Project Reports SS17 Total.pdf

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EDUCATIONAL NETWORK ON SOIL AND PLANT

ECOLOGY AND MANAGEMENT

(EduSapMan)

Summer School Soil & Water 2017

Practical exercises

Content: 1. Stomatal kinetics in response to CO2 and its relation to stomatal size and density 2

2. Soil compaction and oxygen in soil 12

3. Soil zoology 17

4. Land properties influenced by land use and fertilization 25

5. Fast Plant test with various substrates and composts 49

6. Allelopathy experiment with Estonian trees 60

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1. Stomatal kinetics in response to CO2 and its relation to

stomatal size and density

Markus Hügel, Kateřina Kabeláčová, Thomas Link-Hessing, Claude-Eric Marquet and

Patrick Waldhelm

Supervisors: Tiina Tosens, Linda-Liisa Veromann

1 Introduction

Water use efficiency (WUE) is very often topic lately. The level of greenhouse gas CO2 is

rising. That means, inorganic C is more available for plants to assimilate, but for

photosynthesis is important water also. So on the other hand there is problem with water

sufficiency, because of greenhouse gases, the average temperature of the planet is rising and

dry areas are expanding. Plants are under a constant stress, because of drought.

2 Materials and methods

2.1 Measuring stomatal conductance and photosynthesis

Materials:

- Two plants Platanus orientalis (normal and stressed)

- Fern Microsorum diversifolium

- Two instruments GFS-3000 Portable Photosynthesis System

Procedure:

There were used two plants Platanus orientalis and one fern Microsorum diversifolium. One

P. orientalis was not watered for seven days and the second P. orientalis was well watered.

Plants were kept under artificial conditions. Plane trees were kept under the light intensity of

1000 µmol/m²s, 22 °C and 65% humidity. The M. diversifolium was kept under artificial light

at 600 µmol/m²s, 22 °C and 65% humidity. Experiments were done in the plant physiology

laboratory.

The stomatal conductance and the photosynthesis were measured with two instruments GFS-

3000 Portable Photosynthesis System. At first chambers of the machines were adjusted to

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these settings: temperature 25 °C and humidity 65%. The light settings differed: For plane

trees it was set to 1000 µmol/m²s and 600 µmol/m²s for M. diversifolium. The CO2 levels in

chambers were changed during measurements. At first the experiment begun with 400 ppm of

CO2, then 100 ppm and at the end 800 ppm. Changes of CO2 levels were made after a

stabilization of assimilation and stomatal conductance. There was also measured the time of a

stabilization.

Figure 1: GFS-3000 Portable Photosynthesis System

Figure 2: P. orientalis leaf in measurement chamber

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2.2 Stomatal size and density measurement

Materials:

- Transparent nail polish, transparent tape, slides

- Light microscope, PC, Image-J software

Procedure:

The “varnish”-method was used. The leaf which was previously used for measuring, was

coated with clear nail polish on the bottom of the leaf. Leaf-veins were avoided. After the nail

polish was dry, transparent tape was sticked on it and pulled off. The tape with the dry varnish

was sticked on the slides and analysed under the light microscope. Photos of the leaf bottom

imprint were taken in a magnification of 100 and 200. To calculate the stomatal size the

length and width of 10 stomata for each plant were measured with the computer program

ImageJ. The stomatal size was calculated by multiplying the length with the width. To obtain

the stomatal density the amount of stomata on three different parts of the leaf bottom were

counted. To acquire average density the formula: number of stomata / area.

Figure 3: Leaf bottom surface imprint on slides

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Figure 4: Stomata of M. diversifolium at 200 magnification

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Figure 5: Stomata of P. orientalis at 200 magnification

3 Results

During the first experiment gained data about the stomatal conductance and the

photosynthesis are in tables 1 and 2. Graphs in figure 6 and 7 were constructed from these

data and from CO2 levels. We calculated values of WUE according to this formula:

𝑊𝑈𝐸𝑝ℎ𝑜𝑡𝑜𝑠𝑖𝑛𝑡𝑒𝑠𝑖𝑠 =𝐴𝑠𝑖𝑚𝑖𝑙𝑎𝑡𝑖𝑜𝑛

𝑠𝑡𝑜𝑚𝑎𝑡𝑎 𝑐𝑜𝑛𝑑𝑢𝑐𝑡𝑎𝑛𝑐𝑒

Table 1: CO2 Assimilation of the three test plants

CO2

[ppm]

P. orientalis (normal) P. orientalis (drought) M. diversifolium

100 1,07 0,02 0,51

400 6,86 0,97 3,5

800 12,26 2,37 6,84

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Figure 6: CO2 Assimilation of the three test plants

Table 2: Stomata conductance of the three test plants

CO2

[ppm]


100 103,4 9,1 52,2

400 58,6 9,9 33,7

800 42,2 9,6 33,8

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Figure 7: Stomata conductance of the three test plants

WUE values were transferred into the table 3 and figure 8. Stabilization periods of plants was

inserted into the table 4.

Table 3: WUE of the three test plants

CO2

[ppm]


100 10 2 9

400 117 97 103

800 290 246 202

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Figure 8: WUE of the three test plants

Table 4: Stabilization period of the three test plants

Measurement P. orientalis (normal) P. orientalis (drought) M. diversifolium

1. 40 min 8 min 62 min



Summary 75 min 17 min 92 min

The stomatal size and density are shown in figure 9 and 10.

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Figure 9: Stomatal size of the three test plants

Figure 10: Stomatal density of the three test plants

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4 Discussion

We observed reactions of stomata to different levels of CO2. The plant which was stressed by

drought had lower stomata conductance than other plants. It could be related to the

physiological mechanism which tend to protect the plant from complete dryness. The plant

was stressed even more when there was low CO2 level. At the level 100 ppm well-watered

plants managed to open the stomata more than the plant which suffered by drought. At the

level 800 ppm plants had enough of CO2 in the chamber so they could close stomata. Even

though the difference between plane trees is very visible, for better significance and good

strength of the experiment, it would be good to do the experiment with more plants and

different species.

The varnish method showed us how simple it is to observe stomata structures and compare

them between different plant families as gymnosperm and angiosperm. According to the

evolution, we can suggest that angiosperm plants have smaller stomata structure and higher

density compared to ancient gymnosperm family. On the other hand the habitat of the fern is

more humid so it can manage to have bigger stomata than the plane tree. We realized that the

fern needs more time to open stomata and have slower reaction to the changes of CO2 levels.

For better resolution, there would be needed to have wide range of gymnosperm and

angiosperm species to compare.

5 Conclusion

The plane tree which suffered by drought had low stomata conductance during the whole

experiment. The measuring took for 17 minutes in total and gs values were 9,1; 9,9 and 9,6

mmolH2O. WUE values were 2; 97 and 248 µmolCO2∙mmolH2O-1. Well-watered plane tree had

higher values. Stomatal conductance values were 103,4; 58,6 and 42,2 mmolH2O. WUE values

for well-watered plane tree were 10; 117 and 290 µmolCO2∙mmolH2O-1. The measuring took for

75 minutes. The fern had slightly lower values in comparison to well-watered plane tree.

Values of stomatal conductance were 52,2; 33,7 and 33,8 mmolH2O. WUE values were 9; 103

and 202 µmolCO2∙mmolH2O-1. The measuring of the fern took for 92 minutes.

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Summer School Soil & Water

Tartu 2017

Supervisor: Prof. Dr. Marian Kazda, Ahmed Sharif

2. Soil compaction and oxygen in soil

Linda Ahner

Milan Varsadiya

Laurie-May Gonzales

Bernhard Glocker

Contents 1 Introduction

2 Materials and Methods

3 Results

4 Discussion

5 Conclusion

6 References

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1 Introduction Nowadays drip irrigation has gained more important in agriculture and has been

used very intensively on the fields. Although this is a more sustained way to

irrigate the plants, it limits oxygen availability for plant roots by creating a nearly

saturated condition. Furthermore, high soil compaction also affects the amount

of oxygen content in soil which changes gas exchange. In general, gas exchange is

also affecting growth and activity of roots and soil organisms, and leading to an

alteration of chemical processes (Ampoorter et al., 2010). For instance, N fertilization has

significant effect on microbial CO2 respiration and communities functioning. This was also

proved by laboratory incubation experiments (Kowalenka et al., 1978).

In Summary soil compaction involves the compression of pores, which leads to

decreased porosity, increase in dry bulk density and reduce hydraulic conductivity.

The questions of this short experiment were “Soil with organic manure has more oxygen

depletion rate than soil without manure” and “Soil managed with organic fertilizer contains

lower bulk density”.

2 Materials and Methods Six samples were collected on 26th of June 2017 from the long-term fertilization

experiment-site IOSDV in Tartu. Two samples were collected from the same field

without organic fertilizers and without manure, two from the same field but with

manure. And the last two samples were collected from the field with an alternative

organic fertilizer.

The samples were collected in metal cylinders from five centimetres under the surface and

these were put in plastic cups. The second part was in the laboratory. First, the samples were

weighted and after that all samples were flooded with double distilled water (ddH2O) until the

complete soil was saturated. All samples got an oxygen sensor through the plastic lid. For the

measurement, the “FIBOX LCD” was used. Periodically, measurements were recorded. When

the reading came to zero, the water from the plastic cups were removed manually.

Then the samples were kept on filter paper and the plastic lids were left half open to test the

speed of re-aeration. Successively the readings were recorded. After all the measurements

have been taken, all wet samples were re-weighted. To measure dry bulk density, the soil has

to be oven-dry. Therefore, samples were kept in an incubator at 105_C for 24 hours. Finally

the dry soils were weighted again.

3 Results In the following Figure 3.1, the results are shown for the changing of the oxygen levels. The

oxygen concentration in all wet soils decreased in the first 17 hours however, the next two

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days it was stable. On the 1st of July the samples were reaerated at 11 o’clock. The soil without

any fertilizer (beginning value 7.1 mg/l) showed a steep increasing (7.6 mg/l). Then it was on

the same level for a brief time before it drastically declined until 0 mg/l. The Inorganic nitrogen

fertilized sample (beginning value 8.0 mg/l) was stable as well until 1st of July, 16:30. After that

it increased until the end of the experiment (6.2 mg/l). The last sample with organic manure

and nitrogen (beginning value 7.7 mg/l) showed no reaeration until the end (0 mg/l).

Figure 3.1: Changing of the oxygen rate in time. NO – no fertilizer, N 120 –

inorganic nitrogen (120 kg N/ha) fertilizer and ON 120 – alternative

manure nitrogen (120 kg N/ha) fertilizer

Figure 3.2: Dry bulk density. NO – no fertilizer, N 120 – inorganic nitrogen (120

kg N/ha) fertilizer and ON 120 – alternative manure nitrogen (120 kg

N/ha) fertilizer.

Figure 3.2 depicts the dry bulk density (BD). For NO BD the value was 1.4 g/cm3 for the other

two samples the values were smaller. The value for NO 120 was 1.2 g/cm3 and for ON 120

was 1.25 g/cm3.

In Figure 3.3 the maximum water holding capacity is shown. The samples NO, NO 120 and

ON 120 had 30.3 %, 31.3% and 33.3 %, respectively.

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Figure 3.3: Maximum water holding capacity. NO – no fertilizer, N 120 – inorganic

nitrogen (120 kg N/ha) fertilizer and ON 120 – alternative manure

nitrogen (120 kg N/ha) fertilizer.

The Table 3.1 summarizes variables that are important for soil compaction,

which goes respectably in line together. The less the particle density correlated

with higher porosity and maximum water holding capacity.

Table 3.1: Different soil compaction variables. NO – no fertilizer, N 120 – inorganic

nitrogen (120 kg N/ha) fertilizer and ON 120 – alternative manure

nitrogen (120 kg N/ha) fertilizer.

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4 Discussion All three samples showed instant decreasing in oxygen concentration suggested

that water created pressure on the air in the soil which lead to escape it from the

soil. This can be proved also in the stable phase afterwards where there cannot

be any significant different seen.

After reaeration, the air can get inside of the soil and provides more oxygen concentration

increased again. Kowalenka et al., (1978) suggested that nitrogen fertilizer has an influential

effect on microbial communities. This can be seen in the results where the oxygen

concentration increased fast in the sample without fertilizer because the microbial activity was

presumably lower. In the sample with just nitrogen fertilizer the microbial activity was better

than in the sample without fertilizer. But only in the sample with nitrogen and organic manure

did not show any changes in the oxygen concentration due to higher microbial activity.

However, there is still a need to optimize the method as there were problems in enclosing air

bubbles in front of the sensor. Furthermore, moving the sensors during the measurement could

cause its shift into soil parts still anoxic (c.f. Fig. 3.1., sample NO).

The bulk density and the maximum water holding capacity showed a negative

relationship. The higher the bulk density, the less the porosity which lead to less

water holding capacity. The particle density correlated with the porosity.

5 Conclusion Irrespectively the soil properties, in flooded soils the water replaces the air in the

pores so there is nearly no oxygen left. There wasn’t any substantial change in

reaeration for organic manure with mineral N fertilized soil. Its most likely due

to the microbial activities in the soil Addicted to mineral nitrogen fertilizer with

manure, there is a lower bulk density and a higher water holding capacity. , which

depends on soil composition.

In the end, the first hypothesis could not be proved in the depletion rate but

there was no increase in the oxygen rate in the soil managed with manure while

reaeration. Just like the first hypothesis the second was not able to be proved: the

bulk density was not lower in soil with the organic fertilizer.

6 References Ampoorter, E., Van Nevel, L., De Vos, B., Hermyc, M., Verheyena, K. 2010.

“Assessing the effects of initial soil characteristics, machine mass and traffic

intensity on forest soil compaction”. Forest Ecology and Management 260,

1664–1676.

Kowalenko, C. G., Ivarson, K. C., Cameron, D. R. 1978. “Effect of moisture

content, temperature and nitrogen fertilization on carbon dioxide evolution

from field soils”. Soil Biology and Biochemistry 10, 417-423.

Bhattarai, S. P., Pendergast, L., Midmore, D. J. 2006, “root aeration improves

yield and water use efficiency of tomato in heavy clay and saline

soils”. Scientia Horticulturae 108, 278-288.

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Soil and water 2017 in Tartu, Estonia

Julian Fernando Cárdenas Hernandes Docente, Liisi Tonisson, Valentin Mönkemöller, Violetta Volokitina, Paul-Loup Lecomte

3. Soil Zoology Mini Project

mailto:%[email protected]

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Introduction

The soil is alive. The structure of the soil changes during the time and also in short distances.

There are a lot of different species living in this special habitat. Soil fauna are the animals which

live in the soil or on the ground. Every taxon has an important function for the soil structure.

There are two different ways to separate the soil fauna. The first one is based on the habitat.

Earthworms are anecic animals; they live in and on the soil surface. Epigeic animals are usually

found on the soil surface or the litter, for example Coleoptera. Animals, which live only in the

soil, are called endogeic, for example Nematodes.

The other way to separate the soil fauna is based on the size of the organisms. The microfauna

includes organisms, which are smaller than 0.02 mm, for example Nematodes. They are

important for the soil mineralization.

The organisms of the mesofauna are smaller than 2 mm but bigger than an usual organism of

the micro fauna, like Collembolan and Acarida. Their main function is the litter decomposition.

The last group is the macrofauna with organisms with the size from 2 mm to 20 mm, for

example Arachnids and Coleoptera, which are important for the bioturbation.

The function of the soil fauna is the distribution of organic matter, nutrient cycling, soil

formation and many other processes which have a big influence in soil restoration.

In a European forest, you can find between 50.000 and 300.000 organisms in one square meter.

But the number of the soil animals and microorganisms depends on several environmental

influences, which impact the soil fauna. The soil fauna is influenced by vegetation, soil type

and of cause by the anthropogenic use.

Materials and methods

1. Sampling method

In order to study organisms of the soil meso- and macrofauna two different sampling

methods were used. The first method was implemented by pitfall traps.

This kind of trap is a plastic cup buried underground on the top at the soil level. The aim is

to collect the soil organisms that fall into the solution. The solution contains water and an

additional detergent (soap) for reducing the water surface tension to prevent escaping

chances for the animals.

The second method is Tullgren method. Executing this method, the same amount of soil

was collected from several plots. To be sure that the amount of soil of every plot is the same

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a sampling metal ring with a defined volume was used. Afterwards the collected samples

dried in a funnel by light for 48 hours (Figure 1). The purpose is to force the fauna to escape

the dryness and to fall into the solution (water with soap) through the grid (2 mm tight).

2. Sampling plots

One of the main aims of the study is to show the differences of the soil fauna between the

recently cut meadow and the forest indicating by the richness and the composition of soil

communities. Two samples of the meadow soil and 3 samples of the forest soil (lime, spruce

and birch forest) were collected around the University of the Life Sciences in Tartu. The figure

2 shows the map with marked collecting spots. Only one exemplar of each spot was investigated

because of the limited time of the mini project.

Figure 1: Right corner: Collecting of soil sample by a sampling ring and setting of pitfall

traps.

Left corner: Tulgrenn method, illuminating the soil sample

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3. Sorting and identification

For the identification of the animals following equipment was used: microscope, identification

kit including several instruments and the identification key. The taxa were determined to the

order level. The abundance and the richness are noticed.

