Abstract
Knowledge of the spatial variability of soil attributes is an aid to soil management. The aim of this study was to apply fuzzy logic and geostatistics in defining the spatial variability of soil fertility, in soil cultivated with the rubber tree. Soil samples from the 0-0.20 m and 0.20-0.40 m layers were collected in a stratified random sampling grid, with a shortest distance of 6 m, for a total of 60 points. The chemical attributes were P, K, Ca, Mg, BS, CEC and V. The spatial dependence of the attributes was determined, and the maps were constructed using ordinary kriging interpolation. The interpolated values were transformed into degrees of pertinence (FI), with the lower and upper limits previously defined, using an increasing model. The nutrient P displayed values in both layers below the recommended lower limit ( 50%. This methodology reduced the number of maps for interpreting soil fertility in the area, enabling visualisation of the spatial and gradual variability of the needs of the region.
Highlights
IntroductionKnowledge and understanding of soil fertility is of paramount importance for the rational management of agricultural crops
The rubber tree (Hevea brasiliensis L.), native to the Amazon region, has aroused great interest for cultivation in tropical areas, achieving high rubber production when improved clones are used together with adequate crop management (ROQUE et al, 2006).Knowledge and understanding of soil fertility is of paramount importance for the rational management of agricultural crops
The aim of this study was to apply fuzzy logic to observing the gradual variation in levels of soil fertility in an area cultivated with the rubber tree at the initial stage of development, using geostatistical techniques to construct a final map of the area for both sampling layers
Summary
Knowledge and understanding of soil fertility is of paramount importance for the rational management of agricultural crops. Soil attributes often do not reveal a purely random variation throughout the terrain, but show a spatial correlation (GOMES et al, 2007). For this reason, geostatistics has been used as an important tool in data analysis in order to model and study the structure of spatial dependence of soil attributes through adjustment of experimental semivariograms. Vieira et al (2012) studied and correlated dendrometric variables of the rubber tree with physical attributes of the soil, and found a moderate degree of spatial dependence
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