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

Abstract

BackgroundThere 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 discussionAnalyses 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.ConclusionsTo 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.

Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data

“…Such environmental homogeneity may also be achieved by efficient use of experimental design and analysis within the HTPP framework (e.g., choosing suitable block size and/or using appropriate statistical models for the analysis) with less cost than relocating the plants [14]. However, it has been reported that the QTLs identified in controlled environments may not contribute to crop improvement in the field [6].…”

Microclimate conditions in ventilated wet-walled greenhouses in a subtropical climate: spatial variability

“…They also stated that temperature variations inside greenhouses may affect processes such as plant growth and the distribution of pests. Using large climate-controlled greenhouses equipped with computercontrolled conveyor belts carrying up to 600 plants per room, Brien et al (2013) concluded that accounting for the variation in microclimate in a greenhouse is improved using statistical design and analysis rather than rearranging the position of plants during the experiment. However, in many cases, due to financial constraints, greenhouses are not climate-controlled or are only partly so, resulting in microclimate spatial heterogeneity.…”

Replication Concepts for Bioenergy Research Experiments

“…Microclimate effects are common in glasshouse environments and these effects are generally predictable over extended periods of time [18,19]. As such, systematic blocking designs will generally be more effective in accounting for microclimate variation than rearrangement approaches [15].…”

“…We strongly discourage option (b) for four reasons: (1) rearrangement of experimental units increases the workload and creates more opportunities for mistakes in randomization or damage to plants [17]; (2) successful application of this approach requires that experimental units spend similar amounts of time within each microclimate [15]; (3) there is no evidence that this approach will lead to greater precision than the use of formally randomized designs, because the rearrangement either eliminates or creates significant challenges in accounting for spatial or microclimate variation; and (4) there are numerous alternative experimental design options ranging in both complexity and expected effectiveness. Microclimate effects are common in glasshouse environments and these effects are generally predictable over extended periods of time [18,19].…”

“…The completely randomized design, which involves a simple randomization of all experimental units in a single block, is a good idea only under one of two circumstances: (a) there is little likelihood of spatial variation, or microclimate effects, within the glasshouse or (b) the researchers plan to rerandomize or rearrange the experimental units periodically during the course of the experimental time period [15][16][17]. We strongly discourage option (b) for four reasons: (1) rearrangement of experimental units increases the workload and creates more opportunities for mistakes in randomization or damage to plants [17]; (2) successful application of this approach requires that experimental units spend similar amounts of time within each microclimate [15]; (3) there is no evidence that this approach will lead to greater precision than the use of formally randomized designs, because the rearrangement either eliminates or creates significant challenges in accounting for spatial or microclimate variation; and (4) there are numerous alternative experimental design options ranging in both complexity and expected effectiveness.…”