A method for soil management assessment in an unreplicated commercial field

Context: Unreplicated trials are common in agriculture. However, statistical inferences differ from those of traditional experiments based on small, replicated plots. Aims: To present a method to assess management effects on soil carbon (C) storage from unreplicated, side-by-side field trials. Methods: Two estimates of means with spatially correlated errors are compared using a corrected t-statistic. Then causal inference is made by analysing a significant difference between the means (P<0.05) and its changes over time. The use of the method is described in comparing yield and organic C stocks between two large fields. Yield was measured during 1997–2005 with a commercial yield monitor, and soil organic C stocks during 2003–2005. The fields experienced the same tillage practice until autumn 2003 and then with different tillage intensity. Key results: The results show that crop C yield did not differ between the fields when using the same tillage practice but was greater in the tilled than the no-till field. The results also suggest that total and particulate organic matter-C contents depend on tillage history. For comparative purposes, the data were also analysed using standard mixed model analysis with a semivariogram model for spatial autocorrelation among the residuals. The mixed model results were generally similar to those of the corrected t-statistic method. The mixed model was often, but not always, less conservative than the corrected t-statistic model. Conclusions: The method allows analysis of whole-field data and improves our understanding of soil C processes in commercial fields, where agricultural assessment cannot involve replication due to agronomic and economic constraints. Implications: The method complements observational data analyses and can offer a direction towards whole-field management.