Predicted Soil Water Content Research Articles

Soil water content detection based on acoustic method and improved Brutsaert’s model

The traditional Brutsaert’s model does not consider the influence of fixed Poisson’s ratio on the accuracy of the model. Therefore, we extend Brutsaert’s model by introducing Poisson’s ratio dynamic solution model, and obtain the improved Brutsaert’s model which considers the change of soil Poisson’s ratio. The primary objective of this work was to explore whether improved Brutsaert’s model (Poisson’s ratio dynamic solution model is introduced) can more accurately predict soil volumetric water content (SVWC) than traditional Brutsaert’s model. Acoustic velocities (the first compressional wave and shear wave) of natural silica sand (sand) and three different textures of farming soils (loam, sandy loam and clay loam) were measured using pulsed acoustic waves with frequencies less than 900 Hz under the conditions of wet swelling and drying shrinkage, respectively. The traditional Brutsaert’s model and improved Brutsaert’s model are validated by the measured acoustic velocities. We compare the result calculated by improved Brustsaert’s model with that calculcated by traditional Brustsaert’s model. The compared results show that the improved Brustsaert’s model is more suitable for measuring the relationship between the velocity and the soil water content (SWC) than traditional Brustsaert’s model in four different textures of soil samples. In order to verify the feasibility of the improved Brustsaert’s model for predicting soil water content (SWC), field experiments were carried out on three kinds farming soil (loam, sandy loam and clay loam) sampling sites. The results show that the detected precision measured by improved Brustsaert’s model was around 5%.

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.

Assessing Hydrus-2D Model to Investigate the Effects of Different On-Farm Irrigation Strategies on Potato Crop under Subsurface Drip Irrigation

The objective of this paper was to assess the performance of Hydrus-2D model to simulate the effects of different on-farm irrigation strategies applied on potato crop. The ability of the model to simulate the stress coefficient (Ks), obtained as the ratio between actual and maximum transpiration, and to define the productive function of potato crop under the semi-arid conditions of central Tunisia were also evaluated. Experiments were carried out on potato crop under full (FI) and deficit irrigation (DI) and two different water qualities supplied by means of a subsurface drip irrigation system. Results evidenced that the model, despite some discrepancies locally observed, can fairly accurately predict soil water contents and electrical conductivity around buried emitters. Furthermore, under water and salt stress conditions, “measured” Ks, based on crop water stress index (CWSI) obtained on thermal images, resulted in a good correlation with the corresponding estimated by the model (R2 = 0.8). The database collected during the three growth seasons also allowed the definition of the crop productive function represented by a linear relationship between the relative yield loss and Ks. This function represents a useful guidelines for the sustainable use of irrigation water in countries characterized by a semi-arid climate and a limited availability of water for irrigation.

Enhanced Cd transport in the soil-plant-atmosphere continuum (SPAC) system by tobacco (Nicotiana tabacum L.)

The optimal treatment designs of the heavy metal pollution sites and the calculation of the recovery capacity are important in recent studies. In this paper, we aimed to model the accumulation of heavy metals under different artificially Cd added concentrations, and analyzed the various tobacco solute adsorption and fluid flow properties. The finite difference method was used to simulate the heavy metals flux and root absorption in the soil, and the model simulation was compared with the measured values to quantify the uncertainty of the metal transport and modeling parameters. Treatments with different Cd levels were compared, e.g., control tillage (CT), low Cd tillage (LT, 2.0 mg/kg), high Cd tillage (HT, 20.0 mg/kg), ultra-high Cd tillage (UHT, 80.0 mg/kg). The predicted soil water content (SWC) was consistent with observed data. Predicted cumulative root water uptake (mm) ranked as follows: CT (196)>LT (178)>HT (134)>UHT (117). Potential transpiration rates (T r p) under HT and UHT were lower than that of other treatment, because of their lower leaf Area Index (LAI). The predicted root Cd uptake showed a strong correlation within the actual Cd uptake. The predicted root absorption of Cdmax was UHT (180.17)> HT (106.52)> LT (53.20) >CT (0.610). However, deviation of models was added by the Cd effluent trend and the performance of root exudates. This finding would be useful for further investigation into bio-remediation in the agricultural area, not only for Cd ion but for a range of other heavy metal contaminants.

