What’s the best environment for a plant? | DFRobot Science Lab EP09
EP 09 What’s the best environment for a plant?
Search the best PH, light level, and soil humidity of a plant online. Set up contrast experiments using the control variables method to check if the plant grows best when the optimal growth requirements are met.
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We have explored the three factors affecting plant growth: pH, light level, soil humidity. Search the best growth parameters for Pothos online, and fill the form below:
If we control the Potho’s condition in the optimal growth data range, will that be the best environment for its growth?
To find out the answer, you may need to think about the following questions:
●Will light influence soil pH?
●Does light affect soil humidity?
●Will soil humidity affect soil pH?
Design experiments to explore these questions. Take the second one as an example:
●Mark two plastic cups as A and B.
●Fill two cups 3/4 full with potting soil.
●Add the same quantity of water in two cups, wait for a while, then measure their humidity. Spray water onto the soil until the humidity difference of two cups of soil stays in a very small range.
●Put cup A outdoor in sunlight, cup B indoor in shade.
●Measure the soil humidity in two cups at regular intervals.
In this activity your group will use BOSON pH sensor, light sensor, Soil Moisture Sensor to collect data as you plant, water and grow Pothos. The purpose of this activity is to determine whether the best growth environment for Pothos can be constructed when multiple optimal growth requirements are met.
1.Plant 4 pots of Pothos with a similar initial state as the way in the previous activity. Use BOSON pH sensor, light sensor, and soil moisture sensor to detect the corresponding values.
2.Set optimal-condition group: for Pothos pot 1, control the three factors affecting Pothos growth in the best range(the data you got online).
3.Set control group 1: for Pothos pot 2, adjust the soil pH lightly(increase or decrease by 1) and keep the other two factors as the same of the pot 1.
4.Set control group 2: for Pothos pot 3, adjust the light level to 1/2 of the Pot 1, and keep other factors as the same of Pot 1.
5.Set control group 3: for Pothos pot 4, adjust the soil humidity to 1/2 of the Pot 1, other factors are the same as Pot 1.
6.Observe and record the growth situation of Pothos daily for one week.
Copy the Data Table below into your notebook.
Making Sense of the Data:
Create a Multi-Colored Line Graph according to the directions below:
1.Use a different color for each cup to plot your results for plant growth.
2.The manipulated variable is the date and the responding variable is the growth situation(plant height, leaf number). 3.The manipulated variable is graphed along the X axis and the responding variable is graphed along the Y axis.
4.Use the variables to give your graph a title.
5.Include a legend for your graph.
In your group discuss the following questions:
1.Which pot of Pothos grows best? Is it the optimal-condition group(Pot 1)?
2.Which pot of Pothos grows worst? Whether the corresponding control variable is the most important factor for Pothos growth?
3.According to the data you recorded, if the best growth environment for Pothos can be constructed when multiple optimal growth requirements are met.
An experiment has several types of variables, including a control variable (sometimes called a controlled variable). Variables are just values that can change; a good experiment only has two changing variables: the independent variable and dependent variable. Let’s say you are testing to see how the amount of light received affects plant growth:
●The independent variable, in this case the amount of light, is changed by you, the researcher.
●As you change the independent variable, you watch what happens to the dependent variable. In this case you see how much the plants grow.
●A control variable is another factor in an experiment; it must be held constant. In the plant growth experiment, this may be factors like water and fertilizer levels.
The Control Variable and Experimental Design
A confounding variable can have a hidden effect on your experiment’s outcome.
If control variables aren’t kept constant, they could ruin your experiment. For example, you may conclude that plants grow optimally at 4 hours of light a day. However, if your plants are receiving different fertilizer levels, your experiment becomes invalid. As a researcher, you should identify any variables that may affect the outcome of your experiment and you must take steps to keep them constant (“control” them). If you do not, your experiment compromises internal validity, which is just another way of saying your experimental results will not be valid. When control variables run amok and aren’t controlled, they turn into confounding variables, which affect your results and ruin your experiment.
Control Variables vs. Control Groups
In any experiment or research, it can be virtually impossible to account for all variables that may affect the outcome of your experiment. If it’s difficult to identify and control all potential confounding variables, it may be necessary to make a control group. A control group provides a baseline measurement for your experiment.
1.In the experiment, where did we use the control variables method?
2.What are the independent variable and the dependent variable in this experiment?
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