Search results for "météo"

showing 10 items of 19 documents

A genomic map of climate adaptation in Mediterranean cattle breeds

2019

International audience; Domestic species such as cattle (Bos taurus taurus and B. t. indicus) represent attractive biological models to characterize the genetic basis of short term evolutionary response to climate pressure induced by their post-domestication history. Here, using newly generated dense SNP genotyping data, we assessed the structuring of genetic diversity of 21 autochtonous cattle breeds from the whole Mediterranean basin and performed genome-wide association analyses with covariables discriminating the different Mediterranean climate sub-types. This provided insights into both the demographic and adaptive histories of Mediterranean cattle. In particular, a detailed functional…

0106 biological sciences0301 basic medicineMediterranean climateCandidate genehttp://aims.fao.org/aos/agrovoc/c_24002Polymorphisme génétiqueAcclimatizationBreedingMediterraneanFacteur climatiquehttp://aims.fao.org/aos/agrovoc/c_11701 natural sciencesMediterranean Basinhttp://aims.fao.org/aos/agrovoc/c_4397http://aims.fao.org/aos/agrovoc/c_1081http://aims.fao.org/aos/agrovoc/c_3225Phylogeny2. Zero hungerGenomeEcology[SDV.BA]Life Sciences [q-bio]/Animal biologyhttp://aims.fao.org/aos/agrovoc/c_24031Chromosome MappingGenomicsSNP genotypingRace (animal)http://aims.fao.org/aos/agrovoc/c_3373http://aims.fao.org/aos/agrovoc/c_2080http://aims.fao.org/aos/agrovoc/c_4940http://aims.fao.org/aos/agrovoc/c_4026Génotypelocal adaptationBétailThermotoleranceBehavior and SystematicGenotypeP40 - Météorologie et climatologiehttp://aims.fao.org/aos/agrovoc/c_29554EvolutionIntrogressionSNPBiologyhttp://aims.fao.org/aos/agrovoc/c_259010603 evolutionary biology03 medical and health sciencescattle climate genetics local adaptation Mediterranean SNPhttp://aims.fao.org/aos/agrovoc/c_3081GeneticsAnimalsAdaptationhttp://aims.fao.org/aos/agrovoc/c_4697http://aims.fao.org/aos/agrovoc/c_8013climateEcology Evolution Behavior and SystematicsLocal adaptationGenetic diversityhttp://aims.fao.org/aos/agrovoc/c_2503Genetic Variation15. Life on landL10 - Génétique et amélioration des animauxClimat méditerranéen030104 developmental biologyGenetics PopulationEvolutionary biologycattleCarte génétiquehttp://aims.fao.org/aos/agrovoc/c_7273Adaptationgenetic
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Social process of adaptation to environmental changes: How eastern african societies intervene between crops and climate

2014

Abstract Studies on climate change can only be conducted on a long time scale, and observing how societies adapt their sowing practices to climate variability is challenging and costly. As an alternative, a space and time substitution design was used, changes in space corresponding to that induced in time by environmental change. On the eastern slope of Mount Kenya, the Tharaka community, originating from the lowlands (750 m), moved up to the midlands (950 m) with their lowland-adapted resources, whereas the Mwimbi, originating from wetter upland (1100 m), moved down to the midlands with their highland-adapted genetic resources. A weather station was installed at 950 and 1100 m, and a logis…

0106 biological sciencesAtmospheric ScienceEnsemencement010504 meteorology & atmospheric sciencesEnvironmental changeF08 - Systèmes et modes de cultureadaptation aux changements climatiqueshttp://aims.fao.org/aos/agrovoc/c_11701 natural scienceshttp://aims.fao.org/aos/agrovoc/c_2018http://aims.fao.org/aos/agrovoc/c_7142http://aims.fao.org/aos/agrovoc/c_72682. Zero hungerGlobal and Planetary ChangeAgroforestryEcologyAgriculturehttp://aims.fao.org/aos/agrovoc/c_203[ SDE.MCG ] Environmental Sciences/Global ChangesPratique culturaleGeography[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/ClimatologyCrop growth[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/ClimatologyP40 - Météorologie et climatologie[SDE.MCG]Environmental Sciences/Global ChangesClimate changeGrowing seasonSocietal impactsWeather stationAltitudehttp://aims.fao.org/aos/agrovoc/c_1374567058134E50 - Sociologie ruralehttp://aims.fao.org/aos/agrovoc/c_1666AdaptationClimate variability0105 earth and related environmental sciencesChangement climatiquebusiness.industrySowing15. Life on landhttp://aims.fao.org/aos/agrovoc/c_408613. Climate actionAgricultureSociologieAfricaAdaptationbusinessSocial Sciences (miscellaneous)010606 plant biology & botany
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Peaks of in situ N2O emissions are influenced by N2O producing and reducing microbial communities across arable soils

2018

International audience; Introduction Agriculture is the main source of terrestrial N2O emissions, a potent greenhouse gas and the main cause of ozone depletion ((Hu et al., 2015). The reduction of N2O into N2 by microorganisms carrying the nitrous oxide reductase gene (nosZ) is the only known biological process eliminating this greenhouse gas. Recent studies showed that a previously unknown clade of N2O-reducers (nosZII) was related to the potential capacity of the soil to act as a N2O sink (see Hallin et al., 2017 and references therein). However little is known about how this group responds to different agricultural practices. Here, we investigated how N2O-producers and N2O-reducers were …

0301 basic medicine[SDE] Environmental SciencesDenitrification[SDV]Life Sciences [q-bio]Biologie du sol[SHS]Humanities and Social Sciencesnitrogen cyclingF01 - Culture des plantes[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyhttp://aims.fao.org/aos/agrovoc/c_34841General Environmental Science2. Zero hungerAbiotic componentGlobal and Planetary ChangeBiotic componentdenitrificationEcologyEcologyNitrification[SDV] Life Sciences [q-bio]greenhouse gasCycle de l'azote[SDE]Environmental Sciencestillage[SHS] Humanities and Social SciencesArable landGaz à effet de serreP33 - Chimie et physique du solagroecosystemsP40 - Météorologie et climatologie030106 microbiologyhttp://aims.fao.org/aos/agrovoc/c_2793803 medical and health sciencesland-useEnvironmental Chemistryhttp://aims.fao.org/aos/agrovoc/c_12834[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyhttp://aims.fao.org/aos/agrovoc/c_1666Nitrogen cycleChangement climatique[ SDV ] Life Sciences [q-bio]http://aims.fao.org/aos/agrovoc/c_7160P34 - Biologie du sol15. Life on landequipment and suppliesagroecosystems;nitrogen cycling;land-use;tillage;denitrification;nitrification;microbial diversity;greenhouse gasAgronomy13. Climate actionGreenhouse gasmicrobial diversitySoil waterEnvironmental scienceNitrification
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Bulletins météo télévisés et vulgarisation scientifique – Analyse critique des données (non-) disponibles

2020

Les bulletins météo sont, du point du vue linguistique et discursif, des textes très intéressants car ils transmettent une explication scientifique relativement compliquée à la population et représentent donc une forme de vulgarisation scientifique ou de communication scientifique externe. Plusieurs questions apparaissent alors : Quelles différences apparaissent entre les bulletins météo destinés aux scientifiques et ceux destinés au grand public ? D’où proviennent les données scientifiques – ou les structures linguistiques scientifiques – remplacées par des modes d’expression plus expressifs ou plus subjectifs ?... Le projet de recherche « Analyse textuelle et modélisation syntactico-séman…

Analyse mixte[SHS] Humanities and Social SciencesMétéorologieAnalyse textuelle
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The onset of the rainy season and farmers’ sowing strategy for pearl millet cultivation in Southwest Niger

2011

A multi-year (2004-2009) field survey of on-farm sowing practices in 10 villages located in south-west Niger close to Niamey, is analysed to investigate the relationships (i) between rainfall and the sowing date of pearl millet and the risk of sowing failure, (ii) between sowing and meteorological/agro-climatic onset dates, (iii) between sowing/onset dates, and simulated and observed yield/biomass at the end of the season. Even if some villages sow without any synchronous or anterior rainfall, most parcels (73% out of the 1551 available cases) are sown during and just after a 2-day wet spell receiving at least 10 mm. In fact, there is a strong correlation (r = 0.82-0.95 depending on onset d…

Atmospheric Science010504 meteorology & atmospheric sciencesFacteur climatique01 natural sciencesF01 - Culture des plantesYield (wine)Farmers' strategiesNigerPennisetum glaucumdate de semis2. Zero hunger[SDV.EE]Life Sciences [q-bio]/Ecology environmentGlobal and Planetary ChangeBiomass (ecology)MilAgroforestryForestry04 agricultural and veterinary sciencesPearl Millet[ SDE.MCG ] Environmental Sciences/Global ChangesRendement des cultureshttp://aims.fao.org/aos/agrovoc/c_13199[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/ClimatologySowing dateWet seasonP40 - Météorologie et climatologiehttp://aims.fao.org/aos/agrovoc/c_29554[SDE.MCG]Environmental Sciences/Global ChangesBiometeorologySemisBiologyOnset of the rainy seasonPearl milletCrophttp://aims.fao.org/aos/agrovoc/c_10176http://aims.fao.org/aos/agrovoc/c_6437Onset dateGrain yield0105 earth and related environmental scienceshttp://aims.fao.org/aos/agrovoc/c_5181SowingTropics15. Life on landAgronomy13. Climate action040103 agronomy & agriculture0401 agriculture forestry and fisherieshttp://aims.fao.org/aos/agrovoc/c_16208Agronomy and Crop SciencePluviomètre
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PREVISION JOURNALIERE DES POLLENS SUR LE TERRITOIRE NATIONAL FRANÇAIS, AVEC UN OBJECTIF D'INFORMATION SANITAIRE DES POPULATIONS ALLERGIQUES

2008

At present, 16% of French people suffer from allergies to one or several pollens. The corresponding symptoms can be presented as under benign form (rhinitis, conjunctivitis, cough) as under much more serious form : asthma. Forecast of the starting date of an allergic exposure risk to pollens is necessary from a sanitary and preventive standpoint. Forecast has to be more precisely as possible in order to begin anti-allergic treatments at appropriate moment, with a view of effectiveness and reduction of the costs due to this disease. The present study, taking place in all the French territory, concerns four pollen taxa among the most allergenic : ash, birch, grasses and ragweed. This work com…

AérobiologieSantéPréventionAllergy[SHS.GEO] Humanities and Social Sciences/GeographyExposure riskAerobiology[SHS.GEO]Humanities and Social Sciences/GeographyConditions météorologiquesRisque d'exposition[ SHS.GEO ] Humanities and Social Sciences/GeographyAllergieForecast methodsHealthMeteorological conditionsPollenMéthodes de prévision
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The pressure of specialized knowledge structures on the lexicon-grammar continuum: the expression of aspectuality in weather forecasts (GE-FR)

2018

International audience; Der Beitrag reiht sich in drei aktuelle Forschungsstränge ein: (i) korpuslinguistische Zugänge zu Fachdiskursen, wobei quantitative und qualitative Erhebungen parallel durchgeführt werden, (ii) holistische Beschreibungen derselben, wo Lexikon und Grammatik ein Kontinuum bilden und zur Anerkennung globaler Patterns führen (Geldhill/Kübler 2016), und (iii) konstruktionsgrammatische Modelle, die sich in einem solchen methodologischen und theoretischen Rahmen als besonders relevant ergeben und – da sie kognitiv verankert sind – eine Brücke zwischen der kognitiven Architektur eines Faches und deren Versprachlichungsmodi in den fachspezifischen Textsorten schlagen.Untersch…

Langues de spécialitéLinguistique de corpusAllemand[SHS.LANGUE]Humanities and Social Sciences/LinguisticsTerminologie[SHS.LANGUE] Humanities and Social Sciences/LinguisticsFrançaisMétéorologieAnalyse de discours
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Adapter localement les prévisions climatiques saisonnières : désagrégation stochastique et interpolation spatiale.

2013

6 pages; International audience; Un panorama est fait des méthodes de descente d’échelles permettant de passer de prévisions climatiques saisonnières de large-échelle à des séries locales journalières. L’exemple des générateurs stochastiques de temps est appliqué à la prévision des récoltes de sorgho au Kenya, dans le cadre du programme ANR PICREVAT. Une méthode d’interpolation spatiale des paramètres des générateurs est testée, pour obtenir des séries journalières de précipitations en tout point du territoire. Les séries générées sont utilisées en entrée du modèle agronomique SARRA-H.

P40 - Météorologie et climatologieF01 - Culture des plantesU10 - Informatique mathématiques et statistiques[SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry[SDU.STU.GC] Sciences of the Universe [physics]/Earth Sciences/Geochemistrydésagrégationgénérateur stochastiqueprécipitationsrendementsprévision saisonnière[ SDU.STU.GC ] Sciences of the Universe [physics]/Earth Sciences/Geochemistry
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Climatic gradients along the windward slopes of Mount Kenya and their implication for crop risks. Part 2 : crop sensitivity.

2016

16 pages; International audience; Mount Kenya is an equatorial mountain whose climatic setting is fairly simple (two rainy seasons in March–May, the Long Rains, and October–December, the Short Rains) though concealing significant spatial variations related to elevation and aspect (part I, Camberlin et al., 2014). This part II is dedicated to the sensitivity of sorghum yields to climate variability in space and time, with a focus on the intra-seasonal characteristics of the rainy seasons. To that aim we use the crop model SARRA-H calibrated for the region and fed with rainfall, temperature, wind speed, humidity and solar radiation data over the period 1973–2001 at three stations located on t…

P40 - Météorologie et climatologie[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomySARRA-Hintra-seasonal componentsrainy seasonhttp://aims.fao.org/aos/agrovoc/c_9000024http://aims.fao.org/aos/agrovoc/c_10176[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/AgronomyF01 - Culture des planteshttp://aims.fao.org/aos/agrovoc/c_7244ComputingMilieux_MISCELLANEOUSPrécipitationhttp://aims.fao.org/aos/agrovoc/c_24894rainfall variabilityU10 - Informatique mathématiques et statistiquesModélisation des culturescrop modelKenyaVariation saisonnièreRendement des cultureselevation gradientshttp://aims.fao.org/aos/agrovoc/c_4086[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatologyhttp://aims.fao.org/aos/agrovoc/c_6161sorghum[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/Climatology
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Timing and patterns of the ENSO signal in Africa over the last 30 years: insights from normalized difference vegetation index data.

2014

Abstract A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI–Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo…

RainfallSaisonAtmospheric ScienceEquatorhttp://aims.fao.org/aos/agrovoc/c_50098F62 - Physiologie végétale - Croissance et développementhttp://aims.fao.org/aos/agrovoc/c_6734http://aims.fao.org/aos/agrovoc/c_8516http://aims.fao.org/aos/agrovoc/c_7222http://aims.fao.org/aos/agrovoc/c_8038http://aims.fao.org/aos/agrovoc/c_6498http://aims.fao.org/aos/agrovoc/c_24199U10 - Informatique mathématiques et statistiquesIndice de surface foliairehttp://aims.fao.org/aos/agrovoc/c_165VegetationRemote sensing[ SDE.MCG ] Environmental Sciences/Global Changeshttp://aims.fao.org/aos/agrovoc/c_7657El Niño Southern OscillationGeography[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/ClimatologyClimatologyhttp://aims.fao.org/aos/agrovoc/c_6161P01 - Conservation de la nature et ressources foncières[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/Climatologyhttp://aims.fao.org/aos/agrovoc/c_7252http://aims.fao.org/aos/agrovoc/c_7497ENSOModèle mathématiquehttp://aims.fao.org/aos/agrovoc/c_8500http://aims.fao.org/aos/agrovoc/c_1671P40 - Météorologie et climatologieTélédétectionhttp://aims.fao.org/aos/agrovoc/c_29553[SDE.MCG]Environmental Sciences/Global ChangesNormalized Difference Vegetation Indexhttp://aims.fao.org/aos/agrovoc/c_35196Interannual variabilityhttp://aims.fao.org/aos/agrovoc/c_6911Donnée climatiquePrecipitationCombined resulthttp://aims.fao.org/aos/agrovoc/c_8176http://aims.fao.org/aos/agrovoc/c_2676PrécipitationWinter rainfallIntertropical Convergence ZoneVégétation15. Life on landTempérature13. Climate actionVegetation-atmosphere interactionsAfricaClimatologiehttp://aims.fao.org/aos/agrovoc/c_4964Énergie solaire
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