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Identifying agricultural practices that enhance water cycling is critical, particularly with increased rainfall variability and greater risks of droughts and floods. Soil infiltration rates offer useful insights to water cycling in farming systems because they affect both yields (through soil water availability) and other ecosystem outcomes (such as pollution and flooding from runoff). For example, conventional agricultural practices that leave soils bare and vulnerable to degradation are believed to limit the capacity of soils to quickly absorb and retain water needed for crop growth. Further, it is widely assumed that farming methods such as no-till and cover crops can improve infiltration rates. Despite interest in the impacts of agricultural practices on infiltration rates, this effect has not been systematically quantified across a range of practices. To evaluate how conventional practices affect infiltration rates relative to select alternative practices (no-till, cover crops, crop rotation, introducing perennials, crop and livestock systems), we performed a meta-analysis that included 89 studies with field trials comparing at least one such alternative practice to conventional management. We found that introducing perennials (grasses, agroforestry, managed forestry) or cover crops led to the largest increases in infiltration rates (mean responses of 59.2 ± 20.9% and 34.8 ± 7.7%, respectively). Also, although the overall effect of no-till was non-significant (5.7 ± 9.7%), the practice led to increases in wetter climates and when combined with residue retention. The effect of crop rotation on infiltration rate was non-significant (18.5 ± 13.2%), and studies evaluating impacts of grazing on croplands indicated that this practice reduced infiltration rates (-21.3 ± 14.9%). Findings suggest that practices promoting ground cover and continuous roots, both of which improve soil structure, were most effective at increasing infiltration rates.

The physical properties of the soil are studied to understand the influence of soil properties on infiltration rate. The effect of soil physical properties on infiltration rates on logged-over forests was measured with a mini-disk infiltrometer across various levels of soil disturbances. Results of soil analysis suggest are mostly loamy texture and the bulk density has varied from 0.74 - 1.02 g cm −3 , respectively. The basic infiltration rate has varied from a minimum of 0.61 mmhr −1 to a maximum of 45.22 mmhr −1 with an average of 3.81 mmhr −1 . The results of simple regression analyses showed that there was little association between the physical properties of the soil and the infiltration rate. This study suggests that the high variation of infiltration rate in this study site is attributed to the high spatial variability of soil properties.

The air infiltration rate of buildings strongly influences indoor environment and energy consumption. In this study, several traditional methods for determining the air infiltration rate were compared, and their accuracy in different scenarios was examined. Additionally, a method combining computational flow dynamics (CFD) with the Swami and Chandra (S-C) model was developed to predict the influence of the surrounding environment on the air infiltration rate. Two buildings in Dalian, China, were selected: one with a simple surrounding environment and the other with a complex surrounding environment; their air infiltration rates were measured. The test results were used to validate the accuracy of the air infiltration rate solution models in different urban environments. For the building with a simple environment, the difference between the simulation and experimental results was 0.86%–22.52%. For the building with a complex environment, this difference ranged from 17.42% to 159.28%. We found that most traditional models provide accurate results for buildings with simple surrounding and that the simulation results widely vary for buildings with complex surrounding. The results of the method of combining CFD with the S-C model were more accurate, and the relative error between the simulation and test results was 10.61%. The results indicate that the environment around the building should be fully considered when calculating the air infiltration rate. The results of this study can guide the application of methods of determining air infiltration rate.

In the dry–hot valley region of Southwest China, water infiltration exhibits temporal variations due to the combined effects of land use type and the dramatic seasonal dry–wet cycle. To accurately compare and predict the infiltration characteristics, soil water infiltration processes and cumulative infiltration were quantified for five typical land uses—traditional corn (TC), plum orchard (PO), pine forest (PF), grassland (GL), and abandoned cropland (AC)—in a dry–hot valley region during both the rainy (July) and dry (November) seasons using a Mini Disk Infiltrometer (MDI). These data were then statistically analyzed using the Kostiakov, Philip, and Horton models. The results showed that the mean infiltration rate and cumulative infiltration during the rainy season were 1.34 times and 1.31 times higher than in the dry season, respectively. The water infiltration rate and cumulative infiltration for the five land uses generally followed the order of PF > GL/TC > PO/AC during both rainy and dry seasons. The model parameters related to the initial infiltration capability (Kostiakov parameter, a) and the steady infiltration capability (Philip parameter, A; and the Horton parameter, fc) during the rainy season were all greater than those in the dry season. Compared to the Kostiakov and Horton models, the Philip model achieved the highest mean Nash–Sutcliffe efficiency (NSE) values in fitting soil water infiltration processes, the lowest mean relative error (MRE) values, and the highest determination coefficient values (R2) in predicting the cumulative infiltration, with relatively little difference between the two seasons. These results indicate that PF, GL, and TC exhibit superior soil water infiltration capabilities compared to other land uses during both the rainy and dry seasons. The Philip model is more suitable for estimating soil infiltration capacity in the dry–hot valley region during both seasons. Identification of the superior land use types and accuracy determination of the water infiltration model can help guide effective water conservation and vegetation restoration initiatives in the dry–hot valley region of Southwest China.

