Search results for "FAO"

showing 10 items of 91 documents

Nodulating symbiotic bacteria and soil quality

2005

Chapitre 9 : Plant microbe interactions and soil quality Partie : 9-2; International audience

[SDE] Environmental SciencesFixation de l'azotehttp://aims.fao.org/aos/agrovoc/c_7170http://aims.fao.org/aos/agrovoc/c_2736[SDV]Life Sciences [q-bio]Biologie du solSymbioseNITROGEN FIXATIONnodosité racinaireFertilité du solhttp://aims.fao.org/aos/agrovoc/c_27939LégumineuseBactérie fixatrice de l'azotehttp://aims.fao.org/aos/agrovoc/c_7563http://aims.fao.org/aos/agrovoc/c_4255P35 - Fertilité du solhttp://aims.fao.org/aos/agrovoc/c_7160P34 - Biologie du solhttp://aims.fao.org/aos/agrovoc/c_27601[SDV] Life Sciences [q-bio]PLANT ROOTS[SDE]Environmental SciencesÉvaluationU30 - Méthodes de recherchehttp://aims.fao.org/aos/agrovoc/c_5196http://aims.fao.org/aos/agrovoc/c_6563Rhizobium
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Rice cooking and sensory quality

2019

International audience; This chapter provides a state-of-the-art review of the diversity and dynamics of consumer demand with respect to the eating quality of rice worldwide. Quality includes both tangible measurable factors (rice characteristics) and the context of consumption. The main sensory attributes evaluated around the world are described, and their relationship with the diversity of consumer demand is discussed. Instrumental methods for predicting quality measured on either raw or cooked grains are reviewed. The changes that occur in the rice grain during cooking are described along with a modeling approach able to predict the changes and their spatial distribution in the rice grai…

S01 - Nutrition humaine - Considérations généralesCooking quality030309 nutrition & dieteticsmedia_common.quotation_subjectContext (language use)Agricultural engineeringConsumer demandComportement alimentairehttp://aims.fao.org/aos/agrovoc/c_6599[SCCO]Cognitive science03 medical and health sciences0404 agricultural biotechnologyQ02 - Traitement et conservation des produits alimentairesCuissonGrain qualityQuality (business)rizmedia_commonSensory evaluation2. Zero hungerConsumption (economics)Diversity0303 health sciencesConsumer demandModelingConsommation alimentaireQualité des alimentsfood and beveragesRice grain04 agricultural and veterinary sciencescooking quality [EN]040401 food sciencehttp://aims.fao.org/aos/agrovoc/c_330711Environmental sciencehttp://aims.fao.org/aos/agrovoc/c_2840http://aims.fao.org/aos/agrovoc/c_10965http://aims.fao.org/aos/agrovoc/c_3016http://aims.fao.org/aos/agrovoc/c_1851Cooking mode[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
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Assessing agro-hydrological models to schedule irrigation for crops of Mediterranean Environment

2008

Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSWAP FAO Scheduling irrigation
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¿Qué hacer con la OMC?

2003

CNUCDVidal-Beneyto JoséCancúnEstados UnidosComercio mundialNeoliberalismoAltermundistaCrecimientoFAOPublicaciones: Obra periodística: Columnas y artículos de opiniónLiberalismoOrganización Mundial del ComercioUnión EuropeaAltermundialismoOMCGlobalizaciónNaciones UnidasLiberalizaciónNivel de vidaAltermundializaciónONGIdeología
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Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

2018

International audience; Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability ('p-theory'), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types.…

0106 biological sciencesCanopyEarth observationPhoton010504 meteorology & atmospheric sciencesF40 - Écologie végétalehttp://aims.fao.org/aos/agrovoc/c_1920Soil Science01 natural sciencesMeasure (mathematics)http://aims.fao.org/aos/agrovoc/c_7701Multi-angle remote sensingProbability theoryhttp://aims.fao.org/aos/agrovoc/c_718Foliage clumping indexRange (statistics)http://aims.fao.org/aos/agrovoc/c_3081[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyComputers in Earth SciencesLeaf area indexhttp://aims.fao.org/aos/agrovoc/c_4039http://aims.fao.org/aos/agrovoc/c_4116Photon recollision probabilityhttp://aims.fao.org/aos/agrovoc/c_10672http://aims.fao.org/aos/agrovoc/c_32450105 earth and related environmental sciencesMathematicsRemote sensinghttp://aims.fao.org/aos/agrovoc/c_8114GeologyVegetationhttp://aims.fao.org/aos/agrovoc/c_5234http://aims.fao.org/aos/agrovoc/c_7558Leaf area indexhttp://aims.fao.org/aos/agrovoc/c_7273http://aims.fao.org/aos/agrovoc/c_1236http://aims.fao.org/aos/agrovoc/c_1556U30 - Méthodes de recherchehttp://aims.fao.org/aos/agrovoc/c_4026010606 plant biology & botanyhttp://aims.fao.org/aos/agrovoc/c_6124
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Bayesian spatio-temporal discard model in a demersal trawl fishery

