Public-sector crop breeding programmes are crucial for global food security, focusing on various crops based on diverse grower, processor, trader, and consumer needs and preferences. These programmes are increasingly demand-led and data-driven, beginning with product design that requires understanding trait preferences of heterogeneous target populations within specific environments. These preferences are formalized through target product profiles to guide breeding. The term 'trait' is broadly defined here as observable crop characteristics identifiable by stakeholders, essentially phenotypic variables. Trait prioritization is the process of identifying and translating a wide range of crop attributes into actionable breeding decisions. Trait prioritization studies are particularly critical for marginalized smallholder farmers, often excluded from input into breeding decisions. Failure to align released varieties with end-user preferences results in low adoption rates. Therefore, these studies are vital for improving adoption rates and guiding donor and institutional investment strategies. Despite the importance of trait prioritization, the underlying methodologies are seldom examined. Historically, these studies have used direct ranking or choice experimentation to elicit farmer preferences, supplemented by participatory methods yielding rich information. However, systematic bias, such as the underreporting of sex-disaggregated data, exists. This scoping review aims to address this gap by critically reviewing crop trait prioritization tools and methods, providing a resource for improved research design in crop breeding programmes.

The existing literature reveals several gaps in the methods and tools used for trait prioritization studies. A review of stakeholder preference studies focused on rice highlighted shortcomings in stakeholder representation and heterogeneity in trait preferences, lacking focus on the robustness of the data collection tools and methods. Research also demonstrates a systematic bias in socioeconomic factors, with only 25% of studies reporting sex-disaggregated trait preference data, despite evidence on how social differences influence preferences. The lack of systematic attention to methods and tools for collecting and analyzing trait prioritization data hinders the collection of unbiased and representative data, impacting data-driven decision-making in public-sector crop breeding programs.

This scoping review employed the PRISMA-SCR guidelines to evaluate research on trait ranking for major crops from 1980 to 2023. Five electronic databases (Scopus, Web of Science, CAB Direct, AgEcon Search, and BIOSIS) and twelve grey literature sources were searched using keywords related to farmers, consumers, traits, preferences, and prioritization within the domains of plant breeding, agronomy, and seed systems. After deduplication and screening based on titles and abstracts, 1325 potentially relevant studies were identified. Full-text articles were assessed for eligibility, resulting in the inclusion of 657 studies. Data were extracted using a structured framework of 40 questions, focusing on trait prioritization taxa, publication details, institutions, donors, crops, varieties, ranking methods, data disaggregation, timeframe, breeding purposes, geographic areas, data analysis methods, data collection tools, and stakeholder engagement. Data were synthesized descriptively using R and VOSviewer for network analysis. Studies were categorized into four major crop groups: cereals, legumes, root-tuber-bananas (RTB), and vegetables and fruits.

The review revealed uneven geographic distribution of studies, with a concentration in sub-Saharan Africa (57%) and limited representation from South America (6%) and the Middle East and North Africa (2%). Most studies (58%) used direct questions (surveys and focus group discussions) to identify preferred traits, followed by participatory varietal selection (PVS) and demonstration plots (24%). Around 10% employed choice experiments and sensory evaluations. A third of the studies used only one tool, and frequency counts and hypothesis testing were the most common analytical approaches. Qualitative data analysis was underutilized. Trait prioritization studies were predominantly formally led, even those self-reported as participatory. Only 37% of studies collected sex-disaggregated data, more frequent in recent studies and studies on RTB crops, legumes, and vegetables and fruits. Where sex-disaggregated data were collected, 84% found sex-based trait differences. Explanations often invoked gendered roles, while other social demographic factors were rarely considered. Significant heterogeneity in trait descriptions across studies hindered data synthesis. Network mapping of authors and donors revealed clustered collaborations and funding silos. Cereals received significantly more attention than nutritionally critical crops like RTB and vegetables and fruits.

The findings highlight crucial gaps in trait prioritization research. The regional bias, particularly the underrepresentation of South America and the Middle East and North Africa, warrants further investigation, potentially linked to donor priorities and language limitations. The lack of robust study design, including sample size calculations and representative sampling, questions the generalizability of results. The underutilization of qualitative data analysis and overreliance on direct questioning methods may introduce biases. The lack of standardized trait taxonomies prevents the synthesis of data across studies, hindering data-driven decisions at scale. The underrepresentation of sex-disaggregated data limits the ability of breeding programmes to develop new varieties inclusively and equitably. The dominance of formally led participatory approaches also underscores the need for more genuine farmer-led initiatives.

This scoping review reveals significant gaps in the trait prioritization literature relevant to crop breeders, donors, and the plant science community. These gaps, spanning geographic regions, socio-demographic factors, and crop types crucial for nutrition, raise concerns about research development and prioritization inequalities. To address these gaps, future research should prioritize data collection from nutritious crops in underrepresented regions (sub-Saharan Africa, Asia, South America, and the Middle East and North Africa), leverage existing crop breeding databases and ontologies, consistently collect and analyze sex-disaggregated data, and employ rigorous study designs, including representative sampling and appropriate analytical methods. Improved methodologies are crucial for evidence-based decision-making in crop breeding and achieving demand-driven, inclusive variety development.

The review was limited to studies published in English, potentially excluding relevant non-English language publications. The reliance on self-reported data on participatory methodologies and stakeholder engagement introduces potential biases. The descriptive nature of the synthesis limits the ability to make causal inferences. The interpretation of network analysis findings requires careful consideration of the complexities of research collaborations and funding patterns.