A common methodology for the collection of pesticide usage statistics within agriculture and horticulture.
Date: 2008-11-27
Institutions: Eurostat
Subjects: EU Member State, EU candidate countries, EU statistics, data collection, environmental impact, farming sector, health risk, horticulture, pesticide, statistical method

ISSN 1977-0375

M e t h o d o l o g i e s  a n d 
W o r k i n g   p a p e r s

A common methodology for the collection  
of pesticide usage statistics within agriculture 
and horticulture

2008 edition

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Luxembourg: Office for Official Publications of the European Communities, 2008

ISBN 978-92-79-07848-4
ISSN 1977-0375
Cat. No. KS-RA-08-010-EN-N

Theme: Agriculture and fisheries
Collection: Methodologies and working papers

© European Communities, 2008

 
Preface 

The  European  Commission’s  interest  in  statistics  on  pesticide  use  is  not  new.    As  early  as  the 
1990's it set up a task force which included representatives from the United Kingdom, Sweden, 
France and the Netherlands, whose task was - together with Eurostat - to share their experience of 
surveys on pesticide use and to draw up guidelines for the collection of usage statistics within the 
Member States.  At the request of the Organisation for Economic Co-operation and Development 
(OECD), this task force was expanded to include a representative of the United States.  The task 
force considered methods of collection already in use in the European Union and in the member 
countries  of  the  OECD,  and  discussed  at  length  the  minimum  data  requirements  that  a  survey 
should meet. 

This cooperation led to the joint publication by Eurostat and the OECD  of the first "Guidelines 
for the Collection of Pesticide Usage Statistics within Agriculture and Horticulture" (THOMAS, 
1989). 

Since  then,  there  has  been  a  growing  interest  –  particularly  on  the  part  of  the  EU  and  OECD 
member  countries  –  in  monitoring  and  reducing  the  risks  related  to  the  use  of  pesticides.    The 
Member  States,  the  European  Commission  and  the  OECD  have  conducted  a  number  of  studies 
aimed  at  establishing  indicators  to  monitor  these  risks.  Based  on  these  studies,  in  2007  the  EU 
financed a research project as part of the 6th Framework Programme, with the aim of establishing 
a set of "HArmonised environmental Indicators for pesticide Risk" (HAIR).   

Around  the  same  time,  in  July  2006,  as  part  of  the  Sixth  Environment  Action  Programme 
("6EAP"), the Commission adopted its Thematic Strategy on the Sustainable Use of Pesticides, 
which  was  set  out  in  a  Communication  to  the  European  Parliament  and  to  the  Council 
(COM(2006) 372 final) and proposed the adoption of a Directive of the European Parliament and 
of  the  Council  establishing  a  framework  for  Community  action  to  achieve  a  sustainable  use  of 
pesticides (COM(2006) 373 final).   

In parallel with the framework Directive, the Commission proposed to the European Parliament 
and  to  the  Council  to  adopt  a  Regulation  concerning  statistics  on  plant  protection  products 
(COM(2006)  778  final)  to  ensure  that  comparable  data  are  collected  in  all  the  Member  States, 
thereby  making  it  possible  to  calculate  harmonised  risk  indicators  and  to  measure  the  progress 
made towards a more sustainable use of plant protection products throughout the Community. 

In preparation for the adoption of this Regulation, the Commission funded several pilot actions 
on the collection of data on pesticide use.  In particular, two successive rounds of pilot projects 
were financed in 2005 and 2007 in the new EU Member States and in the candidate countries via 
the  Phare  Programme  and  the  Transition  Facility/  multi-beneficiary  statistical  cooperation 
programmes (ADAS, 2005; ASA, 2008; ICON 2008).  These pilot actions, which were based on 
the  methodology  described  in  the  original  guidelines,  demonstrated  the  value  of  adopting  a 
common approach.   

Considering  that  the  initial  guidelines  were  still  relevant  and  might  be  of  great  interest  in  the 
initial stages of the implementation of the Regulation, once adopted, it was thought that it would 
be useful to refresh the guidelines and illustrate them with the results of the pilot actions in the 
form of a case-study. 

Methodology for the collection of pesticide usage statistics 

3 

 
 
 
 
 
 
 
 
As  a  result,  this  methodology  and  working  paper  are  largely  based  on  the  initial  guidelines 
published  in  1989  by  the  Eurostat  Pesticide  Statistics  Task  Force  under  the  leadership  of  Mr 
Miles R. Thomas. They are complemented by explanatory notes prepared by the consultants in 
charge of the coordination of the two successive rounds of pilot surveys - ADAS International in 
the UK and ASA-ICON Institutes in Germany. Lastly, they are illustrated with the main results of 
the pilot surveys on wheat carried out in 2007 in 11 new Member States and candidate countries.  
All  reports  and  documents  on  which  this  paper  is  based  can  be  consulted  on  the  Commission 
Circa website at the following address:  http://circa.europa.eu/Public/irc/dsis/pip/library?l=/indicators_pesticides 

Methodology for the collection of pesticide usage statistics 

4 

 
 
 
Table of contents 

1. 

Methodology for the collection of statistics on pesticide use within agriculture and 

horticulture ................................................................................................................. 7 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

1.10 

1.11 

1.12 

1.13 

1.14 

1.15 

1.16 

1.17 

Types of pesticide statistics ......................................................................................... 7 

Role of usage statistics ................................................................................................. 7 

Methods of data collection ......................................................................................... 10 

Defining the crops to be sampled ............................................................................... 14 

Frequency of surveys ................................................................................................. 17 

Essential Data requirements ....................................................................................... 18 

Additional information ............................................................................................... 21 

Sample design ............................................................................................................ 25 

Establishing a regional breakdown ............................................................................ 27 

Establishing farm size groups .................................................................................... 27 

Establishing the sample .............................................................................................. 28 

Producing national estimates ..................................................................................... 28 

Defining the survey period ......................................................................................... 29 

Training the staff ........................................................................................................ 30 

Carrying out the surveys ............................................................................................ 31 

Data storage and processing ....................................................................................... 32 

Production of results .................................................................................................. 33 

2. 

A case study:  

Pesticide use in the new Member States and candidate countries ....................... 45 

2.1. Key findings ...................................................................................................................... 45 

2.2. A common approach to data collection ............................................................................. 46 

2.3. Survey results .................................................................................................................... 48 

2.4. Variations in the plant protection patterns ........................................................................ 49 

2.5. Main conclusions .............................................................................................................. 50 

3. 

Indicators on pesticide use and risk ....................................................................... 53 

Annexes ................................................................................................................................... 55 

1. 

2. 

Definitions and classification ..................................................................................... 55 

Methodological notes ................................................................................................. 64 

Methodology for the collection of pesticide usage statistics 

5 

 
 
1.  Methodology for the collection of statistics on pesticide 

use within agriculture and horticulture 

Types of pesticide statistics 
1.1 
There are two types of statistics on pesticides:  

•  Sales statistics, which are usually considered as being relatively simple to collect and fairly 
inexpensive.  However, comparisons of sales data over time are, at best, a crude indicator of 
change.    Moreover,  sales  statistics  can  give  rise  to  confidentiality  issues  and  restrictions  on 
the release and  use of  data for commercial reasons.    When  they  are  collected  from  existing 
administrative sources (declarations by producers or sellers), sales data are rarely available at 
the active level and contain no information about the crop, timing, regional variation in use, 
dose applied, number of applications to the crop or percentage of crop treated.  The dividing 
lines between agriculture, amenity, industry, etc. in terms of use are often blurred.  All these 
reasons, plus the fact that the definition of “pesticide” varies from country to country, make 
comparability across countries difficult.  Therefore, for most applications which are covered 
later in the section on the “Role of statistics”, sales statistics alone are virtually useless. 

•  Usage  statistics  in  a  broad  sense  cover  all  kinds  of  data  on  the  actual  use  of  pesticides  by 
farmers  and  growers.    They  can  be  gathered  through  statistically  valid  samples  of  farms 
grossed  up  to  give  national  estimates,  or  through  compulsory  returns  by  all  users.  Their 
accuracy  depends  mainly  on  the  sample  size,  which  is  usually  limited  by  the  resources 
available, the mix of the population by region, farm size, education, etc., the truthfulness of 
respondents, and the quality of the interviews (visits by qualified staff versus unqualified staff 
or postal surveys; questionnaire on ‘average’ treatments or field-by-field treatments, etc. 

1.2  Role of usage statistics 
Collecting  a  reliable  set  of  usage  statistics  has  value  in  many  areas  of  research,  legislation  and 
agricultural support, and should not be regarded simply as a statistical exercise in its own right. 
Within  the  EU  Member  States,  for  instance  in  Great  Britain,  pesticide  use  has  been  surveyed 
cyclically on all crops for over 30 years and the usefulness and availability of the data generated 
far outweigh their cost of collection.  

Areas of use fall into eight main categories, described below; the first two of these are the most 
important for the European Commission (EUROSTAT) and the OECD: 

Provision of annual usage estimates  
In  their  simplest  form,  usage  statistics  provide  information  on  national  and  regional  levels  of 
pesticide inputs to individual crops. Thus, information should be available on the total amount of 
any particular pesticide used annually, together with the areas treated and the range of crops to 
which  it  has  been  applied.  Additionally,  information  on  the  total  inputs  of  all  pesticides  to  any 
one crop would also be available. Both of these can be broken down to provide a seasonal profile 
of use, as dates of application should also be available. Such data are required at several levels: 

•  At a national level, to inform government of the current status of pesticide use 
Following  a  number  of  recent  press  reports  of  “pesticide  scares”  concerning  carcinogenic, 
neurological or other undesirable effects of specific pesticides, it is vital that ministers have up-
to-date  information  on  pesticide  usage.  This  includes  data  on  the  product  range  to  which  they 
belong, the crops on which they are used and the extent to which those crops are treated – all of 
which  ultimately  yields  information  on  the  likely  exposure  of  the  population  to  the  purported 

Methodology for the collection of pesticide usage statistics 

7 

 
 
 
 
 
 
 
hazard.  Without  these  data,  governments  could  find  themselves  in  the  embarrassing  position  of 
being unable to defend the results of their own legislation. Indeed, it is written into most national 
legislations  that  the  government  must  monitor  the  post-registration  use  of  pesticides.  Data  are 
also  freely  passed  on  to  universities,  NGOs  such  as  Greenpeace,  Friends  of  the  Earth  and  the 
World Wide Fund for Nature, and also to members of the general public. 

In addition, detailed statistics on pesticide use will become essential tools to develop and monitor 
the national action plan to be put in place in each Member State in the framework of the Thematic 
Strategy on the sustainable use of pesticides. 

•  Within the EU 
At  EU  level,  the  need  for  harmonised  data  on  pesticide  use  has  been  clearly  recognised.  In 
particular, harmonised and comparable Community statistics on pesticide use are essential for the 
development  and  monitoring  of  Community  legislation  and  policies  in  the  context  of  the 
Thematic Strategy on the Sustainable Use of Pesticides. 

•  Within the OECD 
The Pesticide Forum of the OECD, and in particular the Risk Reduction Group, have expressed a 
need for reliable usage statistics. 

Internationally  

• 
The FAO is attempting to compile annual statistics across all countries pursuant to Article 1 (1) 
of  the  FAO  Constitution,  which  stipulates  that  “the  Organisation  shall  compile,  analyse  and 
disseminate information relating to nutrition, food and agriculture”. 

Monitoring changes over time  
Once  the  regular  collection  of  usage  statistics  has  been  established,  it  is  possible  to  monitor 
changes over time in use on particular crops, or of particular pesticides. These changes may be 
the result of several factors, some or all of which may interact to give annual variations in use: 

•  Annual  differences  in  the  weather,  influencing  the  range  of  pest,  disease  and  weed 
problems requiring control, or affecting the ability of the farmer to apply the pesticide under 
suitable conditions 

•  The introduction of new molecules which may replace older, less active pesticides, and may 

also be applied at much lower rates per hectare 

•  Changes in the price of, or level of support to, crops, thereby altering margins and making 

the use of pesticides more or less economic. 

Environmental protection 
Reliable data on usage are critical for the development of indicators of the effects of pesticides on 
the  environment,  and  data  sets  over  time  are  needed  in  order  to  monitor  the  effects  that  policy 
changes may have on that impact. Pesticide risk and environmental indicators, in particular, need 
time series (i.e. not only sales data) in order to examine changes, regular surveys, data crop by 
crop and related realistic usage.  

Section  3  of  this  report  develops  the  themes  of  risk  and  environmental  impact  indicators  for 
pesticides in greater detail. 

Methodology for the collection of pesticide usage statistics 

8 

 
 
 
 
 
 
 
 
 
 
 
Consumer  protection:  providing 
programmes 
Usage  data  on  individual  crops  are  essential  for  the  planning  of  monitoring  programmes  for 
residue analysis of fresh fruit and vegetables. 

for  residue  monitoring 

information 

•  Where  new  monitoring  programmes  are  being  launched,  usage  data  will  illustrate  the 
range  of  pesticides  currently  used  on  the  crops  to  be  monitored  and  allow  the  analytical 
follow-up  to  be  tailored  to  focus  on  only  those  pesticides  that  are  likely  to  be  encountered. 
For  instance,  usage  data  have  provided  the  foundation  for  the  development  of  residue 
monitoring  follow-up  for  a  wide  range  of  home-grown  produce  within  the  UK  in  order  to 
monitor compliance with Maximum Residue Levels (MRLs). 

•  Where  unusual  or  unexpected  residues  are  found,  usage  data  can  confirm  the  results  or 
point  to  alternative  methods  to  corroborate  or  invalidate  the  findings.  For  example,  an 
analysis of plums using HPLC with UV diode array detection indicated that 50% of samples 
contained residues of diflubenzuron, whereas usage data suggested that only 5% of the crop 
had  been  treated.  These  survey  results  prompted  an  alternative  analysis  by  LC-MS,  which 
revealed  that  suspected  residues  were  artefacts.  In  contrast,  residues  of  chlorothalonil  in 
lettuce – a non-approved use within the UK– were corroborated by survey data where such 
misuse  had  been  encountered  in  the  field.  EU-wide  surveys  would  allow  Member  States  to 
tailor their monitoring programmes for imported produce as well as home-grown foodstuffs. 

•  Contribution  to  studies  of  dietary  risk:  data  on  the  percentage  of  crop  treated  within  the 
country,  on  the  number  and  rate  of  applications,  and  on  harvest  intervals,  are  vital  for  the 
probabilistic risk assessments used to refine exposure models. 

Operator protection  
Data on farmers’ actual use of pesticides may be examined to see where current practices can be 
improved  or  optimised.    Specific  surveys  on  work  rates  for  spray  operators  can  provide  useful 
information on average area treated per day and range, average number of treatments per season 
and range, and amount of product handled per operation.  When used as part of a deterministic 
approach, such data are ideal for probabilistic risk assessment to refine operator exposure models.  
Better data on methods of application used will also improve risk analysis for workers and likely 
exposure estimates in prediction models for operator exposure. 

For  example,  in  Great  Britain,  the  comprehensive  database  of  farmer  practice  with  regard  to 
fungicide and insecticide use on winter wheat is being examined to identify where farmers may 
be  using  pesticide  programmes  inappropriately.  This  examination  is  focusing  in  particular  on 
under-utilisation  of  varietal  resistance  or 
timed  pesticide  applications. 
Furthermore, there would appear to be some scope for reducing pesticide applications in certain 
circumstances. It is hoped that those areas where clear savings can be made will be identified and 
targeted  for  further  advice,  in  an  effort  to  reduce  inputs  of  pesticides  to  those  crops.  The 
technique should be applicable to many crops. 

inappropriately 

Monitoring the potential movement of pesticides into water 
 Data  on  pesticide  usage  can  be  used  to  assist  in  the  monitoring  of  pesticide  contamination  in 
surface and ground waters. For example, the EU seeks to protect drinking water and groundwater 
through  legislation,  leading  to  the  widespread  monitoring  of  pesticide  residues  so  as  to  ensure 
compliance  with  these  directives.  In  Great  Britain,  for  instance,  usage  data  are  used  within  a 
complex geographical information system, which includes maps of soil and groundwater, rivers 
and  other  waterways  and  water  abstraction  points.  This  is  overlaid  with  current  cropping  and 

Methodology for the collection of pesticide usage statistics 

9 

 
 
 
 
 
 
 
pesticide  usage  patterns,  both  geographical  and  seasonal,  and  -  together  with  a  database  of 
pesticide  properties  and  models  of  movement  through  different  soils  -  it  is  used  to  predict  the 
likely  appearance  of  pesticides  at  abstraction  points,  in  order  to  facilitate  the  monitoring  of 
pesticides in water. By so doing, it is hoped to avoid unnecessary monitoring for pesticides which 
are  unlikely  to  appear  at  a  specific  point  or  time  within  a  given  water  body.  It  is  important  to 
note, however, that such methods can only be used to guide the monitoring process rather than be 
a substitute for it. 

Policy advice during review programmes  
An essential part of the review process of a pesticide, which is currently underway for all existing 
pesticides  within  the  EU  (http://ec.europa.eu/food/plant),  is  the  knowledge  of  the  local  and 
national uses and requirements for that pesticide. If monitoring suggests that growers are unable 
to compete without a particular pesticide, and no alternatives are available, this must be borne in 
mind  during  its  review.  Reliable  usage  data  are  fundamental  to  such  appraisals  and  are  an 
appropriate means of quantifying the effect of withdrawal. Alternatively, the demonstrated lack 
of use of a particular pesticide, coupled with the availability and uptake of safer or more benign 
alternatives, may hasten the withdrawal of a pesticide. 

Furthermore, in the USA, in response to the Food Quality Protection Act (1996), the 
Environmental Protection Agency has developed a so-called “Risk Cup” whereby the total area 
of  a  crop  is  assumed  to  be  treated  at  full  label-recommended  rate.  This  is  then  applied  to  the 
tolerance level, and the exposure risk is calculated. If the risk cup is not full, further registration is 
allowed. If the cup is full, on the other hand, exposure risk is recalculated using actual estimates 
of area treated and rates of use from survey data. Without such data, the continued approval of 
products may be significantly affected. 

Providing information as part of the approval process for new pesticides  
During the approval of new active substances, usage data may provide a clear indication of the 
likely uptake of a new pesticide, based on a knowledge of which pesticide(s) it is likely to replace 
and the current extent of their use. Furthermore, with such data it is possible to evaluate likely 
operator exposure, as realistic work rates can be derived from the data collected, such as average 
field size, area sprayed per operator per day, amount of pesticide handled per day, and so on. All 
these factors are vital in developing models of predicted operator exposure. 

1.3  Methods of data collection 
When  drafting  the  first  guidelines,  Eurostat’s  pesticide  task  force  considered  methods  of 
collection  that  were  already  in  use  in  the  EU  and  in  OECD  member  countries.  Five  broad 
methodologies requiring differing levels of input and organisation were examined.  
During  the  discussions  with  the  Member  States  on  the  drafting  of  the  Regulation  concerning 
statistics on plant protection products, the need to allow the Member States enough flexibility in 
the choice of the methodology was stressed.  Countries should thus select the methodology most 
suitable to their resourcing and requirements, amongst the following: 
•  Personal visits to a representative sample of farmers and growers to collect information on 

what they have used; 

•  Telephone interviews with a representative sample of farmers and growers; 
•  Postal or e-mail surveys of a representative sample of farmers and growers; 
•  Compulsory returns of pesticide use from all farmers and growers 

Personal visits 
Personal visits have long been used in a number of countries, such as the United Kingdom (UK), 
France,  Sweden  and  the  United  States  of  America.  Information  is  collected  on  the  pesticides 

Methodology for the collection of pesticide usage statistics 

10 

 
 
 
 
applied  to  specific  crops  over  the  previous  growing  season  or  year  from  a  statistically  derived, 
representative  sample  of  farmers  and  growers.  Such  surveys  have  the  advantage  of  accuracy, 
particularly where trained personnel are used, as the surveyor can scrutinise all the potential uses 
which might have occurred, ensuring that the grower does not omit or forget anything important. 
For example, in the worst case, many growers consider pesticides to include only insecticides (i.e. 
those  which  kill  insect  pests)  and  may  not  include  other  groups  such  as  fungicides,  growth 
regulators  or  desiccants.  Other  areas  which  are  often  not  considered  by  growers  include  seed 
treatments and molluscicides applied at drilling. Pre-drilling and pre- and post-harvest treatments 
to  the  soil  are  also  important  and  are  often  overlooked  by  farmers  if  no  specific  questions  are 
asked about them. 

A further advantage of personal visits is that they enable all the relevant crops to be surveyed on a 
single farm without over-complicating the survey. To cover only one crop or field at each visit 
would result in many more visits having to be undertaken in order to derive a statistically valid 
sample. 
As  with  any  survey,  it  is  vital  to  have  a  well-structured  form  on  which  to  record  the  data,  and 
farmers should be given prior notice of a visit, to allow them time to gather together their records 
and information. 

Telephone interviews 
Telephone  surveys  have  been  used,  particularly  in  Sweden,  to  reduce  the  cost  of  the  survey 
programme. These surveys are similar in structure to personal interviews, but they avoid the time 
and  cost  of  travel.  However,  they  should  not  be  over-complicated  and  it  would  be  unwise  to 
attempt to cover all the cropping on a farm in a single call. To further simplify the interview, such 
surveys may cover only the largest field of each crop grown on the selected farm. 

The  calls  should  be  preceded  by  an  information  letter,  giving  some  indication  of  the  form  the 
interview  will  take.  It  is  also  important  to  have  trained  personnel  making  the  calls,  using  a 
structured questionnaire. 

In  Sweden,  comparative  studies  conducted  in  1990  and  1992  showed  that  the  differences  in 
results between personal visits and telephone interviews are statistically insignificant given that 
the telephone interviews are performed by trained personnel, as long as the information required 
is not over-complex. 

Postal or e-mail surveys 
Postal  surveys  have  been  used  in  the  Netherlands  to  obtain  information  on  pesticide  use  for 
specific crops grown on selected farms. Postal surveys are considerably less expensive than visit 
surveys,  but  can  usually  expect  a  return  of  up  to  only  30%.  However,  experience  in  the 
Netherlands suggests that this does not bias the sample in any way towards those farmers who are 
more conscientious or are more likely to carry out “good farming practice”. Survey numbers can 
be increased to account for the reduction in participation in order to reach the desired number of 
responses. For example, if results from 1,000 farms are needed and the response rate is known to 
be no better than 28%, then the initial sample should comprise a minimum of 3,570 farms. 
Postal  surveys  need  to  be  simpler  than  surveys  undertaken  by  personal  visits  or  telephone 
interviews; this is exemplified by the surveys undertaken in the Netherlands, where only one crop 
per  farm  is  surveyed.  However,  this  enables  survey  forms  to  be  tailored  to  each  crop,  and 
statistical validity is maintained simply by increasing the sample size. 

Postal  surveys  can  be  open  to  abuse,  in  that  respondents  may  only  include  what  they  want  the 
enquirer to know, thereby overlooking or omitting known misuse. Furthermore, these surveys are 
particularly open to misinterpretation, with respondents potentially leaving out specific uses that 

Methodology for the collection of pesticide usage statistics 

11 

 
 
 
 
 
 
 
they might think are excluded from the survey, or even disregarding part of the survey. However, 
just  as  visit  and  telephone  surveys  require  trained  personnel,  the  staff  involved  in  checking  the 
returned  questionnaires  will  also  need  to  be  experienced  in  understanding  what  is  likely  to  be 
used on a crop at a particular time. Further checks may also be included in the questionnaire and, 
as  with  other  survey  methods,  many  error-checking  routines  can  be  built  into  the  data  entry 
programmes and any obvious omissions can be followed up and checked with the grower. 

Additionally,  postal  surveys  can  be  structured  in  such  a  way  that  questionnaires  are  sent  out 
several  times  during  the  year  after  important  periods  during  the  husbandry  of  the  crop  when 
pesticide applications are likely to have occurred. This will remind the grower to fill in the form 
at a time when the information required is fresh in his memory and reduces the burden of filling 
in a form with all the details at the end of the growing year. 

In the Netherlands, before the survey begins, farmers are asked by post whether they still grow 
the crop covered by the survey, and whether they will take part in the survey. If they agree, at the 
beginning of each month they are sent a questionnaire which is tailored to that crop and which 
reflects the practices likely to be undertaken during the following month. 

Each survey covers all of the most important crops, but surveys are only undertaken every two or 
three  years.  This  has  the  advantage  of  providing  a  complete  picture  of  use  of  any  one  active 
substance,  while  lessening  the  burden  on  individual  farmers.  Farmers  who  participated  in  the 
previous survey are also excluded from selection for the next survey. 

Postal surveys can be replaced by or complemented with e-mail or web-form surveys. 

Compulsory returns of spraying records from all users of pesticides 
When  the  guidelines  were  drafted,  the  only  known  example  of  compulsory  returns  from  all 
pesticide  users  of  all  their  spraying  activities  was  in  the  US  State  of  California.  These  returns 
took the form of monthly returns by post. The overheads involved in handling the vast amount of 
data  generated  by  such  a  comprehensive  system  were  very  high  and  the  administration  and 
computing were complicated to set up initially. However, the Californian experience appears to 
illustrate  the  usefulness  of  such  a  comprehensive  database,  once  it  is  established.  This 
methodology may well be particularly appropriate to small countries or for specific crops, as it is 
organised  for  the  production  of  fruit  and  vegetables  in  the  Belgian  producers'  association 
(Veilingen) or by organic farming certification companies.   

It  should  be  noted  that  Regulation  (EC)  No  852/2004  of  the  European  Parliament  and  of  the 
Council of 29 April 2004 on the hygiene of foodstuffs (Annex I, part A, point 9) establishes an 
obligation for food business operators producing or harvesting plant products to keep records on 
any use of pesticides, but does not make it compulsory to return these records. 

Sales statistics as an alternative or a complement to usage data 
Collection of sales data can be used as a substitute for a survey of usage data. One of the obvious 
advantages  is  that  sales  data  are  much  cheaper  to  collect,  and  collection  can  therefore  be 
performed annually. However, it is an advantage if such sales statistics are regularly followed up 
by a survey on usage to verify and clarify sales data.  

Where  countries  do  not  immediately  have  the  resources  to  undertake  surveys  of  pesticide  use 
using  one  of  the  methods  outlined  above,  some  useful  information  can  be  obtained  from  the 
collation of sales statistics, although this is in no way a proper substitute for statistically reliable 
surveys. Some of the advantages and disadvantages are listed below, together with a description 

Methodology for the collection of pesticide usage statistics 

12 

 
 
 
 
 
 
 
 
 
of the process currently used in Sweden, where it is compulsory for manufacturers to return sales 
figures. 

•  Advantages 
There are advantages to using sales statistics as a basis for providing simple statistics on pesticide 
use.  They  are  relatively  inexpensive,  since  they  are  generally  compiled  by  agrochemical 
organisations, or the state, directly from company returns. In some Member States, collection of 
sales data is a statutory requirement. The data are theoretically accurate, as chemical companies 
are likely to know with some degree of precision how much of each product they have sold. Sales 
statistics are therefore quick to produce, as companies should be able to supply quarterly returns, 
or - at worst - annual figures, which can be processed within weeks of receipt. The data may be 
used as a check on usage statistics when sold quantities differ. Thus, statistics on sold quantities 
may be used to adjust and improve surveys on use of pesticides. 

The data may be used to provide estimates for years when surveys are not undertaken. 

•  Disadvantages 
Where agrochemical organisations are involved in the collation of data, unless all the companies 
within a country are members of that organisation, the statistics will represent only a part of total 
sales.  For  example,  the  British  Agrochemicals  Association  comprises  approximately  30  major 
pesticide producers, but there are nearly 200 chemical companies that have pesticides registered 
for  use  in  the  UK.  In  the  Netherlands,  Nefyto  (the  Dutch  Foundation  of  Phytopharmacy)  has 
published  yearly  sales  figures  since  1984,  but  represents  only  90%  of  the  whole  producer 
population. 
However, since 1993, the Dutch government has also received figures from non-members.  The 
same  situation  is  seen  at  EU  level,  where  the  European  Crop  Protection  Association  (ECPA) 
covers only part of the European market. 

Where  products  are  unique  to  individual  companies,  commercially  sensitive  sales  data  are 
unlikely to be released at product level. This, together with the work involved in separating all 
individual active substances, may result in some aggregation of data. For example, all fungicides 
or organophosphates may be grouped etc., thereby masking the use of specific active substances 
or usage on individual crops. 

Sales figures do not accurately represent usage if there is any lag within the chain from sales by 
producer through distributor to end-user, and these lags may be exacerbated by any stock-piling 
within the distribution network or by users. Data from Great Britain would suggest, however, that 
most  growers  buy  in  only  what  they  intend  to  use,  but  the  statistics  for  a  single  year  may  be 
distorted  if  farmers  are  hoarding  pesticides,  e.g.  as  a  hedge  against  expected  price  changes. 
Stocking at the user end of the chain occurs infrequently and only among the smaller producers of 
minor horticultural crops, particularly where annual requirements are less than pack size. 
Most  chemicals  are  not  specific  to  single  crops,  so  sales  data  are  of  no  use  for  anything  more 
sophisticated than total usage figures. 

Sales  figures  often  provide  little,  if  any,  information  on  regional  differences  in  use.  Total  sales 
may  include  sales  to  sectors  outside  agriculture,  for  example  weed  control  in  industry  or  on 
public areas (roads, pavements, parks etc.), sports grounds, homes and gardens. 

Data  on  weights  sold  cannot  be  converted  accurately  into  area  treated.  For  example,  many 
farmers in Great Britain invariably apply pesticides at well below the recommended rate, leading 
to  a  gross  underestimation  of  areas  treated  if  they  were  to  be  calculated  by  taking  the  weight 
applied  and  simply  dividing  it  by  the  recommended  rate.  Furthermore,  experience  in  the  UK 

Methodology for the collection of pesticide usage statistics 

13 

 
 
 
 
 
 
 
 
 
 
indicates that farmer uptake of reduced rates seems to vary from region to region and is definitely 
influenced by enterprise size, thereby complicating any attempt to predict the area treated. 

Finally, unless sales data have been collected using the same classification system for pesticides, 
they will be impossible to interpret meaningfully. 

1.4  Defining the crops to be sampled 
These guidelines are designed to allow assessment of pesticide usage within the broadest range of 
agriculture  and  horticulture,  including  usage  in  food  storage  practice.  For  these  purposes, 
agricultural  crops  include  all  the  major  arable  crops,  grassland  and  fodder  crops.  Horticultural 
crops  include  fruit,  vegetables,  protected  crops,  hops,  mushrooms,  bulbs,  flowers  and  hardy 
nursery stock.  

These  sectors  should  not  include  use  of  amateur  products  in  homes  and  gardens,  or  use  of 
professional products by industry, in amenity situations, on roads, railways or other sectors of the 
transport  industry.  They  are  also  not  intended  to  cover  use  of  wood  preservatives,  anti-fouling 
paints or pesticides used in public hygiene situations, such as insect control in buildings etc. 

Ideally, all agricultural and horticultural sectors should be surveyed, as this will not only account 
for  all  pesticide  use  -  thereby  satisfying  the  requirements  listed  in  the  introduction  -  but  also 
because it is more often in the minor sectors or uses where problems may occur. However, this 
may  be  too  expensive  for  some  countries,  and  crops  should  be  selected  which  represent  the 
majority of pesticide use, both in absolute terms and in terms of rates of application. 

It can be seen from Table1a that the most important crops in Great Britain - which represent, for 
example, 90% of the area grown (sum of ranks 1 to 7) - account for only 73% of the area treated 
(Table 1b – sum of ranks 1, 2, 4, 7, 8 & 11) and only 40% of the weight applied (Table 1c sum of 
ranks 2, 4, 7, 8, 9 & 10). Moreover, they include none of the crops for which the highest rates of 
application  are  found  (Table  1d),  while  the  15  crops  receiving  the  highest  rates  of  application 
account for 45% of the total weight applied. 

Therefore, a sensible selection should be made within each country of the crops which represent 
those  most  commonly  grown,  plus  those  receiving  the  most  treatments,  by  area  treated,  weight 
applied and rate of application.  

It  is  sometimes  within  those  crops  subjected  to  the  highest  rates  of  pesticide  application  that 
problems resulting from pesticide use may occur, and as they tend to have high inputs they may 
belong  to  the  politically  most  interesting  group,  for  which  large  reductions  in  usage  may  be 
stipulated. 

Methodology for the collection of pesticide usage statistics 

14 

 
 
 
 
 
 
 
 
 
 
For example, from the data presented in Tables 1a-d, it would be sensible to include at least the 
following crops: 

Crop  

Importance derived from Table: 

Permanent grass  
Wheat  
Grass < 5 years old  
Winter barley  
Set-aside  
Spring barley  
Oilseed rape  
Sugar beet  
Ware potatoes  
Peas  
Beans  
Mushrooms  
Edible protected crops  
Seed potatoes  

1a  
1a  
1a  
1a  
1a  
1a  

1b  
1b  
1b  
1b  
1b  
1b  
1b  
1b  
1b  
1b  

1c 
1c 
1c 
1c 
1c 
1c 
1c 
1c 

1c 
1c 

1d 

1d 
1d 
1d 

Rough grazing is omitted because it is not listed as important in any of the tables on pesticide use 
(Tables 1b-d). Marrows and flower crops are omitted because of the very small areas grown. For 
flower crops, this would not apply in the case of the Netherlands, where they form a significant 
part of national horticulture. 

Table 1.1a Variation in importance of crop by area grown (UK) 

Rank Crop

Area grown (ha)

% of total 
area grown

Cumulative % 
of area grown

Permanent grass
Rough grazing

1
2
3 Wheat
Grass < 5 years old
4
5
Set aside
6 Winter barley
Spring barley
7
Oilseed rape
8
Sugar beet
9
Beans
10
11 Ware potatoes
12
13 Maize
14
15

Oats

Peas (dry harvested)
Peas (fresh for frozen)

4,714,794
4,286,369
1,989,417
1,093,699
608,100
541,769
530,777
356,780
169,148
164,184
138,004
123,205
120,996
84,765
39,998

31
28
13
7
4
4
3
2
1
1
1
1
1
1
0

31
59
72
79
83
87
90
93
94
95
96
97
97
98
98

Source: ADAS 2005 

Methodology for the collection of pesticide usage statistics 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 1.1b Variation in importance of crop by area treated1

 (UK) 

Rank Crop

Area treated (ha)

% of total 
area treated

Cumulative % 
of area treated

1 Wheat
2 Winter barley
Spring barley
3
Oilseed rape
4
5 Ware potatoes
Sugar beet
6
Set aside
7
Field beans
8
Oats
9
10
Peas (dry harvested)
11 Maize
12
13
14
15

Grass < 5 years old
Permanent grass
Peas (fresh for frozen)
Seed potatoes

23,756,331
4,972,425
3,170,090
2,976,534
2,325,625
2,024,354
1,134,978
990,557
766,852
720,824
446,196
293,082
283,836
249,614
248,648

51
11
7
6
5
4
2
2
2
2
1
1
1
1
1

51
62
68
75
80
84
87
89
90
92
93
93
94
95
95

Source: ADAS 2005 

Table 1.1c Variation in importance of crop by weight of pesticide applied (UK) 

Rank Crop

Weight applied (t)

1 Ware potatoes
2 Wheat
3
Seed potatoes
4 Winter barley
Oilseed rape
5
Spring barley
6
Sugar beet
7
Set aside
8
Field beans
9
Peas (dry harvested)
10
Permanent grass
11
12
Oats
13 Maize
14
15

Grass < 5 years old
Onion - dry

9,202
8,695
3,767
2,060
846
801
629
564
510
332
293
263
234
211
190

% of total 
weight 
applied
30
28
12
7
3
3
2
2
2
1
1
1
1
1
1

Cumulative % 
of weight 
applied
30
58
71
77
80
83
85
87
88
89
90
91
92
93
93

Source: ADAS 2005 

1 Note that the area treated for a crop may exceed the area grown, as this is the sum of all applications made to that crop (e.g. one 
hectare of wheat sprayed six times has an area treated of 6 spray hectares. 

Methodology for the collection of pesticide usage statistics 

16 

 
 
 
 
 
 
 
 
 
 
 
                                                 
Table 1.1d Variation in importance of crop by average rate of pesticide use (UK) 

Rank Crop

Mushrooms
Pinks
Carnation
Flowers, foliage (glass)
Bean - runner
Alstroemeria
Seed potatoes
Asparagus
Lettuce (protected)
Chrysanthemums (glass)
Celery (protected)
Flowers for cutting

1
2
3
4
5
6
7
8
9
10
11
12
13 Ware potatoes
14
15

Horseradish
Strawberry

Average 
application rate 
(kg/ha)
120
42
40
34
19
16
15
12
10
8
7
4
4
3
3

% of total 
weight 
applied
< 0.1
< 0.1
< 0.1
< 0.1
0.1
< 0.1
12.3
0.2
0.1
0.1
< 0.1
< 0.1
30.0
< 0.1
0.5

Cumulative % 
of weight 
applied
< 1
< 1
< 1
1
1
1
13
13
13
13
13
13
43
43
44

Source: ADAS 2005 

Frequency of surveys 

1.5 
Although surveys should ideally be undertaken annually,  it is currently unrealistic to expect all 
countries to embark upon annual surveys of all crops. Even in Great Britain, where monitoring is 
perhaps  at  its  most  sophisticated,  arable  crops  -  which  represent  around  86-90%  of  usage  -  are 
only  surveyed  every  two  years,  while  all  other  crops  are  surveyed  every  four  years.  It  is 
recommended that, if annual surveys are not possible, important crops (as outlined above) should 
be surveyed at least biennially, although an annual programme should be followed for those crops 
where  usage  is  greatest.  The  most  limiting  factor  is  resource  availability,  and  the  various 
ntly  undertaking  surveys  have  established  different  cycles  to  satisfy  their  own 
countries  curre
quirements. 
re

time these guidelines were being prepared, Sweden had biannual surveys of all important 

At the 
rops. 
c

In the USA, major arable crops were surveyed annually because the government was concerned 
 that are being phased 
to monitor how quickly new or alternative products replace the chemicals
ut. Fruit and vegetable crops were surveyed biennially on alternate years. 
o

In Great Britain, arable crops are surveyed at least biennially because of the speed of introduction 
of new active substances, giving rise to a rapidly changing market of use. Furthermore, chemicals 
have  a  two-year  period  of  wind-down  following  partial  revocation,  designed  to  allow  safe 
disposal through normal channels of supply, sales and use. In order to monitor this effectively, it 
would  be  unwise  to  have  a  survey  interval  greater  than  the  average  wind-down  period.  The 
introduction of new products into the horticultural industry, however, is much slower. Owing to a 
lack of resources horticultural surveys cannot be repeated more frequently than once every four 
al, it is accommodated somewhat by the much slower introduction and 
years. Whilst this is not ide
rnover of new products. 
tu

Methodology for the collection of pesticide usage statistics 

17 

 
 
 
 
 
 
 
 
 
 
In the Netherlands, surveys have been undertaken every three years and, while all major crops are 
included in each survey, the work involved means that they cannot be repeated more frequently 
than this. 
Where  surveys  are  not  undertaken  annually,  it  should  be  borne  in  mind  that  differences  in 
weather patterns from year to year may have a greater effect on usage than changes due to other 
reasons,  particularly  on  crops  where  change  is  very  conservative.  Until  a  sequence  of  surveys 
have been undertak
en it would be unwise to explain changes between two surveys as the result of 
any single factor. 

1.6  Essential Data requirements 
The level of complexity of data collection is dependent on the resources available to undertake 
the survey. The more data collected, the more areas outlined in the introduction will be covered. 
However, there are a mi
nimum number of parameters that need to be collected in order to make 
a
ny survey worthwhile. 

The following data are considered essential to collect for each crop to be surveyed; they include 
the crop and its grown area, the product applied and its timing and rate of application or amount 
used, and the area tr
eated. Other data, which may be collected if resources allow, are listed at the 
end of this section. 

Crop 
A  record  of  the  crop  to  which  pesticides  have  been  applied  is  clearly  vital  to  any  realistic 
assessment  of  pesticide  use.  This  should  take  the  form  of  the  crop  name  as  defined  by  the 
Community  Farm  Structure  Survey  (or  the  Crop  Statistics  Regulation).  It  should  additionally 
dy  part  of  the  census 
mention  whether  this  was  a  winter  or  spring  crop,  if  this  is  not  alrea
efinition (e.g. winter or spring wheat, barley, oilseed rape, linseed, etc.).  
d

It would be unrealistic to attempt to survey pesticide use on all crops within a single survey, and 
the limits of the survey with regard to the crops to be covered need to be clearly defined at the 
outset. Some apparently similar crops, or developmental stages of a crop, may be best covered in 
different  surveys.  For  example,  a  survey  of  pesticide  use  on  orchard  crops  may  exclude  trees 
being grown in a nursery, as these may be covered in an alternative survey of all nursery stock. 
Similarly,  peas  grown  for  harvesting  fresh  for  the  frozen  pea  market,  or  carrots,  which  may  be 
grown on arable farms in arable rotations, may be omitted from a survey of arable crops as they 
esting dry may be considered 
would be covered under a survey of vegetable crops. Peas for harv
s combinable crops and would fit best in a survey of arable crops. 
a

It is also necessary to define which developmental stages of a crop will be considered by which 
survey, so as to avoid “double counting” of pesticide applications and thereby inflating the actual 
amount of pesticide used on a crop. Difficulties like this may arise with crops such as lettuces and 
brassicas, which may be raised from seed under glass as small plants, often with quite high inputs 
of pesticide, then sold on to be planted out, either under glass or outside. The seedling production 
stage  and  any  applications  to  the  subsequent  crop  if  planted  under  glass  may  be  covered  by  a 
survey of usage on protected crops, and care must be taken to avoid any double counting of use. 
However, applications made to the crop once planted outdoors would be covered by a survey of 
ents should not be included in this category if 
outdoor vegetable crops,  and any seedling treatm
ey are covered by a survey of protected crops. 
th

Therefore, the survey must clearly define the crops that are to be included and this, in part, may 
also be defined by the census data available. It is relatively easy to multiply up sample data to a 

Methodology for the collection of pesticide usage statistics 

18 

 
 
 
 
 
 
 
 
known  total  area  of  a  crop  grown,  but  difficulties  arise  where  no  census  data  exist  for  a  crop, 
although this is usually only the case for

 minor crops. 

•  A specific case: crop replacement 

For  different  reasons  (frost,  poor  germination,  etc.)  a  crop  may  have  to  be  replaced  by  another 
one  (for  instance,  winter  wheat  by  spring  wheat).  In  such  a  case,  the  treatments  given  before 
sowing  or  to  destroy  the  remaining  part  of  the  crop  (total  herbicide  treatment)  should  be  taken 
into account, as being associated either to the main crop (
in this case, the winter wheat) or to the 
replacement crop (in this case, the spring wheat). Usually they will be attributed to the main crop 
but - whatever th

e case - they should not be overlooked.  

Area grown 
On each surveyed farm, the area grown of each crop to be surveyed must be recorded. This will 
be used to gross up data on pesticide inputs  to give national estimates of  usage. Problems  may 
arise  with  multiple  cropping.  Where  the  principal  interest  is  in  data  for  water  quality  studies, 
clearly  any  multiple  cropping  (e.g.  taking  several  harvests  from  one  field  of  alfalfa)  will  not 
influence the fact that the area grown is equal to the area planted. However, for studies directed 
more specifically at food quality, the number of crops on a single piece of land must be taken into 
consideration. For example, for six crops of lettuce grown on one fiel
d in a year, the area treated 
should be taken as six times the area of the field. Failure to do this would result in the sum of all 
treatments o

n all six crops being attributed to just one crop of lettuce. 

Product 
The  product  actually  used  should  be  collected,  wherever  possible.  This  is  vital  in  order  to 
  its/their  formulation.  Different 
establish  the  active  substance(s)  being  applied,  and  also
rmulations  of  the  same  pesticide  may  have  different  impacts  on  human  health  or  the 
fo
environment, despite containing the same active substance(s). 

In  most  instances,  growers  may  only  know  what  they  have  used  by  its  product  name,  which  is 
ideal,  but  alternatively  they  may  have  known  what  they  wanted  to  use  only  by  its  constituent 
active substance(s), and may not have kept a record of the actual product used. This is often the 
rs by its active substance 
case for chemicals such as cypermethrin, which is well known to farme
nd is frequently a constituent part of the product name in the UK, thereby allowing farmers to 
a
consider it generically (e.g. “Manufacturer’s name” Cypermethrin 10). 

Care must be taken with prefixes and suffixes to product names, where small changes in a name 
can often denote very different constituents. For example, within the UK, Alto 100 SL, Alto Eco, 
r all contain cyproconazole, but at different rates and with 
Alto Elite, Alto Combi and Alto Majo
idely  differing  added  fungicides  (see:  Table  1.2).  It  is  therefore  important  to  collect  the  full 
w
name with as much detail as possible. 

o  collect  only  “Alto”  as  the  product  used  would  lead  to  considerable  confusion  and 

T
misrepresentation. 

Methodology for the collection of pesticide usage statistics 

19 

 
 
 
 
 
 
 
 
Table 1.2 Example of variations in constituent active substances in products with similar 
names 

Name  
Alto 100 SL  
Alto Eco  
Alto Elite  
Alto  
Alto Major  

Active substance(s) 
Cyproconazole 
Cyproconazole + Mancozeb 
Cyproconazole + Chlorothalonil 
Combi Cyproconazole + Carbendazim 
Cyproconazole + Tridemorph  

•  Seed treatment 
Seed  treatments  may  not  be  known  by  the  farmer  and  it  may  be  necessary  to  obtain  this 
information  from  the  merchant  or  supplier.  A  distinction  between  own  treatment  and  bought 
seeds can be made, but the principle is the same. The quantity of seed used per hectare of crop 
will  determine  the  quantity  of  product  of  treatment  applied  to  the  crop.  If  self-treated,  the 
composition  of  the  seed  treatment  should  be  taken  from  the  farmer’s  own  administration.  If 
purchased, the information must be either on the seed label or, if not available, it can be obtained 
from  the  wholesaler,  retailer  or  producer.  Usually  information  about  the  seed  variety  and  the 
supplier is sufficient to identify the treatment applied (as this information is easily retrieved by 
the plant protection services).  

Amount used or rate of application 
The rate of application is crucial to estimating the total amount of pesticide used and, likewise, 
the total amount used and the area treated can be used to derive the rate of application. Either is 
acceptable.  Experience  in  Great  Britain  has  shown  that  it  is  not  sufficient  to  assume  that  the 
farmer/grower  has  applied  the  chemical  at  the  label-recommended  rate.  The  average  rate  for 
applications of fungicide products to wheat in 1996 in Great Britain was, in fact, just over half of 
the  rate  recommended  on  the  label.  Assumptions  that  label  rates  had  been  adhered  to  would 
therefore have overestimated use by almost 100%. 

The grower’s actual rate of application to the crop should be recorded, as litres or kilograms of 
product per hectare. Where the grower is unsure of the rate, a record of the actual amount used 
and  the  area  treated  will  clearly  allow  for  subsequent  calculation  of  rate.  It  would  also  be 
acceptable  to  record  the  grower’s  known  level  of  application,  for  example  “½  or  ¾  of  label 
recommended rate”. 

This  will  also  allow  the  rate  to  be  calculated  from  a  knowledge  of  the  pesticide’s  own  label 
recommendations. 

•  Seed treatment 
In the case of seed treatments (which can be systematic in some crops), the quantity of seed used 
and the type of seed treatment will determine the quantity of product applied to the crop. In order 
to determine the quantity of product applied through seed treatments, it is essential to collect the 
information  about  sowing  density  (number  or  kg  of  seeds  per  hectare)  or  to  apply  standard 
coefficients (average regional sowing density). 

Area treated 
The area treated with each pesticide application should be recorded, as this may not necessarily 
coincide  exactly  with  the  area  of  crop  grown.  Part-field  treatments  to  control  specific  localised 
weed  or  pest  problems,  and  applications  only  to  headlands  or  to  all  parts  of  the  field  except 
headlands,  are  amongst  the  reasons  why  the  whole  of  a  crop  may  not  be  treated.  Additionally, 

Methodology for the collection of pesticide usage statistics 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
there may be enforced buffer zones applied to certain pesticides that prevent application within a 
certain distance of a watercourse, hedge or other boundary. 

Where  spot  treatment  has  occurred,  for  instance  in  grassland  to  control  small  patches  of 
pernicious weeds, the grower should estimate the area treated, if it is not already recorded. Where 
this is not possible, the area should be calculated from the amount used and the application rate. 
Note that when the area treated for a given crop is added up, it will often exceed the area of crop 
grown.  Care  must  be  taken  over  definitions,  for  which  no  accepted  standards  have  yet  been 
established. 

However,  in  order  to  define  more  precisely  what  is  being  referred  to,  a  set  of  definitions  is 
proposed in Section 1.17. 

Timing (date of application) 
The date of application of each pesticide should be recorded. Timing is perhaps the least essential 
of  the  above  data  requirements,  but  recording  the  date  can  prove  useful  for  many  aspects  of 
analysis.  A  record  of  timing  will  make  it  easier  to  quantify  the  number  of  sprays  applied  to  a 
crop, because without timing, or some record of tank-mixing, it would not be possible to separate 
sprays  applied  on  separate  occasions  from  those  applied  together.  More  accurate  data  on  the 
timing of applications help with many of the aspects outlined in the section in the Introduction on 
the  role  of  usage  statistics.  Timing  data  are  particularly  relevant  to  monitoring  potential 
movement  into  water,  monitoring  farmer  practice  with  regard  to  ineffective  or  illegal  timings, 
providing  information  on  harvest  interval  for  residue  monitoring,  and  in  environmental  studies, 
where there may be critical periods during the year affecting the impact on non-target species. 

Biological control methods 
Biological control methods include preparations of fungal, viral and bacterial agents, as well as 
the introduction of natural predators and parasites. Biological control methods should be regarded 
in  the  same  way  as  pesticide  applications.  Changes  in  the  use  of  these  methods,  and  potential 
increases  in  use  at  the  expense  of  conventional  pesticides,  will  be  of  importance  to  schemes 
which aim to monitor the conversion from current practices to methods of integrated pest control. 

The area over which an introduction has been made (area of crop “treated”) should be recorded 
for each introduction, to provide a record of the number of treatments made. There would seem to 
be  little  to  be  gained  from  recording  the  number  or  amount  of  agents  introduced  (e.g.  five 
Encarsia per m2), but this may be relevant in some situations. 

1.7  Additional information 
The  above  guidelines  outline  the  minimum  data  requirements  considered  necessary  in  order  to 
obtain valuable information from a survey – essentially what is being used, where, when and in 
what  quantities.  Whilst  they  are  not  highlighted  as  essential,  many  aspects  of  the  agronomy  of 
crops  may  provide  useful  further  information  on  pesticide  use  or  assist  in  the  analysis  of 
differences  in  use  between  crops.  Countries  should  consider  which  aspects  of  the  demands 
outlined in the introductory section are of most relevance to their situation and consider collecting 
any  further  information  from  the  list  below  which  may  enhance  those  data,  where  resources 
permit. 

Crop type 
In  this  context,  crop  type  may  provide  a  fuller  definition  of  the  crop  beyond  that  broken  down 
within  a  census  orFarm  Structure  survey  definition,  or  as  “winter”  or  “spring”,  as  previously 
defined under “Crop”. For example, the definition may include the words “culinary”, “dessert” or 
“cider” for apples and pears. 

Methodology for the collection of pesticide usage statistics 

21 

 
 
 
 
 
 
Crop  type  is  an  important  parameter  to  collect,  as  pesticide  inputs  may  differ  significantly 
between different crop types. In Great Britain, for example, inputs to many areas of cider apples 
are often low, or zero, compared to apples grown for dessert consumption. Furthermore, dessert 
apples  often  have  higher  inputs  than  culinary  apples  in  Great  Britain,  while  the  Cox  variety 
frequently has higher inputs than other dessert varieties. 

Other important distinctions may exist between crops grown for processing and those grown for 
sale on the fresh market, e.g. blackcurrants, strawberries, and potatoes, which can be grown for 
seed, ware (human consumption) or industrial use. This distinction should be made if such crops 
are  not  separated  at  the  census  level  or  in  the  farm  structure  survey,  because  pesticide  use  can 
differ markedly between the different types. 

Variety 
In  addition  to  crop  type,  there  is  merit  in  recording  the  variety  or  cultivar  of  the  crop  grown 
where  this  may  be  expected  to  influence  pesticide  inputs.  Crops  such  as  wheat,  with  known 
variability  in  disease  resistance,  may  have  very  different  fungicide  regimes  applied  to  different 
cultivars  within  the  same  farm.  By  collecting  information  on  the  variety  of  crop  grown,  this 
variation can be examined, as growers may not be exploiting varietal resistance to the full. Such 
knowledge  can  give  clear  indications  about  where  advisory  work  and  extension  services  may 
suggest changes in practice, which can lead to a reduction or optimisation of pesticide inputs. 

Crop stage 
A record of the developmental stage of the crop may not be necessary if this is implicit from the 
timing  of  the  application  or  the  crop  definition.  However,  it  may  be  necessary  to  record  crop 
stage under  certain circumstances. For example, in the UK, pesticides approved for use  on any 
crop for human or animal consumption may be applied to nursery fruit trees, vines prior to final 
planting out, bushes, canes and non-fruiting strawberries, provided that any fruit harvested within 
one  year  is  destroyed.  It  is  therefore  important  to  record  that  the  crop  stage  was  preproduction 
during  the  nursery  or  maiden  phase,  since  many  applications  would  be  non-approved  to  the 
fruiting crop. If these crops are already defined as nursery stock, then crop stage is unimportant. 

Similarly,  the  crop  stage  may  be  taken  as  “before  planting”  or  “after  harvest”  so  as  to  include 
pesticide applications made to land associated with the production of a crop but not necessarily 
applied  to  that  crop.  Again,  these  may  appear  as  non-approved  uses  if  the  crop  stage  is  not 
recorded. 

Desiccant  or  herbicide  applications  to  ripened  crops,  such  as  glyphosate  applications  to  wheat 
prior  to  harvest,  should  be  recorded  as  “before  harvest”  to  distinguish  them  from  applications 
which would clearly appear to have killed the crop had they been applied earlier. 

Applications  of  insecticides  to  vegetables,  for  example  chlorpyrifos,  will  alter  considerably  as 
crop stage develops. Drenching of compost during propagation of brassicas to control soil pests 
will  be  at  much  higher  rates  per  unit  area  than  later  foliar  applications  against  aphids  or 
caterpillars. 

Formulation and method of application 
A  record  of  the  formulation  will  often  be  implicit  in  the  product  name  (e.g.  granular,  seed 
treatment, etc.), but the method of application of the pesticide(s) should be noted, and the level of 
detail is dependent on the resources available within each county. In its simplest form, this needs 
to  be  no  more  detailed  than  “ground  spray”,  “aerial  application”,  etc.  Within  granular 

Methodology for the collection of pesticide usage statistics 

22 

 
 
 
 
 
 
 
 
applications,  however,  it  is  important  to  know  whether  the  granules  were  broadcast  or 
incorporated, as this may well have significant environmental implications. 

If resources allow, more precise information on the type of spraying equipment used may have 
considerable  implications  for  operator  or  bystander  safety,  drift,  environmental  contamination 
etc. 

Thus, it would be worth recording whether the applications were made by knapsack, air-assisted 
sprayer, ultra-low volume equipment, etc. 

The  range  of  methods  of  application  available  differs  widely  within  the  different  commodities 
surveyed,  with  options  such  as  fogging,  misting  and  smokes  being  common  within  protected 
crops. 
A comprehensive listing of the principal methods of application that are recognised and defined 
within the range of commodities surveyed in Great Britain is given in Appendix V. 

Spray round 
In order to estimate the number of times a crop has been treated, it is necessary to maintain some 
record  of  the  spray  round  within  which  the  product  has  been  applied.  A  spray  round  may  be 
defined  as  a  single  treatment  to  the  crop  to  apply  pesticide(s),  and  in  the  case  of  cereals,  for 
example, may involve the application of a complex tank-mix of chemicals including fungicides, 
herbicides, growth regulators and insecticides within a single treatment. 

Collecting such data will allow subsequent consideration of the average number of times a crop 
has been treated with a fungicide, insecticide etc., and give a clear indication of what products are 
frequently  being  tank-mixed  together.  Thus,  the  first  pesticide  application  should  be  marked  as 
spray round 1. For many annual crops, this may well be any seed treatment applied to the crop, 
and so as to allow an estimate of the proportion of crop not treated with a seed treatment there is 
merit  in  recording  this  first  treatment  as  “Not  treated”  with  a  seed  treatment  where  none  was 
used. 

All  the  products  mixed  together  within  one  application  should  be  linked  using  the  same  spray 
round number, which increases by one for each subsequent application made to the crop. 

Granular applications should be given a unique spray round number, even if they were applied at 
the same time as a sprayer passed over the crop, which is sometimes the case. As they were not 
physically  mixed  in  with  the  other  chemicals  applied,  and  also  require  a  separate  method  of 
application, it is not feasible to include them in with an accompanying spray. 

An example of the use of spray round to link chemicals applied together is given in Table 1.3. 

Table 1.3 Layout of data to illustrate the use of spray round to link chemicals applied 
together 
Date  
Product  
12/9/96   Seed treatment A  
15/10/96   Herbicide B  
15/10/96  Herbicide C  
12/3/97   Herbicide D  
12/3/97   Fungicide E  
12/3/97   Fungicide F  
12/3/97   Growth regulator G  
12/3/97   Molluscicide H  

Method of application  
Seed treatment  
Ground spray  
Ground spray  
Ground spray  
Ground spray  
Ground spray  
Ground spray  
Granular broadcast  

Spray round   
1 
2 
2 
3 
3 
3 
3 
4 

Methodology for the collection of pesticide usage statistics 

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Target species or reason for use 
Where possible, the grower’s perceived reason for use should be recorded. This may be a target 
species,  pest  or  weed(s),  disease  or  range  of  diseases  or,  in  the  case  of  growth  regulators,  for 
reasons such as straw shortening, fruit setting, fruit thinning or ripening. The reason given may 
not  always  appear  appropriate  but  should  be  recorded,  as  this  may  give  a  further  indication  of 
where pesticides may be being used inappropriately. With this knowledge, there may be scope for 
better advice or labelling, thereby reducing inputs. 

Crop rotation 
Crop rotation was not identified as an essential element by the task force, but it was highlighted 
as important during the OECD workshop on Pesticide Risk Indicators in Copenhagen. It is more 
related to pest management outside pesticide use, but may have implications for monitoring the 
development of integrated crop management. Crop rotation will indirectly affect pesticide use, as 
previous cropping history can significantly influence the spectrum of weeds, pests and diseases 
likely to be encountered in the crop. Changes in soil fertility may also influence the requirement 
for  applications  of  growth  regulator.  Recording  the  previous  cropping  history  of  the  land  on 
which  the  surveyed  crop  is  being  grown  is  therefore  the  best  way  of  monitoring  crop  rotation. 
Studies  of  disease  levels  in  major  arable  crops  in  England  &  Wales  (wheat,  winter  barley  and 
oilseed  rape)  have  shown  an  effect  of  previous  crop,  an  effect  of  the  length  of  break  from  the 
current  crop  and  an  effect  of  continuous  cropping  of  up  to  three  years  or  more.  This  would 
therefore indicate a need to record previous crop for at least three years prior to the current crop. 

Drilling method 
The availability of treated seed to birds and mammals will be influenced in part by the method of 
drilling,  which  will  also  influence  the  sowing  rate.  Differences  in  drilling  method  –  e.g.  direct, 
broadcast, broadcast and ploughed in, precision, conventional etc. – may be recorded. 

Sowing date & harvest date 
Sowing date is a useful parameter to record because it can influence crop development, and thus 
the  need  for  and  timing  of  pesticide  applications.  Harvest  date  may  have  implications  for 
applications  made  within  the  harvest  interval  for  some  crop/pesticide  combinations.  It  is 
particularly  important  in  countries  like  the  USA,  where  the  development  of  the  “risk  cup” 
approach to registration may be influenced by the probability of finding residues in edible crops 
because of incorrectly observed harvest intervals. 

Both  of  these  parameters  may  also  be  helpful  in  explaining  odd  or  non-approved  uses  if 
application  dates  prove  to  be  outside  the  cropping  period,  i.e.  before  planting  or  after  harvest 
applications. 

Crop covers 
Use  of  crop  covers  may  have  implications  for  monitoring  the  uptake  of  integrated  pest 
management  techniques.  For  vegetable  crops,  crop  covers  are  sometimes  used  to  protect  crops 
from the weather and pests. These could take the form of polythene or fleece and may influence, 
reduce  or  negate 
the  requirement  for  certain  pesticide  applications  –  for  example, 
organophosphate insecticides to control carrot fly in carrots and parsnips. The type and period of 
cover should be noted. 

Methodology for the collection of pesticide usage statistics 

24 

 
 
 
 
 
Mulches 
Mulches of organic material, such as straw or peat, or artificial mulches in black, white or other 
coloured polythene are often used. These are particularly important in soft fruit production, but 
may also be used on other crops. Although they reduce the need for herbicide applications, such 
covers may exacerbate pest problems, such as vine weevil in strawberries. A note should be made 
of the presence or absence of mulch and its type. 

Age of crop 
This  factor  may  be  unnecessary  where  crop  definition  already  distinguishes  between  crops  of 
different  ages,  for  example  maiden  vs.  fruiting  tress.  Where  this  is  not  the  case,  for  perennial 
crops such as fruit trees, olives, etc. and temperate crops such as rhubarb, cane fruit, bush fruit 
and strawberries, the age of the crop may influence pesticide inputs, and some age structure that 
is suitable for the individual crop and its pesticide programmes should be devised and recorded. 
For example, strawberry crops should be recorded as: maiden, one, two or three years old. Fruit 
trees  may  be  classed  as:  maiden,  less  than  5  years  old  and  5  or  more  years  old,  or  using  some 
system which would distinguish between gross differences in use as crops age, if this is the case. 
Grassland in England & Wales is classified for census purposes as (1) sown within 5 years of the 
survey;  (2) all other grassland  except rough grazing and  (3) rough grazing. For pesticide usage 
purposes, the “within 5 years” category is further broken down into areas sown within 12 months 
of  the  survey  and  those  over  12  months  old.  This  allows  consideration  of  seed  treatments  and 
molluscicide  and  herbicide  applications  during  the  establishment  year,  which  may  be  much 
higher than on established grass. 

Although such surveys are outside the scope of these guidelines, countries may wish to consider 
the value of such surveys as a means of obtaining additional information on pesticides, such as 
handling  practice,  use  of  personal  protective  clothing,  spraying  machinery  maintenance  and 
calibration procedures, spraying machinery filling and washing practices, etc. 

1.8  Sample design  
In  sampling,  it  is  important  that  the  data  of  the  survey  represent  the  required  totals  within 
accepted error margins which are justified in the sampling design. In the design, the number of 
samples  to  be  surveyed  and  their  identification  are  determined  in  such  a  way  that  the  resulting 
data represent the population and their use can be defended. 

It is not the aim of this section to define the sampling method to be used in each country, as this is 
best achieved by using each country’s own statistical offices. However, it is important to ensure 
that  the  data  collected  are  statistically  sound  for  each  crop.  The  methodology  already  in  use 
within some EU Member States is given for guidance purposes. 

Setting up a sample is not a standard routine and several issues need to be determined, e.g.: 
•  The target population: for instance, the total area sown with wheat in a region/country; 
•  The sampled population: the part of the target population that is accessible and available for 

sampling and ideally the same as the target population; 

•  The  sampling  frame:  the  list  of  all  possible  sampling  units  for  which  the  sample  can  be 

selected.  

The basis of a sound sample is a knowledge of the true population. Without an adequate census of 
the  entire  farming  community,  there  would  be  little  point  in  trying  to  undertake  a  survey  of 
pesticide  use,  as  there  would  not  be  enough  data  on  which  to  gross  up  the  sample  to  produce 
national estimates. 

Methodology for the collection of pesticide usage statistics 

25 

 
 
 
 
 
 
 
 
Given  the  resources  available,  sample  selection  should  aim  for  the  largest  sample  that  is 
practically feasible. 

Regional  differences  in  climate,  pest  and  disease  pressure,  farming  intensity  and  general  farm 
practice often lead to significant regional differences in pesticide use, even on the same crop. 
Thus, sampling should initially be stratified by region. 

Here, the concept of the sampling unit should be specified: 

• 

• 

In most cases the sampling unit is a holding (farm) and the list of all possible sampling 
units  is  typically  the  list  of  all  holdings  growing  the  crop(s)  covered  by  the  survey. 
Sampling  frames  are  therefore  list  frames  made  available  through  existing  registers  or 
census lists (Farm Register, FSS, FADN, IACS, Crop Surveys, etc.). 
In specific cases, the sampling unit can be a geographic feature (a physical segment – for 
example,  a  plot  –,  a  squared  segment  or  a  point):  this  is  typical  of  the  area  frames 
approach (used in France). The list of all possible units is then all the points or segments 
where the specified crop(s) is(are) grown. 

Where  the  sampling  unit  is  the  whole  farm,  if  farm  size  is  believed  to  influence  the  degree  of 
pesticide use,  samples should be  stratified by farm size group within the region.  This approach 
has  several  advantages.  Firstly,  farming  practice  -  and  particularly  the  use  of  pesticides  -  may 
vary considerably with enterprise size. In Great Britain, farmers with enterprises of less than 50 
ha are known to be less likely to use pesticides at reduced rates than farmers with enterprises over 
250 ha. In the Netherlands, however, there appears to be no difference in use across farm sizes, 
and stratification by size is not considered necessary. Farms may therefore be selected at random 
within any regional stratification.  

Where the sampling unit is a single crop on a farm, farms should be selected at random within 
any regional stratification for each crop to be surveyed. 

Where  the  sampling  unit  is  a  field  of  a  particular  crop,  a  random  sample  of  fields  should  be 
selected so that the probability of selecting a particular field is directly proportional to the total 
area planted of the crop to be surveyed, within any regional stratification. 

Where the sampling unit is a field, the fields should be selected at random, within any regional 
stratification. 

Sampling unit versus observation unit 
One  of  the  essential  parameters  to  be  identified  in  pesticide  surveys,  besides  the  quantity  of 
product, is the area treated. As explained in the previous section, it is very important to identify 
the area treated with each product as precisely as possible.  Therefore, when the sampling unit is 
the  farm,  it  can  be  useful  to  collect  the  information  at  the  level  of  the  field  (considered  as  an 
observation unit).   

In the most common case (list frame), it should be extremely clear that the sampling unit (which 
will be used as the basis for the calculation of regional-national totals) will always be the holding 
(farm), but observations are to be made at the level of each field (it can also be the main field or 
groups of fields if treatments are similar across the fields) and are aggregated at the level of the 
holding. 

For  area  frames,  the  sampling  unit  should  be  equivalent  to  the  observation  unit,  though 
significantly different processes are involved depending on the type of feature (a point, a physical 
segment or geometric segments can be used as sampling unit). 

Methodology for the collection of pesticide usage statistics 

26 

 
 
 
 
 
 
 
 
 
 
 
1.9  Establishing a regional breakdown 
Stratification  by  region  is  inevitable,  for  example  where  soil  types  vary  regionally,  which  may 
particularly  influence  pesticide  use  and  may  be  essential  where  there  are  regional  variations  in 
pesticide legislation (e.g. the USA). 

Stratification  by  region  should  aim  to  divide  the  country  into  areas  with  similar  agro-
environmental  characteristics,  and  such  a  breakdown  may  already  be  used  in  many  countries. 
Within  the  EU,  there  is  also  the  regional  breakdown  used  by  the  Farm  Accountancy  Data 
Network (FADN), which, for example, recognises 21 distinct regions in Italy, 22 in France and 
17 in Spain etc. 
Where this breakdown is not detailed enough, other systems may be used. For example, FADN 
recognises Scotland as one region, whereas in terms of land use Scotland has been divided into 
11 clear regions, which are used for the purposes of surveying pesticide use within Scotland. 

In England and Wales, six regions are used, corresponding to the original Ministry of Agriculture 
administrative regions, which have a degree of homogeneity with regard to land use and climate, 
and consequent pest and disease pressures. This breakdown provides slightly more detail than the 
four  regions  used  by  FADN:  whereas  the  Netherlands  have  14  agricultural  areas,  Sweden  is 
divided into 102 yield districts. While it may be necessary to select the sample and collect data 
regionally, it is not necessary to present data for every region. However, this approach does allow 
usage to be broken down more easily into areas which may also map catchments, for example. 

1.10  Establishing farm size groups 
Pesticide  usage  on  the  same  crop  may  vary  with  farm  size.  For  example,  larger  farms  may  be 
managed by more highly trained personnel who are prepared to apply pesticides at reduced rates 
when pest pressure is low, or who are more aware of newer products or methods of pest control. 
Where  size  grouping  is  thought  to  be  necessary,  it  should  aim  to  divide  farms  into  size  groups 
such  that  the  total  area  of  holdings  in  each  group  is  roughly  equal.  In  Great  Britain,  farms  are 
generally  grouped  into  five  classes.  This  enables  the  government  to  select  the  right  number  of 
farms in each group for visits, and avoid visiting large numbers of small farms which make little 
contribution to total pesticide use, or visiting too few large farms which contribute significantly. 
For example, groupings of arable farms in England & Wales were adjusted to give the most even 
distribution of areas across size groups, as shown in Table 1.4. 

Table 1.4 Size grouping, numbers of farms and total areas for arable farms in England & 
Wales 

SIZE GROUP  
Area of farms  
% by area  
Number of farms  

< 50 HA  
687,118  
19  
39,629  

50-100 HA  
710,797  
20  
9,972  

100-150 HA   150-250 HA   > 250 HA   TOTAL   
3,608,679 
550,187  
100 
15  
60,256 
4,502  

940,621  
26  
2,367  

719,954  
20  
3,786  

Using  this  breakdown,  it  is  easy  to  divide  farms  by  simple  size  groups,  designed  to  apportion 
approximately 20% of the total arable area to each group. The 100-150 ha group falls below this 
ideal  area,  while  the  largest  size  group  (>  250  ha)  accounts  for  a  larger  than  ideal  area. 
Adjustments could be made to the size of the larger groups to offset this, e.g. by experimenting 
with size groups of 100-180 ha, 180-280 ha and > 280 ha. 

Alternatively,  size  grouping  may  be  based  on  the  European  economic-size  unit  which  is  an 
elaborate size unit based on the cultivated area and price derived for the crop in question. 

Methodology for the collection of pesticide usage statistics 

27 

 
 
 
 
 
 
 
 
 
 
 
1.11  Establishing the sample 
The aim of sampling should be to select farms from a representative number within each regional 
(and  size  group)  cell.  Within  very  small  cells,  a  minimum  of  two  farms  should  be  sampled  to 
ensure statistical validity. As a guide, the numbers of farms and/or fields surveyed for each crop 
within countries already undertaking surveys are listed in Table 1.5. 

Table 1.5 Sample sizes and populations in some countries currently conducting surveys 

Crop  

No of farms  
visited  

No of farms 
growing crop 

43,960  
35,388  
39,208  
28,909  
16,770  
9,543  
16,918  
4,546  
6,218  
221  
2,937  
 1,355  

74,500  

Great Britain 
Winter wheat  
Winter barley  
Set-aside  
Spring barley  
Oilseed rape  
Sugar beet  
Ware potatoes  
Peas  
Beans  
Mushrooms  
Edible protected crops  
Seed potatoes  

864  
710  
875  
517  
516  
190  
201  
127  
182  
90  
250  
45 

Sweden * 

3,775  

USA 
1,757  
Corn  
1,189  
Cotton  
676  
Potatoes  
2,657  
Soybeans  
1,516  
Winter wheat  
 308  
Spring wheat 
122  
Durum wheat 
Selected fruit crops  
7,204  
Selected vegetable crops   6,281  

No of fields  
surveyed 

7,701 
2,766 
2,417 
2,043 
1,761 
768 
590 
286 
494 
288 (crops) 
1,184 (crops) 
77 

3,775 

1,757 
1,189 
676 
2,657 
1,516 
308 
122 
7,204 
6,281 

* N.B. The number of farms visited is not the same as the number of farms in the sample selected. 
Since the frame is updated only once a year, it contains a small number of non-active farms. 

1.12  Producing national estimates 
Essentially,  a  statistically  valid  random  sample  will  give  an  average  use  per  hectare  for  each 
pesticide on each crop (within each region). Total use is obtained by multiplying this average by 
the total area grown (within each region). 
Where  farms  have  additionally  been  stratified  by  size,  and  assuming  that  a  sound  sampling 
procedure has been followed, sample data may be grossed up to produce national estimates which 
correct  for  over-  or  under-sampling  of  a  crop  within  any  region.  A  grossing-up  factor  can  be 
generated  for  each  cell  which  is  equal  to  the  total  area  of  farms  within  that  cell  divided  by  the 
total area of farms sampled within that cell: 

For each cell   Rf1sr =   (total area of farms within size group s in region r)/ 
X 

(total area of farms visited within size group s in region r) 

Methodology for the collection of pesticide usage statistics 

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Any  slight  over-  or  under-sampling  of  a  particular  crop  within  a  region  may  be  corrected  for, 
using a correction factor derived from the total area of that crop grown within the region divided 
by the grossed up estimate of the crop grown in that region: 

For crop c  

Rf2cr =   (total area of crop c grown in region r)/ 

(Σ1n(area of crop c grown on farm n in size groups in region r * RF1 sr)) 

RF2 should approximate to 1. 

1.13  Defining the survey period 
While a standard 12-month period from January through to December is the most logical period 
to survey, any crops other than perennial crops may well be grown in rotation. A particular field 
to be surveyed could have had two different crops growing on  it within a  single calendar year. 
The survey period should therefore cover 12 months and consider all pesticide applications made 
to the land on which the crop is grown over a 12-month period, defined by the cultural practices 
of the crop grown. For example, arable crops grown in Northern Europe are best surveyed over a 
period  following  the  harvest  of  the  previous  season’s  crop,  to  include  any  pre-drilling  clean-up 
treatments to the land, then through drilling of the surveyed crop to harvesting in the survey year. 
Note that the survey year is always considered to be the year in which the harvest was taken. This 
is illustrated in Tables 1.6 and 1.7. 

A decision has to be made on whether to include all pre-drilling treatments to the land in order to 
control weeds prior to sowing, or whether to include these as post-harvest treatments at the other 
end of  the growing year. However, care must be taken not to omit both or include both, as the 
former  would  underestimate  use  while  the  latter  would  lead  to  double  counting  and 
overestimation. 

Table 1.6 Schematic representation of survey period for autumn-drilled crops 

July  

August  

September 

Oct-Dec 

 January-June  

July 

 August 

(cid:197)  

Survey period 

(cid:198) 

  Harvest of 
  previous crop 

Pre-drilling 
clean-up 

Drilling of 
survey crop 

Autumn 

Spring 

pesticide use  pesticide use 

Harvest of 
survey crop 

Pre-drilling 
clean-up 

(cid:197)  Survey year 

 (cid:198) 

For spring-sown crops, where land may have lain fallow since the previous harvest, any weed or 
pest  control  treatments  to  the  land  over  that  period  should  be  associated  with  the  crop  that  is 
grown  subsequently.  While  they  may  not  necessarily  be  appropriate  applications  to  that  crop, 
their omission will lead to an underestimate of national pesticide usage. 

Methodology for the collection of pesticide usage statistics 

29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 1.7 Schematic representation of survey period for spring-drilled crops 

July  

August-December  

January-June  

July  

August 

(cid:197)  

Survey period  

(cid:198) 

  Harvest of 
  previous crop 

Autumn weed 
control ? 
Land lies fallow 

Pre-drilling clean-up 
Drilling of survey crop 
Pesticide use 

Pre-harvest pesticide use 
Harvest of survey crop 

Pre-drilling 
clean-up 

(cid:197)  Survey year 

 (cid:198) 

For short-term crops, such as lettuce, or any crops where more than one cycle is grown within a 
12-month period, the optimum period during which data are recorded may well be influenced by 
the appropriateness or seasonality of other crops within the same survey. Lettuces grown under 
glass are therefore surveyed in Great Britain during the period October to September, as this is 
the  period  appropriate  to  the  growth  of  many  other  protected  crops.  Because  crops  may  grow 
more slowly during the winter than during the summer, it is important to record details of inputs 
during the whole 12-month period, rather than using the inputs for one crop and multiplying them 
up  by  the  number  of  crops  per  year.  Crops  may  require  more  protection  from  disease  or  pests 
during periods of slow growth and therefore have higher inputs at some times of the year than at 
others. Conversely, pest pressure may be higher during warm weather, resulting in inputs to some 
crops that are higher during the summer than in the winter. 

Mushrooms may be considered over a 12-month period from January to December, as there is no 
true  seasonality  to  the  crop.  Pesticide  applications  may  vary  within  the  year  because  of  the 
influences of external temperatures, which may, for example, increase problems from sciarids or 
phorids in the summer months. Again, it is therefore important to record details of inputs during 
the whole 12-month period.  

Perennial  crops  with  a  natural  growing  season,  such  as  fruit  crops,  are  best  considered  over  a 
period commencing after the end of harvest in a given year through to the end of harvest in the 
following year. 

However, it is important to remember to take the whole 12-month period into consideration, and 
any  applications  during  the  dormant  period  -  such  as  winter  washes,  pruning  paints  or  weed 
control - should not be excluded. 

1.14  Training the staff 
The  pilot  surveys  carried  out  according  to  these  guidelines  clearly  showed  the  benefit  of  close 
cooperation  between  the  Statistical  Offices  and  Plant  Protection  Services.    The  role  of  the 
Statistical Offices in establishing the sampling design, programming the surveys and processing 
the data is evident.  The main inputs from the Plant Protection Services concern the provision of a 
complete and updated list of plant protection products related to the crop to be covered, with their 
composition  in  active  substances.    In  addition,  it  is  very  important  that  the  Plant  Protection 
Services provide all relevant information on the crop to be covered in order to facilitate the task 
of the surveyors and the interpretation of the final results. 

Depending on the particular conditions in the different countries involved in the pilot surveys, the 
direct involvement of surveyors from the Plant Protection Services has not always been found to 

Methodology for the collection of pesticide usage statistics 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
     
 
 
 
 
 
 
 
 
be essential.  In some cases it was even considered that direct participation of controllers for the 
Plant  Protection  Services  in  the  interviews  might  give  rise  to  conflicts  of  interest  and 
confidentiality issues.   

However, in any event, the training of the staff  involved in the collection of data, and to some 
extent  in  the  processing  of  data,  has  proved  to  be  crucial  to  the  success  of  the  surveys  and  the 
quality of the data collected.  

A  farming  background  is  useful,  since  it  facilitates  communication  with  and  understanding  of 
farmers.  In the UK surveys, interviewers must have at least a degree in agriculture/horticulture 
and  preferably  a  further  degree  in  crop  protection  or  previous  experience  working  in  crop 
protection  or  with  pesticides.    Transfer  of  know-how  is  ensured  through  the  training  of  new 
recruits by experienced surveyors. 

During  the  interviews,  it  is  also  important  that  the  surveyors  can  rely  on  sound  documentation 
about the crop covered and the specific pesticide treatments it can receive.  For this reason, each 
survey should have: 
• 
•  Full agronomic notes on each crop; 
•  Full instructions to surveyors covering (all) eventualities; 
•  New or unusual practices circulated among surveyors as the work progresses. 

Its own standard forms; 

1.15  Carrying out the surveys 
Visit surveys are usually undertaken retrospectively. Data are collected on all inputs to the crop 
up to the harvest and for a period covering the previous 12 months. In theory, visits could begin 
in autumn, but for practical reasons it is often better to wait for a quieter time of year.   

Considering the complexity of the surveys, it can be relevant to cover different crops during the 
same interview, but this has to be already planned into in the sampling design.  Care should be 
taken  not  to  overburden  farmers  with  long  and  complex  questionnaires.  A  maximum  of 
information  should  be  collected  during  the  interview  so  as  to  avoid  having  to  go  back  to  the 
farmer for additional information or to check the data provided. Using appropriate standard forms 
greatly facilitates this task.   

Overburdening farmers with repetitive surveys can be a particular problem for big farms, which 
usually represent a small stratum in the sample and are covered exhaustively by several surveys.  
To avoid this problem, several countries have experimented with alternatives to survey visits for 
the  big  farms.    Postal  or  e-mail  surveys  have  proved  to  be  efficient,  especially  if  the  relevant 
information is easily accessible for the farmer. 

