Soil Water Balance Simulation Research Articles

Developing a real-time self-organizing algorithm for irrigation planning of rapeseed cultivation

Abstract Sustainable planning of water allocation in the agricultural sector requires attention to soil, plant, climate and their limitations. This study was conducted in order to develop a real-time framework for simulating soil–water balance in the root zone, crop growth curve and irrigation planning of rapeseed cultivation in Henan Province, China during a cropping season from March to October 2022. Simulation of production functions with field information calibration at daily time step was developed to accurately estimate the simulation of crop growth and soil water balance. Particle swarm optimization (PSO) algorithm is incorporated as an efficient tool to evaluate the water productivity as objective function in a self-organizing framework. Choosing the appropriate planting date for rapeseed cultivation at the beginning of the growing season was evaluated to increase the use of precipitation for canopy cover growth and thus reduce irrigation water consumption. The results showed that the proposed model increased water productivity by 23% as the objective function, and evaporation from the soil surface decreased by 16%. The maximum difference between the irrigation depth in the optimal and existing strategies was 41 mm in the germination stages until the seed-filling stage, which caused a decrease in final biomass and plant transpiration.

Simulation of Soil Water and Salt Balance in Three Water-Saving Irrigation Technologies with HYDRUS-2D

Mulch drip irrigation (MDI) technology can effectively solve the problem of insufficient temperature accumulation during the pre-fertility period and facilitate the efficient supplementation of water and fertilizer during the fertility period in spring corn planting. Moreover, this local MDI technology also has impacts on the farmland environment. To investigate the effect of drip irrigation technology on the water and salt environment of farmland, a field study on corn cultivation was carried out at West Liaohe Plain. In addition, the water and salt dynamics of the farmland were simulated using HYDRUS-2D for mulch drip irrigation (MDI), shallowly buried drip irrigation (SBDI), and sprinkler irrigation (SI), with variable rainfall and initial salt content. The results showed that the distribution of and variation in water and salt in the soil were similar under MDI and SBDI. The change near the drip tape was mainly affected by irrigation, while the water and salt in the soil between drip tapes were correlated with irrigation and rainfall. The amount of salt in the topsoil (5 cm) increased with a decrease in rainfall. With an initial EC = 480 μs/cm (soil salt content 0.1%), the salinity of the topsoil under MDI was significantly higher than that under SBDI and SI within two years. The topsoil salinity was similar for all three irrigation technologies with increasing operating life, reaching a relatively stable state, and much lower than the salinity determination threshold of 480 μs/cm. Given the current conditions of rainfall, soil, buried depth, and mineralization in the West Liaohe Plain, the risk of secondary salinization is minimal if irrigation management is reasonable. This study provides data to support the application of drip irrigation technology in the Western Liaohe Plain.

Simulation of soil water balance and crop productivity of long-term continuous maize cropping under high planting density in rainfed agroecosystems

Increasing planting density is one of the main management practices contributing to maize grain yield improvements across the world, but the soil water sustainability after long-term continuous high-density under rainfed farming systems is not clear. Crop simulation models may help explore suitable management systems for increasing crop productivity and economic benefits. The present study calibrated and validated the DSSAT-CERES-Maize model in a four-year field experiment, which coupled four densities from 52,500 to 97,500 plant ha−1 and two cultivars under rainfed conditions. The calibrated CERES-Maize model performed fairly well in simulating the phenological dates, and the average root mean-squared error (RMSE) ranged from 0.7 to 2.8 d for anthesis and 0 to 2.8 d for maturity date. The normalized root mean squared errors (nRMSE) for biomass and grain yield were 17.5% and 12.4%, respectively. The average nRMSE for soil water dynamics in the 0–200 cm soil layers was 15.6% among the different growth stages. The calibrated model was subsequently used to evaluate the soil water regime and crop productivity in response to planting density under 38 years of meteorological data. The results showed that maize water productivity and evapotranspiration (ET) fluctuated with seasonal rainfall, and normal and wet years were significantly higher than dry years. Although ET during the growing season tended to increase with increasing density, the long-term continuous high planting density did not cause excessive soil water consumption. Grain yield and water use efficiency (WUE) tended to exhibit a parabolic relationship with planting density during the long-term simulated seasons between different experimental sites. No significant differences were detected between different cultivars in water productivity under long-term simulation. However, the simulation results suggested that the optimal planting density was often related to variability in climate conditions between sites and years. The scenario simulation results suggest that the optimal density should not exceed 67,500 plant ha−1 when the annual precipitation is less than 500 mm, but it should not exceed 82,500 plant ha−1 in areas where the rainfall is greater than 500 mm. Therefore, this study suggests that moderate planting density has the potential to realize sustainable maize development in dryland farming systems on the Loess Plateau and similar areas.

