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Residues on food items for birds and mammal

Robert Luttik, National Institute for Public Health and the Environment, NLValencia workshop 2007

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History: Nomograms of Kenaga

The EPA food-chain (Kenaga) nomogram used to predict maximum pesticide residues in ppm following application to different categories of plants and plant parts

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History: Nomograms of Kenaga

Premises are that the residues that one can expect after spraying are not the result of the compound but of the crop and that the initial concentration increases proportional with increasing dose.

In 1973 Kenaga proposed, for lack of measurements, to use the residue data of forage crops and cereals for small and large insects, respectively.

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History: European equivalent

6.3 * D1.3 * DFruit

8.9 * D2.7 * D Cereals / large insects

11 * D2.7 * DPods

52 * D29 * DSmall seeds / forage crops/small insects

112 * D31 * D Leaves and leafy crops

98 * D 82 * DLong grass

214 * D112 * DShort grass

Maximum valuesTypical valuesPlant/plant parts

Relationship between "typical" and maximum residue concentrations on plants or parts of plants (in mg/kg fresh weight) and the dosage (D) of plant protection products (in kg active ingredient per hectare) immediately after spraying.

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History: Guidance document 2002

115.1Large insects

5229Small insects

114.8Fruit/pods/large seeds

8740.2Leaves/leafy crops/forage crops/small seeds

6932.1Long grass

14275.7Short grass

RUD 90th

percentile

RUD Arithmetic

meanFood type

Residue unit doses to be used according to the Guidance documentfor birds and mammals (2002) after Fletcher et al. (1994).

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History: Guidance document 2002

Note in guidance document:

The residue estimate for small insects appears unsatisfactory, and as soon as better information becomes available this surrogate should be replaced.

Research is highly desirable to develop more robust data for residues in insects, also with regard to the temporal pattern.

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Revision of guidance document

•Residues on insectsBoth PSD and ECPA have funded much work in this area.PSD funded work has focused on working from the lab to the fieldand has concentrated on issues such as what invertebrates birds eatThe industry approach has been to develop a generic protocol and produced many field based data.

•Residues on vegetationThe existing dataset has been recently reviewed by Canada (Baril et al. 2005)

•MAF and TWAprobably we will provide tables for MAF and a TWA calculator (excel)

•Guidance on how to carry out residue trialsWe will produce a guidance document including to decide on howmuch data are needed before the default values can be replaced. How to deal with studies on degradation time.

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Revision of guidance document

All following presented data are still under debate.

No decision has been made what to propose for the guidance document.

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Insects: ECPA / industry data set

Analysis of field arthropod residue studies from different ECPA companies by RIFCON GmbH to extract suitable initial maximum residue levels

=> Initial residues measured as highest value within 7 days after application (worst-case)

=> Residues per unit doses (RUD)

• Consideration of different substance classes / application and sampling techniques etc. according to a questionnaire provided by industry.

• Consideration of available data from the literature and other sources.

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Insects: ECPA / industry data set

Field studies conducted following GAP and standard agricultural practice

22 Studies (91% GLP) representing 25 different crop / pesticide scenarios from North America and Europe

67 datasets which differ in:study location, active ingredient, crop, method of arthropod sampling.

Database:

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Insects: ECPA / industry data set

ScenariosScenarios coveredcovered

⇒ 16 different active ingredients from 13 different chemical classes made up the dataset. insecticides (n=10), fungicides (n=4) or herbicides (n=2)

⇒ Data were grouped within each of the major crop types (cereals, leafy crops/vegetables and orchards/vines)

-2Grasslandlate / post-harvest4CerealsBare soil / early / late11Miscellaneous Vegetables*early / late6Potatoesearly / late7Cottonduring or after flowering6Vineduring or after flowering31Orchards

Growth stage during application

No of trials (or data sets)Crop types

* Miscellaneous vegetables contain alfalfa, peas, French beans, lentil, endive etc.

