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Simulated tomato yield, soil moisture, and salinity using fresh and saline water: experimental and modeling study using the SALTMED model

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Abstract

This study aimed to calibrate and validate the SALTMED model performance using field data of two growing seasons of tomato during 2017 and 2018, under soil amendment biochar, polymer, and their combination with a rate of 4% and 0.4%, respectively. The experiment included two treatments of deficit irrigation 80% and 60% ETc in addition to 100% ETc with fresh water electrical conductivity ECw = 0.9 dS m−1 and saline water ECw = 3.6 dS m−1. The SALTMED modeling result indicated that the SMC was overestimated and the coefficient of residual mass CRM value ranged from − 0.014 to − 0.032 for biochar and polymer, respectively. The salinity was slightly overestimated; CRM values were − 0.004 to − 0.025, except for biochar treatment, salinity was underestimated; CRM (0.021). In fact, there was a good relationship between the simulated and observed data; R2 values were 0.94–0.95 for soil moisture content, whereas the R2 values for salinity were 0.78–0.99. The model can efficiently simulate the yield of tomato, with fresh and saline water. The correlation between the simulated and observed yield was very good with R2 = 0.99. The result of the calibration and validation of the SALTMED model indicates the ability of the model to predict tomato production, soil moisture content, and salinity under deficit irrigation strategies in semi-arid regions.

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Acknowledgements

The authors wish to thank King Saud University, Deanship of Scientific Research, College of Food and Agricultural Sciences, Research Center for supporting this work. The authors also thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.

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Correspondence to Abdulrasoul Al-Omran.

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Communicated by P. Waller.

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Alkhasha, A., Al-Omran, A. Simulated tomato yield, soil moisture, and salinity using fresh and saline water: experimental and modeling study using the SALTMED model. Irrig Sci 37, 637–655 (2019). https://doi.org/10.1007/s00271-019-00639-1

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