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The E ffect of Soil Percolation on Plant Density Maggie Foot and Oliver Resnick

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T A B L E O F C O N T E N TS Section Author Page

Abstract Maggie Foot 3 Introduction Maggie Foot 3 Materials and Methods Maggie Foot 5 Results Oliver Resnick 6 Discussion Oliver Resnick 10 Acknowledgments Maggie Foot & Oliver Resnick 12 Works Cited Maggie Foot 13 Works Cited Oliver Resnick 14

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A BS T R A C T !Soil percolation is the rate at which water is absorbed by soil. Soil types often

change depending on the distance they are from a pond. When soil types change, the soil percolation rates change due to the fact that more clay based soils have lower percolation rates and sandier soils have higher percolation rates. Using this knowledge, an experiment was conducted at Drumlin Farm in Lincoln, MA to see if there was a correlation between distance from the pond/soil percolation rates and plant density. It was expected that the closer the distance is to the pond, the plant density will be lower, because the soil is more clay based and therefore has lower soil percolation rates. To conduct this experiment, a soup can was inserted three centimeters into soil at specific distances in each cardinal direction at three different ponds and their surrounding areas. A certain amount of water was poured into each soup can, and the time needed for the water to drain was recorded. While this was happening, a 25 cm by 25 cm quadrat was placed around the can that was being drained, and the amount of living, rooted, green plants inside the quadrat were hand counted and this number was then recorded. The r value was 0.01 for the effect of soil percolation on plant density. This allows the conclusion to be drawn that there is little to none correlation between plant density and soil percolation rates. However, there was large room for error during the experiment, making it hard to decide if the information from the experiment was conclusive. !I N T R O D U C T I O N !

Global warming affects everything, from polar ice caps, to rare plants growing in the Amazon. Since there has been such exponential population growth, the human race has started to urbanize, and take up much of the space that used to be for oxygen producing trees and other plants. Mankind needs plants to survive because plants take in carbon dioxide, and release the oxygen humans need to breathe. Being autotrophs, which means they produce their own food, plants are also the ultimate source for all of the

food. In order for plants to grow, they need to have a suitable soil structure, with several factors making this up. One of these is soil percolation. Soil percolation is the rate at which water is absorbed by soil (treepeople.org). It plays an important part in soil structure, and can be a determining factor in whether a species of plants survives or not. Since the human race needs plants to survive in order for their own survival, everyone should pay more attention to what sort of impact their actions have on the soil environment, especially soil percolation.

The experiment set forth in this paper will be looking at the effect of soil percolation on plant density and will be conducted at Drumlin Farm, a Massachusetts Audubon Wildlife Sanctuary, in Lincoln, MA. It consists of several different ecosystems all in the same vicinity, such as forests, fields, and ponds. Once at Drumlin Farm, the focus will be on the ponds and surrounding wetlands. Bathtub Pond, Poultry Pond, and Boyce Pond are the three ponds that the data will be collected from. Poultry Pond lies relatively close to the paved road for outside traffic, and is also directly downhill from a chicken coop. Bathtub Pond is located in an area with an abundance of underbrush and trees. Boyce Pond is near Boyce Field, and is surrounded by forest. Wetland soil, such as the ones around the ponds, is usually much more clay based, and the type of soil is a very important component of soil percolation rates.

Soil percolation is a crucial aspect when looking at plant growth in an ecosystem. It is affected by several factors, such as texture, soil structure, and grain size. Grain size is

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the most prominent factor in soil percolation rates. Gravel, sand, and clay are the most commonly referred to types of grains. Gravel and sand have the largest particles, which means in between soil particles/aggregates. It is hard for soil particles of both gravel and sand to

material (depi.vic.gov.au). This also makes it harder for them to store large amounts of water, because the water enters quickly, and then keeps draining through. Clay based soils, on the other hand, have a very fine texture, and very small pores, also known as

means that the soil can store water for longer, but it can be harmful if there is so much and start to suffocate ( passel.unl.edu). The ideal

growing condition for plants would be if the soil percolation rate is just in the middle, meaning the soil has a little clay and a little sand, but not enough of either one to completely tilt the scales.

The proposed experiment is to explore the effect of distance from the pond (meters), on plant density. The distance from the pond will change soil percolation rates because as the distance from the pond increases, the soil type will change and therefore so will the percolation. The experiment will be performed by running soil percolation tests to the North, South, East, and West of each pond, with each sample being taken at set distances farther and farther away from the pond. There were two hypotheses in this experiment, although the main focus in this paper is on the latter one. The first one is if that the closer the distance is to the pond, then soil percolation rates will be lower, because the soil is more clay based (epa.gov/gmpo/education/pdfs/DesigningWetland.pdf). The independent variable was distance from the pond, and the dependent variable was soil percolation rates. This was more of a preliminary hypothesis to set the stage for the independent variable in the next one. The next hypothesis that this paper mostly focuses on is if the soil percolation rates are lower, then plant density will be lower because the soil is more clay based, and this makes it more difficult for plants to take root and breathe (Lucke, www.earthsciweek.org). The independent variable is the soil percolation, which relates to the first hypothesis because although the soil percolation cannot be controlled, the distance from the pond can be, which in turn makes distance and percolation rates almost equal. The dependent variable is plant density. Some control variables designed for this experiment include distance increment for each measurement, the amount of water used, the amount of soil tested, the same testing day and the diameter of the coffee can.

The data gathered from this experiment could potentially help farmers in the future. When farmers at Drumlin Farm are deciding where to put in a new field, they will want to have the most efficient spot that will produce the largest amount of crops. Depending on the results of this experiment and where the highest plant density is, farmers will be able to determine which distances from the ponds will work the best. Even if their plans do not include a pond, the farmers still should have some idea of the overall effect of soil percolation on plant density, because the percolation will play a big part in plant growth no matter the location. Farmers everywhere should keep the relativity of soil percolation to plant density in mind if they are looking to boost their crop production and have the most efficient farming possible. If there are more plants, there will also be more oxygen given off, which is good for the human race. As more people

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become educated about this relationship, the less space, time, money, and land will be wasted. "M A T E RI A LS A ND M E T H O DS!

The following was completed in the lab before the experiment was performed in the field. Three soup cans, (16 ounces), had their tops and bottoms removed using a can opener. The soup cans then had lines drawn around the inside and outside circumference so when conducting the experiment in the field it was clear how deep to press the can into the ground. These lines were made by marking the inside of the coffee can three centimeters high from the rim with a Sharpie. These marks were repeated in two centimeter increments around the inside circumference of the coffee can until the line could be traced connecting all of them together. This process of marking was then repeated on the outside of the coffee can, on the same end that the inside was marked on, also three centimeters high. A 16 ounce plastic water bottle had its bottom cut out, and a line drawn around the outside to where water should be poured to to get 13 ounces. Two gallon jugs were filled with water.

!F igure 1: These are some of the materials that were used. 1 is a phone opened to a stopwatch. 2 is the permanent marker used to mark the inside of the coffee cans. 3 is the compass that was used to align and locate the different locations at each pond. !

The next part consists of the in-field experimentation that was performed at Drumlin Farm in Lincoln, MA. The three locations that data was collected from were Poultry Pond, Boyce Pond, and Bathtub Pond.

A compass was aligned at each location so that all cardinal directions were known and accurate. The first testing point was found by going to the location facing North, and inserting a flag right next to the pond. A meter tape was then used to measure fifteen meters out, still facing North, and two flags were placed; one at five meters and one at fifteen meters. One of the soup cans was pushed into the ground right next to the flag, until the soil came up to the three centimeter line on the can. The plastic 13oz. bottle was then filled with water to the marked line from one of the two gallon jugs, and was slowly poured into the soup can. As soon as the first drop of water hit the soil, the stopwatch was started. While the first can was draining its water, a second can was pushed into the ground in the same style as how the first one was, but at five meters. Water measured from the 13 oz. bottle was once again poured into the soup can. While the first and second ones were draining, a third can was going through the same procedure fifteen meters away from the first point. Each location was observed and once the water had fully drained into the soil, the stopwatch was stopped and the time was recorded for each point. While the water was draining at the first point, a 25 cm. by 25 cm. quadrat was placed so the flag was in the middle. The number of plants in that area were hand counted and recorded. Only living, green plants that were rooted in the ground were counted. After moving to the next data point, a measured five meters away from the first point,

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still in the northern direction, the procedure of collecting and testing plant density was repeated. The same procedure for collecting and testing was repeated at the third data point, the fifteen meter distance. After finishing one cardinal direction, the next one was tested, until all four had been completed. This entire process was then repeated at each location.!

!F igure 2: A sketch of a generic pond and the general idea of where the samples are going to be collected at each location. !!!!!

R ESU L TS Table 1: Poultry Pond- The effect of distance from pond (meters) on soil percolation time (minutes).

Percolation Time In Minutes

Distance T1 T2 T3 T4 Average Standard Deviation

0 m 1.7 2.5 2.7 5.0 3.0 1.2 5 m 4.3 0.4 10.0 0.2 3.7 4.0 15 m 0.4 1.5 9.8 0.9 3.2 3.9

Table 2: Poultry Pond- The effect of distance from pond (meters) on plant density (plants per quadrat).

Plant Density (plants per quadrat)

Distance T1 T2 T3 T4 Average Standard Deviation

0 m 11 3 0 1 3.8 4.3 5 m 7 4 6 3 5.0 3.1 15 m 1 0 0 2 0.8 0.8

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Table 3: Bathtub Pond- The effect of distance from pond (meters) on soil percolation time (minutes).

Percolation Time In Minutes

Distance T1 T2 T3 T4 Average Standard Deviation

0 m 7.4 4.1 10 6.2 6.9 2.1 5 m 1.6 10 10 10 7.9 3.6 15 m 1.9 8.3 1.6 1.9 3.4 2.8

Table 4: Bathtub Pond- The effect of distance from pond (meters) on plant density (plants per quadrat).

Plant Density (plants per quadrat)

Distance T1 T2 T3 T4 Average Standard Deviation

0 m 2 2 3 0 1.8 1.1 5 m 4 0 2 3 2.3 1.5 15 m 6 1 117 2 31.5 49.4

Table 5: Boyce Pond- The effect of distance from pond (meters) on soil percolation time (minutes).

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Graph 1: The effect of distance from pond (m) on percolation time (minutes).

Graph 2: The effect of distance from pond (m) on plant density (plants per quadrat).

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Graph 3: The effect of soil percolation time (minutes) on plant density (plants per quadrat) in each pond.

Graph 4: The effect of soil percolation (minutes) on plant density (plants per quadrat).

Graph 1 shows effect of distance from pond on soil percolation time. At each distance the data from Poultry Pond and the data from Bathtub Pond are exactly the same. At 0 meters the percolation at Boyce Pond took longer on average than it did at either of the other ponds. At 5 meters it was similar. Poultry Pond and Bathtub Pond remained on level with themselves at eight minutes, however they had a bit longer percolation rates

percolation rates were significantly higher. At 15 meters as well, Poultry and Bathtub stayed on level with each other, however decreasing

the 5 meter distance with a percolation rate of 10 minutes, and remained larger than the other two ponds. The error bars at the 0 meter distance all overlapped. At 5 meters they

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also all overlapped, however at the 15 meter distance Boyce Pond s error bars did not overlap with the other two ponds. Poultry and Bathtub however did overlap. As a whole, all of the data overlapped. The averages for the first ponds percolation rates were three minutes, three point seven minutes, and three point two minutes, making it a precise data set. The average percolation rates for Bathtub Pond were six point nine minutes, seven point nine minutes, and three point four minutes, also making this set imprecise. The average percolation rates for Boyce Pond were nine minutes, ten minutes and ten minutes, making it a fairly precise data set. The highest data point was at Boyce Pond, with a ten minute draining time. The lowest was at Poultry Pond with a three minute draining time Graph 2 showed the effect of distance from pond on plant density. At all distances the plant density appears lowest at Bathtub pond with the exception of the 15 meter data where it is much higher than any other data point with a plant density of 117 plants per quadrat. At 0 meters and 5 meters Poultry Pond seems to have the highest average, however at 15 meters it has less than both Boyce and Bathtub. Every single error bar, in every data set overlapped. eight plants, five plants, and point eight plants, making this a very imprecise data set. The aver ond were one point eight plants, two point three plants and thirty one point five plants making this the most imprecise data set. The

point three plants making this a fairly imprecise data set. The highest data point on this graph was at Bathtub Pond with thirty-one point five plants in the area. The lowest data point was at Poultry Pond with zero point eight plants in the area.! Graph 3 was the effect of percolation time on plant density at each pond. Poultry Pond s data points are spread out pretty evenly; however they seemed to cluster in the area between 2-4 plants and 0-2 secondsspread through the entire thing, and Boyce Pond s are all lined up along the 10 minute mark, however it has a good plant density range. The r squared value for Poultry Pond was .00053, the r squared value for Bathtub Pond was .06, and the r squared value for Boyce Pond was .03. The Poultry Pond trend line only hits two of the data points with most significantly above or below. The trend line for Bathtub Pond hits three of the data points, but still misses a majority of them. The trend line for Boyce Pond also only hits two data points, however it misses a majority of them significantly.! Graph 4 shows the effect of soil percolation on plant density. The r squared value is 0.02. There appear to be clusters of data points around 2.00 minutes and 10.00 minutes. The highest point for plant density was at around 117 plants per quadrat. The lowest included many data points with zero data points at that quadrat. The highest soil percolation time were multiple data points clustered around the 10 minute mark and the lowest points were sub two minutes. The data was very imprecise which is shown through the large spread of the numbers.! DISC USSI O N

The core problem being examined in this experiment was whether or not soil percolation had any affect on plant density. The main hypothesis was: if the distance is closer to the pond, than plant diversity will be lower because the soil is more clay based, and therefore percolation rates are lower (Lucke, http://wwwearthsciweek.org/ncli). The

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hypothesis was not supported because there was no correlation between soil percolation and plant density. There was no correlation between soil percolation and plant density because of a connection drawn incorrectly. It was assumed that areas with higher clay contents would have lesser percolation rates because the particles would be smaller, causing water to run through the spaces in between the particles slower (Lucke, earthsciweek.org/ncli/edact/properties.html). This was also not supported however, because the soil closer to the pond presumably had higher clay levels, due to the moisture of the soil, but there was no correlation between distance from pond and percolation rates. The aforementioned incorrectly drawn conclusion was in the assumption that the moisture of the soil had any effect on plant density. It was assumed that the more water in the soil, then the more plant life could be supported. Assuming this led to the assumption that the percolation of the soil effected the amount of plants grown, however it was later researched that although water, obviously, is necessary in plants survival, there is no immediate correlation between the moisture of the soil and the density of plant life in that area (Veihmeyer, annualreviews.org/doi/abs). I cannot give a new hypothesis because the two variables showed no correlation making it irrelevant. The r squared values show the correlation between the independent and dependent variable in what is being examined. The closer the r squared value is to one, the higher correlation the two have. When examining whether there was a correlation between soil percolation and plant density, the r squared value was .02, which is quite distant from one. This lead us to believe that there was no conclusive correlation between our variables. On graphs 1 and 2, there were also no conclusions that could be made because all the error bars overlapped on both graphs, making it impossible to draw any completely certain assumptions about the data. ! The data set precision was surprisingly high due in part to errors the scientists made. For example, at Boyce Pond the average percolation rates were 9.0 minutes, 10 minutes, and 10 minutes. This was due to the fact that in order to save time a number of tests had to be cut off at the 10 minute mark. This means that they could potentially have gone on for another five minutes anywhere up to a couple hours. This led to very precise, but very inaccurate data points. The most imprecise data set we had was for Bathtub Pond s plant density. The averages were 1.8 plants, 2.3 plants, and 31.5 plants. This was because one of the distances ended up being on grass, so the scientists counted the blades of grass, which heightened that specific data set significantly.! The experiment could have definitively been modified for an improved result. To start, there was no reason to do all three distances, at all four directions, at all four ponds. This was incredibly excessive, and although it seemed like a good idea in order to have all necessary samples and a good, varied data set, it ended up being more harm than good. The amount of time required in order to support these number of samples made it so the thoroughness of the samples that were gotten were not up to scientific standards. On any sample that went above ten minutes, the time was marked to be cut off in order to collect all the data. Cutting out some of these trials would be very beneficial to anyone who wishes to try this experiment at a later date. ! One error that occurred during collection was the can in which the percolation was tested bent and possibly obstructed the trustworthiness of the results. The cans are delicate and should be handled with care. Another error that occurred was perfecting the

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method in which the plant density is counted. For one grassier area, the blades of grass were counted individually, while for others, anything green was counted as a plant and added to the plant density count. In order to maintain the integrity of the experiment these are errors that could be fixed with some preparation and care.!!A C K N O W L E D G M E N TS Maggie Foot:

The Knights of Science project is a very time consuming, important project, and there are many people I would like to thank for helping me get to where I am right now. First, I would like to thank my partner Oliver Resnick, for proof-reading, helping innovate our procedure, detangling my hair from thorns when we were trekking through the wilderness, and being there to create this project with me. Without him, none of this would be possible. I would also like to thank the invaluable help of Ms Schultheis. She is always there to encourage and lend words of wisdom, whether it is editing all the different pieces, or the daily problem solving, from how to make a graph, to how to avoid carrying nine gallons of water with us on the day of the experiment. Another thanks goes out to all of the field guides at Drumlin Farm, who provided insightful knowledge into the workings of all the different environments. I would like to give a special thanks to Barbara and Jonathan Foot, who have the utmost patience and are willing to drive to

the sake of science materials. Finally, I would like to thank everyone that I am not able to name for helping me complete this long and trying process. !"#$%&'!(&)*$+,-!

I would first like to thank Maggie Foot for being nothing short of an amazing partner. The Knights of Science Project is a long term, and at times tedious project, and it takes up a major portion of the eighth grade year. Having a partner that is flexible, forgiving, and always willing to help in any situation you may find yourself in, is fundamental in the success of this project. That is exactly what Maggie was. No amount of gratitude could appease the number of thanks I owe to Maggie. !

Next I would like to thank Ms. Schultheis, our teacher, for not only assisting us in any way we needed, and there were times help was necessary, but also for carrying all the projects, for every group, in both her classes. That is the kind of responsibility and empathetic teaching attitude that all teachers should come to learn and participate in.! I would also like to extend my appreciation to the four people who anonymously reviewed our paper. Although I am unaware of who it was, it was great help in editing the paper. I would also like to thank my parents for reading over the paper and assisting me in my edits, as well as remaining flexible in order to get supplies necessary to complete the project!!! !!

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W O R KS C I T E D!Author 1

"Factors Affecting Plant Growth." Factors Affecting Plant Growth. The

Agricultural Institute College of Agriculture and Life Sciences North Carolina

State University, n.d. Web. 06 Mar. 2014.

<http://broome.soil.ncsu.edu/ssc051/Lec3.htm>.

Gardner, Robert. Soil: Green Science Projects for a Sustainable Planet.

Berkeley Heights, NJ: Enslow, 2011. Print.

"How Do the Properties of Soils Affect Plant Growth." Department of

Environment and Primary Industries. State Government of Victoria,

30 Jan. 2014. Web. 09 Mar. 2014. <http://www.depi.vic.gov.au/agriculture-and-

food/dairy/pastures-management/fertilising-dairy-pastures/how-do-the-properties-

of-soils-affect-plant-growth>.

"How to Do a Percolation Test." Grey Water Action. N.p., n.d. Web. 07 Mar.

2014.

<http://greywateraction.org/content/how-do-percolation-test>.

"Lecture 8: Soils and Percolation." Geology. Western Washington

University, N.d. Web. 9 Mar. 2014.

<http://geology.wwu.edu/rjmitch/L8_soils_percolation.pdf>. PDF.

Lucke, Kristen. "Soil Properties." Earth Science Week. American

Geosciences Institute, 2014. Web. 01 Mar. 2014.

<http://www.earthsciweek.org/ncli/edact/properties.html>.

"Percolation Test." Wikipedia. Wikimedia Foundation, 01 Mar. 2014. Web.

09 Mar. 2014. <http://en.wikipedia.org/wiki/Percolation_test>.

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"Soil Genesis and Development, Lesson 6 - Global Soil Resources and

Distribution."Plant and Soil Sciences ELibrary. National Institute of Food and

Agriculture, n.d. Web. 07 Mar.

2014.<https://passel.unl.edu/pages/informationmodule.php?idinformationmodule

=1130447033&topicorder=3&maxto=12&minto=1>.

"Soil Percolation Rates." Tree People. N.p., n.d. Web. 12 Mar. 2014.

<http://www.treepeople.org/soil-percolation-rates>.

Author 2

Lucke, Kristen. "Soil Properties." Earth Science Weekly. American Geosciences Institute,

2014. Web. 18 Feb. 2014.

<http://wwwearthsciweek.org/ncli/edact/properties.html>.

Veihmeyer, F. J., and A. H. Hendrickson. "Soil Moisture In Relation To Plant Growth."

Annual Reviews. Research4life, n.d. Web. 16 Apr. 2014.

<http://www.annualreviews.org/doi/abs>.

Cover Art

Soil Quotations. Digital image. Urbantext.illinois.edu. NRCS, n.d. Web. 1 May 2014.

<http://urbanext.illinois.edu/soil/quotes/quotes.htm>.

That Slope Makes Your Water Look Turbid!

The Effect of Slope of Pond (degrees) on Turbidity of Pond (NTU)

By: Brooke Shachoy and Olivia Friend

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TABLE OF CONTENTS Section: Author: Page: Abstract Olivia Friend 2 Introduction Brooke Shachoy 2-3 Materials & Methods Olivia Friend 3-4 Results Brooke Shachoy 5-8 Discussion Olivia Friend 8-9

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ABSTRACT Shown by previous experiments, it has been said that increased rainfall on steep

slopes causes higher turbidity readings. This experiment was conducted to test the relationship between pond slope and water turbidity. It was expected that the pond with the steepest slope would end up having the highest turbidity results. Two ponds were used at Drumlin Farm for this study: Ice Pond and Boyce pond. The pond slope was tested at one location at each pond using a Suunto Clinometer and two meter sticks aligned along the slope. The turbidity was then tested using a turbidity sensor. Samples of water were put into the sensor to calculate the data. Two scatter-plot graphs were created to display the results for each pond. Another was formed to show all of the data together along with a bar graph that was created to compare averages. It was found that there was no correlation between pond slope and water turbidity due to a wide range of data and small r2 values.

INTRODUCTION Turbidity is the measurement of the clarity of liquid. It is a visual characteristic of water and is a measurement of light that is scattered throughout the water, when a light is shined into a sample (water.usgs.gov). When turbidity levels are high the water becomes cloudy and foggy and could represent a health hazard. There are many ways to measure turbidity, but it is most commonly measured with a Vernier Turbidity Sensor. Slope is a mathematical term that describes the steepness of a line. Slope is measured with a clinometer, a small device placed with a small “window” that is looked through at a meter stick and then the slope is read. When there is a steeper slope around an area of water, there is thought to be a higher turbidity level, because when rain falls, it could force soil particles from the ground into the water (www.snh.org). At Drumlin Farm, in Lincoln, MA, a member of the Massachusetts Audubon Society, the turbidity and slope of two ponds (Ice & Boyce Ponds) were tested in fourteen different locations around the two ponds. The levels were then compared to determine whether or not the slope of the pond effects turbidity. The ponds that were measured will have designated locations using a randomization technique, and a total of fourteen trials took place at each pond. The ponds are all different shapes and sizes and ultimately will have different turbidity levels and slopes of their banks. An experiment was conducted at the Journal of Sedimentary Research where the comparison of slope of the pond’s effect on turbidity currents. The hypothesis was supported and it showed that turbidity currents are affected by slope, and that slope and turbidity have a connection (http://sabotin.ung.si.) Another experiment was conducted specifically about the rainfall amounts at Duke University testing the effect the slope-flow on the deposition from a continuous turbidity current (scholars.duke.edu). It was found that with a steeper slope the turbidity currents are stronger. Both of these experiments will provide useful information for this study and will be helpful resources to look back at after the Drumlin Farm visit is concluded. The independent variable for the experiment is slope (degrees) of the ponds (Boyce and Ice Ponds) at Drumlin Farm. The dependent variable is the turbidity (NTU) of the ponds water. The turbidity will be tested at equal distance from the pond’s outer edge, with the same turbidity sensor. The weather on the day Drumlin Farm is visited will be an important factor for our analysis because as mentioned before, rainfall can

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affect turbidity. The Clinometer and meter sticks that will be used to determine slope will be used consistently throughout each trial. All of the variables listed above will be controlled during the trials. The hypothesis for the experiment is: If the pond slope is steeper, then the water’s turbidity will be higher, because rainfall moves downward on steeper slopes which will result in the pond having more soil particles suspended in the water thus increasing the turbidity (Giambelluca, Thomas) (A.J. Jakeman) (www.snh.org). Various important lessons will be learned from the experiment. Some lessons that will be learned are that turbidity measures the clarity of the water and slope measures the steepness of the bank of the pond. It could potentially show a correlation between slope and turbidity. Turbidity affects the health of fish. Some direct influences are sediments at the bottoms of lakes and ponds, smothering fish eggs, and affecting organisms living at the bottom of a lake or pond (Newton, David E.). How to use a clinometer and a turbidity sensor will also be mentioned (see materials and methods). New knowledge will be discovered about slope and turbidity and new skills will be developed, in completion of this study. MATERIALS AND METHODS

At Drumlin Farm (Figure 1), located in Lincoln, MA, two wetlands were chosen to test the effect of pond slope (degrees) on water turbidity (NTU). In each location, Ice Pond and Boyce Pond, one spot around the perimeter of the pond was chosen to measure the slope. To do this, “Rand (n)*x” was plugged into the calculator. N stood for the number of random numbers wanted, so 5 was plugged in. X stood for the maximum number, so 360 was plugged in, since this number represents the total degrees within a compass. One of the randomized numbers was chosen to be the primary test location for fourteen trials. At the pond, one person held the compass and moved it around until the random number was pinpointed. The water’s turbidity was tested directly in front of that specific area. These steps were repeated for each of the two ponds. In each wetland, the slope of the pond’s outer edge was measured with a Suunto clinometer (figure 2) measured in degrees. To measure the pond slopes, one individual stood at the top of the slope with a meter stick. The other person stood at the bottom of the slope with another meter stick and the Suunto clinometer. This person held the clinometer up to one eye so that the conversion table was on the right and the spinning dial was on the left. The meter sticks at the top and bottom of the slope were aligned. Then the individual with the clinometer looked through the viewfinder and lined it up with the horizontal line that was created between the meter sticks. This same person noted where the horizontal line crossed the left hand scale inside the viewfinder. Then that number was used on the conversion table located on the other side of the clinometer. The slope measurement was recorded and these steps were repeated for fourteen trials at each pond.

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Water was collected at each sample site using a 50 mL cylinder adjacent to where the slope was measured. The cylinder was filled 2/3 of the way. The Vernier sensor (figure 3) was connected to a TI-Inspire Calculator to become activated. The pond water was transferred from the graduated cylinder into the blank cuvette. The lid was sealed on top and the outside of the cuvette was wiped with a tissue to eliminate any excess water. The mark on the cuvette was aligned with the mark on the turbidity sensor, and the cuvette was placed in the slot inside the sensor (figure 4). The display was watched until the turbidity readings showed. Once the final reading was displayed, the turbidity measurement, measured in Nephelometric Turbidity Units (NTU), was recorded. These two procedures for collecting and testing were repeated at each of the two wetlands for fourteen trials.

RESULTS Table #1: The effect of Slope of Pond on Turbidity of Pond at Boyce Pond

Figure 2: Suunto Clinometer used for calculating the slope at each pond.

Figure 3: Vernier Turbidity Sensor used for measuring turbidity (NTU). Two cuvettes used for putting the samples in.

Figure 4: Cuvette placed in the sensor, ready to be tested.

Figure1: Drumlin Farm, the location where testing took place. Destinations 11, 12, & 13 were used.

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RESULTS Trial Slope (degrees) Turbidity (NTU)

1 17 525.4 2 11 525.4 3 19 395.5 4 17 401.7 5 14 137.9 6 14 404.7 7 11 306.2 8 17 321.9 9 14 162.7

10 9 398.5 11 17 237.2 12 14 341.2 13 17 154.9 14 11 175.3

Average 14 320.6 Standard Deviation 3 131.2

Graph #1: The effect of slope of pond on Turbidity of pond at Boyce Pond

Graphs 1 and 2 show the effect of pond slope versus turbidity at each individual pond. In graph 1, Boyce Pond, the r2 value is 4.9E-05 and at Ice Pond, the r2 value is 0.00059. The highest slope was 39 degrees and the lowest was 24 degrees. The highest

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calculated turbidity was 529.4 NTU and the lowest was 191.0 NTU. This represents a wide range of data with low data set precision. Table #2: The effect of slope of pond on turbidity of pond at Ice Pond Trial Slope Turbidity

1 39 191.0 2 33 525.4 3 35 525.4 4 33 401.7 5 35 276.4 6 35 496.0 7 31 210.6 8 29 525.4 9 37 221.3

10 29 347.0 11 31 252.3 12 35 405.7 13 24 217.5 14 33 529.4

Average 33 360.6 Standard Deviation 4 136.4

Graph #2: The Effect of slope of pond on turbidity of pond at Ice Pond

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In graph 3 the slope and the turbidity are shown at Ice and Boyce Ponds. The data is very similar and has a huge range within it. The smallest slope at each of the ponds was 9 degrees and the largest slope was 39 degrees. The r2 value for both Ice and Boyce Ponds is 0.02785, which is very small. A correlation between pond slope and turbidity cannot be concluded. Graph #4: The effect of a ponds average slope on average turbidity at Ice and Boyce Ponds

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Graph 4 displays the effect of average pond slope on average turbidity. The largest turbidity reading at both ponds was 529.4 NTU and the smallest turbidity reading was 137.9 NTU The error bars do not overlap for the average slope at Boyce Pond with the average slope at Ice Pond, however the average turbidity at Boyce and Ice ponds do overlap. DISCUSSION

This experiment was conducted to test the correlation between pond slope and water turbidity. The hypothesis set forth in this experiment was: If the pond slope is steeper, then the water’s turbidity will be higher, because rainfall moves downward on steeper slopes which will result in the pond having more soil particles suspended in the water thus increasing turbidity (Giambelluca, Thomas) (A.J. Jakeman) (www.snh.org). The hypothesis was not supported due to a wide range of data and small r2 values. As seen in Graphs 1 and 2, there is minimal correlation between the slope of the ponds and the pond’s turbidity. An r2 value was calculated to determine if there was a trend in the data. For Boyce Pond, the r2 value was E-05=10-5. The trend line crossed through only two points of data along with the r2 value being extremely small. Looking at the graph, there are many points that are located far from the trend line, concluding that there is wide range of data. By looking at the r2 value and the trend line, it cannot be concluded that there is a correlation between pond slope and water turbidity.

For Ice pond, the r2 value was 0.00059 and the trend line only hit one of the fourteen points of data. Comparing the two r2 values at each pond, Ice Pond’s r2 value is smaller. The data is spread out, concluding that there is a wide range of data points. Because of the small r2 value and a trend line that doesn’t cross through the majority of the data, it can be concluded that there is no correlation between pond slope and water turbidity. However, if errors did not impact the experiment, the results could have been more conclusive.

For both Ice and Boyce Pond, the graphs display low data-set precision because the data points are scattered throughout the graph, and the majority do not line up with the trend line. There is minimal confidence in the data, because the data ranges are so wide. If the data had been more precise, there would have been more confidence in the results. Sufficient data was not collected due to the fact that no correlation was found. However, the fact that no correlation was found is relevant. If there had been another fourteen data points, a small correlation could have maybe been found.

The average turbidity and slope for both ponds was also compared. This is displayed in graph 4. The error bars for the slope averages did not overlap, showing that Ice pond had a conclusively steeper slope than Boyce. However, the error bars for the turbidity averages overlap, so it cannot be concluded which pond had higher turbidity readings.

A previous experiment was tested to see if canopy coverage effected rainfall. The results showed that the higher percentage of canopy coverage, the less rainfall hitting the ground. At Boyce Pond, one of the ponds being tested in this experiment, there were large trees surrounding the pond, which could have been the cause for an extremely low correlation in the results. The trees overhead could allow less rain to hit the pond slopes, causing fewer soil particles to drift into the pond, thus causing lower turbidity (Xiao, Qingfu). As for Ice Pond, the slopes may have been too stable to have soil drop into the

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pond, creating turbid water. It is said that slopes are created in certain areas to be able to deal with its natural surroundings and weather conditions. It’s best to avoid having loose slopes around areas of water, because it creates more turbid water (http://ntl.bts.gov) The rainfall in the area could have also been light, not putting enough force on the slopes to collapse (Nelson, Stephen A.).

There are aspects of the experiment that could be modified for this experiment to be improved in the future. The first would be to test the water’s turbidity exactly in front of where the slope was tested. This could result in a more precise range of data, because each testing would have been done in the same exact spot each time. Another thing to be modified would be to pick ponds that weren’t covered by large tree canopies because that could decrease the pond’s turbidity (www.itreetools.org).

A few errors occurred while carrying out the two procedures. While trying to test turbidity at Bathtub Pond, the calculator stopped working, so data could not be collected at that pond, so only two ponds were included in the correlation. Next time, it would be helpful to bring an extra calculator just incase it broke down. In the procedure, it said to test the turbidity directly in front of where the slopes were calculated. It was difficult to do that at Boyce pond, because there was so much brush blocking the area of water that needed to be accessed. This was a common error during our data collection and the turbidity was tested two feet from where the slope was measured. For future research of this particular study, scientists could test pond slope on turbidity at different points around the pond, instead of in just one area. This could help farmers at Drumlin Farm know if the water is too turbid in certain areas on one pond. The farmers could then figure out ways to make the pond slopes shallower, so less rainfall would force soil into the pond. ACKNOWLEGDEMENTS I would like to thank multiple people for helping me complete this experiment. At Drumlin Farm, our teacher naturalists, Carol and Danielle, were very helpful with directing us to safe points around the pond to collect our data along with supplying us with information that enhanced our study. Ms. Jamison was a great help at Ice Pond, hanging with my partner and I when we completed our testing. Mrs. Hardy supervised my group at Boyce pond, and helped us complete our data collection in a timely fashion. I would also like to thank Mr. Sarzana for watching over our testing at Bathtub Pond, even though we ended up not collecting data there. Mr. Ewins was very helpful in class when Mrs. Svatek was absent by helping my partner and I fully understand how to use the clinometer. Mrs. Svatek was a huge help; first introducing us to this interesting project and then helping us to get organized and prepared for our Drumlin Farm visit. Lastly, I would like to personally thank Brooke, for being a cooperative and hard working partner who supplied our group with many materials for our poster. --Olivia Friend Many people supplied me with helpful information to help me complete this project. I would like to thank Ms. Jamison, Ms. Hardy, and Mr. Sarzana for chaperoning us at the three ponds we visited and providing us with useful information, and hanging out with us after we had completed all of our testing. I would like to thank Mr. Ewins for helping my partner and I with the clinometer, which enhanced the accuracy of our trials.

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Ms. Svatek was a key component to the success of our project. She supported us through the whole process and I am very grateful for all of her encouragement and suggestions. I would like to thank my partner Olivia, for all of her hard work and commitment through out this project; I never could have done it without her. It was challenging with my absence, but we managed and are very proud of our work. I would also like to thank my parents for allowing Olivia and I to take over our dining room and turn it into a major sparkle fest, and my nanny Susan. for delivering our final printed report. --Brooke Shachoy WORKS CITED: AUTHOR 1 "Common Forestry Tools." SCFC Learn Some. South Carolina Forestry Commission, 2010. Web. 11 Mar. 2014. <http://www.state.sc.us/forest/edutools.htm>. Giambelluca, Thomas. "Rainfall Atlas of Hawaii." Rainfall Atlas of Hawaii. Geography Department- University at Hawai'i Manoa, 2011. Web. 07 Mar. 2014. <http://rainfall.geography.hawaii.edu/rainfall.html> Jakeman, A.J. PDF. Cranberra, Australia: Center for Research and Enviornmental Studies, n.d. <http://www.mssanz.org.au/MODSIM95/Vol%201/Post.pdf> Leong. "The Effect of Antecedent Rainfall on Slope Stability." Springer Link. Springer, Part of Springer Science+Business Media, n.d. Web. 9 Mar. 2014. <http://link.springer.com/article/10.1023%2FA%3A1013129725263>. Nelson, Stephen A. "Slope Stability." Slope Stability. EENS 3050, 10 Dec. 2013. Web. 16 Apr. 2014. <http://www.tulane.edu/~sanelson/Natural_Disasters/slopestability.htm> "Rivers and Their Catchments: Causes and Effects of Turbid Water." Rivers and Their Catchments: Causes and Effects of Turbid Water. Information and Advisory Note Number 22, n.d. Web. 09 Mar. 2014. <http://www.snh.org.uk/publications/on- line/advisorynotes/22/22.htm>. “Slope Stabilization and Stability of Cuts and Fills.” Research and Innovative Technology Administration, RITA, Web. 16 Apr. 2014. <http://ntl.bts.gov/lib/24000/24600/24650/Chapters/M_Ch11_Slope_Stabilization. pdf.> "STEM Inventory." STEM Inventory. The Center for STEM, n.d. Web. 11 Mar. 2014. <http://inventory.stemideas.org/view_item.php?item_id=34>. “Turbidity.” Wikipedia. Wikimedia Foundation, 18 Apr. 2014. Web. 28 Apr. 2014.

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"Vernier Turbidity Sensor." Vernier Turbidity Sensor. VWR, 2014. Web. 11 Mar. 2014. <https://ca.vwr.com/store/catalog/product.jsp?product_id=8891865>. Xiao, Qingfu, Gregory McPherson, James R. Simpson, and Susan L. Ustin. "RAINFALL INTERCEPTION BY SACRAMENTO'S URBAN FOREST." Itreetools., July 1998. Web. 16 Apr. 2014. <http://www.itreetools.org/streets/resources/rainfall_interception_by_sacramentos_uf_xiao.pdf>. WORKS CITED: AUTHOR 2 HORACIO TONIOLO,1* GARY PARKER,1{ VAUGHAN VOLLER,1 AND R. T. BEAUBOUEF2. DEPOSITIONAL TURBIDITY CURRENTS IN DIAPIRIC MINIBASINS ON THE CONTINENTAL SLOPE: EXPERIMENTS: NUMERICAL SIMULATION AND UPSCALING. Houston, Texas: Journal of Sedimentary Research, n.d. PDF. Jakeman, A.J. PDF. Cranberra, Australia: Center for Research and Enviornmental Studies, n.d. <http://www.mssanz.org.au/MODSIM95/Vol%201/Post.pdf> Leong. "The Effect of Antecedent Rainfall on Slope Stability." Springer Link. Springer, Part of Springer Science+Business Media, n.d. Web. 9 Mar. 2014. <http://link.springer.com/article/10.1023%2FA%3A1013129725263>. Newton, David E. Chemistry of the Environment. New York: Facts on File, 2007. Print. Perlman, Howard. "Turbidity." - Water Properties, USGS Water Science School. USGS, 24 Feb. 2014. Web. 27 Feb. 2014. <http://water.usgs.gov/edu/turbidity.html>. Pratson, Lincoln F. "Scholars@Duke." Clinoform Progradation by Turbidity Currents: Modeling and Experiments. Journal of Sedimental Research, 2008. Web. 12 Mar. 2014. <https://scholars.duke.edu/display/pub705216>. "Rivers and Their Catchments: Causes and Effects of Turbid Water." Rivers and Their Catchments: Causes and Effects of Turbid Water. Information and Advisory Note Number 22, n.d. Web. 09 Mar. 2014. <http://www.snh.org.uk/publications/on- line/advisorynotes/22/22.htm>.

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The Effect of Dissolved Oxygen (Mg/L) on Water Turbidity (NTU)

By Kim Vetrano and Lidia Goldberg

Table of Contents

Section Author Page

Abstract Goldberg 1

Introduction Vetrano 1

Materials and Methods Goldberg 3

Results Vetrano 4

Discussion Goldberg 8

Acknowledgements Goldberg & Vetrano 10

Works Cited Goldberg 10

Works Cited Vetrano 11

ABSTRACT At the previous trip to Drumlin Farm in Lincoln, Massachusetts, it was noticed that many of the ponds looked dark and muddy. Also it was noticed that there were many living organisms in the water. The initial question asked here was, does the level of dissolved oxygen affect the turbidity? The experiment tested was the effect of dissolved oxygen (mg/L) on water turbidity (NTU). The idea of the hypothesis of this experiment was, the ponds with the higher dissolved oxygen would have a higher turbidity. This experiment was conducted by taking water sample a meter from the shore, and then they were collected in a plastic cup, attached to the end of a mater stick. Then they were tested for their dissolved oxygen level and turbidity level using a dissolved oxygen probe, and turbidity sensor. Both were Vernier, and the Vernier Lab Quest2 was used to collect the data. It was shown that there were data effects between the variables of the study. All the ponds had around the same average dissolved oxygen, the turbidity varied drastically from pond to pond. INTRODUCTION Turbidity is the measure of the blurriness, or haziness, of water and is caused by small, individual particles suspended in water. One factor that could possibly affect the turbidity of water is dissolved oxygen due to the warming of water, which cannot hold as much oxygen, or the amount of organisms taking in oxygen within the pond (http://water.usgs.gov). Dissolved Oxygen is the measure of the amount of oxygen present in water. Fast moving streams tend to consist of more dissolved oxygen while stationary ponds or lakes tend to contain less. A body of water gains oxygen from the atmosphere, so rushing water will dissolve more oxygen when it meets the surface than still water. Dissolved Oxygen is crucial for organisms living in bodies of water because it is needed in order to breathe. Breathing becomes more difficult when there is less dissolved oxygen in the water (http://water.usgs.gov). A healthy amount of dissolved oxygen within a body of water is a minimum of 4-5 ppm. With too little, dissolved oxygen can cause the death of wildlife, while too much does not have an effect on the organisms. It is important to note the specific factors that could vary the dissolved oxygen level such as water temperature, sunlight, living organisms, plant life and algae (http://www.lenntech.com). This experiment will be conducted at Drumlin Farm in Lincoln, Massachusetts. Drumlin Farm consists of a variety of different environments, including five ponds. The three ponds that are going to be tested are Vernal Pool, Boyce Pond, and Poultry Pond. Vernal Pool is a seasonal body of water located north east of the Drumlin which fills up by melted snow, rainwater and rising groundwater. Boyce Pond is southeast of the drumlin and contains many different organisms and is surrounded by trees. Located northeast of the Drumlin is Poultry Pond which contains a thin layer of duckweed in the Fall and a variety of tree species. A high level of dissolved oxygen is not only beneficial to fish, but it also results in healthier water for humans. However, it is also important to note that if there is too little dissolved oxygen within a body of water it can be extremely harmful to aquatic life

and potentially deadly (http://www.unc.edu). A sign that a pond may include a insignificant amount of dissolved oxygen is if the water temperature is warm. This is usually because the pond is overpopulated with bacteria or aquatic life, causing the dissolved oxygen to be used in great amounts. Another factor that affects the dissolved oxygen level within a pond is over fertilization of water plants by runoff from farm fields containing chemicals that make up fertilizers. When this occurs, it causes excessive water plant growth, causing more dissolved oxygen to be used by them. When these plants eventually die, bacteria multiplies after using it as food causing dissolved oxygen to then be used in a greater amount (http://www.lenntech.com). One factor that can affect water turbidity is plant and animal decay. Once the bodies die, suspended particles are released, making the water more more turbid. Flooding can also be a reason for water turbidity. As the water rises during flooding, it can bring along new, both organic and inorganic, particles from the land surface (http://bcn.boulder.co.us). The experiment that will be conducted is the effect of dissolved oxygen (ppm) on water turbidity (NTU) at Poultry, Vernal, and Boyce Ponds. The objective of this experiment is to test if the dissolved oxygen level affects the water turbidity level at each pond. This will be tested by using a Vernier Dissolved Oxygen Probe and Vernier Turbidity Sensor. The independent variable for this experiment is the dissolved oxygen level of each pond in parts per million (ppm), and the dependent variable is the water turbidity level in nephelometric turbidity units (NTU). Important controlled variables consist of the amount of water tested, number of trials at each pond, method collected, and depth of water tested. The hypothesis set forth for this experiment is: if a pond has a lower dissolved oxygen level, then it will result in a higher turbidity, or blurriness of water, because lower dissolved oxygen indicates more microscopic organisms taking up dissolved oxygen causing the water to be more turbid (http://water.usgs.gov). This experiment and research will demonstrate how dissolved oxygen can influence turbidity which can affect aquatic life significantly. It is very important for the naturalists to be aware of the effects of dissolved oxygen in order to limit the risk of overpopulating ponds. It is essential to understand this concept well so that runoff from farm field fertilizers are reduced in order to not harm the aquatic life active within the pond. It is also important to know why there is more dissolved oxygen and a larger turbidity level within a pond as opposed to another pond. The more people who know about the effect of dissolved oxygen of different ponds on water turbidity, the more beneficial it is to the world because not only will it improve water quality, but it will also keep living organisms healthy. !!!"#$%&"'()"*+)!$#,-+()!Each pond was measured like a compass, 360° around. The ponds that were tested from were Boyce Pond, Poultry Pond and the Vernal Pool at Drumlin Farm. Before the arrival at Drumlin Farm a T-inspire calculator was used to generate 8 random degrees to test from. The sections being tested from were marked with flags. At each section a sample of water was tested for turbidity and dissolved oxygen. In total there was sixteen data points per pond, resulting in 48 total data points.!

!Figure 1: Map of Drumlin Farm

!Figure 2: Vernier Turbidity Sensor (www.vernier.com)

!First the Vernier Turbidity Sensor was connected to the Vernier Lab Quest2. The data collection software was turned on. The sensor was calibrated according to the directions given. The water sample was taken a meter from the shoreline using a plastic cup, attached to the meter stick. The water was collected deep enough for the cup to be submerged. Once the probe was calibrated, the water sample being tested was poured into a ‘cuvette’ (a straight sided, clear container holding liquid samples.) The cuvette was wiped with a tissue and placed into the sensor. The marks were aligned correctly. The lid was closed, and the turbidity was measured, using NTU units. The data was then recorded in the table according to pond, and section. (Out of eight sections) !The Vernier Dissolved Oxygen Probe was connected to the Vernier Lab Quest2. The data collection software was turned on and the probe was calibrated according to the directions specified. The blue cap as removed from the probe. The tip of the probe was

placed into the same water sample that was tested for turbidity. The probe was not fully submerged because the whole probe is not waterproof. The probe was stirred gently in the water. Then the dissolved oxygen was recorded using Mg/L units. If the probe was left un-moving in the water, the dissolved oxygen levels would have begun to drop. !!Figure 3: Vernier Dissolved Oxygen (www.vernier.com) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

!RESULTS According to Graph 4, there was minimal correlation between an increase of dissolved oxygen and an increase of turbidity. Graph 3 shows Poultry Pond had the highest r2 value of 0.16418, compared to Graph 2 where Boyce Pond had a value of 0.03286 and Graph 1 where Vernal Pool had a value of 0.001. The r2 value for all three ponds was 0.12614, which is a stronger correlation than the three ponds individually. Vernal Pool had the highest average turbidity (344.3 NTU) compared to Boyce Pond (72.4 NTU) and Poultry Pond (27.0 NTU). The highest turbidity level at Vernal Pool was 527.3 NTU, while the lowest was 140.5 NTU. Although these data points were relatively high compared to the other ponds, the average dissolved oxygen for this same pond (7.4 mg/L) was close to the average dissolved oxygen level at Boyce Pond (10.6 mg/L) and Poultry Pond (9.3 mg/L). According to Graph 4, out of all three ponds the highest turbidity level was 527.3 NTU (at Vernal Pool) and the lowest was -0.4 NTU (at Boyce Pond). The highest level of dissolved oxygen was 15.8 mg/L and the lowest was -0.4 mg/L (both at Boyce Pond). Data point six at Poultry Pond was considered an outlier because the turbidity level was 79 NTU, which is far off from the other data points. Out of all three ponds, the lowest standard deviation for dissolved oxygen was at Vernal Pool (0.5) and the highest was at Boyce Pond (5.1). The highest standard deviation for turbidity was at Vernal Pool (166.9) while the lowest was at Poultry Pond (15.7).

Each pond consisted of many unique and interesting features. At Poultry Pond there were many thorn bushes and trees surrounding the pond. The Pond also consisted of a large wooden dock and the surface of the water looked dark and cloudy. At Boyce Pond, many dead sticks and bushes surrounded the pond as well, along with narrow streams and muddy pathways. There were also large trees standing in the middle of the pond and towards the shore. Finally, Vernal Pool was the smallest pond out of the three. This pond consisted of somewhat clear pathways along the shore and a large tree that had uprooted the ground and fallen into the muddy water. DISCUSSION !The experiment conducted, tested the effect of dissolved oxygen on water turbidity. The hypothesis for this experiment was: if a pond has a lower dissolved oxygen level, then it will result in a higher turbidity, or blurriness of water, because lower dissolved oxygen indicates more microscopic organisms taking up dissolved oxygen causing the water to be more turbid (water.usgs.gov). The hypothesis was not supported. An alternate hypothesis could have been: if a pond has a higher dissolved oxygen level, then it will result in a higher turbidity, or murkiness of water, because higher dissolved oxygen indicates more microscopic organisms taking up dissolved oxygen causing the water to be more turbid (water.usgs.gov). The ponds with the higher dissolved oxygen had the higher turbidity. This was because the more dissolved oxygen there was, the more pond life there was. If there were more pond life then the water would be more turbid because it is crowded with organisms. At the last pond, Vernal Pool, the water turbidity varied greatly. The lowest water

turbidity was 140.5 NTU, whereas the highest was 527.3 NTU. Vernal Pool had many uprooted trees bending into it. This caused for some water samples to have more soil and mud in them. Since water turbidity is measured by the clarity of the water, those testing locations were affected greatly (www.dnr.mo.gov/.) The other ponds, however, had turbidity averages that were not as high. Boyce Pond’s average turbidity was 72.4 NTU and Poultry Pond’s was, 27 NTU. The total r2 was 0.12614. The r2 value was very low for the data, and it shows a weak correlation. Therefore the trend line does not fit the data well. In conclusion the data seems relative to the pond that it was tested from. For example Vernal Pool had the muddiest shoreline, and the test were taken only a meter from the shore, so it would ultimately have the highest average turbidity (water.usgs.gov.) With research, and an experiment done at USGS water science school, show that rapidly moving water contains more dissolved oxygen. (water.usgs.gov). While collecting results, this was supported because the ponds are all un-moving. The data at Boyce and Poultry Pond is all part of a relative range in Dissolved oxygen and Turbidity. However, at Vernal pool the water turbidity ranges from 140.5 NTU to 527.3 NTU. This impacts the confidence in the data because it’s unclear if the tests were taken too deep underwater, or if it was the actual turbidity, due to the fallen trees. If this experiment were to be done again, one thing would need to be altered. The method for collecting samples a meter from the shoreline would have to be strengthened. Every so often the cup would fall off the end because the tape was not strong enough, and was not waterproof. Sufficient data was collected for the experiment at hand. However, if the test was to be done again, samples could be taken every 30° around the pond instead of random degrees. This is suggested because the tests done at Vernal Pool were more to one side of the pond than the other. This could have affected the results.

Some errors occurred when testing for water samples was taken the cup fell off the meter stick. This could have affected the results because then that water sample was not taken as far from the shoreline as the others. This error could be easily eliminated by using stronger tape. Also, something stronger that measured a meter could be used, and bucket could be attached to that. A question that arose was, does the amount of aquatic life affect the water turbidity? Since dissolved oxygen is needed for aquatic life, the type and number of organisms could be tested. This test affects dissolved oxygen levels because plants go through photosynthesis and release oxygen into the water. (bcn.boulder.co.us) The organisms effect the dissolved oxygen concentration in the water. A future experiment conducted could be, the amount of aquatic life on water turbidity and dissolved oxygen. !"#$%&'()*+),)%-.!!Author 1 !

First off, I would like to thank Ms. Svatek, for making the whole experiment possible, and as easy as possible. Thank you Ms. Hardy for helping us at our first rotation, Boyce Pond. She helped us figure out the weather, and the time. Thank you Mr. Rossiter and Ms. Moon, for attending to the habitats, and making sure everyone was all set to collect data. Thank you naturalist Danielle, you helped us find the easiest way

around Boyce Pond. You told us about some of the factors that affected the pond’s turbidity and dissolved oxygen. Thank you, naturalist Sally for teaching Kim and I about Vernal Pools, and how they are affected. Also, thank you for being eager to collect our results to see how our experiments affected the ponds at Drumlin. Thank you to my mom, for helping me gather some materials from the house, such as tape, and cups. Finally, thank you Kim for being a great partner. You helped me understand things when I was confused, and made the whole testing process much more fun! Author 2 There are a few specific people that I have not yet thanked that I would like to do so for making this experiment possible and productive. Firstly, I would like to thank Ms. Svatek for introducing and teaching us this material and for answering the many questions brought up during the process. Thank you for advising not only us but balancing the whole class. I would also like to thank all the teacher naturalists for helping us to each habitat and answering many questions. Thank you Danielle for guiding me a safe route around the pond in order to collect data in the locations chosen. Thank you Sally for teaching us the basic details of the ponds while throwing in quite a bit of interesting history to the discussion. Next, I would like to thank all the science teachers, Ms. Larocca, Mr. Ewins, and Ms. Schultheis, for keeping us on track and giving helpful advice to us that include the safest and fastest way to collect data. I would also like to thank the department for lending us useful materials for data collection. Thank you to Ms. Hardy, Mr. Rossiter, and Ms. Moon for attending the habitats and making sure everyone was safe and on track. Lastly, I would like to thank my amazing partner, Lidia Goldberg, for being so productive and always making the process exciting and interesting. Thanks for keeping me focused and for lending a helpful hand when needed. I am very grateful for all these people who helped to make this experiment run smoothly. Thank you. WORKS CITED Author 1 Pauley, Sara Parker. “Water Quality Parameters.” Environmental Services Program.

Missouri Department of Natural Resources, n.d. Web. 4 Mar. 2014

http://www.dnr.mo.gov/env/esp/waterquality-parameters.htm

Walker, Pam, and Elaine Wood. “Build and Use of Turbidity Tube.” Environmental

Science Experiments. New York: Facts on File, 2010. 27-28. Print.

“Water Properties: Dissolved Oxygen.” Dissolved Oxygen, from USGS Water Science for

Schools: All about Water. The USGS Water Science School, n.d. Web. 05 Mar.

2014 http://water.usgs.gov/edu/dissolved oxygen.html

"Why Oxygen Dissolved in Water Is Important." Why Is Important the Oxygen Dissolved

in Water. Lenntech, n.d. Web. 08 Mar. 2014.

<http://www.lenntech.com/why_the_oxygen_dissolved_is_important.htm>.

"Turbidity." - Water Properties, USGS Water Science School. N.p., n.d. Web. 11 Mar.

2014. <http://water.usgs.gov/edu/turbidity.html>.

Author 2

“BASIN: General Information on Turbidity.” BASIN: General Information on Turbidity.

USGS Water Quality Monitoring, n.d. Web 01 Apr. 2014

http://bcn.boulder.co.us/basin/data/NEW/info/Turb.html>.

"Chesapeake Bay Program." Bay Blog RSS. N.p., n.d. Web. 08 Mar. 2014.

<http://www.chesapeakebay.net/discover/bayecosystem/dissolvedoxygen>.

Pauley, Sara Parker. “Water Quality Parameters.” Environmental Services Program.

Missouri Department of Natural Resources, n.d. Web. 4 Mar. 2014

<http://www.dnr.mo.gov/env/esp/waterquality-parameters.htm>

Shifflett, Shawn Dayson. “Water and Sustainability: Dissolved Oxygen.” Water and

Sustainability: Dissolved Oxyegn. N.p., n.d. Web. 09 Mar. 2014

<http://www.unc.edu/~shashi/TablePages/dissolvedoxygen.html.>

Walker, Pam, and Elaine Wood. “Build and Use of Turbidity Tube.” Environmental

Science Experiments. New York: Facts on File, 2010. 27-28. Print.

“Water Properties: Dissolved Oxygen.” Dissolved Oxygen, from USGS Water Science for

Schools: All about Water. The USGS Water Science School, n.d. Web. 05 Mar.

2014 http://water.usgs.gov/edu/dissolved oxygen.html

"Why Oxygen Dissolved in Water Is Important." Why Is Important the Oxygen Dissolved

in Water. Lenntech, n.d. Web. 08 Mar. 2014.

<http://www.lenntech.com/why_the_oxygen_dissolved_is_important.htm>.

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The Effect of pH on Nitrate

Owen Hakim S82-6 Spencer Kuldell S82-14

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Table Of Contents Section Author Page Abstract Kuldell 2

Introduction Hakim 2

Materials & Methods Kuldell 3

Results Hakim 5

Discussion Kuldell 8

Acknowledgements Hakim & Kuldell 9

Works Cited Hakim 10

Works Cited Kuldell 12

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ABSTRACT A fine balance between pH and nitrate in pond water is needed for healthy ecosystems.

This study was conducted at Drumlin Farm in Lincoln, MA to investigate whether there was a relationship between the pH and the nitrate in bodies of water. It was expected that at pH values between six and eight, nitrate levels would be highest because plants can grow and produce nitrogen best within that range (allaboutalgae.com). Water samples were collected in ten random intervals around three different ponds: Poultry, Ice, and Vernal. Nitrate and pH were tested in two different beakers from each pond using Vernier sensors. It was found that the hypothesis was not supported because ponds of identical pH levels had statistically significant differences in nitrate levels. The most significant finding in the results was that the relationship between algae and nitrate levels was different than what was researched.

INTRODUCTION A pH or nitrate measurement may seem like just another scientific number, but really they are key to the survival of every plant on earth! Nitrate (NO3) concentration measures the amount of nitrate ions in a substance, while pH measures the acidity of a substance. The concept of pH is defined as the decimal logarithm of the reciprocal of the hydrogen ion activity. The formula for pH is: pH=-log(aH+)=log(1/aH+) if hydrogen ion activity is defined as aH+ (wikipedia.org). A logarithmic increase is comparable to an exponential increase. There are a few ways in which Nitrate can get into the water. The first is nitrite ions (NO2) that are released by plants into the water, react with oxygen in the water to form nitrate (Figari, amersol.edu.pe). Another way is if ozone (O3) ions in the atmosphere react with nitrogen ions in the air to form nitrate, it can be absorbed by water (Keuer, depts.alverno.edu). Another nutrient which is vital Nitrate is measured in parts per million, while pH (the concentration of hydrogen ions per mole) is measured on a scale of 0-14. Fourteen is most basic and zero is most acidic (lusterleaf.com). Most ponds, such as those at Drumlin Farm, maintain a pH of six to eight (sancoind.com). The testing will be conducted at a few of Drumlin Farm’s ponds. Drumlin Farm is a 312 acre Audubon Society Preserve, in Lincoln, MA. There are five ponds, spread out across the area of the preserve. The procedure was conducted at Ice Pond, Vernal Pool, and Poultry Pond. Ice Pond is in between the parking lot and the north side of the drumlin. It is frozen for a large part of the year. The Vernal Pool is to the far east of the preserve, north of Boyce Field, and is the habitat with the most wildlife at Drumlin Farm. Poultry Pond is located just north of the Farm Life Center. This pond gets its name from the nearby poultry and other farm animals that get their water from it. The pH level of the ponds determines algae growth, and whether nearby plants can survive off the pond’s water. There are many variables that can affect water pH. Algae growth, water temperature, soil pH, and surrounding plant life are a few examples (massaudobon.org). The pH value of a pond’s water is essential to algal growth. If a pond’s water is too acidic (about 5 or less) or too basic (about 9 or more), algae won’t grow. This causes pond’s TDS (total dissolved solids) level to become very high (as algae absorb many dissolved solids) which damages the ecosystem by blocking out the sun in the pond (http://allaboutalgae.com). Certain kinds of algae, called planktonic algae, are also a vital first component in the food chain for any pond. These algae are eaten by zooplankton, which are consumed by smaller fish, which are eaten by bigger fish. Many pond owners even supplement their pond’s algal growth in order to promote a healthier stock of fish (www.gotalgae.com/). These algae, along with zooplankton and fish, prefer a pH of six to eight. Furthermore, an experiment on the effect of pH on algal growth conducted by University of Michigan students in 2008 showed that a pond with a pH of 6.2-6.8

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had significantly more algae than one with a pH of 7.6-9.2 (www.gotalgae.com/). Nitrate also has an effect on algal growth. Algae converts nitrogen and other nutrients into energy, and congregate where these nutrients are plentiful. A pond with a lot of leaves, animal excrement, and nitrogen-rich soil nearby will have a large amount of algal growth. This will lead to a rich ecosystem because the pond will have a first step in the food chain (http://marinebio.org/). However, excessive nitrogen levels lead to algal overgrowth, which damages other aquatic plants by blocking sunlight in the pond. This nitrogen excess will also lead to the water being toxic to fish and other organisms (peer.tamu.edu). The experiment being proposed is to test the effect of pond water pH ([H+]/mol) on nitrate levels (mg/L). The experiment will be conducted by taking eight water samples from each of the three ponds. Then, the samples will be tested (separately) for pH and nitrate with the respective probes. There will be 30 total trials. The independent variable for the proposed experiment is pH ([H+]/mol) of the water. The dependent variable will be the nitrate level (mg/L) in the samples collected. Some important controlled variables are the depth of the water from which the samples will be taken, the height in the water at which the samples will be taken, the testing materials, the amount of water taken for each sample, and the shadiness/sunniness of the sample sites. The hypothesis that is proposed is: if the pH of a sample is between six and eight, then it will have the most nitrate because at that pH algal growth increases, and algae turn the nitrate and other nutrients into energy in a process similar to photosynthesis (http://allaboutalgae.com). This research illustrates how Drumlin Farm’s ponds are affected by pH and nitrate levels. Botanists and naturalists at Drumlin Farm need to know the optimal pH range for their pond (most likely six to eight), and what that does to the nitrate value in order to keep the algal growth to a reasonable level. If scientists know more about the effects of certain conditions on nutrients such as nitrate, then these scientists will be able to better monitor the health of the ecosystem at Drumlin Farm. The effect of pH on nitrate is important to understand in order to gain better knowledge of why some ponds are flooded with algae and some are nearly empty despite having nearly identical pH and dissolved oxygen values. Understanding the factors that impact nutrients such as nitrate is critical to the entire field of agriculture because farmers need to understand when to add nitrate-rich fertilizer and when such fertilizer would be excessive, causing elevated levels in nearby bodies of water. The more people who understand the impact of factors such as pH on nitrate, the more efficiently and cost-effectively irrigation water resources can be managed. MATERIALS & METHODS Water samples were collected from three different ponds at Drumlin Farm: Ice, Vernal, and Poultry Ponds. The water samples were large enough to measure both the nitrate levels and the pH. For each pond, eight different samples were collected from the banks of the ponds. Each sample was 20 mL in volume and was collected in two 50 mL beakers labeled A & B. To minimize sampling inconsistencies, each sample was collected from the water’s surface. They were measured when the water’s depth was 10 centimeters, and they were measured in random intervals around the ponds. Notes about the controlled variables such as shade vs. sun were recorded next to the data tables in the Field Note Book.

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Figure 1: Diagram of water sampling in Drumlin Farm pond (Lincoln, MA). Each water sample was collected at random intervals around the pond. Each dark dot represents a sample from one of the three ponds. The depth from the surface represented by the side view shows that the data was collected from samples 10 centimeters above the ground.

Figure 1:

The pH measurements for each sample were made using a Vernier pH sensor. The sensor

was rinsed with distilled water and dried using a paper towel to remove extra water droplets, then submerged into beaker A. The sensor measured the pH, and the pH value was recorded into the data table in the FNB. Values were only recorded 60 seconds after the sensor had been submerged into the sample. After 60 seconds, the reading had to stay steady for three seconds before the data was recorded in the data table to the tenth’s place. Nitrate measurements for each sample were made using a Vernier Nitrate Ion-Selective Electrode. The probe was rinsed with distilled water and dried using a towel to remove extra water droplets and then submerged into beaker B. The probe measured the nitrate and the nitrate value was recorded into the data table in the FNB. Values were only recorded 60 seconds after the probe had been submerged into the sample. After 60 seconds, the reading had to stay steady for three seconds before the data was recorded in the data table to the tenths.

