Comparing test cultivars using reliability functions of test-check differences from on-farm trials

Area of Science:

• Agricultural Science
• Plant Breeding
• Biometrics

Background:

• Traditional crop breeding relies on statistical analysis of trial data.
• Evaluating cultivar performance across diverse environments is crucial for successful crop development.
• Predicting cultivar success in future, unseen environments remains a challenge.

Purpose of the Study:

• To propose a novel selection approach using reliability functions.
• To evaluate the utility of reliability functions for comparing test cultivars against checks.
• To identify cultivar performance characteristics like stability and similarity to checks.

Main Methods:

• Developed a selection method based on probabilities of a test cultivar outperforming a check by a specified amount (d).
• Defined the reliability function as the probability of outperformance for all values of d.
• Applied the method to on-farm maize (Zea mays L.) strip trial data.

Main Results:

• Reliability functions effectively evaluate test cultivar performance relative to checks across environments.
• The location, slope, and shape of reliability functions characterize cultivar performance, similarity, and stability.
• Identified specific environments where test cultivars exhibited performance issues.

Conclusions:

• Reliability functions offer a robust tool for cultivar selection and performance evaluation.
• This approach enhances the understanding of cultivar behavior across varying environmental conditions.
• The method aids in identifying promising cultivars and potential environmental limitations.