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      Accounting for variation in designing greenhouse experiments with special reference to greenhouses containing plants on conveyor systems

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          Abstract

          Background

          There are a number of unresolved issues in the design of experiments in greenhouses. They include whether statistical designs should be used and, if so, which designs should be used. Also, are there thigmomorphogenic or other effects arising from the movement of plants on conveyor belts within a greenhouse? A two-phase, single-line wheat experiment involving four tactics was conducted in a conventional greenhouse and a fully-automated phenotyping greenhouse (Smarthouse) to investigate these issues.

          Results and discussion

          Analyses of our experiment show that there was a small east–west trend in total area of the plants in the Smarthouse. Analyses of the data from three multiline experiments reveal a large north–south trend. In the single-line experiment, there was no evidence of differences between trios of lanes, nor of movement effects. Swapping plant positions during the trial was found to decrease the east–west trend, but at the cost of increased error variance. The movement of plants in a north–south direction, through a shaded area for an equal amount of time, nullified the north–south trend. An investigation of alternative experimental designs for equally-replicated experiments revealed that generally designs with smaller blocks performed best, but that (nearly) trend-free designs can be effective when blocks are larger.

          Conclusions

          To account for variation in microclimate in a greenhouse, using statistical design and analysis is better than rearranging the position of plants during the experiment. For the relocation of plants to be successful requires that plants spend an equal amount of time in each microclimate, preferably during comparable growth stages. Even then, there is no evidence that this will be any more precise than statistical design and analysis of the experiment, and the risk is that it will not be successful at all. As for statistical design and analysis, it is best to use either (i) smaller blocks, (ii) (nearly) trend-free arrangement of treatments with a linear trend term included in the analysis, or, as a last resort, (iii) blocks of several complete rows with trend terms in the analysis. Also, we recommend that the greenhouse arrangement parallel that in the Smarthouse, but with randomization where appropriate.

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          Most cited references13

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          Small sample inference for fixed effects from restricted maximum likelihood.

          Restricted maximum likelihood (REML) is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T2 type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different REML analyses and the methods are illustrated using three examples.
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            On the design of early generation variety trials with correlated data

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              The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines

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                Author and article information

                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central
                1746-4811
                2013
                8 February 2013
                : 9
                : 5
                Affiliations
                [1 ]University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia
                [2 ]University of Adelaide, PMB 1, Glen Osmond, SA, 5064, Australia
                Article
                1746-4811-9-5
                10.1186/1746-4811-9-5
                3630016
                23391282
                f42373df-f1c9-480b-a2f3-ead08ab9feec
                Copyright ©2013 Brien et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 September 2012
                : 30 January 2013
                Categories
                Methodology

                Plant science & Botany
                automated phenotyping,conveyor system,greenhouse experimental design,greenhouse experiments,microclimate variation,plant relocation,statistical analysis,thigmomorphogensis

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