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RESULTS Tables 1-3: The Effect of pH on Nitrate

Poultry Pond (1)

Ice Pond (2)

Vernal Pool (3)

Trial # pH Nitrate (mg/L)

Trial # pH

Nitrate (mg/L)

Trial # pH

Nitrate (Mg/L)

1 5.8 1.6

1 6.3 11.7

1 6.0 7.4 2 6.2 1.6

2 6.2 12.2

2 6.2 7.5

3 6.5 1.8

3 6.0 9.4

3 6.1 6.8 4 6.0 1.8

4 6.2 12.3

4 5.8 2.8

5 6.0 1.8

5 6.2 15.5

5 2.4 3.3 6 6.0 2.0

6 6.2 12.8

6 2.4 5.1

7 6.2 2.1

7 6.3 14.8

7 2.7 5.4 8 6.7 1.9

8 6.2 10.5

8 2.7 6.1

9 6.2 1.8

9 6.2 15.7

9 2.7 6.0 10 6.3 88.61

10 6.3 11.8

10 2.7 6.8

Average 6.19 1.82

Average 6.21 12.67

Average 4.0 5.72 St. Dev. 0.26 27.44 St. Dev. 0.09 2.09 St. Dev. 1.78 1.61

Table 4: The Effect of Nitrate on pH (averages)

Standard Deviation Averages

Poultry Pond

Ice Pond

Vernal Pool

Poultry Pond

Ice Pond

Vernal Pool

pH 0.26 0.09 1.78 6.19 6.21 4.00

Nitrate 0.162 2.09 1.61 1.82 12.67 4.72

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Graph 1: The Effect of pH on Nitrate (mg/L) (Poultry Pond)

Graph 2: The Effect of pH on Nitrate (mg/L) (Ice Pond)

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Graph 3: The Effect of pH on Nitrate (mg/L) (Vernal Pool)

Graph 4: The Effect of Location on Nitrate (mg/L) and pH (average values)

Graph 1 represents the data taken at Poultry Pond. In this graph, there was an r-squared value of 0.07. This shows that at Poultry Pond, there was very little correlation between pH and nitrate levels. For the most part, Poultry Pond contained the lowest nitrate values (1-2 mg/L), however there was one major outlier which was 88.6 mg/L. Poultry Pond samples had a relatively average acidity (around 6 pH). The standard deviation for nitrate was the highest among the three habitats visited, at 27.4. The average pH was 6.19. The average nitrate was 10.5 mg/L. Graph 2 shows the data collected from the Ice Pond. Ice pond had the highest average pH and nitrate values. Unlike in Graph 1, there were no outliers. This graph had an r-squared value of 0.17, the highest out of the three graphs. Despite this, the actual value shows very little

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correlation. The average nitrate value in Ice Pond was 12.7 mg/L, while the average pH was 6.21, both the highest of the three habitats. Ice Pond, in accordance with the averages, had the highest nitrate values (10-16 mg/L), and similar pH values to Poultry Pond. Graph 3 displays the data taken from the Vernal Pool. It had the lowest average nitrate values (5.72 mg/L). In Graph 3, there was an r-squared value of 0.08, a similar result to Poultry Pond, also suggesting no correlation. The data suggested that the Vernal Pool was much more acidic than either of the other pools, with an average pH of 4.0. There were no major outliers in Vernal Pool. Graph 4 was made up of the averages of the three locations. Poultry Pond had the lowest overall error bars, despite having the only “error” measurement. Vernal Pool had the largest overall error bars. Ice Pond had the highest average pH and nitrate. Vernal Pool had the lowest average pH and nitrate. DISCUSSION The experiment was conducted to test the effect of pond water pH ([H+]/mol) on nitrate levels (mg/L). The original hypothesis stated: if the pH of a sample is between six and eight, then it will have the highest nitrate value because algal growth increases as pH increases, and algae turns the nitrate and other nutrients into energy in a photosynthesis-like process (allaboutalgae.com). The hypothesis was not supported in this experiment because between pH values of six and eight, there were many different nitrate values, ranging from 1.8 to 15.5 mg/L. Because of the wide range of nitrate values, a reliable comparison to nitrate values outside that pH range could not be performed, and there was not enough correlation to show a trend in the data.

The correlation between the pH and the nitrate in the experiment conducted was very weak with an r-squared value of 0.07. The error bars for the nitrate had no overlap between the three ponds. The pH error bars had overlaps, however this did not affect the data because the pH between the ponds was not the variable being tested. The three ponds that were sampled all showed an r-squared value below 0.2. Because the r-squared values showed no trends, and because the number of pH measurements outside the range of six to eight was slim, there was little confidence in the data. In a similar experiment conducted by Amersol College using the same variables, the r-squared value was also found to be very low (which was shown in their graph and table) (www2.amersol.edu.pe/). Several explanations could account for the low r-squared value in the pond environments.

Poultry Pond at Drumlin Farm has a greenish hue. This color was thought to be from algae or plant growth. The algae may have affected the data because Poultry Pond, excluding the outlier of 88, had on average the lowest nitrate levels (1.82 mg/L). Ice Pond, which was partially iced over, had the highest average nitrate values (12.67 mg/L). Because the average pH of these two ponds was nearly identical (pH = 6.2), the hypothesis that pH affects nitrate levels was not supported. The two ponds were not likely to have had equal amounts of algae growth because of the temperature difference between the ponds. This notion is based on the ice in Ice Pond. Because of the ice, the temperature in Ice pond could be very low, therefore killing the algae and raising the nitrate levels. The Vernal Pool measurements showed an intermediate nitrate value of 5.72 mg/L. However, the pH measurements of this sample area (average = 4.0) were not the same as those of the other ponds. Comparisons between Vernal Pool and the other two ponds suggest that pH, outside the range of six to eight, does not affect nitrate levels. In extremes of

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pH, according to the research, there would be an absence of nitrate because algae would not be able to grow; therefore the algae could not disperse the nitrate into the water. The data collected at each sample area was precise except for an outlier and an error that are explained below. The pH measurement for Ice Pond had the greatest precision with a standard deviation of 0.08. Its nitrate measurements were also reasonably precise with a standard deviation of 2. This gives confidence to the comparison between Ice Pond and the others. For example the pH of Ice pond and Poultry Pond both average 6.2, while the pH values of Vernal Pool differ because of an error that occurred. A failure to properly clean the pH electrode lead to an error that caused the pH to drop from 6 to 2, and this changed the average of the pH to 4 with a standard deviation of 1.8, and therefore diminished confidence in these results. Another major error that occurred affected the confidence in nitrate concentrations for Poultry Pond. The reading of 88.6 mg/L, which was 45 times higher than any other reading, likely occurred because on that side of the pond, there was a chicken coop that might have drained into the pond therefore changing the reading. Disregarding the outlier, the data was the most precise in Poultry Pond. If this study were repeated, a data set to include should be temperature readings for each sample. Temperature might affect the algae growth in the pond, therefore resulting in more conclusive results. Different ponds could be tested for their pH and nitrate levels. This might reveal differences in ponds that affect data. Sufficient data was collected in this study to draw conclusions because the standard deviation didn’t change that much. Future experiments could include the effect of algal growth on nitrate. It would be interesting to know if algae released more nitrates when they die or when they are living. Complex ecosystems like ponds offer many different experiments to help understand their patterns. ACKNOWLEDGEMENTS It is an enjoyable ego-stroking exercise to say that this report was built upon the pure genius and innovation of Spencer and that, but I would be a lie. The truth is, there are some people who this report would simply not have happened without. First, I would like to thank Danny Kutsovsky and Trevor, who lent us their idea for collecting and testing efficiently in order to avert a late procedural crisis. Also, I would like to acknowledge Ms. LaRocca, for instructing me on the intricate process of writing a lab report. Thirdly, I would like to thank the Drumlin Farm chaperones and staff for guiding us through the data selection process. Lastly, I would like to say that this report couldn’t have happened without The Hitchhiker’s Guide to the Galaxy, by Douglas Adams, which reminded me of my scientific duties every time I tried to procrastinate by reading it, and helping me think outside of the box during the research process. There are many people I would like to thank for help with this project. First of all I would like to thank my partner Owen Hakim for just helping out with the entire project and without him I would not be able to do this project. I would also like to thank Ms. LaRocca for encouraging us and giving us the opportunity to do real science and helping us with the writing of this report. Thanks also to all of the chaperones and the Drumlin Farm staff for facilitating the whole project. Finally I would like to acknowledge my parents for raising me and supporting me through this entire project.

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WORKS CITED Owen Hakim S82-6 "Algae Basics - Benefits of Algae." Algae Basics - Benefits of Algae. Algae Biomass

Organization, n.d. Web. 12 Mar. 2014. <http://allaboutalgae.com/benefits/>.

"Algae Solutions." Algae Solutions. Ed. Ken Rust. Kasco Marine Inc., 2006. Web. 12 Mar. 2014.

<http://www.gotalgae.com/algae_solutions.htm>.

Bergstrom, Carolyn, Casey McKeel, and Suketu Patel. "Effects of PH on Algal Abundance: A

Model of Bay Harbor, Michigan." Deepblue.lib.umich.edu. University of Michigan, 2008.

Web. 13 Mar. 2014.

PDF.<http://deepblue.lib.umich.edu/bitstream/handle/2027.42/57443/Bergstrom_

McKeel_Patel_2007.pdf?se>.

Eutrophication. Digital image. Wheatleyriver.ca. Wheatley River Improvement Group RSS,

16 Aug. 2010. Web. 10 Mar. 2014. <http://www.wheatleyriver.ca/wp-

content/uploads/2011/02/Eutrphication.jpg>.

Figari, Sebastain, Joseph Hagan, Alvaro Marin, and Alvaro Montalvan. "The Effects of Nitrate

Concentration on Conductivity in Four Water Sources." Amersol.edu. Colegio Franklin

Delano Roosavelt American University of Lima, 2 June 2004. Web. 9 Mar. 2014.

<http://www2.amersol.edu.pe/hs/sciences/Projects/FINAL%20G4%20Proj%20M ay04/G

4%20Projects%20May2004%20HTML/G4-Conductivity-vs-

NitrateConc%20HTML/Group4%20AlvaroSebastianJoeAlvaronitrate%20conduct

ivity.htm>.

"Nitrates and Their Effect on Water Quality – A Quick Study." Wheatleyriver.ca.

Wheatley River Improvement Group RSS, 16 Aug. 2010. Web. 07 Mar. 2014.

<http://www.wheatleyriver.ca/current-projects/wrig-pilot-nitrate-study/nitrates-and-their-

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effect-on-water-quality-a-quick-study/>.

Numako, Chiya. "Disordered System." Physica B: Condensed Matter. Vol. 208-209.

Geneva: Izumi Nakai, 1995. 388-89. Print. Physica B.

PEER. "Water's the Matter-- Introduction: Nitrates." Water's the Matter--

Introduction: Nitrates. PEER, n.d. Web. 08 Mar. 2014.

"PH." Wikipedia. Wikimedia Foundation, 03 Mar. 2014. Web. 12 Mar. 2014.

<http://en.wikipedia.org/wiki/PH>.

This source was only used for formulas.

Sanco Industries Inc. "Pond PH." News RSS. Sanco Industries, 8 Apr. 2011. Web. 08

Mar. 2014. <http://www.sancoind.com/news/pond-ph>.

Vernier Software & Technology. "Nitrate Ion-Selective Electrode." Nitrate Ion-selective

Probe. Vernier Software & Data, n.d. Web. 12 Mar. 2014.

"Water Quality." Water Quality. Purdue University & Indiana University, n.d. Web.

10 Mar. 2014.

<http://www.cees.iupui.edu/education/Workshops/Project_Seam/water_quality.h tm>.

"Zooplankton." Marinebio.org. MarineBio Conservation Society, 2014. Web. 12 Mar.

2014. <http://marinebio.org/oceans/zooplankton.asp>.

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WORKS CITED Spencer Kuldell S82-14 "Algae Basics - Benefits of Algae." Algae Basics - Benefits of Algae. Algae Biomass

Organization, n.d. Web. 12 Mar. 2014. <http://allaboutalgae.com/benefits/>.

"Algae Solutions." Algae Solutions. Ed. Ken Rust. Kasco Marine Inc., 2006. Web. 12 Mar. 2014.

<http://www.gotalgae.com/algae_solutions.htm>.

Bergstrom, Carolyn, Casey McKeel, and Suketu Patel. "Effects of PH on Algal Abundance: A

Model of Bay Harbor, Michigan." Deepblue.lib.umich.edu. University of Michigan, 2008.

Web. 13 Mar. 2014.

PDF.<http://deepblue.lib.umich.edu/bitstream/handle/2027.42/57443/Bergstrom_

McKeel_Patel_2007.pdf?se>.

Eutrophication. Digital image. Wheatleyriver.ca. Wheatley River Improvement Group RSS,

16 Aug. 2010. Web. 10 Mar. 2014. <http://www.wheatleyriver.ca/wp-

content/uploads/2011/02/Eutrphication.jpg>.

Figari, Sebastain, Joseph Hagan, Alvaro Marin, and Alvaro Montalvan. "The Effects of Nitrate

Concentration on Conductivity in Four Water Sources." Amersol.edu. Colegio Franklin

Delano Roosavelt American University of Lima, 2 June 2004. Web. 9 Mar. 2014.

<http://www2.amersol.edu.pe/hs/sciences/Projects/FINAL%20G4%20Proj%20M ay04/G

4%20Projects%20May2004%20HTML/G4-Conductivity-vs-

NitrateConc%20HTML/Group4%20AlvaroSebastianJoeAlvaronitrate%20conduct

ivity.htm>.

"Nitrates and Their Effect on Water Quality – A Quick Study." Wheatleyriver.ca.

Wheatley River Improvement Group RSS, 16 Aug. 2010. Web. 07 Mar. 2014.

<http://www.wheatleyriver.ca/current-projects/wrig-pilot-nitrate-study/nitrates-and-their-

! "#!

effect-on-water-quality-a-quick-study/>.

Numako, Chiya. "Disordered System." Physica B: Condensed Matter. Vol. 208-209.

Geneva: Izumi Nakai, 1995. 388-89. Print. Physica B.

PEER. "Water's the Matter-- Introduction: Nitrates." Water's the Matter--

Introduction: Nitrates. PEER, n.d. Web. 08 Mar. 2014.

"PH." Wikipedia. Wikimedia Foundation, 03 Mar. 2014. Web. 12 Mar. 2014.

<http://en.wikipedia.org/wiki/PH>.

This source was only used for formulas.

Sanco Industries Inc. "Pond PH." News RSS. Sanco Industries, 8 Apr. 2011. Web. 08

Mar. 2014. <http://www.sancoind.com/news/pond-ph>.

Vernier Software & Technology. "Nitrate Ion-Selective Electrode." Nitrate Ion-selective

Probe. Vernier Software & Data, n.d. Web. 12 Mar. 2014.

"Water Quality." Water Quality. Purdue University & Indiana University, n.d. Web.

10 Mar. 2014.

<http://www.cees.iupui.edu/education/Workshops/Project_Seam/water_quality.h tm>.

"Zooplankton." Marinebio.org. MarineBio Conservation Society, 2014. Web. 12 Mar.

2014. <http://marinebio.org/oceans/zooplankton.asp>.

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Phunny Canopy(%)

The effect of canopy cover(%) on bark pH. By; Max Kemper & Jamie Hauswirth

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Table Of Contents

Section Author Page Abstract Hauswirth 1 Introduction Hauswirth 1 M&M Hauswirth 2 Results Kemper 3 Discussion Kemper 6 Acknowledgements Kemper 7 Acknowledgements Hauswirth 7 Works Cited Hauswirth 8 Works Cited Kemper 10

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The Effect of Proximity to Water (m) on Soil pH

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Table of Contents Abstract (Armando Hazaveh)………………………..….page 3 Introduction (Armando Hazaveh)…………………....pages 3-4 Materials & Methods (Armando Hazaveh)……………..page 5 Results (Jimin Kang)………………………………..pages 6-10 Discussion (Jimin Kang)…………………………..pages 11-12 Acknowledgements (Kang & Hazaveh)…………...pages 12-13 Works Cited (Kang & Hazaveh)…………………..pages 14-15 Appendix: Pictures (Kang & Hazaveh)………………..page 15

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The Effect of Proximity to Water (m) on Soil pH ABSTRACT

This experiment was conducted in order to define whether there was a relation between soil pH and water pH based on the soil’s proximity to the water. This experiment was conducted at three ponds at Drumlin Farm in Lincoln Massachusetts: Boyce pond1, Ice pond2, and the Vernal pool3. The procedure for this experiment was to collect a water pH sample then collect pH soil samples at different directions around the pond. Using a transect, samples were taken at 1,5,10,and 20m intervals from the water’s shore and tested immediately for their pH. It was thought that the soil would have a pH closer to the water’s pH as its distance from the water was shortened (www.esf.edu/ D. Bickelhaupt and R. Schemedicke). The results displayed no correlation between the distances from the pond to soil pH levels. The r2 value also did not support the point, with the best of all the values at 49%. The ponds had pH levels that ascended in the order Ice, Boyce, and Vernal pool. Despite the ponds touching the soil and equalizing the pH levels, the distances from the pond didn’t have any effect on the soil pH. INTRODUCTION

Does water have a measurable effect on the pH of soil? pH is a measurement of the [H+] ion concentration in a given solution. pH is often tested in soil and water and can determine how acidic or basic the given solution is. Litmus paper and digital detectors are some of the materials used to measure the pH on a scale of 0-14. A soil pH of 14 is basic and a pH of 0 is acidic. pH strongly affects how plants will grow and how well the nutrients are balanced in the soil; optimal pH for plants is about 6-7 (D. Bickelhaupt and R. Schemedicke www.esf.edu/). pH in soil can be strongly affected by rainwater taking away necessary nutrients, and CO2 that comes from decomposed organic materials forming a weak organic acid. More strongly acidic soils are typically formed because of organic matter decay as well as oxidation in the soil which both form strong acids in the soil (D. Bickelhaupt and R. Schemedicke www.esf.edu/). However, pH can also be measured in the water, where it can predict the success of sustaining the aquatic flora and fauna. The death of all fish occurs at 4.2 on the pH scale. (www.epa.gov)

Soil pH can take a very long distance to change (50-100m), and also can change if stepped on a lot because of the compaction of soil i.e. a path (R. McLaughin www.homeguide.sfgate.com). The ponds and pools being tested have paths around them that can affect the longer distance tests. Surrounding plant density also has an effect on

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pH, roots can give off acidic material and photosynthesis removes CO2 from the soil both of which can affect the pH greatly. This is a possible factor in the experiment because it is very possible that different types plants will be along the transect where samples are being taken.

The soil pH can vary a lot from site to site; however, when there is a water source the soil reaches a neutral point as the soil makes contact with the water (D. Bickelhaupt and R. Schemedicke www.esf.edu). This effect comes about through the process of diffusion (Where a solution goes from a location of low concentration to one of high concentration). However the water level does matter because it has an effect on how deep it goes in terms of affecting pH. The soil type also affects how the waters will effect on pH. The soil type determines how resistant the soil is to change of pH. This is dependent on the soil particle size (www.homeguide.sfgate.com, R. McLaughin).

Drumlin Farm presents a great location for a test of how water might affect the pH of soil. It has 312 acres with a lot of diversity throughout the habitats. It also has many water sources and a diversity of soils, which will allow for unique conditions to be averaged.

The proposed experiment is: to test whether the proximity of soil to water will affect the pH of the soil to make it more similar to the water pH measurement. The independent variable will be the distance from the water the soil is collected from (meters). The dependent variable is the pH of the soil. Important controlled variables include: Time between the collection of oil samples, the testing, and the amount of distilled water added to the pH measuring chamber. Other controlled variables are: the plant life in testing area which could affect the pH measurement, and good randomization of locations at which transects are set up around the water source. The hypothesis for the given experiment is: If soil is closer to the water source, then it’s pH will be more similar to the water pH because the water’s interaction with the soil equalizes the [H+] ions through the process of diffusion/ equalization (www.esf.edu/ D. Bickelhaupt and R. Schemedicke).

The experiment would give new knowledge on how exactly water affect the soil pH and if in fact the soil does change. New knowledge could be learned about how long a distance it takes for soil pH to change a measurable amount. If not, however, the opposite could be concluded thus making the pH a more unpredictable measurement. The new found un/predictability could have an effect on how a farmer might fertilize and/or change the type of plants planted in the area closer or farther from the water. (Taking soil types into account as well) The pH could, allow scientists to further predict what flora and/or fauna might blossom in the area, as well as how far as it might be from the water source.

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Materials and Methods The testing took place at three locations in Drumlin Farm: The Ice pond, Vernal pool, and Boyce pond2. Each had four transects to take data from, each one of the transects had five data points taken from it. The water pH was also tested to make the final comparison. The material used in the test was be rinsed between tests with distilled water. To begin the experiment, the TI-nspire (Texas Instruments) calculator was used to generate three numbers between 0 and 360, the numbers were rounded to the nearest whole number and should any repeats occur, another number was randomized. These were used as angles at which to set up transects. Next, the compass was used to determine these angles around the given water source so that transects can be set up along them as they are measured. A 50m long tape measure was then used to take samples from the points along each determined line at 1, 5, 10, and 20 meter intervals. When rolling out the meter tape caution was taken not to step near the sites where the soil was taken from. Soil samples were taken from these locations along each of the lines using a 17cm auger with a 2cm diameter. The auger took samples 5 cm deep (how far it is pushed in). The soil was then quickly put into the Rapidtest Soil Test Kit1 for testing. The soil was tested as quickly as possible so that the soil pH didn’t change in the short time. How the Rapidtest pH kit was used: First the green top was removed from the testing kit, and then the package of capsules was removed (50). Next, the chamber was filled with soil to the marked line. Then, the capsule was held over the chamber and split, pouring the powder into the chamber. Using the dropper, distilled water was added until the mixture was up to the water line. The cap was then put on again and the mixture was mixed thoroughly. The mixture sat for about a minute so that the color could settle. Then the mixture was looked through with sunlight and the color was compared to the pH scale and the pH was then recorded in a pre prepared table. Any plants in the way were noted in the table where data was collected in case of an unusual/outlier measurement. IMAGES: Rapidtest pH tester:

Drumlin Farm:

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Results

Table 1: The effect of proximity to water (m) on soil pH (Ice Pond)

Soil pH

Distance (m) trial 1 trial 2 trial 3 Average Standard Deviation

1 7.5 7.5 6.5 7.2 0.6

5 7.0 7.5 6.0 6.8 0.8

10 6.5 7.0 6.0 6.5 0.5

20 7.0 7.0 6.0 6.7 0.6

Graph 1: The effect of proximity to water (m) on soil pH (Ice Pond)

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Graph 2: The effect of proximity to water (m) on soil pH (Boyce Pond)

Table 2: The effect of proximity to water (m) on soil pH (Boyce Pond) Soil pH

Distance (m) trial 1 trial 2 trial 3 Average Standard Deviation

1 6.5 6.5 6.0 6.3 0.3

5 6.5 6.5 6.5 6.5 0.0

10 7.0 7.0 7.0 7.0 0.0

20 7.0 7.0 6.5 6.8 0.3

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Table 3: The effect of proximity to water (m) on soil pH (Vernal Pool)

Soil pH

Distance (m) trial 1 trial 2 trial 3 Average Standard Deviation

1 7.0 7.0 7.0 7.0 0.0

5 7.0 6.5 8.0 7.2 0.8

10 7.5 6.5 7.0 7.0 0.5

20 7.5 7.0 7.0 7.2 0.3

Graph 3: The effect of proximity to water (m) on soil pH (Vernal Pool)

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In Graph 1, soil samples and pH tests were taken at Ice Pond. One thing that was

unexpected that appeared in the graph is that there is a very big jump from the water pH to the average pH of the soil one meter away from the pond. The water pH was 5.0. The highest pH measured is the pH one meter away from the water. The lowest pH measured is the pH of the water, which is the opposite of what was expected. The trend is as the soil gets farther from the water, the soil pH gets closer to the water pH. The r-squared value is 0.4758. The average soil pH one meter away from the pond was 7.2; the average for five meters away was 6.8, ten meters away 6.5, twenty meters away 6.8. The data collected was the least precise of all graphs. All the error bars for the soil pH measurements were very big. The standard deviation for one meter away was 0.6, five meters away was 0.8, ten meters away was 0.5, and twenty meters away was 0.6. The data sets at different distances were similar, since all the error bars overlapped. Right next to the pond there was an evergreen forest. Since pine needles are acidic, the forest could have possibly affected the data collected.

In Graph 2, soil samples and pH tests were done at Boyce Pond. The water pH and soil pH had more of a trend; there was no average that was drastically above all the other averages. The trend of the graph is as the soil gets farther away from the water, the pH of the soil goes up. The r-squared value is 0.4978, the highest among the graphs. The water pH of the pond was 6.0. The average soil pH one meter away was 6.3, five meters away was 6.5, ten meters away was 7.0, and twenty meters away was 6.8. The pH at one meter overlaps with every average except 7.0 at ten meters away. The average pHs at five and ten meters do not overlap each other, but they both overlap the average pH twenty meters away. Almost all the error bars overlap each other, which means there are similar data sets at different distances in the graph. The data in Graph 2 is much more precise than the other graphs; the error bars and standard deviation are smaller. The standard deviation for one meter and twenty meters away was 0.3. The standard deviation for five and ten meters away was 0.0. At Boyce Pond there were many leaves and branches on the ground, which could have affected the pH of the soil.

In Graph 3, soil samples and pH tests were completed at Vernal Pool. Like Graph 1, it was unexpected that there was a big leap from water pH to the average soil pH measurement only one meter away. The trend in Graph 3 is the farther away the soil is from the pond, the pH of the soil gets higher. The r-squared value is 0.2426, the lowest of the graphs. The average soil pH for one meter and ten meters away from the pond was 7.0. The average soil pH for five meters and twenty meters away from the pond was 7.2. After the pH increase from the water to one meter away from the water, the average soil pH measurements all stay within two tenths of each other, so there is not a big range in data within the soil pH averages. All the error bars of the soil pHs overlap each other, which means the data sets in Graph 3 were very similar to one another. The pH of the water overlaps with every average of the soil pHs. The data in Graph 3 was less precise than Graph 2, more precise than Graph 1. Most of the error bars were big. The standard deviation for one meter away was 0.0, when five meters away it was 0.8, ten meters away 0.5, and twenty meters away 0.3. While at Vernal Pool there was water, but there was not water at the pool during the fall. The soil pH could change depending on the season, since some seasons the pool has water and some seasons the pool has no water.

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Discussion

The purpose of the experiment was to test the effect of soil proximity to water (m) on soil pH. The hypothesis set for this experiment is: If soil is closer to the water source, then it’s pH will be more similar to the water pH because the water’s interaction with the soil equalizes the [H+] ions through the process of diffusion/equalization (www.esf.edu/ D.Bickelhaupt and R.Schemedicke). The hypothesis was not supported because all the error bars overlapped, thus making the data inconclusive of whether the soil pH samples were going away from the water pH as the samples were taken farther from the pond. In Graph 1, the soil pH got closer to the water pH as the samples were taken from farther distances, rather than closer distances. This was the total opposite of the hypothesis.

In Graph 1 (Ice Pond) the trend is as the soil gets farther from the water, the soil pH gets closer to the water pH. One reason this could have happened is because as the soil samples got farther away from the pond, the samples kept getting closer to the evergreen forest that is next to the pond. Evergreen forests have pine needles, which could have affected the soil pH to become more acidic as it got farther from the pond (http://www.gardenguides.com). The results are not conclusively different, all the error bars overlapped. In Graph 2 the trend is as the soil gets farther away from the water, the pH of the soil goes up. Every error bar overlapped except for five and ten meters. The data was most likely very precise because unlike Ice Pond, there were not many plants or trees in the surroundings that affected soil pH. The r-squared value is 0.4978, which is low. In Graph 3 the trend line is almost parallel to the water pH value, but it went up slightly. The data was almost the same as the water pH level because Vernal Pool is not always there the whole year. Some seasons there is water in the pool, other seasons there is no water. The water does not stay long enough to affect the pH of the soil surrounding the pool. All the error bars overlap. The data sets are inconclusively different. The r-squared value for the graph is 0.2426, the lowest of all the graphs. Most of the averages from all the graphs were not precise. This impacts the confidence in the data because the bigger the error bars, the bigger the range in data, so the data is not reliable enough to draw conclusions from.

A lot of sources stated soil pH and water pH are correlated, but the data does not support this. One reason the data could have been so unreliable is that the pH samples were not taken far enough from the pond to actually have significant differences in pH (http://depts.alverno.edu/). Perhaps if the soil samples were taken farther from the pond, the samples would have shown more variety in cultivation the soil has gone through. For example, if the samples were taken very far from the pond, the data would have been different. The surroundings would have been different from the pond’s surroundings, which affects pH. The pond could have had more pine needles, while farther away from the pond there could have been almost no pine needles. Another reason the data could have been inconclusive is that sometimes soil samples had to be taken outside of the pond site into the walking paths or the nearby woods. Having soil samples taken in places very far from the pond could have easily impacted the soil pH, since the ground is much more disturbed in the woods and walking paths from all the footsteps, leaves, and pine needles. In summary, there are going to be different variables that affect soil pH very far from the pond than near the pond. For instance, the pH in the woods could have been less acidic

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than the walking paths since more rain (acidic) would fall on the paths rather than the woods, because of all the leaves covering the ground of the woods (Brimblecombe, Acid Rain).

There are many things that could be done to improve data collection. Instead of having 20 meters as the maximum distance, the maximum distance could have been raised to 50 meters from each pond to see more dramatic differences in the soil pH. Also instead of collecting pH samples where there was a lot of disturbance or leaf coverage, pH samples could have been taken where the soil was not disturbed by any of its surroundings. More trials and data collecting would have helped the graphs be more precise and reliable. Sufficient data was not collected. It was first planned to take sixteen pH samples in all per site. Only twelve pH samples per site were collected because sixteen was too much to do in the time given. One of the errors that occurred in this experiment is that all of the controlled variables could not be controlled. For example, in Ice Pond, there was an evergreen forest on one side of the pond. The forest caused the data to be very different from the water pH, so many of the pH samples were almost as acidic as the water. The forest impacted the data precision. There was no way to eliminate errors of the pond’s surroundings. For future research of this study, people could find out whether the depth of the water has any effect on the water pH or not. Since the ponds most likely had a difference in depth (Vernal Pool was a lot shallower than the other ponds), more research could be done to see if the depth of the pond had any effect on the data around the ponds. Acknowledgements

Jimin Kang First off I would like to thank my partner, Armando Hazaveh, for cooperating

well with me throughout this whole experiment. He always helped clarify whenever I had questions with writing my results section and when I was confused with the procedure. Without his hard work and focus, this experiment would have been much harder to complete. Also I give thanks to the teachers that supervised at our three worksites, Margaret Hardy, Wendy Svatek, Rachel Jamison, and Stephanie Moon. They all helped us with keeping our experiment on track and making sure we were working quick enough to move on to our next site. I would like to thank Michael Ewins for commenting on our work and helping us in many ways to benefit our experiment by getting us the materials we needed and to help polish our procedure. Thanks to all the Drumlin Farm teacher-naturalists we got all of our questions answered that were necessary to our experiment and helped us complete our work. Finally I would like to thank my mom, Jungha Gil, by helping our group buy some of the materials we needed.

Armando Hazaveh To begin with I would like to thank Jimin Kang my partner who was always there

helping me to get through to the hard work. His partnership was always a great tool especially when we finally had to do the experiment. He always made sure that we got everything we needed done. When it became apparent that we would not be able to do everything we had to do Jimin was there to make the plans for the future of our

!"##

experiment. Of course, like Jimin, I would like to thank our site supervisors who kept us in order and on schedule. In my opinion I think that the most important person to thank is our science teacher, Mr. Michael Ewins. He has helped us both through each and every step, giving advice, working through problems, and being a helpful guide through all the steps of writing and experimenting. I would like to also thank my parents who helped me work through the load and were upbeat.

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Works Cited

Armando Hazaveh Ashman, M. R., and G. Puri. "Chapter 6." Essential Soil Science: A Clear and Concise

Introduction to Soil Science. Oxford: Blackwell Science, 2002. N. pag. Print

Bickelhaupt, Donald, and Robert Schmedicke. "Soil PH: What It Means." Soil PH: What

It Means. College of Environmental Science and Forestry, 2014. Web. 09

Mar. 2014. <http://www.esf.edu/pubprog/brochure/soilph/soilph.htm>.

McLaughlin, Randy. "Home Guides." Home Guides. SFGate, 2014. Web. 09 Mar. 2014.

<http://homeguides.sfgate.com/ph-water-affect-ph-soil-74237.html>.

York: Macmillan Reference USA, 2001. Science in Context. Web. 17 Apr. 2014.

United States Environmental Protection Agency. "PH Scale." EPA.

Environmental Protection Agency, n.d. Web. 06 Mar. 2014.

<http://www.epa.gov/acidrain/education/site_students/phscale.html>.

Jimin Kang

Ashman, M. R., and G. Puri. Essential Soil Science: A Clear and Concise Introduction to

Soil Science. Oxford: Blackwell Science, 2002. Print.

Baran, Angie, and Meagan Mecklenburg. "Soil PH." Soil PH. N.p., n.d. Web. 01 May

2014. <http://depts.alverno.edu/nsmt/archive/BaranMeck.htm>.

Bickelhaupt, Donald, and Robert Schmedicke. "Soil PH: What It Means." Soil PH: What

It Means. College of Environmental Science and Forestry, 2014. Web. 01 May

2014. <http://www.esf.edu/pubprog/brochure/soilph/soilph.htm>.

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GardenGuides. "Pine Trees & Acid Soil." GardenGuides. Demand Media, 1997. Web. 01

May 2014. <http://www.gardenguides.com/130318-pine-trees-acid-soil.html>.

(n.d.): n. pag. Web. Peter Brimblecombe 2012. Acid Rain. The Wiley-Blackwell

Encyclopedia of Globalization.

N.p., n.d. Web. "Acidification." Environmental Encyclopedia. Gale, 2011. Science in

Context. Web. 30 Apr. 2014.

