KoS Online Journal 2014

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Knights of Science online Journal 2014

description

The BB&N 8th grade class of 2014 designed and conducted field studies at Drumlin Farm in Lincoln, MA. The Science Knight Web Journal is a compilation of all of those reports

Transcript of KoS Online Journal 2014

  • Knights of Science online

    Journal 2014

  • Table of Contents

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  • ABSTRAC T The objective of this experiment was to determine if Total Dissolved Solids (TDS), or the concentration of dissolved material in water, correlates to the level of the turbidity, or the murkiness of the water. At Drumlin Farms in Lincoln, MA, three ponds were selected to gather and test samples from: Bathtub, Ice, and Poultry. Water was collected and tested for turbidity and TDS, then these measurements were used to determine if there was a signifigant correlation between the two variables. It was expected that the higher the turbidity, the more turbid the water would be because water with many particles dissolved in it allow for many microorganisms to grow, clouding the water. The results showed that there was a trend that supported the hypothesis. Between each of the ponds, the TDS and turbidity conclusively increased or decreased respectively, supporting the correlation. However, because the r2 value was about 0.53, the data was not entirely conclusive. INTRODUCT ION

    Turbidity is the measurement # ' % !!the intensity of light scattered at 90 degrees as a beam of light passes through a sample of water. Turbidity is mainly affected by the amount of total suspended solids in water, such as phytoplankton, sediment from erosion, waste discharge, algae growth, runoff from construction, mining, agriculture, and urban runoff. Because turbidity is a result of substances entering a body of water, it is considered to be an effective way of determining water quality (www.lenntech.com). As a result, water turbidity is important in a manufacturing sense, especially when producing drinking water. As well as being aesthetically unappealing, excessive turbidity in drinking water may be a health concern. Turbidity can promote the regrowth of pathogens by providing food and shelter for them, leading to outbreaks of waterborne diseases. Although turbidity is not a direct cause for health concerns, there is ample evidence supporting a strong correlation between the reduction of turbidity and the removal of protozoa. Data collected in many studies in the past has also suggested that controlling turbidity in drinking water is a safeguard against pathogens and diseases (EPA, www.epa.gov). In response, The World Health Organization (WHO) has recommended turbidity levels under 1 Nephelometric Turbidity Unit (NTU) and no higher than 5 NTU for human consumption (www.lenntech.com). A higher measurement in NTU correlates to a lower measurement in centimeters& less than 10 NTU is equivalent to greater than 54.7 cm.

    A turbidity measurement that is too high can be harmful to aquatic life and organisms as well as humans. The suspended particles in the water absorb heat and scatter light, making the water warmer. This reduces its concentration of dissolved oxygen, decreases the amount of light that reaches farther down in water, and hinders the growth of aquatic plants (www.lenntech.com). Species that may rely on these plants, such as fish and shellfish, are then harmed as well. As aforementioned, high turbidity also may suggest that deadly bacteria is in water, which can hurt the organisms living there. In general, an excessively high turbidity measurement is harmful to most aquatic organisms, but turbidity can also indicate that essential nutrients are in the water, increasing the productivity and prosperity of life (Boyd, Water Quality: An Introduction). In some mangrove areas, high turbidity measurements are necessary in supporting certain species. For example, it can protect juvenile fish from larger predators (U.S. Fish and Wildlife Service, Decline of Submerged Plants in Chesapeake Bay). In addition, some species, such as the Vernal Pool Tadpole Shrimp, can tolerate and even flourish in muddy, highly turbid waters (Vernal Pool Tadpole Shrimp (Lepidurus Packardi)). This is because life may prosper as more essential nutrients are made available due to healthy soil and materials being washed into water by rain (www.snh.org.uk).

    Finding the amount of total dissolved solids (TDS) in a body of water will indicate the amount of inorganic and organic materials dissolved in the water. The right amount of TDS will help organisms maintain a proper density and contribute beneficial nutrients into water, which will increase aquatic life (www.tdsmeter.com). Through an increase of life, such as algae and

  • phytoplankton, turbidity will also increase (www.snh.org.uk). In this case, a higher turbidity measurements would indicate a healthy ecosystem. However, if anything harmful found its way into the water, or algae growth got to the point of depriving other life forms of oxygen, the entire ecosystem could become unhealthy as a result of the high turbidity.

    In a similar experiment that was conducted at these ponds (Evenchik and Yuen, The effect of water turbidity (cm) on water conductivity (), some of the results indicated that there was a correlation between water conductivity and water turbidity. Water conductivity is caused by and related closely to TDS. When the conductivity was 247 S, the turbidity was 34.90 cm; when the conductivity was 494 S, the turbidity was 51.2 cm; when the conductivity was 550 (, the turbidity was 75.0 cm. Although these points cannot accurately represent all of the data, there does seem to be a trend. When there was an increase of the conductivity, meaning that the TDS was also higher, the turbidity was higher as well.

    The objective of this experiment is to determine the correlation between TDS (ppm) and turbidity (cm). The independent variable is the TDS (ppm), and the dependent variable is the turbidity of the water (cm). Eight points along each pond will be randomly selected using a TI Nspire calculator, and water will be collected from each point. The turbidity will be measured using a Water Testing Equipment and Supplies turbidity tube, and the TDS of that sample will be measured using a Hanna Instruments TDS meter. Some important controlled variables include: the distance from the shore the sample is taken from (cm), the depth the sample was taken from (cm), the person doing the testing (eyesight may vary), the measuring tools used, and the data collection tools used. The hypothesis in this experiment is: If the TDS is higher, then the turbidity will also be higher, because higher TDS contributes more nutrients into the water, allowing for an increase in organism growth, which will increase turbidity (EPA, www.uri.edu). The more nutrients in the water, the more opportunity for growth there is. Organisms, such as algae, are main contributors to the turbidity, and a higher TDS promotes their survival and growth (www.lenntech.com).

    The experiment will take place at Drumlin Farm in Lincoln, Massachusetts. Three of the five ponds at Drumlin Farm& Bathtub Pond, Ice Pond, and Poultry Pond& will be testing sites for this experiment. Poultry Pond is downhill of animal pastures, Ice Pond is surrounded by a small forest, and Bathtub Pond has dense thicket surrounding its perimeters and is located in a field. It is hypothesized that Poultry Pond will have the highest TDS and turbidity because it will most likely have the greatest amount of runoff entering its waters. The lowest measurements are hypothesized to come from Bathtub Pond, because the surrounding thicket will prevent erosion and runoff. Ice Pond is located near only a fair amount of trees, and the moderate amount of erosion this results in would cause some materials to enter the pond. Therefore, its data should fall in the middle.

