Light-use Efficiency Research Articles

Photosynthetic carbon and nitrogen metabolism of Camellia oleifera Abel during acclimation to low light conditions

Tea-oil tree (Camellia oleifera Abel) is an important woody oil crop with high economic value. However, it has low photosynthetic production considering the low light intensity of its growth environment. To understand the acclimation mechanism of tea-oil trees to low light conditions, three light intensity treatments were conducted: high light (450–500 μmol. m−2. s−1), medium light (180–200 μmol. m−2. s−1), and low light (45–50 μmol. m−2. s−1). The carbon (C) and nitrogen (N) metabolism network were constructed by investigating the leaf anatomy, photosynthetic characteristics, N partitioning, transcriptome and metabolome. Results demonstrated that a larger proportion light energy was used for photochemical reactions in an environment with lower light intensity, which resulted in an increase in photosystem II photochemical efficiency and instantaneous light use efficiency (LUE) at the leaf level. As the light intensity increased, decreased electron transfer and carboxylation efficiencies, photorespiration and dark respiration rates, LUE at plant level, and N use efficiency (PNUE) were observed. Leaves trended to harvest more light using higher expression levels of light-harvesting protein genes, higher chlorophyll content, more granum and more tightly stacked granum lamella under lower light intensity. At transcriptional and metabolic levels, the TCA cycle, and the synthesis of starch and saccharides were weakened as light intensity decreased, while the Calvin cycle did not show the regularity between different treatments. Less N was distributed in Rubisco, respiration, and cell wall proteins as light decreased. Storage N was prominently accumulated in forms of amino acids (especially L-arginine) and amino acid derivatives as under medium and low light environments, to make up for C deficiency. Therefore, tea-oil trees actively improve light-harvesting capacity and enlarges the storage N pool to adapt to a low light environment, at the cost of a decrease of photosynthetic C assimilation and PNUE.

A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems

Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE (), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of , and (b) separately re-parameterizing in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m−2 d−1) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIRv) were used to re-parameterize the , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.

Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest

Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.

Global Spectrum of Vegetation Light‐Use Efficiency

AbstractVegetation light‐use efficiency (LUE) is a critical parameter of vegetation photosynthesis, but its global variations and the drivers remain poorly understood. Here, we used data from 172 flux tower sites to characterize the global distribution of ecosystem‐level LUE spanning various vegetation and climate. The global growing season average LUE was 1.30 ± 0.55 g C MJ−1 for 2000–2014. The main factors driving LUE variations included environmental conditions (e.g., temperature), plant traits (e.g., foliar nitrogen concentration and isohydricity) and their interactions, which together accounted for about 76% of the observed global variations in LUE. However, an ensemble of 11 dynamic global vegetation models largely underestimated LUE, and hardly captured the key drivers. Our results highlight that a paradigm shift from vegetation functional type‐based scheme to trait‐climate spectrum‐based scheme is necessary for the next‐generation models to accurately simulate the response of vegetation carbon uptake to climate change.

Comparison of Supplemental LED Top- and Interlighting for Year-Round Production of Cherry Tomato

Supplemental lighting is common in northern countries or during winter greenhouse tomato production. We investigated the effect of supplemental lighting treatments on cherry tomato (‘Jun-Ama’) yield, productivity (light-use efficiency (LUE) and energy-use efficiency (EUE)), and fruit quality under high irradiance (average greenhouse daily light integral (DLI) = 14.5 mol m−2 d−1). Supplemental lighting treatments contained average DLIs of 2.7, 4.9, and 7.6 mol m−2 d−1 for interlighting, toplighting, and inter- + toplighting, respectively. Supplemental LED lighting increased fruit yield by 18, 41, and 40% with inter-, top-, and inter- + toplighting, respectively, compared with the control. Interlighting increased fruit number (+11%), and top- and inter- + toplighting also increased the fruit number (+26%, +27%) and weight (+10%, +10%), respectively. LUE and EUE were comparable between inter- and toplighting, while inter- + toplighting decreased LUE by 21 and 38%, and EUE by 38 and 31% compared with inter- and toplighting, respectively. All LED supplemental treatments significantly increased total soluble solids compared with the control. Total acidity and lycopene content were unchanged in all treatments. In conclusion, LED supplemental lighting with inter- or toplighting improved cherry tomato yield and quality, but inter- + toplighting was inefficient under high irradiation.

Variations in abundance, diversity, photo-physiology and estimated productivity of micro-phytoplankton with depth at the Saya de Malha Bank, Mascarene Plateau

The variations in micro-phytoplankton abundance, diversity, photo-physiology, chlorophyll a (Chla) concentration and estimated productivity were assessed at depth ranges of 0-4, 5-10, and 11-29 m with 100, 28 and 11% of irradiance, respectively, in Saya de Malha waters. The total micro-phytoplankton abundance (TMPA) differed significantly (P < 0.001) with depth ranges, and between day and night samples. Out of the 34 genera identified, 27 showed a decrease in abundance of over 40% with depth. Chaetoceros, Coscinodiscus, Navicula, Nitzschia and Ceratium were most dominant. The Shannon-Wiener (H’) diversity index did not differ among depth ranges and between samples collected during day and night, but diatoms were more diverse than dinoflagellates. The effective quantum yield (ΦPSII) and the light-use efficiency factor (α) tended to decrease, while the maximum relative electron transport rate (rETRmax), the photo-inhibitory factor (β) and the maximum non-photochemical quenching (NPQmax) varied insignificantly from morning to afternoon sampling points at all depth ranges studied. The estimated productivity, rETRmax and Chla concentration decreased with depth ranges. The higher diversity of diatoms, better photosynthetic performance in the morning hours and higher near-surface estimated productivity provide new insights into micro-phytoplankton dynamics and productivity in Saya de Malha waters.

Research of Light Use Efficiency for Paddy Rice Using Multi-Angle Hyperspectral Observations

Light use efficiency (LUE) is a critical variable for modeling gross primary production (GPP) of vegetation. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which has great potential in predicting the light use efficiency (LUE) and makes LUE related to plant optical characteristics. Despite the great achievement in understanding the correlation of PRI and LUE on forests, the application of PRI in estimating LUE on crops has almost been ignored. The present study reported the ability of multi-angle PRI to track eddy covariance (EC)-based LUE in a rice field in East China. We found that PRI can estimate LUE in sunny days (R2 = 0.4; p < 0.05). The correlation of PRI and LUE was greater than that at OZA of 0° (R2 = 0.4; p < 0.05) after distinguishing different OZAs and scatter directions and was the greatest in the backscatter direction with an OZA of 60° (R2 = 0.7; p < 0.01). This correlation was also corrected by the bidirectional reflectance distribution function (BRDF), where R2 = 0.72 after correction. We noted that the relationship between PRI and LUE on cloudy days was poor, where R2 = 0.26. Photosynthetically active radiation (PAR) had an influence on the LUE and PRI, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the correlation of PRI and LUE. Our research suggests that PRI can efficiently track the LUE of the rice growth period when considering environmental factors and rice canopy structures.

Dry Matter Production and Light Use Efficiency at Different Developmental Stages of Japanese Cucumber

To investigate whether dry matter production depends on the growth stage of cucumber (Cucumis sativus L.), different growth stage plants of two cultivars (‘Project X’ [PX] and ‘Fresco dash’ [FD]) were cultivated hydroponically, and their growth and yield were evaluated based on dry matter production during the same period. Old and young developmental stage plants (OSP; 200 d after transplanting [DAT], YSP; 43 DAT) were compared for yield components. No significant differences were observed in fruit yield obtained in FD between different developmental stages. However, OSP had a higher yield than YSP in PX. For both cultivars, there was a difference in the LAI between OSP and YSP. In contrast, total dry matter (TDM) showed a decrease in OSP in FD, whereas PX did not show a significant decrease. The fraction of fruit was significantly higher in OSP than in YSP for both cultivars. No significant differences in light use efficiency (LUE) and photosynthetic rate were observed at different developmental stages, while water use efficiency showed improvement in the old developmental stage in both cultivars.

