Modernizing data reporting in agricultural science: A framework for transparent, reproducible, and artificial intelligence‐ready research

Inadequate data reporting in agricultural journals constrains reproducibility, transparent interpretation, cross‐study data reuse, synthesis, and artificial intelligence (AI)‐driven discovery. This study aimed to (1) review current data and results reporting requirements across agricultural sciences journals and (2) propose a comprehensive, standardized framework for reporting sampling methods, datasets, metadata, statistical analyses, and research findings. Sixty journals were categorized into four groups according to their publishing organizations and editorial governance: American, International, American Geophysical Union (AGU)/American Economic Association (AEA), and Multidisciplinary Digital Publishing Institute (MDPI) journals. Substantial differences in data and results reporting requirements were found among the groups. Mandatory public access to raw data varied considerably by publisher group: AGU/AEA (100%), International (21%), American (15%), and MDPI (0%). Explicit guidelines requiring the reporting of variability metrics were present in only 42% of International and 36% of American journals, and were absent from AGU/AEA and MDPI guidelines. Overall, standardized reporting protocols are essential to connect agricultural research with machine‐driven synthesis. At a minimum, manuscripts must consistently report sample sizes, central tendencies, and variability metrics, while raw data and metadata should be deposited in public repositories. This work provides an actionable framework for agricultural journals to strengthen editorial policies and ensure research remains transparent, reproducible, and verifiable.