Stochastic population model of Zea mays L

Area of Science:

• Agricultural Science
• Plant Pathology
• Mathematical Modeling

Background:

• Vector-borne diseases significantly impact maize production.
• Accurate modeling of maize vegetative stages is crucial for understanding disease dynamics.

Purpose of the Study:

• To develop a minimalist stochastic population model for maize vegetative stages.
• To simulate the impact of temperature on maize development and disease propagation.

Main Methods:

• The model incorporates three submodels (A, B, C) to estimate Final Leaf Number (N_FLN) distribution and tassel initiation probability.
• Parameterization utilized laboratory, field experiments, and observational studies across diverse hybrids, weather, and soil conditions.
• Temperature was the sole input variable.

Main Results:

• The model demonstrated good agreement between predicted and observed development times.
• Validation was performed using data from constant temperature lab experiments, field trials in Brazil and Australia, and observational studies in Argentina.

Conclusions:

• The developed model effectively describes the temporal development of maize populations and disease-related events.
• Future enhancements could integrate leaf growth and area estimation for improved disease vector carrying capacity assessment.