A high efficiency CO concentration interval optimization method for lettuce growth.

Sci Total Environ

College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural, Yangling, Shaanxi 712100, China. Electronic address:

Published: June 2023

The decline in carbon fertilization effects has shifted scientific focus toward the efficient and suitable regulation of CO concentration ([CO]) for plant growth. In this study, the rapid A/CO response curve (RAC) data of lettuce were analyzed statistically under nine photosynthetic photon flux densities (PPFDs) and four temperatures. An efficient CO supplementation interval acquisition method based on the frequency distribution characteristics of RACs was proposed. The characteristic subsections of jumping were obtained depending on the frequency distribution of RACs. The cumulative contribution rate (CCR) of the characteristic subsections were >97 %, which showed the efficiency of the method. Additionally, U-chord curvature theory was used to simultaneously obtain the optimal regulated [CO] for the same RAC curves, and the results showed that the [CO] obtained by U-chord length were all within the interval obtained by the method, which proved the rationality of the method. The [CO] interval supplement improved the daily CO exchange rate by 20.27 % and 21.64 % at 150 and 200 μmol·m·s, and increased the lettuce fresh biomass by 26.78 % at 150 μmol·m·s. Based on the interval of [CO] efficient utilization regulation at various temperatures and PPFDs, a genetic algorithm-support vector regression model was built with R of the model was >0.84 and the root mean square error was <35.2256 μmol·mol. In conclusion, the [CO] interval obtained by this method has a positive effect on lettuce growth. This work provides a new method for obtaining high-efficiency supplementary concentration of CO during the growth of lettuce.

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http://dx.doi.org/10.1016/j.scitotenv.2023.162731DOI Listing

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