4. Data analysis

The different communities are compared by the dissimilarity index of Bray Curtis. As defined

by Bray and Curtis, the index of dissimilarity is:

Where Cij is the sum of the less values for only those species in common between both sites.

Si is the total number of specimens counted at both sites. The index reduces to 1-2C/2 = 1-C,

where the abundances at each site are expressed as a percentage. All the data is analyzed with

the software Past.

Results

For each sample, not more than 15 individuals per traps and soil sample were found. As the

following graphs show, the meadow has a poor diversity of taxa with a huge proportion of

Figure 2: Localization of the sampling plots, A and B: cut meadow, C, D; E: forest, Source Google Maps 201

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Collembola (more than 70% of total abundance). The percentage left is shared by beetles,

spiders, ants and larvae. The Tullgren method demonstrates more diversity.

Otherwise, for the forest a relative big diversity of taxa (9 and 6) is observed in contrast of the

meadow sample. A significant proportion of mites and collembola were found in the pitfall traps

followed by spiders, larvae, myriapods, annelids, mollusca. Such results are observed for the

other with myriapods, beetles and spiders who are missing.

In the following graphs, no difference can be noticed between the two methods, in deed less

taxon was found with the Tulgrenn method. Also, a better equitability of kind of organisms is

highlighted for the second method.

Beetle; 1

collembola; 7

Spiders; 0,5

Larvae; 1

Meadow-Tulgrenn

Ants; 1

Beetle; 0,5

collembola; 7,5

Meadow-pitfall

Figure 2: Abundance relation in taxa collected in the cut meadow: on the right by pitfall trap and on the

left by the Tullgren’s method

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According to the figure created by the software a significant dissimilarity between the

communities of the cut meadow and the forest soil can be clearly seen. Also, the heterogeneity

of the communities can be evaluated.

Discussion

Initially, it was expected to find differences in both environments; cut meadow and forest, based

on previous reports (Decaens, 2010) and on the observations made during sampling. The most

obvious difference between the meadow and forest places, in this case, was the high disturbance

of the meadow surface environment caused by the constant and recent transit of the mower,

possibly being the main reason for the low diversity in the cut meadow, especially in the surface

(pitfall trap).

Forest-pitfall Forest-Tulgrenn method

Ants Beetle collembola

mites Spiders Centipod

Mollusca Isopod Larvae

myriapods Lombricidae Annelia

Figure 5: Dissimilarity between the forest community and the meadow community (Software Past),

NMDS (Non-metric multidimensional scale)

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However, additional factors could have influenced these results. The sampling points in the

forest did present different plant species, while the meadow was mainly populated by only one

grass specie. The diversity of plants affects the diversity of fauna in a direct and indirect way.

Depending on the quality of the litter, soils tend to be more diverse in areas with a better litter

quality (easy degradation) (Sauvadet et al., 2017). Additionally, different plant sizes mean more

strata and so: more diversity. Plants tend to have specific communication (secondary

metabolites): repelling or attracting animals, therefore, a higher diversity of plant species

consequently leads to an increasing possibility of ecological interactions. It is well known that

plants drive the diversity of microbes in the soil, in this sense; plants do regulate all the trophic

nets based on microbes (Decaens, 2010).

The soil resources (organic and mineral nutrients, water, etc.) are expected to be higher in the

soil of the forest, and if so, it could be associated with a higher biodiversity. However, in our

case, no soil parameters were measured and that is why there is not much to analyze.

The sampling method will always influence the results of the fauna diversity (Tuf, 2015). In

our case, the pitfall traps were more diverse than the direct soil sampling in the forest, but not

in the meadow, showing the capability of this sampling method to show strong disturbances; in

this case the mower.

Collembola was the most common group among the sites and sampling methods. This group is

widely distributed in the soils of the whole planet and in our case it shows that as group, its

presence is not affected by disturbances, plant species or possible sources differences. However,

inside the group the diversity is high and there are many collembolan species with diverse

functions and interactions, which are very useful as bio indicators (Rusek, 1998); suggesting a

more specific identification for future experiments in order to get more information of the

different environments.

Mites were found only in the forest; which was possible because of the high sensitivity of these

animals to human disturbances as agricultural practices or contamination with no recover

capacity of the communities; even after recovery efforts. However, this group of insects are

very resilient to natural changes of the ecosystems; being one of the first active species after ice

melting at the end of the winter (Gan, 2013). Even though the identification in this experiment

was very general, mite group was associated to the less distributed places, showing a high

potential as a general bio indicator, even at group level.

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Conclusion

First of all, the experiment was very efficient. It shows a general overview of the soil fauna

using simple and cheap methods. But there are some improvements that could make the

methods more precise.

To avoid the over flooding by rain it is important to build a roof over the pitfall traps. Enlarging

the trapping period from 18 h to 72 h as well as the illuminating period up to 48h could possibly

raise the number of caught animals, which can impact the results. To be more random it is highly

recommended to set up more pitfall traps in the several spots. In the described experiment, the

identification was only occurred to the high taxa and focused on the meso- and macro fauna,

but to specify the results it would be interesting to analyze the animals on the species level and

to characterize the micro fauna.

References

Decaëns, T. 2010. Macroecological patterns in soil communities. Global Ecology and

Biogeography. 19, 287–302.

Gan H. 2013 — Oribatid mite communities in soil: structure, function and response to

global environmental change — A dissertation submitted in partial fulfillment of the

requirements for the degree of Doctor of Philosophy (Ecology and Evolutionary

Biology) in the University of Michigan 2013, pp. 164.

Rusek, J. 1998. Biodiversity of Collembola and their functional role in the ecosystem.

Biodiversity & Conservation. 7(9), 1207-1219.

Sauvadet, M., M. Chauvat, N. Brunet and I. Bertrand. 2017. Can changes in litter quality

drive soil fauna structure and functions? Soil Biology & Biochemistry 107 (2).

Tuf, Ivan Hadrián. (2015). Different collecting methods reveal different ecological

groups of centipedes (Chilopoda). Zoologia (Curitiba), 32(5), 345-

350. https://dx.doi.org/10.1590/S1984-46702015000500003

https://dx.doi.org/10.1590/S1984-46702015000500003

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4. Land properties

influenced by land use

and fertilization

Group: Liljana Schmidhäuser, Christina Miehle, Alena Maidel, Jan Smejkal,

Christoph Maier

Supervisor: Alar Astover

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Contents

1. Introduction ...................................................................................... 28

2. Material and Methods ...................................................................... 29

2.1 Soil texture fingering test ............................................................................................... 29

2.2 Moisture content by air drying ....................................................................................... 30

2.3 Soil pHKCl ....................................................................................................................... 30

2.4 Soil electrical conductivity/salinity ................................................................................ 30

2.5 P, K, Ca, Mg by Mehlich-3 method ............................................................................... 31

2.6 Loss on ignition (LOI) .................................................................................................... 31

2.7 C:N by dry combustion .................................................................................................. 31

2.8 Dry bulk density ............................................................................................................. 32

2.9 Calculated soil carbon stock ........................................................................................... 32

3. Results .............................................................................................. 33

3.1 Soil texture fingering test ............................................................................................... 33


3.3 Soil pHKCl ....................................................................................................................... 34





3.8 Dry bulk density ............................................................................................................. 40


3.10 Correlation matrix ........................................................................................................ 42

4. Discussion ........................................................................................ 43

4.1 Soil texture by fingering test .......................................................................................... 43


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4.3 Soil pHKCl ....................................................................................................................... 43





4.8 Dry bulk density ............................................................................................................. 45


4.10 Correlation matrix ........................................................................................................ 46

4.11 Comparison .................................................................................................................. 46

5. Sources ............................................................................................. 48

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1. Introduction

The fact that the population is exponential rising we need more food or a higher yield compared

to hundred years ago. One big point to achieve this is to fertilize the fields to stop the

degradation of the soil. It is not the case, that the humanity has found the perfect way to fertilize

their fields. When the farmers fertilize their fields over decades there are unpredictable changes

in the soil properties. To keep the yield high and stabilize the soil properties with the least

possible amount of fertilizers as possible, long-term field experiments were set up. In this mini-

project we measured different land properties influenced by land use and fertilization. The aim

is to find out and achieve the best way of fertilizing fields to minimalize the degradation of the

soil and get the highest yield.

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2. Material and Methods

The soil samples were collected from the IOSDV (Internationale Organische Stickstoff

Dauerdüngungs Versuchsreihe) long-term fertilization experiment in Tartu. The used

experimental plots are shown in Figure 1. The sample N0 was without organic fertilizer and

without mineral nitrogen. Sample N120 was without organic fertilizer but with a mineral

nitrogen rate of 120 kg N/ha. The samples N120 organic and N0 organic were fertilised with

solid farmyard manure in every third year and with mineral nitrogen rates of 0 kg N/ha for the

N0 organic or 120 kg N/ha for the N120 organic sample. The source for the sample grassland

was permanent grassland next to the experimental plots. All samples were collected from a

depth of 0-15 cm.

