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The ability of soil to sequester carbon and reduce atmospheric CO2 concentrations is limited and depends on the soil minerals and their interaction with the microbiota. Microbial activities are closely associated with the types and amounts of soil organic matter (SOM) and clay minerals that have functional groups that interact with energy in Vis NIR-SWIR and Mid-IR wavelengths. The main objective of this research was to determine, based on these spectral ranges, the relation between mineralogical and organic compounds, as their sequestration and specialization in soils from Brazil. It was possible to map microbiological activity by spectral transfer functions and digital soil mapping reaching R2 from 0.77 to 0.85. Multiple regression equations were constructed to quantify enzymatic activity, microbial biomass carbon (MBC), particulate organic matter (POM), and resistant forms of carbon, and SOM associated with the mineral fraction (MAOM). All these properties were detected by specific bands obtained with the recursive feature elimination (RFE) algorithm, reaching correlations from 0.64 to 0.98 in specific ranges. The prediction model of the carbon sequestration potential was adjusted with microbiological and mineralogical variables from Vis-NIR-SWIR and the Mid-IR spectral range. A SARAR double autoregressive model was adjusted with r 0.61 and to a spatial error model (SEM) with r 0.7. The explanatory variables were associated with kaolinite, hematite, goethite, gibbsite, and the abundance of fungi, actinomycetes, vesico-arbuscular mycorrhizal fungi, enzymatic activity of beta-glucosidase, urease and phosphatase, and POM. Among the microbiological variables, the general abundance of fungi was the most important, in contrast to enzymatic activity that was the least important. The interaction between the different maps constructed and historical land use allowed the identification of areas that contribute to sequestering new carbon and could be the key to climate change mitigation strategies.

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Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.

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Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

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The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot's exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

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Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface ('skin') (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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Characterization of the spatial variability of vegetative vigor in vineyards can help improve the performance of site-specific management practices, or the management of vineyards with different rates. Characterization using canopy proximal sensing has been a widely disseminated technique; however, vineyards in southeastern Brazil, where the utilization of annual double pruning results in a winter harvest, knowledge of the role of variability in improving vineyard management has not yet been applied. This study aimed to determine if post-veraison mapping of a normalized difference vegetation index could be used to assess the variability in grapevine vigor, water status, physiology, yield and berry quality attributes at harvest in an irrigated vineyard in southeastern Brazil. This normalized difference vegetation index was measured with an active canopy sensor, and spatial distribution maps over two growing seasons of a vineyard, managed on an annual double pruning basis, were generated. Attributes of physiological and technological berry maturation, leaf water potential, gas exchange, production, and fresh pruning weight were calculated. These normalized difference vegetation index maps allowed for the determination of variability in vegetative vigor and the productive potential of the vineyard; however, high levels of rainfall during the maturation period may reduce the potential of using these maps for determining berry parameters.

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Characterization of the spatial variability of vegetative vigor in vineyards can help improve the performance of site-specific management practices, or the management of vineyards with different rates. Characterization using canopy proximal sensing has been a widely disseminated technique; however, vineyards in southeastern Brazil, where the utilization of annual double pruning results in a winter harvest, knowledge of the role of variability in improving vineyard management has not yet been applied. This study aimed to determine if post-veraison mapping of a normalized difference vegetation index could be used to assess the variability in grapevine vigor, water status, physiology, yield and berry quality attributes at harvest in an irrigated vineyard in southeastern Brazil. This normalized difference vegetation index was measured with an active canopy sensor, and spatial distribution maps over two growing seasons of a vineyard, managed on an annual double pruning basis, were generated. Attributes of physiological and technological berry maturation, leaf water potential, gas exchange, production, and fresh pruning weight were calculated. These normalized difference vegetation index maps allowed for the determination of variability in vegetative vigor and the productive potential of the vineyard; however, high levels of rainfall during the maturation period may reduce the potential of using these maps for determining berry parameters.(AU)

