A method for soil management assessment in an unreplicated commercial field
- Authors
- Lee, Juhwan; Plant, Richard E.
- Issue Date
- Feb-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
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- SOIL RESEARCH
- Volume
- 60
- Number
- 7
- Start Page
- 743
- End Page
- 754
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/2787
- DOI
- 10.1071/SR21090
- ISSN
- 1838-675X
1838-6768
- 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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Collections - 농업생명과학대학 > 스마트농산업학과 > Journal Articles
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