Postal  or  e-mail  surveys  are  usually  more  flexible  and  can  be  undertaken  during  the  crop  year. 
Data on all inputs to the crop up to the harvest can be collected in one go or at regular intervals 
(monthly) through the life of the crop. 

As already explained, the information on pesticide use should ideally be collected at the level of 
each  field  (observation  unit)  of  the  farm  (sampling  unit)  for  the  crop(s)  under  consideration. 
However, some countries have experimented with grouping all the fields of a farm or the fields 
that have received the same treatments into 'observation clusters' or 'plots'.  Another approach is 
to  cover  only  the  largest  field  of  the  farm.  Such  simplifications  can  reduce  the  length  of  the 
interviews,  but  in  every  case  they  will  result  in  a  loss  of  information  (especially  on  the  area 
treated)  and  their  impact  on  the  quality  of  the  final  results  needs  to  be  properly  assessed.    For 
instance,  it  has  been  demonstrated  that  for  very  common  treatments  with  herbicides  such 

Methodology for the collection of pesticide usage statistics 

31 

 
 
 
 
 
 
 
  
 
 
simplification  have  very  little  impact  on  the  results,  but  will  have  a  significant  effect  on  the 
accuracy of the results for treatments with insecticides, which are usually applied very locally. 

In all cases, for both visits and postal surveys, the keeping of treatment records by farmers can 
greatly facilitate the interviews and improve the quality of the responses.  Farmers that are to be 
surveyed  can  be  issued  with  booklets  prior  to  the  survey  season;  alternatively,  agrochemical 
companies  sometimes  give  away  free  booklets  to  growers,  which  can  be  used  during  the 
interviews. 
Some countries have also developed farm management software packages. 

'Field-by-field' method versus 'farm' method 
The  choice  of  one  or  other  method  will  depend  on  several  considerations,  which  can  arise  at 
different stages in the organisation of the survey.  Economic and practical considerations (cost of 
the survey, human resource availability, length of the interviews) can determine the choice of the 
approach  when  designing  the  survey.    The  availability  of  data  at  field  level  and  the  ability  of 
farmers to separate the treatments between fields can also push the survey in the direction of a 
'farm'  approach.    In  practice,  a  balance  has  to  be  struck  between  the  need  to  achieve  highly 
detailed final results and the practical feasibility of the survey. 

In general, for practical reasons, the field should be considered as the smallest observation unit.  
When treatments are applied locally on one field, the basic area treated should be considered as 
the area of the field. 

A farm survey and a field-by-field survey answer different questions:  

Farm survey: “This is what we do to wheat” 
Field-by-field survey: "This is what we did to each field of wheat” 

Where  the  aim  is  to  collect  data  at  active  substance  level,  the  "field-by-field"  method  will 
always  be  the  most  accurate.  However,  it  is  important  to  ensure  that  the  whole  exercise  of 
processing  individual  data  will  allow  the  distinction  between  different  fields  to  be  maintained 
(there is no intermediate aggregation between fields and farms). 
The  "farm"  method  can  be  a  good  practical  compromise  when  treatments  are  fairly 
homogeneous  on  all  fields  of  a  particular  crop  within  the  farms.  However,  this  method  will 
always involve information losses, since treatments applied on one field are spread onto the basic 
area treated of the whole farm. 

Similarly, when successive crops (of the same or different varieties) occur on the same field, as is 
usually  the  case  for  vegetable  production,  it  is  advisable  to  cover  the  whole  production  period 
(over one year) and to assign a separate code to each successive crop.  

1.16  Data storage and processing 
Our intention is not to describe a single model for the storage and processing of survey data, as 
this aspect will depend very much on national traditions and preferences. 

Within the different pilot projects, some countries collected  the  data on paper forms  and began 
the  actual  data  storage  and  processing  at  a  later  stage.    In  some  cases,  these  two  phases  were 
completely  separate  and  under  the  responsibility  of  different  departments  (Plant  Protection 
Services and Statistical Offices). 

Some  countries  experimented  with  programmes  that  combined  data  storage  and  initial  data 
checks,  based  on  the  identification  of  authorised  plant  protection  products  and  standard 

Methodology for the collection of pesticide usage statistics 

32 

 
 
 
 
 
 
 
 
 
application rates.   These programmes were tested on laptops by interviewers directly during the 
visit or in postal interviews, or after these interviews. 

Such  programmes  are  based  on  relational  databases  and  their  purpose  is  to  optimise  the  entry, 
storage, manipulation and analysis of data. 

The following characteristics are common to programmes of this kind: 
•  They contain a pesticide database (list of approved products for different crops);  
•  Data are collected at product level from the grower; 
•  The product name is related to its active substance(s) in a database; 
•  The amount of product is converted into amount of active substance(s); 
•  Analysis is carried out at active substance level; 
•  The programmes may be used for sophisticated error checking; 
•  They may also include a database on approved uses, application rates, harvest intervals, etc. 

Figure 1. 1 Typical structure of a programme for the collection of pesticide usage data 

Farm table
Farm table
Farm table
Farm number
Farm number
Farm number
Farm details
Farm details
Farm details
Address
Address
Address
Region
Region
Region
Size class
Size class
Size class
Geo reference
Geo reference
Geo reference

Field table
Field table
Field table
Farm number
Farm number
Farm number
Field number
Field number
Field number
Field details
Field details
Field details
Crop
Crop
Crop
Area
Area
Area
Sowing date
Sowing date
Sowing date

Product table
Product table
Product table
Product
Product
Product
Active substance(s)
Active substance(s)
Active substance(s)
Percent a.s.
Percent a.s.
Percent a.s.

Treatment table
Treatment table
Treatment table
Farm number
Farm number
Farm number
Field number
Field number
Field number
Product
Product
Product
Area treated
Area treated
Area treated
Date
Date
Date
Method
Method
Method
Crop stage
Crop stage
Crop stage
Rate
Rate
Rate
Spray round
Spray round
Spray round
Target
Target
Target
Water volume
Water volume
Water volume

1.17  Production of results 
As  explained  above,  the  results  of  pesticide  usage  surveys  can  be  used  at  national  level  for 
different purposes.   Some presentations of the results will require only the data collected in the 
survey to be aggregated at different levels (categories of products, types of farms, regions, etc.). 
More complex presentations,  such  as risk indicators, will  require the  combination  of  individual 
data on pesticide usage with external databases containing specific product properties, agronomic 
or agro-climatic data, parameters on crop development, etc.   

It  is  therefore  important  that  programmes  used  to  store  data  allow  the  data  to  be  aggregated  at 
different levels and to be combined with external databases. 

When  reporting  to  the  Commission,  it  is  important  to  observe  certain  basic  reporting 
requirements and adhere to certain standard definitions and classifications.   

In the framework of the pilot projects on pesticide usage surveys, a standard transmission table 
has  been  prepared  containing  the  essential  parameters  to  be  reported  to  Eurostat.    National 
estimated  values  for  each  individual  active  substance  had  to  be  provided  for  each  of  the 
parameters  set  out  below.  These  pilot  surveys  demonstrated  the  importance  of  defining  basic 

Methodology for the collection of pesticide usage statistics 

33 

 
 
 
 
 
 
 
 
 
 
aggregation  rules  to  produce  the  national  estimates  for  the  different  parameters  (especially  in 
order to aggregate results from different observation units and sampling units). 

•  Quantity of active substance used (g, kg, t) 

This  is  the  easiest  definition  to  understand.  However,  the  questionnaires  do  not  ask  for  this 
information  directly.  It  is  derived  from  the  information  collected  on  commercial  products 
combined  with  the  table  showing  the  content  in  active  substance(s)  of  the  different  products. 
Depending on the kind of product (liquid or solid), concentrations in active substance(s) may be 
expressed  in  g/l,  g/kg  or  %,  but  in  all  cases  the  final  results  have  to  be  expressed  in  grams  of 
active  substance(s)  used  (or  kg  or  tonnes).  If  different  products  containing  one  specific  active 
substance  are  applied  on  one  field  (observation  unit),  then  the  quantity  applied  on  this  field  is 
simply the sum of the quantities contained in the different products. There is thus no aggregation 
problem when the volumes of all products have been converted into active substances, since the 
total quantity of each active substance is simply the sum of all individual applications. 

•  Total area surveyed/cultivated (ha) 

The  total  area  surveyed  is  the  sum  of  the  areas  cultivated  with  the  crop  in  question  for  all  the 
farms or fields covered by the survey. When the data are grossed-up at national (regional) level, 
the  total  area  surveyed  should  correspond  to  the  area  cultivated  with  this  crop  at  country 
(regional) level. The total area surveyed is that which is represented by the survey. It is usually 
given in advance in the sample frame. However, approaches may differ from country to country 
(survey of the whole farm area, a single field, etc). In the final reporting table, it is important that 
the treatments observed are associated with the correct area surveyed. It is important that, when a 
surveyor goes to a farm with a total area of wheat of 100 ha, but only asks questions about 10 ha, 
the report should clearly state that the quantities of products reported have been applied on 10 ha, 
so that the results can be multiplied up to obtain the area corresponding to the farm (which is the 
sampling unit).   

•  Area treated (ha) 
The concept of area treated is the most difficult to embrace, since it can cover different situations 
on the ground. 

Basic area treated 

This is the physical area of the crop treated at least once with a given active substance, product or 
group  of  substances  (e.g.  fungicides,  herbicides,  etc.),  independently  of  the  number  of 
applications.  The basic area treated can be related to active substances or commercial products 
and considered either individually or for all products or active substances (in one specific group 
or  in  total).    Practically,  the  concept  of  basic  area  treated  is  the  most  easily  understood  (and 
calculated),  as  it  is  the  difference  between  the  area  cultivated  and  the  area  not  receiving  that 
particular substance(s) or product(s).  Therefore, it will always be smaller than the area surveyed 
or cultivated. It should be noted that, when only one field per farm is covered by the survey, the 
total  area  surveyed  will  usually  be  identical  or  very  close  to  the  basic  area  treated  (as  the  field 
covered receives the majority of the reported treatments).  

For the purposes of reporting to Eurostat, the basic area treated should be considered exclusively 
in terms of individual active substances. 

When  several  fields  (observation  units)  receiving  different  treatments  are  covered  in  one  farm 
(sampling unit), the basic area treated will normally differ from one active substance to another. 
The  basic  area  treated  has  to  be  calculated  for  each  active  substance  from  the  results  of  the 
different fields. For example, if 100 ha of a farm consist of 10 fields of 10 ha, of which five have 

Methodology for the collection of pesticide usage statistics 

34 

 
 
 
 
 
been treated one or more times with products containing a given active substance, then the basic 
area  treated  with  this  active  substance  is  50  ha.  If  these  same  five  fields  plus  two  others  are 
treated with products containing a second active substance, then the basic area treated with this 
second active substance is 70 ha.  

 It  is  important  to  note  that  the  basic  areas  treated  with  different  active  substances  cannot  be 
grossed up to calculate the basic area treated with a given group or category of substances, since 
this would lead to double counting due to overlapping areas. It is also very important to realise 
that  the  basic  area  treated  on  a  farm  can  only  be  calculated  if  each  treatment  is  attributed  to  a 
single field and if the information on the different fields (observation units) is kept until the data 
are processed.  Any simplifications in the collection of data in the farms (one farm = one field) 
will result in the basic area treated being closer to the area surveyed. 

In  addition  to  the  concept  of  the  basic  area  treated,  which  gives  an  indication  of  the  physical 
dimension on which each single active substance has been applied, it may be useful to measure 
the  'gross'  or  'developed'  area  treated  that  integrates  the  number  of  applications  of  a  single 
substance on the basic area treated.  Whereas the main value of the concept of ‘basic area treated’ 
is to measure the maximum  pressure of a substance on  a  given area, the  concept of 'gross area 
treated' is of particular interest when the risk associated with a given substance is dependent on 
the number of applications (e.g. residues in food).  Different concepts can be used to reflect the 
gross  area  treated,  but  only  the  concept  of  "active  substance  area  treated"  is  of  interest  when 
reporting to Eurostat. 

Active substance area treated includes all multiple applications of a given active substance. It is 
calculated as the number of treatments with this specific substance times the basic area treated. A 
very  simple  example  would  be  that  five  fields  of  the  farm  mentioned  above  have  been  treated 
twice  with  products  containing  the  active  substance  in  question.    The  active  substance  area 
treated with that substance will therefore be twice 50 ha, i.e. 100ha.  

The  original  guidelines  also  described  the  concept  of  application  area  treated,  which  is  the 
product  of  the  basic  area  treated  multiplied  by  the  number  of  different  applications  it  receives, 
irrespective  of  the  number  of  products  in  the  application  (tank-mix)  and  the  concept  of 
formulation area treated, which is the product of the basic area treated multiplied by the number 
of products (formulations) it receives. 

These definitions can be of interest, especially when results are examined from an agronomical 
point  of  view,  for  instance  in  order  to  compare  the  number  of  treatments  involving  fungicides 
applied  to  wheat  in  different  regions.  Since  Eurostat’s  main  objective  is  to  calculate  risk 
indicators, the level of active substances is the only relevant indicator and only the results on the 
basic area treated and on the active substance area treated should be considered. This choice is 
reflected in the standard reporting format, which presents all information at the level of individual 
active substances. 

•  Intensity of treatment 

The intensity of treatment (kg/ha) is reflected by the quantity of active substance applied, divided 
by either the basic area treated or the total cultivated area. It is the result of calculations based on 
the observations in the questionnaires.  

Average  quantity  applied  per  treated  area:  the  total  quantity  of  active  substance  applied 
divided by the basic area treated (kg/ha).  

Methodology for the collection of pesticide usage statistics 

35 

 
 
 
 
 
 
 
 
Average  quantity  applied  per  total  (surveyed)  cultivated  area:  total  quantity  of  active 
substances applied divided by the area (surveyed) cultivated (kg/ha). 

Average  number  of  applications:  for  each  active  substance  this  is  calculated  as  the  ratio 
between  the  active  substance  area  treated  and  the  basic  area  treated.    The  average  number  of 
treatments can also be obtained directly from the individual observations collected in the survey.  

It  should  be  noted  that  it  is  not  possible  to  calculate  the  average  number  of  applications  for 
groups or categories of substances from the average number of applications for each substance.  
In  that  case,  the  basic  area  treated  and  the  grossed-up  area  treated  have  to  be  calculated  at  the 
level of the group or category of substances. 

Methodology for the collection of pesticide usage statistics 

36 

 
 
 
 
 Area of the field: 1 ha 
date 
d/m/y  T1 

1 kg  

d/m/y  T3 

2 kg 

d/m/y  T5 
… 

1 l 

Table 1.9 Field Table F2 

Area of the field: 2 ha 
date 
d/m/y  T2 

2 kg  

d/m/y  T4 

2 kg 

d/m/y  T5 
… 

1 l 

Table 1.10 Farm table  

How to produce results from field observation tables? 

FIRST EXAMPLE 

First, let us take the simple example of a farm growing 3 ha of wheat on two fields of 1 ha and 2 
ha  respectively.    The  example  shows  how  to  calculate  the  basic  reporting  parameters  from  the 
observation tables 8, 9, and 10. 

Table 1.8 Field Table F1  

treatment  Qt (kg or l)  AS (g/kg or l) Area treated (ha) Qt AS (g) 

X (30) 
Y (20) 
Z (10) 
X (30) 
W (30) 
X (60) 

1 
1 
1 
1 
1 
1 

30 
20 
10 
60 
60 
60 

treatment  Qt (kg or l)  AS (g/kg or l) Area treated (ha) Qt AS (g) 

X (30) 
Y (20) 
V (40) 
W (40) 
X (60) 

2 
2 
2 
2 
2 

60 
40 
80 
80 
120 

Total area cultivated with wheat: field F1 + Field F2 = 3 ha 

AS  Qt AS 

(kg) 

Basic area 
treated  (ha) 

Average N° 
applications  

V  80 
W  140 
X  330 
Y  60 
Z 
10 
… 

2 
3 
3 
3 
1 

2/2=1 
3/3=1 
7/3=2.3 
3/3=1 
1/1=1 

Average quantity 
applied per treated area 
(g/ha) 
40 
47 
110 
20 
10 

Average quantity applied 
per total cultivated area 
(g/ha) 
27 
47 
110 
20 
3 

Calculation of the basic areas treated 
Basic area treated with active substance V: 2 ha on F2 = 2 ha 
Basic area treated with active substance W: 1 ha on F1 + 2 ha on F2 = 3 ha 
Basic area treated with active substance X: 1 ha on F1 + 2 ha on F2 = 3 ha 
Basic area treated with active substance Y: 1 ha on F1 + 2 ha on F2 = 3 ha 
Basic area treated with active substance Z: 1 ha on F1 = 1 ha 

Methodology for the collection of pesticide usage statistics 

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
Calculation of the active substance area treated  
Active substance area treated with V: 2 ha on F2 = 2 ha 
Active substance area treated with W: 1 ha on F1 + 2 ha on F2 = 3 ha 
Active substance area treated with X: 3x(1 ha on F1) + 2x(2 ha on F2)= 7 ha 
Active substance area treated with Y: 1 ha on F1 + 2 ha on F2 = 3 ha 
Active substance area treated with Z: 1 ha on F1 = 1 ha 

Calculation of the average number of applications:  
First step: calculate the Active substance area treated which is the sum of the area treated with 
the different products containing the relevant active substance (gross area treated). 
Second step: divide this sum by the basic area treated with this active substance. 
Example for X, which is applied on field F1 in treatments T1, T3, T5 
And on parcel YYY in treatments T2 and T5: 
Average number of applications for X: (1ha+1ha+1ha+2ha+2ha)/3ha or (3x1ha) + (2x2ha)/3ha 
= 2.3 

Methodology for the collection of pesticide usage statistics 

38 

 
 
 
 
SECOND EXAMPLE 

This second example is based on realistic field observation tables, and it illustrates the different 
steps  necessary  to  produce  harmonised  results  that  are  compatible  with  Eurostat’s  standard 
reporting format. 

Step 1: Compile field sheets 

In  this  example,  field  data  compilation  tables  are  based  on  the  model  of  a  booklet  used  by  a 
French farmers’ association to keep records on pesticide and fertiliser applications.  In that case, 
data are very easy to retrieve since each field is covered by one sheet. 

Table 1.11 Models of field tables 

Nam e of the producer

EXAMPLE

Crop

Production year

Field Nam e

IACS N°

2006

Hill

11

Variety
specify if GMO

WHEAT

Charger

Seed treatm ent

Kinto TS 0.15 l/q

Sowing date

28/09/2005

Name of the producer

Total Field Area (ha)

EXAMPLE

6 ha 50

Crop

sowing density 

WHEAT

300

Production year

Area that can be treated (ha)
(without edges, buffer zones, etc)

2006

Variety
specify if GMO

6 ha 50

Nam e of the producer

Production year

Field Nam e

IACS N°

Field Nam e

IACS N°

Date

Total Field Area (ha)
20-Oct-05
Area that can be treated (ha)
20-Oct-05
(without edges, buffer zones, etc)
21-Mar-06

EXAMPLE

Big F ield

Crop

Seed treatment

WHEAT

2006

Product

Village

Matara

Plant protection treatments

Sowing date

4
Variety
specify if GMO
5 ha 40
Seed treatm ent

Apache

area treated (ha)

sowing density 

6 ha 50

Celest 0.2 l/q

First

7
Atlantis WG

5 ha 35

Sowing date

Harvest date

Caphorn

30/07/2006

Previous crop

Celest 0.2 l/q

Beet

25/09/2005

Dose/ha

2.4 l/ha

300

Observations

0.75 l/ha

25/07/2006

Total Field Area (ha)

21-Mar-06

12 ha 20

Vegelux

sowing density 

Area that can be treated (ha)
(without edges, buffer zones, etc)

Date

30-Mar-06

Product

29-Mar-06

12 ha 20

Starane

Plant protection treatments
Bell

Harvest date

area treated (ha)

25/09/2005

6 ha 50

Harvest date

6 ha 50

Previous crop
300

6ha 50

2 ha 50

0.25 kg/ha

Beet

0.8 l/ha

0.4 l/ha

6 ha 50

Dose/ha

26/07/2006

1.5 l/ha
Observations

10-Oct-05

20-Oct-05

20-Oct-05

21-Mar-06

Product

21-Mar-06

Decis expert

29-Mar-06

Matara

30-Mar-06

First

15-Apr-06

Starane

Bell

Opus

15-Apr-06

Decis expert

Opus

Previous crop
5 ha 35

Matara
Plant protection treatments
First

5 ha 35

5 ha 35

Atlantis W G

area treated (ha)

Vegelux

Starane

Bell

Opus

8 ha

12 ha 20

12 ha 20

4 ha

12 ha 20

4 ha

Dose/ha

5 ha 35

0.075 l/ha

5 ha 35

5 ha 35

2.4 l/ha

0.75 l/ha

5 ha 35

5 ha 35

0.4 l/ha

1.5 l/ha

1 l/ha

Date

10-Oct-05

20-Oct-05

20-Oct-05

29-Mar-06

30-Mar-06

15-Apr-06

6 ha 50

0.075 l/ha

Beet

1 l/ha

2.4 l/ha

0.75 l/ha
Observations
0.25 kg/ha

0.8 l/ha

0.4 l/ha

1.5 l/ha

1 l/ha

Methodology for the collection of pesticide usage statistics 

39 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Step 2: Group together all the fields in a farm 

Since the farm is usually the sampling unit, data on the different fields should be gathered for the 
whole farm.  The farm table presented below groups all the information about the farm, but still 
enables the different treatments on the different fields to be identified. 

Table 1.12 Model of farm table 

Name of the producer

EXAMPLE

Production year

CROP

Summary table

Plant protection treatments

Date

10-Oct-05

20-Oct-05

20-Oct-05

21-Mar-06

21-Mar-06

29-Mar-06

30-Mar-06

15-Apr-06

Product

Decis expert

Matara

First

Atlantis WG

Vegelux

Starane

Bell

Opus

2006

WHEAT

Dose/ha

0.075 l/ha

2.4 l/ha

0.75 l/ha

0.25 kg/ha

0.8 l/ha

0.4 l/ha

1.5 l/ha

1 l/ha

Field Name

Variety
specify if GMO

IACS N°

Big Field

Village

Hill

Caphorn

Apache

Charger

4

7

11

Seed treatment

Celest 0.2 l/q

Celest 0.2 l/q

Kinto TS 0.15 l/q

Total Field Area (ha)

5 ha 40

12 ha 20

6 ha 50

Area that can be treated (ha)
(without edges, buffer zones, etc)

5 ha 35

12 ha 20

6 ha 50

Previous crop

Beet

Beet

Beet

Observations

area treated (ha)

area treated (ha)

area treated (ha)

5 ha 35

5 ha 35

5 ha 35

5 ha 35

5 ha 35

5 ha 35

5 ha 35

5 ha 35

8 ha

12 ha 20

12 ha 20

-

-

4 ha

12 ha 20

4 ha

-

6 ha 50

6 ha 50

6 ha 50

6ha 50

2 ha 50

6 ha 50

6 ha 50

Step 3: Convert doses into quantities of products 

Date of harvest

25/07/2006

26/07/2006

30/07/2006

Doses  of  products  are  multiplied  by  the  area  treated  to  obtain  the  quantity  (in  litres  or  kg)  of 
product applied in each treatment. 

Table 1.13 Conversion table doses-quantities 

Date

Product

dose/ha

Field 1
5.35 ha

Field 2 
12.20 ha

Field 3 
6.5 ha

seed
seed
10-Oct-05
20-Oct-05
20-Oct-05
21-Mar-06
21-Mar-06
29-Mar-06
30-Mar-06
15-Apr-06

Celest
Kinto TS
Decis expert
Matara
First
Atlantis W G
Vegelux
Starane
Bell
Opus

0.3 l/ha
0.225 l/ha
0.075 l/ha
2.4 l/ha
0.75 l/ha
0.25 kg/ha
0.8 l/ha
0.4 l/ha
1.5 l/ha
1 l/ha

Area 
treated (ha) 
5.35
-
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35

Quantity 
(kg or l)
1.6 l
-
0.4 l
12.84 l
4.01 l
1.34 kg
4.28 l
2.14 l
8 l
5.35 l

Area 
treated (ha) 
12.2
-
8
12.2
12.2
-
-
4
12.2
4

Quantity 
(kg or l)
3.7 l
-
0.6 l
29.28 l
9.15 l
-
-
1.6 l
97.9 l
4 l

Area 
treated 
(ha) 
-
6.5
-
6.5
6.5
6.5
6.5
2.5
6.5
6.5

Quantity 
(kg or l)
-
1.5 l
-
15.6 l
4.88 l
1.63 kg
5.2 l
1 l
9.75 l
6.5 l

Methodology for the collection of pesticide usage statistics 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Step 4: Convert products into active substances 

Converting  the  quantity  of  products  into  their  active  substance  component  requires  a  product 
database  that  contains  the  composition  of  all  products  authorised  for  use  on  wheat.    Since 
products are authorised at national level and can have different names and compositions from one 
country to another, these databases should be compiled at national level. 

Table 1.14 Product-active substance conversion table  

Active substances

Product
Décis expert  Deltamethrin
Matara
First

Isoproturon
Bromoxynil
Diflufenanil
Ioxynil
Atlantis W G Metsulfuron-methyl

Vegelux
Starane
Bell

Opus
Seed treatment
Celest

Kinto TS

Iodosulfuron
Mefenpyr
Paraffinic oil
Fluroxypyr
Boscalid
Epoxiconazole
Epoxiconazole

Anthraquinone
Fludioxonil
Anthraquinone
Prochloraz
Triticonazole 

Qt

100
500
125
40
75
3
0.6
9
946
200
233
100
125

250
25
333
100
23.3

g/l
g/l
g/l
g/l
g/l
%
%
%
g/l
g/l
g/l
g/l
g/l

g/l
g/l
g/l
g/l
g/l

Step 5: Calculate quantities of active substances and area treated at farm level 

Table 1.15 Active substance calculation table 

Treatment

Product

Active substances

Field 1

Field 2 

Field 3 

Celest

Kinto TS

Anthraquinone
Fludioxonil
Anthraquinone
Prochloraz
Triticonazole 
Decis expert Deltamethrin
Isoproturon
Matara
Bromoxynil
First
Diflufenanil
First
First
Ioxynil
Atlantis WG Metsulfuron-methyl
Atlantis WG Iodosulfuron
Atlantis WG Mefenpyr
Vegelux
Starane
Bell
Bell
Opus

Paraffinic oil
Fluroxypyr
Boscalid
Epoxiconazole
Epoxiconazole

Area 
(ha) 
5.35
5.35
-
-
-
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35
5.35

Quantity 
grams

-
-
-
40

Area 
(ha) 
400 12.2
40 12.2
-
-
-
8
6420 12.2
501 12.2
160 12.2
300 12.2
-
-
-
-
4
1872 12.2
535 12.2
4
669

40
8
121
4048
428

Quantity 
grams

925
92.5
-
-
-
60
14640
1144
366
686
-
-
-
-
320
4270
1220
500

Area 
(ha) 
-
-
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.5
2.5
6.5
6.5
6.5

Quantity 
grams

Total Qt a.s. 
applied (grams)

-
-
500
150
35
0
7800
610
195
366
49
10
147
4919
200
2275
650
812

1325
132.5
500
150
35
100
28860
2255
721
1352
89
18
268
8967
948
8417
2405
1981

Total area treated
17.55
17.55
6.5
6.5
6.5
13.35
24.05
24.05
24.05
24.05
11.85
11.85
11.85
11.85
11.85
24.05
24.05
24.05

N°
1

2

3
4
5
5
5
6
6
6
7
8
9
9
10

Total 10 treatments

58523.5

24.05

Methodology for the collection of pesticide usage statistics 

41 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Step 6: Group data at active substance level  
The  following  table  presents  a  typical  calculation  sheet  allowing  the  main  indicators  to  be 
calculated, based on quantities of active substance applied and areas treated. Such a table can be 
used at farm level or with data grossed up at national, regional or any other aggregation level. In 
this case, farm data from the previous example are presented. 

Table 1.16 Active substance calculation sheet 

Area surveyed (in ha) =

24.1

= sum across all applications of (area treated x grower's rate of application)

Note: as the data are unraised, comparison is made 
against the area surveyed, not the national census area

Active substance

Active substance W eight of
a.s. applied
area treated (ha)

Proportion of total
pesticide active 
substance
area treated

Proportion of 
census area 
treated

Average number
of applications

Basic area
treated (ha)

Anthraquinone
Fludioxonil
Prochloraz
Triticonazole 
Deltamethrin
Isoproturon
Bromoxynil
Diflufenican
Ioxynil
Metsulfuron-methyl
Iodiosulfuron
Mefenpyr
Paraffinic oil
Fluroxypyr
Boscalid
Epoxiconazole
Total

24.05
17.55
6.5
6.5
13.35
24.05
24.05
24.05
24.05
11.85
11.85
11.85
11.85
11.85
24.05
48.1
295.55

1825
132.5
150
35
100
28860
2255
721
1352
89
18
268
8967
948
8417
4386
58523.5

0.08
0.06
0.02
0.02
0.05
0.08
0.08
0.08
0.08
0.04
0.04
0.04
0.04
0.04
0.08
0.16
1.00

1.00
0.73
0.27
0.27
0.55
1.00
1.00
1.00
1.00
0.49
0.49
0.49
0.49
0.49
1.00
1.00
1.00

1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
2.0
12.3

24.05
17.55
6.5
6.5
13.35
24.05
24.05
24.05
24.05
11.85
11.85
11.85
11.85
11.85
24.05
24.05
24.05

= sum of areas 
treated with 
different a.s.

= sum of 
weights 
applied for 
different a.s.

= Bn/B26

= basic area 
treated/B3        
= Gn/B3

= B10/basic area 
treated         
=Bn/Gn

this is the sum of areas treated with epoxiconazole + sum of areas treated with epoxiconazole/boscalid

Methodology for the collection of pesticide usage statistics 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
Step 7: Gross up data at national level and complete the reporting sheet. 
The next table presents a simple example of the standard reporting format that was used to report 
to Eurostat on the results of pilot surveys on pesticide use in 2007.  In this example, although the 
data  have  not  been  grossed  up  at  national  level,  they  relate  to  the  same  farm  example  as  that 
shown above. 

Table 1.17 Active substance standard reporting format 

MAJOR GROUPS &
Categories of products

Chemical Class

Active substances 
(BCPC)

WHEAT 

Common Nomenclature Total Area 
Surveyed 
(ha)

Basic area 
treated  (ha)

Average N° 
applications 
(facultative)

quantity a.s. 
applied (g)

Average 
quantity 
applied per 
treated area 
(g/ha)

Fungicides 
imidazoles and 
imidazoles and 
Other fungicides
Other fungicides
Other fungicides
Herbicides
amides and anilides
urea, uracil or of 
urea, uracil or of 
urea, uracil or of 
Other herbicides
Other herbicides
Other herbicides
Other herbicides

Insecticides 
pyrethroids
Other PPP
Mineral oils
All other plant 
Total Plant Protection 

CONAZOLE FUNGICIDES
CONAZOLE FUNGICIDES
AMIDE FUNGICIDES
PHENYLPYRROLE
AMIDE FUNGICIDES

TRITICONAZOLE
EPOXICONAZOLE
PROCHLORAZ
FLUDIOXONIL
BOSCALID

24.10

ANILIDE HERBICIDES
SULFONYLUREA
SULFONYLUREA 
UREA HERBICIDES
NITRILE HERBICIDES
NITRILE HERBICIDES
PYRIDYLOXYACETIC-ACID  FLUROXYPYR
OTHER HERBICIDES

DIFLUFENICAN
IODOSULFURON
METSULFURON
ISOPROTURON
BROMOXYNIL
IOXYNIL

MEFENPYR

PYRETHROID 

DELTAMETHRIN

MINERAL OIL
OTHER PPP

PETROLEUM OILS
ANTHRAQUINONE

24.05
6.50
24.05
6.50
17.55
24.05
24.05
24.05
11.85
11.85
24.05
24.05
24.05
11.85

11.85
24.05
13.35
24.05
11.85
24.50

2.70
1.00
2.00
1.00
1.00
1.00
3.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

1.00
0.60
1.00
1.50
1.00
1.00

13120.50
35.00
4386
150.00
132.50
8417.00
34511.00
721.00
18.00
89.00
28860.00
2255.00
1352.00
948.00

268.00
100.00
100.00
10792.00
8967.00
1825.00
58523.50

545.55
5.38
182.37
23.08
7.55
349.98
1434.97
29.98
1.52
7.51
1200.00
93.76
56.22
80.00

22.62
4.16
7.49
448.73
756.71
74.49
-

Average 
quantity 
applied per 
total 
cultivated area 
(g/ha)

544.42

1431.99

4.15

447.80

2428.36

Methodology for the collection of pesticide usage statistics 

43 

 
 
 
 
 
 
 
2.  A case study: Pesticide use in the new Member States 

and candidate countries 

/

g
k

a
h

1.60

2.00

1.40

1.00

1.20

1.80

0.80

13 

2008, 

States 

transition 

Between  March  2007  and 
new 
March 
Member 
and 
candidate  countries  to  the 
EU conducted  pilot surveys 
on  the  use  of  pesticides 
under  the  multi-beneficiary 
and 
facility 
cooperation 
statistical 
programmes  for  2005.    For 
most 
this 
experience  was  a  follow-up 
to  a  similar  initiative  in 
2005  and 
the 
collecting  of  representative 
national  data  on  the  use  of  pesticides  on 
wheat.    This  section  presents  the  main 
results  and  methodology  improvements 
resulting  from  this  example  of  fruitful 
international co-operation. 

them 

led 

of 

to 

0.00

0.60

0.20

0.40

PL

HR

LV

LT

HU

EE

SK

SI

TR

RO

BG

Fungicides Herbicides

Insecticides Growth Regulators

 Figure 2.1 Average dose of application of 
plant protection products (kg of active 
substance per hectare of wheat cultivated) 

2.1. Key findings 
Beyond the detailed analysis of the results of the national surveys, this pilot project demonstrated 
the interest of a harmonised methodology.  First of all, it facilitated the exchange of experience 
between the participating countries, but it also allowed easier comparison of the national results 
by calculating similar indicators.  So far, only indicators of the intensity of use of plant protection 
products at the national level have been calculated, but the ultimate aim of pesticide use surveys 
is to monitor the trends of the risk to human health and the environment that are associated with 
pesticides.   

This  section  presents  the  main  results  obtained  on  wheat,  as  this  crop  was  covered  by  most 
participating countries and therefore provides the most  comparable  data  at  this  stage.  The  most 
obvious indicator of the intensity of use is the quantity of active substance applied by hectare of 
cultivated area. Figure 2.1 shows a comparison of the average dose per hectare for the different 
categories  of  plant  protection  products  used  in  wheat  production.    Although  the  indicator  is 
independent of the size of the area cultivated, it should be noted that the results are representative 
of the whole country in most cases, but limited to the region of the survey in the case of Croatia, 
Turkey and to some extent Poland (limited to three provinces. Moreover, no results are presented 
for  Cyprus  and  Malta,  since  wheat  is  not  a  significant  crop  in  Malta  and  in  Cyprus  the  wheat 
cultivation  is  important  especially  in  the  arid  areas  but  the  production  of  the  island  is  not 
significant  in  the  European  wheat  production.  The  results  show  some  similarities  between 
countries within the same region (the Baltic countries, Slovenia and Slovak Republic, etc) albeit 
with  major  variations  between  the  different  countries.      These  variations  reflect  different 
strategies  for  the  protection  of  wheat  which  are  partly  determined  by  the  production  objectives 
and climate, but which can also be explained in part by the main active substances used, which 
are applied at significantly different rates. 

Pesticide use in the new Member States and candidate countries 

45 

 
 
 
 
 
 
 
2.2. A common approach to data collection 
The  surveys  carried  out  under  the  2005  co-operation  programme  were,  for  most  of  the 
participating  countries,  a  continuation  of  pilot  projects  initiated  in  2005;  they  focused  on  the 
collection  of  meaningful  data  on  the  use  of  plant  protection  products  that  enabled  further 
estimates to be made of the risk to the environment and human health. 

The first pilot project in 2005 focused on staff training, setting up institutional cooperation and 
familiarisation  with  the  methodology  for  data  collection  through  small-scale  surveys  on  a 
selection  of  crops.    Eleven  of  the  12  new  Member  States  took  part  in  this  first  project,  the 
exception  being  Cyprus.  In  2007,  all  12  new  Member  States  except  the  Czech  Republic 
participated in the project, and they were joined by Turkey and Croatia. The aim of this second 
initiative was to consolidate the institutional network and construct a sustainable national survey 
system that would meet the requirements of the future Regulation. 

Nine of the already experienced Member States conducted a national survey on the use of plant 
protection  products  on  wheat.    Malta,  which  had  already  carried  out  a  full  national  survey  in 
2005,  reproduced  a  similar  survey  paying  particular  attention  to  the  methodological  aspects.  
Cyprus, Turkey and Croatia, for whom this was the first experience in this field, were asked to set 
up the administrative structure and to experiment with the methodology by organising small-scale 
pilot  surveys  on  a  small  selection  of  crops,  including  wheat.  The  success  of  this  broad  co-
operation  was  ensured  by  the  active  participation  of  all  beneficiary  countries  in  regular 
workshops, and short-term country visits by experts from ASA-ICON Institutes. 

This section describes and compares the results of the national surveys on wheat, focusing on the 
main aspects developed during the project: 

•  The definition of the sampling unit at farm or field level; 

•  The  possibility  of  stratifying  the  samples  according  to  farm  size  and/or  technical 

orientation, and by region; 

•  The production of national estimates; 

•  The definition of the accuracy of the final results. 

The three workshops organised in the course of this project provided an excellent opportunity to 
exchange experience and to develop different aspects of the methodology described in the initial 
"Guidelines  for  the  collection  of  pesticide  usage  statistics  within  agriculture  and  horticulture". 
Specific  definitions  and  concepts,  such  as  sampling  unit  (usually  the  farm)  versus  observation 
unit (the farm or the field parcel), and area treated (sprayed once or several times with a specific 
pesticide)  versus  area  surveyed,  had  to  be  refined.    Progress  in  the  project  and  in  the  different 
country surveys was reported regularly in a bi-monthly newsletter sent to those taking part in the 
project.  

Given  the  technical  complexity  of  the  subject  under  investigation,  close  collaboration  between 
statistical  services  and  plant  protection  services  was  strongly  recommended.  An  administrative 
cooperation network was therefore put in place and worked very well in almost all participating 
countries. The statistical services were generally involved in the design of the sampling approach 
and  in  the  collection,  processing  and  interpretation  of  the  data.  The  role  of  the  plant  protection 
services was principally to identify the plant protection products to be covered by the survey, to 
check the reliability of the information provided by famers and to interpret the final results.  In 
most cases, the preparation of the questionnaire was a joint venture between both services and, 
very  often,  surveyors  either  were  from  –  or  had  been  trained  by  –  the  plant  protection 
administration.  

Pesticide use in the new Member States and candidate countries 

46 

 
 
 
 
 
 
 
The quality of the questionnaire was acknowledged as a key element in the success of the survey 
and the quality of the data.  In some cases, the paper questionnaire was supplemented or replaced 
by an electronic form connected directly to product databases.   

Regarding the sampling approach, as Table 2.1 shows, most countries opted for a simple random 
sampling approach (Neyman type) based on a sample frame derived usually from their national 
farm  registers.  The  sample  size  varied  significantly  from  country  to  country,  ranging  from  250 
units  to  4000  units  (farms  in  all  cases).  This  can  of  course  be  explained  by  the  size  of  the 
countries, but also by the fact that a similar budget was allocated to each country to observe the 
impact of optimal allocation of the resources on the quality of the results. 

Ideally, the observation unit should be as close as possible to the size of the plots on which the 
different plant protection treatments are applied.  This approach allows a better estimation of the 
actual area treated with each single product or substance.  However, as a compromise, which was 
necessitated  by  the  limited  resources  allocated  to  this  project,  there  was  an  almost  equal  split 
between field and farm as the chosen observation unit. It was possible to show that the impact of 
this  choice  on  the  quality  of  the  final  results  depends  very  much  on  the  variability  of  the 
treatments  applied  by  the  farmers  to  the  different  fields  of  a  crop  in  their  farm.  Amongst  the 
different  types  of  treatments,  insecticides  –  which  are  usually  applied  on  limited  areas  –  were 
most  affected  by  the  aggregation  of  different  fields  into  a  single  observation  unit  and  by  the 
resulting loss of information. 

