Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models

Hassanli, Mohammad; Ebrahimian, Hamed; Mohammadi, Ehsan; Rahimi, Amirreza; Shokouhi, Amirhossein

doi:10.1016/j.agwat.2016.05.003

Cited by 53 publications

(29 citation statements)

References 23 publications

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“…The development stage was assumed to be linear with a growth period from emergence to harvest (0 < development stage < 2) (Ma et al., ). Extinction coefficients for diffuse and direct visible light were used as the model default values, which were 0.60 and 0.75, respectively (Hassanli et al., ; Jiang et al., ). Root growth was simplified to a static development process unrelated to climatic conditions (Kroes et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…SWAP has performed well for simulations of farmland water and heat flow under different soil conditions (Martínez-Ferri, Muriel-Fernández, & Díaz, 2013). The SWAP model has also been used to schedule irrigation (Jiang, Feng, Ma, Huo, & Zhang, 2016;Ma, Feng, & Song, 2015;Rallo, Agnese, Minacapilli, & Provenzano, 2012) and estimate crop yield at the field and regional scale (de Jong van Lier, Wendroth, & van Dam, 2015;Hassanli, Ebrahimian, Mohammadi, Rahimi, & Shokouhi, 2016;Mokhtari, Noory, & Vazifedoust, 2018). Some research has shown that SWAP simulation results are superior to other models (Bonfante et al, 2010;Eitzinger et al, 2004;Hassanli et al, 2016).…”

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confidence: 99%

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Modelling root water uptake under deficit irrigation and rewetting in Northwest China

et al. 2020

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The spatial and temporal distribution of root water uptake (RWU) under deficit irrigation are critical factors for crop growth. The SWAP (soil–water–atmosphere–plant) model was applied to analyze the pattern of RWU for winter wheat (Triticum aestivum L.) under three irrigation levels: no water deficit (100% evapotranspiration [ET]), moderate water deficit (80% ET) and severe water deficit (60% ET). The 2–yr experiments indicated that SWAP was highly accurate (mean relative error [MRE] <21.7%, root mean square error [RMSE] <0.07 cm3 cm−3) in simulating the soil water content (SWC). Root water uptake was significantly (P < 0.01) different in the 0‐ to 60‐cm soil layer. The 0‐ to 60‐cm soil layer was the main source of RWU, and the average value accounted for 89.4% of the total root zone. Water stress had the greatest adverse effect on heading to grain filling, reducing RWU by 0.0026 cm3 cm−3 d−1. The critical SWC was 67.9% of the field capacity, when the RWU dropped to 95% of the control treatment. After rewetting, compensation and hysteresis effects on RWU were observed. The ranking of RWU recovery ability after rewetting was: emergence to jointing > jointing to heading > grain filling to maturity > heading to grain filling. Recovery time of RWU was 2 to 11 d and gradually increased with growth stage. The simplified RWU model established using path analysis and regression performed well (R2 = 0.836; P < 0.01) for RWU. This provided a more convenient way to accurately estimate RWU with fewer variables.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

et al. 2020

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“…Predictive science and numerical models such as HYDRUS-2D (Šimůnek et al, 2016) present an excellent opportunity to gauge the impacts of irrigation practices (Kandelous and Šimůnek, 2010;Ramos et al, 2012;Phogat et al, 2012;González et al, 2015), water quality (Hassan et al, 2005;Ramos et al, 2011;Hassanli et al, 2016) and climate change (Austin et al, 2010) on potential water and salinity hazards and to control offsite movement of costly inputs into the surface and subsurface water bodies (Phogat et al, 2014). Moreover, the partial wetting pattern and irregular root water uptake in a drip-irrigated orchard make it difficult to apply and interpret standard water balance techniques and require a large number of measurements (BenAsher, 1979).…”

Section: Introductionmentioning

confidence: 99%

Soil water and salinity dynamics under sprinkler irrigated almond exposed to a varied salinity stress at different growth stages

Phogat

Pitt

Cox

et al. 2018

Agricultural Water Management

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“…The capability of simulating changes in plant growth and yield in saline conditions was added to AquaCrop versions 4.0 (Raes et al, ) and 5.0 (Raes et al, ). The model has exhibited a good ability with regard to yield response to saline conditions in wheat (Kumar et al, ), rice (Mondal et al, ) and corn (Hassanli et al, ). However, Hassanli et al () reported that the performance of AquaCrop for wheat under irrigation with saline water has not been as good as that of the SALTMED and SWAP models.…”

Section: Introductionmentioning

confidence: 99%

“…The model has exhibited a good ability with regard to yield response to saline conditions in wheat (Kumar et al, ), rice (Mondal et al, ) and corn (Hassanli et al, ). However, Hassanli et al () reported that the performance of AquaCrop for wheat under irrigation with saline water has not been as good as that of the SALTMED and SWAP models. One of the advantages of the AquaCrop model over others is its ability to evaluate salt and water stresses simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Improving Irrigation Scheduling of Wheat to Increase Water Productivity in Shallow Groundwater Conditions Using Aquacrop

Goosheh

Pazira

Gholami

et al. 2018

Irrigation and Drainage

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Irrigation scheduling plays a key role in increasing crop production and controlling soil salinity in arid and semi‐arid regions. Inappropriate irrigation scheduling is considered an effective factor in low water productivity and soil salinization in central and southern parts of Khuzestan. Therefore, the aim of this research was to improve irrigation scheduling in order to increase water productivity and maintain the water and salt balance in a district in central Khuzestan. AquaCrop was selected as the research model, and the effects of the scenarios on wheat grain yield, water productivity, salt and water content in an effective root zone, and salt content moved upward from the groundwater table by capillary rise were compared with each other. Various scenarios of irrigation scheduling were simulated for a 12‐year period (2003–2014). To collect the required data for calibration and validation of the model, a field experiment was carried out during the wheat growing season (2014–2015). The results of the model evaluation indicated that it was an appropriate one for simulating the water and salt balance in the soil and wheat grain yield under different irrigation scheduling scenarios in experimental conditions. Finally, the recommended irrigation scheduling for wheat was seven times of irrigation with 550 mm water consumption. © 2018 John Wiley & Sons, Ltd.

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Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models

Cited by 53 publications

References 23 publications

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

Soil water and salinity dynamics under sprinkler irrigated almond exposed to a varied salinity stress at different growth stages

Improving Irrigation Scheduling of Wheat to Increase Water Productivity in Shallow Groundwater Conditions Using Aquacrop

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