Mohammad Bagher Lak; Saeid Minaei; Saeid Soufizadeh; Ahmad Banakar
Abstract
Climate change and essentiality of the food security have motived scientists to try innovative approaches, among which, crop growth models can help to predict crop yield. In order to simulate tomato (Solanum lycopersicum) growth, phenological characteristics of a short-life variety of tomato were assessed. ...
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Climate change and essentiality of the food security have motived scientists to try innovative approaches, among which, crop growth models can help to predict crop yield. In order to simulate tomato (Solanum lycopersicum) growth, phenological characteristics of a short-life variety of tomato were assessed. Phenologic characteristics included leaf area index (LAI), specific leaf area (SLA), crop height (H), leaf fresh and dry weight (LFW and LDW), and stem fresh and dry weight (SFW and SDW). These parameters were measured at four different times (i.e. 33, 45, 55, and 87 days after planting) during tomato growth and development. Fruit fresh and dry weight (FFW and FDW), harvest index (HI), and water efficiency () were measured at the end of the crop season. This study was done in a randomized complete block design with three levels of irrigation (i.e. at 48h (i1), 72h (i2), and 96h (i3)) in three replications. Irrigation treatment had significant effects on LAI1, LAI2, H2, FLW1, FLW2, DLW1, DLW2, DL2, FSW1, DSW1, DSW2, and DS2 at the 0.01 level, while its effect on SLA1, SLA2, H1, and FSW2 was significant at the level 0.05. Two-tailed correlations among characteristics were investigated and regression models developed for DFW. Dry fruit weight was simulated using both AquaCrop and regression models, separately. It was found that regression model could predict DFW of tomatoes under different treatment better than AquaCrop. It was also concluded that the phenologic characteristics measured at 55 DAP provide good criteria for predicting tomato fruit production.
