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The application of precision agriculture considers the values of non-sampled places by the interpolation of sample data. The accuracy with which the maps of spatial distribution of yield and the soil attributes are produced in the interpolation process influences their application and utilization. This paper aimed to compare three interpolation methods (inverse of the distance, inverse of the square distance, and ordinary kriging) in the construction of thematic maps of soybean yield and soil chemical attributes. A set of data referred to 55 sampling units for the construction maps of soybean yield and of eight soil chemical attributes, by different interpolation methods. The comparison was made based on the error matrix, by calculating the Kappa and Tau indices, beyond the relative deviation coefficient (RDC). It was noticed that the inverse of the square distance was the interpolator that less influenced the data behavior, and the best interpolation method dependent of the variability of the studied attribute. The kriging and the inverse of the square distance were considered the methods that presented the best results in the interpolation of data. Key words: Geostatistics. Precision agriculture. Spatial variability. ResumoA aplicação da agricultura de precisão considera os valores mensurados para lugares não amostrados obtidos por meio da interpolação dos dados amostrais. A precisão com que os mapas de distribuição espacial da produtividade e atributos do solo são produzidos no processo de interpolação influencia a sua aplicação e utilização. Assim, este trabalho teve como objetivo comparar três métodos de interpolação (inverso da distância, inverso do quadrado da distância e krigagem ordinária) na construção de mapas temáticos de produtividade de soja e atributos químicos do solo. Um conjunto de dados referentes a 55 unidades de amostragem foi utilizado para a construção dos mapas de oito atributos químicos do solo e da produtividade de soja, por diferentes métodos de interpolação. A comparação foi feita com base na matriz de erro, por meio do cálculo dos índices Kappa e Tau, além do coeficiente de desvio relativo

Spatial variability management of soil chemical attributes is one of the approaches to be employed in the face of the constant challenge of increasing agricultural yield to meet world demand. In this sense, precision agriculture has as one of its tools the application of inputs at varying rates, which seeks to determine the ideal amount of fertilizer at each point of the crop, contrary to the conventional recommendation approach based on average values. In this context, this work studied the fertilizer recommendation methods used in site-specific nutrient management and the calculation methodologies for N, P, and K recommendations. For this purpose, a systematic literature study (SLS), consisting of systematic literature mapping, snowballing, and systematic literature review was performed. The analyzed studies were grouped into five domains (precision agriculture, soil fertility, site-specific nutrient application, fertilizer recommendation methods, and recommendation software for site-specific nutrient application). As a result, the SLS identified 12 methods for recommending N, nine for recommending P, and six for recommending K, in addition to five computer programs for precision agriculture that perform fertilizer recommendations at varying rates.

Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran's bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.KEYWORDS: precision agriculture, spatial correlation, relative efficiency. VARIÁVEIS REDUNDANTES E A QUALIDADE DE ZONAS DE MANEJORESUMO: A agricultura de precisão proporciona aos agricultores identificar e tratar de forma adequada as variações encontradas na área agrícola. As zonas de manejo (ZMs) permitem a implantação da agricultura de precisão de forma viável e relativamente mais econômica. A forma mais importante para definir ZMs é usando o algoritmo fuzzy C-means. Um problema consiste em como selecionar a variável a ser usada como layer de entrada no processo fuzzy. Assim, o objetivo deste trabalho, foi testar a hipótese de que variáveis redundates podem prejudicar o processo de delineamento de ZMs. Este trabalho foi desenvolvido em uma área de 19,6 ha e 15 agrupamentos de ZMs foram gerados por meio do o algoritmo fuzzy C-means, dividindo-se em duas a cinco classes. Cada agrupamento usou uma composição diferente de variáveis, que são os atributos cobre, silte, argila, e altitude. Foi encontrado que algumas combinações dessas variáveis produziu melhores ZMs. Nenhuma combinação de variáveis produziu desempenho estatisticamente melhor que a ZM gerada apenas com as variáveis não redundantes. Assim, as variáveis redundantes podem ser descartadas. O agrupamento com todas as variáveis não forneceu maior separação e organização dos dados entre as classes de ZM, não sendo recomendado. PALAVRAS-CHAVE: agricultura de precisão, correlação espacial, eficiência relativa.

The study aimed to identify and evaluate the spatial variability in laminar erosion in areas using precision agriculture tools. Soil data from three properties in the western region of Paraná state, Brazil, were used: one in the municipality of Céu Azul (area A) and two in Serranópolis do Iguaçu (areas B and C). To identify discrepant data (outliers), analysis of the dispersion of quartiles was performed using a box-plot graph. Data normality was verified using the Kolmogorov-Smirnov test. A spatial analysis was performed using AgDataBox-Map software. The parameters of the universal soil loss equation were estimated and used with map algebra to produce a model to identify areas susceptible to erosion. Area A (soil loss estimate = 0-200 t ha-1 per year) presented greater susceptibility to erosion than areas B and C (soil loss estimate = 0-150 t ha-1 per year); however, all areas had a low susceptibility to erosion.

Yield mapping technologies can help to increase the quantity and quality of agricultural production. Current systems only focus on the quantification of the harvest, but the quality has equal or greater importance in some perennial crops and impacts directly on the financial profitability. Therefore, a system was developed to quantify and relate the quality obtained in the classification line with the plants of the orchard and for decision-making. The system is comprised of hardware, which obtains the location of the harvester bag during harvesting and unloading at the unloading site, and software that processes the collected data. The cloud of real-time data contributed from the different collectors (bins) allows the construction of yield maps, considering the multi-stage harvesting system. Further, the system enables the creation of a detailed map of the plants and fruits harvested. As the harvest focuses on quality, it takes place in stages, depending on the ripening of the fruits. In addition to the yield maps, the system allows identification of the efficiency of each worker undertaking the harvest by the number of performed discharges and by the time spent. The system was developed in partnership with the Federal Technological University of Paraná and Embrapa Uva & Vinho and was tested in apple orchards in southern Brazil. Although the system was evaluated with only data from apple cultivation, monitoring the quality and quantifying other orchard fruits can positively impact the fruit sector.

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