146 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
Table 2 Summary of decision rules describing the effect of the input
variables Rate of application and DT50 on the hypothetical module Environmental effect
a
Rate of application DT50
Environmental effect F
F 0.0
F U
0.5 U
F 0.5
U U
1.0
a
F: Favourable; U: Unfavourable.
conclusions of the decision rules to obtain a value for the module. Fig. 1 shows the decision rules from Table
2. This mode of presentation is followed in this paper.
3. Results
3.1. Modification of Ipest The modifications made had as their objective the
transformation of the expert system Ipest into Ipest-B, adapted to the hydrogeological conditions of Brittany.
No change was made to the modules Presence and Rair, which remain as described by Van der Werf and
Zimmer 1998. Presence depends on the rate of ap- plication of active ingredient only. Rair reflects the po-
tential of a pesticide to volatilise and contaminate air. Its value depends on four input variables: 1 pesticide
volatility; 2 the position of application of the pesti- cide on the crop, on the soil, in the soil; 3 the field
half-life of the pesticide average values and 4 the toxicity of the pesticide to humans based on Accept-
able Daily Intake. Fig. 2 shows the decision rules for
Fig. 1. Summary of decision rules. The effects of the input variables Rate of application and Field half-life DT50 on the value of the conclusions of the decision rules for the hypothetical indicator module Environmental effect according to their membership to the fuzzy
sets Favourable and Unfavourable. Fig. 2. The effect of the input variables Volatility, Position of
application, Field half-life DT50 and Human toxicity on the value of the conclusions of the decision rules for the indicator module
Rair according to their membership to the fuzzy sets Favourable non-shaded boxes and Unfavourable shaded boxes. For details,
see Van der Werf and Zimmer 1998.
the calculation of Rair. The modules Rgro and Rsur have been modified.
3.1.1. The module Rgro The indicator module Rgro reflects the potential of
a pesticide to reach groundwater through leaching and to affect its potential use as a source of drinking wa-
ter for humans. Rgro as proposed by Van der Werf and Zimmer 1998 depends on four input variables:
1 pesticide leaching potential; 2 the position of ap- plication of the pesticide on the crop, on the soil, in
O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158 147
the soil; 3 soil leaching risk and 4 the toxicity of the pesticide to humans based on Acceptable Daily
Intake. In Ipest, the estimation of soil leaching risk is up to ‘user-expertise’; for Ipest-B this was replaced
by two new input variables.
Leaching risk depends on characteristics of the soil, the unsaturated zone above the water table and the sat-
urated aquifer. Van der Werf and Zimmer 1998 cite several methods which can be used to assess this risk
as a function of soil characteristics only e.g. Goss and Wauchope, 1990, or of overall hydrogeologic settings
including soil characteristics e.g. Aller et al., 1985; Hollis, 1991. However, because of a lack of soil data
for the fields in the present study neither of these meth- ods was appropriate. Instead, estimates were made of
the risk of pesticide loss to groundwater from two vari- ables: Soil organic matter content and Geologic sub-
stratum. Soil organic matter content is considered the single most important soil characteristic affecting pes-
ticide leaching Van der Zee and Boesten, 1991. Geo- logic substratum affects movement of pesticides once
they have left the soil. In the hydrogeologic context of Brittany geologic substratum is considered to be
more important than soil organic matter content with respect to pesticide loss from a field to groundwater
Carré, 1997, personal communication.
The choices of the values of input variables lim- iting the transition interval define the fuzzy subsets.
Reus 1993 defines five classes for soil leaching potential based on organic matter content: 1.5,
1.5–3, 3–6, 6–12 and 12. Hollis 1991 identifies three classes for organic matter content: low=1.1,
intermediate=2.5 and high=4.7–7. Considering the range of soil organic matter contents found in
Brittany Walter et al., 1995, the present authors de- fine the limits of the transition interval by assigning
complete membership to U if Soil organic matter content 2 and complete membership to F if Soil
organic matter content 5.
Carré et al. 1994 have shown that contamination risk of groundwater depends on geologic substratum.
The character of the mechanism involved may be phys- ical protection of the groundwater, clay content or
chemical pH. This study considered particularly the physical mechanisms in estimating the effect of geo-
logic substratum on groundwater contamination risk. On a 0 no contamination risk to 1 major contamina-
tion risk scale Carré 1997, personal communication attributes the following scores to the input variable
Geologic substratum: •
0.3 for a schist substratum where aquifers are pro- tected because they are generally confined,
• 0.5 for granite where aquifers generally are free and,
therefore, less protected, the breakdown of pesti- cides being favoured because of their slow transit
in these substratums,
• 1.0 for a calcareous substratum or an alluvium
which allow rapid hydrologic infiltrations. The transition interval is defined by assigning com-
plete membership to F if Geologic substratum=0 and complete membership to U if Geologic substratum=1.
These modifications lead to a new module Rgro which depends on the input variables GUS, Position of ap-
plication, Geologic substratum, Soil organic matter content and Human toxicity according to a set of
32 decision rules not shown. These decision rules summarised in Fig. 3 reflect the authors’ ‘expert’
perception of the Rgro as a result of a pesticide application.
3.1.2. The module Rsur The indicator module Rsur reflects the potential of
a pesticide to reach surface water through runoff or drift and to harm aquatic organisms. Rsur as proposed
by Van der Werf and Zimmer 1998 depends on five input variables: 1 the runoff risk of the field site;
2 the drift percentage depends on application tech- nique and distance to surface water; 3 the position
of application of the pesticide on the crop, on the soil, in the soil; 4 the field half-life of the pesti-
cide average values and 5 the toxicity of the pes- ticide to three aquatic organisms algae, crustaceans
and fish. For Ipest-B, the present authors implemented the input variable 1 in Ipest, its estimation was up
to ‘user-expertise’ and added a sixth input variable: Human toxicity.
The runoff risk of the field site should reflect the risk of pesticide transport in solution or adsorbed on
soil particles from a field to surface water by runoff. It depends on many factors, e.g. slope steepness, slope
length, soil texture, surface condition, soil particle ag- gregation and stability, crop cover and distance to sur-
face water Leonard, 1990; Simon, 1995. Most of the factors affecting runoff risk are taken into account
in a method for the evaluation of the risk of pesti- cide runoff proposed by Aurousseau et al. 1996. A
148 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
Fig. 3. The effect of the input variables GUS, Position of application, Geologic Substratum, Soil organic Matter content OM, and Human toxicity H.T. on the value of the conclusions of the decision rules for the indicator module Rgro according to their membership to the
fuzzy sets Favourable non-shaded boxes and Unfavourable shaded boxes. Boxes with a dotted outline indicate modifications relative to the proposal by Van der Werf and Zimmer 1998.
more recent version of this method was published by Aurousseau et al. 1998.
Aurousseau et al. use a scoring method called SIRIS System of Integration of Risk with Interac-
tion of Scores, Vaillant et al., 1995 to implement their method for the evaluation of the risk of pesti-
cide runoff. The SIRIS method can be used for any problem which requires taking into account a number
of criteria or input variables in order to rank options in the present case field sites are ranked with re-
spect to runoff risk. The method involves defining two to four classes for each criterion and attribut-
ing modalities to the classes. Quantitative as well as qualitative data can be used, the modalities are:
f favourable, m median, u unfavourable and U very unfavourable.
Aurousseau et al. propose eleven criteria which af- fect the runoff risk of a field. In this study data were
available for six of these criteria Table 3: 1. Distance between the zone of application and the
river system: This criterion was defined as the dis- tance between the lowest part of the field and the
river system. Its value was obtained by interview- ing the farmer.
2. Presence of a man-made draining system: On shallow soils or in winter the presence of a draining
system will facilitate infiltration and thus decrease runoff risk. However, a drainage system favours
transfers by preferential flow, in particular of par- ticles, and therefore, its presence is considered
unfavourable. Obviously this criterion does not concern runoff risk in the strict sense but rather
the risk of transfer to surface water in general.
3. Soil organic matter content: This was obtained by interviewing the farmer.
4. Slope steepness: This criterion is estimated quali- tatively by the farmer.
5. Geologic substratum: This criterion is estimated from a soil map. Granite is considered favourable
and schist or sandstone unfavourable because drainage is better on granite than on schist and
sandstone.
6. Existence of an embankment downhill from the field between the field and the river system is con-
sidered to be favourable. The five criteria not taken into account data were
not available are: •
the length of the slope, •
the presence of permanent vegetation wood, pas- ture adjacent to the lower side of the field,
O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158 149
Table 3 List of the input variables, classes and modalities incorporated in this version of the Aurousseau indicator assessing the runoff risk of a field
a
Input variables Classes
Modalities Distance between the zone of application and the river system
Adjacent to the field U
50 m u
50–200 m m
200 m f
Draining system Presence
u Absence
f Soil organic matter content
2.5 u
2.5–5 m
Over 5 f
Slope steepness Moderate to strong
u Weak
m Null
f Nature of the geologic substratum
Schist and sandstone u
Metamorphic or granite and schist m
Granite f
Embankment between the field and the river system Absence
u Presence
f
a
f: Favourable; m: median; u: unfavourable and U: very unfavourable.
• the shape of the slope presence of a concave zone,
• the risk of surface crusting, an index of susceptibil-
ity to crusting calculated from granulometric data and organic matter content,
• the risk of an intense rainstorm shortly after appli-
cation calculated from historic meteorological data and sowing date.
The method proposed by Aurousseau et al. allows
the calculation of a rank with respect to runoff risk for each field. The rank is transformed into a value
between 0 and 1 by dividing it by the highest rank number. The transition interval is defined by assign-
ing complete membership to F if Runoff risk=0 and complete membership to U if Runoff risk=1.
The indicator Ipest was constructed assuming that groundwater rather than surface water is the raw ma-
terial for drinking water, so Human toxicity was an input variable for Rgro and not for Rsur. In Brittany,
80 of drinking water is made from surface water, so for Ipest-B Human toxicity was added as an input
variable for Rsur, with the same weight as Aquatic toxicity Fig. 4.
3.1.3. Aggregation of the modules: Ipest-B The indicator Ipest-B results from the aggregation
of the four modules. For Ipest Van der Werf and Zim- mer 1998 gave a similar weight to the modules Rsur,
Rgro and Rair. For Ipest-B, it was decided to give less weight to the module Rgro Fig. 5, as in Brit-
tany groundwater is much less important than surface water.
3.2. Application of Ipest-B, a comparison of pesticide environmental effect of beet, wheat and maize
In the survey, pesticides were applied 31 crops of winter wheat, 16 crops of silage maize and 14 fodder
beet crops, and each crop was grown on a different field. These crops were compared with respect to pes-
ticide environmental effect. As the value of Ipest-B depends, amongst others, on site-specific conditions,
it should be known to what extent the crops in this study differed for input variables reflecting these con-
ditions. Soil organic matter content and Runoff risk are rather similar for the three crops Fig. 6 . For Ge-
ologic substratum and Drift percentage the fields on which wheat and maize are grown are quite similar.
The fields carrying beet crops have a larger proportion of granite substratum implying more risk for ground-
water and more of these fields are unfavourable with respect to the drift percentage implying more risk for
surface water Fig. 6.
150 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
Fig. 4. The effect of the input variables Runoff risk, Drift percentage, Position of application, Field half-life DT50, Human toxicity H.T., and Aquatic toxicity A.T. on the value of the conclusions of the decision rules for the indicator module Rsur according to their
membership to the fuzzy sets Favourable non-shaded boxes and Unfavourable shaded boxes. Boxes with a dotted outline indicate modifications relative to the proposal by Van der Werf and Zimmer 1998.
3.2.1. Average and cumulative values of the four modules and of Ipest-B
The number of pesticide applications varied from 1 to 14 for beet, from 1 to 13 for wheat and from
2 to 5 for maize Fig. 7. The median and average
Fig. 5. The effect of the modules Presence, Rsur, Rgro and Rair on the value of the conclusions of Ipest-B indicator of environmental effect according to their membership to the fuzzy sets Favourable non-shaded boxes and Unfavourable shaded boxes.
number of applications were 8 and 8.2 for beet, 5 and 5.2 for wheat and 2 and 2.8 for maize. The av-
erage amount applied was 294 g ha
− 1
per application for beet, 333 g ha
− 1
for wheat, and 559 g ha
− 1
for maize.
O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158 151
Fig. 6. Distribution of Soil organic matter content, Runoff risk, Geologic substratum and Drift percentage for each crop. Diagrams for Soil organic matter content and Runoff risk present the distribution of values: vertical line shows the range of values from minimum to
maximum, box contains 50 of values, excluding the lowest and highest 25, histogram bar shows median value.
