RESUMO
The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km2 area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation.
RESUMO
Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis, sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color.
RESUMO
Brazil is one of the major passion fruit producers worldwide. Viral diseases are among the most important constraints for passion fruit production. Here we identify and characterize a new passion fruit infecting-virus belonging to the family Geminiviridae: passion fruit chlorotic mottle virus (PCMoV). PCMoV is a divergent geminivirus unlike previously characterized passion fruit-infecting geminiviruses that belonged to the genus Begomovirus. Among the presently known geminiviruses, it is most closely related to, and shares ~62% genome-wide identity with citrus chlorotic dwarf associated virus (CCDaV) and camelia chlorotic dwarf associated virus (CaCDaV). The 3743 nt PCMoV genome encodes a capsid protein (CP) and replication-associated protein (Rep) that respectively share 56 and 60% amino acid identity with those encoded by CaCDaV. The CPs of PCMoV, CCDaV, and CaCDaV cluster with those of begomovirus whereas their Reps with those of becurtoviruses. Hence, these viruses likely represent a lineage of recombinant begomo-like and becurto-like ancestral viruses. Furthermore, PCMoV, CCDaV, and CaCDaV genomes are ~12-30% larger than monopartite geminiviruses and this is primarily due to the encoded movement protein (MP; 891-921 nt) and this MP is most closely related to that encoded by the DNA-B component of bipartite begomoviruses. Hence, PCMoV, CCDaV, and CaCDaV lineage of viruses may represent molecules in an intermediary step in the evolution of bipartite begomoviruses (~5.3 kb) from monopartite geminiviruses (~2.7-3 kb). An infectious clone of PCMoV systemically infected Nicotiana benthamina, Arabidopsis thaliana, and Passiflora edulis.
