1. Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions is expensive and time-consuming to collect. Furthermore, relationships derived from small-scale plot data may not apply to ecosystem services operating at larger spatial scales (fields, landscapes).
2. Precision yield data can be used to improve the accuracy and geographic range of ecosystem service studies, but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis.
3. We provide examples of analysis using simulated and real precision yield data and outline two case studies of ecosystem services using precision yield data. Ecologists and agronomists should consider using precision yield data more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes.

1. The combined yield monitor data for Supplemental 1 was donated by Trent Clark (the absolution location of the spatial data has been anonymized for privacy).
2. Supplemental 2 uses entirely generated data (see script for details).
3. Supplemental 3 uses a correlation matrix created from unpublished yield data collected by Hector Cárcamo.

All scripts were written in RMarkdown (Allaire et al 2023) using R version 4.3.1 (R Core Team 2023).

Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2023). rmarkdown: Dynamic Documents for R. R package version 2.22, https://github.com/rstudio/rmarkdown.

 R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.