How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

Core Ideas Ensemble Kalman filter data assimilation was used to predict soil water content. Analyzed data assimilation frequencies were 1, 2, 3, 5, 7, 9, 11, and 14 d. Assimilation of observed data every 7 d or more yielded better results. Data assimilation (DA) is a promising alternative to infer soil hydraulic parameters from soil water dynamics data. Frequency of measurements and updates are important controls of DA efficiency; however, no strict guidance exists on determining the optimal frequency. In this study, DA was performed with the ensemble Kalman filter (EnKF) with a state augmentation approach to update both model states and parameters. We analyzed updates every 1, 2, 3, 5, 7, 9, 11, and 14 d. Two soil types (sandy loam and loam) and four climates (hot semiarid [Bwh], cold semiarid [Bsk], humid continental [Dfa], and humid subtropical [Cfa]) were considered. Results demonstrate that DA with high update frequencies does not provide better results than results obtained when using low frequencies. For sandy loam soil, assimilation of data every seven or more days yields better results for whatever climate considered. For loam soil, the same is true after 9 mo of assimilation. The chosen performance metric may affect the results, but the general trend of better results with low assimilation frequencies does not change.

Evaluation of Calibration Method for Field Application of UAV‐Based Soil Water Content Prediction Equation

The objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higher spatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed a water content estimation equation using the color of the soil and suggested a calibration method for field application. Since the resolution of the images taken by the UAV is different according to the altitude, the water content estimation formula is developed by using the images taken at each altitude. In order to calibrate the color difference according to lighting conditions, the calibration method using field data were proposed. The results of the study showed an altitude‐specific estimation equation using RGB values of the UAV image through linear regression. The appropriate number of field data needed for calibration for site application of the estimation equation was found between 4 and 10. On‐site application results of the proposed calibration method showed RMSE accuracy of 1.8 to 2.9%. Thus, the water content estimation and calibration method proposed in this study can be used in effectively monitoring the water content of soil using UAVs.

Functional Extremum Solution and Parameter Estimation for One‐Dimensional Vertical Infiltration using the Brooks–Corey Model

Core Ideas Least action and variational principles were used to simulate unsaturated vertical infiltration. A functional extremum explicit solution to Richards' equation was obtained. A new method describes nonlinear relationships between cumulative infiltration, infiltration rate and wetting‐front depth. The method could be used to accurately predict soil water content and estimate soil hydraulic parameters. A simple analytical solution of one‐dimensional vertical infiltration of soil water is of great importance for the estimation of soil hydraulic properties and for precision irrigation. We use a new approximate analytical method based on the principle of least action and the variational principle for simulating the vertical infiltration of water in unsaturated soil for a constant‐saturation upper boundary and for estimating infiltration parameters. The method proposes the functional extremum problem of one‐dimensional vertical infiltration of water in unsaturated soil using the Brooks–Corey model. A functional extremum solution (FES) to Richards' equation was obtained using the Euler–Lagrange equation and the integral mean‐value theorem. A modified functional extremum solution (MFES) was also proposed empirically to improve precision based on the FES of horizontal absorption and numerical data simulated by Hydrus‐1D. The relationships between a parameter derived by the value theorem of the integral (α), the air‐entry suction (hd), and the wetting front depth (zf) were analyzed by comparing MFES with the numerical distribution of soil water content. The MFES was used to describe the nonlinear relationship between cumulative infiltration and zf, with the relative error (Re) of < 4.1% and the coefficient of determination (R2) > 0.986. The MFES was also used to describe the nonlinear relationship between the infiltration rate (i) and the reciprocal of zf for soils with hd ≥10 cm, with Re < 6.0% and R2 > 0.8453. A simple method for estimating soil saturated hydraulic conductivity (Ks) was proposed from the nonlinear relationship between i and zf, and the sensitivities of parameters in the Brooks–Corey model were also analyzed.

