References of "El Jarroudi, Moussa"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailMathematical modelling of non-local spore dispersion of wind-borne pathogens causing fungal diseases
El Jarroudi, Mustapha; Karjoun, Hasan; Kouadiou, Louis et al

in Applied Mathematics and Computation (2020), 376(7), 1-11

Theoretical description of epidemics of plant diseases is an invaluable resource for their efficient management. Here we propose a mathematical model for describing the dispersal by wind of fungal ... [more ▼]

Theoretical description of epidemics of plant diseases is an invaluable resource for their efficient management. Here we propose a mathematical model for describing the dispersal by wind of fungal pathogens in plant populations. The dispersal of pathogen spores was modelled using a non-local diffusion equation which took into account variations in wind velocity components and contained a threshold in the convolution kernel defining the non-local diffusion term. The model was analyzed and the epidemic levels and patterns of the plant disease were derived, based upon defined assumptions of the time and space variables (i.e., represented by continuous parameters), and the host population (i.e., fixed population size). Numerical applications were then performed using reported characteristic values for wheat leaf rust, stripe rust and stem rust. [less ▲]

Detailed reference viewed: 23 (1 ULiège)
Full Text
Peer Reviewed
See detailQuantitative Ordinal Scale Estimates of Plant Disease Severity: Comparing Treatments Using a Proportional Odds Model
Chiang, Kuo-Szu; Liu, H.I.; Chen, Y.L. et al

in Phytopathology (2020), 101(4), 734-743

Studies in plant pathology, agronomy and plant breeding requiring disease severity assessment often use a special type of ordinal scale based on defined numeric ranges (a quantitative ordinal scale). This ... [more ▼]

Studies in plant pathology, agronomy and plant breeding requiring disease severity assessment often use a special type of ordinal scale based on defined numeric ranges (a quantitative ordinal scale). This form of the ordinal scale is generally based on the percent area with symptoms [e.g. the Horsfall-Barratt (HB) scale]. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from disease scales directly, without converting ratings to percentages using range midpoints of quantitative ordinal scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for the purpose of comparing treatments (e.g. varieties, fungicides, etc.) using ordinal estimates of disease severity to midpoint conversions (MCs) and nearest percent estimates (NPEs) using a t-test. A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison was the power of the hypothesis test (the probability to reject the null hypothesis when it is false). Most often NPEs had superior performance. The performance of the POM was never inferior to using the midpoint of the severity range at severity <40%. Especially at low disease severity (≤10%), the POM is superior to using the midpoint conversion method. Thus, for early onset of disease, or for comparing treatments with severities <40%, the POM is preferable for analyzing disease severity data based on quantitative ordinal scales when comparing treatments, and at severities >40% is equivalent to other methods. [less ▲]

Detailed reference viewed: 32 (3 ULiège)
Full Text
Peer Reviewed
See detailWeather-Based Predictive Modeling of Wheat Stripe Rust Infection in Morocco
El Jarroudi, Moussa ULiege; Lahlali, Rachid; Kouadio, Louis et al

in Agronomy (2020), 10(2), 1-18

Predicting infections by Puccinia striiformis f. sp. tritici, with su cient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR ... [more ▼]

Predicting infections by Puccinia striiformis f. sp. tritici, with su cient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR) epidemics that might otherwise lead to yield loss. Despite the increasing threat of WSR to wheat production in Morocco, a model for predicting WSR infection events has yet to be developed. In this study, data collected during two consecutive cropping seasons in 2018–2019 in bread and durum wheat fields at nine representative sites (98 and 99 fields in 2018 and 2019, respectively) were used to develop a weather-based model for predicting infections by P. striiformis. Varying levels of WSR incidence and severity were observed according to the site, year, and wheat species. A combined e ect of relative humidity > 90%, rainfall 0.1 mm, and temperature ranging from 8 to 16 C for a minimum of 4 continuous hours (with the week having these conditions for 5% to 10% of the time) during March–May were optimum to the development of WSR epidemics. Using the weather-based model, WSR infections were satisfactorily predicted, with probabilities of detection 0.92, critical success index ranging from 0.68 to 0.87, and false alarm ratio ranging from 0.10 to 0.32. Our findings could serve as a basis for developing a decision support tool for guiding on-farm WSR disease management, which could help ensure a sustainable and environmentally friendly wheat production in Morocco. [less ▲]