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The effect of soil texture on soil percolation By: Trevor Khanna and Danny Kutsovsky

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TABLE OF CONTENTS Section Author Page Abstract Khanna 2 Introduction Kutsovsky 2 Materials and Methods Khanna 4 Results Kutsovsky 5 Discussion Khanna 11 Acknowledgements Khanna & Kutsovsky 14 Work Cited Khanna 15 Work Cited Kutsovsky 16 Appendix: Pictures Khanna & Kutsovsky 17

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ABSTRACT The goal of this experiment was to find the effect of soil texture on percolation. The experiment was conducted at Bathtub Pond, Boyce Field, and Red Pine Forest in Drumlin Farm, Lincoln, Massachusetts. The hypothesis for this experiment said if the ratio of sand to silt to clay is closer to 6:3:1 respectively, then the percolation rate will be closer to 30 mL/sec, because it will balance both soil permeability and surface yield (www.lagunahillsnursery.com). The independent variable was the soil texture and the dependent variable was the percolation. To test the percolation, a can was placed into the ground and it was filled with water. The time the water took to seep through the ground was measured. Then, back at the science lab, the soil samples from each site were mixed with water and left to sit. The soil settled overnight and the percentages of each layer of soil (sand on the bottom, silt in the middle, and clay on the top) were recorded. At the end of the experiment, the results stated that there was a slight correlation between clay and percolation, and silt and percolation. The hypothesis was not supported due to a lack of samples. Along with low data samples, there was a low R! value. Valid conclusions were able to be made from the results and therefore, allowed a new hypothesis to be made. INTRODUCTION

Soil is almost everywhere. If one would look out a window, then one would most likely find some soil, whether it be in a nearby park or in the cracks of a sidewalk. Soil is also necessary to sustain plant growth, which contains key element for life. Soil is the mixture of minerals, organic matter, gases, liquids, and a great amount of microorganisms (http://en.wikipedia.org). However, most of the soil is composed of different types of particles. These particles are almost as important as the soil itself. This is because if a soil’s particle type is not adequate, then organisms will not be able to survive in that particular area because water, an essential substance for plants, will not be able to reach the roots. When water falls onto the surface of soil, it will either absorb into the soil well or the particles will not hold onto the water. These particles are sand, silt, and clay and are important because of their different sizes which affect have different size and space between the particles and in turn, as this experiment will explore, the ability to retain and transport water. The ratio of these three particles (sand to silt to clay) essentially defines the soil’s texture, which directly affects soil water retention. This is how well soil retains water and nutrients as well as water permeability, which is how well soil transports water and nutrients (www.co.portage.wi.us). These small particles that go unnoticed and are not visible to the naked eye, play a crucial part in the healthiness of soil, which as proved above, can mean the difference between the life and death of the human race.

This experiment was conducted at Drumlin Farm, a Massachusetts Audubon Wildlife Sanctuary in Lincoln, Massachusetts. The sanctuary is about 312 acres wide and has four different forests, three wetlands, and five fields. For this experiment one of each type of area was tested. The forest area that was selected was the Red Pine Forest. In this area there were many White Pines and Hemlock trees. On the forest floor there were many decomposing plant types. The wetland area that was selected was Bathtub Pond. Bathtub Pond had many flora and fauna that lived there such as duckweed and daphnia. Lastly, the field that will be tested will be the Boyce field. Boyce field is a fertile land that grows many vegetables and has colored flowers. These areas are being measured because the areas are different types of habitats, and will most likely have different soil

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textures since they foster different types of plants which need different soil growing conditions (www.co.portage.wi.us).

Sand is the largest particle out of the three types because it the particle has a diameter of approximately 2.0 - 0.05 mm. Silt is the second largest particle because it has a diameter of approximately 0.05 - 0.002 mm. Clay is the smallest particle because it has a diameter of less than 0.002 mm. This means that water and nutrients goes through quickly through pure sand, making the permeability for it horrible, while on the other side of the spectrum, clay is so small, water and nutrients go through pure clay slow, making the permeability horrible, and silt is in between these effects of sand and clay, but is leaning more towards the speed of clay (http://broome.soil.ncsu.edu).

When water enters the soil from above, it is called infiltration. This infiltration intake must be controlled or smoothed over or the soil will suffer from over hydration. This is where the percolation effect comes in. Percolation is the movement of water within the soil. It controls the infiltration rate because the friction and space between the particles can speed up or slow down the infiltration based on the size of the particles. A healthy percolation rate is between five and fifteen minutes, but the closer the soil percolation to ten minutes, the better. This effect is important because it determines how fast water and different nutrients will pass through the soil, and if the percolation is too high then the water will not get absorbed causing the soil to be unhealthy. If the soil has too low of a percolation then the water will not be able to get through quickly and efficiently, causing some places to be overhydrated and some places would be dehydrated. The percolation rate is determined by the grain size (or pore size) which determines the amount of frictional resistance and the area available for flow. The smaller the grains, means smaller pores, causing more frictional resistance, and lower hydraulic conductivity. This lower or higher hydraulic conductivity means either a lower or higher percolation rate (ftp://ftp.fao.org).

The shape and arrangement of these soil particles help determine porosity. Porosity is the amount of air space or void space between soil particles. Infiltration occurs in these void spaces. The soil porosity can also affect the permeability which means that if the porosity is not good, then the soil’s health is worse. Not all the water stored in pore spaces becomes part of flowing or moving groundwater. Water clings to soil particles due to surface tension. Clay has a greater surface area than sand; therefore, more water will remain behind clinging to the clay particle surface. This implies that sand will not cling to water well, but clay has too low of a surface yield causing too much water to cling. Healthy soil has just enough surface yield to hold on to water but not so much as to create overhydration (http://www.co.portage.wi.us/).

Combining all of these facts, the optimal soil should have a balance of specific yield so the amount of water absorption is good. The ratio of sand to silt to clay should be so that the water flows through the soil at an optimal rate so that the water does not drain too fast out of the soil but slow enough that all of the soil is hydrated. From the experience of farmers, they have discovered that the optimal ratio of sand to silt to clay so that these effects transpire is close to six to three to one (www.lagunahillsnursery.com).

The object of this experiment is to determine how the ratio of the three different types of particles (sand, silt, and clay) the soil percolation, and in other words, the permeability of the soil. This question was tested by collecting soil samples from three

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different habitats at Drumlin Farm, eight times in each habitat. The independent variable was the soil texture. To measure the percolation a can was placed in the ground and the time for water to pass through was recorded. A few controlled variables were the amount of soil collected, amount of water added for percolation test, the amount of water used for texture test, and how far the can is placed into the ground for the percolation test. The hypothesis set forth in this experiment is: If the ratio of sand to silt to clay is closer to 6:3:1 respectively, then the percolation rate will be closer to 30 mL/s, because it will balance both soil permeability and surface yield (www.lagunahillsnursery.com).

This experiment is useful because the soil permeability and its surface yield are of utmost importance. If the ratio of sand to silt to clay is off, it would damage the permeability and the surface yield which can lead to not hydrated soil that is unsuitable for plant growth. If the farmers at Drumlin farm are not aware of the effects of the different soil particles, then they may be planting the crops in the wrong places. This can cause the organisms to die or be unhealthy, which can waste time and money. Also, the more knowledgeable farmers are of the effects of different soil particles, the healthier plants they will grow because the soil will be able to contain water better, producing riper and better fruits, producing a higher level product. MATERIALS AND METHODS

For this experiment, there were two procedures: one to find the percolation of the soil and the water retention and another to find the soil texture. The percolation of the soil was tested in the field, opposed to having tested the independent variable in the field. A procedure was found at www.planetseed.com. This was adapted to the purposes of this experiment. Ten coordinates were found Bathtub Pond using a Ti-nspire calculator random location generator. The first coordinate on the calculator’s list was then chosen and a hole about 5 centimeters deep was dug with a trowel. One of the 300 mL cans was placed into this hole (see fig. 1), and then, about 250 mL of water was poured into the can. While the water was percolating through the soil, four more of these tests were started in the next four locations on the list on the calculator by following the same steps. Each test was carefully examined until the water had completely drained out. When the water had percolated through the soil, the time it took was recorded. Once the time was recorded, a simple texture test was performed. The same was done for the other four tests. This entire procedure was repeated once more in Bathtub pond and then everything was done again in Boyce Field and Red Pine Forest. A total of thirty data points were collected (ten at each site).

To test the texture, 100 mL of soil were gathered from each site where the other tests had taken place. Each sample was placed in a Ziploc bag and brought back to the lab. After the samples were brought to the lab, each sample was placed into their own clear container (a total of thirty). Additionally, 120 mL of water was added to this and three drops of Dawn dishwashing soap. Then the mixture was stirred with a glass rod for thirty seconds. This new mixture was then left overnight in the lab. The next day each layer of soil was measured. The sand was on the bottom, then silt, and on the top was clay. The ratio of sand to silt to clay was found and recorded (http://www.gardeners.com).

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Figure 1

RESULTS Table 1: The effect of soil texture on soil percolation: Bathtub Pond (BP) Sample Sand % Silt % Clay % Texture Percolation (mL/sec)

BP 1 15 85 0 silt loam 21.26

BP 2 0 100 0 silt 32.07

BP 3 0 100 0 silt 59.95

BP 4 25 75 0 silty clay loam 8.95

BP 5 20 80 0 silt loam 15.72

BP 6 40 60 0 silty clay loam 31.18

BP 7 0 100 0 silt 10.73

BP 8 0 100 0 silt 11.84

BP 9 60 30 10 silt loam 19.29

BP 10 40 60 0 sandy loam 58.47

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Figure 2

Table 2: The effect of soil texture on soil percolation: Boyce Field (BF) Sample Sand % Silt % Clay % Texture Percolation (mL/sec)

BF 1 0 0 100 clay 4.42 BF 2 0 0 100 clay 3.73 BF 3 0 5 95 clay 8.83 BF 4 0 0 100 clay 8.21 BF 5 0 0 100 clay 4.79 BF 6 0 0 100 clay 7.61 BF 7 0 25 75 clay 12.58 BF 8 0 0 100 clay 6.41 BF 9 0 15 85 clay 6.64 BF 10 15 0 85 clay 7.74

Figure 3

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Table 3: The effect of soil texture on soil percolation: Red Pine Forest (RPF)

Sample Sand % Silt % Clay % Texture Percolation (mL/sec) RPF 1 10 90 0 silt 20.79 RPF 2 0 100 0 silt 57.86 RPF 3 15 85 0 silt loam 43.85 RPF 4 0 100 0 silt 76.89 RPF 5 10 90 0 silt 32.82 RPF 6 20 80 0 silt loam 37.92 RPF 7 0 100 0 silt 21.59 RPF 8 10 90 0 silt 33.21 RPF 9 0 100 0 silt 30.01 RPF 10 60 40 0 silty clay 28.93

Table 4: The effect of soil texture on soil percolation averages Habitat Sand % Silt % Clay % Texture Percolation (mL/sec) Standard Dev

BP 20.0 79 1 Silt Loam 26.9 17.8 BF 1.5 4.5 94.0 Clay 7.1 2.4

RPF 12.5 87.5 0.0 Silt 38.4 16.4 Graph 1: The effect of soil particle on percentage in area

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Graph 3: The effect of texture on percolation

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Graph 4: The effect of percent of silt on percolation

Graph 5: The Effect of Percent of Clay on Percolation

WRITTEN RESULTS In Graph 1, the averages of the dispersion of different soil particle percentages at Bathtub Pond, Boyce Field, and Red Pine Forest are shown. What is strange in this graph is that in Bathtub Pond and Red Pine Forest, there is almost no clay and almost all silt. However, in Boyce Field, it is the opposite- clay is the most while silt is almost none. In general, in Graph 1, at Bathtub Pond, silt was by far the most common with clay and sand being small. At Boyce Field, there is little silt and sand, and it is mostly clay. At Red Pine Forest, it is the same paradigm as Bathtub Pond; however there is absolutely no clay. It

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can be visible that in Bathtub Pond the sand average was around twenty percent, the silt average was seventy-nine percent, and the clay average was one percent. This implies that the silt particle was the most common in the soil type. However, one cannot say that clay was the least common particle because the error bars overlap with silt. In Bathtub Pond, the amounts of clay was the most precise with a standard deviation of three, followed by sand with a much larger standard deviation of 20, and then silt with a standard deviation of 22. This means that silt was the least precise as well. At Boyce Field, the sand average was 1.5 %, the silt average was 4.5 %, and the clay average was 94 %. The standard deviation of the silt is 8.2, the sand is 4.5 and the clay is 8.6. Therefore the most precise is sand, followed by silt, and then clay. There was no clay at Red Pine Forest. The average silt was 87.5 % and the sand average was 12.5 %. Silt had a standard deviation of 31.5, and the sand had a standard deviation of 17.0, making silt the least precise and sand the most precise. In Graph 2, the effect of habitat on average percolation is displayed. This graph displays the average percolation for each of the three habitats. At Bathtub Pond, the average percolation was 26 mL/sec. It had a standard deviation of 17.8. This made it the least precise in the data set. At Boyce Field, the average percolation was 7.1 mL/sec. It had a standard deviation of 2.4, making it the most precise of the data set. At Red Pine Forest, the average percolation was 38.4 mL/sec. It had a standard deviation of 16.4, making the precision in the middle. Boyce Field conclusively had the lowest percolation. Even though the error bars between Boyce Field and Bathtub pond overlapped by .4 mL/sec which is not significant enough to be important. In Graph 3, the effect of the texture classes found at Drumlin Farm on percolation is displayed. There were four prominent texture categories found at Drumlin Farm: silt loam, silt, silty clay loam, and clay. Also, the error bars here were large. Samples consisting of silt loam had a percolation of 30.58 mL/sec. The standard deviation for this was 10.5, making it more precise than the norm in this data set. Samples of silt had a percolation of 35.25 mL/sec. It had a standard deviation of 20.1, making it the least precise in the data set. Silty clay loam had an average percolation of 20.1 mL/sec and a standard deviation of 11.1. Clay had an average percolation of 7.1 mL/sec and had a standard deviation of 2.43, making it by far the most precise in the data set. The pattern here is that soils with higher silt levels were more permeable and soils with more clay were less permeable. In Graph 4, an XY scatter plot is shown depicting the effect of percent of silt on percolation. It displays that as the percent of silt increases, percolation increases as well. The rate at which it increases is y = 0.2922x + 7.486. The r2 value is .39 in this graph. Here, there is a large cluster at the zero point, indicating that there were points with only clay and sand. Also, at the 100% mark, there are many points but with a wide array of percolation values, which resulted in bad precision. At this point this lack of precision led to values ranging from 10.73-76.89 mL/sec. In between these zero and 100 markers there are some points but these points in between 0 and 100 is less than the sum of the points at those two places. This creates a semi-large room for error that has a decent margin of error. In Graph 5, an XY scatter plot is show depicting the effect of percent of clay on percolation. It displays a negative trendline. The equation of this trendline is y = -0.2744x + 32.832. This implies that as the percent of clay in soils increase, the permeability

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decreases. This has a better r2 value than Graph 4. The majority of the points in this graph are at the 0 point. The remaining points on the graph go from 75%-100% clay. The overall precision of the data set was decreased mostly by the scattered values on the 0% axis. These values ranged from 8.95-76.89 mL/sec. DISCUSSION

The goal of the experiment was to find out what the ratio of sand to silt to clay would be to have an optimal percolation rate. The hypothesis for the experiment was if the ratio of sand to silt to clay is closer to 6:3:1 respectively, then the percolation rate will be closer to 30 mL/sec, because it will balance both soil permeability and surface yield (www.lagunahillsnursery.com). The results go against the hypothesis because they state that the optimal rate of 30 mL/sec requires the soil to be 20% sand, 50% silt, and 30% clay. This, in a ratio, is 2:5:3, instead of the original theory of 6:3:1.

This may be due to fact that silt varies greatly in size, and can measure from .002 to .05 millimeters in diameter (http://www.co.portage.wi.us). Not only is there a variation in the size of silt, but there was also a lack of sand. The results had very little samples of sand and therefore, conclusions for sand could not have been very accurate.

For the first graph on the percentage of sand, silt, and clay, Bathtub Pond was mostly silt and partly sand, because in order for a pond to form, there must be a slower percolation rate, hence the silt levels. Then in order for the water to not overflow, there cannot be a very low percolation rate either, which explains the sand levels. Clay tends to have a smaller surface yield which causes water to have a very slow percolation rate (http://www.co.portage.wi.us). The low clay levels make sense because if there was a large amount of clay, the pond would be more likely to overflow in almost every shower. The error bars and the low precision are because not all of the soil that was tested came from near the pond. Some samples came from near the dirt road, and others closer to a compost heap. Each area would have different samples and range of data was collected.

Boyce Field was mostly clay, but had a mix of silt and sand. The best types of soils for farms tend to be a mixture of two or more of the three types of soils called loams (http://cru.cahe.wsu.edu). These loams tend to not have a very fast percolation rate, which allows for plants to take in the water, and they also tend to not have a very slow percolation rate, which would allow the water to run-off and not drown the plants. The area that was chosen was not in use recently and this would explain the higher percentage of clay. Since there was no reason to tend the field, fertilizer would not have to be placed. Though as it is a farm, there was a slight mixture of silt and a little sand too. Once again, the data is not precise as shown by the large error bars, but this is because the surrounding area that was being used, were two different fields. To the east was a field that had crops in it which would explain the mixing of loams, because the crops would need a mixed soil to survive. To the west was a patch of dead plants which again, would affect the results from the habitat because of the decay.

The samples from Red Pine Forest were mostly silt, but had a decent amount of sand. The large amount of silt and sand could be from the glacial deposit from thousands of years ago (http://www.daviesand.com). These glacial tills tend to leave coarser soils and silt that can reach a diameter of .05 millimeters. Sand is also very coarse and can range up to 2 millimeters in diameter (http://www.co.portage.wi.us). These soils would

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have been dragged to Red Pine Forest by the glacier that created the Drumlin (http://www.daviesand.com).

In Graph 2, there is a very small overlap between Bathtub Pond and Boyce Field. There were two outliers; the one in Boyce was only 75% clay whereas the other sample were at least 85% clay and at Bathtub Pond, the silt in the sample could have been very fine and more compact, which would cause the water to drain slowly. There is a large overlap in error bars between Red Pine Forest and Bathtub Pond, because both habitats recorded a large amount of silt and around the same amount of sand. Since Bathtub Pond was mostly silty loams, the average percolation rate was around 27 mL per second. Red Pine Forest, which was made up of sand and silt that was most likely coarse due to the passing of a glacier, had a higher percolation rate because the void between the coarser soils would allow water the seep through more quickly. Boyce Field was nearly conclusively the slowest habitat to percolate due to the fact that it was made up of mostly clay which tends to have smaller voids (http://www.co.portage.wi.us) and therefore, a slower percolation rate.

Graph 3 shows that clay, on average, has the lowest percolation rate and this is because clay particles are very small. This causes the clay particles to pack closely together which does not leave a lot of room for water to drain. Also, clay has a lower surface yield and instead of water percolating, the water sticks to the surface of the clay (http://www.co.portage.wi.us). Silty clay loam had an error bar that slightly overlapped that of clay. It is also, on average, the second slowest percolation soil texture. Silty clay loam is a mixture of silt and clay. The clay would cause the water to percolate more slowly, but since not all of the soil was clay and some was silt, it would not have the fastest or slowest percolating time. The difference in the silt particle size would account for the lack of precision in the data too. The silt loam is, on average, the second fastest percolation time. The error bar overlaps with the silty clay loam error bar because both most likely contain clay due to the fact that they are loams. However, the silt loam allows water to pass through more easily, because it contains less clay and more sand. The silt loam does not have the quickest percolation rate because it was averaged between both Red Pine Forest and Bathtub Pond. The silt loam could have been packed differently in the habitats and caused water to drain more slowly, than the silt which was mostly found in Red Pine Forest. Silt had, only by average, the fastest percolation rate. This can be due to the fact that the soil was differently packed to allow more water to pass through it, or that the silt had a larger diameter. The large error bar can be explained because of the large area tested in Red Pine. Each sample was collected in different places. One was by a fallen tree which could have compacted the soil, another by a live tree whose roots could have created air pockets in the soil, and another in a clearing.

In graph number 4 on how silt affected soil percolation showed that there was very low R! value between silt and percolation, though as silt increased, the percolation rate also tended to increase. This is because silt varies in many different ways. The way that the soil is structured causes the percolation rate to change along with the variety in the size. The only places where silt was found, was by a pond and by a forest. A pond and forest are two very different habitats. In a forest, the roots of the trees plough into the ground and change the structure of the soil, whereas by a pond, there aren’t many ways to change the structure of the soil once it is placed.

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In Graph 5, there is a R2 value of .40 which signifies a low to no correlation between clay and the percolation rate. However, if the data points of clay that were measured at 0% clay were removed, the R2 value increases to .84 which does signify a correlation. The trend line indicates that as the clay increases, the percolation rate decreases. Once again, this is because of the small surface yield and the compactness of the soil.

Overall, silt was the most abundant soil in Red Pine Forest and Bathtub Pond, while sand also made up a fair amount of both habitats. Clay made up most of Boyce Field and if the section that was tested was to be used, then it would be helpful for the plants if a little bit of sand and silt was mixed into it. Clay had (almost conclusively) the slowest percolation rate, and then by average, silty clay loam, silt loam, and finally silt. There was little correlation between the amount of silt and the effect on percolation, but this is because of the different sizes in the silt particles. Clay, on the other hand, affected the percolation by an incredible amount. Even though sand was measured, there were only a few samples that had sand in them and those few samples were not enough to make a conclusion off of. Due to the lack of sand, a proper hypothesis could not be formed because it is hard to find an optimal rate from a ratio soil without sand. However a new hypothesis was formed and it is that if the ratio of sand to silt to clay is closer to 2:5:3 respectively, then the percolation rate will be closer to 30 mL/sec, because it will balance both soil permeability and surface yield (www.lagunahillsnursery.com). This was formed by averaging out the percolation rate of all the three types of soil and then finding the ratio that was the closest to 30 ml/sec.

To conduct this experiment again, a few improvements could be made. First and foremost, there would be more areas and more time to collect the samples. There was a lack of sand in the experiment which could have made the hypothesis unsupported, because the hypothesis was made to include sand in the tests. Also time was short and things had to be rushed which again, would have caused errors in the procedure. There were also roots in some samples, leaves in another, grass in yet another, and just soil in others. The errors in the procedure could have caused the lack of correlation in the results and therefore, changed the conclusions. To eliminate these future problems, more time could be allotted or more habitats could be allowed for use. Also the samples could be examined before placing them in Ziploc bags. Several questions that still remain are that would there have been a difference if all the samples were collected in the same habitat? Did the trees and roots affect the outcome? Or would the results stay the same? To improve upon this experiment, a more detailed texture test could be made, because most of the tests in this experiment were done by eye. Also, areas that have very different soil texture could be measured. This would help determine the rate of percolation that each soil type has. ACKNOWLEGEMENTS Trevor Khanna: I would like to thank Carol, the staff member at Drumlin Farm, for helping us get our experiment started and giving us a place to work I would like to also thank Danielle for helping us find a suitable place to conduct our experiment in Boyce Field that did not hinder the growth of the plants.

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I would like to thank the science teachers and BB&N faculty for helping us conduct the experiment and to answer question when need be without them, we would still be stuck on square one. Danny Kutsovsky: First of all, I would like to thank my father, Yakov Kutsovsky, for helping me form this experiment in the first place. He guided my thoughts to this experiment. He helped me figure out different ways sort my information. I would also like to thank him for creating amusement in my life when I was anxious about this project. All through my life he was the main influence for my level of intelligence and creativity, which led to this experiment. Also, I would like to thank my mother, Marina Kutsovskaya. She helped drive me to school every day so that I could complete this experiment. She also raised me with common sense which helped me solve problems that were encountered in the field as well in the laboratory. I would like to thank the Drumlin Farm attendants who graciously allowed our group to enter the Mass Audubon's Drumlin Farm Wildlife Sanctuary, and conduct our experiment. Without that area, we would have never been able to complete the experiment. The staff there helped acquaint us with the area, and gave instructions on where to conduct our tests which led to a successful experiment. In addition, I would like to thank my brother, Mikhail Kutsovsky, who inspired me to think outside of the box when creating this unique experiment. Lastly, I would like to give the largest thanks to my supervisor and educator, Heather LaRocca. She supervised the formation of this experiment and was with our group, advising on ways to improve, as well as tell us what not to do.

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WORKS CITED

Trevor Khanna

Allaby, Michael. "permeability (geology)." Science Online. Facts On File, Inc. Web. 10

Mar. 2014.

<http://www.fofweb.com/activelink2.asp?ItemID=WE40&SID=5&iPin=EWCR05

37&SingleRecord=True>

Carter, Martin R. Soil Sampling and Methods of Analysis. Boca Raton: Lewis, 1993.

Print.

Cogger, Craig. "Soil Management For Small Farms." Soil Management For Small Farms.

Washington State University, July 2000. Web. 15 Apr. 2014.

<http://cru.cahe.wsu.edu/CEPublications/eb1895/eb1895.pdf>.

"Experiment: The Permeability of Soil." Permeability Of Soil. SEED, 2014. Web. 10

Mar. 2014. <http://www.planetseed.com/laboratory/experiment-permeability-

soil>.

Davies, Karl. "Forest Soils." Forest Soils. Davies and Company, 1999. Web. April 2014.

<http://www.daviesand.com/Perspectives/Forest_Soils/>.

LaLebarte, Kathy. "Building Healthy Soil." Gardeners Supply. Gardening Company, n.d.

Web. 05 Mar. 2014. <http://www.gardeners.com/Building-Healthy-

Soil/5060%2Cdefault%2Cpg.html>

Soil Permeability." Food and Agriculture Organization. Food and Agriculture

Organization, 2 Sept. 2008. Web. 9 Mar. 2014.

<ftp://ftp.fao.org/fi/CDrom/FAO_Training/FAO_Training/General/x6706e/x6706e

09.htm>.

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Danny Kutsovsky

Carter, Martin R. Soil Sampling and Methods of Analysis. Boca Raton: Lewis, 1993.

Print.

Drier, Patty. "Soil Properties That Affect Groundwater." Soil Properties That Affect

Groundwater. Porter County, 7 Jan. 2012. Web. 09 Mar. 2014.

<http://www.co.portage.wi.us/groundwater/undrstnd/soil.htm>.

"Lecture 8: Soil and Percolation." Geology Western Washington University Geology

Department. Web. 11 Mar. 2014.

<http://www.geol.wwu.edu/rjmitch/L8_soils_percolation.pdf>.

Soil Permeability." Food and Agriculture Organization. Food and Agriculture

Organization, 2 Sept. 2008. Web. 9 Mar. 2014.

<ftp://ftp.fao.org/fi/CDrom/FAO_Training/FAO_Training/General/x6706e/x6706e

09.htm>.

"Soil." Wikipedia. Wikimedia Foundation, 03 Oct. 2014. Web. 10 Mar. 2014.

<http://en.wikipedia.org/wiki/Soil>.

"The Soil." Laguna Hills Nursery. Laguna Hills, N.d. Web. 9 Mar. 2014.

<www.lagunahillsnursery.com/lecture-GROG-soil_only.doc >.

"Topic 8 Soil Physical Properties." Topic 8 Soil Physical Properties. North Carolina State

University, 14 Dec. 2009. Web. 09 Mar. 2014.

<http://broome.soil.ncsu.edu/ssc012/Lecture/topic8.htm>.

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APPENDIX

Figure 1

Figure 2

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Figure 3

The Effect of Nitrogen Level on Tree Height

Christina Knight & Caroline Scheer

2

TABLE OF CONTENTS

Section Author Page

Abstract Scheer 3

Introduction Knight 3

Materials & Methods Scheer 4

Results Knight 5

Discussion Scheer 10

Acknowledgements Knight & Scheer 11

Works Cited Knight 12

Works Cited Scheer 13

3

ABSTRACT The objective of this experiment was to find if nitrogen levels affected tree

growth. This experiment took place at Drumlin Farm in Lincoln, MA. The testing was done at three forests: Red Pine, MAS and Hemlock forest. The tree height and circumference were tested with a tape measurer. The nitrogen levels were tested using a Vernir Nitrogen Probe and a graphing calculator. The expected hypothesis was: if the nitrogen levels are higher in the soil, then the tree will be taller because of nitrogen fixation, the process of converting nitrogen gas into ammonium, nitrites and nitrates. These can then be used as vital nutrients by plants to stimulate their growth (ctahr.hawaii.edu). The results were in line with what was predicted in the hypothesis. The results for the tree height were not conclusive because the error bars were large and overlapped. The data for the nitrogen levels, however, was conclusive and precise because the error bars were very small with no overlap. INTRODUCTION

Nitrogen is a vital nutrient for all plants. Plants require nitrogen more than any other element in soil. Nitrogen is found in all proteins, chlorophyll and many other organic compounds and is critical for plant growth (Gardner 70-71). Nitrogen in soil exists in many different forms, such as ammonium, nitrate and nitrite, and can be found in a variety of soil conditions. However, 98% of the nitrogen found in soil has not undergone nitrogen fixation and therefore cannot be used by plants. Many different plant species use the small amount of nitrogen that is found in the fixed form to grow (Dechorgnat, jxb.oxfordjournals.org).

This experiment will take place at Drumlin Farm. Drumlin Farm is a Mass Audubon Wildlife Sanctuary with over 206 acres in Lincoln, MA. This experiment will be conducted in three different forests: Hemlock Forest, Red Pine Forest and MAS (Mass Audubon Society) Forest. The Red Pine Forest mostly contains red and white pines, but also has a few varying tree species. The Hemlock Forest is located directly to the right of the Spruce Forest and has mostly white pines and hemlocks. The MAS Forest is primarily a pine oak forest, but, like the Red Pine Forest, has many white and red pine trees. The tree height in these locations may vary slightly due to sunlight and space. Depending on the weather conditions, the trees can be exposed to varying amounts of nutrients and varying soil quality, which could change the height of a tree. Also, spacing can affect growth. Trees need a certain amount of space to grow, and so if trees are too close together, they will not be able to grow as tall. The trees being tested, Pinus strobus, or more commonly known as white pines, are thought of to be some of the tallest trees in North America. They can grow as tall as 70 m, although the trees tested in this experiment are only in the 10-30 m range. White pines are found in a variety of different habitats: ranging from highly acidic, dry soils to wet, swampy areas (Minnis, bioweb.uwlax.edu)

Nitrogen fixation is a complicated process in which nitrogen gas is transformed into ammonia, nitrate or nitrite so it can be used by plants. Nodules, small lumps, form on the roots of legume plants. Inside the nodules there are bacteria that are protected by plant tissue. In exchange for staying in the nodule, the bacteria take the nitrogen gas and convert it so that it becomes useful for the plant (Wiedenhoeft, fofweb.com). They take the nitrogen from the air and turn the nitrogen into ammonia, or NH3. After that, the

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ammonia is converted into nitrite, NO2-, and then further still into nitrate, NO3-. The plants then utilize this modified nitrogen as a fertilizer to help stimulate growth (elmhurst.edu). Nitrogen levels are increased by organic matter, crop residues, animal manures and commercial fertilizers. Nitrogen levels are decreased by leaching, denitrification (reducing nitrites/nitrates by bacteria into the air) soil erosion and run-off (O’Leary, extension.umn.edu).

The goal of this experiment is to determine whether trees planted in soil with higher levels of nitrogen will be taller. The independent variable is the nitrogen level (mg/L) in the soil, the dependent variable is the tree height (m), and the control variables are the method of measurement, climate conditions, distance of soil from tested tree, type of tree tested, distance of tested tree to other trees and the circumference of the tree, which determines the age (approximately). In normal circumstances, trees add one ring per year, increasing the width of the tree. Depending on the growth conditions from year to year, the distance between the rings may vary slightly. The process of determining the history of the tree through its rings is called dendrochronology (Morris, icr.org). For this experiment, the trees tested will all have similar diameters so the trees will all be approximately the same age. The possibility that the trees have slightly different ages due to the distance of rings from each other because of climate change, rainfall etc is a possible source of error in this experiment. The hypothesis tested in this experiment is: if the nitrogen levels are higher in the soil, then the tree will be taller because of nitrogen fixation, the process of converting nitrogen gas into ammonium, nitrites and nitrates. These can then be used as vital nutrients by plants to stimulate their growth (ctahr.hawaii.edu).

