Variabilidade espacial de atributos físico-químicos de um Latossolo Vermelho-Amarelo distrófico em sistema plantio direto

Neto, João Vidal de Negreiros; Santos, Antônio Clementino dos; Guarnieri, Analu; Souza, Djalma Junior de Almeida Tavares; Daronch, Douglas José; Dotto, Michel Antônio; Araújo, Aridouglas dos Santos

doi:10.5433/1679-0359.2014v35n1p193

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…The spatial dependency index (SDI) corresponded to a strong spatial dependency only for the Cu attribute, classifying it as a moderate spatial dependency for the yield, C, pH, Mg, and K data. The attributes Fe, Mn, and Ca presented weak spatial dependency (Table 2), corroborating the results of Mondo et al (2012) andNegreiros Neto et al (2014). The data were interpolated by the ISD, ID, and ordinary kriging methods, and thematic maps were constructed (Figure 2).…”

mentioning

confidence: 57%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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The application of precision agriculture considers the values of non-sampled places by the interpolation of sample data. The accuracy with which the maps of spatial distribution of yield and the soil attributes are produced in the interpolation process influences their application and utilization. This paper aimed to compare three interpolation methods (inverse of the distance, inverse of the square distance, and ordinary kriging) in the construction of thematic maps of soybean yield and soil chemical attributes. A set of data referred to 55 sampling units for the construction maps of soybean yield and of eight soil chemical attributes, by different interpolation methods. The comparison was made based on the error matrix, by calculating the Kappa and Tau indices, beyond the relative deviation coefficient (RDC). It was noticed that the inverse of the square distance was the interpolator that less influenced the data behavior, and the best interpolation method dependent of the variability of the studied attribute. The kriging and the inverse of the square distance were considered the methods that presented the best results in the interpolation of data. Key words: Geostatistics. Precision agriculture. Spatial variability. ResumoA aplicação da agricultura de precisão considera os valores mensurados para lugares não amostrados obtidos por meio da interpolação dos dados amostrais. A precisão com que os mapas de distribuição espacial da produtividade e atributos do solo são produzidos no processo de interpolação influencia a sua aplicação e utilização. Assim, este trabalho teve como objetivo comparar três métodos de interpolação (inverso da distância, inverso do quadrado da distância e krigagem ordinária) na construção de mapas temáticos de produtividade de soja e atributos químicos do solo. Um conjunto de dados referentes a 55 unidades de amostragem foi utilizado para a construção dos mapas de oito atributos químicos do solo e da produtividade de soja, por diferentes métodos de interpolação. A comparação foi feita com base na matriz de erro, por meio do cálculo dos índices Kappa e Tau, além do coeficiente de desvio relativo

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confidence: 57%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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“…Segundo Lemos Filho et al (2017) a normalidade dos dados é uma condição desejável para o ajuste dos semivariogramas pela metodologia da máxima verossimilhança, devido ao fato de o ajuste do modelo de semivariograma por essa metodologia não se basear nos pontos do semivariograma, mas sim, nas diferenças dos dados originais. Porém, neste estudo, os valores de mediana e média estão próximos, destacado pelo coeficiente de curtose que deve ser preferencialmente nulo, contudo é aceito valores entre -2 e +2 (NEGREIROS NETO et al, 2014).…”

Section: Resultsunclassified

“…O restante dos atributos químicos utilizando o método de espectrofotometria de absorção atômica (ALKEMADE; MILATZ, 1955;WALSH, 1953). As análises comporam seis teses (CÁRDEÑAS, 2007;JUMBO, 2007;MORENO, 2007;PINEDA, 2007;SEDAMANOS, 2007e UZHO, 2007.…”