    Once the data is collected, Drumlin Farm can have a better understanding of how the human activities that take place on the Farm affect their aquatic life. The Farm will be able to realize the best way to control what enters their ponds in order to create the optimal living conditions for the organisms that live there. Since a higher turbidity measurement may prevent the sterilization of water using chlorine or ultraviolet rays, this makes it harder for water to be sanitized. Human activities like construction, mining, and agriculture can cause sediment to get in water through runoff during a rainstorm, and storm water can carry pollution from bridges, roads, and sidewalks (EPA, National Management Measures to Control Nonpoint Source Pollution from Urban Areas). If how the TDS specifically affects turbidity is discovered, then efforts can be made to decrease the amount of runoff and pollution that enters the water, causing the higher TDS. Ultimately, the spreading of disease will be prevented, and organisms living in bodies of water will have healthier living conditions.

  • M A T E RI A LS A ND M E T H O DS The effect of total dissolved solids (TDS) on turbidity of water (cm) was tested at three separate

    locations at Drumlin Farm in Lincoln, MA: Bathtub Pond, Poultry Pond, and Ice Pond. To determine the exact point of sample collection along the perimeter of the pond, the center of the pond was determined. Eight numbers -one for each trial- from zero to three hundred and sixty were randomly generated on a TI Nspire cx calculator from Texas Instruments. Each of those numbers represented where the sample would be taken from in relation to the aforementioned center of the pond. The compass was used to determine the specific points at which those angles intersected with the shore. The angles for Bathtub Pond were: 16, 53, 72, 122, 146, 185, 264, and 358 degrees; for Ice Pond the angles were: 2, 3, 39, 80, 113, 287, 337, and 343 degrees; and for Poultry Pond, the angles for the sample collection were: 19, 44, 99, 100, 198, 260, 308, and 352 degrees. Those locations, 50 centimeters from the edge of the pond, were where the water samples were collected. To test the turbidity, which was tested first, the open end of the turbidity tube was placed at the sample point. The tube was filled to the top with the sample water. The turbidity (cm) was then measured by looking down from the top of the tube while the tube was slowly emptied from the valve at the bottom. A small portion of the water sample was then poured into a 50 mL beaker to measure the TDS. With the Hanna Instruments TDS meter (Figure 1), all of the instructions accompanying the device were followed to produce the most accurate response, which entailed placing one end of the meter in the water and waiting for the measurement to stabilize. Both of these measurements were recorded in the field notebooks of the scientists involved, and each of the steps were repeated first for the individual trial, and then for the separate sites.

    Figure 1: Hanna Instruments HI 98311 EC/TDS/Temperature Tester (www.hydrogalaxy.com) !

  • R ESU L TS Table 1: The effect of TDS (ppm) on turbidity (cm) all three ponds.

    TDS (ppm) Turbidity (cm)

    BP Trial 1 0 107.1

    BP Trial 2 14 121.0

    BP Trial 3 20 85.0

    BP Trial 4 14 69.8

    BP Trial 5 16 60.1

    BP Trial 6 14 97.8

    BP Trial 7 21 53.2

    BP Trial 8 14 97.2

    IP Trial 1 212 121*

    IP Trial 2 239 26.0

    IP Trial 3 148 13.2

    IP Trial 4 182 40.0

    IP Trial 5 217 14.0

    IP Trial 6 214 69.1

    IP Trial 7 154 60.0

    IP Trial 8 157 52.1

    PP Trial 1 339 16.4

    PP Trial 2 395 42.6

  • Table 2: The effect of TDS (ppm) on turbidity (cm) averages and standard deviation at each pond.

    TDS (ppm) Averages

    TDS (ppm) Standard Deviation

    Turbidity (cm) Averages

    Turbidity (cm) Standard Deviation

    Bathtub 14 3.2 86.4 23.7

    Ice 190 36.2 49.4 22.3

    Poultry 337 68 27.7 7.6

    G raph 1: The effect of TDS (ppm) on turbidity (cm) at all three ponds.

    PP Trial 3 258 28.8

    PP Trial 4 250 24.3

    PP Trial 5 349 24.7

    PP Trial 6 346 29.5

    PP Trial 7 301 24.1

    PP Trial 8 454 31.2

    BP = Bathtub Pond

    IP = Ice Pond

    PP = Poultry Pond

    *error

  • G raph 2: The effect of TDS (ppm) on turbidity (cm) at all three ponds.

    Graph 1 shows the combined data collected from all the pond locations: Bathtub Pond, Ice Pond, and Poultry Pond. With an r2 value of about .53, the trend line exhibits an increase in the turbidity (cm) where there was an increase in the TDS (ppm). The r2 value indicates that there was a relatively low correlation between the two variables. In the case of turbidity, a lower measurement in centimeters means that it the overall turbidity is higher. Although the turbidity measurement decreased, causing the line to slope downwards on Graph 1, this still indicated an upward trend in the turbidity. In Graph 2, the turbidity error bars of Bathtub Pond and Ice Pond, and of Ice Pond and Poultry Pond both overlap slightly, but their averages indicate a downward trend. For TDS, no error bars overlap, and there is a clear upward trend.

    Bathtub Pond, which had the lowest average TDS and turbidity (14 ppm, 86.4 cm), was surrounded by highly concentrated thorn bushes, trees, and thicket. The surrounding land was mostly flat and muddy, except for the occasionally drier, elevated Southwest and South sides of the partially frozen over pond. Poultry Pond had the highest average TDS and turbidity measurements (337 ppm, 27.7 cm). It was downhill of a busy road on its Southwest side, and it was situated closely by a farmyard that housed animals and was most likely fertilized. Ice Pond had the average TDS and turbidity levels (395 ppm, 42.6 cm) that were in the middle of the three ponds, and it was seen to be was bordered by slightly elevated ground with evenly spaced out trees.

    The three outliers that affected Graph 1 were mostly caused by malfunctioning equipment. In an attempt to make the graph more accurately represent the data collected, an outlier of 121.0 cm in Trial 1 from Ice Pond was removed from the final graph. Removing this data point ultimately brought the r2 up from .44, improving the two variables correlation in this experiment. In Trial 1, a malfunctioning turbidity tube resulted in a measurement of 121.0 cm, and in Trial 2, a malfunctioning TDS meter resulted in a measurement of 0 ppm. The two other errors from Trial 1 and 2 were not removed from the data, because doing so would have decreased the r2 value.

