Evidence for Resilient Agriculture Dataset
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
The Evidence for Resilient Agriculture (ERA) dataset now synthesizes evidence from 2,916 agricultural studies conducted across Africa, providing a comprehensive foundation for evaluating the performance of agronomic technologies and management strategies in diverse contexts. <br><br> ERA v1.0.1 contains 112,859 observations from 2,011 agricultural studies published between 1934 and 2018. These studies examine the efficacy of 363 practice combinations across 87 environmental, social, and agricultural-economic outcome indicators. Observations are geolocated and can be linked to open-source environmental, economic, and social datasets, enabling analysis of how local context shapes the performance of agricultural practices. ERA v1 provides a foundational evidence base for the design of policies, programs, and investments supporting African agricultural development. <br><br> As part of the 2024–25 update (ERA v2), we expanded the dataset to include studies published between 2018 and 2024. This additional search identified approximately 900 new eligible studies, bringing the total number of studies represented in ERA to 2,916. The expansion substantially strengthens the evidence base for understanding climate-resilient agronomy across Africa, particularly in areas such as soil fertility management, climate adaptation, intercropping, and sustainable intensification. <br><br> In addition to extending the temporal coverage, ERA v2 modernized and enhanced its methodology through the integration of AI-powered tools. Literature screening was supported by OpenAlex, an open research graph enabling scalable, automated discovery and filtering of scientific publications (see vignette: https://eragriculture.github.io/AI-Powered-Meta-Analysis-Automation/docs/OA-vignette.html). <br><br> Data extraction workflows were augmented using OpenAI APIs, which support semi-automated extraction of numerical results and metadata from tables, text, and figures (https://eragriculture.github.io/AI-Powered-Meta-Analysis-Automation/docs/Use_of_AI_for_Extraction.html). <br><br> These innovations substantially increased throughput, consistency, and reproducibility in evidence synthesis, reducing human extraction time while improving dataset structure and reliability. <br><br> The ERA dataset includes bibliographic metadata, geographic coordinates, environmental context, experimental design variables, treatment comparisons, and outcome indicators. Each row corresponds to a unique combination of article, site, treatment contrast, commodity, outcome, and time period. Supporting documentation provides definitions, hierarchies, and data structures for all coded fields: <br><br> -ERA_Compiled.csv – compiled ERA dataset (wide format) <br> -ERA_Compiled_Fields.csv – descriptions of dataset fields <br> -ERA_Bibliography.csv – bibliographic metadata <br> -ERA_Search_Terms.csv – search terms for literature discovery <br> -Practice_Codes.csv – hierarchical definitions of agronomic practices <br> -Outcome_Codes.csv – outcome definitions and hierarchies <br> -EU_Codes.csv – enterprise unit definitions <br><br> To support users, a fully updated ERA User Guide has been published: <br> https://eragriculture.github.io/ERA_Agronomy/ERA-User-Guide.html <br> https://eragriculture.github.io/ERL/Guide-to-Livestock-Data-Analysis-in-the-ERA-Dataset--STATIC.html <br><br> Additional vignettes from the ERAg and ERAgON R packages illustrate workflows for data exploration, analysis, and reproducibility: <br> -ERA-Introduction.pdf <br> -ERAdev – a collection of scripts illustrating how the ~2,900 studies in ERA were systematically compiled, standardized, and harmonized (AI-assisted workflows included) <br> -ERA-Explore-and-Analyze.pdf <br> -ERA-Search-Protocols.pdf <br> -ERA-Yield-Stability.pdf <br><br> ERA v1 have received support from the CGIAR Excellence in Agronomy Initiative, the Livestock and Climate Initiative, the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), and partner agencies including FAO, the EU, IFAD, USDA-FAS, and CIFOR’s Evidence-Based Forestry program. <br><br> The ERA v2 update was additionally funded by the CGIAR Sustainable Farming Program (SFP). <br>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,447 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle