Combined Analysis to Characterize Yield Pattern of Greenhouse-grown Red Sweet Peppers

Abstract

Understanding the irregular yield pattern of greenhouse-grown sweet peppers (Capsicum annuum L.) has been a challenge to researchers and greenhouse producers. Experimental data from 4 years, each consisting of 26 production weeks, were used in a time series analysis, neural network (NN) modeling, and regression analysis. Time series analysis revealed that weekly yield was influenced by yields from the preceding 2 weeks (Yd_1 and Yd_2), cumulative light 2 and 4 weeks prior (L_2 and L_4), and average 24-h air temperature 5 weeks prior (T_5). Cumulative light (L) data were transformed into kL by dividing by 1000 for subsequent NN modeling and regression analysis. These five inputs were used to establish a NN model, which illustrated the positive influence of Yd_1, kL_4, and kL_2 and negative influence of Yd_2 and T_5. Again, these five inputs were used in a regression analysis illustrating the positive influence of Yd_1 and the negative influence of Yd_2. Each input was further modified to include its squared value before entering the regression, which resulted in significant inputs of Yd_1, Yd_1 squared, and Yd_2 squared. Among these three analyses, the most consistent parameters were Yd_1 and Yd_2, confirming that the irregular yield pattern of greenhouse-grown peppers is of a biological nature. Environmental factors kL_2, kL_4, and T_5 did not show a consistent effect on yield in all three analyses, indicating yield pattern is less influenced by growing environment.