A comprehensive evaluation of pedotransfer functions for predicting soil water content in environmental modeling and ecosystem management

Many pedotransfer functions (PTFs) have been developed for predicting the soil water content at different matric potentials. The use of these functions has been encouraged because of the time and work typically required for measuring it, while the PTFs require commonly measured soil properties such as sand, silt, clay, organic matter content, or bulk density for predicting water retention. In addition, several environmental and ecosystem management simulation models such as DRAINMOD, HYDRUS, EPIC, SPAW, and WEPP use PTFs for computing soil hydraulic properties. Because of the increasing use of the PTFs and their effect in many soil water simulation and transport models, this study revised and tested 13 different PTFs for predicting soil water content at −33 and −1500 kPa, values usually known as field capacity and wilting point. Three of these PTFs were derived from tropical soils while the rest were developed with soil samples collected across the United States. These PTFs were evaluated in Chilean soils as an independent dataset and their improvement after calibration was assessed with this new data. The results demonstrate that the PTFs performance depends on the soils used for their development as the estimates showed a significant improvement after calibration. When predicting water content, Rawls et al. (2004) was the best function before calibration (RMSE = 0.08 for −33 and −1500 kPa), while Gupta and Larson (1979) was the best after calibration (RMSE of 0.06 and 0.05, and r2 values of 0.69 and 0.66 at −33 and −1500 kPa, respectively). Nonlinear PTFs performed better than linear PTFs when predicting water content at field capacity. Finally, bulk density proved to be the key variable and can be used as footprint for soils changes through time. Organic matter content was also a significant input but improved the estimates for some specific matric potentials and PTFs.

Evaluation of point and parametric pedotransfer functions for predicting soil water retention and availability in soils of southwestern Saudi Arabia

Simulation of water flow and solute transport in the soil environment requires accurate estimates of the soil water retention characteristics (SWRC). In this study, multiple linear regression (MLR) was used to develop two site-specific pedotransfer functions (PTFs), a point (MLRP) and a parametric (MLRF), using soil properties of 43 soil samples collected from the Jazan region, southwest of Saudi Arabia. The accuracy of the developed PTFs and four existing PTFs to determine the SWRC, predict soil water content (SWC) at − 10, − 33, and − 1500 kPa, and estimate available water content (AWC) was assessed. The MLRP and the Schaap PTFs produced the best estimate of SWC, with smaller root mean square error (RMSE) (0.023–0.053 cm3 cm−3), and larger D-index (0.8–0.9) and Akaike information criterion (AIC) (− 303.7 to − 240.7), respectively. The largest prediction errors in the estimation of SWC were observed at matric potential close to field capacity (FC). For the AWC, the Schaap PTF provided the best prediction (RMSE = 0.014 cm3 cm−3, D-index = 0.93, AIC = − 359.9), followed by the MLRP PTF (RMSE = 0.027 cm3 cm−3, D-index = 0.83, AIC = − 302.1), whereas the Vereecken, Gupta and Larson, and the MLRF PTFs produced less accurate predictions of the AWC. The MLRP PTF proved to be more accurate compared to other tested PTFs in the prediction of SWC at both FC and permanent wilting point (PWP). In contrast, the MLRF PTF produced a relatively large error in the estimation of SWC at FC.

Predicting soil water content at - 33kPa by pedotransfer functions in stoniness 1 soils in northeast Venezuela.

Soil water content is a key property in the study of water available for plants, infiltration, drainage, hydraulic conductivity, irrigation, plant water stress and solute movement. However, its measurement consumes time and, in the case of stony soils, the presence of stones difficult to determinate the water content. An alternative is the use of pedotransfer functions (PTFs), as models to predict these properties from readily available data. The present work shows a comparison of different widely used PTFs to estimate water content at-33kPa (WR-33kPa) in high stoniness soils. The work was carried out in the Caramacate River, an area of high interest because the frequent landslides worsen the quality of drinking water. The performance of all evaluated PTFs was compared with a PTF generated for the study area. Results showed that the Urach's PTF presented the best performance in relation to the others and could be used to estimate WR-33kPa in soils of Caramacate River basin. The calculated PTFs had a R2 of 0.65. This was slightly higher than the R2 of the Urach's PTF. The inclusion of the rock fragment volume could have the better results. The weak performance of the other PTFs could be related to the fact that the mountain soils of the basin are rich in 2:1 clay and high stoniness, which were not used as independent variables for PTFs to estimate the WR-33kPa.

