ABSTRACT Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

ABSTRACT. Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

ABSTRACT: The adaptation of the Global Navigation Satellite Systems (GNSS) technology to fit the needs of farmers requires knowledge of the accuracy level delivered by a GNSS receiver in working conditions. To date, no methodology indicates the minimum number of replications to perform a statistical comparison. This study aims to advance knowledge on the methodological approach for evaluating the static and dynamic performance of GNSS receivers commonly used in agricultural operations. For the static test, a supporting frame in the ground carried all the receivers with coordinates properly transported. In the dynamic test, a circular rail with a 9.55 m radius was installed at ground level with a platform driven by an electric motor to carry the receivers at a constant speed. The transversal error of the receiver to the circular reference line was measured. The error with 95 % probability (E95) to receivers without differential correction ranged between 4.22 m and 0.85 m in the static test, and 2.25 m and 0.98 m in the dynamic test. Receivers with differential correction had E95 values below 0.10 m in the static test and 0.16 m in the dynamic test. Receivers with C/A code require five replications at minimum and 13 replications are needed for L1/L2 with differential correction signals in the dynamic test. The static test needs nine replications for C/A and five for L1/L2 with differential correction signals.

ABSTRACT: The mapping of sugarcane yield is still not as widely available as it is for grain crops. Sugarcane harvesters cut and process the cane in a single or maximum of two rows, facilitating the monitoring of cane yield and its behavior on a small scale. This study tested a method for sugarcane yield data cleaning, investigating if the data recording frequency influences the characterization of yield variations in mapping high-resolution spatial data within a single row. Four data sets from yield monitors of single row harvesting were used. A cleaning process with global and anisotropic filtering in a single sugarcane row was applied. The local outlier cleaner compares the yield value of a point with its nearest neighbors within the same row. Even after the elimination of outliers, there is great variation in yield between the rows, and this variation is much smaller in a single row. A frequency of 2 Hz was required for identifying and characterizing small yield variations within the sugarcane rows whilst other frequencies tried (0.2 and 0.1 Hz) resulted in loss of information on yield variability within the row. The difference between the root mean square error (RMSE) of ordinary kriging (OK) and inverse distance weighting (IDW) techniques is large enough to suggest the use of an individual yield line. Individual yield lines saved information in the data generated by the yield monitor unlike IDW and OK interpolation methods which omitted information over short distances within the rows and compromised the quality of high-resolution maps.

RESUMO As práticas de gestão localizada têm sido possibilitadas devido à ampla variedade de soluções para a aquisição de dados e as intervenções em nível de talhão. Diferentes abordagens devem ser consideradas para a coleta de dados, como os sensores de solo e de planta dedicados ou mesmo associados à capacidade das máquinas agrícolas gerarem dados relevantes que permitam ao agricultor ou ao gerente agrícola inferir a variabilidade espacial dos talhões. No entanto, elevados recursos computacionais são necessários para converter os extensos bancos de dados em informações úteis ao gerenciamento localizado da lavoura. Assim, as tecnologias oriundas da indústria, como a Internet das Coisas e a Inteligência Artificial, aplicadas à produção agrícola, têm direcionado o processo de tomada de decisão das práticas de agricultura de precisão. A interpretação e a integração de informações de diferentes fontes de dados permitem o aprimoramento do manejo agrícola devido à sua capacidade de predizer atributos de planta e de solo por meio de ferramentas avançadas baseadas em dados. Alguns exemplos são o monitoramento de cultivos, aplicações localizadas de insumos, e detecção de doenças por meio de sistemas baseados em nuvem nas plataformas digitais, previamente elaborados para os sistemas de apoio à decisão. Nesta revisão, discutimos as diferentes abordagens e recursos tecnológicos, popularmente denominados como Agricultura 4.0 ou Agricultura Digital, que se inserem no contexto do gerenciamento da variabilidade espacial das lavouras considerando diferentes fontes de dados de planta e de solo.

ABSTRACT Site-specific management practices have been possible due to the wide range of solutions for data acquisition and interventions at the field level. Different approaches have to be considered for data collection, like dedicated soil and plant sensors, or even associated with the capacity of the agricultural machinery to generate valuable data that allows the farmer or the manager to infer the spatial variability of the fields. However, high computational resources are needed to convert extensive databases into useful information for site-specific management. Thus, technologies from industry, such as the Internet of Things and Artificial Intelligence, applied to agricultural production, have supported the decision-making process of precision agriculture practices. The interpretation and the integration of information from different sources of data allow enhancement of agricultural management due to its capacity to predict attributes of the crop and soil using advanced data-driven tools. Some examples are crop monitoring, local applications of inputs, and disease detection using cloud-based systems in digital platforms, previously elaborated for decision-support systems. In this review, we discuss the different approaches and technological resources, popularly named as Agriculture 4.0 or digital farming, inserted in the context of the management of spatial variability of the fields considering different sources of crop and soil data.

