Over the past few decades, a significant amount of agricultural land has been lost due to soil degradation/desertification. In addition, the increasing frequency of extreme events, such as intense droughts and forest fires, has negatively impacted various ecosystem services. Two of the main Brazilian biomes—the Cerrado and the Caatinga—have been affected by increased rainfall variability, leading to desertification, increased fire frequency, and, consequently, rising concerns regarding the water and food security of the local population. In this study, we develop a methodology to assess these impacts using a Socio-Environmental Vulnerability Index (SEVI) that combines physical, environmental, and socio-economic indicators related to exposure, sensitivity, and adaptation, as well as including socio-environmental feedback. The developed SEVI is then applied to the São Francisco and Parnaíba river basins. The proposed index is based on the MEDALUS methodology and is adapted to include multiple biological, physical, and socio-economic indicators, allowing for the discrimination of areas characterized by different levels of vulnerability. We also analyze the effectiveness of governmental policies, such as the creation of conservation areas and the rural registration of properties, in reducing vulnerability. The SEVI analysis highlights that adaptive capacity is the main constraint for reducing socio-environmental vulnerability in the Parnaíba basin, while exposure and sensitivity are the greater challenges in the São Francisco basin. The results of this study are crucial for the prioritization of recovery actions in degraded areas.

In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.

Losses in agricultural production, mainly caused by extreme temperature, rainfall and drought, have demanded the adaptation of the Brazilian agriculture to the ongoing climate change. This study aimed to evaluate, through an integrative review, the effects of agricultural practices established by two Brazilian public policies (ABC Plan and Planaveg) on the resilience and adaptation of the country’s agricultural systems, considering the effects on local climate and extreme events. All the Embrapa library collections were used as main sources, and the Google Scholar was used as a secondary source. The search included publications from 1998 to April 2022 and resulted in 334 data, extracted from 51 references, covering at least one factor and one practice. The review showed that the proposed practices may contribute to the resilience and adaptation of the Brazilian agricultural and forestry sectors to climate change in the main production regions. Studies have indicated that the incorporation of the arboreal component into production systems may provide an environment more resilient to droughts, increased air humidity, reduced temperature impact and reduced fire risks, among other benefits. The adoption of the ABC Plan and Planaveg practices by the agricultural sector is an option feasible, strategic and well-aligned with a low-carbon economy, while it increases the resilience to present and future climate change impacts.