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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 detailEconomics of a decision-support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg
El Jarroudi, Moussa ULg; Kouadio, Louis; Beyer, Marco et al

in Field Crops Research (2015), 172(2), 32-41

We evaluated the cost effectiveness of a decision-support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew ... [more ▼]

We evaluated the cost effectiveness of a decision-support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in the Grand-Duchy of Luxembourg (GDL). The study was conducted in replicated field experiments located in four agricultural locations (representative of the main agro-ecological regions of the country) over a 10-year period (2003-2012). Three fungicide spray strategies were compared: a single DSS-based system and two commonly used spray practices in the GDL, a double- (2T)- and a triple- spray (3T) spray treatment; there was also a non-treated control. In years with a high disease pressure, the DSS-based recommendation resulted in protection of the three upper leaves comparable to that achieved with the 2T and 3T treatments, with significant grain yield increases (P > 0.05) compared to the control (a 4 to 42% increase, depending on the site and year). Overall, the financial gain in treated plots compared with the control ranged from 3 to 16% at the study sites. Furthermore, in seasons when dry weather conditions precluded epidemic development, no the DSS-basedDSS recommended no fungicide spray was recommended, reducing use of fungicide, and thus saving the cost of the product. The gain in yield for the 2T and 3T plots (compared with control) did not necessarily result in a financial gain during the duration of the experiment. This study demonstrates the potential advantages and profitability of using a DSS -based approach for disease management. [less ▲]

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See detailA comparison between visual estimates and image analysis measurements to determine Septoria leaf blotch severity in winter wheat
El Jarroudi, Moussa ULg; Kouadio, Louis; Mackels, Christophe et al

in Plant Pathology (2015)

Methods to estimate disease severity vary in accuracy, reliability, ease of use and cost. Severity of Septoria leaf blotch (SLB, caused by Zymoseptoria graminicola) was estimated by four raters and by ... [more ▼]

Methods to estimate disease severity vary in accuracy, reliability, ease of use and cost. Severity of Septoria leaf blotch (SLB, caused by Zymoseptoria graminicola) was estimated by four raters and by image analysis (assumed actual values) on individual leaves of winter wheat in order to explore accuracy and reliability of estimates, and to ascertain whether there were any general characteristics of error. Specifically, (i) we determined the accuracy and reliability of visual assessments of SLB over the full range of severity from 0 to 100%, and we investigated (ii) whether certain 10% ranges in actual disease severity between 0 and 100% were more prone to estimation error compared with others, and (iii) whether leaf position affected accuracy within those ranges. Lin's concordance correlation analysis of all severities (0 to 100%) demonstrated that all raters had estimates close to the actual values (agreement: ρc = 0.92-0.99). However, agreement between actual SLB severities and estimates by raters was less good when compared over short 10% subdivisions within the 0-100% range (ρc = -0.12 to 0.99). Despite common rater imprecision at estimating low and high SLB severities, individual raters differed considerably in their accuracy over the short 10% subdivisions. There was no effect of leaf position on accuracy or precision of severity estimate on separate leaves (L1-L3). Pursuing efforts in understanding error in disease estimation should aid in improving the accuracy of assessments, making visual estimates of disease severity more useful for research and applied purposes. [less ▲]

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See detailCadres méthodologiques et outils de gestion des eaux et terres pour l'agriculture irriguée en zones périurbaines au Burkina Faso
Sauret, Elie; Wellens, Joost ULg; Guyon, Francis et al

in Bogaert, Jan; Halleux, Jean-Marie (Eds.) Territoires périurbaines - Développement, enjeux et perspectives dans les pays du Sud (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 detailLIVESTOCK SYSTEMS--TECHNICAL REPORT
Minet, Julien ULg; Diouf, Abdoul Aziz; Garba, Issa et al

Report (2015)

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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 detailDisease severity assessment in epidemiological studies: accuracy and reliability of visual estimates of Septoria leaf blotch (SLB) in winter wheat.
El Jarroudi, Moussa ULg; Kouadio, Louis; Mackels, Christophe et al

in Phytopathology (2014), 104(11), 37

The accuracy and reliability of visual assessments of SLB severity by raters (i.e. one plant pathologist with extensive experience and three other raters trained prior to field observations using standard ... [more ▼]

The accuracy and reliability of visual assessments of SLB severity by raters (i.e. one plant pathologist with extensive experience and three other raters trained prior to field observations using standard area diagrams and DISTRAIN) was determined by comparison with assumed actual values obtained by digital image analysis. Initially analyses were performed using SLB severity over the full 0-100% range; then, to explore error over short ranges of the 0-100% scale, the scale was divided into sequential 10%-increments based on the actual values. Lin’s concordance correlation (LCC) analysis demonstrated that all raters were accurate when compared over the whole severity range (LCC coefficient (ρc)= 0.92-0.99). However, agreement between actual and visual SLB severities was less good when compared over the short intervals of the 10×10% classes (ρc= -0.12-0.99), demonstrating that agreement will vary depending on the actual disease range over which it is compared. Inter-rater reliability between each pair of raters over the full 0-100% range (correlation analysis r= 0.970-0.992, P<0.0001), and inter-class correlation coefficient (ρ≥ 0.927) were very high. This study provides new insight into using a full range of actual disease severity vs limited ranges to ensure a realistic measure of rater accuracy and reliability, in addition to contributing to the ongoing debate on the use of visual disease estimates based on the 0-100% ratio scale for epidemiological research. [less ▲]

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See detailDifferentiating between influencing factors land use and climate to assess drought effects on groundwater recharge in a temperate context
Verbeiren, Boud; Huysmans, Marijke; Vanderhaegen, Sven et al

Conference (2014, November)

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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 detailFonctions d’ajustement pour l’estimation de la production fourragère herbacée des parcours naturels du Sénégal à partir du NDVI s10 de SPOT-vegetation
Diouf, Abdoul Aziz ULg; Djaby, Bakary ULg; Diop, Mouhamadou Bamba et al

in XXVIIe Colloque de l’Association Internationale de Climatologie (2014, July 04)

Face à la situation actuelle de changement climatique et ses conséquences sur l’homme et les ressources naturelles, les Systèmes d’Alerte Précoce (SAP) sur le disponible fourrager en zones pastorales ... [more ▼]

Face à la situation actuelle de changement climatique et ses conséquences sur l’homme et les ressources naturelles, les Systèmes d’Alerte Précoce (SAP) sur le disponible fourrager en zones pastorales constituent des stratégies essentielles dans la lutte contre l’insécurité alimentaire, notamment au niveau des pays du Sahel ouest-africains comme le Sénégal. L’évaluation du stock de fourrage s’y effectue habituellement à partir d’une régression linéaire entre les données de biomasse mesurée sur le terrain et l’indice de végétation par différence normalisée (NDVI) issu du satellite SPOT VEGETATION. Mais, compte tenu de la nature non-linéaire de la relation NDVI-biomasse herbacée, cinq autres fonctions d’ajustement sont testées afin de déterminer celles qui traduisent au mieux cette relation.Les données de biomasse ont été collectées au niveau de cinquante-et-un Sites de Contrôle au Sol (SCS) dont trente-six ont servi à la calibration et quinze pour évaluer la précision des modèles. Les variables utilisées sont le NDVI moyen et le NDVI maximum, enregistrés au cours de la saison. Les résultats obtenus montrent que les modèles Exponentiel et Puissance sont les plus cohérents et précis pour l’estimation de la biomasse herbacée à partir du NDVI. Toutefois, cette approche empirique par régression simple reste globalement imprécise pour l’évaluation de la biomasse herbacée au Sénégal vu les valeurs relativement élevées du RMSE qui varient entre 324,07 et 858 kg/ha selon l’année. [less ▲]

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See detailMapping of vegetation water content using Shortwave Infrared SPOT5 data to monitor irrigation in semi-arid regions.
Benabdelouahab, Tarik ULg; Balaghi, Riad; Lionboui, Hayat et al

Conference (2014, May 28)

Half of world’s food comes from irrigated area that uses about 72% of available water resources. In Morocco, water availability is the main limiting factor for crop growth and final yield and it is ... [more ▼]

Half of world’s food comes from irrigated area that uses about 72% of available water resources. In Morocco, water availability is the main limiting factor for crop growth and final yield and it is becoming a national priority for the agricultural sector. This situation leads the stakeholders to define most favorable strategies in planning and management of available water resources, on one hand, and to assess accurately vegetation water content status, on the other hand, in order to improve irrigation scheduling and prevent water stress adversely affecting yield. Remotely sensed reflectance has been used to estimate vegetation water content for different crops and to monitor water irrigation per surface unit, considering its high temporal and spatial resolution. In this study, we used two spectral indices of vegetation water content indicator (the Normalized Difference Infrared Index (NDII) and the Moisture Stress Index (MSI)) developed using Near Infrared (NIR) and Short Wave Infra-Red (SWIR) bands. The study area is the irrigated perimeter of Tadla in Morocco (35% dominated by irrigated wheat crop). In a first step, we compared observed vegetation water content of 16 studied plots of wheat and derived spectral indices NDII and MSI at the end of cropping season. The two images used at this step were acquired on March 26, 2013 and on April 11, 2013 when soil was fully covered by vegetation. Statistical analyses showed that the two spectral indices, NDII and MSI, simulated accurately vegetation water content. The statistical indicators, r, R², RMSE, nRMSE and MAE were -0.81, 0.65, 3.26% of water content (≈0.13 kg/m²), 4.26% and 2.69% for the NDII and 0.81, 0.65, 3.27% of water content (≈0.14 kg/m²), 4.27% and 2.72% for the MSI, respectively. To validate these results, we compared observed vegetation water content values and those predicted using the k-fold CV method. The errors were minimal for NDII and MSI, and the indicators of model evaluation obtained for predicted vegetation water content from NDII were: RMSE = 3.17%, nRMSE = 4.13%, MAE = 2.52% and R²=0.64. For MSI, these indicator were RMSE = 3.28%, nRMSE = 4.29%, MAE = 2.68% and R²=0.61. In a second step, we delimited the cereal area in the studied perimeter using a supervised classification method. The classification has been validated and the overall accuracy and Kappa coefficient were estimated respectively at 96.7% and 0.9545. Based on the regression model resulting from the comparison between NDII and measured vegetation water content, we produced maps of vegetation water content of wheat over the whole Beni-Moussa East irrigated area (41,000 hectares). The results of this work demonstrated the potential of spectral indices (NDII and MSI) derived from SPOT5 satellite images data to quantify and map vegetation water content of wheat. It showed also the potential of the SWIR band to improve the monitoring of irrigation by mapping water stress of wheat at field and regional level. [less ▲]

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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 and 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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