Soil Water Content Measurements Research Articles

Simulating the effects of conventional versus conservation tillage on soil water, nitrogen dynamics, and yield of winter wheat with RZWQM2

The movement and distribution of the soil water and nitrogen are significantly influenced by tillage management. However, the dynamics of soil water and nitrogen due to changes in tillage and surface residue cover can be difficult to characterize due to limitations in field experimentation. The objective of this study was to quantify the differences in the soil water and nitrogen balance in the 0–100 cm soil profile, winter wheat (Triticum aestivum L.) yield, water productivity (WP) and nitrogen use efficiency (NUE) that occur when conventional tillage (CT) was changed to four types of conservation tillage treatments (no-tillage [NT], subsoiling tillage [ST], no-tillage with straw [NS] and subsoiling tillage with straw [SS]). Experimental data from 2011 to 2016 collected in Henan province, China on a sandy loam soil was used to calibrate and validate the Root Zone Water Quality Model (RZWQM2). Then the model was used to simulate four tillage systems (NT, NS, ST, SS) that were then compared with CT. The agreement index (d) between simulated and measured soil water content, soil nitrate concentration, and grain yield ranged between 0.71 and 0.93. The root means square error (RMSE) of the soil water content and nitrate concentration was in the range 0.02–0.03 cm cm−1and 5.5–10.3 mg kg−1, respectively. When CT was converted to NT from 2011 to 2016, the model simulated a 9.1 % reduction in annual water loss and a 55.6 % decrease in nitrogen leaching loss and simulated significant increase in average grain yield, WP and NUE. Replacing CT with NT appears to be the best of the four alternative conservation tillage conversion strategies in Henan Province of China.

Estimating the Soil Water Content Using Electrical Conductivity, Oven Method and Speedy Moisture Tester

Soil samples were taken in order to investigate the spatial soil water content changes at 40 points in a field of 675 m2 located on the old Istanbul-Izmit road in Kocaeli-Turkey. The water content values of soil samples collected were obtained using oven and Speedy Moisture Tester (SMT) in the laboratory to correlate with measured electrical conductivity of soil by electrical resistivity method with a 2-electrode system. The results showed a reasonably good correlation between electrical conductivity and water content. The determination coefficient between the water content and the electrical conductivity found by the oven method (R2 ≈ 0.8) was higher than that of the SMT (R2 ≈ 0.6). The correlation between the water content values of oven and SMT is quite affordable with the determination coefficient (R2 ≈ 0.7). Considering the results obtained from Oven and SMT method, it can be concluded that the resistivity method is also successful in measuring soil water content. It is foreseen that resistivity method can be preferred both because it is easy to use, and large areas are faster and economical in measuring soil water content.

Comparative Study of Statistical, Numerical and Machine Learning-based Pedotransfer Functions of Water Retention Curve with Particle Size Distribution Data

The water retention curve (WRC) describes the nonlinear relation of soil water content (SWC) and matric potential. Since direct measurement of SWC and matric potential is difficult and time consuming, indirect approaches including statistical, numerical, and pattern recognition-based pedo-transfer functions (PTFs) that relate basic soil properties to the WRC have been developed during the last few decades. Although several studies reporting the performance of these models can be found in literature, it seems that an extensive investigation which compares the available models and introduces a reliable method to soil hydrologists can be useful. Therefore, in this study, the performance of multiple linear regressions (MLR) models, scaled numerical models and machine learning methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are compared using 98 UNSODA codes with various soil textures to estimate WRC. Results showed that regardless of the soil texture, ANN (RMSE = 0.029) predicts the WRC more accurately than ANFIS (RMSE = 0.035), scaled model (RMSE = 0.060) and MLR (RMSE = 0.071), respectively. Considering the soil texture, ANFIS performance is the best in the moderate and fine textured soils, while scaled numerical model predicts with acceptable performance in sandy soils. WRC prediction using easily available soil characteristics particularly when there is a lack of data, shows that newly developed machine learning methods are capable of predicting WRC considerably accurate for sustainable water flow and solute transport management.

Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor.

Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m3 m−3) as compared to laboratory experiments (RMSE = 0.06 m3 m−3). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K’ reading, failed to improve the Hilhorst model for ECp > 3 dS m−1 for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K0, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

Estimations of rooting depths and sources of plant-available water (PAW) in flatland petrocalcic soils under different land uses

Plant-available water (PAW) is one of the most important issues in productive landscapes around the world. Besides climatic conditions, it depends on several factors, among them, land uses, the rooting and water table depths, and the presence of soil heterogeneities (e.g., petrocalcic horizons). In the present work, PAW evolution was assessed over 441 days in a vertical 1D domain of a monitored Pampean petrocalcic soil under two different land uses and contrasted water demand (i.e., natural and afforested grasslands). For this purpose, rooting depths were estimated using a process-based numerical model of the aquifer-soil-plant-atmosphere continuum constrained by both in situ measured atmospheric demand and water table fluctuations. This model was calibrated against soil water content measurements at different depths and finally, as validation, computed transpiration rates were compared with the sap flow measurements. Modeling results, and calibrated rooting depths, suggest that petrocalcic horizons induce a limited hydraulic connection between the shallow soil portions and the phreatic aquifer. Thus, water storage is enhanced above the petrocalcic horizon in the natural grassland plot particularly during the periods of low atmospheric demand, whereas it is immediately consumed by forest to soil wilting point below the afforested grassland (hydric stress). The numerical methodology followed here appears as an alternative to estimate the forest groundwater usage in afforested grasslands, a key issue in soil salinization research in great plain areas.

Design and characterization of capacitive sensor for soil water content measurement

Soil water content is one of the measurable parameters which can be used as a reference in the selection of efficient land use. The methods used for measure the soil water content is gravimetric method which is commonly used for direct measuring and indirectly method with the measurement of traits related to water content, namely the dielectric constant (relative permittivity). Interdigital capacitor sensor is designed as a unit of measurement of soil water content. Interdigital capacitor sensor (IDC-S) is a capacitive sensor which is able to change the value of the physical parameters to electrical quantities (capacitance). The measurement of IDC-S capacitance value of soil water content variety was done. It is shown that greater percentage of soil water content cause greater value of capacitance. The greater number of sensor electrode increases its sensitivity. IDC-S has sensitivity of 5.589 pF for N=200 electrodes, 4.6362 pF/% for N=150 electrodes and 3.8865 F/% for N=100 electrodes.

Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

Crop evapotranspiration (ETc) is a complex and non-linear process difficult to measure and estimate accurately. This complexity can be solved applying the machine learning techniques with different meteorological input variables. This study investigated the performance of k-Nearest Neighbour (kNN), Artificial Neural Networks (ANN) and Adaptive Boosting (AdaBoost) models to predict daily potato ETc using four scenarios of available meteorological data as: air temperature (scenario 1), air temperature and solar radiation (scenario 2), air temperature, solar radiation and wind speed (scenario 3), and air temperature, solar radiation, wind speed and relative humidity (scenarios 4). The analysis was based on the results of experimental trials carried out in Southern Italy in 2009 and 2010 and focussed on the potato crop cultivation under optimal water supply. The results of ETc estimation with different machine learning techniques were compared with ETc obtained from the soil water balance model, based on the FAO Penman Monteith approach, and gravimetric measurements of soil water content in the crop root zone. The best performances were observed with the kNN model with R2 of 0.813, 0.968 and 0.965, slope of regression 0.947, 0.980 and 0.991, modelling efficiency (EF) of 0.848, 0.970 and 0.972, root mean square error (RMSE) of 0.790, 0.351 and 0.355 mm day−1, mean absolute error (MAE) of 0.563, 0.263 and 0.274 mm day−1 and mean squared error (MSE) of 0.623, 0.123 and 0.126 mm day−1 for scenarios 1, 2 and 3, respectively. When all meteorological variables were available (scenario 4), the ANN model produced slightly better statistical indicators. Therefore, the kNN model could be recommended for the estimation of ETc when limited meteorological data are available. Otherwise, the ANN model should be applied.

