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Accounting for spatial variability in field experiments on tea
- Peiris, T.U.S., Samita, S., Veronica, W.H.D.
- Experimental agriculture 2008 v.44 no.4 pp. 547-557
- Camellia sinensis, tea, cultivars, field experimentation, spatial variation, methodology, covariance, mathematical models, equations, plant micronutrients, crop yield, fertilizer application, experimental design
- Spatial variability among experimental units is a common problem in field experiments on tree crops such as tea (Camellia sinensis). Spatial variability is partly accounted for by blocks, but a substantial amount remains unaccounted for and this may lead to erroneous conclusions. In order to capture spatial variability in field experiments on tea, six commonly used spatial analysis techniques were investigated: Covariate method with pre-treatment yield as the covariate, Papadakis and the Modified Papadakis nearest neighbour adjustments, Moving means and Modified moving means methods, and Autoregressive method. The data from long-term fertilizer experiments and cultivar evaluation trials, conducted at different locations by the Tea Research Institute of Sri Lanka, were used in the study. Spatial techniques were evaluated by means of their relative efficiency at each location and year. Evaluation of the four neighbour methods analysed in conjunction with the pre-treatment yield, revealed that spatial variability due to both past and current conditions are operative, especially in experiments with large blocks, and could be captured simultaneously. Relative efficiencies averaging 141% clearly indicated that the neighbour techniques in combination with pre-treatment yield would be effective in controlling the experimental error in tea experiments with large blocks (nine plots per block or more). Experiments with small blocks were not affected by spatial variability due to past conditions and only that due to current conditions need to be addressed. Neighbour techniques, on their own, were found to be adequate to capture spatial variability due to current conditions. The modified Papadakis technique was found to be the best with an average relative efficiency of 145%. The techniques investigated in the study can easily be implemented using standard statistical software. The precision of tea experiments could be increased by using covariate analysis with pre-treatment yield and any one of the four nearest neighbour adjustments tested, when the block size is large; and modified Papadakis technique, on its own, when the block size is small.