A high efficiency CO₂ concentration interval optimization method for lettuce growth.

Area of Science:

• Plant Physiology
• Agricultural Science
• Environmental Science

Background:

• Declining carbon fertilization effects necessitate optimized CO2 regulation for plant growth.
• Understanding plant responses to varying CO2 concentrations under different environmental conditions is critical.

Purpose of the Study:

• To develop an efficient method for determining optimal carbon dioxide (CO2) supplementation intervals for lettuce growth.
• To validate the proposed method using U-chord curvature theory and assess its impact on lettuce physiology and biomass.
• To build a predictive model for CO2 regulation based on environmental factors.

Main Methods:

• Statistical analysis of rapid A/CO2 response curve (RAC) data under varying photosynthetic photon flux densities (PPFDs) and temperatures.
• Development of a CO2 supplementation interval acquisition method based on RAC frequency distribution characteristics.
• Application of U-chord curvature theory to determine optimal CO2 levels and validate the proposed interval method.
• Construction of a genetic algorithm-support vector regression model for predicting optimal CO2 intervals.

Main Results:

• The proposed method efficiently identified characteristic CO2 subsections with >97% cumulative contribution rate.
• CO2 interval supplementation significantly improved daily CO2 exchange rates (up to 21.64%) and lettuce fresh biomass (26.78%).
• The predictive model achieved an R² > 0.84 and a root mean square error < 35.2256 μmol·mol⁻¹.

Conclusions:

• The developed CO2 interval regulation method positively impacts lettuce growth and physiological parameters.
• This study provides a novel and efficient approach for high-efficiency supplementary CO2 concentration in lettuce cultivation.
• The findings contribute to optimizing controlled environment agriculture strategies for enhanced crop production.