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  • When the outliers were disregarded, the highest TDS recorded was 454 ppm from Trial 8 from Poultry Pond. The lowest recorded TDS was 14 ppm, shared by three trials: Trial 3 from Bathtub Pond, Trial 6 from Bathtub Pond, and Trial 8 from Bathtub Pond. The highest turbidity belonged to Trial 3 from Ice Pond with the measurement of 13.2 cm. The lowest turbidity recorded was from Trial 6 with 97.8 cm.

    DISCUSSION This experiment was conducted to determine the correlation between total dissolved solids (TDS) and turbidity in the water of Bathtub Pond, Ice Pond, and Poultry Pond at Drumlin Farms. The hypothesis for this experiment was: If the TDS is higher, then the turbidity will also be higher, because higher TDS contributes more nutrients into the water, allowing for an increase in organism growth, which will increase turbidity (EPA, www.uri.edu). This hypothesis was supported, because as the measurement of TDS (ppm) increased the measurement of turbidity (cm) increased. The decrease in turbidity (cm) indicates a higher turbidity, because it is harder to see through the water. As the level of TDS in the water rises, the turbidity levels will also increase because of the organisms in the water (Maczulak, www.fofweb.com). As the concentrations of inorganic and organic particles in the water increased, which causes the TDS to increase, the amount of microorganisms that could grow also increased (Maczulak, www.fofweb.com). The more surface area of particles floating in the water there were, the more plant life could grow. The TDS of the ponds ranged from 0 ppm to 454 ppm, and none of the error bars overlapped for each pond. This shows that each of the ponds had a conclusively different level of TDS. This significant difference in TDS level was caused by many factors. In Poultry Pond, a fairly large road moved close to the pond, and runoff from roads and road salts caused higher TDS (www.safewater.org). The runoff from the chicken pens that were nearby, and uphill of, the pond also contributed to the high TDS levels (www.safewater.org). Ice Pond was not as close to a main road nor a chicken pen and Bathtub Pond was even further from both, so the TDS levels were significantly lower at both locations. The r2 value for this experiment was 0.528. This shows a correlation, but it is not conclusive. Although a trend was visually represented, the r2 value does not suggest a strong correlation. The measurements of TDS from one of the ponds all clustered around 15 ppm, which may have disrupted the r2 value, making the data less conclusive. In the bar graph, the TDS levels from each of the ponds were was conclusively different, increasing from Bathtub, to Ice, to Poultry Pond. The turbidity levels also decreased in the same order, showing the trend continues. Sufficient data was not collected at Drumlin farm. There were gaps in the data between the ponds, which provided uncertainty in the trend. A wider range of data, perhaps from ponds with an even greater range of TDS and turbidity, would close those gaps. Taking more samples from each site, and therefore having more data points, would eliminate errors and outliers from reducing the r2 value. Changes to the procedure may yield more accurate results. Removing the opportunity for human error would make this data more reliable, if not more conclusive. Using a meter that automatically measured the turbidity, instead of relying on the eyes of the scientist, could improve the accuracy of the turbidity readings. Also, taking the measurements in a more controlled environment, instead of trying to do so while avoiding getting caught in brambles would make the experiment more reliable. There were a few errors that may have impacted the outcome of the experiment. The turbidity tube was subject to human error and interpretation. There was some confusion over when the flow of water from the bottom of the tube should be stopped, leading to turbidity reading that may have been slightly higher or lower than they would have been otherwise. The TDS meter was also slightly faulty. When the measurement for the first trial was taken from Bathtub Pond, the turbidity meter would not move from zero, even though the level of TDS was obviously higher. This could have been caused by an error on the part of the scientists, such as not setting the meter properly, but it was more likely the result of a mechanical malfunction, because a similar error occurred during the preliminary tests. For two trials, the turbidity tube was not tall enough to measure accurately. The measurements for those trial were recorded as 121 cm, and noted that there was an error. This error occurred in trial 2 at Bathtub Pond and trial 1 at Ice Pond.

  • Performing this experiment raised many questions. The variation in the TDS between the ponds led to the question: What factors caused these locations to be so thoroughly different? The trees around each pond were of differing species. Does that have anything to do with the TDS or turbidity? The differences in algae growth appeared to correlate with the turbidity being higher, but an experiment could be designed and completed to prove or disprove that. A C K N O W L E D G E M E N TS I, Addie Millman Bevis, would like, first and foremost, to thank my partner, Rachel Avram, for all her # "' #!! hroughout the %! ' $Catherine, our three teacher naturalists at Drumlin Farms, for guiding us through the stressful day of collecting samples. Finally, I'd like to thanks everyone who helped us to edit, and make this the best it can be.

    I, Rachel Alexandra Avram, would like to thank my lab partner, Addie Millman Bevis, for being extremely helpful, reliable, and flexible throughout this entire process. I also appreciate Mr. Ewins for helping us out whenever we had a question or needed advice, as it was essential to our success. I want to thank him for editing our work during the project as well. I also want to thank all of the teachers who went on the Drumlin Farm field trip and worked hard to make it run smoothly. In particular, I thank Ms. Jamison, Mr. Dwyer, and Ms. Brooks, who all were at our different testing stations. Thank you to Ms. ! !##!' naturalists at Drumlin Farm for aiding us in the navigation around the farm and making sure that we all could collect data in the most efficient way possible. Another huge thank you to everyone at Drumlin Farm for allowing our class to experiment on their farm& #!' " %## ! assistance and willingness to have us there.

  • W O R KS C I T E D Author 1: Daphne, Low Hui Xiang, Handojo Djati Utomo, and Lim Zhi Hao Kenneth. "Correlation

    between Turbidity and Total Suspended Solids in Singapore Rivers." Journal of Water

    Sustainability 1.3 (2011): n. pag. JWSP Online. Division of Civil Engineering, School of

    Architecture and the Built Environment, Singapore Polytechnic, Dec. 2011. Web. 28 Feb.

    2014..

    Hydro Galaxy. "Hanna Instruments DiST 5 Waterproof EC/TDS Temperature

    Tester HI 98311 716825." Hydro Galaxy. Hydro Galaxy, 2014. Web. 13 Mar. 2014.

    .

