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A method for soil management assessment in an unreplicated commercial field

Authors
Lee, JuhwanPlant, Richard E.
Issue Date
2022
Publisher
CSIRO PUBLISHING
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
Citation
SOIL RESEARCH, v.60, no.7, pp.743 - 754
Indexed
SCIE
SCOPUS
Journal Title
SOIL RESEARCH
Volume
60
Number
7
Start Page
743
End Page
754
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/2787
DOI
10.1071/SR21090
ISSN
1838-675X
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.
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농업생명과학대학 (스마트농산업학과)
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