The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0-0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over-or under-grazing.

Este artigo apresenta e discorre sobre os atributos da agricultura de precisão (ap) e da agricultura digital (ad), expondo as particularidades e sinergias de cada uma delas. Explica como a ap vem sendo empregada nos sistemas de produção vegetal e animal em vários países desde a década de 1990, com uma intensidade e abrangência em relação à área e aos tipos de sistemas de produção que a adotam e que evoluem gradualmente. A ap compreende o uso de procedimentos e de equipamentos, implementos e/ou sensores que avaliam a variabilidade espacial e temporal de atributos do solo, planta, animal ou clima, com o intuito de fornecer informações que subsidiam a tomada de decisão pelo produtor ou profissional quanto à realização de uma prática ou manejo agrícola de modo diferenciado ou variável. Em muitas das atividades realizadas dentro do contexto da ap, a coleta, o armazenamento, a análise e a transmissão de dados ou informações sobre solo, planta, animal ou clima de um específico sistema de produção agrícola, são realizadas por hardwares e softwares, os quais se enquadram dentro do contexto de ad. Muitos desses procedimentos também podem ser realizados com diferentes graus de automação, parcial ou total. Termos usualmente utilizados na produção vegetal e animal, como “tecnologias de informação e comunicação”, “conectividade”, “internet das coisas”, “nuvem”, “algoritmo”, “aplicativo”, “base de dados”, entre outros, podem estar relacionados entre si e com a ap e/ou com a ad. No Brasil, como em muitos outros países, a ap e a ad encontram-se em um processo dinâmico de discussão crítica, desenvolvimento, adaptação, validação e aplicação.Palavras-chave: Tecnologias de Informação e Comunicação. Conectividade. Internet das Coisas. Nuvem. Algoritmo. Aplicativo. Base de Dados.

This work has base in the demand of research and development of data communication networks (fieldbus) to support the integration of control and automation devices for applications in agricultural systems. Agricultural systems related with the Precision Agriculture practices, with the embedded systems in agricultural machinery and with the greenhouses control and livestock systems. It is also guided by the efforts on the implementation of ISO11783 standard. The ISO11783 (also called ISOBUS) standard communication link is a common tendency to integrated different devices on agricultural machinery through an embedded control network. The ISOBUS use the Controller Area Network (CAN) as a data link protocol to perform the data communication. The correct definition of the data link configuration parameters represents one of the main challenges related to the design of CAN-based networks. The definition of these parameters has influence in the performance of the analyzed network. This work presents the research and the development of a performance analysis tool of CAN-based networks for applications in agricultural systems. This development consists of the systematization and validation of a CAN mathematical model. An analysis methodology is proposed to use the mathematical model. A simulation software was built and implements the methodology. It is expected that the implemented methodology facilitates the analysis tasks of the configuration parameters of the applications. The result obtained may assist in the performance evaluation and in the definition of an optimized configuration for the network based on CAN protocol and ISO11783 standard.

O conhecimento da variabilidade espacial das propriedades do solo e das culturas é importante para as tomadas de decisão sobre o manejo agrícola. Objetivou-se, neste trabalho, avaliar a variabilidade espacial de parâmetros químicos e físicos do solo e biofísicos de superfície de área cultivada com sorgo. O estudo foi conduzido em área de 12 ha de um Argissolo Vermelho Amarelo distrófico. A amostragem de solo georreferenciada e a medição da condutividade elétrica do solo foram realizadas antes do plantio do sorgo. As imagens do satélite Landsat 5 foram utilizadas para calcular os parâmetros biofísicos de superfície. Ferramentas de geoestatística foram utilizadas para se determinar e modelar a variabilidade espacial dos atributos em estudo em que os resultados mostraram que a densidade de amostragem adotada foi insuficiente para uma caracterização adequada da variabilidade espacial de parâmetros do solo. Ocorreu dependência espacial de grau, moderada a forte para CE e parâmetros biofísicos da superfície da imagem de satélite com alcances variando de 74,4 a 181,1 m; por outro lado, o sensoriamento remoto orbital também foi útil para o mapeamento da variabilidade espacial da cultura do sorgo e tem grande potencial para aplicação da Agricultura de Precisão.