The Use of Spatial Interpolation to Improve the Quality of Corn Silage Data in Case of Presence of Extreme or Missing Values

Thomas M Koutsos,Ilias G Eleftherohorinos,Georgios C Menexes

doi:10.3390/ijgi11030153

Thomas M Koutsos, Ilias G Eleftherohorinos + Show 1 more

Open Access

https://doi.org/10.3390/ijgi11030153

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Abstract

Agricultural spatial analysis has the potential to offer new ways of analyzing crop data considering the spatial information of the measurements. Moving from farmers’ estimates and crop-cuts techniques to interpolation is a new challenge, and a promising path to achieving more reliable results, especially in the case of field data with extreme or missing values. By comparing the main descriptive statistics of three types of crop parameters (fresh weight, dry weight, and ear weight) in three randomly taken maize plots, we found that the issue of missing values can be addressed by using interpolation to calculate estimated values of given parameters in non-sampling locations. Moreover, based on the descriptive statistics, the implementation of interpolation can reduce crop field variability (extreme values) and achieve an improvement of coefficient of variation (CV) values up to 30%, compared with other methods used, such as the replacing of missing values by the average of all data, or the average of the row or column, with an improvement of only up to 15%. These findings strongly suggest that the implementation of an interpolation method in case of extreme or missing values in crop data is an effective process for improving their quality, and consequently, their reliability. As a result, the application of spatial interpolation to existing crop data can provide more dependable estimations of average crop parameters values, compared to the usual farmers’ estimates.

Highlights

In three randomly taken maize plots, we found that the issue of missing values can be addressed by using interpolation to calculate estimated values of given parameters in non-sampling locations
We aim to explore the way in which interpolation can manage extreme and missing values in experimental plot data, and its potential to provide more reliable and representative mean values as productivity metrics for crop parameters
Given the most commonly measured maize parameters of three randomly taken experimental plots, we examined the benefit of applying an interpolation method to the original set of measurements and how safe it is to draw conclusions for larger cultivating areas based on small experimental plots

Summary

Introduction

In three randomly taken maize plots, we found that the issue of missing values can be addressed by using interpolation to calculate estimated values of given parameters in non-sampling locations. Based on the descriptive statistics, the implementation of interpolation can reduce crop field variability (extreme values) and achieve an improvement of coefficient of variation (CV) values up to 30%, compared with other methods used, such as the replacing of missing values by the average of all data, or the average of the row or column, with an improvement of only up to 15%. The existence of missing values and outliers, along with the arbitrary process of removing measurements from field areas with no adequate plant density, are the most important issues that further affect the expected shortcomings in the field estimates Data quality issues, such as completeness, consistency, accuracy, and validity [14–18], are needed before the analysis, because data with low quality lead to poor decision-making, and to misleading conclusions, and, in some cases, do not reflect actual or real-world situations. The statistical analysis of data with many missing values

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