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Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon−1, and the multiyear mean maximal LUE was 0.30 g C mol photon−1 during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation. [ABSTRACT FROM AUTHOR]

Background: Controlled environment agriculture, particularly vertical farms (VF), also called plant factories, is often claimed as a solution for global food security due to its ability to produce crops unaffected by weather or pests. In principle, essential macronutrients of the human diet, like protein, could technically be produced in VF. This aspect becomes relevant in the era of protein transition, marked by an increasing consumer interest in plant‐based protein and environmental challenges faced by conventional farming. However, the real question is: what does the cultivation of protein crops in VF imply in terms of resource use? To address this, a study was conducted using a VF experiment focusing on two soybean cultivars. Results: With a variable plant density to optimize area use, and because of the ability to have more crop cycles per year, protein yield per square metre of crop was about eight times higher than in the open field. Assuming soy as the only protein source in the diet, the resources needed to get total yearly protein requirement of a reference adult would be 20 m2 of crop area, 2.4 m3 of water and 16 MWh of electricity, versus 164 m2, 111 m3 and 0.009 MWh in the field. Conclusions: The study's results inform the debate on protein production and the efficiency of VF compared to conventional methods. With current electricity prices, it is unlikely to justify production of simple protein crops in VF or promote it as a solution to meet global protein needs. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices. [ABSTRACT FROM AUTHOR]

Temperature and water stress are important factors limiting the gross primary productivity (GPP) in terrestrial ecosystems, yet the extent of their influence across ecosystems remains uncertain. This study examines how surface air temperature, soil water availability (SWA) and vapor pressure deficit (VPD) influence ecosystem light use efficiency (LUE), a critical metric for assessing GPP, across different ecosystems and climatic zones at 80 flux tower sites based on in situ measurements and data assimilation products. Results indicate that LUE increases with temperature in spring, with higher correlation coefficients in colder regions (0.79–0.82) than in warmer regions (0.68–0.78). LUE reaches a plateau earlier in the season in warmer regions. LUE variations in summer are mainly driven by SWA, exhibiting a positive correlation indicative of a water‐limited regime. The relationship between the daily LUE and daytime temperature shows a clear seasonal hysteresis at many sites, with a higher LUE in spring than in fall under the same temperature, likely resulting from younger leaves being more efficient in photosynthesis. Drought stress influences LUE through SWA in all ranges of water availability; VPD variation under moderate conditions does not have a clear influence on LUE, but extremely high VPD (exceeding the threshold of 1.6 kPa, often observed during extreme drought‐heat events) causes a dramatic reduction of LUE. Our findings provide insight into how ecosystem productivities respond to climate variability and how they may change under the influence of more frequent and severe heat and drought events projected for the future. Plain Language Summary: The terrestrial ecosystem assimilates carbon through photosynthesis, and its ability to convert sunlight energy to primary production during photosynthesis is commonly measured by light use efficiency (LUE). To understand how temperature, water in the soil, and atmospheric aridity impact carbon assimilation through photosynthesis, we analyzed the drivers of LUE at 80 sites across different ecosystems and different climate regimes in the United States. We found that temperature is the main factor influencing LUE of grassland and forest in spring, and has a greater influence on LUE in colder regions than in warmer regions. Under the same temperature, LUE in the spring is higher than in the autumn season, likely because of new leaves. In summer, deep soil water availability is the primary driver of LUE variations. In addition, extreme aridity of the atmosphere contributes to a dramatic decrease in LUE during compound heat‐drought events. Our findings contribute to advancing our understanding of how climatic factors influence LUE across different ecosystems in a changing climate. Key Points: Light use efficiency (LUE) variation is generally driven by temperature in spring and by water stress in summerSoil water availability dominates the LUE response to moderate droughts, but vapor pressure deficit becomes important under extreme droughtThe primary drivers of LUE variation also depend on background climate and vegetation types [ABSTRACT FROM AUTHOR]

Efficient solar energy utilization is a crucial determinant of crop yield formation. Moreover, various planting methods have dissimilar impacts on crop solar energy utilization and its measurement methods. This study examined the differences in solar energy utilization between monoculture and intercropping by considering density configuration, plant type arrangement, timing schedule, and spatial layout. We further evaluated the traditional methodologies versus remote sensing technology for solar energy measurements and described the differences in calculation methods for monoculture and intercropping, drawing from the photosynthesis model. Additionally, we discussed the potential advantages and limitations of employing remote sensing technology for the monitoring and prediction of solar energy utilization in field crops.

To achieve high-density tomato seedlings in a plant factory with artificial lighting, tomatoes (Solanum lycopersicum Mill. cv. "Zhongza NO.9") were used as the experimental material. This study expected to analyze the effects of light intensity (150, 200, 250, and 300 μmol·m−2·s−1) and light time (12 and 14 h), as well as daily light integral (DLI, 10.80, 12.60, and 12.96 mol·m−2·d−1) and sowing density (50, 72, and 105 holes per tray), on seedling quality. The results indicated that biomass accumulation, seedling quality, and energy use efficiency of seedlings significantly improved with an increase in DLI. At a DLI of 12.96 mol·m−2·d−1, seedlings sown at a density of 72 holes per tray exhibited comparable growth characteristics and biomass accumulation to those sown at 50 holes per tray. However, under lower DLIs, seedlings at 50 holes per tray displayed superior growth morphology and seedling quality compared to those at 72 holes per tray. This indicates that increasing the DLI can partially mitigate the negative effects of higher sowing density on seedling quality. Light use efficiency (LUE) and energy use efficiency (EUE) were not significantly different between seedlings at 72 and 105 holes per tray but were higher than those at 50 holes per tray. Therefore, optimizing parameters such as DLI and sowing density can effectively enhance the seedling quality, spatial use efficiency, and light use efficiency in industrial seedling production. Based on the results of this study, a DLI of 12.96 mol·m−2·d−1 (achieved with a light intensity of 300 μmol·m−2·s−1 and a light time of 12 h) and sowing density of 72 holes per tray are recommended for cultivating high-quality tomato seedlings while reducing energy consumption. [ABSTRACT FROM AUTHOR]

Pea shoots is a popular vegetable in certain regions of the world due to their unique taste and abundance of health-promoting phytochemicals. The perishable nature and susceptibility to root rot of pea shoots necessitate a new soilless production system located close to the market. This study compared the growth of pea shoots using various cultivation methods in an LED plant factory. The results showed that early topping (4 days after transplanting, ET) promoted early harvest compared to later topping (20 days after transplanting, LT) and increased the number of harvested shoots by extending the harvest time to 2.8 times, ultimately resulting in a substantial yield improvement. Moreover, the yield of ET with a lower planting density (72 plants m−2, ET-LD) was 8.7% higher than ET with a higher planting density (126 plants m−2, ET-HD). Particularly, the average shoot fresh weight (AFW) under ET-LD exceeded that of ET-HD by 48.9%. It is advisable to consider adopting ET-LD for the cultivation of pea shoots in LED plant factories. Based on ET-LD, the yield, nutritional quality, and light use efficiency of pea shoots were further explored at different stages under three levels of light intensity (50, 100, and 150 μmol m−2·s−1). Contrasted against a light intensity of 50 μmol m−2·s−1, AFW, number of harvested shoots, and total fresh yield under a light intensity of 150 μmol m−2·s−1, increased by 60.2%, 62.8%, and 165.1%, respectively. Meanwhile, AFW, photosynthetic capacity, soluble sugar and vitamin C levels in leaves, as well as light use efficiency and photon yield, initially increased and then decreased with the extension of the planting period. Among these, soluble sugar, light use efficiency, and photon yield started to decrease after reaching the maximum value at 60–70 days after transplanting. In conclusion, a light intensity of 150 μmol m−2·s−1 with a photoperiod of 16 h d−1 using LEDs, combined with early topping within a planting period of 60–70 days, proves to be suitable for the hydroponic production of pea shoots in LED plant factories. [ABSTRACT FROM AUTHOR]

Anthocyanins are secondary metabolites classified as water-soluble, non-photosynthetic pigments with the potential ability to shield chloroplasts from excess light energy. This study investigated the morphological and physiological responses of six lettuce (Lactuca sativa) cultivars with different leaf anthocyanin contents grown in a greenhouse under different supplemental photosynthetic photon flux densities (PPFD). Cultivars 'Cherokee', 'Teodore', 'Rex', and 'Rouxai' decreased in specific leaf area with increasing PPFD, respectively. We observed that growth in cultivars with higher leaf anthocyanin content ('Cherokee' and 'Rouxai') increased with increasing PPFD. Light use efficiency (LUE) is an important physiological parameter affecting biomass accumulation, and cultivars 'Cherokee', 'Rex', 'Teodore', and 'Rouxai' had the highest LUE and shoot weight. We found that red lettuce varieties, especially 'Cherokee' and 'Rouxai', showed increased shoot dry weight as light intensity increased. Interestingly, these varieties also had the highest anthocyanin levels in their leaves. This suggests that higher anthocyanin content might contribute to the increased shoot dry weight under higher light intensity, although this was mainly evident in 'Cherokee'. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product. Plain Language Summary: Gross primary productivity (GPP) is crucial for terrestrial carbon cycle study. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP data is perhaps the most widely used product, but many studies have indicated evident error patterns in it. These errors can be mainly explained by the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. As a result, this paper developed a novel and simple model (CTL‐MOD17) to obtain accurate GPP by reusing two inputs data from the MODIS GPP product, based on a two‐leaf (TL) theory. The CTL‐MOD17 model is evaluated at 153 global FLUXNET eddy covariance (EC) sites, and compared with other TL GPP products and models. The results indicate that CTL‐MOD17 can significantly improve the accuracy of MODIS GPP products at different time scales, to be similar to other TL models but with fewer data inputs. The obvious underestimation/overestimation of MODIS GPP in the high/low leaf area index (LAI) regions are all effectively corrected. This study proves the possibility of bridging the BL and TL models for the first time, which can be migrated to other BL products and models. Key Points: A novel product‐based model (CTL‐MOD17) corrects Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product bias, achieving results similar to two‐leaf models with fewer inputsIt is the first time to prove the possibility of bridging the big‐leaf and two‐leaf models, which can be migrated to other big‐leaf GPP products and modelsThe obvious underestimation/overestimation of MODIS GPP in high/low LAI regions are all effectively corrected by the CTL‐MOD17 model [ABSTRACT FROM AUTHOR]

High planting densities achieve high light interception and harvestable yield per area but at the expense of product quality. This study aimed to maintain high light interception without negative impacts on fruit quality. Dwarf tomato was grown at four densities in a climate-controlled room--at two constant densities (high and low) and two dynamic spacing treatments (maintaining 90% and 75% ground coverage by decreasing planting density in 3-4 steps)--resulting in ~100, 19, 54, and 41 plants/m² averaged over 100 days of cultivation, respectively. Constant high density resulted in the highest light use efficiency (LUE; 7.7 g fruit fresh weight per mol photons incident on the canopy) and the highest harvestable fruit yield (11.1 kg/m²) but the lowest fruit size and quality. Constant low density resulted in the lowest LUE and yield (2.3 g/mol and 3.2 kg/m², respectively), but higher fruit size and quality than high density. Compared to low density, maintaining 90% ground coverage increased yield (9.1 kg/m²) and LUE (6.4 g/mol). Maintaining 75% ground coverage resulted in a 7.2 kg/m² yield and 5.1 g/mol LUE. Both dynamic spacing treatments attained the same or slightly reduced fruit quality compared to low density. Total plant weight per m² increased with planting density and saturated at a constant high density. Assimilate shortage at the plant level and flower abortion lowered harvestable fruit yield per plant, sweetness, and acidity under constant high density. Harvestable fruit yield per plant was the highest under dynamic spacing and low density. Under constant high density, morphological responses to lower light availability per plant--i.e., higher specific leaf area, internode elongation, and increased slenderness--coincided with the improved whole-plant LUE (g plant dry weight per mol photons). We conclude that a constant high planting density results in the highest harvestable fruit yield per area, but with reduced fruit quality. Dynamic spacing during cultivation produces the same fruit quality as constant low density, but with more than double the harvestable yield per area. [ABSTRACT FROM AUTHOR]

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C3 species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

Summary: Theoretically, the PEP‐CK C4 subtype has a higher quantum yield of CO2 assimilation (ΦCO2) than NADP‐ME or NAD‐ME subtypes because ATP required for operating the CO2‐concentrating mechanism is believed to mostly come from the mitochondrial electron transport chain (mETC). However, reported ΦCO2 is not higher in PEP‐CK than in the other subtypes. We hypothesise, more photorespiration, associated with higher leakiness and O2 evolution in bundle‐sheath (BS) cells, cancels out energetic advantages in PEP‐CK species.Nine species (two to four species per subtype) were evaluated by gas exchange, chlorophyll fluorescence, and two‐photon microscopy to estimate the BS conductance (gbs) and leakiness using a biochemical model.Average gbs estimates were 2.9, 4.8, and 5.0 mmol m−2 s−1 bar−1, and leakiness values were 0.129, 0.179, and 0.180, in NADP‐ME, NAD‐ME, and PEP‐CK species, respectively. The BS CO2 level was somewhat higher, O2 level was marginally lower, and thus, photorespiratory loss was slightly lower, in NADP‐ME than in NAD‐ME and PEP‐CK species. Differences in these parameters existed among species within a subtype, and gbs was co‐determined by biochemical decarboxylating sites and anatomical characteristics.Our hypothesis and results partially explain variations in observed ΦCO2, but suggest that PEP‐CK species probably use less ATP from mETC than classically defined PEP‐CK mechanisms. [ABSTRACT FROM AUTHOR]