The Effect of Diffuse Radiation on Ecosystem Carbon Fluxes Across China From FLUXNET Forest Observations

Aerosol loading and cloud cover can alter the composition of radiation reaching the Earth’s surface and affect the ecosystem’s carbon cycle. In this study, we established an empirical model of the diffuse radiation fraction (Kd) based on a clearness index (Kt) to obtain the Kd of four FLUXNET forest sites in China. We focused on the relationships among the Kd, photosynthetically active radiation (PAR), light-use efficiency (LUE) and gross primary productivity (GPP) through mechanistic analysis. The relationships between carbon fluxes [including GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE)] and the Kd were explored. Furthermore, we investigated the influence of environmental factors on carbon fluxes. The results showed that the Kd models were accurate in estimating Kd (R2= 0.88–0.93). Overall, the GPP first increased and then decreased with increasing Kd. When Kd< Ko (Ko, the diffuse radiation fraction corresponding to the maximum value of GPP), the direct PAR decreased as Kd increased, while the diffuse PAR increased rapidly. At this stage, the diffuse fertilization effect led to an increase in GPP. When Ko<Kd<Kdiff-max (Kdiff-max, the diffuse radiation fraction corresponding to the maximum value of diffuse PAR), as Kd increased the direct PAR still decreased and the diffuse PAR still increased, but the GPP declined. When Kd>Kdiff-max, the diffuse PAR began to decrease, and the reduction in the superimposed direct PAR caused the GPP of the canopy to drop rapidly. The LUE of the vegetation canopy was higher under diffuse light conditions than under direct light. Furthermore, with an increase in the Kd, the negative value change of the NEE was consistent with the GPP, but the ER was less affected by the Kd. Finally, the impact of temperature (TA) and vapor pressure deficit (VPD) on the GPP was unimodal, and the impact on the NEE was U-shaped. In addition, latent heat (LE) had a significant positive effect on GPP and NEE. Our study emphasized the relationship between the change in PAR composition and the Kd, as well as its impact on the carbon fluxes change, which is highly important to the study of carbon neutralization.

Exploring the interrelated effects of soil background, canopy structure and sun-observer geometry on canopy photochemical reflectance index

Photochemical reflectance index (PRI) is an intriguing avenue for monitoring photosynthetic light use efficiency (LUE), which is a crucial physiological variable and the primary source of uncertainty in gross primary production (GPP) prediction. The correlations between PRI and LUE induced by the energy-dependent xanthophyll cycle are overall convincing at the leaf scale. However, their relationship becomes complicated and less reliable at the canopy scale due to several confounding factors, such as soil background, canopy structure and sun-observer geometry. A fundamental understanding of the interrelated effects of these factors is still missing. In this study, the roles of soil reflectance, canopy structure and sun-observer geometry were examined by investigating radiative transfer processes that determine canopy PRI. Theoretical analysis shows that the deviation between canopy- and leaf-level PRI is mainly attributed to soil background, namely the fraction of sunlit soil in the scene and the discrepancy between leaf and soil reflectance. Canopy structure and sun-observer geometry are not the direct drivers of variations in canopy PRI. They affect canopy PRI by altering the contribution of soil to canopy reflectance. Furthermore, several numerical experiments based on 1D and 3D radiative transfer models, which compare PRI of canopies with black soil (i.e., soil reflectance is zero) and non-black background, were conducted to confirm and validate the decisive role of soil background in explaining the deviation between canopy and leaf PRI. Multi-angular canopy PRI from PROBA-CHRIS data was employed to examine the angular dependency of canopy PRI changing with vegetation coverage. The numerical experiments demonstrate that canpy PRI differs from leaf PRI as large as 150% when canopy leaf area index (LAI) or the fraction of vegetation coverage is small. Canopy PRI is almost identical to soil PRI (i.e., PRI computed from soil reflectance) when the contribution of soil background to canopy reflectance is large. In contrast, when the soil reflectance is zero or LAI is greater than four, canopy PRI is almost identical to leaf PRI and independent of canopy structure and sun-observer geometry. Nonetheless, when leaf and soil reflectance are similar, canopy PRI is always close to leaf PRI regardless canopy structure and sun-observer geometry. The analysis of multi-angular PRI reveals that the angular dependency decreases with vegetation coverage due to weaker soil background effect. This study narrows down the causes of variation in canopy PRI to the fraction of observed soil background and soil reflectance. It suggests that estimating and accounting for the soil contribution to canopy PRI are necessary to mitigate angular and canopy structural effects on canopy PRI and eventually extract leaf physiological information from canopy PRI.