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Insects: ECPA / industry data set

ArthropodArthropod categoriescategories and and samplingsampling methodsmethods

-car netting, light traps and malaise trapsFlying arthropods

-mainly inventory spray and beating method -collected arthropods (sweep nets)-exposure methods of larvae and adults at foliage level

Foliage dwelling arthropods

-mainly pitfall traps-collected arthropods (aspirators, by hand)-crickets placed in exposure chambers at soil level

Ground dwelling arthropods

Sampling techniquesArthropod class

Arthropods were classified as ground dwelling, foliage dwelling and flying arthropods according to sampling technique

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– flying insects (6 data sets)

• car netting, light traps

– leaf dwelling arthropods (23 data sets)

• inventory spray, beating on vegetation

– ground dwelling arthropods (37 data sets)

• pitfall traps

Arthropod Sampling and Arthropod Sampling and ForagingForaging StrataStrata

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Summary of Insect RUD valuesSummary of Insect RUD values

47.89.5all pesticides all crops

Leaf dwellersfoliage application

15.66.7herbicides (orchards/vines), all pesticides early growth stages of leafy crops

Ground dwellersground application

9.8*3.6*insecticides and fungicides: orchards/vines, late growth stages of leafy crops

Ground dwellersfoliage application

6.61.4all pesticides all crops

Flying insects90%tileMedianPesticide and CorpArthropods

RUD [mg/(kg*h)]

* value for fungicides (covering insecticides)

Preliminary results

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ECPA / industry data set

RUD current SANCO vs. experimental residue data from ECPA database

Difficult use of RUD for small and large arthropods (as recommended by current SANCO/4145) for refined risk assessments because no clear definition of what is ‘small’ and ‘large’

A differentiation into ecological groups (e.g. foliage vs. ground dwelling arthropods) is more appropriate instead of arbitrary and undefined size classes (e.g. small vs. large).

The default values (52 / 29 mg/kg small insects) from the guidance document (SANCO/4145/2000) appear to overestimate actual arthropod residues.

Proposed RUD values for ecological categories of arthropods fromthe ECPA database can be updated as new data become available

Summary Results and ConclusionsSummary Results and Conclusions

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Residues on insects –PSD study

• Extended lab studies for cereal and broad-leafed crops

• Pot grown plants (cereal, cabbage, beans) infested with invertebrates (aphids, syrphids, caterpillar identified as major food items for birds and mammals) and oversprayedusing boom sprayer with pesticides

• Range of 9 pesticides- insecticides, herbicides, fungicides with range of formulations

• Samples taken immediately after drying

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Residues on insects – PSD study

3837(0-48)

18(0-47.8)

27(0-57)

109(0-186)

Overall 90th

percentile (range)

4.613(0-35)

3.4(0-8.5)

11(0-40)

31(0.008-150)

Overall mean

(range)

MeanPierisbrassicae

on cabbage

Episyrphus

balteatus

on cereal

Acyrthosiphon pisum

on bean

Rhopalosiphumpadi

on cereal

RUD

Preliminary results

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Residues on insects – PSD study

• Plot studies• Cereal and cabbage grown on plots• Infested with aphids and Pieris brassicae larvae

(as highest residues in extended lab studies)• Oversprayed with boom sprayer with prochloraz,

chlorpyrifos, tebuconazole and L-cyhalothrin• Samples taken when dry• Beetles placed in plots and oversprayed

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Residues on insects – PSD study

8747226155Overall

88532391970.01L-cyhalothrin

62352021550.25Tebuconazole

153782131870.9Chlorpyrifos

3122103810.4Prochloraz

Pierisbrassicae

90th

Pierisbrassicae

Mean

Rhopalosiphumpadi

90th

Rhopalosiphumpadi

mean

Rate (kg ai/ha)

Pesticide

Preliminary results

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Residues on insects – PSD study

1514(1.4-17)

28(1.9-134)

15(1.5-29)

90th percentile (range)

9.04.9(1.1-13)

16 (1.3-56)

6.0(1.0-19)

Mean (range)

All beetlesBeetleCereal

Beetlecabbage

Beetlebare soil

Preliminary results

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Residues on vegetationNew research available:

Baril, Whiteside and Boutin (2005) ETC 24: 360-371

Database contains 1488 residue values originating from 314 sources

From literature but also 25 field studies submitted by manufacturers in support of registration.

Much larger database compared to Fletcher et al (1994), which was already much larger than the database of Kenaga.

Still under discussion within the working group whether the values given in the paper are representative for the European agricultural practice.

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Residues on vegetation

57423523101671916Pods1035813118212515Grains128474125101893419Large fruits

14127434234Small fruits

99th 95th 90th 75th50thnstdmean

RUDS on fruits

and seeds

Small fruits are berries and small fruits from orchards (apricot, cherry, plum, etc.)

Large fruits are fruit from orchards like apples, tomato, eggplant and gourds.

Pods are pulse crops and okra.

Categories proposed by authors

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Residues on vegetation

1951863333

9

n

BBBBA

AB

sign.

