Recommendation for working out a new soil ranking system based on the

results of the SOILMAP project

László ManczingerDepartment of Microbiology, Faculty of Science and Informatics,

University of Szeged, Hungary

László Manczinger, Isidora Radulov, Adina Berbecea, Enikő Sajben-Nagy, Andrea Palágyi, Dorin Tărău, Lucian Dumitru Niţă, Csaba Vágvölgyi

HU-1 Intensive wheat culture (Öthalom)

HU-2 Forest (Kiszombor)

HU-3 Intensive wheat culture (Kiszombor)

HU-4 Meadow (Kiszombor)

HU-5 Bio-wheat (Kiszombor)

HU-6 Intensive wheat culture (Szentes)

HU-7 Intensive wheat culture (Sándorfalva)

HU-8 Intensive wheat culture (Derekegyház)

HU-9 Intensive wheat culture (Újszeged)

HU-10 Intensive wheat culture (Makó)

RO-1 Bio-wheat culture (Cenad)

RO-2 Intensive wheat culture ICAR (Cenad)

RO-3 Meadow (Cenad)

RO-4 Forest (Cenad)

RO-5 Intensive wheat culture (Sânnicolau Mare)

RO-6 Intensive wheat culture (Sânnicolau Mare)

RO-7 Intensive wheat culture (Lovrin)

RO-8 Intensive wheat culture (Clarii)

RO-9 Intensive wheat culture (Săcălaz – Beregsana)

RO-10 Intensive wheat culture (COMAGRA)

Two type sampling from every places

A: upper layer : 0-20 cm

B: lower layer : 20-40 cm

SAMPLING TIMES

SPRING - MarchSUMMER- AugustAUTUMN- November

SAMPLING PLACES

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8

3

7

5

1 4

9

2

10

7

24

5

6

9

10

8

3

1

Two type sampling from every placesA: upper layer : 0-20 cmB: lower layer : 20-40 cm

The sampling places on the map of the region

The investigated parameters of the soil samples

Rough sand ( 2,0 - 0,2 mm)

Fine sand ( 0,2 –0,02 mm )

Dust ( 0,02 – 0,002 mm )

Colloid clay ( sub 0,002 mm )

Physical clay ( sub 0,01 mm )

pH in water

Carbonate ( CaCO3 )

Humus

Phosphorus mobile ( P mobile )

Phosphorus mobile ( P mobile )recalc pH

Potassium mobile (K mobile )

Zinc

Copper

Manganese

Nickel

Cadmium

Iron

Lead

Physical-chemicalparameters

Biochemicalparameters

Microbiologicalparameters

E1 Phosphatase

E2 β-glucosidase

E3 Cellobiohydrolase

E4 β-xylosidase

E5 Trypsin-like protease

E6 Chymotrypsin-like protease

E7 Palmitoyl-esterase

E8 Chitinase

1. Species richnessof bacteria

2. Species richnessof fungi

3. Diversity of important bacterial genera

4. Diversity of toxinogenic fungi

Some important results regarding the physical-chemical parameters, processed with Excel and OpenStat softwares

0

1

2

3

4

5

6

7

8

9

HU RO

pH

pH

Some important results regarding the physical-chemical parameters

0

1

2

3

4

5

6

HU RO

%

Humus

Some important results regarding the physical-chemical parameters

(phosphorus and potassium)

0

20

40

60

80

100

120

140

160

180

200

HU RO

Ph

osp

ho

rus

(pp

m)

0

100

200

300

400

500

600

700

800

900

1000

HU RO

Po

tass

ium

(p

pm

)

In the Romanian soils theamount of both P and Kis frequently much more less in the lower layer thanin the upper layer.

Some important results regarding the physical-chemical parameters

(cadmium and copper)

0

1

2

3

4

5

6

HU RO

Cd

(p

pm

)

0

20

40

60

80

100

120

140

HU RO

Cu

(p

pm

)

Some important results regarding the physical-chemical parameters(zinc and lead)

0

20

40

60

80

100

120

140

HU RO

Zn

(p

pm

)

0

10

20

30

40

50

60

70

80

HU RO

Pb

(p

pm

)

Some important results regarding the physical-chemical parameters

(manganese and iron)

0

200

400

600

800

1000

1200

HU RO

Mn

(p

pm

)

0

10000

20000

30000

40000

50000

60000

HU RO

Fe

(pp

m)

Regression analysises

0

100

200

300

400

500

600

700

0 50 100 150 200

P-mobile

K-m

ob

ile

X versus Y Plot

X = VAR1, Y = VAR2 from file: Temporary.TEX

Variable Mean Variance Std.Dev.VAR1 89.31 1923.73 43.86VAR2 295.80 19081.85 138.14Correlation = 0.7066, Slope = 2.23, Intercept = 97.03Standard Error of Estimate = 97.74Number of good cases = 20

P-mobile – K-mobile regression in the Hungarian soil samples

0100

200300400500

600700800

9001000

0 20 40 60 80 100 120 140 160

P-mobile

K-m

ob

ile

X versus Y Plot

X = VAR1, Y = VAR2 from file: Temporary.TEX

Variable Mean Variance Std.Dev.VAR1 58.88 2031.53 45.07VAR2 262.85 45963.82 214.39Correlation = 0.5739, Slope = 2.73, Intercept = 102.10Standard Error of Estimate = 175.57Number of good cases = 20

P-mobile – K-mobile regression in the Romanian soil samples

Multiple regression of heavy metals in the Romanian samples

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80

Zn (ppm)

Cu

(p

pm

)

0

0,5

1

1,5

2

2,5

0 20 40 60 80 100 120

Cu (ppm)

Cd

(p

pm

)

Variables Cu Mn Ni Cd Pb Zn

Cu 1.000 -0.311 0.031 0.411 0.453 0.488 Mn -0.311 1.000 0.225 0.371 0.099 -0.279 Ni 0.031 0.225 1.000 0.301 0.359 0.235 Cd 0.411 0.371 0.301 1.000 0.674 0.163 Pb 0.453 0.099 0.359 0.674 1.000 0.259 Zn 0.488 -0.279 0.235 0.163 0.259 1.000

0

5

10

15

20

25

30

35

40

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

Cd (ppm)

Pb

(p

pm

)

Correlation matrix

Multiple regression of heavy metals in the Hungarian samples

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120 140

Cu (ppm)

Pb (p

pm)

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60

Ni (ppm)

Zn

(p

pm

)Variables Cu Mn Ni Cd Pb Zn

Cu 1.000 0.323 -0.091 0.279 0.418 -0.006 Mn 0.323 1.000 -0.395 -0.235 -0.336 -0.551 Ni -0.091 -0.395 1.000 -0.174 0.315 0.706 Cd 0.279 -0.235 -0.174 1.000 0.544 0.119 Pb 0.418 -0.336 0.315 0.544 1.000 0.269 Zn -0.006 -0.551 0.706 0.119 0.269 1.000

0

20

40

60

80

100

120

140

0 200 400 600 800 1000

Mn (ppm)

Zn (p

pm)

Correlation matrix

Analysis of soil enzyme data

We worked on microtiter plates with chromogenic substrates

A Phosphatase

B β-glucosidase

C Cellobiohydrolase

D β-xylosidase

E Trypsin-like protease

F Chymotrypsin-like protease

G Palmitoylesterase

H Chitinase

0

0,5

1

1,5

2

2,5

1/F 2/F 3/F 4/F 5/F 6/F 7/F 8/F 9/F 10/F

A

B

C

D

E

F

G

H

-0,5

0

0,5

1

1,5

2

2,5

HU-1A HU-2A HU-3A HU-4A HU-5A HU-6A HU-7A HU-8A HU-9A HU-10A

A

B

C

D

E

F

G

H

Relative activitiesof soil enzymes in thespring and summer sample series.Hungarian soils, upperlayer.

SPRING

SUMMER

The other sample seriesshowed very like pictures.

The summer samples, asbeing most diverse, werestatistically analysed and used for soil qualifying.