Effect of agronomic treatments on the accuracy of soil moisture mapping by electromagnetic induction

Electromagnetic induction (EMI) is an established method for mapping field-scale soil water content (SWC). However, the correlation between the recorded apparent electrical conductivity (ECa) and SWC is affected by several factors that can vary across test sites and with environmental conditions. As agricultural practices affect both, ECa and SWC, it is likely that the mismatch in SWC predictions using ECa can be directly or indirectly attributed to agronomic treatment effects. Hence, EMI based SWC predictions are often limited to sites with one soil amendment and strong ECa – SWC correlations. However, non-invasive SWC mapping is particularly desirable for larger agricultural fields, covering different soil amendments. We hypothesized that different agronomic treatments altered the ECa – SWC correlations and consequently the EMI based SWC prediction accuracy. We further hypothesized that a model established on areas with high positive ECa – SWC correlation could be used to predict SWC for areas with unsuitable ECa – SWC correlations. A field-scale experiment was conducted to investigate the effects of six agronomic treatments (including biochar, BC) on SWC, ECa, and ECa – SWC correlation in a silage corn field. We tested the accuracy of three models to predict the SWC of independent data sets using data from i) all treatments (Tall), ii) plots with lowest and iii) plots with highest ECa – SWC correlations. We found statistically significant treatment effects on both, ECa and SWC, although overlapping data ranges were given. Furthermore, the correlations between ECa and SWC were affected by the treatments. Correlations were found to be lowest on nutrient-rich dairy manure plots (T2) and highest on the control plots (T6), likely due to differences in the ionic strength of pore water. BC mitigated the effect of ionic strength for T2 while it showed no measurable effects on ECa on plots receiving inorganic fertilizers. Most accurate SWC predictions were reached by employing Tall data (RMSEP 1.40–3.13% vol.). However, models based on T6 data provided similar accuracies (RMSEP 1.46–3.96% vol.) using only 12.5% of the area. The T2 based model performance failed (RMSEP 3.02–7.21% vol.). Results suggest that ECa – SWC models established on non-manured areas could provide best possible SWC predictions and are recommended as training areas if soil texture and mineralogical composition can be expected to be relatively homogeneous.

Exploring Scale‐Specific Controls on Soil Water Content across a 500‐Kilometer Transect Using Multivariate Empirical Mode Decomposition

Core Ideas We used MEMD to explore scale‐specific controls of SWC at a 500‐km transect. The largest mean scale of SWC and environmental factors along the transect was 121 km. Precipitation and temperature were large‐scale factors controlling SWC. Elevation and sand were moderate‐scale factors controlling SWC. Clay content was the dominant factor controlling SWC at all scales. Soil water content (SWC) varies both spatially and temporally and is highly controlled by various factors operating at different intensities and scales. In this study, we investigated the scale‐specific controls of SWC along a 500‐km transect using multivariate empirical mode decomposition (MEMD) at 42 sites. Soil water content and six environmental factors were divided into different intrinsic mode functions (IMFs) and residuals to represent different scales. Different values of IMFs for SWC and environmental factors were obtained throughout the whole profile and within five soil layers (0–1, 1–2, 2–3, 3–4, and 4–5 m). The largest scales (i.e., the scales that explain largest portion of variances) of SWC and environmental factors depended on the soil layer from which SWC was involved in MEMD analysis, and they were 272, 126, 134, 126, 117, and 121 km for soil layers from 0 to 1, 1 to 2, 2 to 3, 3 to 4, 4 to 5, and 0 to 5 m, respectively. The residuals accounted for a majority (33–78.1%) of the variance of the original data. At large scales (>250 km), precipitation and temperature were the controlling factors on SWC, whereas at moderate scales (65 km), elevation and sand were the factors determining SWC. In contrast, at all scales, clay content affected SWC distribution. The scale‐specific prediction of SWC on the basis of IMFs and residuals contained more information on environmental factors than the results obtained at the measurement scale. Overall, SWC prediction from IMFs and their residuals were superior to those based on the original data. Using information obtained from MEMD could improve our understanding of the scale‐specific characteristics of soil water and environmental factors across a long transect scale.

Importance of soil organic carbon in near-surface soil water content estimation: A simple model comparison in dry-end Canadian Prairie soils

The relationship of soil organic carbon (SOC) to soil water content (SWC) is primarily included in hydrological models through porosity where SOC is known to influence soil structure. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) field sampling campaign in southern Manitoba provided both SWC and SOC data over three soil textural classes and a range of soil wetness conditions. Linear regressions were developed to predict time-averaged SWC from SOC in the surface 5 cm of 50 cropland fields during SMAPVEX12 when averaged across three wetness ranges. The prediction of SWC from SOC was tested on fields in the same vicinity as SMAPVEX12 that were sampled for SWC in 2008 when soils were very dry. The measured SWC on the five sampling dates in 2008 was compared to SWC estimated for those dates from (1) the single-parameter regression equation from SOC during SMAPVEX12 dry days (1pSOC); (2) a ‘decay’ formula (5% reduction of a declining balance) (2pDM); and (3) a model based on field mean SOC with daily computation (3pSOCM). Both daily iteration models (2 and 3) required initial field capacity and rainfall data from 1 April. Models were compared with root-mean-square error (RMSE) and delta Akaike information criterion (∆AIC). The two-parameter model (2pDM) had the lowest RMSE and most favorable ∆AIC scores for mean SWC of the five sampling dates over all fields. The single-variable SOC equation was equally viable in RMSE and ∆AIC to the other models on sand soils. On clay soils, 3pSOCM was advantageous on some sampling dates, demonstrating a benefit of including SOC with field capacity and precipitation. The predictive ability of SOC to explain time-averaged SWC variability in dry soils would be valuable for SWC model initialization and as a covariate for geostatistical downscaling from satellite SWC scales.

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

AbstractScale‐ and location‐dependent relationships between soil water content (SWC) and individual environmental factors have been widely explored. SWC is controlled by multiple factors concurrently; however, the multivariate relationship is rarely explored at different scales and locations. Multivariate controls of SWC at different scales and locations in two seasons within a hummocky landscape of North America were identified using bivariate wavelet coherency and multiple wavelet coherence. Results showed that depth to CaCO3 layer, which was correlated with elevation over all locations at scales of 36–144 m and cos(aspect), provided the best individual factor for explaining SWC variations in spring (May 2) and summer (August 23), respectively. Although spatial patterns of SWC were temporally stable, different topographic indices affected spatial distribution of SWC in different seasons (elevation in spring and aspect in summer) due to different dominating hydrological processes. These varying hydrological processes also resulted in the distinct role of soil organic carbon (SOC) content in different seasons: a positive correlation in spring and a negative correlation in summer. Multiple wavelet coherence identified a combination of depth to CaCO3 layer and SOC in spring and a combination of cos(aspect) and SOC in summer that controlled SWC at different scales and locations, respectively. This indicated a combined effect of soil and topographic properties on SWC distribution and a clear need for these two factors in developing scale‐dependent prediction of SWC in the hummocky landscape of North America.

Evaluating AquaCrop model for simulating production of amaranthus (Amaranthus cruentus) a leafy vegetable, under irrigation and rainfed conditions

