A lettuce growth model responding to a broad range of greenhouse climates

文献类型： 外文期刊

第一作者： Sun, Weituo

作者： Sun, Weituo;Sun, Weituo;Coules, Anne;Lu, Chungui;Zhao, Chunjiang

作者机构：

关键词： Crop growth model; Lettuce; Greenhouse climate; Dry weight; Leaf area index; Photosynthesis inhibition; Symbols

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.3； 五年影响因子：5.9 ）

ISSN： 1537-5110

年卷期： 2025 年 250 卷

页码：

收录情况： SCI

摘要： Crop models serve as a basis for optimal management of greenhouse climate, while the current simulations for lettuce growth are incomplete. This study presents a lettuce growth model that describes the effects of a broad range of greenhouse climates, including air temperature with extreme conditions, humidity, CO2 concentration, and shortwave radiation on dynamics of the single state variable, structural crop dry weight. The proposed model framework performs two parallel sets of mass flows: dry matter accumulation and buffer evolution. The buffer carbohydrates flow to growth conversion based on the temperature-dependent sink strength. The inhibition of canopy assimilation occurs when the carbohydrate storage approaches the buffer capacity. The humidity effects are incorporated by describing stomatal resistance and specific leaf area of new leaves. The model was first calibrated at both sub-model and model levels and then validated against data collected in three experiments, covering a broad range of greenhouse climates. Results demonstrated that the model performance was good and acceptable; the simulated crop dry weights closely mirrored the measured values, with the RRMSE of 10.5-24.9% and the RMSE of 0.0070-0.0131 kg m- 2. The model predicted the leaf area index with an RRMSE of 12.1-54.7% and performed well for the vegetative growth stage concerned by commercial production. The photosynthesis inhibition time accounted for 27-41% of the total photosynthesis time, indicating that the model framework and underlying hypothesis worked in simulations. The developed model, simulating instantaneous lettuce dynamics for the potential situation, can be applied to low-tech greenhouses and enables optimal control of all four climate factors.

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