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SYNOPS_ 2
- Volkmar Gutsche and Dietmar Rossberg

1. Name of the partner

Federal Biological Research Centre for Agriculture and Forestry (BBA)
Institute for Technology Assessment in Plant Protection
Stahnsdorfer Damm 81, D-14532 Kleinmachnow, Germany
Tel. +33203/482265 Fax +33203/48424
e-mail: v.gutsche@bba.de
Internet: http://www.bba.de

 

2. Name of the indicator

SYNOPS_2 (Synoptisches Bewertungsmodell für Pflanzenschutzmittel)

 

3. Purpose of the indicator

The purpose of version 2 of the SYNOPS-model is to assess the environmental risk potential of a plant protection strategy in a region and to compare different strategies using different plant protection agents.

4. Environmental compartments and ecotoxicological effects

SYNOPS_2 considers the compartments soil, surface water and (optional) the air. The ground water is not involved in the risk assessment since if a pesticide has problems concerning leaching to ground water it would not be registered in Germany. The compartment air can optionally be taken into account but it has no real mass balance and no ecotoxicological impacts. On the basis of exposure in the compartments soil and surface water the ecotoxicological effects on soil organisms (earthworms) and on aquatic organisms (algae, daphnia, fish) are considered.

 

5. Method

The risk assessment procedure consists of five main steps.

 

 

Step 1

The starting point is the determination of the crop and the different pest management strategies which should be compared. This means that for each chosen strategy the chemicals (herbicide(s), fungicide(s), insecticide(s)) which are intended for application have to be fixed. So each strategy is characterized by a number of active ingredients (a. i.) and their dose rates and application times related to the developmental stage of the crop (BBCH-code). Repeated applications of an a. i. are allowed (e. g. Late Blight Disease on potatoes). Table 1 gives an example of a strategy for pest management in potatoes. Here two herbicides (pre-emergence and post-emergence application), one insecticide (against the Colorado Beetle) and six times the same fungicide (against the Late Blight Disease) are applied. The example is only made for demonstration purposes and does not reflect an appropriate management strategy. Concerning the application techniques SYNOPS only recognizes spraying equipment. For arable crops (including vegetables) downwards directed spraying equipment and for fruit trees, vine and hops (excluding weed treatments) cross-flow-fan equipment are assumed. So environmental risks arising from seed treatment or other non spray techniques are not involved in the assessment.

 

The following steps 2 - 4 have to be carried out for each strategy.

 

Table 1: Pest management strategy in potatoes (Demonstration example)

 

Application

number

 

Active

ingredient

Application time

Day number BBCH

Dose rate

g / hectare

1

Glufosinate

120 0

549

2

Bentazon

140 20

960

3

Mancozeb

166 40

1440

4

Mancozeb

176 47

1440

5

Methamidophos

180 51

720

6

Mancozeb

186 55

1449

7

Mancozeb

196 65

1440

8

Mancozeb

206 75

1440

9

Mancozeb

216 85

1440

 

 

Step 2

For each a. i., which the strategy table contains, its environmental concentration is calculated in the following manner.

 

a) Calculation of direct loads

The calculation of direct loads of the compartments soil and surface water has to be made for each application of the considered a. i. according to the formulas:

DRIFT = DOSE RATE · VGT (distance, crop) / 100 (g / hectare)

SOILLOAD =( DOSE RATE - DRIFT) · VDT (crop) / 100 (g / hectare)

WATER LOAD = DRIFT · WATERINDEX (g / hectare)

where

VGT (distance, crop) - spray drift value (%) from the Ganzelmeier-table (Ganzelmeier, 1997) depending on the crop and the distance between the spraying device and the surface water body. The last parameter offers the opportunity to take into consideration possible distance conditions required on the label of the pesticide (as default value SYNOPS uses 5 m distance).

 

VDT (crop) - value (%) from a table which describes the distribution of the remaining dose rate between the plant surface and the soil surface depending on the developmental stage of the crop.

