Light and Temperature Effects on Agronomic Indices of <i>Brassica chinensis L.</i> in a Simulated Greenhouse Test

CAI Shufang; WU Baoyi; LIAO Shuilan; LIU Xian; LEI Jingui

doi:10.19303/j.issn.1008-0384.2020.06.006

Volume 35 Issue 6

Jun. 2020

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Article Navigation > Fujian Journal of Agricultural Sciences > 2020 > 35(6): 611-617

CAI S F, WU B Y, LIAO S L, et al. Light and Temperature Effects on Agronomic Indices of Brassica chinensis L. in a Simulated Greenhouse Test [J]. Fujian Journal of Agricultural Sciences，2020，35（6）：611−617 doi: 10.19303/j.issn.1008-0384.2020.06.006

Citation:

CAI S F, WU B Y, LIAO S L, et al. Light and Temperature Effects on Agronomic Indices of Brassica chinensis L. in a Simulated Greenhouse Test [J]. Fujian Journal of Agricultural Sciences，2020，35（6）：611−617 doi: 10.19303/j.issn.1008-0384.2020.06.006

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Light and Temperature Effects on Agronomic Indices of Brassica chinensis L. in a Simulated Greenhouse Test

doi: 10.19303/j.issn.1008-0384.2020.06.006

Institute of Digital Agriculture, Fujian Academy of Agriculture Sciences, Fuzhou, Fujian　350003, China

Received Date: 2020-04-03
Rev Recd Date: 2020-05-09
Publish Date: 2020-08-10

Abstract

Abstract

Objective By studying changes on the agronomic properties of Brassica chinensis L. in response to the light and temperature conditions in a greenhouse, morphology and yield of the plant were investigated. Method A hydroponics experiment with a heat-resistant B. chinensis in a greenhouse was carried out. The environmental data were collected in real time, and the agronomic properties of the plants monitored continuously. Relationship between the plant agronomy and the greenhouse temperature and photosynthetically active radiation (or, the light and temperature function, LTF) was used to compare with other dynamic simulation models. Result The agronomic indices including plant height, leaf number, leaf area, stem diameter, root length, and fresh weight of B. chinensis increased with increasing LTF in a fitting equation of an s-shaped function. Prediction by the LTF-based model was better than either TEP- or GDD-based model. The LTF model was not only more precise but also better fitted between the simulated and measured agronomic indices. It showed a R² of 0.907-0.984, a RMSE of 0.540-34.393, and a RE of 6.79-12.66%, which were superior to the RMSE and RE of 5.29-59.98% and 31.30-96.23% for TEP and GDD models, respectively. Conclusion The LTF-based model was found to more accurately predict the growth and yield of B. chinensis than did the other models.
- Greenhouse,
- Brassica chinensis L.,
- light and temperature function,
- agronomy,
- simulation

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References(21)

References

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