Growth Dynamics and Production of Kara Oncet Bean (Ficia Faba L.) Through Plant Growth Analysis Approach To Get Optimum Planting Distance

Setting the spacing with a specific density allows each plant to grow well. Spacing will affect the density and efficiency of light use and competition between plants in the use of water and nutrients to affect crop production. At low densities, plants compete less with other plants so that individual plant performances are better. Conversely, at high densities, the competition between plants for light, water, and nutrients is getting tighter so that plant growth can be stunted. Each kara oncet bean cultivar responds differently to different plant density levels. The experiment was conducted in farmers' rice fields in Lembang with Andosol soil. The altitude is 1500 m above sea level. Cangar and Bromo kara oncet bean cultivars with populations of 83,333 plants per ha, 66,667 plants per ha, and 55,556 per ha were studied using factorial block design with four replications. The response surface methodology and snugness test responses to find the highest population of each kara oncet bean cultivar. From the experimental results, it turns out that different plant populations cause differences in (ILD), (LTT), (LAB), and results. The optimum population to obtain the highest yield of two faba bean cultivars has not yet been determined. The components of plant organs that can be relied upon as a determinant of the yield of kara oncet beans are the number of pods per plant, the number of seeds per plant, and the weight of 25 dry seeds.

Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe

Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (Df) can enhance GPP globally. We quantified the aerosol diffuse fertilization effect (DFE) and incorporated it into a light use efficiency (LUE) model, EC-LUE. The new model is driven by aerosol optical depth (AOD) data and is referred to as AOD-LUE. The eddy correlation variance (EC) of the FLUXNET2015 dataset was used to calibrate and validate the model. The results showed that the newly developed AOD-LUE model improved the performance in simulating GPP across all ecosystem types (R2 from 0.6 to 0.68), with the highest performance for mixed forest (average R2 from 0.71 to 0.77) and evergreen broadleaf forest (average R2 from 0.34 to 0.45). The maximum LUE of diffuse photosynthetic active radiation (PAR) (3.61 g C m−2 MJ−1) was larger than that of direct PAR (1.68 g C m−2 MJ−1) through parameter optimization, indicating that the aerosol DFE seriously affects the estimation of GPP, and the separation of diffuse PAR and direct PAR in the GPP model is necessary. In addition, we used AOD-LUE to quantify the impact of aerosol on GPP. Specifically, aerosols impaired GPP in closed shrub (CSH) by 6.45% but enhanced the GPP of grassland (GRA) and deciduous broadleaf forest (DBF) by 3.19% and 2.63%, respectively. Our study stresses the importance of understanding aerosol-radiation interactions and incorporating aerosol effects into regional and global GPP models.

Unraveling Forest Complexity: Resource Use Efficiency, Disturbance, and the Structure‐Function Relationship

AbstractStructurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)‐derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem‐scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure‐function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water‐sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies.

Photoenergy Harvesting by Photoacid Solution (Adv. Mater. 24/2022)

Photoenergy Harvesting In article number 2201734, Il-Doo Kim and co-workers develop a photoacid-driven liquid-state photoenergy harvester (PLPH) as the first demonstration of a photoacid-solution-based device that generates electric energy from light. Asymmetric light exposure on the PLPH generates electric energy by the light-induced pH gradient (ΔpH = 2) along the two electrodes This unique approach offers a new way to harness solar energy in a form-factor-free design that maximizes space and light-use efficiency.

Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981–2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m−2 d−1 and 1.28 g C m−2 d−1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

Evaluating Cumulative Drought Effect on Global Vegetation Photosynthesis Using Numerous GPP Products

The increasing trend in drought events under the background of global warming makes it more important to understand the drought effect on vegetation photosynthesis. While diverse global gross primary production (GPP) datasets were adopted to investigate the drought impact on photosynthesis, few studies focused on the discrepancies of drought response among different GPP datasets, especially for the cumulative drought impact. Therefore, a total of twenty-six global GPP datasets based on process, machine learning (ML), and light-use efficiency (LUE) model schemes were obtained to appraise the cumulative impact of drought stress on photosynthesis from 2001 to 2010. Moreover, a relatively reliable global pattern of drought’s cumulative effect on vegetation photosynthesis was acquired from these global GPP products through probability analysis. The results illustrated that the cumulative impact of drought existed in 52.11% of vegetation cover land with the cumulative time scales dominantly at a short term (1–4 months, 31.81%). Obvious heterogeneity of the drought cumulative effect in space and different vegetation functional types was observed, as the reliability of the drought effect decreased with latitude decreasing and a higher sensitivity to drought in herbaceous vegetation than woody plants. Our findings highlighted the importance of ways in characterizing moisture conditions across vegetation types among various GPP models and the necessity of GPP dataset selection in investigating drought effect on photosynthesis.

Pinus tabulaeformis Forests Have Higher Carbon Sequestration Potential Than Larix principis-rupprechtii Forests in a Dryland Mountain Ecosystem, Northwest China

Carbon sinks in terrestrial ecosystems can be significantly increased by afforestation, which will slow global warming. However, it is still unclear how different plantations influence the carbon sink and how they respond to environmental factors, especially in drylands. In this study, eddy correlation method (EC) was used to measure carbon and water fluxes and environmental factors of two artificial forests (Larix principis-rupprechtii and Pinus tabulaeformis) in the dryland of Northwest China, and the responses of evapotranspiration (ET), net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO) to environmental factors were also assessed. Results showed that the L. principis-rupprechtii forest ecosystem had higher water use efficiency (WUE), light use efficiency (LUE), GPP, and RECO than the P. tabulaeformis forest ecosystem. However, the proportion of net ecosystem production (NEP) to GPP in the P. tabulaeformis forest ecosystem (62.89%) was higher than that in the L. principis-rupprechtii forest ecosystem (47.49%), indicating that the P. tabulaeformis forest ecosystem had the higher carbon sequestration efficiency. In addition, the CO2 and H2O fluxes in the L. principis-rupprechtii forest ecosystem were more sensitive to environmental factors, compared with the P. tabulaeformis forest ecosystem. Further, the RECO of the L. principis-rupprechtii forest ecosystem was more sensitive to temperature changes, which implies that the L. principis-rupprechtii forest ecosystem will release more CO2 than the P. tabulaeformis forest ecosystem with a warming climate. Therefore, the P. tabulaeformis forest ecosystem may have better carbon sequestration potential. These results are important for understanding the effects of climate change on the CO2 and H2O cycles in coniferous plantation ecosystems in drylands.

Photoenergy Harvesting by Photoacid Solution.

Solar energy has seen 180years of development since the discovery of the photovoltaic effect, having achieved the most successful commercialization in the energy-harvesting fields. Despite its long history, even the most state-of-the-art photovoltaics remain confined to solid-state devices, limiting spatial and light utilization efficiencies. Herein, a liquid-state photoenergy harvester based on a photoacid (PA), a chemical that releases protons upon light irradiation and recombines with them in the dark through a fully reversible reaction, is demonstrated. Asymmetric light exposure on a PA solution contained in a transparent tube generates a pH gradient (ΔpH=2) along the exposed and dark regions, which charges the Nernst potential up to 0.7V across the two electrodes embedded at each end, as if a capacitor. Owing to the reversibility of PAs, a PA-driven liquid-state photoenergy harvester (PLPH) generates capacitive currents up to 0.72mAm-2 on an irradiation. Notably, the transparent nature of the PLPH enables vertical stacking up to 25units, which multiplies the light-harvesting efficiencies by over 1000%. This unique approach provides a new route to harness solar energy with a form-factor-free design that maximizes spatial and light-use efficiencies.