ABSTRACT Soil fertility attributes have different scales and forms of spatial and temporal variations in agricultural fields. Adequate spatiotemporal characterization of these attributes is fundamental to the successful development of strategies for variable rate application of fertilizers, enabling the classic benefits of precision agriculture (PA). Studies on Brazilian soil have shown that at least 1 sample ha-1 is required for the reliable mapping of key fertility attributes. However, this sampling density is difficult owing to the operational challenges of sample collection and the cost of laboratory analyses. Given this limitation, soil sensors have emerged as a practical and complementary technique for obtaining information on soil attributes, at high spatial density, without the production of chemical residues and at a reduced cost. Scientists worldwide have devoted their attention to the development and application of sensor systems for this purpose. The concept of proximal soil sensing (PSS) was established in 2011 and involves the application of soil sensors directly on the field. PSS techniques involve different disciplines, such as instrumentation, data science, geostatistics, and predictive modeling. The integration of these different disciplines has allowed successful sensor application for the spatial diagnosis of soil fertility attributes. The present work aimed to present a bibliographic review of the concepts involved and main techniques used in soil sensing to predict fertility attributes. We sought to present a broad view of the challenges, advances, and perspectives of sensor application in Brazilian tropical soils in the context of PA.

Resumo: O objetivo deste trabalho foi avaliar o desempenho de um conjunto sensor direto, de íon seletivo (ISE) de antimônio, usando os modos de funcionamento manual e automático, na determinação do potencial hidrogeniônico (pH), em tempo real, em Latossolos com diferentes características. Amostras foram coletadas com trado e encaminhadas a laboratório para a determinação do pH em água e do cloreto de cálcio, cujos valores foram utilizados como referência. Um segundo conjunto de amostras foi coletado automaticamente utilizando-se um equipamento disponível no mercado, e foram executadas leituras em seu modo manual. Parâmetros de desempenho do conjunto foram calculados e avaliados. Parte dessas amostras de solo foi utilizada para avaliar a influência da umidade na determinação do pH. Retas de calibração foram construídas. As análises estatísticas apresentaram melhor resultado para o modo manual. Ambos os modos de operação apresentaram erros superiores aos aceitos como limite em técnicas laboratoriais, porém, com a possibilidade de elevada densidade de dados e com resultados positivos e promissores. Verificou-se que a umidade do solo interferiu no resultado das leituras. O conjunto sensor fornece uma densidade de amostragem de 45 dados por hectare, o que representa uma grande contribuição para a intensificação de dados para melhor avaliação espacial da informação do solo.

Abstract: The objective of this work was to assess the performance of an antimony ion-selective electrode (ISE) sensor system, using manual and automatic operating modes, for measuring the potential of hydrogen (pH), in real time, in Oxisols with different characteristics. Samples were manually collected and sent to a laboratory for determination of pH in water and calcium chloride, whose values were used as references. Another set of samples was also automatically collected using a commercial equipment, and readings were performed in manual mode. The performance parameters of the sensor system were calculated and evaluated. Part of the soil samples was used to assess the degree of influence of moisture in determining pH. Calibration lines were constructed. The statistical analysis revealed better results for the manual mode. Both modes of operation had errors superior to those accepted as a limit in laboratory techniques, however, with the possibility of high data density and with positive and promising results. Soil moisture interfered with the result of the readings. The sensor system yields a sampling density of 45 data per hectare, which represents a great contribution to the intensification of data for a better spatial evaluation of soil information.

RESUMO. Não existe consenso na literatura sob qual é o número de subamostras necessário para realizar o mapeamento da resistência do solo à penetração (RSP) de modo acurado. Dessa forma, o objetivo deste estudo foi definir o número de subamostras por ponto amostral para quantificar a RSP. O experimento foi realizado em uma área de 4,7 ha seguindo uma malha amostral de 50 × 50 m (18 pontos amostrais). A RSP foi avaliada com auxílio de um penetrômetro digital em dois anos seguidos utilizando-se 1, 2, 3, 4, 5, 6, 9, 12 e 15 subamostras por ponto amostral. Os mapas de RSP gerados com diferente número de subamostras foram comparados com o mapa referência (15 subamostras) utilizando o coeficiente de desvio relativo e de correlação linear de Pearson. A redução do número de subamostras por ponto amostral promove aumento da variabilidade da RSP. De maneira geral, esse estudo sugere o uso de pelo menos quatro subamostras por ponto amostral para atingir mapas de RSP com coeficiente de desvio relativo inferior a 10% (erro máximo de 30% em torno da média por ponto amostral) e correlação significativa com os mapas referência (15 subamostras).