Simulation of dryland maize growth and evapotranspiration using DSSAT‐CERES‐Maize model

AbstractEvapotranspiration (ET) is a crucial component of plant water use and ecosystem energy balance. This study aims to simulate maize (Zea mays L.) ET using a crop modeling system and compare that to ET measured using an eddy‐covariance (EC) system. An EC system was used to collect growing season ET data from a rainfed maize field in 2017, 2018, and 2019. Supporting soil and plant growth data were also collected. The Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Maize model was used in this study. Results from DSSAT's ET simulation methods, FAO‐56 and Priestley–Taylor, were compared to measured ET. Results indicated a low coefficient of determination (R2) between simulated and measured ET, due to overestimation of simulated ET in all 3 yr. The average percent root mean square error for the daily ET simulation across all 3 yr was 34.6% for the Priestley–Taylor method and 30.4% for the FAO‐56 method. For the FAO‐56 simulation, the average seasonal overestimation compared to measured ET was 59 mm and the average R2 was .59. For the Priestley–Taylor method, the average seasonal overestimation was 72 mm and the average R2 was .57. Possible sources of error contributing to this overestimation include DSSAT's potential ET estimations, crop coefficient estimation, and the simulation of soil water balance in vertisols with high swell‐shrink clay minerals. The mixed simulation results, particularly compared to those seen in studies of irrigated crops, indicate a greater need to improve ET estimation capabilities in rainfed systems.

Optimization of Irrigation Scheduling for Maize in an Arid Oasis Based on Simulation–Optimization Model

In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distribution of soil types and irrigation districts in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. The results indicated that the model is applicable for reflecting the complexities of simulation–optimization for maize irrigation scheduling. The optimization results showed that the irrigation water-saving potential of the study area was between 97 mm and 240 mm, and the average annual optimal yield of maize was over 7.3 t/ha. The simulation–optimization model of irrigation schedule established in this paper can provide a technical means for the formulation of irrigation schedules to ensure yield optimization and water productivity or water saving.

Simulation of soil water balance and partitioning of evapotranspiration of maize grown in two growing seasons in Southern Brazil

ABSTRACT: The objective of this study was to simulate the variation of the available soil water during maize crop growth, in two different sowing times (first and second growing season), using a drip irrigation system. The treatments consisted of different irrigation strategies (full to deficit). The SIMDualKc simulation model was used to determine the daily soil water balance and crop evapotranspiration using the dual crop coefficient approach. Soil, climate, crop and irrigation parameters were used as input data. Two experiments were carried out in a rainout shelter composed of two metallic structures (16x10m) in the city of Santa Maria, Rio Grande do Sul, Brazil, during 2010/11 (second crop, season 1) and 2011/12 (first crop, season 2) growing seasons, under no-tillage system. The simulations showed that all the irrigation management strategies used in season 2 resulted in soil water deficit, while only two strategies showed deficit in season 1. Results showed good agreement between observed and simulated soil water data, with an R2 ranging from 0.86 to 0.99 and the root mean square error ranging from 2.7 to 5.6% of the total available water for seasons 1 and 2, respectively. The observed results of water balance showed that maize grown in season 2 presented higher water consumption compared to season 1, due to the higher atmospheric demand of season 2. The SIMDualKc model allowed the partitioning of crop evapotranspiration into soil evaporation and crop transpiration, demonstrating that the vegetative growth subperiod presented the greatest differences between the two seasons compared to the others growth phases.

Improvements to the Hybrid-Maize model for simulating maize yields in harsh rainfed environments

This paper reports revisions in formulation and new features of the Hybrid-Maize model (released as HM2016), to better simulate yields in harsh rainfed environments. Revisions include updated subroutines for root growth and distribution within the soil profile, greater sensitivity of canopy expansion and senescence to water deficits, an expanded kernel setting period, and soil evaporation as influenced by surface cover with crop residues. The updated model also includes routines for simulating surface runoff and estimating soil water content at sowing based on simulation of soil water balance during the preceding fallow period. Revisions of model functions were based on recent advances in understanding and quantification of maize response to environmental factors and management practices, as well as characteristics of new maize hybrids. More robust simulation of maize yield was obtained with the updated model under rainfed conditions, especially in years and locations with severe drought or on soils with limited water holding capacity. Capability to quantify soil water content at sowing and to perform batch simulations makes HM2016 more useful for pre-season yield projections in years with below-normal soil water recharge and for in-season yield forecasting across a wide range of environments. Revisions to routines governing root distribution and kernel setting make HM2016 a more powerful tool for evaluating hybrid-specific traits and crop management practices for ability to mitigate yield loss from water deficits and for identifying management options for individual production fields.

Parameterization of AquaCrop model for vining pea biomass and yield predictions and assessing impacts of irrigation strategies considering various sowing dates

The AquaCrop model was parameterized and evaluated using data relative to supplemental irrigated vining pea for industry using observations in farmers’ fields located in the Ribatejo region, Portugal. Data refers to field observations relative to leaf area index (LAI), soil water content, biomass and final yield relative to two contrasting rainfall years, 2011 and 2012, a wet and a dry year, respectively. LAI data were used to parameterize the canopy cover (CC, %) curve. Results showed that after parameterization of the CC curve the respective root mean square errors (RMSE) were smaller than 15 % of the fraction of ground cover by the crop. The model performance relative to the soil water balance simulation revealed a slight over-estimation bias for the wet year but estimation errors were small, with RMSE representing <5 % of the average soil water content observations. Good accuracy for biomass and yield predictions was obtained, with deviations between predictions and observations ranging from 1 to 7 % of the average observed biomass and up to 11 % of the average observed yield. Overall, results have shown that AquaCrop is appropriate for vining pea biomass and yield estimation. Therefore, the model was explored to assess impacts of sowing dates and alternative supplemental irrigation strategies on water use and yield. Results have shown that sowing by early January is appropriate in terms of improved water use and yield and that a very mild deficit irrigation schedule may be adopted without significantly affecting yields.

Spatial and temporal variations of, and the impact of climate change on, the dry season crop irrigation requirements in Bangladesh

Sustaining irrigation is vital for ensuring future food security in the face of population growth and a changing climate in Bangladesh. In this study, a daily soil water balance simulation model was used to estimate the net irrigation requirements of nine crops including Boro rice for the historical period of 1985–2010 and for future climate scenarios of 2030 and 2050 dry and average conditions using the A1B emission scenario. The average net irrigation requirement of Boro rice, the main crop, is 676 mm with temporally averaged spatial variation of 644–779 mm and spatially averaged temporal variation of 570–755 mm for base case planting on clay loam soil. The variations are due to the variation in crop evapotranspiration and rainfall during the cropping period. Changing planting or sowing date affects the net irrigation requirement which for Boro rice is lower in early (October–November) or late planting (January–February). The net irrigation requirement of Boro rice is about twice that required by wheat, maize, potato, tomato and sunflower, three times that of pulses and 5–6 times that required by oilseeds. The impact of climate change on irrigation requirements of Boro rice is small. The average irrigation requirement is projected to increase by a maximum of 3 % for the 2050 dry scenario. For other crops this is projected to increase by 1–5 % depending on the crop and the time of sowing/planting.

Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization

Several maize field experiments, including deficit and full irrigation, were performed in Ribatejo region, Portugal and were used to assess water stress impacts on yields using the AquaCrop model. The model was assessed after its parameterization using field observations relative to leaf area index (LAI), crop evapotranspiration, soil water content, biomass and final yield data and also using default parameters. LAI data were used to calibrate the canopy cover (CC) curve. Results showed that when the CC curve is properly calibrated, with root mean square errors (RMSE) smaller than 7.4%, model simulations, namely relative to crop evapotranspiration and its partition, show an improved accuracy. The model performance relative to soil water balance simulation revealed a bias in estimation but low estimation errors, with RMSE<13% of the total available soil water. However the model tends to overestimate transpiration and underestimate soil evaporation. A good model performance was obtained relative to biomass and yield predictions, with RMSE lower than 11% and 9% of the average observed biomass and yield, respectively. Overall results show adequacy of AquaCrop for estimating maize biomass and yield under deficit irrigation conditions, mainly when an appropriate parameterization is adopted. The model showed less good performance when using the default parameters but errors are likely acceptable when field data are not available.