Amaranthus (Amaranthus spp.), a leafy vegetable in South Africa, has the potential to be cultivated as a crop, but is rarely cultivated because it easily grows naturally on any waste land. The crop tolerates adverse environmental conditions, but performs better with application of water and soil organic or inorganic fertilizers. The AquaCrop crop model was calibrated and validated for amaranthus under irrigation and rainfed conditions for this study. Field experiments were carried out during the 2008–09 and 2009–10 seasons under line source sprinkler system while pot experiment was carried out during the 2010–11 season. The pot and field data sets were used for parameterisation, calibration and validation of the model. The model was adequately calibrated for biomass and cumulative evapotranspiration (ET) for amaranthus under irrigation and rainfed conditions. However, pooled data across irrigation and rainfed conditions showed canopy cover (CC) was moderately simulated (root-mean-square error (RMSE) =20.8%; model efficiency (ME)=0.11; R2=0.577; d index of agreement (d)=0.746; mean absolute percentage error (MAPE)=43.4%). During validation, the model was able to adequately predict biomass and cumulative evapotranspiration (ET) for amaranthus for pooled data of irrigation (Full irrigation=W5 & Moderate irrigation=W3) and rainfed (W1) with RMSE of 1.96tha−1 and 75.64mm, ME of 0.89 and 0.76, R2 of 0.92 and 0.91, d index of agreement of 0.91 and 0.91 and MAPE of 24.1 and 37.6% respectively. The prediction of soil water content by the model was moderate (RMSE=50.62mm; ME=0.19; R2=0.30; d=0.67; MAPE=40.09) and needs improvement. It is recommended that datasets from other agro-ecological regions be used to improve calibration and validation for this crop.

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

AbstractNonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the ‘optimal’ soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the ‘optimal’ solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

Water Use and Yield of Soybean under Various Irrigation Regimes and Severe Water Stress. Application of AquaCrop and SIMDualKc Models

Data relative to two soybean seasons, several irrigation scheduling treatments, including moderate and severe deficit irrigation, and rain-fed cropping were used to parameterize and assess the performance of models AquaCrop and SIMDualKc, the latter combined with the Stewart’s yield model. SIMDualKc applies the FAO56 dual crop coefficient approach for computing and partitioning evapotranspiration (ET) into actual crop transpiration (Tc act) and soil evaporation (Es), while AquaCrop uses an approach that depends on the canopy cover curve. The calibration-validations of models were performed by comparing observed and predicted soil water content (SWC) and grain yield. SIMDualKc showed good accuracy for SWC estimations, with normalized root mean square error (NRMSE) ≤ 7.6%. AquaCrop was less accurate, with NRMSE ≤ 9.2%. Differences between models regarding the water balance terms were notable, and the ET partition revealed a trend for under-estimation of Tc act by AquaCrop, mainly under severe water stress. Yield predictions with SIMDualKc-Stewart models produced NRMSE < 15% while predictions with AquaCrop resulted in NRMSE ≤ 23% due to under-estimation of Tc act, particularly for water stressed treatments. Results show the appropriateness of SIMDualKc to support irrigation scheduling and assessing impacts on yield when combined with Stewart’s model.

Approximate solution of a one-dimensional soil water infiltration equation based on the Brooks-Corey model

The solution of a one-dimensional unsaturated soil water infiltration equation can provide theoretical support when simulating soil water distribution. To solve the equation for one-dimensional vertical infiltration of soil moisture in unsaturated soil with initially uniform moisture content distribution, a new method is proposed based on the Principle of Least Action and the Variational Principle. We used second-, third- and fourth-order Taylor series expansions to obtain an approximate analytical solution for a one-dimensional, constant water head, vertical infiltration equation. The results were compared with a numerical solution obtained using the HYDRUS-1D software. Our results showed that the soil water content profile, accumulated infiltration and infiltration rate can be calculated by the new method, and the soil water content calculated by the second-, third- and fourth-order approximate analytical solutions was closer to the numerical solution in the higher water content areas. The relative error of the soil water content for clay loam, silt, loam and sandy loam ranged from 0.7187% to 3.0848%, 0.9667% to 2.2847% 0.6310% to 2.2943%, 1.7504% to 6.8823%, respectively. In four soils modeled numerically, the fourth-order results were the best, with the smallest relative errors and the best coefficients of determination. The longer the wetting front distance, the smaller the calculated errors. Thus, the fourth-order approximate analytical solution can be used to simulate the long-term vertical infiltration process. If the results do not require the highest error precision, the second-order approximate analytical solution was simple in form and also sufficient to be used to describe the water content of the vertical infiltration process. Our results show that the approximate analytical solution proposed provides an effective approach to predict soil water content in the vertical infiltration process.