ABSTRACT. There is no consensus in the literature regarding how many subsamples are needed to perform accurate on-farm soil penetration resistance (SPR) mapping. Therefore, the objective of this study was to define the number of subsamples per sampling point needed to quantify the SPR. The experiment was performed in a 4.7 ha area and employed a 50 × 50 m grid system (18 sampling points). The SPR was evaluated using a digital penetrometer in two different years with 1, 2, 3, 4, 5, 6, 9, 12, and 15 subsamples per sampling point. The SPR maps produced with increasing numbers of subsamples were compared to the reference maps (15 subsamples) using the relative deviation coefficient and Pearson´s linear correlation. A reduction in the number of subsamples promoted an increase in the variability of the SPR data. Generally, the results from this study suggest the use of at least four subsamples per sampling point to achieve SPR maps with a coefficient of relative deviation less than 10% (30% maximum error per point around the mean) and significant correlation with the reference maps (15 subsamples).

RESUMO. Sphenophorus levis tem se tornado uma praga importante na cultura da cana-de-açúcar, causando perdas expressivas na produtividade. São raros os estudos sobre a distribuição espacial desta praga, e tais estudos são imprescindíveis para o desenvolvimento de planos de amostragem. O objetivo deste trabalho foi caracterizar a distribuição espacial de S. levis definindo um plano de amostragem confiável, prático e viável, assim como gerar mapas de aplicação localizada para poder intervir em áreas de produção, possibilitando comparar o consumo de insumo de uma aplicação convencional com uma aplicação localizada. O estudo se desenrolou em áreas na região de Piracicaba, Brasil. Primeiramente foi realizada uma amostragem para avaliar a melhor densidade amostral para caracterizar a dependência espacial da praga. Os resultados indicaram que onde os avaliadores entravam a cada 20 fileiras de cana e faziam uma amostragem a cada 20 m foi suficiente para caracterizar a dependência espacial. Em outras áreas foi realizado a amostragem seguindo o padrão das avalições prévias e foram gerados os mapas para aplicação localizada de inseticida. Mesmo utilizando o nível de controle mais conservador, houve uma considerável redução na quantidade de defensivo aplicado, a porcentagem de área sem a infestação da praga foi 56,1 e 80,7%.

ABSTRACT. Sphenophorus levis has become a major pest of sugarcane plantations in Brazil, causing significant losses in productivity. Few studies have been conducted on the spatial distribution of this pest in the field, which is essential for the development of sampling plans. We aimed to characterize the spatial distribution of S. levis in sugarcane, defining a reliable, practical and feasible sampling plan, as well as generating application maps for production areas, allowing for comparison of the consumption of inputs in a conventional system with site-specific application. The study was conducted in areas around Piracicaba, Brazil. First, we conducted sampling to evaluate the best sampling density to characterize the spatial dependence. The results showed that the procedure in which the evaluators entered every 20 rows of sugarcane and sampled every 20 m was sufficient to characterize the spatial dependence. In separate areas, the sampling was performed similarly to the previous step, and after generating the application maps, insecticide was applied on a site-specific basis. Even using the more strict control level defined by the producer, there was considerable saving for the pesticide used. In the two fields, the percentage of the infestation-free area was 56.1 and 80.7%.

RESUMO: A soja é o principal produto do agronegócio Brasileiro, em volume e geração de renda. Considerando o aumento da demanda por alimentos e energia, bem como a busca por sistemas de produção mais sustentáveis, o presente estudo teve como objetivo analisar o uso de energia oriunda de insumos agrícolas em área de possível expansão de produção de soja: sistema de produção sob subirrigação em área de várzea no Cerrado, região Norte do Brasil. Seu desempenho ambiental foi comparado a outros locais no Brasil, entre os quais regiões tradicionalmente produtores de soja. A avaliação e comparação foram feitas por meio do uso de ferramentas de fluxo de materiais e energia, a fim de determinar a quantidade de insumos utilizados por área, bem como a demanda, disponibilidade e eficiência do uso de energia no sistema de produção avaliado. A demanda (IE) e disponibilidade (OE) de energia foram de 7.6 e 57.1 GJ ha-1, respectivamente. O balanço energético (BE), o retorno de energia sobre o investimento (EROI) e a energia incorporada dos grãos (EE) foram 49.5 GJ ha-1, 7.5 e 2.2 MJ kg-1, respectivamente. O maior consumo de energia foi devido à utilização de fertilizantes, herbicidas e combustível. O sistema analisado é eficiente no uso da energia, uma vez que fornece mais energia do que é demandado, e eficiente quando comparado a sistemas de produção usuais em outras regiões, embora seja altamente dependente de energia de origem não-renovável.