135Tomato1920Large fruits from orchards

3419Gourds229Eggplant

333Small fruits from orchards

86Berries

stdmeanRUDS on fruits

Other possibilities for food categories

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Residues on vegetation

876543424Thin plants

183140674922654337Tall grasses

388165121642633317261Vegetable row crops

2021801526725275951Oilseeds

2071601167236345056Short plants

22059493381255090403170Orchard/vineyard

69738418910760309153100Field crops

21519516311684595490Shrubs

99th 95th 90th 75th 50th nSTDmeanRUDs on leaves

Field crops: cereals, forage grasses, forages legumes and turf

Categories proposed by authors

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Residues on vegetation

significanceUnderlying cropsBaril et al choice

Pulse crops

Large whorled leaves

Medium-sized plants

Woody vines

Fruit trees

Turf

Forages legumes

Forage grasses

Cereals

CD

CD

ABCD

ABC

ABC

ABCD

Thin plants

Tall grasses

Vegetable row crops

Oilseeds

Short plants

Orchard/vineyard

Field crops

Shrubs

E

D

BCD

ABCD

ABCD

ABCD

AB

A

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Residues on vegetation

9650.640.2Fletcher values for leaves

21947.862.7Small plants

2759.451.2ABCDOilseeds

3449.856ABCDShort leafy plants

902956.6ABCForage legumes

3768.677.8ABCDForage grasses

3142.879.4ABCTurf

nstdmean“leaf type”

Other possible choices that can be made

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MAF and TWA

MAF = Multiple Application FactorReplaces a series of individual applications by one virtual ‘total application’.Underlying assumption: single 1st order degradation kinetics (SFO)acute scenario:specific calculation to achieve 90th percentile (or other percentile)long-term scenario:based on standard equation for SFOc = c0 × exp(-k × t) with k = ln(2)/DT50

TWA = time-weighted averageReplaces a non-static concentration curve by a constantUnderlying assumption: single 1st order degradation kinetics (SFO)acute scenario:not applicablelong-term scenario:based on standard equation for SFOctwa = c0 × (1 - exp(-k × t))/(k × t) with k = ln(2)/DT50

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0

0,25

0,5

0,75

1

1,25

1,5

1,75

2

0 5 10 15 20 25 30 35

time

conc

entr

atio

nMAF and TWA

working example:

3 applicationsinterval 7 d

DT50 = 7 d

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MAF and TWA

0

0,25

0,5

0,75

1

1,25

1,5

1,75

2

0 5 10 15 20 25 30 35

time

conc

entr

atio

n

MAFtransforms 3 singleapplications into onevirtual ‘total application’here: MAF = 1.75

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0

0,25

0,5

0,75

1

1,25

1,5

1,75

2

0 5 10 15 20 25 30 35

time

conc

entr

atio

nMAF and TWA

twareplaces a non-staticconcentration curveby a constanthere: ftwa = 0.736

areas match

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0

0,25

0,5

0,75

1

1,25

1,5

1,75

2

0 5 10 15 20 25 30 35

time

conc

entr

atio

nMAF and TWA

MAF × twastepwise calculationfor multiple applicationshere: MAF × ftwa = 1.022

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0

0,25

0,5

0,75

1

1,25

1,5

1,75

2

0 5 10 15 20 25 30 35

time

conc

entr

atio

nMAF and TWA

MAF × twaselection of appropriatetime window

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a MAF can bederived

read outMAF × twa

here

selection ofinput parameters

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Future use of degradation kineticscalculations to be based on single 1st order kinetics (SFO)volatilisation, wash-off or photolysis of residues might result in non-SFO best fit kinetics, but use of SFO ensures conservative approach for the relevant time intervals shortly after application

Future use of MAFgives maximum concentration to be expected for acute exposure scenarionew GD will include equations to calculate 90th percentile MAF (or other percentile when appropriate)no longer to be used alone for long-term exposure scenario

Future use of ftwa

to be used in long-term exposure scenario as a combined factorMAF × ftwacalculation using a moving time-windowcheck applicability of averaging for certain endpoints first

MAF and TWA

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Guidance for field experiments

• It is intended to provide methodological recommendations how to perform arthropod residue studies under field conditions, considering:– Study site selection, plot size, replicates– Sampling methods for different strata (foliage, ground

dwellers etc.)– Sample size and frequency (for determination of residue

decline data)• Recommendations may be provided as an annex /

supplement to the new GD

Will this be helpful for authorities (study interpretation) and notifiers / CRO‘s (study performance)?