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O zoneamento agrícola é uma ferramenta que utiliza variáveis climáticas, edáficas e socioeconômicas de cada cultura, para identificar áreas de menor risco ao estabelecimento e cultivo de espécies vegetais de uma determinada região. Os primeiros zoneamentos agrícolas no Brasil foram publicados na década de 1950. A partir dos anos 90, intensificam-se as pesquisas em metodologias específicas para dar suporte ao Programa de Zoneamento Agrícola de Risco Climático do MAPA. O objetivo deste trabalho foi avaliar o estado atual da pesquisa científica na área do zoneamento agrícola no Brasil. A metodologia empregada foi a análise bibliométrica implementada no R. A análise foi conduzida usando duas bases de dados indexadas Web of Science e Scopus e bases não indexadas. A matriz de dados foi submetida a análise descritiva. Foram analisados um total de 84 artigos oriundos de 26 fontes bibliográficas, 266 autores com 0,27 artigos por autor e índice de colaboração de 3,76. No período analisado, observou-se um crescimento no número de publicações. Constatou-se uma concentração de trabalhos publicados com autores principais provenientes dos Estados do Sul e Sudeste. Observou-se forte colaboração científica entre os grupos de pesquisa brasileiros. As fontes de dados mais citadas foram INMET, ANA, IAPAR, EPAGRI, FEPAGRO.

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.

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 (R2 = 0.73; root mean square error = 10.60 %; ratio of the performance to the interquartile range = 2.36) and MSC (R2 = 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.

Made available in DSpace on 2019-10-06T16:42:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-15 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The present study was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350–2500 nm range of Brazilian soils. The Brazilian Soil Spectral Library (BSSL) began in 1995, creating a protocol to gather soil samples from different locations in Brazil. The BSSL reached 39,284 soil samples from 65 contributors representing 41 institutions from all 26 states. Through the BSSL spectra database, it was possible to estimate important soil attributes, such as clay, sand, soil organic carbon, cation exchange capacity, pH and base saturation, resulting in differences among the multi-scale models taking Brazil (overall), regional and state scale. In general, spectral descriptive and quantitative behavior indicated important relationship with physical, chemical and mineralogical properties. Statistical analyses showed that six basic patterns of spectral signatures represent the Brazilian soils types and that environmental conditions explain the differences in spectra. This study demonstrates that spectroscopy analyses along with the establishment of soil spectral libraries are a powerful technique for providing information on a national and regional levels. We also developed an interactive online platform showing soil sample locations and their contributors. As soil spectroscopy is considered a fast, simple, accurate and nondestructive analytical procedure, its application may be integrated with wet analysis as an alternative to support the sustainable management of soils. Department of Soil Science Luiz de Queiroz College of Agriculture (ESALQ) University of São Paulo (USP), Ave. Pádua Dias 11, Cx. Postal 9 Department of Soil Federal University of Santa Maria, Av. Roraima 1000 Geographical Sciences Department Federal University of Pernambuco, Av. Ac. Hélio Ramos, s/n Department of Agronomy State University of Maringá, Av. Colombo 5790 Department of Agriculture Biodiversity and Forestry Federal University of Santa Catarina, Rodovia Ulysses Gaboardi 3000 - Km 3 Federal Rural University of Amazon, Ave. Presidente Tancredo Neves 2501 Faculty of Agronomy and Veterinary Medicine University of Brasília EMBRAPA - Solos, R. Antônio Falcão, 402, Boa Viagem Center of Nuclear Energy in Agriculture (CENA) USP, Av. Centenário 303 CDRS/Secretary of Agriculture of São Paulo State, R. Campos Salles 507 Department of Soils Federal University of Viçosa, Ave. Peter Henry Rolfs s/n EMBRAPA – Informática Agropecuária, Ave. André Tosello, 209 Department of Nuclear Energy Federal University of Pernambuco, Av. Prof. Luis Freire 1000 Department of Geography Federal University of Rio Grande do Norte, R. Joaquim Gregório s/n Agronomic Institute of Campinas (IAC), Ave. Barão de Itapura 1481 Institute of Agricultural Sciences Federal Rural University of Amazônia, Ave. Presidente Tancredo Neves 2501, 66.077-830 Department of Soil Science Federal University of Lavras Federal University of Mato Grosso, Cuiabá, Av. Fernando Corrêa da Costa 2367 Department of Soils Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 07 s/n Soil and Water Sciences Department University of Florida, 2181 McCarty Hallr, PO Box 110290 EMBRAPA - Solos, R. Jardim Botânico, 1024 Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n Federal University of Sergipe, Av. Marechal Rondon s/n Graduate Program in Earth Sciences (Geochemistry) Department of Geochemistry Federal Fluminense University, Outeiro São João Batista, s/n Federal Institute of the Southeast of Minas Gerais, R. Monsenhor José Augusto 204 Federal University of Rio Grande do Norte, R. Joaquim Gregório s/n Federal University of Piauí EMBRAPA Milho e Sorgo, Rod MG 424 Km 45 Institute of Agricultural Sciences Federal University of Jequitinhonha e Mucuri Valleys, Ave. Ver. João Narciso 1380 Department of Biosystems Engineering ESALQ USP, Ave. Pádua Dias 11, Cx. Postal 9 Federal University of Acre, Rodovia BR 364 Km 04 Federal University of Amazonas, Av. General Rodrigo O. J. Ramos 1200 EMBRAPA Clima Temperado, BR-392, km 78 Department of Agronomy Federal Rural University of Pernambuco, R. Manuel de Medeiros s/n EMBRAPA Cocais, Quadra 11, Av. São Luís Rei de França 4 Paraense Emílio Goeldi Museum, Av. Gov. Magalhães Barata 376 Exata Laboratory, Rua Silvestre Carvalho Q 11 Federal University of Rondônia, BR 364, Km 9.5 Nacional Institute for Amazonian Research, Ave. André Araújo 2936 Department of Forestry Sciences ESALQ-USP, Ave. Pádua Dias 11, Cx. Postal 9 Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n FAPESP: 2014/22262-0 FAPESP: 2016/26176-6 FAPESP: 2017/03207-6

