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Dwarf tomatoes are advantageous when cultivated in a plant factory with artificial light because they can grow well in a small volume. However, few studies have been reported on cultivation in a controlled environment for improving productivity. We performed two experiments to investigate the effects of photosynthetic photon flux density (PPFD; 300, 500, and 700 μmol m−2 s−1) with white light and light quality (white, R3B1 (red:blue = 3:1), and R9B1) with a PPFD of 300 μmol m−2 s−1 on plant growth and radiation-use efficiency (RUE) of a dwarf tomato cultivar (‘Micro-Tom’) at the vegetative growth stage. The results clearly demonstrated that higher PPFD leads to higher dry mass and lower specific leaf area, but it does not affect the stem length. Furthermore, high PPFD increased the photosynthetic rate (Pn) of individual leaves but decreased RUE. A higher blue light proportion inhibited dry mass production with the same intercepted light because the leaves under high blue light proportion had low Pn and photosynthetic light-use efficiency. In conclusion, 300 μmol m−2 s−1 PPFD and R9B1 are the recommended proper PPFD and light quality, respectively, for ‘Micro-Tom’ cultivation at the vegetative growth stage to increase the RUE.

• Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. • To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. • PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI–ɛ and CCI–photosynthesis and NIRV–photosynthesis. • PRI-, CCI- and NIRV-based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.

Light‐use efficiency (LUE) is at the core of mechanistic modeling of global gross primary production (GPP). However, most LUE estimates in global models are satellite based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower‐based LUE from 237 FLUXNET towers, and scaled up LUEs from in situ tower level to global biome level. We integrated the tower‐based LUE estimates with key environmental and biological variables at 0.5° × 0.5° grid‐cell resolutions, using a random forest regression (RFR) approach. Then, we developed a RFR‐LUE‐GPP model using the grid‐cell LUE data. In order to calibrate the LUE model, we developed a data‐driven RFR‐GPP model using RFR method only. Our results showed LUE varies largely with latitude. We estimated a global area‐weighted average of LUE at 1.23 ± 0.03 g C·m−2·MJ−1 APAR, which led to an estimate of global GPP of 107.5 ± 2.5 Gt C/yr from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e., 20° S–40° S and 5° N–40° N) due to the lack of available data. Model results were improved by incorporating Köppen climate types to represent climate/meteorological information in machine‐learning modeling. This brought a new understanding to the recognized problem of climate dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broadleaf forests (EBF). The divergent responses of GPP to temperature and precipitation at middle to high latitudes and at middle to low latitudes echo the necessity of modeling GPP separately by latitudes.

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 ( ε max ), 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 ε max , and (b) separately re-parameterizing ε max in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art ε max –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 ε max –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 ε max –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 (NIR v ) were used to re-parameterize the ε max , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of ε max and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.

We investigated how radiation conditions within a tundra canopy were linked to canopy photosynthesis, and how this linkage explained photosynthetic sensitivity to sky conditions, that is total radiation and its diffuse fraction. We measured within canopy radiation at leaf scales and net CO₂ exchanges at canopy scales, under varied total irradiance and diffuse fraction, in Alaskan shrub tundra. Normalised mean radiation profiles within canopies showed no significant differences with varied diffuse fractions. However, radiation density distribution was non‐normal, being more unimodal under diffuse conditions and distinctly bimodal under direct sunlight. There was a nearly three‐fold increase in the proportion of the canopy in deep shade under direct illumination, compared to diffuse conditions. Under diffuse conditions the canopy had higher light‐use efficiency (LUE), resulting in up to 17% greater photosynthesis. The enhancement in LUE under diffuse illumination was not related to differences in the mean light profiles, but instead was due to significant shifts in the density distribution of light at leaf scales, in particular a reduced fraction of the canopy in deep shade under diffuse illumination. These results provide unique information for testing radiative transfer schemes in canopy models, and for better understanding canopy structure and trait variation within plant canopies.

We measured light‐related patterns of primary productivity within a topographically complex Oregon watershed over a 30‐year period. Second‐growth conifer densities were experimentally altered in 1981. Plots receiving at least 3434 MJ m−2 over a 6‐month growing season averaged 40% greater aboveground net primary productivity (ANPP) than those receiving less light (p = 0.000). Unthinned stands potentially built enough LAI to compensate for low light, but risked mortality that exceeded resilience. The two light levels acted as basins of attraction for other physiological and ecological processes, including size–density relationships and limiting foliar nutrients. Initial (1981) LAI and the irradiation step (above or below 3434 MJ m−2) explained 60% of variation in a 30‐year ANPP. Irradiation within each light group did not affect ANPP. At high irradiation, foliar N/Ca and slope steepness (both negative) explained 58% of the variation in residuals from the initial models, while at low irradiation on north, east, and west aspects, 83% of residual variation was explained by foliar Mg (+), understory cover (+), and 30‐year mortality (−). Light use efficiency (LUE) of fully stocked stands correlated with LAI and foliar N/K. Results suggest that understory influence on tree foliar N (+ or −) enhances ANPP by regulating critical nutrient ratios. Mortality reduced or eliminated differences among thinning levels, which did not vary at low light and only between unthinned and heavily thinned at high light. Values associated with relatively open forests (biodiversity, resilience) may be attained without large sacrifice of long‐term carbon sinks. In our study, light interacts with topography to produce nonlinear dynamics in which small changes in irradiation can have large consequences. Reduced sunlight has been suggested as a geoengineering option to combat global warming. Ecological changes out of proportion to lowered irradiation are a distinct possibility, including sharp reductions in terrestrial carbon sinks.