Mixed-species forests can provide higher levels of ecosystem functions and services and can be more resistant and resilient in the face of global change. While many studies focus on the growth and yield of mixed forests, fewer have examined the underlying processes. Inter- or intra-specific differences and interactions influence tree- and stand-level light absorption by determining the vertical structure of stratified canopies, stand density, leaf area index, and the size or allometry of trees. While canopy light absorption is a very important process, it is difficult to quantify it for individual species within a mixture and is rarely examined. A detailed tree-level model (MAESTRA) was used in combination with measurements of tree sizes and stand structures to examine effects of mixing on absorbed photosynthetically active radiation (APAR) in 41–63-year-old stands of Pseudotsuga menziesii and Fagus sylvatica at three sites in Bavaria, Germany. The effects of initial stand density on APAR were analysed in 46-year-old P. menziesii stands of a spacing experiment at two sites. At the tree level, mixing increased mean height and leaf area, growth (185% higher) and APAR (85% higher) of P. menziesii at all sites. Mean tree heights and crown sizes of F. sylvatica were larger in mixtures, while recent growth rates and APAR were not significantly different to monocultures. Planting density did not influence mean tree variables (e.g. height, leaf area, crown volume), because any initial spacing effects had been gradually removed by thinning across all treatments. At the stand level, there were no differences in growth, basal area or in the annual growth per annual APAR (light use efficiency, LUE) between monocultures and mixtures. The highest APAR values were observed in P. menziesii monocultures, while the lowest APAR values were observed in F. sylvatica monocultures. While mixing these species may not increase stand-level growth during later phases of development, mixing accelerated initial growth of individual trees and reduced the time to reach target diameters, which are both important aspects in adapting forests to global change. [ABSTRACT FROM AUTHOR]

Solar radiation is scattered by cloud cover, aerosols and other particles in the atmosphere, all of which are affected by global changes. Furthermore, the diffuse fraction of solar radiation is increased by more frequent forest fires and likewise would be if climate interventions such as stratospheric aerosol injection were adopted. Forest ecosystem studies predict that an increase in diffuse radiation would result in higher productivity, but ecophysiological data are required to identify the processes responsible within the forest canopy. In our study, the response of a boreal forest to direct, diffuse and heterogeneous solar radiation conditions was examined during the daytime in the growing season to determine how carbon uptake is affected by radiation conditions at different scales. A 10‐year data set of ecosystem, shoot and forest floor vegetation carbon and water‐flux data was examined. Ecosystem‐level carbon assimilation was higher under diffuse radiation conditions in comparison with direct radiation conditions at equivalent total photosynthetically active radiation (PAR). This was driven by both an increase in shoot and forest floor vegetation photosynthetic rate. Most notably, ecosystem‐scale productivity was strongly related to the absolute amount of diffuse PAR, since it integrates both changes in total PAR and diffuse fraction. This finding provides a gateway to explore the processes by which absolute diffuse PAR enhances productivity, and the long‐term persistence of this effect under scenarios of higher global diffuse radiation. [ABSTRACT FROM AUTHOR]

This study compared supplemental white light-emitting diode (LED) light provided on top of the canopy (top-light) or partially on top and partially as intracanopy light (ICL) in high-wire cucumber (Cucumis sativus) and tomato (Solanum lycopersicum) crops. The aim was to determine the effects of partially substituting top-light by ICL on fruit yield and its underlying yield components. For each crop, three replicate Venlo glasshouse compartments were used. Two cucumber (HiPower and Skyson) and two tomato cultivars (Brioso and Merlice) were planted in the second half of Oct 2020 and grown on stone wool for a period of 15 weeks (cucumber) or 20 weeks (tomato). Light was supplied at either a light intensity of 250 or 375 mmol.m22.s21, provided either as 100% top-light or as 67% (2/3) top-light and 33% (1/3) ICL. For cucumber at the higher light intensity, 50% more fruits were retained and for tomato at the higher light intensity, planting density was 50% higher to keep the plants balanced in terms of source-to-sink ratio. Substituting 33% of top-light with ICL resulted on average in an increase of 17% in fresh fruit yield for both cucumber and tomato. This increase was twice as high at the higher light intensity (20% to 24%) compared with the lower light intensity (10% to 12%). For both cucumber and tomato, the higher yield for ICL treatments resulted mainly from higher total plant dry weight, whereas partitioning to the fruits was hardly affected. For both crops, the higher plant dry weight resulted from a higher light use efficiency. Increasing light intensity from 250 to 375 mmol.m22.s21 resulted in 38% higher total daily light integral (including solar radiation) and 36% to 37% higher total plant dry weight in cucumber. In tomato, the higher light intensity resulted in 33% higher daily light integral and 36% to 40% total plant dry weight. These values are in agreement with the rule of thumb that 1% increment in light results in 1% increase in plant growth. For cucumber, partially substituting top-light by ICL as well as increasing light intensity resulted in longer and greener fruits, whereas tomato fruit quality (Brix, pH) was unaffected by ICL or light intensity. In conclusion, partially substituting top-light by intracanopy light increased fruit yield and this was even more so at higher than at lower supplemental light intensities. [ABSTRACT FROM AUTHOR]

Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (F q'/ F m'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the F q'/ F m' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of F q'/ F m' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon−1, and the multiyear mean maximal LUE was 0.30 g C mol photon−1 during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation.

The study aimed to establish the nexus among biological efficiency and forage production by analyzing the canopy structure and light interception potential in orchard grass (Dactylis glomerata), alfalfa (Medicago sativa), and white clover (Trifolium repens). The measurements were performed in Gherghita Plain, at Pucheni village on large plots in 2023 by using the Delta-T Devices Sunscan Analysis system. The leaf area index (LAI), light parameters, and microclimate indicators were retrieved in each canopy of the studied species at various layers of 10 cm from the bottom to the top of the canopy. Consequently, a close relationship was observed between the biological efficiency, the leaf area distribution, and potential forage production for both grass species and legumes. The results are useful for planning biometrical parameters when developing new performant cultivars. [ABSTRACT FROM AUTHOR]

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India. The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing–based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing–based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions. [ABSTRACT FROM AUTHOR]

