ReviewAgronomy for sustainable development2020
Data synthesis for crop variety evaluation. A review.
Review in Agronomy for sustainable development, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Harnessing plant-exuded prebiotics as a next-generation strategy for sustainable agriculture.Journal of integrative plant biology · 2026Review
- Adaptability and Stability Analyses of Root Yield and Micronutrient Concentrations in Sweetpotato (Plant-environment interactions (Hoboken, N.J.) · 2026Article
- Plasticity of source-sink dynamics contributes to wheat yield stability.Nature communications · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- CERES-Maize (DSSAT) Model Applications for Maize Nutrient Management Across Agroecological Zones: A Systematic Review.Plants (Basel, Switzerland) · 2025Review
- Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models.Frontiers in research metrics and analytics · 2025Article
- Innovative technology integration: E tongue, near infrared grain tester & machine vision approaches for amylose content & quality characterization.Food chemistry: X · 2024Article
- A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties.Frontiers in plant science · 2024Article
- The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective.Agronomy for sustainable development · 2024Review
- Development of a multicriteria decision-making model for evaluating hybrid offspring in the sweetpotato (Breeding science · 2023Article
- gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation.SoftwareX · 2023Article
- Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A variety test platform for the standardization and data quality improvement of crop variety tests.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.