Search Results (11)

As China’s third-largest lead–zinc ore field, the Xicheng Ore Field has significant potential for discovering concealed deposits. In this study, a tectono-geochemical survey was conducted, and 1329 composite samples (comprising 5614 subsamples) were collected from the central part of the field. The dataset was analyzed using staged factor analysis (SFA) and concentration–area (C–A) fractal model. Four geochemical factors were extracted from centered log-ratio (CLR)-transformed data: F_2-1 (Ag–Pb–Sb–Hg), F_2-2 (Mo–Sb–(Zn)), F_2-3 (Au–Bi), and F_2-4 (W–Sn). Known Pb–Zn deposits coincide with positive F_2-1 and negative F_2-2 anomalies, as identified by the C–A fractal model, suggesting these factors are reliable indicators of Pb–Zn mineralization. Five Pb–Zn exploration targets were delineated. Statistical analysis and anomaly maps for F_2-3 and F_2-4 also indicate the potential for Au and W mineralization. Notably, some anomalies from different factors spatially overlap, indicating the possibility of epithermal Pb–Zn mineralization at shallow depths and mesothermal to hyperthermal Au and W mineralization at great depths. Overall, the integration of tectono-geochemistry, targeted and composite sampling, SFA, and C–A fractal modeling proves to be an effective and economical approach for identifying and enhancing ore-related geochemical anomalies. Full article

The N fertilization to reach high cranberry (Vaccinium macrocarpon) yields resulted in high proportions of soft berries. Our objective was to define the mineral nutrient balance of cranberry to reach a high yield of firm berries. The database comprised 393 observations on cv. ‘Stevens’. Berries were analyzed for total S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe, Al, and Si. Random Forest and XGBoost machine learning models were run to predict yield and firmness classes using raw concentrations, centered log ratios (clr) accounting for nutrient interactions, and weighted log ratios (wlr) that also considered the importance of each dual interaction. The wlr returned the most accurate models. The wlr standards elaborated from the high-yielding and nutritionally balanced subpopulation most often differed between the high-yield class and the high-firmness class. The wlr Cu level was significantly (p ≤ 0.01) too high to reach the high-yielding class in the nutritionally imbalanced subpopulation. There was excessive Al and shortage of Si and Mg to reach high berry firmness in the nutritionally imbalanced subpopulation (p ≤ 0.01), indicating the large influence of soil genesis on berry firmness. Despite statistical evidence, cranberry response to Al and Si corrective measures should be tested to elaborate site-specific recommendations based on soil and tissue tests. Full article

India is a major producer of mandarin oranges. However, the average fruit yield remains below potential due in part to multiple nutrient deficiencies. Our objective was to elaborate compositional nutrient diagnosis (CND) log-ratio standards accounting for nutrient interactions and the dilution the leaf tissue. We hypothesized that equally or unequally weighted dual nutrient log ratios integrated into centered log ratios (clr) or weighted log ratios (wlr) influence the accuracy of the CND diagnosis. The database comprised 494 observations on ‘Nagpur’, ‘Khasi’, and ‘Kinnow’ cultivars surveyed in contrasting agroecosystems of India. Weights were provided by gain ratios that indicated the importance of the dual log ratio on crop performance. The cutoff yield was set at the upper high-yield quarter for each variety. Centered log ratios (clrs) and weighted log ratios (wlrs) returned accuracies of 0.7–0.8 depending on the machine learning classification model. The gain ratios were not contrasted enough to make a difference between clr and wlr. We derived clr and wlr nutrient standards following the Gradient Boosting model. In a case study, the clr and wlr returned similar diagnoses. The capacity of clr and wlr to generalize to unseen cases and correct nutrient imbalance should be further verified in fertilizer trials. The diagnosis could also be conducted at a local scale, thanks to the Euclidian geometry and additivity of clr and wlr variables. Full article