High planting densities achieve high light interception and harvestable yield per area but at the expense of product quality. This study aimed to maintain high light interception without negative impacts on fruit quality. Dwarf tomato was grown at four densities in a climate-controlled room—at two constant densities (high and low) and two dynamic spacing treatments (maintaining 90% and 75% ground coverage by decreasing planting density in 3–4 steps)—resulting in ~100, 19, 54, and 41 plants/m2 averaged over 100 days of cultivation, respectively. Constant high density resulted in the highest light use efficiency (LUE; 7.7 g fruit fresh weight per mol photons incident on the canopy) and the highest harvestable fruit yield (11.1 kg/m2) but the lowest fruit size and quality. Constant low density resulted in the lowest LUE and yield (2.3 g/mol and 3.2 kg/m2, respectively), but higher fruit size and quality than high density. Compared to low density, maintaining 90% ground coverage increased yield (9.1 kg/m2) and LUE (6.4 g/mol). Maintaining 75% ground coverage resulted in a 7.2 kg/m2 yield and 5.1 g/mol LUE. Both dynamic spacing treatments attained the same or slightly reduced fruit quality compared to low density. Total plant weight per m2 increased with planting density and saturated at a constant high density. Assimilate shortage at the plant level and flower abortion lowered harvestable fruit yield per plant, sweetness, and acidity under constant high density. Harvestable fruit yield per plant was the highest under dynamic spacing and low density. Under constant high density, morphological responses to lower light availability per plant—i.e., higher specific leaf area, internode elongation, and increased slenderness—coincided with the improved whole-plant LUE (g plant dry weight per mol photons). We conclude that a constant high planting density results in the highest harvestable fruit yield per area, but with reduced fruit quality. Dynamic spacing during cultivation produces the same fruit quality as constant low density, but with more than double the harvestable yield per area.

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices.

To achieve high-density tomato seedlings in a plant factory with artificial lighting, tomatoes (Solanum lycopersicum Mill. cv. “Zhongza NO.9”) were used as the experimental material. This study expected to analyze the effects of light intensity (150, 200, 250, and 300 μmol·m−2·s−1) and light time (12 and 14 h), as well as daily light integral (DLI, 10.80, 12.60, and 12.96 mol·m−2·d−1) and sowing density (50, 72, and 105 holes per tray), on seedling quality. The results indicated that biomass accumulation, seedling quality, and energy use efficiency of seedlings significantly improved with an increase in DLI. At a DLI of 12.96 mol·m−2·d−1, seedlings sown at a density of 72 holes per tray exhibited comparable growth characteristics and biomass accumulation to those sown at 50 holes per tray. However, under lower DLIs, seedlings at 50 holes per tray displayed superior growth morphology and seedling quality compared to those at 72 holes per tray. This indicates that increasing the DLI can partially mitigate the negative effects of higher sowing density on seedling quality. Light use efficiency (LUE) and energy use efficiency (EUE) were not significantly different between seedlings at 72 and 105 holes per tray but were higher than those at 50 holes per tray. Therefore, optimizing parameters such as DLI and sowing density can effectively enhance the seedling quality, spatial use efficiency, and light use efficiency in industrial seedling production. Based on the results of this study, a DLI of 12.96 mol·m−2·d−1 (achieved with a light intensity of 300 μmol·m−2·s−1 and a light time of 12 h) and sowing density of 72 holes per tray are recommended for cultivating high-quality tomato seedlings while reducing energy consumption.

Pea shoots is a popular vegetable in certain regions of the world due to their unique taste and abundance of health-promoting phytochemicals. The perishable nature and susceptibility to root rot of pea shoots necessitate a new soilless production system located close to the market. This study compared the growth of pea shoots using various cultivation methods in an LED plant factory. The results showed that early topping (4 days after transplanting, ET) promoted early harvest compared to later topping (20 days after transplanting, LT) and increased the number of harvested shoots by extending the harvest time to 2.8 times, ultimately resulting in a substantial yield improvement. Moreover, the yield of ET with a lower planting density (72 plants m−2, ET-LD) was 8.7% higher than ET with a higher planting density (126 plants m−2, ET-HD). Particularly, the average shoot fresh weight (AFW) under ET-LD exceeded that of ET-HD by 48.9%. It is advisable to consider adopting ET-LD for the cultivation of pea shoots in LED plant factories. Based on ET-LD, the yield, nutritional quality, and light use efficiency of pea shoots were further explored at different stages under three levels of light intensity (50, 100, and 150 μmol m−2·s−1). Contrasted against a light intensity of 50 μmol m−2·s−1, AFW, number of harvested shoots, and total fresh yield under a light intensity of 150 μmol m−2·s−1, increased by 60.2%, 62.8%, and 165.1%, respectively. Meanwhile, AFW, photosynthetic capacity, soluble sugar and vitamin C levels in leaves, as well as light use efficiency and photon yield, initially increased and then decreased with the extension of the planting period. Among these, soluble sugar, light use efficiency, and photon yield started to decrease after reaching the maximum value at 60–70 days after transplanting. In conclusion, a light intensity of 150 μmol m−2·s−1 with a photoperiod of 16 h d−1 using LEDs, combined with early topping within a planting period of 60–70 days, proves to be suitable for the hydroponic production of pea shoots in LED plant factories.

Increasing evidence shows that silicon possesses important physiological functions in plants, thus, silicon fertilizers are used widely in agriculture. However, few attentions were paid on the effects of silicon dioxide nanoparticles (SiO2NPs) on plant growth and resistance to abiotic stresses. In this present study, different amount of SiO2NPs were applied in pots with Cunninghamia lanceolata seedlings, and their root development, gas exchange, and drought resistance were investigated. These results showed that SiO2NPs treatment increased total root length, root volume, and the numbers of root tips of these seedlings under drought stress. Specific root area (SRA) and specific root length (SRL) also increased. SiO2NPs treatment altered anatomical structures of the first-order lateral roots of C. lanceolata seedlings under drought stress (i.e., increase in cortex thickness and the number of cortex layers). Under drought stress, SiO2NPs treatment increased net photosynthetic rates, stomatal conductance, intercellular CO2 concentration, and transpiration. SiO2NPs treatment also led to increase in light use efficiency and instantaneous Rubisco carboxylation rate and decrease in water use efficiency under drought stress. Under drought stress, SiO2NPs treatment resulted in increased activities of antioxidant enzymes (superoxide dismutase, catase, and peroxidase) and concentrations of antioxidants (ascorbate and glutathione), decreasing oxidative damage caused by drought stress. All together, these results suggested that SiO2NPs could be used for seedling culture of forest trees. [ABSTRACT FROM AUTHOR]

