Upcycling rice yield trial data using a weather-driven crop growth model

Area of Science:

• Agricultural Science
• Plant Breeding
• Climate Change Adaptation

Background:

• Efficient plant breeding is crucial for food security amidst climate change.
• Multi-environment yield trials generate valuable data, but it's often underutilized.
• Existing methods struggle to effectively leverage accumulated cultivar performance data.

Purpose of the Study:

• To develop a novel method for quantifying cultivar-specific productivity using existing yield trial data.
• To enable the effective upcycling of historical yield data for advanced breeding applications.
• To guide the development of new rice cultivars adapted to changing climatic conditions.

Main Methods:

• Proposed the "YpCGM method" utilizing two regression coefficients: yield-ability (β) and yield-plasticity (α).
• Expressed individual cultivar yields as a linear regression against theoretical potential yield (Yp) from a crop growth model.
• Applied the method to a large dataset of 72,510 rice yield trials across 110 Japanese locations over 38 years.

Main Results:

• Successfully quantified cultivar-specific yield-ability and yield-plasticity.
• Demonstrated the YpCGM method's capability to upcycle extensive historical rice yield data.
• Validated the method's applicability across a diverse range of cultivars and environments.

Conclusions:

• The YpCGM method offers a powerful tool for analyzing historical yield data.
• This approach can significantly enhance genetic-gain analysis and genome-wide association studies.
• The method provides valuable insights for future plant breeding programs focused on climate adaptation.