    Maczulak, Anne. "water quality." Science Online. Facts On File, Inc. Web. 15 Apr. 2014.

    .

    Safe Drinking Water Foundation. "TDS AND PH." Safewater.org. Safe Drinking Water

    Foundation, n.d. Web. 12 Apr. 2014.

    .

    United States. Environmental Protection Agency. Why Test Your Well Water For Turbidity?

    N.p.: n.p., n.d. Print.

    "Water Pollution." The New Book of Popular Science. 16th ed. Vol. 3. Danbury, CT: Grolier,

    2006. 85. Print.

    Author 2: Boyd, Claude E. (1999). Water Quality: An Introduction. The Netherlands: Kluwer Academic Publishers

    Group.

    "Fathead Minnow (Pimephales Promelas)." RSS. Texas Parks and Wildlife, N.d. Web. 15 Apr. 2014.

  • "Rivers and Their Catchments: Causes and Effects of Turbid Water." Rivers and Their Catchments:

    Causes and E ffects of Turbid Water. Scottish Natural Heritage, N.d. Web. 30 Mar. 2014.

    .

    "Turbidity." Turbidity. Lenntech, N.d. Web. 02 Mar. 2014. .

    United States. Environmental Protection Agency. Why Test Your Well Water For Turbidity?N.p.: N.p.,

    N.d. Print. .

    U.S. Environmental Protection Agency (EPA). Washington, D.C. "National Management Measures to

    Control Nonpoint Source Pollution from Urban Areas." Chapters 7 and 8. Document No. EPA

    841-B-05-004. November 2005.

    "Vernal Pool Tadpole Shrimp (Lepidurus Packardi)." Beacham's Guide to the Endangered Species of

    North America. Ed. Walton Beacham, Frank V. Castronova, and Suzanne Sessine. Vol. 3.

    Detroit: Gale, 2001. Science in Context. Web. 15 Apr. 2014.

    "What Is TDS?" - HM Digital. HM Digital, N.d. Web. 28 Jan. 2014. .

  • APPE NDI X

    TDS (ppm) Turbidity (cm)

    Trial 1 212 error 121 error

    Trial 2 239 26.0

    Trial 3 148 13.2

    Trial 4 182 40.0

    Trial 5 217 14.0

    Trial 6 214 69.1

    Trial 7 154 60.0

    Trial 8 157 52.1

    Average 190 49.4

    St. Deviation 36.2 22.3

    TDS (ppm) Turbidity (cm)

    Trial 1 0 error 107.1 error

    Trial 2 14 error 121 error

    Trial 3 20 85.0

    Trial 4 14 69.8

    Trial 5 16 60.1

    Trial 6 14 97.8

    Trial 7 21 53.2

    Trial 8 14 97.2

    Average 14 86.4

    St. Deviation 3.2 23.7

    G raph 3: The effect of TDS (ppm) on turbidity (cm) at Ice Pond

    G raph 4: The effect of TDS (ppm) on turbidity (cm) at Bathtub Pond Pond

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  • TDS (ppm) Turbidity (cm)

    Trial 1 339 16.4

    Trial 2 395 42.6

    Trial 3 258 28.8

    Trial 4 250 24.3

    Trial 5 349 24.7

    Trial 6 346 29.5

    Trial 7 301 24.1

    Trial 8 454 31.2

    Average 337 27.7

    St. Deviation 68.0 7.6

    Table 3: The effect of TDS (ppm) on turbidity (cm) at Ice Pond

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  • The Effect of Type of Animal Manure on Soil Conductivity By Ezra Berg and Avi Madsen

  • 1

    Table of Contents Section Author Page Abstract Berg 2 Introduction Madsen 2 Materials and Methods Berg 3 Results Madsen 7 Discussion Berg 8 Acknowledgements Berg & Madsen 10 Works Cited Berg 10 Works Cited Madsen 11

  • 2

    ABSTRACT Soil in different habitats contains various levels of soil conductivity. This experiment was conducted in order to discover whether animal manure has an effect on soil conductivity, and which animals manure effects it the most. The procedure for this experiment was to determine and compare the soil conductivity between the cow, goat, chicken and a non inhabited field. These locations were at Drumlin Farm in Lincoln, MA. The names of the locations are Farmyard, and Overlook Field. It was expected that the manure would increase the soil conductivity at each habitat because manure makes the soil more acidic. The results of the experiment showed that only the chicken habitat had a conclusively higher soil conductivity from the non inhabited field, however, the soil conductivity increased in areas where the most manure was excreted. It was discovered that chickens have a different diet than cows and goats. Chickens are fed corn, oats, weeds, vegetables, and any bugs they can find, whereas goats and cows are fed grains and hay. Hay was found on the non inhabited field, which could be the cause for the similar soil conductivity. Vegetables are more acidic than grains, and acid increases soil conductivity, therefore, vegetables increase soil conductivity. INTRODUCTION Soil conductivity is the measure of how well soil conducts electricity and this has an effect on plant growth and health. It also correlates with particle size and soil texture because of the relationship between high soil conductivity and clay soils. (Barbosa, www.lsuagcenter.com/) Soil conductivity is measured in micro Siemens per centimeter (!S/cm) and comes from a variety of sources. The main source of conductivity is rainwater or pond and ocean water. However, it can also be increased with the addition of manure to the soil. The manure mineralizes and releases salts into the soil, therefore increasing the soil conductivity. Therefore, the correlation between types of animal manure and the soil conductivity was investigated. Prior research found a correlation was found between animal manure and soil conductivity. Taylor and Francis (http://dx.doi.org/) conducted where these researchers compared the soil conductivity of four fields that were exposed to different types animal manure. The experiment showed that fields that had been exposed to animal manure had a higher soil conductivity than those that did not. These findings support the hypothesis of this experiment as the scientists here at BBN believe that the exposure of any type of animal manure will result in higher soil conductivity than the control field, (http://dx.doi.org/) which did not have any manure based fertilizer for about a year. (Stone, Martha. Personal interview. 7 Apr. 2014.) This experiment was conducted at the fields in Drumlin Farm in Lincoln, MA, specifically, fields that are the residence of cows, chickens, goats, and no animals. These fields were