Automated and intelligent light supplement systems have been widely applied in greenhouses in recent years. Among them, the light environment regulation model can be the core content of the system. However, the existing models cannot consider the comprehensive influence of light quality and light intensity, as well as the double optimization of net photosynthetic rate and light use efficiency. In this study, a collaborative control method was proposed for the light quality and light intensity using multi-objective optimization, particularly for the efficient supplemental illumination of cucumbers in greenhouses. Firstly, a multi-factor coupled photosynthetic experiment was designed to obtain the net photosynthetic rate of cucumber leaves. The model of net photosynthetic rate was then established using support vector regression with temperature, carbon dioxide concentration, photosynthetic photon flux density and light quality ratio as the input, while the net photosynthetic rate as the output. Furthermore, the light use efficiency was calculated at the leaf scale, according to the definition. Secondly, a multi-objective optimization model was constructed with the light use efficiency and net photosynthetic rate as optimization targets, while the light quality and light intensity as control variables. The non-inferior solution set was solved using the multi-objective particle swarm optimization. Technique for order preference by similarity to ideal solution was used to select the control single point of light quality and light intensity, in order to narrow the regulation interval for the less subjectivity of manual selection. Finally, the red and blue light demand were calculated according to the multiple relationship of light quality and light intensity. And then the red and blue light models were fitted by support vector regression with the temperature and carbon dioxide concentration as the input. The control experiments were carried out to compare with the fixed light quality supplement and the photosynthetic maximum supplement, in order to verify the superiority. The theoretical verification experiment showed that the net photosynthetic rate decreased by 21.39%, whereas, the light demand decreased by 59.40%, compared with the photosynthetic maximum supplement. The net photosynthetic rate increased by 3.66% and 9.69%, respectively, compared with the fixed light quality of 0.5 and 0.8. The practical verification experiment was also carried out to further verify the energy efficiency. The results showed that the physiological indicators were better than the fixed light quality supplement under similar power consumption, indicating significant differences in the stem diameter, dry weight and strong seedling index. There was no significant difference in physiological indicators, but the power consumption decreased by 27.43%, compared with the photosynthetic maximum supplement. The consumption of light and electrical energy resources was effectively saved to keep the physiological indicators almost unchanged. The model construction can be expected to serve as the new perspective for the greenhouse light supplement. This study can provide a new light supplement strategy for the facility's agricultural regulation and the efficient utilization of agricultural production resources. [ABSTRACT FROM AUTHOR]

We rarely consider light limitation in ecosystem productivity, yet light limitation is a major constraint on river autotrophy. Because the light that reaches benthic autotrophs must first pass through terrestrial vegetation and an overlying water column that can be loaded with sediments or colored organic material, there is strong selection for river autotrophs to have high light use efficiencies (LUEs), that is, the efficiency at which light energy is converted to biomass. In contrast to prior studies that have estimated river LUE on single days, we calculated continuous LUE over more than 6 full years for 64 free‐flowing rivers across the United States. This dataset represents the largest compilation of continuous estimates of daily rates of gross primary productivity (GPP) and daily light inputs from which we calculated daily estimates of LUE. Early estimates of LUE in rivers found that clearwater springs with stable flows could achieve LUEs of 4%, much higher than LUEs reported for terrestrial plants. We found that 53% of the rivers in our dataset have LUEs that exceed 4% on at least one day of their time series. Because of the high variability in daily LUE, measurements taken on any given day may misrepresent a river ecosystem's annual LUE. Though most rivers share a high potential, the mean annual LUE of all rivers in our dataset is much lower, only 0.5%. We found that rivers with more variable flow regimes had lower annual LUEs, which indicates that LUE is constrained by hydrologic disturbances that remove, bury, or shade autotrophic biomass. Comparisons of LUE across ecosystems allow us to reframe our view of rivers, by recognizing the high efficiency with which they convert light to biomass compared with lentic, marine, and terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) plays an important role in gross primary production (GPP), so accurate estimation of LUE is crucial. The estimation of LUE using the photochemical reflectance index (PRI) is considerably promising, but on the other hand there is always uncertainty in obtaining the PRI due to the changing viewing angle and sun position in remote sensing observations. The objective of this study was to distinguish between sunlit and shaded leaves to improve the performance of PRI estimating LUE, and we used rice canopy reflectance data collected by the automatic multi-angle remote sensing platform and flux data for each half hour from 9:00 to 15:00 in Shouxian, Anhui Province, China. We modified the two-leaf model based on the characteristics of the rice canopy, P R I t was thought of by distinguishing between sunlit and shaded leaves PRI by two-leaf model on the rice canopy. The results showed that the P R I t -LUE correlation improved relative to PRI-LUE on clear days and was essentially negligible on cloudy days. We found that the photosynthetically active radiation (PAR), temperature, and humidity had some influence on P R I t , the P R I t -LUE correlation was strongest at higher temperature and vapour pressure deficit (VPD) and was linear with the change of temperature. We conclude that the modified two-leaf model improves the integrity of the PRI remote sensing observation and increases the accuracy of its LUE estimation by adequately separating sunlit and shaded leaves in the rice canopy. [ABSTRACT FROM AUTHOR]

Edible halophyte Portulaca oleracea L., known as purslane, was grown in two percentages of artificial seawater (ASW) under two combined red (R) and blue (B) LED spectra. High salinity (40% ASW) negatively affected shoot productivity and leaf growth of purslane compared to those grown in 10% ASW. Photosynthetic pigment and total reduced nitrogen concentrations were significantly higher in purslane grown in 10% ASW than in 40% ASW. However, LED spectral quality did not markedly influence these parameters. Grown in 10% ASW under R/B 2.2, purslane had the highest maximum nitrate reductase activity, while those in 40% ASW under R/B 2.2 had the highest activation state. Under both light qualities, purslane had a sevenfold increase in proline concentration in 40% ASW than in 10% ASW. Total phenolic compounds' concentration was the highest in 10% ASW under R/B 0.9, while there were no significant differences in the accumulation of total soluble sugars and ascorbic acids among all plants. Antioxidant enzymes activities were lower in 40% ASW under R/B 2.2 compared to the other conditions. In conclusion, salinity affected the yield, physiology and nutritional quality of purslane. The impacts of LED spectral quality on purslane were only reflected by certain physiological and nutritional parameters. [ABSTRACT FROM AUTHOR]

Summary: In natural ecosystems, plants compete for space, nutrients and light. The optically dense canopies limit the penetration of photosynthetically active radiation and light often becomes a growth‐limiting factor for the understory. The reduced availability of photons in the lower leaf layers is also a major constraint for yield potential in canopies of crop monocultures. Traditionally, crop breeding has selected traits related to plant architecture and nutrient assimilation rather than light use efficiency. Leaf optical density is primarily determined by tissue morphology and by the foliar concentration of photosynthetic pigments (chlorophylls and carotenoids). Most pigment molecules are bound to light‐harvesting antenna proteins in the chloroplast thylakoid membranes, where they serve photon capture and excitation energy transfer toward reaction centers of photosystems. Engineering the abundance and composition of antenna proteins has been suggested as a strategy to improve light distribution within canopies and reduce the gap between theoretical and field productivity. Since the assembly of the photosynthetic antennas relies on several coordinated biological processes, many genetic targets are available for modulating cellular chlorophyll levels. In this review, we outline the rationale behind the advantages of developing pale green phenotypes and describe possible approaches toward engineering light‐harvesting systems. [ABSTRACT FROM AUTHOR]

