1. Multi-Beneficiary Statistical Co-operation Programme 2005
Transition Facility
Multi-Beneficiary Statistical
Co-operation Programme 2005
Lot 2: Pesticide Indicators
Survey on Pesticide Use on Wheat Crops 2006 in Estonia
Kaia Oras

2. ...history:
Statistical Office of Estonia started the collection of data on the use of pesticides about a decade ago
Survey was worked out as a response to the demand of Estonian Agricultural Ministry Plant Production Inspectorate and has been financed from the state budget
Quantity of pesticide used - by the crop
Area treated with pesticide by the crop

3. Pesticide Usage in Estonia, Kg of active substance per agricultural land hectare Statistics Estonia has carried out the pesticide use survey for a decade now but the quality of data is still a major problem.

4. Herbicides use per hectar

5. General data structure
NUTS 4

6. Are there a direct correlations from purely statistical viewpoint
between the yield and the use of pesticides ?
CEREALS
Use of fungicides
Production
Use of herbicides
Use of seed treatment preparations
kg per ha, NUTS 4

7. Extrapolation in annual survey
Pesticide quantity in holding: Ph= St*Pr
Extrapolated quantity:
Pt = Ph *L
Area sown: Sg
Treated area: St
Used pesticides: kg/ha Pr
Extrapolated quantity: Pt
Extrapolation factor: L

8. Yearly publications on environmental indicators
Dissemination of data
Data on pesticides are available on Statistical Office website database program PC-AXIS gives the user a possibility to download data in different formats
Yearly publications on environmental indicators
Environmental strategy review

9. Use of pesticides in agriculture, 2006kg

10. Use of pesticides in agriculture, 2006
Kg/ha of cultivated area

11. Transition Facility Multi-Beneficiary Statistical Co-operation Programme Lot 2: Pesticide Indicators Survey on Pesticide Use on Wheat Crops 2006 in Estonia We made the attempt to stratify on a crop type level (routine has been on a household group level) Aim to get for one crop total of active substances used with incorporated error estimations

12. Project Time Scale April May June July Aug Sept Oct Nov Dec Sampling
Questionnaire design
Postal survey
Telephone interviews
Data entry
Data analysis

13. Sample characteristics
Area group
Frame
Sample
Routine survey
Pilot study
0…1 ha
774 75 33 29
1…2 ha
543 66 26 30
2…5 ha
748
227 45
162
5…10 ha
470
238 53
165
10…20 ha
406
347 80
264
20…30 ha
191 49
142
30…50 ha
202 71
131
50…100 ha
228
107
121
> 100 ha
231
175 56
Total
3793
1805
639
1100

14. Sampling method: simple random stratified
Sampling unit is agricultural holding growing wheat.
Sampling frame is the Estonian Agricultural Registers and Information Board database of agricultural supports.
Frame is stratified according to area of wheat. Holdings with area greater or equal to 20 ha are surveyed completely. Holdings with wheat area less than 20 ha were divided into 5 strata as follows: Sample survey: 0-1, 1-2, 2-5, 5-10, ha
Simple random sample is drawn in each strata with different inclusion probabilities.
Method of permanent random numbers is used for sample selection.
Sample is designed using Neyman optimal allocation method according to variability of the area of wheat.

16. Reasons for non-response
Not co-operative 23
Did not grow wheat 43
Incorrect contact details 70
Total number of non-response
136

17. Sample characteristics
Area group
Frame
Sample
Responses %
Imputation
Weight
0…1 ha
774 75 74
98,7
12,6885
1…2 ha
543 66 62
93,9
10,4423
2…5 ha
748
227
207
91,2
4,0000
5…10 ha
470
238
217
2,3858
10…20 ha
406
347
314
90,5
1,3055
20…30 ha
191
176
92,1
1,0852
30…50 ha
202
186
1,0860
50…100 ha
228
213
93,4 15
1,0000
> 100 ha
231
220
95,2 11
Total
3793
1805
1669
92,5

18. Compensating non-response
Non-response was compensated by imputation – over 50 ha
“Hot deck” imputation method
Nearest neighbour as a donor
Nearest neighbour is the one who has the most similar wheat area in respective geographical category (county)

19. State of the Art
Sown areas, treated areas, products used, units used, number of treatments – are checked for mistakes (more than six thousand lines in database to validate still)
Checking of the databases consistency
The analyses of grossed up results will start with help of ESO´s Methodological Service in Februaryr.

20. Use of pesticides on wheat

21. Volatility of the staff
Specific problems:
Volatility of the staff
Quality of the data: mistakes, mistakes, mistakes
3. No proper software,
4. Difficulties in consistency of the two databases
General problems:
No proper vision from the policy demand side
Difficult regarding motivation
Expensive

22. Some lessons learned Carry out the survey in winter
Provide more channels for information delivery (e g internet)
Do not correct all “mistakes”, for example over and under dose uses and the use of the several forbidden products are the case in real life

23. Identification of the logic of the errors
Results:
Attempt to stratify on a crop type level (routine has been on a household group level)
For one specific crop to get the total of active substances used with incorporated error estimations
Identification of the logic of the errors
Maping of the workload to be invested to get the similar results on all crops

24. This study gives the possibility for production of more relevant statistics for the objective “to minimize hazards and risks to health and environment”
The risk incorporation requires the binding of information what is existing on chemical compound level
The bottleneck is the binding of information what is available on single compound level with the information on the use patterns.
Our previously produced grossed up figures of pesticide use by pesticides types do not allow the measurement of risks. The reason is the low representation of single active substance in

25. Treated wheat area by categories of danger

26. Glyphosate counts for 15-50 % of herbicides used on wheat

27. Next steps… Finalize the consistency check of the databases
Reparing the mistakes
Gross up analyses : error estimations for active substances
Quality analyses
Final report …April 30

30. Permanent Random Numbers (PRN)
To each unit in the register we associate a random number generated from uniform distribution U(0,1) and it is not changed, it is the permanent random number (PRN).
A new unit (a birth) is assigned a random number which is independent of the existing ones.
Closed down unit (a death) is withdrawn from the register together with its random number.
On each sampling occasion we take a new sample using PRN-s

31. Permanent Random Numbers (PRN)
Permanent random numbers allow at the same time
to get samples from the up-to-date register
to get a large amount of overlap with the latest sample since units have the same random numbers (PRN) on both occasions.

32. Neyman allocation Nh – number of units in stratum h
n – predetermined overall sample size
nh – sample size of stratum h

33. Comparison of sales and use
The comparison of the sales and use figures. Use figures have the breakdown by plant or crop type used. The sales figures have the further breakdown by pesticides types.

34. Reporting format categories
Total area surveyed
Basic area treated
Average number of treatments
Quantity of active substance applied
Average quantity applied per treated area
Average quantity applied per total cultivated area

35. Open questions: definition of treated area
Four categories listed in “Guidelines for the collection of pesticide usage statistics within agriculture & horticulture” by Miles R. Thomas:
Basic area treated
Application area treated
Formulation area treated
Active substance area treated
Which of the definitions is applicable
and most relevant?

36. Use of Pesticides with Different Categories of Danger on Wheat
Dangerous : * Carcinogenic, mutagenic, teratogenic, sensitising
Irritant: ** Irritant, sensitising