Measurement Of Soil Hydraulic Properties Research Articles

Measurements of Soil Water Potential and Conductivity based on a Simple Evaporation Experiment using a Hydraulic Property Analyzer.

The measurement of soil hydraulic properties is critical in understanding the physical components of soil health as well as integrated knowledge of soil systems under various management practices. Collecting reliable data is imperative for informing decisions that affect agriculture and the environment. The simple evaporation experiment described here uses instrumentation in a laboratory setting to analyze soil samples collected in the field. The soil water tension of the sample is measured by the instrument, and tension data is modeled by software to return soil hydraulic properties. This method can be utilized to measure soil water retention and hydraulic conductivity and give insight into differences in treatments or environmental dynamics over time. Initial establishment requires a user, but data acquisition is automated with the instrument. Soil hydraulic properties are not easily measured with traditional experiments, and this protocol offers a simple and optimal alternative. Interpretation of results and options for extending the data range are discussed.

Analyzing the role of soil and vegetation spatial variability in modelling hydrological processes for irrigation optimization at large scale

The main purpose of this paper was to study the effect of spatial variability of soil hydraulic properties (HP) and vegetation parameters (VP) (e.g., leaf-area index, LAI, and crop coefficient, Kc) on modelling agro-hydrological processes and optimising irrigation volumes at large scale. Based on this analysis, the effect of partly overlooking the spatial variability of soil HP and/or VP inputs was verified on a 140 ha irrigation sector in “Sinistra Ofanto” irrigation system in Apulia Region, Southern Italy. Five soil profiles were excavated and the HP were measured in all the soil horizons. Additionally, measurements of soil HP were taken in the surface soil layer in ninety sites distributed over the whole irrigation sector. All the HP measurements were carried out using tension infiltrometer. Remote sensing applications were used to obtain LAI and Kc using European Space Agency’s (ESA) Sentinel-2 images with 10 m resolutions. First, distributed (on ninety polygons with an average area of about 1.5 ha) optimal irrigation volumes and related deep percolation volumes at a depth of 80 cm, were computed using an agro-hydrological model and accounting for the actual observed variability of soil HP and VP inputs. The sector scale irrigation and deep percolation volumes were obtained by aggregating the distributed irrigation volumes. This was considered as the reference scenario (hereafter DVS—Detailed Variability Scenario). Then, reduced variability scenarios (hereafter RVS—Reduced Variability Scenario) were considered, where the information on the actual spatial variability of the soil HP and VP was gradually overlooked to find the minimum data set needed to still have sector scale irrigation volumes and related deep percolation volumes comparable to those obtained under the DVS. Results showed that overlooking VP (RVS-VP) variability did not significantly change the optimal irrigation volumes and the deep percolation fluxes. By contrast, neglecting the HP variability (RVS-HP) showed significant effects on both the irrigation and percolation volumes compared to the DVS. The main practical finding was that, at least for the area investigated in this study, hydraulic characterization of one soil profile in an area of approximately 30 ha provides sector scale irrigation volumes and percolation fluxes comparable to those obtained under the DVS, thus by accounting for all the observed local variability.

Modeling moisture redistribution from selective non-uniform application of biochar on Palouse hills

Precision agriculture is most effective in areas where significant in-field variation occurs. The Palouse region of the Pacific Northwest in the US, a vast area of undulating fertile farmland, has relatively high in-field variation in water retention and crop yield due to regional topography and uneven soil erosion. The regional agricultural systems depend on the soil at or near field capacity towards the end of a wet spring to support crops throughout the summer drought period. Dryland agricultural systems and high in-field variation and changing climate make water retention management practices throughout the region critical. A finite element vadose zone transport model was developed and used to understand the benefits of the targeted application of biochar on water retention and water redistribution in a representative hillslope. The model utilizes measured soil hydraulic properties to predict soil moisture distribution over the dry season. A Redwood Sawdust and Wheat Straw biochar was amended at 4% and 7% concentrations by mass. Biochar amended soils showed an increase in water retention and apparent reduction in unsaturated hydraulic conductivity as the soil approached saturated conditions. After two months of bare field evaporation, the model showed that biochar impacts water redistribution in the soil profile, contributing to positive and negative changes and a net increase in water retention. Model outputs with biochar showed increased retention in and around the amendment area, although the magnitude between outputs varied, with some samples showing minimal effectiveness. Despite the differences in magnitude with targeted biochar amendment, these results indicate that biochar can change water redistribution (up to 0.5%) in a soil profile. Additionally, the developed model shows promise as a field and regional level management tool to determine the best return on investment from biochar application when applied in a targeted manner.