WATERINDEX - bank length of surface water body that borders on fields in the region per 100 meter site length of the fields in the region in per cent. In Germany this WATERINDEX can be estimated by means of GIS-technology on base of the ATKIS data (Authorized Topographical Map Information System). If these data are not available, the percentage of fields in the region, which are in neighborhood with surface water can serve as a first approximation.

For using the model the water index can also be set to default values. Then all the results are related to the surface water conditions that are assumed by the default values (for example: regions with few ditches, rivers, lakes etc. versus regions with a high number of surface water elements).

 

Table of soil loads (%) depending on the developmental stages of the crops

BBCH

 

Sugar beets

Rape

Maize

Potatoes

Cereals

0-9

100

100

100

100

100

10-14

85

85

85

80

93

15-20

71

71

71

65

85

21-30

50

50

50

40

50

31-39

23

36

36

20

32

40

20

35

35

20

30

41-49

5

21

21

17

28

50

5

20

20

17

28

51-99

5

5

5

5

20

 

GANZELMEIER - table of spraydrift (per cent of the applied dose)

Distance

(meter)

 

vine

early

vine

late

fruit trees

early

fruit trees

late

arable crops

early

arable crops

late

hops

early

hops

late

1

23.2

20

46.2

26.7

4

5

47.6

23.4

2

8

12

34.5

22.3

1.6

1.8

39.9

19.9

3

4.9

7.5

29.6

19.6

0.9

1.4

32.3

17.7

4

2.6

5.8

23.8

15.3

0.6

1.0

26.1

15.4

5

1.6

5.2

19.5

10.1

0.5

0.7

18

12.7

7.5

1

2.6

14.1

6.4

0.3

0.5

8.5

10.8

10

0.4

1.7

10.6

4.4

0.3

0.4

4.8

8.9

15

0.2

0.8

6.2

2.5

0.2

0.2

1.7

4.7

20

0.1

0.4

4.2

1.4

0.1

0.1

0.8

3.8

30

0.1

0.2

2.0

0.6

0.1

0.1

0.3

2.1

40

0.1

0.2*

0.4

0.6*

0.1*

0.1*

0.1

0.3

50

0.1

0.2*

0.2

0.6*

0.1*

0.1*

0.1

0.3

* no experimental data available

 

b) Calculation of the concentration in soil for each a.i. as a function of time

In case of repeated applications of the same a.i. each application is separately considered at first. The time t is measured in days, the time period for calculation is one year (365 days). A first order kinetic is assumed for degradation of the substance in soil:

(4) y(t) = yo · exp (- l · t)

(5) with l = ln2 / DT50_s

where

D50_s - Disappearance time in soil from the standard laboratory experiments.

The dependence of the parameter l on the organic content of soil is not explicitly reflected in the model. It can implicitly be done by modifying the DT50-value (supposing that the information is available). But the influence of temperature (Temp) on degradation is taken into consideration as follows:

(6) l * (t) = (exp (0,08 · Temp (t) - 20)) · l

So the concentration in soil as function of time CS_a.i. (t) (measured in mg / kg_soil) is calculated by the following recurrent formulae:

(7) CS_a.i .(0) = yo

(8) t* = 1

(9) t = 1

(10) dy = yo (exp (-l * (t) · (t*-1)) - exp (-l *(t) · t*))

(11) CS_a.i. (t) = CS_a.i. (t-1) - dy

(12) t* = - ln (CS_a.i. (t) / (yo · l ))

(13) t = t + 1

goto (10)

where

yo - initial concentration in soil caused by the soil loading (mg/kg_soil):

(14) yo = SOILLOAD / 375

The formula is subject to the assumptions:

- Soil density is 1,5 g/cm3

- Soil depth is 2,5 cm.