Detailed reference viewed: 62 (10 ULiège)
Full Text
Peer Reviewed
See detailEpidemic models for plants infection under mixed effects of temperature and wetness
Soufi, Adil; Ait Rami, Mustapha; El Jarroudi, Mustapha et al

in Advanced Intelligent Systems for Sustainable Development (2020)

This paper deals with modeling and fitting for epidemic models and their applications to the field of plants disease. For this purpose, two models are proposed that are expressed as a blend of two ... [more ▼]

This paper deals with modeling and fitting for epidemic models and their applications to the field of plants disease. For this purpose, two models are proposed that are expressed as a blend of two functions which reflect the effect of the temperature and the wetness. In addition, we provide an original method to _t the proposed models by employing simple techniques that can constitute an easy-to-use tool for simulation, prediction and/or control. Moreover, the method accuracy and efficiency are evaluated for some reported works in the literature. Computational results are provided to show the validity and effectiveness of the proposed epidemic models for some plant infections. [less ▲]

Detailed reference viewed: 43 (4 ULiège)
Full Text
Peer Reviewed
See detailEmploying Weather-Based Disease and Machine Learning Techniques for Optimal Control of Septoria Leaf Blotch and Stripe Rust in Wheat
El Jarroudi, Moussa ULiege; Lahlali, Rachid; El Jarroudi, Haifa et al

in Advanced Intelligent Systems for Sustainable Development (2020)

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host ... [more ▼]

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host cultivars, favorable environmental conditions, and sufficient quantities of pathogen inoculum. Thus, to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss, weatherbased systems as stand-alone or combined with other disease or agronomic variables have been implemented in decision-support systems (DSS). Given the economic importance of wheat in Morocco and increasing concerns caused by fungal plant pathogens in wheat-growing regions, DSS integrating a disease risk model would help to limit potentially harmful side effects of fungicide applications while ensuring economic benefits. Here we describe the use of an artificial intelligence algorithm, i.e. the artificial neural network, within a weatherbased modelling approach to predict the progress of STB in wheat in Luxembourg. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. Hence, we explore the potential of coupling artificial intelligence algorithms with weather-based model for predicting in-season progress of a major economically important fungal disease – wheat stripe rust – in selected wheatproducing regions in Morocco. [less ▲]

Detailed reference viewed: 75 (14 ULiège)
Full Text
Peer Reviewed
See detailEmploying weather-based disease model and machine learning techniques for optimal control of wheat stripe rust in Morocco
El Jarroudi, Moussa ULiege; Lahlali, Rachid; El Jarroudi, Haifa et al

in AI2SD2019 (Ed.) Advanced Intelligent Systems for Sustainable Development (2019, July 08)

Wheat stripe rust (WSR, caused by Puccinia striiformis Westend) is among the most important crop diseases causing a continuous threat to wheat production worldwide. In most seasons in temperate countries ... [more ▼]

Wheat stripe rust (WSR, caused by Puccinia striiformis Westend) is among the most important crop diseases causing a continuous threat to wheat production worldwide. In most seasons in temperate countries, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of P. striiformis, which are dispersed from distances of a few centimeters to thousands of kilometers, where they might reach a susceptible host plant. Weather-based systems, or weather-based systems combined with other disease or agronomic variables have been implemented in decision-support systems (DSS) to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss. Given WSR is becoming a major threat in wheat-producing regions in Morocco, a DSS integrating a disease risk model would help limiting potentially harmful side effects of fungicide applications while ensuring economic benefits. The main objective of this study is to develop a threshold-based weather model for predicting in-season WSR progress in selected wheat-producing regions (i.e., Sais, Gharb, Middle Atlas, Tadla, Zair, Zemmour, Pre-Rif, High Atlas and Oasis) in Morocco. The threshold-based weather modelling approach has been successfully applied for predicting WSR in Belgium and Luxembourg. Data collected during two consecutive crop seasons in 2010-2011 at the selected sites will be used to test the modelling approach in Morocco. Machine learning techniques including Random Forest, Multivariate Adaptive Regression Splines, and Naïve Bayes Algorithm will also be investigated to improve the model. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. As such, this study is a validation case study of the threshold-based weather modelling approach. Moreover, it explores the potential utility of coupling artificial intelligence algorithms with plant disease models in decision support systems as an effort to improve sustainable wheat production in Morocco. [less ▲]

Detailed reference viewed: 61 (8 ULiège)
Full Text
Peer Reviewed
See detailEpidemic models for plants infection under mixed effects of temperature and wetness
Soufi, Adil; Ait Rami, Mustapha; El Jarroudi, Mustapha et al

in AI2SD2019 (Ed.) Advanced Intelligent Systems for Sustainable Development (2019, July 08)

This paper deals with modeling and tting for epedimic models and their ap- plications to the eld of plants disease. For this purpose, two models are proposed that are expressed as a blend of two functions ... [more ▼]

This paper deals with modeling and tting for epedimic models and their ap- plications to the eld of plants disease. For this purpose, two models are proposed that are expressed as a blend of two functions which re ect the e ect of the temperature and the wetness. In addition, we provide an original method to t the proposed models by employing simple techniques that can constitute an easy-to-use tool for simulation, prediction and/or control. Moreover, the method accuracy and e ciency are evaluated for some reported works in the literature. Computational results are provided to show the validity and e ectiveness of the proposed epidemic models for some plant infections. [less ▲]

Detailed reference viewed: 40 (4 ULiège)
Full Text
See detailLutte intégrée : Solution pour remédier aux problèmes de résistance des parasites des cultures dans l’optique des changements climatiques.
El Jarroudi, Moussa ULiege

Scientific conference (2019, April 26)

Les cultures sont confrontées aux soucis de rentabilité, ce qui les met en face de nombreux défi relative aux coûts des intrants, aux problèmes des résistances des souches aux pesticides. L’agriculteur ... [more ▼]

Les cultures sont confrontées aux soucis de rentabilité, ce qui les met en face de nombreux défi relative aux coûts des intrants, aux problèmes des résistances des souches aux pesticides. L’agriculteur est obligé d’abaisser les coûts d’intrants de fongicides en même temps qu’il doit réduire au minimum le risque de dégâts par suite aux attaques des maladies cryptogamiques des cultures. La grande irrégularité du développement des maladies rend néanmoins difficile l’appréciation du risque de dégâts par l’agriculteur. Ceci l’incite à des traitements d’assurance souvent inutiles voire à impact nuisible sur l’environnement et sur le développement de problèmes de résistance des souches parasitaire. Ainsi, diminuer les charges fongicides est non seulement devenu une nécessité économique mais également une nécessité environnementale suite aux fortes pressions de l’opinion publique. Désormais, les objectifs passent de « produire plus » à « produire plus propre » afin de promouvoir une agriculture durable. Un des enjeux majeurs actuels de l’agriculture est ainsi de concilier rentabilité, les produits de traitement des cultures. Il devient donc indispensable d’instaurer la lutte intégrée et de raisonner la lutte chimique en diminuant quantitativement les apports et en choisissant des dates pertinentes de traitement. Moduler le nombre d’applications fongicides en fonction de la pression parasitaire dans l’optique des changements climatiques est donc une stratégie qui peut s’avérer à la fois économiquement intéressante et plus respectueuse de l’environnement. De ce fait, des aides d’outils à la décision tels que des modèles prédictifs de développement de maladies deviennent incontournable pour l’agriculteur de façon à optimiser les traitements chimiques. Des modèles de simulation ont été développés pour les principales maladies qui affectent la céréaliculture. Avec un pourcentage de réussite oscillant entre 85% et 95%, ses modèles sont devenue une plateforme indispensable pour une agriculture durable à l’échelle parcellaire en Belgique, Grand-Duché de Luxembourg et France et peuvent être transféré au bassin méditerranéen dont le Maroc qui connaît de fortes pressions de maladies cryptogamiques en céréaliculture, principalement la septoriose et tout récemment de nouvelles souches de rouille jaune [less ▲]

Detailed reference viewed: 52 (7 ULiège)
Full Text
Peer Reviewed
See detailYellow rust does not like cold winters. But how to find out which temperature and time frames could be decisive in vivo?
Aslanov, Rufat; El Jarroudi, Moussa ULiege; Gollier, Mélanie et al

in Journal of Plant Pathology (2019)

Yellow rust epidemics caused by Puccinia striiformis f. sp. tritici were monitored in winter wheat grown without fungicides at four locations over the years 2010–2016 in the Grand Duchy of Luxembourg (GDL ... [more ▼]

Yellow rust epidemics caused by Puccinia striiformis f. sp. tritici were monitored in winter wheat grown without fungicides at four locations over the years 2010–2016 in the Grand Duchy of Luxembourg (GDL) and were observed at increased frequency since 2014. A total of 29 field case studies were subdivided into epidemic and non-epidemic cases based on the control threshold of the disease defined in the framework of integrated pest management (IPM). Significant air temperature differences were found between the time courses of epidemic and non-epidemic cases during seven periods and seven individual days. The longest periods with significantly higher temperatures for epidemic cases were found between 21 and 28 days after sowing (DAS) and between 132 and 134 DAS, corresponding approximately to the time of winter wheat emergence, when the disease may infect the newly sown crop, and to the coldest period of the year, respectively. Average daily temperatures were 7.33 ± 0.32 °C and 10.79 ± 0.26 °C between 21 and 28 DAS for non-epidemic and epidemic cases, respectively. Between 132 and 134 DAS, average daily temperatures were − 1.62 ± 0.74 °C and 1.58 ± 0.43 °C for non-epidemic and epidemic cases, respectively. Based on the significant temperature differences detected, up to 86.7% of correct classifications were obtained by leave-one out cross-validation, suggesting that some of the temperature differences identified here have considerable prognostic value for forecasting if an economically relevant yellow rust epidemic must be expected or not. [less ▲]

Detailed reference viewed: 42 (3 ULiège)
Full Text
Peer Reviewed
See detailA lubricant boundary condition for a biological body lined by a thin heterogeneous biofilm
El Jarroudi, Mustapha; Hajjami, r.; Lahrouz, a. et al

in International Journal of Biomathematics (2019)

We study the asymptotic behavior of an incompressible viscous fluid flow in a biological body lined by a thin biological film with a cellular microstructure, varying thickness, and a heterogeneous ... [more ▼]

We study the asymptotic behavior of an incompressible viscous fluid flow in a biological body lined by a thin biological film with a cellular microstructure, varying thickness, and a heterogeneous viscosity regulated by a time random process. Letting the thickness of the film tend to zero, we derive an effective biological slip boundary condition on the boundary of the body. This law relates the tangential fluxes to the tangential velocities via a proportional coefficient corresponding to the energy of some local problem. This law describes the ability of the biological film to function as a lubricant reducing friction at the wall of the body. The tangential velocities are functions of the random trajectories of a finely concentrated biological particle. [less ▲]

Detailed reference viewed: 37 (7 ULiège)
Full Text
Peer Reviewed
See detailModeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions
El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege; Tychon, Bernard ULiege et al

in Advances in Plant Pathology (2018)

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen ... [more ▼]

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg. [less ▲]

Detailed reference viewed: 40 (11 ULiège)
Full Text
Peer Reviewed
See detailImproving fungal disease forecasts in winter wheat: A critical role of intra-day variations of meteorological conditions in the development of Septoria leaf blotch
El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege; El Jarroudi, Mustapha et al

in Field Crops Research (2017), 213

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine ... [more ▼]

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine timing for efficacious fungicide application. This study uses the Fourier transform method (FTM) to characterize temporal patterns of meteorological conditions between two neighbouring experimental sites used in a regional fungal disease monitoring and forecasting experiment in Luxembourg. Three meteorological variables (air temperature, relative humidity, and precipitation) were included, all conducive to infection of wheat by Zymoseptoria tritici cause of Septoria leaf blotch (STB) in winter wheat, from 2006 to 2009. The intraday, diurnal, dekadal and intra-seasonal variations of the meteorological variables were assessed using FTM, and the impact of existing contrasts between sites on the development of STB was analyzed. Although STB severities varied between sites and years (P ≤ 0.0003), the results indicated that the two sites presented the same patterns of meteorological conditions when compared at larger temporal scales (diurnal to intra-seasonal scales, with time periods >11 h). However, the intraday variations of all the variables were well discriminated between the sites and were highly correlated to STB severities. Our findings highlight and confirm the importance of intraday meteorological variation in the development of STB in winter wheat fields. Furthermore, the FTM approach has potential for identifying microclimatic conditions prevailing at given sites and could help in improving the prediction of disease forecast models used in regional warning systems. [less ▲]

Detailed reference viewed: 51 (8 ULiège)
Full Text
Peer Reviewed
See detailA threshold-based weather model for predicting stripe rust infection in winter wheat
El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege; Bock, Clive et al

in Plant Disease (2017), 101(693-703),

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions ... [more ▼]

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999-2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity > 92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤ 0.1 mm (with the dekad having these conditions for 5-20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥ 0.90 and false alarm ratios were ≤ 0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications. [less ▲]

Detailed reference viewed: 82 (13 ULiège)
Full Text
Peer Reviewed
See detailEffects of regional climate change on brown rust disease in winter wheat
JUNK, Jürgen; Kouadio, Amani Louis ULiege; Delfosse, Philippe et al

in Climatic Change (2016)

Projected climate changes will affect wheat crop production both in the main processes of plant growth and development but also in the occurrences and severities of plant diseases. We assessed the ... [more ▼]

Projected climate changes will affect wheat crop production both in the main processes of plant growth and development but also in the occurrences and severities of plant diseases. We assessed the potential infection periods of wheat leaf rust (WLR) at two climatologically differentsites in Luxembourg. A threshold-based model, taking hourlyvalues of air temperatures, relative humidity and precipitation during night-time into account, was used for calculating favourable WLR infection days during three periods throughout the cropping season. Field experiments were conducted during the 2003–2013 period at the selected sites. Projected climate data, from a multi model ensemble of regional climate models (spatial resolution 25 km) as well as an additional projection with a higher spatial resolution of 1.3 km, were used for investigating the potential WLR infection periods for two future time spans. Results showed that the infections of WLR were satisfactorily simulated during the development of wheat at both sites for the 2003–2013 period. The probabilities of WLR detection were close to 1 and the critical success index ranged from 0.80 to 0.94 (perfect score = 1 for both). Moreover, the highest proportions of favourable days of WLR infection were simulated during spring and summer at both sites. Regional climate projections showed an increase in temperatures by 1.6 K for 2041–2050 and by 3.7 K for 2091–2100 compared to the reference period1991–2000. Positivetrends infavourableWLR infection conditions occur at both sites more conducive than in the reference period due to projected climatic conditions. [less ▲]

Detailed reference viewed: 65 (10 ULiège)
Full Text
Peer Reviewed
See detailEffects of rater bias and assessment method on disease severity estimation with regard to hypothesis testing
Chiang, Kuo-Szu; Bock, Clive; El Jarroudi, Moussa ULiege et al

in Plant Pathology (2016)

Detailed reference viewed: 62 (4 ULiège)
Full Text
Peer Reviewed
See detailDo single, double or triple fungicide sprays differentially affect the grain quality in winter wheat?
El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege; Junk et al

in Field Crops Research (2015), 183(257-266),

Foliar fungicides in wheat are typically used to safeguard against economic losses from diseases. In this study, we assessed the effects of three fungicide spray regimes [single, double, and triple ... [more ▼]

Foliar fungicides in wheat are typically used to safeguard against economic losses from diseases. In this study, we assessed the effects of three fungicide spray regimes [single, double, and triple treatments] on four different grain quality parameters [thousand grain weight (TGW), test weight (TW), grain protein content (GPC), and Zeleny sedimentation volume (ZSV)] during the 2006–2009 period at two sites in Luxembourg. The fungicides used were generally a mix of chlorothalonil and triazoles. At Burmerange, (cultivar Cubus), the values of TGW, TW, GPC and ZSV ranged from 38 to 62 g, 67 to 83 kg hl−1, 12.0% to 14.7% dry matter (DM), and 27 to 54 ml, respectively. Whereas, at Everlange (cultivar Achat), the ranges of TGW, TW, GPC and ZSV were 42 to 65 g, 65 to 81 kg hl−1, 11.0% to 15.0% DM, and 21 to 66 ml, respectively. In more than 75% cases, the results indicate that fungicides did not significantly affect TW or ZSV at either sites (P > 0.05). However, there was a significant and positive fungicide effect on GPC in 2006 and 2009 at Burmerange, and only in 2006 at Everlange (P < 0.05). On the contrary, TGW was significantly affected at Burmerange in all years, except 2008 when a positive increase was observed compared to control plots; and in 2006 and 2007 at Everlange. Interestingly, when there was an effect of fungicides on a quality parameter, there was no difference among different fungicide treatments. Thus under conditions prevailing in Luxembourg, a single fungicide treatment applied with judicious timing generally resulted in statistically similar grain quality parameters when compared with a double or triple fungicide treatment. [less ▲]

Detailed reference viewed: 38 (6 ULiège)
Full Text
Peer Reviewed
See detailDynamics of hybrid switching diffusions SIRS model
Settati, Adel; Lahrouz, Aadil; El Jarroudi, Mustapha et al

in Journal of Applied Mathematics and Computing (2015)

The main aim of this paper is to study the effect of the environmental noises in the asymptotic properties of a stochastic version of the classical SIRS epidemic model. The model studied here include ... [more ▼]

The main aim of this paper is to study the effect of the environmental noises in the asymptotic properties of a stochastic version of the classical SIRS epidemic model. The model studied here include white noise and telegraph noise modeled by Markovian switching. We obtained conditions for extinction both in probability one and in pth moment. We also established the persistence of disease under different conditions on the intensities of noises, the parameters of the model and the stationary distribution of the Markov chain. The highlight point of our work is that our conditions are sufficient and almost necessary for extinction and persistence of the epidemic. The presented results are demonstrated by numerical simulations. [less ▲]

Detailed reference viewed: 46 (1 ULiège)
Full Text
Peer Reviewed
See detailDisease Severity Estimates – Effects of Rater Accuracy and Assessment Methods for Comparing Treatments
Bock, Clive; El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege et al

in Plant Disease (2015), 99(1104-1112),

Assessment of disease severity is required for several purposes in plant pathology; most often the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable ... [more ▼]

Assessment of disease severity is required for several purposes in plant pathology; most often the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data; partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from non-treated and fungicide treated plots were assessed in both 2006 and 2007 by four raters and compared to assumed true values measured using image analysis. Lin’s concordance correlation (LCC, ρc) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) mid-points and again compared for agreement with true values. The estimates of SLB severity from fungicide-treated and non-treated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good agreement with image analysis (ρc = 0.986 to 0.999), while raters 3 and 4 had less good agreement (ρc = 0.205 to 0.936). Conversion to the HB scale had little effect on bias or accuracy, but reduced both precision and agreement for most raters on most assessment dates (precision, r = -0.001 to -0.132; and agreement, ρc = -0.003 to -0.468). Inter-rater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3 and 4, and there were frequently significant differences among raters (F=151 to 1260, P=0.001 to <0.0001). Conversion to the HB scale changed the means separation ranking of rater estimates on 26 June 2007. Nonetheless, image analysis and all raters were able to differentiate control and treated plots treatments (F=116 to 1952, P=0.002 to <0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F-values slightly (2006: NPEs, F=116 to 276, P=0.002 to 0.0005, and for the HB converted values F=101 to 270, P=0.002 to 0.0005, and in 2007, NPEs, F=164 to 1952 P=0.001 to <0.0001, and for HB converted values F=126 to 1633 P=0.002 to <0.0001). The results demonstrated the need for accurate and reliable disease assessment to minimize over or underestimates compared to actual disease, and where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis. [less ▲]

Detailed reference viewed: 47 (8 ULiège)
Full Text
Peer Reviewed
See detailFodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series
Diouf, Abdoul Aziz ULiege; Brandt, Martin; Verger, Aleixandre et al

in Remote Sensing (2015), 7(9122-9148),

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship ... [more ▼]

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables—large seasonal integral (LINTG), length of growing season, and end of season decreasing rate—performed best (MAE = 605 kg·DM/ha; R2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999–2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg·DM/ha; R2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg·DM/ha; R2 = 0.77), indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R2 = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands. [less ▲]

Detailed reference viewed: 49 (8 ULiège)