Soil water content and water balance simulation of Caragana korshinskii Kom. in the semiarid Chinese Loess Plateau

Abstract In this paper, to evaluate the hydrological effects of Caragana korshinskii Kom., measured data were combined with model-simulated data to assess the C. korshinskii soil water content based on water balance equation. With measured and simulated canopy interception, plant transpiration and soil evaporation, soil water content was modeled with the water balance equation. The monthly variations in the modeled soil water content by measured and simulated components (canopy interception, plant transpiration, soil evaporation) were then compared with in situ measured soil water content. Our results shows that the modeled monthly water loss (canopy interception + soil evaporation + plant transpiration) by measured and simulated components ranges from 43.78 mm to 113.95 mm and from 47.76 mm to 125.63 mm, respectively, while the monthly input of water (precipitation) ranges from 27.30 mm to 108.30 mm. The relative error between soil water content modeled by measured and simulated components was 6.41%. To sum up, the net change in soil water (ΔSW) is negative in every month of the growing season. The soil moisture is approaching to wilting coefficient at the end of the growth season, and the soil moisture recovered during the following season.

Usability of calcium carbide gas pressure method in hydrological sciences

Summary Soil moisture is a key engineering variable with major influence on ecological and hydrological processes as well as in climate, weather, agricultural, civil and geotechnical applications. Methods for quantification of the soil moisture are classified into three main groups: (i) measurement with remote sensing, (ii) estimation via (soil water balance) simulation models, and (iii) measurement in the field (ground based). Remote sensing and simulation modeling require rapid ground truthing with one of the ground based methods. Calcium carbide gas pressure (CCGP) method is a rapid measurement procedure for obtaining soil moisture and relies on the chemical reaction of the calcium carbide reagent with the water in soil pores. However, the method is overlooked in hydrological science applications. Therefore, the purpose of this study is to evaluate the usability of the CCGP method in comparison with standard oven-drying and dielectric methods in terms of accuracy, time efficiency, operational ease, cost effectiveness and safety for quantification of the soil moisture over a wide range of soil types. The research involved over 250 tests that were carried out on 15 different soil types. It was found that the accuracy of the method is mostly within ±1% of soil moisture deviation range in comparison to oven-drying, and that CCGP method has significant advantages over dielectric methods in terms of accuracy, cost, operational ease and time efficiency for the purpose of ground truthing.

Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step

AbstractCalculation of actual crop evapotranspiration under soil water stress conditions is crucial for hydrological modeling and irrigation water management. Results of actual evapotranspiration depend on the estimation of water stress coefficient from soil water storage in the root zone, which varies with numerical methods and time step used. During soil water depletion periods without irrigation or precipitation, the actual crop evapotranspiration can be calculated by an analytical method and various numerical methods. We compared the results from several commonly used numerical methods, including the explicit, implicit and modified Euler methods, the midpoint method, and the Heun's third‐order method, with results of the analytical method as the bench mark. Results indicate that relative errors of actual crop evapotranspiration calculated with numerical methods in one time step are independent of the initial soil water storage in the range of soil water stress. Absolute values of relative error decrease with the order of numerical methods. They also decrease with the number of time step, which can ensure the numerical stability of successive simulation of soil water balance. Considering the calculation complexity and calculation errors caused by numerical approximation for different time step and maximum crop evapotranspiration, the explicit Euler method is recommended for the time step of 1 day (d) or 2 d for maximum crop evapotranspiration less than 5 mm/d, the midpoint method or the modified Euler method for the time step of up to one week or 10 d for maximum crop evapotranspiration less than 5 mm/d, and the Heun's third‐order method for the time step of up to 15 d. Copyright © 2011 John Wiley &amp; Sons, Ltd.

