| Texto completo | |
| Autor(es): |
Fernandes, Marcia Helena Machado da Rocha
;
Tedeschi, Luis Orlindo
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF ANIMAL SCIENCE; v. 103, p. 16-pg., 2025-01-01. |
| Resumo | |
Integrating modeling innovations and satellite remote sensing technology offers a transformative approach to sustainable grazing cattle management. Mathematical models, which translate real-life situations into mathematical formulations, are becoming critical components in livestock production, especially for describing patterns and predicting behaviors. Mathematical models are categorized by their purpose and methodology and include descriptive, prescriptive, static, dynamic, deterministic, and stochastic types. Grazing lands, covering 24.6% of the world's land area, provide essential ecosystem services such as soil stability, nutrient cycling, and climate regulation. Sustainable management of these lands is necessary to optimize grazing performance and prevent degradation. Given its ability to rapidly scan vast expanses, satellite remote sensing has become indispensable for monitoring grassland conditions over large areas, surpassing traditional field methods in coverage and efficiency. Modeling approaches using satellite imagery include parametric and nonparametric artificial intelligence-based regression and physically based models. Parametric models, such as those based on vegetation indices, offer simplicity but may struggle with high vegetation cover and soil background interference. Nonparametric models, including machine learning algorithms like random forest and support vector regression, provide flexibility and improved accuracy in estimating forage mass and nutritional attributes. Physically based models, like canopy radiation transfer models, integrate satellite data to simulate vegetation dynamics. Practical applications of satellite-based vegetation data support real-time, continuous grazing management by adjusting stocking rates and predicting average daily gain. Studies demonstrate that integrating satellite data with field observations and mechanistic models can optimize forage use, improve livestock productivity, and enhance the sustainability of grazing systems. This comprehensive review highlights the pivotal role of satellite remote sensing in revolutionizing grazing cattle management, providing a detailed exploration of the technologies and models that drive sustainable practices in this field. Through continuous advancements, satellite-based approaches promise to enhance precision livestock farming further, contributing to ecological and economic sustainability. Integrating modeling innovations and satellite remote sensing technology offers a transformative approach to sustainable grazing cattle management. This comprehensive review highlights the pivotal role of satellite remote sensing in revolutionizing grazing cattle management, providing a detailed exploration of the technologies and models that drive sustainable practices in this field. Advancements in satellite technology and mathematical modeling are revolutionizing how we manage grazing cattle and their environments. Grazing lands, which cover a significant portion of the Earth's surface, are essential for maintaining soil health, cycling nutrients, and regulating the climate. To manage these lands sustainably, it is crucial to monitor them regularly. By using satellites to capture detailed images of grazing lands, we can now monitor vast areas efficiently and accurately. These satellite images, combined with advanced mathematical models, help predict and analyze the health and productivity of grazing lands. For instance, models can estimate the amount and quality of forage available for cattle, allowing farmers to adjust the number of animals grazing on the land to prevent overuse and degradation. Additionally, these tools can help predict how changes in management practices or environmental conditions will affect grazing lands in the future. This innovative approach enhances the productivity and sustainability of grazing systems and supports farmers in making informed decisions that benefit their livestock and the environment. Through ongoing technological advancements, satellite data promises to improve precision in livestock farming further. (AU) | |
| Processo FAPESP: | 20/14367-7 - Sistema de suporte à decisão para pecuária de precisão: predição de massa de forragem e atributos nutricionais usando algoritmos de aprendizagem de máquina |
| Beneficiário: | Márcia Helena Machado da Rocha Fernandes |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |