Incorporation of temperature and solar radiation thresholds to modify a lettuce downy mildew warning system

Evaluating the Performance of a Relative Humidity-Based Warning System for Sooty Blotch and Flyspeck in Iowa

A warning system for the sooty blotch and flyspeck (SBFS) fungal disease complex of apple, developed originally for use in the southeastern United States, was modified to provide more reliable assessment of SBFS risk in Iowa. Modeling results based on previous research in Iowa and Wisconsin had suggested replacing leaf wetness duration with cumulative hours of relative humidity (RH) ≥97% as the weather input to the SBFS warning system. The purpose of the present study was to evaluate the performance of a RH-based SBFS warning system, and to assess the potential economic benefits for its use in Iowa. The warning system was evaluated in two separate sets of trials-trial 1 during 2010 and 2011, and trial 2 during 2013-2015-using action thresholds based on cumulative hours of RH ≥97% and ≥90%, respectively, in conjunction with two different fungicide regimes. The warning system was compared with a traditional calendar-based system that specified spraying at predetermined intervals of 10 to 14 days. In trial 1, use of the RH ≥97% threshold caused substantial differences between two RH sensors in recording number of hours exceeding the threshold. When both RH thresholds were compared for 2013-2015, on average, RH ≥90% resulted in a 53% reduction in variation of cumulative hours between two identical RH sensors placed adjacent to each other in an apple tree canopy. Although both the SBFS warning system and the calendar-based system resulted in equivalent control of SBFS, the warning system required fewer fungicide sprays than the calendar-based system, with an average of 3.8 sprays per season (min = 2; max = 5) vs. 6.4 sprays per season (min = 5; max = 8), respectively. The two fungicide regimes provided equivalent SBFS control when used in conjunction with the warning system. A partial budget analysis showed that using the SBFS warning system with a threshold of RH ≥90% was cost effective for orchard sizes of >1 ha. The revised warning system has potential to become a valuable decision support tool for Midwest apple growers because it reduces fungicide costs while protecting apples as effectively as a calendar-based spray schedule. The next step toward implementation of the SBFS warning system in the North Central U.S. should be multiyear field testing in commercial orchards throughout the region.

Evaluation of Forecasting Models for Fusarium Head Blight of Wheat Under Growing Conditions of Quebec, Canada

Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum L.) causing frequent economic losses to farmers under growing conditions of Eastern Canada. To assess risks associated with this disease and guide fungicide use decisions, many researchers from numerous countries have developed weather-based forecasting models. This work aims at evaluating which model produces the most accurate predictions of disease infection or deoxynivalenol (DON) content under climatic conditions occurring in Quebec. Spring wheat was grown during two seasons and winter wheat during one season at four experimental sites located in Quebec. Nine selected models for evaluation produced predictions of DON content (Canada and Italy), disease incidence (Argentina and Italy), and probability of epidemics (United States). Data from plots without fungicide (52 samples) were used to test the models listed above. Reliability of the selected forecasting models was evaluated with receiver operating characteristic (ROC) curve analysis. DON content (≥1 ppm) was the best crop damage indicator to differentiate epidemic (cases) and nonepidemic (controls) situations. Two American and the Argentinean forecasting models were more reliable than the others when the thresholds recommended in the literature were adjusted using the results for the ROC curve analyses. Those models are a good starting point for the implementation of an FHB forecasting system adapted to wheat production in Quebec.

A Quantitative Dynamic Simulation of Bremia lactucae Airborne Conidia Concentration above a Lettuce Canopy

Lettuce downy mildew, caused by the oomycete Bremia lactucae Regel, is a major threat to lettuce production worldwide. Lettuce downy mildew is a polycyclic disease driven by airborne spores. A weather-based dynamic simulation model for B. lactucae airborne spores was developed to simulate the aerobiological characteristics of the pathogen. The model was built using the STELLA platform by following the system dynamics methodology. The model was developed using published equations describing disease subprocesses (e.g., sporulation) and assembled knowledge of the interactions among pathogen, host, and weather. The model was evaluated with four years of independent data by comparing model simulations with observations of hourly and daily airborne spore concentrations. The results show an accurate simulation of the trend and shape of B. lactucae temporal dynamics of airborne spore concentration. The model simulated hourly and daily peaks in airborne spore concentrations. More than 95% of the simulation runs, the daily-simulated airborne conidia concentration was 0 when airborne conidia were not observed. Also, the relationship between the simulated and the observed airborne spores was linear. In more than 94% of the simulation runs, the proportion of the linear variation in the hourly-observed values explained by the variation in the hourly-simulated values was greater than 0.7 in all years except one. Most of the errors came from the deviation from the 1:1 line, and the proportion of errors due to the model bias was low. This model is the only dynamic model developed to mimic the dynamics of airborne inoculum and represents an initial step towards improved lettuce downy mildew understanding, forecasting and management.

Bremia lactucae Infection Efficiency in Lettuce is Modulated by Temperature and Leaf Wetness Duration Under Quebec Field Conditions

More than 80% of Canadian lettuce production is located in the province of Quebec. Yet most of our knowledge on the epidemiology of lettuce downy mildew (Bremia lactucae) is derived from controlled-condition experiments or field experiments conducted in subtropical climates and, thus, cannot readily be applied to Quebec lettuce production. The influence of temperature and leaf wetness duration on the infection efficiency (IE) of B. lactucae was studied for 4 years (2003, 2004, 2012, and 2013) under field and growth-chamber conditions. IE was defined as the ratio of the number of lesions/leaf to the airborne conidia concentration (ACC). B. lactucae ACC was measured with rotating-arm samplers three times/week. In addition, 72 lettuce trap plants/sampling day were exposed to the potential airborne B. lactucae inoculum and disease intensity was assessed after 7 days of incubation in greenhouse. Under growth-chamber conditions, an ACC of 1 conidium/m³ was sufficient to cause 1 lesion/leaf, and IE ranged from 0.25 to 1.00. Under field conditions, an ACC of 10 to 14 conidia/m³ was required to cause 1 lesion/leaf, and IE ranged from 0.02 to 0.10, except in 2004, when IE ranged from 0.03 to 1.00. IE increased with increasing leaf wetness duration but decreased with increasing temperature. Also, considering an observed average temperature range from 10 to 20°C in the area of Quebec, 2 h of leaf wetness was sufficient for infection by B. lactucae. Therefore, under Quebec lettuce production conditions, a leaf wetness period of 2 h and an ACC of 10 to 14 conidia/m³ can be used as risk indicators to facilitate disease management decisions. Also, under typical Quebec weather conditions, measuring both morning and evening leaf wetness events could be used to improve the reliability of leaf wetness duration as a downy mildew risk indicator. Further research is needed to validate these risk indicators for integration into management strategies.

Impact of Diurnal Periodicity, Temperature, and Light on Sporulation of Bremia lactucae

ABSTRACT We evaluated direct and interactive effects of light quality and intensity, temperature and light, diurnal rhythms, and timing of high relative humidity during long day lengths on sporulation of Bremia lactucae, the causal agent of lettuce downy mildew, using inoculated lettuce seedlings and detached cotyledons. Suppression of sporulation by light was strongly dependent upon temperature and there was little suppression at </=10 degrees C. The most suppressive waveband was in the range from 400 to 450 nm, although a lesser effect of wavebands from 450 to 500 and 500 to 550 nm could be detected. At 15 degrees C, near the lower threshold for suppression of sporulation by light, a clear diurnal pattern of sporulation was observed independent of light and darkness. This diurnal rhythm potentially could interact with light and temperature to confound the results of controlled environment studies, and may be the controlling factor in timing of sporulation at low temperatures. Forecasting models that currently use sunrise and sunset to delimit periods conducive to sporulation can be adapted to short nights and extended twilight conditions by incorporating the effects reported herein. Additionally, models of sporulation could be adapted to better reflect a decrease or absence of the suppressive effect of light at <15 degrees C.

Disease cycle approach to plant disease prediction

Plant disease cycles represent pathogen biology as a series of interconnected stages of development including dormancy, reproduction, dispersal, and pathogenesis. The progression through these stages is determined by a continuous sequence of interactions among host, pathogen, and environment. The stages of the disease cycle form the basis of many plant disease prediction models. The relationship of temperature and moisture to disease development and pathogen reproduction serve as the basis for most contemporary plant disease prediction systems. Pathogen dormancy and inoculum dispersal are considered less frequently. We found extensive research efforts evaluating the performance of prediction models as part of operation disease management systems. These efforts appear to be greater than just a few decades ago, and include novel applications of Bayesian decision theory. Advances in information technology have stimulated innovations in model application. This trend must accelerate to provide the disease management strategies needed to maintain global food supplies.

Analyses of the Relationships Between Lettuce Downy Mildew and Weather Variables Using Geographic Information System Techniques

Previous studies in coastal California suggested that morning leaf wetness duration and temperature immediately after the prolonged leaf wetness period affect infection of lettuce by the downy mildew pathogen, Bremia lactucae. In this study, spatial analysis tools in a geographic information system were used to interpolate disease assessment data and then relate them to weather variables measured in 1995 and 1997 at weather stations in the Salinas Valley. Among the variables monitored at these weather stations, midday temperature (10:00 A.M. to 2:00 P.M.) was related most strongly to the interpolated downy mildew incidence in a circular area (radius = 5 km) around each station (r = 0.52, P < 0.0001); the higher the midday temperature, the lower the disease incidence. High humidity and prolonged morning leaf wetness duration also were associated with high downy mildew incidence. Cluster analysis resulted in distinct regions with different midday temperatures, which overlapped well (92.2% of the total area) with regions distinguished in previous cluster analyses of downy mildew incidence. Clusters of morning relative humidity showed similar patterns, although they overlapped less well with clusters of disease incidence. These results confirmed that midday temperature is an important determining factor for lettuce downy mildew, and its effects should be incorporated into a disease warning system for coastal California. Cluster analyses based on the effects of temperature have great potential for use in regional downy mildew risk assessment.