Data and R Code for Systematic Review and Meta-analysis of Adoption, Acceptance, and Perceptions of Precision Livestock Farming Technology

This article describes the datasets and R analytical code underpinning a systematic review and random-effects meta-analysis of technology adoption, acceptance, and perception outcomes in precision livestock farming (PLF). Five cleaned comma-separated datasets were compiled from a corpus of peer-reviewed studies identified through a structured database search: (i) adoption proportions, (ii) acceptance Likert-scale means, (iii) acceptance proportions, (iv) perception Likert-scale means, and (v) perception proportions. Each dataset contains effect-size inputs, variance estimates, and moderator variables, including stakeholder group, country/region, livestock species, and PLF technology type. A self-contained R script implements restricted maximum-likelihood (REML) random-effects meta-analysis for each dataset, moderator meta-regression, Egger publication-bias tests, and publication-quality forest and funnel plots. All data and code are deposited in a public repository to promote transparency and reproducibility in PLF technology research. The datasets and code will be of value to researchers, policymakers, and technology developers seeking evidence-based insights into the drivers and barriers of PLF adoption across different production systems and world regions.