BSc Botany 3rd Year Genetic Plant Breeding Notes 2023

BSc Botany 3rd Year Genetic Plant Breeding Notes 2023.Plant breeding, application of genetic principles to produce plants that are more useful to humans. Plant breeding dates to the very beginnings. BSc Botany, sometimes known as a Bachelor of Science in Botany, is an undergraduate degree that takes three years to complete. Cell biology, genetics, plant anatomy, and plant Embryology are some of the courses covered during this time.

Plant breeding is the use of genetic principles to create plants with greater human utility. This is accomplished by selecting plants found to be economically or aesthetically desirable, first by controlling the mating of selected individuals, and then by selecting certain individuals among the progeny.

BSc Botany 3rd Year Genetic Plant Breeding Notes
BSc Botany 3rd Year Genetic Plant Breeding Notes

Genetic Basis of Plant Breeding

His investigation used self-pollinating (autogamous) plants and he assessed simply- inherited, qualitative traits in a controlled environment. Plant breeders seldom have such luxury, as most agronomically significant traits are determined by complex gene interactions.

What role does genetics play in plant breeding?
Advancements in plant genetics and genomics, when employed in breeding, help encourage higher production and cultivation of crops resistant to pests, diseases, and drought.

Analysis of Statistics in Plant Biology
A statistical tutorial for plant biology is provided here. It will provide you with the basics of various common statistical methods and examples of how to perform these tests using SPSS statistical software available in York’s computer labs and accessible from home using York’s remote Web-based File Access System.


Hypothesis Building
Null hypothesis/alternate hypothesis
Hypothesis Testing
Visual summary
Common Statistical tests and how to run them
Summary statistics
Setting Up a T-test
Paired versus independent t-tests
1-tailed versus 2-tailed t-tests
Running a T-test in SPSS
Importing the data and analysis in SPSS
Reporting t-test results
How to present your findings
Types of graphs and usage

1. Hypothesis Building

  • Creating a testable hypothesis is central to the scientific method

1a. Null hypothesis/alternate hypothesis

  • Null (H0) hypothesis – ‘no effect’ or ‘no difference’ between samples or treatments
  • Alternative (HA) hypothesis – experimental treatment has a certain statistically significant effect
  • A claim for which we are trying to find evidence

Some Examples

  • H0: “Different light spectra have no effect on photosynthetic activity” (H0: x2=x1 or x2-x1=0)
  • HA: “Pollen treated with chloramphenicol grow faster than untreated pollen” (HA: x2>x1 or x2-x1>0)

2. Hypothesis Testing

  • Either reject or fail to reject the H0 based on statistical testing
  • Statistical testing compares the p-value of observed data to an assigned significance level (α — alpha)
    • p-value – the frequency or probability with which the observed event would occur
    • α = the probability that the outcome did not occur by chance
      • Popular levels of significance are 5% (0.05), 1% (0.01), and 0.1% (0.001)
  • If the p-value is SMALLER than α, reject the null hypothesis (H0)

3. Common Statistical Tests

3a. Summary statistics

You should already be aware of the basic summary statistics. Usually, scientific data are summarized by reporting the mean, the standard deviation and the sample size.

3b. T tests

For this course you are expected to understand and use t-tests

T-tests are used to determine if two sets of data (2 means) are significantly different from each other. It assumes that the data are normally distributed and samples are equal.

Two decisions must be made when selecting a t-test:

  • Are the samples paired or independent?
  • Is the comparison 1-tailed or 2-tailed?

4. Setting Up a T-Test

4a. Paired versus independent t-tests

A One-sample (paired) t-test compares two samples in cases where each value in one sample has a natural partner in the other (data are not independent) (data are not independent). It can be used during pre- or post- data analysis. It is also used to compare a sample mean to a specified value.One example of paired t-test analysis is comparing patient performance before and after the application of a drug. Because the same patient is compared before and after treatment, the data are matched.
A two-sample (independent) t-test compares the means for two groups of cases.An example of independent t-test analysis is comparing patient performance in a group receiving a drug versus a separate group receiving a trial drug.

4b. 1-tailed versus 2-tailed t-tests

  • One-tailed/sided t-test expects the effect to be in a certain direction.
  • Is the sample mean greater than μ? (μ is the population mean, the greek letter ‘mu‘)
  • Is the sample mean less than μ?
  • Two-tailed/sided t-test tests for different means regardless of whether it is greater or smaller.
  • Is there a significant difference?

A carefully state experimental hypothesis will indicate the type of effect you are looking For example, the hypothesis that “Coffee improves memory” suggests paire, one tail because you will repeatedly measure the same participants and expect an improvement”Men weigh a different amount from women” suggests an independent two taile test as no direction is implie.So remember, don’t be vague with your hypothesis if you are looking for a specific effect! Avoid saying “A does not affect B” if what you really mean is “A does not improve B” when using the null hypothesis.

5. Running a T-test in SPSS

Question: Do two photosynthetic organisms have the same oxygen evolution capability?

Null Hypothesis: HA: μ1 = μ2 (Both photosynthetic organisms produce the same amount of oxygen)

An independent 2-tailed t-test!

Alternative Hypothesis: HA: μ1 ≠ μ2 (the two photosynthetic organisms DO NOT produce equal amounts of oxygen)

5a. Importing the data and analysis in SPSS

In your browser, ‘viewing image’ should enlarge the screenshots

  • Make sure your spreadsheet is save on the C: drive of your computer
  • Make sure excel file types are select
  • Click ‘Data view’ tab rather than ‘Variable view’
  • Notice the variable names in the column headers
  • All raw data is list (SPSS will calculate means for you)
  • Data is list in one column (all with the same units) with the first column indicating the grouping
  • Select Analyze –> compare means –> independent samples t testO2evo is the test variable, species is the grouping variable
  • Click on define groups, then
  • type the two names used in the data view
  • We first check Levene’s test –which assesses if variances are equalif p > 0.05, then the variances are equal and you can interpret the t results
  • The t-test result is p = 0.014so we can reject the null hypothesis, thus the two photosynthetic organsims DO NOT produce equal amounts of oxygen.

5b. Reporting t-test results

  • All perform statistics MUST be referr to in the text of your report
  • You must indicate:
  • The type of test performed
  • The data the test was performe on
  • The α (– alpha) level used (0.05 is the default)
  • The p-value outcome of your t-test
  • Whether you accept or reject the null hypothesis

For purposes of this course you are require to take a print screen of your SPSS output (as in the previous slide) and attach this in your report.

6. Graphing

Choosing Graphs

  • Some tests are related to specific figuresFor example, correlations and scatter plots
  • The following examples outline the basic use of several common graphsScatter plotsLine GraphsBar graphsHistograms

Scatter plots

  • Displays 2 variables for a set of data
  • Dependent vs. independent — one variable is under the control of the other variable

Line graphs

  • Shows relationship between values plotted on each axis (dependent vs. independent)
  • Used on continuous variables

Bar graphs

  • Used for discrete quantitative variables which are similar but not necessarily related
  • Often used to display t-test results


  • Used exclusively for showing the distribution of data that are continuous.

7. Correlations

Pearson’s Correlation Coefficient (r) measures the relationship between two variables
Positive r-values indicate a positive correlation between the two variables. They are decreasing with one another if the r-value is negative. There is no association between the variables when the r-value is close to zero, but there is a substantial relationship when the r-value is close to either -1 or 1.
R2 (coefficient of determination) is used to assess how well a regression line fits the data: 0 means no fit, 1 means a perfect fit.

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