High-density planting is an effective technique to optimize yields of mulched cotton. On the other hand, deficit irrigation is an emerging water-saving strategy in cotton cultivation, especially suitable for arid and water-scarce areas. However, the relationships between deficit irrigation, high-density planting, and regulation mechanisms of canopy light radiation and light use efficiency (LUE) in cotton is not yet clear. To clarify the mechanism of light interception (LI) and the LUE of cotton canopies, three irrigation treatments [315 (50% Fc), 405 (75% Fc, farmers' irrigation practice), and 495 mm (100% Fc), where Fc was the field capacity] with three plant densities [13.5, 18.0 (farmers' planting practice), and 22.5 plants m2] were applied. The findings of this research revealed that, under deficit irrigation, the above-ground dry matter (ADM) was reduced by 5.05% compared to the farmers' irrigation practice. Over both years and across all plant densities, LI and LUE under deficit irrigation decreased by 8.36% and 4.79%, respectively, relative to the farmers' irrigation practices. In contrast, LI and LUE for the highest irrigation level increased by 10.59% and 5.23%, respectively. In the case of the interaction (plant density and irrigation level), the ADM under deficit irrigation and high-density combination increased by 7.69% compared to the control (farmers' irrigation × sowing practices interaction effects). The LI and LUE also exhibited an increase in 1.63% and 6.34%, respectively. Notably, the LI effect of the middle and upper cotton canopy under film drip irrigation reached 70%. A lower irrigation level resulted in a higher percentage of LI in the lower canopy region. The leaf area index, light interception rate, and extinction coefficient escalated with the increase in plant density. Under deficit irrigation treatment, the LI of the 0–30 cm canopy in high plant density settings increased by 8.6% compared to the control (farmers' irrigation × sowing practices interaction effects). In conclusion, deficit irrigation and increased plant density improved the interception of LI and LUE of cotton canopy. These findings may help the farmers to optimize their agricultural management strategies in water-deficient areas. [ABSTRACT FROM AUTHOR]

Light and its spectral characteristics are crucial for plant growth and development. The far-red photon flux mediates many plant processes through the action of phytochrome and also accelerates the photosynthetic electron transfer rate. In this study, we assessed the effects of far-red addition on butterhead lettuce morphology, light use efficiency, optical properties, and phytochemical characteristics. Three-week-old lettuce plants (Lactuca sativa L. cv. Alyssa) were grown for up to 28 days under a 10% blue and 90% red light spectrum (200 µmol m-2 s-1, 16 h photoperiod) to which five different intensities of far-red light (peak at 735 nm) were added (0-9-18-36-72 µmol m-2 s-1). White light-emitting diodes were included as a proxy for sunlight. Increasing supplemental far-red photon flux from zero to 21% increased the light use efficiency (g per mol) by 37% on day 14; 43% on day 21; and 39% on day 28. Measurements of projected head area suggest that this was associated with an increase in leaf expansion and photon capture and not necessarily a direct effect on photosynthesis. Moreover, vegetation indices based on leaf reflectance showed a decrease in chlorophyll-related indices under a high far-red photon flux. This decrease in pigment content was confirmed by chemical analyses, suggesting that the plants may not reach their full potential in terms of photon capture, limiting the overall photosynthetic performance. Furthermore, the stress-related Carter 1 index increased in plants grown under a high far-red photon flux, indicating early plant stress. Far-red tended to decrease the content of total phenolics and increase soluble sugars. The higher sugar levels can be attributed to an improved photochemical efficiency due to photosystem I excitation by far-red wavelengths, also known as the Emerson Enhancement effect. Despite these higher sugar levels, no effect on foliar nitrate content was observed. Our results show that far-red supplementation has the potential to enhance light interception at the early growth stages, although higher intensities of far-red may cause plant stress. [ABSTRACT FROM AUTHOR]

Anthocyanins are secondary metabolites classified as water-soluble, non-photosynthetic pigments with the potential ability to shield chloroplasts from excess light energy. This study investigated the morphological and physiological responses of six lettuce (Lactuca sativa) cultivars with different leaf anthocyanin contents grown in a greenhouse under different supplemental photosynthetic photon flux densities (PPFD). Cultivars ‘Cherokee’, ‘Teodore’, ‘Rex’, and ‘Rouxai’ decreased in specific leaf area with increasing PPFD, respectively. We observed that growth in cultivars with higher leaf anthocyanin content (‘Cherokee’ and ‘Rouxai’) increased with increasing PPFD. Light use efficiency (LUE) is an important physiological parameter affecting biomass accumulation, and cultivars ‘Cherokee’, ‘Rex’, ‘Teodore’, and ‘Rouxai’ had the highest LUE and shoot weight. We found that red lettuce varieties, especially ‘Cherokee’ and ‘Rouxai’, showed increased shoot dry weight as light intensity increased. Interestingly, these varieties also had the highest anthocyanin levels in their leaves. This suggests that higher anthocyanin content might contribute to the increased shoot dry weight under higher light intensity, although this was mainly evident in ‘Cherokee’.

Abstract We rarely consider light limitation in ecosystem productivity, yet light limitation is a major constraint on river autotrophy. Because the light that reaches benthic autotrophs must first pass through terrestrial vegetation and an overlying water column that can be loaded with sediments or colored organic material, there is strong selection for river autotrophs to have high light use efficiencies (LUEs), that is, the efficiency at which light energy is converted to biomass. In contrast to prior studies that have estimated river LUE on single days, we calculated continuous LUE over more than 6 full years for 64 free‐flowing rivers across the United States. This dataset represents the largest compilation of continuous estimates of daily rates of gross primary productivity (GPP) and daily light inputs from which we calculated daily estimates of LUE. Early estimates of LUE in rivers found that clearwater springs with stable flows could achieve LUEs of 4%, much higher than LUEs reported for terrestrial plants. We found that 53% of the rivers in our dataset have LUEs that exceed 4% on at least one day of their time series. Because of the high variability in daily LUE, measurements taken on any given day may misrepresent a river ecosystem's annual LUE. Though most rivers share a high potential, the mean annual LUE of all rivers in our dataset is much lower, only 0.5%. We found that rivers with more variable flow regimes had lower annual LUEs, which indicates that LUE is constrained by hydrologic disturbances that remove, bury, or shade autotrophic biomass. Comparisons of LUE across ecosystems allow us to reframe our view of rivers, by recognizing the high efficiency with which they convert light to biomass compared with lentic, marine, and terrestrial ecosystems.

