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Plant Science Lab Notes

Course Overview and Grading

  • Class participation: 40\ ext{points}
  • Quizzes and assignments: 20\% of the final grade
  • Skills tests linked to class participation (performed during lab time)
  • Group project: 20\% of the final grade; largest component of the lab
  • Group project goal: conduct a plant science experiment in a group setting
  • The instructor emphasizes early planning and group selection; groups are student-chosen and not assigned

The Group Project

  • You must do a plant science experiment within a group
  • Group size guidelines:
    • Recommended: 2–3 people
    • Maximum: 4 people
    • Occasionally a student may work alone if others drop
    • Groups are formed by the students themselves
  • The project is designed to be challenging and requires care for living plants (they die if neglected)
  • You will decide what plant-based experiment to run, taking into account seasonal timing and plant needs
  • Seasonal considerations: fall, changing seasons, temperature changes affect plant growth and survival
  • Plant selections discussed include warm-season vs cool-season grasses:
    • Bermuda grass = warm-season
    • Ryegrass = cool-season
  • Timing and environment matter: choose plants appropriate to the season and climate window
  • The project demands planning ahead to avoid last-minute, one-night efforts (e.g., growing plants in a dorm room overnight is not realistic)
  • The group must plan for potential disasters (e.g., weather, pests) and discuss contingencies
  • Project can involve a variety of activities: plant growth, germination tests, soil sampling, fertilizer recommendations, plant identification, etc.
  • Weather dependence: labs may shift order based on weather; flexibility is expected

Planning and Time Management

  • Procrastination is discouraged due to plant growth requirements
  • Students should anticipate the time it takes for plants to grow or produce results
  • The instructor highlights the need to plan what to study and test early in the semester
  • The concept of “disasters” in experiments (e.g., pests) is acknowledged; plan for contingencies

Plant Growth and Environmental Considerations

  • Plants depend on humans for survival in many cases, and humans depend on plants for food and resources
  • As seasons change, some plants will die while others will begin to grow; timing is critical for the experiment
  • Plant environment compatibility is essential: use species suited to the given season and facilities
  • The instructor notes personal experience with gardening and uses it to illustrate planning and care requirements

Group Formation Rules

  • Groups are student-chosen rather than assigned by the instructor
  • Size guidelines reiterated:
    • 2–3 preferred; up to 4 may be acceptable
    • Sometimes a student ends up alone if others drop
  • You should decide who works in your group and who does the tasks
  • The instructor will not assign groups

What is a Scientific Experiment? The Scientific Method

  • Purpose: solve a problem using scientific inquiry
  • Steps of scientific inquiry/experimental process:
    1. Identify the problem or question
    2. Do background research
    3. Form a hypothesis (educated guess)
    4. Test the hypothesis by conducting an experiment or study
    5. Analyze the data
    6. Draw a conclusion
    7. Report results
  • Big-picture goal: address problems relevant to agriculture, e.g., how to feed a growing population with limited resources
  • The process is iterative and aims to derive plausible descriptions consistent with data from experiments
  • Controlled experiments are emphasized as the most reliable way to obtain information for decision making

Formulating a Hypothesis

  • Definition: an educated guess about how the dependent variable will respond to changes in the independent variable
  • Key points:
    • The hypothesis should be testable
    • Avoid questions you already know the answer to
    • Do not use first-person pronouns like "I" in the hypothesis
    • Example: \text{The rate of reaction } R \uparrow \text{ when } T \uparrow. (Rate increases with temperature)
    • If the hypothesis is not proven by the experiment, explain why in the conclusion
    • Do not change the hypothesis after starting the experiment; if new insights arise, you may start a different experiment but keep the original hypothesis fixed for the current study
  • The instructor notes that occasionally surprising or novel discoveries can occur, but emphasize documenting the rationale and results

Experimental Design: Variables, Controls, and Groups

  • Independent variable (IV): the variable intentionally changed to test its effect (e.g., amount of fertilizer, type of fertilizer, or presence of diesel exhaust fluid)
  • Dependent variable (DV): the measured outcome that responds to the IV (e.g., plant height, biomass, germination rate)
  • Constants: factors kept the same across all treatments to avoid confounding effects (e.g., same soil type, same light exposure, same watering schedule, etc.)
  • Control group: standard or baseline treatment used for comparison (e.g., plants with no fertilizer or no DEF)
  • Experimental group: the group(s) where the IV is applied/altered
  • Replication: multiple experimental units within each treatment to improve reliability
  • Randomization: place treatment units randomly to avoid positional effects and biases
  • Blocking: group units into blocks to control for known sources of variation (e.g., shade or soil differences)
  • Example discussion: a ryegrass growth experiment with shade and soil type as blocks
  • A note on common misuse: failing to rotate plants or failing to keep constants truly constant can undermine validity

Reliability and Validity

  • Reliability: if the procedure is followed, the same results should be obtained when repeated by someone else
  • Internal validity: the degree to which the experiment actually tests the intended variable and yields trustworthy results
  • External validity: the extent to which laboratory results translate to real-world settings
  • The instructor uses examples (e.g., plants near a window grew taller due to more sunlight) to illustrate threats to internal validity and the need for proper controls
  • In some introductory labs, external validity may be of secondary concern, but internal validity and replicability are critical

