Hostname: page-component-84b7d79bbc-g78kv Total loading time: 0 Render date: 2024-07-28T17:21:52.821Z Has data issue: false hasContentIssue false
  • English
  • Français

ACCOUNTING FOR SPATIAL VARIABILITY IN FIELD EXPERIMENTS ON TEA

Published online by Cambridge University Press:  01 October 2008

T. U. S. PEIRIS ,
S. SAMITA  and
W. H. D. VERONICA
T. U. S. PEIRIS*
Affiliation:
Division of Biometry, Tea Research Institute of Sri Lanka, St Coombs Estate, Talawakelle, Sri Lanka
S. SAMITA
Affiliation:
Department of Crop Science, Faculty of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka
W. H. D. VERONICA
Affiliation:
Division of Biometry, Tea Research Institute of Sri Lanka, St Coombs Estate, Talawakelle, Sri Lanka Department of Crop Science, Faculty of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka
*
†Corresponding author. usp71@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Summary

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.

Type
Research Article
Information
Experimental Agriculture , Volume 44 , Issue 4 , October 2008 , pp. 547 - 557
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Akaike, H. (1974). A new look at the statistical model identification. Institute of Electric and Electronic Engineers Transactions on Automatic Control 19:716–723.CrossRefGoogle Scholar
Ball, S. T., Mulla, D. J. and Konzak, C. F. (1993). Spatial heterogeneity affects variety trial interpretation. Crop Science 33:931–935.CrossRefGoogle Scholar
Bartlett, M. S. (1978). Nearest neighbour models in the analysis of field experiments. Journal of Royal Statistical Society B 40:147–174.Google Scholar
Basford, K. E. and Turkey, J. W. (1995). Inter comparing residuals to find outliers in randomized blocks. Australian Journal of Agricultural Research 46:451–461.CrossRefGoogle Scholar
Brownie, C., Bowman, D. T. and Burton, J. W. (1993). Estimating spatial variation in analysis of data from yield trials: A comparison of methods. Agronomy Journal 85:1244–1253.CrossRefGoogle Scholar
Cullis, B. R. and Gleeson, A. C. (1989). The efficiency of neighbour analysis for replicated variety trials in Australia. Journal of Agricultural Science 113:223–239.Google Scholar
Cullis, B. R. and Gleeson, A. C. (1991). Spatial analysis of field experiments – an extension to two dimensions. Biometrics 47:1449–1460.CrossRefGoogle Scholar
Gleeson, A. C. and Cullis, B. R. (1987). Residual maximum likelihood (REML) estimation of a neighbour model for field experiments. Biometrics 43:277–288.CrossRefGoogle Scholar
Glimour, A. R., Cullis, B. R. and Verbyla, A. P. (1997). Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological and Environmental Statistics 2:269–293.CrossRefGoogle Scholar
Kempton, R. A., Seraphin, J. C. and Sword, A. M. (1994). Statistical analysis of two dimensional variations in variety yield trials. Journal of Agricultural Science. Cambridge 122:335–342.CrossRefGoogle Scholar
Lawrence, T. and Townley-Smith, T. F. (1975). Use of moving means in grass yield trials. Canadian Journal of Plant Science 55:587–592.CrossRefGoogle Scholar
Mak, C., Harvey, B. L. and Berdahl, J. D. (1978). An evaluation of control plots and moving means for error control in barley nurseries. Journal of Crop Science 18:870–873.CrossRefGoogle Scholar
Papadakis, J. S. (1984). Advances in the analysis of field experiments. Proceedings of the Athens Academy 59:326–342.Google Scholar
Pearce, S. C. (2002). The control of environmental variation within the experimental area. Experimental Agriculture 38:445–456.CrossRefGoogle Scholar
Qiao, C. G., Basford, K. E., Delacy, I. H. and Cooper, M. (2000). Evaluation of experimental designs and spatial analysis in wheat breeding trials. Theoretical and Applied Genetics 100:9–16.CrossRefGoogle Scholar
Quinn, G. P. and Keough, M. J. (2002). Experimental Design and Data Analysis for Biologists. Cambridge University Press.CrossRefGoogle Scholar
Rong-Cai, Y., Terrance, Z. Ye., Stanford, F. B. and Bandara, M. (2004). Efficiency of spatial analysis of field pea variety trials. Journal of Crop Science 44:49–55.Google Scholar
Rosielle, A. A. (1980). Comparison of lattice designs, check plots and moving means in Wheat breeding trials. Euphytica 29:129–133.CrossRefGoogle Scholar
Scharf, P. G. and Alley, M. M. (1993). Accounting for spatial yield variability in field experiments increases statistical power. Agronomy Journal 85:1254–1256.CrossRefGoogle Scholar
Singh, M., Malhotra, S., Ceccarelli, S., Sarker, A., Grando, S. and Erskine, W. (2003). Spatial variability models to improve dry land field trials. Experimental Agriculture 39:151–160CrossRefGoogle Scholar
Smith, K. F. and Casler, M. D. (2004). Spatial analysis of forage grass trials across locations, years and harvests. Crop Science 44:56–62CrossRefGoogle Scholar
Townley-Smith, T. F. and Hurd, E. A. (1973). Use of the moving means in wheat yield trials. Canadian Journal of Plant Science 53:447–450.CrossRefGoogle Scholar
Wilkinson, G. N., Eckert, S. R., Hancock, T. W. and Mayo, O. (1983). Nearest neighbour analysis of field experiments. Journal of Royal Statistical Society B 45:151–211.Google Scholar