Livestock intensification and environmental sustainability: An analysis based on pasture management scenarios in the brazilian savanna.

Brazil's major beef production occurs in the Cerrado, predominantly as extensive pastures that covers ∼50 Mha of the biome, of which approximately 2/3 show signs of degradation. Pasture recovery is now a key environmental policy, as it improves land use efficiency and soil carbon sequestration. However, as intensification leads to higher cattle stocking rates and external inputs (fertilizers, liming, etc.), the impact of improved pastures on greenhouse gas mitigation is still debatable. This study focused on the Cerrado biome and aimed to (i) quantify soil carbon stock changes under different scenarios of management and recovery of degraded pastures. In addition, (ii) the potential for capturing carbon in the soil to mitigate emissions of greenhouse gases (GHG) by the intensification of pastures was evaluated. Soil C stock changes (0-20 cm) were assessed using the Century model version 4.5, which had been previously validated for the region. Model parameters were adjusted for three pastures classes (degraded, intermediately degraded, and not degraded) within a time-series (1985-2020) of land use maps for the Cerrado, serving as baselines. Scenarios of pasture intensification were modeled against these baselines, and an analysis was carried out on the estimated changes in soil C stock and greenhouse gas balance. Before the intensification scenario (year 2020), the total carbon stock was estimated to be ∼1830 Mt for the whole pasture area, whose spatial distribution corresponded to edaphoclimatic contrasts and pasture conditions. The highest soil organic carbon stocks were observed in the non-degraded pastures. With the increase in carrying capacity, beef production is estimated to potentially increase by 1/3 due to the recovery of degraded areas through intensive management. This increase would be sufficient to meet the projected ∼12% increase in Brazil's livestock production by the end of the decade if all pasture areas are restored, which is much possibly an unrealistic scenario as not all degraded areas are suitable for crops or can successfully improve pasture yield. In addition, the increase in soil C stocks was only sufficient to compensate for 27% and 42% of the GHG emissions resulting from intensification in areas with intermediate and severe degradation, respectively. Therefore, to strike a balance between economic considerations and environmental impact, additional strategies are needed to reduce GHG emissions and/or enhance C sinks, such as increasing tree density on farms. From this perspective, implementing livestock intensification at the landscape scale can promote C stocks and the diversity of ecosystem services, opening the possibility of ecosystem restoration.

Discriminação de fitofisionomias de floresta de várzea a partir do algoritmo Iterated Conditional Modes aplicado aos dados SAR/R99 (QUAD-POL/Banda L) / Discrimination among flooded forest phytophysiognomies from Iterated Conditional Modes algorithm applied to SAR Data R99 (QUAD POL/L-Band)

Utilizando-se dados do sensor aerotransportado SAR R99, adquiridos na banda L (1,28 GHz) em amplitude e com quatro polarizações (HH, VV, HV e VH), avaliou-se a distinção de fitofisionomias de floresta de várzea existentes nas Reservas de Desenvolvimento Sustentável Amanã e Mamirauá e áreas adjacentes, com a aplicação do algoritmo Iterated Conditional Modes (ICM) de classificação polarimétrica pontual/contextual. Os resultados mostraram que o uso das distribuições multivariadas em amplitude, conjuntamente com uma banda de textura, produziu classificações de qualidade superior àquelas obtidas com dados polarimétricos uni/bivariados. Esta abordagem permitiu a obtenção de um índice Kappa de 0,8963, discriminando as três classes vegetacionais de interesse, comprovando assim o potencial dos dados do SAR R99 e do algoritmo ICM no mapeamento de florestas de várzea da Amazônia.

This study seeks to evaluate the capability of data generated by the synthetic aperture radar SAR R99 sensor to map phytophysiognomies found in the Amanã and Mamirauá Sustainable Development Reserves (RDSA and RDSM). By means of L-band (1.28 GHz), full polarimetric (HH, VV, VH, HV), amplitude data acquired with the SAR R99 sensor, distinctions among flooded forest phytophysiognomies in the RDSA and RDSM and around were achieved. The Iterated Conditional Modes (ICM) algorithm was employed to perform the local/contextual polarimetric classification of the data. Results showed that the use of multivariate distributions in amplitude with a band of texture produced classifications of superior quality in relation to those obtained with the uni/bivariate polarimetric data. This approach allowed to obtain a Kappa index of 0,8963 and the distinction of three vegetation classes of interest, demonstrating the potential of SAR R99 and the ICM algorithm to map flooded vegetation of the Amazon.

Técnicas avançadas de sensoriamento remoto aplicadas ao estudo de mudanças climáticas e ao funcionamento dos ecossistemas amazônicos / Advanced remote sensing techniques for global changes and Amazon ecosystem functioning studies

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.