAL WEEDINGSoil SciencePlant ScienceVULPIN DES CHAMPS01 natural sciencesPloughATV Farm Technologyseed characteristicsCropping system[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciencesMathematics2. Zero hungerCROPPING SYSTEMSowingpopulation-dynamics04 agricultural and veterinary sciences15. Life on landCrop rotationWeed controlPE&RCsimulationCrop protectionTillageMODELsoil climateAgronomyINTEGRATED CROP PROTECTION040103 agronomy & agriculturetillage0401 agriculture forestry and fisheriessystemsWEED DYNAMICSWeedbusinessAgronomy and Crop Sciencemanagement010606 plant biology & botany
researchProduct

Etudes d'objets combinatoires : applications à la bio-informatique

2011

This thesis considers classes of combinatorial objects that model data in bioinformatics. We have studied two methods of mutation of genes within the genome : duplication and inversion. At first,we study the problem of the whole mirror duplication-random lossmodel in terms of pattern avoiding permutations. We prove that the class of permutations obtained with this method after p duplications from the identity is the class of permutations avoiding alternating permutations of length 2p + 1.We also enumerate the number of duplications that are necessary and sufficient to obtain any permutation of length n from the identity. We also suggest two efficient algorithms to reconstruct two different …

[SDV.SA]Life Sciences [q-bio]/Agricultural sciencesCompositions d’entiers[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[SDV.SA] Life Sciences [q-bio]/Agricultural sciencesBioinformaticsDuplicationcompositions d'entiersCompositions of integersInversionDuplicationsPermutationsInversionsGray codes[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationCodes de Gray[ INFO.INFO-CY ] Computer Science [cs]/Computers and Society [cs.CY][INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY][INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]CombinatoricsBio-informatiqueCombinatoire[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences
researchProduct

Modeling and energy optimization of the operative parts of an air assisted drill

2016

In the context of sustainable farming, the optimization of the energy costs of agricultural operations allows shorter working times and high quality of the agricultural operations. This question relates particularly to the seeding. This operation one is decisive for the quality of the future harvest. The modern high capacity seed drills must be compatible with all the constraints. The main goal of this PhD thesis is thus to develop an innovative methodology, integrating the modeling tools, in order to reduce the energy consumption of the heavy seeding equipment. Thus, we explored four key aspects concerning air seed drill design: maneuverability of poly-articulated seed drills; establishmen…

[SDV.SA]Life Sciences [q-bio]/Agricultural sciences[SDV.SA] Life Sciences [q-bio]/Agricultural sciencesSensorsManœuvrabilitéPneumatic seed drillAir-seederSemoir pneumatiquePoly-articulated seedersPneumatic conveying conditionsOptimisation énergétiqueCapteursDivider headRépartition des semencesAir-stream loadingConditions de transport pneumatiqueIntroduction de la matière dans un circuit pneumatiqueEnergy optimizationManeuverabilitySemoirs poly-articulés[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciencesSimulationTête de distributionDistribution accuracy
researchProduct

Impact sur le long terme d'une pulvérisation localisée sur la flore adventice. Une étude de Simulation

2017

AGROSUPEAGESTADINRA; International audience; Afin d’évaluer l’intérêt sur le long terme d’une pulvérisation localisée d'herbicides, nous comparons l’évolution d’indicateurs d’impact de la flore adventice (rendement, biomasse des adventices, …) pour une pulvérisation en plein et localisée. Les simulations sont conduites à l’aide du logiciel FLORSYS pour une monoculture de maïs de 30 ans et 10 répétitions climatiques, avec des adventices réparties en agrégats ou de manière uniforme. En cas de pulvérisation localisée, le rang est intégralement traité et le traitement des adventices de l’inter-rang est réalisé en fonction de taux de détection des adventices. Ces derniers dépendent de la taille …

[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]agriculture de précisionsimulationmodèlesystème de cultureadventice
researchProduct

Mieux prédire les conditions hydriques dans les premiers centimètres du sol pour déterminer la germination des adventices

2017

EAGESTADAGROSUPINRA; La germination des adventices est sensible aux conditions hydriques des premiers centimètres du sol. Il apparaît donc nécessaire de simuler avec précision ces variables avec le modèle FLORSYS. Nous proposons une méthode de calage du module « eau du sol » de FLORSYS basé sur le formalisme de STICS à partir d’une chaine de modélisation par forçages successifs : (1) simulation du développement de la biomasse aérienne et racinaire de la culture dans STICS; (2) intégration de ces variables dans le modèle HYDRUS pour simuler la dynamique des conditions hydriques de surface (3) ; calage des paramètres hydriques de STICS à partir des résultats d’HYDRUS. Cette méthodologie sera …

[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio]germinationnon laboureau[SDV]Life Sciences [q-bio]simulationlabour
researchProduct