The outcome of this experiment can help farmers and wildlife professionals with conservation of trees. The goal of this experiment is to explore the correlation of nitrogen levels in soil to tree growth. Fertilizer companies can take this information into consideration when deciding how much nitrogen to include in the fertilizer. If Drumlin Farm knows the level of nitrogen in each forest they should be able to determine which areas can tolerate additional planting and whether some areas may need additional natural sources of nitrogen such as manure or commercial sources such as fertilizer. Also, they can use this information for land management by being able to tell how fast and tall certain trees will grow in their forests. MATERIALS AND METHODS The procedure for this experiment consisted of nine steps. First, ten trees of the same species were located in one of the forests being tested (Red Pine, Hemlock, Spruce). A tape measurer was used to find the circumference of the trees in order to make sure there was no more than a one-third of a meter difference between them. This ensured that every tree was roughly the same age. Then the trees were measured using a technique where partner one bent down and then walked forward until the top of the tree was just visible. Then, partner two measured the distance from tester one’s hands to the base of the tree. The data was recorded. Next, the soil auger was used to take a soil sample of 300 cm, one half of a meter away from the base of the tree and put into Tupperware container. All of the soil testing was done back in the lab at BB&N and not at Drumlin Farm. The soil sample was mixed with 50 m/L of CaCl2 solution and stirred with a rod to get the most accurate nitrogen reading. The nitrogen levels of these samples were tested using a

5

Vernir nitrogen probe. In order to read the nitrogen levels, a Golink was needed, connecting the probe to a graphing calculator. When using the nitrogen probe, it was necessary to soak it in high standard solution for 30 minutes in the short term soaking bottle before testing. These steps were repeated with every tree in the same location. The final step was to repeat all of the above steps for each of the three different locations being tested. Figure 1: Vernir nitrogen probe

Figure 2: Tape measurer

RESULTS

Table 1: The Effect of Nitrogen Level on Tree Height (MAS Forest):

Tree Number

Tree Height (m)

Nitrogen Level (mg/L)

1 20.9 2.7 2 20.8 2.4 3 17 2.5 4 21.5 2.7 5 21.1 2.8 6 19.5 2.5 7 21.3 2.8 8 22.4 3.3 9 18.9 2.4

10 21.5 2.6 Average 20.5 2.7 Stand. Dev. 1.5 0.3

6

Table 2: The Effect of Nitrogen Level on Tree Height (Red Pine Forest):

Tree Number

Tree Height (m)

Nitrogen Level (mg/L)

1 22.1 4.2 2 19.3 3.8 3 26.5 3.1 4 18.3 3 5 16.4 2.8 6 25.1 3.4 7 22.3 4.1 8 18.5 3.6 9 24.1 5.8

10 20.7 3.4 Average 21.3 3.7 Stand. Dev. 3.1 0.8

Table 3: The Effect of Nitrogen Level on Tree Height (Hemlock Forest):

Tree Number

Tree Height (m)

Nitrogen Level (mg/L)

1 19.5 2.2 2 18.5 2.1 3 24.6 2 4 14.9 1.9 5 13.5 1.5 6 12.5 2.3 7 15.7 2.6 8 18.3 2.4 9 16.4 2.2

10 15.3 2.2 Average 16.9 2.1 Stand. Dev. 3.3 0.3

Table 4: The Effect of Nitrogen Level on Tree Height (all locations):

Forest Average Tree Height

Average Nit. Level

Stand. Dev. Tree Height

Stand. Dev. Nitrogen

MAS Forest 20.5 2.7 1.5 0.3 Red Pine Forest 21.3 3.7 3.1 0.8 Hemlock 16.9 2.1 3.3 0.3

7

Graph 1: The Effect of Nitrogen Level on Tree Height (overall):

Graph 2: The Effect of Nitrogen Level on Tree Height (all Locations):

R! = 0.28155

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7

Tree

Hei

ght (

m)

Nitrogen Level (mg/L)

R! = 0.41884 R! = 0.13139

R! = 0.00482

0

5

10

15

20

25

30

0 2 4 6 8

MAS forest

Red Pine forest

Hemlock forest

Tre

e H

eigh

t (m

)

Nitrogen Level (mg/L)

8

Graph 3: The Effect of Nitrogen Level on Tree Height (averages):

Graph 1 demonstrates that an increase of nitrogen in the nearby soil (mg/L) did correspond with the tree height (m). This graph compares the data from all three of the locations: MAS forest, Red Pine Forest, and Hemlock forest. The R! value, a measure of correlation between the variables, was fairly low, only 0.28155. The highest nitrogen level was found near tree #9 in the Red Pine forest (5.8 mg/L) and was 24.1 m tall. The lowest nitrogen level was near tree #4 at Hemlock forest (1.9 mg/L) and this tree was 14.8 m tall. The tallest tree was tree #6 at Red Pine forest (25.1 m) and had a nitrogen level of 3.4 mg/L in the soil. The shortest tree was tree #6 at Hemlock forest (12.5 m) and the soil had a nitrogen level of 2.3 mg/L). Overall, the data had a trend line that portrayed the correlation of the nitrogen levels in the soil to the tree height, but the data didn’t fit the trend line very well.

Graph 2 shows the comparison of the three habitats and the correlation between the nitrogen levels and tree heights within each site. The MAS forest has a R! value of 0.41884. The tallest tree in the MAS forest was 22.4 m tall and the shortest was 17 m tall. The lowest nitrogen level in the MAS forest was 2.4 mg/L and the highest nitrogen level was 3.3 mg/L. The Red Pine forest has a R! value of 0.13139, the data fit the trend line even less well that it did in the MAS forest. The tallest tree in the Red Pine forest was 26.5 m tall, and the shortest tree was 16.4. The tree with the highest nitrogen level in the soil was 5.8 mg/L and the lowest level was 2.8 mg/L. The Hemlock forest’s data fits its trend line the worst out of all three of the sites; the R! value was 0.00482. The tallest tree in the Hemlock forest was 19.5 m tall, and the shortest tree was 12.5 m tall. The highest nitrogen level in the soil was 2.6 mg/L and the lowest was 1.9 mg/L. Overall, the nitrogen level data in the Hemlock forest was the lowest all together, then the MAS forest, and finally the Red Pine forest had the highest overall nitrogen levels.

0

5

10

15

20

25

30

MAS Forest Red Pine Forest Hemlock

Tree Height (m)

Nitrogen Level (mg/L)

Tre

e H

eigh

t (m

) and

Nitr

ogen

Lev

el (m

g/L

)

9

Graph 3 shows the comparison of the average tree height (m) and nitrogen level (mg/L) from each habitat. Interestingly, the Red Pine forest has the highest average tree height (21.33 m), as well as the highest average nitrogen level (3.72 mg/L). Also, the Hemlock forest has the lowest average tree height (16.92 m), and the lowest average nitrogen level (2.14 mg/L). Finally, the MAS (Mass Audubon Society) forest has both values in between the other two sites. The average tree height was 20.49 m, and the average nitrogen level was 2.67 mg/L. The error bar length for the Red Pine forest was 6.2 m for tree height, and 1.61 mg/L for nitrogen level. The error bar length for the Hemlock forest was 6.62 m for tree height, and 0.56 mg/L for nitrogen level. The error bar length for the MAS forest was 3 m for tree height, and 0.5 mg/L for nitrogen level. This shows that in terms of height, the MAS forest had the most accurate data, then the Red Pine forest then the Hemlock forest. For nitrogen levels, the MAS forest had the most accurate data, then the Hemlock forest, and then the Red Pine forest. This concludes that overall the MAS forest had the most accurate data results. All three of the error bars overlapped in terms of tree height. None of the error bars overlapped for the nitrogen levels. In the MAS forest, the trees were all planted in straight rows. Also, it was on a fairly small hill and the topography was a bit uneven. The Hemlock forest was on a huge slant and had very uneven ground. The trees were very close together and the ground was completely covered in sticks and pine cones. Also, most of the trees were bare. The Red Pine forest had very moist soil and the ground was completely covered in pine needles. There were also many red pine trees in addition to the white pines. The trees were slightly further apart than the Hemlock forest. Due to these qualitative observations, it becomes apparent that the data could’ve been fairly inaccurate due to these nuances in the different forests. Figure 1: The Rows of Trees in the MAS Forest:

10

DISCUSSION The purpose of this experiment was to test if nitrogen levels affect tree height. The hypothesis was: if the nitrogen levels are higher in the soil, then the tree will be taller because of nitrogen fixation, the process of converting nitrogen gas into ammonium, nitrites and nitrates. These can then be used as vital nutrients by plants to stimulate their growth (http://www.ctahr.hawaii.edu). The hypothesis was supported because the data was partially conclusive and accurate. The trees in forests with more nitrogen had conclusively taller heights than in the forests with less nitrogen. The results for this experiment supported what was expected. Nitrogen levels stimulate plant growth and make the trees taller. The places with higher nitrogen levels in the soil had taller trees than places that had lower nitrogen levels. (www.tetrachemicals.com). Nitrogen is also in the atmosphere and biological material, but the only thing measured in this experiment was soil. The nitrogen levels in the air or the tree could have been factored in to this experiment, and it might have made the results more accurate (msucares.com). The error bars for the different forests in this experiment overlapped. A bar graph was used for measuring the averages of tree height and nitrogen level. When measuring the tree heights, the error bars overlapped which made for weak correlation among the tree heights. The error bars for the nitrogen levels however, did not overlap at all which resulted in a strong correlation between them. The error bars for the tree height’s averages were large meaning that the data was not very precise. The error bars were the highest on the tree averages in the Hemlock Forest and the smallest for the trees in the MAS Forest. The error bars were in the middle for the trees in the Red Pine Forest but all of the error bars overlapped with each other. The error bars for the nitrogen levels were very small, indicating that the data was very precise. The error bars were the largest in the Red Pine Forest, and the smallest in the MAS forest. These were logical conclusions for this experiment because the trees tested may not have had a large variety in height, which made the data correlate less. There was no conclusive data in this experiment so therefore a conclusion cannot be drawn. This experiment could be improved in many ways. One being that the experiment was not done at an ideal time of year because the trees being tested were mostly the same species, but it was more difficult to distinguish tree type without the leaves. If the experiment had been done in a time of year when the leaves were on the trees, it would have improved the accuracy of the experiment. This would make the experiment easier to conduct because the leaves would still on the trees, and a dichotomous key would be used to distinguish tree type. There was a sufficient amount of data collected in this experiment. At each of the three locations that were tested, ten different trees were tested. This amounted to a total of 30 data points, which was plenty of data to get a result. To improve the data collection it would be logical to get trees that estimated to be relatively similar in height within each location so that when comparing each location the results would be more precise and conclusive. Some errors that occurred during this experiment were, the bushes in the forests. This was an error because when measuring the height of the trees the partner has to walk away from the tree and if the bushes and sticks were in the way it made this task especially hard. Another error that occurred was when the soil samples were taken the soil auger still had pieces of soil from other locations still on it. The soil auger was

11

cleaned when at the sight, but soil from other locations was still on it anyways. In the future, it would be easy to avoid these errors. This could be done by choosing trees that have a clearing near them, which would be easy to walk to for measuring purposes. To avoid cross contaminating the different soils, the soil auger would need to be cleaned more carefully and made sure that there were no traces of other soils on the auger. AWKNOWLEDGEMENTS Christina Knight:

Thank you Mrs. LaRocca for helping Caroline and me immensely throughout this entire process. You helped us brainstorm ideas for our hypothesis, supervised our experiment and edited all of our work. I also owe much gratitude to my mother, who helped edit my Introduction and Results sections of my lab report. My grandfather, Poppi, has always kept me enthusiastic about science, and was there for constant support throughout this project. My father helped me flesh out my experiment and figure out the details of my procedure. But, mostly, I need to thank my lab partner, Caroline Scheer. She edited all my sections dutifully, worked very hard collecting data at Drumlin Farm, and is just an overall amazing partner. Caroline Scheer: For our experiment, I would like to acknowledge and thank all of the staff at Drumlin farm for all of their help in this project. Without them none of this would be possible and they helped out a lot with all of our experiments. For our experiment in particular, it required a lot of time, and Drumlin Farm provided plenty of time for the experiment to get done. Drumlin farms and all of it’s staff provided much help to us for our experiment and without them, none of this would have been possible. I would also like to thank my partner Christina for all of her help. She was an amazing partner and we stayed very focused and organized throughout the entire process which benefited our project greatly.

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WORKS CITED Christina Knight: Dechorgnat, Julie. "Journal of Experimental Botany." From the Soil to the Seeds: The

Long Journey of Nitrate in Plants. Oxford University Press, 19 Nov. 2010. Web.

11 Mar. 2014. <http://jxb.oxfordjournals.org/content/62/4/1349.full>.

Gardner, Robert. Soil: Green Science Projects for a Sustainable Planet. Berkeley

Heights, NJ: Enslow, 2011. Print.

Minnis, Ashley. "Pinus Strobus." Pinus Strobus - Eastern White Pine Tree. University of

Wisconsin - La Crosse, n.d. Web. 02 May 2014.

Morris, John D., Ph.D. "Tree Ring Dating." Tree Ring Dating. Institute for Creation

Research, 2012. Web. 10 Mar. 2014. <https://www.icr.org/article/7058/>.

"Nitrogen Cycle." Nitrogen Cycle. Elmhurst College, n.d. Web. 10 Mar. 2014.

http://www.elmhurst.edu/~chm/onlcourse/chm110/outlines/nitrogencycle.html

"Nitrogen." Soil Management. University of Hawaii at Manoa, 2014. Web. 13 Apr. 2014.

O'Leary, Mike. "Understanding Nitrogen in Soils." University of Minnesota Extension.

University of Minnesota, 2002. Web. 03 Apr. 2014.

<http://www.extension.umn.edu/agriculture/nutrient-

management/nitrogen/understanding-nitrogen-in-soils/>.

Wiedenhoeft, Alex C. “Root Nodules, Nitrogen Fixation, and Endophytes.” Science

Online. Facts On File, Inc. Web. 11 Mar. 2014.

http://www.fofweb.com/activelink2.asp?ItemID=WE40&SID=5&iPin=GWPN00

09&SingleRecord=True

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Caroline Scheer:

"Calcium: A Central Regulator of Plant Growth and Development." Calcium: A Central

Regulator of Plant Growth and Development. American Society of Plant

Biologists, 2014. Web. 17 Apr. 2014.

<http://www.plantcell.org/content/17/8/2142.full>.

"The Importance of Calcium." The Importance of Calcium. Tetra Technologies, 2010.

Web. 17 Apr. 2014.

<http://www.tetrachemicals.com/Products/Agriculture/The_Importance_of_Calci

um.aqf>.

"Nitrogen Fertility." Nitrogen Fertility. University of Mississippi, 2010. Web. 17 Apr.

2014. <http://msucares.com/crops/soils/nitrogen.html>.

"Nitrogen." Soil Management. University of Hawaii, 2014. Web. 16 Apr. 2014.

`<http://www.ctahr.hawaii.edu/mauisoil/c_nutrients01.aspx>.

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!"#$%!&$'(During past visits to Drumlin Farm in Lincoln, MA it was looked at that there was

various plant growth and soil compaction. This experiment was conducted to test the effect of soil percolation on phosphorus levels. The original hypothesis was: if there is a faster soil percolation, then there will be less phosphorus in the soil, because phosphorus is a non-metal, and can be carried away by water (Lowell Busman, John Lamb, Gyles Randall, George Rehm, and Michael Schmitt, http://www.extension.umn.edu/). The data was collected from three different sites (Farmyard, Boyce Field, Overlook Field) at nine points within those sites. A can was used to test the percolation and a phosphorus testing kit was used to measure phosphorus levels. It was found that there was not a strong correlation between soil percolation and phosphorus levels.

)*$%+,-&$)+*: Phosphorus is a necessary nutrient for plant life. Phosphorus is a nucleic and acidic

component in the structure of plants (Brittanica Encyclopedia). It helps to develop new protein tissues which aid plant growth (unknown, passel.unl.edu). When the plant dies, the phosphorus is released back into the soil, and then can be reused by many other plants (Busman, Lowell, Lamb, Randall, Rehm, and Schmitt, extension.umn.edu). Phosphorus is a strong nutrient, but if there isn’t enough, or in some cases if there is too much, there can be outcomes which result in insufficient plant growth and health. If there is not enough, the plants will die, lacking the nutrients phosphorus provides, which should have helped their growth and protein production (unknown, passel.unl.edu). If there is too much phosphorus in the environment, this will take away the plants ability to take up micronutrients such as iron and zinc, which will eventually lead to the death of the plant (Provin and Pitt, aggieturf.tamu.edu). A little comparison that may help to understand how phosphorus works in the environment is the human intake of water. Think of phosphorus as the liquid the human is drinking. Humans drink the liquid, and it hydrates their bodies. Once the body no longer needs the liquid, it is released in the form of urine. This urine, if treated correctly can once again be used for hydration, and can be reused multiple times. In terms of phosphorus, this means that plants take in the phosphorus to help with their growth. The levels of phosphorus specifically depend on the amount of plant life, the amount of fertilizer used on each of the areas, and the amount of water flow through the location. If there are a lot of plants in the desired field of testing the phosphorus levels will be taken up by the plants, as they need the nutrients to survive (unknown, passel.unl.edu). Soil percolation, the other part of this experiment, is the speed at which water moves through soil (unknown, treepeople.org). The soil percolation is really only affected by two things, which are the size of the particles, and how compact the soil is. If there are large particles, then there will be larger gaps, which the water can travel through, and if there is smaller particles, then there will be much smaller gaps for the water to move through (unknown, treepeople.org). If water is able to flow through the soil easily, then sufficient plant growth will occur. When water runs through soil, the phosphorus easily dissolves because its a non-metal, and therefore attracted to the water (unknown, extension.unm.edu).

This experiment will take place at Drumlin Farm, a Massachusetts Audubon Wildlife Sanctuary, located in Lincoln Massachusetts. This sanctuary has an approximate size of 206 acres, and has six fields. These fields are used for agriculture, animal life, and grass/walking fields. The fields that will be tested are Overlook Field, Boyce Field, and Farmyard Field. These

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fields were chosen, to help the scientists get the largest range of data. For example, at Boyce Field, the soil will have a very fast soil percolation, due to the tilling in the cropping area; as opposed to Farmyard Field, where sheep are grazing over it all day. Then, in Overlook Field, because people rarely walk over it, the soil percolation will be quick too. So, in the end, three completely different fields will be used in order to get a larger range of data. The proposed experiment is to try to find out the effect of soil percolation on the amount of phosphorus in the soil. This experiment will be tested by taking eight random samples from three selected fields at Drumlin Farm. On the day of the testing, only soil percolation will be tested in the field. The day after the testing day the phosphorus levels will be tested in the lab. The hypothesis for the experiment is, if there is a faster soil percolation, then there will be less phosphorus in the soil, because phosphorus is a non-metal, and can be carried away by water (unknown, extension.unm.edu). In order to make sure that the best results are taken, there will be five control variables. These are, day tests are taken, weather, amount of water used to test the soil percolation, depth of soil, and lastly the phosphorus tester. Soil percolation is a huge part of gardening, and if the gardener knows the rate that water moves through the soil, then they may in turn plant much healthier plants, and know exactly where the soil might need to be less compact. Using this same technique, farmers can make these same decisions, but in a much larger scale.

!"#$%&"'()"*+)!$#,-+(.)This experiment was completed in Lincoln, MA. At Drumlin Farm. The sites for testing

are Farmyard, Boyce, and Overlook. The procedure is in two parts. The first is the test of soil percolation. In the lab, find the location of testing spots, create a grid on the field, using the contour and the random function on the TXI-Inspire calculator. After that, at the location, the can, with both ends cut off and a line three fourths up from the bottom, needs to be placed in the ground with the can up to the line in the soil. Then 75mL of water in a 200mL beaker should be measured and

poured into the can. While the water is poured the partner starts the timer and stops it when there is no more visible water. The time should then be recorded in the appropriate data table. First, 20mL of soil needs to be collected per sample using a soil auger. This sample should be taken at the percolation testing

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location. Along with that, 100 mL of distilled water need to be collected per sample. Then the soil and water is mixed and shaken heavily for sixty seconds in a tupperware container. Then wait for thirty minutes to an twenty-four hours for the mixture to settle. Then get the phosphorus testing kit and capsules. Fill kit to fill mark then very carefully open the capsule above the kit and pour the powder in. Wait ten minutes and record the data in the appropriate table.

!"#$%&#: Table 1: The effect of soil percolation on phosphorus level at farmyard field

Graph 1: The effect of soil percolation on phosphorus level at farmyard field

Farmyard ended up having the most phosphorus containing soil; having two more

samples than Boyce field, and one more than Overlook Field. Not only this, but Farmyard also had very slow soil percolation, which resulted beyond the maximum time of five minutes every time. This would mean that the soil was very compact, due to sheep walking over it all day. On the test day, the scientists noticed that the soil had very weird soil patches. In certain places,

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there would be circular lumps, while in other places it would be very flat, and soft. This could affect the data, because this may mean that the soil is more or less compact in different places. Table 2: The effect of soil percolation on phosphorus level at boyce field

Graph 2: The effect of soil percolation on phosphorus level at Boyce field

Sadly on the test day, the scientists were told that samples could not be collected from the actual cropping portion of the field. Instead, the scientists were told to test on the side of the walking path. Also, because this was right next to the walking path, the soil was very compact. The soil was dried out and cracking on the top layer, and very wet about an inch underneath. Looking at the data, there was absolutely no correlation between the two variables. The only real exception in the data was the eighth test, where there was only a result because of the large rocks in the location.

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Table 3: The effect of soil percolation on phosphorus level at overlook field.

Graph 3: The effect of soil percolation on phosphorus level at overlook field

On the test day, because all of the field’s soil percolation was going over five minutes every single time, the experiment had to be altered a little bit. The can that was being put into the soil was put in deeper (until the ! mark, no longer the " mark), and half the amount of water was put in. Not only this, but the scientists let the time run until all of the water was drained out. Now that the testing was done differently, much more sufficient data was concluded. The data was much more precise, and in line with how it should be. Comparatively, Boyce Field had an r

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squared value of three percent, while Overlook Field had an r square value of twenty-four. This would have been much higher if there had not been that singular outlier in the bottom left corner.

!"#$%##"&': This experiment tested the correlation between soil percolation and phosphorus levels.

The hypothesis was: if there is a faster soil percolation, then there will be less phosphorus in the soil, because phosphorus is a non-metal, and can be carried away by water (Lowell Busman, John Lamb, Gyles Randall, George Rehm, and Michael Schmitt, http://www.extension.umn.edu/). The hypothesis was not supported because all phosphorus levels were depleted or deficient.

At the first two fields (Farmyard and Boyce) every soil percolation test was over five minutes. Along with that all phosphorus levels were between zero and one. The data did not vary between the two sites. The only slightly sufficient data was collected at Overlook field. The phosphorus at each field were precise but the soil percolation was not precise at all.

According to a lot of sources, phosphorus should commonly be found in places where plants are meant to be grown (Lowell Busman, John Lamb, Gyles Randall, George Rehm, and Michael Schmitt, http://www.extension.umn.edu/), but at Boyce field, which is used for agriculture, the phosphorus levels were depleted. There could be a lot of reasons why this happened. A big problem could have been the phosphorous testing kit, it was not a precise kit and the capsules containing the powder was not always the same. At the same time, the plant intake of phosphorus in that field could have been higher and depleted the soil of all phosphorous or the phosphorus could have been in a unavailable form (Schatchman, Daniel P., Robert J. Reid, S.M. Ayling, http://www.plantphysiol.org/). At the third testing site the phosphorus was also depleted. This could be because of again, dense brush growth. The phosphorus could have been in an unavailable state, which it often is (Philip J. White, John Hammond, 224).

A possible reason the soil percolation was inconclusive was because it had rained the night before. The experiment only tested the soil on one day out of 365 days. The soil at Farmyard and Boyce was very compact. It was compact at Farmyard because the goats walked on it all day. At Boyce the experiment had to be conducted on the side of the field because students were not permitted in the middle of the field. The soil was very wet and compact. At Overlook the soil was very dense because of all the hay and grass that had a thick root system throughout.

There are a lot of things that could be changed about this experiment. The biggest one was time, there was not enough time for all the samples to be collected and the percolation tests were often cut short. Along with that the procedure could have had less water and it would have been easier to take percolation tests. Also with the phosphorus, if there was a more precise tool to measure phosphorus it would be a lot easier to conclude ideas about soil percolation and phosphorus.

There were some errors while conducting this experiment. First was that the original plan was for the soil percolation to be tested with 150mL of water. After testing the first two fields like this, it was realized that it was taking too much time. The procedure had to be revised to be taken with the can three fourths of the way covered in soil, instead of half, and the soil

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percolation be tested with 75mL. Also the phosphorus tests were all collected and created on different days. This was because of insufficient planning. For future studies, other elements could be tested against soil percolation because percolation might not affect the phosphorus levels.

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!"#$%&'()*(+($,-: Author 1: I’d like to thank my partner Chris Lang (83-12) for helping me complete this experiment even when I was out for two days during an important testing period. I would also like to thank my science teacher Mr. Ewins for helping me write and understand all the data and assignments. My parents also played a big part in helping me edit and revise my report. I also want to thank Mr. Sarzana, Ms. Canaday, and Mr. Senabre for supervising my partner and I on the trip. Also, I want to thank Debbie and Martha for being extremely knowledgeable about all nature topics and tricks at our testing locations.

Author 2: I like to thank the environmental specialists, Debbie and Martha for teaching me more about the environments I was testing in, including the treatments used on it, and what the fields were used for. I would also like to thank Mr. Sarzana, Mrs. Canaday, and Mr. Senabre for being there when either my partner or I may need some help. And my parents for getting me all of the materials I needed for this experiment. I would especially like to thank my science teacher Mr. Ewins, and my lab partner Isaac Glotzer-Martin. I’d like to thank Mr. Ewins for providing my partner and I with the majority of the materials my partner and I needed, and for advising me with some major decisions for the written portion and field portion of this experiment. I would like to thank Isaac for just being a great partner to work with.

! ""!

!"#$%&'()*+: Author 1: Beaker with 150 mL of water in it. Digital image. Lab 7: Preparation of Oxygen,

Properties of Oxygen, and Behavior of Oxides. N.p., n.d. Web. 11 Mar. 2014.

<http://www.chemistryland.com/CHM130FieldLab/Lab7/Beaker150mLwater.jpg>.

Busman, Lowell, John Lamb, Gyles Randal, George Rehm, and Michael Schmitt. "The

Nature of Phosphorus in Soils." : Nitrogen : University of Minnesota Extension. Regents

of the University of Minnesota, n.d. Web. 02 Mar. 2014.

<http://www.extension.umn.edu/agriculture/nutrient-management/phosphorus/the-nature-

of-phosphorus/>.

Gordon, Lisa K. Soi Test Kit. Digital image. House Logic. N.p., 13 Apr. 2012. Web. 11

Mar. 2014. <http://www.houselogic.com/blog/gardens/soil-testing/#.>.

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Apr. 2014. Web. 16 Apr. 2014.

<http://www.fofweb.com/activelink2.asp?ItemID=WE40&SID=5&iPin=PTNM0006&Si

ngleRecord=True>.

Schatchman, Daniel P., Robert J. Reid, and S.M. Ayling. "Phosphorus Uptake by Plants:

From Soil to Cell." Phosphorus Uptake by Plants: From Soil to Cell. American Society

of Plant Physiologists, n.d. Web. 14 Apr. 2014.

<http://www.plantphysiol.org/content/116/2/447.full>.

Western Washington University. Http://geology.wwu.edu/. N.p.: Western Washington

University, n.d. PDF.

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White, Philip J., and John Hammond. The Ecophysiology of Plant-phosphorus

Interactions. Dordrecht: Springer, 2008. Print.

Author 2: Busman, Lowell, John Lamb, Gyles Randall, George Rehm, and Michael Schmitt. "The Nature

of Phosphorus in Soils." : Nitrogen : University of Minnesota Extension. N.p., 2009.

Web. 16 Apr. 2014. <http://www.extension.umn.edu/agriculture/nutrient-

management/phosphorus/the-nature-of-phosphorus/>.

Daniels, Mike, Tommy Daniel, Dennis Carman, Robert Morgan, John Langston, and Karl

VanDevender. "Soil Phosphorus Levels: Concerns and Recommendations." University of

Arkansas. Agriculture and Natural Resources, n.d. Web. 17 Apr. 2014.

<http://www.sera17.ext.vt.edu/Documents/Soil_P_Levels_Concerns_and_Recommendati

ons.pdf>.

Provin, T. L., and J. L. Pitt. "Phosphorus: Too Much and Plants May Suffer." Texas Cooperative

Extension. Aggie Turf, n.d. Web. 17 Apr. 2014. <http://aggieturf.tamu.edu/files-

2005/phosphorus_Provin.pdf>.

Sanderson, R. Thomas. "Phosphorus (P) (chemical Element)." Encyclopedia Britannica Online.

Encyclopedia Britannica, n.d. Web. 17 Apr. 2014.

<http://www.britannica.com/EBchecked/topic/457568/phosphorus-P>.

"Soil Percolation Rates." Home Page. N.p., n.d. Web. 17 Apr. 2014.

<http://www.treepeople.org/soil-percolation-rates>.

"Soils - Part 6: Phosphorus and Potassium in the Soil." Plant and Soil Sciences ELibrary. N.p., n.d. Web. 17 Apr. 2014. !

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ABSTRACT

This experiment was directly focused on the effect of plant density on water turbidity. By controlling the amount of plants around a pond scientists could limit erosion, thus lowering the water turbidity. With lower water turbidity, animals and plants within the pond would be able to thrive. The hypothesis for this experiment was if the plant density around the pond is the highest, then the turbidity in the pond will be the lowest because the plants stop soil erosion which means less particles will come into the water making the water turbidity less. (http://water.epa.gov/) Samples were gathered from Bathtub Pond, Ice Pond and Poultry Pond. To do this, random cardinal directions were determined and then water turbidity was sampled by using water turbidity tube. Then using the same cardinal direction, plant density was recorded by creating square meters and recording the plant density by using a plastic square meter grid. Major results included a plant density ranging from a 4.83% coverage to a 18.33% coverage and a water turbidity ranging from 7cm to a very clear 110cm. In addition, the R2

value between the two variables was a very small 0.216, showed a very slight correlation. Thus, the results collected were incapable of determining a valid conclusion to the hypothesis.

INTRODUCTION

Water turbidity is a measure of how much matter is floating in the water. Turbidity is defined as the amount of sediment or particles that are suspended in the water (http://www.thefreedictionary.com). The turbidity of a body of water is mainly affected by soil erosion, waste discharge and urban runoff. High water turbidity can clog fish gills, lower growth rates and affect egg and larval development. (http://water.epa.gov).

The experiment will be conducted at Drumlin Farm in Lincoln, MA. Drumlin Farm spans 206 acres and acts as both a wildlife sanctuary and a working farm, producing naturally grown fruits and vegetables for the public to purchase (www.massaudubon.org). Testing the water turbidity at each of the three ponds will indirectly dictate it’s ability to sustain life by showing how much the particles in the water will interfere with animal life. In this experiment, the following three ponds will be tested: Ice pond, Bathtub pond and Poultry pond. Ice pond is located downhill from a parking lot and contains a drain meaning the water will be running. The running water could affect the turbidity because the movement could be stirring up particles from the ground, which would contribute to a higher turbidity. Bathtub pond is surrounded by many trees and bushes and is south of the drumlin. This affects the turbidity in two ways. First, all of the plants’ roots would slow down erosion, bringing less particles into the water. At the same time, water running down from the drumlin could pick up particles and deliver them to the pond resulting in a higher turbidity, however, having more plants would also slow this down. Finally, Poultry pond is located downhill from the chicken coop and grazing fields meaning that animal excrements could be leaking into the pond affecting turbidity. This will mean that the plant density could play a large role as the roots could help filter the excrement leaking into the pond.

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The main impact that water turbidity has on the wildlife is that high water turbidity will decrease photosynthesis in underwater plants, because it lets less light through. The reduced photosynthesis will both decrease the amount of nutrients a plant is generating, which could lead to it’s death, and the amount of dissolved oxygen in the water (http://water.epa.gov). Because animals in the water need the D.O. to breathe, it would definitely affect the aquatic life, also, the higher water turbidity will lead to the destruction of both the flora and the fauna in the pond. In extreme cases, the amount of suspended particles can become so high that the particles will clog fish gills making it impossible for them to breathe resulting in death. However, in most cases, such effects are only observed over the course of a larger period time (http://www.watercenter.org/). Because one of the main factors that contribute to water turbidity is erosion (http://water.epa.gov), a larger density of plants, which would stop erosion (http://www.kalkaskacounty.net), would contribute to a lower water turbidity.

The proposed experiment is to test the effect of the plant density around a pond on the turbidity of the water in the pond. The objective of this experiment is to determine whether or not the amount of plants actually does slow down erosion which leads to a lower water turbidity. The question will be tested by measuring the plant density around the pond and water turbidity sample from within the pond to see if there is a correlation between them. Three turbidity samples and nine plant density samples will be taken at each pond. The independent variable is the plant density around the pond, which will be measured by seeing how much of the ground is exposed in the quadrat. The dependent variable is the water turbidity in the pond. Important controlled variables include the same number of samples at each site, the distance from the pond at which the plant samples are taken, the depth where the water turbidity sample will be taken, and the time of year when the samples are taken. The hypothesis for this experiment is: if the plant density around the pond is higher, then the water turbidity will be lower because more plants means less erosion and less erosion means that there will be less particles coming into the water making the turbidity lower (water.epa.gov/).

The purpose of this experiment is to help the naturalists and farmers at Drumlin Farm, and people who keep ponds at home, regulate a healthy cleanliness of their ponds by showing how to plant plants to help regulate turbidity. This experiment is to show that when deforestation occurs, nearby water sources will also be affected. Also, if land is cleared near water sources that are used for obtaining drinking water or similar, the deforestation could have negative effects on the entire publics health.

MATERIALS & METHODS

Before traveling to Drumlin Farm, in Lincoln MA, a random number between one and 360 was created by using the random number generator on a TI-nspire CX calculator (rand(1)•360). This number was then rounded and used as a cardinal direction. Once that direction was located using a compass, the Water Monitoring Equipment & Supplies water turbidity tube (pre-rinsed with sample water) was filled with 120 cm3 of water from the edge of the pond edge (measured with a meter stick). Then one partner stood and looked

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down the tube at the secchi disk while the other released the valve at the bottom. When the secchi disk was visible, the valve was closed and the water turbidity, as indicated by the amount of water still left in the tube, was recorded in the Field Notebook. Now, using the same random cardinal direction, a one by one meter square or quadrat was constructed on the edge of the pond by using a meter stick and four colored flags. First, one side was created by using the meter stick while two flags were placed on the ends. Then the two sides were measured out and two more flags were placed on the last two corners of the square. Then, if at all possible, the plastic square meter was used to help determine the plant density, which was then recorded in the FNB (both scientists wrote down their estimates to be averaged later). Then another square meter was created right on the edge of the first and the plant density was recorded using the same procedure above. The third and final square meter was then created on the edge of the second and the plant density was recorded once again in the Field Notebook. Once the plant density was recorded for all of the square meters, the procedure was repeated three times at the cardinal directions at the site. This procedure was then repeated at Ice, Bathtub and Poultry Pond. For an example of the sampling procedure refer to the diagram below. !!!!!!!!!!!!!!!!!!!!!!