Section: Methodsunclassified

Distribuição espacial de atributos do solo na região de El Oro, Equador

et al. 2020

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The objective of this study was to generate information through the analysis of variability and given structure of spatial dependence as well as the linear correlation of physical and chemical attributes of the soil, in order to assist in the planning of agricultural activities in six cities in the province of El Oro, Ecuador. The soil sample collections were carried out every two kilometers in the form of a grid with smallchanges in the field with a Dutch-type auger and GPS for georeferencing, with a depth of 0-0.20 m, thus totaling 368 points in the sample grid. Chemical and physical attributes of six municipalities in the province of El Oro were evaluated: Huaquillas, Machala, Chilla, Atahualpa, Portovelo and Las Lajas. The sampling was carried out in each city, but notbetween them. Distances between cities were 20,000 km to 180,000 km. Each soil attribute was subjected to an initial descriptive analysis, geostatistics and simple linear correlation matrix. Only pH and sand showed a high variation coefficient (22.02% and 25.42%)andall other attributes showed very high variation. Most of the semivariograms were adjusted to the spherical model.The longest range was indicated by magnesium (41,500 m) and the shortest by copper (7,740 m). The Mg pair versus sum of bases was the highest linear correlation among all the analyzed attributes. The pH stood out with a greater linear correlation between the other chemical attributes of the soil and its specific management zones. In view of the great taxonomic variation of the soil, climatic types, cultures used and management adopted, a specific analysis in each city is recommended for betteragriculturalrecommendations.

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“…The soil, classified as Latossolo Vermelho distrófico (Santos et al, 2014) equivalent to the Rhodic Hapludox of the USDA soil classification (Soil Survey Staff, 2010), contains 680 g kg -1 clay and has been under no-tillage and conventional tillage since 1981. The conventional tillage system consists of soil preparation with the plow (Ø 0.60 m) after the harvest of the summer crop and another preparation to the depth of 0.20 m after the harvest of the winter crop, followed by two harrowings (Ø 0.40 m).…”

Section: Methodsmentioning

confidence: 99%

“…Geostatistics focuses on the spatial variability of soil nutrient levels in specific areas represented by isoline maps. Models of spatial distributions have shown that the range varies among soil attributes (DALCHIAVON et al, 2012;ARTUR et al, 2014) and tillage systems (VIEIRA et al, 2011;NEGREIROS NETO et al, 2014). Such models help optimize fertilization without harming the environment and the natural resources on which they originate.…”

Section: Introductionmentioning

confidence: 99%

Soil chemical attributes variability under tillage and no-tillage in a long-term experiment in southern Brazil

Malvezi¹,

Júnior²,

Guimarães³

et al. 2019

Biosci. J.

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Spatial variability of chemical attributes in soils tends to increase under the no-tillage system. However, data regarding this variability under this system for a long time are scarce. This study aimed to evaluate the spatial variability of phosphorus (P), organic matter (OM), pH, aluminum (Al), calcium (Ca), magnesium (Mg), potassium (K), base saturation (V%), sulfur (SO 4 2-), and micronutrients: boron (B), iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn) in soil with conventional tillage and no-tillage treatments for more than three decades in order to optimize fertilization and maintain soil conservation. Samples of Rhodic Hapludox were collected at 0.00-0.20 m depth. Sampling points were spaced 5 m apart, totaling 100 points. All sampling points were georeferenced according to the tillage system. Samples were analyzed chemically. Semivariograms and kriging were used in descriptive statistics and geostatistics analyses of the results. Only micronutrients and P presented distinct spatial variability between the treatments. Most of the analyzed chemical attributes presented spatial dependency in both tillage systems, which may help determine the best form of sampling and indicate adequate fertilization. According to semivariogram range values of OM, Mg, and K, the same sampling strategy may be adopted for analysis in both tillage systems, yet for Al, Ca, and pH the strategy should be specific.

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Variabilidade espacial de atributos físico-químicos de um Latossolo Vermelho-Amarelo distrófico em sistema plantio direto

Cited by 14 publications

References 17 publications

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Distribuição espacial de atributos do solo na região de El Oro, Equador

Soil chemical attributes variability under tillage and no-tillage in a long-term experiment in southern Brazil

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