Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.

The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability.

The Brazilian Cerrado (tropical savanna) is the second largest biome in South America and the main region in the country for agricultural production. Altitude is crucial information for decision-makers and planners since it is directly related to temperature that conditions, for example, the climatic risk of rainfed crop plantations. This study analyzes the conformity of two freely available digital elevation models (DEMs), the NASADEM Merged Digital Elevation Model Global 1 arc second (NASADEM_HGT) version 1 and the Advanced Land Observing Satellite Global Digital Surface Model (ALOS AW3D30), version 3.1, with the altitudes provided by 1695 reference stations of the Brazilian Geodetic System. Both models were evaluated based on the parameters recommended in the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD), which defines error tolerances according to eight different scales (from 1:1000 to 1:250,000) and classes A (most strict tolerance, for example, 0.17 m for 1:1000 scale), B, C, and D (least strict tolerance, for example, 50 m for 1:250,000 scale). Considering the class A, the NASADEM_HGT meets 1:250,000 and lower scales, while AW3D30 meets 1:100,000 and lower scales; for class B, NASADEM_HGT meets 1:100,000 scale and AW3D30 meets 1:50,000. AW3D30 presented lower values of root mean square error, standard deviation, and bias, indicating that it presents higher accuracy in relation to the NASADEM_HGT. Within eight of Cerrado’s municipalities with the highest grain production, the differences between average altitudes, measured by the Cohen’s effect size, were statistically insignificant. The results obtained by the PEC-PCD for the Cerrado biome indicate that both models can be employed in different DEM-dependent applications over this biome.

Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping production. In this study, we analyzed the potential of six PS monthly mosaics from the 2021/2022 crop growing season to discriminate double cropping areas in the municipality of Goiatuba, Goiás State, Brazil. The four multispectral bands of the PS images were converted into normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red normalized difference index (GRNDI), and textural features derived from the gray-level co-occurrence matrix (GLCM). The ten most important combinations of these attributes were used to map double cropping systems and other land use and land cover classes (cultivated pasture, sugarcane, and native vegetation) of the municipality through the Random Forest classifier. Training and validation samples were obtained from field campaigns conducted in October 2021 and April 2022. PS mosaic from February 2022 was the most relevant data. The overall accuracy and Kappa index of the final map were 92.2% and 0.892, respectively, with an accuracy confidence of 81%. This approach can be expanded for mapping and monitoring other agricultural frontiers in the Cerrado biome.

This study analyzes the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) of the digital elevation model (DEM) from Copernicus DEM program, denominated COP-30, with the ellipsoidal altitudes provided by 317 reference stations of the Brazilian Geodetic System located in federative units of Goiás and Distrito Federal (Brazil). The PEC-PCD defines error tolerances according to eight different scales (from 1:1000 to 1:250,000) and four quality classes (ranging from A to D). Considering class A, the DEM COP-30 meets the 1:50,000 scale and lower scales, while for class B, the DEM COP-30 meets the 1:25,000 scale and lower scales. DEM COP-30 presented lower values of root mean square error (RMSE=1.98 m), indicating that it presents higher accuracy compared to other global free DEM.

Os sistemas de produção animal no Semiárido brasileiro podem ser vulneráveis às mudanças climáticas. Cenários futuros para a produção de Brachiaria brizantha, Panicum maximum, Cenchrus ciliaris e Opuntia fícus indica foram analisados para a identificação de áreas mais vulneráveis. A partir dos modelos já disponíveis, foram identificadas algumas áreas de maior vulnerabilidade, o que possibilitou a indicação de alternativas de adaptação dos sistemas de produção para mitigação de possíveis impactos negativos das mudanças climáticas globais sobre a produção animal do Semiárido brasileiro. O desenvolvimento de técnicas para o uso sustentável da Caatinga como pasto nativo contribuirá para reduzir a degradação deste bioma e os riscos de desertificação da região. Novas variedades de plantas forrageiras, tanto de exóticas quanto de espécies nativas, melhor adaptadas para as condições de cenários climáticos futuros poderão ser desenvolvidos através do melhoramento vegetal. O uso de irrigação de pastagens e o aproveitamento de co-produtos na alimentação dos animais contribuirão para o aumento de produtividade e para a redução da pressão sobre a vegetação nativa. Diversos grupos de pesquisa têm desenvolvido modelos melhores para projeção regionalizada de chuva e de temperatura. O desenvolvimento desses modelos permitirá uma investigação mais precisa dos possíveis impactos das mudanças climáticas globais sobre a pecuária no semiárido brasileiro. Palavras - chave: pastagem; pastagem nativa; produção animal; sistemas de produção. Global Climatic Changes and Animal Production: Future Scenarios for the Brazilian Tropical Semiarid ABSTRACT The animal production systems on semi-arid areas of Brazil may be vulnerable to global climatic changes. Future scenarios for the production of Brachiaria brizantha, Panicum maximum, Cenchrus ciliaris and Opuntia fícus indica were analysed to identify more vulnerable areas. Using the models already available, areas with higher vulnerability were identified and alternatives for adaptation of current animal production systems were proposed to mitigate possible negative impacts of global climatic changes. The development of techniques for the sustainable use of Caatinga as native pasture may contribute to reduce degradation of the Caatinga biome and to reduce risks of desertification. New genotypes of exotic and native forage species, better adapted to future climatic conditions, may be developed through plant breeding. Pasture irrigation and use of residues from industries as animal feed will improve productivity and reduce pressure over Caatinga biome. Several research groups are developing better models to a better regionalized prediction of rainfall and temperature. The development of this models will allow a more precise investigation of the possible impacts of the climatic changes on animal production systems on semi-arid area of Brazil. Keywords:grassland; rangeland; animal production; production systems.