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<p>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.</p>
</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.

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

Huazhong University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A hybrid physics-data-driven optimization model for grassland grazing management"}

This paper presents a hybrid physics-data-driven optimization model for grassland grazing management. It comprehensively assesses essential factors in the...

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

Online veröffentlicht: 10. März 2024

Seitenbereich: 3215 - 3228

Eingereicht: 28. Juli 2023

Akzeptiert: 23. Aug. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01125

Schlüsselwörter
Grassland grazing management, nonlinear mathematical models, genetic algorithm, grazing pattern, grazing intensity

© 2023 Bo Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 10. März 2024

Seitenbereich: 3215 - 3228

Eingereicht: 28. Juli 2023

Akzeptiert: 23. Aug. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01125

SchlüsselwörterGrassland grazing management, nonlinear mathematical models, genetic algorithm, grazing pattern, grazing intensity

© 2023 Bo Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Grassland grazing management, nonlinear mathematical models, genetic algorithm, grazing pattern, grazing intensity