Multiple covariance: its utility in analysing forest fertilizer experiments |
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| Affiliation: | 1. RHP, Galgeweg 38, 2691, MG S-Gravenzande, Netherlands;2. LEAF - Linking Landscape, Environment, Agriculture and Food Research Centre, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal;3. Forest Research Centre, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal;1. Department of Ecology and Environmental Science, Umeå University, SE-901 87, Umeå, Sweden;2. Biomaterials Division, Stora Enso AB, SE-131 04, Nacka, Sweden;3. EMBRAPA Genetic Resources and Biotechnology—EPqB, 70770-910, Brasilia, DF, Brazil;4. Universidade Católica de Brasília- SGAN, 916 modulo B, Brasilia, DF, 70790-160, Brazil;5. Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden;6. Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Sciences, SE-750 07, Uppsala, Sweden;1. Bullard Laboratories, Department of Earth Sciences, Madingley Rise, Madingley Road, University of Cambridge, Cambridge, CB3 0EZ, UK;2. School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Bristol BS8 1RJ, UK;3. Department of Earth and Planetary Sciences, Nanjing University, 163 Xianlin Road, Nanjing 210023, China;1. Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, BE-9090 Melle-Gontrode, Belgium;2. Flemish Institute for Technological Research (VITO), Boeretang 200, BE-2400 Mol, Belgium;3. Research Institute for Nature and Forest (INBO), Kliniekstraat 25, BE-1070 Brussels, Belgium;1. Department of Chemistry, Faculty of Natural and Agricultural Science, University of the Free State, Bloemfontein, 9301, South Africa;2. Department of Microbial, Biochemical and Food Biotechnology, University of Free State, Bloemfontein, 9301, South Africa |
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| Abstract: | Forest fertilizer trials are often impaired by high experimental variability. Multiple covariance analysis provides a means of removing not only the confounding effect of differences in initial growing stock between experimental plots but also the influence of other variables such as initial foliar and soil nutrient levels, and inter-tree competition. Unblocked fertility gradients can sometimes be partially controlled by using plot position or dummy variables as covariates. Considerable gains in precision and additional information from available data can be achieved by use of multiple covariance methodology. Three examples are presented to illustrate the efficacy of the technique and the specific benefits its application confers. |
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