Compass direction found (black lines coming from center), then water turbidity sampled and recorded (circle), and plant density sampled and recorded from one-meter squares (squares at edge of circles)

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RESULTS !Table 1: Effect of Average Plant Density on Water Turbidity !"#$%&#'()%*+',#*-.+/' 0%+#$'12$3.4.+/'

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Table 2: Effect of Plant Density at edge of Pond on Water Turbidity !"#$%&'($)*%+& ,#%(-&./-0*1*%+&

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Table 4: Effect of Plant Density two meters from Pond on Water Turbidity !"#$%&'($)*%+& ,#%(-&./-0*1*%+&

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Graph 1 shows the average Plant Density vs. the Water Turbidity at the

tested ponds. In the graph, Water Turbidity values ranged from a murky 7 cm to an extremely clear 110 cm. Average Plant Density at zero to three meters from the pond ranged from a 4.83% coverage to an 18.33% coverage. The r2 value of 0.216 shows that while there is a slight increase in Water Turbidity as the plant density gets higher, it is low enough that one can see no significant effect of one on the other. There were no major trends or highly unusual patterns.

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Graphs 2,3, and 4 show the Plant Density at different distances from the pond vs. the water turbidity. In all of the graphs the Water Turbidity had a maximum value of 110 cm and a minimum value of 7 cm while the plant densities varied in each one. In Graph 2, two significant outliers have been removed, making a very high r2 value of 0.937, indicating a significant correlation. In the graph, the minimum plant coverage is 2.5% and the maximum is 19%. In Graph 3, the lowest Plant Density was a 1.5% coverage with the highest being a 27.5% plant coverage. The r2 value of 0.261 was the highest of all the graphs but was still low enough to indicate that there is very little correlation, even though the Water Turbidity does get higher on average as the plant density gets higher. Once again there are no irregular patterns. Graph 4 has the lowest r2 value of all the graphs, with a value of 0.00683. This shows that there is no discernable correlation between the two variables. DISCUSSION #$%&!'()'*%+',-!-'&-'.!-$'!/00'1-!20!)3/,-!.',&%-4!/*25,.!/!)2,.!2,!-$'!6/-'*!-5*7%.%-4!20!-$/-!&/+'!)2,.8!#$'!)5*)2&'!6/&!-2!.'-'*+%,'!-$'!12**'3/-%2,!7'-6'',!-$'&'!-62!0/1-2*&9!-$5&!0%,.%,:!25-!%0!)3/,-!.',&%-4!*'/334!.2'&!/00'1-!6/-'*!-5*7%.%-48!#$'!$4)2-$'&%&!02*!-$%&!'()'*%+',-!6/&;!%0!-$'!)3/,-!.',&%-4!/*25,.!-$'!)2,.!%&!-$'!$%:$'&-9!-$',!-$'!-5*7%.%-4!%,!-$'!)2,.!6%33!7'!-$'!326'&-!7'1/5&'!-$'!)3/,-&!&-2)!&2%3!'*2&%2,!6$%1$!+'/,&!3'&&!)/*-%13'&!6%33!12+'!%,-2!-$'!6/-'*!+/<%,:!-$'!6/-'*!-5*7%.%-4!3'&&8!=$--);>>6/-'*8')/8:2?@8!#$'!*'&53-&!:/-$'*'.!0*2+!-$%&!-'&-!.2!,2-!.'0%,%-%?'34!/,&6'*!-$'!2*%:%,/3!A5'&-%2,;!.2'&!)3/,-!.',&%-4!/00'1-!6/-'*!-5*7%.%-4!20!/!)2,.B!C,!/?'*/:'9!-$'!*'&53-&!&$26!/&!-$'!)3/,-!.',&%-4!%,1*'/&'&9!-$'!6/-'*!-5*7%.%-4!.'1*'/&'&8!D26'?'*9!-$'!EF!?/35'&!20!-$'!*'&53-&!/*'!-22!326!-2!&$26!/,4!&%:,%0%1/,-!'00'1-!20!2,'!2,!-$'!2-$'*8!!

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ACKNOWLEDGEMENTS Author 1 Acknowledgments:

I, Theodor Lukin-Yelin , would first and foremost like to thank my lab partner James McCarey. He always stood by me even when I was having trouble. I would also like to thank Mr. Ewins for teaching us techniques and helping us when we were stuck. I would like to thank Mr. Dwyer, Ms. Jamison and Ms. Brooks for supervising us on the day and the drumlin farm staff for helping us out around the habitats. Author 2 Acknowledgements: $%!&'()*!+,-'.)/%!01234!356)!71!78'96!):)./19)!081!,197.5;27)4!71!,.)'759<!'94!,'../59<!127!785*!)=>).5()97?!@85*!59,324)4!(/!>'.79).!@8)141.!A2659BC)359!081!8)3>)4!('6)!78)!>.1,)42.)!<1!*(11783/%!+.?!D059*%!081!>.1:54)4!'33!78)!8)3>!71!,.)'7)!78)!)=>).5()97%!'94!@)',8).!E'72.'35*7!-'.13!081!8)3>)4!12.!<.12>!F594!*57)*!'94!0'/*!71!9':5<'7)!78)(?!G78).!>)1>3)!$!01234!356)!71!78'96!59,324)!+.?!H0/).%!+.*?!&'(5*19%!'94!+.*?!I.116*!081!*2>).:5*)4!12.!>.1,)42.)!'7!45FF).)97!*57)*?!! !

!

! ""!

WORKS CITED: Works Cited for Author 1: "5.5 Turbidity." Home. United States Environmental Protection Agency, n.d.

Web. 28 Feb. 2014.

<http://water.epa.gov/type/rsl/monitoring/vms55.cfm>.

"Factors Influencing Erosion." Factors Influencing Erosion. N.p., n.d. Web. 30

Mar. 2014. <http://www.kalkaskacounty.net/planningeduc0043.asp>.

Kalkaska County. "Factors Influencing Erosion." Factors Influencing Erosion.

Kalkaska County, n.d. Web. 01 May 2014.

<http://kalkaskacounty.net/planningeduc0043.asp>.

Mass Audubon. "About Drumlin Farm." About Drumlin Farm. Mass Audubon,

n.d. Web. 13 Mar. 2014. <http://www.massaudubon.org/get-

outdoors/wildlife-sanctuaries/drumlin-farm/about>.

The Free Dictionary. "Turbidity." The Free Dictionary. Farlex, n.d. Web. 13 Mar.

2014. <http://www.thefreedictionary.com/turbidity>.

"Water Turbidity Effects on Fish and Aquatic Life." Watercenter. Water Center,

n.d. Web. 13 Mar. 2014. <http://www.watercenter.org/physical-water-

quality-parameters/water-turbidity/water-turbidity-effects-on-fish-and-

aquatic-life/>.

Works Cited for Author 2:

Chinn, Lisa. "Soil Erosion Control Plant List." Home Guides. Demand Media, n.d.

Web. 15 Apr. 2014. <http://homeguides.sfgate.com/soil-erosion-control-

plant-list-68961.html>.

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! "#!

"Effects of Erosion on Water Quality." Effects of Erosion on Water Quality. The

Mill Creek Watershed Group, n.d. Web. 15 Apr. 2014.

<http://www.millcreek.20m.com/page5.html>.

"Measuring Plant Cover." Uidaho. N.p., n.d. Web. 15 Apr. 2014.

<http://www.webpages.uidaho.edu/range357/notes/cover.pdf>.

"Turbidity: Description, Impact on Water Quality, Sources, Measures."

Minnesota Pollution Control Agency. MPCA, n.d. Web. 15 Apr. 2014.

<http%3A%2F%2Fwww.pca.state.mn.us%2Findex.php%2Fview-

document.html%3Fgid%3D7854>.

"What's a Good Value for R-squared?" What's a Good Value for R-squared?

Duke, n.d. Web. 15 Apr. 2014.

<http://people.duke.edu/~rnau/rsquared.htm>.

1

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!!!!

7

!!!

!!!!!!

! !!

!!!

!!!!

!!!!!!!!!!!!!!!!!!!!!!!!!

F igure 1 This is a picture of the soil moisture sensor

inserted into the ground to get our soil moisture percentage readings.

F igure 2 This is a map of Drumlin Farms. The locations marked

above are the three forest habitats visited.

M AS

R E D PIN E

H E M L O C K

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12

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13

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14

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16

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pHunductivity

The E ffect of Land Use and Soil pH on Soil Conductivity

By Ian McJohn (S84-8) and Victor Chu (S84-2)

1

T A B L E O F C O N T E N TS

Section Author Page

Abstract Chu 2

Introduction McJohn 2

Material & Methods Chu 4

Results McJohn 7

Discussion Chu 9

Acknowledgements Chu & McJohn 10

Works Cited McJohn 12

Works Cited Chu 13

2

A BST R A C T

During the previous visit to the three sites (Farmyard, Boyce Field and Overlook) at Drumlin Farm in Lincoln, MA, an experiment was conducted. The experiment was aimed to discover the correlation between land use, soil conductivity and soil pH. Soil pH is the measure of how acidic or basic soil is and soil conductivity is the measurement of how well soil conducts electricity. With a randomization method involving a map from each site on with XY coordinates using a TI-Nspire CX, nine random spots were selected from each site. Soil conductivity and pH data from each spot was collected. There were three hypotheses: First, for the relationship between land use and soil conductivity, Farmyard would have the highest soil conductivity because of the nutrients from farming (Riseng, 2011). Second, for the relationship between land use and pH, if the land is heavily used, the land would be acidic because of the higher amount of metals such as aluminum in the soil (Hanlon, http://edis.ifas.ufl.edu). Lastly, for the relationship between pH and soil conductivity, if the soil is acidic, the conductivity of the soil would be high because both pH and soil conductivity depend on amount of nutrient and correlate negatively (Zhao et al., 2013). Only the hypothesis concerning the relationship between land use and pH was supported as Farmyard, the site that is used the most, had the lowest pH. IN T R O DU C T I O N

If an agricultural business wants to know how to keep their crops alive, they have to measure soil conductivity. By measuring soil conductivity, it is possible to tell what factors may

s chemical contents, as those same factors influence conductivity. Some of the primary influences on soil conductivity are dryness, the amount of metal in the soil, and which nutrients are in the soil. Substances such as phosphorus, nitrogen, and some metals that corrode greatly influence pH and can alter conductivity. All of these factors can be affected by land use, because using land often adds or subtracts substances that get mixed in with the soil (www.sepa.org.uk). Another influence on soil conductivity is pH. Soil pH is the measure of how acidic or basic soil is. As soil pH is influenced by many of the stated factors, it is a measurement often correlated with soil conductivity. Soil pH is measured on a scale from zero to fourteen, with seven being neutral and anything below seven an acid, and anything above it a base. Aluminum can become toxic and make soil more acidic, lowering the pH, while the same metallic content can increase the conductivity of the soil (Bickelhaupt, www.esf.edu). Soil conductivity is measured in mS/m, or milliSiemens per meter. By combining the data from soil pH and conductivity, farmers can learn more about the contents of their soil. (Barbosa/Overstreet, www.lsuagcenter.com). This experiment attempts to provide a more detailed summary of what is in specific regions of fields. With more data about the relationship between soil pH and conductivity, it will be possible to measure one of those factors and determine the other one based on past knowledge of the correlation.

As previously discussed, farmers often use soil pH and soil conductivity to learn about their land. Many farms, including Drumlin Farm in Massachusetts, could use conductivity and pH data to tell which crop to place in particular regions of their fields to maximize growth. The three sites examined at Drumlin Farm (the Overlook, Boyce Field, and the Farmyard) are used in different ways, making them ideal test grounds to see how land use and soil pH influence soil conductivity. While not all of this land is farmland, it is required to use multiple types of land so as to get larger scale correlation data and to provide farmers data on land they may potentially convert into farmland. The Farmyard is a typical animal pasture, while Boyce Field is used for growing crops, and Overlook was natural land, for hiking and other unaltered purposes. Soil pH

3

is used to indicate which plant types are best situated for a particular habitat. This is because some plants tolerate acidic soil, while some require basic or close to neutral soil. Soil conductivity can create a semi-accurate reference map for where nutrients are in a particular body of soil, making positioning crops and fertilizing the soil easier (Hanlon, edis.ifas.ufl.edu). Because of the varying chemical content between farm, urban, and other types of land, what land is used for can greatly influence the pH and conductivity of the soil.

The method of determining the contents of land via sampling the soil pH and conductivity has one major problem. Inside a habitat, there can be major fluctuations in pH and conductivity between subdivisions. This is because different nutrients can be concentrated in particular areas. The only way to record accurate data on pH and conductivity for an entire region is to divide it up into habitats based on the usage of the land (grazing, farmland, etc.) and to do multiple trials throughout each habitat (RapiTest instruction packet). Different habitats create different soil conditions and nutrient levels. For instance, the experiment done by Zhao et al. (2013) demonstrated that urban land use has a very acidic pH, mostly due to the metal contents of the soil. This study shows that both types and levels of nutrients influence soil pH and conductivity, making the land use an important independent variable to be tested (Zhao et al., 2013).

The objective of this experiment is to find the relationship between land use, pH and soil conductivity. Soil pH and soil conductivity are highly correlated values (Riseng, 2011). For this reason soil pH will be used as a secondary independent variable. The primary independent variable is land use. Soil samples will be taken from three different sites, one where land is used as farmland, one as animal grounds, and one where it is mostly used by wildlife. The primary dependent variable is soil conductivity. This will be measured in mS/m. Some of the more important control variables are the time the samples are collected, the method of measurement, the amount of time the sample is tested for (e.g. how long the probe sits in the cup), the chemicals added to test pH, the quantity of sample collected, and the amount of time the sample is stored before testing. Due to the fact that this experiment has several variables involved, there will be three hypotheses, one for each combination of variables.

The first hypothesis relates to the primary independent variable and the dependent variable. This is that if the land is used as farmland, such as the Farmyard, then the land will have a high conductivity level, because the nutrients from farming will greatly increase the conductivity of the soil (Riseng, 2011). Another hypothesis is used to predict the outcome of the correlation between the independent variables, stating that if the land is heavily used, such as the Farmyard or Boyce Field, then the land will be acidic, because the intense usage of the land, including farming and urban use, causes higher amount of metals such as aluminum in the soil, increasing the toxicity and lowering the pH of the soil (Hanlon, http://edis.ifas.ufl.edu). The third and final hypothesis tests the relationship between the secondary independent variable and the dependent variable. If there is a low (acidic) pH, then the conductivity of the soil will be high, because both factors are related to the amount of nutrients in soil, and correlate positively (Zhao et al., 2013).

4

The primary application of the data from this experiment is to be applied to farms such as Drumlin Farm. Knowing the relationship between land use, pH, and soil conductivity will allow farmers to learn about new aspects of their land. Farmers can learn what parts of their land are best suited for farmland by comparing its conductivity and pH ratings to those of the ideal conditions for farming (Riseng, 2011). Another thing farmers can learn is the influence of crops on soil. This will allow them to figure out the best place to plant particular crops. Also, this particular field of science can use the experiment to advance the amount of data known about the fluctuations of pH and conductivity within a habitat. M A T E RI A LS A ND M E T H O DS The objective of this experiment was to find the relation between land use, pH and soil conductivity. The primary independent variable was land use, the secondary independent variable was pH, and the dependent variable was soil conductivity. To carry out this experiment accurately, controlled variables such as the method of measurement, the temperature of the sample, the time the samples are collected, the method of collecting the sample, and the quantity of each sample collected were applied. In this experiment, nine data points of both pH and soil conductivity were collected from each of the three sites at Drumlin Farm in Lincoln, MA. The three sites were Overlook (Diagram 5), Boyce Field (Diagram 3) and Farmyard (Diagram 5). The experiment was conducted at each of the three sites, first, Overlook, second, Boyce Field and third, Farmyard. The map of each site was then put into an XY coordinate system. Then, nine random spots from each site were chosen by the random coordinate generating function of the TI-Nspire CX calculator (Diagram3,4,5). Each of the nine random spots from each of the three sites were visited and data, including pH and soil conductivity, was collected. The following procedure was used on each of the nine spots to examine the soil conductivity and pH. From each spot, 50 mL of soil was obtained using a small soil auger. The collected soil was poured into the 200 mL plastic cup and mixed with 100 mL of distilled water. The solution was thoroughly stirred to make sure the soil and the distilled water were uniformly mixed. Then, the Vernier Conductivity Probe (Model Name: CON-BTA) was connected to the TI-Nspire calculator via the USB connector. It was placed into the solution for a minute. Then, the Vernier Conductivity Probe measured the conductivity of the soil. Then, the probe was rinsed carefully by distilled water for future use. The data was recorded in a table. Next, the following experiment was conducted on each spot to get the pH value of the soil. Using the soil auger, a small amount of soil was collected and poured into the test chamber of the RapiTest Soil Test Kit until the soil fill line. To complete the process of measuring pH of the soil, a small amount of distilled water was poured into the test chamber of the RapiTest Soil Test Kit until the water fill line, followed by the powder from the capsule from the RapiTest Soil Test Kit. The test chamber was shaken thoroughly until the three substances were uniformly mixed. The solution was left untouched for a minute until color appeared in the test chamber. Then, the pH value was obtained by using the pH chart and recorded in the data table.

5

The Setup of the Vernier Conductivity Probe

Rapitest Soil Test Kit

Boyce Field (locations where samples were collected)

6

Farmyard (locations where samples were collected)

Overlook (locations where samples were collected)

7

R ESU L TS Table 1: The effect of land use on soil pH !! "#$%!&'!

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Graph 3: The effect of soil pH on soil conductivity (mS/cm)

For graph one, there were several unexpected results. The first was that all of the

averages were about the same, with large error bars. The Boyce Field and Overlook error bars overlapped, making it impossible to tell which one was higher. Due to the overlap, the highest data point cannot be determined. If the data is being looked at without error bars, the highest average pH is Boyce Field (6.83). The lowest average pH is Farmyard (6.03).

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Graph two had large standard deviations. The highest was Farmyard, with a standard deviation of 94.18 mS/cm. The Farmyard column overlapped with both of the other columns.

it could be determined that Boyce Field had the higher conductivity. Disregarding the error bars, the highest was Boyce Field (197.54 mS/cm), and the lowest was Overlook (75.96 mS/cm). The trend differs from the soil pH graph, but the highest for both is Boyce Field. Both the graphs have different lowest values, with pH as Farmyard and conductivity as Overlook. One important point to note is there was an outlier in the Farmyard data. It was known to have been caused by artificial influence of rusted metal deposited into the ground. The third graph was the hardest to analyze, as different habitats showed different trends. The first point (Farmyard) had the lowest pH (6.03), but the mid-range conductivity (96.5 mS/cm). The only correlation was where Boyce Field had the highest pH (6.83), and conductivity (198.1 mS/cm). The r2 values are not enough to indicate correlation between any of the three habitats. For Farmyard the r2 is 0.0053, for Boyce Field it is 0.0833, and for Overlook it is 0.1729. This provides an unusual outlook, as the highest correlation value has a positive trend line, while the others have the trend line showing the opposite. Multiple qualitative observations were made throughout the experiment. One was due to the outlier in the Farmyard data set. The teacher naturalist stated that there was a drywell where we did our testing, meaning that some of the metal could have flaked off of it and caused the conductivity outlier. Another observation was at Boyce Field. Places with different crops had different conductivity (and pH) levels, even if the soil looked and felt identical. This influence was seen mostly with conductivity, but on a small scale with pH also.

A Map of Drumlin Farm, where the data was collected from DISC USSI O N

This experiment was conducted to test the correlation between the land use, pH and conductivity of soil. There were three hypotheses for this experiment: If the land is heavily used, such as Farmyard and Boyce Field, then the land will be acidic, because the intense usage of the land, including farming and urban use, causes higher amount of metals such as aluminum in the soil, increasing the toxicity and lowering the pH of the soil (Hanlon, http://edis.ifas.ufl.edu); If the land is used as a farmland, then the land, it will have a high conductivity level, because the excessive amount of nutrients from farming will greatly increase the conductivity of the soil

10

(Riseng, 2011); and If there is a low (acidic) pH, then the conductivity of the soil will be high, because both factors are related to the amount of nutrients in soil, and correlate positively (Zhao et al., 2013). The experiment did not support any hypothesis since there was not any correlated trend. The experiment showed that the initial hypothesis about the correlation between the soil pH and land use was not supported. The experiment was well conducted and the data for soil pH was valid since the error bars were small, thus yielding precise data. According to the hypothesis, Overlook would have the lowest soil pH, but Graph 1 showed that Farmyard had the lowest soil pH. This initial hypothesis was wrong, but by looking at the fact that Boyce Field had conclusively lower pH than Farmyard, a new hypothesis concerning the correlation between soil pH and land use can be established. If the land is used for animal grazing, then the land will be more acidic than land used for plants growing because denser nitrogen concentration in animal grazing field results in lower soil pH. (Zhao et al., 2013) The experiment did not support the initial hypothesis about the correlation between the land use and the soil conductivity. The only conclusive data for soil conductivity was that Overlook was less conductive than Boyce. Given the length of the error bars for soil conductivity at Farmyard and Boyce Field, the data was highly imprecise. This impreciseness of data may have been caused two incidents. A metal drywell at Farmyard caused an outlier, since metal is highly conductive. Also, sudden malfunction of the soil conductivity probe at Boyce Field recorded large variance among measured data within the site. Therefore, a different probe was used to measure the conductivity of soil at Overlook. Nine data points for each site were sufficient because the data points successfully covered various parts of each site, such as the dry part, wet part and grassy part. The research showed the initial hypothesis about the correlation between soil pH and soil conductivity was not supported. The data points of pH and soil conductivity on the three sites had low r squared values, (0.0833 on Boyce Field, 0.0872 on Farmyard and 0.1729 on Overlook) showing that correlation between pH value and soil conductivity does not exist (graph 3). To the contrast of the relatively consistent data for pH across all three sites, data for soil conductivity was inconsistent due to the sudden malfunction of the conductivity probe at Boyce Field. The malfunctioning conductivity probe resulted in inconsistent soil conductivity data at Boyce Field. This may have caused low r squared values and large error bars in graphs showing the soil conductivity. This problem can be fixed by using a working conductivity probe. Plus, not enough data was collected for Overlook. Overlook was approximately three times larger than Boyce Field and Farmyard, so more than nine data points were needed at Overlook. This problem can be solved by collecting samples from twenty seven sites at Overlook to make sure enough data points are achieved. The correlation between the land use and pH can be further justified by a future research concerning the correlation between the land use and the nitrogen level. A future experiment that measures nitrogen level of each site can be conducted. The hypothesis for the experiment would be if the land is heavily used for growing crops, then the land will have low nitrogen level because the crops consume the nitrogen in the soil.

A C K N O W L E D G E M E N TS

Without the help of several people, this KoS projeWithout the help of our teacher naturalist at Drumlin Farms, Carol, we never would have learned several key points about the habitat. This would have made interpreting the data harder, or causing us to arrive at different conclusions. As there were several outliers, Carol helped us

11

understand that these were because of particular artificial land additions (such as a dry well). Another person who made our project a success was Kelley Schultheis, our science teacher, who helped us understand the project requirements and properly conduct our experiment. This never could have happened without my partner Victor Chu, who was invaluable in both the brainstorming and experimenting processes. Thank you all for making this experiment possible. There are many people supported our Knights of Science project successfully, who should be recognized. Special thanks to Ms. Kelley Schultheis for broadening our view and fostering our curiosity in science by expanding our perimeter from land use and soil conductivity to land use, soil conductivity and pH. Many thanks to Ms. Carol, who supervised us in Overlook and gave us valuable explanations and advices when our conductivity probe malfunctioned. She led us to Ms. Wendy Svatek so that our conductivity probe could be replaced in timely fashion. Also, many thanks to Ian McJohn for successfully carrying out his part in this project, including measuring conductivity of soil and organizing the data. Thanks to my parents, Mrs. Mi Lee and Mr. Samuel Chu for encouraging me and providing a shovel for collecting soil sample.

12

W O R KS C I T E D (A U T H O R 1) Barbosa, Roberto, and Charles Overstreet. "What Is Soil Electrical Conductivity." LSU

AgCenter. Louisiana State University Agricultural Center, 2011. Web.

<https://www.lsuagcenter.com/NR/rdonlyres/E57E82A0-3B99-4DEE-99B5-

CF2AD7C43AEF/77101/pub3185whatissoilelectricalconductivityHIGHRES.pdf>.

Bickelhaupt, Donald. "Soil PH: What It Means." Soil PH: What It Means. Environmental

Information Series, n.d. Web. 09 Mar. 2014.

<http://www.esf.edu/pubprog/brochure/soilph/soilph.htm>.

Hanlon, E. A. "Soil PH and Electrical Conductivity: A County Extension Soil Laboratory

Manual 1." EDIS New Publications RSS. University of Florida, 2009. Web. 07 Mar. 2014.

<http://edis.ifas.ufl.edu/ss118>.

United States of America. U.S. Geological Survey. Impacts of Agricultural Land Use on

Biological Integrity: A Causal Analysis. By C. M. Riseng. USGS, 2011. Web. 8 Mar.

2014. <http://wa.water.usgs.gov/neet/Riseng%20et%20al_2011_Ecol%20App.pdf>.

Unknown. Trails at Drumlin Farm. Digital image. MassAudubon.org. Mass Audubon, 2014.

Web. 30 Apr. 2014. <http://www.massaudubon.org/get-outdoors/wildlife-

sanctuaries/drumlin-farm/about/trails>.

Zhao, D., Li, F., Yang, Q., Wang, R., Song, Y. and Tao, Y. (2013), The Influence of Different

Types of Urban Land Use on Soil Microbial Biomass and Functional Diversity in Beijing,

China. Soil Use and Management, Doi: 10.1111/sum.12034

13

W O R KS C I T E D (A U T H O R 2) Aguayo, Albert, and E. A. Howes. The Jthenal of Experimental Biology. Cambridge:

Company of Biologists Limited, 1990. 273-81. Print.

Grisso, Robert, Mark Wysor, David Holshouser, and Wade Thomason. "The Why and How to

Testing the Electrical Conductivity of Hanlon, E. A. "Soil PH and Electrical

Conductivity: A County Extension Soil Laboratory Manual 1." EDIS New Publications

RSS. University of Florida, 2009. Web. 07 Mar. 2014. <http://edis.ifas.ufl.edu/ss118>.

"NITROGEN: AN ESSENTIAL ELEMENT IN CROP PRODUCTION."NITROGEN: AN

ESSENTIAL ELEMENT IN CROP PRODUCTION. Nachurs Alpine Solutions, 2010.

Web. 15 Apr. 2014. <http://www.nachurs.com/nitrogen.html>.

United States of America. U.S. Geological Survey. Impacts of Agricultural Land Use on

Biological Integrity:A Causal Analysis. By C. M. Riseng. USGS, 2011. Web. 8 Mar.

2014. <http://wa.water.usgs.gov/neet/Riseng%20et%20al_2011_Ecol%20App.pdf>.

Zhao, D., Li, F., Yang, Q., Wang, R., Song, Y. and Tao, Y. (2013), The Influence of Different

The Types of Urban Land Use on Soil Microbial Biomass and Functional Diversity in

Beijing,China. Soil Use and Management, 29: 230 239. Doi: 10.1111/sum.12034

"Threats to Soil Quality." Threats to Soil Quality. Scottish Environment Protection

Agency, n.d. Web. 09 Apr. 2014.

<http://www.sepa.org.uk/land/soil/threats_to_soil_quality.aspx>.

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!

Diving into Diversity

The effect of salinity on the diversity of macro invertebrates

By Carly Newell 84-9 & Lily Denton 84-3

http://fullserviceaquatics.com/wp-content/uploads/2012/04/crayfish3.jpg

2

TABLE OF CONTEXT ABSTRACT 3 INTRODUCTION 3 MATERIALS AND METHODS 4 RESULTS 7 DISCUSSION 13 ACKNOWLEDGEMENTS 14 WORKS CITED 15

3

ABSTRACT Antecedent to the second trip to Drumlin Farm in Lincoln, Massachusetts it was

discovered that there was a main road behind a pond. The experiment was conducted to see the effect salinity has on the diversity of macro invertebrate organisms. It was assumed that the salt used on the main road behind Poultry Pond partially drained into the pond. The hypothesis was that the higher the salinity level, the less diversity there would be, because salts absorb the dissolved oxygen, which many organisms rely on for survival (Schaffner, web.vims.edu). The salinity was tested with a Vernier Salinity Sensor. The sensor was attached to a calculator and then placed into a cup full of pond water. The reading from the calculator was recorded. The organisms were collected with a net and put into a bucket to identify with a key. During the experiment, the homemade net broke and was replaced with a net provided by the farm. The salinity sensor was also not working for twenty to thirty minutes at the first pond, but the sensor was replaced once a teacher was notified. All the data was inconclusive, except Ice Pond’s data had conclusively the lowest level of salinity. INTRODUCTION Water environments can be heavily impacted by salinity. Salinity is the measurement of dissolved salts in water. It is measured in ppt. Salts are known to absorb dissolved oxygen, which most water organisms need to survive. Certain organisms are more tolerant to different salt levels. Chloride is a salt, which can be harmful to certain types of organisms. Due to the fact that there is a low flow of water during the summer and fall, chloride levels can be dangerously high, and negatively impact aquatic organisms (des.nh.gov). Once salt has entered a body of water, it cannot be removed until it is flushed downstream (des.nh.gov). The natural level of salinity in freshwater is .001 ppt (des.nh.gov). Road salts can be harmful to organisms if they enter freshwaters. Drumlin Farm, which is located in Lincoln, Massachusetts, contains ponds that are located near main roads.

At Drumlin farms there are five ponds. For this particular experiment, only three of the ponds will be used. Poultry Pond has a large exposure to salinity, due to the fact that it is located near a main road. This past winter there was a surplus of snow. To melt the ice and snow, road salts were used. There is a possibility that those salts, as well as other salts, could have leaked into the pond, increasing the salinity level. Another pond that will be tested is Ice Pond, which is located near a parking lot. This parking lot may have also used salts to melt this winter’s ice. Like Poultry Pond, the salt could have entered the water, affecting the salinity level. The last pond that will be tested is Bathtub pond, which is isolated from streets and possible road salts. It is next to Bathtub Field and Town Trail. The locations of the ponds could affect how much salinity it has.

If salinity levels are too high, they can absorb a majority of the dissolved oxygen that organisms need to survive. It has been found that road salts make some types of fish more tolerant to salinity (des.nh.gov). In other types of fish it has been found that the longer exposure the fish has to salt, the less tolerant it becomes. Some kinds of organisms are more tolerant to higher levels of chloride while other kinds are more tolerant to lower levels (Eckenfelder, www.env.gov.bc.ca). Invertebrates are more sensitive to salt than vertebrates (Siegel, www.rebulidingi93.com). Road salts can make more species less tolerant to salt, which would minimize the reproduction, and therefore be decreasing diversity (des.nh.gov). The most dangerous salts are potassium chloride and magnesium chloride. These road salts are most harmful to the organisms living in ponds. They cause organisms to have lower osmosis pressure.

4

Osmosis is the water transfer from the lower salinity of a pond to the tissue of fish. By lowering the osmotic pressure it will decrease the efficiency of the fish to remove excess water. If the salts get close to the internal tissue of the fish, the osmosis pressure will reverse and dehydrate the fish (Nimbusponds.incorporated).

