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

Modeling the dynamic changes in Plasmopara viticola sporangia concentration based on LSTM and understanding the impact of relative factor variability

Reliable disease management can guarantee healthy plant production and relies on the knowledge of pathogen prevalence. Modeling the dynamic changes in spore concentration is available for realizing this purpose. We present a novel model based on a time-series modeling machine learning method, i.e., a long short-term memory (LSTM) network, to analyze oomycete Plasmopara viticola sporangia concentration dynamics using data from a 4-year field experiment trial in North China. Principal component analysis (PCA)-based high-quality input screening and simulation result calibration were performed to ensure model performance, obtaining a high determination coefficient (0.99), a low root mean square error (0.87), and a low mean bias error (0.55), high sensitivity (91.5%), and high specificity (96.5%). The impact of the variability of relative factors on daily P. viticola sporangia concentrations was analyzed, confirming that a low daily mean air temperature restricts pathogen development even during a long period of high humidity in the field.

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants

Fungi in the genus Colletotrichum cause serious pre- and post-harvest losses to several agricultural crops worldwide. Through a systematic literature review, we retrieved the published information on Colletotrichum anthracnose diseases on different host plants and developed a mechanistic model incorporating the main stages of the pathogen's life cycle and the effect of weather. The model predicts anthracnose progress during the growing season on the aerial organs of different crops, and was parameterized for seven Colletotrichum clades (acutatum, dematium, destructivum, gloeosporioides, graminicola, and orbiculare) and the singleton species, C. coccodes. The model was evaluated for the anthracnose diseases caused by fungi belonging to five clades on six hosts by using data from 17 epidemics that occurred in Italy, the USA, Canada, and Japan. A comparison of observed versus predicted data showed a concordance correlation coefficient of 0.928 and an average distance between real data and the fitted line of 0.044. After further validation, the model could be used to support decision-making for crop protection.

A Mechanistic Model Accounting for the Effect of Soil Moisture, Weather, and Host Growth Stage on the Development of Sclerotinia sclerotiorum

The fungus Sclerotinia sclerotiorum causes serious losses to several agricultural crops worldwide. By using systems analysis, we retrieved the available knowledge concerning S. sclerotiorum from the literature and then analyzed and synthesized the data to develop a mechanistic, dynamic, weather-driven model for the prediction of epidemics on different crops. The model accounts for i) the production and survival of apothecia; ii) the production, dispersal, and survival of ascospores; iii) infection by ascospores; and iv) lesion onset. The ability of the model to predict the occurrence of apothecia was evaluated for epidemics observed with different climates, soil types, and host crops (soybean, white bean, and carrot) using independent data obtained from trials conducted in Ontario (Canada) in 1981, 1982, and from 1999 to 2002; in Michigan (U.S.A.) in 2015 and 2016; and in Wisconsin (U.S.A.) in 2016. The model showed 0.82 accuracy and 0.73 specificity in predicting the presence of apothecia, with a posterior probability of correctly predicting apothecia to be present or absent of 0.804 and 0.876, respectively. The model was also validated for its ability to predict disease progress on soybean and sunflower in Ontario in 1981 and 1982, in Manitoba (Canada) in 2001 and 2002, and in Michigan in 2015 and 2016. Comparison of model output with observations showed a concordance correlation coefficient of 0.948, and a root mean square error of 0.122. The model represents an improvement of previous S. sclerotiorum models and could be useful for making decisions on disease control.

Measurements of Aerial Spore Load by qPCR Facilitates Lettuce Downy Mildew Risk Advisement

The lettuce downy mildew pathogen, Bremia lactucae, is an obligate oomycete that causes extensive produce losses. Initial chlorotic symptoms that severely reduce the market value of the produce are followed by the appearance of white, downy sporulation on the abaxial side of the leaves. These spores become airborne and disseminate the pathogen. Controlling lettuce downy mildew has relied on repeated fungicide applications to prevent outbreaks. However, in addition to direct economic costs, heterogeneity and rapid adaptation of this pathogen to repeatedly applied fungicides has led to the development of fungicide-insensitivity in the pathogen. We deployed a quantitative PCR assay-based detection method using a species-specific DNA target for B. lactucae coupled with a spore trap system to measure airborne B. lactucae spore loads within three commercial fields that each contained experimental plots, designated EXP1 to EXP3. Based upon these measurements, when the spore load in the air reached a critical level (8.548 sporangia per m3 air), we advised whether or not to apply fungicides on a weekly basis within EXP1 to EXP3. This approach saved three sprays in EXP1, and one spray each in EXP2 and EXP3 without a significant increase in disease incidence. The reduction in fungicide applications to manage downy mildew can decrease lettuce production costs while slowing the development of fungicide resistance in B. lactucae by eliminating unnecessary fungicide applications.

UV-B Induced Flavonoids Contribute to Reduced Biotrophic Disease Susceptibility in Lettuce Seedlings

Biotrophic disease is one of the largest causes of decreased yield in agriculture. While exposure to ultraviolet B (UV-B) light (280-320 nm) has been previously observed to reduce plant susceptibility to disease, there is still a paucity of information regarding underlying biological mechanisms. In addition, recent advances in UV-LED technology raise the prospect of UV light treatments in agriculture which are practical and efficient. Here, we characterized the capability of UV-B LED pre-treatments to reduce susceptibility of a range of lettuce (Lactuca sativa) cultivars to downy mildew disease caused by the obligate biotroph Bremia lactucae. Innate cultivar susceptibility level did not seem to influence the benefit of a UV-B induced disease reduction with similar reductions as a percentage of the control observed (54-62% decrease in conidia count) across all susceptible cultivars. UV-B-induced reductions to conidia counts were sufficient to significantly reduce the infectivity of the diseased plant. Secondary infections caused by UV-B pre-treated plants exhibited yet further (67%) reduced disease severity. UV-B-induced flavonoids may in part mediate this reduced disease severity phenotype, as B. lactucae conidia counts of lettuce plants negatively correlated with flavonoid levels in a UV-B-dependent manner (r = -0.81). Liquid chromatography-mass spectrometry (LC-MS) was used to identify metabolic features which contribute to this correlation and, of these, quercetin 3-O-(6"-O-malonyl)-b-D-glucoside had the strongest negative correlation with B. lactucae conidia count (r = -0.68). When quercetin 3-O-(6"-O-malonyl)-b-D-glucoside was directly infiltrated into lettuce leaves, with those leaves subsequently infected, the B. lactucae conidia count was reduced (25-39%) in two susceptible lettuce cultivars. We conclude that UV-B induced phenolics, in particular quercetin flavonoids, may act as phytoanticipins to limit the establishment of biotrophic pathogens thus delaying or reducing their sporulation as measured by conidia count. These findings highlight the opportunity for UV-B morphogenesis to be exploited through the application of UV-LED technology, as part of the development of next-generation, sustainable disease control tools.