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The existing models for estimating cation exchange capacity (CEC) from easy‐to‐measure hygroscopic water content (θh) were based on a single water activity (aw) value rather than on the processes that govern soil water vapor adsorption for a distinct aw range. Here, we present a new CEC estimation model based on θh data of 119 soils with different clay mineralogy (i.e., illitic [IL], montmorillonitic [ML], and kaolinitic [KA] samples) and organic carbon (OC) contents for the aw range from 0.23 to 0.57 (Δθ0.23–0.57) and validate its performance. Based on the hypothesis that multilayer adsorption exhibits a higher correlation with CEC than monolayer adsorption and capillary condensation, the aw range from 0.23 to 0.57 was chosen with CEC calculated as CEC = k × Δθ0.23–0.57. The performance of the new model is compared to the Arthur (2017) model and the Torrent (2015) model, which considers a single θh value. The average proportionality coefficient (k) varied with the dominant clay mineralogy of the investigated soils. For soils dominated by 2:1 clay minerals (i.e., IL and ML), the new model showed a good estimation accuracy (Nash‐Sutcliffe model efficiency [E] ≥ 0.85; root mean squared error [RMSE] ≤ 4.18 cmol(+) kg−1). The new model performed better for IL and ML samples than for KA samples, and yielded more accurate CEC estimations than the Arthur model and Torrent model for soils with 2:1 clay minerals. For soil with high OC content (>23.2 g kg−1), the new model slightly underestimated CEC (E = 0.66; RMSE = 5.87). [ABSTRACT FROM AUTHOR]

Soil shrinkage characteristic curves are used to describe the shrinkage behavior and hydraulic properties of unsaturated soils. To construct soil shrinkage characteristic curves, a high‐data‐density measurement method is needed that relates water content to soil volume changes. We present a fully automated soil shrinkage measurement setup, based on the simplified evaporation method, to characterize the shrinkage behavior of undisturbed natural expansive clay soils. The high data density creates the opportunity to produce soil shrinkage characteristic curves without the need for a mathematical model. The technique allows for resaturation of the samples after drying, enabling differentiation between reversible and irreversible shrinkage. The setup consists of the commercialized HYPROP2 apparatus combined with optical distance sensors to measure the horizontal and vertical dimensions of the samples, yielding data on the sample volume, weight, and soil water suction. The measurement frequency is once per 10 min, and the measurement period is up to 4 weeks, providing a detailed time series of the drying and shrinkage characteristics. The setup can capture the different shrinkage phases and offers the opportunity to relate the soil shrinkage characteristic curve to soil water suction. The measurement data acquisition rate and accuracy enable detailed interpretation of soil water retention curves for nonrigid soils and are shown to be essential for understanding and quantifying the shrinkage potential of several types of deposits. [ABSTRACT FROM AUTHOR]

Conservation management practices often produced positive but limited desirable outcomes in US Southeast sandy soils, likely due to their intrinsically low clay contents that constrain the soil’s capacity to preserve organic carbon (C) and nutrients. In the field, we tested the effectiveness of a novel approach, that is, clay soil amendment, to improve sandy soils. In October 2017, clay-rich soils (25% clay) were spread at 25 metric tons ha−1 and tilled onto a sandy soil (1.9% clay) in the field, which was further mixed by light tillage at 0- to 15-cm depth, followed by planting winter cover crop mixtures (cereal rye, crimson clover, and winter pea). The crop rotation was cotton and corn with cover crop mixtures planted in the winter fallow season. Soils (0–15 cm) were collected in August 2021 and subjected to physiobiochemical analyses. Clay amendment increased soil clay content to 3.4%, which improved nitrogen (N) availability by 51% but inhibited the activities of C (β-Dcellubiosidase [CB]; β-xylosidase [BX]; N-acetyl-β-glucosaminidase [NAG]) and N (leucine aminopeptidase [LAP]) cycling enzymes, resulting in up to 78% reduction in microbial respiration. A follow-up kinetic study on BG and LAP enzymes suggested that clay addition can have different impacts on enzymes with diverse biological origins through distinct mechanisms. Clay addition can potentially improve sandy soils by stabilizing the organic inputs in soils. However, more research is required to understand its long-term impacts making this approach practical. [ABSTRACT FROM AUTHOR]

The soil mantle of the tropical karstic landscapes of Southern Mexico was shaped by specific processes of pedogenesis and long-term human impacts of ancient Maya agriculture. To understand the interaction between natural and human-induced soilforming processes in the calcareous mountains of Chiapas state, we studied soil toposequences around the Classic Maya site of Budsilhá and related them to the archaeological evidence of settlement and land-use distribution. Soil chemical analysis, micromorphological observations, and clay mineral identification were carried out in key soil profiles at the main geoforms. Limestone hills are occupied by shallow Rendolls which are usually perceived as incipient soils. However, high content of silicate clay composed of kaolinite and vermiculite and ferruginous clayey soil material observed at macro- and microscale backed the hypothesis that these soils were formed from the residues of thick Terra Rossa after their erosion. Swampy lowlands are occupied by thick clayey gleyic soils with clay mineral assemblages similar to those in the upland Rendolls. We suppose that the mineral matrix of the lowland soils is largely derived from the pedosediments of eroded upland Terra Rossa, which lost original ferruginous pigmentation and aggregation due to redoximorphic processes. Some wetland soils contain neoformed gypsum that is atypical for humid tropics; sulfidesulfate transformation under fluctuating redox conditions could promote gypsum synthesis. Ancient Maya land use was closely related to soil-geomorphic conditions: settlements with homegardens occupied calcareous hills, whereas the primary agricultural domain was developed on lowland soils after their drainage by artificial canals. [ABSTRACT FROM AUTHOR]

The present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models' performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models. Core Ideas: Prediction of clay from visible and near infrared spectroscopy was analyzed based on soil color.Models were calibrated from regional database and subsets stratified by soil color.Global model yielded over‐ and underestimation of accuracy over red and black soils, respectively.Predictions are highly dependent on the calibration dataset.Model performance evaluation is highly dependent on validation data stratification. [ABSTRACT FROM AUTHOR]

Crop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha−1; r2 = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine‐textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha−1; r2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions. Core Ideas: An improved N mineralization model predicts unfertilized corn yield for a wide variety of conditions.The new model provides realistic estimates of microbial humification efficiency across a range of soil textures.Humification efficiency is affected more by soil clay content than sand content.The updated coefficients account for the influence of precipitation on corn yield.The improved model provides a foundation for site‐specific N fertilizer recommendations. [ABSTRACT FROM AUTHOR]