A database of soil physical properties for the Kansas Mesonet

AbstractThis study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine‐textured soils, and sandy loam and sandy clay loam soils comprising the only coarse‐textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d–1 in the clay soils to 47.1 ± 119 cm d–1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near real‐time in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates.

Smartphone-based tension disc infiltrometer for soil hydraulic characterisation

The tension disc infiltrometer has become a widespread method to measure soil hydraulic properties under unsaturated conditions. Although several automated devices have been developed, most designs include an elongated water reservoir and a pressure transducer with electronic components to register the water level, increasing its cost and limiting its application. The objective of this work is to present a new tension disc infiltrometer with a compact design of 10 cm diameter and height, where the water level is monitored by a smartphone camera. The infiltration curve is determined from the automated analysis of the images recorded by the smartphone without additional electronic components. The device was first validated in the laboratory by comparing visual measurements (V), the camera imagery (Ca) and a pressure transducer (PT). Next, it was tested on field infiltration experiments. Robust fits (R2 = 0.99) were found between the water level measured with Ca in the laboratory and those obtained with V and PT procedures. The Ca method is accurate, robust and independent of the relative camera position. Good fits were also observed between Ca water level and those obtained with PT in the field experiments. Similar hydraulic conductivity and sorptivity values were obtained with both sensors using the numerical solution of the Haverkamp (NSH) equation. The compact infiltrometer, in conjunction with the smartphone camera, is an accurate, accessible, portable and easy-to-use field-based device for soil hydraulic characterisation.

Time Since Burning and Rainfall Characteristics Impact Post-Fire Debris-Flow Initiation and Magnitude

ABSTRACTThe extreme heat from wildfire alters soil properties and incinerates vegetation, leading to changes in infiltration capacity, ground cover, soil erodibility, and rainfall interception. These changes promote elevated rates of runoff and sediment transport that increase the likelihood of runoff-generated debris flows. Debris flows are most common in the year immediately following wildfire, but temporal changes in the likelihood and magnitude of debris flows following wildfire are not well constrained. In this study, we combine measurements of soil-hydraulic properties with vegetation survey data and numerical modeling to understand how debris-flow threats are likely to change in steep, burned watersheds during the first 3 years of recovery. We focus on documenting recovery following the 2016 Fish Fire in the San Gabriel Mountains, California, and demonstrate how a numerical model can be used to predict temporal changes in debris-flow properties and initiation thresholds. Numerical modeling suggests that the 15-minute intensity-duration (ID) threshold for debris flows in post-fire year 1 can vary from 15 to 30 mm/hr, depending on how rainfall is temporally distributed within a storm. Simulations further demonstrate that expected debris-flow volumes would be reduced by more than a factor of three following 1 year of recovery and that the 15-minute rainfall ID threshold would increase from 15 to 30 mm/hr to greater than 60 mm/hr by post-fire year 3. These results provide constraints on debris-flow thresholds within the San Gabriel Mountains and highlight the importance of considering local rainfall characteristics when using numerical models to assess debris-flow and flood potential.

Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin

AbstractWater retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426‐km2 subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R2, mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR‐PTF and ML‐PTFs, the R2 of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN‐PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application.Highlights Developed machine learning and multiple linear regression pedotransfer functions (PTFs). The Saxton & Rawls PTFs are not recommended for use in the Zambezi River Basin. PTFs were functionally evaluated through use of estimated soil hydraulic properties in AquaCrop. More accurate PTFs have better functional performance, although differences are small.

Efficiency of The Spaw Model in Estimation of Saturated Hydraulic Conductivity in Calcareous Soils

