Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Ikeda K

PROVIDER: S-EPMC7678443 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

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

Ikeda Kentaro K Osawa Takeshi T

PeerJ 20201117

<h4>Background</h4>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 targ ...[more]

PMID: 33240630

Similar Datasets

Project description:BackgroundVerticillium wilt of olive (VWO), caused by Verticillium dahliae Kleb, is one of the most threatening diseases affecting olive cultivation. An integrated disease management strategy is recommended for the effective control of VWO. Within this framework, the use of biological control agents (BCAs) is a sustainable and environmentally friendly approach. No studies are available on the impact that the introduction of BCAs has on the resident microbiota of olive roots. Pseudomonas simiae PICF7 and Paenibacillus polymyxa PIC73 are two BCAs effective against VWO. We examined the effects of the introduction of these BCAs on the structure, composition and co-occurrence networks of the olive (cv. Picual) root-associated microbial communities. The consequences of the subsequent inoculation with V. dahliae on BCA-treated plants were also assessed.ResultsInoculation with any of the BCAs did not produce significant changes in the structure or the taxonomic composition of the 'Picual' root-associated microbiota. However, significant and distinctive alterations were observed in the topologies of the co-occurrence networks. The introduction of PIC73 provoked a diminution of positive interactions within the 'Picual' microbial community; instead, PICF7 inoculation increased the microbiota's compartmentalization. Upon pathogen inoculation, the network of PIC73-treated plants decreased the number of interactions and showed a switch of keystone species, including taxa belonging to minor abundant phyla (Chloroflexi and Planctomycetes). Conversely, the inoculation of V. dahliae in PICF7-treated plants significantly increased the complexity of the network and the number of links among their modules, suggestive of a more stable network. No changes in their keystone taxa were detected.ConclusionThe absence of significant modifications on the structure and composition of the 'Picual' belowground microbiota due to the introduction of the tested BCAs underlines the low/null environmental impact of these rhizobacteria. These findings may have important practical consequences regarding future field applications of these BCAs. Furthermore, each BCA altered the interactions among the components of the olive belowground microbiota in idiosyncratic ways (i.e. PIC73 strongly modified the number of positive relations in the 'Picual' microbiota whereas PICF7 mostly affected the network stability). These modifications may provide clues on the biocontrol strategies used by these BCAs.

Dataset Information

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

Background

Methods

Results

Publications

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

Similar Datasets

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