The dual crop coefficient approach using a density factor to simulate the evapotranspiration of a peach orchard: SIMDualKc model versus eddy covariance measurements

The dual crop coefficient approach accounts separately for plant transpiration and soil evaporation by using the basal crop coefficient and the evaporation coefficient, respectively. The SIMDualKc model, which performs the soil water balance simulation with estimation of the actual crop evapotranspiration (ET) with the dual crop coefficient approach, was applied to a drip-irrigated peach orchard under Mediterranean conditions. Orchard ET was obtained with the eddy covariance technique, which was subsequently correlated with tree transpiration estimated from sap flow measurements and soil evaporation determined with microlysimeters, thus providing ET for the whole irrigation season. Two years of field observations were used for model calibration and validation using those ET measurements and taking into account the fraction of ground covered by trees through a density factor which adjusts the basal crop coefficient. Model fitting relative to ET observations during calibration and validation provided indices of agreement averaging 0.90, coefficients of regression close to 1.0, root mean square errors around 0.41 mm and average absolute errors of 0.32 mm. Model fitting relative to transpiration and to soil evaporation produced similar results, so showing the adequateness of modelling.

Hydrologic Evaluation of a Mediterranean Watershed Using the SWAT Model with Multiple PET Estimation Methods

The Penman-Monteith (P-M) method suggested by the Food Agricultural Organization in irrigation and drainage paper 56 (FAO-56 P-M) was used in the Soil and Water Assessment Tool's (SWAT) water balance simulation at the outlet of an experimental watershed in Sicily, Italy. A sensitivity analysis determined that the model was more sensitive to this potential evapotranspiration (PET) parameter than to the other six parameters impacting surface runoff in this small Mediterranean watershed. The FAO-56 P-M method was compared to the three existing SWAT PET methods from 1997 to 2003. The watershed's water balance was more realistically simulated by the FAO-56 P-M method than by the other PET methods. The traditional P-M method incorporated into SWAT overestimated total (surface and base flow) runoff volumes observed during the entire period by approximately 50%; however, total runoff volumes were underestimated by only 17% when the FAO-56 P-M method was used. The surface runoff simulation results using the FAO-56 P-M PET equation for calculating daily values was sufficient at the monthly time interval (Nash-Sutcliffe efficiency >0.75) during the calibration and validation periods. The incorporation of the FAO-56 P-M method has broadened the SWAT model's applicability to watersheds that are in semi-arid environments with high-intensity, short-duration rainfall events.

Climate sensitivity of soil water regime of different Hungarian Chernozem soil subtypes

In this study the possible effects of two predicted climate change scenarios on soil water regime of Hungarian Calcic Chernozem soils has been investigated. Soil profiles classified as Calcic Chernozem — in total 49 — were selected from the MARTHA soil physical database that incorporates soil data at national scale. These profiles were subdivided into three groups (sandy loam, loam and clayey loam) in accordance with their mechanical composition. Soil water retention curves were scaled separately for each of the three textural groups, using similar media scaling in order to represent the variability of soil hydrophysical data with one parameter, the scaling factor (SF). Reference soil profiles were chosen according to the cumulative distribution function of the scaling factor, six for each textural group. Daily downscaled meteorological data from A2 and B2 climate scenarios of the Hadley Centre (2070–2100) and data from a reference period (RF, 1961–1990) were used in this study to characterize different climatic situations. Nine representative years were selected in case of all the three scenarios, using the cumulative probability function of the annual precipitation sum. Scenario analyses were performed, validating the SWAP soil water balance simulation model for the 18 reference soil profiles and 27 representative years in order to evaluate the expected changes in soil water regime under different from the present (RF) climatic conditions (A2 and B2 scenarios). Our results show that the scaling factor could be used as a climate sensitivity indicator of soil water regime. The large climate sensitivity of the majority of Chernozem soil subtypes water regime has been proven.

A Chernozem soil water regime response to predicted climate change scenarios

Climate, hydrology and vegetation are closely linked at local, regional and global scales. The recent land use and plant production systems are adapted to the present climatic conditions. Thus, studies on the influence of possible climate change scenarios on the water and heat regimes of the soil-plant-atmosphere system are important in order to work out plant production strategies, adjusted to changed conditions. In this study the effect of two possible climate change scenarios on the soil water regime of a Chernozem soil was estimated for a Hungarian site. Soil water content dynamics simulated for different conventional and soil conserving soil tillage systems were evaluated, using the SWAP soil water balance simulation model. The combined effect of different soil tillage systems and climate scenarios was analysed. Climate scenarios were represented through the cumulative probability function of the annual precipitation sum. The SWAP model was calibrated against the measured in the representative soil profiles soil water content data. The site- and soil-specific parameters were set and kept constant during the scenario studies. According to the simulation results, increase in the average growing season temperature showed increase in climate induced soil drought sensitivity. The evaluated soil water content dynamics indicated more variable and less predictable soil water regime compared to the present climate. It was found that appropriate soil tillage systems that are combined with mulching and ensure soil loosening could reliably decrease water losses from the soil. From this aspect cultivator treatment created the most favourable for the plants soil conditions. It was concluded that soil conserving soil management systems, adapted to local conditions could contribute to soil moisture conservation and could increase the amount of plant available water under changing climatic conditions.

