Adding green component to growth light had a profound effect on biomass accumulation in lettuce. However, conflicting views on photosynthetic efficiency of green light, which have been reported, might occur due to nonuniform light sources used in previous studies. In an attempt to reveal plausible mechanisms underlying the differential photosynthetic and developmental responses to green light, we established a new way of light treatment modeled according to the principle of gene "knock out". Lettuce (Lactuca sativa L. var. youmaicai) was grown under two different light spectra, including a wide spectrum of light-emitting diode (LED) light (CK) and a wide spectrum LED light lacking green (480-560 nm) (LG). Total PPFD was approximately 100 µmol(photon) m-2 s-1 for each light source. As compared to lettuce grown under CK, shoot dry mass, photosynthetic pigment contents, total chlorophyll to carotenoids ratio, absorptance of PPFD, and CO2 assimilation showed a remarkable decrease under LG, although specific leaf area did not show significant difference. Furthermore, plants grown under LG showed significantly lower stomatal conductance, intercellular CO2 concentration, and transpiration compared with CK. The plants under CK exhibited significantly higher intrinsic quantum efficiency, respiration rate, saturation irradiance, and obviously lower compensation irradiance. Finally, we showed that the maximum ribulose-1,5-bisphosphate-saturated rate of carboxylation, the maximum rate of electron transport, and rate of triosephosphate utilization were significantly reduced by LG. These results highlighted the influence of green light on photosynthetic responses under the conditions used in this study. Adding green component (480-560 nm) to growth light affected biomass accumulation of lettuce in controllable environments, such as plant factory and Bioregenerative Life Support System., H. Liu, Y. Fu, M. Wang, H. Liu., and Obsahuje bibliografii
When we apply ecological models in environmental management, we must assess the accuracy of parameter estimation and its impact on model predictions. Parameters estimated by conventional techniques tend to be nonrobust and require excessive computational resources. However, optimization algorithms are highly robust and generally exhibit convergence of parameter estimation by inversion with nonlinear models. They can simultaneously generate a large number of parameter estimates using an entire data set. In this study, we tested four inversion algorithms (simulated annealing, shuffled complex evolution, particle swarm optimization, and the genetic algorithm) to optimize parameters in photosynthetic models depending on different temperatures. We investigated if parameter boundary values and control variables influenced the accuracy and efficiency of the various algorithms and models. We obtained optimal solutions with all of the inversion algorithms tested if the parameter bounds and control variables were constrained properly. However, the efficiency of processing time use varied with the control variables obtained. In addition, we investigated if temperature dependence formalization impacted optimally the parameter estimation process. We found that the model with a peaked temperature response provided the best fit to the data., H. B. Wang, M. G. Ma, Y. M. Xie, X. F. Wang, J. Wang., and Obsahuje bibliografii