Light and its spectral characteristics are crucial for plant growth and development. The far-red photon flux mediates many plant processes through the action of phytochrome and also accelerates the photosynthetic electron transfer rate. In this study, we assessed the effects of far-red addition on butterhead lettuce morphology, light use efficiency, optical properties, and phytochemical characteristics. Three-week-old lettuce plants (Lactuca sativa L. cv. Alyssa) were grown for up to 28 days under a 10% blue and 90% red light spectrum (200 µmol m-2 s-1, 16 h photoperiod) to which five different intensities of far-red light (peak at 735 nm) were added (0-9-18-36-72 µmol m-2 s-1). White light-emitting diodes were included as a proxy for sunlight. Increasing supplemental far-red photon flux from zero to 21% increased the light use efficiency (g per mol) by 37% on day 14; 43% on day 21; and 39% on day 28. Measurements of projected head area suggest that this was associated with an increase in leaf expansion and photon capture and not necessarily a direct effect on photosynthesis. Moreover, vegetation indices based on leaf reflectance showed a decrease in chlorophyll-related indices under a high far-red photon flux. This decrease in pigment content was confirmed by chemical analyses, suggesting that the plants may not reach their full potential in terms of photon capture, limiting the overall photosynthetic performance. Furthermore, the stress-related Carter 1 index increased in plants grown under a high far-red photon flux, indicating early plant stress. Far-red tended to decrease the content of total phenolics and increase soluble sugars. The higher sugar levels can be attributed to an improved photochemical efficiency due to photosystem I excitation by far-red wavelengths, also known as the Emerson Enhancement effect. Despite these higher sugar levels, no effect on foliar nitrate content was observed. Our results show that far-red supplementation has the potential to enhance light interception at the early growth stages, although higher intensities of far-red may cause plant stress.

The product of near-infrared radiation reflected by vegetation (NIRv) and PAR (NIRvP) is a promising proxy for the remote estimation of gross primary production (GPP). However, the efficiency of NIRvP in estimating the GPP and its limitations across multiple biomes and climate zones remain unclear. In this study, we aimed to evaluate the performance and limitations of NIRvP in estimating the GPP in comparison to absorbed photosynthetically active radiation (APAR), solar-induced chlorophyll fluorescence (SIF), and the MOD17A2H GPP product. Overall, the correlation between NIRvP and eddy covariance (EC) GPP was stronger than that of APAR, SIF, and MOD17A2H GPP across most biomes with usually similar seasonal variations in radiation, air temperature (TA), and precipitation. The near-​infrared (NIR) reflectance (ρNIR) and light use efficiency (LUE) exhibited a covarying relationship under these environmental conditions, which suggested that the ρNIR contributed positively to the NIRvP-GPP relationship under such climatic conditions. However, the performance of NIRvP was poor in some biomes and climate zones, which exhibited different variations in the seasonal patterns of radiation, TA, and precipitation. The resulting inconsistencies between ρNIR and LUE implied that the ρNIR contributed negatively to the NIRvP-GPP relationship in these regions. Altogether, the findings demonstrated that the NIRvP-GPP relationship was robust but attained a moderate overall relationship across ecosystems (R2

Net primary production (NPP) is one of the approaches used to estimate the amount of carbon sequestration by plants. This research aims to estimate the total carbon flux exchanged from different ages of oil palm using remote sensing. The study site was at the PTPN VI Batang Hari, Jambi, Sumatra, Indonesia. The amount of carbon sequestration by oil palm plantations at PTPN VI Batang Hari, Jambi can be estimated using remote sensing based on the light use efficiency (LUE) model. The results showed that the oil palm age affects the amount of carbon sequestrated. The lowest Net primary production value was found at one year of planting 4.28 gCm-2day-1, and the highest was 9.38 gCm-2day-1 at 20 years of planting. The model LUE output was validated using Eddy covariance data and the results showed a low error and a high accuracy rate with RMSE = 0.05 gCMJ-1, R² = 92%, and p-value = 0.04. We concluded that the LUE model can be used with high accuracy to estimate the amount of carbon absorption of oil palm when direct measurement is unavailable. [ABSTRACT FROM AUTHOR]

[Objective] Resource use efficiencies are key indicators for acclimation of a plant to environmental conditions, which is one of the main concerns in the area of climate change ecology. The controlling machanisms of light use efficiency (LUE) and water use efficiency (WUE) of understory shrub species in response to environmental changes remain unclear. Met hods] The seasonal variation in LUE and WUE and its envrionmental controls for an understory shrub, Spiraea salicifolia, in a typical deciduous forest in Songshan of Beijing were examined through in-situ measurements of leaf photosynthetic parameters and environmental factors in the full leafing stage from June to September 201 9. Results] LUE and WUE showed obvious seasonal variations, averaging ().03 mol/mol and 8.32/imol/mmol, respectively. Photosynthetically active radiation (PAR) and air temperature (Ta) were the main factors controlling the LUE, showing an exponential and a linear negative relationship, respectively. PAR and vapor preassure difference (VPD) were the main factors controlling WUE, with which both showed the linear negative relationship. The VPT) limited transpiration by affecting stomatai conductance (g5), which affected WUE. The seasonal variation in both LUE and WUE was not affected by soil water content (SWC). There was con ver gen cy between LUE and WUE in response to environmental factors. Conclusion], Ta and VPD, rather than SWC: were the main limiting factors for resource use efficiencies of the shrub Spiraea salicifolia in understory of the research area. The shrub Spiraea salicifolia in understory showed a resource conservation strategy under the condition of limited resources, specifically, under the condition of limited light, the shrub Spiraea salicifolia in understory maximized the use of light energy for photosynthesis and thus had a high light energy utilization efficiency. [ABSTRACT FROM AUTHOR]

Sugarcane is a major cash crop grown in India. Several empirical, semi-empirical and mechanistic models have been used to compute and predict sugarcane yield using satellite data. Reliable data predictions are necessary for important decision-making situations. This study attempted a semi-empirical Light Use Efficiency (LUE) model to compute the field-level sugarcane yield for a mill catchment in Uttar Pradesh, India. The phenology parameters, Start of Season and End of Season, for each field were derived by fitting the Sentinel-1 VH backscatter data using the Savitzky–Golay filter. Later these fields were grouped into four classes based on their key phenology parameters. Photosynthetically Active Radiation (PAR) was computed from INSAT- 3D data and calibrated based on the ground information. The fraction of absorbed PAR was computed using Sentinel-2 images and ground truth Leaf Area Index observations. The model was limited to using temperature and water stress factors. Water stress was computed using the Land Surface Water Index from Sentinel-2. ERA5's 2 m temperature data were used to compute the stress caused by temperature. LUE and Harvest index were used as constants. The study yielded very satisfying results. The observed and predicted data had an agreement index of 0.91 and a Root Mean Square Error of 46.6 q/ha. The results improved substantially after the calibration of the INSAT-3D insolation. This study overcomes the limitation of varying planting and harvesting dates which are usually not accounted for in LUE-based yield estimation. [ABSTRACT FROM AUTHOR]

