PBMs and the Simulation of Plant Growth

PBMs focus on plant functioning. The goal is to assess the crop production per square meter based on environmental

Plant Growth Simulation Software

Figure 9 Simulations of geometrical plant models simulated with AMAPsim software (J. F. Barczi, CIRAD): (a) wild cherry tree (Fournier), (b) zelkova tree (Barthelemy), (c) young Aleppo pine (Carraglio), (d) coffee tree (de Reffye), (e) cotton plant (de Reffye), (f) ornamental tobacco plant (Rey). The parameters for plant development and geometry have been assessed from measurements on real plants.

Figure 9 Simulations of geometrical plant models simulated with AMAPsim software (J. F. Barczi, CIRAD): (a) wild cherry tree (Fournier), (b) zelkova tree (Barthelemy), (c) young Aleppo pine (Carraglio), (d) coffee tree (de Reffye), (e) cotton plant (de Reffye), (f) ornamental tobacco plant (Rey). The parameters for plant development and geometry have been assessed from measurements on real plants.

Figure 10 Flowchart of a PBM for plant growth.

conditions, and not its 3-D representation. For such an attempt, the plants are considered only at the minimal level of organ compartments. PBMs rely more on computation than on computer simulation and represent the engineer's point of view of agronomists.

PBMs are used to simulate crops production in either fields (i.e., Ceres model) or greenhouse conditions (i.e., Tomsim model; Figure 10). They take account of the simplifications seen previously:

• light interception based on the Lambert-Beer law: this way of using LAI is quite efficient to compute and skip the cumbersome plant leaf canopy;

• biomass production according to eqn [1] (see above): parameter Yg and processes Pg and Rm are computed from empirical knowledge and relationships with the environmental parameters (radiation, temperature, CO2);

• using the common pool of biomass (transport resistance for assimilates is neglected), the biomass partitioning is

Measure Tomato Growth Hourly

Figure 11 Measured (symbols) and simulated (model TOMSIM: lines) dry matter production for five tomato crops differing in planting date (first data point). Hourly averages for measured global radiation outside the greenhouse, greenhouse temperature, and CO2 concentration were input to the model. Details are given in Heuvelink E (1995) Dry matter production in a tomato crop: Measurements and simulation. Annals of Botany 75: 369-379.

Figure 11 Measured (symbols) and simulated (model TOMSIM: lines) dry matter production for five tomato crops differing in planting date (first data point). Hourly averages for measured global radiation outside the greenhouse, greenhouse temperature, and CO2 concentration were input to the model. Details are given in Heuvelink E (1995) Dry matter production in a tomato crop: Measurements and simulation. Annals of Botany 75: 369-379.

performed according to the relative values of the sink strength of organs (some experiments allow assessing them directly);

• sources and sinks have no significant direct interaction.

PBMs perform the computation of both biomass production (dry matter) and biomass partitioning. They are dynamical models that follow the step-by-step plant functioning. They rely on eqn [2] and on the assessed sinks of the different plant compartments thanks to direct measurements.

Such models work fairly well in normal conditions (Figure 11). Nevertheless, some bottlenecks of PBMs and FSMs appear for different reasons:

• For ornamental crops, a 3-D output is relevant to assess the resulting plant architecture (plant external quality).

• A lack of prediction of the SLW according to different environmental conditions (climate, density) prevents a good estimation of the leaf area.

• There is neither a good prediction about organ abortion, nor about dry matter contents.

• No consideration is taken of the statistical variations among the yield.

PBMs often summarize the whole plant architecture in four compartments (leaf, stem, fruit, and root) for samples collected on the crop at each step of time. This makes fitting models easy and misleading, since an infinite number of plant architectures could correspond to a plant fitted with a PBM.

It is generally recognized that these drawbacks come from ignoring the plant architecture and its plasticity, such as the interactions between growth and development at phytomer level. Typically, the number of phytomers produced by a bud building an annual shoot depends on the biomass production of the previous year in temperate trees.

model parameters by model inversion, and to perform system optimization and control. For this reason, there is mainly a focus on this approach in this article.

Worm Farming

Worm Farming

Do You Want To Learn More About Green Living That Can Save You Money? Discover How To Create A Worm Farm From Scratch! Recycling has caught on with a more people as the years go by. Well, now theres another way to recycle that may seem unconventional at first, but it can save you money down the road.

Get My Free Ebook


Post a comment