Water flow and nitrate transfer simulations in rice cultivation under different irrigation and nitrogen fertilizer application managements by HYDRUS-2D model

Rezayati, Samaneh; Khaledian, Mohammadreza; Razavipour, Teymour; Rezaei, Mojtaba

doi:10.1007/s00271-020-00676-1

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…The equilibrium equation for the motor drive voltage is shown in Eq (1). Where U d (t) is the DC motor's drive voltage, R is the internal resistance,…”

Section: Mathematical Modelling Of Fertilizer Application Systemsmentioning

confidence: 99%

“…Agriculture accounts for more than 65% of total water consumption, and 95% of water application of agriculture is applied for irrigation of large zones of farmland, where water is severely scarce [1]- [5].Variable fertiliser application is an important area of research in the development of precision that farms, and this technology is a excellent solution to the high labour intensity, low efficiency of fertiliser application operations and the unevenness of manual fertiliser application. Most of the current fertiliser application models are still based on ready approach and a rough, with water's concentration proportion to fertiliser being ignored in the fertiliser application process and irrigation, leading to depressed fertiliser uptake by crops and plenty of wasted water and labour [6] [7].…”

Section: Introductionmentioning

confidence: 99%

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Application of PID control based on bacterial foraging-particle swarm hybrid algorithm optimization in variable fertilization system

Zhang

et al. 2022

Preprint

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In the irrigation process and fertilization process of agricultural production, the accuracy of fertilizer application and water use is kept at a relatively low level, which leads to soil slumping and waste of resources. In order to solve the above problems, this study investigates the demand for fertilizer application accuracy in the field and builds a mathematical model of variable fertilizer application system. Considering a large number of defects of the system, such as poor fertilization accuracy, long response time, and unstable regulation process, combined with MATLAB/Simulink platform, this study designs a PID algorithm based on bacterial foraging-particle swarm hybrid algorithm to control the fertilization and irrigation process of the variable fertilizer applicator. The precise fertilization control of the variable fertilizer system is mainly studied and the fuzzy proportional integral differential gain parameters of the variable fertilizer system by the hybrid algorithm are optimized. By comparing with the control algorithms (proportional-integral differential control, fuzzy proportional-integral differential control, and proportional-integral differential control with particle swarm optimization) commonly applied in the current fertilizer application system, the comparison results demonstrate that the control algorithm in this paper has better regulation, smaller overshoot, excellent stability and more rising steady-state time deficit, which can realize the precise control of the fertilizer system. Finally, this study designed fertilizer application accuracy tests under different pressure values. The results show that the algorithm proposed in this study has high fertilizer application accuracy under different pressures, which greatly reduces the waste of resources in the process of fertilizer application for crop irrigation.

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“…The equilibrium equation for the motor drive voltage is shown in Eq (1). Where U d (t) is the DC motor's drive voltage, R is the internal resistance,…”

Section: Mathematical Modelling Of Fertilizer Application Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of PID control based on bacterial foraging-particle swarm hybrid algorithm optimization in variable fertilization system

Zhang

et al. 2022

Preprint

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“…Rice is characterized by shallow roots. The root zone occupies about 60 cm of the soil domain while the maximum root density lies 25 cm below the ground surface [37,38]. Root growth function is used to express root evolution during the growing season by specifying given parameters.…”