Fig. 7. Distributions of the number of pesticide applications per crop for a growing season. The vertical line shows the range from
minimum to maximum value, the box contains 50 of values, excluding the lowest and highest 25, the histogram bar shows
the median value.
For each field the values for the individual appli- cations applied during the growing season were used
to calculate averages for the four modules and Ipest Fig. 8. The average values of Presence and Rgro are
higher for maize than for beet and wheat Fig. 8a and b; with respect to Rair wheat and maize are similar
and values are smaller for beet Fig. 8c. The average values of Rsur show a wide range within each of the
three crops, the median values being similar Fig. 8d. The average values of Ipest-B are smallest for beet and
largest for maize Fig. 8e, despite more unfavourable site-specific conditions substratum and drift percent-
age for beet.
For each field the values for the individual ap- plications applied during the growing season were
summed to obtain cumulative values for the four
152 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
Fig. 8. a–e Distribution of the average values of the four modules and of Ipest-B for each crop. The vertical line shows the range from minimum to maximum value, the box contains 50 of values, excluding the lowest and highest 25, the histogram bar shows the median
value.
modules and Ipest-B Fig. 9. These values give an indication of the potential environmental effect of
overall pesticide use in a crop. Cumulative Presence values are largest for beet and smallest for maize
Fig. 9a. For Rgro, the crops do not differ much with respect to the distribution of cumulative values
Fig. 9b. For Rair, wheat shows higher values than the other two crops Fig. 9c. Rsur values for maize
are much lower than for beet and wheat Fig. 9d. With respect to the cumulative values of Ipest-B,
O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158 153
Fig. 9. a–e Distribution of the cumulative values of the four modules and of Ipest-B for each crop. The vertical line shows the range from minimum to maximum value, the box contains 50 of values, excluding the lowest and highest 25, the histogram bar shows the
median value.
beet and wheat are quite similar; maize has a similar median value but its maximum value for Ipest-B is
less than 2; whereas for beet and wheat it exceeds 3 Fig. 9e.
3.2.2. Some selected pesticide treatment sequences Interestingly, the cumulative Ipest-B values show a
wide range: between 0.2 and 3.4. The variability of this value within each crop is very large, in partic-
154 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
ular for beet and wheat Fig. 9e. To examine this variability, some fields were selected for each crop
having particularly low or high cumulative Ipest-B scores.
Table 4 Cumulative Ipest-B scores for selected pesticide sequences on beet crops, data from survey of CEDAPA members
a
Beet Pesticides
Rate g ha
− 1
Indicator modules Applied
Type Presence
Rair Rgro
Rsur Ipest-B
Field 1
b
Ethofumesate H
200 0.40
0.00 0.13
0.15 0.13
Phenmedipham H
334 0.51
0.00 0.00
0.20 0.12
Ethofumesate H
200 0.40
0.00 0.13
0.15 0.13
Phenmedipham H
334 0.51
0.00 0.00
0.20 0.12
Total 1068
1.82 0.00
0.26 0.70
0.50 Field 2
c
Desmedipham H
22 0.03
0.00 0.00
0.76 0.26
Ethofumesate H
628 0.65
0.00 0.15
0.77 0.48
Fluazifop-P-butyl H
112 0.27
0.03 0.00
0.60 0.25
Metamitron H
315 0.50
0.00 0.26
0.76 0.42
Phenmedipham H
84 0.22
0.00 0.00
0.78 0.29
Clopyralid H
90 0.23
0.00 0.42
0.75 0.34
Desmedipham H
11 0.00
0.00 0.00
0.76 0.26
Ethofumesate H
87 0.22
0.00 0.14
0.77 0.31
Fluazifop-P-butyl H
112 0.27
0.03 0.00
0.60 0.25
Phenmedipham H
42 0.10
0.00 0.00
0.78 0.27
Total 1503
2.49 0.06
0.97 7.33
3.13 Field 3
d
Glyphosate H
540 0.62
0.00 0.23
0.15 Chlorpyrifos
I 1350
0.81 0.80
0.00 0.20
0.58 Lindane
I 711
0.68 0.56
0.40 0.22
0.47 Metamitron
H 350
0.52 0.00
0.30 0.23
0.24 Ethofumesate
H 200
0.40 0.00
0.14 0.15
0.14 Phenmedipham
H 167
0.36 0.00
0.00 0.21
0.11 Phenmedipham
H 167
0.36 0.00
0.00 0.19
0.10 Triflusulfuron-methyl
H 15
0.00 0.00
0.00 0.07
0.00 Ethofumesate
H 140
0.32 0.00
0.13 0.14
0.11 Fluazifop-P-butyl
H 175
0.37 0.03
0.00 0.12
0.07 Metamitron
H 350
0.52 0.00
0.25 0.19
0.20 Phenmedipham
H 250
0.45 0.00
0.00 0.18
0.10 Total
4415 5.41
1.39 1.22
2.13 2.27
a
Cumulative Total scores are preceded by the scores for each application H: Herbicide, I: Insecticide; pesticides not separated by space were applied as one treatment; treatments are in chronological order; site-specific conditions and characteristics of the application:
Geol. Subst.: Geologic substratum and OM: Soil organic matter content; a blank for Rair corresponds to a lack of K
H
data; it was considered as a 0.00 score for the Ipest-B calculation.
