Abstract
ABSTRACT The elaboration of maps to characterize the spatial variability of soil attributes assists in the strategic planning and decision making of agricultural managers. Precision and accuracy of maps are related to the ideal sampling density to characterize the variability pattern. This study was conducted with the aim of identifying the sampling density to represent the variability of soil physical quality using attributes with different magnitudes of variation in an area cultivated with coffee. Three approaches were used to find the most adequate sampling density (geostatistical analysis, percentage of error associated with the sampling density, and coefficient of variation). A total of 145 soil samples were collected at a depth of 0-0.20 m at the crossing points of a regular grid with a spacing of 50 m. The percentage of clay, silt, and sand, macroporosity, microporosity, total pore volume, and soil density were determined. The data were submitted to descriptive statistical analysis. For elaborating the variability maps with up to 15% error and soil attributes with a coefficient of variation close to 50%, a sampling density of 3 points ha−1 is suggested.
Highlights
Technological advances observed in the last decades have allowed an improvement in crop management and productivity
One of these main deficiencies is the knowledge on the spatial variability of soil attributes, which forms the basis of precision agriculture (PA) (Montanari et al, 2012)
This study aimed to define an appropriate sampling density to represent the variability of soil physical quality using three approaches: a) semivariogram range, b) the methodology proposed by Cline (1944), and c) the methodology proposed by Ding-Geng & Peace (2011), in addition to an estimate of possible sampling costs
Summary
Technological advances observed in the last decades have allowed an improvement in crop management and productivity. Implements, and sensors for use in agriculture have been perfected over the years in order to provide better conditions for producers to manage crops. Some deficiencies still hinder the correct implementation of precision agriculture (PA) despite these advances. One of these main deficiencies is the knowledge on the spatial variability of soil attributes, which forms the basis of PA (Montanari et al, 2012). Variability of soil attributes occurs naturally in soils as a result of the performance of soil formation factors (Jenny, 1941) or as a product of anthropogenic interference that conditioned its existence. The knowledge, understanding, and interpretation of how soil attributes vary in space have a great influence on crop productivity (Fortes et al, 2015)
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