Nested row–column designs for near-factorial experiments with two treatment factors and one control treatment

Bailey, R. A.; Łącka, Agnieszka

doi:10.1016/j.jspi.2015.04.003

Cited by 8 publications

(10 citation statements)

References 24 publications

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“…The experimental design was randomised in blocks, with four repetitions, in a 2 × 2 + 1 factorial scheme (Bailey and Łacka, 2015). The factorial was determined by two intensities of vibration, two times of exposure to vibrations (minimum and maximum) and a control treatment.…”

Section: Methodsmentioning

confidence: 99%

Mechanical vibrations in the transport of hatching eggs and the losses caused in the hatch and quality of broiler chicks

Donofre

Silva

Nazareno

et al. 2017

J Agricult Engineer

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Many factors present in eggs' transportation from incubator to growing confinements can damage their integrity and, consequently, the results of the incubation. This study aimed to evaluate whether the intensity and duration of exposures of hatching eggs to mechanical vibrations were capable of affecting the hatch and the quality of broiler chicks. Four treatments, determined by two intensities of vibrations and two durations of exposures were evaluated and compared with a control group in a randomised design in blocks with four repetitions. The vibrations were applied by a mechanical shaker before incubation. Then the eggs were incubated and as the following responses were evaluated: hatchability, the type of embryonic mortality, the quality of the birds and the total of losses. The analyses were carried out by a binomial regression model, with application of the Wald test at 5% significance level. The results showed that the exposure of eggs to mechanical vibrations harmfully affects the hatchability and reduces the ratio (percentage) of high quality chicks. Highest vibration level together with longer duration of exposure resulted in worst hatchability and losses. It is argued that the mechanical vibrations are potentially damaging and should be regarded as an important factor in the management of hatching eggs.

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Section: Methodsmentioning

confidence: 99%

Mechanical vibrations in the transport of hatching eggs and the losses caused in the hatch and quality of broiler chicks

Donofre

Silva

Nazareno

et al. 2017

J Agricult Engineer

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show abstract

“…Therefore, the experiment investigated v = 7 treatments: the control treatment and 6 experimental combinations of two spray systems (SV300 and PSV)-factor F 1 -and adjuvant additive (NO, MULTI or PMH)-factor F 2 . This type of arrangement gives a treatment structure called a near-factorial experiment [14,15]. Numerous construction methods for this type of experimental situation in the case of NRC designs are given by Bailey and Łacka [14] and Bose and Mukerjee [15], and it is from the first of these papers from which the plan for the distribution of treatments on experimental units comes-see Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…The problem of choosing a plan for different types of experiments has been discussed in numerous publications. For instance, some methods of designing factorial experiments with a control treatment in a block design with nested rows and columns are widely considered by Bailey and Łacka [14] and Bose and Mukerjee [15]. An example of this type of experiments is also presented in Section 4.…”

Section: Insurance Policy In Agricultural Experimentsmentioning

confidence: 99%

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NRC Designs—New Tools for Successful Agricultural Experiments

Łącka

2021

Agronomy

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In a nested row–column design (NRC), the experimental units in each of n blocks are grouped into n1 rows and n2 columns. Due to its structure, this experimental design allows full control of the experimental material and a relatively simple feedback loop within the “statistical triangle”. By applying such designs in agricultural experiments, we provide an insurance policy against future unexpected problems. Until now, the cost of this policy has been a complex statistical analysis of experimental data. This paper proposes a new “direct” approach to ANOVA based on the latest literature on the subject. The paper provides the theoretical foundations of this approach, indicates the possibility of applying it to factorial and near-factorial experiments, and supplements the theory with a familiar letter-based representation of all-pairwise comparisons, which has so far been lacking in the literature. The methodology is illustrated by the analysis of a field experiment carried out to improve the use of fungicides against late blight in tomato processing. The presented analytical tools are supplemented with code in R.

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“…As Bailey and Lacka (2015) report, it is not uncommon to include a "do nothing" control treatment in experiments, especially in factorial experiments where one of the factors is quantitative. Yates (1937) called this a dummy treatment.…”

Section: Dummy Treatmentsmentioning

confidence: 99%

Hasse diagrams as a visual aid for linear models and analysis of variance

Bailey

2020

Communications in Statistics - Theory and Methods

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The expectation part of a linear model is often presented as a single equation with unknown parameters, and the reader is supposed to know that this is shorthand for a whole family of expectation models (for example, is there interaction or not?). It is helpful to list the whole family of models separately and then represent them on a Hasse diagram. This shows which models are sub-models of others, which helps the user to respect marginality when choosing the most parsimonious model to explain the data.Each row in an analysis-of-variance table corresponds to an edge in the Hasse diagram. In the scaled version of the Hasse diagram, the length of each edge is proportional to the appropriate mean square. This gives a visual display of the analysis of variance. For some people, this is easier to interpret than the standard analysis-of-variance table. Moreover, the scaled Hasse diagram makes clear the difficulties in model choice that can occur under non-orthogonality.The ideas are illustrated using some familiar families of models defined by crossed and nested factors, possibly including polynomial terms for quantitative factors, as well as some more recently introduced families of models for experiments in biodiversity.

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Nested row–column designs for near-factorial experiments with two treatment factors and one control treatment

Abstract: This paper presents some methods of designing experiments in a block design with nested rows and columns. The treatments consist of all combinations of levels of two treatment factors, with an additional control treatment.

Cited by 8 publications

References 24 publications

Mechanical vibrations in the transport of hatching eggs and the losses caused in the hatch and quality of broiler chicks

Mechanical vibrations in the transport of hatching eggs and the losses caused in the hatch and quality of broiler chicks

NRC Designs—New Tools for Successful Agricultural Experiments

Hasse diagrams as a visual aid for linear models and analysis of variance

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