Measurement of Volumetric Water Content by Gravimetric and Time Domain Reflectometry Methods at Field Experiment with Biochar and N Fertilizer

Abstract There are many methods used for soil water content measurement which we can divide into direct gravimetric methods from using soil samples or indirect methods that are based on the measurement of another soil property which is dependent on soil moisture. The paper presents the findings of volumetric water content measurements with gravimetric and time domain reflectometry (TDR) methods. We focused on four variants in the field experiment in Dolná Malanta (Slovakia): control variant (B0+N0), variant with biochar at dose 20 t.ha−1 without N fertilizer (B20+N0), variant with biochar 20 t.ha−1 and N fertilizer 160 kg.ha−1 (B20+N160) and variant with biochar 20 t.ha−1 and N fertilizer 240 kg.ha−1 (B20+N240). TDR is nowadays a well-established dielectric technique to measure volumetric water content; however, its accuracy is influenced by high concentration of salts in soil. In this paper, we evaluated the effect of added N fertilizer on the measuring accuracy of HydroSense II device that is operating under the TDR principle.

Crack closure and flow regimes in cracked clay loam subjected to different irrigation methods

Soil cracks, which are common in farmland, typically serve as preferential paths for water movement or solute transport and potentially result in deep percolation and groundwater contamination during crop production. The major objectives of this study were to investigate crack variation and flow regimes in cracked soil under different irrigation methods. Dye experiments were conducted in cracked soil columns using three different irrigation methods, i.e. surface irrigation, spray irrigation and drip irrigation. Superficial crack networks and dye patterns in soil profiles were photographed and quantified by image processing techniques. Soil water content was measured by Time Domain Reflectometry. Evaluation of irrigation efficiency was conducted based on the dye patterns and measured soil water content. The results showed that the cracks rapidly closed under surface irrigation but partially closed under spray irrigation, which was mainly attributed to the difference in application rate. Crack flow occurred under surface irrigation and caused striking ‘V’-shaped regions of preferential flow in profiles, markedly increasing the infiltration depth compared with spray irrigation. Under drip irrigation, crack closure degree decreased with the distance from drip center. Cracks restricted the horizontal infiltration, giving rise to an inverted pear-shaped wetted zone in soil. Our results indicate that application rate and infiltration geometry of irrigation have substantial effect on crack closure degree, and flow regimes in upper soil are closely associated with both irrigation methods and soil cracks, which can appreciably affect the irrigation efficiency. These findings could bear management recommendation for irrigation in cracked fields to promote irrigation efficiency. Besides, this study implies that spraying at appropriate application rates may be able to facilitate crack closure without inducing large water loss via crack flow, which needs to be further investigated.

Comparison of a stand-alone surface renewal method to weighing lysimetry and eddy covariance for determining vineyard evapotranspiration and vine water stress

Surface renewal (SR) is a biometeorological technique that uses high-frequency air temperature measurements above a plant canopy to estimate sensible heat flux. The sensible heat flux is then used to estimate latent heat flux as the residual of a surface energy balance equation. SR previously relied on calibration against other methods (e.g., eddy covariance) to obtain accurate measurements of sensible heat flux, and this need for calibration limited the use of SR to research applications. Our group recently showed that compensating for the frequency response characteristics of SR thermocouples causes the calibration factor to converge near the theoretically predicted value of 0.5 (Shapland et al., Agric For Meteorol 189:36–47, 2014). This led to the development of an inexpensive, stand-alone SR system to measure sensible heat flux without the need for calibration, and here we evaluated the SR system in a mature vineyard containing a weighing lysimeter. Vineyard evapotranspiration (ET) measured with SR was strongly and positively correlated with that from the lysimeter, eddy covariance, and a soil water budget approach. ET measured with the various techniques responded similarly to changes in the microclimatic conditions (i.e., day to day variability) and when water was withheld from the entire vineyard for an extended period. A stress index, calculated using reference and actual ET from SR and lysimetry, was correlated to leaf water potential, stomatal conductance, and volumetric soil water content measurements, but some of these relationships were more variable than others. Our results suggest that the new SR method could potentially be used as a low-cost tool to provide growers with field-specific estimates of crop water use and stress for irrigation management in vineyards.

Unsaturated Soil Hydraulic Properties Identification using Numerical Inversion and In-Situ Experiments from Mnasra Area, Morocco

In soil physics, accurate estimation of hydrodynamic properties is essential for effective groundwater management. Experimentally, determination of these properties in the laboratory or in-situ conditions is difficult step, long and may involve uncertainties significant for the vast majority practice use. Frequently, inverse methods were established to determine accurate parameters in the field scale based on measured soil moisture and hydraulic conductivity over time and depth. These inversions methods involve hydraulic soil-models using adapted optimization algorithms that ensure a best combination of parameters minimizing a cost function. In this work, direct and inverse optimization approaches has been proposed to estimate the effective hydraulic parameters, based on the temporal description of the soil physical and hydraulic parameters using field water content measurements from located in Mnasra area (northwest of Morocco). The Levenberg Marquardt optimization algorithm coupling with the vadose zone model (VZM), numerical code in Fortran, has been used to inverse estimation of hydraulic parameters based on transients soil water content measurements. These uses have been conducted using temporal measurements of soil moisture in depths: 20, 40, 60 and 80 cm. The estimated properties are in good agreement with those measured in-situ. Overall, it was concluded that this technique can greatly widen the range of hydrological problems admissible for inversion and can be used for many applications in hydrologic engineering, either alone or in conjunction with traditional techniques.

Pedoclimate monitoring in the periglacial high mountain soils of the Atacama Desert, northern Chile

AbstractPedoclimate monitoring in the Central Andes is key to understanding climatic change in arid high‐mountain low‐latitude environments. We carried out a study of the thermal–hydric regimes of three soils along an altitudinal gradient in the Licancabur Volcano, northern Chile. In situ measurements of soil and air temperature and water content were collected over 19 months, via temperature and moisture probes installed from 5 to 100 cm depths, at 5,061, 4,728 and 4,426 m a.s.l. All soils show a periglacial thermal regime, but without permafrost. The contrasting behavior regarding soil water content and temperature regime along the gradient was attributed to climate, geomorphic features and varying soil attributes. Altitude‐related factors (air temperature and precipitation) drive the overall thermal regime of soils, but the mid‐altitude site showed the coldest soil thermal regime. Here, local landform characteristics (south aspect, mid‐slope location) impose lower solar radiation, influencing the soil thermal regime. Aridity drives unusual periglacial processes, with low frost shattering, no cryoturbation and particular landscape evolution. The thermal behavior affects soil moisture, with the lowest water contents recorded in the winter period due to water freezing at colder sites. Higher clay content results in greater soil moisture, despite the lower rainfall, shallow depth of the wetting front and large variability in water content. The study period included an extreme climatic event, and as such the monitored data do not represent the typical regional seasonal variation. Pedoclimate is more important in driving soil development than altitude‐related factors.

Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska Under Full and Deficit Irrigation

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 < 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.

In-situ estimation of unsaturated hydraulic conductivity in freezing soil using improved field data and inverse numerical modeling

Hydraulic property determination in freezing soils continues to be a substantial challenge. Our overall objective was to present a novel method for in-situ estimation of unsaturated hydraulic conductivity in freezing soil using a combination of improved field data and a simplified inverse modeling approach. Dielectric sensor readings in the field were corrected using knowledge of ice permittivity, temperature and water redistribution to achieve higher accuracy in determination of liquid soil water content and soil ice content. The equation describing unsaturated hydraulic conductivity in freezing soil with an added impedance parameter was inversely estimated by firstly fitting soil freezing and thawing characteristic curves using the measured liquid soil water content and soil temperature, and then minimizing differences of the measured and simulated total soil water content with a coupled heat- and water-transfer model in frozen soil. Field measurements were made over two winters between 2011 and 2013 (year-1: winter of 2011–2012; year-2: winter of 2012–2013) in Beijing, China. Results suggested that (i) the relative error of liquid soil water content measurement in freezing soil was up to 53% if the knowledge of ice permittivity, temperature and water redistribution was neglected; (ii) the estimated impedance parameter in year-1 (1.152) was an order of magnitude higher than in year-2 (0.117), possibly because a faster freezing rate generated more fine ice in year-2, resulting in reduced tortuosity; (iii) the estimated impedance parameter uncertainty likely comes from the model assumptions, the measurement accuracies of model inputs and the inverse modeling parameter estimates. Based on these results and analysis, we conclude that the impedance parameter in frozen soil is strongly related to both the soil ice content and the size of formed ice, which is affected by the freezing rate and by the size of soil pores related to the soil texture.

