Effect of Ammonia on Fast Plant Root Growth

Effect of Ammonia on Plant Root Growth

Authors:  Faraji, T., Imobersteg, A., Karimi, B., Kerr, A.

12/05/2018

Cal Poly Biological Sciences Dept., Biology 263: Ecology and Evolution

Fall 2018

Abstract:

Bodies of water near animals typically receive runoffs of animal waste which contain high concentrations of ammonia. At high levels of ammonia, important nutrients are not able to reach plant roots and thus it is hypothesized that ammonia will have a negative effect on plant growth. We collected pond water from a grazing field and regular tap water to observe the impact of ammonia on the root growth of  Salvia officinalis, Spinacia oleracea, Trifolium repens, and Lavandula angustifolia plant seeds grown in petri dishes over the course of two weeks. Our raw data showed that spinach did not germinate with either type of water, while clover and lavender grew more in the tap water, and sage grew more in the pond water. The individual t-tests for each plant type did not show significant results between root length and type of water. The ANOVA test showed a significant difference among the different types of plants compared to the type of water used and the tukey test showed no significant difference.

Introduction:

In the current environment, water pollutants pose one of the biggest threats to growing crops and maintaining the agricultural ecosystem. In fact, around the world, agriculture is the leading cause of water degradation and the main pollutant coming from agricultural environments is ammonia, commonly found in manure (Denchak 2018). In many facilities where cattle are present, measures are taken to prevent high ammonia concentrations from polluting water and affecting plant growth and crop yield: constructed wetland systems are designed to utilize water quality improvement processes occurring in natural wetlands, including high primary productivity, low flow conditions, and oxygen transfer to anaerobic sediments (Anthonisen et al., 1976). Although these precautions are taken, the ammonia levels are often still too extreme for plants to handle. These extreme conditions can lead to a decrease in plant survivorship and quality.

This study was conducted at California Polytechnic University, San Luis Obispo, an institution that boasts a large agricultural department and has many grazing fields in close proximity to an orchard and small plots of land used for growing crops. During rainfall, the ammonia from the pastures washes into common waterways and can cause nutrient pollution. Nutrient pollution, caused by excess nitrogen and phosphorus in water or air, is the number-one threat to water quality worldwide and can cause algal blooms, a toxic soup of blue-green algae that can be harmful to people and wildlife. (Denchak, 2018). Ammonia has the potential to affect the ecosystem as a whole.  In order to further study the effect of ammonia on plant growth and sustainability, we conducted an experiment by exposing four different plant species to water from a grazing field pond and compared it to plant growth in controlled tap water. We used a variety of plants, each with their own significance to the ecosystem surrounding our university: Salvia officinalis and Spinacia oleracea used for human consumption, Trifolium repens, used for animal consumption, and Lavandula angustifolia, used for pollen by bees. This was intentionally done to demonstrate how excess ammonia can affect multiple aspects of the ecosystem.

Through some research, use of literature, and previous knowledge from biology courses, we hypothesized that areas near grazing cattle which are prone to high levels of ammonia will have a negative effect on the surrounding plant life. We predicted that all plants grown in pond water from the grazing field would have lower root lengths than plants grown in tap water.

Methods and Materials

In order to conduct this experiment, we collected approximately 1L of pond water for the variable group and 1L of normal tap water as a control group. We then assembled two separate containers large enough to fit four petri dishes vertically and labeled one for control and one for the variable group. To increase visibility and allow the seeds to adhere to the petri dishes, we cut eight circles out of paper towels big enough to comfortably fit inside the petri dish. One paper towel disk was placed inside each petri dish and a line was drawn across the paper towel disk approximately one inch from the top to indicate where the seeds would be placed. Each petri dish was then labeled with each plant and variable (control and variable for lavender, sage, clover, spinach). We then placed nine seeds corresponding to the plant labeled on the petri dish along the line in each dish making sure to evenly space the seeds for maximum growth. The petri dishes were then placed vertically in their respective container. The containers were then filled about half-way with the appropriate water (tap water for the control group and pond water for the variable group). We ensured that the water level was not overflowing the petri dish but was rather at a comfortable level where it would trickle up the petri dish via capillary action. The two containers were then left in a sunny controlled environment to ensure that no third variables were introduced into the experiment. Throughout the two-week experiment, the seeds were monitored to determine which germinated. This observation was recorded in addition to the length of the root once the seed germinated.

Once all of the data was collected, statistical analysis was performed through R. A t-test was done for each plant type to determine the significance of the length of the seed roots in tap water compared to the pond water. This test was chosen because our independent variable is categorical (type of water), and our dependent variable was continuous (length of root), therefore a t-test was the most appropriate statistical analysis. An anova test was also performed to determine to determine the significance of the results among each plant type. A publication quality bar graph was then created to compare the length of the seed root to the type of water. Standard error bars were added which represented to standard error of the mean.

Results:

The pH of the tap water was 7 while the pH of the pond water was around 9. Overall, there is no significant difference between the type of water the seeds were grown in (pond vs. tap) and the root length of the plant. The spinach seeds showed no growth in both the control and variable group. There was no significant difference in root length of plants grown in tap water compared to pond water for sage (p-value = 0.178) (Fig. 3), lavender (p-value = 0.5606) (Fig. 4) , clover (p-value = 0.159) (Fig. 2), or spinach as there was no growth. There was a significant difference among the root lengths of the plant species tested (sage, lavender, clover, spinach) and the type of water used (p=0.0149) (Table 1). We accept the null hypothesis that there is no significant difference between growing plants (sage, lavender, clover, spinach) in high levels of ammonia (pond water) and tap water (Fig. 1).

Discussion:

The experiment returned with results that proved failure in rejecting the null hypothesis. The data from the study showed insignificant differences between pond and control water on plant root growth. The hypothesis was not necessarily incorrect, but our experiment was unable to prove a significant difference between pond and control water sources in the statistical analysis. Sage was the only species to show impressive growth in both the pond and control water, a result which was unexpected due to the known hazardous effects of high ammonia concentrations (Anthonisen et al., 1976). In this group, pond water caused the sage to grow over three times the amount of the control water. This could be due to an increase in the nitrification process where ammonia is oxidized to nitrate for plant use. While too much ammonia can overflow this process, some studies have shown that at low concentrations, ammonia can promote plant growth as it is an important source of nitrogen (Anthonisen et al., 1976). Clover and Lavender both had seeds which grew longer roots in tap water compared to pond water, though not to a significant extent. Nonetheless, the raw data clearly showed more growth in the control water source than in pond water. Our threshold for the significance level was very low, using a p value < .05 as significant however if the threshold was moved up to .10, it could be a more accurate representation of the data considering the amount of outliers in the raw data set. Ignoring the raw data could lead to a type II error in which we assume there is no difference when there truly is one and increasing the minimum p value could remediate this error.

In one study similar to ours, the optimal water levels and ammonia levels for a wetlands environment were proposed. The conclusions of the study were that the effectiveness of a wetlands system may be limited by a high ammonia concentration. The study considered that management of ammonia concentration may enhance total plant and biomass growth alongside system functionality levels (Clarke & Baldwin, 2002). This could explain our results, where clover and lavender grew longer in control water than in the ammonia concentrated pond water since high concentrations of ammonia can be detrimental to plant growth and can cause a pause in the growth cycle. The scientists also concluded that the amount of ammonia in the water was significant to the effect it had on the plant growth. Plants in concentrations of ammonia over 200 mg/l were the only ones which experienced the hinderance on their growth.

As studies continue, it is imperative to determine what safe levels of ammonia are for plants and to employ those standards wherever agricultural practices take place. With the number of cattle increasing, the issue of manure management becomes a more pressing problem (USDA, 1995). Though our study did not present significant results, it is important to keep in mind that more populated agricultural fields could produce higher levels of ammonia which could affect crop yield and growth leading to a global epidemic. To improve upon our experiment, we would recommend using increments of ammonia concentrations to determine the level at which ammonia becomes toxic to plants. For example, it would be beneficial to isolate the variable such that ammonia is the only compound found in the water and grow plants using accurate concentration measurements of ammonia and increase the concentration at small increments until the plants no longer exhibit growth.

In conclusion, our findings supported the null hypothesis that there was no significant difference between root length and type of water used to grow the plants (pond vs. tap). It is possible that the concentration of ammonia present in the pond water was simply not high enough to significantly negatively affect the growth of plants but more research is needed to specify the ammonia level which warrants toxicity in plant growth. When a Tukey HSD test was run on all variables, it showed that only three were significantly related. This showed that almost no matter what comparisons we made, it would be insignificant. This is the main reason we were unable to compare the total growth in control water sources vs. pond water sources.

References:

Anthonisen, A. C., Loehr, R. C., Prakasam, T. B. S., and Srinath, E. G. 1976. Inhibition of nitrification by ammonia and nitrous acid. Journal (Water Pollution Control Federation). 48(5): 835-852.

Clarke, E., and Baldwin, A. H. 2002. Responses of wetland plants to ammonia and water level. Ecological Engineering. 18: 257-264.

Denchak, M. 2018. Water Pollution: Everything You Need To Know. Natural Resources Defense Council  https://www.nrdc.org/stories/water-pollution-everything-you-need-know#common

United States, Congress, Natural Resources Conservation Service. “Animal Manure Management.” Animal Manure Management, Brief #7 ed., United States Department of Agriculture, 1995. RCA Edition. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/null/?cid=nrcs143_014211

📷

Figure 1:  Comparison of root length (cm) of clover, sage, lavender, and spinach plants compared to water type (control, pond).  Error bars are shown to represent the standard error. Dots show statistical outliers.

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Figure 2: Comparison between root length of clover plant and type of water (tap, pond). There was no significant difference between root length and type of water (t-test, t = 1.4774, df = 16, p-value = 0.159)

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Figure 3: Comparison between root length of sage plant and type of water (tap, pond). There was no significant difference between root length and type of water (t-test, t = -1.4091, df = 16, p-value = 0.178). Standard error bars represent standard error of the mean.

📷

Figure 4: Comparison between root length of lavender plant and type of water (tap, pond). There was no significant difference between root length and type of water (t-test, t = 0.59428, df = 16, p-value = 0.5606).

Source

df

SS

MS

F

p

Treatment

7

76.68

10.955

2.74

0.0149

Length Valves

64

255.84

3.998

Total

71

332.62

Table 1: There is a significant difference among the root lengths of the tested plants (sage, lavender, clover, and spinach)  and the type of water used (control, pond) (ANOVA, F= 2.74, df= 7, 64, p= 0.0149).

ANOVA Test Input:

pd <- read.csv("PondData.csv", header=TRUE)

attach(pd)

str(pd)

a7 <- aov(Length..cm.~Treatment)

summary(a7)

TukeyHSD(a7)

install.packages("ggplot2")

library(ggplot2)

ggplot(pd, aes(Treatment, Length..cm.)) + geom_boxplot() + labs(x="Treatment Type", y="Length (cm)")

Code Output:

> pd <- read.csv("PondData.csv", header=TRUE)

> attach(pd)

> str(pd)

'data.frame': 72 obs. of  2 variables:

$ Treatment  : Factor w/ 8 levels "Clover Control",..: 5 5 5 5 5 5 5 5 5 6 ...

$ Length..cm.: num  0 0 0 0 0 0 2.3 4.1 3.3 0 ...

> a7 <- aov(Length..cm.~Treatment)

> summary(a7)

           Df Sum Sq Mean Sq F value Pr(>F)  

Treatment    7 76.68 10.955    2.74 0.0149 *

Residuals   64 255.84 3.998                 

---

Signif. codes:  

0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> TukeyHSD(a7)

 Tukey multiple comparisons of means

   95% family-wise confidence level

Fit: aov(formula = Length..cm. ~ Treatment)