Figure 3 sketch of the used plots for the soil samples at IOSDV

Each sample had three replicates. For every replicate out of the field, five specimens were taken

while for the replicates out of the grassland ten specimens were taken.

The soil samples were dried overnight. Then, the organic material and small stones were

removed before the samples were sieved to homogenise them.

2.1 Soil texture fingering test

Water was added to the soil samples to get an even soil. The samples were rolled to get a ball

and then, a wire was formed. Afterwards, a ring out of the wire. During the whole process, it

was observed when the material broke apart.

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2.2 Moisture content by air drying

For each sample, 10 g air-dry soil was weighed into a previously weighed metal can. Overnight,

the soil samples were put in an oven at 105 °C. Afterwards, they were removed from the oven

and put in a desiccator for at least 30 minutes. Then, the samples were re-weighed.

The water content in the soil samples was calculated with Formula Ⅰ.

𝑤𝑎𝑡𝑒𝑟 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 𝑖𝑛 𝑠𝑜𝑖𝑙 𝑠𝑎𝑚𝑝𝑙𝑒 [%] = 100 𝑥 [𝑚1 − 𝑚2

𝑚1 − 𝑚0]

(I)

𝑚0 = 𝑒𝑚𝑝𝑡𝑦 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠𝑙𝑦 𝑑𝑟𝑖𝑒𝑑 𝑚𝑒𝑡𝑎𝑙 𝑐𝑎𝑛

𝑚1 = 𝑎𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑒𝑙𝑦 10 𝑔 𝑎𝑖𝑟 𝑑𝑟𝑦 𝑠𝑜𝑖𝑙 + 𝑚𝑒𝑡𝑎𝑙 𝑐𝑎𝑛

𝑚2 = 𝑟𝑒 − 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑑𝑟𝑖𝑒𝑑 𝑠𝑜𝑖𝑙 + 𝑚𝑒𝑡𝑎𝑙 𝑐𝑎𝑛

2.3 Soil pHKCl

Out of each sample, 5 g air-dried soil was weighed into a 100 mL glass beaker. 12.5 mL 1M

KCl solution was added to each beaker using a graduated cylinder. The beakers were placed on

to a shaker for 30-60 minutes and the pH was measured by putting the combined electrode about

3 cm deep in the suspensions.

2.4 Soil electrical conductivity/salinity

Approximately 5g air-dried soil of each sample was weighed into a 100 mL plastic beaker. With

the help of a graduated cylinder, 25 ml distillate water was added to each and the beakers were

placed on to the shaker for 30-60 minutes. Afterwards, the suspensions were let to settle for a

few minutes before the conductivity cell was immersed in the solutions to measure the electrical

conductivity.

31

2.5 P, K, Ca, Mg by Mehlich-3 method

For the measurement of phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) by

the Mehlich 3 method, 5 g air-dried soil of each sample was weighed into a 100 mL plastic

flask. 50 mL of the prefabricated Mehlich 3 extracting solution was added to each flask. The

flasks were placed on the mechanical shaker for 10 minutes (200, 4 cm recipes/minute).

Afterwards, the suspensions were filtered through paper filters and the extracts were collected

in new 100 mL plastic flaks. The extracts were re-filtrated into 15 mL plastic tubes using syringe

filters.

The samples were analysed by the MP-AES (Microwave Plasma-Atomic Emission

Spectrometer).

2.6 Loss on ignition (LOI)

To determine the soil organic matter, 20 g dried soil of each sample was weighed into a

porcelain crucible. The crucibles were placed into the muffle furnace, the temperature was

increased to 400 °C. After 24 hours, the crucibles were cooled down and weighed again.

The organic matter content was calculated with Formula Ⅱ.

𝐿𝑂𝐼 [%] =[(𝑊𝑜𝑑 − 𝑊𝑖) 𝑥 100]

𝑊𝑜𝑑

(II)

𝑊𝑜𝑑 = 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑑𝑟𝑖𝑒𝑑 𝑠𝑎𝑚𝑝𝑙𝑒

𝑊𝑖 = 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒 𝑎𝑓𝑡𝑒𝑟 𝑖𝑔𝑛𝑖𝑡𝑖𝑜𝑛 𝑎𝑡 400 °𝐶

𝐿𝑂𝐼 = 𝐿𝑜𝑠𝑠 𝑜𝑛 𝑖𝑔𝑛𝑖𝑡𝑖𝑜𝑛 = 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑚𝑎𝑡𝑡𝑒𝑟 𝑐𝑜𝑛𝑡𝑒𝑛𝑡

2.7 C:N by dry combustion

A small amount of each sample was put into a fully automated machine to get the content of

total nitrogen (N) and carbon (C). The machine heated to a temperature of 900 °C.

32

2.8 Dry bulk density

The dry bulk density was measured in cooperation with working group 2. They took the

undisturbed soil samples with cylinders and cleaned the cylinders from the outside. All

cylinders were put into the oven to dry them at 105 °C for 12 hours. After the drying, the

samples were weighed. Afterwards, the cylinders were cleaned and weighed without the soil

and the volume of the cylinders was measured.

The oven dry soil weight p was calculated with Formula Ⅲ.

𝑝 = 𝑡𝑜𝑡𝑎𝑙 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑐𝑦𝑙𝑖𝑛𝑑𝑒𝑟 + 𝑠𝑜𝑖𝑙) − 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑒𝑚𝑝𝑡𝑦 𝑐𝑦𝑙𝑖𝑛𝑑𝑒𝑟 (III)

The dry soil bulk density Dm was calculated according to Formula Ⅳ.

𝐷𝑚 [𝑔

𝑐𝑚3] = 𝑝

𝑉

(IV)

𝑉 = 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑐𝑦𝑙𝑖𝑛𝑑𝑒𝑟 [𝑐𝑚3]

2.9 Calculated soil carbon stock

To calculate the soil carbon stock, it was necessary to first calculate the bulk density for all the

samples. This was done according to Formula Ⅴ.

𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑠𝑜𝑖𝑙 𝑏𝑢𝑙𝑘 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 [𝑔

𝑐𝑚3] = 1,775 − 0,173 𝑥 𝐿𝑂𝐼

12

(V)

The carbon stock was then calculated with Formula Ⅵ.

𝑐𝑎𝑟𝑏𝑜𝑛 𝑠𝑡𝑜𝑐𝑘 [𝑡

ℎ𝑎] = 𝑠𝑜𝑖𝑙 𝑣𝑜𝑙𝑢𝑚𝑒 𝑥 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑠𝑜𝑖𝑙 𝑏𝑢𝑙𝑘 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑥 𝐶 %

(VI)

𝑠𝑜𝑖𝑙 𝑣𝑜𝑙𝑢𝑚𝑒 = 𝑑𝑒𝑝𝑡ℎ 𝑥 𝑎𝑟𝑒𝑎 = 0,15 𝑚 𝑥 10000 𝑚2

33

3. Results

Table 1 shows the entire results of the experiments. All following results will refer to this table.

Table 1 All measured values results

P (mg/kg) Mg (mg/kg) Ca (mg/kg) N (%) C (%) pH

Grasland 1 122 130 973 0,17 2,23 5,00

Grasland 2 108 131 789 0,16 2,15 4,73

Grasland 3 102 147 1 006 0,17 2,12 4,84

N0/1 133 149 1 332 0,08 1,25 6,39

N0/2 128 146 1 258 0,06 1,14 6,21

N0/3 135 163 1 360 0,07 1,17 6,23

N120/1 126 112 787 0,07 1,01 5,38

N120/2 124 90 738 0,07 0,99 5,11

N120/3 161 134 1 119 0,07 0,96 5,38

N0 org fert/1 136 167 1 294 0,07 1,10 6,28

N0 org fert/2 143 147 1 110 0,07 1,19 6,25

N0 org fert/3 141 163 1 336 0,08 1,17 6,18

N120 org fert/1 148 121 1 036 0,08 1,11 5,85

N120 org fert/2 154 152 1 148 0,07 1,11 6,09

N120 org fert/3 123 131 892 0,09 1,09 6,09

3.1 Soil texture fingering test

Conductivity

(µS) Moisture (%)

Bulk density

(g/cm3)

Calc. Bulk

density

(g/cm3)

SOC stock

(t/ha)

Grasland 1 58 1,19 1,51 50,67

Grasland 2 45 1,51 1,51 48,71

Grasland 3 43 1,60 1,50 47,66

N0/1 70 1,10 1,61 1,55 29,05

N0/2 67 1,00 1,54 1,55 26,57

N0/3 63 0,90 1,56 27,39

N120/1 87 1,10 1,44 1,57 23,83

N120/2 39 1,10 1,34 1,57 23,25

N120/3 98 -19,00 1,58 22,81

N0 org fert/1 32 1,00 1,56 25,72

N0 org fert/2 60 0,90 1,57 28,00

N0 org fert/3 27 1,01 1,56 27,38

N120 org fert/1 69 0,90 1,43 1,58 26,37

N120 org fert/2 75 1,00 1,54 1,56 26,03

N120 org fert/3 90 1,10 1,58 25,80

34

The soil texture type was determined to

be sandy loam with clay content of 10 –

25% according Food and agriculture

organization of the United Nations

(2006) (Fig. 2). The result was equal for

all the samples.