b
Runoff risk=0.25, drift=0; Geol. Subst.=0.5, OM=5; low number of applications, moderate runoff risk, no drift, application rates are 30–50 of recommended rates.
c
Runoff risk=0.62, drift=1; Geol. Subst.=0.3, OM=4.8; many applications, high runoff risk, very high drift percentage resulting in very high risk for surface water.
d
Runoff risk=0.25, drift=0; Geol. Subst.=0.5, OM=5.5; many applications, moderate runoff risk, no drift, both insecticides yield high scores.
The beet crop on Field 1 has a low cumulative Ipest-B score Table 4. This is the result of a lim-
ited number of applications, at low rates, in a low risk environment. Field 2 has a high cumulative Ipest-B
O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158 155
Table 5 Cumulative Ipest-B scores for selected pesticide sequences on wheat crops, data from survey of CEDAPA members
a
Wheat Pesticides
Rate g ha
− 1
Indicator modules Applied
Type Presence
Rair Rgro
Rsur Ipest-B
Field 4
b
MCPA H
800 0.70
0.00 0.40
0.00 0.18
Metconazole F
90 0.23
0.00 0.00
0.01 Total
890 0.93
0.00 0.40
0.00 0.19
Field 5
c
Isoproturon H
750 0.69
0.00 0.24
0.06 0.14
Metsulfuron methyl H
6 0.00
0.17 0.37
0.06 0.08
Thifensulfuron-methyl H
6 0.00
0.00 0.38
0.05 0.04
Fenoxaprop-P-ethyl H
55 0.14
0.00 0.00
0.08 0.02
Fenpropidin F
375 0.54
0.13 0.00
0.07 0.11
Hexaconazole F
250 0.45
0.00 0.13
0.07 0.08
Total 1442
1.82 0.30
1.12 0.39
0.47 Field 6
d
Diflufenican H
125 0.29
0.53 0.00
0.69 0.43
Ioxynil H
90 0.23
0.40 0.00
0.57 0.34
Isoproturon H
1625 0.84
0.00 0.23
0.71 0.53
Mecoprop H
270 0.46
0.00 0.44
0.66 0.40
Tebuconazole F
250 0.45
0.00 0.34
0.82 0.45
Total 2360
2.27 0.93
1.01 3.45
2.15
a
Cumulative Total scores are preceded by the scores for each application H: Herbicide, F: Fungicide; pesticides not separated by space were applied as one treatment; treatments are in chronological order; site-specific conditions and characteristics of the application:
Geol. Subst.: Geologic substratum, OM: Soil organic matter content; a blank for Rair corresponds to a lack of K
H
data; it was considered as a 0.00 score for the Ipest-B calculation.
b
Runoff risk=0.01, drift=0; Geol. Subst.=0.5, OM=6; low number of applications, very low runoff risk, no drift; a spring-tined weeder harrow was used for additional weed control.
c
Runoff risk=0.14, drift=0; Geol. Subst.=0.5, OM=4.9; low runoff risk, no drift, pesticides applied at low doses.
d
Runoff risk=0.68, drift=1; Geol. Subst.=0.3, OM=3.5; high runoff risk and very high drift percentage.
score, the total amount applied and cumulative score for Presence being somewhat higher than for Field
1. However, the real problem for this field is its high Runoff risk and Drift percentage causing high Rsur
values. Field 3 also has a high cumulative Ipest-B score, despite a low Runoff risk and a Drift percent-
age of 0. The large number of applications and, in particular, the two insecticide treatments explain the
score.
