Trait prioritization studies have guided research, development, and investment decisions for public sector crop breeding programs since the 1970s, but the research design, methods and tools underpinning these studies are not well-understood. We used PRISMA-ScR (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) to evaluate research on trait ranking for major crops over the past forty years (1980 – 2023). Data extraction and descriptive analysis on 657 papers show uneven attention of crops, lack of systematic sex-disaggregation, and regional bias. The lack of standardized trait data taxonomy across studies, inconsistent research design, and data collection practices make cross comparison of findings impossible. In addition, network mapping of authors and donors shows patterns of concentration and presence of silos within research areas. This study contributes to the next generation of innovation in trait preference to produce more inclusive, demand-driven varietal design that moves beyond trait prioritization focused on productivity and yield.
- Crop groups critically important for food security and nutrition such as root, tubers and bananas, or fruits and vegetables, receive less research attention than cereals.
- There is a gender bias in the trait prioritization literature, with only a third of studies collecting sex-disaggregated data.
- It is impossible to aggregate trait preferences across studies due to the lack of a standardized taxonomy of traits
- Tools that have the potential to provide high quality trait prioritization data are commonly used, but are applied inconsistently and unevenly across diverse stakeholders
- There is a dearth of information on trait priorities in South America and Middle East and North Africa, reflecting a regional bias in trait prioritization research.
- Network mapping of authors and donors shows patterns of concentration that intensifies over time, raising questions on increased research siloing.
- Data, which contains raw data for the work.
- R script, which contains the script for the descriptive graphs and figures of the work.
- The map present in the paper (Fig. 2) has been done in Google Sheet and it is available at this link.
- Network data are in Data, but the analysis has been done in VOsviewer [Van Eck, N. and Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2), 523-538 (2010)].
The protocol for this scoping review has been registered in Open Science Framework in July 8, 2022 (https://osf.io/ayw8q). Amendments to the protocol are available at the same link.
Please contact Martina Occelli at mo386(at)cornell.edu