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Funded Project

Funding Program: Enhancement Grants - Special Projects

Project Title: Testing and deployment of a web-based yield loss prediction tool for risk management of Soybean Rust

Project Directors (PDs): Joe A Omielan [1] Donald E. Hershman [2] Saratha Kumudini-Vandoren [3]

Lead State: KY Lead Organization: University of Kentucky

Undesignated Funding: $25,000

Start Date: Mar-01-2008 End Date: Feb-28-2009

Pests Involved: Soybean Rust

Site/Commodity: Soybean

Summary: Soybean rust (SBR) is a serious disease of soybean that has the potential to reduce crop yields by as much as 80%. In the fall of 2004, the first case of SBR in the continental United States was confirmed. In the fall of 2005, our group began an international, multi-disciplinary study to develop a yield loss prediction tool for SBR. The utility of such a model is that potential yield losses may be weighed against cost of pesticide control in order to make more informed management decisions on the farm. However, the model must be validated using independent data sets from SBR-infected plots and the utility of the tool must be demonstrated for growers to adopt its use in IPM decisions. The tool must also be accessible and easy to use. The SBR Sentinel network in North America has proved to be invaluable in monitoring the survival and spread of SBR. Information on the occurrence and severity of SBR has facilitated the formulation of SBR risk assessments and recommendations communicated to soybean growers. As a direct result of SBR surveillance and educational activities, the majority of soybean producers in the U.S. have yet to apply a fungicide for SBR control. For example, it is estimated that only 1.7% of the 63.3 million soybean acres produced in the U.S. during 2007 were sprayed for soybean rust. The 1.34 million acres that were sprayed were all in the southern region where SBR intensity was greatest. It is highly probable that a portion of these 1.34 million acres were inappropriately sprayed, but we currently lack the ability to make more precise fungicide use recommendations to growers for specific settings. Users of IPM tools need to have confidence in the accuracy, reliability, and utility of the predicted outcomes and recommendations. If they are confident then they are more likely to implement the recommendations and promote its use to others. We need to do more extensive beta testing before going mainstream with the web-based yield loss prediction tool. Adjustments may be necessary. The yield loss depends on when the disease epidemic begins, the rate of disease progress over time, and the disease severity at the end of the season. The tool has to take these into account in order to make credible predictions. A soybean rust risk index would be an essential input to the model. The objectives are as follows: 1. Test the output of our yield loss prediction tool against actual data from trials where yield was limited by soybean rust. 2. Develop and test an interactive website for the yield loss prediction tool. For the first objective, we will obtain data from previous SBR fungicide trials from different states in the region. We will also collect all the background information, such as variety, seeding date, row spacing, disease progress during the season, and final yield. We will also make arrangements for obtaining data from the 2008 trials. Parameters to be input by the user in the tool include maturity group, row spacing and growth stage of the crop, expected yield (based on 5 year average), expected sale price of the crop and cost of the fungicide application. The key disease parameter input will be a measure of soybean rust risk (high, medium, or low). The outputs from the tool (predicted yield loss and economics of spraying) will be compared with the actual trial data. For replicated trials the output will be compared to the mean and whether it falls within the confidence limits of the trial. The frequency of correct spraying decisions will also be compared. If necessary the model generating the tools output will be adjusted. We will solicit input from likely users of the tool about the utility of the predictions, based on our tests. Further adjustments or more regional predictions may be required. For the second objective we will collect input on whats needed for the website. The focus will be on the yield loss prediction tool and will include the evaluation information. The design team and other potential users will be asked to evaluate how well the website functions. The testing will include different browsers, as well as ensuring it is useable by people on a dialup connection. Groups will be asked to do usability testing on the site. For this we would set out tasks to complete. How long it takes and the frustration involved will be some of the data collected. These results will be used to improve the site or confirm that it is good enough. The beta testing of the yield loss prediction tool and development of an interactive website will increase the utilization and effectiveness of this component of soybean IPM in the southern U.S. This will improve the profitability of the crop and reduce the environmental impact of unnecessary fungicide applications. Objectives: The objectives are as follows: 1. Test the output of our yield loss prediction tool against actual data from trials where yield was limited by soybean rust. 2. Develop and test an interactive website for the yield loss prediction tool.

