Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil ...characterization, this work aimed to: (i) characterize sandy soils’ profiles from proximal sensing; (ii) assess the ability of visible, near, and short-wave infrared (Vis-NIR-SWIR) as well as mid-infrared (MIR) spectroscopy to distinguish soil classes of highly sandy content; (iii) quantify physical and chemical attributes of sandy soil profiles from Vis-NIR-SWIR and MIR spectroscopy as well as X-ray fluorescence (pXRF). Samples were described and collected from 29 sandy soil profiles. The 127 samples went under Vis-NIR-SWIR and MIR spectroscopy, X-ray fluorescence, and chemical and physical analyses. The spectra were analyzed based on “Morphological Interpretation of Reflectance Spectrum” (MIRS), Principal Components Analysis (PCA), and cluster methodology to characterize soils. The integration of different information obtained by remote sensors, such as Vis-NIR-SWIR, MIR, and Portable X-ray Fluorescence (pXRF), allows for pedologically complex characterizations and conclusions in a short period and with low investment in analysis and reagents. The application of MIRS concepts in the VNS spectra of sandy soils showed high potential for distinguishing pedological classes of sandy soils. The MIR spectra did not show distinct patterns in the general shapes of the curves and reflectance intensities between sandy soil classes. However, even so, this region showed potential for identifying mineralogical constitution, texture, and OM contents, assuming high importance for the complementation of soil pedometric characterizations using VNS spectroscopy. The VNS and MIR data, combined or isolated, showed excellent predictive performance for the estimation of sandy soil attributes (R2 > 0.8). Sandy soil color indices, which are very important for soil classification, can be predicted with excellent accuracy (R2 from 0.74 to 0.99) using VNS spectroscopy or the combination of VNS + MIR.
•New information on the frost prediction for forest.•Frost forecasting from spatial data.•Machine learning algorithms to classify frost risk probability.•High prediction performance using Random ...Forest classifier.•Transferability of the predictive approach for other agricultural plantations.
Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest – RF; Support Vector Machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is essential for planning strategies of plantation at the landscape scale.
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Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.) can take several years as these traits must be evaluated under multiple harvests. Therefore, genotype‐by‐harvest ...interaction should be incorporated into genomic prediction models to explore genotypes’ adaptability and stability. In this study, we investigated how enviromics could help to predict the genotypic performance under multiharvest alfalfa breeding trials by evaluating 177 families across 11 harvests under four cross‐validation scenarios. All scenarios were analyzed using six models in a Bayesian mixed model framework. Our results demonstrate that models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance, indicating better biological explanation when the enviromic information was incorporated. Furthermore, models that accounted for enviromic data led to higher predictive ability (PA) in a reduced number of harvests used in the training data set. The best enviromic models (M2 and M3) outperformed the base model (GBLUP model—M0) for predicting adaptability and persistence across all cross‐validation scenarios. Incorporating environmental covariates also provided higher PA for persistence compared with the base model, as predictions increased from 0 to 0.16, 0.20, 0.56, and 0.46 for CV00, CV1, CV0, and CV2. The results also demonstrate that GBLUP without enviromics term has low power to predict persistence, thus the adoption of enviromics is a cheap and efficient alternative to increase accuracy and biological meaning.
Core ideas
Enviromics can increase the predictive ability for genotypic performance across harvests.
The use of enviromic information can reduce the number of harvests needed to train genomic prediction models.
Models including enviromic data outperformed the base model for predicting adaptability across all scenarios.
GBLUP models without incorporating enviromics had low power to predict persistence.
Accounting for G×E by inclusion of multiharvest data increased the predictive ability for most traits.
ABSTRACT High accuracy in timber volume estimation in tropical forests is required to support sustainable management. Terrestrial laser scanners (TLS) can provide high-quality estimates from tree ...structural variables. We compared stem variable estimations obtained by TLS and traditional methods at tree level and adjusted volume equations using data of a secondary seasonal semideciduous forest (Atlantic Forest). We also discuss the feasibility of TLS in hyperdiverse and secondary forest fragments. Traditional measurements (Method I) and TLS-based measurements (Method II) were performed on 29 trees belonging to 10 species. Volume equations based on the Schumacher and Hall (SH) and Spurr models were generated. DBH (diameter at breast height) was equal for both methods. Total height (TH) was overestimated by Method II, and commercial height (CH) showed a low correlation between the two methods. The adjusted volumetric equations were different for both methods, and those based on the SH volume model showed the best fit. Our results lead us to infer that in hyperdiverse secondary forests, tree structural variables should be obtained via TLS. However, attention should be given to the occlusion of target trees by the regenerating understory and to height estimates, which can be biased by the crown characteristics of the dominant species.
