PLoS ONE (Jan 2021)

Die-off of plant pathogenic bacteria in tile drainage and anoxic water from a managed aquifer recharge site.

  • Carina Eisfeld,
  • Jan M van der Wolf,
  • Boris M van Breukelen,
  • Gertjan Medema,
  • Jouke Velstra,
  • Jack F Schijven

DOI
https://doi.org/10.1371/journal.pone.0250338
Journal volume & issue
Vol. 16, no. 5
p. e0250338

Abstract

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Managed aquifer recharge (MAR) can provide irrigation water and overcome water scarcity in agriculture. Removal of potentially present plant pathogens during MAR is essential to prevent crop diseases. We studied the die-off of three plant pathogenic bacteria in water microcosms with natural or filtered tile drainage water (TDW) at 10 and 25°C and with natural anoxic aquifer water (AW) at 10°C from a MAR site. These bacteria were: Ralstonia solanacearum (bacterial wilt), and the soft rot Pectobacteriaceae (SRP) Dickeya solani and Pectobacterium carotovorum sp. carotovorum (soft rot, blackleg). They are present in surface waters and cause destructive crop diseases worldwide which have been linked to contaminated irrigation water. Nevertheless, little is known about the survival of the SRP in aqueous environments and no study has investigated the persistence of R. solanacearum under natural anoxic conditions. We found that all bacteria were undetectable in 0.1 mL samples within 19 days under oxic conditions in natural TDW at 10°C, using viable cell counting, corresponding to 3-log10 reduction by die-off. The SRP were no longer detected within 6 days at 25°C, whereas R. solanacearum was detectable for 25 days. Whereas in anoxic natural aquifer water at 10°C, the bacterial concentrations declined slower and the detection limit was reached within 56 days. Finally, we modelled the inactivation curves with a modified Weibull model that can simulate different curve shapes such as shoulder phenomena in the beginning and long tails reflecting persistent bacterial populations. The non-linear model was shown to be a reliable tool to predict the die-off of the analysed plant pathogenic bacteria, suggesting its further application to other pathogenic microorganisms in the context of microbial risk assessment.