Final Report:

Impacts The impact of the yield loss prediction tool on soybean rust (SBR) management decisions has yet to be determined. SBR in the 2010 growing season has spread more slowly and less extensively than in previous years plus the tool website has not been widely publicized yet. It is currently linked on the following websites: - United Soybean Board (www.unitedsoybean.org) under Production Resources; it will be in the Tools section on the refreshed USB site (due to launch in Feb. 2011). They are also planning to include it as a sponsored link on the Plant Management Network (www.plantmanagementnetwork.org). - Kentucky Soybean Board (www.kysoy.org) - Southern Soybean Research Program (kysoy.org/ssrp/) Don Hershman will be reporting on the new tool at the Regional Soybean Rust meeting (NCERA-208) in early December and will suggest linking to it from the soybean rust public website and individual state websites. Cláudia Godoy has requested a version of the tool for Brazilian growers using units (yield and cost) they are familiar with. The tool allows producers to see what the yield impact (and economic ramifications) will be under three SBR progress scenarios. Of course, applications targeting SBR must be made BEFORE anyone knows how the epidemic will play out. So, the options are given in order to provide the producer with at least some information on possible outcome depending on how things play out. But ultimately the grower will have to make a best guess as to which epidemic type is most likely to play out based on the disease situation at a given time, recommendations being made by the state specialist, and past experience with SBR (grower and/or consultant). In most cases, the worst case scenario will NOT be an accurate reflection of disease progress, but it could be. The tool should become a part of the SBR management toolkit in future growing seasons.

Outcomes For the first objective, independent studies were conducted to validate the accuracy of model predictions over a range of environments, cultivar maturity groups, and row widths in the U.S. Trials were planted in Quincy, FL in 2007, Tifton, GA in 2008 and southern Brazil in 2006 and 2009. The Brazil trials had severe SBR epidemics and large reductions in yield. However, we have learned over the past five years that lesser SBR epidemics are much more common in the U.S. Consequently, reduced rates of disease progress (and associated yield loss damage) were estimated and incorporated into the model. The expanded model assumes that severe, moderate, and light SBR epidemics have rapid, moderate, and slow disease progress, respectively. The estimated yield losses were calculated for SBR starting at different growth stages and for different epidemic scenarios. These estimates were compared with actual research data from 47 U.S. and 39 Brazil fungicide trials. The yields from the unsprayed control plots were expressed as a percentage of the yields from the best fungicide treatment. In general, the yield estimates were within the range of yield outcomes from the fungicide trials (see attached extension fact sheet for more details). Significant variability in how estimated yield loss compared to actual yield achieved was anticipated due to the presence of many confounding factors in the tests. For the second objective, estimated yield losses were combined with economic calculations on an interactive website to assist growers making fungicide spray decisions (http://dept.ca.uky.edu/sbrtool/). Our design team expanded the range of economic calculations with the assistance of Kevin Dhuyvetter from Kansas State University. Its important that users are aware of the limitations of the model and the predicted yield losses when making management decisions. Therefore, a message box with this information appears when the tool calculation page is first accessed. The user has to click through it before any information can be entered and predictions calculated. Information is also provided on the assumptions used for the yield loss predictions and the calculations used for the economic estimates. One of the assumptions is that the best fungicides are about 90% effective for SBR control. Therefore, the yield with fungicide application is reduced by 10% of the yield reduction if no control is applied. The functions and output of the website have been tested and refined by the design team and a select group of extension plant pathologists. The website was announced to the larger community working with soybean rust in August, 2010.

Leveraged Funds This grant helped fund the project which was largely funded by USDA-RMA. We also received funds from the Southern Soybean Research Project and the Kentucky Soybean Board.

Outputs This project developed an interactive website (http://dept.ca.uky.edu/sbrtool/) for soybean growers and an extension fact sheet, "Soybean Yield Loss Prediction Tool for Managing Soybean Rust", describing the development and use of the tool.

Potential Impacts The use of the yield loss prediction tool by growers as a fungicide application decision aide should lead to fewer uneconomical (unnecessary) applications against soybean rust.

Report Appendices Soybean Yield Loss Prediction Tool for Managing Soybean Rust [PDF]

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