Skip to main content
SpringerLink
Log in

Risk factors for bacterial spot on peach in Okayama Prefecture, Japan

  • Bacterial and Phytoplasma Diseases
  • Published:
Journal of General Plant Pathology Aims and scope Submit manuscript

Abstract

Bacterial spot, caused by Xanthomonas arboricola pv. pruni, is the most important disease that affects peach production in Okayama Prefecture, Japan. Currently, this disease is managed mainly with copper compounds applied at two stages, before flowering and after harvesting, or with antibiotics applied in May and June. Here we identified the disease risk factors that affect peach at harvest and developed a disease-forecasting model to help growers decide when to apply bactericides. The model was based on parameters for weather data collected for September and October of 2001 through 2012 and for April, May, and June of 2002 through 2013, combined with data on bacterial leaf spot incidence obtained from 28 to 30 fields per year in August from 2001 to 2012 and in May to July from 2002 to 2013. The model, developed using a logistic regression analysis, included the percentage of fields with a bacterial spot incidence (BSI) ≥1 % in mid-August of the previous season and the number of rainy days (≥5 mm/day) during the current June as predictors, and explained 75.0 % of the variability. These results suggest that the previous season’s BSI and weather variables in the present season can be used to predict the risk of bacterial spot.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Caski F (eds) Proceedings of the 2nd international symposium on information theory. Akadimiai Kiado, Budapest, pp 267–281

    Google Scholar 

  • De Wolf ED, Madden LV, Lipps PE (2003) Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data. Phytopathology 93:428–435

    Article  PubMed  Google Scholar 

  • Ernster VL (1994) Nested case–control studies. Prev Med 23:587–590

    Article  CAS  PubMed  Google Scholar 

  • Esker PD, Harri J, Dixon PM, Nutter FW (2006) Comparison of models for forecasting of Stewart’s disease of corn in Iowa. Plant Dis 90:1353–1357

    Article  Google Scholar 

  • Harikrishnan R, del Río LE (2008) A logistic regression model for predicting risk of white mold incidence on dry bean in North Dakota. Plant Dis 92:42–46

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (1989) Applied logistic regression. Wiley, New York, pp 1–392

    Google Scholar 

  • Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 48:452–458

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kawaguchi A, Inoue K, Inoue Y (2014) Biological control of bacterial spot on peach by nonpathogenic Xanthomonas campestris strains AZ98101 and AZ98106. J Gen Plant Pathol 80:158–163

  • Mila AL, Carriquiry AL, Yang XB (2004) Logistic regression modeling of prevalence of soybean Sclerotinia stem rot in the north-central region of the United States. Phytopathology 94:102–110

    Article  CAS  PubMed  Google Scholar 

  • Morimoto R (2011) Occurence and control of bacterial shot hole on peach (in Japanese). Plant Prot 65:210–214

    Google Scholar 

  • Nekoduka S, Hada H, Iwadate Y, Ishiguro K (2009) A nested case–control study on disease severity of Alternaria blotch of apple at early and late phases of epidemics (in Japanese). Jpn J Phytpathol 75:314–322

    Article  Google Scholar 

  • Paul PA, Munkvold GP (2004) A model-based approach to preplanting risk assessment for gray leaf spot of maize. Phytopathology 94:1350–1357

    Article  CAS  PubMed  Google Scholar 

  • Ritchie DF (1995) Bacterial spot. In: Ogawa JM, Zehr EI, Bird GW, Ritchie DF, Uriu K, Uyemoto JK (eds) Compendium of stone fruit diseases. APS Press, St. Paul, pp 50–52

    Google Scholar 

  • Sistrom CL, Garvan CW (2004) Proportions, odds, and risk. Radiology 230:12–19

    Article  PubMed  Google Scholar 

  • Stefani E (2010) Economic significance and control of bacterial spot/canker of stone fruits caused by Xanthomonas arboricola pv. pruni. J Plant Pathol 92(Suppl 1):99–103

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Science and Technology Promotion Program for Agriculture, Forestry, Fisheries and Food Industry from the Ministry of Agriculture, Forestry and Fisheries, Japan (23037). I am grateful for the comments provided by the journal’s anonymous reviewers.

Author information

Authors and Affiliations

  1. Research Institute for Agriculture, Okayama Prefectural Technology Center for Agriculture, Forestry and Fisheries, 1174-1 Koudaoki, Akaiwa City, 709-0801, Japan

    Akira Kawaguchi

Authors
  1. Akira Kawaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akira Kawaguchi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawaguchi, A. Risk factors for bacterial spot on peach in Okayama Prefecture, Japan. J Gen Plant Pathol 80, 435–442 (2014). https://doi.org/10.1007/s10327-014-0532-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10327-014-0532-4

Keywords

Search

Navigation