Prognostication algorithms and predictability ranges of mass reproduction of harmful insects according to the method of nonliner dynamics
Abstract
S. V. Stankevych*, Ye. M. Biletskyj, I. V. Zabrodina, M. D. Yevtushenko, H. V. Baidyk, I. P. Lezhenina, M. O. Filatov, L. Ya. Sirous, D. D. Yushchuk, V. O. Melenti, O. A. Molchanova, L. V. Zhukova, I. V. Nepran, O. V. Romanov, T. A. Romanova and O. M. Bragin
The authors have analysed the theoretical possibilities of prognostication the dynamics in the number and mass reproduction of some species of harmful insects. A theoretical synthesis of the information on the regularities of the population dynamics of the most common insect pests of agricultural plants from the point of view of the methodology of nonlinear dynamics and synergetics has been done. Based on the past and present an analysis of the many-year dynamics in the number of the insect populations has been carried out and an attempt to develop the algorithms for prognostication the seasonal and annual changes in the number of the insects has been made. To do this the authors recommend a scenario-based method of prognostication and making decisions in plant protection. Using the phytosanitary monitoring they determine the beginning of the regular mass reproduction (the appearance of an aggravated rate) and then, based on the phytosanitary prognosis, an aggravation of the situation that has developed or is being developing on the farm, in the district or in the region is made; after that on the base of the short-term prognosis (signaling) it is recommended to make the optimal decision to protect a particular crop taking into account the economic threshold of harmfulness. According to the authors this approach, based on the methodology of nonlinear dynamics (synergetic paradigm), makes it possible to determine in advance the breeding grounds of the aggravated rates and make the optimal decisions in plant protection. The predicted scenario will not be a prognostication of the future, but the elements of an evolutionary process inherent in nature.