Skip navigation

Optimal threshold density in a stochastic resource management model with pulse intervention

Optimal threshold density in a stochastic resource management model with pulse intervention

Tan, Yi, Ning, Lijuan, Tang, Sanyi and Cheke, Robert A. ORCID: 0000-0002-7437-1934 (2019) Optimal threshold density in a stochastic resource management model with pulse intervention. Natural Resource Modeling:e12220. ISSN 0890-8575 (Print), 1939-7445 (Online) (In Press) (doi:https://doi.org/10.1111/nrm.12220)

Full text not available from this repository. (Request a copy)

Abstract

Human activities and agricultural practices are having huge impacts on the development of fishery and land resources through different ways. To model such systems that involve harvesting, an impulsive model of natural resources with a stochastic noise perturbation element is formulated to study the relationship between (a) the maximal expectation of biomass after harvesting or fishing events and (b) the minimal expectation of pest biomass and the number of times pesticide is applied. Using a detailed analytical treatment, time estimation, and numerical demonstrations, we establish that the proposed mechanism is capable of maximizing fish populations at the end of a fishing season and minimizing pest numbers after a crop harvesting season once the intensity of the noise is relatively small. Investigations of the effects of different parameters reveal that theoretical predictions from the new stochastic model accord with those from the deterministic case.

Item Type: Article
Uncontrolled Keywords: fishing time, maximal biomass expectation, optimal threshold density, pulse perturbation, stochastic logistic equation
Subjects: Q Science > QA Mathematics
S Agriculture > SH Aquaculture. Fisheries. Angling
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 26 Jun 2019 12:30
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24370

Actions (login required)

View Item View Item