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An Environmental Model Predicting Bacterial Ring Rot Symptom Expression

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Abstract

Bacterial ring rot (BRR) is a regulated disease of potato caused by the gram-positive bacterium Clavibacter michiganensis subsp. sepedonicus. Many countries have seed certification programs that involve pre- and post-harvest inspections for disease symptoms supplemented by laboratory immunological and nucleic acid diagnostics. Several studies have shown that environmental factors and other parameters may affect the severity of BRR symptoms. Collection and analysis of field data from 154 cultivated potato genotypes over 15 years indicates that moisture, temperature, and cultivar are major factors influencing BRR symptom expression. Sensitivity analysis showed that late season temperatures were more important than mid-season moisture. Contrary to expectations, cultivar susceptibility was of less importance in our models than weather parameters in determining BRR symptoms. A neural network model was successfully deployed that predicts severity of BRR symptom expression based on late season temperature, precipitation, and cultivar susceptibility.

Resumen

La pudrición anular bacteriana (BRR) es una enfermedad regulada de la papa causada por la bacteria gram positiva Clavibacter michiganensis subsp sepedonicus. Muchos países tienen programas de certificación de semillas que involucran inspecciones pre y post-cosecha para síntomas de la enfermedad, suplementados con diagnósticos inmunológicos y de ácidos nucleicos de laboratorio. Varios estudios han demostrado que los factores ambientales y otros parámetros pudieran afectar la severidad de los síntomas de BRR. La colección y análisis de datos de campo de 154 genotipos de papa cultivada durante 15 años indica que la humedad, temperatura y la variedad son los factores principales influenciando la expresión de síntomas de BRR. Análisis de sensibilidad mostraron que las temperaturas al final del ciclo fueron más importantes que la humedad a la mitad del ciclo. Contrario a las expectativas, la susceptibilidad varietal era de menor importancia en nuestros modelos que los parámetros meteorológicos en la determinación de los síntomas de BRR. Se instaló con éxito un modelo de red neural que predice la severidad de la expresión de los síntomas con base en la temperatura al final del ciclo, precipitación y susceptibilidad varietal.

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Acknowledgements

We thank Dr. D. Lambert, F. Kulcsar, T. Hulstein, and B. Lee for assistance. This project was funded in part by the Potato Growers of Alberta, Saskatchewan Agriculture Development Fund, and the Federal Matching Investment Initiative 53536. LRC contribution 387–1025.

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Authors and Affiliations

  1. Agriculture and Agri-Food Canada, Lethbridge Research Centre, Lethbridge, Alberta, T1J 4B1, Canada

    Bernard D. Hill, Melanie Kalischuk & Lawrence M. Kawchuk

  2. Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5A8, Canada

    Doug R. Waterer

  3. Agriculture and Agri-Food Canada, Potato Research Centre, Fredericton, New Brunswick, E3B 4Z7, Canada

    Benoit Bizimungu

  4. Alberta Agriculture and Rural Development, Crop Diversification Centre South, Brooks, Alberta, T1R 1E6, Canada

    Ron Howard

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  1. Bernard D. Hill
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  2. Melanie Kalischuk
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  4. Benoit Bizimungu
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  5. Ron Howard
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  6. Lawrence M. Kawchuk
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Correspondence to Lawrence M. Kawchuk.

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Hill, B.D., Kalischuk, M., Waterer, D.R. et al. An Environmental Model Predicting Bacterial Ring Rot Symptom Expression. Am. J. Pot Res 88, 294–301 (2011). https://doi.org/10.1007/s12230-011-9193-4

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  • DOI: https://doi.org/10.1007/s12230-011-9193-4

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