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Related Experiment Video

Updated: Jul 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Stochastic and deterministic models for agricultural production networks.

P Bai¹, H T Banks, S Dediu

¹Department of Statistics, University of North Carolina, Chapel Hill, NC, USA. pbai@email.unc.edu

Mathematical Biosciences and Engineering : MBE

|July 31, 2007

Summary

This study introduces a new model to assess agricultural network disruptions. It simulates disease impacts, aiding in better risk management and production planning.

Related Experiment Videos

Last Updated: Jul 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Agricultural economics
Systems biology
Mathematical modeling

Background:

Agricultural production networks face unpredictable disturbances.
Modeling these impacts is crucial for supply chain stability.
Existing models may not fully capture stochastic elements.

Purpose of the Study:

To develop a novel modeling approach for agricultural production networks.
To analyze the effects of various disturbances, including diseases.
To provide tools for enhanced risk assessment and management.

Main Methods:

Development of a stochastic model for network dynamics.
Creation of an approximate deterministic model for average-case analysis.
Implementation of simulations and sensitivity analyses.
Modeling disease introduction and its network-wide effects.

Main Results:

The stochastic and deterministic models effectively represent network behavior.
Sensitivity analyses identify key factors influencing network resilience.
Simulations demonstrate the propagation and impact of diseases.
The approach provides quantitative insights into disturbance effects.

Conclusions:

The presented modeling framework offers a robust method for analyzing agricultural network vulnerabilities.
This approach can inform strategies to mitigate the impact of disturbances and diseases.
Further research can refine the models for specific agricultural systems.