Section: Root Water Uptake Parametersmentioning

confidence: 99%

A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation

Eltarabily

Berndtsson

Abdou

et al. 2021

Water

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Root distribution during rice cultivation is a governing factor that considerably affects soil water content (SWC) and root water uptake (RWU). In this study, the effects of activating root growth (using growth function) and assigning a constant average root depth (no growth during simulation) on SWC and RWU for rice cultivation under four deficit drip irrigation treatments (T90, T80, T70, and T60) were compared in the HYDRUS-2D/3D model version 3.03. A secondary objective was to investigate the effect of applied deficit irrigation treatments on grain yield, irrigation water use efficiency (IWUE), and growth traits of rice. The simulated DI system was designed to reflect a representative field experiment implemented in El-Fayoum Governorate, Egypt, during two successive seasons during 2017 and 2018. The deficit treatments (T90, T80, T70, and T60) used in the current study represent scenarios at which the first irrigation event was applied when the pre-irrigation average SWC within the upper 60 cm of soil depth was equal to 90%, 80%, 70%, and 60% of plant-available water, respectively. Simulation results showed that as water deficiency increased, SWC in the simulation domain decreased, and thereby, RWU decreased. The average SWC within the root zone during rice-growing season under different deficit treatments was slightly higher when activating root growth function than when considering constant average root depth. Cumulative RWU fluxes for the case of no growth were slightly higher than for the case of root growth function for T90, T80, and T70 accounting for 1289.50, 1179.30, and 1073.10 cm2, respectively. Average SWC during the growth season (24 h after the first irrigation event, mid-season, and 24 h after the last irrigation event) between the two cases of root growth was strongly correlated for T90, T80, T70, and T60, where r2 equaled 0.918, 0.902, 0.892, and 0.876, respectively. ANOVA test showed that there was no significant difference for SWC between treatments for the case of assigning root growth function while the difference in SWC among treatments was significant for the case of the constant average root depth, where p-values equaled 0.0893 and 0.0433, respectively. Experimental results showed that as water deficiency decreased, IWUE increased. IWUE equaled 1.65, 1.58, 1.31, and 1.21 kg m−3 for T90, T80, T70, and T60, respectively. Moreover, higher grain yield and growth traits of rice (plant height, tillers number plant−1, panicles length, panicle weight, and grain number panicles−1) were obtained corresponding to T90 as compared with other treatments. Activating the root growth module in HYDRUS simulations can lead to more precise simulation results for specific dates within different growth stages. Therefore, the root growth module is a powerful tool for accurately investigating the change in SWC during simulation. Users of older versions of HYDRUS-2D/3D (version 2.05 and earlier) should consider the limitations of these versions for irrigation scheduling.

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“…A calibration of the model with an independent dataset of measurements in the same soil can be used to reduce the differences between measured and simulated soil water contents [18,57]. Nevertheless, calibration requires an independent dataset of soil water measurements obtained in similar conditions which is not always feasible.…”

Section: Validation Of the Modelmentioning

confidence: 99%

Modeling Approaches for Determining Dripline Depth and Irrigation Frequency of Subsurface Drip Irrigated Rice on Different Soil Textures

Arbat

Cufí

Duran–Ros

et al. 2020

Water

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Water saving techniques such as drip irrigation are important for rice (Oriza sativa L.) production in some areas. Subsurface drip irrigation (SDI) is a promising alternative for intensive cropping since surface drip irrigation (DI) requires a higher degree of labor to allow the use of machinery. However, the semi-aquatic nature of rice plants and their shallow root system could pose some limitations. A major design issue when using SDI is to select the dripline depth to create appropriate root wetting patterns as well as to reduce water losses by deep drainage and evaporation. Soil texture can greatly affect soil water dynamics and, consequently, optimal dripline depth and irrigation frequency needs. Since water balance components as deep percolation are difficult to estimate under field conditions, soil water models as HYDRUS-2D can be used for this purpose. In the present study, we performed a field experiment using SDI for rice production with Onice variety. Simulations using HYDRUS-2D software successfully validated soil water distribution and, therefore, were used to predict soil water contents, deep drainage, and plant water extraction for two different dripline depths, three soil textures, and three irrigation frequencies. Results of the simulations show that dripline depth of 0.15 m combined with one or two daily irrigation events maximized water extraction and reduced percolation. Moreover, simulations with HYDRUS-2D could be useful to determine the most appropriate location of soil water probes to efficiently manage the SDI in rice.

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Water flow and nitrate transfer simulations in rice cultivation under different irrigation and nitrogen fertilizer application managements by HYDRUS-2D model

Cited by 15 publications

References 14 publications

Application of PID control based on bacterial foraging-particle swarm hybrid algorithm optimization in variable fertilization system

Application of PID control based on bacterial foraging-particle swarm hybrid algorithm optimization in variable fertilization system

A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation

Modeling Approaches for Determining Dripline Depth and Irrigation Frequency of Subsurface Drip Irrigated Rice on Different Soil Textures

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