state that light intensity, carbon dioxide concentration and temperature are examples of limiting factors of photosynthesis

Investigation of Limiting Factors in Photosynthesis

Learning Objectives (Cambridge AO)

• AO1: State that light intensity, carbon‑dioxide (CO₂) concentration and temperature are limiting factors of photosynthesis.
• AO2: Design, carry out and analyse an experiment that investigates one limiting factor while keeping the other two in excess.
• AO3: Evaluate the reliability of the data, identify sources of error and suggest realistic improvements.

Syllabus Overview (Cambridge IGCSE/A‑Level Biology 9700 – Topic 13.2)

What Is a Limiting Factor?

A limiting factor is any variable that, when changed, alters the rate of a biological process. In photosynthesis the overall rate is dictated by the factor that is most restrictive at that moment; the other two factors are present in excess.

Key Limiting Factors for Photosynthesis

• Light intensity – number of photons reaching the chloroplasts.
• Carbon‑dioxide (CO₂) concentration – substrate for the Calvin cycle (Rubisco).
• Temperature – influences enzyme activity (Rubisco, ATP‑synthase), membrane fluidity and the balance between photosynthesis and photo‑respiration.

How Each Factor Affects the Rate of Photosynthesis

Typical Experimental Design (Investigating ONE Limiting Factor)

1. Choose a test organism: fast‑growing aquatic plant (Elodea), or leaf discs from a broad‑leaf plant (e.g., spinach).
2. Set up a sealed gas‑collection system:

   • Inverted graduated cylinder (or gas‑evolution tube) filled with water.
   • Connect to a light source and, where required, to a gas‑mixing inlet.

3. Measure the rate of O₂ evolution:

   • Record the volume of gas collected at 1‑minute intervals for 5‑10 min.
   • Express the rate as mL min⁻¹ (or µmol m⁻² s⁻¹ if a gas sensor is used).

4. Vary ONE factor while keeping the other two in excess:

   • Light intensity: use neutral‑density filters or an adjustable LED lamp; measure irradiance with a light‑meter (µmol m⁻² s⁻¹).
   • CO₂ concentration: bubble a known mixture of CO₂‑enriched air (0 %, 0.04 %, 0.1 %, 0.5 %); maintain constant light and temperature.
   • Temperature: place the whole chamber in a thermostatically controlled water bath (10 °C, 20 °C, 30 °C, 40 °C); keep light and CO₂ constant.

5. Control of non‑tested variables (box):

   • Use a sealed chamber with a CO₂‑buffer solution (e.g., NaHCO₃) to keep CO₂ constant when varying light.
   • Maintain temperature with a water bath or a temperature‑controlled stage when varying CO₂.
   • Keep light intensity constant (e.g., 1 000 µmol m⁻² s⁻¹) when varying temperature.

6. Replication: repeat each treatment ≥3 times; randomise the order of treatments to minimise systematic bias.
7. Data analysis:

   • Plot the varied factor (x‑axis) against photosynthetic rate (y‑axis).
   • Identify the region of linear increase, the saturation/optimum point and, where relevant, any decline (photoinhibition or thermal denaturation).
   • For CO₂ data, fit a Michaelis–Menten curve to estimate V_max and K_m.

Practical Skills Integrated with AO2 & AO3

• Planning: formulate a clear hypothesis, list independent, dependent and controlled variables, and design appropriate controls (see box above).
• Data collection: use consistent units (mL min⁻¹), record ambient conditions (room temperature, humidity), and ensure replication.
• Data analysis:

  • Construct scatter plots with best‑fit curves (hyperbolic or bell‑shaped).
  • Calculate mean, standard deviation and, where required, perform a t‑test or one‑way ANOVA to compare treatments.

• Evaluation:

  • Systematic errors – gas leakage, inaccurate light‑meter calibration, temperature drift.
  • Random errors – timing inaccuracies, bubble coalescence, variation between leaf discs.
  • Improvements – digital gas‑sensor, thermostatically regulated cuvette, use of a spectrophotometer to monitor chlorophyll fluorescence as an indirect rate indicator.
  • Real‑world link – how knowledge of limiting factors guides greenhouse lighting, CO₂ enrichment strategies and the selection of C₄ crops for warming climates.

Mathematical Interlude (AO2)

Short calculations that frequently appear in exam questions:

• Surface‑area‑to‑volume ratio (SA:V) – important for diffusion of CO₂ into leaf tissue.

  
  Example: For a spherical leaf disc of radius r, SA = 4πr² and V = (4/3)πr³, so SA:V = 3/r.

• Respiratory Quotient (RQ) – ratio of CO₂ produced to O₂ consumed.

  
  Example: 8 mL CO₂ released while 10 mL O₂ taken up → RQ = 0.8 (mixed carbohydrate‑fat metabolism).

• Michaelis–Menten kinetics for Rubisco:

  
  v = (V_max [CO₂])/(K_m + [CO₂])

  
  Lineweaver‑Burk plot (1/v vs 1/[CO₂]) gives a straight line; the intercepts provide V_max and K_m, which correspond to the CO₂‑saturation plateau observed experimentally.

Interpreting the Data

When the plotted curve reaches a plateau, the factor being varied has become the limiting factor; the other two factors are in excess. A decline after the optimum (high light or high temperature) indicates photoinhibition or thermal denaturation, respectively. Repeating the experiment for each factor allows you to determine which factor is most restrictive under the chosen environmental conditions.

Key Points to Remember

• Only one factor can be limiting at any one time; the others must be present in excess.
• Light saturation occurs when all photosystems operate at maximum capacity; beyond ≈2 000 µmol m⁻² s⁻¹ photoinhibition may reduce the rate.
• CO₂ saturation reflects Rubisco’s V_max; the hyperbolic curve is a manifestation of Michaelis–Menten kinetics.
• Temperature optimum is species‑specific: C₃ plants ≈25‑30 °C, C₄ plants ≈30‑35 °C.
• Above the optimum, enzyme denaturation and increased photo‑respiration lower the photosynthetic rate.

Real‑World Applications

• Greenhouse production: optimizing light (LED spectra, photoperiod), CO₂ enrichment (≈1 000 ppm) and temperature control to maximise yield.
• Agricultural breeding: selecting C₄ crops (e.g., maize, sorghum) for regions where high temperature limits C₃ productivity.
• Climate‑change modelling: predicting how rising atmospheric CO₂ and temperature will shift the limiting factor for different ecosystems.
• Policy: informing carbon‑sequestration strategies by understanding the temperature ceiling for photosynthetic carbon uptake.

Summary

Light intensity, carbon‑dioxide concentration and temperature each have a range over which they increase the rate of photosynthesis. Beyond their respective saturation or optimum points the rate no longer rises, and the factor becomes limiting. Recognising these limits enables you to design robust investigations, interpret data in biochemical terms (e.g., Rubisco kinetics, photo‑respiration), and apply the knowledge to real‑world challenges such as crop improvement and climate‑change mitigation—key competencies for both AS and A‑Level examinations.