Introduction: Conductance-photosynthesis (Gs-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (Gs) and transpiration (Tc) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (gsu and gsh) and maximum LUE (ϵmsu and ϵmsh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in Tc estimation errors, as it contradicts field observations. Methods: In this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and Tc were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA). Results: Our results show a cyclical variability of ϵmsu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for gsu and gsh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced Tc simulation errors in terms of RMSE by 3.7 ± 4.4%. Discussion: These findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests. [ABSTRACT FROM AUTHOR]

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a key parameter connecting light absorption and carbon fixation and understanding the relationship between fluorescence and photosynthesis. Based on the absorbed energy dissipation pathways, LUE can be divided into light use efficiency of photosynthetic (LUEp) and light use efficiency of fluorescence (LUEf). The relationship between LUEp and LUEf of poplar and cork oak and the differences between species were explored in this study, to better understand the mechanism links between solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) in deciduous broadleaf forests. In this study, the poplar (Populus alba x Populus gllandulosa) and cork oak (Quercus variabilis BI.) seedlings were used as the objects to measure the divergences in SIF and net photosynthetic rate (Pn) under the same soil moisture and nutrient conditions, to explore the responses of LUEp/LUEf to photosynthetically active radiation (PAR). The results showed that, (1) SIF was driven by incident PAR, SIF and PAR were significantly positively correlated. The coefficient of determination (R²) of poplar and cork oak were 0.965 and 0.973, respectively, and LUEf was higher in poplar. Pn gradually increased with increasing PAR. The light saturation point (LSP) of poplar was significantly higher than that of cork oak, which were 998.59 and 674 μmolCO2·m-2·s-1, respectively. (2) Pn first increased with the increasing SIF, and then the Pn of poplar was gradually saturated, while that of cork oak continued to slowly increase. (3) LUEp and LUEf showed significant negative linear correlation, and LUEp/LUEf decreased exponentially with increasing PAR, there was no significant difference in the change pattern and amplitude between tree species. During the daytime, with the increase of PAR, LUEf changed slightly, while LUEp decreased significantly, and the proportion of light energy allocation to carbon assimilation decreased. Therefore, Pn saturated under high light intensity, SIF and GPP showed exponential correlation in intraday scale, and the exponential relationship was consistent across tree species. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary production (GPP) represents the magnitude of CO2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively. Plain Language Summary: Plants uptake carbon dioxide through photosynthetic process from atmosphere, and the amount of carbon fixed is called terrestrial gross primary production (GPP) which is a key factor in global carbon cycle. People have developed many process‐based models to simulate GPP at regional or global scale, and Light use efficiency (LUE) GPP models have been widely used for they are easy to implement and the input data could be conveniently accessed. How to improve the capacity of GPP model to get more precise estimation remains a change for a long time. In this study, for the purpose of improving single model capacity, we ran five popular individual GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight plant types based on site‐measured and remote sensed data, and then fuse them together using three machine learning methods: Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF). We found that EC‐LUE performed best to simulate GPP as an individual model. RF was the best fusion method to improve estimation of GPP, followed by SVM and BMA, with the increased average accuracy of estimation by 8%, 18%, and 19%. Key Points: The performance of individual gross primary production (GPP) models varied to capture interannual variability of GPP across different plant functional typesMachine learning algorithm could significantly improve the capacity of estimating GPP by combining the advantages of all individual models [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions.

Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass (R2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982–2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle. [ABSTRACT FROM AUTHOR]

Vertical farming is a relatively new fresh fruit and vegetable production system, where lamps (mostly light emitting diodes [LED]) are the sole light source. A high light use efficiency (LUEinc), defined as shoot dry weight per incident photosynthetic photon flux density (PPFD; g mol−1) integral, is crucial for the economic viability of vertical farming. Very different values for LUEinc have been reported in the literature and it is not clear whether LUEinc is higher in vertical farming than in greenhouse or open field cultivation. Values of LUEinc of lettuce grown in a vertical farm (53 studies), greenhouse (13 studies) and open field (8 studies) were collected from literature, as well as relevant cultivation aspects such as lettuce weight at harvest, cultivation period (plant age at harvest), daily light integral, cumulative daily light integral for the whole cultivation period, planting density and CO2 concentration. The average LUEinc for lettuce grown in a vertical farm was 0.55 g mol−1 which was higher than 0.39 g mol−1 for greenhouse‐grown lettuce. Both were substantially higher than for field‐grown lettuce (0.23 g mol−1). The maximum measured LUEinc for lettuce grown in a vertical farm (1.63 g mol−1) is close to the published maximum theoretical value, which ranges from 1.26 to 1.81 g mol−1. Since all environmental factors can be fully controlled, vertical farming has the capability to achieve the theoretical maximum LUEinc. Using the highest reported LUEinc based on shoot fresh weight (44 g mol−1 at 200 μmol m−2 s−1 PPFD and 16 h photoperiod), it is estimated that each layer of a vertical farm can potentially produce annually up to 700 kg of lettuce per m2 at 500 μmol m−2 s−1 of continuous light. [ABSTRACT FROM AUTHOR]

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks. [ABSTRACT FROM AUTHOR]

Vegetation production is an important variable in terrestrial ecosystems, playing crucial roles in sustaining carbon balance, reducing atmospheric CO2 concentration, and mitigating global climate change. Satellite-based models, which benefit from spatially and temporally continuous remote sensing observations of vegetation growth conditions, are widely used for quantifying regional and global vegetation production. Satellite-based vegetation production models were initially simple statistical models, but later, process-based light use efficiency models were developed. The latest models are based on the relationship between solar-induced chlorophyll fluorescence and vegetation production. An increasing number of satellite-based studies are being conducted by Chinese scientists, who are developing and implementing plant production models, particularly by self-developing a number of light consumption efficiency models and establishing a long-term worldwide dataset of vegetation production. Furthermore, Chinese scientists have investigated the spatial and temporal patterns of vegetation using diverse models, significantly improving our understanding of terrestrial functions and structures. However, current models and estimation techniques need further improvement, and Chinese scientists had the opportunity to improve model capability and our understanding of vegetation production patterns and their regulating elements. [ABSTRACT FROM AUTHOR]