Autor(es)

Fernandes, Marcia Helena Machado da Rocha ; Fernandes Jr, Jalme de Souza ; Adams, Jordan Melissa ; Lee, Mingyung ; Reis, Ricardo Andrade ; Tedeschi, Luis Orlindo

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 14, n. 1, p. 14-pg., 2024-04-15.

Resumo

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management. (AU)

• Processo FAPESP: 15/16631-5 - Estratégias de manejo para redução de impactos ambientais em sistemas de produção de bovinos de corte
• Beneficiário: Ricardo Andrade Reis
• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Temático
• Processo FAPESP: 17/18750-7 - Impacto da intensificação sustentável da produção de bovinos de corte em áreas tropicais na mitigação das mudanças climáticas
• Beneficiário: Márcia Helena Machado da Rocha Fernandes
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• 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