Averaging Techniques in Processing the High Time-resolution Photosynthesis Data of Cherry Tomato Plants for Model Development
| Main Authors: | ROMDHONAH, Yayu, FUJIUCHI, Naomichi, SHIMOMOTO, Kota, TAKAHASHI, Noriko, NISHINA, Hiroshige, TAKAYAMA, Kotaro |
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| Format: | Article PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists
, 2021
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| Subjects: | |
| Online Access: |
https://eprints.untirta.ac.id/9877/2/en https://eprints.untirta.ac.id/9877/3/Averaging%20Techniques%20in%20Processing%20the%20High%20Time-resolution%20Photosynthesis%20Data.pdf https://eprints.untirta.ac.id/9877/ https://www.jstage.jst.go.jp/article/ecb/59/3/59_107/_article/-char/en |
Daftar Isi:
- 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.