Development and implementation of the BlightPro decision support system for potato and tomato late blight management

A web-based DSS for potato and tomato late blight management has been developed.Disease dynamics are predicted based on weather, crop, and management information.The web-tool enables well-informed decisions regarding fungicide use.The BlightPro DSS can help users improve the efficiency of their disease management. A web-based decision support system (DSS) for potato and tomato late blight management has been developed which links several models into a system that enables prediction of disease dynamics based on weather conditions, crop information, and management tactics. Growers identify the location of their production unit of interest (latitude and longitude of field) and the system automatically obtains observed weather data from the nearest available weather station, and location-specific forecast weather data from the National Weather Service - National Digital Forecast Database. The DSS uses these weather data along with crop and management information to drive disease forecasting systems and a validated mechanistic model of the disease to generate location-specific management recommendations for fungicide application. An integrated alert system allows users to receive notification of upcoming critical thresholds via e-mail or text message. This system provides producers, consultants, researchers, and educators with a tool to obtain management recommendations, evaluate disease management scenarios, explore comparative epidemiology, or function as a teaching aid. In field and computer simulation experiments, DSS-guided schedules were influenced by prevailing weather and host resistance and resulted in schedules that improved the efficiency of fungicide use and also reduced variance in disease suppression when compared to a weekly spray schedule. In situations with unfavorable weather, the DSS recommended fewer fungicide applications with no loss of disease suppression. In situations of very favorable weather, the DSS recommended more fungicide applications but with improved disease suppression. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

[1]  William E. Frye,et al.  Late Blight of Potatoes and Tomatoes , 1998 .

[2]  P. Hamm,et al.  Cost of Fungicides Used to Manage Potato Late Blight in the Columbia Basin: 1996 to 1998. , 2000, Plant disease.

[3]  R. Hijmans,et al.  Simulation of Potato Late Blight in the Andes. I: Modification and Parameterization of the LATEBLIGHT Model. , 2005, Phytopathology.

[4]  R E Thornton,et al.  Fungicide Spray Programs for Defender, A New Potato Cultivar with Resistance to Late Blight and Early Blight. , 2007, Plant disease.

[5]  D. Shtienberg.  Will decision-support systems be widely used for the management of plant diseases? , 2013, Annual review of phytopathology.

[6]  William E. Fry,et al.  Stability of resistance to Phytophthora infestans in potato: an international evaluation , 2005 .

[7]  C. Smart,et al.  The 2009 Late Blight Pandemic in the Eastern United States - Causes and Results. , 2013, Plant disease.

[8]  Clyde W. Fraisse,et al.  Original paper: Development of a web-based disease forecasting system for strawberries , 2011 .

[9]  W. Fry,et al.  Disease management implications of age-related changes in susceptibility of potato foliage toPhytophthora infestans , 2008, American Potato Journal.

[10]  D. Shtienberg,et al.  MODELLING: THE BASIS FOR RATIONAL DISEASE MANAGEMENT , 2000 .

[11]  S. B. Goodwin,et al.  Re-emergence of Potato and Tomato Late Blight in the United States. , 1997, Plant disease.

[12]  Marjorie McGuirk,et al.  Toward Regional Climate Services:The Role of NOAA’s Regional Climate Centers , 2010 .

[13]  William E. Fry.  Principles of Plant Disease Management , 1982 .

[14]  Megan Kennelly,et al.  Evaluation of fungicides for control of dollar spot of fairway height creeping bentgrass in Kansas, 2012 , 2013 .

[15]  William E. Fry,et al.  Evaluation of potato late blight forecasts modified to incorporate host resistance and fungicide weathering , 1983 .

[16]  Arthur T. DeGaetano,et al.  Efficient Access to Climate Products using ACIS Web Services , 2015 .

[17]  A. J. Haverkort,et al.  Societal Costs of Late Blight in Potato and Prospects of Durable Resistance Through Cisgenic Modification , 2008, Potato Research.

[18]  L. Madden,et al.  FAST, a Forecast System for Alternaria solani on Tomato , 1978 .

[19]  J. A. Bruhn,et al.  A Statistical Model of Fungicide Deposition on Potato Foliage , 1982 .

[20]  Alan L. Jones,et al.  A microcomputer-based instrument to predict primary apple scab infection periods. , 1980 .

[21]  B. J. Nielsen,et al.  Epidemiology and Integrated Control of Potato Late Blight in Europe , 2011, Potato Research.

[22]  J. A. Bruhn,et al.  A Mathematical Model of the Spatial and Temporal Dynamics of Chlorothalonil Residues on Potato Foliage , 1982 .

[23]  W. Fry,et al.  Stability of disease expression in the potato late blight pathosystem: A preliminary field study , 1992, American Potato Journal.

[24]  R. A. Krause,et al.  Blitecast: a computerized forecast of potato late blight [Phytophthora infestans, fungus diseases]. , 1975 .

[25]  William E. Fry,et al.  Relative resistances of potato clones in response to new and old populations of Phytophthora infestans , 1996 .

[26]  C. Smart,et al.  Differential Susceptibility of 39 Tomato Varieties to Phytophthora infestans Clonal Lineage US-23. , 2014, Plant disease.

[27]  J. C. Jenkins,et al.  Classifying the Relative Host Reaction in Potato Cultivars and Breeding Lines to the US-8 Strain of Phytophthora infestans in Minnesota. , 2003, Plant disease.