RESUMOOs assentamentos rurais no Brasil foram criados para responder a pressões localizadas e estão marcados pela falta de planejamento prévio de implantação e diagnóstico dos recursos naturais relativos a aptidão agrícola, distribuição das classes de relevo, hidrografia, vegetação e mecanismos de apoio. O objetivo desta pesquisa foi avaliar a aptidão agrícola das terras destinadas ao assentamento de famílias rurais do Instituto Nacional de Colonização e Reforma Agrária (INCRA), aplicando o Sistema de Avaliação da Aptidão Agrícola das Terras (SAAT) associado a um Sistema de Informações Geográficas (SIG) no Projeto de Assentamento Eldorado dos Carajás (PAEC), localizado no município de Lebon Régis/SC. No desenvolvimento da pesquisa, optou-se por utilizar mapas temáticos em sistemas de informações geográficas (SIG) para armazenar e integrar os dados espaciais. O uso do SIG permitiu a análise e integração dos temas com significativa redução de tempo e subjetividade nos cruzamentos. O uso do método forneceu duas respostas básicas ao planejamento de uso dos recursos naturais das áreas rurais: as classes de aptidão agrícola, apontando os principais fatores limitantes e a viabilidade de melhoramento das terras no nível tecnológico B; e, quando confrontado com os mapas temáticos e os dados cadastrais do assentamento, revelou as regularidades e irregularidades no uso das terras em cada parcela imobiliária.Termos de indexação: avaliação de terras, planejamento agrícola, sustentabilidade e conservação de recursos naturais.(1) Parte da Dissertação de Mestrado do primeiro autor apresentada ao Programa de Pós-Graduação em Engenharia Civil, Universidade Federal de Santa Catarina -UFSC. Recebido para publicação em setembro de 2009 e aprovado em maio de 2010.
The success of soil prediction by VIS-NIR-SWIR spectroscopy has led to considerable investment in large soil spectral libraries. The aims of this study were 1) to develop a soil VIS-NIR-SWIR spectroscopy approach using legacy soil samples to improve spectral soil information in a regional scale; (2) to compare six spectral preprocessing techniques; and (3) to compare the performance of linear and non-linear multivariate models for prediction of sand and clay content. A total of 1,534 legacy soil samples, stored by Epagri, were collected from agricultural areas in 2009 on a regional scale, covering 260 municipalities of Santa Catarina. Six spectral preprocessing techniques were applied and compared with reflectance spectra (control treatment) in the development of sand and clay prediction models. Five multivariate regression models, Support Vector Machines, Gaussian Process Regression, Cubist, Random Forest, and Partial Least Square Regression were compared. The scatter-corrective preprocessing groups produced similar or better performance than spectral-derivatives. In addition, preprocessing spectra prior to regression analysis does not improve sand prediction, since reflectance spectra achieved the best performance using Cubist, SVM, and PLS models. In general, clay content presented better prediction accuracy than sand content. The best multivariate model to predict sand and clay content from soil VIS-NIR-SWIR spectra was Cubist. The best Cubist performance was achieved combined with reflectance spectra (R 2 = 0.73; root mean square error = 10.60 %; ratio of the performance to the interquartile range = 2.36) and MSC (R 2 = 0.83; root mean square error = 7.29 %; ratio of the performance to the interquartile range = 3.70) for sand and clay content, respectively. Considering the mean RMSE values of the validation set, the predictive ability of the multivariate models decreased in the following order: Cubist>PLS>RF>GPR>SVM for both properties. The predictive ability of VIS-NIR-SWIR reflectance spectroscopy achieved in this study for sand and clay content using legacy soil data and heterogeneous samples confirmed the potential of the spectroscopy approach.
Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables -slope, landforms, and topographic wetness indexwere correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar.
A Agrometeorologia é uma ciência multidisciplinar que, além das atuações bem consolidadas na área agronômica, contribui para o desenvolvimento territorial regional e das cadeias produtivas, apoiando tecnicamente a implementação das Indicações Geográficas (IG). O objetivo desse trabalho foi apresentar os conhecimentos da agrometeorologia utilizados durante o processo de delimitação da Indicação Geográfica (IG) dos Vinhos de Altitude de Santa Catarina. Para descrever as etapas da aplicação dos conhecimentos agrometeorológicos nesse processo, foi utilizada a proposta da hierarquia Data-Information-Knowledge-Wisdom (DIKW). Entre os conhecimentos da agrometeorologia utilizados na implementação de uma IG estão: consistência de dados climáticos, estimativas de variáveis meteorológicas, cálculo de variáveis agrometeorológicas, relação solo-água-planta, agrometeorologia dos cultivos e ecofisiologia vegetal.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.