The measured values are shown in Table 1. They are generally between 0.9 and 1.6 %. The

lowest value showed moisture of -19 %.

3.3 Soil pHKCl

The pH of the grassland soil is 4.86. The field without any fertilizers used, N0, is 6.28 which is

also the highest measured pH. The N0 organic is treated with organic fertilizers and has a pH

of 6.24. N120 has a pH of 5.29 and the pH of N120 organic is 6.01 (Fig. 3).

Figure 4 Comparison of sample taken for fingering

test with the guideline picture

35

Figure 5 Soil pHKCl depending on land use and fertilisation. N0: without organic fertilizer and without mineral nitrogen, N0 organic: with orgnic fertilizer every third year (manure) and without mineral nitrogen, N120: without orgnic fertilizer and with 120 kg N/ha, N120 organic: with orgnic fertilizer every third year (manure) and with 120 kg N/ha


The Figure 4 and the Table 1 show the soil electrical conductivity of the soil solution of the

different samples. The N0 organic has the lowest soil electrical conductivity with 39.67 µS and

the N120 the highest with 74.67 µS.

Figure 6 Electric conductivity of soil solution depending on land use and fertilisation. N0: without organic fertilizer and without mineral nitrogen, N0 organic: with orgnic fertilizer every third year (manure) and without mineral nitrogen, N120: without orgnic fertilizer and with 120 kg N/ha, N120 organic: with orgnic fertilizer every third year (manure) and with 120 kg N/ha

4,86

6,28 6,24

5,29

6,01

-

1,00

2,00

3,00

4,00

5,00

6,00

7,00

Grassland N0 N0 organic N120 N120 organic

pH

48,67

66,67

39,67

74,67 78,00

-

20,00

40,00

60,00

80,00

100,00

120,00

Grassland N0 N0 organic N120 N120 organic

µS

36


Figure 5 represent the measured amount of macro-nutrients in different fertilized crops, no

fertilized crop and grassland.

The received data was compared with reference values and it turned out that phosphorus has a

high amount in each case. In-between the different crops there have been no big differences in

the Phosphorus amount, what can be seen in figure 1. Just the grassland soil has a little bit less

phosphorus than the arable soils, with approximate 110 mg/kg. The highest amount with

approximate 145 mg/kg phosphorus can be found in the soil of N120, which has been treated

with organic fertilizer.

For magnesium, there is a medium content to the reference values and a high content in the zero

fertilized crops, plus in the crops which has been treated with organic fertilizer. The highest

amount is almost 160 mg/kg Magnesium in the NO crops with organic fertilizer, and the lowest

amount can be found in the N 120 crop with approximate 110 mg/kg.

Potassium reached a higher amount with over 180 mg/kg in grassland, than in the fertilized

crops. The lowest amount is shown in the N 120 crop with less than 60 mg/kg of potassium.

Figure 7 Measured P, K, Mg by Mehlich-3 method depending on land use and fertilization. N0:

without organic fertilizer and without mineral nitrogen, N0 organic: with orgnic fertilizer every

third year (manure) and without mineral nitrogen, N120: without orgnic fertilizer and with 120

kg N/ha, N120 organic: with orgnic fertilizer every third year (manure) and with 120 kg N/ha

37

Figure 6 shows the measured Calcium amount by the Mehlich-3 method in each arable soil.

There is the highest amount of Calcium in the no fertilized crop (NO) with approximate 1350

mg/kg Calcium. The lowest amount can be found in the crop which has been treated with

mineral fertilizer (N 120). It indicates an amount of approximate 900 mg/kg Calcium.

There is a positive correlation between the pH and the phosphorus, what can be seen on figure

7.

0

200

400

600

800

1000

1200

1400

Grassland N 0 N 120 N 0 organic N 120 organic

mg/

kg

Ca, mg/kg

0

20

40

60

80

100

120

140

160

0

1

2

3

4

5

6

7


mg/

kg

pH pH

P

Figure 8 Measured P, K, Mg by Mehlich-3 method depending on land use and fertilization

Figure 9 Phosphorus correlated with pH depending on land use and fertilization

38


The results of loss on ignition show the highest loss in permanent grassland and very similar

values in the other fields while the lowest values are measured in the field fertilized with

nitrogen (Fig. 8).

Figure 10 Average loss on ignition of different experimental field managements.

0

0,5

1

1,5

2

2,5

3


LOI

%

Average Loss-on-ignition

39


The results show strong positive correlation between C and N percentage in soil samples. The

result which is far from the others is the grassland with significantly higher percentage of both

carbon and nitrogen (Fig. 9).

Figure 11 C:N Correlation in soil samples from different management of field and grassland measured by Dumas dry combustion.

y = 0,086x - 0,0215R² = 0,9685

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

0 0,5 1 1,5 2 2,5

N in

%

C in %

40


Table 2 is showing the calculated dry bulk density with the equation _ and the average.

Table 2 Measured dry bulk density and calculated average

The average was used to create the graph in Figure 10. In Figure 10 the dry bulk density

depending on land use and fertilization is shown. The difference between the maximum at N0

and minimum at N 120 is 0.18 g cm-3.

Figure 12 Measured dry bulk density depending on land use and fertilization

-

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

N 0 N 120 N 120 organic

g/cm

³

dry bulk density

bulk density

Sample Bulk density Bulk density average

N0 1,61

1,57 N0 1,54

N 120 1,44

1,39 N120 1,34

N 120 organic 1,43

1,48 N120 organic 1,54

41


Because of the little amount of values, the bulk density was calculated with a formula which is

shown in the material and method part. The grassland too, because then the different values can

be compared.

The calculated average of the soil organic carbon stock depending on land use and fertilization

is shown in Figure 11. The soil organic carbon stock is indicated in t h-1. There is more carbon

stock in the grassland and less in the agricultural fields. Between the agricultural fields is no

mentionable difference of the soil organic carbon stock.

Figure 13 Calculated soil carbon stock depending on land use and fertilization

0

10

20

30

40

50

60

Grassland N0 N120 N0 organic N120 organic

SOC

in t

/ha

Soil organic carbon

42

3.10 Correlation matrix

The correlation matrix shows strong correlation between all measurements connected to soil

carbon. There is quite strong correlation between carbon and nitrogen and also between nitrogen

and phosphorus. There is quite strong correlation between phosphorus available and pH too

(Tab. 3).

Table 3 Correlation matrix of all collected data

P, mg/kg Mg, mg/kg K, mg/kg Ca, mg/kg N%

P, mg/kg 1,00

Mg, mg/kg 0,22 1,00

K, mg/kg - 0,56 0,25 1,00

Ca, mg/kg 0,47 0,86 - 0,05 1,00

N% - 0,69 - 0,08 0,90 - 0,38 1,00

C% - 0,68 0,03 0,92 - 0,26 0,97

pH 0,56 0,59 - 0,54 0,75 - 0,73

µS 0,37 - 0,23 - 0,42 - 0,13 - 0,29

% water content in soil sample - 0,52 0,05 0,17 - 0,07 0,19

LOSS ON IGNITION - 0,75 0,11 0,92 - 0,16 0,90

bulk density 0,27 0,94 0,82 0,91 0,04

C% pH µS

% water content in soil sample

LOSS ON IGNITION

bulk density

P, mg/kg

Mg, mg/kg

K, mg/kg

Ca, mg/kg

N%

C% 1,00

pH - 0,65 1,00

µS - 0,35 0,11 1,00

% water content in soil sample 0,25 0,13 - 0,48

1,00

LOSS ON IGNITION 0,95 - 0,60 - 0,47

0,31 1,00

bulk density 0,88 0,93 0,51

0,00 0,70

1,00

43

4. Discussion

4.1 Soil texture by fingering test

The result of the soil texture was similar on the field as on the grassland because soil texture

doesn’t change so quickly, thus no changes can be observed after 30 years of experimental

agricultural use of this particular site. This result was expected and was described in the

guideline of the experimental site (Astover Alar, Estonian University of Life Science, 2017).


The moisture of the soil according the numbers is quite low comparing to climate conditions in

Estonia, where the precipitation is higher than evaporation. All the samples showed quite the

same humidity and didn’t show any significant difference between various management types.

The lowest humidity value shows unrealistic result and was probably caused by typing error.

4.3 Soil pHKCl

There are differences between the pH of the grassland and the pH of the fields. The moderate

acid pH of the grassland is as expected because of the natural process of acidification and no

presence of carbonates in the soil. The pH of the field-samples is higher because of the liming

made in year 2000 on the field. If there are no fertilizers added as in the Plot N0 there is no

anthropogenic influence on the pH. The higher amount of mineral fertilizers in N120 causes a

lower pH than the N0, because the bacteria oxidase the ammonium and they release hydrogen-

ion during this process. Organic fertilizers contain calcium and magnesium which neutralizes

the pH because they improve soil buffering-capacity. This also could be a reason for the higher

pH than in the grassland.


The general trend shown in Figure 3 is that the more fertilizer is used, the higher is the soil

electrical conductivity of our samples. That is totally what was expected because when there

are some mineral fertilizers added, ions responsible for electrical conductivity are added the

44

same time. The N0 organic sample doesn’t fit in the trend because the soil electrical

conductivity is lower than the N0 without any fertilizer used. The high standard deviation of

N0 organic shows that the measured result is not very representative. Also the outer standard

deviations are high, because of the fluctuations of the values shown by the instrument while

measuring.


The phosphorus concentration is quite high in all arable soils, because it has been fertilized with

phosphorus 20 years ago, so an amount can still be found.

As the phosphorus assimilation benefits by the mutualism with mycorrhiza, a lower amount of

phosphorus in soil could be expected for grasslands. This might due to a higher density of intact

root systems in grassland, so more phosphorus is taken out by the plants. Moreover, the

grasslands haven´t been fertilized, why the phosphorus source in the soil decreases over the

years. Phosphorus is an important primary macronutrient for plants to build the DNA and to

guarantee the membrane development and function. For that reason, fertilization is essential to

assure a good yield.

The higher amount of potassium in grassland can be explained by the absent harvest. Thus,

there has been no potassium taken out of the grassland over the years. In contrast to that, on the

fertilized crops the potatoes have a high demand of potassium, which is taken out more and

more by every harvest.

In acidic conditions phosphorus is more available in a free mineral form, thus it can be used by

microorganism. When the pH is to low ore to high, phosphorus is fixed for example with iron

as a complex, for which reason it is not available any more.


The highest loss on ignition measured in grassland soil sample shows the highest amount of

soil organic matter. The management of mowing only enables soil organic carbon to accumulate

in the upper soil layers. Although the other results differences are slightly insignificant, the

lowest carbon content should be observed in the nitrogen only fertilized fields because nitrogen

45

addition increase decomposition by saturating the decomposers’ need for nitrogen. The organic

only fertilized field shows relatively high loss on ignition because organic manure contents high

amount of organic carbon which can be quite stable in the soil.


The strong correlation between C and N amount shows that nitrogen in soil is mainly bound in

organic particles.


The values are between 1.39 g cm-3 and 1.57 g cm-3 which is typically, because the density of

mineral soils commonly ranges from 1.1 to 1.5 g cm-3 in surface horizons [1]. The little

deviations can come from samples, which were taken with compaction or crumbling.

The difference between the maximum and minimum from the fields with 0.18 g cm-3 is very

low. The reason for this is that the used samples are from the same field with the same soil. It’s

no surprise because the soil changes slowly in the landscape. Maybe there would be a difference

when grassland samples are compared. In fact of this, in the grassland should be more carbon

than in the other samples.


There is less soil organic carbon stock in the agricultural fields because plants are taking carbon

from the atmospheric CO2. The SOC in the grassland is much higher because there are no plants

which use a high amount of carbon from the atmospheric CO2 In fact of this the amount of

carbon in the soil is higher. Between the different agricultural fields isn’t a significant

difference. It doesn’t matter which fertilization, the organic carbon left in the soil is always

quite the same.

46

4.10 Correlation matrix

The correlation between organic carbon related measurements (loss on ignition, carbon by dry

combustion, bulk density, calculated bulk density, total carbon stock) is obvious. The

correlation between C:N:P shows that the main source of active N and P in soil is the soil

organic matter which contains all these elements.

The correlation between pH and Phosphorus shows that soil phosphorus is available in very pH

neutral conditions only because too high (to low) pH cause binding of P into unsolvable

complexes. This is why in grassland (where pH is low) the P available is low too.

4.11 Comparison

The results of this study are comparable with the results of other analyses on the effect of

mineral and organic fertilization on soil. Körschens et al. [2] compared the results of 20

European long-term experiments concerning the impact of fertilization on crop yield, carbon

balance, soil organic carbon content and dynamics. In Figure 12, their work is compared to the

work at hand. The figure demonstrates the effect of fertilization and clay content on the soil

organic carbon content for 18 European long-term experiments and the results for Tartu. From

the left to the right side, the clay content of the sites is increasing. The lighter part of the columns

shows the soil organic carbon content without fertilization and the darker part shows the content

with organic (10 t/ha FYM) and mineral (NPK) fertilization. The results concerning the clay

content and the carbon content for Tartu are added in red. The red frame contains the range of

the clay content for Tartu according to the fingering test.

47

Figure 14: Comparison of our measurements with the result of Körschens et al. [1]

For Tartu, we measured a clay content of 10-25 %. In comparison, Speyer has a clay content of

9 % and Wien a clay content of 25 % [2]. For the treatment without fertilization, Tartu got a

result of 1.19 % of carbon, Speyer 0.58 % and Wien 2.06 % of SOC. So the result of Tartu lies

in between the results of Speyer and Wien, what is consistent with the expectations. The result

for the treatment with mineral and organic fertilizer for Tartu was 1.10 % whereas the result for

Speyer was 0.81 % and for Wien 2.24 % SOC. Again, the value of Tartu lies in between the

values of Speyer and Wien. However, according to [2], for Speyer and Wien, the carbon content

for the treatment with fertilization was higher than for the treatment without fertilization while

for Tartu, it was the other way round. To summarize, the values for Tartu are in accordance

with the results by Körschens et al. even if the fact that for Tartu the carbon content decreased

while using organic and mineral fertilizer is surprising.

48

5. Sources

[1] Thien and Graveel, Laboratory Manual for Soil Science

[2] Martin Körschens et al. (2012) Effect of mineral and organic fertilization on crop yield,

nitrogen uptake, carbon and nitrogen balances, as well as soil organic carbon content and

dynamics: results from 20 European long-term field experiments of the twenty-first century.

Archives of Agronomy and Soil Science, 59:8, 1017-1040

49

5. Fast Plant test with various

substrates and composts.

PROTOCOL By Laura Böe, Aneta Flekalova, Donatus Mmodum, Arthur

Naimowitsch.

OUTLINE

1. Introduction


3. Results

4. Discussion

5. Future perspectives

1. Introduction

Worldwide peat is decreasing especially in Europe. This made us work on the alternative for this and

Compost could be the alternative for this peat growing media therefore Compost is good for

Agriculture because of its resource usage efficiency especially in Nutrients and Organic matter. This is

an important project because it centers on the possible replacement of peat growing media if we

eventually lose our peat especially in Europe. In this our study we compare and test different quality

of composts by using fast growing plant tests to find out the difference on compost quality. We try to

50

find out if different quality of compost from different source can cause growth in Plants. And also if

the compost quality determines the growth in plant. Below is the distribution of peat in Europe.

PEAT DISTRIBUTION IN EUROPE

This paper derives the distribution of peat land in Europe as the extent of peat and peat-topped soils

indicated by soil databases. The data sources were the 1:1,000,000 European Soil Database (v1.0)

and a data set of organic carbon content (%) for the top soils of Europe at 1km x 1km resolution that

was recently published in map form.

The strong influences of vegetation and land use on soil organic carbon (OC) content were taken

into account in computing the 1km (OC) data set, as was the influence of temperature.

The areas of peat and peat-topped soils estimated from the European Soil Database are generally in

close agreement with those obtained using the Map of OC in Top soils of Europe. The results reveal a

strong northern bias in the distribution of organic soils across Europe. Almost one-third of the peat

land resource of Europe is in Finland, and more than a quarter is in Sweden. The remainder is in Poland,

the UK, Norway, Germany, Ireland, Estonia, Latvia, The Netherlands and France. Small areas of peat

and peat-topped soils also occur in Lithuania, Hungary, Denmark and the Czech Republic. For most

European countries, the distribution of peat and peat-topped soils is probably more accurately

portrayed by the Map of OC in Top soils of Europe than by the European Soil Map and Database. Such

baseline data are important for the conservation of peat and for making much more precise estimates

of carbon stocks in topsoil than have been possible hitherto.

