Development of Real-Time Isothermal Amplification Assays for On-Site Detection of Phytophthora infestans in Potato Leaves

Real-time loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) assays were developed targeting the internal transcribed spacer 2 region of the ribosomal DNA of Phytophthora infestans, the potato late blight causal agent. A rapid crude plant extract (CPE) preparation method from infected potato leaves was developed for on-site testing. The assay's specificity was tested using several species of Phytophthora and other potato fungal and oomycete pathogens. Both LAMP and RPA assays showed specificity to P. infestans but also to the closely related species P. andina, P. mirabilis, P. phaseoli, and P. ipomoeae, although the latter are not reported as potato pathogen species. No cross-reaction occurred with P. capsici or with the potato pathogens tested, including P. nicotianae and P. erythroseptica. The sensitivity was determined using P. infestans pure genomic DNA added into healthy CPE samples. Both LAMP and RPA assays detected DNA at 50 fg/μl and were insensitive to CPE inhibition. The isothermal assays were tested with artificially inoculated and naturally infected potato plants using a Smart-DART platform. The LAMP assay effectively detected P. infestans in symptomless potato leaves as soon as 24 h postinoculation. A rapid and accurate on-site detection of P. infestans in plant material using the LAMP assay will contribute to improved late blight diagnosis and early detection of infections and facilitate prompt management decisions

Decision Trees to Forecast Risks of Strawberry Powdery Mildew Caused by Podosphaera aphanis

Powdery mildew (Podosphaera aphanis) is a major disease in day-neutral strawberry. Up to 30% yield losses have been observed in Eastern Canada. Currently, management of powdery mildew is mostly based on fungicide applications without consideration of risk. The objective of this study is to use P. aphanis inoculum, host ontogenic resistance, and weather predictors to forecast the risk of strawberry powdery mildew using CART models (classification trees). The data used to build the trees were collected in 2006, 2007, and 2008 at one experimental farm and six commercial farms located in two main strawberry-production areas, while external validation data were collected at the same experimental farm in 2015, 2016, and 2018. Data on proportion of leaf area diseased (PLAD) were grouped into four severity classes (1: PLAD = 0; 2: PLAD > 0 and <5%; 3: >5% and <15%; and 4: PLAD > 15%) for a total of 681 and 136 cases for training and external validation, respectively. From the initial 92 weather variables, 21 were selected following clustering. The tree with the best balance between the number of predictors and highest accuracy was built with: airborne inoculum concentration and number of susceptible leaves on the day of sampling, and mean relative humidity, mean daily number of hours at temperature between 18 and 30 °C, and mean daily number of hours at saturation vapor pressure between 10 and 25 mmHg during the previous 6 days. For training, internal validation, and external validation datasets, the sensitivity, specificity, and accuracy ranged from 0.70 to 0.90, 0.87 to 0.98, and 0.82 to 0.97, respectively. The classification rules to estimate strawberry powdery mildew risk can be easily implemented into disease decision support systems and used to treat only when necessary and thus avoid preventable yield losses and unnecessary treatments

Fungicides

Plant and plant products are affected by a large number of plant pathogens among which fungal pathogens. These diseases play a major role in the current deficit of food supply worldwide. Various control strategies were developed to reduce the negative effects of diseases on food, fiber, and forest crops products. For the past fifty years fungicides have played a major role in the increased productivity of several crops in most parts of the world. Although fungicide treatments are a key component of disease management, the emergence of resistance, their introduction into the environment and their toxic effect on human, animal, non-target microorganisms and beneficial organisms has become an important factor in limiting the durability of fungicide effectiveness and usefulness. This book contains 25 chapters on various aspects of fungicide science from efficacy to resistance, toxicology and development of new fungicides that provides a comprehensive and authoritative account for the role of fungicides in modern agriculture

Effect of physical environment on cercospora carotae and development of a model to predict cerscospora blight of carrot

The effect of interrupted leaf wetness (IWP) and %RH on infection by Cercospora carotae (Pass.) Solh. was studied by inoculating carrot leaves (Daucus carota var sativa DC. L.) and subjecting the plants to different IWP treatment, continuous leaf wetness (CWP) and to different combinations of %RH and temperature with and without an initial wet period of 6 hr. IWP significantly reduced infection as compared to CWP. Infection was optimal under leaf wetness and decreased with decrease in percent RH. The effect of temperature and duration of moist period on sporulation of C. carotae was studied on carrot plants under leaf wetness, 96%RH, and 96%RH with an initial 12 hr of leaf wetness. For all types of moisture conditions, sporulation increased with the increase in temperature up to the optimum (28$sp circ$C) and then declined. Logistic and polynomial models were used to describe the effect of temperature and time on sporulation under these moisture conditions. The incubation period of Cercospora carotae was studied in the field. First lesions were observed 6 to 8 days after inoculation and new lesions appeared until the 10th to 14th day. The beginning, mean, and end of incubation period was modelled as a function of mean daily temperature and mean daily RH $ge$ 90%. A model describing lesion appearance as a function of time was developed using a logistic function (R$sp2$ = 0.84). A prediction model containing series of equations that described mathematically the interaction among predicted inoculum, infection and sporulation equivalents for the environment was developed and validated. In general, the model predicted adequately Cercospora blight progress. A weather-based forecasting system was developed to time the first fungicide spray to manage Cercospora blight of carrot based on the accumulation of critical number of disease severity units

Spatiotemporal relationships between disease development and airborne inoculum in unmanaged and managed botrytis leaf blight epidemics

International audienceComparatively little quantitative information is available on both the spatial and temporal relationships that develop between airborne inoculum and disease intensity during the course of aerially spread epidemics. Botrytis leaf blight and Botrytis squamosa airborne inoculum were analyzed over space and time during 2 years (2002 and 2004) in a nonprotected experimental field, using a 6 × 8 lattice of quadrats of 10 × 10 m each. A similar experiment was conducted in 2004 and 2006 in a commercial field managed for Botrytis leaf blight using a 5 × 5 lattice of quadrats of 25 × 25 m each. Each quadrat was monitored weekly for lesion density (LD) and aerial conidium concentration (ACC). The adjustment of the Taylor's power law showed that heterogeneity in both LD and ACC generally increased with increasing mean. Unmanaged epidemics were characterized in either year, with aggregation indices derived from SADIE (Spatial Analysis by Distance Indices). For LD, the aggregation indices suggested a random pattern of disease early in the season, followed by an aggregated pattern in the second part of the epidemic. The index of aggregation for ACC in 2002 was significantly greater than 1 at only one date, while it was significantly greater than 1 at most sampling dates in 2004. In both years and for both variables, positive trends in partial autocorrelation were observed mainly for a spatial lag of 1. In 2002, the overall pattern of partial autocorrelations over sampling dates was similar for LD and ACC with no significant partial autocorrelation during the first part of the epidemic, followed by a period with significant positive autocorrelation, and again no autocorrelation on the last three sampling dates. In 2004, there was no significant positive autocorrelation for LD at most sampling dates while for ACC, there was a fluctuation between significant and non-significant positive correlation over sampling dates. There was a significant spatial correlation between ACC at given date (ti) and LD 1 week later (ti + 1) on most sampling dates in both 2002 and 2004 for the unmanaged and managed sites. It was concluded that LD and ACC were not aggregated in the early stage of epidemics, when both disease intensity and airborne conidia concentration were low. This was supported by the analysis of LD and ACC from a commercial field, where managed levels of disease were low, and where no aggregation of both variables was detected. It was further concluded that a reliable monitoring of airborne inoculum for management of Botrytis leaf blight is achievable in managed fields using few spore samplers per field

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

Epidemiology and population dynamics: Modelisation, monitoring and management

Understanding how populations of microbial pathogens and arthropod pests develop over time is critical for timely and effective intervention to control disease epidemics and pest infestations in agricultural production systems. Various elements including the pathogen or pest, host plant, natural enemies or competitors, environment, and human activity interact in complex ways, and some of these elements can be factored into mathematical models for pest population increase and disease progress. Greenhouse production affords a level of control over climate and growth environment, as well as the opportunity to release biological control agents, and thus the potential to influence pathogen and arthropod pest populations and their development to a much greater degree than in field production. To this end, thresholds for intervention must be derived based on the relationship between losses and yields weighed against the cost of intervention. In the context of integrated pest management, monitoring of pathogen and pest populations, as well as of the environment and the development of resistance to chemical pesticides such as fungicides and insecticides, is necessary to estimate the risk to the crop posed by these diseases and pests and to select the optimal method for their control