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László Manczinger Department of Microbiology, Faculty of Science and Informatics,

Recommendation for working out a new soil ranking system based on the results of the SOILMAP project. László Manczinger, Isidora Radulov, Adina Berbecea, Enikő Sajben-Nagy, Andrea Palágyi, Dorin Tărău, Lucian Dumitru Niţă, Csaba Vágvölgyi. László Manczinger

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László Manczinger Department of Microbiology, Faculty of Science and Informatics,

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  1. Recommendation for working out a new soil ranking system based on the results of the SOILMAP project László Manczinger, Isidora Radulov, Adina Berbecea, Enikő Sajben-Nagy, Andrea Palágyi, Dorin Tărău, Lucian Dumitru Niţă, Csaba Vágvölgyi László Manczinger Department of Microbiology, Faculty of Science and Informatics, University of Szeged, Hungary

  2. SAMPLING PLACES Two type sampling from every places A: upper layer : 0-20 cm B: lower layer : 20-40 cm SAMPLING TIMES SPRING - March SUMMER- August AUTUMN- November

  3. 6 The sampling places on the map of the region 8 7 1 9 10 5 2 4 3 3 4 1 2 Two type sampling from every places A: upper layer : 0-20 cm B: lower layer : 20-40 cm 5 6 7 10 8 9

  4. The investigated parameters of the soil samples Physical-chemical parameters Biochemical parameters Microbiological parameters • Species richness • of bacteria • 2. Species richness • of fungi • 3. Diversity of important bacterial genera • 4. Diversity of toxinogenic fungi

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

  6. Some important results regarding the physical-chemical parameters Humus

  7. Some important results regarding the physical-chemical parameters(phosphorus and potassium) In the Romanian soils the amount of both P and K is frequently much more less in the lower layer than in the upper layer.

  8. Some important results regarding the physical-chemical parameters(cadmium and copper)

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

  10. Some important results regarding the physical-chemical parameters(manganese and iron)

  11. Regression analysises P-mobile – K-mobile regression in the Hungarian soil samples X versus Y Plot X = VAR1, Y = VAR2 from file: Temporary.TEX Variable Mean Variance Std.Dev. VAR1 89.31 1923.73 43.86 VAR2 295.80 19081.85 138.14 Correlation = 0.7066, Slope = 2.23, Intercept = 97.03 Standard Error of Estimate = 97.74 Number of good cases = 20

  12. P-mobile – K-mobile regression in the Romanian soil samples X versus Y Plot X = VAR1, Y = VAR2 from file: Temporary.TEX Variable Mean Variance Std.Dev. VAR1 58.88 2031.53 45.07 VAR2 262.85 45963.82 214.39 Correlation = 0.5739, Slope = 2.73, Intercept = 102.10 Standard Error of Estimate = 175.57 Number of good cases = 20

  13. Multiple regression of heavy metals in the Romanian samples Correlation matrix 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

  14. Multiple regression of heavy metals in the Hungarian samples Correlation matrix 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 -0.5510.706 0.119 0.269 1.000

  15. Analysis of soil enzyme data

  16. We worked on microtiter plates with chromogenic substrates

  17. SPRING Relative activities of soil enzymes in the spring and summer sample series. Hungarian soils, upper layer. SUMMER The other sample series showed very like pictures. The summer samples, as being most diverse, were statistically analysed and used for soil qualifying.

  18. Soil type – soil enzyme correlations calculated with OpenStat software HU-Enzyme-Lower soil layer X VERSUS MULTIPLE Y VALUES PLOT X= VAR1: 1=non fertilized soils, 2= fertilized soils CORRELATION 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 We made the corre- lation matrices in every soil sample series

  19. Correlation of soil enzyme activities with the use of fertilizers and pesticides The enzyme activities in the lower layers of intensively cultivated Hungarian soils are higher, except of palmitoylesterase (G).

  20. Correlation of soil enzyme activities with the use of fertilizers and pesticides The enzyme activities in the upper layers of fertilizer and pesticide treated Hungarian soils are frequently higher, than in the soils of nonintensive fields (forest, meadow, biocultivation).

  21. Correlation of soil enzyme activities with the use of fertilizers and pesticides In the Romanian soils all enzyme activities were strongly less in the intensively cultivated fields both in the upper and lower layers exept of phosphatase.

  22. Determination of soil microbial diversity

  23. 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

  24. RISA Ribosomal Intergenic Spacer Analysis

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

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

  27. 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’

  28. 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

  29. Some results obtained with the SOILMAP samples M 1/1 1/2 2/1 2/23/1 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.

  30. 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.

  31. As the fungal fingerprints were not enough diverse we used for soil qualifying the bacterial fingerprints only. Forest Meadow M 1/1 1/2 2/1 2/23/1 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 Pf Ba Str Bs 200 100 Bacterial RISA fingerprints of Hungarian soil samples

  32. 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 UPPER VAR3 0.351 1.000 -0.642 LOWER VAR1 -0.249 -0.642 1.000 VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils Means Variables VAR2 VAR3 VAR1 11.950 14.550 1.700 Standard Deviations Variables VAR2 VAR3 VAR1 8.224 8.841 0.470 No. of valid cases = 20

  33. VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils • RISA-UPPER-HU • 0-20 cm

  34. VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils RISA-LOWER-HU 20-40 cm

  35. 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: 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 the soil value points were summed in all cases of samples.

  36. The quality values of Hungarian and Romanian soils A: 0-20 cm , B. 20-40 cm Forest soils: HU2 and RO4

  37. 6 8 7 1 9 10 5 2 4 3 3 4 1 2 5 6 7 = above +40 = 0- +40 = 0- -40 = below -40 0-20 cm 10 8 9 The worst soils are besides the road and railway of Szeged-Makó.

  38. 6 8 7 1 9 10 5 2 4 3 3 4 1 2 5 6 7 = above +40 = 0- +40 = 0- -40 = below -40 20-40 cm 10 8 9

  39. The averaged quality values of Hungarian and Romanian soils

  40. 6 8 7 1 9 10 5 2 4 3 3 4 1 2 5 6 7 = above +40 = 0- +40 = 0- -40 = below -40 Averaged 10 8 9

  41. Thank you for your attention!

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