Development of a Predictive Model to Assess the Risk of Sclerotinia Stem Rot in Soybeans.
by Mehdi Kabbage and Damon Smith, Soybean Plant Pathologists, University of Wisconsin
Apothecia forming on the soil surface
Sclerotinia stem rot (SSR, also called white mold) can substantially reduce soybean yield in the North Central region 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) within the top two inches of soil profile, generally when the canopy closes. 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 SRR quite variable from year to year.
Soybean flowering, apothecia formation, 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 support 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 are developing a predictive model to assess the risk of SSR development in soybeans. The goal is to help farmers decrease unnecessary fungicide input in low-risk environments and to optimize the timing of application of fungicides in high-risk environments.
Sclerotinia forecasting models have been developed for other crop systems — including peanut, carrot, lettuce, dry bean, and canola. We know from these models and previous research that temperature and moisture, over a period of 30-50 days, influence the development of apothecia and that the distribution of SSR is highly correlated with the presence of apothecia.
We used newer statistical methods to develop a model that predicts the probability of apothecial presence in soybean fields during the flowering period, based on 30-day moving averages of gridded, remotely-accessible weather variables and nine site-years of apothecia observations in soybean fields with a history of SSR. The weather data we used are publicly available, so that the model can be widely accessible to farmers, and functional in virtually any growing location.
In preliminary validation tests in Wisconsin, Michigan, Iowa, and Nebraska the model predicted the presence of apothecia with 82-91% accuracy during the soybean flowering period. Furthermore, model predictions explained end-of-season disease observations in all fields and would have resulted in both the reduction of fungicide applications in low-risk areas and improved timing of critical applications in high-risk fields.
In Wisconsin, the model successfully predicted the risk of apothecia presence during susceptible soybean growth stages in 14 of 19 scouted fields, and the predictions were verified by the resulting presence or absence of disease.
In the 2017 season, the model will be available to soybean researchers only, for field-level validation. We will continue multi-state testing and validations of the
model in irrigated and non-irrigated soybean fields. The validations
will be used to determine action thresholds for a variety of scenarios,
with consideration of common fungicide programs and regional cultivar
In 2018, we plan to release the model to the public. We will integrate the model into a smartphone-based risk assessment tool which will also incorporate soybean flowering and canopy closure parameters to assist farmers in making informed integrated management decisions for sclerotinia stem rot.
For the current recommendations on scouting and management of sclerotinia stem rot, please read the latest checkoff-funded publications:White MoldScouting for White Mold