Averaging Techniques in Processing the High Time-resolution Photosynthesis Data of Cherry Tomato Plants for Model Development

Abstract

We evaluated averaging techniques in data processing for the estimation of canopy net photosynthetic rates (P_n) of two cherry tomato plants using a multiple linear regression analysis with variables of aerial environmental factors. Whole canopy P_n and the environmental factors were measured in a high time resolution with a 5-minute interval under a commercial greenhouse by using a novel photosynthesis chamber. We processed the data by using a moving average (MA) and simple average (SA) with several time frames (30-minute, 1-hour, 2-hour). The canopy Pn was expressed as a general linear function of PAR irradiance (I), air temperature (T), relative humidity (RH), vapor pressure deficit (VPD), and CO₂ concentration (C). Model accuracy generally increased with longer time frames; however, it can be varied depending on the datasets and the variables used in the models. The 2-hour-SA datasets gave the best accuracy for both 5-variable model (I, T, RH, VPD, C) and 3-variable model (I, VPD, C) with R² of 0.81 and 0.67, respectively. This study indicates that datasets of 2-hour time frame with simple average are promising to make a practical general linear regression model for the estimation of P_n of cherry tomato by using the high time-resolution P_n data.