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 index - were 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.

Os 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.

Soil particle size is an attribute of fundamental importance when defining soil horizons. Proximal soil sensors can facilitate the acquisition of a larger amount of soil data using a faster and less laborious technique. Thus, the objective of this study is to evaluate the capacity of a limited spectral acquisition region (325–1075 nm) for estimating soil texture. Soil samples were collected in the southwest part of Marombas river watershed located near the center of Santa Catarina State, south of Brazil. A total of 42 soil profiles were sampled according to the GlobalSoilMap specification. A dataset of 166 samples was used for model calibration and another set of 71 samples was used for model validation. Diffuse reflectance spectroscopy of sieved samples (2 mm) was collected with a spectrometer FieldSpecHandHeld II (ASD Inc.). Savitzky–Golay second derivatives were calculated and used in partial least-squares regression modeling. Calibration and validation datasets showed statistically similar mean and variance. The root-mean-square error of prediction for sand, silt, and clay content is 5.47, 5.18, and 5.39 g 100 g−1, respectively. The R2 for validation is 0.30, 0.59, and 0.69 for the same attributes. Partitioning the model by depth did not improve the predictions significantly. The results show that estimating soil texture from a limited spectral region is promising and can contribute toward the development of cheaper spectrometers or infrared cameras that can be used for digital soil morphometrics.

This doctorate thesis developed advanced research regarding digital soil mapping (DSM) and its potential for improving soil surveys using multiple and complex digital information. For the first chapter, it is presented a general introduction to the developed research, a general contextualization of the topic\'s importance and its applications for the advancement of soil science. The second chapter presents the insertion of new covariates for the DSM related to drainage networks (DN). The work combined satellite, relief, and DN information to map clay, sand, and soil organic carbon contents for the municipality of Piracicaba in the state of São Paulo, Brazil. The third chapter applies the DSM methodology to refine (enhance the scale) a geological map for the Pereira Barreto region in the state of São Paulo, Brazil. The work showed that even with the soil surface spectra, it is possible to analyze the characteristics of the subsurface. In the fourth chapter, the bare soil detection methodology (Synthetic Soil Image SySI) was applied to identify and classify soils with hydromorphism. The reflectance in the visible, near-infrared and short-wave infrared (Vis-NIR-SWIR) spectral bands of the exposed soil was able to identify features related to hydromorphic soils, which were used to predict hydromorphic soils for a region of 863,577.9 km2 between the Southeast and Midwest of Brazil. The results showed a high occurrence of hydromorphism in agricultural areas, indicating the need for greater monitoring. The last chapter presents a general conclusion of the work with the main results and their applications for soil science. A presente tese de doutorado desenvolveu pesquisa sobre o mapeamento digital de solos (MDS) e seu potencial para o aprimoramento dos levantamentos de solo a partir de informações digitais. No primeiro capítulo há uma introdução geral sobre a pesquisa desenvolvida, uma contextualização geral sobre a importância do tema e suas aplicações para o avanço da ciência do solo. O segundo capítulo apresenta a inserção de novas covariáveis para o MDS relacionadas a redes de drenagem (RD). O trabalho combinou informações de satélite, relevo e RD para mapear os teores de argila, areia e carbono orgânico do solo para o município de Piracicaba no estado de São Paulo, Brasil. O terceiro capítulo aplica a metodologia do MDS para realizar o refinamento (aumento de escala) de um mapa geológico para a região de Pereira Barreto no estado de São Paulo, Brasil. O trabalho mostrou que mesmo com a reflectância espectral da superfície do solo é possível fazer relações e análises sobre as características da subsuperfície. No quarto capítulo, foi aplicada a metodologia de detecção de solo exposto (imagem sintética de solo exposto SySI) para a identificação e classificação de solos com ocorrência de hidromorfismo. A reflectância nas faixas espectrais do visível, infra-vermelho próximo e infra-vermelho de ondas curtas (Vis-NIR- SWIR) do solo exposto foi capaz de identificar feições relacionadas aos solos hidromórficos, as quais foram utilizadas para classificar estes solos para uma região de 863,577.9 km2 entre o sudeste e centro-oeste do Brasil. Os resultados indicaram alta ocorrência de hidromorfismo em áreas agrícolas, indicando a necessidade de maior monitoramento. O ultimo capítulo apresenta uma conclusão geral do trabalho com os principais resultados e suas aplicações na ciência do solo.