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

Abstract

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

Productivity of soybean in management zones with application of different sowing densities

“…The inverse square distance (ISD) interpolation was the method used for the interpolation of data as proposed by BETZEK et al (2017). Productivity maps were then constructed using the ArcGIS software.Maps were constructed for each sowing density.…”

Spatialization of chemical attributes, penetration resistance and magnetic susceptibility of the soil in a Cerrado area

“…According to Betzek et al (2017), kurtosis coefficients are used to evaluate the flattening degree of a distribution compared to the normal curve. Kurtosis was classified as leptokurtic for H+Al, OM, CEC, V% and Sand and as platykurtic for the other attributes.…”

Spatial Variability in Leaf Analysis and Productivity of Fertirrigated Açaí

“…The spatial dependence index (SDI) was analyzed by the C/(C0 + C) ratio, following the proposed interpretation of Dalchiavon & Carvalho (2012), where SDI < 20 % indicated very low spatial dependence, 20 % ≤ SDI < 40 % indicated low spatial dependence, 40 % ≤ SDI < 60 % indicated average spatial dependency, 60 % ≤ SDI < 80 % indicated high spatial dependence, and SDI ≥ 80 % indicated very high spatial dependence. Following spatial dependence analysis, the ordinary kriging interpolation method was used, according to Betzek et al, (2017), in order to estimate values in unmeasured locations. Then, two multivariate statistical methods were applied: the principal component analysis (PCA) and the non-hierarchical k-mean clustering using the software Statistica, version 10 (Statsoft, 2010).…”