In case of repeated applications the concentration of the considered a.i. is got by summing up the concentrations of the single applications:

 

napp

(15) CS_a.i. (t) = å CS_a.i. (t, i)

i = 1

 

where

napp - number of applications of the considered a.i.

 

Figure 1 shows the concentration curves in soil of the four active ingredients of our demonstration example.

 

 

 

c) Calculation of the concentration in surface water for each a.i. as a function of time

All calculations are carried out similarly formulae (4) - (15) for soil concentration with the following changed terms:

DT50_w - Disappearance time 50 for water (only water body) from the standard laboratory experiments.

CW_a.i. (t) - Concentration in water (measured in mg / l)

(14a) yo = WATERLOAD / 3000

(This formula is subject to an assumed water depth of 30 cm).

Additional to the concentration in water caused by spraydrift, a concentration peak caused by a default run-off event 3 days after application is taken into consideration. The amount of the peak (RUN_a.i.) is calculated by submodel (I) (see Annex). So for t = 3 the formula (11) has to be modified to

(11a) CW_a.i. (t) = CW_a.i. (t-1) - dy + RUN_ a.i.

 

The results of the demonstration expample are given in figure 2. Only for Glufosinate and Bentazon does the submodel calculate a clear additional concentration peak for run off.

 

 

 

Step 3

For each a.i., which the strategy table contains, four mandatory and three optional indices, characterising environmental exposure, are calculated:

 

 

- Short term predicted environmental concentration in soil (mandatory):

 

365

(16) sPEC_a. i = max CS_a. i. (t) (mg / kg_soil)

t=1

 

- Short term predicted environmental concentration in water (mandatory):

365

(17) sPECW_a. i. = max CW_a. i. (t) (mg / l_water)

t=1

 

- Long term predicted environmental concentration in soil (mandatory):

365

(18) lPECS_a.i. = å CS_a. i. (t) (mg · d / kg_soil)

t=1

 

- Long term predicted environmental concentration in water (mandatory):

 

365

(19) lPEC_a.i. = å CW_a. i. (t). (mg · d / l_water)

t=1

 

- Adsorbent index for soil (optional):

(20) AS_a. i. = KdS · sPECS_a. i./(KdS + 1)

 

- Adsorbent index for water sediment (optional):

(21) AW_a. i. = KdW · sPECW_a. i./(KdW + 1)

 

- Air exposure index (optional):

(22) AIR_a.i. = DOSERATE· min (DT50_hyd, DT50_phot) · K_henry

where

log Koc = 1.029 logKow-0.18 (Bonazountas and Wagner, 1984)

Koc = 0.66069 Kow1.029

KdS = %OCS · Koc/100

KdW = %OCW · Koc/100

K_henry = VP · MOLW/(R · SOL· T)

with

log Kow - Logarithmic partition coefficient n-octanol-water of a.i.

%OCS - Organic carbon content of soil (default: 2 %)

%OCW - Organic carbon content of water sediment (default: 5 %)

VP - Vapor pressure of a.i.

MOLW - Molecular weight of a.i.

SOL - Solubility of a.i.

R - Gas constant (= 8.314 Pa * m3/(md * k))

T - Default temperature = 20°C = 293 K

In table 2 are given the results of the demonstration example.

 

Table 2: Indices of the environmental exposure (Demonstration example)

a.i.

 

sPECS

sPECW

lPECS

lPECW

AS

AW

AIR *

Glufosinate

1.455

0.00135

24.123

0.241

0.0240

0.0000543

0.0500 E-4

Bentazon

1.654

0.00228

65.511

0.480

0.00731

0.0000250

0.847 E-4

Mancozeb

0.754

0.0137

4.043

1.437

0.00180

0.000790

4.003 E-4

Methamidophos

0.095

0.00144

0.503

0.0359

0.00188

0.00000711

0.00439 E-4

Mean value

0.9895

0.00773

23.545

0.548

0.00832

0.000219

1.226 E-4

Maximum

1.654

0.01370

65.511

1.437

0.0240

0.000790

4.003 E-4

* 0.0500 E-4 = 0.0500 · 10 -4 etc.

