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 (Pn) of two cherry tomato plants using a multiple linear regression analysis with variables of aerial environmental factors. Whole canopy Pn 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 Pn of cherry tomato by using the high time-resolution Pn data.