Impact of Biochar Reapplication on Physical Soil Properties

The Biochar is getting attention of agronomist worldwide due to its positive effect on physical soil properties in last years. The influence of biochar on physical soil properties has been studied, but only few results was obtained from the field experiments. The main objective of this paper was to study the effect of repeated dose of the biochar on physical soil properties (water content, temperature). The biochar used for the field experiment was produced from paper fiber sludge and grain husks (1:1 w/w) by pyrolysis at 550 degrees C for 30 minutes. In this study, the soil water content (SWC) and temperature was measured in 0.05-0.10 m depth at experimental plots which received dose 20 t.ha−1 (in 2014); repeated dose 20 t.ha−1 (in 2018) and 0 t/ha (control) of biochar amendment at the Malanta area, Slovakia (48°19’23”N 18°09’01”E) in the year 2018. Our field measurements show the positive effect of biochar on SWC. SWC was higher (statistically significant difference) at plots with older dose of the biochar compared to plots with repeated dose and control plots. No statistically significant difference was between plots after reapplication of the biochar and control plots. It could be caused by short time period between biochar reapplication and SWC measurements which means that biochar incorporation to soil was limited. There was no significant difference between average daily temperatures on plots with variant dose of the biochar. Soil warming up offset could be observed during sunrise. It could be caused by darker colour of biochar.

Assessment of the Physically-Based Hydrus-1D Model for Simulating the Water Fluxes of a Mediterranean Cropping System

In a context characterized by a scarcity of water resources and a need for agriculture to cope the increase of food demand, it is of fundamental importance to increase the water use efficiency of cropping systems. This objective can be meet using several currently available software packages simulating water movements in the “soil–plant–atmosphere” continuum (SPAC). The goal of the paper is to discuss and optimize the strategy for implementing an effective simulation framework in order to describe the main soil water fluxes of a typical horticultural cropping system in Southern Italy based on drip-irrigated watermelon cultivation. The Hydrus-1D model was calibrated by optimizing the hydraulic parameters based on the comparison between simulated and measured soil water content values. Next, a sensitivity analysis of the hydraulic parameters of the Mualem–van Genuchten model was carried out. Hydryus-1D determined simulated soil water contents fairly well, with an average root mean square error below 9%. The main fluxes of the SPAC were confined in a restricted soil volume and were therefore well described by the one-dimensional model Hydrus-1D. Water content at saturation and the fitting parameters α and n were the parameters with the highest impact for describing the soil/plant water balance.

Effect of traditional check dams (jessour) on soil and olive trees water status in Tunisia

In the arid regions of south eastern Tunisia, the land use is predominated by olive trees cropping, where two main cultivation strategies can be found: using of water harvesting techniques to overcome the scarcity and variability of rainfall (in the Matmata mountains) and dryland farming (in the Jeffara plain). In these arid areas, soil moisture is the main limiting factor for crop growth and it should be monitored to benchmark different management options. Different conventional methods are available for point soil moisture monitoring, but the increased availability of remotely sensed data offers major opportunities for spatial analyses. The aim of this paper is to perform a comparative study on the soil water status for rainfed olive tree growing in three major landscape areas: in the mountains with traditional water harvesting check dams (called jessour), in the piedmont on floodwater harvesting (called tabias), and in the plain with full dryland farming conditions. Time series of Normalized Difference Infrared Index (NDII), derived from Landsat 7 satellite, were retrieved from the novel Google Earth Engine platform. NDII values were related to measured soil water content, which was taken at non-regular time intervals between 2009 and 2017. The analysis of NDII data, indicating the water content of the vegetation, shows that jessour can adequately ensure water supply for olive trees. Increased soil moisture conditions in the jessour areas are visible both in the dry and the humid seasons, indicating the effectiveness of this traditional water harvesting system. Moreover, our results show that Landsat 7 NDII values are correlated with the root-zone soil moisture in the monitoring sites (r2 ranging from 0.62 to 0.67), allowing the use of NDII to estimate soil water contents in our study area.