 

Step 4

Now the acute and subchronic biological risk arising from the plant protection strategy as an whole is assessed for the reference organisms (earthworms, algae, daphnia, fish). The acute biological risk indices (abr) of the strategy are obtained by:

 

m

(23) abr_e.w. = max (sPECS_a.i. / LC50_e.w.)

a.i.=1

 

m

(24) abr_w.o. = max (sPECW_a.i ./ LC50_w.o.)

a.i. =1

 

with

e.w. - earthworm

w.o. - reference water organisms

LC50 - Lethal concentration 50 for the reference organisms. If within one organism-group values of different species (e. g. different fish species) are available, then SYNOPS uses the geometric mean value.

m - number of active ingredients of the considered strategy

 

Next, the time depending subchronic risk indices (scr) are calculated in the following manner:

m t

(25) scr_e.w.(t) = å ( å (CS_a.i.(t)/(NOEC_e.w. · t_e.w.(a.i.)))

a.i.=1 t - t_e.w.(a.i.)

 

m t

(26) scr_w.o.(t) = å ( å (CW_a.i.(t)/(NOEC_w.o. · t_w.o.(a.i.)))

a.i.=1 t - t_w.o.(a.i.)

 

with

NOEC_e.w., NOEC_w.o. - No Observed Effect Concentration for the reference organisms

t_e.w.(a.i.), t_w.o.(a.i.) - experimental time in the relevant NOEC-experiments. Usually the time is standardized (earthworm: 14 d, algae: 4 d, daphnia and fish: 21 d) but in some cases the experimental time deviates from the standard. Therefore, in general it depends on the design of the according NOEC-experiment of the a.i..

 

Figure 3 shows the subcronic risk indices during a year resulting from our demonstration example.

 

 

Finally, the maximum values of the time depending subchronic risk from the total subchronic risk indices:

365

(27) tscr_e.w. = max scr_e.w.(t)

t=1

 

365

(28) tscr_w.o. = max scr_w.o.(t)

t=1

 

 

Step 5

 

The last step serves to visualize the risk potentials in order to compare different strategies. As the result of step 4 the biological risk potential of each strategy is characterized by 8 discrete indices: the acute biological risk for earthworms, algae, daphnia, fish and the total subchronic risk for the same reference organisms. Furthermore, each strategy is characterized by a figure of 4 curves showing the time depending subchronic risk of the four organisms. To compare different strategies the relationship between the corresponding single indices can be visualized by means of risk graphs.

 

A risk graph is a circle divided into sectors. Each sector is divided into segments corresponding to the individual indices. The value of each index determines the length of the radius of the corresponding segment such that large risk graphs suggest a high risk potential of the strategy and small risk graphs suggest a lower risk potential. The values of the corresponding indices are represented in relation to each other. The largest value has the maximum circular arc radius, the smaller values have proportionally smaller circular arc radiuses.

Figure 4 shows a demonstration example of the comparison of three strategies.

 

Figure 4: Visualization of the biological risk potentials of different plant protection strategies by means of risk graphs

 

 

6. Active ingredients and brand names

 

SYNOPS considers only active ingredients. If an applied product contains more than one active ingredient all these chemicals have to be put into the strategy table. The corresponding dose rates have to be calculated on base of the information on the product label (g_a.i. per kg_product etc.).

 

7. Practical use and success of the indicator

 

The first version of the indicator ( SYNOPS_1) was used for two scientific and political purposes up to now:

- a investigation of the risk trend on national level in Germany on base of the ten most sold herbicides, fungicides and insecticides in 1987 and 1995, respectively.