Remotely sensed carotenoid dynamics improve modelling photosynthetic phenology in conifer and deciduous forests

Detecting the phenology of photosynthesis, which conveys the length of the growing season, is key for terrestrial ecosystem models to constrain total annual carbon uptake and estimate gross primary productivity (GPP). However, some of the vegetation indices that are widely used for modelling GPP lack the ability to represent changes in the magnitude of photosynthesis, leading to errors in detecting phenology and large uncertainties. Crucially, remotely sensed vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) can detect changes in foliar carotenoid composition that represent adjustments in photosynthetic light-use-efficiency (LUE) and changes in phenology. We modeled GPP from remote sensing data using PRI and CCI to represent foliar carotenoid changes as proxies for LUE. GPP values estimated from these PRI and CCI modified LUE models were compared against GPP from eddy covariance flux tower measurements, MODerate Resolution Imaging Spectroradiometer (MODIS) GPP product, conventional meteorological driven LUE-model, and process-based dynamic global vegetation model (ie. JULES) in an evergreen needleleaf and a deciduous broadleaf forest in the Great Lakes region. Overall, and in particular for evergreen needleleaf forests, estimates of start and end of growing season using PRI and CCI LUE-models showed less year-to-year variability than estimates obtained by process-based meteorological models. Although many process-based models provide reasonable estimates of start and end of growing season, our results demonstrate that using regulatory carotenoids and photosynthetic efficiency can improve remote monitoring of the phenology of forest vegetation.

Responses of butter leaf lettuce to mixed red and blue light with extended light/dark cycle period

To investigate the effects of extended light/dark (L/D) cycle period (relative to the diurnal L/D cycle) on lettuce and explore potential advantages of abnormal L/D cycles, butter leaf lettuce were grown in a plant factory with artificial light (PFAL) and exposed to mixed red (R) and blue (B) LED light with different L/D cycles that were respectively 16 h light/8 h dark (L16/D8, as control), L24/D12, L48/D24, L96/D48 and L120/D60. The results showed that, all the abnormal L/D cycles increased shoot dry weight (DW) of lettuce (by 34–83%) compared with the control, and lettuce DW increased with the L/D cycle period prolonged. The contents of soluble sugar and crude fiber in lettuce showed an overall upward trend with the length of L/D cycle extended, and the highest vitamin C content as well as low nitrate content were both detected in lettuce treated with L120/D60. The light use efficiency (LUE) and electric use efficiency (EUE) of lettuce reached the maximum (respectively 5.37% and 1.76%) under L120/D60 treatment and so were DW, Assimilation rate (A), RC/CS, ABS/CS, TRo/CS and DIo/CS, indicating that longer L/D cycle period was beneficial for the assimilation efficiency and dry matter accumulation in lettuce leaves. The highest shoot fresh weight (FW) and nitrate content detected in lettuce subjected to L24/D12 may be related to the vigorous growth of root, specific L/D cycle seemed to strengthen root growth and water absorption of lettuce. The openness level of RC in PSII (Ψo), ETo/CS, and PIabs were all the highest in lettuce treated with L24/D12, implying that slightly extending the L/D cycle period might promote the energy flowing to the final electron transfer chain. In general, irradiation modes with extended L/D cycle period had the potential to improve energy use efficiency and biomass of lettuce in PFAL. No obvious stress or injury was detected in lettuce subjected to prolonged L/D cycles in terms of plant growth and production. From the perspective of shoot FW, the optimal treatment in this study was L24/D12, while L120/D60 was the recommended treatment as regards of the energy use efficiency and nutritional quality.