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The investigation into the impact of gravel on water infiltration process and hydraulic parameters in stony soil could offer a theoretical basis to enhance water availability in rocky mountain area. A one-dimensional vertical infiltration experiment was used in this study. Six groups of gravel content of 0% (CK), 10% (W1), 20% (W2), 30% (W3), 40% (W4) and 50% (W5) were established to explore the changes in the wetting front, cumulative infiltration volume and infiltration rate. Then the accuracy of four infiltration models in simulating soil water infiltration processes was evaluated. Finally, Hydrus-1D was used to perform numerical inversion of the soil water content after infiltration. The findings revealed that: (1) When the infiltration time reached 300 min, the wetting front of the W1 , W2 , W3, W4 and W5 treatments was 11.00%, 17.00%, 32.25%, 38.75% and 54.50% lower than CK, the cumulative infiltration volume was 29.80%, 38.97%, 45.62%, 54.74% and 73.17% lower than CK, and the stable infiltration rate was 50.98%, 52.94%, 66.67%, 68.63% and 86.27% lower than CK. (2) The soil–water infiltration processes were accurately described by the Horton model, the coefficient of determination ( R 2 ) > 0.935. (3) The simulation results of Hydrus-1D showed that with the increase of gravel content, the values of the retention water content ( θ r ), saturated water content ( θ s ), shape coefficient ( n ) and saturated hydraulic conductivity ( K s ) were decreased, the values of the reciprocal of air-entry ( α ) were increased. The value of R 2 was more than 0.894, the root mean square error ( RMSE ) and mean absolute error ( MAE ) were less than 2%, which demonstrated that the Hydrus-1D model exhibited superior capability in simulating the changes of water content in stony soil in rocky mountain area. The findings of this study demonstrated that gravel could decrease the water infiltration process and affect the water availability. It could provide data support for the water movement process of stony soil and rational utilization of limited water resources in mountainous area.

Plants grown in stony soils have better developed root systems and higher crop yields. The roles of physical and chemical effects of stony soils on plant growth have been published, but the roles of soil microbiota have not been investigated. In this study, Tetrastigma hemsleyanum plants were cultivated for 2 years in stony soils and the same soils after removing rock fragments (non-stony soils). The composition of bacterial community and the tuber transcriptome were analyzed, using multiple bioinformatics methods. Compared with non-stony soils, stony soils supported a significantly different bacterial community, which was associated with larger tuber size of T. hemsleyanum. Stony soils had greater bacterial diversity, co-occurrence network complexity, and greater abundance of bacterial taxa belonging to Actinobacteria, Rokubacteria, Rhizobiales, Desulfarculaceae, and Chthoniobacteraceae than the corresponding microbiota in non-stony soils. The differential soil bacterial communities between stony and non-stony soils may be mainly driven by soil physicochemical properties, such as available S, organic matter, and pH. The discriminatory bacterial taxa of soils shaped the bacterial communities of rhizosphere soil, and tuber surface soil (TS), which was strongly correlated with tuber size parameters. In addition, the potential functions of the discriminatory bacterial taxa in TS corresponded closely with gene pathways of the host, like phytohormone biosynthesis, photosynthesis, and biotic stress resistance, which are crucial for the initiation and development of tubers. These results not only help us to better understand the role of stony soils in improving plant growth but also provide a reference for increasing tuber yield through the use of microbial inocula.

Sowing is critical for successful crop establishment and productivity, particularly in precision agriculture management strategies. However, topsoil characteristics directly affect agribusiness maximization (i.e., crop-yield increase, machinery efficiency, operating-cost reduction) even in the most advanced farming management techniques. The excessive presence of coarse fractions or stones in arable soil layers prevents modern machinery from reaching optimal efficiency. This work focuses on sowing to verify whether the vibration and noise arising during this operation significantly change with varying soil conditions according to the stoniness degree of disturbance on soil workability. To make this assessment, an experimental sowing activity was carried out on four soil plots with two different disturbance degrees. The results confirmed that the noise and acceleration of the sowing machine significantly correlated with the soil disturbance degree and related workability profile.

Mountainous forest soils usually contain a large number of rock fragments (particle diameter >2 mm), which influence soil properties. Data characterizing hydraulic properties of these soils usually describe only the fine soil fraction (particle diameter <2 mm) properties. To quantitatively describe soil water movement in stony soils, it is necessary to evaluate effective hydrophysical characteristics, involving the influence of stones, that is, the effective hydraulic conductivity and retention capacity should be known. Properties of evaporating surface (plant canopy) also play important role in formation of soil water movement and retention. This work presents results of the study of rock fragments (stoniness) effect on soil water content profiles and soil water dynamics during the season. Stony and homogeneous soil behavior is compared. The effect of different canopies (spruce forest, low vegetation) and bare soil in both types of soils on soil water dynamics is also studied. Stones as a part of soil are decreasing its water capacity and hydraulic conductivity as well. This is expressing in the decrease of stony soil water content retention capacity. High interception capacity of trees, followed by the low undercanopy precipitation, leads to the decreased soil water content of the upper soil layer. Combination of stony soil and dense forest canopy led to the low undercanopy precipitation, to relatively low infiltration totals into soil, and to decreased outflow.

Determining soil hydraulic properties is of major concern in various fields of study. Although stony soils are widespread across the globe, most studies deal with gravel-free soils, so that the literature describing the impact of stones on the hydraulic conductivity of a soil is still rather scarce. Most frequently, models characterizing the saturated hydraulic conductivity of stony soils assume that the only effect of rock fragments is to reduce the volume available for water flow, and therefore they predict a decrease in hydraulic conductivity with an increasing stoniness. The objective of this study is to assess the effect of rock fragments on the saturated and unsaturated hydraulic conductivity. This was done by means of laboratory experiments and numerical simulations involving different amounts and types of coarse fragments. We compared our results with values predicted by the aforementioned predictive models. Our study suggests that it might be ill-founded to consider that stones only reduce the volume available for water flow. We pointed out several factors of the saturated hydraulic conductivity of stony soils that are not considered by these models. On the one hand, the shape and the size of inclusions may substantially affect the hydraulic conductivity. On the other hand, laboratory experiments show that an increasing stone content can counteract and even overcome the effect of a reduced volume in some cases: we observed an increase in saturated hydraulic conductivity with volume of inclusions. These differences are mainly important near to saturation. However, comparison of results from predictive models and our experiments in unsaturated conditions shows that models and data agree on a decrease in hydraulic conductivity with stone content, even though the experimental conditions did not allow testing for stone contents higher than 20 %.

Drought is a serious constraint on crop growth and production of important staple crops such as maize. Improved understanding of the responses of crops to drought can be incorporated into cropping system models to support crop breeding, varietal selection, and management decisions for minimizing negative impacts. We investigate the impacts of different soil types (stony and silty) and water regimes (irrigated and rainfed) on hydraulic linkages between soil and plant, as well as root : shoot growth characteristics. Our analysis is based on a comprehensive dataset measured along the soil–plant–atmosphere pathway at field scale in two growing seasons (2017 and 2018) with contrasting climatic conditions (low and high vapor pressure deficit). Roots were observed mostly in the topsoil (10–20 cm) of the stony soil, while more roots were found in the subsoil (60–80 cm) of the silty soil. The difference in root length was pronounced at silking and harvest between the soil types. Total root length was 2.5–6 times higher in the silty soil than in the stony soil with the same water treatment. At silking time, the ratios of root length to shoot biomass in the rainfed plot of the silty soil (F2P2) were 3 times higher than those in the irrigated silty soil (F2P3), while the ratio was similar for two water treatments in the stony soil. With the same water treatment, the ratios of root length to shoot biomass of silty soil were higher than for stony soil. The seasonally observed minimum leaf water potential (ψleaf) varied from around −1.5 MPa in the rainfed plot in 2017 to around −2.5 MPa in the same plot of the stony soil in 2018. In the rainfed plot, the minimum ψleaf in the stony soil was lower than in the silty soil from −2 to −1.5 MPa in 2017, respectively, while these were from −2.5 to −2 MPa in 2018, respectively. Leaf water potential, water potential gradients from soil to plant roots, plant hydraulic conductance (Ksoil_plant), stomatal conductance, transpiration, and photosynthesis were considerably modulated by the soil water content and the conductivity of the rhizosphere. When the stony soil and silt soil are compared, the higher “stress” due to the lower water availability in the stony soil resulted in fewer roots with a higher root tissue conductance in the soil with more stress. When comparing the rainfed with the irrigated plot in the silty soil, the higher stress in the rainfed soil resulted in more roots with a lower root tissue conductance in the treatment with more stress. This illustrates that the “response” to stress can be completely opposite depending on conditions or treatments that lead to the differences in stress that are compared. To respond to water deficit, maize had higher water uptake rate per unit root length and higher root segment conductance in the stony soil than in the silty soil, while the crop reduced transpired water via reduced aboveground plant size. Future improvements in soil–crop models in simulating gas exchange and crop growth should further emphasize the role of soil textures on stomatal function, dynamic root growth, and plant hydraulic system together with aboveground leaf area adjustments.

How much water can be taken up by roots and how this depends on the root and water distributions in the root zone are important questions that need to be answered to describe water fluxes in the soil–plant–atmosphere system. Physically based root water uptake (RWU) models that relate RWU to transpiration, root density, and water potential distributions have been developed but used or tested far less. This study aims at evaluating the simulated RWU of winter wheat using the empirical Feddes–Jarvis (FJ) model and the physically based Couvreur (C) model for different soil water conditions and soil textures compared to sap flow measurements. Soil water content (SWC), water potential, and root development were monitored noninvasively at six soil depths in two rhizotron facilities that were constructed in two soil textures: stony vs. silty, with each of three water treatments: sheltered, rainfed, and irrigated. Soil and root parameters of the two models were derived from inverse modeling and simulated RWU was compared with sap flow measurements for validation. The different soil types and water treatments resulted in different crop biomass, root densities, and root distributions with depth. The two models simulated the lowest RWU in the sheltered plot of the stony soil where RWU was also lower than the potential RWU. In the silty soil, simulated RWU was equal to the potential uptake for all treatments. The variation of simulated RWU among the different plots agreed well with measured sap flow but the C model predicted the ratios of the transpiration fluxes in the two soil types slightly better than the FJ model. The root hydraulic parameters of the C model could be constrained by the field data but not the water stress parameters of the FJ model. This was attributed to differences in root densities between the different soils and treatments which are accounted for by the C model, whereas the FJ model only considers normalized root densities. The impact of differences in root density on RWU could be accounted for directly by the physically based RWU model but not by empirical models that use normalized root density functions.

Soil surface roughness (RR) and its spatiotemporal variations are important for understanding soil erosion process, but how the erosion‐induced evolution of RR affects flow pathways and sediment connectivity patterns remains unclear. This study used high‐resolution topographic data sets from a long‐term rainfall simulation experiment in semi‐arid environments. A total of approximately 1,500–2,400 mm of rainfall was applied on 2.0 m by 6.1 m plots with stony soil at three slope treatments (5%, 12%, and 20%). The temporal variations in RR, flow pathway length (FL), sinuosity (SS), and longitudinal profile roughness (PR) were investigated. The ratio of RR to slope relief, λ, was incorporated into a newly proposed sediment connectivity index (RIC). The results showed: (a) RR and PR continuously increased by 73.9% and 34.6% as compared to the initial surfaces, respectively, and steeper slopes developed greater RR and PR; (b) FL and SS increased due to the combined effects of increased RR and surficial rock fragment coverage; (c) RIC decreased because of the increases in FL, RR, and PR; (d) λ, SS and RIC were found to be appropriate predictors for sediment yield rates with determination coefficients of 0.50, 0.72 and 0.38, respectively, signifying that SS would be a promising parameter for modeling sediment yield. This study also highlighted the important roles of RR and λ in regulating flow and sediment connectivity, and suggested that stony hillslopes might evolve in a way wherein sediment connectivity and the effects of slope gradient on connectivity will decrease as erosion progresses.

Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards.