Moussa El Jarroudi

Fusarium head blight and associated mycotoxin occurrence on winter wheat in Luxembourg in 2007/2008.

Fusarium head blight (FHB) is among the major causes of reduced quality in winter wheat and its products. In addition, the causal fungi produce a variety of toxins. A relatively high FHB infection rate in winter wheat was observed in 2007 and 2008 in Luxembourg. A fusariotoxin survey was carried out in 17 different geographical locations. Three groups of Fusarium mycotoxins (trichothecenes A and B and zearalenone) were analysed by a multi-detection HPLC–MS/MS method. Fusarium strains were also investigated by morphological and molecular methods. In addition, questionnaires relating to cultural practices were sent to the farmers managing the 17 fields investigated. FHB prevalence ranged from 0.3 to 65.8% (mean: 8.5%) in 2007 and from 0 to 24.5% (mean: 8.3%) in 2008. Results of morphological and molecular identification showed that the most common species isolated from diseased wheat spikes was F. graminearum (33.1%), followed by F. avenaceum (20.3%) and F. poae (17.8%). The chemical analysis revealed that 75% of the investigated fields were contaminated by deoxynivalenol (DON, range 0–8111 µg/kg). The preceding crop was highly and significantly correlated to the number of grains infected and had a significant impact on disease prevalence (p = 0.025 and 0.017, respectively, Fishers F-test). A trend was found for maize as the preceding crop (p = 0.084, Tukeys test) to predict the amount of DON in the fields. This is the first report on the occurrence of DON and ZON in naturally infected wheat grains sampled from Luxembourg.

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

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; R 2 = 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; R 2 = 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; R 2 = 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

Brown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system

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.

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 simulated probability of detection (POD) of infection events was high (0.83 on average), and the simulated false alarm ratio (FAR) of infection events was not negligible (0.24 on average). For most stations, simulation-observed FAR decreased to 0 and simulation-observed POD 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 3 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.

Disease Severity Estimates – Effects of Rater Accuracy and Assessment Methods for Comparing Treatments

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 nontreated and fungicide-treated plots were assessed in both 2006 and 2007 by four raters and compared with assumed actual values measured using image analysis. Lins concordance correlation (LCC, ρc) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) midpoints and were compared with actual values. The estimates of SLB severity from fungicide-treated and nontreated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good accuracy (ρc = 0.986 to 0.999), while raters 3 and 4 were less accurate (ρc = 0.205 to 0.936). Conversion to the HB scale had little effect on bias but reduced numerically both precision and accuracy for most raters on most assessment dates (precision, r = -0.001 to -0.132; and accuracy, ρc = -0.003 to -0.468). Interrater 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 1,260, P = 0.001 to P < 0.0001). Only on 26 June 2007 did conversion to the HB scale change the means separation ranking of rater estimates. Nonetheless, image analysis and all raters were able to differentiate control and treated-plot treatments (F = 116 to 1,952, P = 0.002 to P < 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; 2007: NPEs, F = 164 to 1,952, P = 0.001 to P < 0.0001; and, for HB-converted values, F = 126 to 1,633, P = 0.002 to P < 0.0001). The results reaffirm the need for accurate and reliable disease assessment to minimize over- or underestimates compared with actual disease, and the data we present support the view that, where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis.

A threshold-based weather model for predicting stripe rust infection in winter wheat

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 to 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 to 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.

First report of wheat leaf rust in the Grand Duchy of Luxembourg and the progress of its appearance over the 2003-2008 period.

Wheat leaf rust caused by Puccinia triticina Eriks. was identified for the first time in 2000 in the Grand Duchy of Luxembourg on the basis of orange-to-brown, round-to-ovoid, erumpent uredinia (1 to 1.5 mm in diameter) scattered on the upper and lower leaf surfaces and producing orange-brown urediniospores that are subgloboid, approximately 20 μm in diameter, and with up to eight germ pore scattered in thick, echinulate walls. In a second phase, wheat was monitored weekly (starting from Zadoks growth stage 30, pseudo stem erection) during the 2003-2008 cropping seasons for wheat leaf rust. Disease severity (percentage of leaf area with symptoms) was recorded in four, replicated field experiments located in three villages (Diekirch District: Reuler; and Grevenmacher District: Burmerange and Christnach), which are representative of the different agroclimatological zones of Luxembourg. A significant difference in severity was observed between the sites (P < 0.01) and the years (P < 0.05). Over the 6-year period, Burmerange and Reuler consistently showed the highest and lowest disease severity, respectively. In 2003 and 2007, Burmerange (a southern site with the highest average spring temperatures of 13.6 and 14.0°C, respectively) showed the highest disease severity with 66 and 57%, respectively, whereas the lowest severity (<1% for both years) was observed in the north at Reuler (site with the lowest average spring temperatures of 12.0 and 12.4°C, respectively). Christnach, located midway between Reuler and Burmerange, showed an intermediate disease severity with 7% (2003) and 22% (2007). The disease appeared at growth stages 77 (late milk) and 87 (hard dough) in the period 2003-2005, but at an earlier stage (45, boots swollen) for 2006-2008 (P < 0.001). In 2005, low severity was recorded due to a severe drought during May, June, and July. A reason for this earlier appearance of leaf rust occurrences in the two districts may be related to an increase in the average spring temperature (average March to May temperature for Luxembourg was 8.3°C for the 1971-2000 period, 9.5°C for the 2003-2005 period, 9.9°C for the 2006-2008 period, 2007 was exceptional with 11.9°C, P < 0.01). In the past, cereal disease management strategies were oriented toward the control of predominant and yield-reducing diseases such as that caused by Septoria tritici Desm. Because the succession of mild winters and warm springs during the last 5 years allowed the early occurrence and the fast development of wheat leaf rust in the Grand Duchy of Luxembourg, it is advisable to take this disease into account in fungicide application schemes.