  • 3

    Farmyard field, which houses cows, goats and chickens, and Overlook field which does not have any animals making it the control field. These fields will be tested to find whether there is a correlation between animal manure and soil conductivity. The hypothesis for this experiment is: If animal manure is present on a field then the soil conductivity of that field will increase because when an animal defecates, the salt that was in its feed stays in the manure and therefore increases the soil conductivity because the measure of soil conductivity is the measure of salts in a soil sample. (http://dx.doi.org/) (http://www.dpi.nsw.gov.au/) The independent variable in this experiment was the presence of animal manure and the type of animal manure. The dependent variable was the soil conductivity in all the manure and non-manure habitats. The variables that were controlled on the day of collection of data, procedure for collection, the type of probe, and the amount of soil collected. The data were collected from the fields at Drumlin Farm

    This experiment can potentially help farmers buy the most effective manure to encourage crop growth. This information can also help manure producers produce the most effective manure for healthy plants and help those farmers choose the correct amount of manure to reach a desired level of soil conductivity. These data could potentially aid farmers worldwide and take the mystery out of buying different types of manure based fertilizer. MATERIALS AND METHODS Data was collected from different habitats at Drumlin Farm in Lincoln Massachusetts in order to compare the level of soil conductivity (!S/cm) with different types of manure. The habitats were Overlook field, the cow habitat at Farmyard, the goat habitat at Farmyard, and the chicken habitat at Farmyard (see pictures). At each habitat, measurements were taken at 25 different random locations. The random locations were found by laying a 15 by 9 numbered grid over each habitat, and using a TI-nspire Cx calculator to give 25 random squares on the grid. Before any data was taken from the habitats, a Drumlin Farm naturalist was asked about the soil in Overlook field and how often it is fertilized because that was presumed to be the field that is manure free. The soil at Overlook was found to be fertilized once a year and had not been fertilized since last spring so the data was not affected by that factor.

  • 4

    Figure 1: Map of Drumlin Farm Data was collected from Sandpit (2), and different habitats within Farmyard (6).

    Figure 2: Map of Overlook Field This is a map of randomized areas to collect data at Overlook field.

  • 5

    Figure 3: Map of Farmyard cow habitat This is a map of randomized areas to collect data at the cow habitat at Farmyard.

    Figure 4: Map of Farmyard goat and chicken habitat This is a map of randomized areas to collect data at the goat and chicken habitat at Farmyard.

    To begin the test, a Hanna HI 98331 soil conductivity probe was put together (see figure 5), and then placed approximately 4 centimeters into the soil at one of the 25 random locations given from the TI-nspire Cx calculator. Once the Hanna HI 98331 soil conductivity probe

  • 6

    received a measurement of the soil conductivity (!S/cm), the measurement of soil conductivity (!S/cm) was then read, and recorded. Once the measurement had been read, the Hanna HI 98331 soil conductivity probe was taken out of the soil. The HI 98331 soil conductivity probe was then rinsed off with distilled water so it could be used at the next location. After the last sample was taken at the habitat, a soil smudge was put into a field notebook to later observe and compare with soil smudges at other habitats. These steps were repeated for all 100 trials (25 at each habitat). Figure 5: Hanna HI 98331 conductivity probe This is a picture of the Hanna HI 98331 conductivity probe.

  • 7

    RESULTS Table 1: The Effect of Animal Manure Type on Soil Conductivity

    Graph 1: The Effect of Type of Animal Manure on Soil Conductivity

    Graph #1 and Table #1 shows the data that was collected at Drumlin Farm. There are several trend that can easily be seen by looking at the graph. The chicken manure location had much higher soil conductivity (!S/cm) than any other location. The goat manure location was much less precise than the rest of the locations. The chicken had the highest average (0.44 ms/cm)

  • 8

    and the goat field the lowest average (0.05 ms/cm). The goat habitat was very large and spread out compared to other habitats even the area that the goats mostly grazed around in (and defecated in) was very small. The chicken field at Drumlin Farm had housed the chickens all winter long and compared to other habitats was tiny. None of the sites were very precise and the cow and control had almost the exact same average and standard deviation. DISCUSSION This experiment was conducted to test the effect that manure has on soil conductivity, and also to find out if one animal species manure makes soil more conductive than other animal manure. The hypothesis for this experiment was: If animal manure is on a field then the soil conductivity of that field will increase because when an animal excretes, the salt that was in its feed stays in the manure and therefore increases the soil conductivity (http://dx.doi.org/). This hypothesis was supported because two of the three fields with manure on it (cow and goat habitat at Farmyard), had close to the same conductivity levels as the field with no manure on it (Overlook), but there appeared to be an increase in soil conductivity when closer to where the animals spend most of their time, which is where more manure was found, therefore it can be concluded that manure increases soil conductivity. The manure that the chickens produced caused the soil conductivity to increase to an average of 0.44 mS/cm. When measuring the conductivity around the chicken house, it was extremely high, versus when measuring the soil conductivity from further away from the chicken pen, it decreased. This was presumably due to the fact that the chickens spend a lot more time around their pen, and therefore produced more manure in that area which raised the conductivity. The chickens diet also had something to do with the increase in soil conductivity. Chickens are fed corn, oats, weeds, vegetables, and any bugs they can find (Martha Stone, personal communication). This is a different diet from the cows and goats at Farmyard. The diet of the chickens also is more acidic than the other animals, and higher levels of acid increases the soil conductivity (Rail, http://www.livestrong.com). The cows diet of hay and grains is the same as the goats diet. The soil conductivity is very similar in the two habitats with the cow habitat at an average of 0.07 mS/cm, and the goat habitat at an average 0.05 mS/cm, which were both much less conductive than the chicken habitat. The goat habitat had an increase in soil conductivity when closer to the barn, where the goats live and seemed to spend more time at than the rest of the habitat. This means that goat manure increases the soil conductivity. The cows, however, had a much more equal level of soil conductivity throughout the habitat, except along the edge near the forest. Taking away the data taken near the Red Pine forest because it is assumed that it affected the measurement of soil conductivity (Zoltak, http://depts.alverno.edu), the whole cow habitat had fairly equal levels of soil conductivity. The cows spend a lot more time all over the habitat, and do not stay in a single area as much (Martha Stone, personal communication), which means that more

