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See detailModelling carbon fluxes of forest and grassland ecosystems in Western Europe using the CARAIB dynamic vegetation model: evaluation against eddy covariance data.
Henrot, Alexandra-Jane ULg; François, Louis ULg; Dury, Marie ULg et al

in Geophysical Research Abstracts (2015, April), 17

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface ... [more ▼]

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface and vegetation models at regional and global scale. In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), vegetation dynamics and carbon fluxes of forest and grassland ecosystems simulated by the CARAIB dynamic vegetation model (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) are evaluated and validated by comparison of the model predictions with eddy covariance data. Here carbon fluxes (e.g. net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO)) and evapotranspiration (ET) simulated with the CARAIB model are compared with the fluxes measured at several eddy covariance flux tower sites in Belgium and Western Europe, chosen from the FLUXNET global network (http://fluxnet.ornl.gov/). CARAIB is forced either with surface atmospheric variables derived from the global CRU climatology, or with in situ meteorological data. Several tree (e.g. Pinus sylvestris, Fagus sylvatica, Picea abies) and grass species (e.g. Poaceae, Asteraceae) are simulated, depending on the species encountered on the studied sites. The aim of our work is to assess the model ability to reproduce the daily, seasonal and interannual variablility of carbon fluxes and the carbon dynamics of forest and grassland ecosystems in Belgium and Western Europe. [less ▲]

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See detailunité: Eau-Environnement-Développement (ULg Campus Arlon): la télédétection au service de l'agriculture
Wellens, Joost ULg; Lang, Marie ULg; Benabdelouahab, Tarik et al

Diverse speeche and writing (2015)

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See detailRemote sensing enables high discrimination between organic and non-organic cotton for organic cotton certification in West Africa
Denis, Antoine ULg; Tychon, Bernard ULg

in Agronomy for Sustainable Development (2015)

One of the challenges of organic crop certification is the efficient targeting of the relatively small percentage of risk-sensitive fields that have to be controlled during the regulatory annual in situ ... [more ▼]

One of the challenges of organic crop certification is the efficient targeting of the relatively small percentage of risk-sensitive fields that have to be controlled during the regulatory annual in situ inspection. A previous study carried out on wheat and maize in Germany has shown that organic and non-organic crops can be efficiently distinguished by remote sensing. That study pointed to the possibility that these techniques could be used for helping organic crop certification bodies to better target risk-sensitive fields. This study is a first adaptation of that research on organic cotton in southwestern Burkina Faso, West Africa. This study assumed that organic and non-organic cotton, primarily because of their different approaches to fertilization and pest control, would result in bio-chemico-physical differences measurable by both in situ and remote sensing indicators. This study included 100 cotton fields, of which 50 were organic, 28 conventional, and 22 genetically modified. In situ indicators were derived from chlorophyll content, canopy cover, height, and spatial heterogeneity measurements. Remote sensing spectral and spatial heterogeneity indicators were derived from two SPOT 5 satellite images. Discriminant models were then computed. The results show statistically highly significant differences between organic and non-organic cotton fields for both in situ and satellite indicators, using univariate and multivariate linear models, with up to 86 % discrimination performance. This is the first time that the efficiency of using remote sensing to discriminate between organic and non-organic crops is evaluated in a developing country, particularly for cotton, with good discrimination being achieved. Pending further validation, it therefore seems that remote sensing could be used to enhance organic cotton certification in West Africa by enabling more efficient targeting of suspect fields and consequently could contribute to a better development of this sector. [less ▲]

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See detailBayesian inversions of a dynamic vegetation model at four European grassland sites
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

in Biogeosciences (2015), 12(9), 2809--2829

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters ... [more ▼]

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m−2 day−1 and 0.50 to 1.28 mm day−1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. [less ▲]

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See detailOutcomes from the MACSUR grassland model inter-comparison with the model CARAIB
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, October 15)

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See detailAquaCrop: Manuel d'utilisation
Raes, Dirk; Steduto, Paquale; Hsiao, Theodore et al

Learning material (2014)

Le logiciel AquaCrop est un modèle de bilan d’eau qui permet d’évaluer les efficiences en irrigation, l’élaboration des calendriers d’irrigation au niveau de la parcelle et l'estimation des rendements ... [more ▼]

Le logiciel AquaCrop est un modèle de bilan d’eau qui permet d’évaluer les efficiences en irrigation, l’élaboration des calendriers d’irrigation au niveau de la parcelle et l'estimation des rendements. AquaCrop a été sélectionné en raison de sa simplicité et de sa robustesse, et du nombre limité de variables à introduire. Il existe bien des modèles déterministes, mais pour fonctionner correctement, ils exigent des paramètres d’entrée très détaillés qui ne sont pas toujours disponibles quand il s’agit de recherche en milieu rural (‘on-farm’). [less ▲]

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See detailBrown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system
El Jarroudi, Moussa ULg; kOUADIO, Louis; Giraud, Frédéric et al

in Environmental Science & Pollution Research (2014), 21(4), 4809-4818

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall ... [more ▼]

