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.

中文翻译：

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利用物种分布模型预测单作甘蓝黄萎病病害发生

背景 虽然综合虫害管理 (IPM) 对于保护性农业至关重要，但这种方法对于严重感染的田地可能是不够的。预测可能发生的严重土传疾病侵扰的能力将使农民能够在高风险地区采用合适的方法，例如土壤除害，并在低风险地区采用其他选择。最近，研究人员利用物种分布模型（SDM）根据各种环境变量预测目标动植物物种的发生。在这项研究中，我们以卷心菜为例，应用这种技术来预测和绘制土传病害黄萎病的发生概率。方法 对嬬恋村（本州中部、日本）从 1997 年到 2013 年每年进行两到三次。选择道路密度、海拔和地形湿度指数（TWI）作为疾病发生潜力的解释变量。使用MaxEnt软件构建黄萎病发生概率模型进行SDM分析。由于疾病调查主要在农业区进行，该区域被加权为“偏差网格”，除农业区外的区域被设置为背景。结果 疾病发生的网格与发生概率高的网格具有高度的一致性。对疾病发生预测的最大贡献是可变道路密度为 97.1%，其次是 TWI，为 2.3%，海拔为 0.5%。排列重要性最高的是道路密度，为 93.0%，其次是 TWI，为 7.0%，而可变高程为 0.0%。这种预测疾病发生概率的方法可以帮助在高概率发生地区进行疾病监测，并告知农民控制措施的选择。