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
6
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
(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
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
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
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
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.