The measurement of soil hydraulic properties is tedious, time-consuming, and costly. An alternative approach is to formulate models that utilize the physical and chemical properties of the soil as input variables to predict soil saturated hydraulic conductivity (Ks). However, the previous studies have not paid attention to the calcium carbonate content in their models; it can lead to reducing the size and number of the pores in the soil which, in turn, can lead to reduction Ks . Here we evaluated the ability of the Soil, Plant, Atmosphere, and Water (SPAW) model to predict Ks under different states of compaction for calcareous soils with wide-ranging textures sampled along a precipitation gradient in northwestern Iraqi Kurdistan. The results revealed that the best match occurred under loose to normal state of compaction for these soils. Among the soil properties, sand content was high significantly correlated with Ks followed by CaCO3, clay, organic matter content, silt and Electrical conductivity. A pedotransfer function (PTF) was proposed using these data and its results were compared to these from the SPAW model. Root mean square error (RMSE) and coefficient of variation (CV) for the comparison between measured Ks values and those predicted by the SPAW model were very high 2.7×10-4 cm s−1 and 166% respectively, that due to the values of Ks predicted by the SPAW model are overestimated for calcareous soils, for these reasons the accuracy of the SPAW model was improved via calibration. The RMSE and CV of the calibrated SPAW model were dropped to 9.8×10-5 cm s−1 and 61.2%, respectively. Additionally, the accuracy of our best PTF that constructed from sand, clay, and CaCO3 was slightly higher than the calibrated SPAW model. Therefore, it is recommended to use the calibrated SPAW model for predicting Ks in calcareous soils.

Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: I. The Soil Water Retention Curve

Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess the accuracy of the pseudo continuous neural network PTF (PCNN-PTF) approach for estimating the soil water retention curve of 153 international soils (a total of 12,654 measured water retention pairs) measured via the evaporation method. In addition, an independent data set from Turkey (79 soil samples with 7729 measured data pairs) was used to evaluate the reliability of the PCNN-PTF. The best PCNN-PTF showed high accuracy (root mean square error (RMSE) = 0.043 cm3 cm−3) and reliability (RMSE = 0.061 cm3 cm−3). When Turkish soil samples were incorporated into the training data set, the performance of the PCNN-PTF was enhanced by 33%. Therefore, to further improve the performance of the PCNN-PTF for new regions, we recommend the incorporation of local soils, when available, into the international data sets and developing new sets of PCNN-PTFs.

Water Balance of Two Major Soil Types of the Texas High Plains: Implications for Dryland Crop Production

Crop production in the Texas High Plains is shifting from irrigated to dryland due to the increase of the depth to the water table from the Ogallala aquifer in regions where the saturated thickness of 9 m, the minimum to sustain irrigation, has been reached. Our objective was to use the mechanistic model ENWATBAL to evaluate the daily and annual water balance for three scenarios of rainfall in this region, a dry (189 mm), an average (449 mm) and a wet (669 mm) year. These three scenarios were applied to two major soil series of this region, Pullman and Amarillo. In all simulations, we used hourly input weather data for a location near Lubbock, Texas and used measured soil hydraulic properties to simulate the water balance for each soil series and the three rainfall scenarios. Results showed that in years with average and wet rain, storage of rainfall occurred in the Pullman but not in in the Amarillo soil series. However, storage of water could be enhanced by combining furrow dikes with minimum tillage along with crop covers that provide a surface residue. The implications of our results for dryland crop production in the semiarid climate of the THP suggest that for years with average and wetter rainfall soils in the Pullman series could store water that would be available for crop use. However, this was not the case for the Amarillo soil series and these soils represent a higher risk for dryland crop production.

Sources of inherent infiltration variability in postwildfire soils

AbstractAn automated disc infiltrometer was developed to improve the measurements of soil hydraulic properties (saturated hydraulic conductivity and sorptivity) of soils affected by wildfire. Guidelines are given for interpreting curves showing cumulative infiltration as a function of time measured by the autodisc. The autodisc was used to measure the variability of these soil hydraulic properties in three different sample sets: (a) a reference soil consisting of a nonrepellent, uniform, fine sand; (b) soils with the same soil textural classification derived from the same bedrock geology but having different initial burn severities; and (c) soils from different bedrock geology but having the same burn severity. The autodisc infiltrometer had greater sampling rates and volume resolution when compared with the visual minidisc infiltrometer from previous studies. There was no statistical difference in the mean values measured using the autodisc and visual minidisc, but the variability of the autodisc measurements was significantly less than the visual minidisc for a given set of samples. The greatest variability of soil hydraulic properties in reference samples with uniform particle size was attributed to different pore geometries (coefficient of variation [COV] = 0.28–0.34). Unburned field samples (same soil type) with heterogeneous particle sizes had greater variability (COV = 0.57–0.78) than the reference samples. However, this basic variability decreased or remained constant in these field samples as burn severity increased. Additional sources of variability (COV = 0.53–1.99) were attributed to multiple layers resulting from ash or sediment deposition. Results indicate that resolving differences in soil hydraulic properties from different sites requires more than the common 10 random samples because of the multiple sources of variability.

Can measured soil hydraulic properties simulate field water dynamics and crop production?

