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Apr 2019
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Experiments for in silico evaluation of Optimality of Photosynthetic Nitrogen Distribution and Partitioning in the Canopy: an Example Using Greenhouse Cucumber Plants
树冠中光合氮分配的最适生物信息学评估实验:温室黄瓜应用实例   

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Abstract

Acclimation of leaf traits to fluctuating environments is a key mechanism to maximize fitness. One of the most important strategies in acclimation to changing light is to maintain efficient utilization of nitrogen in the photosynthetic apparatus by continuous modifications of between-leaf distribution along the canopy depth and within-leaf partitioning between photosynthetic functions according to local light availability. Between-leaf nitrogen distribution has been intensively studied over the last three decades, where proportional coordination between nitrogen concentration and light gradient was considered optimal in terms of maximizing canopy photosynthesis, without taking other canopy structural and physiological factors into account. We proposed a mechanistic model of protein turnover dynamics in different photosynthetic functions, which can be parameterized using leaves grown under different levels of constant light. By integrating this dynamic model into a multi-layer canopy model, constructed using data collected from a greenhouse experiment, it allowed us to test in silico the degree of optimality in photosynthetic nitrogen use for maximizing canopy carbon assimilation under given light environments.

Keywords: Functional partitioning (功能划分), Light fluctuation (光脉动法), Mechanistic model (机理模型), Nitrogen reallocation (氮再分配), Optimality (最优性), Photosynthetic acclimation (光合适应)

Background

Intra-canopy nitrogen distribution in response to light has been intensively studied (Hirose and Werger, 1987; Werger and Hirose, 1991; Anten et al., 1995; Dreccer et al., 2000; Moreau et al., 2012; Hikosaka, 2016) and many studies demonstrated that, although the actual nitrogen distribution resulted in higher canopy photosynthesis than uniform nitrogen distribution, it was still suboptimal (Field, 1983; Evans, 1993; Hollinger, 1996; Hirose et al., 1997; Meir et al., 2002; Wright et al., 2006; Hikosaka, 2016). This discrepancy between optimum and reality could, on one hand, be explained by physiological limitations (Niinemets, 2012; Hikosaka, 2016). On the other hand, it might result from incorrect predictions by over-simplified models, where the effects of variations in the structural characteristics on light interception, age-dependent modifications of leaf biochemistry and photoacclimation in functional nitrogen partitioning were neglected. To incorporate these factors into the acclimation processes, a mechanistic model based on the concept of protein turnover (synthesis and degradation) was proposed to simulate the dynamics of photosynthetic nitrogen in carboxylation, electron transport and light harvesting functions along the development and ageing of the leaf (Pao et al., 2019a and 2019b). Leaf elevation angle and leaf area distribution in the canopy was measured to construct a multi-layer canopy model for simulating more realistic intra-canopy light distribution, which is used as input for the protein turnover model. By manipulating the parameters controlling nitrogen distribution and partitioning, it is possible to quantify the degree of optimality in photosynthetic nitrogen use for maximizing canopy carbon assimilation in silico.

Materials and Reagents

  1. 25-L plastic boxes, as container for hydroponic system
  2. Polystyrene foam boards for fixing plants onto plastic boxes
  3. Rockwool cubes (10 cm × 10 cm × 6.5 cm), as growth medium in the hydroponic system (Grodan Delta; Grodan, Roermond, The Netherlands)
  4. Rockwool cubes (3.6 cm × 3.6 cm × 4 cm), as seed sowing medium (Grodan A-OK Starter Plugs; Grodan, Roermond, The Netherlands)
  5. Plastic plant support clips
  6. N-P-K fertilizer (Ferty 2 MEGA; Planta, Regenstauf, Germany)
  7. P-K fertilizer (Ferty Basisdünger 1; Planta, Regenstauf, Germany)
  8. N fertilizer (YaraLiva Calcinit; Yara, Oslo, Norway)
  9. Paper bags (size should be enough to contain a single cucumber lamina, which is about 15 cm × 20 cm or larger)
  10. Rockwool slabs (100 cm × 20 cm × 7.5 cm), as growth medium in the greenhouse (Grodan GT Expert; Grodan, Roermond, The Netherlands)
  11. Seed (Cucumis sativus ‘Aramon’, Rijk Zwaan, De Lier, The Netherlands)
  12. 1% H2SO4 (96% H2SO4, Carl Roth, catalog number: 4623 ; preparation: 6 ml 96% H2SO4 mix with 1 L H2O)

Equipment

  1. Walk-in growth chambers with aeration system and controlled air temperature and humidity (Vötsch Industrietechnik, Balingen, Germany) and light source using metal halide lamps (HQI-BT 400 W/D PRO; Osram, Munich, Germany)
  2. Quantum sensor LI-190R coupled with light meter LI-250A (LI-COR, Lincoln, NE, USA)
  3. Light sensor logger (LI-COR, model: LI-1000 , LI-1400 or LI-1500)
  4. Temperature data logger (Tinytag; Gemini Data Loggers, Chichester, UK)
  5. Portable photosynthesis system LI-6400XT (coupled with 6400-40 leaf chamber fluorometer) or LI-6800 (LI-COR, Lincoln, NE, USA)
  6. Chlorophyll meter (Konica Minolta Sensing, model: SPAD-502 )
  7. Leaf area meter (LI-COR, model: LI-3100C)
  8. Laboratory balance with resolution of 0.01 g (Sartorius, model: ED4202S ) or with resolution of 0.1 mg for mass below 1 g (Sartorius, model: ED224S)
  9. Vacuum freeze dryer (Alpha 1-4 LSC; Martin Christ Gefriertrocknungsanlagen, Osterode am Harz, Germany)
  10. 3D digitizer (Fastrak; Polhemus, Colchester, USA)

Software

  1. R (ver. 3.3.0 or later; R Foundation for Statistical Computing, https://www.r-project.org/); R scripts and simulated example data sets are provided to facilitate data analysis (https://github.com/yichenpao/bio-protocol/)
    1. R script 1 [data processing] (see section Data analysis A, B)
    2. R script 2 [model parameterization] (see section Data analysis C, D); required packages are ‘DEoptim’, ‘deSolve’, ‘ggplot2’, ‘reshape2’, ‘xlsx
    3. R script 3 [simulation and in silico test] (see section Data analysis E-H); required packages are ‘DEoptim’, ‘deSolve’, ‘dplyr’, ‘ggplot2’, ‘magrittr’, ‘xlsx
  2. Digitool (customized software for 3D-digitizer, availability upon request)

Procedure

  1. Raising seedlings for experiments
    1. Sow one cucumber seed (Cucumis sativus ‘Aramon’, Rijk Zwaan, De Lier, The Netherlands) in each rock-wool cube (3.6 cm × 3.6 cm × 4 cm, Figures 1A and 1B) and water sufficiently until the cubes are completely wet.
    2. Sow 20-40% more seeds (lower the germination rate and quality, more the additional amount) than the number of plants required for the experimental design in order to select for uniform seedlings in 7-10 days.
    3. Set environmental conditions to 10-15 mol m-2 d-1 photosynthetically active radiation (PAR) at the seedling level with 12 h light period, 24 °C day/20 °C night air temperature and 70% relative humidity.
    4. Eight days after sowing, transfer each rockwool cube into a larger rockwool cube (10 cm × 10 cm × 6.5 cm, Figure1C) and irrigate with nutrient solution of N-P-K fertilizer (0.5 g L-1 Ferty 2 MEGA; Planta, Regenstauf, Germany; 5.7 mM N, 2.7 mM K, 0.35 mM P, 0.45 mM Mg in working solution) once every day.

  2. Growth chamber experiment to parameterize the protein turnover model
    1. Transplanting and starting experiment
      1. Prepare chambers with at least three constant light intensities (one < 5, one between 10-15 and one > 25 mol photon m-2 d-1 PAR for cucumber) at the plant level, which should cover most variation found in the light environment during crop production.
      2. Prepare nutrient solution with at least three levels of nitrogen (one < 2.8, one 3.5-5 and one > 8.5 mM NO3- for cucumber Aramon) using N fertilizer (YaraLiva Calcinit; Yara, Oslo, Norway) and P-K fertilizer (Ferty Basisdünger 1; Planta, Regenstauf, Germany; 5.2 mM K, 1.3 mM P, 0.82 mM Mg in working solution) if the effect of nitrogen is of interest.
      3. Transplant the seedlings when their second true leaves reach a length of 3 cm (ca. eight days grown in the larger rockwool cubes) into hydroponic system (Figure 1D), consisting of a 25-L plastic box and a piece of polystyrene foam board that fixes a rockwool cube containing a plant.
      4. Fill 25-L boxes with nutrient solution and supply the solution with air from aeration system (Figure 1E).
      5. Prepare polystyrene foam boards for supporting the plants in the hydroponic system.
        1. Cut polystyrene foam boards to make them fit onto 25-L boxes.
        2. Cut a squared opening (9.5 cm × 9.5 cm) in the middle of the boards.
        3. Fix the plants into the openings in polystyrene foam boards and position them into the 25-L boxes.
      6. Select the healthy and unshaded leaves within leaf ranks four to eight (counted acropetally) as sampled leaves in each plant and record their dates of appearance.
      7. Record light condition at the level of the sampled leaves using quantum sensor LI-190R and light meter LI-250A (LI-COR, Lincoln, NE, USA).
    2. Plant care and monitoring environmental conditions
      1. Prepare custom-made leaf holders.
        1. Make leaf holders using plastic coated metal wires to form a loop structure, consisting of a circular part which supports the leaf and a stick part which can be fixed to the stem and petiole (Figure 1F).
        2. Combine each holder with two plastic plant support clips at the stick part.
        3. Prepare leaf holders in different sizes and lengths in order to support leaves at various developmental stages.
      2. Allow plants to establish vegetative growth by removing flowers below the seventh node.
      3. Keep plants to single stem by cutting all side shoots and train the rest of the shoot above the sampled leaves downward to avoid mutual shading (Figure 1G).
      4. Renew the nutrient solution completely and record the nitrogen level in the nutrient solution once a week, fill the solution once between two times of solution renewal, and adjust pH value to 6.0-6.5 by 1% H2SO4 twice a week.
      5. Place data loggers Tinytag (Gemini Data Loggers, Chichester, UK) around the sampled leaves and record daily mean air temperature (Tmean, °C).
      6. Measure and record the PAR at the center of the sampled leaves (Figure 1a in Wiechers et al., 2011) weekly and adjust the angle of the leaves using leaf holders (Figure 1F) to make sure they are horizontally and fully exposed to the light, not shaded by other leaves, in order to achieve target PAR level at the leaves.


