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Background  Although integrated pest management (IPM) is essential for conservation agriculture, this method can be inadequate for severely infected fields. The ability to predict the potential occurrence of severe infestation of soil-borne disease would enable farmers to adopt suitable methods for high-risk areas, such as soil disinfestation, and apply other options for lower risk areas. Recently, researchers have used species distribution modeling (SDM) to predict the occurrence of target plant and animal species based on various environmental variables. In this study, we applied this technique to predict and map the occurrence probability of a soil-borne disease, Verticillium wilt, using cabbage as a case study.    Methods  A disease survey assessing the distribution of Verticillium wilt in cabbage fields in Tsumagoi village (central Honshu, Japan) was conducted two or three times annually from 1997 to 2013. Road density, elevation and topographic wetness index (TWI) were selected as explanatory variables for disease occurrence potential. A model of occurrence probability of Verticillium wilt was constructed using the MaxEnt software for SDM analysis. As the disease survey was mainly conducted in an agricultural area, the area was weighted as “Bias Grid” and area except for the agricultural area was set as background.    Results  Grids with disease occurrence showed a high degree of coincidence with those with a high probability occurrence. The highest contribution to the prediction of disease occurrence was the variable  road density  at 97.1%, followed by  TWI  at 2.3%, and  elevation  at 0.5%. The highest permutation importance was  road density  at 93.0%, followed by  TWI  at 7.0%, while the variable  elevation  at 0.0%. This method of predicting disease probability occurrence can help with disease monitoring in areas with high probability occurrence and inform farmers about the selection of control measures.

Predicting disease occurrence of cabbage Verticillium wilt in monoculture using species distribution modeling