A mathematical model for simulating water balances in cropped sandy soil with conventional flood irrigation applied

In this paper, a model that integrates various complex model components for the purposes of water balance modeling throughout crop development in arid inland region under the conventional flood irrigation practiced is presented. These components are modules for calculating dynamic soil water content based Richard's equation, potential and actual evapotranspiration, and crop root water uptake. Soil water content in the active root zone and soil evaporation simulation obtained from the model were test using field data in 2003. The low values of MARE and high values of R 2 and PE in the active root zone of soil profile as well as daily soil evaporation indicated that the soil water balance simulation model presented in the paper can be used with reliable accuracy to simulate the components of water balance in cropped sandy soil under the conventional flood irrigation condition in arid inland regions. The model simulation on components of water balance using observed field data in 2004 indicated that large quantities – about 43% of irrigation water (amounting to 840 mm) – were consumed by deep percolation, only small (less than 41%) proportions of irrigation water used by the plants for transpiration. The current irrigation scheme is characterized by the unreasonable agricultural water management with the waste of water in the irrigational system in this region. The impact of irrigation scheduling on water balance presented in this paper showed that the reasonable irrigation scheme with more frequent irrigation and less amounts is more suitable for the irrigation of spring wheat in Heihe River basin, northwest China. Therefore, to establish a decision-making system for agricultural irrigation scheme and to utilize the limited water resources in this region have become an urgent problem that needs to be solved.

Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the O pus model—A case study

Over a period of 5 years, the agro-ecosystem model O pus was used to simulate soil water and nitrogen balance as well as crop growth for three experimental field plots. At these plots, different agricultural management practices were applied. The data set obtained from these plots consists of automatically recorded time series of daily volumetric soil water contents measured by TRIME-probes as well as daily pressure heads measured by tensiometer. Aboveground total biomass, yield, nitrogen-uptake by crops as well as nitrate contents in the soil were measured at 6–10 sample times per year. The objective of this study was an evaluation of the accuracy of O pus regarding the simulation of crop growth, soil water and nitrogen balance. The simulations of soil water contents and pressure heads correspond with the commonly measured trends in soil depths shallower than 60 cm. In depths deeper than 60 cm, some differences between measured and simulated soil water contents as well as pressure heads could be observed. Nitrate contents in the root zone and the aboveground total biomass were simulated satisfactorily. In contrast to that, simulated and observed yields show greater discrepancies. This indicates the need of a site specific calibration of crop growth parameters within the O pus model.

Field test of a soil water balance simulation model

A simple soil water balance model is used to simulate the soil water content in the active root zone of mustard crop ( Brassica juncea) as well as is tested with field experimental data of 2 years (1998 and 1999) under rain-fed (no irrigation) and irrigated conditions. The model used includes physical process like infiltration from rainfall or irrigation, redistribution in soil water zone, plant water uptake in the form of actual evapotranspiration and percolation out of the soil reservoir. In addition, the model considers dynamics of crop root growth model that affect the plant water uptake and hence the soil water in the unsaturated zone. The model satisfactorily simulates the soil water content in the active root zone of the crop on daily basis. Values of the mean absolute relative error (MARE) index between the observed and simulated soil water content of the rain-fed mustard in 1998 and 1999 are found to be 0.046 and 0.058, respectively, whereas for irrigated mustard, it is 0.051 in 1999. Prediction efficiency (PE) index is found to be 0.98, 0.97 and 0.97 for rain-fed mustard of 1998 and 1999 and irrigated mustard of 1999, respectively. Since the MARE index is low and PE index is high for both rain-fed and irrigated mustard, the model can be used to simulate the soil water content in the active root zone of the crop.