We have recently described an increased lymphocytic infiltration rate in breast carcinoma tissue is a significant response predictor for anthracycline/taxane-based neoadjuvant chemotherapy (NACT). The aim of this study was to prospectively validate the tumor-associated lymphocyte infiltrate as predictive marker for response to anthracycline/taxane-based NACT. The immunological infiltrate was prospectively evaluated in a total of 313 core biopsies from HER2 negative patients of the multicenter PREDICT study, a substudy of the neoadjuvant GeparQuinto study. Intratumoral lymphocytes (iTuLy), stromal lymphocytes (strLy) as well as lymphocyte-predominant breast cancer (LPBC) were evaluated by histopathological assessment. Pathological complete response (pCR) rates were analyzed and compared between the defined subgroups using the exact test of Fisher. Patients with lymphocyte-predominant breast cancer (LPBC) had a significantly increased pCR rate of 36.6%, compared to non-LPBC patients (14.3%, p<0.001). LPBC and stromal lymphocytes were significantly independent predictors for pCR in multivariate analysis (LPBC: OR 2.7, p = 0.003, strLy: OR 1.2, p = 0.01). The amount of intratumoral lymphocytes was significantly predictive for pCR in univariate (OR 1.2, p = 0.01) but not in multivariate logistic regression analysis (OR 1.2, p = 0.11). Confirming previous investigations of our group, we have prospectively validated in an independent cohort that an increased immunological infiltrate in breast tumor tissue is predictive for response to anthracycline/taxane-based NACT. Patients with LPBC and increased stromal lymphocyte infiltration have significantly increased pCR rates. The lymphocytic infiltrate is a promising additional parameter for histopathological evaluation of breast cancer core biopsies.

Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R 2 ) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.

Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge for sound design of rain gardens is still very limited, particularly the accurate modeling of infiltration rate and how much it differs from infiltration of natural ground surface. The present study uses experimentally observed infiltration rate of rain gardens with different types of vegetation (grass, candytuft, marigold and daisy with different plant densities) and flow conditions. After that, modeling has been done by the popular infiltration model i.e. Philip's model (which is valid for natural ground surface) and soft computing tools viz. Gradient Boosting Machine (GBM) and Deep Learning (DL). Results suggest a promising performance (in terms of CC, RMSE, MAE, MSE and NSE) by GBM and DL in comparison to the relation proposed by Philip's model (1957). Most of the values predicted by both GBM and DL are within scatter limits of ±5%, whereas the values by Philips model are within the range of ±25% error lines and even outside. GBM performs better than DL as the values of the correlation coefficients and Nash-Sutcliffe model efficiency (NSE) coefficient are the highest and the root mean square error is the lowest. The results of the study will be useful in selection of plant type and its density in the rain garden of the urban area.

Hydraulic conductivity (K) as a parameter in surface and subsurface water interaction is an important study to research. Field observations using geoelectrics with the Schlumberger configuration and using infiltrometers with double ring were chosen as methods to estimate the (K) which aims to recognize the characteristics of the relationship between (K) obtained from different observation results. The estimated (K) obtained from infiltrometer observations are quite significant compared to geoelectric observations which range from 2.715 × 10-7 m/s to 6.132 × 10-7 m/s, while geoelectrical values range from 1.965 × 10-8 m/s to 3.896 × 10-9 m/s. In this study, the soil conditions in geoelectric observations were carried out in an unsaturated state and infiltrometer observations were in a saturated state. This soil condition is used as one of the reasons for interpreting the research results in this study, that the hydraulic conductivity in unsaturated soil conditions decreases compared to saturated soil.

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

Prescribed fire is commonly used to reduce the wildfire risk in Mediterranean forests, but the soil’s hydrological response after fire is contrasting in literature experiences. The mulch treatment can limit the increases in runoff and erosion in the short term after a fire. The use of fern is preferable to straw, due its large availability in forests. However, no experiences of post-fire treatment with fern mulch have been found in the literature and therefore the mulching effectiveness has not been evaluated. This study has measured water infiltration rate (IR) and water repellency (SWR) using a rainfall simulator in three Mediterranean forest stands (pine, oak and chestnut) of Calabria (Southern Italy) after a prescribed fire and mulching treatment with fern in comparison to unburned soil. Prescribed fire reduced water infiltration in all forests in the short term compared to the unburned conditions, and increased SWR in pine and oak forests. These reductions in IR in the time window of disturbance after fire increased the runoff generation capacity in all soils, but had a lower effect on peak flows. However, soil mulching with fern limited the runoff rates and peak flows compared to the burned soils, but this treatment was less effective in pine forest. One year after fire, IR increased in burned soils (treated or not) over time, and SWR disappeared. The effects of mulching have disappeared after some months from fire. The study confirms the usefulness of mulching in broadleaves forest in the short term, in order to control the hydrological effects of prescribed fire in Mediterranean forests. Both post-fire management techniques should be instead adopted with caution in conifer forests.