New Procedures for Identifying High Rates of Pesticide Use

Transcription

1 New Procedures for Identifying High Rates of Pesticide Use Larry Wilhoit Department of Pesticide Regulation February 14, 2011

2 1 Detecting Outliers in Rates of Use 2 Current Outlier Criteria 3 New Outlier Criteria 4 Criteria Evaluation

3 Importance of Error Screening It is critical that the PUR be as accurate and complete as possible The PUR is screened for about 40 errors The amount of pesticide used is one of the most important data fields One big error in one record among thousands or even millions can affect an analysis

4 Errors in Amount Used or Area Treated Errors in amount or area can be found most easily by unusually high or low rates of use Difficulties in finding errors in rates of use Rates vary by AI, product, site treated, target pest May need to search millions of records Rates may have complex frequency distributions

5 Distributions in Rates of Use Glyphosate

6 Distributions in Rates of Use Abamectin

7 Distributions in Rates of Use Chlorpyrifos

8 Distributions in Rates of Use Propiconazole

9 General Methods of Detecting Outliers Maximum label rates Compare rate to rates of previously reported applications with similar kinds of use Similar kinds of uses could be applications with the same: Active ingredient Pesticide product Product and site or crop treated Product, site, and pest treated

10 Current Outlier Criteria A rate of use is considered an outlier if it is greater than: Fixed limit, not considering rates of similar use Median of rates of similar use times 50 Median of rates of similar use plus measure of dispersion Limit determined by neural networks Similar uses are all applications in the previous year with same product, site, record type (ag or non-ag), and unit treated.

11 Measures of Disperson Traditional statistical measure of dispersion Standard deviation Robust statistical measures Trimmed standard deviation (SD): standard deviation after removing a small percent of the highest and lowest values Median absolute deviation (MAD): median of the absolute deviation about the median Interquartile range (IQR): the difference between the third and first quartile

12 Two Levels of Oultierness 1 Rates greater than fixed limit or median 50 are: Flagged as possible errors Replaced with median rates Sent back to the counties for error checking 2 Rates greater than other criteria are: Flagged as possible errors Left unchanged Not sent back to the counties

13 Advantages and Disadvantages with Each Criterion Fixed Limits Flag records where the pounds of AI per acre of any non-fumigant active ingredient is greater than 200 or the pounds per acre of a fumigant is greater than 1000 Advantages Does not depend on previously reported uses Easy to understand Disadvantages Uses same outlier limit for pesticides used at very different rates Used only when unit treated is acres

14 Advantages and Disadvantages with Each Criterion Median of rates of similar use times 50 Flag records where the pounds of pesticide product per unit treated is greater than 50 times the median rate of similar uses Advantages Outlier limit is more appropriate to particular product and site treated Easy to understand Disadvantages Does not consider dispersion or range of rates used Sometimes based on few previously reported rates

15 Advantages and Disadvantages with Each Criterion Median of rates of similar use plus measure of dispersion Flag records where the pounds of a pesticide product per unit treated is greater than the median plus 10 times the median absolute deviation Advantages Outlier limit is more appropriate to particular product and site treated Considers the dispersion in rates Disadvantages Sometimes based on few previously reported rates Fails when most of the rates have nearly identical values

16 Advantages and Disadvantages with Each Criterion Limit from a neural network Flag records where the pounds of a pesticide product per unit treated is greater than a limit value from a neural network procedure A neural network is a mathematical model inspired by the behavior of neurons in the brain Advantages Outlier limit is more appropriate to each product and site treated Handles unusual frequency distributions Disadvantages Sometimes based on few previously reported rates Difficult to understand Difficult to implement

17 Ways to Improve the Criteria Define similar uses as applications of each AI rather than each product and site Use rates from last 5 years rather than just last year Use log of rates, rather than just rates, in defining criteria Develop fixed criteria for all units, not just acres Convert square feet to acres, 1000 cubic feet to cubic feet, and tons to pounds Use other robust measures of dispersion

18 Proposed New Outlier Criteria fixed: Limits at 100 lbs/acre for most AIs, 1000 lbs/acre for high rate AIs, and other limits for other units treated med 50: Median log (rates of similar use times 50) med + 10 MAD: Median log (rates of similar use) + 10 median absolute deviation q3 + 5 IQR: 3rd quartile log (rates of similar use) + 5 interquartile range mean + 8 SD: Trimmed mean log (rates of similar use) + 8 trimmed standard deviation Similar uses are applications with same AI, record type (ag or non-ag), and unit treated.

19 Grouping Similar Rates Problem For many AIs there are two or more distinct groups, each group with very different rates. How can we determine what it is that distinguishes these groups? Solution Regression trees

20 Regression Trees Definition A Regression tree is a data-analysis method that partitions data into sets based on values of different predictor variables. Example Here, regression trees are used to determine distinct sets of similar rates based on values of variables such as site treated, product, formulation, AI type, application month, region, application method, and percent of AI in product

21 Regression Tree Applied to Abamectin Rates

22 Criteria Evaluation 1 Visual inspections of distributions with the different outlier limits 2 Relative ranking of outlier limits 3 Percent of records identifed as outliers by each criterion 4 Applying criteria to data with known correct and incorrect rates 5 Applying criteria to data with known maximum label rates In discussing evaluations, I will focus on agricultural records with unit treated in acres

23 Visual Inspections of Distributions Cyprodinil

24 Visual Inspections of Distributions Azoxystrobin

25 Visual Inspections of Distributions Fosetyl-al

26 Relative Ranking of Outlier Limits

27 Percent of PUR Records Greater Than Each Limit

28 PUR data with known correct and incorrect rates Such data does not exist. However, we have something close: data from PUR error reports. Rates flagged as errors by the current fixed and med*50 criteria were sent to county staff for checking.

29 PUR data with known correct and incorrect rates There are several problems with the results from these error checks: Error reports do not show the rates of use, just individual fields, such as amount used, area treated, and their units Making a correction to one of these fields does not necessarily mean all fields were correct We don t know how well county staff do in making corrections They check only a small percent of the errors found The error could have been in the label database

30 Applying Criteria to Data With Known Correct and Incorrect Rates Outlier Pct > Limit Pct Errors Rank > Limit Rank Errors Limit That are That are That are That are Errors > Limit Errors > Limit Fixed Limit Med* Med+10*MAD Q3+5*IQR Mean+5*SD Mean+8*SD Mean+10*SD Fixed Current Med*50 Current Neural Net

31 Applying Criteria to Data With Known Maximum Label Rates Outlier Pct > Limit Pct > Label Rank > Limit Rank > Label Limit That are That are That are That are > Label > Limit > Label > Limit Fixed Limit Med* Med+10*MAD Q3+5*IQR Mean+5*SD Mean+8*SD Mean+10*SD Fixed Current Med*50 Current Neural Net

32 Conclusion By most evaluations, the best criteria were mean + 7 SD and mean + 8 SD Proposal Rates greater than fixed limit, med 50, and mean + 8 SD should be replaced with estimates and sent to counties for correction Flag rates that exceed mean + 3 SD, mean + 5 SD, and mean + 7 SD as possible errors