The ceteris paribus assumption that all features are equal except the one(s) being examined limits the reliability of nutrient diagnosis and fertilizer recommendations. The objective of this paper is to review machine learning (ML) and compositional data analysis (CoDa) tools to make nutrient management feature specific. The accuracy of the ML methods averaged 84% across the crops. The additive and orthogonal log ratios of CoDa reduce a D-parts soil composition to D-1 variables, alleviating redundancy in the predictive ML models. Using a Brazilian onion (Allium cepa) database, the combined CoDa and ML methods returned crop response patterns, allowing feature-specific fertilizer recommendations to be made. The centered log ratio (clr) diagnoses plant nutrients as a compositional nutrient diagnosis (CND). Using a Quebec database of vegetable crops, the mean variance of clr variables ($\overline{VAR}$) allowed comparing total variance among species and growth stages. While clr is the summation of equally weighted dual log ratios, dual nutrient log ratios may show unequal importance regarding crop performance. The RReliefF scores, gain ratios or gini inequality coefficients can provide weighting coefficients for each dual log ratio. The widely contrasting coefficients of weighted log ratios (wlr) improved the accuracy of the ML models for a Quebec muck onion database. The ML models, $\overline{VAR}$ and wlr, are advanced tools to improve the accuracy of nutrient diagnosis. Full article

Identifying the local geochemical anomalies from stream sediment samples is challenging in regional-scale exploration programs. For this purpose, some robust and reliable techniques must be applied to distinguish the geochemical targets from the background values. In this research, a procedure of several tools, including singularity mapping (SM), random forests (RF), success-rate curves, and the t-Student method, were employed to analyze the geochemical anomalies within the intrusive-plutonic Torud-Chahshirin belt (TCB), northeast Iran. In this regard, the success-rate curves were initially applied to extract efficient geochemical signatures. Then, singularity analysis was used on the selected geochemical elements (Au, Cu, Pb, and Zn), which were transformed via centered log-ratio (clr) transformation. In the next step, due to the complexity of the ore-forming processes in the TCB, the structural factors (e.g., fault intersection and faults with different orientations) were determined. Based on the success-rate curves, NE-trending faults and fault density were distinguished as critical structural criteria. Afterward, the RF model as a robust machine learning algorithm was executed on the four efficient SM-based geochemical layers and two efficient structural factors. The anomaly map derived by the RF model (Accuracy = 98.85% and Error = 1.15%) illustrates a very high relationship with Cu ± Au mineral occurrences. Therefore, the RF algorithm assisted by the singularity method is more trustworthy for highlighting the weak geochemical prospectivity areas in the TCB. Full article

The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility. Full article

The world-renowned Jinchuan Cu-Ni-(PGE) sulfide deposit consists of four mainly independent intrusive units from west to east, namely Segments III, I, II-W, and II-E, and the main sulfide types are the disseminated, net-textured, massive, and Cu-rich ores. Due to the similar geochemical characteristics of each segment, there is no convenient method to distinguish them and explain their respective variations. Meanwhile, considering that the division of different types of ores is confusing and their formation is still controversial, direct classification using elemental discrimination maps can facilitate subsequent mining and research. In this paper, we report the new major and trace elements data from the Jinchuan deposit and collect the published data to construct a database of 10 major elements for 434 samples and 33 trace elements for 370 samples, respectively, and analyze the data based on multivariate statistical analysis for the first time. Robust estimation of compositional data (robCompositions) was applied to investigate censored geochemical data, and the input censored data were transformed using the centered log-ratios (clr) to overcome the closure effect on compositional data. Exploratory data analysis (EDA) was used to characterize the spatial distribution and internal structural features of the data. The transformed data were classified by partial least squares-discriminant analysis (PLS-DA) to identify different compositional features for each segment and ore type. The receiver operator characteristic (ROC) curve was used to verify the model results, which showed that the PLS-DA model we constructed was reliable. The main discriminant elements were obtained by PLS-DA of the major and trace elements, and based on these elements, we propose the plot of SiO₂ + Al₂O₃ vs. CaO + Na₂O + K₂O and Cs + Ce vs. Th + U to discriminate the different segments of the Jinchuan deposit, and the Al₂O₃ + CaO vs. Fe₂O₃^T + Na₂O and Co + Cu vs. Rb + Th + U to discriminate the different ore types. In addition, we predict that there are still considerable metal reserves at the bottom of Segment I. Full article

