Managing Foliar Blights on Carrot Using Copper, Azoxystrobin, and Chlorothalonil Applied According to TOM-CAST

Evaluating the Applicability of the TOMCAST Model for the Control of Potato Early Blight in China

To explore the applicability of different fungicide application schemes to control potato early blight (mainly caused by Alternaria solani) in China, field trials were conducted from 2020 to 2022, combining different fungicides with the tomato forecaster (TOMCAST) model and using weather variables to adjust the minimum temperature of TOMCAST to 7 °C. To effectively manage potato early blight, the TOMCAST model combines relative humidity (>88%) and air temperature to calculate daily severity values (DSVs). The application of fungicides (fungicide schedule) is as follows: untreated; two standard treatments, Amimiaoshou SC and Xishi SC, applied at the first appearance of disease symptoms; and two different TOMCAST treatments, in which fungicides are applied when the physiological days add up to 300 days and DSVs add up to 15. This study quantifies the intensity of early blight as the area under the disease progression curve and the final disease severity. Additionally, a progress curve for early blight is constructed to compare the development of early blight in different years and treatments. The TOMCAST-15 model reduces the number of fungicide applications in addition to significantly suppressing the development of early blight. Furthermore, fungicide application significantly increases the dry matter and starch contents of potatoes, and TOMCAST-15 × Amimiaoshou SC has similar enhancement effects on dry matter, protein, reducing sugar, and starch contents compared with Amomiaohou SC and Xishi SC. As a result, TOMCAST × Amimiaoshou SC may be an effective alternative to the standard treatment and have good applicability in China.

Evaluation of the TOM-CAST Forecasting Model in Asparagus for Management of Stemphylium Leaf Spot in Ontario, Canada

In the last 5 years, asparagus acreage in Canada has increased by over 25%. Stemphylium leaf spot, caused by Stemphylium vesicarium, has emerged as the predominant foliar pathogen of asparagus. Typically, contact fungicides are applied every 14 days; however, regardless of the number of applications, growers are not achieving adequate control of the disease. The TOM-CAST forecasting model is used widely in Michigan asparagus fields, but it has never been assessed for suitability in Ontario or in the popular cultivar, Guelph Millennium. Six field trials were conducted in 2012 and 2013 to evaluate the TOM-CAST forecasting model in two asparagus cultivars. The fungicides chlorothalonil or azoxystrobin/difenoconazole were applied according to the forecasting model or on a 14-day interval. The effectiveness of the forecasting model differed between sites and cultivars. Even though TOM-CAST is used in all cultivars in Michigan, TOM-CAST was not effective on Guelph Millennium. In the cultivar Jersey Giant, however, TOM-CAST with a 20 disease severity value spray interval improved control of Stemphylium leaf spot without increasing the number of sprays, compared with a 14-day treatment. The results in Guelph Millennium differed between sites. At one site, TOM-CAST maintained similar levels of Stemphylium leaf spot, but increased the number of applications, compared with a 14-day application interval. Of more concern, none of the fungicide treatments differed greatly from the untreated control at the other site. Our results show that forecasting models need to be validated locally in asparagus cultivars relevant to production today.

Some consequences of using the Horsfall-Barratt scale for hypothesis testing

Comparing treatment effects by hypothesis testing is a common practice in plant pathology. Nearest percent estimates (NPEs) of disease severity were compared with Horsfall-Barratt (H-B) scale data to explore whether there was an effect of assessment method on hypothesis testing. A simulation model based on field-collected data using leaves with disease severity of 0 to 60% was used; the relationship between NPEs and actual severity was linear, a hyperbolic function described the relationship between the standard deviation of the rater mean NPE and actual disease, and a lognormal distribution was assumed to describe the frequency of NPEs of specific actual disease severities by raters. Results of the simulation showed standard deviations of mean NPEs were consistently similar to the original rater standard deviation from the field-collected data; however, the standard deviations of the H-B scale data deviated from that of the original rater standard deviation, particularly at 20 to 50% severity, over which H-B scale grade intervals are widest; thus, it is over this range that differences in hypothesis testing are most likely to occur. To explore this, two normally distributed, hypothetical severity populations were compared using a t test with NPEs and H-B midpoint data. NPE data had a higher probability to reject the null hypothesis (H0) when H0 was false but greater sample size increased the probability to reject H0 for both methods, with the H-B scale data requiring up to a 50% greater sample size to attain the same probability to reject the H0 as NPEs when H0 was false. The increase in sample size resolves the increased sample variance caused by inaccurate individual estimates due to H-B scale midpoint scaling. As expected, various population characteristics influenced the probability to reject H0, including the difference between the two severity distribution means, their variability, and the ability of the raters. Inaccurate raters showed a similar probability to reject H0 when H0 was false using either assessment method but average and accurate raters had a greater probability to reject H0 when H0 was false using NPEs compared with H-B scale data. Accurate raters had, on average, better resolving power for estimating disease compared with that offered by the H-B scale and, therefore, the resulting sample variability was more representative of the population when sample size was limiting. Thus, there are various circumstances under which H-B scale data has a greater risk of failing to reject H0 when H0 is false (a type II error) compared with NPEs.