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This study aims at identifying the influence of soil surface roughness from small to large aggregates (random roughness) on runoff and soil loss and to investigate the interaction with soil surface seal formation. Bulk samples of a silty clay loam soil were sieved to four aggregate-size classes of 3 to 12, 12 to 20, 20 to 45, 45 to 100 mm, and packed in soil trays set at a 5% slope. Rainfall simulations using an oscillating nozzle simulator were conducted for 90 min at an average rainfall intensity of 50.2 mm h(-1). Soil surface roughness was measured using an instantaneous profile laser scanner and surface sealing was studied by macroscopic analysis of epoxy impregnated soil samples. The rainfall simulations revealed longer times to initiate runoff with increasing soil surface roughness. For random roughness levels up to 6 mm, a decrease in final runoff rate with increasing roughness was observed. This can be attributed to a decreased breakdown of the larger roughness elements on rougher surfaces, thus keeping infiltration rate high. For a random roughness larger than 6 mm, a greater final runoff rate was observed. This was caused by the creation of a thick depositional seal in the concentrated flow areas, thus lowering the infiltration rates. Analysis of impregnated soil sample blocks confirmed the formation of a structural surface seal on smooth surfaces, whereas thick depositional seals were visible in the depressional areas of rougher surfaces. Therefore, from our observations it can be learned that soil surface roughness as formed by the presence of different aggregate sizes reduces runoff but that its effect diminishes due to aggregate breakdown and the formation of thick depositional seals in the case of rough soil surfaces. Sediment concentration increased with increasing soil surface roughness, due to runoff concentration in flow paths. Nevertheless, final soil loss rates were comparable for all soil roughness categories, indicating that random roughness is only important in influencing runoff rates and the time to initiate runoff, but not in influencing sediment export through soil loss rates.

Bulk density (BD) is a key soil property for sustainable land management and C stock assessment. However, data on BD are often missing in various soil survey reports in Central Africa because its measurement is laborious, time consuming at large scales, and may pose expense and facilities challenges, particularly in resource-poor countries. Therefore, there was a need for developing pedotransfer functions (PTFs) to predict BD for soils of Central Africa as an alternative solution. To do so, two approaches, namely multiple linear regression (MLR) and a pattern-recognition approach (k-nearest neighbor [k-NN]) were tested to predict BD for 196 soils of the Lower Congo. Based on their needs and familiarity with one or both of the two approaches, potential users interested in predicting the BD of strongly weathered soils of Lower Congo in particular or of Central Africa in general can immediately use the proposed equations or later apply the k-NN algorithm. Using an independent data set of low-activity clay soils from different tropical countries, nine individual PTFs and three ensemble PTFs were tested for their predictive capability. The results showed that despite the use of various modeling approaches—MLR, k-NN, or ensembles—BD remains a soil property that is difficult to predict with a satisfactory level of reliability because it can have substantial short-term variability in response to natural as well as human-induced causes. We achieved a minimum root mean square difference value of 0.179 Mg m⁻³.

The soil physical quality (SPQ) index S can provide inconsistent designations of SPQ and has a lack of consistency with other physical indicators for some soils. The aim of this study was to compare the suitability of S in identifying SPQ against 12 SPQ indicators, including water-release-related indicators, physical properties, and visual examinations. This study was conducted on medium-textured soil samples taken from tropical and temperate soils. Comparisons of SPQ class and relationships between indicators were used to judge the S SPQ designation. For the studied soils, S classified SPQ in the same way as other indicators when the condition of the soil was optimal or degraded but not when it was intermediate. This demonstrates that the proposed critical limits for S are not generally valid and do not apply for all soil conditions. Porosity parameters from the water release curve were more consistent indicators of SPQ than S. Our work also demonstrates that scores from visual examinations have at least similar resolution (P > 0.05) to the other indicators of SPQ evaluated. The use of S as an indicator to be considered as part of a minimum data set of indicators of SPQ assessment is less viable when other indicators such as bulk density, porosity, and visual examination are much more easily determined and more consistent than S. Therefore, it is too ambitious to consider that a unique indicator such as the S index could be used to evaluate SPQ as such.

