Marcelo Henrique Procópio Pelegrino

Ensuring sustainable crop nutrition requires improving Zn phytoavailability in weathered and leached tropical soils, which depends not only on available Zn but also on soil pH, texture and P levels. This study developed a digital soil mapping framework for the 200‐million‐hectare Cerrado using 32,272 topsoil (0–20 cm) samples of available Zn, 42,163 of available P, and 42,417 for pH, mostly from precision‐management systems, applying spatial machine learning models for Zn and key soil properties (pH, texture and available P) and formalizing Zn deficiency conditionals to map Zn phytoavailability. Higher accuracy was obtained for Zn, followed by P and pH, with RFE–random forest models achieving R ² values of 0.61 for Zn, 0.41 for P and 0.43 for pH, with corresponding RMSEs of 1.65, 5.75 mg dm ⁻³ and 0.37. All predictions showed low uncertainty. Both conventional and newly developed environmental covariates, designed to capture anthropogenic influence, were ranked among the most important predictors. Overall, 78.3% of the Cerrado showed a medium Zn deficiency risk. The highest deficiency occurred under Zn‐pH interaction, affecting 4% (~7.7 million ha), followed by Zn‐Clay (3%, ~5 million ha). Medium risk was most pronounced for Zn‐pH (77%, ~158 million ha) and Zn‐P _rainfed (43.6%, ~88.5 million ha). Low risk was observed in 91%, 89%, and 55% of the area for Clay, P _irrigated and P _rainfed interactions, respectively. Accounting for soil properties such as pH, clay, and P rather than relying solely on extractable Zn is essential for accurately diagnosing Zn deficiency and estimating fertilizer needs. Zn demand across high‐risk areas was estimated at ~38.5 billion kg when considering other soil factors, compared with only ~7 billion kg when based solely on Mehlich‐1 Zn levels, a difference more than fivefold.

Airborne gamma-ray spectroscopy measures natural gamma radiation from uranium-238, thorium-232, and potassium-40 within the upper 30 cm of the Earth’s surface. In Brazil, there is an extensive spatial coverage of such information. Given the technique’s potential to trace soil parent materials and the scarcity of studies under tropical conditions, this study aimed to investigate the relationships among %K, eTh, and eU across contrasting geological, topographical, and climatic settings, focusing on the dominant soil class in southern Minas Gerais, Brazil. Representative areas included rock outcrops and Udoxes associated with pedopsamitic, poor pedopelitic, pedomafic, and pedogneissic groups. Gamma-ray channels (K, eTh, eU), their ratios (eTh/K, eU/K, U/Th), and F parameter were pre-processed and interpolated. Rock outcrops showed lower eTh (14.34 ± 6.03 ppm) and eU (1.54 ± 0.81 ppm) but higher K (1.01 ± 0.74%) compared to Udoxes, which had eTh of 15.98 ± 4.92 ppm, eU of 1.45 ± 0.43 ppm, and K of 0.39 ± 0.40%. Potassium proved to be the most effective geophysical tracer, exhibiting higher values in rock outcrops than in Udoxes, except over poor pedopelitic rocks rich in muscovite. The parameters K and F showed positive correlations with texture, slope, and terrain ruggedness index (TRI), while Udoxes correlated positively with paleoclimate (annual precipitation ~6,000 years ago). Airborne gamma-ray spectroscopy was capable of differentiating soil parent materials, providing valuable insights into the geological and pedological controls on soil formation, even when there is a large cover of a thick soil compared to rock outcrops (~ 9.5 times smaller).

The need to maintain soil health and produce more food worldwide has increased, and soil analysis is essential for its management. Although spectroscopy has emerged as an important tool, it is important to focus primarily on predictive modeling procedures rather than specific protocols. This article aims to contribute to a routine work sequence in a hybrid laboratory that seeks to provide the best data for its users. In this study, 18,730 soil samples from the state of Paraná, Brazil, were analyzed using three different laboratories, sensors and geometries for data acquisition. Thirty soil properties were analyzed, some using different chemical methodologies for comparison purposes. After a spectral reading, two literary protocols were applied, and the final prediction results were observed. We applied cubist models, which were the best for our population. The combination of different spectral analysis systems, with a standardized protocol using LB for the ISS detection of discrepant samples, was shown to significantly improve the accuracy of predictions for 21 of the 30 soil properties analyzed, highlighting the importance of choosing the extraction methodology and improving data quality, which have a significant impact on laboratory analyses, reaffirming spectroscopy as an essential tool for the efficient and sustainable management of soil resources.

