Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Application of moment-based methods for parameter inference to nonlinear stochastic population growth model


B. Mottet

Semester Thesis, HS14 (10378)

Aphids are a notorious agricultural pest of many vegetable and field crops. Among them, the importance of cotton aphid is well documented (Leclant and Deguine, 1994) and its significant economic impact has been reported worldwide (e.g. Rummel et al., 1995; Xia et al., 1999; Sharma, 2001). Consequently accurate modeling of cotton aphid population growth is of economic importance. One of the first attempt was the nonlinear deterministic model proposed by Prajneshu (1998) which incorporated population dynamic mechanisms that apply to aphids and that was successfully fitted to aphid population data in Indian mustard crop. Eversince several approaches have been investigated, such as dispersion models (Celini and Vaillant, 2004) and computer simulation models (Giarola et al., 2006). Matis et al. (2005) extended Prajneshu’s model to account for stochastic variability, whose effects are of particular importance when population sizes are small (Zheng and Ross, 1991). Such models do not only allow to predict the aphid abundance but also to gain a better understanding of the underlying dynamics. In our case study for example the combined effects of irrigation water and nitrogen fertility on the cotton aphid growth are of specific interest since these are the two primary factors affecting cotton production in Texas. Designing successful treatments programs requires deep understanding of multiple factors affecting the cotton ecosystem.

Supervisors: Francesca Parise, Jakob Rüess, John Lygeros


Type of Publication:

(13)Semester/Bachelor Thesis

F. Parise

File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5110
Permanent link