This document discusses the principles of experimental design and analysis in agricultural experiments. It covers key topics such as experimental error, assumptions of analysis of variance, data transformation techniques, and the basic principles of experimental design including replication, randomization, blocking, and selection of experimental designs. The purpose of the document is to introduce fundamental concepts for the design and statistical analysis of agricultural experiments.

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Design and Analysis of Agricultural Experiments - Dr. Awadallah Belal Dafaallah
Principles of
Experimental Designs
and Analysis

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Principles of Experimental Designs and
Analysis
 Experimental error
 Basic principles of experimental designs
 Assumptions of analysis of virulence
 Data transformation

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Design and Analysis of Agricultural Experiments - Dr. Awadallah Belal Dafaallah
Experimental error:
Experimental error is a measure of the variation
which exists among experimental units treated
alike.

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Design and Analysis of Agricultural Experiments - Dr. Awadallah Belal Dafaallah
Source of experimental error:
 Inherent variability.
 Lack in uniformity in the physical conduct
of the experiment.
 Technical error in collection data.

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Design and Analysis of Agricultural Experiments - Dr. Awadallah Belal Dafaallah
Minimization of experimental error:
 Select homogenous experimental units.
 Increase number of replicates per treatment.
 Make random distribution.
 Use blocking.
 Select proper experimental design.
 Improve methods of conduction of the
experiment and collection of data.

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Basic principles of experimental designs:

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Replication:
Any treatment is repeated two or more in the
experiment.

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Advantages of replication:
 To provide an estimate of experimental error.
 To improve precision of experiment by
reducing experimental error through increasing
number of replicates per treatment.
 To increase the scope of interference of the
experimental results through replication over
time and location.

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Selection of number of replicates:
 Degree of required accuracy.
 Size variation in experimental units.
 Type of experimental design.
 Availability of resources.
 Size and shape of experimental unit.

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Random distribution:
It is assignment of treatments of experimental
units so that all units have an equal chance for
receiving treatments.

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Objectives of random distribution:
 Provide valid estimate of experimental error.
 Prevent biasness of assigning treatments to
the experimental units.

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Methods of
randomization
Hat
Cards
Dice
Flapping
coin
Table s of
randomization
Computer

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Local control:
The principle of experimental design allows
for certain restrictions on randomization in form
of blocking or grouping to reduce experimental
error.

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Advantages of replication:
 Provide valid estimate of experimental error.
 Prevent biasness of assigning treatments to
the experimental units.

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Local control and selection of experimental design

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Assumptions of analysis of virulence:
 The main effects are additive.
 Normal distribution of the experimental error.
 Homogeneity in variance of samples.
 Means and variance of treatments should not be
related or correlated.

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Data transformation:
The following transformation methods are
suggested for solving the problem associated with
assumptions underlying analysis of virulence.

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Logarithmic transformation:
Arcsine transformation:
Reciprocal transformation:
Square root transformation:

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Reference:
Dafaallah, A. B. (2017).Fundamentals of Design and
Analysis of Agricultural. Experiments
(Observation – Experimentation –Discussion), Part
One. First Edition. University of Gezira House for
Printing and Publishing, Wad Medani, Sudan. Pp
246.
Dafaallah, A. B. (2017). Fundamentals of Design and
Analysis of Agricultural Experiments (Observation
– Experimentation –Discussion), Part Two. First
Edition. University of Gezira House for Printing
and Publishing , Wad Medani, Sudan. Pp 204.

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Thanks
Dr. Awadallah Belal Dafaallah
E-mail: awadna@hotmail.com;
awadna@uofg.edu.sd
Tel: +249902295166