Summary
This methodological paper presents a multilevel meta-analytic workflow that extends beyond conventional mean-effect synthesis to simultaneously capture within-study variability and context-dependent heterogeneity. Using an organic versus conventional crop-yield dataset as exemplar, the authors demonstrate how location-scale meta-regression can disaggregate heterogeneity components to improve interpretability and transferability of ecological findings. The approach provides a framework for assessing both the typical direction and magnitude of effects and the consistency and predictability of outcomes across contexts.
Regional applicability
The methodology is context-neutral and applicable to United Kingdom farming system comparisons; however, the exemplar dataset (organic versus conventional yields) and its findings' applicability to UK arable conditions would require examination of the underlying primary studies included in the meta-analysis. The workflow itself is transferable to any meta-analytic synthesis comparing UK farming systems.
Key measures
Log response ratio (lnRR), log variability ratio (lnVR), log coefficient of variation ratio (lnCVR), study-level heterogeneity, residual heterogeneity
Outcomes reported
The study demonstrates a methodological workflow that simultaneously synthesises mean effects (log response ratio) and within-group variability (log variability ratio and coefficient of variation ratio) from organic versus conventional crop-yield data. It separates study-level heterogeneity from residual heterogeneity and tests whether fertiliser type modifies both mean and variability effects.
Topic tags
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