Background: The effectiveness of combining resistant tomato with BioAct WG (Purpureocillium lilacinum strain 251, Pl251) against Meloidogyne incognita was assessed in a tomato-cucumber rotation in a greenhouse over 2 years. Additionally, the enzymatic activity of the fungus, the percentage of fungal egg and juvenile parasitism, cardinal temperatures and the effect of water potential on mycelial growth and the soil receptivity to Pl251 were determined in vitro.
Results: Plant resistance was the only factor that suppressed nematode and crop yield losses. Percentage of egg parasitism in plots treated with BioAct WG was less than 2.6%. However, under in vitro conditions, Pl251 showed protease, lipase and chitinase activities and parasitised 94.5% of eggs, but no juveniles. Cardinal temperatures were 14.2, 24-26 and 35.4 °C. The maximum Pl251 mycelial growth was at -0.25 MPa and 25 °C. Soil temperatures and water potential in the greenhouse were in the range of the fungus. However, soil receptivity was lower in greenhouse soil, irrespective of sterilisation, than in sterilised sand.
Conclusion: Plant resistance was the only factor able to suppress nematode densities, disease severity and yield losses, and to protect the following cucumber crop. Environmental factors involved in soil receptivity could have negatively affected fungus effectiveness. © 2016 Society of Chemical Industry.
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http://dx.doi.org/10.1002/ps.4357 | DOI Listing |
New Phytol
December 2024
State Key Laboratory for Crop Stress Resistance and High-Efficiency Production/Shaanxi Key Laboratory of Apple, College of Horticulture, Northwest A&F University, Yangling, 712100, China.
The clustered distribution of genes involved in metabolic pathways within the plant genome has garnered significant attention from researchers. By comparing and analyzing changes in the flanking regions of metabolic genes across a diverse array of species, we can enhance our understanding of the formation and distribution of biosynthetic gene clusters (BGCs). In this study, we have designed a workflow that uncovers and assesses conserved positional relationships between genes in various species by using synteny neighborhood networks (SNN).
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College of Life Science and Technology, Harbin Normal University, Harbin, China.
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