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A by-product of industrialization and population growth, automobile scrap yards are a potential source of metal contamination in soil. This study evaluated the use of portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (χ) analysis in assessing metal soil contamination in scrap yards located in Brazil. Five automobile scrap yards were selected in Curitiba, Paraná State (CB1, CB2, and CB3) and Lavras, Minas Gerais State (LV1 and LV2). By evaluating metal concentrations and geoaccumulation index values, we verified moderate Cu, Pb, and Zr contamination and moderate to high Zn contamination, primarily in the topsoil (0-10 cm). Soil Zn concentrations in automobile scrap yards were on average four times higher than in reference soils, suggesting that galvanized automobile parts may be the primary source of this soil contaminant. Although other elements (i.e., As, Cr, Fe, Nb, Ni, and Y) were slightly increased compared to reference values in one or more soils, concentrations did not constitute contamination. Automobile scrap yard topsoil had higher χ values (5.8 to 52.9 × 10-7 m3 kg-1) at low frequency (χlf) compared to reference soil (3.6 to 7.5 × 10-7 m3 kg-1). The highest values of χlf occurred in LV soils, which also represented the highest Zn contamination. Magnetic multidomain characteristics (percent frequency-dependent susceptibility between 2 and 10) indicated magnetic particle contributions of anthropogenic origin. The use of pXRF and χlf as non-destructive techniques displays potential for identifying soil contamination in automobile scrap yards.

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When the harvesting of sugarcane involves a mechanized process, plant residues remain on the soil surface, which makes proximal and remote sensing difficult to monitor. This study aimed to evaluate, under laboratory conditions, differences in the soil spectral behavior of surface layers Quartzipsamment and Hapludox soil classes due to increasing levels of sugarcanes dry (DL) and green (GL) leaf cover on the soil. Soil cover was quantified by supervised classification of the digital images (photography) taken of the treatments. The spectral reflectance of the samples was obtained using the FieldSpec Pro (350 to 2500 nm). TM-Landsat bands were simulated and the Normalized Difference Vegetation Index (NDVI) and soil line were also determined. Soil cover ranged from 0 to 89 % for DL and 0 to 80 % for GL. Dry leaf covering affected the features of the following soil constituents: iron oxides (480, 530 and 900 nm) and kaolinite (2200 nm). Water absorption (1400 and 1900 nm) and chlorophyll (670 nm) were determinant in differentiating between bare soil and GL covering. Bands 3 and 4 and NDVI showed pronounced variations as regards differences in soil cover percentage for both DL and GL. The soil line allowed for discrimination of the bare soil from the covered soil (DL and GL). High resolution sensors from about 50 % of the DL or GL covering are expected to reveal differences in soil spectral behavior. Above this coverage percentage, soil assessment by remote sensing is impaired.

Assuntos

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When the harvesting of sugarcane involves a mechanized process, plant residues remain on the soil surface, which makes proximal and remote sensing difficult to monitor. This study aimed to evaluate, under laboratory conditions, differences in the soil spectral behavior of surface layers Quartzipsamment and Hapludox soil classes due to increasing levels of sugarcanes dry (DL) and green (GL) leaf cover on the soil. Soil cover was quantified by supervised classification of the digital images (photography) taken of the treatments. The spectral reflectance of the samples was obtained using the FieldSpec Pro (350 to 2500 nm). TM-Landsat bands were simulated and the Normalized Difference Vegetation Index (NDVI) and soil line were also determined. Soil cover ranged from 0 to 89 % for DL and 0 to 80 % for GL. Dry leaf covering affected the features of the following soil constituents: iron oxides (480, 530 and 900 nm) and kaolinite (2200 nm). Water absorption (1400 and 1900 nm) and chlorophyll (670 nm) were determinant in differentiating between bare soil and GL covering. Bands 3 and 4 and NDVI showed pronounced variations as regards differences in soil cover percentage for both DL and GL. The soil line allowed for discrimination of the bare soil from the covered soil (DL and GL). High resolution sensors from about 50 % of the DL or GL covering are expected to reveal differences in soil spectral behavior. Above this coverage percentage, soil assessment by remote sensing is impaired.(AU)

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