- a comparison of the risk potential of organophosphates versus pyrethroides

 

The main difference between the first version of the model and the version described here is that SYNOPS_1 considers each potential application of all active ingredients involved in the comparison singularly (no balance of environmental concentration in case of repeated applications). So each potential application derived from the „Register of Authorized Plant Protection Products" gets a series of the discrete exposure and biological risk indices and on this base SYNOPS_1 calculates weighted mean values for each index. The weights are related to the application probability, to the national crop area and to the national sale data. A detailed description of this indicator version can be found in GUTSCHE and ROSSBERG 1997 and 1998.

 

The second version of the assessment model (SYNOPS_2) explained here was used in an eco-bilance procedure to compare different crop management strategies in vineyards in the Bundesland Rheinland/Pfalz. The results of the full procedure will be published this year.

 

Appendix

Run-off submodels

(I)

The submodel for the calculation of the run-off peak of the concentration curve in surface water is based on the formulas of BEINAT and VAN DEN BERG, 1996. They have been modified on the base of experimental results published by KLÖPPEL, HAIDER and KORDEL 1994.

It is assumed that the run-off event takes place 3 days after the application of the active ingredient. The run-off concentration depends on three parameters:

P - amount of precipitation of the run-off event [mm].

If the precipitation is less than 17 mm, no run-off is assumed.

SLOPE - average of the slope of the fields in the region [%].

logKow - logarithmic partition coefficient n-octanol-water of the a.i. considered. If the value is greater than 4.5, the run-off water does not contain the chemical.

 

Three formal coefficients are calculated according to the formulas:

fs = (0.124 · SLOPE + 0.0082 · SLOPE2) / 100

fr = 0.0208 · (P-17) + 0.00011 · (P-17)2 P ³ 17 mm

fp = (87.7071 - 0.5424 · log kow + 0,6465 · log kow2 log Kow < 4.5

- 1.0777 log kow3) / 100

but fp = 1 if log kow = - 2

By multiplication and introducing a correction factor we obtain:

f = fs · fr · fp · 0.9

Than the submodel calculates the additional peak of the concentration curve on the third day after application by:

RUN_a.i. = f · SOILLOAD · exp (-l · 3) / 3000

where SOILLOAD and l are obtained according to the formulas (2) and (5), respectivly.

The submodel requires a further validation.

 

(II)

The second version is based on the model of LUTZ and MANIAK (1984, 1992) for calculation of run-off volumes depending on soil type, crop type, week number, soil moisture and precipitation. It also needs a further validation.

 

L%runoff = (Q / P) · f(slope) · exp(-3 · ln2 / DT50soil) · 100 / (1+Kd)

 

Where:

L%runoff - Percentage of application dose being available in run-off water as dissolved

compound

Q - Run-off volume (mm) calculated according the model of Lutz & Maniak

P - Precipitation volume (mm)

DT50soil – Half-life time of a.i. in soil

f(slope) – Factor, that reflects the influence of field slope on L% (see figure 1b)

 

f(slope) = 0.02153 · slope + 0.001423 · slope2 if slope < 20%

= 1 if slope ³ 20%

Kd = KOC · %OC / 100 with KOC – Sorption coefficient of a.i. to organic carbon

%OC – Organic carbon content of soil

 

In the formula is assumed that 3 days after application a run-off event happens. For simplification the run-off volumes for 3 scenarios calculated by means the Lutz & Maniak model are given in table 4b.

 

Scenario I represents a bare soil with a high soil moisture, scenario II a bare soil with a low soil moisture and scenario III a covered soil with a low soil moisture, respectively. Within each scenario the run-off values for more sandy soils and for more loamy soils are calculated.

 

To demonstrate the effects of intrinsic attributes of the compounds two examples are shown in table 4c and 4d, respectively. The active ingredients isoproturon and bifenoxin are considered in the example. As physico-chemical parameters are used:

 

Isoproturon: DT50soil = 29d, logKow = 2.48, %OC = 1.3%, estimated Kd = 3.089

Bifenox: : DT50soil = 7d, logKow = 4.48, %OC = 1.3%, estimated Kd = 349.8.

