Soil sampling is a fundamental stage for recommending agricultural correctives and fertilizers, estimating the nutritional demands of plants, and consequently maximizing productivity. Therefore, this study aimed to assess the performance of three soil samplers in different management systems in terms of sample quality and operational efficiency. A completely randomized experimental design was used in a factorial scheme. Three samplers and two sampling depths (3 × 2) were used with four replicates. At each sampling location, eight single samples were taken at a varying sampling depth of 0.0-0.2 and 0.2-0.4 m, and the collection time was recorded. Samples were analyzed for chemical attributes and granulometry. Statistically significant differences were observed for specific attributes (organic matter, K, Ca, CEC, pH, and S). In terms of operational efficiency, the hydraulic sampler was more efficient than the other samplers, being three times faster than the combustion drill and six times faster than the manual probe. Thus, it is suitable and reliable for soil sampling purposes.

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R² of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture.

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