Soil type – soil enzyme correlations calculated with OpenStat software

HU-Enzyme-Lower soil layerX VERSUS MULTIPLE Y VALUES PLOTX= VAR1: 1=non fertilized soils, 2= fertilized soilsCORRELATION MATRIX Correlations VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR2 1.000 -0.593 -0.407 -0.081 0.039 0.363 VAR3 -0.593 1.000 0.617 0.261 0.483 0.032 VAR4 -0.407 0.617 1.000 0.717 -0.024 -0.095 VAR5 -0.081 0.261 0.717 1.000 0.136 0.309 VAR6 0.039 0.483 -0.024 0.136 1.000 0.821 VAR7 0.363 0.032 -0.095 0.309 0.821 1.000 VAR8 -0.717 0.414 -0.026 -0.402 0.111 -0.339 VAR9 0.379 -0.354 0.038 0.534 -0.217 0.042 VAR1 0.403 0.158 0.142 0.239 0.194 0.264 Correlations VAR8 VAR9 VAR1 VAR2 -0.717 0.379 0.403 VAR3 0.414 -0.354 0.158 VAR4 -0.026 0.038 0.142 VAR5 -0.402 0.534 0.239 VAR6 0.111 -0.217 0.194 VAR7 -0.339 0.042 0.264 VAR8 1.000 -0.475 -0.536 VAR9 -0.475 1.000 0.237 VAR1 -0.536 0.237 1.000

A Phosphatase VAR2

B β-glucosidase VAR3

C Cellobiohydrolase VAR4

D β-xylosidase VAR5

E Trypsin-like protease

VAR6

F Chymotrypsin-like protease

VAR7

G Palmitoylesterase VAR8

H Chitinase VAR9

We made the corre-lation matrices in everysoil sample series

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

A B C D E F G H

The enzyme activities in the lower layers of intensively cultivated Hungarian soils are higher, except of palmitoylesterase (G).

A Phosphatase VAR2

B β-glucosidase VAR3

C Cellobiohydrolase VAR4

D β-xylosidase VAR5

E Trypsin-like protease

VAR6

F Chymotrypsin-like protease

VAR7

G Palmitoylesterase VAR8

H Chitinase VAR9

Correlation of soil enzyme activities with the use of fertilizers and pesticides

A Phosphatase VAR2

B β-glucosidase VAR3

C Cellobiohydrolase VAR4

D β-xylosidase VAR5

E Trypsin-like protease

VAR6

F Chymotrypsin-like protease

VAR7

G Palmitoylesterase VAR8

H Chitinase VAR9-0,1

0

0,1

0,2

0,3

0,4

0,5

0,6

A B C D E F G H

The enzyme activities in the upper layers of fertilizer andpesticide treated Hungarian soils are frequently higher, than inthe soils of nonintensive fields (forest, meadow, biocultivation).

Correlation of soil enzyme activities with the use of fertilizers and pesticides

A Phosphatase VAR2

B β-glucosidase VAR3

C Cellobiohydrolase VAR4

D β-xylosidase VAR5

E Trypsin-like protease

VAR6

F Chymotrypsin-like protease

VAR7

G Palmitoylesterase VAR8

H Chitinase VAR9

In the Romanian soils all enzyme activities were strongly less in the intensivelycultivated fields both in the upper and lower layers exept of phosphatase.

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

A B C D E F G H

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

A B C D E F G H

Correlation of soil enzyme activities with the use of fertilizers and pesticides

Determination of soil microbial diversity

The new molecular diversity methods

- DGGE = Denaturing Gradient Gelelectrophoresis- TGGE = Temperature Gradient Gelelectrophoresis- TTGE= Temporal Temperature Gradient

gelelectrophoresis - SSCP= Single Strand Conformational Polymorphism- RISA, ARISA (Automated) Ribosomal Intergenic Spacer

Analysis- Community ARDRA, Community ITS RFLP- T-RFLP= Terminal Restriction Fragment Length

Polymorphism

RISA

Ribosomal Intergenic Spacer Analysis

Variability of the size of the ITS region in distinct bacterial groups

Variability of the size of the ITS region in distinct fungal groups

Multiplication of the ITS region and electrophoresis of the PCR products

PCR was carried out in a final volume of 50 μl containing 5 μl of Taq polymerase 10x puffer, 1.6 mM MgCl2, 200 μM for each of the dNTPs, 10 pM primers, 5 μl of template DNA (app. 100 ng) in distilled water and 1 U Taq DNA polymerase (Fermentas). The PCR product was visualized with gelelectophoresis, and the DNA fragments in the gels were stained with SYBR Green and analyzed under UV light.