(For estimation of Koc the formula logKoc=1.029 logKow – 0.18 (Bonanzountas & Wagner, 1984) was chosen).

 

The examples show that in the worst case 7.37% of the applied dose of isoproturone is lost by a heavy rainfall of 50mm and a field slope of 15% whereas in case of bifenoxin de facto no loss is calculated by the formula.

 

 

 

Figure 1b: Slope depending factor f(slope) for the calculation of L%runoff

Table 4b: Run-off volume according the model of Lutz & Maniak

Precipitation (mm)

Run-off volume (mm)

Scenario I

Scenario II

Scenario III

sandy soil

loamy soil

sandy soil

loamy soil

sandy soil

loamy soil

6

0.10

0.45

0.04

0.19

0.02

0.13

8

0.28

0.82

0.12

0.35

0.07

0.24

10

0.54

1.29

0.23

0.56

0.13

0.38

12

0.88

1.86

0.38

0.81

0.21

0.55

14

1.29

2.51

0.56

1.11

0.32

0.76

16

1.78

3.24

0.78

1.45

0.44

0.99

18

2.32

4.05

1.03

1.83

0.59

1.26

20

2.92

4.93

1.31

2.25

0.75

1.55

22

3.58

5.88

1.63

2.72

0.94

1.88

24

4.29

6.88

1.98

3.22

1.14

2.23

26

5.04

7.95

2.35

3.76

1.36

2.61

28

5.84

9.06

2.76

4.34

1.59

3.02

30

6.69

10.23

3.19

4.95

1.85

3.45

32

7.57

11.45

3.65

5.59

2.12

3.91

34

8.48

12.70

4.13

6.27

2.40

4.39

36

9.44

14.00

4.65

6.99

2.71

4.90

38

10.42

15.34

5.18

7.73

3.03

5.44

40

11.43

16.71

5.75

8.51

3.36

6.00

42

12.47

18.11

6.33

9.31

3.71

6.58

44

13.53

19.54

6.94

10.14

4.08

7.18

46

14.62

21.01

7.57

11.01

4.46

7.81

48

15.73

22.50

8.22

11.90

4.85

8.46

50

16.87

24.01

8.90

12.81

5.26

9.13

55

19.78

27.89

10.67

15.22

6.34

10.89

60

22.79

31.90

12.57

17.78

7.50

12.79

65

25.89

36.01

14.57

20.48

8.74

14.80

70

29.06

40.20

16.68

23.31

10.06

16.92

75

32.29

44.47

18.89

26.26

11.44

19.15

80

35.57

48.80

21.19

29.33

12.89

21.48

85

38.90

53.18

23.58

32.51

14.40

23.91

90

42.26

57.60

26.04

35.79

15.97

26.44

95

45.65

62.06

28.58

39.16

17.60

29.04

100

49.07

66.55

31.18

42.61

19.29

31.74

 

 

Table 4 c: Loss of isoproturon in per cent caused by run-off events

Precipitation (mm)

Slope

(%)

Scenario I

  

Scenario II

  
   

Sandy soil

Loamy soil

Sandy soil

Loamy soil

10

5

0.18

0.44

0

0.20
 

15

0.83

1.98

0.35

0.86

30

5

0.76

1.16

0.36

0.53
 

15

3.42

5.23

1.63

2.53

50

5

1.15

1.64

0.61

0.87
 

15

5.18

7.37

2.73

3.93

 

 

Table 4d: Loss of bifenoxin in per cent caused by run-off events

Precipitation (mm)

Slope

(%)

Scenario I

  

Scenario II

  
   

Sandy soil

Loamy soil

Sandy soil

Loamy soil

10

5

0

0

0

0
 

15

0.01

0.02

0

0.01

30

5

0.01

0.01

0

0
 

15

0.03

0.05

0.01

0.02

50

5

0.01

0.01

0

0.01
 

15

0.05

0.06

0.02

0.03

 

