Abstract
Environmental monitoring is increasingly used to assess spatial and temporal trends in agricultural sustainability, and test the effectiveness of farm management policies. However, detecting changes in environmental variables is often technically and logistically challenging. To demonstrate how survey effort for environmental monitoring can be optimised, we applied the new statistical power analysis R package simr to pilot survey data. Specifically, we identified the amount of survey effort required to have an 80% chance of detecting specified trends (−1 to −4% pa) in 13 environmental variables on New Zealand kiwifruit orchards within an 11-year period. The variables assessed were related to soil status, agricultural pests (birds), or ecosystem composition (birds). Analyses were conducted on average values (for each orchard and year combination) to provide a consistent scale for comparison among variables. Survey frequency varied from annual (11 surveys) to every 5 years (3 surveys). Survey size was set at either 30, 60, 150 or 300 orchards. In broad terms, we show the power to detect a specified range of trends over an 11-year period in this sector is much higher for ‘soil status’ than for ‘agricultural pest’ or ‘ecosystem composition’. Changes in one subset of native bird species (nectar-feeders) requiring a particularly high level of relative survey effort to detect with confidence. Monitoring soil status can thus be smaller and less frequent than those which also want to detect changes in agricultural pests or ecosystem composition (with the latter requiring the most effort) but will depend on the magnitude of changes that is meaningful to detect. This assessment thus allows kiwifruit industry in New Zealand to optimise survey design to the desired information, and provides a template for other industries to do likewise. Power analyses are now more accessible through the provision of the simr package, so deploying and integrating them into design and decision-making should be routine to reduce the risk of inefficiencies and opportunity costs.