The results are also relevant to the planning of effective soil protection measures at European level

(Montanarella etal., 2006)

https://www.researchgate.net/publication/26841884_The_distribution_of_peatland_in_Europe


Four different compost were used in the project. Composition of these compost you can see in the

Tab. 1.


51

Table 1: Different composition of the compost, year of formation.

Name of Compost Content Year Shortnote

Compost 5 Leaves, grass, branches 2015 Compost was made at EULS, no aeration or

mixing

Compost 7 Made of Waste Water sludge,

mixed with Peat

2016 Tartu, passed thermophilic phase, mixed

during composting process

Compost 9 Waste water sludge, mixed

with wood chips

2015 Jõgeva, mixed during composting

Compost 12 Waste from animal slaughter,

general biowaste

2014 Väätsa, mixed several times during

composting.

Control (Growing

media)

Sphagnum peat Commercially produced, contained mineral

fertilizers, limed

Compost were mixed in different ration with the growing media for 4 pots to each mixture of:

100 % compost

80 % compost and 20 % growing media (GM)

50 % compost and 50 % GM

10 % compost and 90% GM

Into each pot were added 1 g cress (Lepidium sativum). The pots were covered by plastic and put under

uniform lamp. The duration last 1 week. After the duration the cress was harvested and weighed.

For the closed chamber test different composts were put into plastic buckets and 1 g of seed was

added into each bucket. Buckets were covered. The duration last 1 week.

52

For the pH measurement were used 10 g compost and 100 ml of 0.01M CaCl2 solution. The solution

was mixed for 1 hour in the shaker and then pH was measured by combined electrode.

For the electrical conductivity were used 10 g of compost and 100 ml distilled water. The solution was

mixed for 1 hour in the shaker and then electrical conductivity was measured.

For soil moisture content the aluminum container was weighed empty, then with approximately half

full of compost. The samples were put into an oven ( 150 °C) for 24 hours. After that the container

were removed and weighed again.

For soil organic matter by loss on ignition tared porcelain crucible was weighed empty, then with

approximately half full of compost. The samples were put into an oven ( 560 °C) for 24 hours. After

that the container were removed and weighed again.

3. Results

Cress test – Pot

After the duration of one week, the pots were removed of their artificial environment. All samples

showed a sign of overgrowth except compost 12 the 100% samples, which showed an inhibited growth,

by developing slower and dark green leaves. The biomass was established by weighed the harvested

cress. The results were summarized in figure 1.

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The results show, that the GM has the highest amount of biomass in comparison to the samples.

Compost 9 and 12 yielded the lowest biomass, while compost 7 and 5 were relatively close the control.

Cress test – closed chamber

After the duration of one week, the closed chambers were removed of their condition and observed.

All plants of the compost number 5 showed some state of germination, but soon died after that. The

same condition were observed for the other three composts. Two samples of compost 7 were showed

some state developing with sign of green leaves. Same for one sample of compost 9. For compost 12

showed one sample a typical developing of a plant. After opening the closed chambers a smell of

decomposition was experienced.

Figure 1: Results of the average biomass, of the different compost mixtures

54

pH

The results of the pH-Test are summarized in

figure 2. The GM had the lowest pH, closely

followed my compost 7 with 6.0 and compost 9

with 6.7. The highest pH was observed of

compost 5 with 7.6 and compost 12 with 7.3.

Electrical conductivity (EC)

The results of the EC test are summarized in

image 3. All compost except compost 12 showed a low conductivity. Compost 12 was nearly three

times higher compared to the others with a conductivity of 1.39 mS. The GM was the highest of the

four lowest values, with a conductivity of 0.5 mS, followed by compost 7 with 0.38 mS, then compost

9 with 0.32 mS and at last compost 5 with 0.2.

Figure 3: Results of the average EC of the four different composts and growing media using three

samples

Soil Moisture Content

Figure 2: The average pH of the four different

composts and control of three samples

55

The results of the soil moisture content were

summarized in figure 4, the formula for the

water content in percent was calculated as

follows:

% water content = 100∗ ((m1− m2)

(m1− m0))

m0 = weight of empty container

m1 = weight of container with compost before

the heating process

m2 = weight of container with compost

after the heating process

After calculating the water content, it is also possible to calculate the dry soil in percent as follows:

dry soil= 100− % water content

Compost 12 showed the highest amount of dry soil with 76.6% and 23.4 % water content. The highest

amount of water content is visible for compost 7 with 72.6 % water content and 27.4 % dry soil. The

soil content for control was 37.1 % and water content 61.9 %. The soil content for compost 5 was 23

% and water content 67 %. Compost 9 had soil content of 37.3 % and water content of 62.7 %.

Loss on ignition

The Results of the loss on ignition test are shown in table 2. Control showed the highest amount of

organic matter with 96.1 % followed by compost 7 and compost 5 with 91.9 % and 91.4 %. Compost 9

showed a rather low amount with 82 % and compost 12 the lowest amount of organic matter with

65.5%.

Table 2: Soil organic matter by loss on ignition in percent

Control Compost 5 Compost 7 Compost 9 Compost 12

96.1 % 91.4 % 91.9 % 82.0 % 65.5 %

C/N ratio

Figure 3: average dry soil and moisture content in

percent of the four different composts and growing media

measurement by three sample each

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After analyzing the content of the five samples the results regarding the carbon and nitrogen content

are listed in table 3. The highest nitrogen content is visible for compost 7 with 2.41 % and 34.42 %

carbon resulting to C/N ratio of 14. The second highest nitrogen content was observed for compost 12

with 2.06 % and 24.16 % carbon which resulted into the lowest C/N ratio of 12. Compost 9 got a

nitrogen content of 1.61 % in one mg per kg and 26.35 % carbon, resulting into a C/N ration of 16. The

second lowest nitrogen content was spotted in compost 5 with 1.36 % and 24.04 % carbon, leading to

a C/N ratio of 18. The lowest amount of nitrogen was observed for control with 1.02 % and 47.21 %

carbon, resulting into the highest C/N ratio of 46.

Table 3: The content of control and the four compost, listed are the carbon and nitrogen

content in percent in one mg per kg and the corresponding C/N ratio

Samples Control Compost 5 Compost 7 Compost 9 Compost 12

N% 1.02 1.36 2.41 1.61 2.06

C% 47.21 24.04 34.42 26.35 24.16

C/N ratio 46 18 14 16 12

Macroelements

The results of macroelements content are shown in table 4, the numbers were calculated with the

average of three samples of each compost. Showing Compost with highest amount of Calcium 8046.80

mg per kg and highest amount of potassium with 4088.50 mg per kg. The lowest amount of calcium

was observed for compost 7 with 2614.03 mg per kg and the lowest potassium content with 499.8 mg

per kg. The highest phosphorus content is visible in compost 9 with 880.93 mg per kg and lowest in

control with 266.73 mg/kg.

Table 4: Macroelement content for control and the four compost samples, showing the total

amount of P, Mg, K and Ca in mg per kg

Sample Control Compost 5 Compost 7 Compost 9 Compost 12

P [mg/kg] 266.73 441.30 702.93 880.93 546.87

Mg [mg/kg] 793.53 766.30 1083.50 1014.97 1434.47

K [mg/kg] 786.73 1001.73 499.87 601.87 3110.40

Ca [mg/kg] 4484.03 5074.73 2614.03 4088.50 8046.80

57

For visualizing and to compare the composts all the numbers in table 4 were added into bar graph

figure 5.

Figure 4: Macroelement content for control and the four compost samples, showing the total

amount of P, Mg, K and Ca in mg per kg visualized in bar graph

58

4. Discussion

The compost experiments showed positive results regarding all composts. However, the cress was

overgrown in each sort of compost besides the control that grew on growing media (GM). In

comparison to the control (biomass of 29.1 g) composts 5 and 7 had the highest biomass, but still

below the control and composts 9 and 12 had the lowest biomass.

The composts 5, 7, 9 and 12 showed an increased growth because of their specific C/N ratio. In

composts the optimal ratio of these two elements is normally between 15:1 and 35:1. The ratio of

compost 5 and compost 12 is below that optimum and the composts 7 and 9 are within the optimum

but nevertheless in a low range compared to the maximum of 35:1. It so can be concluded that the

concentration of nitrogen is higher in comparison to the concentration of carbon.

Both elements are essential for the development of plants but a high N content that is primary received

in form of nitrate and ammonium, leads to fast formation of amino acids and proteins and due to that

to a high growth rate.

The pH of each compost is in an optimal range also in the control. This shows that the formation has

finished because at the beginning of composting the pH decreases at first due to the formation of

organic acids, CO2 and nitrification.

The measurements of the electric conductivity (EC) resulted in similar values for composts 5, 7, 9 and

the control. Compost 12 was a statistical outlier with an EC of 1.39 mS. The reason for that is the high

amount of calcium in compost 12. The calcium reacts with chloride to CaCl and more salts in the media

correlate with a higher EC.

