Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Abstract

Eggplant is an important product in greenhouse cultivation in the world. However, much of its production is done on the farm. Few studies have been conducted on modelling of its growth and yield. Considering the simplicity of the logistic models, only the temperature and dry matter data during the growing season are needed to calibrate them. Hence, we evaluated the performance of the logistic model in growth and yield prediction of eggplant in greenhouse and farm conditions. Eggplants were planted in 2012 and 2013 under different treatments of irrigation and salinity with a complete randomized block statistical design. Irrigation frequency treatments consisted of: daily (I₁), weekly, (I₂) and every 2 weeks of irrigation (I₃). Each pot was irrigated to field capacity level. Four levels of salinity treatments are as follows: electrical conductivities (EC) of 0.8 (J₁); 2.5 (J₂); 5.0 (J₃) and 7.0 (J₄) dS∙m^-1. The amount of plant dry matter was measured during the growing season (DM) and the amount of product (Y) at the end of the growing season. The logistic model was calibrated with the first-year data and validated with the second-year data. Logistic equation coefficients and harvest index were estimated as a function of the depth and electrical conductivity of irrigation water. The results showed that the accuracy of the logistic model for estimating DM during the growing season was good and predicted the product at the end of the growing season with acceptable accuracy. Also, the model's agreement with the measured DM and Y was good.