Modelling soil water balance and root water uptake in cotton grown under different soil conservation practices in the Indo-Gangetic Plain

Although soil conservation practices are being promoted as better environmental protection technologies than traditional farmers’ practice, limited information is available on how these practices affect soil water balance and root water uptake. The root water uptake (RWU) patterns of cotton grown under soil conservation practices and soil water balance in cotton (Gossypium hirsutum L.) fieldsunder a cotton-wheat (Triticum aestivum L.) cropping system were analyzed using the Hydrus-2D model. The treatments were: conventional tillage (CT), zero tillage (ZT), permanent narrow beds (PNB), permanent broad beds (PBB), ZT with residue (ZT+R), PNB with residue (PNB+R) and PBB with residue (PBB+R). Results in the third year of the cotton crop indicated that the surface (0–15cm layer) field saturated hydraulic conductivity in both PNB and PBB plots were similar and were significantly higher than in the ZT plots. Computed potential transpiration rates (Trp) under CT were lower than in other treatments, due to less radiation interception and lower Leaf Area Index (LAI). Both PNB and PBB plots had higher Trp and crop yields than CT plots, which were further improved by residue retention. Predicted soil water content (SWC) patterns during the simulation periods of third and fourth years showed strong correlation (R2=0.88, n=105, P<0.001, the root mean square error (RMSE)=0.025, and the average relative error (AVE)=7.5% for the third year and R2=0.81, n=105, P<0.001, RMSE=0.021, and AVE=9% for the fourth year) with the actual field measured SWCs. Cumulative RWU (mm) were in the order: ZT (143)<CT (157)<PNB (163)<ZT+R (174)<PBB (188)<PNB+R (198)<PBB+R (226). Thus, PBB+R and PNB+R practices could be adopted for cotton cultivation, as these enhanced root growth and improved radiation interception and LAI. The Hydrus-2D model may be adopted for managing efficient water use, as it can simulate the temporal changes in SWC and actual transpiration rates of a crop/cropping system.

Estimating the impacts of climate change on crop yields and N2O emissions for conventional and no-tillage in Southwestern Ontario, Canada

Accurately predicting the impacts of higher temperatures, different precipitation rates and elevated CO2 concentrations on crop yields and GHG emissions is required in order to develop adaptation strategies. The objectives of this study were to calibrate and evaluate a regionalized denitrification-decomposition (DNDC) model using measured crop yield, soil temperature, moisture and N2O emissions, and to explore the impacts of climate change scenarios (Representative Concentration Pathways (RCP) 4.5 and RCP 8.5) on crop yields and N2O emissions in Southwestern Ontario, Canada. This simulation study was based on a winter wheat-maize-soybean rotation under conventional tillage (CT) and no tillage (NT) practices at Woodslee, Ontario, Canada. The model was calibrated using various statistics including the d index (0.85–0.99), NSE (Nash-Sutcliffe efficiency, NSE>0) and nRMSE (normalized root mean square error, nRMSE<10%) all of which provided “good” to “excellent” agreement between simulated and measured crop yields for both CT and NT practices. The calibrated DNDC model had a “good” performance in assessing soil temperature. However, there were no differences in simulated soil temperatures between CT and NT treatments and this was attributed to deficiencies in the temperature algorithm which does not consider the insulation effect of surface crop residues in the DNDC model. The DNDC model provided a reasonable prediction of soil water content in the 0–0.1m depth, but it overestimated soil water content during dry conditions mainly because the model was unable to characterize preferential flow through clay cracks. Under future climate scenarios, soybean and maize yields were significantly increased compared to the baseline scenarios due to the benefits from higher optimum temperature for maize and increased CO2 for soybean. The mean annual N2O emissions for winter wheat significantly increased by about 38.1% for CT and 17.3% for NT under future RCP scenarios when using the current crop cultivars. However, when a new cultivar with higher TDD (thermal degree days) was used, the mean winter wheat yield increased by 39.5% under future climate scenarios compared to current cultivars and there were significant reductions in N2O emissions. The higher crop heat units cultivars and longer growing season length would contribute to increased biomass accumulation and crop N uptake. Hence there would be co-benefits with the development of high TDD cultivars in the future as they would not only increase crop yields but also reduce N2O emissions.