Download presentation

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

Similar presentations

© 2019 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google