      Figure 1. Raising seedlings and setup of growth chamber experiment. A-C. Raising seedlings in rockwool cubes. D-E. Transplanting seedlings into hydroponic system. F. Custom-made leaf holders fixed to a plant on its stem and petiole with support clips. G. Avoiding shading of the sampled leaf (marked yellow) by training rest of the shoot downward.

    3. Collecting data of the sampled leaves
      1. Gas exchange
        1. Conduct measurements for each environmental condition at an interval of three to four days, starting with the youngest leaves (ca. three days after leaf appearance) and then the older ones to obtain data from leaves with a wide range of ages (ca. 40-550 °Cd).
        2. Estimate age (t, °Cd) of individual leaves days from the day of its appearance to the day of measurement using daily mean temperature and a base temperature (Tbase, 10 °C for cucumber):



        3. Measure net photosynthesis rate (An, μmol CO2 m-2 s-1, Table 1), intercellular CO2 concentration (Ci, µmol mol-1), photosynthetic photon flux density (PPFD, µmol m-2 s-1) and quantum efficiency of photosystem II electron transport (ϕPSII) using a portable photosynthesis system LI-6400XT or LI-6800 (LI-COR, Lincoln, NE, USA).

          Table 1. Labeling of variables in the output file from portable photosynthesis systems LI-6400XT and LI-6800 (LI-COR, Lincoln, NE, USA). Necessary variables for data processing are net photosynthesis rate (An, μmol CO2 m-2 s-1), intercellular CO2 concentration (Ci, µmol mol-1), photosynthetic photon flux density (PPFD, µmol m-2 s-1) and quantum efficiency of photosystem II electron transport (ϕPSII).


        4. Collect data to a .csv filefile (Figure 2 and Table 2), which will be used in the data analysis sections A and B for data processing (in this example ‘example_chamber_gas_exchange_data.csv’).


          Figure 2. Format of gas exchange data file. See Table 2 for explanation for column name, description and unit used.

          Table 2. Column name, description and unit used in the gas exchange data file


        5. Cut the lamina directly after the measurement for further analyses.

      2. Harvest data
        1. i. Measure relative chlorophyll content (SPAD value) using chlorophyll meter SPAD-502 (Minolta Camera, Japan) and leaf area by area meter LI-3100C (LI-COR, Lincoln, NE, USA) of the harvested lamina.
        2. Keep each lamina in individual paper bag and freeze them under -20 °C for storage.
        3. Precool sample shelves in the vacuum freeze dryer (Alpha 1-4 LSC; Martin Christ Gefriertrocknungsanlagen GmbH, Osterode am Harz, Germany) to 10 °C and the ice condenser to -50 °C. Freeze dry lamina samples for 48 h under pressure of 1.030 mbar and then measure the mass of freeze-dried lamina. Please note that most samples can be dried to 1-5% residual moisture; therefore, the measured dry mass should be corrected to exclude the weight of residual moisture.
        4. Grind the lamina into fine powder and analyze total nitrogen (e.g., Nelson and Sommers, 1980) and chlorophyll (e.g., Lichtenthaler, 1987) content.
        5. Collect data to a .csv filefile (Figure 3 and Table 3; in this example ‘example_chamber_harvest_data.csv’), which will be used in data analysis sections A and B for data processing.


          Figure 3. Format of harvest data file. See Table 3 for explanation for column name, description and unit used.

          Table 3. Column name, description and unit used in the harvest data file


      3. Quantify the empirical relationship between SPAD value and leaf chlorophyll concentration per area to facilitate non-destructive estimation in the greenhouse experiment, using a linear (Chl = a + b × SPAD) or power (Chl = a × SPADb) function.

  3. Gas exchange measurement using portable photosynthesis system LI-6400XT or LI-6800 (LI-COR, Lincoln, NE, USA)
    1. Allow leaves to adapt for 10-20 min under measurement conditions of:
      1. Photosynthetic photon flux density (PPFD) 1,300 µmol m-2 s-1,
      2. Sample CO2 400 µmol mol-1,
      3. Leaf temperature 25 °C,
      4. Relative humidity 55-65%,
      until Rubisco is fully activated and photosynthesis rate, stomatal conductance and fluorescence (F’) equilibrate to steady states, then read light-saturated net photosynthesis rate (Asat, μmol CO2 m-2 s-1).
      Measure maximum chlorophyll fluorescence (Fm’) using the multiphase flash (MPF) approach 
    2. (Loriaux et al., 2013; Moualeu-Ngangue et al., 2017):
      1. Phase 1 with constant maximum irradiance for 320 ms,
      2. Phase 2 with irradiance attenuation (30% ramp depth) over 350 ms,
      3. Phase 3 with constant maximum irradiance as in phase 1 for 200 ms.
    3. Measure light response curves of net photosynthesis rate (An, μmol CO2 m-2 s-1) under PPFD 900, 500, 250, 150, 100, 85, 70, 60, 50, 40, 0 µmol m-2 s-1.
    4. Total duration of this measurement is 30-40 min per leaf; notice that for old leaves or leaves grown under low light, the time of adaptation is generally longer than for young and high light-grown leaves.
    5. Quantum efficiency of photosystem II electron transport (ϕPSII) is computed using fluorescence data (Murchie and Lawson, 2013):



  4. Greenhouse experiment to obtain canopy structural information and data to evaluate the protein turnover model
    1. Transplanting and starting experiment
      1. Record daily mean air temperature (Tmean, °C) near the seedlings using data logger Tinytag and transplant the seedlings when their third true leaves reach a length of 3 cm (ca. two weeks grown in the larger rockwool cubes).
      2. Transfer two plants onto one rockwool slab (100 cm × 20 cm × 7.5 cm) with a distance of 50 cm between them and 150 cm between rows (density of 1.33 plants m-2 in a greenhouse with 96 m2 of cultivation area).
      3. Supply plants with nutrient solution by drip irrigation system with nitrogen levels of interest.
    2. Plant care and monitoring environmental conditions
      1. Train the plants vertically onto wires and remove all side shoots as well as flowers below the seventh node.
      2. Record daily mean air temperature using data logger Tinytag in the greenhouse and daily integral of PAR above the canopies using quantum sensor LI-190R and light meter LI-250A.
      3. Analyze nitrate (Navone, 1964) and ammonium (following German standard methods for the examination of water, waste water and sludge, DIN 38406-5) in the nutrient supply and nitrogen concentration remained in the rockwool slabs weekly.
      4. Collect data to a .csv filefile (Figure 4 and Table 4; in this example ‘example_greenhouse_environment_data.csv’), which will be used in data analysis sections E-H for simulation and in silico test.


        Figure 4. Format of greenhouse environmental data file. See Table 4 for explanation for column name, description and unit used.

        Table 4. Column name, description and unit used in the greenhouse environmental data file

        *ID of light and nitrogen treatments are named by users and should be identical as the treatment ID in greenhouse structural data.

    3. Collecting plant data
      1. Measure leaf number, leaf elevation angle, leaf area and leaf area index non-destructively using a 3D digitizer (Chen et al., 2014) at a weekly interval (equates to roughly 100 °Cd difference between two measurements under the greenhouse condition described above) to obtain static canopy structures at various developmental stages.
      2. Estimate age (t, °Cd) of individual leaves in the canopy.
        1. Calculate total growing degree days (GDDcanopy) from the day of transplanting into greenhouse (when leaf x appeared; in this example x = 3) to the day of measurement using Eq. G1.
        2. Divide GDDcanopy by the number of leaves appeared after transplanting (excluding the first x-1 leaves) to estimate phyllochron (°Cd per leaf, interval between appearance of successive leaves), assuming constant phyllochron during the experimental period:



        3. Estimate the age of leaf n using phyllochron in relation to leaf x:



      3. Measure gas exchange and relative chlorophyll content (SPAD value, used to estimate Chl non-destructively) to evaluate the performance of the functional model of photosynthetic protein turnover in the leaf.
      4. Conduct digitization and gas exchange measurement for the same plants within 2-3 days.
      5. Collect data to a .csv filefile (Figure 5 and Table 5; in this example ‘example_greenhouse_structure_data.csv’), which will be used in data analysis sections E-H for simulation and in silico test.


        Figure 5. Format of greenhouse plant structural data file. See Table 5 for explanation for column name, description and unit used.

        Table 5. Column name, description and unit used in the greenhouse plant structural data file


  5. Digitizing plant structure and converting coordinates into structural data
    1. Digitize the structures of at least two representative plants for each treatment using a 3D digitizer (Fastrak; Polhemus, Colchester, USA).
    2. Obtain the structural information as Cartesian coordinates in a standardized sequence of points on the individual plant organs from bottom to top of a plant (modified from Kahlen and Stützel, 2007; Wiechers et al., 2011):
      1. Digitize ‘node 0’ at the base of the stem at its insertion point to the rockwool cube.
      2. Digitize ‘node 1’ opposite to the base of petiole of the first true leaf (‘Node’ in Figure 6A).
      3. Digitize ‘axil 1’ at the insertion point of the first true leaf to the stem (‘Axil’ in Figure 6A).
      4. Digitize ‘leaf 1’ with a predefined sequence and spatial arrangement of 13 points on the lamina surface (Figure 6).
      5. Continue digitizing in the sequence of ‘node n - axil n - leaf n’ until all leaves are digitized.
      6. Neglect flowers and fruits.
    3. Convert Cartesian coordinates into structural data.
      1. Leaf area: area sum of a predefined structure of triangles (Figure 6A).
      2. Leaf elevation angle (EA , °): the angle between the orientation of the leaf tip with respect to the base of the leaf (points 1 and 2 in Figure 6B) and the horizontal plane.


        Figure 6. Configurations for extracting leaf area and elevation angle from digitized data. A. Predefined positions of digitized points on the cucumber stem for a node, leaf axil, lamina and structure of triangles defined on the lamina. B. Leaf elevation angle (EA ).

    4. Quantify the empirical relationships between leaf area index (LAI ), EA and leaf age (t, °Cd) to simulate the dynamics of canopy structure in the in silico experiment, for example:


Data analysis

R script 1 [data processing] (Figure 7)



Figure 7. Overview of R script 1 for data processing. Input data files for this script are ‘example_chamber_harvest_data.csv’ and ‘example_chamber_gas_exchange_data.csv’ from growth chamber experiment.


  1. Estimate photosynthetic parameters using gas exchange data (Figure 7, # 1.3.0)
    1. Estimate leaf absorptance (abs, unitless) using leaf chlorophyll concentration (Chl, mmol m-2) given by Evans (1993):



    2. Estimate electron transport rate (J, μmol e- m-2 s-1) under various photosynthetic photon flux  density (PPFD, µmol m-2 s-1):



      where β (0.5, unitless) is the partitioning fraction of photons between photosystem II and I.
    3. Estimate maximum electron transport (Jmax) by least squares fitting to a nonrectangular hyperbola:



      where ϕ (0.425 µmol e µmol-1 photon; Chen et al., 2014) is the conversion efficiency of photons to J, and θ (0.7, unitless; Chen et al., 2014) is a constant convexity factor describing the response of J to PPFD.
    4. Estimate daytime respiration rate (Rd, μmol CO2 m−2 s−1) using the linear portion (40 ≤ PPFD ≤ 100 µmol m-2 s-1) of the light response curve (Kok, 1948) since the light compensation point in cucumber leaf is observed at ca. 40 µmol photon m-2 s-1.
    5. Estimate mesophyll conductance to CO2 (gm, mol m−2 s−1) using the variable J method (Harley et al., 1992):



      where Г* is CO2 compensation point in the absence of mitochondrial respiration (43.02 µmol mol-1 for cucumber; Singsaas et al., 2003) and Ci is intercellular CO2 concentration (µmol mol-1).
    6. Estimate chloroplastic CO2 concentration (Cc, μmol mol-1):



    7. Estimate maximum carboxylation rate (Vcmax, μmol CO2 m-2 s-1) using the one-point method (De Kauwe et al., 2016):



      where Km (mmol mol-1) is given by Kc (404 μmol mol-1) and Ko (278 mmol mol-1), Michaelis-Menten constants of Rubisco for CO2 and O2, and Oc (210 mmol mol-1) is the mole fraction of O2 at the site of carboxylation:



    8. Parameterize empirical relationships between Rd, gm and leaf age (t, °Cd, estimated using Eq. G1), mean daily photon integral over the last four days of leaf growth (DPI4d, mol m-2 d-1) and leaf photosynthetic nitrogen (Nph, mmol m-2) using, for example, Eq. 10 and Eqs.16 and 17 in Pao et al. (2019a):





  2. Estimate photosynthetic nitrogen pools using photosynthetic parameters (Figure 7, # 1.3.1)
    1. Estimate nitrogen involved in carboxylation (NV, mmol N m-2), electron transport (NJ, mmol N m-2) and light harvesting (NC, mmol N m-2) following Buckley et al. (2013):
      1. NV includes Rubisco and represents the nitrogen investment in carboxylation capacity:



      2. NJ includes electron transport chain, photosystem II core and Calvin cycle enzymes other than Rubisco:



      3. NC includes photosystem I core and light harvesting complexes I and II:



        where χV (μmol CO2 mmol−1 N s−1) is the carboxylation capacity per unit Rubisco nitrogen, and χJ (μmol e- mmol−1 N s−1) is the electron transport capacity per unit electron transport nitrogen. χCJ (mmol Chl mmol-1 N) and χC (mmol Chl mmol-1 N) are the conversion coefficients for chlorophyll per electron transport nitrogen and per light harvesting component nitrogen, respectively.
    2. Photosynthetic nitrogen (Nph, mmol N m-2) is defined as biologically active nitrogen in the proteins involved in photosynthetic functions, including nitrogen involved in carboxylation, electron transport and light harvesting:



    3. Photosynthetic nitrogen partitioning fraction of a pool X (pX) is determined as the ratio of nitrogen in the pool X (NX, mmol N m-2) to Nph:



    4. Output processed data to a .csv filefile (Figure 8; in this example ‘chamber_processed_data.csv’) (Figure 7, # 1.4.0), which will be used in data analysis sections C and D for model parameterization.


      Figure 8. Format of processed data file output from R script 1. This file will be used for model parameterization.

  3. R script 2 [model parameterization] (Figure 9)



    Figure 9. Overview of R script 2 for model parameterization. Input data file for this script is ‘chamber_processed_data.csv’ from script 1.


  4. Description of protein turnover model (Figure 9, # 2.3.0)
    The rate of change of a functional nitrogen pool NX is determined by the instantaneous protein synthesis rate (SX(t), mmol N m-2 °Cd-1) and degradation rate (DX(t), mmol N m-2 °Cd-1) of the corresponding enzymes and protein complexes at a given leaf age (t, °Cd):



    Protein synthesis as an age-dependent and zero-order process (Li et al., 2017), is described by a logistic function and independent of the current NX state:



    where Smax,X (mmol N m-2 °Cd-1) is the maximum protein synthesis rate of NX which occurs at the early stage of leaf development. The constant td,X (°Cd-1) describes the relative decreasing rate of the protein synthesis over time. At age of 1/td,X, SX reduces to 53.8% of Smax,X.
      The degradation rate Dx is governed by first-order kinetics (Li et al., 2017) with a degradation Dr,X (°Cd-1):



    The variable Smax,X is a function of daily leaf PAR interception (DPIinterceptLeaf, mol photon m-2 d-1): 



    where Smm,X (mmol N m-2 °Cd-1) is potential maximum protein synthesis rate and kl,X is rate constant describing the increase of Smax,X with light. The factor rN,X increases with nitrogen level in the nutrient solution (NS, mM) by a Michaelis-Menten constant, kN,X (mM):



  5. Parameterizing protein turnover model using data from growth chamber experiment (Figure 9)
    Solve differential Eqs. M4-M6 to obtain Smax,X, td,X and Dr,X in R using an algorithm programed with lsoda() function from ‘deSolve’ package and DEoptim() function from ‘DEoptim’ package, which minimizes the sums of squares of the residuals between observations and simulations (Figure 9, # 2.4.0). There are three steps to quantify the parameters in Eqs. M5-M8:
    1. Quantify td,X (Eq. M5) and Dr,X (Eq. M6) for each photosynthetic nitrogen pool using data of all environmental conditions, assuming Dr,X and td,X being species- and function-specific and not influenced by the light and nitrogen availabilities (Figure 9, # 2.4.1).
    2. Quantify Smax,X (Eq. M5) with the determined values of td,X, and Dr,X for each environmental condition (Figure 9, # 2.4.2).
    3. Determine Smm,X, kl,X (Eq. M7) and kN,X (Eq. M8) from Smax,X by nonlinear least squares fitting using nls() function from ‘stats’ package, and the standard errors (se) and P values (pv) for the estimates are calculated as well (Figure 9, # 2.4.3).
    4. Output results (Figure 9, # 2.5.0) to a .csv filefile (Figure 10; in this example ‘parameterize_result_output.csv’), which will be used in Data analysis sections E-H for simulation and in silico test.


      Figure 10. Format of parameterization result file output from R script 2. This file will be used for simulation and in silico test.

  6. R script 3 [simulation and in silico test] (Figure 11)


    100
    Figure 11. Overview of R script 3 for simulation and in silico test. Input data files for this script are ‘example_greenhouse_structure_data.csv’ and ‘example_greenhouse_environment_data.csv’ from greenhouse experiment and ‘parameterize_result_output.csv’ from script 2.