  • 9

    equal amounts of manure was spread out across the field, which explains the similar levels of conductivity within the cow habitat. The hay and grains that are fed to the goats and cows, which is then excreted onto the soil, is similar to the hay found at Overlook field (Debby, personal communication). This is the reason for the similarity in the cow, goat, and Overlook habitat. The similar hay is on all three of those fields. The chickens different diet is the reason for such high levels of conductivity. They excrete bugs, vegetables, and weeds which then goes into the soil and increases the soil conductivity. When food is digested, the stomach acid breaks down the food and causes the manure to be acidic. Vegetables, something that the cows and goats are not fed, are higher in acidity than grains. Acid increases the level of soil conductivity because when ions are added to soil, the conductivity increases, and acids add ions to the soil (Benoit, http://environmentalet.hypermart.net/), which is why the chicken habitat has higher soil conductivity than the other habitats. The similarity in the cow, goat, and Overlook field are shown on the bar graph as well. The error bars are all overlapping, which means that the data is similar and inconclusive. However, the chicken habitat on the graph has no overlaps and is above all the other habitats, which means that the soil conductivity is conclusively higher in the chicken habitat than the other three locations. The most precise data was collected at the cow habitat. The next most precise data was collected at Overlook and then the chicken habitat. The data collected at the goat habitat was the least precise. The only error that occurred during the testing was that there was a rock at one of the points for testing. This was easily solved by testing right next to the rock, which was about 2 feet away from the correct testing location. The field study could be improved by testing habitats that are inhabited by animals with different diets. Only one habitat stood out in the experiment, and it was the only habitat with a species that had a different diet. More conclusions could be drawn if testing soil conductivity in animal enclosures with different diets. The data collected was sufficient because the amount of trials taken at each habitat was enough to be able to accurately compare the soil conductivity of each habitat. The chicken habitat and the increase in soil conductivity when near areas with more manure is proof that the animals diet affects the soil conductivity. Data collection could be improved by staying away from the borders of the habitats because the data around the edges could be affected by surrounding variables, as was the cow habitat. The tests taken around the borders of the cow habitat at Farmyard could not provide data about the effect manure has on soil conductivity because the Red Pine forests soil was too close, and possibly was affecting the trial. For future experiments, the diet of the animals should be figured out before collecting data, and it would be interesting to test a wider variety of diets.

  • 10

    ACKNOWLEDGEMENTS Avi Madsen I would like to thank my parents for helping with ideas for the experiment and editing the drafts. I would also like to thank the helpful naturalists at Drumlin Farm, especially Martha Stone and Debbie. Last of all my science teacher Ms. Svatek who helped with ideas, conducting the experiment, writing, formatting, and presenting this experiment. Ezra Berg I would like to thank my parents for going out to buy materials to make this possible and help with ideas for the experiment. I would also like to thank the naturalists at Drumlin Farm, especially Debby and Martha Stone for giving us information to use for testing, and allowing us to use their farm habitats. My science teacher Ms. Svatek is the one who made this all possible. I would like to thank her for walking us through the steps to successfully doing an experiment and also giving us permission to use materials from the science lab. We could not have done our experiment without her. WORKS CITED Ezra Berg:

    Benoit, Anthony. "PH and Conductivity." PH and Conductivity. N.p., n.d. Web. 17 Apr. 2014.

    .

    "Dynamics of Soil PH and Electrical Conductivity with the Application of Three Animal

    Manures." Taylor and Francis. Department of Crop Sciences , Tshwane University of

    Technology , Pretoria , South Africa, 27 Mar. 2012. Web. 14 Apr. 2014.

    .

    Pein, David V. Hannah Soil EC & Temp Probe. Digital image. Soil PH and Conductivity

    Meter Range. 2002-2014 David Von Pein, n.d. Web. 12 Mar. 2014.

    .

    Rail, Kevin. "High Acidic Foods List." LIVESTRONG.COM. LIVESTRONG.COM, 21 Oct.

    2013. Web. 14 Apr. 2014. .

  • 11

    xx, Debby. Plants in soil at overlook. Personal interview. 07 Apr. 2014.

    xx, Stone Martha. "Chicken Feeding Habits." Personal interview. 07 Apr. 2014.

    Zoltak, Wendy. "Comparisons of PH and Phosphorus Levels in Pine and Deciduous Soils."

    Http://depts.alverno.edu/. Alverno College. Web. 5 Apr. 2014.

    .

    WORKS CITED Avi Madsen: Barabosa, Roberto N. What Is Soil Electrical Conductivity? Lsuagcenter. Lsuagcenter, n.d.

    Web. 17 Apr. 2014. .

    Eignberg, R.A,. Electrical Conductivity Monitoring of Soil Condition and Available N with

    Animal Manure and a Cover Crop. Digitalcommons. Digitalcommons, n.d. Web. 17 Apr.

    2014.

    .

    Spaulding, A.D. "The VALUE of SOIL ELECTRICAL CONDUCTIVITY and

    TOPOGRAPHICAL INFORMATION for VARIABLE RATE NITROGEN APPLICATION:

    FIRST ASSESSMENT." (n.d.): n. pag. Castonline. Web. 17 Apr. 2014.

    .

    Stone, Martha. Personal interview. 7 Apr. 2014.

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  • The Effect of Tree Species on soil pH in the pHorest

    By: Benjamin Blackburn & Brendan Donovan

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    TABLE OF CONTENTS Section Author Page(s) Abstract Blackburn 3 Introduction Blackburn 3-4 Materials & Methods Blackburn 4 Results Donovan 5-6 Discussion Donovan 6-7 Acknowledgements Blackburn & Donovan 8 Works Cited Blackburn 9 Works Cited Donovan 10-11 Appendix: Pictures Blackburn & Donovan 11-14

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    ABSTRACT

    Soil pH is the measurement of active hydrogen ions collected within the soil. The more hydrogen ions the more acidic the soil will be. This experiment was conducted to discover whether or not certain types of trees affect the pH of the soil and also potentially affect the growth of other surrounding plants. To conduct this experiment a soil sample was taken from under a tree with a soil auger and then tested with a Rapitest pH testing kit. The original hypothesis was: If a Red Pine tree is tested, then the soil pH will be the lowest, because in the northern forest region Red Pines need a 4.5 to 6.0 range in order to absorb enough nutrients (Rudolf, www.na.fs.fed.us). In the end there was no conclusive data found and it was not possible to determine whether or not the coniferous trees were more acidic because all of the error bars overlapped, despite the differentiating averages. INTRODUCTION

    Soil pH measures how acidic soil is due to contributing factors from surrounding sources. The definition of pH is the measurement of active hydrogen (H) ions in any given substance or the power of hydrogen ions within the soil (Knapp, Brian J., and Mary Sanders, Acids, Bases, Salts, 182). The pH levels range from 1 to 14 with 14 being the most basic, 1 being the most acidic and 7 being neutral. Organisms like trees and flowers contribute hydrogen ions into the soil, making it more acidic. Most soils have a pH in the 3 to 9 range (Londo, Andrew J., John D. Kushla, and Robert C. Carter, www.lsuagcenter.com). Many scientists believe that more acidic trees tend to live where levels of pH are lower and often contribute to those conditions. The pH levels can also affect the amounts of other nutrients that a plant can absorb. The reactions caused by pH can either allow a plant to uptake more or less nutrients, ultimately leading to better or worse plant health.