This experiment will examine the effect of salinity (ppt) on the diversity of macro invertebrates in a pond. The objective of the experiment is to see if the different levels of salt affect which types of macro invertebrates live in the pond. The independent variable for this experiment is the salinity level (ppt), and the dependent variable is the diversity of macro invertebrates. Some controlled variables are the same net speed, the same net, the same person holding the net, and the testing will be done in the same day. To test the hypothesis the salinity will be measured at each of the three ponds eight times. Then the macro invertebrates will be placed into a bucket to observe. Using a key, the organisms will be identified and recorded. The hypothesis is if the salinity is high, then the macro invertebrates diversity will be low because salts absorb dissolved oxygen, which many of the macro invertebrates need to survive (Schaffner, web.vims.edu). It was predicted that Poultry Pond would have the highest salinity levels and least macro invertebrate diversity, because it is the closest pond to a road. This experiment could help teach people about the impacts that road salts have on ponds specifically in New England. It could encourage people to find other alternatives for road salts in order to melt ice. This could lessen negative human impact on animals. Eventually, someone one could create an effective way to flush salinity out of ponds more quickly. MATERIALS AND METHODS

The experiment was conducted at Drumlin Farm in Lincoln, Massachusetts. The three habitats tested were: Poultry Pond, Ice Pond, and Bathtub Pond. These three ponds were chosen to provide a variety of salinity levels based on their location and potential human and environmental influences. Poultry Pond was situated on the north side of a major road. It was possible that road salts, used this past winter to melt ice, could have leaked into the water. The second location, Ice Pond, was located near a parking lot far from the street near Poultry Pond. This parking lot, like the road by Poultry Pond, could have also contained road salts that leaked into the water. The final pond, Bathtub Pond, was next to a field and trail. This location was not near roads, which might cause its salinity level to differ from that of the other two bodies of water. In order to have no bias regarding which area of the ponds to sample, a randomization method was used. This method is called “Selecting Individuals at Random.” The ponds were circular, so the areas around the perimeter of the pond were labeled from 1-12 similar to a clock. Then a formula was plugged into the TI nspire cx calculator, which generated areas to collect the data from. In order to test the salinity levels, a Vernier salinity sensor was used. After randomizing the pond, a bucket and a plastic cup were filled up half way with pond water (Gibbs, http://www.dpi.nsw.gov.au). Once those steps were completed, the Vernier salinity sensor was attached to the top outlet of the TI-nspire cx calculator. The tip of the salinity sensor was rinsed with distilled water and dipped into the plastic cup for 30 seconds. As soon as the time was up, a reading appeared on the calculator and the salinity level was recorded. After all those steps were completed, they were repeated eight times for Poultry Pond, rinsing the probe in between each trial. The different locations in the pond, where the salinity testing was done was also where the organisms were collected. All the steps were repeated at the other two ponds, except the randomizing technique. Randomizing was difficult with the other two ponds because its surrounding bushes and trees made it hard to get to areas that were originally generated on the calculator. Due to this problem, salinity samples as well as the macro invertebrate data were

5

collected from easy to access locations on the pond. Once all the salinity testing was finished, the diversity data was collected. In order to collect macro invertebrates, a net was placed in the water and turned so that it was on its side. Then the net was moved back and forth so it was pushed to each side two times. Finally the net was brought out of the pond and placed over the bucket with pond water. The macro invertebrates were carefully dropped into the bucket. When all the organisms were in the bucket, a key was used to group and identify the different types of macro invertebrates. Then the data was recorded in a table. After that the organisms were dumped back into the pond. Next, a clean yogurt tub was filled up with pond water and poured into the bucket that had the macro invertebrates. This was done to wash out the organisms that were still in the bucket after the bucket was dumped out the first time. Then the bucket was dumped out one more time. Once all those steps were completed they were repeated eight times per pond using the same exact materials, refilling the bucket before each new trail. After all the testing was finished, the tip of the salinity sensor was rinsed off with distilled water and then dried with a paper towel.

Diagram 1: The areas where data was collected from at Poultry Pond

Diagram 2: The areas where data was collected from at Ice Pond

6

Diagram 3: The areas where data was collected from at Bathtub Pond

Diagram 4: Vernier Salinty Sensor

Diagram 5: Distilled water

7

Diagram 6: TI nspire calculator RESULTS Table 1: The effect of pond on salinity levels (ppt)

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Table 2: The effect of organism types on amount of organisms in Poultry Pond

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Table 3: The effect of organism type on amount of organisms in Bathtub Pond

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Table 4: The effect of organism type on amount of organisms in Ice Pond

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Graph 1: The effect of pond on salinity levels (ppt)

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Graph 3: The effect of organism type on amount of organisms in Bathtub Pond

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Graph 4: The effect of organism type on amount of organisms in Ice Pond

Graph 5: The effect of pond on total number of organisms

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Graph 6: The effect of salinity levels (ppt) on total number of organisms in each pond

Graph 1 shows the salinity levels of three ponds: Ice Pond, Bathtub Pond, and Poultry Pond. Ice Pond has the lowest salinity average of 0.0019 ppt and the smallest error bar with a standard deviation of 0.0006 ppt. Bathtub Pond has the highest salinity average of 0.0194 ppt. Poultry Pond had an average of 0.0160 ppt; only 0.0034 ppt smaller than the average of Bathtub.

Graph 2 illustrates the number of each type of macro invertebrates in Poultry Pond. The water in this pond was a murky green. There were trees that had fallen into the water (See figure 1 below). The most common type of organism in Poultry Pond was copepods (average of 13.1). Copepods also have the largest error bars with a standard deviation of 6.6. Dalpha has the second highest average of 2.4 organisms. Its standard deviation of 2.3 is also the second highest. Its error bar overlaps with all the other organisms’ bars except Copepods. Mayfly, Damselfly, and Water Boatman all have the lowest average (0.1 organisms). Graph 3 displays the amount of each type of macro invertebrate in Bathtub Pond. This pond had the most frozen water surface out of the three ponds that were visited (see figure 2 below). There were only three organisms found in the pond, and all of their averages were under one. The highest average (0.4 organisms) was that of the Phantom Midge Larva. Next were the Copepods with 0.3 organisms, then the Soldier Fly Larva with 0.1. Unlike Poultry Pond’s data, all of the error bars overlapped with one another. The greatest standard deviation belonged to the Copepods. The Soldier Fly Larva had the smallest error bar with a standard deviation of 0.4. The Phantom Midge Larva was in the middle with a standard deviation of 0.5. Graph 4 exhibits the number of the types of organisms in Ice Pond. Ice Pond, like Bathtub Pond, had ice covering about a third of the surface water. Unlike Bathtub Pond, most of the ice was melting away (see figure 3 below). Four organisms were found at this pond. The Phantom Midge Larva had the highest average (1.6 organisms) and also the largest standard deviation of 2.4. The Copepods were second with a final average of 1.5 organisms, only a .1

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12

difference from the Phantom Larva. The third lowest average (0.8 organisms) belonged to the Isopods. The organism with the lowest average (0.4 organisms) and smallest standard deviation of 0.7 belonged to the Crayfish. Ice Pond’s data was similar to the data of Bathtub Pond as all the error bars overlapped with one another. Graph 5 demonstrates the correlation between the total number of organisms and pond. Poultry Pond had the largest average (2.2 organisms), and the largest standard deviation of 4.9. This ends up making it have the largest error bar, which overlaps with the error bars of the other two ponds. The second largest average (1.1 organisms) belonged to Ice Pond. Bathtub Pond had the smallest average of 0.3 organisms, but also ends up having the lowest standard deviation of 0.5. Graph 6 represents the relationship between the total numbers of organisms found at each pond on salinity levels. The r2 values for the ponds were all below 0.01. Poultry Pond’s points on the graph were not consistent and did not seem to fit the trendline. Poultry Pond, however, seemed to have highest number of organisms (highest point above 25). The lowest point for Poultry Pond was just above 5. The r2 value for Poultry Pond was 0.01201. Ice Pond, as stated before, had the lowest amount of salinity, and therefore the pond’s points are all below 0.005 ppt. This also makes them have the smallest trendline and r2 value (0.00236). Ice Pond and Poultry Pond both have eight data points while Bathtub only has seven. Bathtub Pond has the largest range of salinity levels. It has the second highest r2 value of 0.00617. This pond has the lowest total amount of organisms, the highest being just below five.

Figure 1: Poultry Pond Figure 2: Bathtub Pond

13

Figure 3: Ice Pond DISCUSSION

The goal of this experiment was to see if there was a correlation between the salinity levels and the diversity of organisms in Drumlin Farm ponds. The hypothesis for this experiment was: if the salinity is high, then the macro invertebrates diversity will be low because salts absorb the dissolved oxygen in water which many of the macro invertebrates need to survive (Schaffner, web.vims.edu). It was assumed that Poultry Pond would have the highest salinity levels. This hypothesis was not supported fully by the collected data. Salinity depletes the amount of dissolved oxygen, which has a direct affect on the diversity of organisms in fresh water. Most organisms need dissolved oxygen to live (Gross, Oceanography: A View of Earth). A study done in 2012, by the Institute of Maine Research, testing the effect temperature and salinity have on macro invertebrate diversity discovered that salinity does affect the macro invertebrate species (Brucet, plosone.org). The study stated that too much salinity resulted in a lower macro invertebrate diversity (Brucet, plosone.org). The data gathered in this experiment did not support the research conclusions. Ice Pond had conclusively the lowest salinity, and the second lowest average of number of organisms. The low salinity Ice Pond exhibited may be due to the cold temperatures of the season, and the remote location of the pond as there could be a reduced amount of salt run off absorption (Arnold, eesc.columbia.edu). Located near a busy road, Poultry Pond had the second highest average salinity, in contrast with the highest number of organisms. Since road salts run off congested streets, it was expected that Poultry Pond would have the highest salinity levels (des.nh.gov). A partially frozen Bathtub Pond had the highest average of salinity, while also having the lowest number of organisms. Only Bathtub Pond’s levels matched the hypothesis, but not conclusively. In graph 6, the effect of salinity levels on total number of organisms in each pond, there was little correlation between the two variables. Poultry Pond’s plots were the most scattered of the three. The r2 value for this pond was 0.012, which is too low for any conclusions to be made. The data points for Ice Pond were all on the low end of the graph, which means this pond had the lowest amount of salinity. Bathtub Pond’s data points were consistently low, reflecting the pond’s low organism count. The salinity points, however, showed a variation of values ranging from 0.0371 ppt to 0.0037 ppt. Both the r2 values for Ice and Bathtub pond were lower than 0.007 ppt, which reflects a weak correlation. The results of the data collection were heavily compromised by investigator errors and geographic challenges. Almost all of the graphs were inconclusive since the error bars

14

overlapped. For example, in graph 4, the effect of organism type on amount of organisms in ice pond, all the bars’ error bars overlapped with each over, which means that no strong conclusions can be made. The validity of the results should be viewed with caution. Many scientists would not be confident in drawing conclusion from the data. If this experiment were to be replicated, several modifications would need to be made. To begin, the investigator should make sure that the salinity sensor is working correctly. In the original experiment during our data collection, the salinity sensor was faulty and needed to be replaced. This caused the sensor to give incorrect readings for some of the data collections. An accurate instrument would have guaranteed more reliable data. Season of data collection should also be taken into consideration. Cold temperatures and residual ice on the ponds will affect the number of living organisms found (Brucet, plosone.org). Perhaps the experiment could be conducted in late spring when the ice on the ponds has fully melted. Colder water temperatures can limit the number of macro invertebrates found in a body of water (Brucet, plosone.org). The terrain affected the investigators’ access to all data collection spots. Specifically, thorns bushes and fallen trees had to be navigated. If these challenges had been addressed earlier, the investigators could have collected data from a broader range of locations. There are several lingering questions to address such as: If the procedure were followed correctly, would the experimenters have gotten more conclusive data? Also, if the experimenters had been able to reach all the data collection spots, would the experimenters find different macro invertebrate species? Despite collection challenges, sufficient data (24 data points) was collected for analysis. It is not clear that additional data points would have lead to different conclusions in this case. Environmentalists need accurate data about salinity and the effect salinity has on the organisms living in ponds. This could lead to road salt regulations in areas where bodies of fresh water are surrounded by roads. Lincoln, Massachusetts, the town where Drumlin Farm is located, does use road salts (lincolntown.org). If the data from this experiment could be compared with data collected from another town’s pond that does not use salts, then scientists could see if the amount of road salt used does actually affect the diversity of macro invertebrates. Researchers may be interested in using this type of study as a springboard for more research about low impact road salt practices. ACKNOWLEDGMENTS: Author 1

I would like to thank all of the people who helped me with my project, Mr. Rossiter, Ms. Schultheis, Lily Denton, Mr. Dwyer, and Ms. Svatek. Whether it was calling another teacher for me, or bringing me a new salinity probe, or something else. They each helped me in one way or another and I would not have been able to do the project without them. Thanks again to all those who helped. ACKNOWLEDGMENTS: Author 2 I would like to thank my partner Carly for her contributions to this project. She wrote half the final report and took all the salinity samples for our data. Also, I would like to thank the environmentalist at Poultry Pond, Catherine. Without her, I would have not been able to identify half of the macro invertebrates I ended up catching. At last, I want to thank my science teacher Ms. Schultheis. She got our group prepared for our data collection day at Drumlin Farm. When our salinity sensor broke, she came to our data collection spot and helped us fix it. Without these three people, this experiment / final paper could not have been possible.

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WORKS CITED: Author 1 Eckenfelder, Margaret. "Ambient Water Quality Guidelines for Chloride."

Ambient Water Quality Guidelines for Chloride. Province of British Columbia, 2013. Web. 7 Mar. 2014. <http://www.env.gov.bc.ca/wat/wq/BCguidelines/chloride/chloride.html>.

"The Free Automatic Bibliography and Citation Generator." EasyBib. N.p., n.d. Web. 12 Mar.

2014. <http://www.easybib.com/>. Gibbs, Simon. How Do I Test Water Salinity? Condobolin: NSW Agriculture, 4 Oct. 2000. PDF. Gross, Grant, and Elizabeth Gross. A View Of Earth;. Saddle River: Simon & Schuster A

Viacom, 1972. Print. "Rebuilding I93: Salem to Manchester (NHDOT) — Home." Rebuilding I93: Salem to

Manchester (NHDOT) — Home. NHDOT Home, 12 Feb. 2014. Web. 7 Mar. 2014. <http://www.rebuildingi93.com/>.

"Salinity." Salinity. Department of Biological Sciences, n.d. Web. 7 Mar. 2014.

<http://web.vims.edu/bio/shallowwater/physical_characteristics/salinity.html>. "Water Quality Impacts - Environmental, Health and Economic Impacts of Road Salt - New

Hampshire Road Salt Reduction Initiative - Watershed Assistance Section - NH Department of Environmental Services." Water Quality Impacts - Environmental, Health and Economic Impacts of Road Salt - New Hampshire Road Salt Reduction Initiative - Watershed Assistance Section - NH Department of Environmental Services. Imagine Easy Solutions, LLC, 2012. Web. 7 Mar. 2014. <http://des.nh.gov/organization/divisions/water/wmb/was/salt-reduction-initiative/impacts.htm>.

"Water Testing » Pond Water Chemistry » Salinity in Ponds." Salinity in Ponds. Nimbus Pond

Inc, 2014. Web. 7 Mar. 2014. <http://www.nimbusponds.com/Pond-Water-Chemistry/Salinity-in-Ponds-p-48.html>.

WORKS CITED: Author 2 Brucet, Sandra. "Effects of Temperature, Salinity and Fish in Structuring the Macroinvertebrate

Community in Shallow Lakes: Implications for Effects of Climate Change." PLOS ONE:. PLOS, 2012. Web. 15 Apr. 2014. <http://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.p one.0030877>.

Gordon, Arnold L. "The Climate System." Ocean Stratification. Columbia University, 2004. Web. 30 Apr. 2014. <http://eesc.columbia.edu/courses/ees/climate/lectures/o_strat.html>.

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Gross, Grant, and Elizabeth Gross. Oceanography: A View of Earth. Upper Saddle River: Simon

& Schuster, 1972. Print. Schaffner, Linda. "Salinity." Salinity. Virginia Institute of Marine Science, 2013. Web. 15 Apr.

2014. <http://web.vims.edu/bio/shallowwater/physical_characteristics/salinity.html>. "Welcome | NH Department of Environmental Services." Welcome | NH Department

of Environmental Services. New Hampshire Department of Environmental Services, 2014. Web. 15 Apr. 2014. <http://des.nh.gov/>.

"Welcome to an Engaged Community." Lincoln, MA. Town Of Lincoln, 2014. Web.

16 Apr. 2014. <http://www.lincolntown.org/index.aspx?nid=403>.

Percol-H

The effect of soil percolation on soil pH

Elisa Tabor and Shayan Olumi

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TABLE OF CONTENTS

SECTION AUTHOR PAGE _______________________________________________________________________________________________________________________________________ ======================================================================================================================

Abstract Olumi 3

Introduction Olumi 3

Materials & Methods Olumi 4

Results Tabor 7

Discussion Tabor 10

Acknowledgements Olumi & Tabor 11

Works Cited Olumi 12

Works Cited Tabor 13

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ABSTRACT Soil pH can greatly affect how a plant grows. Some plants will lose crucial elements from the soil being too acidic. The experiment tested whether soil percolation can affect the soil pH, which could also potentially affect plant growth. Soil samples were taken and tested for their pH levels along with their percolation times. This data was then analyzed to see if there is a connection between soil percolation and soil pH in Hemlock Forest and the Farmyard, which are within Drumlin Farms, Lincoln, MA. It was thought that if the percolation times were lower then the soil would be more acidic in the locations that were tested. The results displayed that there is no significant correlation between the two variables. Hemlock Forest and the Farmyard had a very close average pH level; Hemlock had an average pH level of 6.8, and the Farmyard had an average pH of 7. Both of the habitats had r2 values of less than 0.1 in the graph that shows the effect of percolation on pH. INTRODUCTION

For a number of years soil acidity has been an issue within the Northeast Region in the United States and Eastern Canada. The Northeast Region tends to have soil that has a pH level of six, which is acidic. If need be there are ways to adjust the pH, however these ways can have drawbacks. If soil is too acidic then elements, such as aluminum, can dissolve more easily, which blocks the water intake from the roots to the plant or tree, due to the blocked nutrients that cannot get out of the roots. Soil pH is the measure of how acidic or alkaline soil is (Rengel, Handbook of Soil Acidity; Bruulsema, Northeast Soil Fertility). Soil percolation is a soil’s ability to let water drain through. If a cup of water is poured into soil and it drains rather quickly than the percolation is very high. If the time is very high than the percolation is low. One factor that affects percolation is the amount of pore space in the soil.

Soil pH levels can greatly affect the trees that grow in a habitat. If the pH levels are too low then the trees will not be able to take in the necessary nutrients such as nitrogen, phosphorus, and potassium. If the levels are too high then other elements won’t be able to be absorbed such as, iron, manganese, and phosphorus. The appropriate levels for pH are 6.0 - 7.5 (Unknown, http://www.agriinfo.in). There are many other factors that affect the soil pH such as rainfall, climate temperature, and vegetation. When there is heavy rainfall, the soil tends to be more acidic due to the more alkaline nutrients the rain takes out, which are replaced by more acidic nutrients. (Unknown, http://www.agriinfo.in). The more vegetation there tends to be, the lower the percolation (Unknown, http://www.visualrealization.com).

The experiment will be conducted at Drumlin Farm, which is located in Lincoln, MA. Drumlin Farms is a wildlife sanctuary that is devoted to protecting the nature of Massachusetts. There are multiple different locations at Drumlin Farms such as forests, ponds, and open fields. These locations contain various types of soil that have large ranges in textures and colors. The experiment will be done at these locations: Hemlock Forest and the Farmyard. Based on the inhabitants of these locations, the infiltration of the water is affected. When animals, humans, or cars go over the top layer of the soil, it becomes more compact which makes it harder for the water to infiltrate the beginning levels of the soil (Unknown, www.organicgardening.com).

The plan of this experiment is to test the effect of soil percolation on soil pH. The objective is to learn whether soil percolation will affect the levels of pH within the soil. When more water passes through the soil, more acidic elements be dropped in the soil. More acidic material from passing water means a lower pH count. Soil samples will be collected from Hemlock Forest and the Farmyard. The independent variable is the soil percolation, while the

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dependent variable is the soil pH. Some controlled variables are the depth at which the auger collects soil from the ground (cm), and the pH test pills used (RapiTest). Other control variables are the procedures for measuring the pH and the percolation, along with the materials used during the data collection. The hypothesis for this experiment is if the soil percolation is higher, then the pH will be lower, because when more water percolates through the soil it becomes more acidic due to the basic nutrients dissolved in the water that are taken out of the soil (Unknown, www.organicgardening.com). When more rain passes through the soil, acidic nutrients are dissolved in the soil while the rainwater absorbs the basic nutrients (Unknown, http://hubcap.clemson.edu). This experiment demonstrates how soil percolation can affect the soil pH. There are many farms across the country that could greatly benefit from this research. When the soil pH isn’t within the levels of 6.0-7.5 then the trees will not be able to gain the necessary nutrients to thrive. There are many methods to make the soil more acidic, such as liming the soil, or the farmers could spread wood ashes, which contain calcium carbonate, potassium, and phosphorus to make the soil more basic. Understanding the percolation effect on soil pH could help farmers decide where to plant their crops without doing numerous tests. Tests could be conducted, then the correlation between the pH and percolation would be discovered which would potentially benefit several farmers. MATERIALS & METHODS

The locations that were visited were Hemlock Forest and the Farmyard which are located at Drumlin Farms in Lincoln, MA. Twenty-four locations were numbered in the forest at random within an area of fifteen by fifteen meters at both Hemlock Forest and the Farmyard. The horizontal and vertical lengths of the enclosed area were taken then put into the randomizer function on the TI-nspire calculator to formulate a grid. The points were then used as a graph to find out which specific spots would be visited.

After the specific spots were chosen, 7.57 liters of water was gathered. Two three hundred eighteen mL tin cans that had the top and bottom removed were collected. Next, marks were put on the tin can that were three centimeters above the bottom. The tin can was then put in the ground until the soil reached the three centimeter mark. Two hundred seventy-six mL of water was poured into the can, and the timer was started. Once all the water was drained, the timer was stopped, and the time was recorded in the data table. If the water did not percolate into the soil within twenty minutes, the time was stopped in the interests of time. These steps were repeated twelve times at each forest location for a total of twenty-four data points.

Next, soil was collected from about seven centimeters deep in the ground with an auger. Soil was filled in the testing comparator up until the “fill soil line” on both sides. The capsule was then broken over the pH testing container, pouring the powder into the smaller side. Next distilled water was added until the “fill water line” on both sides, and the container was shaken for a minute. After the color developed, it was compared with the chart on the comparator. These steps were repeated twelve times at each of the locations.

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Figure one contains all the materials needed for this experiment: -Ruler -Timer -Auger -Liter bottles -Tin can with top and bottom removed -Soil pH testing kit -Green pH testing capsules

Figure 2 shows the first testing area, Hemlock Forest, which is located at Drumlin Farms, Lincoln, MA

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Figure 3 shows the percolation test being conducted at Hemlock Forest

Figure 4 shows the soil pH test being conducted at Hemlock Forest

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RESULTS Table 1: The effect of location on soil percolation time (seconds) and soil pH.

Table 2: The effect of soil pH on soil percolation time (seconds).

Soil Percolation Time

(seconds)

Soil pH Average Standard deviation

6.5 684 443

7 643 496

7.5 750 508

Location Variables Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 6

Trial 7

Trial 8

Trial 9

Trial 10

Trial 11

Trial 12 Average Standard

Deviation

Hemlock Forest

Soil Percolation

Time (seconds)

420 3 22 270 337 160 450 1200 28 90 1177 826 415 431

Soil pH 6.5 6.5 7.0 7.0 6.5 7.0 7.0 6.5 7.5 7.0 7.0 6.5 6.8 0.3

Farmyard

Soil Percolation

Time (seconds)

1200 1210 510 800 1200 971 380 921 1200 448 1200 1200 937 326

Soil pH 6.5 6.5 6.5 7.5 7.0 7.5 7.0 7.0 7.0 6.5 7.5 7.0 7.0 0.4

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Graph 1: The effect of soil percolation time (seconds) on soil pH based on location.

Graph 2: The effect of soil pH on soil percolation time (seconds).

R! = 0.08216 R! = 0.03592

6.4

6.6

6.8

7.0

7.2

7.4

7.6

0 200 400 600 800 1000 1200 1400

Soil

pH

Soil Percolation time (seconds)

Hemlock

Farmyard

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Graph 3: The effect of habitat on soil pH.

Graph 4: The effect of habitat on soil percolation time (seconds).

Graph 1 shows the relationship between soil percolation and soil pH in each habitat. At the Hemlock Forest, the soil pH was more acidic when the percolation time increased, while at the Farmyard the soil pH was more basic with increasing percolation time. The r2 values were less than 0.1 for both habitats. The percolation test results ranged from 3 to 1200 seconds, and the soil pH values were between 6.5 and 7.5. At the Farmyard the soil was very compact, resulting in multiple percolation times of 1200 seconds.

Graph 2 shows the change in soil percolation times based on the pH. The three error bars are large and overlapping, which indicates that the data are imprecise. The soil with a pH of 7 had the shortest average percolation time of 643 seconds, while the soil with a pH of 7.5 had the longest average percolation time of 750 seconds. The data for the soil with a pH of 6.5 had the smallest error bar, and therefore the most precise data.

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Graphs 3 and 4 display the effect of habitat on the two variables, soil pH and percolation, independently. In both graphs the error bars are large and overlapping, which indicates the data were imprecise. In graph 3, the pH average for Hemlock Forest was 6.8, while for the Farmyard it was 7.0; this shows the Hemlock soil had a more acidic average pH. Graph 4 shows that the soil percolation average time was 415 seconds in the Hemlock Forest, and 937 seconds in the Farmyard. The average soil percolation time at the Farmyard was more than twice that at Hemlock Forest. The error bar for the Hemlock Forest was larger; in fact, it extends below 0 because it is larger than the average percolation time. DISCUSSION

In this experiment, the objective was to discover whether the percolation of soil affected its acidity. The hypothesis was that if the soil percolation is higher then its pH will be lower, because when more water percolates through the soil it becomes more acidic due to the basic nutrients dissolved in the water that are taken out of the soil (Unknown, www.organicgardening.com). This hypothesis was not supported due to the imprecision of the data collected, shown the overlap of all error bars and the low r2 values.

The results shown in graph 1 were inconclusive, since the trend-lines have opposite slopes. The Hemlock Forest trend-line supported the hypothesis whereas the Farmyard trend-line did not. The soil pH was higher at the Farmyard, while it was lower in the Hemlock Forest (graph 3). This could be because the Hemlock trees, being conifers, emit chemicals that lower the pH of the soil (Bickelhaupt, www.esf.edu). A new hypothesis can therefore be formed: if the Hemlock Forest soil is tested, then the soil pH will be lower because it consists of conifer trees, which emit chemicals to increase the acidity of the surrounding soil (Bickelhaupt, www.esf.edu).

Percolation is affected by the texture of the soil, e.g. whether it is mostly clay, which is less percolating, or sand, which is more percolating, or silt, which has intermediate percolation (Prowebs, www.agriinfo.in). Another factor affecting the percolation time is when silt and clay particles are mixed to form soil grains, micro-organisms decompose the organic material, which increases percolation rates (Reeves, www.walterreeves.com). The soil in the Farmyard consisted of a higher amount of clay (graph 4). It was also less percolating because animals such as chicken and sheep graze over the field, walking repeatedly over the soil and compacting it. This leads to a new hypothesis: if the Farmyard soil is tested, then the soil will have a higher percolation time because the Farmyard soil is compact from the repeated grazing of animals (Hümann, www.sciencedirect.com).

None of the results have high precision, as indicated by the fact that all the error bars are large and overlap, and the r2 values are less than 0.1. This impacts the confidence in the data, and shows that there aren’t sufficient data to have a high degree of confidence in the results. The low precision is due to the variety of the soil within each habitat; e.g., measurements observed just a few meters apart varied from percolating entirely in 28 seconds to taking over 1200 seconds. This was probably due to the dry leaves and twigs covering the area, which can change the moisture in the soil and its exposure to the elements.

In retrospect, the location of the measurements should have been one of the independent variables, and the pH should have been the independent variable rather than the percolation. A greater amount of data should have been collected to increase the accuracy of the results. Additional scientists and increased time and resources would be helpful in order to have obtained additional data. The methods used for this experiment were laborious and inherently imprecise, which resulted in many errors. In the method used to measure percolation, if a larger can were

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used along with a greater amount of water this would increase the accuracy of the experiment because it would cover a larger testing area. Also, during this experiment, an error occurred when the water poured into the can overflowed and some dripped out of the can. This would not happen with a larger vessel. However, this modification would have increased the time required for each measurement. For the pH testing, using a probe to measure the pH digitally would have been much more precise, rather than relying on the judgment of the scientists to match the color of the pH solution with the labels on the container.

Several questions remained unanswered, such as why the soil percolation time varied so much within one meter (from 28 to 1200 seconds), how different would the results be if the experiment had been conducted in the fall, and what else affects the percolation and the pH?

This experiment could be repeated in different locations, with different types of soil, such as near a pond or in a different forest, where the soil might have a greater variation in pH. Other variables could also be tested relative to soil percolation or pH, such as soil conductivity or the amount of soil nutrients to discover what effect those variables have on the environment and the nutrients available to plants. ACKNOWLEDGEMENTS

This whole project would not have been possible if it wasn’t for the amazing people who helped us along the way. Kelley Schultheis provided many materials such as the soil pH test kits along with vital knowledge that helped us conduct the experiment. Aria Olumi and Sareh Parangi helped with collecting materials such as the water and tin cans. Ms. Bomfim watched over us and helped when it came to fixing some materials. The biggest thank you goes out to Drumlin Farms for allowing us to use their facilities and providing such a nice learning environment.

This project would not have been possible without the help of individuals who supported us in our preparation for Drumlin Farm and during our time there. Kelley Schultheis, our science teacher, helped us throughout the experiment with brainstorming, writing of the initial drafts, and with providing us with all necessary materials. At Drumlin Farm, the BB&N teachers and the on-site naturalists provided indispensable help with any questions we had concerning the experiment or the habitats. Finally, I would like to thank my partner, Shayan, for contributing greatly to the entire experiment.

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WORKS CITED AUTHOR 1

Bruulsema, T.W. Ipni. N.p.: n.p., n.d. PDF.

Group 1D. Soil Properties. N.p.: n.p., n.d. PDF.

Mitchell, Charles C. "Soil Acidity and Liming (Overview)." Soil Acidity and Liming (Overview).

Hubcap, n.d. Web. 08 Mar. 2014.

<http://hubcap.clemson.edu/~blpprt/acidity2_review.html>.

"My Agriculture Information Bank - Absorption and Movement of Water in Soil - Water

Intake." My Agriculture Information Bank - Absorption and Movement of Water in Soil -

Water Intake. My Agricultural Information Bank, n.d. Web. 08 Mar. 2014.

<http://www.agriinfo.in/?page=topic&superid=1&topicid=5>.

Rengel, Zdenko. Handbook of Soil Acidity. New York: Marcel Dekker, 2003. Print.

"Soil Percolation Rates." Home Page. Tree People, n.d. Web. 02 Apr. 2014.

<http://www.treepeople.org/soil-percolation-rates>.

Soils and Percolation. Washington: Washington University, n.d. PDF.

"Soils Part 2." Plant & Soil Sciences ELibrary. Plant & Soil Sciences ELibrary, n.d. Web. 7 Mar.

2014.

<http%3A%2F%2Fpassel.unl.edu%2Fpages%2Finformationmodule.php%3Fidinformati

onmodule%3D1130447039%26topicorder%3D10%26maxto%3D10>.

Taylor, Carrie. Investigating Soil. Montana: Montana State University, n.d. PDF.

"Understanding PH." What Is Soil PH and What Does It Mean?: Organic Gardening. Organic

Gardening, n.d. Web. 08 Mar. 2014. <http://www.organicgardening.com/learn-and-

grow/understanding-ph>.

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What Are Soils. University of St. Thomas, n.d. Web. 7 Mar. 2014.

<http://www.stthomas.edu/geography/faculty/kelley/physgeog/soils/soil%20intro/physica

lproperties.html#waterholdingcapacity>.

AUTHOR 2

Bickelhaupt, Donald. "Soil PH: What It Means." ESF. State University of New York College of

Environmental Science and Forestry, 2014. Web. 12 Mar. 2014.

<http://www.esf.edu/pubprog/brochure/soilph/soilph.htm>.

Hümann, Marco. "Identification of Runoff Processes – The Impact of Different Forest Types and

Soil Properties on Runoff Formation and Floods." ScienceDirect. Elsevier B. V., 2011.

Web. 15 Apr. 2014.

<http://www.sciencedirect.com/science/article/pii/S0022169411006329>.

Leung, Anthony K. "Effects of Soil Density on Grass-induced Suction Distributions in

Compacted Soil Subjected to Rainfall." Canadian Geotechnical Journal. 17 Dec. 2013.

Web. 15 Apr. 2014. <http://www.nrcresearchpress.com/doi/abs/10.1139/cgj-2013-

0221#.U02nq15aacc>.

Londo, Andrew J., John D. Kushla, and Robert C. Carter. N.p.: Southern Regional Extension

Forestry, Jan. 2006. PDF. <http://www.lsuagcenter.com/NR/rdonlyres/3E784F3F-0B26-

44E9-958D-3C31CB911EFD/69963/SoilpH.pdf>. PDF File.

Prowebs. "Absorption and Movement of Water in Soil." My Agriculture Information Bank.

AgriInfo, 2011. Web. 02 Mar. 2014.

<http://www.agriinfo.in/?page=topic&superid=1&topicid=5>.

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Reeves, Walter. "Soil – Percolation Rate." Walter Reeves: The Georgia Gardener. The Simple

Gardener, Inc., 2011. Web. 02 Mar. 2014.

<http://www.walterreeves.com/landscaping/soil-percolation-rate/>.

"Understanding PH." Organic Gardening. Rodale Inc., 2014. Web. 12 Mar. 2014.

<http://www.organicgardening.com/learn-and-grow/understanding-ph?page=0,0>.