RESUMO A alta precisão na estimativa do volume de madeira em florestas tropicais é necessária para apoiar o manejo sustentável. Os scanners a laser terrestres (TLS) podem fornecer estimativas de alta qualidade a partir de variáveis estruturais de árvores. Comparamos estimativas de variáveis do fuste obtidas por TLS e métodos tradicionais em nível de árvore e equações de volume ajustadas usando dados para uma floresta semidecídua sazonal secundária (Mata Atlântica). Também discutimos a viabilidade do TLS em fragmentos florestais hiperdiversos e secundários. Medições tradicionais (Método I) e medidas baseadas em TLS (Método II) foram realizadas em 29 árvores pertencentes a 10 espécies. Foram geradas equações de volume baseadas nos modelos de Schumacher e Hall (SH) e Spurr. O DAP (diâmetro à altura do peito) foi igual para ambos os métodos. A altura total foi superestimada pelo Método II, e a altura comercial apresentou baixa correlação entre os dois métodos. As equações volumétricas ajustadas foram diferentes para ambos os métodos, e aquelas baseadas no modelo de volume SH apresentaram o melhor ajuste. Nossos resultados nos levam a inferir que em florestas secundárias hiperdiversas, as variáveis estruturais das árvores devem ser obtidas via TLS. No entanto, atenção deve ser dada à oclusão das árvores alvo pelo sub-bosque em regeneração e às estimativas de altura, que podem ser influenciadas pelas características da copa das espécies dominantes.
Despite the success of using soil spectroscopy in studies to predict soil attributes, like soil organic carbon (SOC), recent work has revealed several limitations to this approach: a tendency for ...model overfitting and a lack of transparency of machine learning (ML) methods. Thus, we aimed to both test the ability to improve the generalizability of the models to predict SOC using a cross-validation (CV) strategy oriented to soil profiles and to test the gain in model interpretability by using the least absolute shrinkage and selection operator (LASSO) regression method instead of the commonly used partial least squares (PLS) method. We used one soil spectral library composed of 127 soil profiles (n = 701), from Northeast Brazil, containing reflectance data from the visible, near, and short-wave infrared (VNIR) and the mid-infrared (MIR) spectral regions. We tuned the ML models to predict SOC via two CV strategies: the standard k-fold CV and the leave-soil-profile-out (LSPO) CV. We found that LSPO CV can produce models with better generalizability, as they lose less accuracy than the ones trained with k-fold CV. We conclude that disregarding the autocorrelation of SOC within the soil profile can produce models that are prone to overfitting. In addition, LASSO used 105 covariables from VNIR and 190 from MIR for a total of 8604 and 13,336 covariables, respectively. Moreover, a few LASSO covariables correlated with SOC and are associated with both electronic transitions and vibrational bonds in organic compounds, so the possibility and ease of identifying spectral bands and their correlation with organic carbon indicate that the LASSO models presented more transparent models than the PLS models.
•We have studied regression models between soil organic carbon and soil spectroscopy.•We propose to use the Least Absolute Shrinkage and Selection Operator (LASSO).•LASSO has shown to be more accurate and transparent than Partial Least Squares.•When using soil profile samples, the SOC's autocorrelation should be addressed.•Soil profile-oriented cross-validation improved the models' generalization ability.
The great geological and soil variation in the state of Minas Gerais, Brazil, indicates the need for regional studies to understand the geochemical background of soils. The Rio Doce Basin became a ...priority area for geochemical background determination after the rupture of the tailings dam of Fundão in 2015. In this context, the objectives of this study were to propose Reference Values of Soil Quality in the Rio Doce Basin, to define variables that can predict metal(loid) concentrations in the soil, and to examine the correlation between metal(loid) concentrations determined by X-ray fluorescence and by the traditional method. One hundred and seven samples were collected from minimally disturbed areas, representing the main soils and source materials. Metal(loid)s were determined by acid digestion and X-ray fluorescence. Descriptive statistics of the data, as well as the calculation of the Randomized Dependence Coefficient (RDC) and Principal Component Analysis (PCA) were carried out. The soils were found to be acidic, dystrophic with low Mehlich-1 extracted P contents, and have a variable texture. The coefficient of determination ranged from 0.4 to 0.9, suggesting X-ray fluorescence as a promising technique for determining metal(loid) concentrations in soils. The absence of correlation between clay and organic matter contents with metal(loid) concentrations suggests that the latter were inherited exclusively from the parent material, with little influence of pedogenesis. Metal mineralization in the highlands that constitute the topographic drainage divide of the basin increase the reference values of soil quality to higher values than established for the State of Minas Gerais.
