Permafrost warming has the potential to amplify global climate change, because when frozen sediments thaw it unlocks soil organic carbon. Yet to date, no globally consistent assessment of permafrost temperature change has been compiled. Here we use a global data set of permafrost temperature time series from the Global Terrestrial Network for Permafrost to evaluate temperature change across permafrost regions for the period since the International Polar Year (2007–2009). During the reference decade between 2007 and 2016, ground temperature near the depth of zero annual amplitude in the continuous permafrost zone increased by 0.39 ± 0.15 °C. Over the same period, discontinuous permafrost warmed by 0.20 ± 0.10 °C. Permafrost in mountains warmed by 0.19 ± 0.05 °C and in Antarctica by 0.37 ± 0.10 °C. Globally, permafrost temperature increased by 0.29 ± 0.12 °C. The observed trend follows the Arctic amplification of air temperature increase in the Northern Hemisphere. In the discontinuous zone, however, ground warming occurred due to increased snow thickness while air temperature remained statistically unchanged.

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The concept of soil health has evolved over the past several decades, recognizing that dynamic soil property response to management and land use is highly dependent on sitespecific factors that must be considered when interpreting soil health measurements. Initially, the Soil Management Assessment Framework (SMAF) and Comprehensive Assessment of Soil Health (CASH) were developed and used globally for scoring soil health indicators. However, both SMAF and CASH frameworks were developed using a relatively small dataset and their interpretation curves were not validated at the nationwide scale. Expanding upon these concepts, we propose the Soil Health Assessment Protocol and Evaluation (SHAPE) tool. SHAPE was developed using 14,680 soil organic carbon (SOC) observations from across the United States and accounts for edaphic and climate factors at the continental scale. Data were compiled from the literature, the Cornell Soil Health Laboratory, and the Kellogg Soil Survey Laboratory. In this approach, scoring curves are Bayesian model-based estimates of the conditional cumulative distribution function (CDF) for defined soil peer groups reflecting five soil texture and five soil suborder classes adjusted for mean annual temperature and precipitation. Specifically, SHAPE produces scores between 0 and 1 (0 to 100%) for measured SOC values that reflect the quantile or position within the conditional This article is protected by copyright. All rights reserved. 4 CDF along with measures of uncertainty. Herein, we focus on development of the SHAPE scoring curve for SOC with four case studies. SHAPE is a flexible, quantitative tool that provides a regionally relevant interpretation of this key soil health indicator.

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Development of more efficient (rapid) and cost-effective methodologies are needed in soil survey to meet the demand for quantitative data in digital soil mapping and updates. The objective of this study was to pilot the application of mid-infrared (MIR)-diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) in a soil survey field office where soil samples are processed, MIR spectra are acquired, and predictions are obtained using calibration models developed and validated from the Kellogg Soil Survey Laboratory spectral library. Mid-infrared models were built for total C, organic C, CaCO 3 equivalent, total clay, cation exchange capacity, 1500 kPa water, and pH in water and CaCl 2 for Mollisols of the central United States. Validation results (from the MIR library) using Lin's concordance correlation (r c) of measured versus predicted values showed that most properties predicted very well (r c = 0.967-0.996), whereas models for total clay in B horizons and 1500 kPa water in B horizons predicted fairly well (r c = 0.844-0.955). Models for pH predicted the least well (r c = 0.750-0.921). The MIR-DRS coupled with PLSR was successful in predicting soil properties for completely independent samples that were collected, processed, and MIR scanned in a soil survey field office. Predicted results using r c ranged from 0.697 to 0.992, with pH in water having the lowest r c and CaCO 3 having the highest r c. All properties except pH had an acceptable level of accuracy for use in soil survey and a marginal level of acceptable accuracy for total clay. Direct calibration transfer was feasible.

The half-lives, degradation rates, and metabolite formation patterns of atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine) and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamide] were determined in an anaerobic wetland soil incubated at 24 degrees C for 112 d. At 0, 7, 14, 28, 42, 56, and 112 d, the soil and water were analyzed for atrazine and metolachlor, and their major metabolites. The soil oxidation-reduction potential reached -200 mV after 14 d. Degradation reaction rates were first-order for atrazine in anaerobic soil and for metolachlor in the aqueous phase. Zero-order reaction rates were best fit for atrazine in the aqueous phase and metolachlor in anaerobic soil. In anaerobic soil, the half-life was 38 d for atrazine and 62 d for metolachlor. In the aqueous phase above the soil, the half-life was 86 d for atrazine and 40 d for metolachlor. Metabolites detected in the anaerobic soil were hydroxyatrazine and deethylatrazine for atrazine, and relatively small amounts of ethanesulfonic acid and oxanilic acid for metolachlor. Metabolites detected in the aqueous phase above the soil were hydroxyatrazine, deethylatrazine, and deisopropylatrazine for atrazine, and ethanesulfonic acid and oxanilic acid for metolachlor. Concentrations of metabolites in the aqueous phase generally peaked within the first 25 d and then declined. Results indicate that atrazine and metolachlor can degrade under strongly reducing conditions found in wetland soils. Metolachlor metabolites, ethanesulfonic acid, and oxanilic acid are not significantly formed under anaerobic conditions.

Summary Statistical validation of spatial predictions of soil properties requires assessment of errors against measured values. The objective of this study was to assess the size of errors in the measurement of soil pH from different sources in the United States of America national databases and implications of the size of errors for prediction, validation and management decision making under uncertain conditions. Error sources included measurement methods, laboratory conditions, pedotransfer functions, database manipulations, location accuracy, and spatial and polygon methods of interpolation. The databases consisted of measured soil pH values from the US National Cooperative Soil Survey Characterization Database (NCSS–SCDB) and estimated values from US Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO2) databases. The degree of agreement between measurement methods ranged from poor to substantial, with Lin's concordance correlation coefficients (ρc) varying from 0.83 (pH 1:1W against 1:5CaCl2) to 0.95 (pH 1:1W against pH 1:5W) and root mean square error (RMSE) varying from 0.27 to 0.43. The degree of agreement between pH 1:1W, 1:2CaCl2 and mid‐infrared spectroscopy (MIR) ranged from poor to moderate. The RMSE for MIR was 0.40 for pH 1:1W and 0.32 for soil pH 1:2CaCl2. The RMSE for between‐laboratory reproducibility varied from 0.50 (pH 1:1W) to 0.68 (pH 1:2CaCl2) and was greater than within‐laboratory reproducibility (pH 1:1W, 0.34; pH 1:2 CaCl2, 0.22) and repeatability (pH 1:1W, 0.19; pH 1:2CaCl2, 0.04). The RMSE for the relations for profile depth slicing (weighted mean against equal‐area spline) was 0.36. The RMSE for the relation between soil pH 1:1W for the Global Positioning System and Public Land Survey System was 0.57. Predictions based on polygon or spatial interpolation had the largest RMSEs, 0.78 and 0.62, respectively. Soil liming recommendations based on 0.1 pH increments do not reflect error measurements or the uncertainty of spatial prediction. Although it was not possible to establish consistent trends in the size of error (progressively increasing from measurement to aggregation), its assessment can improve modelling and management at various scales. Highlights We assessed sources of errors and uncertainty for measured and spatial predictions of soil pH. The smallest error was reported for measured pH (0.06). Polygon or spatial interpolation resulted in the largest error (0.68). Differences in error size influenced rates of liming and cost.

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