Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH, and VV) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover.

This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg⁻¹ to 45.63 g kg⁻¹, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method.

Segmenting agricultural fields into management zones (MZ) is a core principle of precision agriculture (PA). However, the widespread adoption of PA remains limited, partly due to operational barriers in MZ segmentation. These barriers often involve the necessity for advanced programming skills and a strong statistical background, in addition to the lack of a free, integrated and straightforward tool that executes the entire workflow. Addressing this gap required the development of the open-source QGIS plugin Precision Zones. The plugin reproducibly implements the entire MZ segmentation pipeline: (i) raster layers preprocessing; (ii) dimensionality reduction via Principal Component Analysis (PCA); (iii) multivariate clustering using K-Means ++, with integrated support for determining the optimal number of zones through the Elbow and Silhouette methods; (iv) spatial filtering of MZ to mitigate noise; and (v) assessment of MZ agronomic effectiveness using statistical metrics (i.e., within-zone variance reduction). This tool enables practical MZ segmentation for a wide range of agricultural applications, eliminating the need for programming knowledge. Despite its robust architecture, as a novel tool, it has not yet been formally characterized and presented to the scientific community. Therefore, this study describes the Precision Zones plugin, address the step-by-step user decisions and presents its validation. In a reproducible case study, the plugin produced agronomically coherent MZ and reduced within-zone variability (VR%) for most soil attributes analyzed. The study concludes that Precision Zones provides a reproducible, user-friendly workflow that bridges the gap between advanced spatial analysis and practical precision agriculture applications for growers, consultants and researchers.

Introdução e Objetivo. Este estudo objetivou analisar a cobertura de Mata Atlântica na subprefeitura de Parelheiros, cidade de São Paulo, de 2014 a 2022. Metodologia. Utilizou-se o Índice de Vegetação Melhorado (EVI), imagens Landsat-8 e dados do portal GeoSampa. O EVI, que mostra o vigor vegetativo, passou por uma limiarização na qual se dividiu a classe de vegetação arbórea das demais, de modo a contabilizar a proporção de mata em Parelheiros e nas duas Áreas de Proteção Ambiental (APA) situadas na subprefeitura. Resultados. Apesar de estudos mostrarem a ocorrência de desmatamentos, esse trabalho não obteve resultados expressivos quanto à diminuição da vegetação local. A cobertura arbórea se manteve em torno de 75% ao longo dos anos em Parelheiros e na APA Bororé-Colônia, e 80% na APA Capivari-Monos. Conclusão. A metodologia foi validada por meio da métrica acurácia, atingindo o valor mínimo de 93% e o índice Kappa com valores acima de 86%.

Soil sampling for fertility mapping remains a challenge in agricultural management, mainly due to high costs. Several techniques have been employed to mitigate this issue, each with its own advantages and limitations. This study proposes an approach based on the delineation of management zones, considering soil classification, topography, and Apparent Magnetic Susceptibility (MSa). MSa was combined with each covariate to create two distinct sampling-zone configurations. We evaluated this methodology in a 107-ha area, assessing the representativeness of the zones for mapping potassium (K), phosphorus (P), and clay and seeking to determine the minimum number of subsamples required within each zone. We considered three error levels relative to the reference mean (10%, 20%, and 30%). The results indicated that zone-based sampling is effective for attributes with a well-defined spatial pattern, but limited for attributes with random distribution, such as phosphorus. Both zone configurations showed good capacity to represent variability. A 20% error level provided a balance between cost and accuracy, with about 12 subsamples per zone, which is considered feasible and consistent with field practice.