Life tables and a physiologically based model application to Corcyra cephalonica (Stainton) populations

Rossini, L. ORCID: https://orcid.org/0000-0003-2558-7111, Speranza, S., Severini, M., Locatelli, D. P. and Limonta, L. (2021) Life tables and a physiologically based model application to Corcyra cephalonica (Stainton) populations. Journal of Stored Products Research, 91. 101781. ISSN 0022-474X doi: 10.1016/j.jspr.2021.101781

Abstract/Summary

The rice moth Corcyra cephalonica is a harmful insect pest for grains stocked in storage systems. Its infestations represent a serious concern among producers, because of the non-marketability of the products affected by it and subsequent economic losses. New technologies are improving the supply chain of the stored grains, in line with the integrated pest management framework. However, a prompt control action also requires an in-depth knowledge of insect pests’ biology and their response to environmental parameters. If this information is available, it can also be translated into mathematical language. The modelling of insect pest populations is increasing in utility, particularly if the models are included in decision support systems. The aim of this work concerns two aspects of a model application and validation. Since physiologically based models require information about interactions between species and environment, C. cephalonica individuals were reared at different constant temperatures: 18, 21, 24,26, 28, 30,34 and 36 °C. This first part provided the life tables, whose data were used to estimate the parameters of the Logan, Briére, and Sharpe and De Michele development rate functions. The second part of the work concerns the application of a physiologically based model described by a first order partial differential equation. The validation of the simulations was conducted with a semi-field experimentation with three repetitions. Results showed that the life tables are well represented by the Sharpe and De Michele development rate function, and that the physiologically based model proposed is reliable in representing field populations.