józsef prokisch, dóra hovánszky, Éva széles, béla kovács, zoltán győri university of...

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József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science, Quality Assurance and Microbiology 4032 Debrecen Böszörményi út 138. Hungary Inhomogeneity of the agricultural soils in Hungary Basic terms: Homogenous, heterogenius, inhomogenous Inhomogenity, Guide to the expression of uncertainty in measurement (GUM) Representative sampling Question of this study: What is the real inhomogeneity of our soils in the practice?

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Page 1: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri

University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science, Quality Assurance and Microbiology 4032 Debrecen Böszörményi út 138. Hungary

Inhomogeneity of the agricultural soils in Hungary

Basic terms:

Homogenous, heterogenius, inhomogenousInhomogenity, Guide to the expression of uncertainty in measurement (GUM)Representative sampling

Question of this study:What is the real inhomogeneity of our soils in the practice?

Page 2: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

Bottle with the certified reference material.Mass: 1.00 gParticle size distribution and concentration distribution are same than the real sample.

Sample intake for the measurement. Sample mass: (e.g.) ~10 mgConcentration of sample is calculated from analyte concentration in the individual particles

particle

analyte

Bottle with the certified reference material.Mass: 1.00 gParticle size distribution and concentration distribution are same than the real sample.

Sample intake for the measurement. Sample mass: (e.g.) ~10 mgConcentration of sample is calculated from analyte concentration in the individual particles

particle

analyte

0

0.5

1

1.5

2

2.5

1 10 100 1000

particle size [µm]

den

sity

dis

trib

uti

on q

3lg(

x)

"Sample A"

"Sample B"

"Sample C"

0

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particle size [µm]

CD

NA [µ

g/m

L]

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160

180

0.01 0.1 1 10 100

sample mass [mg]

CD

NA

g/m

l]

Sample CSample BSample A

Page 3: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

The scale and theinhomogeneity

Page 4: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

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JPconv.exe

Page 5: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

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The scale and the measuredinhomogeneity

Page 6: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

Questions of this study1. How many sample should I take from an arable field for getting results with a

certain confidence level?

2. Who is responsible for the uncertainity? Sampling or the laboratory? What is the acceptable uncertainity of a repeated measurement of soil from an arable field (5-30 ha)

35.6 ± 12.5 mg/kg25.3 ± 5.2 mg/kg45.6 ± 22.5 mg/kg

An example:

Page 7: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

The sampling site and sampling strategy

Example:Nádudvar, Hungary, 47o26’36.8”N 21o13’37.9”E

1000 m * 300 m = 30 ha147 sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

Measured parameters:pH, CaCO3, N, P, K, „total” and „plant available” metals, pesticides

Page 8: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

55-60

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

10-15

5-10

0-5

Cr („total”) [mg/kg](ICPOES)

1000 m * 300 m = 30 ha147 sample

Cost more than 6000€

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

36000-39000

33000-36000

30000-33000

27000-30000

24000-27000

21000-24000

18000-21000

15000-18000

12000-15000

9000-12000

6000-9000

3000-6000

0-3000

Al („total”) [mg/kg](ICPOES)

30

35

40

45

50

55

60

20000 25000 30000 35000 40000

Al [mg/kg]

Cr

[mg

/kg

]

Results:Spatial distribution of total aluminum and chromium in

the soil at the sampling site

0

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10 100 1000 10000

Particle size [µm]

cc.H

NO

3 +H

2 O2

solu

ble

chro

miu

m [m

g/kg

]

Page 9: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

3800-40003600-38003400-36003200-34003000-32002800-30002600-28002400-26002200-24002000-22001800-20001600-18001400-16001200-14001000-1200800-1000600-800400-600200-4000-200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

2000-21001900-20001800-19001700-18001600-17001500-16001400-15001300-14001200-13001100-12001000-1100900-1000800-900700-800600-700500-600400-500300-400200-300100-2000-100

P („total”) [mg/kg](ICPOES)

P („Ammonium lactate soluble”) [mg/kg]

(Photometry)

1000 m * 300 m = 30 ha

Spatial distribution of total and AL soluble phosphorous in the soil at the sampling site

600

1100

1600

2100

2600

3100

3600

4100

20000 25000 30000 35000 40000

Al [mg/kg]

P [

mg

/kg

]

Page 10: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

The developed and applied Monte-Carlo model for the sampling

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

55-60

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

10-15

5-10

0-5

Random selection of certain number of sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

55-60

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

10-15

5-10

0-5

Calculation of average

Repeating 100 times

Calculation of relative standard deviation of average values

number of sample RSD%1 10,942 6,953 5,884 5,385 4,996 4,698 4,1610 3,7320 1,9040 1,1250 0,7575 0,64100 0,56

Page 11: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

0

5

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0 10 20 30 40 50 60 70 80 90 100

number of samples

RS

D %

0

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40

45

0 10 20 30 40 50 60 70 80 90 100

number of samples

RS

D %

P Cr

Results of the Monte-Carlo model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

55-60

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

10-15

5-10

0-5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21S1

S2

S3

S4

S5

S6

S7

3800-40003600-38003400-36003200-34003000-32002800-30002600-28002400-26002200-24002000-22001800-20001600-18001400-16001200-14001000-1200800-1000600-800400-600200-4000-200

Page 12: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

Conclusions

0

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40

45

0 50 100 150

number of samples

RS

D %

Comparison of standard procedures and practical results

5 ha = 1 sample (what was created by mixing 10-20 individual point)

30 ha = 6 sample (60 sample)

A repeated procedure should produce a results less than 3 % RSD% for the phosporous and less than 1 % for chromium.

1 or not enough sample can resulted high uncertainity

Correct sampling acceptable uncertainity reason of high uncertainity (>10%) in the land scale sampling is caused by the

wrong measurement in the laboratory or very high antropogenic contamination

Good sampling is not impossible!

Page 13: József Prokisch, Dóra Hovánszky, Éva Széles, Béla Kovács, Zoltán Győri University of Debrecen, Centre of Agricultural Sciences, Institute of Food Science,

Thanks for you attention!