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Using Conditional Probability and a Nonlinear Kriging Technique to Predict Potato Early Die Caused by Verticllium Dahliae

  • Luke SteereEmail author
  • Noah Rosenzweig
  • William Kirk
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 582)

Abstract

Verticillium dahliae is a plant pathogenic fungus that can be devastating to commercial potato production. Potato growers in the state of Michigan have experienced yield declines and decreased marketability as a direct result of the persistence of V. dahliae in soil. A team of researchers at Michigan State University conducted a soil evaluation using geostatistics and geographic information systems (GIS). The use of a nonlinear Kriging method allowed the team to predict where infection may occur. Nonlinear Kriging is a useful tool for creating conditional probability maps based on a threshold, which can be built into the equation. Verticillium dahliae has an inoculum threshold needed to cause infection in a potato plant. Using this threshold, maps can be created based on a probability of any point in space being greater than the threshold. The methods used in this paper show how geostatistics can be a valuable tool for commercial growers.

Keywords

Geostatistics Indicator Kriging Potato early die Soilborne disease 

Notes

Acknowledgements

This research was supported by funding provided by the Michigan Potato Industry Commission through a USDA NIFA Specialty Crop Block Grant Program (Grant #791N1300). Additional funding and resources were provided by the Michigan Potato Industry Commission and the Michigan State University Project GREEEN (Generating Research and Extension to Meet Economic and Environmental Needs). The authors wish to thank Rob Schafer and the potato growers of Michigan.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Michigan State UniversityEast LansingUSA

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