Determination of the field-saturated hydraulic conductivity (Kfs) can result in very high variability due to soil heterogeneity, the measurement method, the number of replications, and the Kfs calculation method used. Especially for dryland soils, stoniness can influence infiltration rates significantly. To identify this variability as well as its source, six widely used measurement methods were compared: single-ring (SR) and double-ring (DR) infiltrometers, the constant head well infiltrometer (CH), the inverse auger hole method (IA), the tension infiltrometer (TI), and the rainfall simulator (RFS). The six methods were applied at three locations in a semiarid part of Chile that showed moderate (15%) to high (55%) stoniness. Additionally, Kfs variations due to different calculation techniques for the same measurement method were thoroughly investigated. Results showed that different calculation techniques sometimes gave significantly different estimates of Kfs when using the same data set, and those relative differences were conserved among measurement locations. The borehole methods (IA and CH) showed high discard rates due to stoniness, making these methods less appropriate. The SR and DR methods gave considerably higher Kfs estimates, while the RFS and TI proved good candidates as reference methods for stony soils, with low failure rates and coefficients of variation.

Nonparametric techniques are of interest for soil and environmental sciences because they enable to effectively predict soil data from basic soil properties without the need of a priori selected equations. We applied two nonparametric techniques, k-nearest neighbor (k-NN) and boosted regression tree (BRT), on data of an existing soil survey database to predict topsoil bulk density (BD) of a tropical mountain forest (Nyungwe) in Rwanda. Soil particle size distribution, organic carbon (OC) content, pH, and cation exchange capacity (CEC) were used as input data and soil depth (topsoil or subsoil), land use (forest or nonforest) and soil horizon notation were tested as possible grouping and limiting factors for model training. The k-NN and BRT techniques showed a comparable performance and predicted BD for an independent data set equally well as the Adams-Minasny-Hartemink and Adams-Rawls-Brakensiek pedotransfer function (PTF) but significantly better than the Adams-De Vos-et al. PTF developed for tropical nonforest soils, nontropical (United States) nonforest soils and soils in the tropics, and nontropical (Belgian) forest soils, respectively. Adding particle size distribution, pH, and cation exchange capacity (CEC) as input variables or grouping samples by different limiting factors did not enhance the predictive capacity significantly compared to a model that used OC content as the sole input. Thus, it appears that robust soil OC data is essential for successfully predicting soil BD in African tropical forests, which in turn is an essential parameter for soil fertility assessments and drives many biogeochemical models. Despite this, OC levels still remain largely unknown for such areas. High throughput analyses based on infrared (IR) spectroscopy might help in collecting OC data for data poor areas.

Soil erosion processes are affected by the erodibility of the soil and by the erosivity of the rain. The effects of rain characteristics and invariant soil properties such as texture and organic matter content on soil erosion processes are well documented. However, the effect of antecedent soil-water content (θa) on aggregate breakdown, seal formation and subsequent soil erosion is much more disputable as opposing effects have been reported. We conducted lab experiments with a rainfall simulator on a Belgian silt loam soil. The objectives were to determine the effect of θa on seal formation, runoff and soil loss and to evaluate its effect on an empirical sediment transport equation. Air-dried soil aggregates were subjected to antecedent soil-water contents of 0.04 (air-dry aggregates), 0.12 and 0.19 m3 m-3. No runoff occurred on the soils with highest antecedent soil-water content, highest total runoff values were observed for the intermediate θa, while intermediate amounts of total runoff were noticed for the air-dry aggregates. Soil loss, however, showed a different trend: highest values were found for the lowest θa, intermediate values for the intermediate θa and no soil loss for the highest θa. We further observed that θa had no influence on the final runoff rates and on the final infiltration rate through the soil surface. In using a water discharge and stream power equation to predict sediment transport, the intercept and exponent of the regression equations were found to be lower for θa of 0.19 m3 m-3 compared to θa of 0.12 m3 m-3, indicating a decreasing erodibility with increasing θa. We therefore suggest including θa as an additional variable to assess soil erodibility in deterministic event-based water erosion models.

