A method for soil management assessment in an unreplicated commercial field
Juhwan Lee
A * and Richard E. Plant B
+ Author Affiliations
- Author Affiliations
A Department of Smart Agro-industry, Gyeongsang National University, Jinju, Gyeongsangnamdo 52725, Republic of Korea.
B Departments of Plant Sciences and Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA.
Soil Research 60(7) 743-754 https://doi.org/10.1071/SR21090
Submitted: 10 April 2021 Accepted: 21 January 2022 Published: 15 February 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
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.
Keywords: crop management, crop yields, observational data, particulate organic carbon, precision agriculture, soil carbon dynamics, soil management, spatial model, tillage, total carbon, unreplicated trials, yield monitor.
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