Abstract
Aim of study: To reduce the sample size in an agricultural area of 167.35 hectares, cultivated with soybean, to analyze the spatial dependence of soil penetration resistance (SPR) with outliers.Area of study: Cascavel, BrazilMaterial and methods: The reduction of sample size was made by the univariate effective sample size ( ) methodology, assuming that the t-Student model represents the probability distribution of SPR.Main results: The radius and the intensity of spatial dependence have an inverse relationship with the estimated value of the . For the depths of SPR with spatial dependence, the highest estimated value of the reduced the sample size by 40%. From the new sample size, the sampling redesign was performed. The accuracy indexes showed differences between the thematic maps with the original and reduced sampling designs. However, the lowest values of the standard error in the parameters of the spatial dependence structure evidenced that the new sampling design was appropriate. Besides, models of semivariance function were efficiently estimated, which allowed identifying the existence of spatial dependence in all depth of SPR.Research highlights: The sample size was reduced by 40%, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in the next mappings in the agricultural area. The spatial t-Student model was able to reduce the influence of outliers in the spatial dependence structure.
Highlights
The Brazilian economy is directly related to agribusiness, and soybean (Glycine max (L.) Merrill) lead this scenario, which figures as the main grain exported by Brazil
Considering the 100 simulations of each variable, the graph with the means and standard deviations of the estimated values of the univariate effective sample size showed that the variation of the value of the nugget effect did not generate a relevant change in the estimated value of the effective sample size (Fig. 3)
The high difference in the estimated EEEESStt values (Fig. 3) can be explained by the discrepancy between the variables concerning the values of the parameters of spatial dependence, mainly regarding the practical range, which variation was of 0.3 to 1.2 km
Summary
The Brazilian economy is directly related to agribusiness, and soybean (Glycine max (L.) Merrill) lead this scenario, which figures as the main grain exported by Brazil. Given the economic importance of this commodity, to preserve the productivity and increase it, it is important to know the spatial variability of soybean yield and its relationship with the physical and chemical properties of the soil (Sobjak et al, 2016). From this perspective, precision agriculture (PA) techniques use the knowledge of the spatial variability of grain yield and the physical and Letícia E.
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