References

Beinat, E., van den Berg, R., 1996: EUPHIDS, a decision support system for admission of pesticides. Report no. 712405002, National Institute of Public Health and the Environment, Bilthoven, The Netherlands

Bonazountas, M., Wagner, J., 1984. SESOIL: A Seasonal Soil Compartment Model. Designed for the U.S. Environmental Protection Agency, Washington D.C., Arthur D. Little, Inc., Cambridge, Mass., p. AD-10

Ganzelmeier, H., 1997: Abtrift und Bodenbelastung beim Ausbringen von Pflanzenschutzmitteln. Mitt. Biol. Bundesanstalt Land- u. Forstwirtschaft, Berlin-Dahlem, H.328, S. 115-124

Gutsche, V., 1995. The influence of pesticides and pest management strategies on wildlife. BCPC Symposium Proceedings No 63, Farnham, UK, pp. 469-480

Gutsche, V., Rossberg, D.,1996. SYNOPS 1.1 - a model to assess and to compare the environmental risk potential of active ingredients in plant protection products. Agriculture, Ecosystems & Environment 64(1997) 181-188

Gutsche, V., Rossberg, D.; 1997: Die Anwendung des Modells SYNOPS1.2 zur synoptischen Bewertung des Risikopotentials von Pflanzenschutzmittelwirkstoffgruppen für den Naturhaushalt. Nachrichtenbl. Deut. Pflanzenschutzd., 49 (11), S. 273-285

Gutsche, V., Enzian, S. 1998: Quantitative Untersuchungen zur geografischen Nachbarschaft von Ackerland und Oberflächengewässer am Beispiel von Schleswig-Holstein und Sachsen-Anhalt. Nachrichtenbl. Deut. Pflanzenschutzd., 50 (4), S. 73-78

Klöppel, H., Haider, J., Kördel, W. 1994: Herbicides in surface runoff: A rainfall simulation study on small plots in the field. Chemosphere, 28 (4), pp. 649-662

Lutz, W. 1984: Berechnung von Hochwasserabflüssen unter Anwendung von Gebietskenngrößen. Mittlg. Inst. Hydrologie Wasserwirtschaft, Univ. Karlsruhe, Heft 24

Maniak, U. 1992: Regionalisierung von Parametern für Hochwasserabflußganglinien. In: Regionalisierung der Hydrologie (H.B. Kleeberg), DFG, Mittlg. Senatskomm. für Wasserf. 11, S. 325-332

 

 

 

Volkmar Gutsche, DE

 

Ideas on the approach of risk indicators

 

 

 

 

 

 

 

 

Prof. Dr. Volkmar Gutsche

Address:
Biologische Bundesanstalt für Land- und Forstwirtschaft
Institut für Folgenabschätzung im Pflanzenschutz
Stahnsdorfer Damm 81
14532 Kleinmachnow
Germany

Phone: +49 33203 48205
Fax: +49 33203 48425
E-Mail: V.Gutsche@bba.de
Internet: http://www.bba.de

Prof. Dr. Gutsche has received his appointment as Director and Professor by the Ministry for Food, Agriculture and Forestry in Germany in 1983. He earned his first doctorate in 1971 at the Technical University Chemnitz with a specialization in Operations Research. He completed his studies by gaining the first academic degree in mathematics. In 1976, he earned the Doctorate A at the German Academy of Agricultural Sciences. The title of dissertation was "Theoretical basis for modelling and forecasting of population dynamics." In 1986 he received his Doctorate B (doctor scientae naturalium) at the German Academy of Agricultural Sciences and the title of his dissertation was "Development and usage of epidemic and pest insect models in research and practice of plant protection."

As Director of the Ministry for Food, Agriculture and Forestry, his responsibilities include: