Performance of leaf wetness sensor used in winter wheat disease management

Wetness on crop leaves has particular epidemiological significance because many fungal diseases affect plants only when free moisture is present on leaves. The leaf wetness sensor detects the presence of wetness on a leaf’s surface, enabling researchers and producers to forecast disease and protect plant canopies, and consequently to optimize fungicide application and often reduce environmental load. This research project aimed at better understanding the leaf wetness duration and its influence in winter wheat disease. Measurement of surface wetness duration by three electronic flat-plate sensors (Model 237-Campbell Scientific, Inc) in wheat fields were compared with tactile and visual observations in replicated field experiments at the site of Arlon (Belgium) during the period May-July 2006 and April-July 2007. Performances of the sensor were evaluated against SWEB model outputs and visual observations of disease symptoms. On the field, dew-onset and dry-off of wetness on leaves were observed visually (with a flash light for dew-onset) at 15-minute intervals. Each sensor was placed close the flag leaf. For the three sensors, the two dew-onset and dry-off times measured in both 2006 and 2007 crop seasons gave a leaf wetness duration (LWD) which was on average one hour less than visual observations. In order to establish a relationship between the surface wetness periods and wheat foliar diseases, LWD was compared with the Septoria leaf blotch (SLB) development risk (main winter wheat disease). A minimal surface wetness duration favourable to infection for SLB was established.Weather-radar data ; Rainfall ; Septoria tritici ; Forecasting system ; Winter wheat ; Epidemiological model ; Surface wetness duration ; Spatializatio

Spatial heterogeneity of leaf wetness duration in winter wheat canopy and its influence on plant disease epidemiology

peer reviewedLeaf wetness duration (LWD) is an important factor influencing the occurrence of plant disease epidemiology. Despite considerable efforts to determine LWD, little attention has been given to study its variability within the canopy. The objective of this study was to evaluate its spatiotemporal variability in wheat fields in a heterogeneous landscape. The spatiotemporal variability of LWD was evaluated in a site close to Arlon (Belgium) during the period May to July 2006 and 2007. LWD measurements were made using a set of flat plate sensors deployed at five different distances from a 18 m high hedge (5, 10, 20, 50, 100 m). Each set of two sensors was placed horizontally close the flag leaf. In addition, we collected the amount of dew water that deposited on rigid epoxy plates placed next to each sensors. Experimental results showed that LWD measurements revealed substantial heterogeneity among sensor positions. LWD is longer for sensors closer to the hedge mainly because of its shadowing effect. 3 to 4 hours of difference was observed between sensors located at 5 m and those located at 100 m, and besides, a significant quantitative difference (p < 0.0001) of dew deposit was observed between area beside hedge and those placed at 100 m. In summary, this study provides new information on how wetness is distributed on wheat leaves according to the distance from a hedge. This leads to local microclimate conditions that will contribute to the disease spatial heterogeneity

Weather-Radar Rainfall Measurement and Simulated Surface Wetness Duration for Septoria Leaf Blotch Risk Assessment

