Cucumber seedling growth under constant and fluctuating photosynthetic photon flux density (PPFD) light (CPL and FPL, respectively) was examined to discuss whether plants can acclimate to CPL versus FPL. The FPL was created in a growth chamber by artificially reproducing fluctuations in ground-level sunlight PPFD using phosphor-converted white LEDs. The daily mean PPFD at the canopy surface and photoperiod were the same for the CPL and FPL. The plants were grown for two weeks, with the former and latter half periods under either CPL or FPL, respectively; there were two treatments for the first week and four with the 2 × 2 combinations for the last week. According to the growth analysis for the first week, the net assimilation rate (NAR) tended to be lower under FPL than under CPL, while the leaf area ratio and specific leaf area (SLA) were greater under FPL than under CPL. The lower NAR may be due to a delayed response of photosynthesis to a PPFD increase, i.e., photosynthetic induction, and the non-linearity of the PPFD-response curve of photosynthetic rate. The greater SLA was consistent with previous reports, although the mechanism underlying the response was unclear. For the last week, there was no significant difference in those parameters among treatments, possibly because of a change in canopy structure accompanying self and mutual leaf shading. The relative growth rate and shoot dry mass were also not significantly different among treatments. Thus, although cucumber seedlings initially showed physiological and morphological responses to CPL versus FPL distinctively, the responses did not improve growth under continued respective light conditions and may not necessarily be acclimative at the whole-plant growth level.

　We examined whether the method that we have recently proposed for quantitative estimation of leaf mass per area (LMA) in leaves grown under day-to-day changing photosynthetic photon flux density (PPFD) could be applied to conditions where PPFD also fluctuated during the daytime. The method utilizes the time-weighted average PPFD (Q_wl) in which a greater weight was assigned to PPFD levels in recent days than in past days. Cucumber seedlings were treated for six days with seven light treatment patterns of day-to-day PPFD changes, each combined with two diurnal PPFD conditions: constant- and fluctuating-PPFD light during a 16-h d^-1 photoperiod. The LMA tended to increase with increasing Q_wl and was greater under diurnal constant-PPFD light than fluctuating-PPFD light in each of the seven day-to-day light treatment patterns. Regression analysis revealed a significant and highly linear relationship between LMA and Q_wl in each of constant- and fluctuating-PPFD light conditions. Our result demonstrates that the concept of Q_wl for quantitative estimation of LMA can be applied to light conditions where the PPFD level fluctuates not only day to day but also during the daytime, although some adjustments would be required to incorporate the effect of daytime PPFD fluctuations properly.

　Measurements of greenhouse gas (GHG) emissions from paddy fields can often include flux measurement errors due to either instrument errors or unfavorable weather. Therefore, data post-processing, including the gap-filling process, is required to improve data quality and quantify the GHG flux budget. This study applied machine learning (ML) techniques with polynomial and multivariate polynomial regression models for gap-filling methane (CH₄) and carbon dioxide (CO₂) fluxes from closed chamber (CC) method measurements and compared results with mean diurnal variation (MDV) and look-up table (LUT) techniques. The most influential factors affecting methane emissions in the paddy field were used for input variables in the models: air temperature, soil temperature, soil redox potential, soil water content, solar radiation, and days after transplanting. The models' performances were compared using mean absolute error (MAE) and root mean square error (RMSE). The results showed that MAE and RMSE for gap-filling CH₄ fluxes were 1.299-2.984 and 2.499-4.981 mg CH₄ m^-2 h^-1, respectively. Also, the multivariate polynomial regression models performed better for gap-filling CH₄ fluxes (RMSE = 2.499 mg CH₄ m^-2 h^-1) than the polynomial regression models, MDV (RMSE = 3.210 mg CH₄ m^-2 h^-1), and LUT (RMSE = 3.339 mg CH₄ m^-2 h^-1) techniques. The MAE and RMSE for gap-filling CO₂ fluxes were 0.282-0.949 and 0.435-1.078 g CO₂ m^-2 h^-1, respectively. The ML techniques with polynomial regression using solar radiation (RMSE = 0.435 g CO₂ m^-2 h^-1) and multivariate models (RMSE = 0.445 g CO₂ m^-2 h^-1) perform better on gap-filling CO₂ fluxes than MDV (RMSE = 0.544 g CO₂ m^-2 h^-1), and LUT (RMSE = 0.553 g CO₂ m^-2 h^-1) techniques. The gap-filling using the multivariate polynomial regression models used in this study improved the reliability of the diurnal variation in GHG fluxes. Therefore, ML techniques could be a proper alternative for gap-filling GHG fluxes.