  7. Simulating leaf photosynthesis (Figure 11, # 3.6.0)
    In order to evaluate daily canopy carbon assimilation, net photosynthesis rate (An, μmol CO2 m-2 s-1) of individual leaves in the canopy should be simulated. An is defined as the minimum of RuBP carboxylation-limited (Ac, mmol CO2 m-2 s-1) and RuBP regeneration-limited (Aj, mmol CO2 m-2 s-1) net photosynthesis rate (Farquhar et al., 1980). The steady-state Ac can be solved analytically with Eqs. 9b, 14 and 15, and Aj with Eqs. 9c, 14 and 15 in Pao et al. (2019a) with given values of leaf-to-air vapor pressure deficit (D, kPa), atmospheric CO2 concentration (Ca, μmol mol-1), photosynthetic photon flux density (PPFD, µmol m-2 s-1) at leaf level and photosynthetic parameters.
    1. Leaf level PPFD is simulated (Figure 11, # 3.4.1) following Beer-Lambert’s law (Monsi and Saeki, 2005) with canopy light extinction coefficient (k) and leaf area index (LAI ) and adjusted by the cosine of leaf elevation angle (EA , °), which are estimated with leaf age using Eqs. G4 and G5 (Figure 11, # 3.3.0):



      where diurnal PPFD above the canopy (PPFDaboveCanopy, μmol m-2 s-1) at a given time (thour, h) during the day is calculated by a simple cosine bell function (Kimball and Bellamy, 1986) with daily PAR integral above the canopy (DPIaboveCanopy, mol m-2 d-1) and day length (DL, h):



    2. Photosynthetic parameters Jmax, Vcmax, abs, Rd and gm
      1. Electron transport rate Jmax under a given PPFD is calculated using Eq. P4.
      2. Carboxylation rate (Vc, μmol CO2 m-2 s-1) is calculated based on the amount of activated Rubisco under a given PPFD (Qian et al., 2012):



      3. abs is calculated using Eq. P2.
      4. Rd and gm are simulated using empirical relationships Eqs. P9 and P10 (Figure 11, # 3.3.1).

  8. Simulating daily canopy carbon assimilation (Figure 11, # 3.6.0)
    Daily canopy carbon assimilation during daytime (DCA, mol d-1) on day d is simulated with input data of:
    1. environmental information (from the appearance the leaf three until day d): Tmean (Eq. G1), DPIaboveCanopy (Eq. P10), and nitrogen concentration in the supply solution and rockwool slabs;
    2. greenhouse canopy characteristics (on day d): leaf area (digitized data, Figure 7A) and leaf age (Eqs. G1-G3; Figure 11, # 3.5.1).
    Each leaf in a canopy is first simulated for its photosynthetic nitrogen pools until day d using Eqs. M4-M8 (Figure 11, # 3.7.0 and # 3.8.0) and photosynthetic parameters using Eqs. M1a-M1c. DPIinterceptLeaf during the growth is simulated using Eq. P11 (Figure 11, # 3.5.1). The mean value of DPIaboveCanopy during the plant growth (from transplanting to measurement day) is used as DPIaboveCanopy on day d to simulate DCA. Nitrogen level in the nutrient solution (NS) is assumed to be the mean value of nitrogen concentration in the supply solution and rockwool slabs (Figure 11, # 3.4.1). In order to test the effect of incoming light condition on day d on the optimality of Nph distribution and partitioning, DPIaboveCanopy is multiplied by a factor ‘DPI multiplier’ assigned by users (Figure 11, # 3.7.2 and # 3.8.3).
    Leaf net photosynthesis is simulated for a time step of 0.1 h on day d, and summed up for every 0.1 h over the daytime to obtain daily leaf carbon assimilation (DLA, mol d-1). DCA is calculated as the sum of DLA of all leaves in the canopy.

  9. In silico experiment to test the optimality of nitrogen distribution in the canopy (Figure 11)
    To evaluate the effects of between-leaf distribution of Nph on DCA, a distribution factor fd is introduced into Eq. M5 to create variations in the rate of protein synthesis (Figure 11, # 3.7.0):



    A control condition is defined with fd = 1. Increasing fd accelerates the decrease in the rate of protein synthesis and enhances acropetal Nph reallocation, but it also reduces total Nph in a canopy (Ncanopy). To obtain the leaf photosynthetic nitrogen content (Nleaf,i, mmol N in leaf i) with comparable Ncanopy, simulated Nleaf,i with fd = n (denoted as N'leaf,i) is adjusted proportionally to the ratio between Ncanopy obtained with fd = 1 and Ncanopy obtained with fd = n:



    Photosynthetic nitrogen partitioning fraction of a pool X in leaf i (pX,i) is set equal to the control value:



    These adjustments assure the same amount of Ncanopy while changing the distribution pattern. The factor fd is varied from 0.5 to 5.0 (Figure 11, # 3.7.1), which gives values of Nph comparable to those observed in cucumber leaves (< 150 mmol N m-2). Values of DCA produced by various fd (Figure 11, # 3.7.2) are then compared with the control DCA (fd = 1) under a given environmental condition and output (Figure 11, # 3.7.3) to a .xlsx file (in this example ‘Test_fd_result.xlsx’) and plotted (Figure 11, # 3.7.4 and Figure 12). Detailed results with Nph and pX at leaf level can be output (Figure 11, # 3.7.5) to a .xlsx file (in this example ‘Test_fd_result_detailed.xlsx’).


    Figure 12. Example results of percentage change in daily canopy carbon assimilation during daytime (DCA, mol d-1) with various values of photosynthetic nitrogen (Nph) distribution factor fd under a given daily photosynthetically active radiation integral above the canopy (DPI, mol m-2 d-1). A. DPI = mean DPI during plant growth multiplied by 0.25. B. DPI = mean DPI during plant growth. C. DPI = mean DPI during plant growth multiplied by 2. Positive change in DCA resulted from varying fd indicates that the control Nph distribution (fd = 1) is sub-optimal.

  10. In silico experiment to test the optimality of nitrogen partitioning in the leaf (Figure 11)
    To evaluate the effects of within-leaf partitioning of Nph on DCA, a partitioning factor fp,X is introduced into Eq. M7 to modify maximum protein synthesis Smax,X, in order to create variations in partitioning pattern between the three photosynthetic nitrogen pools (Figure 11, # 3.8.0):



    A control condition is defined by fp,X = 1. An increase in fp,X results in a higher rate of synthesis of NX and increases the partitioning to pool X. The potential maximal protein synthesis rate for pool X (Smm,X) is modified by a factor fp,X, ranging from 0.2 to 2.0, to find the optimal within-leaf Nph partitioning between functions which maximizes DCA (Figure 11, # 3.8.3). Partitioning pattern which maximizes DCA under a given environmental condition is identified as ‘optimal’ and then compared with the control DCA (fp,X = 1), and the results are output (Figure 11, # 3.8.4) a .xlsx file (in this example ‘Test_fp_result.xlsx’) and plotted (Figure 11, # 3.8.5 and Figure 13). Detailed results with optimal Nph partitioning at leaf level can be output (Figure 11, # 3.8.6) to a .xlsx file (in this example ‘Test_fp_result_detailed.xlsx’).


    Figure 13. Example results of percentage change in daily canopy carbon assimilation during daytime (DCA, mol d-1) with various values of photosynthetic nitrogen (Nph) partitioning factor fp,X under mean incoming daily photosynthetically active radiation integral above the canopy (DPI, mol m-2 d-1) during plant growth multiplied by DPI multiplier between 0.25 and 2.0. Positive change in DCA resulted from optimal fp,X indicates that the control Nph partitioning (fp,X = 1) is sub-optimal. In this example, Nph partitioning of the canopy grown under light treatment H and nitrogen treatment L is sub-optimal under its growing light environment, and DCA can be improved almost 15% if Nph partitioning is optimized.

Acknowledgments

This work was supported by Deutsche Forschungsgemeinschaft (DFG). This protocol is modified and appended referencing the original, as featured in Pao et al. (2019a).

Competing interests

The authors declare no conflict of interest.

References

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简介

[摘要 ] 使叶片性状适应不断变化的环境是最大化适应性的关键机制。适应光变化的最重要策略之一是通过不断修改沿冠层深度的叶间分布以及根据局部光的可用性在光合功能之间进行叶内分配来保持光合装置中氮的有效利用。叶片间氮分配 在过去的三十年中,人们对该技术进行了深入研究,其中在最大程度地提高冠层光合作用的同时,不考虑其他冠层的结构和生理因素,认为氮浓度和光梯度之间的比例协调是最佳的。我们提出了不同光合功能下蛋白质更新动态的力学模型,该模型可以使用在不同水平的恒定光照下生长的叶片进行参数化。通过将此动态模型集成到使用从温室实验收集的数据构建的多层冠层模型中,它使我们能够在计算机上测试光合作用氮的最佳程度,以在给定的光照环境下最大程度地利用冠层碳同化。