L’humectation des surfaces végétales, due principalement aux précipitations sous forme de pluie ou de rosée, joue un rôle déterminant lors de la phase de contamination des plantes par de nombreux agents phytopathogènes. La connaissance de la pluie et de la rosée constitue un élément fondamental pour l’étude et la compréhension du fonctionnement des modèles de simulation des épidémies et des systèmes d'avertissements agricoles. L’objectif de cette recherche est de contribuer à l’amélioration du système d’avertissement des principales maladies cryptogamiques affectant le blé d’hiver au sud de Belgique et au G-D de Luxembourg. Notre démarche a consisté, dans un premier temps à évaluer les potentialités du radar météorologique de Wideumont. Nous avons décrit son fonctionnement général ainsi que son principe de mesure et nous avons détaillé les différentes sources d’erreur qui affectent les estimations de précipitations dérivées des observations radar. Les mesures radar sont moins précises que les mesures de précipitations par des pluviomètres. Néanmoins, le radar permet d’observer en temps réel les précipitations sur un large domaine avec une très bonne résolution spatiale et temporelle. La comparaison quantitative et qualitative des précipitations mesurées au sol avec celles estimées par le radar a été faite sur une période de trois ans (2003, 2004 et 2005). Les résultats de la validation des cumuls mensuels font apparaître que le radar a tendance à sous-estimer les précipitations. L’erreur calculée pour l’ensemble des stations varie entre -50% et +12%. La validation qualitative du radar a été réalisée sur des occurrences de cumuls horaires. Les indices calculés à partir des tables de contingence donnent des valeurs de POD (Probability Of Detection) entre 0.44 et 0.80 durant la période étudiée. L’impact des estimations radar sur les périodes d’infection de Septoria tritici simulées par PROCULTURE a été évalué durant trois saisons culturales (2003, 2004 et 2005) par comparaison entre les données de sortie du modèle (alimenté par des estimations radar de précipitations horaires) et les estimations visuelles du développement des symptômes de la maladie sur les trois dernières feuilles. Les outputs de PROCULTURE via les données radar ont montré un grand accord entre la simulation et l’observation. Le radar météorologique devrait dès lors être bénéfique pour des régions où le réseau des pluviomètres est inexistant (ou moins dense) et où l’incidence de la septoriose est importante. Dans un deuxième temps, sur base d’une recherche bibliographique, un modèle d’humectation a été choisi. Le modèle sélectionné, appelé SWEB, se base sur le bilan énergétique et le bilan hydrique. Il simule la durée d’humectation due à la pluie et à la rosée sur l’ensemble du couvert végétal à partir des données issues des stations agrométéorologiques. Le modèle a été ensuite testé et validé sur différentes variétés de blé d’hiver. Les données de sortie du modèle ont été comparées statistiquement aux mesures des capteurs (préalablement calibrés) et aux données d’observation obtenues sur des parcelles expérimentales et au champ durant les saisons culturales 2006 et 2007. Sur base des résultats obtenus, le modèle SWEB semble sous-estimer la durée d’humectation et plus particulièrement pour les événements de la fin d’humectation (dryoff). L’erreur moyenne en général est inférieure à 90 minutes. Dans un troisième temps, afin d’obtenir une relation entre les périodes d’humectation et le développement de la septoriose sur les trois dernières feuilles, les périodes d’humectation simulées par SWEB ont été comparées d’une part aux périodes d’infection de Septoria tritici simulées par PROCULTURE et d’autre part aux estimations visuelles. Le modèle de la durée d’humectation simule avec succès des périodes d’humectations, dues à la fois à la rosée et à la pluie, qui ont déclenché l’infection de la septoriose observée sur des parcelles expérimentales. Une durée minimale d’humectation favorable à l’infection des feuilles de blé par Septoria tritici a été déterminée. Il est donc désormais nécessaire d’élaborer un système opérationnel intégrant le radar météorologique, le modèle de la durée d’humectation et le modèle épidémiologique. Notre travail a permis d’acquérir via l’analyse des données agrométéorologiques et des données phytopathologiques, les connaissances nécessaires à l’élaboration d’un tel système et de participer ainsi à l’amélioration des modèles d’avertissements existants. En effet, nous avons analysé les avantages et les limites du système radar comme données d’entrée aux modèles et son aptitude dans la spatialisation des données. Nous avons également testé le modèle d’humectation pour la détermination des périodes d’infection nécessaires au développement de la septoriose. Dans une perspective d’une meilleure opérationnalisation du système, l’approche envisagée pourrait facilement être intégrée dans le système existant pour la simulation d’autres maladies comme les rouilles, l’oïdium et la fusariose à l’échelle régionale. En définitive, ce travail aura prouvé une fois de plus l’intérêt du "mariage" entre l’agrométéorologie et la phytopathologie.Summary - Weather-Radar Rainfall Measurement and Simulated Surface Wetness Duration for Septoria Leaf Blotch Risk Assessment. The persistence of free moisture on leaves, mainly as a result of precipitation in the form of rainfall or dew, plays a major role during the process of plant infection by most fungal pathogens. Acquiring rainfall and leaf moisture information is needed for accurate and reliable disease prediction and management. The objective of this research is to contribute to improve forecasting Septoria leaf blotch and other fungal pathogens on winter wheat in Belgium and Luxembourg. In the first part of this work, the potential of weather-radar rainfall estimates for plant disease forecasting is discussed. At first step, we focused on assessing the accuracy and limitations of radar-derived precipitation estimates, compared with rain-gauge data. In a second step, the Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on the simulated infection rate of using, as input data, rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar-derived estimates and reference rain gauge measurements, the probability of detection (POD) of infection events was high (0.83 on average), and the false alarm ratio (FAR) of infection events was not negligible (0.24 on average). FAR decreased to 0 and POD increased (0.85 on average) for most stations, when the model outputs for both datasets were compared against visual observations of Septoria leaf blotch symptoms. Analysis of 148 infection events observed over three years at four locations showed no significant difference in the number of simulated infection events using either radar assessments or gauge measurements. This suggests that, for a given location, radar estimates are just as reliable for predicting infection events as rain gauges. As radar is able to estimate rainfall occurrence over a continuous space, unlike weather station networks that do observations at only a limited number of points, it has the great advantage of being able to predict the risk of infection at each point within an area of interest with an accuracy equivalent to rain gauge observations. This gives radar an important advantage that could significantly improve existing warning systems. In the second part, a physical model based on the energy balance, known as the Surface Wetness Energy Balance (SWEB), was applied for the simulation of Surface Wetness Duration (SWD) on winter wheat canopy. The model, developed in the United States on grapes canopies, was adapted for the winter wheat cultivars and was applied for use with agrometeorological data easily available from standard weather stations and weather-radar rainfall estimates. The SWEB model simulates surface wetness duration for both dew and rain events. The model was validated with data measured by sensors and with visual observations of SWD conducted in experimental plots during two cropping seasons in 2006 and 2007. The wetness was observed visually by assessing the presence or absence of surface water on leaves. Based on the results, the SWEB model appeared to underestimate surface wetness duration and especially for the dry-off events when compared statistically to visual observations. The error, on average, is generally less than 90 minutes. In order to establish a relationship between the surface wetness periods and Septoria leaf blotch development risk on the top three leaves, the SWEB model SWD outputs were compared with the number of hours of high probability of infection simulated by PROCULTURE as well as with visual plant diseases observations. A minimal surface wetness duration of favourable infection conditions for Septoria tritici was established. It is now required to develop an operational system that would integrate weather radar, surface wetness duration and foliar epidemic model. In this work, we have analyzed the advantages and limitations of the radar system as input to models and its ability for spatial interpolation of rainfall. We also tested the model for the determination of surface wetness periods required for Septoria Leaf Blotch Risk development. The proposed approach could be integrated in the existing system. Finally this approach shows once more the "happy marriage" between agrometeorology and plant disease management

