Development and validation of a spatially explicit individual-based mixed crop growth model

Area of Science:

• Agricultural Science
• Ecology
• Computational Biology

Background:

• Intercropping success depends on plant spatial and temporal arrangements, influencing ecological interactions.
• Empirical testing of various arrangements is resource-intensive.
• Individual-based dynamic models offer a powerful simulation tool for intercropping research.

Purpose of the Study:

• To enhance a published spatially explicit, individual-based mixed crop growth model.
• To experimentally validate the model's predictions for intercropping systems.
• To investigate the impact of neighborhood scales on plant growth and performance.

Main Methods:

• Developed computer programs for simulating individual plant growth and statistical analysis.
• Parametrized the model using experimental data from a complex diculture.
• Tested predictive capacity with independent spatio-temporal experimental data.
• Modified the model to include nonlinear neighborhood competition effects.

Main Results:

• The enhanced model demonstrated efficient and general predictive capabilities for plant growth under competition.
• Introducing nonlinear terms in the neighborhood competition index improved predictive power.
• Stochastic versions of both the original and modified models were compared using goodness-of-fit measures.

Conclusions:

• Spatially explicit, individual-based modeling is a valuable heuristic tool for understanding and optimizing intercropping systems.
• The modified model, incorporating nonlinear competition, offers superior predictive accuracy.
• This approach facilitates reduced time and investment in empirical intercropping research.