A B S T R A C TThis study aimed to apply precision agriculture techniques in coffee production, using correlation analysis in the definition of management zones. This work was carried out in a 22-ha area of coffee (Coffea arabica L.), cv. 'Topázio MG 1190' , which was sampled on a regular grid, using a topographic GPS, totaling 64 georeferenced samples (on average, 2.9 points per ha). Descriptive analysis was used in the data, followed by Pearson's correlation analysis at 0.05 significance between soil chemical attributes, agronomic characteristics of the plants and altitude. It was possible to verify the correlation of soil chemical attributes, agronomic characteristics of the plants and altitude with coffee yield. Altitude was the variable most correlated with coffee yield through correlation analysis. Therefore, it was chosen as the best variable to define management zones and thematic maps capable to support coffee farmers. Three maps were generated to characterize the area in two, three and four management zones. There was a direct influence on mean yield.Definição de zonas de manejo para cafeicultura R E S U M O Objetivou-se, com o presente estudo, aplicar técnicas de agricultura de precisão no cultivo do café utilizando análise de correlação na definição de zonas de manejo. O trabalho foi desenvolvido em uma área de 22 ha de lavoura de cafeeiro (Coffea arabica L.) da cultivar Topázio MG 1190. Demarcou-se na área em estudo e com a utilização de GPS topográfico, uma malha amostral regular totalizando 64 pontos amostrais georreferenciados (em média 2,9 pontos por ha). Utilizou-se o método de análise descritiva dos dados seguido da análise de correlação de Pearson a 0,05 de significância entre os atributos de solo, características agronômicas da planta e altitude. Foi possível verificar a correlação dos atributos do solo, das características agronômicas das plantas e da altitude com a produtividade. Através da análise de correlação observou-se que a altitude foi a variável que mais se correlacionou com a produtividade sendo, assim, selecionada como variável mais propícia para geração de zonas de manejo e de mapas temáticos capazes de auxiliar os cafeicultores. Foram gerados três mapas que caracterizam a área em duas, três e quatro zonas de manejo. Verificou-se que houve influência direta na média da produtividade.

This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

Precision agriculture is based on a set of techniques that explore the spatial variability of properties related to a determined area. The aim of this study was to develop and test a methodology to evaluate the quality of grid sampling. The experiment was performed in three areas of 112, 50 and 26 ha, in coffee plantations (Coffea arabica) with cultivar Catuai 144, in the Três Pontas Farm, located in Presidente Olegário, MG, Brazil, in 2014 and 2015. A total of 224, 100, and 52 georeferenced points (2.0 points/ha) were plotted in the areas regarding the soil chemical properties, respectively: phosphorus, potassium, calcium and magnesium. For the application methodology the standardized accuracy index (SAI), the standardized precision index (SPI) and the standardized optimal grid indicator (SOGI) were developed and tested. From grid 1 (2 points/ha), another three sampling grids (1.0, 0.7 and 0.5 point/ha) were adopted. The indexes were important to analyze the grid quality, whereas the SOGI allowed selecting the grid that best represented the properties.

The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.

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