Chapter 2 Growth or Storage

What the polysaccharide is

The slime that makes a fresh D. officinale stem sticky when chewed is mainly a glucomannan: a long chain of mannose and glucose units, many of them carrying acetyl groups. Its structure was described from the stem as a 2-O-acetylglucomannan (Hua et al. 2004). The Chinese Pharmacopoeia judges the dried herb partly by this fraction: total polysaccharide, measured by the phenol–sulfuric acid method, must be at least 25 % of the dry stem, with limits on the proportion of mannose (国家药典委员会 2020; Dubois et al. 1956).

Evidence

Polysaccharide content varies widely between growing places and methods. In one survey of 380 samples, 42 % met the 25 % standard; most lay between 20 % and 35 %, and plants grown in sun had more than plants grown in shade (Tan et al. 2023). In a comparison of three cultivation methods, stems grown on living trees had significantly less polysaccharide than stems grown on stone or in greenhouses, which did not differ from each other (Yang et al. 2023). In four strains held at 4 °C for 28 days, stem polysaccharide rose in two strains and fell in two (Bao et al. 2024).

These results are a warning against a simple story. “Wild-like” cultivation on trees did not give more polysaccharide in that comparison, and the response to cold depended on the strain. The polysaccharide is a storage product, and what decides how much is stored is the balance between carbon coming in and carbon used for growth.

The growth–differentiation balance

Evidence

The growth–differentiation balance hypothesis of Herms and Mattson starts from the observation that growth is more sensitive than photosynthesis to shortages of water and nutrients. When moderate stress slows growth more than it slows photosynthesis, carbon that growth cannot use accumulates and is available for storage and for secondary products. When nutrients and water are plentiful, growth uses most of the carbon (Herms and Mattson 1992). A related idea, the carbon–nutrient balance hypothesis, makes the same prediction for nitrogen: plants short of nitrogen tend to have more carbon-based compounds (Bryant et al. 1983). Experiments across many species confirm that water deficit uncouples growth from photosynthesis, so that carbohydrate concentrations rise in growing organs under mild drought (Muller et al. 2011).

The hypothesis was framed for defensive secondary compounds, and its application to a storage polysaccharide is an extension, not a test. Its logic, though, is a simple budget, and a budget can be written down.

A carbon budget

Calculation

A clump gains carbon each day by photosynthesis. Growth takes what it can use, limited by nitrogen, water and night temperature; the surplus is stored as polysaccharide; respiration at night draws on everything. Four regimes over 240 days, with illustrative parameters.

def run(nitrogen, water_cycle, night_temp, days=240):
    """Daily carbon budget of a clump, in relative units (starting dry mass 1). Photosynthesis
    supplies carbon; growth (the sink) uses it first; what growth cannot use is stored as
    polysaccharide; night respiration draws on both pools in proportion. Water deficit and
    cool nights slow growth more than they slow photosynthesis (Muller et al. 2011)."""
    S, P = 0.9, 0.1                                    # structural dry matter, stored polysaccharide
    for day in range(days):
        w = 1.0
        if water_cycle:                                # 12-day cycle: 6 days watered, 6 days drying
            phase = day % 12
            w = 1.0 if phase < 6 else 1.0 - 0.1 * (phase - 5)   # falls to 0.4 by day 12
        size = S / (1 + S / 20)                           # self-shading: gains level off
        supply = 0.060 * size * w ** 0.3                  # photosynthesis, mildly water-limited
        cool = 1.3 ** ((night_temp - 22) / 10)            # growth slows on cool nights more than photosynthesis
        demand = 0.056 * size * nitrogen * w * cool       # growth, strongly N-, water- and temperature-limited
        growth = min(supply, demand)
        S += 0.75 * growth                                # a quarter of growth carbon is respired in synthesis
        room = max(0.0, 1 - P / (S + P) / 0.5)             # storage slows as the stem fills (cap ≈ 50 %)
        P += 0.9 * (supply - growth) * room               # surplus stored as polysaccharide
        r = 0.006 * 2.0 ** ((night_temp - 20) / 10)       # night respiration per unit mass, Q10 = 2
        S, P = S * (1 - r), P * (1 - r)
    return S + P, P / (S + P)

