DELINEATION OF HOMOGENEOUS ZONES BASED ON GEOSTATISTICAL MODELS ROBUST TO OUTLIERS

Danilo Pereira Barbosa,Domingos Sárvio Magalhães Valente,Nerilson Terra Santos,Eduardo Leonel Bottega,Wellington Donizete Guimarães

doi:10.1590/1983-21252019v32n220rc

Danilo Pereira Barbosa, Domingos Sárvio Magalhães Valente + Show 3 more

Open Access

https://doi.org/10.1590/1983-21252019v32n220rc

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Abstract

ABSTRACT Measures of the apparent electrical conductivity (ECa) of soil are used in many studies as indicators of spatial variability in physicochemical characteristics of production fields. Based on these measures, management zones (MZs) are delineated to improve agricultural management. However, these measures include outliers. The presence or incorrect identification and exclusion of outliers affect the variogram function and result in unreliable parameter estimates. Thus, the aim of this study was to model ECa data with outliers using methods based on robust approximation theory and model-based geostatistics to delineate MZs. Robust estimators developed by Cressie-Hawkins, Genton and MAD Dowd were tested. The Cressie-Hawkins semivariance estimator was selected, followed by the semivariogram cubic fit using Akaike information criterion (AIC). The robust kriging with an external drift plug-in was applied to fitted estimates, and the fuzzy k-means classifier was applied to the resulting ECa kriging map. Models with multiple MZs were evaluated using fuzzy k-means, and a map with two MZs was selected based on the fuzzy performance index (FPI), modified partition entropy (MPE) and Fukuyama-Sugeno and Xie-Beni indices. The defined MZs were validated based on differences between the ECa means using mixed linear models. The independent errors model was chosen for validation based on its AIC value. Thus, the results demonstrate that it is possible to delineate an MZ map without outlier exclusion, evidencing the efficacy of this methodology.

Highlights

With the development of precision agriculture, mapping of soil heterogeneity has become a relevant tool in agricultural management
The present study aims to delineate management zones (MZs) without excluding outliers, using methods that are insensitive to outliers
Similar CV values for electrical conductivity (ECa), ranging from 17.61% to 44.49%, and a higher mean ECa, ranging from 12.79 to 27.42, were obtained by Peralta et al (2013). These differences are due to different sample depths (0 to 90 cm), a higher sand content and lower clay content, which lead to higher water accumulation and, lower ECa values (PERALTA et al, 2013)

Summary

Introduction

With the development of precision agriculture, mapping of soil heterogeneity has become a relevant tool in agricultural management. Precision agriculture may be defined as a systematic procedure to inspect and incorporate soil spatial variability in field management (HAGHVERDI et al, 2015). Such spatial variability may be caused by climatic, topographical and biological factors (CÓRDOBA et al, 2013). Management of field spatial variability is performed through the delineation of management zones (MZs). MZs are field sub-regions that have similar needs based on soil physicochemical features. The delineation of these sub-regions allows specific input needs to be identified and decreases their usage, increasing agricultural sustainability (CÓRDOBA et al, 2013; BOTTEGA et al, 2017)

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