In most cases, the countries opted for the face-to-face interview method, which is considered to 
be most effective, as it leads to high response rates and good quality responses.  However, some 
countries demonstrated the interest of other methods, such as phone or e-mail interviews, which 
in some cases can even improve the quality of the results. 

Almost  all  countries  were  able  to  calculate  national  estimates  for  the  consumption  of  plant 
protection products in the crop covered, except those countries that were carrying out a small-size 
pilot survey for the first time. In most cases, it was also possible to calculate the accuracy of the 
national estimates. 

Table 2.1 Description of the survey methodology for the different participating countries 
Crop

Data collection

Sampling

Frame

Method

Stratification

Total number of 
farms

Surveyed 
farms

Mode 

14230

3793

:

14279

34533

485

Face to face

1739

Postal, Phone

250

904

483

Face to face

Face to face

Face to face

:

590 Face to face, e-mail

8617

400

Face to face

Unit

Farm

Farm

Parcel

Farm

Parcel

Farm

Parcel

National
 estimates

Yes

Yes

No

Yes

Yes

Yes

Yes

Wheat

Wheat

5 crops

Wheat

Wheat

Wheat

15 crops

710176

510

Face to face

Parcel

Estimate

Wheat

National level

9 size classes

No
26 districts, 6 size 
classes
7 size classes
20 regions, 3 size 
classes

6 size classes

3 regions, 5 size 
classes

BG

EE

CY

LV

LT

IACS

FR

FR 03

Optimal precision 
Simple random 
sampling
Selection

FR 01, IACS

Optimal allocation

IACS

HU

FSS 05

MT

FR 01

PL

FR 02

RO

FR 03

SI

FSS 07

SK

FR 07, 
census area sown 
07

Optimal allocation
1-stage stratified 
random
optimal allocation, 
Random sampling
Optimal allocation, 
systematic random 
sampling 
Optimal allocation, 
systematic random 
sampling
Stratified simple 
random sampling

Optimal allocation

National level

1036135

4000

Face to face

Parcel

Yes

Wheat

12 regions

21000

2900

Face to face, postal, 
phone

Parcel

Yes

Wheat

3 regions, 6 size 
classes

3039

1000

Postal

Farm

Yes

Wheat

HR

FR 03

Selection

TR

FR

Stratified random 
sampling

No

No

10708

260 Face to face, postal

Farm

:

260

Face to face

Farm

No

No

Wheat

5 crops

Pesticide use in the new Member States and candidate countries 

47 

 
 
 
 
 
 
 
 
 
 
2.3. Survey results 

 Most of the participating countries covered the use of plant protection products in the production 
of wheat for the harvest year 2006 or 2007.  All countries for which it was the second experience 
of pesticide usage surveys were requested to cover a sample of farms that was large enough to 
allow an extrapolation of the results at national level.  Most of the results presented in this paper 
can  thus  be  regarded  as  being  representative  of  the  whole  country  for  the  production  of  wheat. 
Croatia and Turkey - for whom it was the first pilot survey - were not required to produce such 
estimates.    Owing  to  the  size  of  its  territory  and  limited  budget,  Poland  decided  to  restrict  the 
survey  to  three  provinces.    The  results  presented  for  Poland  concern  the  whole  country,  but 
should be treated with caution, since the three provinces are not fully representative of the whole 
country.   

Beyond the broad distribution in the volumes of plant protection products used on wheat in the 
different  countries,  which  is  directly  linked  to  the  importance  of  the  crop  (Table  2.2),  it  is 
possible to observe variations in the intensity of use for the different categories of products and in 
the list of the main active substances used. 

Intensity  of  use,  expressed  as  the  quantity  of  active  substances  used  per  hectare  of  wheat 
cultivated,  ranges  from  0.27  kg/ha  in  Bulgaria  to  1.84  kg/ha  in  Poland  (Figure  2.1,  Table  2.3).  
Such variations can be explained by the different approaches adopted in respect of the protection 
of  wheat  in  each  country.    These  are  largely  influenced  by  climatic  conditions,  incidence  of 
diseases, differences in economic objectives, and - to some extent - by the price and availability 
of plant protection products on national markets. 

Table 2.2 Main results by country 

Crop

Year

Area

Number

quantity of active substances used (kg)

(ha)

of AS

All

Fungicides

Herbicides

Insecticides

BG

EE

CY

LV

LT

HU

MT

PL

RO

SI

SK

HR

Wheat

Wheat

2007

2006

Wheat*

2006

Wheat

Wheat

Wheat

2006

2006

2006

All crops

2007

Wheat

Wheat

Wheat

Wheat

2006

2007

2007

2006

Wheat*

2007

975153

90842

351

223403

307185

933789

8100

1753401

2017537

32858

326633

38146

55

72

9

61

58

65

85

116

63

61

106

41

260,082

106,194

127,238

26,650

83,025

1,929

229,707

303,572

9,982

0

46,301

82,968

54,956

1,721

95,486

143,267

872,469

559,315

244,062

120,730

116,721

2,958

3,220,925

1,270,443

1,323,038

603,346

243,540

326,957

21,489

8,873

12,407

265,638

95,457

107,843

43,722

11,040

23,402

1,614

208

2,975

3,774

3,987

930

24,011

28,091

174

2,228

2,997

Growth 
Regulators

0

16,043

0

84,946

73,563

64,880

570,153

950

29

59,977

518

2006

Wheat*

TR
* The countries marked have carried out limited-scale surveys on five different crops, including 
wheat.  The data presented for these countries concern wheat only. 

1499

929

946

14

17

0

0

However,  national  averages  give  only  a  very general  view  and  can  cover  different  phenomena, 
such as a variation in the proportion of crops without pesticide treatment or regional variations in 

Pesticide use in the new Member States and candidate countries 

48 

 
 
 
 
 
 
 
 
 
 
the intensity of treatment.  Such detailed analysis would require regionally representative surveys 
for each individual plot, which were not carried out in all countries.  

An  initial  finding,  looking  beyond  the  broad  distribution  of  the  average  dose  for  all  plant 
protection products, is the wide variation in the contribution of the different types of products.  In 
general,  the  highest  volumes  of  fungicides  and  insecticides  are  explained  by  higher  pest  and 
disease pressures and more intensive production patterns, and a necessity to maintain the highest 
potential yields.   

Differences  in  the  use  of  herbicides  are  partly  explained  by  the  importance  of  weed  control  in 
wheat in the different countries, but they were also clearly related to the preparation of the fields 
before sowing and to the replacement of frozen crops in specific cases. 

The use of growth regulators is mainly related to climatic conditions and to the selection of wheat 
varieties that make it necessary to limit the growth of the crop. 

Table 2.3: Average intensity of treatment (kg active substance/ha wheat cultivated)  

Fungicides

Herbicides
Insecticides

Growth Regulators

Total

BG
0.11

0.13
0.03

0.00

0.27

EE
0.11

0.60
0.02

0.18

0.91

HR
0.29

0.61
0.08

0.01

1.15

HU
0.60

0.26
0.08

0.07

0.93

LT
0.27

0.47
0.01

0.24

0.99

LV
0.21

0.43
0.01

0.38

1.03

PL
0.72

0.75
0.01

0.33

1.84

RO
0.12

0.16
0.01

0.00

0.30

SI
0.27

0.38
0.01

0.00

0.65

SK
0.29

0.30
0.01

0.18

0.82

TR
0.00

0.62
0.01

0.00

0.63

2.4. Variations in the plant protection patterns 
Active  substances  are  the  components  of  plant  protection  products  that  mainly  determine  both 
their  biological  activity  and  their  potential  environmental  impact.    All  relevant  analyses  of 
pesticide use should be based on the individual active substances.  Tables 2.4 and 2.5 show two 
different approaches to analyse the results of the pesticide use surveys at the level of the active 
substances.  Table 2.4 presents the top-5 lists of active substances in each category of products 
according  to  the  quantity  used  in  each  country.    Table  2.5  looks  at  the  main  substances  used 
according to the area of wheat treated. Both approaches are important as a way of analysing the 
different plant protection patterns, since the quantity of an active substance used depends on its 
frequency of use and on the dosage applied.   

'sulfonylurea'  share  top  place  on  the  list  for  herbicides. 

A  comparison  of  the  top-5  lists  for  the  different  categories  of  active  substances  reveals  some 
similarities in the choice of products. A predominance of substances from the chemical classes of 
the  'conazoles',  'morpholines'  and  'benzimidazoles'  is  observed  for  fungicides.  The  'phenoxy', 
'urea'  and 
'Pyrethroid'  and 
'organophosphorus' insecticides are the most commonly used, though usually sprayed on limited 
areas depending on the presence of pests. Growth regulation treatments - when they are used - are 
based  on  a  very  limited  list  of  substances,  with  a  general  preference  for  'chlormequat'.  Beyond 
these  common  patterns,  however,  the  overall  picture  is  largely  dominated  by  the  range  of 
different  products,  both  in  quantitative  terms  and  as  regards  the  proportion  of  crop  treated;  the 
Baltic countries are the notable exception, as they all exhibit very similar profiles.  

Since no standard approach had been agreed to express the accuracy of the results, it is difficult to 
compare  the  quality  of  the  different  national  estimates.  The  accuracy  of  the  national  estimates 
depends, of course, on both the size of the sample and on the correct estimation of the total area 
cultivated with the respective crop.  The quality of the farm registers that usually constitute the 
sampling  reference  is  therefore  very  important.    Another  aspect  that  influences  the  accuracy  of 

Pesticide use in the new Member States and candidate countries 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
the results as regards quantities of active substances used is their frequency of use; extrapolations 
are much more precise for the most widely used substances. Two countries have calculated the 
error on the extrapolated dosage of active substances per hectare.  Poland estimated the relative 
standard  error  for  the  average  pesticide  dosage  per  province  at  between  4.3  and  6.3%.    In 
Bulgaria,  the  maximum  relative  standard  error  on  average  dosages  calculated,  without 
considering  the  stratification  of  the  sample,  ranged  from  18%  for  fungicides  to  50%  for 
insecticides, with an overall error of 15%.  Estonia's calculations of the relative standard error on 
the  quantity  of  each  active  substance  showed  that  it  varies  considerably  from  one  substance  to 
another and can be as high as 40% for substances used in small quantities.  Estonia estimated the 
percentage  error  at  0.3%  for  fungicides,  0.8%  for  herbicides,  1.1%  for  insecticides,  0.4  %  for 
growth regulators, and 0.58% overall. Lithuania estimated the relative error on the quantities and 
areas treated with the main active substances used: the error for both quantities and areas is below 
1%.    In  Slovenia,  the  relative  error  on  the  area  treated  with  individual  active  substances  varied 
from 2.6% to 99% depending on the number of plots treated.  80% of the estimated areas treated 
with herbicides had a relative error of less than 5%; 60% had a less than 8% error for fungicides, 
and almost all areas treated with insecticides had an error rate of under 13%. Similar values were 
found in respect of the quantities applied. 

2.5. Main conclusions 
It is of course difficult to draw major conclusions from a survey that is limited to a single crop in 
13  countries.    However,  this  project  has  demonstrated  that,  with  a  harmonised  approach,  it  is 
possible to collect comparable data that enable a comparison of plant protection patterns between 
countries.  Such surveys – if repeated over time and on different crops - would build up a broader 
picture of the overall use of plant protection products.   

This study also indicates the limited usefulness of comparing very general indicators, such as the 
quantity  of  product  used  or  the  average  quantity  of  active  substance  applied  per  hectare.  Such 
comparisons  are  of  limited  value  and  can  be  misleading  if  details  of  active  substances  are  not 
analysed.  One method of comparing countries with slightly different plant protection patterns or 
of keeping track of changing patterns over time is to calculate risk indicators. 

As  indicated  in  the  results  of  the  HAIR  project  (HAIR,  2007),  the  calculation  of  pesticide  risk 
indicators involves combining data on toxicity, which mainly depends on the intrinsic properties 
of individual active substances, with data on the exposure to these substances or their residues for 
humans or different parts of the environment.  Detailed data on the use of pesticides constitute an 
essential  input  for  the  calculation  of  such  indicators  in  combination  with  data  sets  on  the 
properties of active substances and agro-climatic information.   

Collecting  meaningful  data  on  the  use  of  plant  protection  products  so  as  to  allow  further 
estimation  of  the  risk  for  the  environment  and  human  health  was  the  main  objective  of  these 
surveys.  It  can  definitely  be  concluded  from  this  project  that  such  data  can  be  collected  with 
sufficient accuracy by means of sample surveys. 

As  a  follow-up  to  this  project,  Eurostat  -  together  with  a  consultant,  ARCADIS  Belgium  -  is 
currently testing the HAIR programme with a selection of data collected under this project. The 
results of these calculations will be the subject of a future publication. 

While  this  project  clearly  highlighted  the  value  of  using  a  harmonised  methodology  to  collect 
data, it also showed the need to better harmonise the quality criteria used to estimate the accuracy 
of the national estimates and to investigate further the impact of different stratification methods 
on the quality of the final results. 

Pesticide use in the new Member States and candidate countries 

50 

 
 
 
 
 
 
 
 
 
Table 2.4 Top-5 list of active substances used on wheat as a percentage of the total quantity of active substances used in each group. 
%

BG

HR

EE

%

Fungicides

Herbicides

Insecticides

Growth regulators

Fungicides

Herbicides

Insecticides

Growth regulators

CARBOXIN
THIRAM
TEBUCONAZOLE
TRIBASIC COPPER SULPHATE
SPIROXAMINE
2,4-D
ISOPROTURON
DICAMBA
METSULFURON
FENOXAPROP-P
CHLORPYRIFOS
CARBOFURAN
DIMETHOATE
CYPERMETHRIN
ACETAMIPRID

LV
FENPROPIMORPH
TEBUCONAZOLE
THIRAM
SPIROXAMINE
EPOXICONAZOLE
GLYPHOSATE
MCPA
2,4-D
DICAMBA
AMIDOSULFURON
DIMETHOATE
ALPHA-CYPERMETHRIN
LAMBDA-CYHALOTHRIN
ZETA-CYPERMETHRIN
THIACLOPRID
CHLORMEQUAT
ETHEPHON
MEPIQUAT
TRINEXAPAC-ETHYL

31.70 TEBUCONAZOLE
31.61 FENPROPIMORPH
5.65 EPOXICONAZOLE
5.06 SPIROXAMINE
4.39 PROPICONAZOLE

77.49 MCPA

6.11 GLYPHOSATE
5.47 METSULFURON
2.41 2,4-D
2.22 DICAMBA

54.66 DIMETHOATE
23.82 CYPERMETHRIN
11.78 ALPHA-CYPERMETHRIN
7.50 LAMBDA-CYHALOTHRIN
0.90 TAU-FLUVALINATE

CHLORMEQUAT
ETHEPHON
TRINEXAPAC-ETHYL
MEPIQUAT

%

PL

17.62 SULFUR
12.50 CARBENDAZIM
9.83 THIRAM
8.41 FENPROPIMORPH
8.06 FENPROPIDIN
57.78 ISOPROTURON
30.65 MCPA
4.46 2,4-D
1.95 CHLORTOLURON
0.97 PENDIMETHALIN
94.12 DIMETHOATE
3.20 PIRIMICARB
1.24 ALPHA-CYPERMETHRIN
0.66 CHLORPYRIFOS
0.28 CYPERMETHRIN
90.27 CHLORMEQUAT
6.28 TRINEXAPAC-ETHYL
2.84 ETHEPHON
0.61

30.07 PROPICONAZOLE
16.76 CARBENDAZIM
12.19 EPOXICONAZOLE
7.46 TEBUCONAZOLE
4.30 AZOXYSTROBIN
50.61 CHLORTOLURON
31.69 ISOPROTURON
6.70 2,4-D
4.75 DICAMBA
3.03 DIFLUFENICAN
86.57 CHLORPYRIFOS
7.33 DIMETHOATE
4.30 OXYDEMETON-METHYL
0.66 CYPERMETHRIN
0.58 LAMBDA-CYHALOTHRIN
97.58 CHLORMEQUAT
1.74 TRINEXAPAC-ETHYL
0.59
0.08
%

RO
23.76 CARBENDAZIM
16.13 PROCHLORAZ
16.07 DIFENOCONAZOLE
7.31 THIRAM
4.63 THIOPHANATE-METHYL
36.65 2,4-D
17.64 TRIBENURON
13.65 BROMOXYNIL
8.04 Not identified
6.58 CHLORSULFURON
70.30 IMIDACLOPRID
12.49 Not identified
7.12 CYPERMETHRIN
6.14 DIMETHOATE
2.35 THIAMETHOXAM
97.35 CHLORMEQUAT
1.81
0.84

HU

%
29.23 EPOXICONAZOLE
21.68 SULFUR
19.34 FENPROPIMORPH

9.65 KRESOXIM-METHYL
8.02 CARBENDAZIM

79.88 MCPA

6.74 DICHLORPROP-P
5.83 2,4-D
4.04 TRIBENURON
1.35 MECOPROP-P

46.83 LAMBDA-CYHALOTHRIN
23.83 CYPERMETHRIN
16.86 ZETA-CYPERMETHRIN
4.68 OXYDEMETON-METHYL
2.69 ESFENVALERATE

62.52 CHLORMEQUAT
37.48 TRINEXAPAC-ETHYL

%

SI
60.15 FENPROPIDIN
7.52 PROPICONAZOLE
7.39 THIRAM
6.20 FENPROPIMORPH
6.12 AZOXYSTROBIN
81.43 CHLORTOLURON
7.84 ISOPROTURON
3.25 BENTAZONE
2.46 DIFLUFENICAN
1.54 2,4-D
40.58 LAMBDA-CYHALOTHRIN
14.96 ALPHA-CYPERMETHRIN
14.65 BETA-CYFLUTHRIN
13.96 DELTAMETHRIN
8.97 CHLORPYRIFOS-METHYL

100.00 TRINEXAPAC-ETHYL

CHLORMEQUAT

LT

%
28.54 THIRAM
17.69 TEBUCONAZOLE
10.34 FENPROPIMORPH
7.12 PROPICONAZOLE
6.32 SPIROXAMINE
22.35 GLYPHOSATE
22.24 MCPA
19.51 DICAMBA
12.34 2,4-D
6.19 TRITOSULFURON
21.68 ZETA-CYPERMETHRIN
19.19 ALPHA-CYPERMETHRIN
19.19 DELTAMETHRIN
16.79 THIACLOPRID
10.36 LAMBDA-CYHALOTHRIN
97.28 CHLORMEQUAT
2.72 ETHEPHON
MEPIQUAT
TRINEXAPAC-ETHYL

SK

THIOPHANATE-METHYL

%
19.51 TEBUCONAZOLE
16.31 CARBENDAZIM
"c"
10.07 PROCHLORAZ
7.01 PROPICONAZOLE
57.90 MCPA
16.25 2,4-D
5.73 GLYPHOSATE
2.80 DICAMBA
2.59 ISOPROTURON
32.33 CHLORPYRIFOS
22.73 CYPERMETHRIN
13.05 DIMETHOATE
5.56 LAMBDA-CYHALOTHRIN
2.42 ALPHA-CYPERMETHRIN
58.60 CHLORMEQUAT
41.40 ETHEPHON

TRINEXAPAC-ETHYL

%
40.52
16.80
7.53
7.22
5.46
49.33
25.90
8.50
7.22
3.80
80.39
9.43
2.49
2.38
2.31
76.80
22.19
0.68
0.32
%
10.84
10.59
9.83
9.12
8.68
28.68
14.97
10.69
10.11
8.99
41.74
16.77
14.36
10.72
7.14
96.92
2.62
0.46

Pesticide use in the new Member States and candidate countries 

51 

 
 
 
 
 
 
Table 2.5 Top-5 list of active substances used on wheat as a percentage of the total area of the crop treated. 
EE

BG

HR

%

%

Fungicides

Herbicides

Insecticides

Growth regulators

Fungicides

Herbicides

Insecticides

Growth regulators

TEBUCONAZOLE
CARBOXIN
THIRAM
DIFENOCONAZOLE
DINICONAZOLE
METSULFURON
TRIBENURON
Not identified
FLORASULAM
2,4-D
CYPERMETHRIN
CHLORPYRIFOS
FIPRONIL
DIMETHOATE
ACETAMIPRID