Vertical farming is gaining popularity as a sustainable solution to global food demand, particularly in urban areas where space is limited. However, optimizing key factors such as planting density remains a critical issue, as it directly affects light interception, energy efficiency, and crop yield. Lettuce and basil, the most commonly grown crops in vertical farms, were chosen for this study, with the aim of addressing the impact of planting density on light interception and overall productivity for improving the performance and sustainability of vertical farming systems. Plants were grown in an ebb-and-flow system of a fully controlled experimental vertical farm, where light was provided by light-emitting diode fixtures delivering a photoperiod of 16 h d−1 and 200 µmol m−2 s−1 of photosynthetic photon flux density. Experimental treatments included three planting densities, namely 123 (low density, LD), 237 (medium density, MD), and 680 (high density, HD) plant m−2. At the final harvest (29 days after sowing), the adoption of the highest planting density (680 plant m−2) resulted in greater fresh yield (kg FW m−2), leaf area index (LAI, m2 m−2), light use efficiency (LUE, g DW mol−1) and light energy use efficiency (L-EUE, g FW kWh−1) for both lettuce (+207%, +227%, +142%, +206%, respectively), and basil (+312%, +316%, +291, +309%, respectively), as compared to the lowest density (123 plant m−2). However, the fresh and dry weights of the individual plants were lowered, probably as a result of the reduced light availability due to the highly dense plants’ canopy. Overall, these findings underscore the potential of increasing planting density in vertical farms to enhance yield and resource efficiency.

Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAIG) measurements were used to revise the key inputs, the maximum light use efficiency (εmax) and the fractional photosynthetically active radiation (FPARM) in the MOD17 algorithm. Using two approaches to determine the site-specific εmax value, we suggested that the suitable εmax was about 1.61 g C MJ−1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ−1 for grassland. The FPARM underestimated 22.2% of the actual FPAR (FPARG) simulated from the LAIG during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the εmax and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the εmax and FPARG, are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow.

As a desert shrub, Haloxylon ammodendron combines ecological, economic, and social benefits and plays an important role in the ecological conservation of arid desert areas. Understanding its physiological characteristics and its mechanism of light energy utilization is important for the conservation and utilization of H. ammodendron . Therefore, we selected five stands (5-, 11-, 22-, 34-, and 46-year-old) of H. ammodendron as research objects in the study and measured their photosynthetic light response curves by a portable open photosynthesis system (Li-6400) with a red-blue light source (6400-02B). Then, we measured the leaf chlorophyll parameters in the laboratory, calculated the photosynthetic characteristics by using Ye Zipiao’s photosynthetic model, analyzed their variation patterns across stand ages, and explored the relationships between leaf chlorophyll parameters and photosynthetic characteristics. The results showed that leaf chlorophyll parameters and photosynthetic characteristics of H. ammodendron at different stand ages were significantly different. Chl content, P nmax , and LUE max of H. ammodendron were V-shaped with the increase of stand age. The 5-year-old H. ammodendron was in the rapid growth period, synthesized more Chl a+b content (8.47 mg g −1 ) only by using a narrower range of light, and the P nmax and LUE max were the highest with values of 36.21 μmol m −2 s −1 and 0.0344, respectively. For the 22-year-old H. ammodendron , due to environmental stress, the values of Chl a+b content, P nmax , and LUE max were the smallest and were 2.64 mg g −1 , 25.73 μmol m −2 s −1 , and 0.0264, respectively. For the older H. ammodendron , its Chl content, P nmax , and LUE max were not significantly different and tended to stabilize but were slightly higher than those of the middle-aged H. ammodendron . On the other hand, the other photosynthetic parameters did not show significant variation patterns with stand age, such as R d , AQE, LSP, LCP, and I L-sat . In addition, we found that the relationships between Chl a+b content and P nmax and between Chl a+b content and LUE max were highly correlated, except for the older H. ammodendron . Thus, using leaf chlorophyll content as a proxy for photosynthetic capacity and light use efficiency should be considered with caution. This work will provide a scientific reference for the sustainable management of desert ecosystems and vegetation restoration in sandy areas.

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key vegetation biophysical variable in most production efficiency models (PEMs). Operational FAPAR products derived from satellite data do not distinguish between the fraction of photosynthetically active radiation (PAR) absorbed by nonphotosynthetic and photosynthetic components of vegetation canopy, which would result in errors in representation of the exact absorbed PAR utilized in photosynthesis. The possibility of deriving only the fraction of PAR absorbed by photosynthetic elements of the canopy (i.e. FAPARps) was investigated. The approach adopted involved inversion of net ecosystem exchange data from eddy covariance measurements to calculate FAPARps. The derived FAPARps was then related to three vegetation indices (i.e. Normalized Difference Vegetation Index (NDVI), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Enhanced Vegetation Index (EVI)) in an attempt to determine their potential as surrogates for FAPARps. Finally, the FAPARps was evaluated against two operational satellite data-derived FAPAR products (i.e. MODIS and CYCLOPES products). The maximum FAPARps from the inversion approach ranged between 0.6 and 0.8. The inversion approach also predicted site-specific Q10-modelled daytime respiration successfully (R 2> 0.8). The vegetation indices were positively correlated (R 2= 0.67–0.88) to the FAPARps. Finally, the two operational FAPAR products overestimated the FAPARps. This was attributed to the two products deriving FAPAR for the whole canopy rather than for only photosynthetic elements in the canopy.