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Novel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets. Core Ideas: Soil properties can be predicted via proximal sensors using multinational datasets.This study encompassed soil samples from Brazil, France, Mozambique, India, and United States.Soil organic carbon and texture were accurately predicted via proximal sensors.The broad application of proximal sensors to aid soil characterization is encouraged. [ABSTRACT FROM AUTHOR]

Mollisols are dark colored, carbon‐rich mineral soils occupying a large proportion (836 soil series) of the soils of the Central Plains of the United States. By contrast, only eight official soil series of Mollisols have been mapped in Alaska (six Haplocryolls, one Calcicryoll, one Haplogeloll). Little information exists about Geloll pedogenesis, taxonomic variability, and extent. In this study, 39 horizons were morphologically described across ten Geloll pedons in northern Alaska. Based on analogous taxonomic structure in Cryolls, two pedons would meet the criteria for a Fluventic subgroup as the organic carbon content was ≥0.3% (1.47% and 0.88%) at a depth of 125 cm below the mineral soil surface. Three pedons would meet the criteria for a Pachic subgroup because the mollic epipedon was thicker than 40 cm (52 cm, 53 cm, and 54 cm) and the texture class was finer than loamy fine sand (sandy loam). However, no Fluventic or Pachic subgroups currently exist for Haplogelolls. Two pedons were classified as Cumulic Haplogelolls, and three pedons were Typic Haplogelolls. Field and laboratory characterization allowed for the development of a Geloll pedogenic concept generally represented by well‐drained soils with limestone and/or dolomitic parent material. Such soils generally feature thin or no surficial organic horizons and large quantities of coarse fragments and coarse textured materials. Soil organic carbon calculations for suspected areas of Alaska Gelolls total 1.18 Pg; closely aligning with estimates of previous studies. Future work should expand the explanatory taxonomy based on new morphological expressions observed throughout gelic temperature regime regions. [ABSTRACT FROM AUTHOR]

The effects of crop residue on soil conditions depend on the quality and management of the residue. Management practices that do not use physical disturbance (prescribed fire and no-till) were investigated for their potential impacts on soil chemical and biological properties. Surface soil samples (0-5 cm) were collected across no-till and prescribed fire treatments of wheat (Triticum spp.) in 2014 and 2015 and in 2014 in corn (Zea mays L.) stubble residue at 0 h (before management) and at 1 h, 6 h, 24 h, 7 d, 1 mo, and 6 mo after management. There was a lack of difference between prescribed fire and no-till management responses in soil chemical and biological properties. However, similar responses in prescribed fire and no-till management over sampling time of NO3--N, NH4+-N, enzyme activity, and absolute abundance of ester-linked fatty acid methyl ester biomarkers suggest that abiotic factors and systems management (i.e., fertilizer applications, crop rotation) had a greater influence in these humid subtropical production systems. Additions of soybean [Glycine max (L.) Merr.] residue and N fertilizers in a wheat (Triticum aestivum L.)-soybean rotation increased NH4+-N concentrations by 153%, N-acetyl-β-D-glucosaminidase activity by 247%, and relative abundance of Gram-negative bacteria and saprophytic fungi regardless of residue management. Nitrogen fertilizer applications in corn systems also increased NH4+-N concentrations (322%) along with relative abundance of actinomycetes and saprophytic fungi. These results suggest that no-till and prescribed fire management are viable residue management options that maintained soil organic matter and inorganic N concentrations; however, further investigation is needed to evaluate the long-term (>2 yr) impacts on soil health. [ABSTRACT FROM AUTHOR]

• Proximal sensors were used to determine calcic horizons in-situ. • PXRF in isolation was adept at differentiating chernozems and phaeozems. • Combined VisNIR-PXRF models modestly increased predictive model stability. The Transylvanian Plain (TP), Romania is widely used for agronomic production. The Chernisol soil class covers a vast majority of the TP as defined by the Sistemul Roman De Taxonomie A Solurilor (SRTS). Chernisols are fertile, dark soils similar to Mollisols in the United States. Chernisols have four key soil types, two of them occur in the TP: chernozems and phaeozems. While these two classifications appear similar to those found in the World Reference Base (WRB) for Soil Resources , only the Romanian system (SRTS) is applied in this paper. Chernozems require a chroma of ≤2 in the Am horizon when moist and a calcic horizon or secondary CaCO 3 within 125 cm. Phaeozems require chroma of ≤3.5 when wet and a calcic horizon or secondary CaCO 3 deeper than 125 cm. Traditionally, morphological assessment in combination with laboratory data has been used to assess the depth of secondary CaCO 3 , thus establishing the taxonomic classification. Herein, the efficacy of portable X-ray fluorescence (PXRF) and visible near infrared spectroscopy (VisNIR) were evaluated to make such determinations in lieu of laboratory data on 25 soil cores collected across five toposequences. Cores were scanned on-site with both sensors at 10 cm increments to determine depth to CaCO 3 accumulation. Comparing Ca percentages from only PXRF with traditional laboratory pressure calcimetry via simple linear regression (SLR), the following model validation data (based on whole core splitting, Core val) were obtained: R2 = 0.92; root mean squared error (RMSE) = 0.81%; residual prediction deviation (RPD) = 3.27: ratio of performance to interquartile range (RPIQ): 5.34. Thus, most of the Ca in soils of the TP is associated with secondary CaCO 3. Using the three most prominent latent variables, VisNIR spectra (smoothed to 10 nm bands) were combined with PXRF data via partial least squares regression (PLSR) to determine if any improvements could be achieved by the combined approach. Combined Core val models produced the following: R2 = 0.89; RMSE = 0.98%; RPD = 2.73; RPIQ = 4.46. Boosted regression tree Core val modeling produced similar results (R2 = 0.90; RMSE = 0.89%; RPD = 2.99; RPIQ = 4.87). With deference to the law of parsimony, use of PXRF data without VisNIR for calcic horizon identification and quantification in conjunction with morphological assessment and interpretation appears preferable for most Pedological applications given its robust, strong performance. Minimal differences were observed using two different sample splitting schemes (whole core vs. full sample set) relative to PXRF data predictive models for CaCO 3 prediction, especially for PXRF with no VisNIR contribution. Of the sites investigated, PXRF identified six phaeozems (P) and nineteen chernozems (C). Specifically, the following were identified on different slope profiles: summits (1P/4C), shoulders (1P/2C), backslopes (3P/7C), footslopes (0P/4C), and toeslopes (1P/2C). Localized landslides and erosion precluded the identification of a common landscape model differentiating P/C on landscapes. Nonetheless, proximal sensors were adept at informing soil properties needed for taxonomic classification on-site, with minimal need for traditional pressure calcimetry. [ABSTRACT FROM AUTHOR]