Primers used in bacteria:

For the amplification of the bacterial ITS region, the Eub-ITSF as forward and Eub-ITSR as reverse primers were used.

Eub-ITSF: 5’-GTCGTAACAAGGTAGCCGTA-3’Eub-ITSR: 5’- GCCAAGGCATCCACC-3’

Primers used in fungi: the best is the ITS5 –forward ITS4-reverse combination.

ITS5: 5’-GGAAGTAAAAGTCGTAACAAGG-3’ ITS4: 5’-TCCTCCGCTTATTGATATGC-3’

Some results obtained with the SOILMAP samples

M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2

Bacterial RISA fingerprints of Romanian soil samples

Some results obtained with the SOILMAP samples

M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2

Bacterial RISA fingerprints of Hungarian soil samples

The fingerprints were very peculiar to the given sample collecting places and there was no significant distinction between the upper and lower layers of the same sampling place.

M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2

Fungal RISA fingerprints of Romanian soil samples made with

ITS5-ITS4 primer pair. 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2

Fungal RISA fingerprints of Hungarian soil samples

made with ITS5-ITS4 primer pair.

As the fungal fingerprints were not enough diverse we used for soil qualifying the bacterial fingerprints only.

M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2

Bacterial RISA fingerprints of Hungarian soil samples

PfBaStrBs200

100

Forest Meadow

Correlation analysis with the bacterial species richness values

RISA fingerprints, summer bacterial species richness, RO+HU+upper+lower

X VERSUS MULTIPLE Y VALUES PLOT WITH OPENSTAT SOFTWARE

CORRELATION MATRIX Correlations VAR2 VAR3 VAR1 VAR2 1.000 0.351 -0.249 UPPERVAR3 0.351 1.000 -0.642 LOWERVAR1 -0.249 -0.642 1.000

VAR1=1 Non intenzively cultivated soilsVAR1=2 Intenzívely cultivated soils

MeansVariables VAR2 VAR3 VAR1 11.950 14.550 1.700 Standard DeviationsVariables VAR2 VAR3 VAR1 8.224 8.841 0.470 No. of valid cases = 20

1. RISA-UPPER-HU0-20 cm

VAR1=1 Non intenzively cultivated soilsVAR1=2 Intenzívely cultivated soils

RISA-LOWER-HU20-40 cm

VAR1=1 Non intenzively cultivated soilsVAR1=2 Intenzívely cultivated soils

The synthesis of the data: establishment a new complex soil qualifying system

Six positive and six negative soil parameters were selected from the summer collected soil samples:

Trypsin-like protease +40

Palmitoylesterase (PE) +40

Bacterial species richness (SR) +40

Humus +40

Phosphorus, mobile (P mobile ) +40

Potassium, mobile (K mobile ) +40

Zinc -40

Copper -40

Manganese -40

Nickel -40

Cadmium -40

Lead -40

The maxima of + parameters get +40 „soil value points”The maxima of negative ones( the heavy metals) get -40 .

All measured parameters had been proportioned to these +40, -40 values, after that thesoil value points were summed in all cases of samples.

The quality values of Hungarian and Romanian soils

-125

-75

-25

25

75

125

A B A B A B A B A B A B A B A B A B A B

HU1 HU2 HU3 HU4 HU5 HU6 HU7 HU8 HU9 HU10So

il v

alu

e (U

nit

s)

-125

-75

-25

25

75

125

A B A B A B A B A B A B A B A B A B A B

RO1 RO2 RO3 RO4 RO5 RO6 RO7 RO8 RO9 RO10

A: 0-20 cm , B. 20-40 cm

Forest soils: HU2 and RO4

6

8

3

7

5

1 4

9

2

10

7

24

5

6

9

10

8

3

1

= above +40 = 0- +40 = 0- -40 = below -40

0-20 cmThe worst soils are besides theroad and railway of Szeged-Makó.

6

8

3

7

5

1 4

9

2

10

7

24

5

6

9

10

8

3

1

= above +40 = 0- +40 = 0- -40 = below -40

20-40 cm

The averaged quality values of Hungarian and Romanian soils

6

8

3

7

5

1 4

9

2

10

7

24

5

6

9

10

8

3

1

= above +40 = 0- +40 = 0- -40 = below -40

Averaged

Thank you for your attention!