$`Treatment`

                                         diff

Clover Pond-Clover Control       -8.111111e-01

Lavender Control-Clover Control   4.444444e-02

Lavender Pond-Clover Control     -4.111111e-01

Sage Control-Clover Control       1.333333e-01

Sage Pond-Clover Control          2.433333e+00

Spinach Control-Clover Control   -9.444444e-01

Spinach Pond-Clover Control      -9.444444e-01

Lavender Control-Clover Pond      8.555556e-01

Lavender Pond-Clover Pond         4.000000e-01

Sage Control-Clover Pond          9.444444e-01

Sage Pond-Clover Pond             3.244444e+00

Spinach Control-Clover Pond      -1.333333e-01

Spinach Pond-Clover Pond         -1.333333e-01

Lavender Pond-Lavender Control   -4.555556e-01

Sage Control-Lavender Control     8.888889e-02

Sage Pond-Lavender Control        2.388889e+00

Spinach Control-Lavender Control -9.888889e-01

Spinach Pond-Lavender Control    -9.888889e-01

Sage Control-Lavender Pond        5.444444e-01

Sage Pond-Lavender Pond           2.844444e+00

Spinach Control-Lavender Pond    -5.333333e-01

Spinach Pond-Lavender Pond       -5.333333e-01

Sage Pond-Sage Control            2.300000e+00

Spinach Control-Sage Control     -1.077778e+00

Spinach Pond-Sage Control        -1.077778e+00

Spinach Control-Sage Pond        -3.377778e+00

Spinach Pond-Sage Pond           -3.377778e+00

Spinach Pond-Spinach Control      8.881784e-16

                                       lwr

Clover Pond-Clover Control       -3.7643642

Lavender Control-Clover Control  -2.9088087

Lavender Pond-Clover Control     -3.3643642

Sage Control-Clover Control      -2.8199198

Sage Pond-Clover Control         -0.5199198

Spinach Control-Clover Control   -3.8976976

Spinach Pond-Clover Control      -3.8976976

Lavender Control-Clover Pond     -2.0976976

Lavender Pond-Clover Pond        -2.5532531

Sage Control-Clover Pond         -2.0088087

Sage Pond-Clover Pond             0.2911913

Spinach Control-Clover Pond      -3.0865865

Spinach Pond-Clover Pond         -3.0865865

Lavender Pond-Lavender Control   -3.4088087

Sage Control-Lavender Control    -2.8643642

Sage Pond-Lavender Control       -0.5643642

Spinach Control-Lavender Control -3.9421420

Spinach Pond-Lavender Control    -3.9421420

Sage Control-Lavender Pond       -2.4088087

Sage Pond-Lavender Pond          -0.1088087

Spinach Control-Lavender Pond    -3.4865865

Spinach Pond-Lavender Pond       -3.4865865

Sage Pond-Sage Control           -0.6532531

Spinach Control-Sage Control     -4.0310309

Spinach Pond-Sage Control        -4.0310309

Spinach Control-Sage Pond        -6.3310309

Spinach Pond-Sage Pond           -6.3310309

Spinach Pond-Spinach Control     -2.9532531

                                       upr

Clover Pond-Clover Control        2.1421420

Lavender Control-Clover Control   2.9976976

Lavender Pond-Clover Control      2.5421420

Sage Control-Clover Control       3.0865865

Sage Pond-Clover Control          5.3865865

Spinach Control-Clover Control    2.0088087

Spinach Pond-Clover Control       2.0088087

Lavender Control-Clover Pond      3.8088087

Lavender Pond-Clover Pond         3.3532531

Sage Control-Clover Pond          3.8976976

Sage Pond-Clover Pond             6.1976976

Spinach Control-Clover Pond       2.8199198

Spinach Pond-Clover Pond          2.8199198

Lavender Pond-Lavender Control    2.4976976

Sage Control-Lavender Control     3.0421420

Sage Pond-Lavender Control        5.3421420

Spinach Control-Lavender Control  1.9643642

Spinach Pond-Lavender Control     1.9643642

Sage Control-Lavender Pond        3.4976976

Sage Pond-Lavender Pond           5.7976976

Spinach Control-Lavender Pond     2.4199198

Spinach Pond-Lavender Pond        2.4199198

Sage Pond-Sage Control            5.2532531

Spinach Control-Sage Control      1.8754754

Spinach Pond-Sage Control         1.8754754

Spinach Control-Sage Pond        -0.4245246

Spinach Pond-Sage Pond           -0.4245246

Spinach Pond-Spinach Control      2.9532531

                                    p adj

Clover Pond-Clover Control       0.9884756

Lavender Control-Clover Control  1.0000000

Lavender Pond-Clover Control     0.9998484

Sage Control-Clover Control      0.9999999

Sage Pond-Clover Control         0.1818080

Spinach Control-Clover Control   0.9725078

Spinach Pond-Clover Control      0.9725078

Lavender Control-Clover Pond     0.9842733

Lavender Pond-Clover Pond        0.9998739

Sage Control-Clover Pond         0.9725078

Sage Pond-Clover Pond            0.0215005

Spinach Control-Clover Pond      0.9999999

Spinach Pond-Clover Pond         0.9999999

Lavender Pond-Lavender Control   0.9996996

Sage Control-Lavender Control    1.0000000

Sage Pond-Lavender Control       0.2000542

Spinach Control-Lavender Control 0.9646567

Spinach Pond-Lavender Control    0.9646567

Sage Control-Lavender Pond       0.9990330

Sage Pond-Lavender Pond          0.0671283

Spinach Control-Lavender Pond    0.9991541

Spinach Pond-Lavender Pond       0.9991541

Sage Pond-Sage Control           0.2403760

Spinach Control-Sage Control     0.9443730

Spinach Pond-Sage Control        0.9443730

Spinach Control-Sage Pond        0.0142530

Spinach Pond-Sage Pond           0.0142530

Spinach Pond-Spinach Control     1.0000000

> install.packages("ggplot2")

trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.5/ggplot2_3.1.0.zip'

Content type 'application/zip' length 3622449 bytes (3.5 MB)

downloaded 3.5 MB

package ‘ggplot2’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in

C:\Users\Owner\AppData\Local\Temp\RtmpGafuP5\downloaded_packages

> library(ggplot2)

> ggplot(pd, aes(Treatment, Length..cm.)) + geom_boxplot() + labs(x="Treatment Type", y="Length (cm)")