Xu, Xiang-Ming and Salama, N. and Jeffries, Peter and Jeger, Michael J. (2010) Numerical studies of biocontrol efficacies of foliar plant pathogens in relation to the characteristics of a biocontrol agent. Phytopathology, 100 (8). pp. 814-821. ISSN 0031-949X. (doi:https://doi.org/10.1094/PHYTO-100-8-0814) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
|The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)|
A previously published generic mathematic model has been used in a numerical study to understand the dynamics of foliar pathogens in relation to mechanisms, and timing and coverage of biocontrol agent (BCA) applications. With the model parameter values used, it was demonstrated that a BCA possessing either competition or induced resistance as the main mechanism of biological control was more effective in reducing disease development than a BCA with either mycoparasitism or antibiosis as its mechanism. Application coverage, ranging from 50 to 90%, had little effect on biocontrol efficacy, particularly for a BCA with competition and induced resistance as the main mechanism of biocontrol. Conversely, delayed application of BCA had more profound effects on biocontrol efficacy for those with competition or induced resistance as their main mechanism than those with mycoparasitism and antibiosis. Biocontrol efficacy was greatest for a single BCA combining competition with mycoparasitism or antibiosis. The efficacy for a single BCA combining induced resistance with competition critically depended on application time; the efficacy was greatly reduced for delayed applications. The present study suggests that development of an effective strategy for BCA application is critically dependent upon our quantitative understanding of several key biocontrol processes and their interactions. Without reliable quantitative estimation of these processes, it is impossible to make quantitative predictions about biological control and hence to optimize BCA application strategies.
|Divisions:||Faculties > Sciences > School of Biosciences|
|Depositing User:||Sue Davies|
|Date Deposited:||23 Nov 2011 11:49 UTC|
|Last Modified:||01 May 2014 15:53 UTC|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/28479 (The current URI for this page, for reference purposes)|