    This experiment was conducted at Drumlin Farm, in Lincoln, Massachusetts. Drumlin Farm is part of the Audubon Wildlife Sanctuary, a program to keep and protect natural habitats in Massachusetts. The Drumlin covers 312 acres of land and has 4 different forests from which BB&N has access to. The experiment was conducted at Hemlock forest located on the Northern part of the drumlin. When conducting the experiment it was helpful to note what signs the tree might show to signify too high or too low pH levels. If the pH levels are too high then there will be signs of deficiencies in nutrients because the pH levels can prevent the tree from receiving enough of a specific nutrient based on the amount of reaction with the soil (Alvey, Alexis, www.ccesuffolk.org). If the pH levels are too low then the tree wont be able to grow as well because of the more acidic soil, resulting in smaller limbs and dying leaves. The pH in the soil also translates to the health of the animals and organisms around it. If a plant is consumed by an animal, then all of the nutrients in the plant will go to the animal. If the soil pH is too high or too low the plant wont have as many essential nutrients and it will impact the health of the organism that relies on the nutrients from plants.

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    The proposed experiment is to test how the type of tree affects pH levels in the soil. The objective of the experiment is to find out whether or not the type of tree actually affects the acidity of the soil that surrounds it leading to the growth or stagnation of the plant. This question was answered by collecting soil samples from soil found beneath different trees. Then the soil acidity was determined using a Rapitest pH testing kit and a chemical reagent mixed with distilled water. These results showed whether or not the tree does impact the soil. The independent variable is the type of tree that we take the soil samples from. The dependent variable for this test was the level of pH tested from the soil. Some variables that were controlled are the climate, the forest from which the experiment was conduct in, the day in which the experiment is conducted, the way in which data was collected, and the type of soil. The hypothesis states: If a red pine tree is tested then the soil pH will be the lowest because in the northern forest region red pines need a 4.5-6.0 range, in order to absorb enough nutrients (Rudolf,www.na.fs.fed.us).

    This research will demonstrate how the type of tree is affecting the acidity of the soil that surrounds it. The volunteers from the Audubon Wildlife Sanctuary program can use this data to further help preserve the health of their plants. It will help them understand what plants can tolerate the living conditions of another plant and which can not. It is important to understand how the levels of pH affect a plant because then it is easier to help plants grow to become more healthy. This experiment will help contribute to the understanding of plant life and how each plant can affect one another. This will allow people to be able to create ideal living spaces for different species of plants and will help many societies like the Audubon Wildlife Sanctuary program preserve the natural forests. MATERIALS AND METHODS

    The previously proposed experiment was conducted in Hemlock Forest. Hemlock Forest was selected because it had many different deciduous and coniferous groups of trees. To select what areas of Hemlock Forest this experiment would be conducted on it was necessary to run randomization method number 2 to select which areas the soil samples would be taken from. First and area of Hemlock forest with a dense population of one species of tree was identified. The type of trees that this experiment was conducted upon was Spruce, Ash, Oak and Red Pine. There were two areas with deciduous trees, two cites with coniferous trees and one cite where there were no trees to affect the soil pH. Then all of the surrounding trees were marked and numbered. Then all of the numbers of the marked trees were randomized with a calculator to fairly select which six trees the experiment was going to be conducted upon. After the trees were selected an auger was used to scoop a two-inch soil sample from under the tree. Then soil from the B-horizon was put in a pH test kit and mixed with distilled water and a chemical reagent. Then after a minute of letting the substance sit the substance color was compared to the acidity chart on the side of the test kit.

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    RESULTS TABLE 1: The effect of tree species on soil pH

    Tree Species

    pH Level

    Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 6 Average St.Dev.

    White Spruce 6.0 7.0 6.0 4.5 6.5 5.5 5.9 0.9

    White Ash 6.0 6.5 6.5 5.5 5.5 6.0 6.0 0.4

    Red Pine 6.0 4.5 6.0 6.0 5.5 5.5 5.6 0.6

    White Oak 6.6 6.0 6.5 6.0 6.0 6.5 6.3 0.3

    Control Run 5.5 6.5 7.0 6.0 6.5 6.0 6.3 0.5

    GRAPH 1: The effect of tree species on soil pH

    Graph 1 shows all of the tree species and their corresponding averages, from the data that was collected at Hemlock Forest. The graphed data shows that the type of tree species and the surrounding soil pH level did not correspond. The data collected was

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    unusual; all of the error bars overlap, yet the data was still fairly precise. The only trends in the data came from the size of the error bars. The Red Pine trees, White Spruce and the control run had very large error bar sizes; these were the least precise data. From the data collected at Hemlock Forest, depicted in Graph 1, the highest average soil pH level was the same with the White Oaks and the control run (6.3). The lowest average pH level came from the Red Pine trees (5.6). The White Spruce tree was by far the least precise. The Red Pine trees and the control run error bar sizes were very similar, but the White Ash and White Oak trees were the most precise. The data as a whole was largely precise, with one very big outlier. An important observation was: there were many groves of trees that were occupied by only one species during the testing. DISCUSSION

    This experiment was conducted in order to test the correlation between tree species and the soil pH. The hypothesis for this experiment states: If a red pine tree is tested then the soil pH will be the lowest because in the northern forest region red pines need a 4.5-6.0 range in order to absorb enough nutrients (Rudolf,www.na.fs.fed.us). This hypothesis was not supported by the results of the experiment because all of the error bars overlapped.

    Based on the averages, the Red Pine trees were the most acidic, followed by the White Spruce, then White Ash, control run and finally White Oak. All of the trees tested are native to the Northeast Forest Region. Therefore, they need similar soil conditions in order to survive. All of these samples were taken from the same forest, where the soil is very similar. At each spot where data was collected at exactly 6.1 cm. below the surface, the color and texture of the soil was very similar. The soil was a dark brown and crumpled easily when touched. Hemlock Forest is located on a hill. As a result, each spot was elevated and some spots were higher than others. Also, each location was a grove that was predominantly occupied by the species that was being tested (with a few outliers). Many sources said that all of these trees have a similar need for pH so the readings will be alike, because of the similarity in the ranges, within these conditions (Rudolf,www.na.fs.fed.us).