[背景 ] 帧内篷Ñ itrogen分布响应于光已被广泛研究(广濑和Werger ,1987; Werger 和广濑,1991; Anten 等人,1995; Dreccer 等人,2000;莫罗等人,2012; Hikosaka ,2016)和许多研究表明,尽管实际的氮分布导致冠层的光合作用高于均匀的氮分布,但仍然不是最优的(Field,1983; Evans,1993; Hollinger,1996; Hirose et al 。,1997; Meir 等人,2002; Wright 等人,2006;Hikosaka ,2016)。一方面,最佳与现实之间的这种差异可以通过生理上的限制来解释(Niinemets ,2012;Hikosaka ,2016)。另一方面,这可能是由于过简化模型的错误预测所致,在该模型中,结构特征的变化对光的截留,叶片生化的年龄依赖性修饰以及功能性氮分配中的光适应作用的影响被忽略了。为了将这些因素纳入驯化过程,提出了一种基于蛋白质更新(合成和降解)概念的机械模型,以模拟光合氮在羧化过程中的动力学,电子传递和光收集功能,以及蛋白质的发展和衰老。叶(报等人。,2019a 一个第二2019b)。测量冠层中的叶仰角和叶面积分布,以构建多层冠层模型,以模拟更真实的冠层内部光分布,用作蛋白质更新模型的输入。通过操纵控制氮分布和分配的参数,可以量化光合氮的最佳利用程度,以最大化硅层的冠层碳同化。

关键字:功能划分, 光脉动法, 机理模型, 氮再分配, 最优性, 光合适应

材料和试剂


 


25升塑料盒,作为水培系统的容器
用于将植物固定在塑料盒上的聚苯乙烯泡沫板
岩棉立方体(10厘米×10厘米*6.5厘米),如在水培系统的生长培养基(Grodan 德尔塔; Grodan ,鲁尔蒙德,T 他荷兰)
岩棉块(3.6厘米×3.6厘米×4厘米),用作种子播种介质(Grodan A-OK入门塞子;Grodan ,鲁尔蒙德,荷兰)
塑料工厂支撑夹
氮磷钾肥(Ferty 2 MEGA;植物中,雷根斯陶夫,德国)
PK肥料(Ferty Basisdünger 1; 植物中,雷根斯陶夫,德国)
氮肥(YaraLiva Calcinit ;挪威奥斯陆亚拉)
纸袋(尺寸应足以容纳单个黄瓜薄片,约15厘米×20厘米或更大)
岩棉板坯(100毫升×20厘米* 7.5厘米),如在温室生长培养基(Grodan GT专家; Grodan ,鲁尔蒙德,T 他净herlands )
种子(黄瓜黄瓜' Aramon ',Rijk的Zwaan酒店,德利尔,荷兰)
1%H 2 SO 4 (96%H 2 SO 4 ,卡尔·罗斯,目录号:46 23;制备:6毫升96%H 2 SO 4 与1 LH 2 O 混合)
 


设备


 


走在生长室与曝气系统和控制的空气的温度和湿度(Vötsch INDUSTRIETECHNIK ,巴林根使用金属卤化物灯,德国)和光源(HQI-BT 400 W / d PRO ; 欧司朗,慕尼黑,德国)
量子传感器LI-190R与测光表LI-250A(LI-COR,Lincoln,NE,USA)耦合
光传感器记录器(LI-COR,型号:LI-1000,LI-1400或LI-1500 )
温度数据记录仪(Tinytag ;英国奇切斯特双子座数据记录仪)
P ortable光合作用系统LI-6400XT(加上6400-40叶室荧光计)或LI-6800(LI-COR,林肯,NE,USA)
叶绿素计(柯尼卡美能达传感,型号:SPAD-502 )
叶面积计(LI-COR,型号:LI-3100C )
分辨率为0.01 g的实验室天平(Sartorius,型号:ED4202S )或质量小于1 g的分辨率为0.1 mg的实验室天平(Sartorius,型号:ED224S )
真空冷冻干燥机(Alpha 1-4 LSC;德国哈茨Osterode的Martin Christ Gefriertrocknungsanlagen)
3D数字转换器(Fastrak; Polhemus,科尔切斯特,美国)
 


软件


 


R (3.3.0版或更高版本; R统计计算基金会,https://www.r-project.org/);提供了R 脚本和模拟示例数据集以促进数据分析(https://github.com/yichenpao/bio-protocol/)
R脚本1 [数据处理](请参阅数据分析A,B部分)
R脚本2 [模型参数化](请参阅数据分析C,D部分);所需的软件包为' DEoptim ',' deSolve ',' ggplot2 ',' reshape2 ',' xlsx '
R脚本3 [模拟和计算机模拟测试](请参阅数据分析EH部分);所需的软件包为' DEoptim ',' deSolve ',' dplyr ',' ggplot2 ',' magrittr ',' xlsx '
Digitool (3D数字化仪的定制软件,可根据要求提供)
 


程序


 


育苗实验
播下一个黄瓜种子(黄瓜黄瓜“ Aramon ”,Rijk的Zwaan酒店在每个岩棉立方体,德利尔,荷兰)(3.6厘米× 3.6厘米× 4厘米,图1A和1B),并用水充分直到立方体是完全湿润。
播种的种子要比实验设计所需的植物数多20-40%(发芽率和质量更低,更多的添加量),以便在7-10天内选择均匀的幼苗。
将环境条件设置为10-15 mol m -2 d -1 在幼苗水平的光合有效辐射(PAR),光照时间为12 h,每天24 °C / 20 °C夜间空气温度和70%相对湿度。
播种后八天,在每次传送岩棉立方体到一个较大的岩棉立方体(10厘米× 10厘米× 6.5厘米,Figure1C)和灌溉用氮磷钾肥(0.5克L-的营养液-1 Ferty 2 MEGA;植物中,雷根斯陶夫,德国; 5.7 毫N,2.7 毫K,0.35 毫P,0.45 毫镁在工作溶液)每天一次。
 


生长室实验以参数化蛋白质周转模型
移植和开始实验
在植物水平上准备至少具有三个恒定光照强度的腔室(黄瓜,其中一个<5,一个在10-15之间,另一个> 25 mol 光子m -2 d -1 PAR),该范围应涵盖在光照环境中发现的大多数变化在农作物生产期间。
准备有至少三个水平的氮的营养液(一个<2.8,一个3.5-5和一个> 8.5 毫NO 3 - 黄瓜Aramon )使用氮肥(YaraLiva Calcinit ; 屋,奥斯陆,挪威)和PK肥料(Ferty Basisdünger 1; 植物中,雷根斯陶夫,德国; 5.2 毫K,1.3 毫P,0.82 毫镁在工作溶液)如果氮的效果是令人感兴趣的。
当幼苗的第二片真叶长3厘米(在较大的岩棉块中长约8天)时,将其移植到水培系统中(图1D),该系统由25升塑料盒和一块聚苯乙烯泡沫板组成,修复包含植物的岩棉方块。
向25-L箱中填充营养液,并从曝气系统向溶液供气(图1E)。
准备用于在水培系统中支撑植物的聚苯乙烯泡沫板。
切割聚苯乙烯泡沫板,使其适合25 L的盒子。
在木板中间切一个正方形的开口(9.5厘米×9.5厘米)。
将植物固定在聚苯乙烯泡沫板上的开口中,然后将其放入25 L的盒子中。
在每棵植物的取样叶片中,选择四到八片(垂直计数)的健康且无阴影的叶片,并记录它们的出现日期。
使用量子传感器LI-190R和光度计LI-250A(LI-COR,林肯,内布拉斯加州,美国)记录采样叶水平的光照条件。
植物护理和监测环境状况
准备定制的叶子固定器。
使用涂有塑料的金属线制成叶片支架,形成一个环结构,该环结构由支撑叶片的圆形部分和可以固定在茎和叶柄上的杆部分组成(图1F)。
将每个支架与两个塑料植物支撑夹结合在杆部。
准备不同大小和长度的叶片支架,以在各个发育阶段支撑叶片。
通过去除第七个节点以下的花朵,使植物建立营养生长。
修剪所有侧生芽,使植物保持单茎生长,并在取样的叶片上方向下训练其余的芽,以避免相互遮蔽(图1G)。
每周彻底更新营养液并记录一次营养液中的氮含量,两次更新之间应填充一次溶液,并每周两次用1%H 2 SO 4 将pH值调节至6.0-6.5 。
将数据记录仪Tinytag (Gemini Data Loggers,英国奇切斯特,英国)放在采样叶周围,并记录每日平均气温(T mean ,°C)。
测量和取样的叶子的中心记录PAR(图1 一在Wiechers 等人。,2011)每周和调节使用叶保持器叶片的角度(图1F) ,以确保它们被水平地和完全暴露于光线,不要被其他树叶遮蔽,以便在树叶上达到目标PAR水平。
D:\ Reformatting \ 2020-2-7 \ 1902785--1312 Pao-Chen Pao 683288 \ Figs jpg \ Fig1.jpg


图1. 育苗和生长室实验的设置。AC。在岩棉块中育苗。DE。将种苗转入水培系统。F.定制的叶子固定器,用支撑夹固定在茎和叶柄上的植物上。G.通过向下训练其余的枝条,避免取样叶片的阴影(标记为黄色)。


 


采集采样叶数据
气体交换
在三到四天的间隔内对每种环境条件进行测量,从最年轻的叶子(出现叶子后大约三天)开始,然后从较老的叶子开始,以获取年龄范围广泛(大约40- 550 °Cd)。
使用日平均温度和基准温度(T base ,黄瓜为10°C ),估计从出现之日到测量之日的各个叶片的年龄(t ,°Cd ):
 


 (G1)


 


测量净光合速率(甲Ñ ,微摩尔CO 2 米-2 小号-1 ,表1),胞间CO 2 浓度(ç 我,μ 摩尔摩尔-1 ),光合光子通量密度(PPF d ,μ 摩尔米-2 小号-1 光合的)和量子效率II电子传输(φ PSII )使用p ortable光合作用系统LI-6400XT或LI-6800(LI-COR,林肯,NE,USA)。
 


表1. 便携式光合作用系统LI-6400XT和LI-6800(LI-COR,林肯,NE,美国)在输出文件中标记变量。用于数据处理的必要的变量是净光合速率(甲Ñ ,微摩尔CO 2 米-2 小号-1 ),胞间CO 2 浓度(ç 我,μ 摩尔摩尔-1 ),光合光子通量密度(PPFD ,μ 摩尔米- 2 s -1 )和光系统II电子传输的量子效率(ϕ PSII )。


系统


一个ñ


ç 我


聚四氟乙烯


ϕ PSII


LI-6400XT


照片





帕里


PhiPS2


LI-6800


n








PhiPS2


 


收集数据到。CSV FIL E(图2和表2),这将在数据分析部分A和B中的数据一起使用p rocessing (在这个例子“example_chamber_gas_exchange_data.csv” )。
 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312包一晨683288 \ Figs jpg \ Fig2.jpg


˚F igure 2.格式气EXCHAN GE数据文件。有关列名称,描述和所用单位的说明,请参见表2。


 


表2.气体交换数据文件中使用的列名称,描述和单位


栏名


描述


单元


ExpID


实验编号


无单位


测量日期


测量日期


无单位


叶ID


被测叶片的ID


无单位


一个


净光合速率


微摩尔CO 2 m -2 s -1





细胞间CO 2 浓度


µmol CO 2 mol -1


聚四氟乙烯


光合光子通量密度


µmol光子m -2 s -1


PhiPS2


光系统II电子传输的量子效率


无单位


 


测量后直接切下椎板以进行进一步分析。
 


收集资料
使用叶绿素计SPAD-502(日本美能达相机公司)测量叶绿素含量(SPAD值),并使用收获的叶片的面积计LI-3100C(LI-COR,林肯,NE,美国)测量叶面积。
将每个薄片放在单独的纸袋中,并在-20°C下冷冻保存。
在真空冷冻干燥机预冷样品架子(阿尔法1-4 LSC;马丁基督Gefriertrocknungsanlagen GmbH公司奥斯特罗德是哈茨,德国),以10 ℃,冰冷凝器至-50℃。将干燥的薄片样品在1.030 mbar的压力下冷冻48小时,然后测量冻干薄片的质量。请注意,大多数样品都可以干燥至1-5%的残留水分。因此,应校正测得的干重,以排除残留水分的重量。
将叶片研磨成细粉,然后分析总氮(例如Nelson和Sommers,1980年)和叶绿素(例如,Lichtenthaler ,1987年)含量。
收集数据到。csv 文件(图3和表3;在本示例中为“ example_chamber_harvest_data.csv” ),该文件将在数据分析部分A和B中用于数据处理。
 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312 Pao-Chen Pao 683288 \ Figs jpg \ Fig3.jpg


˚F igure 3 HARV的格式EST数据文件。有关列名,说明和所用单位的说明,请参见表3。


 


表3. 收获数据文件中使用的列名称,描述和字段


栏名


描述


单元


ExpID


实验编号


无单位


叶ID


收获叶片的ID


无单位


品种ID


品种编号


无单位


LightID


灯光处理的ID


无单位


氮ID


氮处理的ID


无单位


出现日期


收获叶片出现的日期


无单位


收获日期


收获日期


无单位


LightLevel_mol_m2_d


光照水平


摩尔光子m -2 d -1


氮水平_毫米


氮处理水平


毫米


均值温度


收获叶片生长期间的平均气温


°C


垃圾邮件


相对叶绿素含量(SPAD值)


无单位


LeafArea_cm2


收获叶的叶面积


厘米2


DryMass_g


收获叶片的叶片干重


G


总计N_mg_g


收获叶片的总氮含量


mg N g -1 干质量


Chl_a_mg_g


收获叶片的叶绿素a含量


mg Chl ag -1 干物质


Chl_b_mg_g


收获叶片的叶绿素b含量


mg Chl bg -1 干质量


 


 


使用线性(Chl = a + b×SPAD)或幂(Chl = a × SPAD b )函数,量化SP AD值与每区域叶片叶绿素浓度之间的经验关系,以促进温室实验中的无损估计。
 


使用便携式光合作用系统LI-6400XT或LI-6800(LI-COR,林肯,内布拉斯加州,美国)进行气体交换测量
在以下条件下让叶子适应10-20分钟:
光合光子通量密度(PPFD )1,300 µ mol m -2 s -1 ,
样品CO 2 400 µ mol mol -1 ,
叶温度25℃ ,
相对湿度55-65% ,
直到Rubisco的完全激活和光合速率,气孔导度和荧光(˚F “)equili 定到稳定状态,然后读出光饱和的净光合速率(甲饱和,微摩尔CO 2 米-2 小号-1 )。


使用多相闪光(MPF)方法测量最大叶绿素荧光(F m ')(Loriaux 等,2013; Moualeu - Ngangue 等,2017):
具有恒定最大辐照度320 m s的第一阶段,
第2阶段在350 毫秒内具有辐照衰减(斜坡深度为30%),
与阶段1相同的阶段3具有恒定的最大辐照度,持续200 ms。
测量净光合速率的光响应曲线(甲Ñ ,微摩尔CO 2 米-2 小号-1 )下PPFD 900,500,250,150,100,85,70,60,50,40,0μ 摩尔米-2 s -1 。
此测量的总持续时间为每片叶子30-40分钟;请注意,对于老叶或弱光下生长的叶子,适应时间通常比年轻和高光下生长的叶子更长。
光系统II的电子传输(量子效率φ PSII )是利用荧光数据(计算穆歇尔和Lawson,2013年):
 


 (P1)


 


通过温室实验获得冠层结构信息和数据以评估蛋白质更新模型
移植和开始实验
使用数据记录仪Tinytag 记录幼苗附近的日平均气温(T 平均值,°C),并在第三片真叶长3 cm(在较大的岩棉块中生长约两周)时移栽幼苗。
将两株植物转移到一块岩棉平板上(100 cm×20 cm×7.5 cm),它们之间的距离为50 cm,行之间的距离为150 cm (在种植面积为96 m 2 的温室中,密度为1.33 m 2 -2 ) 。
通过滴灌系统向植物提供所需氮水平的营养液。
植物护理和监测环境状况
垂直将植物训练到电线上,并除去第七节以下的所有侧生芽和花朵。
使用数据记录器Tinytag 在温室中记录每日平均气温,并使用量子传感器LI-190R和光度计LI-250A 记录冠层上方PAR的每日积分。
每周分析营养液中的硝酸盐含量(Navone ,1964)和铵盐(按照德国关于水,废水和污泥的标准检测方法,DIN 38406-5),并保持岩棉板中氮的含量。
收集数据到。csv 文件(图4和表4;在此示例中为“ example_greenhouse_environment_data.csv” ),该文件将在数据分析部分EH中用于仿真和计算机测试。
 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312鲍一晨683288 \ Figs jpg \ Fig4.jpg


˚F igure 4格式温室ENVIR的onmental数据文件。有关列名,说明和所用单位的说明,请参见表4。


 


表4. gre enhouse环境数据文件中使用的列名称,描述和单位


栏名


描述


单元


ExpID


实验编号


无单位


日期


日期


无单位


DPI_L_ L


轻型奶嘴ID L 下的日光子积分*


摩尔光子m -2 d -1


DPI_L_ H


摘录ID H 下的每日光子积分*


摩尔光子m -2 d -1


供应_N_ L


氮处理下营养供应中的氮水平ID L *


毫米


底物_N_ L


氮气处理下底物中的氮水平ID L *


毫米


供应_N_ H


氮处理下养分供应中的氮水平ID H *


毫米


基板_N_ H


氮气处理下底物中的氮水平ID H *


毫米


Tmean_L_ L


光照处理下的日平均气温ID L *


°C


Tmean_L_ H


光照下日平均气温ID H *


°C


*光和氮处理的ID由用户命名,并且应与温室结构数据中的处理ID相同。


 


收集工厂数据
使用3D数字化仪(Chen et al 。,2014)以每周间隔(相当于上述温室条件下两次测量之间的差值约100°Cd)无损测量叶片数量,le af仰角,叶片面积和叶片面积指数以上),以获得处于不同发育阶段的静态树冠结构。
估算冠层中各个叶片的年龄(t ,°Cd)。
计算总生长度日(GDD 篷从天○)˚F移植到温室(当叶X 出现;在该示例中X = 3),以测量电一天EMENT 利用方程 G1
将GDD 冠层除以移植后出现的叶子数量(不包括前x-1个叶子),以估计叶同步(每叶°Cd,相继出现的叶片之间的间隔),假设在实验期间叶同步恒定:
 


 (G2)
 


使用phyllochron 估计相对于叶子x 的叶子n 的年龄:
 


 (G3)
 


测量气体交换和相对叶绿素含量(SPAD值,用于非破坏性地估算Chl ),以评估叶片中光合蛋白质更新功能模型的性能。
在2-3天内对同一工厂进行数字化和气体交换测量。
收集数据到。csv 文件(图5和表5;在此示例中为“ example_greenhouse_structure_data.csv” ),该文件将在数据分析部分EH中用于模拟和计算机测试。
 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312包一成683288 \ Figs jpg \ Fig5.jpg


˚F igure 5.格式温室植物S的tructural数据文件。有关列名,说明和所用单位的说明,请参见表5。


表5.温室植物结构数据文件中使用的列名称,描述和单位


栏名


描述


单元


ExpID


实验编号


无单位


测量日期


测量日期


无单位


植物ID


数字化工厂的ID


无单位


品种ID


数字化品种的ID


无单位


LightID


灯光处理的ID


无单位


氮ID


氮处理的ID


无单位


叶号


数字化的叶子的等级数


无单位


EA_学位


数字化叶片的仰角


 


LA_cm2


叶面积数字化


厘米2


 


数字化工厂结构并将坐标转换为结构数据
使用3D数字化仪(Fastrak ;Polhemus ,科尔切斯特,美国)对每种处理方法至少对两个代表性植物的结构进行数字化处理。
从植物的底部到顶部,以标准化的直角坐标顺序获取各个植物器官上的点的结构信息(改编自Kahlen 和Stützel ,2007;Wiechers 等,2011):
数字化“节点0”在杆在其插入点到基部岩棉立方体。
数字化与第一个真叶的叶柄的根部相对的“节点1”(图6A中的“节点”)。
在第一个真叶插入茎的位置将“ axil 1”数字化(图6A中的“ Axil”)。
以预先定义的序列和层板表面上13个点的空间排列数字化“叶子1”(图6)。
继续按照“节点n- 轴n- 叶n ” 的顺序进行数字化,直到所有叶均被数字化。
忽视鲜花和水果。
将笛卡尔坐标转换为结构数据。
叶面积:trian gles 的预定结构的面积总和(图6A)。
叶仰角(EA ,°):所述角度相对于所述叶的基部(叶尖端的方位之间的点1和2在图6B)和水平面。
 


 


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312鲍一晨683288 \ Figs jpg \ Fig6.jpg


图6. 从数字化数据中提取叶面积和仰角的配置。A.黄瓜茎上数字化点的预定位置,用于结,叶腋,叶片和叶片上定义的三角形结构。B.叶片仰角(EA)。


 


量化叶面积指数(LAI ),EA 和叶龄(t ,°Cd)之间的经验关系,以模拟计算机模拟实验中的冠层结构动态,例如:
 


 (G4)
 


 (G5)


 


数据分析


 


R脚本1 [数据处理] (图7)


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312鲍一晨683288 \ Figs jpg \ Fig7.jpg


˚F igure 7.概述ř 脚本1进行数据处理。该脚本的输入数据文件是来自生长室实验的“ example_chamber_harvest_data.csv” 和“ example_chamber_gas_exchange_data.csv” 。


 


使用气体交换数据估算光合作用参数(图7,#1.3.0)
使用Evans(1993)给出的叶绿素浓度(Chl ,mmol m -2 )估算叶子的吸收率(abs ,无单位):
 


 (P2)


 


估计电子传递速率(Ĵ ,微摩尔ë - 米-2 小号-1 )各种光合光子通量密度下(PPFD ,μ 摩尔米-2 小号-1 ):
(P3)
 
其中β (0.5,无单位)是光子II和I之间光子的分配分数。


通过拟合非矩形双曲线的最小二乘估计最大电子传输(J max ):
 
 (P4)
 


其中ϕ (0.425 µmol e – µmol -1 光子; Chen 等,2014)是光子向J 的转换效率,而θ (0.7,无单位; Chen 等,2014)是一个常数凸率因子,描述了J 对PPFD的响应。


估计白天呼吸率([R d ,微摩尔CO 2 米-2 小号-1 使用线性部分(40≤)PPFD ≤100μ 摩尔米-2 小号-1 )的光响应曲线的(角,1948),因为光黄瓜叶片的补偿点大约在。40 µ mol 光子m -2 s -1 。
使用可变J 方法估算对CO 2 (g m ,mol m -2 s -1 )的叶肉电导(Harley 等,1992):
 
(P 5)             
 


其中Г *是在不存在线粒体呼吸的情况下的CO 2 补偿点(黄瓜为43.02 µmol mol -1 ;Singsaas 等人,2003年),C i 为细胞间CO 2 浓度(µ mol mol -1 )。


估计叶绿体CO 2 浓度(ç Ç ,微摩尔摩尔-1 ):
 
 (P6)
 


估计最大羧速率(V 的C max ,微摩尔CO 2 米-2 小号-1 使用单点法)(德Kauwe 等人,2016):
 
 (P7)
 


其中ķ 米(毫摩尔摩尔-1 )由下式给出ķ Ç (404 微摩尔摩尔-1 )和ķ Ô (278 毫摩尔摩尔-1 ),米氏的Rubisco的常数CO 2 和O 2 ,并ö Ç (210 mmol mol -1 )是O 2 在羧化位点的摩尔分数:
 


 (P8)
 


参数化R d ,g m 和叶龄(t ,°Cd,使用等式G1估算)之间的经验关系,叶生长的最后四天的平均日光子积分(DPI 4d ,mol m -2 d -1 )和叶片光合氮(N ph ,mmol m -2 )使用例如 Pao 等人的第10和Ess.16和17 。(2019a):
 
 (P9)
 


 (P10)


 


使用光合参数估算光合氮池(图7,编号1.3.1)
根据Buckley 等人的估计,参与羧化反应的氮(N V ,mmol N m -2 ),电子传输(N J ,mmol N m -2 )和光收集(N C ,mmol N m -2 )。(2013):
N V 包括Rubisco,代表氮对羧化能力的投资:
 


 (M1a)
 


N J 包括Rubisco以外的电子传输链,光系统II核心和卡尔文循环酶:
 (M1b)
 


N C 包括光系统I核心和光收集复合体I 和II:
 
 (M1c)
 


其中χ V (微摩尔CO 2 毫摩尔-1 N s个-1 )是每单位的Rubisco氮羧化能力,和χ Ĵ (微摩尔ë - 毫摩尔-1 N s个-1 )是每单位电子传输氮的电子传输能力。χ CJ (毫摩尔叶绿素毫摩尔-1 N)和χ Ç (毫摩尔叶绿素毫摩尔-1 N)分别是用于每电子传输氮和每捕光组分氮叶绿素的转换系数,。


光合氮(N ph ,mmol N m -2 )被定义为参与光合功能的蛋白质中的生物活性氮,包括参与羧化,电子传输和光收集的氮:
 
               (M2)
 


池的光合氮分配分数X (p X )被确定为氮的池中的比率X (Ñ X ,毫摩尔N×M个-2 )到Ñ pH值:
 


               (M3)
 


将处理后的数据输出到。cs v 文件(图8;在此示例中为“ chamber_processed_data.csv” )(图7,#1.4.0),该文件将在数据分析C和D部分中用于模型参数化。
 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312鲍一晨683288 \ Figs jpg \ Fig8.jpg


˚F igure 8.格式化处理的数据文件输出的从- [R 脚本1. 该文件将被用于模型参数化。


 


R脚本2 [模型参数化] (图9)


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312包一晨683288 \ Figs jpg \ Fig9.jpg


˚F igure 9.概述ř 脚本2为模型参数化。该脚本的输入数据文件是脚本1中的“ chamber_processed_data.csv” 。


 


蛋白质周转模型的说明(图9,#2.3.0)
的官能氮池的变化率Ñ X 是determ的INED由瞬时蛋白合成率(小号X (吨),毫摩尔N×M个-2 °镉-1 )和降解率(d X (吨),毫摩尔N×M个在给定的叶龄(t ,°Cd)下相应酶和蛋白质复合物的-2 °Cd -1 :
 


               (M4)
 


蛋白质合成是一个与年龄相关的零阶过程(Li et al 。,2017),通过逻辑函数描述且与当前的N X 状态无关:
 


               (M5)
 


其中S max,X (mmol N m -2 °Cd -1 )是N X 的最大蛋白质合成速率,发生在叶片发育的早期。常数t d ,X (°Cd -1 )描述了蛋白质合成随时间的相对下降速率。以1 /年龄吨d ,X ,小号X 降低到53.8%小号最大值,X 。


  降解速率D x 由具有降解常数D r ,X (°Cd -1 )的一级动力学控制(Li et al 。,2017 ):
 


               (M6)
 


变量S max ,X 是每日叶子PAR拦截的函数(DPI 拦截叶,mol 光子m -2 d -1 ):
 


               (M7)
 


其中S mm,X (mmol N m -2 °Cd -1 )是潜在的最大蛋白质合成速率,k I,X 是速率常数,描述了S max,X 随着光的增加。因子- [R Ñ ,X 与在营养液(氮水平增加Ñ 小号,毫)由米氏常数,ķ N,X (毫):
 


 (M8)
 


使用生长室实验的数据对蛋白质更新模型进行参数设置(图9)
解决差分方程。M4-M6以获得小号最大值,X ,吨d,X 和d R,X 在ř 使用具有程序性的算法lsoda ()从“功能deSolve ”包和DEoptim ()从“功能DEoptim ”包,其中总和最小化观察和模拟之间的残差平方(图9,#2.4.0)。有三个步骤来量化的参数方程。M5-M8:


使用所有环境条件的数据,对每个光合氮池的t d,X (等式M5)和D r,X (等式M6)进行定量,假设D r,X 和t d,X 是特定于物种和功能的,不受光和氮利用率的影响(图9,#2.4.1)。
用每种环境条件的确定值t d,X 和D r,X 量化S max ,X (等式M5)(图9,#2.4.2)。
使用' stats '包中的nls ()函数通过非线性最小二乘法拟合,从S max,X 确定S mm,X ,k I,X (方程M7)和k N,X (方程M8),以及标准误差估计的(se)和P 值(pv )也被计算出来(图9,#2.4.3)。
将结果(图9,#2.5.0)输出到。csv 文件(图10;在本示例中为“ parameterize_result_output.csv” ),该文件将在数据分析部分EH中用于仿真和计算机测试。
D:\ Reformatting \ 2020-2-7 \ 1902785--1312包一晨683288 \ Figs jpg \ Fig10.jpg


˚F igure 10.格式参数化结果文件输出的从- [R 脚本2. 该文件将被用于模拟和在计算机芯片上测试。


 


R脚本3 [模拟和计算机模拟测试] (图11)


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312 Pao-Chen Pao 683288 \ Figs jpg \ Fig11.jpg


˚F igure 11.ř脚本3的概述模拟和在计算机芯片上测试。该脚本的输入数据文件是温室实验中的“ example_greenhouse_structure_data.csv ”和“ example_greenhouse_environment_data.csv ”,以及脚本2中的“ parameterize_result_output.csv ”。


 


模拟叶片的光合作用(图11,#3.6.0)
为了评价每日篷的碳同化作用,净光合速率(甲Ñ ,微摩尔CO 2 米-2 小号-1 在树冠个体叶子)应该被模拟。甲Ñ 被定义为最小的RUBP 羧受限(甲Ç ,毫摩尔CO 2 米-2 小号-1 )和RUBP 再生限制(甲Ĵ ,毫摩尔CO 2 米-2 小号-1 )净光合速率(夸尔等人,1980)。稳态A c 可以用等式解析解。9b,14和15,以及带有等式的A j 。Pao 等人的图9c,14和15 。(2019a)用的给定值叶子- (对空气的蒸气压赤字d ,千帕),大气CO 2 浓度(ç 一个,微摩尔摩尔-1 ),光合光子通量密度(PPFD ,μ 摩尔米-2 小号-1 )在叶片水平和光合参数上。


叶片水平PPFD 遵循Berer-Lambert定律(Monsi 和Saeki,2005)进行模拟(图11,#3.4.1),其冠层消光系数(k )和叶片面积指数(LAI )并通过叶片高程余弦进行调整角度(EA ,°),使用等式估算叶龄。G4和G5 (图11,#3.3.0):
 
 (P11)
 


其中昼夜PPFD 罩盖(上面的PPFD aboveCanopy ,微摩尔米-2 小号-1 在给定的时间)(吨小时白天,H)通过简单的余弦钟形函数(金博尔和贝拉米,1986)来计算每日PAR积分冠层以上(DPI 高于上层,DPI ,mol m -2 d -1 )和日长(DL ,h):
 


 (P12)


 


光合参数J max ,V cmax ,abs ,R d 和g m
在给定的PPFD 下的电子传输速率J max 使用公式(1)计算。P4。
羧化速率(V Ç ,微摩尔CO 2 米-2 小号-1 )是基于激活的Rubisco的下一个给定的量来计算PPFD (钱等人,2012。) :
 
 (P13)
 


abs 使用公式计算。P2 。
使用经验关系式重新模拟R d 和g m a 。P 9和P10(图11,#3.3.1)。
 


模拟日常冠层碳吸收(图11,#3.6.0)
使用以下输入数据来模拟第d 天白天的日冠层碳同化(DCA,mol d -1 ):


环境信息(从叶子的外观到第3 天直到第d天):T 均值(方程式G1),高于上盖的DPI (方程式P10)以及供应溶液和岩棉板中的氮浓度;
温室冠层特征(在第d天):叶面积(数字化数据,图7A)和叶龄(方程G1-G3;图11,#3.5.1)。
首先使用等式模拟树冠中的每片叶子的光合氮库,直到第d 天。M4-M8(图11,#3.7.0和#3.8.0)和使用等式的光合参数。M1a-M1c。使用公式可模拟生长过程中的DPI 截获叶。P11(图11,编号3.5.1)。的平均值DPI aboveCanopy 所述植物生长(从移栽到测量日)中被用作DPI aboveCanopy 上天d 模拟DCA。在营养液(氮水平Ñ 小号)被假定为的平均值在供应溶液和氮浓度岩棉SL ABS(图11中#3.4.1)。为了测试的效果入射光条件一天d 上的最优Ñ pH值分布和分区,DPI aboveCanopy 被系数“乘以DPI乘数” 由用户(分配图11中,#3.7.2和3.8.3# )。


在第d 天以0. 1 h 的时间步长模拟叶片净光合作用,并在白天每0.1 h进行汇总,以获取每日的叶片碳同化作用(DLA,mol d -1 )。DCA计算为冠层中所有叶片的DLA的总和。


 


在计算机模拟实验中测试冠层中氮分布的最佳状态(图11)
为了评估的叶之间分布的影响Ñ pH值上DCA,分配因子˚F d 被引入到公式 M5产生蛋白质合成速率的变化(图11,#3.7.0):
 


               (S 1 )
 


控制病症与定义˚F d = 1。增加˚F d 加速了蛋白质的合成和提高的速度的降低向顶Ñ pH值重新分配,但它也降低了总Ñ pH值在天篷(Ñ 篷)。为了获得叶片光合氮含量(Ñ 叶,我,毫摩尔N的叶我)具有可比Ñ 篷,模拟Ñ 叶,我与˚F d = Ñ (表示为N” 叶,我)按比例调整,以之间的比率ñ 篷与获得˚F d = 1和ñ 篷与获得˚F d = ñ :


 


               (S2)


 


叶i (p X ,i )中的池X的光合氮分配分数设置为等于控制值:


 


               (S3)


 


这些调整可确保在更改分配模式时具有相同数量的N 冠层。因子f d 在0.5到5.0之间变化(图11,#3.7.1),其N ph 值可与黄瓜叶片(< 150 mmol N m -2 )中观察到的N ph 相媲美。然后,在给定的环境条件下,将由各种f d (图11,#3.7.2)产生的DCA值与对照DCA(f d = 1)进行比较,并输出到a (图11,#3.7.3)。xlsx 文件(在此示例中为' Test_fd_result.xlsx ')并绘制(图11,#3.7.4和图12)。可以在叶级输出N ph 和p X的详细结果(图11,#3.7.5)到a。xlsx 文件(在此示例中为' Test_fd_result_detailed.xlsx ')。


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312鲍一晨683288 \ Figs jpg \ Fig12.jpg


图12. 在给定的树冠上方每日光合作用活性辐射积分(DPI )下,不同光合氮(N ph )分布因子f d 值下,白天树冠碳同化量(DCA,mol d -1 )的百分比变化的示例结果,mol m -2 d -1 )。A. DPI =植物生长期间的平均DPI乘以0.25。B. DPI =植物生长期间的平均DPI。C. DPI =植物生长期间的平均DPI乘以2。f d的变化导致DCA的正向变化表示对照N ph 分布(f d = 1)不理想。


 


在计算机实验中测试叶片中氮分配的最佳状态(图11)
为了评估N ph 的叶内分配对DCA的影响,将分配因子f p,X 引入等式。M7修改最大蛋白质合成S max,X ,以便在三个光合氮库之间分配模式的变化(图11,#3.8.0):


 


               (S4)


 


的控制条件由下式定义˚F p ,X 在= 1的增加˚F p ,X 在更高的速率的合成的结果Ñ X ,并增加了分割以池X 。池X (S mm ,X )的潜在最大蛋白质合成速率被系数f p,X修改,范围为0.2到2.0,以找到使DCA最大化的功能之间的最佳叶内N ph 分配(图11,#3.8.3)。将在给定环境条件下最大化DCA的分区模式识别为“最佳”,然后与控制DCA(f p,X = 1)进行比较,然后输出结果(图11,#3.8.4)a。xlsx 文件(在此示例中为' Test_fp_result.xlsx ')并绘制(图11,#3.8.5和图13)。具有最佳详细结果Ñ pH值在叶级分区可以被输出(图11中的#3.8.6)到一个。xlsx 文件(在此示例中为' Test_fp_result_detailed.xlsx ')。


 


D:\ Reformatting \ 2020-2-7 \ 1902785--1312包一晨683288 \ Figs jpg \ Fig13.jpg


FIGUR Ë13.实施例结果白天日常篷碳同化百分比变化(DCA,摩尔d -1 )与光合氮(的各种值Ñ pH值)分配因子˚F P,X 下平均流入每日光合有效辐射上面的积分植物生长期间的冠层(DPI ,mol m -2 d -1 )乘以DPI乘数在0.25和2.0之间。最优f p 导致DCA的正变化,X 表示控制N ph 分区(f p,X = 1)次优。在该示例中,在光照处理H和氮气处理L下生长的树冠的N ph 分配在其生长的光照环境下次优,如果优化N ph 分配,则DCA可以提高近15%。


 


致谢


 


这项工作得到了Deutsche Forschungsgemeinschaft (DFG)的支持。如Pao 等人所述,对本协议进行了修改并附加了原始协议。(2019a )。


 


利益争夺


 


作者宣称没有利益冲突。


 


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引用:Pao, Y., Chen, T., Moualeu-Ngangue, D. P. and Stützel, H. (2020). Experiments for in silico evaluation of Optimality of Photosynthetic Nitrogen Distribution and Partitioning in the Canopy: an Example Using Greenhouse Cucumber Plants. Bio-protocol 10(6): e3556. DOI: 10.21769/BioProtoc.3556.
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