Understanding the weathering intensities can provide answers for environmental issues, soil, and geoscience studies. Recently, geophysical approaches and machine learning techniques have been applied ...in soil science to access weathering. This study aimed to model weathering intensity using combined data from geophysical sensors, satellite images, and morphometry associated to machine learning algorithms. We also we evaluated the efficiency of nested-leave one out cross-validation applicability in a small geodata set evaluated the importance of covariates and the resulting weathering intensity map in relation to pedogeomorphological processes. Our study focused on a 184-ha area in southwest Brazil, where we conducted soil analysis at 71 sites. We applied principal component analysis and determined the ideal number of clusters to determine the classes of weathering intensity. We used six geophysical sensor parameters, including equivalent uranium, equivalent thorium, potassium 40, magnetic susceptibility, and soil apparent electrical conductivity, along with the weathering index to create the clusters. Then, four machine learning algorithms were used to infer different weathering intensities in soils formed from the same parent materials. To validate our results, we used the nested-leave-one-out-cross-validation (“nested-LOOCV”) method, which is suitable for small datasets. Our findings showed that the random forest model performed the best with three clusters as the ideal number. We also found that the geophysical data, clusterization, and machine learning algorithm contributed significantly to identifying different weathering intensities. The results indicated that weathering operated at different intensities on both the diabase/Rhodic-Nitisols and the siltite/metasiltite Rhodic and Xanthic Lixisols areas, with the highest intensities occurring in the west Xanthic Lixisols and the lowest intensities occurring in the Rhodic and Lixisols in the east area. The siltite/metamorphosed siltite and Lixisols areas presented moderate weathering rates. We found that the all-geophysical variables used were related and affected by weathering intensity, which contributed to the modeling and clusterization processes.
•Combined use of geophysical sensors in modeling soil weathering intensity.•Nested Leave One Out Cross-Validation for external validation in small dataset.•Use pedoenvironmental covariates and satellite image to determine ideal number of clusters.•Geophysical data, machine learning and clustering identified different weathering intensities.•Use of clusters in modeling processes.
El objetivo de este estudio fue analizary comparar la composición corporal y el somatotipo de atletas ciegos de fútbol 5 de alto rendimiento de diferentes posiciones de juego. Participaron de esta ...investigación 63 atletas (28,0±5,8años) de diferentes equipos masculinos defútbol5 de alto rendimiento. Los atletas fueron sometidos a una evaluación antropométrica a fin de para obtener las medidas: espesor de pliegues cutáneos, perímetros corporales, diámetros óseos, estatura y masa corporal total. A partir de estas medidas fueron calculados los siguientes parámetros: porcentaje de grasa corporal y porcentaje de masa magra, Σ de 9 pliegues cutáneos, índice de masa corporal y somatotipo. Los jugadores Alas presentaron valores inferiores en porcentaje de grasa (%G=17,4%) en comparación con los atletas Cierre (23,1%) y Pivot (21,5%), ambos con diferencia significativa (p<0,05). Se obtuvo un perfil somatotpio meso-endomorfo con predominancia del componente muscular, tanto para el grupo general como separadamente por posiciones de juego. El conocimiento de la composición corporal y del somatotipo de estos atletas de fútbol5 podrá contribuir hacia la orientación y el monitoreo de entrenamientos, favoreciendo el rendimiento deportivo.
Palabras Clave: Discapacidad visual; Atletas paralímpicos; Somatotipo; Antropometría, Fútbol 5.
Abstract. The aim of this study was to analyze and compare the body composition and the somatotype of blind athletes of 5-a-side football of high-performance in different positions in play. Participated in this research 63 blind athletes (28.0 ± 5.8 years) from male of teams of 5-a-side football of high-performance.The athletes underwent an anthropometric evaluation to obtain the following measurements: skinfold thickness, body perimeters, bone diameters, height and total body mass. From these measurements, the following parameters were calculated: body fat percentage, lean mass percentage, Σ of the 9 skinfolds, body mass index and somatotype. Wing athletes showed a significant difference (p <0.05) with lower values for % G (17.4%) compared to fixed athletes (23.1%) and pivots (21.5%). A meso-endomorphic somatotype profile was obtained, with a predominance of the muscular component in the team and in all positions in play. The knowledge of body composition and somatotype of these blind athletes of 5-a-side footballcan contribute to guidance and monitoring of training, favoring sports performance.
Key words: Visual impairment; Paralympic athletes; Somatotype; Anthropometry, Five-a-side football.
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