    There is definitely a connection between all of the species because the ranges are very similar, with a moderately precise range of data. The White Ash and White Oak trees were extremely precise, the control run and Red Pine trees were moderately precise, and the White Spruce trees were not precise at all. Although there was a large difference in error bar size, all of the error bars overlapped. There is no conclusion to be made from this experiment because the trees in this region need soil that is slightly acidic (4.5-6.0) in order to survive, especially the winter. Slightly acidic soil allows trees to soak up the largest amount of nutrients (Harrington, Tree pH Ranges). Although there are no conclusions to be made, the data is still precise. Confidence in data comes from the size of the error bars and how much outlying data there is. In this experiment there was not a lot of outlying data within each species type, therefore there is a lot of confidence.

    The field study was well thought out and proved very successful in the field, and it needed little modifications. This allowed for sufficient data collection. Perhaps, if the soil was collected deeper at around 25.4 cm., this could have provided a much different range of pH levels because at this depth there is a horizon that contains much more

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    nutrients that the trees absorb. Although there was confidence in the data, maybe if more samples were collected other outliers would appear.

    Errors occurred very rarely throughout this experiment. The only errors that occurred were: once dropping the container with the solution, this error could have been eliminated by being more careful; rushing through the process at the end and not getting samples deep enough, and this could have been eliminated by better time management. Future ideas for this experiment are: the correlation between groves of mixed species of trees and non-mixed species on soil pH, and tree height on soil pH around the tree. This experiment, if researched again, could be improved by collecting soil samples deeper and collecting samples from a variety of forests. If this experiment was conducted on a much larger scale, it could help farmers and arborists plant their trees in prime locations and maintain them efficiently.

  • !"

    Acknowledgements This project wouldnt have been a success without the help from many different

    people throughout the way. I would like to thank Drumlin Farm and all of its volunteers for giving their time to supervise the testing and helping to make everything run smoothly. I would also like to thank the teacher chaperones for volunteering their day to accompany us on the trip to Drumlin Farm and providing us with guidance whenever possible. And last but certainly not least I would like to thank the BB&N Middle School science department for organizing and developing the entire trip while helping each and every person with this extensive project. Without the help of all of these people this project wouldnt have been nearly as successful and I am very thankful for their support.

    Throughout the course of this experiment, from brainstorming to concluding, several people have helped my partner and I. First, I would like to thank Mrs. Larocca for teaching us about all of the different topics that we could explore and for helping us revise our experiment. Mrs. Larocca played a key role in getting the basics down for us and even guiding us through our experiment. Next, I would like to thank the entire Drumlin Farm crew for keeping the Farm in such a beautiful condition, and for their great knowledge on all the outdoor topics that came up in our many questions. Also, I would like to thank Mr. Rossiter, Ms. Bomfim and Mrs. Brooks for being in our general location and keeping watch over us and ready to help us in any way possible. Lastly, I would like to thank my entire class for making science such an amazing experience for me and also for providing my partner and I with help/advice, especially on the writing pieces. Thank you so much to everyone involved in our experiment and God Bless.

  • !"

    WORKS CITED: Benjamin Blackburn: Alvey, Alexis. "Trees Tolerant of High Soil PH* (pH up to 8.2)." Www.ccesuffolk.org.

    Cornell Cooperative Extension of Suffolk County, 2011. Web. Apr. 2014.

    .

    Knapp, Brian J., and Mary Sanders. Acids, Bases, and Salts. Danbury, CT: Grolier

    Educational, 1998. Print.

    Londo, Andrew J., John D. Kushla, and Robert C. Carter. Soil pH and Tree Species

    Suitability in the South. www.lsuagcenter.com. Southern Regional

    Extension Forestry, Jan. 2006. Pdf. 2014 Apr. 2.

  • !"#

    #WORKS CITED: Brendan Donovan: Blumm, Barton M. "Picea Rubens Sarg." Picea Rubens Sarg. Http://www.na.fs.fed.us/,

    n.d. Web. 15 Apr. 2014.

    .

    Harrington, Harry. "Tree PH Ranges." Www.bonsai4me.com. Harry Harrington, 2004.

    Web. 15 Apr. 2014.

    .

    Innovation! Big Green Cartoon Tree. N.d.

    Http://innovation.kpru.ac.th/web17/551121712/innovation/index.php/1-1-5-

    0. Innovation.kpru.ac.th. Web. 1 May 2014.

    .

    Jett, John W. "Horticulture." Www.wvu.edu. Extension Service West Virginia University,

    May 2005. Web. 15 Apr. 2014.

    .

    Rudolf, Paul O. "Red

    Pine."Http://www.na.fs.fed.us/pubs/silvics_manual/Volume_1/pinus/resinosa.htm.

    Www.na.fs.fed.us, n.d. Web. 15 Apr. 2014.

    .

    Sander, Ivan L. "Quercus Muehlenbergii Engelm." Quercus Muehlenbergii Engelm.

  • !!"

    Www.na.fs.fed.us, n.d. Web. 15 Apr. 2014.

    Schlesinger, Richard C. "White

    Ash.http://www.na.fs.fed.us/pubs/silvics_manual/volume_2/fraxinus/am

    Trails at Drumlin Farm. 2014. Www.massaudubon.org.Www.massaudubon.org. Web. 1

    May 2014. .

  • !"#

    APPENDIX

    Figure 1: Red Pine and White Oak testing sites. Many different types of leaves filled the forest floor. The soil was damp and very close to Ice Pond.

    Figure 2: The White Ash testing site. Ashes, can be identified by their triangular shaped trunks. Only White Ash leaves covered the ground, the ground was damp and there were several fallen trees.

  • !"#

    Figure 3: The White Spruce testing site. Spruces, can be identified by the several notches

    in their bark. This ground was not so damp, but still a lot of fallen trees.

    Figure 4: The control run at Hemlock Forest. This was extremely close to Ice Pond, there

    were many varieties of leaves on ground and not many trees nearby.

  • !"#

    Figure 5: This is a pH Rapitest testing kit with the chemical reagent creating a color to compare with the color chart (Right).

    Figure 6: This is an overview map of Drumlin Farm (left).

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