From field trials to meta-analysis: A practical guide for agricultural synthesis

Agricultural field experiments often produce context-dependent results because treatment effects vary across crop systems, soil properties, climate conditions, management practices, experimental durations, and measurement methods. Meta-analysis offers a powerful way to synthesize such evidence, increase statistical power, and identify the conditions under which agricultural practices are effective. However, agricultural meta-analysis involves a series of practical decisions that are not always fully addressed in general methodological guidelines. These decisions include framing field-relevant research questions, developing reproducible search strategies, extracting data from heterogeneous reports, defining independent records, selecting appropriate effect-size metrics, handling missing variance information, accounting for dependent effect sizes, modeling heterogeneity, and interpreting results in agronomic terms. This review presents a field-trial-to-meta-analysis framework for agricultural research, emphasizing common pitfalls and agriculture-specific considerations. We highlight the importance of defining research questions around crop or soil system, management practice, comparator, outcome, and context; extracting both core statistics and agronomic moderator variables; choosing effect-size metrics that match the outcome and intended interpretation; and using model structures that reflect heterogeneity and dependence in agricultural datasets. We also provide practical guidance for transparent reporting, sensitivity analysis, and interpretation of statistical results in relation to field relevance, trade-offs, and management implications. By linking general meta-analytic principles with the realities of agricultural field research, this guide aims to support syntheses that are statistically robust, reproducible, and agronomically meaningful.