1. Chapter 9 Normalisation of Field Half-lives
Ian Hardy Battelle AgriFood Ltd., Ongar, UK

2. Overview Overview / Basic Processes
Availability of data for soil temperature and moisture content
Approaches to normalisation
Average temperature and moisture content
Time-step normalisation
Rate constant optimisation
Conclusions

3. Why do we want to normalise field data ?
Overview
Why do we want to normalise field data ?
Degradation is investigated under more realistic use conditions for the product
Enables use in risk assessments – e.g. FOCUS groundwater models
Large amounts of useful information are generated during the field studies which are not fully utilised in evaluations

4. Assessment of Study Design and Results
A preliminary check of the field study should be made to assess the suitability for its use in normalisation procedures:
Assess the significance of dissipation processes such as photodegradation and volatilisation. If they are unimportant, or can be properly addressed during the evaluation, then the use of the data in normalisation procedures is possible
The soil should be well characterised at different depths
The sampling depth and analytical method should allow for the bulk of the applied material to be evaluated
Daily meteorological data should be available (rainfall, air temperatures etc.)
Cropping and pesticide use history

5. Basic Processes
Normalisation techniques should be consistent with the process implementation in the subsequent model used for risk assessment
For temperature: Standard FOCUS Q10 (2.2) or Arrhenius approaches can be used
For moisture: Walker B-factor (0.7) approach typically used
Can normalise to any reference conditions e.g. 20oC/pF2 for EU or 25oC/75% pF2.5 for US

6. Data Availability What data should we use for normalisation ?
Field half-lives are normalised to reference conditions reflecting the major influence factors on field dissipation – soil temperature and soil moisture
The normalisation is conducted using daily measured or simulated values for soil temperature and moisture
A number of algorithms are available for calculating soil temperatures from min/max air temperatures
Soil moisture can be readily estimated using the FOCUS groundwater models

7. Soil Temperature Estimation

8. Approaches to Normalisation
Three approaches considered:
Average soil temperature and moisture content
Time-step normalisation
Rate constant optimisation

9. Average Temperature and Moisture Content
Good approximation for short-term kinetics when the mean temperature is relatively stable
The average soil temperature and moisture content are determined over an appropriate period and normalisation conducted as for laboratory studies
Useful for older studies with limited measurement data and can give comparable results to the more complex methods
Not suitable for long periods e.g. over several seasons where the conditions vary significantly

10. Average Temperature and Moisture Content
Appropriate over this period
Not appropriate over this period

11. Average Temperature and Moisture Content
Advantages
Good approximation for ‘short-term’ kinetics (i.e. over 1-month) where there is no big variation in conditions
Easy to calculate
Same methodology as for laboratory studies
Disadvantages
Not appropriate for ‘long-term’ kinetics

12. Time-step Normalisation
Concept:
Daily variation in soil temperature and moisture content is accounted for using a normalised day-length (NDL) approach:
1 day at 15oC and 80%FC is equivalent to 0.58 days at 20oC and 100%FC
1 day at 25oC and 90%FC is equivalent to 1.38 days at 20oC and 100%FC
Cumulative NDL is then calculated between sampling points
Standard kinetic tools used for evaluations

13. Time-step Normalisation

14. Time-step Normalisation
Data points ‘regressed’ along the time axis

15. Time-step Normalisation
Plot cumulative NDL vs residue

16. Time-step Normalisation

17. Time-step Normalisation
Real example:
2 year field dissipation study conducted in Northern Europe
Winter application

18. Time-step Normalisation

19. Time-step Normalisation

20. Time-step Normalisation
Site 1
Sampling time (days)
Timestep (days)
0.0 61
25.0
184
59.4
274
105.5
327
145.2
428
193.0
544
229.8
604
256.4
671
289.5
726
321.9

21. Time-step Normalisation
Normalised DT50 = 102 days
Min χ2 =6.0
Significant at >99%

22. Time-step Normalisation
Advantages
Easy to calculate daily factors from available data
No restriction on time periods or parameter variation (i.e. whole year / season can be modelled)
Applies the correction to the whole dataset at once
Standard kinetic modelling schemes and tools can be used for the subsequent analysis of the data
Disadvantages
Same correction factors (Q10, B) applied to whole dataset – although multiple regressions can be made

23. Rate Constant Optimisation
Uses the same assumptions and input data as the timestep approach
The reference rate constant is adjusted on a daily basis for soil temperature and moisture content and fitted to the measured data

24. Rate Constant Optimisation

25. Rate Constant Optimisation

26. Rate constant optimisation
Normalised DT50 = 99 days
Min χ2 =5.8
Significant at >99%

27. Rate Constant Optimisation
Advantages
No restriction on time periods or parameter variation (i.e. whole year / season can be modelled)
Good ‘visualisation’ of the effects of soil temperature and moisture content on the dataset and kinetics
Individual Q10 and B factors can be applied
Disadvantages
Requires higher level model to implement (e.g. ModelMaker)
Sometimes difficult to optimise complicated metabolite schemes

28. Conclusions
A number of approaches can be used to robustly derive normalised degradation rates from field studies for use in risk assessments
The methodology can be used to evaluate data from different seasons and application timings and to understand the processes important for degradation