ABSTRACT: Soybean is the main product of Brazilian agribusiness, both production and income. Considering the increase in food and energy demand and the search for more sustainable production systems, this study aimed to analyze inputs and energy use of a possible area of expansion of soybean production: a system under sub irrigation management located in a lowland area of Cerrado biome, northern region of Brazil. Its environmental performance was compared to other Brazilian locations among them traditionally soybean producers. The evaluation and comparison was made through material and energy flow tools in order to determine the inputs embodied per area, as well as energy demand, availability and efficiency in the analyzed production system. Energy demand (IE) and energy availability (OE) of the analyzed production system were 7.6 and 57.1 GJ ha-1, respectively. Energy balance (EB) was 49,5 GJ ha-1, energy return over investment (EROI) was 7.5 and embodied energy in grains (EE) was 2,2 MJ kg-1, respectively. Highest energy consumption was due to the use of fertilizers, fuel and herbicide. The system is energy efficient, since it provides more energy than demands, and efficient when compared to usual production systems in other regions, however it is highly dependent on non-renewable energy.

RESUMO A cana-de-açúcar é uma cultura muito importante para o Brasil, usada para a produção, tanto de alimentos, quanto de biocombustíveis, provendo 18% do total de energia primária no país. Um importante requisito para uma produtividade alta é uma boa uniformidade do canavial, a qual é comprometida devido a falhas que aparecem nas linhas de cana causadas por falhas no plantio, danos durante a colheita, tráfego de máquinas, pragas e doenças entre outros fatores. O objetivo deste estudo é desenvolver um sistema baseado em um sensor fotoelétrico para examinar o canavial, georreferenciando as falhas e apresentando-as em um mapa. Um coletor de dados integrou um sensor fotoelétrico, um encoder um Sistema de Navegação Global por Satélites (GNSS em inglês) mensurando a distância entre as plantas, definindo as falhas. Testes em situação controlada, utilizando estacas, mostraram erros entre 0,02 e 0,03 m variando a velocidade de 1,3 a 3,0 m s-1 e as estacas de 0,1 a 1,0 m de espaçamento, os resultados encontrados foram próximos aos mensurados manualmente. Testes em campo utilizando amostras de 6,0 x 6,0 m (quatro linhas de cana) em cana planta e soqueira, também mostraram uma boa relação com a mensuração manual. Os dados brutos fornecem o tamanho de cada falha individualmente ou em porcentagem e uma interpolação entre falhas pontuais produz um mapa que evidencia áreas com baixa ou alta densidade de falhas, criando uma ferramenta útil no gerenciamento da cultura de cana-de-açúcar para que sejam tomadas as medidas necessárias a manutenção do canavial.

ABSTRACT Sugarcane is a very important crop in Brazil, used as food production and biofuel, providing 18% of the total primary energy in the country. An important requirement to high yield is a good uniformity in the sugarcane field, which is impaired due to row gaps that appear since the first year of sugarcane crop caused by planting failures, harvesting damages, machinery traffic, pests, diseases and others. The aim of this study is to develop a system based on a photoelectric sensor to scan the field, georeferencing gaps representing them as a map. A data logger integrates the data from the photoelectric sensors, an encoder and a Global Navigation Satellite System (GNSS) and measures the distance between plants, defining the gaps. Tests under controlled conditions, using regular obstacles simulating stalks, showed errors between 0.02 and 0.03 m under speeds varying from 1.3 to 3.0 m s-1 and obstacles ranging from 0.1 to 1.0 m of gap. Results of tests performed at sugar cane rows were close to manual measurement. Field tests were performed using sampling plots of 6.0 x 6.0 m (four crop rows) along the area, on newly planted and on ratoon areas, showed a good relation with manual measurements. The raw data provides the length of the individual gaps or its local percentage and the interpolation among punctual gaps produces a map highlighting areas with low and high gap intensity as a useful tool in the sugarcane management for decision making for local or integral replanting.