Spatial variability of soil thermal properties and their relationships with physical properties at field scale

Characterizing the spatial variability of soil thermal properties and quantifying their relationships with commonly measured soil physical properties is important for successful implementation of precision agriculture practices. However, such information at field scale is scarce. This study aimed to characterize the spatial variability of soil thermal properties and to quantify their spatial relationships with soil physical properties at a field scale. Measurements of soil water content (SWC) and thermal properties were carried out at 80 locations using a time domain reflectometry (TDR) probe and a heat pulse probe (Decagon KD2 Pro), respectively in a cultivated cornfield in southwestern QC, Canada. Soil texture and total organic carbon (TOC) at those locations were also determined in the laboratory. Both coefficient of variation (CV%) and variogram range showed a weak to moderate variability and spatial dependency for the soil properties which was attributed to the homogeneity of the soil, crop, and land management practices. Results of principle component analysis (PCA) indicated that the soil thermal properties and soil water content (SWC) had strong correlations with principle component 2 (PC2). The variogram ranges indicated that the soil thermal properties varied on a similar spatial scale as the SWC in the field. Cross variograms indicated strong spatial correlations between SWC and thermal properties, particularly thermal conductivity (k) and thermal diffusivity (D). Overall, results of this study showed that the SWC had a stronger influence on the spatial variability of soil thermal properties than the other properties such as TOC and soil texture at the field scale. Therefore, the results of this study could be useful for accurate modeling of the soil thermal regimes and mass-energy exchange processes at the field scale as well as developing successful agricultural management strategies such as precision irrigation. Future studies should explore the relationships between the thermal properties and other soil properties such as bulk density under various cropping systems.

Soil Salinity Resistance Effect on Evaporation

Soil salinity is an increasing threat to agriculture and is a major factor in reducing plant productivity in arid and semi-arid regions. Knowledge of the transport process of mass and energy in subsurface soil is vital for understanding the environmental and economic impact of agricultural practices in these areas. A finite difference model, WASH_1D, was applied to evaluate various transport mechanisms associated with temporal variations in soil water content and soil salinity in 5 cm sandy soil columns. Experiment was conducted in a laboratory with fixed temperature and relative humidity. Soil columns were initially saturated with distilled water or 3000 ppm NaCl solution. The accuracy of the model was evaluated using measured soil water content and soil salinity at seven depths 0.2, 0.6, 1.2, 1.7, 2.4, 3.4 and 4.4 cm at the end of the experiment. Simulated cumulative evaporation was compared with the data measured with an electronic balance. Results showed that the liquid flux of distilled water treatment was sharply declined sooner than NaCl solution treatment which was associated with decreasing in moisture diffusivity. The contribution of the water vapor flux to the total moisture flux was 0.01 and 0.2% for distilled water and solution treatments, respectively. The RMSE values between simulated and measured cumulative evaporation lines were 0.77 and 1.08 (g) for the distilled water and solution treatments, respectively. It was observed that the solution treatment significantly reduced total cumulative evaporation up to 11% as compared to the distilled water treatment. Simulated results demonstrated that the model predicted soil water content and salinity with adequate accuracy.

BEETSOIL: a decision support tool for forecasting the impact of soil conditions on sugar beet harvest

Sugar beet in the UK is harvested in autumn and winter, when soil moisture is usually close to field capacity. This, together with the heavy machinery used can lead to serious environmental problems such as topsoil disturbance, subsoil compaction and soil erosion. BEETSOIL is a decision support tool (DST) developed to help plan the sugar beet harvest campaign by assessing if soil conditions are suitable for harvest whilst minimising the occurrence of soil damage. The core of BEETSOIL is a soil water balance model that, using a rainfall source selected by the user, predicts soil water content in a determined prediction window. The resulting soil water content is used to predict soil trafficability, wheel sinkage, soil stickiness and soil loss due to harvest on a daily basis. The soil water balance module was validated with measured soil water content at three field sites with contrasting clayey, silty and sandy textures and showed RMSE of 0.91%, 0.96% and 0.52%, respectively. The sensitivity of the trafficability modules of BEETSOIL were tested using several scenarios using different initial soil water contents at the start of the harvest campaign combined with rainfall amounts that simulate wet, median and dry conditions during the harvest period. Analysis of the scenarios showed the trafficability module was very sensitive to changes in texture, initial soil water content of the simulation and rainfall. This information can be used to assess the suitability of new sugar beet growing areas, where the proportion of time during which fields can be trafficked by vehicles (harvested effectively) can be predicted under different scenarios and therefore give an indication of any consistent harvest difficulties. The model outputs of sinkage, trafficability and soil loss by harvest have yet to be validated, but the first outputs provide indications of how the DST can be used across the whole growing area to schedule harvest operations to target areas that can be harvested most effectively.