Searcharxiv⌕ Search

arXiv · 2609.39809

A Stage-Structured Deterministic Model of Fall Armyworm Infestation on Maize Farming

Abstract

Fall Armyworm (FAW) poses a serious threat to maize production in many regions due to its aggressive feeding habits and rapid development cycle. In this study, we developed and analyzed a stage-structured mathematical model to evaluate the impact of different FAW larval instars on maize dynamics during the vegetative and reproductive stages. Analytical results indicate that the two models have unique and positively bounded solutions for all time $t\geq 0$ and admit four equilibrium points: the trivial, non-trivial, maize extinction and coexistence equilibria. The behavior of the model was studied using stability analysis to find conditions under which FAW dies out or continues to spread. Furthermore, sensitivity analysis and numerical simulations were conducted to examine how key parameters affect FAW population dynamics and maize. Numerical simulations of the model in both stages indicate that there is high destruction of maize plants in both vegetative and reproductive stages of maize production due to increased egg production and larval population density. The extensive damage caused by large populations of eggs, larvae and adult moths motivated an extension of the two models to include intervention measures such as traditional methods like handpicking and chemical pesticides. Numerical results indicate that these control strategies significantly suppressed the FAW population with a resultant increase in the maize plant population towards its maximum capacity during the vegetative and reproductive stages, respectively.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Donald Okoth Ojwang, Mamadou Pathe Ly, Shaibu Osman. 2026-09-30. A Stage-Structured Deterministic Model of Fall Armyworm Infestation on Maize Farming. https://arxiv.org/abs/2609.39809

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks

There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition. We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled ``help''/``harm''), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals regime changes in both connectivity and resilience. Our findings extend classical random threshold and Erdős-Rényi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil.

q-bio.PE↗

Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes

How search strategies evolve in finite, depletable landscapes remains a question in foraging theory. We study this problem with an evolutionary simulation in which agents forage on a two-dimensional toroidal lattice containing non-renewable resources distributed uniformly or as Lévy dust. Each agent carries a heritable genome encoding step lengths, velocities, and turning angles, and selection acts on a fitness function combining energetic gain, movement cost, and coverage efficiency. By allowing movement traits to evolve without imposing a prescribed power-law step-length distribution, we test whether evolved trajectories are better described by intermittent-search or Lévy-walk dynamics. Our results indicate that evolved search is more consistent with intermittent dynamics than with strict scale-free Lévy motion in the finite depletion-driven landscapes considered here. We characterize the dynamics by fitting second- and fourth-order displacement moments to intermittent-search and Lévy-walk models. While a Lévy-like random walk fits the evolutionary trajectories well (mean adjusted $R^2$ > 0.9 in most tested conditions), intermittent search achieves a closer fit (mean adjusted $R^2$ > 0.99) for all tested resource distributions. This preference holds across the tested grid sizes and resource densities. Five independent evolutionary runs per environment on a 503 x 503 grid at nominal resource density $ρ$ = 0.15 reproduce this preference for the uniform environment and five Lévy-dust environments. Evolution rapidly reshapes the movement genome toward short displacements while retaining a sparse tail of longer relocations, consistent with local exploitation punctuated by occasional transfer. The framework provides a controlled setting for studying how search rules emerge under resource limitation and may inform resource-constrained exploration in autonomous systems.

q-bio.PE↗

Fluctuating growth rate and spatial diffusion shape plankton diversity

Planktonic communities exhibit ubiquitous population distributions and patchy spatial structures, yet the fundamental mechanisms driving them remain debated. Here, we derive these regularities from a minimalistic theoretical description that incorporates stochastic fluctuations in growth rates and effective ocean dispersal. We combine global metabarcoding, microscopy, and high-resolution chlorophyll datasets and show that the decay of spatial correlations, the crossover regimes of Taylor's law, the patterns of local species diversity and biomass distributions agree with common underlying dynamics. These results suggest that the intertwined effect of diffusivity and fluctuating growth rate, captured by an emergent correlation length, shapes plankton spatial heterogeneity from local to long-range scales, reconciling local variability with macroecological patterns.

q-bio.PE↗