Investigating long-term effects of subsurface drainage on soil structure in paddy fields

Talukolaee, Mehdi Jafari; Naftchali, Abdullah Darzi; Parvariji, Lotfullah Zare; Ahmadi, Mirkhalegh Z.

doi:10.1016/j.still.2017.12.012

Cited by 25 publications

(12 citation statements)

References 25 publications

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“…The effectiveness of the subsurface drainage systems in improving the soil structure of the study field by providing better conditions for soil aeration and increased saturated hydraulic conductivity was reported by Jafari‐Taloukolaee et al . (2018). Such improvement could finally result in formation of flow paths from the soil surface to drains which could be a major reason for increasing variation in NO 3 − ‐N concentrations.…”

Section: Resultsmentioning

confidence: 99%

Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II

Hashemi

Darzi‐Naftchali

2020

Irrigation and Drainage

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In this study, the effects of various drainage systems on water and nitrate-N losses were investigated using DRAINMOD-N II. Required field data were obtained during three growing seasons of canola in a subsurface drainage pilot in Mazandaran Province, northern Iran. The calibrated model was used to assess the effects of different drain depths (D = 0.10-0.90 m with 0.10 m intervals) and spacing (L = 10-90 m with 10 m intervals) on seasonal drainage water and NO 3 −-N concentration in drainage effluents. DRAINMOD-N II performance was assessed using different criteria including absolute deviation (AD), root mean square error (RMSE) and determination coefficient (R 2). The simulated and observed drainage discharges (0.97 vs 0.96 mm day −1) and NO 3-N concentrations (9.1 vs 14.1 mg l −1) were in good agreement in the calibration process. The model performance was also acceptable in the validation process (AD = 0.59-0.79 mm day −1 ; RMSE = 1.01-1.28 mm day −1 ; R 2 = 0.59-0.79 for drainage discharge and AD = 8.3-16.3 mg l −1 ; RMSE = 12.4-27.6 mg l −1 ; R 2 = 0.4 for NO 3-N). Based on the scenario analyses, the D0.40L50 drainage system was the best one, resulting in fewer environmental effects from the nitrate-N and water loss viewpoints.

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Section: Resultsmentioning

confidence: 99%

Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II

Hashemi

Darzi‐Naftchali

2020

Irrigation and Drainage

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“…In canola cropping, nitrate losses in 2016-2017 were considerably higher than in 2011-2012. Part of these increased losses may be related to the improved soil structure and formation of flow paths in the soil profile, which occurred after the installation of the subsurface drainage systems [35]. As mentioned earlier, nitrate concentration in the drainage effluents were much higher in the 2016-2017 canola season than in 2011-2012 season ( Table 6).…”

Section: Nitrate Losses Through the Drainage Systemsmentioning

confidence: 73%

“…The highest seasonal average nitrate concentrations occurred under FD in the 2016-2017 canola season, six years after the introduction of the subsurface drainage systems, with the D.90L30 drainage system having significantly higher concentrations than the other systems. These high concentrations can be explained by improved soil structure [35] and the formation of stable flow paths to the subsurface drains. During the 2011, 2012, and 2014 rice seasons, maximum nitrate concentration was observed in the shallow drains, while in the 2015 rice season and canola growing seasons, the maxima were found in the deeper drains.…”

Section: Nitrate Concentration In the Drainage Watermentioning

confidence: 99%

Integrating Irrigation and Drainage Management to Sustain Agriculture in Northern Iran

Darzi‐Naftchali

Ritzema

2018

Sustainability

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In Iran, as in the rest of the world, land and water for agricultural production is under pressure. Integrating irrigation and drainage management may help sustain intensified agriculture in irrigated paddy fields. This study was aimed to investigate the long-term effects of such management strategies in a newly subsurface drained paddy field in a pilot area in Mazandaran Province, northern Iran. Three strategies for managing subsurface drainage systems were tested, i.e., free drainage (FD), midseason drainage (MSD), and alternate wetting and drying (AWD). The pilot area consisted of subsurface drainage systems, with different combinations of drain depth (0.65 and 0.90 m) and spacing (15 and 30 m). The traditional surface drainage of the region’s consolidated paddy fields was the control. From 2011 to 2017, water table depth, subsurface drainage system outflow and nitrate, total phosphorous, and salinity levels of the drainage effluent were monitored during four rice- and five canola-growing seasons. Yield data was also collected. MSD and AWD resulted in significantly lower drainage rates, salt loads, and N losses compared to FD, with MSD having the lowest rates. Phosphorus losses were low for all three practices. However, AWD resulted in 36% higher rice yields than MSD. Subsurface drainage resulted in a steady increase in canola yield, from 0.89 ton ha−1 in 2011–2012 to 2.94 ton ha−1 in 2016–2017. Overall, it can be concluded that managed subsurface drainage can increase both water productivity and crop yield in poorly drained paddy fields, and at the same time reduce or minimize negative environmental effects, especially the reduction of salt and nutrient loads in the drainage effluent. Based on the results, shallow subsurface drainage combined with appropriate irrigation and drainage management can enable sustained agricultural production in northern Iran’s paddy fields.

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“…Each irrigation plan will be successful if in the plan the soil drain issue is considered (Bhattacharya and Michael 2004). In the other words, irrigation and drainage are interdependent (Talukolaee et al 2017). Generally, drainage means removal of excess water and solute from the soil (Ziccarelli and Valore 2018).…”

Section: Introductionmentioning

confidence: 99%

Computation of subsurface drain spacing in the unsteady conditions using Artificial Neural Networks (ANN)

Ostad‐Ali‐Askari

Shayannejad

2021

Appl Water Sci

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Artificial neural networks are a tool for modeling of nonlinear systems in various engineering fields. These networks are effective tools for modeling the nonlinear systems. Each artificial neural network includes an input layer, an output layer between which there are one or some hidden layers. In each layer, there are one or several processing elements or neurons. The neurons of the input layer are independent variables of the understudy issue, and the neurons of the output layer are its dependent variables. Artificial neural system, through exerting weight on inputs and by suing an activation function attempts to achieve a desirable output. In this research, in order to calculate the drain spacing in an unsteady state in a region situated in the north east of Ahwaz, Iran with different soil properties and drain spacing, the artificial neural networks have been used. The neurons in the input layer were: Specific yield, hydraulic conductivity, depth of the impermeable layer, height of the water table in the middle of the interval between the drains in two-time steps. The neurons in output layer were drain spacing. The network designed in this research was included a hidden layer with four neurons. The distance of drains computed via this method had a good agreement with real values and had a high precision in compare with other methods.

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Investigating long-term effects of subsurface drainage on soil structure in paddy fields

Cited by 25 publications

References 25 publications

Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II

Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II

Integrating Irrigation and Drainage Management to Sustain Agriculture in Northern Iran

Computation of subsurface drain spacing in the unsteady conditions using Artificial Neural Networks (ANN)

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