regimes = [("high N, constant water, warm nights 22 °C", 1.0, False, 22),
           ("high N, constant water, cool nights 14 °C", 1.0, False, 14),
           ("low N, drying cycles, warm nights 22 °C", 0.7, True, 22),
           ("low N, drying cycles, cool nights 14 °C", 0.7, True, 14)]
print("after 240 days (illustrative parameters, not fitted to Dendrobium data):")
for label, n, cyc, tn in regimes:
    mass, frac = run(n, cyc, tn)
    print(f"  {label:42s} dry mass {mass:4.1f}, polysaccharide {100 * frac:4.1f} % = {mass * frac:3.1f}")
# after 240 days (illustrative parameters, not fitted to Dendrobium data):
#   high N, constant water, warm nights 22 °C  dry mass 55.4, polysaccharide  6.9 % = 3.8
#   high N, constant water, cool nights 14 °C  dry mass 59.0, polysaccharide 19.3 % = 11.4
#   low N, drying cycles, warm nights 22 °C    dry mass 21.8, polysaccharide 28.5 % = 6.2
#   low N, drying cycles, cool nights 14 °C    dry mass 21.6, polysaccharide 32.9 % = 7.1

Dry mass (left) and polysaccharide share (right) over 240 days in the illustrative carbon budget. Low nitrogen and drying cycles raise the polysaccharide share but cut the mass; cool nights alone raise the share while keeping the mass.

The model reproduces the direction that the hypothesis predicts: less nitrogen, drying cycles and cool nights all raise the polysaccharide share. It also shows the cost. Low nitrogen and drying cut the dry mass by more than half, so that the total polysaccharide per clump is lower than under cool nights with ordinary feeding. For a grower who sells stems by weight, the best regime is not the one with the highest percentage. Which regime gives the most valuable crop depends on prices and on parameters that this model only guesses at, and it can be settled only by field trials.

Cool nights

Calculation

Two night-time effects, with simple assumptions: respiration rising with temperature (Q₁₀ = 2), and the water cost of carbon fixed at night by the CAM part of the plant.

import math

def es(t):
    return 0.6108 * math.exp(17.27 * t / (t + 237.3))

# 1. Night respiration. Respiration roughly doubles for every 10 K (Q10 ≈ 2; Atkin & Tjoelker 2003).
#    Suppose a 12-hour night at 20 °C costs 20 % of the day's net carbon gain.
for t_night in (26, 22, 18, 14):
    share = 0.20 * 2.0 ** ((t_night - 20) / 10)
    print(f"night at {t_night} °C: respiration ≈ {100 * share:4.1f} % of the day's carbon gain")

# 2. CO2 taken up at night (the CAM part of a C3–CAM plant) costs less water, because the
#    evaporative demand (VPD) is lower. Water lost per unit of CO2 fixed scales with VPD
#    at the same stomatal opening (ratio of diffusivities 1.6, taken as constant here).
day = es(26) * (1 - 0.65)
print(f"\nday 26 °C, 65 % RH: VPD {day:.2f} kPa")
for t, rh in ((14, 85), (20, 85), (22, 70)):
    night = es(t) * (1 - rh / 100)
    print(f"night {t} °C, {rh} % RH: VPD {night:.2f} kPa -> water per CO2 fixed at night is "
          f"{100 * night / day:3.0f} % of the daytime cost")
# night at 26 °C: respiration ≈ 30.3 % of the day's carbon gain
# night at 22 °C: respiration ≈ 23.0 % of the day's carbon gain
# night at 18 °C: respiration ≈ 17.4 % of the day's carbon gain
# night at 14 °C: respiration ≈ 13.2 % of the day's carbon gain
#
# day 26 °C, 65 % RH: VPD 1.18 kPa
# night 14 °C, 85 % RH: VPD 0.24 kPa -> water per CO2 fixed at night is  20 % of the daytime cost
# night 20 °C, 85 % RH: VPD 0.35 kPa -> water per CO2 fixed at night is  30 % of the daytime cost
# night 22 °C, 70 % RH: VPD 0.79 kPa -> water per CO2 fixed at night is  67 % of the daytime cost

Cool nights save carbon and, in a C3–CAM plant, make night-time uptake cheap in water. Two cautions apply. Plants acclimate: after days or weeks at a new temperature, respiration partly returns towards its former rate, so the short-term Q₁₀ overstates the long-term saving (Atkin and Tjoelker 2003). And very cool nights with high humidity bring back the dew problem of Chapter 1.

Note

The day and night temperatures proposed for D. officinale in grower manuals, such as 26 °C by day and 14 °C by night, are working rules, not optima established by controlled trials. The models in this chapter show which way each factor pushes; they do not say how far.

Hypothesis · not established

Not established. In greenhouse D. officinale, a day–night difference of at least 10 K, compared with 4 K at the same mean temperature and feeding, raises stem polysaccharide by at least 3 percentage points of dry mass without lowering fresh yield by more than 10 %. The hypothesis predicts that, in a randomised trial of matched greenhouse bays over one growing season, the wide-difference bays show both results. If polysaccharide does not rise by 3 points, or yield falls by more than 10 %, the hypothesis is refuted.

References

Atkin, Owen K., and Mark G. Tjoelker. 2003. “Thermal Acclimation and the Dynamic Response of Plant Respiration to Temperature.” Trends in Plant Science 8 (7): 343–51.

Bao, H., H. Bao, Y. Wang, et al. 2024. “Variations in Cold Resistance and Contents of Bioactive Compounds Among Dendrobium Officinale Kimura Et Migo Strains.” Foods 13 (10): 1467.

Bryant, John P., F. Stuart Chapin, and David R. Klein. 1983. “Carbon/Nutrient Balance of Boreal Plants in Relation to Vertebrate Herbivory.” Oikos 40 (3): 357–68.

Dubois, Michel, K. A. Gilles, J. K. Hamilton, P. A. Rebers, and Fred Smith. 1956. “Colorimetric Method for Determination of Sugars and Related Substances.” Analytical Chemistry 28 (3): 350–56.

Herms, Daniel A., and William J. Mattson. 1992. “The Dilemma of Plants: To Grow or Defend.” The Quarterly Review of Biology 67 (3): 283–335.

Hua, Y. F., M. Zhang, C. X. Fu, Z. H. Chen, and G. Y. S. Chan. 2004. “Structural Characterization of a 2-O-Acetylglucomannan from Dendrobium Officinale Stem.” Carbohydrate Research 339 (13): 2219–24.

Muller, Bertrand, Florent Pantin, Michel Génard, et al. 2011. “Water Deficits Uncouple Growth from Photosynthesis, Increase C Content, and Modify the Relationships Between C and Growth in Sink Organs.” Journal of Experimental Botany 62 (6): 1715–29.

Tan, Daopeng, Yao Zeng, Jiangmei Wang, et al. 2023. “Effect of Environmental Factors on the Polysaccharide Content of Dendrobium Officinale.” Food Science and Technology (Campinas) 43. https://doi.org/10.1590/fst.127422.

Yang, D., Y. Song, A. Lu, et al. 2023. “Metabolomics-Based Analysis of the Effects of Different Cultivation Strategies on Metabolites of Dendrobium Officinale Kimura Et Migo.” Metabolites 13 (3): 389.

国家药典委员会. 2020. 中华人民共和国药典（2020 年版）一部. 中国医药科技出版社.

Fan, Y. (2026). Ecological Cultivation, Stress-Induced Polysaccharide Synthesis and Fresh-Extract Rheology of Dendrobium officinale: CAM Photosynthesis, VPD Water Control, Growth-Differentiation Balance and Nocturnal Core-Body-Temperature Regulation [铁皮石斛的生态栽培、逆境多糖合成与鲜汁流变学] (Version 1.0.0). 5D Library. https://doi.org/10.5281/zenodo.23729740 · Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).