RESUMO As adições minerais são empregadas no concreto com o objetivo de melhorar algumas de suas propriedades mecânicas, bem como a sua durabilidade. Atualmente, tem-se cada vez mais adotado o uso de pozolanas de alta reatividade como ferramenta para obtenção do concreto de alta resistência (CAR). O metacaulim de alta reatividade (MCAR) é oriundo do processo de calcinação em temperaturas entre 400ºC e 950ºC e posterior processo de moagem de argilas com altos teores de caulinita, como a argila caulinítica e o caulim. O objetivo principal deste trabalho foi avaliar o uso do (MCAR) no concreto, visando verificar a sua influência no módulo de elasticidade do concreto. Para a obtenção desta adição mineral adotou-se como matéria-prima o rejeito proveniente da indústria do papel, que após um processo de calcinação e moagem, transforma-se no MCAR. Para a realização deste trabalho variou-se os teores de substituições de MCAR, as relações água/cimento e as idades de rompimento dos corpos-de-prova. A metodologia utilizada foi baseada em ensaios laboratoriais e no uso de ferramentas estatísticas para validação dos resultados. O estudo indicou que o efeito isolado das variáveis controladas e algumas interações entre essas variáveis influenciam nesta propriedade. Dentre esses efeitos isolados, constatou-se que as variações das porcentagens de MCAR de 0 a 20% resultam em acréscimos de até 9% no valor obtido no ensaio de módulo de elasticidade do concreto.

ABSTRACT The mineral additives are employed in the concrete in order to improve some of its mechanical properties as well as its durability Currently, there has been increasingly adopted the use of highly reactive pozzolans as a tool for obtaining high-strength concrete (HSC). The high reactivity metakaolin (HRM) comes from the calcination process at temperatures between 400 ° C and 950 ° C and subsequent grinding process clays kaolinite with high yields, as the kaolinitic clay and kaolin. This paper aims at assessing the use of HRMK within the concrete and verifying its influence within the modulus of concrete elasticity. To obtain this mineral addition a paper industry tailing was adopted as raw material, which was then calcinated and processed in a mill to be transformed into HRMK. To accomplish this work, the content of the HRMK replacements has been varied, as well as the water/concrete ratio and the ages of disruption of the body-test. The methodology used has been based on laboratorial assays and statistical tools in order to validate the outcomes. The study concludes that the isolated effect of the controlled variables and some interactions between these variables influenced the concrete's elasticity.

Resumo:O objetivo deste trabalho foi avaliar se um sensor de dossel é capaz de estimar a resposta da cana-de-açúcar ao N, bem como propor estratégias para tratar os dados gerados pelo equipamento durante o processo de tomada de decisão para a adubação nitrogenada da cultura. Foram conduzidos quatro experimentos de dose-resposta ao N, com doses de N que variaram de 0 a 240 kg ha-1. Duas avaliações foram realizadas com o sensor de dossel quando as plantas alcançaram altura média de colmos de 0,3 e 0,5 m. Apenas dois experimentos mostraram resposta ao N aplicado em termos de produtividade de colmos. O sensor foi capaz de identificar a resposta da cultura a diferentes doses de N e a relação do nutriente com a produtividade da cana. Os índices de resposta obtidos pelas leituras do sensor foram úteis em avaliar a resposta da cana-de-açúcar ao N aplicado. Sensores de dossel podem auxiliar na identificação de áreas responsivas à aplicação de N e, consequentemente, melhorar o manejo da fertilização nitrogenada da cana-de-açúcar.

Abstract:The objective of this work was to evaluate whether a canopy sensor is capable of estimating sugarcane response to N, as well as to propose strategies for handling the data generated by this device during the decision-making process for crop N fertilization. Four N rate-response experiments were carried out, with N rates varying from 0 to 240 kg ha-1. Two evaluations with the canopy sensor were performed when the plants reached average stalk height of 0.3 and 0.5 m. Only two experiments showed stalk yield response to N rates. The canopy sensor was able to identify the crop response to different N rates and the relationship of the nutrient with sugarcane yield. The response index values obtained from the canopy sensor readings were useful in assessing sugarcane response to the applied N rate. Canopy reflectance sensors can help to identify areas responsive to N fertilization and, therefore, improve sugarcane fertilizer management.

Soil electrical conductivity (ECa) is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, coefficients of determinations between soil moisture and clay content were above 0.70. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil ECa and soil moisture. Data repetition over the years, suggested that ECa is a qualitative indicator in areas with high spatial variability in soil texture.