Agricultural system models simulate soil water dynamics and crop evapotranspiration (ETc) and growth to enhance soil and crop management. To achieve this, simulations must be critically evaluated against field experimental data in different field management conditions. In many evaluations, simulations deviated from field measurements, which can be due to the quality of model input parameters. Replacing soil hydraulic properties indirectly derived from soil textural data by measured soil hydraulic property may decrease the discrepancy between measured and simulated soil water status. What is the benefit of using measured soil hydraulic properties in a model instead of pedo-transfer-function (PTF) based approaches to estimate the hydraulic properties indirectly? The objective of this study was to investigate the effect of using measured soil hydraulic property input parameters (SHPIP) as Root Zone Water Quality Model (RZWQM2) inputs compared to PTF-based indirectly derived hydraulic parameter inputs with and without calibration. A field experiment with soybean, corn, and fallow soil was conducted. Five model scenarios using measured SHPIP and SHPIP derived from soil texture as model inputs were created. The results indicate that, RZWQM2 showed a high sensitivity to the SHPIP calibration for fallow and corn season. Uncalibrated measured SHPIP yielded better simulation results than other SHPIP scenarios with regard to soil water flux, crop evapotranspiration, and soybean yield during the validation. While, the calibration of the SHPIP in corn and fallow slightly helped soybean soil water prediction at the surface depth. Hence, with representative measurements of SHPIP, it was possible to improve model simulations even without calibrating the input parameters.

Impacts of successive wildfire on soil hydraulic properties: Implications for debris flow hazards and system resilience

AbstractClimate and land use changes have led to recent increases in fire size, severity, and/or frequency in many different geographic regions and ecozones. Most post‐wildfire geomorphology studies focus on the impact of a single wildfire but changing wildfire regimes underscore the need to quantify the effects of repeated disturbance by wildfire and the subsequent impacts on system resilience. Here, we examine the impact of two successive wildfires on soil hydraulic properties and debris flow hazards. The 2004 Nuttall‐Gibson Complex and the 2017 Frye Fire affected large portions of the Pinaleño Mountains in southern Arizona, creating a mosaic of burn severity patterns that allowed us to quantify differences in wildfire‐induced hydrologic changes as a function of burn severity and recent fire history (i.e. burned in only the Frye Fire or burned in both fires). Field observations after the 2017 Frye Fire indicated debris flow activity in areas burned predominantly at low severity. Many of these areas, however, were also affected by the 2004 Nuttall‐Gibson Complex, suggesting that the relatively short recovery time between the two wildfires may have played a role in the geomorphic response to the most recent wildfire. Field measurements of soil hydraulic properties suggest that soils burned at moderate severity in 2004 and low severity in 2017 have a lower infiltration capacity relative to those that remained unburned in 2004 and burned at low severity in 2017. Simulations of runoff demonstrate that measured differences in infiltration capacity between once‐ and twice‐burned soils are sufficient in some cases to influence the rainfall intensities needed to initiate runoff generated debris flows. Results quantify the impact of wildfire history and burn severity on runoff and debris flow activity in a landscape affected by successive wildfires and provide insight into how the resilience of geomorphic systems may be affected by successive wildfires. © 2019 John Wiley & Sons, Ltd.

Field techniques for measuring soil hydraulic properties in unsaturated soils

Field techniques for measuring soil hydraulic properties in unsaturated soils

Functional Evaluation Of Pedotransfer Functions For Simulation Of Soil Profile Drainage

AbstractDirect measurement of soil hydraulic properties is time consuming and costly. Therefore indirect methods, such as pedotransfer functions (PTFs), may provide an alternative. Some PTFs have been incorporated into stand‐alone computer programs like Rosetta and Soilpar, which are used in this study. The aim of this study was to compare different PTFs to use for modelling soil free drainage at three different soil textures including loamy sand, loam and clay loam. Statistics showed relatively good performance of Rosetta in the simulation of free drainage of clay loam but its function was not good as clay loam for loam and loamy sandy soils. The results showed that simulation of free drainage using Rosetta's hydraulic parameters was better than Soilpar. The results also indicated that a PTF might be accurate enough for estimating soil hydraulic properties, but was not able to simulate the soil profile drainage process using an input in the numerical code and using them might lead to large errors in simulating soil water drainage. © 2019 John Wiley & Sons, Ltd.

