Research Highlights

Research Highlights
Development of Predictive Models for Management of Sclerotinia sclerotiorum

by Mehdi Kabbage and Damon Smith, soybean plant pathologists, University of Wisconsin-Madison

Sclerotinia stem rot (SSR, also called white mold) can substantially reduce soybean yield in the north-central region of the U.S. and Canada, especially when management practices and weather favor high yield potential.

The disease cycle begins when the fungal pathogen, Sclerotinia sclerotiorum, forms mushroom-like structures (apothecia) in early summer within the top two inches of soil. Spores are released from the apothecia and infect senescing soybean flowers. This process is very sensitive to weather conditions — which makes the incidence and severity of SSR quite variable from year to year.

Soybean flowering, germination of apothecia, and conducive weather conditions must occur simultaneously for SSR to occur. Due to this complex array of factors, and the difficulty for farmers to assess these factors during the season, fungicide applications are often ineffective due to improper application timing. Fungicide applications might also be unnecessary if the required environmental factors do not converge for disease to develop.

With checkoff funding provided by the Wisconsin Soybean Marketing Board, the Michigan Soybean Promotion Council, the United Soybean Board, and the North Central Soybean Research Program, a group of soybean plant pathologists in Wisconsin, Michigan, and Iowa developed a predictive model to assess the risk of SSR development in soybeans. The goal is to help farmers decrease unnecessary fungicide use in low-risk environments and to optimize the timing of fungicide application in high-risk environments.

We examined 9 site-years of apothecia observations in soybean fields with a history of Sclerotinia stem rot to determine which agronomic and weather variables best explain the development of apothecia and subsequent disease.

In nonirrigated soybean, the presence of apothecia was best explained by daily maximum air temperature and wind speed. In irrigated soybean fields, apothecial presence was best explained by row width, daily maximum air temperature, and daily maximum relative humidity. These variables are likely representative of increased moisture in the canopy microclimate which is favorable for apothecial development.

We used newer statistical methods to develop a model that predicts the probability of apothecial presence in soybean fields during the R1-R3 flowering period, using site-specific 30-day moving averages of gridded, remotely-accessible weather data. Other variables such as crop development stage and canopy closure are integrated into the model.

The performance of the model has been evaluated in irrigated and non-irrigated research and commercial soybean fields by intensive monitoring for the presence of apothecia and subsequent disease development. In the 2016 and 2017 growing seasons, we found that the model predicted the presence of apothecia with 80% accuracy during the soybean flowering period.

Prediction examples and spray recommendations (R1-R3) from validation tests are shown in the following two figures. The results indicate that the prediction model can both reduce unnecessary fungicide input and improve application timing during the flowering period.

Figure 1: At this location no sprays would be recommended between the R1-R3 flowering period. Low levels of apothecia (blue lines) were mostly observed after the R3 growth stage and very low disease was observed.
Figure 2. At this location, one spray would be recommended at the R3 stage. Apothecia were mostly present during flowering and disease was observed. This year, the model predicted that a spray at R3 would provide better protection than a standard application at R1.

For non-irrigated or irrigated soybean with 15-inch row spacing, the model uses a 35% risk-threshold. This means that when flowers are present on soybeans (potentially R1-R4), fungicide applications should be applied according to an action threshold of 35-40% for effective SSR management.

A smart-phone application is currently being developed that utilizes this model for farmers’ use.

Published: Feb 26, 2018

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