Designing the Experiment and Procedure

  • Start with a clear purpose statement describing what you want to find out; be specific
  • Preliminary research and literature review are encouraged before finalizing the design
  • Example purpose: "What are the effects of diesel exhaust fluid on the growth and height of ryegrass?" (DEF described as water and urea; urea is a fertilizer)
  • Independent variable identification: specify what will be changed (e.g., presence/absence of DEF, or DEF concentration)
  • Dependent variable identification: specify what will be measured (e.g., plant height in cm, biomass)
  • Measurement plan: the change in the variable must be measurable and quantifiable
  • Procedure: present step-by-step methods (bullet points preferred) so that another researcher can replicate the experiment
  • The number of trials: typically 3–4 (n = 3 to 4)
  • Data type: aim for quantitative data (numerical measurements) rather than vague qualitative judgments
  • Example: a fertilizer treatment with a specific rate (e.g., 50\ ext{lb/acre}) to connect with practical farming decisions
  • Hypothesis testing in practice: may yield demonstrated results or not; include discussion of results and limitations in the conclusion
  • Discussion of potential discipline-specific measurement strategies: e.g., height, leaf number, biomass, germination rate, etc.
  • Avoid vague language such as "+> more green"; strive for objective, measurable outcomes
  • The instructor emphasizes the importance of economic relevance when possible (e.g., yield or cost implications)

Data, Analysis, and Conclusion

  • Data can be qualitative or quantitative, but quantitative data is preferred for clarity and replicability
  • In this course, statistics are noted but not required to perform extensive statistical analysis
  • When presenting results, include a conclusion that restates the problem, the hypothesis, and the data analysis
  • Explain the reasoning behind whether the hypothesis was supported or refuted
  • Acknowledge any mistakes or limitations encountered in the lab and discuss potential sources of error
  • If issues like pests (e.g., fall armyworms) affect results, report them and consider how to proceed (e.g., restarting or adjusting the design)
  • The conclusion should reflect whether the experiment was reliable and valid, and propose possible improvements for future runs

Practical Examples Discussed in Class

  • Hay analysis (Skills Test): order hay from highest to lowest quality
    • Criteria: Total Digestible Nutrients (TDN) and protein content
    • Example ranges encountered: \text{TDN} \approx 60\%; \text{Protein} \approx 18\%; historically, protein around 14\%–18\% depending on management and season
    • Highest-nutrient/higher-protein hay is valuable for animal nutrition; differences can be due to maturity, fertilizer, and harvest practices
  • The concept of nitrogen and protein in hay:
    • Nitrogen content influences protein level in hay
    • A protein is described in class as a "carbohydrate with nitrogen" (not chemically exact, but this is how it was presented)
  • Other activities mentioned for the lab: soil samples, fertilizer recommendations, germination tests, plant identification, and seasonal plant trials
  • Weather dependence and real-world constraints impact lab planning and outcomes

Additional Concepts and Anecdotes

  • The instructor emphasizes the broader goal of agricultural science: producing more with less to feed a growing population
  • The role of weather, soil type, shade, and moisture as factors that complicate ecological experiments
  • The difference between controlled experiments (artificially controlled environments) and ecological/field studies (more complex variables)
  • The importance of documenting errors and acknowledging unforeseen events (e.g., pests) in the final report
  • The importance of clarity and reproducibility in the written procedure; the grader will assess whether another person can repeat the experiment and obtain similar results
  • Some light humor and storytelling to illustrate concepts (e.g., group dynamics, common misinterpretations) used to reinforce key points, not to replace formal content

Key Formulas and Notable Values

  • Hypothesized relationship: \text{Rate of reaction} \uparrow \iff T \uparrow
  • Example fertilizer rate: 50 \ \text{lb/acre}
  • Group size constraints (guidance):
    • Recommended: 2 \le n \le 3
    • Maximum: n \le 4
  • Trials:n = 3 \text{ to } 4
  • Hay quality indicators:
    • \text{TDN} \approx 60\%
    • \text{Protein} \approx 18\% (range often 14\%\text{–}18\% depending on factors)
  • Variable definitions (summary):
    • Independent Variable: the variable intentionally changed by the investigator
    • Dependent Variable: the measured outcome
    • Constants: factors kept the same across all treatments
    • Control Group: baseline for comparison
    • Experimental Group: receives the treatment/IV

Quick Reminders for Exam Preparation

  • Always define independent, dependent, constants, control, and experimental groups clearly in your design
  • Plan for replication and randomization to strengthen reliability
  • Strive for quantitative data when possible; connect results to practical, real-world implications (economics, yield, etc.)
  • Be prepared to discuss internal and external validity and how you would address threats to validity in your own experiment
  • Prepare to articulate a testable hypothesis in third person and explain the rationale in your conclusion
  • Be ready to describe potential sources of error and how you would mitigate them in future iterations