Modelling plant responses to elevated CO2: how important is leaf area index?

Abstract

Background and aims: The problem of increasing CO(2) concentration [CO(2)] and associated climate change has generated much interest in modelling effects of [CO(2)] on plants. While variation in growth and productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated [CO(2)] on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including [CO(2)] concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated [CO(2)] and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising [CO(2)] and discusses related implications for process-based modelling.

Model: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of [CO(2)]. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of [CO(2)] elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply.

Conclusions: Progress in estimating plant growth and productivity under rising [CO(2)] is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.

Fig. 1. Growth and developmental processes that are affected by elevated [CO₂] and are commonly used in process‐based productivity models. Processes and relationships that are shown in white boxes and bold arrows are the ones primarily emphasized in the text. Grey areas indicate the temporal resolution of different processes ranging from seconds‐to‐hours (dark grey), to days (grey) and to decades‐to‐months (light grey).

Fig. 2. Diurnal course of (A) measured air temperature, incident radiation and vapour pressure deficit (VPD) used as model input; (B) observed and simulated (WIMOVAC) instantaneous net assimilation rate of two wheat canopies with LAI = 1 (L1) and LAI = 4 (L4) at 360 µmol mol^–1 [CO₂] (A) and 720 µmol mol^–1 [CO₂] (AE); and (C) the simulated relative effects of [CO₂] elevation on canopy assimilation (AE/A) for L1 and L4. Measured assimilation rates are shown in (B) for A and LAI = 4·2. Information about experimental conditions and measurements are available in Rodriguez et al. (2001) and Manderscheid et al. (2003).

Fig. 3. Relationships between simulated (WIMOVAC) and observed (only for A and LAI = 4·2) instantaneous canopy net assimilation rate and intercepted photosynthetically active radiation (IPAR) for LAI = 1 (L1) and LAI = 4 (L4) at ambient (A) and elevated (AE) [CO₂]. Data refer to simulations and observations presented in Fig. 2.

Fig. 4. Simulated (WIMOVAC) relationships between daily canopy net assimilation rate per unit ground area (GA) and per unit leaf area (LA) and LAI for ambient (A) and elevated (AE) [CO₂]. Relative [CO₂] effects are calculated from AE/A. Climate input data and [CO₂] concentrations were the same as in Figs 2 and 3.

Fig. 5. Simulated (AFRCWHEAT2‐O3) vs. measured IPAR (A, B) and biomass at harvest maturity (C, D) for spring wheat ‘Minaret’ grown at ambient (A) and elevated (2 × ambient, AE) [CO₂] at eight location across Europe between 1994–1996. Simulations were performed using simulated LAI (A, C) or observed LAI (B, D) as model input. Original simulations of biomass (C) that used model estimates of LAI were unsatisfactory (see also Ewert et al., 1999) but improved substantially when observed LAI data were used as model input (D). The remaining unexplained variability was due to factors mainly associated to the use of open‐top chambers that were not considered in the model (see Ewert and Porter, 2000; Ewert et al., 2002).

Fig. 6. Relationships between effects on biomass (BM) and effects on LAI of limited (A) water and (B) N supply for ambient (A) and elevated (1·5 × ambient, AE) [CO₂] of wheat ‘Yecora Rojo’ grown at Maricopa, Arizona in 1992–93 and 1993–94 (H₂O limitation) and 1995–96 and 1996–97 (N‐limitation). Ratios were calculated from measurements of biomass and LAI at different occasions throughout each growing season in water‐stressed (H₂O–) and well‐watered (H₂O+) and N‐limited (N–) and non‐limited (N+) treatments, respectively. References about data sources with information about experimental performances can be obtained from Jamieson et al. (2000) and Ewert et al. (2002).