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opencc-by-4.0Dec 2022View details →

Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"

Dataset used in the paper Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895. Please cite the paper presenting this datase. <h1>Plant Species and Inbred Lines:</h1> <ul> <li>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </li> <li>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</li> <li>Brassica rapa L. (inbred line ‘R-o-18’)</li> <li>Arabidopsis thaliana (accession Columbia)</li> </ul> <h1>Growth Conditions:</h1> <ul> <li>Media: Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li>Fertigation: Nitrogen-rich nutrient solution via automated dripping system.</li> <li>Light Conditions: 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li>Temperature: Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li>Relative Humidity: 70%</li> </ul> <h1>Codes</h1> Species: <ul> <li>Hirschfeldia incana L. - H. incana</li> <li>Brassica nigra L. - B. nigra</li> <li>Brassica rapa L. - B. rapa</li> <li>Arabidopsis thaliana - A. thaliana</li> </ul> Light conditions: <ul> <li>High light - HL</li> <li>Low light - LL</li> </ul> Replicates: <ul> <li>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</li> </ul> <h1>Measurements</h1> <h2>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2>Optical Properties Measurement (Absorbance & chlorophyll.zip):</h2> <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2>Stomatal Density and Size Analysis (Stomata.zip):</h2> Sampling: <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Plants: Samples taken from three different plants.</li> <li>Leaf-side: abaxial and adxial leaf side.</li> </ul> Microscopy Setup: <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> Data Output: <ul> <li>Imaging Results: .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2>Light and Electron Microscopy of Leaf Sections (CellwallChloroplast.zip):</h2> Sampling: <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Plants: Samples taken from four different plants.</li> </ul> Sample preparation <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections  of 70 nm were double stained for TEM</li> </ul> Microscopy Setup: <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2>Mesophyll Chlorophyll (ConfocalData.zip):</h2> Sampling: <ul> <li>Leaves: Three leaves per species per treatment.</li> <li>Plants: Samples taken from three different plants.</li> <li>Thickness: 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> Microscopy Setup: <ul> <li>Microscope: Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li>Excitation: 490 nm excitation laser line</li> <li>Fluorescence Recording: Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li>Objective: Leica objective ×10/0.4 NA.</li> <li>Z-Stacks:  85–112 µm depth, two random positions per sample</li> </ul> Data Output: <ul> <li>Imaging Results: Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li>Spectral Information: Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> Analysis: <ul> <li>Software: The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>

Winter wheat yield analysis using the Light Use Efficiency model in LandKlif project

The crop yield of Winter wheat is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.86 and RMSE of 5.03 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Oil Seed Rape yield analysis using the Light Use Efficiency model in LandKlif project

The crop yield of Oil Seed Rape (OSR) is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.82 and RMSE of 2.14 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Biomass estimation of oilseed rape using the light use efficiency model in Bavaria (Atlas) in LandKlif project

This dataset shows the predicted biomass (g/m2) of oilseed rape (OSR) using the light use efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.79 with an RMSE of 2.21 dt/ha.  This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project

This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha.  This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

publicAug 2024View details →

dryad40/100

Data from: Gross primary productivity from leaf-age-dependent light use efficiency (LA-LUE) model over pantropical evergreen broadleaved forests

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eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI

Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO2) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to March 2024. Cross-validation with 202 flux tower sites (1494 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPPeLUE) (R2 = 0.71, RMSE = 2.11 g C m-2 d-1). The uncertainty associated with GPPeLUE is comparatively lower than that of the other global GPP datasets (MOD17, FluxSat, VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2023, was 138.46±13.92 Pg C yr-1. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.28±0.05 Pg C yr-1 (p < 0.001) from 2001 to 2023, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.

opencc-zeroJun 2024View details →

zenodo36/100

Data from: Enhanced light interception and light use efficiency explain overyielding in young tree communities

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publicApr 2021View details →

dryad36/100

eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI

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publicJan 2025View details →

opencc-zeroAug 2020View details →

Technical note: Estimating light-use efficiency of benthic habitats using underwater O2 eddy covariance

This datafile contains all data required to recreate figures presented in Attard KM & Glud RN (2020) Technical Note: Estimating light-use efficiency of benthic habitats using underwater O2 eddy covariance. Biogeosciences https://doi.org/10.5194/bg-2020-140 Paper abstract Light-use efficiency defines the ability of primary producers to convert sunlight energy to primary production and is computed as the ratio between the gross primary production and the intercepted photosynthetic active radiation. While this measure has been applied broadly within the atmospheric sciences to investigate resource-use efficiency in terrestrial habitats, it remains underused within the aquatic realm. This report provides a conceptual framework to compute hourly and daily light-use efficiency using underwater O2 eddy covariance, a recent technological development that produces habitat-scale rates of primary production under unaltered in situ conditions. The analysis, tested on two benthic flux datasets, documents that hourly light-use efficiency may approach the maximum theoretical limit of 0.125 O2 photon-1 under low light conditions but it decreases rapidly towards the middle of the day and is typically tenfold lower on a 24 h basis. Overall, light-use efficiency provides a useful measure of habitat functioning and facilitates site comparison in time and space.

opencc-zeroDec 2014View details →

Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based Vegetation Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

opencc-zeroDec 2017View details →

Data from: Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation

Global syntheses of leaf trait scaling relationships report an increase in light interception costs or 'diminishing returns' with increase in leaf area. However, variation in light interception costs across ecological gradients and plant strategies to cope up with these costs are not adequately understood. We analyzed leaf area (A) - leaf dry mass (M), leaf water mass (W) - M and W - A scaling relationships in plants occurring in a high altitude region of western Himalaya across environmental gradients to understand changes in light interception cost and metabolic mass component. M represents light interception cost, whereas, W is considered as a proxy of metabolic mass component for liquid phase being the ultimate source of metabolic activity. Trait values were measured from 9278 leaves belonging to 136 dominant species occurring at different sites, slope aspects, elevations and habitat types. Overall, light interception cost increased with increasing A (scaling exponent (α) <1 in A-M relationship) and metabolic mass component increased disproportionately high with increasing M and A. We found significant differences in scaling exponents of leaf trait relationship between sites, elevations, slope aspects and habitat types, indicating that increase in light interception cost was more evident at higher elevations, southern slopes and open habitats. Further, with increase in light interception cost, metabolic mass component also increased (α>1 in W-M and W-A relationships). The changes in scaling exponents of various leaf trait relationships across ecological gradients indicated that vegetation of different regions have differences in light interception cost and metabolic mass component. Moreover, increasing light interception cost (increase in mechanical and hydraulic tissues) with increasing A and increasing metabolic mass (leaf thickness) with increasing A and M are favored in high altitude vegetation. This could be a key strategy of high altitude plants for efficient resource capture and use in harsh environments.

zenodo32/100

A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020

The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.     Variable: GPP, GPPsh, and GPPsu Spatial coverage: global Temporal coverage: 2002 to 2020 Spatial resolution: 0.05×0.05° Temporal resolution: eight-day Unite: g C m−2 d−1 Data format: raster (.tif)

opencc-by-4.0Aug 2024View details →

publicDec 2015View details →

Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

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publicSep 2018View details →

Data from: Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation

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