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""""""T A B L E O F C O N T E N TS "

Section Author(s) Page #

Abstract Michelle Tang 2

Introduction Ben Ross 2-3

Materials & Methods Michelle Tang 3-6

Results: Tables, Graphs, Summary Ben Ross 7-8

Discussion Michelle Tang 8-9

Acknowledgements Ben Ross 9-10

Acknowledgements Michelle Tang 10

Works Cited Ben Ross 11

Works Cited Michelle Tang 12-13

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A BST R A C T

This experiment was conducted in order to discover how human influence affects the potassium level of soil, and therefore have an impact on the environment. The procedure for this experiment, conducted at Drumlin Farm in Lincoln, MA, was to take samples of soil from habitats maintained by humans in different ways, and test its potassium levels. The points examined at each habitat were randomly generated using a TI-Nspire CX graphing calculator. It was expected that the soil from Boyce Field would have the least amount of potassium, because when plants are harvested, the nutrients are not returned to the soil to replenish the potassium.

was that the Farmyard had conclusively more potassium than the Hemlock Forest. This may be due to the fact that chicken and sheep manure are a good fertilizer and add organic matter into

relatively long because each soil sample was collected in different sections with varying human influence, whereas the error bars for the Hemlock Forest were short because the habitat was mostly uniform. IN T R O DU C T I O N

Humans alter soil to be more fit for plant growth, grazing, or just to keep the land healthy with different nutrient supplements. Potassium is a soft silver metal and a macronutrient in soil, meaning a greater volume of it is needed to sustain healthy plant life (yale.edu). When potassium levels in soil get too low plants cannot grow at a healthy rate and at a point plant life is not possible (home.howstuffworks.com). Potassium can be affected by composts, chemical fertilizers, manure, wood ashes, and granite and organic matter in soil (scifun.org; Penhallegon, extension.oregonstate.edu). Farmers and gardeners can add these substances to get a potassium level fit for how the land is being used. In nature, potassium levels are most likely to be lower in areas with high plant density because it is being used up, and higher in a rockier soil (Dye, Britannica.com). On farms, humans add fertilizers and irrigation systems that effect the potassium level. Potassium is a nutrient essential to plant life that can be altered and controlled in soil by people. Drumlin Farm, in Lincoln, Massachusetts, uses land for farming, raising livestock, natural forests. At Drumlin Farm different areas that are influenced to different degrees were tested. Boyce field, Farmyard area, and Hemlock forest will all be examined. Boyce field is used for gardening and is constantly managed. Only green fertilizers and cover crops are used to add nutrients to the soil at drumlin farm (www.massaudubon.org). Farmyard field is mowed for sheep to graze. Hemlock forest is a dense forest with lots of shrubs and minimal human influence. How these areas are treated will affect the potassium levels in the soil. If potassium levels get to be too low, the plant life in that area will not be healthy. For this reason farmers try to control potassium levels in soil, so their crops will grow and their livestock can graze. This is difficult for farmers because when crops are harvested no nutrients are returned to the soil as they would be in the wild (Shakhashiri, scifun.chem.wisc.edu). When farmers are adding fertilizers to soil, they are replenishing the nutrients that in nature would be returned through dead plants, so the potassium is similar or lower than the potassium level would be in nature. Grass fields, similar the farmyard area, usually have consistently higher potassium because when the fields are mowed the nutrients from the grass clippings help keep the potassium level steady when they decompose and are absorbed into the soil (MacKintosh, www.snh.org.uk). The sheep manure also gives back some of the potassium retracted from the

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soil. Forest habitats that have been left to grow need lots of potassium to survive because 0.5-2% of a britannica.com). If the plants are bigger than they will use more potassium. The rocks in the soil will add potassium, as well as dead shrubbery, but this potassium is quickly used up. The objective of the experiment is to explore to find out how humans affect potassium the potassium levels in soil. The independent variable is the degree of human influence. The dependent variable is the potassium level in the soil. To conduct the experiment soil samples will be taken from randomly selected points at each site. The soil samples will then be brought back to the lab and the potassium levels will be tested. While conducting the experiment the day of collection, the depth the soil is collected at, the storage temperature of the samples, the time samples sit, and amount of distilled water added to soil samples must be controlled to get more accurate results. The hypothesis is if the soil from Boyce Field is tested, then it will have the least amount of potassium, because plants are harvested and the nutrients in the plants are not returned to the soil to replenish the potassium (Morgan and Connolly, www.nature.com).

The workers and volunteers at Drumlin farm need to know how they are effecting the soil and if the soil is unhealthy. All farmers need to know if they are creating a healthy soil ecosystem. By finding trends in different levels of influences, farmers can know if their soil is healthy without having to do as many soil tests. This experiment could also determine in humans need to care for the soil in forests because the soil lacks proper nutrients, causing unhealthy plant growth. If people know what types of land have higher or lower potassium levels and people can choose different fertilizers to fertilize the soil accordingly. This experiment will allow for a healthier soil ecosystem and healthier plants in farms. M A T E RI A LS & M E T H O DS

The three habitats (Boyce Field, Farmyard, Hemlock Forest) that were tested at Drumlin Farm in Lincoln, MA are affected and managed by human actions in different ways. The farmyard (figure 1) is mowed and maintained for sheep s, whereas Boyce field (figure 2) is reserved for vegetables and fruits to grow and prosper naturally, and lastly, Hemlock Forest (figure 3each habitat using a TI-4).The numbers randomly generated were the amount of strides taken from the starting point. The pairs of randomly generated numbers were the xy coordinates used to determine the soil sample locations (Wilson, http://oregonstate.edu). This randomizing technique was used to

www.ma.utexas.edu). For every one of the ten random points, a 30 cm by 8 cm hand shovel, inserted 5 cm beneath the ground, was used to collect the soil samples. The soil from the shovel was then put into a 118 mL plastic Glad container until it reached the 40 mL mark. Afterward, 80 mL of distilled water was poured into a container and applied into the container of soil. Subsequently, the Vernier Potassium Ion-Selective Electrode (figure 5) was submerged into the mixture past the white dot, and was stirred in the soil sample for 15 seconds (www.vernier.cz). When the numbers on the Vernier Labquest 2 (figure 6) leveled out, the results were recorded in a data table (www2.vernier.com). In each habitat, ten trials were conducted. All these steps were repeated with all thirty of the soil samples from Boyce Field, Hemlock Forest, and Farmyard.

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Figure 1: The Farmyard with chickens and sheep.

Figure 2: Boyce Field. It is split into many strips of land reserved for different crops.

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Figure 3: Hemlock Forest. The forest is mostly uniform.

Figure 4: TI-

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"Figure 5: The Vernier Potassium Ion-Selective Electrode. The probe was submerged into the soil samples past the white dot.

"Figure 6: The Vernier Labquest 2.

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R ESU L TS Table I: The effect of human influence on potassium (mg/L) in soil

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Graph I: The effect of human influence on potassium (mg/L) in soil

" " "" Graph I shows the effect of human influence on the potassium level in the soil. the average potassium level for high human influence (Boyce Field) was 2.4 mg/L, the average potassium level for medium human influence (Farmyard) was 4.7 mg/L, and the average potassium level for low human influence (Hemlock) was 1.4 mg/L. Hemlock (low) was the most precise with a standard deviation of

r bars. Farmyard (medium) was the least precise with a standard deviation of 2.3 and had error bar overlap with Boyce field (high). Boyce

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field was more precise than farmyard, but still not very precise with a standard deviation of 1.7. Boyce raph.

During the experiment unique aspects of each site were observed. At Boyce field the area was split up into sections with different crops and soil types. At the farm yard the area varied. One part had chickens in it, on part was along a fence with a variety of animals on the other side, and one part was covered with dead leaves and had to trees. Hemlock forest was much more uniform, the soil was covered with a layer of dead leaves and there were dense trees. Hemlock was also on a slope that lead to Ice pond.

DISC USSI O N interacted, and in the process, have altered the environment. Human life revolves around agriculture and natural resources, and growing populations and higher standards of living has put increasing pressure on the environment. Human activity can result in consequences that are hard to reverse. Therefore, it is crucial to understand how to protect and sustain the environment. In order to further understand the effects of human actions on the land, an experiment was conducted focusing on the level of potassium, an essential element for soil and plant life. The proposed hypothesis was: if the soil from Boyce Field is tested, then it will have the least amount of potassium, because plants are harvested and the nutrients in the plants are not returned to the soil to replenish the potassium (Morgan and Connolly, www.nature.com).This hypothesis was

average. As shown in graph 1, the error bars for Farmyard were the longest, and the results were the least precise possibly because each sample was taken from a different section of the field, and each part of the field was a different distance from the sheep and chickens. A pattern that consistently occurred was that the samples taken near the animals had higher potassium, whereas the soil that was collected farther away had clearly less potassium. When chickens eat pests, weeds and other scraps that damage the richness of the soil, these scraps are digested and converted into manure (Duncan, http://seattletilth.org). The manure adds organic matter and supplies energy to organisms (like earthworms) that further decompose components in the soil and improve soil quality (Duncan, http://seattletilth.org). In addition, the manure is a fertilizer and overall, boosts the levels of essential elements like potassium and nitrogen (Duncan, http://seattletilth.org). Other organic materials added to the Farmyard like straw bedding, wood shavings, and spilled feed may also be consumed by the chickens, and this produces a high concentration of nitrogen, phosphorus and potassium in the manure and urine (Lovejoy, http://homeguides.sfgate.com). Animal fertilizers also loosen clay soils that are tightly bounded together, making more room for air, moisture, and nutrients to enter. Additionally, it improves drainage, and helps nutrients in the soil be retained for longer amounts of time (http://web.extension.illinois.edu). Hence, the soil near the chickens and sheep had more potassium. Similarly to the Farmyard, the error bars for Boyce Field were relatively long, and the

each sample was taken from a different strip of land that made up the field, and each portion is maintained for different crops. Many sources and experiments conducted show that different crops have different levels of potassium because the potassium absorbance time differs with each plant, vegetable, and fruit (http://www.cropnutrition.com/).Therefore, the error bars overlapped because both the Farmyard

s varied throughout. Trial two, the highest level of potassium

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recorded at Boyce Field (6.0 mg/L), was conducted near bright, tall green grass, which usually has high potassium content because when the fields are mowed, the nutrients from the grass clippings left to sit increases the amount of potassium (MacKintosh, www.snh.org.uk). Trial four, the lowest level of potassium recorded at Boyce Field (1.0 mg/L), was conducted near a strip of land with sandy loam soil. This soil texture resulting in the low potassium level (Thompson, http://homeguides.sfgate.com). Moreover, potassium deficiencies are most likely found in sandier soils (Koenig, http://extension.usu.edu). The results for Hemlock forest were the most precise because the whole forest was mostly uniform. The only conclusion that could be drawn from the results overall was that the Farmyard had conclusively more potassium than the Hemlock Forest. Based on these supporting facts, a new hypothesis can be formed: If the soil from Hemlock Forest is tested, then it will have less potassium than Farmyard, because there are many ways forests can suffer from nutrient deficiencies; like frost damage, lack of weed control, drainage, and vermin damage (http://www.teagasc.ie/). These factors are not fixed or prevented by human actions and there is no additional fertilizer added because humans do not sustain this specific forest. The testing day was conducted after the long winter season, so the Hemlock Forest was shielded in a layer of dead leaves that prevented oxygen and sunlight from reaching the soil (Reynolds, http://homeguides.sfgate.com). Since earthworms wither in sunlight, the forest floor was largely inhabited by these organisms (www.learner.org). Earthworms are fairly detrimental to forests because the leaf litter and duff layer is consumed by these creatures (http://www.magma.ca).

elements like potassium to the soil (Bose, http://sundayfarmer.wordpress.com). Some questions still unanswered are: if the samples were collected on the Boyce Field itself, would the results have been any different? If the procedure was tested in a different season, how would the results change? If humans tried to sustain the Hemlock Forest, how

especially at Boyce Field and Farmyard, because for each section of the area, only one sample was collected. Therefore, the confidence in the results is fairly low. If this experiment were repeated, each patch of land should have multiple trials to eliminate any errors and outliers, and as a result, make the outcome more credible and accurate. A few errors occurred while carrying out the procedure. While gathering the soil at Boyce Field, it was originally planned that the soil

was collected from around the field. ThField. The first soil sample that got tested at Boyce Field sat in the distilled water for a longer amount of time than the others because the procedure was still getting figured out. There are many things that could be modified for this experiment to be improved. The

for the white dots on the Vernier Potassium Probe to be below the surface. For future research of this study, other nutrients in the soil could be tested and compared to human influence because potassium is not the only element that plays a vital role in soil and plant life. Also, based on these results, the biodiversity of the plants in each habitat could be tested to see the negative and positive effects of humans on plant life.

A C K N O W L E G E M E N TS (B E N R OSS)

Completing this project and paper was not something that I could have done by myself. Throughout the whole process I received advice, information and ideas from many people. First I want to thank my mom for helping me and encouraging me throughout the project. I would like

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to thank Martha, the naturalist who helped us in the farmyard area, answered our questions, gave us background information, and allowed us to access the area. Mr. Rossiter, Mrs. Canaday, and Mr. Sarzana for chaperoning us at Drumlin farm. I would like to thank Mrs. Schultheis!who helped edit the paper, helped with our equipment, organization of the trip to Drumlin farm, and about anything else that you could think of. And lastly I would like to thank Michelle for being a great partner throughout this whole project. A C K N O W L E D G E M E N TS (M I C H E L L E T A N G) I would first like to thank my Knights of Science partner, Ben Ross for working cooperatively with me, peer editing my work, and helping me through the process of it all. I also want to thank Ms. Canaday, Mr. Sarzana, and Ms. Brenner, the naturalist at Farmyard, for supervising and providing assistance during the Drumlin Farm collecting day. In addition, I would like to say a special thanks to Ms. LaRocca for calibrating the Vernier Potassium Probe. Lastly, I would like to say a huge thank you to my science teacher, Ms. Schultheis for guiding me through the project, offering me help whenever I needed it, and giving me constructive feedback on my work.

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W O R KS C I T E D (B E N R OSS)

AGRICULTURAL F ERTILIZERS: NITROGEN, POTASSIUM, AND PHOSPHORUS. N.p.: Scifun.org, n.d. PDF.

Dye, James L. "Potassium (K)." Encyclopaedia Britannica Online Academic Edition. Encyclopædia Britannica Inc., 2014. Web. 9 Mar. 2014. <http://www.britannica.com/EBchecked/topic/472373/potassium-K/>.

"Growing Practices." Growing Practices. Mass Audubon Society, 2014. Web. 7 Mar. 2014. <http://www.massaudubon.org/get-outdoors/wildlife-sanctuaries/drumlin-farm/farming/growing-practices>.

"HowStuffWorks "What Is Fertilizer and Why Do Plants Need It?"" HowStuffWorks. Discovery, 1 Apr. 2000. Web. 13 Apr. 2014.

MacKintosh, Jane. "The Management of Unimproved Lowland Grassland for Nature Conservation." The Management of Unimproved Lowland Grassland for Nature Conservation. Snh.org, n.d. Web. 9 Mar. 2014. <http://www.snh.org.uk/publications/on-line/advisorynotes/11/11.htm>.

Miller, Ron. The Elements: What You Really Need to Know. Minneapolis: Twenty First Century, 2006. Print.

Morgan, Jennifer B., and Erin L. Collony. "Plant-Soil Interactions: Nutrient Uptake." Nature.com. Nature Publishing Group, 2013. Web. 12 Mar. 2014. <http://www.nature.com/scitable/knowledge/library/plant-soil-interactions-nutrient-uptake-105289112>.

"Movement of Metals in the Soil of a Pitch Pine Forest." Elements. Yale, n.d. Web. 9 Mar. 2014. <http://www.yale.edu/fes519b/pitchpine/elements.html#K>.

Penhallegon, Ross. NITROGEN--PHOSPHORUS--POTASSIUM VALUES O F ORGANIC F ERTILIZERS . Eugene, OR: Oregon State University Extension Service, May 2003. PDF.

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W O R KS C I T E D (M I C H E L L E T A N G) "Benefits and Uses." Composting for the Homeowner. University of Illinois Extension, n.d. Web. 16 Apr. 2014. <http://web.extension.illinois.edu/homecompost/benefits.cfm>.

Bose, Hiren. "Importance of Leaf Litter." Sundayfarmer. N.p., 5 June 2012. Web. 02 May 2014. <http://sundayfarmer.wordpress.com/2012/06/05/importance-of-leaf-litter/>.

Duncan, Judy. "Composting Chicken Manure." Seattle Tilth. WSU Cooperative Extension, Fall 2005. Web. 16 Apr. 2014. <http://seattletilth.org/learn/resources-1/city- chickens/compostingchickenmanure>.

"Forests Build Soil. Ours Is Losing It! What's Going On?" Earthworms Damage Forests. Malcoun Field Club, Aug.-Sept. 2008. Web. 02 May 2014. <http://www.magma.ca/~bambie/mfc/msa/worms.html>.

"Frequently Asked Questions About Earthworms." Earthworms. Journey North, n.d. Web. 02 May 2014. <http://www.learner.org/jnorth/search/WormNotes3.html>.

Ion Selective Electrodes Manual. Oregon: Vernier, 19 Oct. 2006. PDF.

Koenig, Rich, Mark Nelson, James Barnhill, and Dean Miner. FERTILIZER MANAGEMENT FOR GRASS AND GRASS-LEGUME MIXTURES. Utah: Utah State University Cooperative Extension, Aug. 2002. PDF. LabQuest 2 User Manual. Oregon: Vernier, 8 Nov. 2013. PDF. Lovejoy, Rachel. "What Does Manure Do to Soil?" SFGate. Home Guidesby Demand Media, n.d. Web. 16 Apr. 2014. <http://homeguides.sfgate.com/manure-soil-70424.html>. MacKintosh, Jane. "The Management of Unimproved Lowland Grassland for Nature

Conservation." The Management of Unimproved Lowland Grassland for Nature Conservation. Snh.org, n.d. Web. 9 Mar. 2014. <http://www.snh.org.uk/publications/on-line/advisorynotes/11/11.htm>.

Moore and McCabe (2006), Introduction to the Practice of Statistics, Third edition, p. 219. Nutrient Deficiencies in Forest Crops. Carlow, Ireland: Teagasc Agriculture and Food Development Authority, July 2007. PDF.

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"Potassium." Crop Nutrition. Mosaic, n.d. Web. 02 May 2014. <http://www.cropnutrition.com/efu-potassium#in-plants>. Reynolds, Laura. "What Happens If You Don't Rake Leaves?" Home Guides by Demand Media. SFGate, n.d. Web. 02 May 2014. <http://homeguides.sfgate.com/happens-dont-rake- leaves-75096.html>.

Siskin, Daisy. "Chickens and Gardens: A Perfect Match?" Horticulture- The Art & Science of Smart Gardening. Smart Gardening ENewsletter, 26 June 2012. Web. 16 Apr. 2014. <http://www.hortmag.com/blogs/editors-blog/chickens-and-gardens>.

Thompson, Daniel. "Characteristics of Sandy Loam Soil." Home Guides by Demand Media. SFGate, n.d. Web. 16 Apr. 2014. <http://homeguides.sfgate.com/characteristics-sandy- loam-soil-50765.html>. Wilson, Mark V. "Simple Random Sampling in the Field." Field Methods in Vegetation Science. Oregon State University, 2005. Web. 02 May 2014. <http://oregonstate.edu/instruct/bot440/wilsomar/Content/SRS.htm>.

The Effect of Distance on Soil Acidity

Jay Symonds Cooper Wolff

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TABLE OF CONTENTS

Section Author Page Abstract Jay Symonds 2 Introduction Jay Symonds 2 Materials & Methods Jay Symonds 3 Results Cooper Wolff 6 Discussion Cooper Wolff 11 Acknowledgements Wolff & Symonds 12 Works Cited Cooper Wolff 13 Works Cited Jay Symonds 13

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ABSTRACT Acidity can have a very big impact on an ecosystem. The objective of the experiment at Drumlin Farm was to find out if the acidity of water can have an affect on the acidity of the soil in or around it as the samples become farther and farther away. The procedure for this experiment was; to first test the acidity of the water itself, and then test the acidity of the soil as samples were taken from increasing distances. The information that was taken down was then used to see if the soil acidity either increased or decreased due to the acidity of the water. The results show that the soil samples had approximately the same acidity and were primarily basic (alkaline). INTRODUCTION Acidity (pH) is represented in form of the hydrogen ion (H+). It is measured on the pH scale, ranging from 0 to 14. A pH under 7 is acidic, while anything above 7 is alkaline (basic). Many different factors can affect the pH of soil, for example: rain and pollutants such as “mine spoiling’s” (http://en.wikipedia.org). The same factors apply to the pH of water. In soil, crops usually do best when the acidity (pH) is or is about 7.0, which is neutral (http://www.morningsun.net). The experiment will take place at Drumlin Farm, a wildlife sanctuary in Lincoln, Massachusetts. Within the overall farm, there are three different ponds that will be visited: Poultry Pond, Bathtub Pond, and Ice Pond. Each pond has around the same general surroundings, for example the soil around each is very muddy and wet. They also consist of various types of plant life. Poultry Pond, however, has a layer of algae-like or plant-like substances covering the pond itself, while Ice pond is surrounded by various types of trees that are located all around the pond, with some that have fallen into it. Ice Pond also has a covering of ice on its surface. Also, Bathtub Pond is more of a swamp than a pond. It has a large amount of vegetation that grows in and around the “pond”, and is covered with a thin layer of ice on half of its surface, similar to Ice Pond. One type of plant that grows is Duckweed, which is a small, bud-like plant that does not have a stem (http://en.wikipedia.org/wiki/Lemnoideae). It is hard to maneuver due to the thick briars around the pond and there is a small hill on the opposite side of where you enter. The acidity of the water and soil has a great impact on animal and plant life in and around the pond. For aquatic animals, if the water of the pond is too acidic, it can affect various things. For example, if the acidity of the water reaches below 5, the unhealthy water can cause fish to die (http://aqua-culture.blogspot.com). Also, vital nutrients that plants need such as calcium and potassium are alkaline elements and they can be depleted and lost with high acidity because the low pH transforms the elements into solid substances, making it harder for the plant to absorb them (http://www.gardeningsingapore.org/). There are also many factors that can play a role in the acidity (pH) of the water, with some being natural and some being unnatural (man-made). For example, Poultry Pond is located just a few feet away from a busy road. The excess runoff can impact the acidity greatly of not only the water but the soil as well because the runoff could contain oil or gas from passing cars and trucks. The runoff could then lead straight to the pond or seep into the soil. In Ice Pond, one factor is the abundance of trees surrounding the pond. Each

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tree has its own level of acidity, if some are right on the pond’s edge, the tree’s roots will grow in the water and affect the acidity of the water. For example, the Silver Maple thrives in a wide range of mainly acidic soil (4-7.5 pH), which can indicate the type of soil around the tree and it can have a slight effect on the soil p by making it more acidic. The proposed experiment is the effect of distance from the pond’s edge on the acidity of the soil. The independent variable for this experiment is the distance from the pond and the dependent variable is the acidity of the soil samples collected. The objective of this experiment is to determine if acidity in soil increases or decreases as the distance from the pond increases, which then can affect plant and animal life in and around the soil and pond. The proposed hypothesis is: If soil pH samples are collected farther away from the pond then they will have higher pH levels because of the average pH level of a natural pond being generally more acidic due to runoff from the rain (acidic) and the runoff from the water cycle (Skinner, The Blue Planet) as well the general pH levels of soil surrounding a pond being more basic (7-9 pH) (depts.alverno.edu). At each pond, a certain number of points will be randomized. Once the points are determined, samples of the soil will be collected from those points. The acidity of the samples will be tested. All of the testing will be within the increments (m) that are laid out. This experiment shows how acidity can greatly affect an ecosystem, especially at Drumlin Farm. The volunteers at the farm need to know how it affects different crops and wildlife. They must know the dangers of acidity so that they can protect the various species of plants and animals that live in the sanctuary. Once they gain a better understanding of the topic, they can inform people who visit the farm about the importance of keeping care of pollution, which affects acidity. As more people know about the dangers of high pH levels, they can start to protect and save many different crops and aquatic life.

MATERIALS AND METHODS This experiment was conducted at Drumlin Farm in Lincoln, Massachusetts. The ponds that were tested were Ice Pond, Bathtub Pond and Poultry Pond (see Figure 1: locations #11, #12, #13). At each pond a distance of 3 meters was measured from the pond’s edge outward, using a meter stick and small flags were put into the ground to mark each meter. Once this was done, a TI- Nspire CX calculator was used to randomize 10 different points within each increment. After the points were found, samples of pond water were tested with litmus paper (see Figure 3) and soil samples were taken from the set points using a soil auger, filling approximately 2 inches of the auger. After all of the soil samples were collected, they were put into the RapiTest Soil Test Kit to test and determine the acidity of the soil.

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How to use the RapiTest Soil Test Kit: Once all of the samples were collected, take out the soil test kit. When the kit is out, take off the green cap of the comparator (see Figure 2) and remove the package of capsules. The soil samples were filled to the fill line. Get a capsule and hold it horizontally then separate the two halves and pour powder into the test chamber. Using a dropper, add distilled water to the water’s fill line. Then once the comparator was filled, the green cap was put back onto the top securely and the comparator was shaken. Shake for one minute and let soil settle and color to develop. Then compare the color of sample to the color indicator in the front of the comparator to determine the acidity of sample.

Figure 1: Numbered Map of Drumlin Farm used for finding each habitat within the Farm

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Figure 2: pH RapiTest Soil Test Kit with comparator used for finding acidity of the samples

Figure 3: Litmus Paper used to measure/test the acidity (pH) of water samples

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RESULTS: TABLES AND GRAPHS

Table 1: The effect of distance away from pond (m) on acidity (pH) at Bathtub Pond

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Table 2: The effect of distance away from pond (m) on acidity (pH) at Ice Pond

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Table 3: The effect of distance away from pond (m) on acidity (pH) at Poultry Pond

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Table 4: The effect of distance away from pond (m) on average acidity (pH) at All Ponds

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Graph 1: The effect of distance away from pond (m) on average acidity (pH) Bathtub Pond

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Graph 2: The effect of distance away from pond (m) on average acidity (pH) at Ice Pond

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Graph 3: The effect of distance away from pond (m) on average acidity (pH) at Poultry Pond

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Graph 4: The effect of distance away from pond (m) on average acidity (pH) at All Ponds Combined

RESULTS: WRITTEN PARAGRAPHS

Graph 1 shows that the errors bars for the water didn’t overlap with any of the soil levels of the independent variable; however, all of the soil error bars did overlap. It should be noted, that two of the trials caused the error bars to be slightly larger for soil (3m) those trials having a pH of 7; these were the highest results overall. The lowest results were found in the water and had a pH level of 4. This data set had medium degree of precision. Bathtub pond had a lot of shrubbery and trees surrounding the pond with minimal openings surrounding the pond.

Graph 2 represents the data that was collected at was Ice pond. Ice pond had the smallest error bars, making it the most precise of the three ponds tested at. The only unusual thing about this data set was the range in the water. The water pH ranged heavily, but still had the smallest average by a large margin. The water pH was more acidic than the other three levels of the independent variables. Unlike Bathtub pond, the soil collected at (2m) had a smaller average then both (1m) and the soil at (3m) which both averaged the same (6.3 pH). This pond had a thin layer of ice stretching throughout the pond and the water was cold and brisk to touch.

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The third and final pond that was tested was Poultry pond (Graph 3). Poultry pond had the largest range of data and thus the largest errors bars by a noticeable amount (.49-.83). This data set was the least precise and all of the error bars overlapped with the only exception being water and soil (3m). The highest value of the averages was 5.90 for the soil collected at (3m) while the lowest average was the average for water (4.65). This pond was murky and the dirtiest of the three ponds.

Graph 4 represented the three ponds combined and their r2 values. When combined, the r 2 values of bathtub pond (.72) and poultry pond (.76) were higher then ice pond (.59) making them more accurate representations of the data. The r2 values of poultry and bathtub ponds showed higher correlations between the independent variables and the dependent variables. DISCUSSION:

The objective of this experiment was to find out whether or not acidity increases or decreases distance from edge of water. The hypothesis of this experiment was: If soil pH samples are collected farther away from the pond then they will have higher pH levels because of the average pH level of a natural pond being generally more acidic due to runoff from the rain (acidic) and the runoff from the water cycle (Skinner, #10) as well the general pH levels of soil surrounding a pond being more basic (7-9pH) (depts.alverno.edu). This hypothesis was supported due to the strong correlation of several ponds. However when all the data was added together, the error bars for water pH did not overlap with any of the soil pH and was smaller than the rest of the independent variables.

The r2 value of Bathtub pond was .71254; showing strong correlation and accuracy in the variables. The r2 value of Poultry pond was .75789; also showing a strong correlation and accuracy in the independent variables. The data that was collected was generally precise due the shortness of error bars at each pond and the aggregate data (strong trends) when all of the data was combined. The r2 value when all of the independent variables were combined was .71507 showing a clear correlation between the distance away from the pond and the acidity (pH). The data that was collected was generally precise due to the shortness of error bars at each pond. The r2 value when all of the independent variables were combined was: .71507, showing a clear correlation between the independent variable and the dependent variable. Ice pond however was the only pond that was consistently not precise. Ice pond had a standard deviation of (.59-.83) which was much higher than the other two ponds and it’s data may have skewed the rest of the averages. The r2 value of Ice pond was: .59211, which was much lower than the other two ponds.

The hypothesis was supported because of the runoff from the water cycle making pond water more acidic than the soil; dirt is generally more basic and how more acidic runoff ends up getting into water causing it to be more acidic than the soil around it; which is a mixture of soil and pond water mixed together (Skinner, The Blue Planet). Another reason the hypothesis wasn’t supported was because the soil pH levels

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overlapped causing the data to be inconclusive. This was because soil tends to be more basic while the pond water tends to be more acidic causing the soil-water mixture that we get near ponds to be more basic than the actual pond water itself (http://www.gardeningsingapore.org/). That information could’ve caused all of the soil distances error bars to overlap, while the water variable didn’t overlap with any of them when combining all the data.

With 120 total trials collected, sufficient data was collected to perform this experiment and make solid conclusions as to whether or not there was correlation with distance of soil from a pond. This experiment could have been improved if the full pH capsule was used at every location while collecting data; only half of the capsule was used while collecting data at Bathtub pond, which was a major error in this experiment. In order to avoid this error, it would have been important to read the instruction manual thoroughly for the pH testing kit. Another way this experiment could have been improved was to make a specific depth in which soil was collected at so it stayed consistent throughout the experiment. This impacted the results because the soil pH could have been varied deeper into the soil affecting the overall pH level of the soil. A way this could’ve been prevented would’ve been to read the instruction manual thoroughly for the pH testing kit. Another idea for further research of this topic would be to test the pH levels of different ponds in different habitats and see how each different habitat compares to one another showing the environmental impact on acidity (pH). Acknowledgments We would like to thank many people for helping us with our experiment. We would like to thank Ms. Jamison, Mr. Rossiter, Mr. Sarzana, Mr. Ewins, and Ms. Schultheis for helping manage our habitats on the day of the experiment without you, this couldn’t be possible. We would also like to thank Carol and Catherine (our Drumlin farm naturalists) on giving us the needed information. A huge thanks goes to the entire science department for helping us every step along the way and organizing everything in the fantastic way that they did, especially Ms. Svatek who helped Jay and I a ton along the way, without you the experiment would not be where it is today. I would also like to thank my partner Jay Symonds for putting in hard work on his parts of the KoS experiment and helping me edit everything along the way as well as being a great partner – Cooper I would like to thank my partner Cooper for working extremely hard on our journal article as well as being a great partner. – Jay

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WORKS CITED

Jay Symonds

Encyclopedian Dictionary (Aquaculture). Mammuth, n.d. Web. <http://aqua-culture.blogspot.com/2007/01/effects-of-high-and-low-ph-levels-in.html>. "Singapore Gardening Society." Singapore Gardening Society. N.p., n.d. Web. 13 Mar. 2014. <http://www.gardeningsingapore.org/>.

"Soil PH." Wikipedia. Wikimedia Foundation, 22 Feb. 2014. Web. 13 Mar. 2014. <http://en.wikipedia.org/wiki/Soil_pH>.

Skinner, Brian J. The Blue Planet (2nd Edition). Page 10-12 New York: Wiley, 1999. Print.

Cooper Wolff Encyclopedian Dictionary (Aquaculture). Mammuth, n.d. Web. <http://aqua-culture.blogspot.com/2007/01/effects-of-high-and-low-ph-levels-in.html>. "Singapore Gardening Society." Singapore Gardening Society. N.p., n.d. Web. 13 Mar. 2014. <http://www.gardeningsingapore.org/>.

"Soil PH." Wikipedia. Wikimedia Foundation, 22 Feb. 2014. Web. 13 Mar. 2014. <http://en.wikipedia.org/wiki/Soil_pH>.

Skinner, Brian J. The Blue Planet (2nd Edition). Page 10-12 New York: Wiley, 1999. Print. Stites, Dean. "The Effect of Soil PH on Crop Yield." Morning Sun. Morning Sun, 30 Jan. 2011. Web. 13 Mar. 2014. <http://www.morningsun.net/x286173897/The-effect-of-soil-pH-on-crop-yield>. !