2014

Spatial management of discards has recently been proposed as a useful tool for the protection of juveniles, by reducing discard rates and can be used as a buffer against management errors and recruitment failure. In this study Bayesian hierarchical spatial models have been used to analyze about 440 trawl fishing operations of two different metiers, sampled between 2009 and 2012, in order to improve our understanding of factors that influence the quantity of discards and to identify their spatio-temporal distribution in the study area. Our analysis showed that the relative importance of each variable was different for each metier, with a few similarities. In particular, the random vessel eff…

0106 biological sciencesPerteSpatial correlationhttp://aims.fao.org/aos/agrovoc/c_28840Computer scienceProcess (engineering)Bayesian probabilitySede Central IEOAquatic ScienceOceanography01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_2173Abundance (ecology)Component (UML)http://aims.fao.org/aos/agrovoc/c_4438Pesquerías14. Life underwaterM11 - Production de la pêchehttp://aims.fao.org/aos/agrovoc/c_7881Ecology Evolution Behavior and SystematicsChalutageU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyhttp://aims.fao.org/aos/agrovoc/c_2801204 agricultural and veterinary sciencesDiscardsFisheryRessource marineVariable (computer science)Théorie bayésienneM40 - Écologie aquatique040102 fisheries0401 agriculture forestry and fisherieshttp://aims.fao.org/aos/agrovoc/c_2942Fisheries managementPêche démersale
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Agro-hydrological models and field measurements to assess the water status of a citrus orchard irrigated with micro-sprinkler and subsurface drip sys…

2021

Compared to the micro-sprinkler irrigation, traditionally used in citrus orchards, subsurface drip systems (SDS) allow increasing the water use efficiency (WUE); when coupled with water-saving strategies, like regulated deficit irrigation (RDI), further increase of WUE are possible. Combining measurements of soil water content (SWC) and weather data with measurements of midday stem water potential (MSWP) makes it possible to identify irrigation scheduling parameters for the RDI. However, measurements of MSWP are destructive and time-consuming, and also require skilled operators. For all these reasons, the use of the agro-hydrological models, such as the FAO-56 model, can be considered a sur…

Settore AGR/03 - Arboricoltura Generale E Coltivazioni ArboreeHydrologyCitrusFAO 56 modelField (physics)Midday Stem Water PotentialSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliEnvironmental scienceHorticultureRegulated deficit irrigationSubsurface drip irrigationWater stress functionCitrus orchardActa Horticulturae
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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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Effects of PET packaging on the quality of an orange juice made from concentrate

2004

An orange juice made from concentrate was conditioned in three different PET (PolyEthylene Terephtalate) and glass packagings. Influence of storage conditions (length of storage, light, oxygen) on vitamin C content, browning index, and colour were measured. Results show that permeability to oxygen of PET packagings is the major factor detrimentally affecting the above parameters.

OxygèneConcentrationConditionnementhttp://aims.fao.org/aos/agrovoc/c_28269[SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineeringhttp://aims.fao.org/aos/agrovoc/c_1801http://aims.fao.org/aos/agrovoc/c_25492StockageQ02 - Traitement et conservation des produits alimentaireshttp://aims.fao.org/aos/agrovoc/c_7427[SDV.IDA]Life Sciences [q-bio]/Food engineeringJus d'orange[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process EngineeringComputingMilieux_MISCELLANEOUSPerméabilitéAcide ascorbiquehttp://aims.fao.org/aos/agrovoc/c_661[SDV.IDA] Life Sciences [q-bio]/Food engineeringhttp://aims.fao.org/aos/agrovoc/c_5718Brunissement enzymatiquehttp://aims.fao.org/aos/agrovoc/c_6400http://aims.fao.org/aos/agrovoc/c_5477Couleurhttp://aims.fao.org/aos/agrovoc/c_1773Qualitéhttp://aims.fao.org/aos/agrovoc/c_5495
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Sean O'Faolain's Creative Marginality

2001

International audience

[SHS.LITT] Humanities and Social Sciences/LiteratureContemporary Irish literatureexile[SHS.LITT]Humanities and Social Sciences/Literaturemarginalitycreation[ SHS.LITT ] Humanities and Social Sciences/LiteratureSean O'FaolainComputingMilieux_MISCELLANEOUS
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