Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

March 10, 2024


Pages


DOI

Article

A hybrid physics-data-driven optimization model for grassland grazing management

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Authors

Bo Yu Affiliation:
Anhui Vocational College of Defense Technology, Lu’an 237011, Anhui Province, China
and Yulong Li Affiliation:
Huazhong University of Science and Technology, Wuhan 430074, Hubei Province, China


Abstract

This paper presents a hybrid physics-data-driven optimization model for grassland grazing management. It comprehensively assesses essential factors in the Abaga Banner grassland ecosystem, including soil moisture, vegetation biomass, desertification degree index, and soil compaction. Through a thorough analysis, the impacts of grazing patterns and intensity on the grassland’s physical characteristics and biomass are studied. Employing genetic algorithms, an optimal grazing model is formulated to minimize soil desertification throughout the year. The paper aims to contribute to grassland ecology restoration and ensure sustainable livelihoods for local herdsmen, offering a scientific foundation for promoting the sustainable growth of grassland husbandry.


Keywords

Grassland grazing management, nonlinear mathematical models, genetic algorithm, grazing pattern, grazing intensity, 91B76


Citation

Yu, B. & Li, Y. (2023). A hybrid physics-data-driven optimization model for grassland grazing management. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.01125

Published by: Engineering Journals

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