Abstract
Several equipments and methodologies have been developed to make available precision agriculture, especially considering the high cost of its implantation and sampling. An interesting possibility is to define management zones aim at dividing producing areas in smaller management zones that could be treated differently, serving as a source of recommendation and analysis. Thus, this trial used physical and chemical properties of soil and yield aiming at the generation of management zones in order to identify whether they can be used as recommendation and analysis. Management zones were generated by the Fuzzy C-Means algorithm and their evaluation was performed by calculating the reduction of variance and performing means tests. The division of the area into two management zones was considered appropriate for the present distinct averages of most soil properties and yield. The used methodology allowed the generation of management zones that can serve as source of recommendation and soil analysis; despite the relative efficiency has shown a reduced variance for all attributes in divisions in the three sub-regions, the ANOVA did not show significative differences among the management zones.
Highlights
The continued growth of precision agriculture technology (PA) promoted the emergence of machines equipped with sensors and equipment aiming at reducing costs and improving performance of production processes as well as allowing more detailed analysis of both soil and plants
Among the questions asked by the researcher, there are included some as: 1) Is it possible to determine the spatial variability of attributes in a less costly way? 2) Is the PA economically feasible? These questions are the focus of much research worldwide, aiming at answering them, but developing techniques and procedures that make affirmative answers to these questions
In order to enable the use of PA technology and evaluate aspects that can influence the process of soybean nutrition, this study aimed at defining management zones using physical and chemical data of soil and soybean yield data to define sub-regions which can be taken as a source of recommendation and optimized sampling
Summary
The continued growth of precision agriculture technology (PA) promoted the emergence of machines equipped with sensors and equipment aiming at reducing costs and improving performance of production processes as well as allowing more detailed analysis of both soil and plants (chlorophyll meter, penetrometer, electrical conductivity meter, yield monitors, meters of vegetation index, among others). According to KHOSLA et al (2008), who conducts a research related to PA viability, there are some unknowns from producers that hinder its use in a more expressive way. Physical and chemical data of soil, electrical conductivity, topography and combination of them are routinely used to define these sub-regions, in addition to the use of statistical modeling of such attributes Physical and chemical data of soil, electrical conductivity, topography and combination of them are routinely used to define these sub-regions, in addition to the use of statistical modeling of such attributes (RODRIGUES JR. et al, 2011)
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