Most Brazilian municipalities do not have regulated areas for solid waste disposal in civil construction. Usually, residues are disposed of vacant lots and dumps, posing risks to the population health and the environment. Soils are the primary means for the disposal or recycling of waste, highlighting the importance of well-characterized soils and their respective landscape. This study aimed to establish a land suitability system for solid residues in civil construction and apply such information in a case study in Southeastern Brazil. An unprecedented digital soil map with a resolution of 30 m was created using the random forest classifier algorithm and soil field prospection information. A guide listing favorable soil-landscape attributes that most prevent soil erosion, water bodies or water table contamination was elaborated and discussed. Thus, such information was linked through a suitability system to classify areas with potential for receiving waste on a daily volume basis as follows: large size: >500 m³ day ^-1 , medium size: >100 m³ day ^-1 and -1 , and small size -1 . Topography and soil depth were the most limiting factors of the areas in the case study. The proposed attributes as criteria for the suitability system complement the current state legislation. A total of 236 ha closer to the urban perimeter connected by roads in good condition were classified as suitable for managing medium- and small-scale daily volume, whose destination might reduce transportation and installation costs in the study area.

Portable X-ray fluorescence (pXRF) spectrometry offers valuable information for prediction models of soil fertility attributes spatial variation, although this approach is yet scarce in tropical regions. This study aims to predict and build spatial variability maps of soil pH, remaining phosphorus (P-Rem), soil organic matter (SOM) and sum of bases (SB) using pXRF results through stepwise multiple linear regression (SMLR) and Random Forest (RF) in a highly variable tropical area. Composite samples from soil A horizon were collected at 90 points throughout the campus of the Federal University of Lavras, Minas Gerais, Brazil, for pH, P-Rem, SOM, SB and pXRF analyses. RF predictions showed the highest accuracies, especially for P-Rem and SB (R² values of 0.66 and 0.55, respectively). Attributes that showed higher R² in punctual predictions also exhibited higher R² in spatial predictions. Data obtained from pXRF in tandem with RF can be used to assist prediction models for soil fertility attributes, consequently enabling the digital mapping of such attributes and helping to improve the knowledge about the spatial variability of such attributes in soils of tropical climate. This technique can therefore assist in the identification and orientation of adequate management practices in tropical agricultural practices.

Existing soil maps (legacy data) associated with digital mapping techniques are alternatives to obtain information at lower costs, however, tests are required to do it more efficiently. This study had as objectives to compare different methods to extract information from detailed scale soil maps using decision trees for mapping soil classes at two watersheds in Minas Gerais, validate these maps in the field and use the best method to extrapolate information to larger areas, also validating these maps of larger areas. Detailed soil maps of Vista Bela creek (VBW) and Marcela creek (MCW) watersheds were used as source of information. Seven methods to extract information from maps were compared: the whole polygon, eliminating 20 and 40 m from the polygon boundaries, and with buffers around the sampled points with radii of 25 m, 50 m, 75 m, and 100 m. The Classification and Regression Trees (CART) algorithm was employed to create decision trees and enable creation of soil maps. Accuracy was assessed through overall accuracy and kappa index. The best method was used to extrapolate information to larger areas and maps were validated. The best methods for VCW and MCW were, respectively, eliminating 20 m from polygon edges and buffer of 25 m of radii from points. Maps for larger areas were obtained using these methods. Removing uncertainty areas from legacy soil maps contribute to better modeling and prediction of soil classes. Information generated in this work allowed for validated extrapolation of soil maps for regions surrounding the watersheds.