Statistical methods for identifying anisotropy in the Spodoptera frugiperda spatial distribution

Daniela T. Nava, Orietta Nicolis, Miguel A. Uribe-Opazo, Fernanda De Bastiani

Abstract


Corn is a very important agricultural product, however, some pests may cause damage to the corn productivity such as Spodoptera frugiperda, which prevents the plant from growing in a regular manner. Since the indiscriminate use of the pesticide may cause an increasing resistance of the insect besides an environmental damage, it is important to estimate the areas and the dominant directions where the insect may propagate. The main aim of this work was to study the spreading of the fall armyworm in a commercial agricultural area in the South of Brazil. For this, we considered a set including the location of each corn plant attacked by the insect. In particular, we assumed that the spatial locations given by the geographic coordinates constitute a spatial point pattern following a stationary Poisson point process. In order to detect the presence of possible dominant directions in the distribution of the fall armyworm infestation we studied the anisotropic features of the data by using some second-order spatial point-pattern analysis techniques such as the K directional test, the wavelet-based test, and the quadrat counting test. All the results showed that spatial distribution of fall armyworm may follow a clustered Poisson point process with the presence of an evident anisotropy mainly due to the shape and the distance between corn plants of the experimental area. These preliminary results could be used for reducing and optimizing the use of pesticides with a consequent decrease of the environmental impact.


Keywords


cluster processes; corn pests; spatial point pattern; directional K function; wavelet based test

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References


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DOI: 10.5424/sjar/2018161-11916