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Related Experiment Video

Updated: Jul 2, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

A scoping review on tools and methods for trait prioritization in crop breeding programmes.

M Occelli¹, R Mukerjee^2,3, C Miller⁴

¹School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.

|February 22, 2024

Summary

Trait prioritization studies lack standardization, hindering effective crop breeding. This review highlights uneven crop focus, data inconsistencies, and biases, calling for more inclusive, demand-driven varietal design.

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Area of Science:

Agricultural Science
Genetics
Plant Breeding

Background:

Trait prioritization studies have guided public-sector crop breeding since the 1970s.
Understanding the research design, methods, and tools of these studies is crucial.
Past research has focused on productivity and yield, potentially overlooking other important traits.

Purpose of the Study:

To systematically evaluate trait ranking research for major crops over the past 40 years (1980-2023).
To identify limitations and biases in existing trait prioritization studies.
To inform the next generation of trait preference studies for more inclusive, demand-driven varietal design.

Main Methods:

Systematic literature review using PRISMA-ScR (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols).
Data extraction and descriptive analysis of 657 relevant papers.
Network mapping of authors and donors to identify research concentrations and silos.

Main Results:

Uneven attention to different crops was observed.
Lack of systematic sex disaggregation and significant regional bias were identified.
Inconsistent research design, data collection practices, and lack of standardized trait data taxonomy prevent cross-comparison.

Conclusions:

Current trait prioritization studies have significant limitations, including data inconsistency and bias.
There is a need for standardized methodologies and data taxonomy in trait ranking research.
Future research should focus on inclusive, demand-driven varietal design, moving beyond solely yield-focused trait prioritization.