Optimizing Deficit Irrigation Management to Improve Water Productivity of Greenhouse Tomato under Plastic Film Mulching Using the RZ-SHAW Model

Haomiao Cheng, Shu Ji, Hengjun Ge, Mohmed A. M. Abdalhi, Tengyi Zhu (), Xiaoping Chen, Wei Ding and Shaoyuan Feng
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Haomiao Cheng: School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
Shu Ji: School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
Hengjun Ge: Yangzhou Survey Design Research Institute Co., Ltd., Yangzhou 225007, China
Mohmed A. M. Abdalhi: Department of Agricultural Engineering, Faculty of Agricultural Technology and Fish Sciences, Al-Neelain University, Khartoum 12702, Sudan
Tengyi Zhu: School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
Xiaoping Chen: School of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225127, China
Wei Ding: Yangzhou Survey Design Research Institute Co., Ltd., Yangzhou 225007, China
Shaoyuan Feng: School of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225127, China

Agriculture, 2022, vol. 12, issue 8, 1-13

Abstract: Deficit irrigation (DI) is a widely recognized water-saving irrigation method, but it is difficult to precisely quantify optimum DI levels in tomato production. In this study, the Root Zone Water Quality-Simultaneous Heat and Water (RZ-SHAW) model was used to evaluate the potential effects of different DI levels on tomato growth in a drip-irrigated field. Combinations of five DI scenarios were tested in greenhouse field experiments under plastic film mulching according to the percentage of crop evapotranspiration (ET), i.e., ET50, ET75, ET100, ET125, and ET150. The model was calibrated by using the ET100 scenario, and validated with four other scenarios. The simulation results showed that the predictions of tomato growth parameters and soil water were in good agreement with the observed data. The relative root mean square error (RRMSE), the percent bias (PBIAS), index of agreement (IoA) and coefficient of determination (R 2 ) for leaf area index (LAI), plant height and soil volumetric water content (VWC) along the soil layers were <23.5%, within ±16.7%, >0.72 and >0.56, respectively. The relative errors (REs) of simulated biomass and yield were 3.5–8.7% and 7.0–14.0%, respectively. There was a positive correlation between plant water stress factor (PWSF) and DI levels ( p < 0.01). The calibrated model was subsequently run with 45 different DI scenarios from ET0 to ET225 to explore optimal DI management for maximizing water productivity (WP) and yield. It was found that the maximum WP and yield occurred in ET95 and ET200, with values of 28.3 kg/(ha·mm) and 7304 kg/ha, respectively. The RZ-SHAW demonstrated its capacity to evaluate the effects of DI management on tomato growth under plastic film mulching. The parameterized model can be used to optimize DI management for improving WP and yield based on the water stress-based method.

Keywords: RZ-SHAW; drip irrigation; water productivity; plastic mulch; tomato production; water stress; irrigation scheduling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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