The wheat crop on Field 4 has a very low cumu- lative Ipest-B score Table 5. Only one herbicide
application and one fungicide application were car- ried out, mechanical weed control was used. The
low score results from a limited number of applica- tions, at low rates in a low risk environment. In the
wheat crop of Field 5, four herbicide applications and two applications of fungicides were made. The
cumulative Ipest-B score is low, because low doses are used in a low risk environment. On Field 6, four
herbicide applications and one fungicide application were made, giving a much higher cumulative Ipest-B
score than Field 5, which had a similar number of applications. The total amount applied is somewhat
higher, but the problem is the high Runoff risk and Drift percentage causing high Rsur values; in addition
two of the herbicide applications yield a high Rair score.
The maize crops on Fields 7 and 8 received a sim- ilar amount of herbicide Table 6. Runoff risk and
Drift percentage are low in both cases. On Field 7 cumulative Ipest-B score is 0.28 and on Field 8 it is
0.60. This difference results from the characteristics of the pesticides used, producing higher values for
Rgro and Rair on Field 8. On Field 9, one insecticide
156 O. Roussel et al. Agriculture, Ecosystems and Environment 80 2000 143–158
Table 6 Cumulative Ipest-B scores for selected pesticide sequences on maize crops, data from survey of CEDAPA members
a
Maize Pesticides
Rate g ha
− 1
Indicator modules Applied
Type Presence
Rair Rgro
Rsur Ipest-B
Field 7
b
Bromoxynil octanoate H
300 0.49
0.00 0.10
0.07 Dicamba
H 480
0.59 0.00
0.50 0.09
0.21 Total
780 1.08
0.00 0.50
0.19 0.28
Field 8
c
Atrazine H
750 0.69
0.00 0.65
0.00 0.29
Dinoterb H
250 0.45
0.37 0.36
0.00 0.31
Total 1000
1.14 0.37
1.01 0.00
0.60 Field 9
d
Lindane I
1500 0.82
0.56 0.37
0.46 0.62
Atrazine H
750 0.69
0.00 0.65
0.46 0.42
Dinoterb H
200 0.40
0.37 0.36
0.40 0.47
Nicosulfuron H
40 0.10
0.29 0.23
0.12 Pyridate
H 675
0.67 0.00
0.00 0.30
0.20 Total
3165 2.68
0.93 1.67
1.85 1.83
a
Cumulative Total scores are preceded by the scores for each application H: Herbicide, I: Insecticide; pesticides not separated by space were applied as one treatment; treatments are in chronological order; site-specific conditions and characteristics of the application:
Geol. Subst.: Geologic substratum, OM: Soil organic matter content; a blank for Rair corresponds to a lack of K
H
data; it was considered as a 0.00 score for the Ipest-B calculation.
b
Runoff risk=0.14, drift=0; Geol. Subst.=0.5, OM=4.9; low number of applications, low runoff risk, no drift.
c
Runoff risk=0.02, drift=0; Geol. Subst.=0.5, OM=6; low number of applications, very low runoff risk, no drift but risk for groundwater.
d
Runoff risk=0.25, drift=0.3; Geol. Subst.=0.5, OM=5.6; some high rates of application, moderate runoff risk and drift percentage.
and four herbicides were applied. Cumulative Ipest-B score is high, from several causes: Runoff risk and Drift
percentage are higher than on Fields 7 and 8, total amount applied is high, several pesticides pose a risk
for air and groundwater and all pose a risk to surface water.
3.3. Feedback of Ipest-B to the farmers of the CEDAPA network
The results of this study have been presented to the CEDAPA farmers on whose fields they were obtained.
The farmers were greatly interested in these results, as they are looking for ways to reduce the environ-
mental effects of their farming practices. Its use as a tool for a retrospective diagnostic of pesticide en-
vironmental effect was well understood and appreci- ated. The farmers said that they would like to use the
Ipest-B decision aid tool to simulate the potential en- vironmental effect of different sequences of pesticide
applications.
4. Discussion and conclusions