Infiltration measurements in and, stony soils are notoriously variable in visually homogeneous areas, and have been reported to be influenced by embedded stone fragments. This Study aimed to identify the influence of rock fragment contents, orientation, and position within a small and watershed on hydraulic conductivity in northern Chile. Two different measurement techniques were used, a single-ring infiltrometer with constant ponding head and a tension infiltrometer, which were applied at both an undisturbed field site (44 locations along three transects) and on the disturbed < 2-mm soil fraction from the same locations. Variations in saturated and unsaturated hydraulic conductivities were observed when using different calculation methods, adding to the observed variability For saturated conditions, only small differences in conductivities were observed between two calculation methods, whereas unsaturated hydraulic conductivities calculated by five different methods showed more important variations. Stone fragment content correlated significantly with both saturated and unsaturated conductivities, probably due to a positive correlation between stone content and coarse lacunar pore space. Slope orientations with higher amounts of stone fragments gave higher infiltration rates, as well as transects with steeper slopes and more, but smaller, rock fragments. When evaluating differences in infiltration rates observed along three transects in the watershed, variability could be mainly attributed to stone fragment content influences.

A crucial parameter in predicting wind erosion is the deflation threshold shear velocity, which is highly dependent on near-surface soil water. The empirical and theoretical models to predict the deflation threshold as affected by near-surface water that have been reported in literature all suffer from a weak physical background and very large differences between the predicted results can be observed. The present study was conducted to develop a new conceptual model to predict the deflation threshold as affected by near-surface wetness and to contribute to a better understanding of the role of the latter on deflation of sediment. The model was developed by solving the moment balance equation for entrainment of soil particles by wind, including the moments associated with drag forces, lift forces, aerodynamic moment forces, gravitational forces, and interparticle forces due to dry and wet bonding. The wet bonding force, which represents the effect of near-surface soil water, is due to liquid bridge bonding or capillary forces, and adsorbed layer bonding or adhesive forces. It was related to the particle diameter squared, surface tension squared and the inverse of matric potential. The latter was then converted to water content by assuming a logarithmic relationship, which was shown to be valid between oven dryness and a matric potential of -1.5 MPa, a range that is of interest in the light of deflation of soil particles by wind. The conceptual model presented in this paper is relatively simple and predicts, for a given particle diameter and surface tension, the deflation threshold shear velocity as a function of the ratio between water content and the water content at a matric potential of -1.5 MPa.

The soil moisture retention curve (MRC) is time consuming and expensive to measure directly. Several attempts have been made to establish a relation between readily available soil properties, like particle-size distribution, organic matter content, and bulk density, and the soil moisture retention curve. Those relationships are referred to as pedotransfer functions (PTFs). The objective of this study was to evaluate some PTFs with respect to their accuracy in predicting the soil moisture retention curve. Five widely used and four more recently developed PTFs were selected for evaluation. Seven of the selected PTFs predict moisture retention function parameters, whereas the other two predict the moisture content at certain matric potentials. In order to quantify the prediction accuracy, the mean of the absolute value of mean differences (MAMD), the mean and the standard deviation of the root of mean squared differences (MRMSD and SDRMSD, respectively), and the mean of the Pearson correlation coefficient (Mr) were used. The evaluated PTFs were finally ranked based on these validation indices. The PTFs showed good to poor prediction accuracy with MAMD values ranging from 0.0312 to 0.0603 m 3 m -3 and with MRMSDs between 0.0412 and 0.0774 m 3 m -3 , The SDRMSDs and Mrs ranged from 0.0212 to 0.0349 m 3 m -3 , and from 0.9468 to 0.9980, respectively. The validation indices computed by the PTF of Vereecken and coworkers gave the best results. Moreover, it predicts moisture retention function parameters, and therefore, this PTF is recommended most to predict the moisture retention curve from readily available soil properties.