Variabilidade espacial de atributos físicos do solo em terra preta de índio sob cultivo de café conilon

Júnior, Pedro Cardoso Mota; Campos, Milton César Costa; Mantovanelli, Bruno Campos; Franciscon, Uilson; Cunha, José Maurício da

doi:10.25186/cs.v12i2.1277

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Dominance of coarse particles in these areas of ADEs (forest and grassland) can be explained by the contribution of lithic and ceramic fragments, thus presenting higher levels for the sand fraction. Dominance of the sand and silt fraction was also reported by Mota Júnior et al (2017) in anthropic soils in the city of Apuí-AM, associating this granulometric characteristic with the formation of these soils and constituent materials. In addition, it is important to note that the use of fire retardants in the presence of sand is more likely to occur in the sand (2000).…”

Section: Soil Particle-size Distribution Fractal Features and Physical Attributessupporting

confidence: 61%

Variability and Spatial Correlation of Aggregates and Organic Carbon in Indian Dark Earth in Apuí Region, Am

Gomes¹,

Campos²,

Brito³

et al. 2021

Terra Preta Arqueológica: Atributos Morfológicos, Físicos, Químicos E Efluxo De CO2 Em Solos Sob Diferentes Usos Na Região Sul

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Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

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Section: Soil Particle-size Distribution Fractal Features and Physical Attributessupporting

confidence: 61%

Variability and Spatial Correlation of Aggregates and Organic Carbon in Indian Dark Earth in Apuí Region, Am

Gomes¹,

Campos²,

Brito³

et al. 2021

Terra Preta Arqueológica: Atributos Morfológicos, Físicos, Químicos E Efluxo De CO2 Em Solos Sob Diferentes Usos Na Região Sul

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“…Of the evaluated models (spherical, exponential, and gaussian), the spherical semivariogram model had the lowest ME for both evaluated periods; thus, it was the fit model chosen. The authors of [40] state that there is a predominance of the spherical mathematical model for studies in soil science. The leaf water potential had a range of 15 m for both the sampling in the dry season and the sampling in the rainy season, i.e., Ψw had a similar area of influence for both sampling periods.…”

Section: Geostatistical Analysismentioning

confidence: 99%

“…By identifying water balance variables and their relationship between vegetation indices obtained by the MODIS sensor (Terra satellite) for coffee growing, Ref. [40] evaluated the NDVI and EVI vegetation indices obtained by the MODIS sensor (Terra satellite). The authors claim that the relationships between climate-topological water balance variables and vegetation indices were verified, but with weak statistical correlation.…”

Section: Geostatistical Analysismentioning

confidence: 99%

Evaluation of the Water Conditions in Coffee Plantations Using RPA

et al. 2022

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The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.

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Spatial variability of chemical indicators of Amazon agricultural soils through geomultivariate statistics, Brazil

Martins,

de Souza,

Campos

et al. 2023

Environ Monit Assess

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Variabilidade espacial de atributos físicos do solo em terra preta de índio sob cultivo de café conilon

Cited by 11 publications

References 10 publications

Variability and Spatial Correlation of Aggregates and Organic Carbon in Indian Dark Earth in Apuí Region, Am

Variability and Spatial Correlation of Aggregates and Organic Carbon in Indian Dark Earth in Apuí Region, Am

Evaluation of the Water Conditions in Coffee Plantations Using RPA

Spatial variability of chemical indicators of Amazon agricultural soils through geomultivariate statistics, Brazil

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