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall) and a mechanistic model for leaf emergence and development simulation (i.e., PROCULTURE) was tested in order to schedule fungicide time spray for controlling leaf rust progress in wheat fields. Experiments including a single fungicide treatment based upon the DSS along with double and triple treatment were carried out over the 2007–2009 cropping seasons in four representative Luxembourgish wheat field locations. The study showed that the WLR occurrences and severities differed according to the site, cultivar, and year. We also found out that the single fungicide treatment based on the DSS allowed a good protection of the three upper leaves of susceptible cultivars in fields with predominant WLR occurrences. The harvested grain yield was not significantly different from that of the double and triple fungicide-treated plots (P < 0.05). Such results could serve as basis or be coupled to cost-effective and environmentally friendly crop management systems in operational context. [less ▲]

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See detailBayesian inference of a dynamic vegetation model for grassland
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, April 02)

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic ... [more ▼]

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations. [less ▲]

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See detailSoil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness
Denis, Antoine ULg; Stevens, Antoine; Van Wesemael, Bas et al

in Geoderma (2014), 226-227(August 2014), 94102

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC ... [more ▼]

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect. A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas. Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350–2500 nm] spectral range was computed to translate this differential influence. Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa. [less ▲]

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See detailCan a global dynamic vegetation model be used for both grassland and crop modeling at the local scale?
Minet, Julien ULg; Tychon, Bernard ULg; Jacquemin, Ingrid ULg et al

Poster (2014, February)

We report on the use of a dynamic vegetation model, CARAIB, within two modeling exercises in the framework of MACSUR. CARAIB is a physically-based, mechanistic model that calculates the carbon ... [more ▼]

We report on the use of a dynamic vegetation model, CARAIB, within two modeling exercises in the framework of MACSUR. CARAIB is a physically-based, mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions. Within MACSUR, it was used in the model intercomparison exercises for grassland and crop modeling, in the LiveM 2.4 and CropM WP4 tasks, respectively. For grassland modeling, blind model runs at 11 locations were performed for various time ranges (few years). For crop modeling, a sensitivity analysis for building impact response surfaces (IRS) was performed, based on a bench of model runs at different levels of perturbation in the temperature and precipitation input data over 30 years. For grassland modeling, specific management functions accounting for the cutting or grazing of the grass were added to the model, in the framework of the MACSUR intercomparison. Initially developed for modeling the carbon dynamics of the natural vegetation, CARAIB was already adapted for crop modeling but further modifications regarding the management, i.e., yearly-dependent sowing dates, were introduced. For grassland modeling, simulation results will be further intercompared with other modeling groups, but preliminary results showed that the model could cope with the introduction of the grass cutting module. For crop modeling, building the IRS over 30 years permitted to assess the sensitivity of the model to temperature and precipitation changes. So far, the participation of CARAIB in the intercomparison exercises within MACSUR resulted in further improvements of the model by introducing new functionalities. [less ▲]

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See detailImplementing agricultural land-use in the CARAIB dynamic vegetation model
François, Louis ULg; Jacquemin, Ingrid ULg; Fontaine, Corentin et al

Conference (2014)

CARAIB (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) is a state-of-the-art dynamic vegetation model with various modules dealing with (i) soil hydrology, (ii) photosynthesis/stomatal ... [more ▼]

CARAIB (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) is a state-of-the-art dynamic vegetation model with various modules dealing with (i) soil hydrology, (ii) photosynthesis/stomatal regulation, (iii) carbon allocation and biomass growth, (iv) litter/soil carbon dynamics, (v) vegetation cover dynamics, (vi) seed dispersal, and (vii) vegetation fires. Climate and atmospheric CO2 are the primary inputs. The model calculates all major water and CO2/carbon fluxes and pools. It can be run with plant functional types or species (up to 100 different species) at various spatial scales, from the municipality to country or continental levels. Within the VOTES project (Fontaine et al., Journal of Land Use Science, 2013, DOI:10.1080/1747423X.2013.786150), the model has been improved to include crops and meadows, and some modules have been written to translate model outputs into quantitative indicators of ecosystem services (e.g., evaluate crop yield from net primary productivity or calculate soil erosion from runoff, slope, grown species and various soil attributes). The model was run over an area covering four municipalities in central Belgium, where land-use is dominated by crops, meadows, housing and some forests and was introduced in the model at the land parcel level. Simulations were also performed for the future. In these simulations, CARAIB was combined with the Aporia Agent-Based Model, to project land-use changes up to 2050. This approach is currently extended within the MASC project (funded by Belgian Science Policy, BELSPO) to the whole Belgian territory (at 1 km2) and to Western Europe (at 20 km x 20 km). [less ▲]

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See detailThe Agrometeorological crop yield forecasting System of Armenia - User Manual - December 2013
Tychon, Bernard ULg; Denis, Antoine ULg; Djaby, Bakary ULg et al

Learning material (2013)

« This document was produced under the Agrometeorological component of the “EC/FAO Programme on Information Systems to Improve Food Security Decision-Making in ENP East Area” in Armenia. The objective of ... [more ▼]

« This document was produced under the Agrometeorological component of the “EC/FAO Programme on Information Systems to Improve Food Security Decision-Making in ENP East Area” in Armenia. The objective of the Programme in Armenia is to support government’s priorities to reduce food insecurity and poverty by improving the quality and sharing of information across institutions, and promoting evidence-based analyses and assessments. This User Manual of Crop Yield Forecasting System used in Armenia may be used as a reference document for the future calculation of yield forecasts that HYDROMET will use in the coming years. [less ▲]

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