The measurements of the elements phosphorus, magnesia, potassium and calcium showed the highest

amount of P in compost 7 and 9. These composts consisted partially of waste water sludge. This

explains the Pcontent because of the use of phosphoric cleaning agents in the household and

phosphoric fertilizer in the agriculture. Compost 12 had the highest potassium values because it

consists of organic waste. Furthermore, this compost also has the highest amount of calcium for the

same reason.

The closed chamber experiments did not work because all samples died against the expectation.

Reasons for that could be too much water that maybe disturbed the aerobic processes. Hence, this

experiment is not representative and needs to be repeated.

All in all, the hypothesis of the beginning cannot be confirmed by now because all mixtures of composts

led to growth indeed but the complete biomass was below the biomass of the control in each sample.

59

So there need to be more studies and investigations to find alternatives that actually replace peat in

future agriculture.

5. Future perspectives

We did those experiments because we tried to find an alternative to the common use of peat as a

growing media that will certain come close in future. The ambition was to find a composition of

compost that results in the same or better biomass of cress in the cress test than the control.

By now such a composition could not be found and more studies are necessary.

References

Montanarella, L., Jones, R.J.A., Hiederer, R. 2006. The distribution of peatland in Europe. Mires and

Peatland, Volume 1 (2005), Article 01, http://www.mires-and-peatland.net. 2005 International Mire

Conservation Group and International Peat Society.


http://www.mires-and-peatland.net/


60

6. Allelopathy experiment

with Estonian trees

Nora Tomsová, Axel Bergeon, Sophie Baur, Ingmar Pappel

Supervisors: Virginie Baldy, Ilja Reiter, Jordane Gavinet

Erasmus - Soil and Water - 2017

61

Introduction

Allelopathy is a chemical interaction between plants, and between plants and microorganisms. Plants produce allelochemicals (secondary metabolites), which are released into the environment. These secondary metabolites have influence on growth, germination, reproduction, distribution of vegetation.

In our experiment, we have been observing allelopathy of 3 common Estonian plant species: Acer platanoides, Picea abies and Quercus robur. From scientific articles, we know that allelopathy of boreal shrubs has been evidenced, but we have low knowledge about allelopathy of Estonian trees (Gallet 1994).

We raised 2 main hypotheses:

- H1: Macerate of leaves or needles of Estonian common trees (Acer platanoides, Picea abies, Quercus robur) have allelopathic effect on germination of Lactuca sativa, Lepidium sativum.

- H2: allelopathic effect of young needles of Picea abies is higher compared to old needles of Picea abies.

Our experiment was mainly done by germination tests.

62

Material and method

1. First experimentation

The first experimentation is carried out to observe the potential allelopathic effect of the macerates

of needles of Picea abies and of leaves of Acer platanoides.

i. Preparation of macerates

We put 20g (fresh weight) of needles of Picea abies or leaves of Acer platanoides in a glass with

100ml of distilled water. After 17 hours we filtered the macerate, this was the mother solution at

10% (mass/volume). Another solution of 5% was realized by mixing 50ml of the solution at 10% with

50ml of distilled water.

A control was also realized with only distilled water (0%).

6 treatments were tested: 2 species (P. abies and A. platanoides) at 3 concentrations (5%, 10% and

control at 0%).

ii. Germination test

For the germination test we used Petri dishes with a substrate of filter paper. In this filter paper, we

put 25 seeds of the target species: Lepidium sativum.

There were 24 Petri dishes: 6 treatments and 4 replicates per treatment.

We watered each Petri dish with 2ml of one solution (distilled water as 0% control, 5% or 10% of

macerate of each species).

48 hours after watered the Petri dishes, we counted the number of germinated seeds, and the

germination stage, according to the Figure 1.”.

Figure 1: the 4 germination stages considered for the experiment. 1: no germination, 2: germinated

without leaves, 3: germinated with yellow leaves, 4: germinated with green leaves.

Data analysis was done with Chi² tests.

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2. Second experiment

The first experiment demonstrated no significant inhibition effect of the tested treatments. We

performed a second experiment, with the same protocol but we used 2 target species (Lepidium

sativum and Lactuca sativa), and we selected two tree species Picea abies and Quercus robur and

two phenological stages for Picea abies (young and old needles).

i. Preparation of macerate

We put 20g (fresh weight) of leaves or needles of Quercus robur and young or old needles of Picea

abies in a glass with 100ml of distilled water. After 17 hours, we filtered the macerates.

A control for the macerate is also realized with only distilled water (0%).

8 treatments were tested: 3 macerates (young or old needles of Picea abies or leaves of Quercus

robur) at 10% and 1 control (0%), with 2 target species (Lepidium sativum and Lactuca sativa).

ii. Germination test

For the germination test we used Petri dishes with a substrate of filter paper. In this filter paper, we

put 25 seeds of the target species: Lepidium sativum and Lactuca sativa.

There were 24 Petri dishes: 8 treatments and 3 replicates per treatment.

We watered each Petri dish with 2ml of one solution (distilled water as control or macerate).

48 hours after watered the Petri dishes, we counted the number of germinated seed, and the

germination stage, according to the Figure 1.

Data analysis was done by using Chi² tests.

Table 1: Summary table of both experiments:

First experimentation Second experimentation

Number of 0%

control (distilled

water)

8 3

Donor plants 2 (Picea abies and Acer

platanoides)

3 (young and old needles of Picea

abies and leaves of Quercus robur)

Concentrations 5% and 10% 10%

Target species 1 (Lepidium sativum) 2 (Lepidium sativum and Lactuca

sativa)

64

Replicates 4 3

Results

1. First experiment

Figure 2: Germination rates of Lepidium sativum with 0% control with distilled water, with 5 and 10%

macerates for the two donor plant species (C=conifer=Picea abies, D=deciduous=Acer platanoides).

Results showed that most of the seeds germinated (Figure 2). All treatments lead to all the

germination stages, except D5 (Acer 5%), which had no stage 0 (all seeds have germinated). D10

(Acer 10%) treatment showed the highest proportion of non-germinated seeds. Both Picea

macerates concentrations (C5, C10) showed comparable results.

The statistical test Chi² realized gave 6.5 and showed no significant difference between the

treatments, reporting no allelopathic effect of donor plants on target plants.

65

2. Second experiment

Figure 3: Germination rates of Lepidium sativum. with 0% distilled water (CW) and 10% macerates of

two species, Quercus robur (CQ) and Picea abies (CPY=young needles, CPO=old needles).

Again, most of the seeds germinated (Figure 3). In this experiment, only a few proportion of seeds

germinated until the stage « yellow leaves ». For CPO, no seeds reached this stage. The statistical

test Chi² realized was 0.45 and showed no significant difference between the treatments, reporting

no allelopathic effect.

Figure 4: Germination rates of Lactuca sativa with 0% distilled water (LW) and 10% macerates of two

species, Quercus robur (LQ) and Picea abies (LPY=young needles, LPO=old needles).

As it was observed previously, most of the seeds germinated (Figure 3). However, we did not observe

any « yellow leaves » germination stage except a really low proportion with Quercus. The statistical

66

test Chi² realized was 1.05 and showed no significant difference between the treatments, indicating

no allelopathic effect.

67

Discussion We reported no allelopathic effects during these experiments. We hypothesized that donor plants

produced not enough allelochemicals for inhibiting germination rates of target species.

Possible reasons, why there was not enough allelochemicals could be raised:

● if plants are not under stress then they produce less secondary metabolites;

● allelochemicals were emitted by using different ways (e.g. root exudates, litter decomposition)

than leaf or needles leachates;

● target species used in these experiments are not competitors for trees and trees do not release

allelochemicals harmful for these target species.

Possibilities to improve the method:

● repeat experiments with needles and leaves from trees under environmental stress - bogs,

sandy areas etc.,

● let more time for macerates (standard is 24 hours)

● use soil instead of distilled water in Petri dishes

● use native Estonian species as targets in germination tests

● count rate of germination earlier (12 or 24 hours)

● use fallen needles under the Picea

● use target species which grow in Estonian spruce or oak forests.

68

Conclusion Our main conclusion is that we have not observed any allelopathy in leaves or needles

leachates. The reasons why we have not observed any allelopathic effect can be different.

Firstly, there is no allelopathy of the donor plants used on these target species (Lactuca sativa,

Lepidium sativum). These species do not growth in Estonian ecosystem and, they are very

resistant species. Secondly, we have not observed allelopathy, because these species in natural

environment do not grow together, so there is no competition between them, because they live

in different ecological niches.

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Bibliography

Gallet, C. (1994) Allelopathic potential in bilberry-spruce forests, influence of

phenolic compounds on spruce seedlings. J. Chem. Ecol. 20, 1009–1024.

Project Reports SS17 Total.pdf

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