Title:	Estimating forage mass in Brazilian pasture-based livestock production systems through satellite and climate data integration.
Authors:	SILVA, G. B. S. da NOGUEIRA, S. F. ADAMI, M. SANO, E. E. NUÑEZ, D. C. SANTOS, P. M. PEZZOPANE, J. R. M. GREGO, C. R. TEIXEIRA, A. H. de C. SKAKUN, S.
Affiliation:	GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPMA; SANDRA FURLAN NOGUEIRA, CNPMA; MARCOS ADAMI, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; EDSON EYJI SANO, CPAC; DANIEL COAGUILA NUÑEZ, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA GOIANO; PATRICIA MENEZES SANTOS, CPPSE; JOSE RICARDO MACEDO PEZZOPANE, CPPSE; CELIA REGINA GREGO, CNPTIA; ANTÔNIO HERIBERTO DE CASTRO TEIXEIRA, UNIVERSIDADE FEDERAL DE SERGIPE; SERGII SKAKUN, UNIVERSITY OF MARYLAND.
Date Issued:	2025
Citation:	Computers and Electronics in Agriculture, v. 237, part A, 110496, 2025.
Description:	Abstract: Grasslands are vital for global food security, making reliable monitoring of forage mass (FM) essential for sustainable pasture management. The availability and quality of FM are key factors in determining the profitability of pasture-based farms. This study presents a replicable methodology for estimating FM using multi-sensor satellite data and an agrometeorological modeling framework. Conducted at the Brazilian Agricultural Research Corporation Southeast Livestock Center (Embrapa Pecuária Sudeste) in São Carlos, Brazil, the research integrates NASA’s Harmonized Landsat and Sentinel-2 (HLS) imagery with climate data processed through the Simple Algorithm for Evapotranspiration Retrieving (SAFER) and Monteith’s Light Use Efficiency (LUE) models. The SAFER model explained over 67 % of FM variability in three pasture-based livestock systems. A key factor in achieving accurate FM estimates was the differentiation between field green matter (GM) and total dry matter, as GM represents the most nutritious and consumable forage component. The model performed best in extensive systems, where minimal management intervention resulted in stable forage conditions. In integrated crop-livestock systems, the accuracy remained high, though fertilization and crop residue decomposition influenced FM estimates. In intensive systems, model performance was slightly lower due to higher management variability. This study contributes to the development of automated, scalable FM assessment methods, enabling systematic pasture monitoring and data-driven grazing management. The SAFER model allowed simultaneous processing of satellite imagery and climate data, increasing the accuracy of FM estimations. Future research should explore the use of higher-resolution imagery (e.g., CBERS-4A, PlanetScope) to better capture within-field variability and consider increasing the frequency of field sampling frequency (from 32 days to 15 or even 7 days) to further improve FM estimation accuracy, particularly in intensive systems.
Thesagro:	Forragem Pecuária Sistema de Produção Integração Satélite
NAL Thesaurus:	Pastures Agriculture Environmental sustainability Food security
Keywords:	Digital agriculture Agricultura digital
ISSN:	0168-1699
DOI:	https://doi.org/10.1016/j.compag.2025.110496
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPMA)