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Hydraulic characteristics of rill incipience on steep slopes with sandy soils are different from those of gentle slopes in the mountainous watersheds. In other to better understand the processes of the incision and expansion of rill, rainfall simulations were conducted in a laboratory soil box with an inflow device installed at the top of the steep slope. The location and shape of the rill incision were tested with respect to different slopes (20, 24, and 28°), rainfall intensity (80 and 100 mm/h), and segment distances with various inflow rates (0.0, 8.8, 17.5, and 26.3 ml/s). The result showed that rills with heatcut were mostly incised on the 2.4 m segments and progressively moved upslope on the 3.2 m segments. The steps of rill evolution with increase of inflow are identified as four stages; (1) sheet flow with impact of raindrops, (2) overland flow with flowpaths, (3) microrill with headcut incision, and (4) rill enhanced by headcut expansion. It appears that incision and headcut of rill is prevalent when the Froude number (Fr) changes from subcritical flow to supercritical flow and the maximum velocity of rill reaches over 0.065 m/s. Unit stream power using the maximum velocity of rill was the best parameter for estimating rill erosion as rill evolution greatly depends on maximum velocity of the local flow enhanced by rill incision and expansion As a result, it was found that critical conditions for rill incipient of sandy soils on steep slope are observed greater than those of loamy soils or gentle slopes. Full article

The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (EC_a) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R²_cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources. Full article