‘Esmeralda’ is an orange fleshed peach cultivar primarily used for juice extraction and secondarily used for the fresh fruit market. Fruit yield and quality depend on several local environmental and managerial factors, mainly on nitrogen, which must be balanced with other nutrients. Similar to other perennial crops, peach trees show carryover effects of carbohydrates and nutrients and of nutrients stored in their tissues. The aims of the present study are (i) to identify the major sources of seasonal variability in fruit yield and qu Fruit Tree Department of Federal University of Pelotas (UFPEL), Pelotas 96010610ality; and (ii) to establish the N dose and the internal nutrient balance to reach high fruit yield and quality. The experiment was conducted from 2014 to 2017 in Southern Brazil and it followed five N treatments (0, 40, 80, 120 and 160 kg N ha⁻¹ year⁻¹). Foliar compositions were centered log-ratio (clr) transformed in order to account for multiple nutrient interactions and allow computing distances between compositions. Based on the feature ranking, chilling hours, degree-days and rainfall were the most influential features. Machine learning models k-nearest neighbors (KNN) and stochastic gradient decent (SGD) performed well on yield and quality indices, and reached accuracy from 0.75 to 1.00. In 2014, fruit production did not respond to added N, and it indicated the carryover effects of previously stored carbohydrates and nutrients. The plant had a quadratic response (p < 0.05) to N addition in 2015 and 2016, which reached maximum yield of 80 kg N ha⁻¹. In 2017, harvest was a failure due to the chilling hours (198 h) and the relatively small number of fruits per tree. Fruit yield and antioxidant content increased abruptly when foliar clr_Cu was >−5.410. The higher foliar P linearly decreased total titratable acidity and increased pulp firmness when clr_P > 0.556. Foliar N concentration range was narrow at high fruit yield and quality. The present results have emphasized the need of accounting for carryover effects, nutrient interactions and local factors in order to predict peach yield and nutrient dosage. Full article

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha⁻¹. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders. Full article

The lipid fraction of human milk provides the infant with the fatty acids that are necessary for optimal growth and development. The aim of this study was to investigate the fatty acid composition of human milk at three time points during lactation and its change over time using appropriate statistical methods. Human milk samples from breastfeeding mothers at 6 weeks (n = 706), 6 months (n = 483), and 12 months (n = 81 with all three time points) were analyzed. Centered log-ratio (clr) transformation was applied to the fatty acid data. Principal component analysis (PCA) and generalized linear model-based repeated measure analysis were used to assess changes over time. The total lipid content was significantly higher at 6 months (β = 0.199, p < 0.029) and 12 months of lactation (β = 0.421, p < 0.001). The constituents of C20:3n-6 and C20:3n-3 were lower at 6 months (p < 0.001). Four distinct sub-compositional fatty acid groups were only identified at 12 months of lactation. The inclusion of small fatty acids of small constituent size in the analysis resulted in a shift in the balance between fatty acid constituents. Human milk fatty acid composition during prolonged lactation is different from that of human milk during a short duration of lactation. Our findings support the hypothesis that a combination of multiple fatty acids is important in fatty acid profiling beyond the presentation of individual fatty acids. Furthermore, the high variability of small fatty acids warrants attention because a compositional analysis may show more pronounced changes. Full article

Predicting the future climate and its impacts on the global environment is model based, presenting a level of uncertainty. Alternative robust approaches of analyzing high volume climate data to reveal underlying regional and local trends are increasingly incorporating satellite data. This study uses a centered log-ratio (clr) transformation approach and robust principal component analysis (PCA), on a long-term Normalized Difference Vegetation Index (NDVI) dataset to test its applicability in analyzing large multi-temporal data, and potential to recognize important trends and patterns in regional climate. Twenty five years of NDVI data derived by Global Inventory Modeling and Mapping Studies (GIMMS) from 1982 to 2006 were extracted for 88 subwatersheds in central Kenya and statistically analyzed. Untransformed (raw) and clr transformed NDVI data were evaluated using robust PCA. The robust PCA compositional biplots of the clr transformed long-term NDVI data demonstrated the finest spatial-temporal display of observations identifying climate related events that impacted vegetation activity and observed variations in greenness. The responses were interpreted as normal conditions, El Niño Southern Oscillation (ENSO) events of El Niño and La Niña, and drought events known to influence the moisture level and precipitation patterns (high, low, normal) and therefore the level of vegetation greenness (NDVI value). More drought events (4) were observed between 1990 and 2006, a finding corroborated by several authors and linked to increasing climate variability. Results are remarkable, emphasizing the need for appropriate data transformation prior to PCA, dealing with huge complex datasets, to enhance pattern recognition and meaningful interpretation of results. Through improved analysis of past data, uncertainty is decreased in modeling future trends. Full article