The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14932251

Ramoni Perazzi, Paolo; Passamani, Marcelo; Thielen, Dirk; Padovani, Carlos Roberto; Arizapana, Marco Aurelio (2021): Supplementary Material C. Spatial visualization of the discrepancies and the predictions. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/14932251.v1

Dataset

• Horticultural Crops
• Ecological Applications
• Remnant Vegetation and Protected Conservation Areas
• Natural Hazards
• Horticultural Production
• Environmental and Natural Resource Evaluation
• Forestry Sciences
• Plant Biology
• Ecology
• Environmentally Sustainable Animal Production
• Environmental Science and Management
• Industrial Crops
• Ecosystem Assessment and Management
• Animal Production
• Evolutionary Biology
• Agriculture, Land and Farm Management
• Environmentally Sustainable Plant Production
• Flora, Fauna and Biodiversity
• Control of Pests, Diseases and Exotic Species
• Forestry
• Rehabilitation of Degraded Environments
• Pasture, Browse and Fodder Crops
• Climate and Climate Change
• Crop and Pasture Production
• Zoology
• Other Earth Sciences

Bioclimatic covariates, Brazil, Machine learning, Precipitation, Spatial interpolation, Temperatue

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Paolo Ramoni Perazzi , Marcelo Passamani , Dirk Thielen , Carlos Roberto Padovani , Marco Aurelio Arizapana

Spatially-oriented graphic comparisons of the monthly precipitations (Ppt), as well as maximum and minimum temperatures (T_max and T_min, respectively) provided by the meteorological weather system managed by the Brazilian National Institute of Meteorology (INMET) relative to the information provided by several bioclimatic databases currently available.

• 2021-07-16 first online, published, posted

4TU.ResearchData

compressed .rar file, containing an html webpage

BrazilClim: The overcoming of limitations of preexisting bioclimate data

Federal University of Lavras, Laboratory of Ecology and Conservation of Mammals