Delaying bud-break on pecan trees: a Bayesian longitudinal multinomial regression approach.

Area of Science:

• Agricultural Science
• Statistical Modeling
• Horticulture

Background:

• Pecan bud break timing is critical for yield, with early growth risking frost damage.
• Existing statistical methods may not fully capture complex longitudinal ordinal data in agricultural experiments.
• Understanding treatment effects on bud growth is vital for optimizing orchard management.

Purpose of the Study:

• To adapt and apply a multivariate Bayesian Probit model for analyzing longitudinal multiclass-ordinal pecan bud growth data.
• To evaluate the efficacy of different treatments in delaying pecan bud growth to mitigate cold temperature damage.
• To present a robust statistical methodology for common agricultural research data structures.

Main Methods:

• Utilized a multivariate Bayesian Probit model with a linear plateau longitudinal component.
• Analyzed data from two randomized complete block designs with pecan bud growth measurements on an ordinal scale at irregular intervals.
• Conducted a simulation study to validate the model's implementation and reliability.

Main Results:

• Treatment 3 in Experiment 1 demonstrated the most significant reduction in pecan bud growth rate.
• Treatments 2 and 3 in Experiment 2 showed a notable effect in delaying bud growth.
• While not statistically significant, observed trends suggest potential for specific treatments to manage bud development.

Conclusions:

• The adapted Bayesian Probit model provides a practical and efficient technique for analyzing longitudinal multinomial ordinal data in agricultural studies.
• The study highlights potential treatments for delaying pecan bud growth, offering insights for frost damage mitigation strategies.
• This modeling approach can be valuable for researchers dealing with similar complex response variables in applied agricultural research.