Sensitivity of simulated surface wetness duration to meteorological variations in three different regions of Grand-Duchy of Luxembourg

Surface wetness duration (SWD) is an important factor influencing the occurrence of winter wheat diseases. For this reason, SWD is extremely important for the management of crop protection activities. In order to understand the SWD variability and its influence on winter wheat disease, the objective of this study was to (i) determine the sensitivity of our model on varying input plant parameters and (ii) to evaluate the influence of simulated SWD to meteorological variations in three different climatic regions of the Grand-Duchy of Luxembourg (EVERLANGE, OBERCORN and SCHIMPACH). In this work, an agrometeorological model known as the Surface Wetness Energy Balance (SWEB) was applied for the simulation of SWD. The model was previously applied in another study for winter wheat cultivars and was adapted for use with agrometeorological data easily available from standard meteorological monitoring stations. Based on weather data and simulated SWD data, sensitivity analyses were performed to compare the effects of relative humidity, air temperature, wind speed and net radiation on wetness duration over one growing season (March-July) at three test sites. The results indicated that the sensitivities were very similar at three sites and there was no spatial trend (i.e. difference between locations) in the sensitivities. However, the model is most sensitive to relative humidity and differences between 0.5 and 25 h (per month) SWD were found when increasing/decreasing relative humidity by 10%. The model was least sensitive to changes in air temperature, showing differences of only 0.5–2 h (per month) in SWD. Intermediate sensitivity is found for rainfall, net radiation and wind speed. Among the input plant parameters values, SWD was most sensitive to the maximum fraction of canopy allowed as wet surface area, leaf area index, maximum water storage per unit area and least sensitive to crop height. The sensitivity to parameter values was less important compared to the sensitivity to the meteorological variable relative humidity

Site-specific Septoria Leaf Blotch Risk Assessment in Winter Wheat using Weather-Radar Rainfall Estimates

The Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on simulated infection rates when using rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar- and gauge-derived data, the probability of detection (PODs) of infection events was high (0.83 on average), and the false alarm ratio (FARs) of infection events was not negligible (0.24 on average). For most stations, FARso of infection events decreased to 0 and PODso increased (0.85 on average) when the model outputs for both datasets were compared against visual observations of disease symptoms. An analysis of 148 infection events over three years at four locations showed no significant difference in the number of infection events of simulations using either dataset, indicating that, for a given location, radar estimates were as reliable as rain gauges for predicting infection events. Radar also provided better estimates of rainfall occurrence over a continuous space than weather station networks. The high spatial resolution provides radar with an important advantage that could significantly improve existing warning systems