ASAS-NANP symposium: mathematical modeling in animal nutrition: application of modeling innovations to support satellite remote sensing for sustainable grazing cattle management
Abstract
Abstract 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…
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Evidence Hub reads this as a review, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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