The roles of competition and climate in tree growth variation in northern boreal old‐growth forests

AbstractQuestionsHow have tree growth and growth trends varied in structurally complex old‐growth forests? What determines this variation and how this differs among species?LocationNatural boreal old‐growth forests, northern Finland.MethodsWe sampled trees over 10‐cm diameter on 48 randomly located 0.1‐ha plots in three landscapes and compiled a data set of 1,565 trees including all three main species, and a full range of sizes and competitive states in northern boreal old‐growth forests. We extracted tree ring samples from every tree and used ring widths to reconstruct tree sizes and their neighbourhood for the past 50 years for Pinus sylvestris and Picea abies, and 30 years for Betula pubescens. We used mixed‐effects models to study variation in tree growth. In the models, we used annually varying environmental variables, forest and tree structural variables and site variables as predictors. We also measured soil hydraulic properties for a water balance model to specifically include soil moisture deficits as predictors.ResultsTree growth rates had increased during the analysis period for all three species, with the highest increases for P. sylvestris and B. pubescens. All three species grew well during warm summers, but P. sylvestris and P. abies growth was also negatively influenced by soil moisture deficits. Competition in these sparse forests had a small influence on growth. However, its influence increased over time.ConclusionsThe trees showed positive growth trends, but the increasing competition means that such strong positive trends are unlikely to continue over the long term. For year‐to‐year variability, negative influence of droughts implied that increasing temperatures are not only beneficial to tree growth even in these high‐latitude forests. Overall, the findings demonstrate the site‐ and species‐dependent complexity in tree responses to environmental and structural changes we may expect in the future.

Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike’s information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes.

Improved evaporation method for the measurement of the hydraulic conductivity of unsaturated soil in the wet range

The evaporation method (EM) is an effective technique for measuring soil hydraulic properties. However, the EM generally fails to measure hydraulic conductivity curves (K–h; K and h are the hydraulic conductivity and the pressure head, respectively) in the wet (low suction) range when the hydraulic gradient within soil samples exceeds the minimum accuracy of the pressure transducer. In this study, we developed a new experimental device that uses the EM to perform conductivity measurements in the wet range. By making two major improvements, we successfully expanded the measurable range of K–h to cover the wet range, enabling accurate and effective measurements. Firstly, the accuracy of the hydraulic gradient measurement was remarkably improved by the use of a differential pressure transducer. Assuming the minimum evaporation speed (0.18 cm/day at the center of soil sample), the estimated measurable range of K–h is up to 27 cm/day for any type of soil. That is, our device has expanded the range of K–h measurable by the EM by more than two orders of magnitude on the wet end. Secondly, our device can execute the saturated/unsaturated steady-state infiltration method (SSIM/USIM) and the EM on the same sample. This diminishes not only time and cost but also errors between different methods, ensuring good accuracy and representativeness. The range of K–h measurable by the USIM was determined to be more than 21 cm/day, which overlaps with the range measurable by the EM. As a result, our device can obtain consecutive K–h curves from saturation to the medium pressure range (−500 < h ⩽ 0 cm) for any type of soil.

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

An Analysis of Hydraulic Properties of Soil Based on Soil Texture in Chiti Areas of Lamjung District In Nepal

Soil hydraulic properties play crucial role for having sustainable growth of vegetation and also required for carrying out environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been used to estimate hydraulic properties. Soil hydraulic properties: bulk density, available water, permanent wilting point, field capacity and water retention capacity and saturated hydraulic conductivity are essential to be studied. These factors affect soil productivity and the environment. The knowledge of soil water properties for different land-use and land cover types based on soil textural classes are important for re-growth of plants and agriculture crops and ultimately efficient soil and water management. The aim of this paper is to measure soil hydraulic properties based on soil textural classes for different land use types. Furthermore, the use of the soil texture triangle: hydraulic properties calculator has been assessed. The comparison of the soil hydraulic properties obtained from textural triangle for different land use land and cover types has also been analyzed. Moreover, an analysis of variance (ANOVA) of soil hydraulic properties among different land use types has been performed. The soil reaction (pH) value ranges from 4.69 to 6.69 and as a mean of 5.51 indicating slight acidic. The analysis showed barren land occupying 14.3 percent of total land use/land cover is found to be suitable for plant growth since it has loam texture having bulk density less than 1.6 g/cm3.The analysis of bulk density showed that barren land is significantly different at the 0.05 level from both agriculture and forest, whereas agriculture and forests are not different even at 0.05 significant levels. It verifies the fact that mean bulk density at the depth of 0.15 cm is affected by land use land cover types.The Geographical Journal of NepalVol. 11: 63-76, 2018