Multivariate spatial sample reduction of soil chemical attributes by means of application zones

Abstract

Aim of study: In precision agriculture, the definition of Application Zones (AZs) in agricultural areas consists in delimiting the area in subareas with similar characteristics, using soil chemical attributes. To such end, the use of clustering methods is common. Therefore, the AZs make up a database that can be used to target future soil sampling, thus seeking a possible sample reduction. The objective of this paper is to assess the acquisition of sample configurations, with reduced sample size, contained in application zones generated by spatial multivariate clustering. The sampling protocol proposed in this work evaluated five clustering methods (C-means, Fanny, K-means, Mcquitty, and Ward) for the creation of AZs, and, through these AZs, to obtain reduced sample configurations with 50% and 75% of the initial sampling points. Area of study: Commercial agricultural area, Cascavel, Brazil. Material and methods: Data of the soil chemical attributes from a commercial agricultural area were used, referring to three soybean harvest years (2013-2014; 2014-2015; and 2015-2016). The clustering methods considered a dissimilarity matrix that aggregates the information about the Euclidean distance between the sample elements and the spatial dependence structure of the attributes. Main results: The results indicated division of the agricultural area into two or three AZs for the aforementioned harvest years, considering the K-means method. Comparing all the reduced sample configurations with the initial one, it was observed that the one proportionally reduced by 25% was the most effective to obtain a reduced sample configuration. Research highlights: The sampling protocol using AZs showed that it is possible to reduce the sample size.

Keywords

• clustering
• dissimilarity matrix
• precision agriculture
• sampling design.