Defying the flat-Earth myth: Soil organic carbon monitoring in topographically complex temperate pasture landscapes

• Topographical complex landscapes challenge sampling designs for soil organic carbon. • We explored the vertical, spatial and temporal distribution of soil organic carbon. • Fit-for-purpose methods were applied to capture soil organic carbon variability. • Hybrid methods captured spatial and temporal variability of soil organic carbon. • More pragmatism is required to monitor soil organic carbon for integrated farm plan. Soil sampling design to capture the spatiotemporal variability of soil organic carbon (SOC) stocks in complex landscapes is challenged by the influence of topographical features on primary production, animal behaviour, and carbon and nutrient return to grasslands. Here we explore a range of approaches for streamlining the sampling process using measures of SOC stock data (2003, 2020, 2021) from a long-term hill-country fertilizer experiment. The stepwise procedure included a) evaluation of sampling efficiency between stratified and random soil sampling designs to capture spatial variability of SOC stocks, b) semi-parametric regression analysis to evaluate the representativeness of certain strata (e.g. medium slopes), c) exploring the appropriateness of using shallow SOC stock estimates to represent the full sampling depth, and d) the use of a process-based model to determine expected SOC stock changes and temporal variability to estimate the number of representative soil samples required to achieve a significant minimum change. Our analyses suggest that a) the stratified sampling design was more efficient reflected in the number of samples required to identify 5 % deviation in SOC stocks, b) estimates obtained on the medium slope were a reasonable approximation of the SOC stocks and its spatial variation across the farmlets, c) shallow (0–75 mm) SOC stock estimates from an adjusted regression model offered an accurate inference of its vertical distribution (0–300 mm), and d) the Grass-NEXT model provided insights on where more intensive sampling is required to detect a significant minimum change in SOC stocks over time. Tailored, stratified soil sampling and context-specific strategies, such as using representative strata and shallow sampling as effective steps for estimating SOC stocks and addressing data gaps, are crucial for simplifying monitoring and minimizing the cost of on-farm soil sampling designs in topographically complex landscapes covered by long-term pastures.