LV

FLUDIOXONIL
TEBUCONAZOLE
CYPROCONAZOLE
PROTIOCONAZOLE
FLUOXASTROBINE
TRIBENURON
FLORASULAM
AMIDOSULFURON
GLYPHOSATE
IODOSULFURON
ALPHA-CYPERMETHRIN
DIMETHOATE
LAMBDA-CYHALOTHRIN
ZETA-CYPERMETHRIN
BETA-CYFLUTHRIN
CHLORMEQUAT
ETHEPHON
MEPIQUAT
TRINEXAPAC-ETHYL

35.62 TEBUCONAZOLE
22.39 EPOXICONAZOLE
22.17 FENPROPIMORPH
21.99 CYPROCONAZOLE
18.92 FLUDIOXONIL
43.88 MCPA
20.57 AMIDOSULFURON
19.93 IODOSULFURON
19.93 DICAMBA
18.01 FLORASULAM
11.53 DIMETHOATE

8.60 ALPHA-CYPERMETHRIN
2.64 CYPERMETHRIN
2.20 LAMBDA-CYHALOTHRIN
1.54

CHLORMEQUAT
TRINEXAPAC-ETHYL
ETHEPHON
MEPIQUAT

%

PL
34.65 CARBENDAZIM
29.05 THIRAM
28.98 PROPICONAZOLE
26.26 EPOXICONAZOLE
25.52 TEBUCONAZOLE
29.61 ISOPROTURON
28.86 2,4-D
22.85 CHLORSULFURON
20.24 IODOSULFURON
18.16 DICAMBA

4.04 ALPHA-CYPERMETHRIN
3.47 DIMETHOATE
1.99 PIRIMICARB
0.76 LAMBDA-CYHALOTHRIN
0.53 CYPERMETHRIN
34.88 CHLORMEQUAT
10.23 TRINEXAPAC-ETHYL

4.44 ETHEPHON
2.54

18.82 PROPICONAZOLE
10.70 EPOXICONAZOLE
10.12 CYPROCONAZOLE
10.02 CARBENDAZIM

7.75 TEBUCONAZOLE
33.43 TRIASULFURON
18.26 CHLORTOLURON
17.75 DICAMBA
17.39 AMIDOSULFURON
16.38 2,4-D

5.74 LAMBDA-CYHALOTHRIN
4.67 ALPHA-CYPERMETHRIN
2.61 DELTAMETHRIN
1.61 CYPERMETHRIN
0.49 CHLORPYRIFOS

30.42 TRINEXAPAC-ETHYL

1.62 CHLORMEQUAT
1.10
0.05
%

RO

69.23 TEBUCONAZOLE
57.21 CARBENDAZIM
25.79 DIFENOCONAZOLE
21.25 DINICONAZOLE
19.82 THIOPHANATE-METHYL
29.01 2,4-D
25.40 TRIBENURON
24.37 CHLORSULFURON
21.33 METSULFURON
19.98 Not identified

8.34 CYPERMETHRIN
3.79 IMIDACLOPRID
1.08 Not identified
0.97 THIAMETHOXAM
0.90 LAMBDA-CYHALOTHRIN

31.61 CHLORMEQUAT

8.04
1.31

HU
%
48.83 CARBENDAZIM
43.12 CYPROCONAZOLE
42.81 PROPICONAZOLE
31.27 EPOXICONAZOLE
23.34 TEBUCONAZOLE
49.36 TRIBENURON
41.60 AMIDOSULFURON
14.72 2,4-D
10.60 FLORASULAM

9.57 TRIASULFURON

33.35 LAMBDA-CYHALOTHRIN
11.36 ESFENVALERATE
10.52 CYPERMETHRIN

8.84 ZETA-CYPERMETHRIN
8.84 DELTAMETHRIN
4.98 CHLORMEQUAT
0.58 TRINEXAPAC-ETHYL

SI

%
47.62 PROPICONAZOLE
22.83 TEBUCONAZOLE
17.43 CYPROCONAZOLE
14.68 THIRAM
10.26 FENPROPIDIN
25.28 IODOSULFURON
13.88 AMIDOSULFURON
6.46 TRIASULFURON
4.55 CHLORTOLURON
3.87 ISOPROTURON

14.00 LAMBDA-CYHALOTHRIN
6.39 ALPHA-CYPERMETHRIN
3.33 BETA-CYFLUTHRIN
1.30 DELTAMETHRIN
1.15 PIRIMIPHOS-METHYL
0.07 TRINEXAPAC-ETHYL
CHLORMEQUAT

%

LT

100.00 TEBUCONAZOLE
100.00 THIRAM
100.00 CYPROCONAZOLE

98.46 FLUDIOXONIL
98.46 PROPICONAZOLE
100.00 AMIDOSULFURON
96.80 IODOSULFURON
94.34 DICAMBA
90.84 TRITOSULFURON
89.40 MCPA
96.50 ZETA-CYPERMETHRIN
68.96 ALPHA-CYPERMETHRIN
65.22 LAMBDA-CYHALOTHRIN
65.22 DELTAMETHRIN
45.47 BETA-CYFLUTHRIN
85.81 CHLORMEQUAT
21.35 ETHEPHON

TRINEXAPAC-ETHYL
MEPIQUAT

SK

CARBENDAZIM

%
30.87 CYPROCONAZOLE
29.60 Not identified
28.97 PROPICONAZOLE
"c"
19.98 TEBUCONAZOLE
51.73 DICAMBA
38.96 FLORASULAM
21.43 2,4-D
18.32 IODOSULFURON
7.85 TRITOSULFURON
22.56 LAMBDA-CYHALOTHRIN
9.94 CYPERMETHRIN
6.63 ZETA-CYPERMETHRIN
4.37 ALPHA-CYPERMETHRIN
1.33 DELTAMETHRIN
0.64 CHLORMEQUAT
0.02 ETHEPHON

TRINEXAPAC-ETHYL

%
39.71
20.99
20.40
18.86
14.77
32.55
31.96
23.06
18.48
17.37
11.37
6.65
4.03
3.89
2.35
31.69
13.26
1.37
0.91
%
21.80
20.92
20.53
19.58
19.57
27.61
23.34
22.11
20.92
18.37
11.13
4.45
3.90
3.33
1.85
16.71
1.09
0.82

Pesticide use in the new Member States and candidate countries 

52 

 
 
 
3. 

Indicators on pesticide use and risk 

The past few decades have seen the development of a broad diversity of pesticide indicators.  
A review of the literature (VAN BOL, 2002) listed more that a hundred pesticide indicators.  
Three types of indicators have been identified according to their complexity and the type of 
data that were taken into account: 
•  Pesticide use indicators (PUI): total amounts or total number of sprayings on a given area; 
•  Pesticide risk indicators: risk induced for one particular category of risk.  By definition, a 

PRI is a combination of hazard and exposure parameters; 

•  Pesticide  impact  assessment  systems  (PIAS):  evaluation  of  the  impact  of  a  set  of  risks 
(involves not only toxicology but also ascribing relative importance to different categories 
of  non-target  organisms,  which  is  outside  the  realm  of  objective  science  and  enters  the 
realm of value judgement). 

PUI are very widely used and give an indication of the intensity of chemical crop protection.  
They are usually simple indicators, such as the volume of pesticide applied per hectare, and 
do not incorporate any measure of risk (toxicity). 

In order to better control pesticide use, several countries have adopted a number of indicators 
based on the comparison between the quantities of pesticides applied and officially authorised 
doses.    For  instance,  the  Danish  authorities  have  developed  the  concept  of  Frequency  of 
Application,  Germany  is  using  a  comparable  Application  Index,  and  various  countries  are 
using a similar Pesticide Treatment Frequency, Treatment Frequency or Treatment Frequency 
Index. Such indicators are regarded as indicators of the spraying intensity as well as overall 
indicators of the environmental impact of pesticides. It is assumed that there is a link between 
dose rates (and hence the effects on target organisms) and the effects on non-target organisms; 
however, this is not always the case. 

Pesticide  risk  indicators  are  more  complex  and  they  typically  integrate  the  dimensions  of 
hazard (toxicity) and exposure, which are the two components of risk.  Confronted with the 
broad  diversity  of  pesticide  risk  indicators,  the  OECD  and  the  European  Commission  have 
supported  various  programmes  for  the  development  of  harmonised  indicators.    One  of  the 
main recent initiatives in this field was the HAIR project, financed under the EU’s 6th research 
framework programme to develop a set of HArmonised environmental Indicators for pesticide 
Risk. 

HAIR developed risk indicators in five areas: 
•  Aquatic environment; 
•  Groundwater; 
•  Terrestrial environment; 
•  Consumers; 
•  Operator / Bystander. 

In these five areas, risk is expressed via "Exposure to Toxicity Ratios" (ETRs) which compare 
predictable  concentrations  or  exposure  to  the  measure  of  the  effects  (e.g.  LD50,  LC50, 
NOEC).  HAIR  models  work  with  relational  databases  which  include  pesticide  usage  data, 
compound  properties,  GIS  information  (soil,  climate)  and  different  transport  and  exposure 
models for the different compartments considered.  

Definitions and classification 

53 

 
 
 
 
 
 
 
 
 
HAIR  also  integrates  aggregation  tools.    Various  levels  of  aggregation  are  possible  in  the 
different  dimensions  of  the  indicators:  the  component  (e.g.  terrestrial  environment),  active 
substances, space, time, and hazard types or events. For instance, an ETR can be calculated 
for a group of terrestrial organisms in different regions during a given year. 

Since aggregations cannot be based on sound scientific justification, they should reflect policy 
orientations  (e.g.  interest  in  water  protection,  consumer  health,  etc.)  and  allow  sufficient 
disaggregation  to  be  able  to  identify  and  interpret  the  specific  pesticide  use  patterns  that 
influence  the  risk  level.    One  way  of  aggregating  the  indicators  is  by  comparing  individual 
results  to  the  (regulatory)  threshold  values  for  the  risk  considered.  An  example  is  the 
evolution  of  aquatic  risk  adopted  by  the  UK  pesticide  forum,  with  risk  being  expressed  in 
terms of the area treated where risk exceeds different levels of acceptability. 

Figure 3.1 Model of presentation of the aquatic risk indicator adopted by the UK 
Pesticides Forum. 

Definitions and classification 

54 

 
 
 
 
 
 
 
 
 
Annexes 

1. 

Definitions and classification 

Pesticide is a generic term covering: 
(a)   plant protection products as defined in Council Directive 91/414/EEC of 15 July 1991 
concerning the placing of plant protection products on the market, and the new proposal 
for a Regulation COM(2006) 388 final; 

(b)   biocidal  products  (also  called  "biocides")  as  defined  in  Directive  98/8/EC  of  the 
European Parliament and of the Council of 16 February 1998 concerning the placing of 
biocidal products on the market. 

Classification of plant protection products 
For  the  purposes  of  these  guidelines,  and  to  allow  meaningful  comparisons  of  usage  data 
between  countries,  plant  protection  products  should  be  classified  into  the  major  groups  of 
fungicides,  herbicides,  insecticides,  molluscicides,  growth  regulators  and  “other  pesticides”, 
within which usage of certain chemicals is specifically defined. Each group is outlined below. 

General classification 
• Fungicides 
Include all chemicals used as fungicides, including the fungicidal elements of seed treatments, 
but excluding any non-fungicidal seed treatments. Because of the very large amounts applied 
to  some  commodities  in  some  countries  that may  distort  inter-country  comparisons,  sulphur 
should be reported individually within the fungicide category. 
• Herbicides 
Include  all  chemicals  used  as  herbicides,  including  herbicides  used  for  the  purposes  of 
desiccation  (e.g.  diquat  &  glufosinate-ammonium).  Exclude  sulphuric  acid,  however,  which 
may form a major part of all herbicide usage in some countries (approx. 13,000 tonnes or 
57% of all herbicides by weight applied in Great Britain in 1996).  
• Insecticides 
Include  all  chemicals  used  as  insecticides,  including  the  insecticidal  elements  of  seed 
treatments,  but  excluding  any  non-insecticidal  seed  treatments.  Include  all  nematicides, 
together  with  all  acaricides  such  as  fenbutatin  oxide,  cyhexatin,  dicofol  &  tetradifon,  not 
recognised as having any insecticidal activity. Exclude molluscicides, which will be reported 
separately in their own section. 
• Molluscicides 
Include  all  chemicals  used  as  molluscicides,  including  the  molluscicidal  elements  of  seed 
treatments, but excluding any non-molluscicidal seed treatments. 
• Growth regulators 
Include  all  chemicals  used  as  growth  regulators,  including  carbaryl  where  this  was 
specifically used for fruit thinning rather than insect control. 
• Other pesticides 
Include  all  chemicals  not  included  in  the  above  five  categories,  but  are  registered  as  plant 
protection products in one or several Member States. 

Definitions and classification 

55 

 
 
 
 
 
 
Other  plant  protection  products  will  include  soil  sterilants  such  as  dazomet,  metam-sodium, 
chloropicrin and 1,3-dichloropropene and chemicals such as dichlorophen and formaldehyde, 
together with the rodenticides and talpicides. 

Harmonised list of active substances 
In  order  to  harmonise  the  presentation  of  data  to  be  reported  to  Eurostat  and  to  allow 
comparable  aggregations  by  categories  of  products  or  groups  of  substances,  a  harmonised 
classification  of  active  substances  has  been  included  in  the  proposal  for  a  Regulation 
concerning statistics on plant protection products.  Substances are identified according to the 
nomenclature used in the Pesticide Manual published by the British Crop Protection Council 
(BCPC,  2000)  and  by  reference  numbers  published  by  the  Chemical  Abstracts  Service 
Registry  Numbers  (CAS)  and  the  Collaborative  International  Pesticides  Analytical  Council 
CIPAC).  The chemical classes and categories of substances have been agreed by the Eurostat 
pesticide  expert  task  force  on  the  basis  of  the  classification  proposed  by  the  Alan  Wood 
Compendium of Pesticide Common Names (http://www.alanwood.net/pesticides).  

MAJOR GROUPS 
& 
Categories of 
products 

Group 

Chemical Class 

Active substances (BCPC) 

CAS 

CIPAC 

Common Nomenclature 

Fungicides and 
Bactericides: total. 
Fungicides and 
Bactericides: total 
excluding sulphur. 
Inorganic 
fungicides 

F0 

F0.1 

F1 

  F1.1 
  F1.1 
  F1.1 
  F1.1 
  F1.1 
  F1.1 
  F1.1 
  F1.2 

  F1.3 

COPPER COMPOUNDS 

INORGANIC SULFUR 
OTHER INORGANIC 
FUNGICIDES 

ALL COPPER COMPOUNDS 
BORDEAUX MIXTURE 
COPPER HYDROXIDE 
COPPER OXYCHLORIDE 
TRIBASIC COPPER SULPHATE 
COPPER (I) OXIDE 
OTHER COPPER SALTS 
SULFUR 

ZZ-OTHER-INORG-FUNGICIDES 

8011-63-0 
20427-59-2 
1332-40-7 
1333-22-8 
1319-39-1 

7704-34-9 

Fungicides based 
on carbamates 
and 
dithiocarbamates 

Fungicides based 
on benzimidazoles 

Fungicides based 
on imidazoles and 
triazoles 

F2 

F2.1 
F2.2 
F2.2 

F2.3 

F2.3 
F2.3 
F2.3 
F2.3 
F2.3 

F3 

F3.1 
F3.1 
F3.1 
F3.1 

F4 

F4.1 
F4.1 
F4.1 
F4.1 

CARBANILATE FUNGICIDES 
CARBAMATE FUNGICIDES 

DIETHOFENCARB 
PROPAMOCARB 
IPROVALICARB 

DITHIOCARBAMATE 
FUNGICIDES 

MANCOZEB 

MANEB 
METIRAM 
PROPINEB 
THIRAM 
ZIRAM 

BENZIMIDAZOLE FUNGICIDES 
BENZIMIDAZOLE FUNGICIDES 
BENZIMIDAZOLE FUNGICIDES 
BENZIMIDAZOLE FUNGICIDES 

CARBENDAZIM 
FUBERIDAZOLE 
THIABENDAZOLE 
THIOPHANATE-METHYL 

CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 

BITERTANOL 
BROMUCONAZOLE 
CYPROCONAZOLE 
DIFENOCONAZOLE 

87130-20-9 
24579-73-5 
140923-17-7 

8018-01-7 

12427-38-2 
9006-42-2 
12071-83-9 
137-26-8 
137-30-4 

10605-21-7 
3878-19-1 
148-79-8 
23564-05-8 

55179-31-2 
116255-48-2 
94361-06-5 
119446-68-3 

Definitions and classification 

56 

44 
44 
44 
44 
44 
44 
44 
18 

513 
399 
620 

34 

61 
478 
177 
24 
31 

263 
525 
323 
262 

386 
680 
600 
687 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
 
 
 
  
  
  
  
 
 
 
 
 
 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.1 
F4.2 
F4.2 
F4.2 

F5 

F5.1 
F5.1 
F5.1 

Fungicides based 
on morpholines 

Other fungicides  F6 

F6.1 

F6.1 
F6.1 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.2 
F6.3 
F6.3 

F6.4 

F6.4 
F6.4 
F6.5 
F6.5 
F6.6 
F6.6 
F6.7 
F6.8 

F6.9 

F6.9 
F6.10 
F6.10 
F6.10 
F6.11 
F6.12 
F6.12 
F6.13 
F6.13 
F6.13 
F6.13 
F6.13 
F6.14 

CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
CONAZOLE FUNGICIDES 
IMIDAZOLE FUNGICIDES 
IMIDAZOLE FUNGICIDES 
IMIDAZOLE FUNGICIDES 

DINICONAZOLE 
EPOXICONAZOLE 
ETRIDIAZOLE 
FENBUCONAZOLE 
FLUQUINCONAZOLE 
FLUSILAZOLE 
FLUTRIAFOL 
HEXACONAZOLE 
IMAZALIL (ENILCONAZOLE) 
METCONAZOLE 
MYCLOBUTANIL 
PENCONAZOLE 
PROPICONAZOLE 
TEBUCONAZOLE 
TETRACONAZOLE 
TRIADIMENOL 
TRICYCLAZOLE 
TRIFLUMIZOLE 
TRITICONAZOLE 
CYAZOFAMIDE 
FENAMIDONE 
TRIAZOXIDE 

MORPHOLINE FUNGICIDES 
MORPHOLINE FUNGICIDES 
MORPHOLINE FUNGICIDES 

DIMETHOMORPH 
DODEMORPH 
FENPROPIMORPH 

ALIPHATIC NITROGEN 
FUNGICIDES 

AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
AMIDE FUNGICIDES 
ANILIDE FUNGICIDES 
ANILIDE FUNGICIDES 
ANTIBIOTIC FUNGICIDES-
BACTERICIDES 

CYMOXANIL 

DODINE 
GUAZATINE 
BENALAXYL 
BOSCALID 
FLUTOLANIL 
MEPRONIL 
METALAXYL 
METALAXYL-M 
PROCHLORAZ 
SILTHIOFAM 
TOLYLFLUANID 
ZOXAMIDE 
CARBOXIN 
FENHEXAMID 

KASUGAMYCIN 

POLYOXINS 
STREPTOMYCIN 
CHLOROTHALONIL 
DICLORAN 
IPRODIONE 
PROCYMIDONE 
FLUAZINAM 

AROMATIC FUNGICIDES 
AROMATIC FUNGICIDES 
DICARBOXIMIDE FUNGICIDES 
DICARBOXIMIDE FUNGICIDES 
DINITROANILINE FUNGICIDES 
DINITROPHENOL FUNGICIDES   DINOCAP 
ORGANOPHOSPHORUS 
FUNGICIDES 

FOSETYL 

TOLCLOFOS-METHYL 
HYMEXAZOL 
OXAZOLE FUNGICIDES 
FAMOXADONE 
OXAZOLE FUNGICIDES 
OXAZOLE FUNGICIDES 
VINCLOZOLIN 
PHENYLPYRROLE FUNGICIDES  FLUDIOXONIL 
PHTHALIMIDE FUNGICIDES 
PHTHALIC ACID FUNGICIDES 
PYRIMIDINE FUNGICIDES 
PYRIMIDINE FUNGICIDES 
PYRIMIDINE FUNGICIDES 
PYRIMIDINE FUNGICIDES 
PYRIMIDINE FUNGICIDES 
QUINOLINE FUNGICIDES 

CAPTAN 
FOLPET 
BUPIRIMATE 
CYPRODINIL 
FENARIMOL 
MEPANIPYRIM 
PYRIMETHANIL 
QUINOXYFEN 
8-HYDROXYQUINOLINE 
SULFATE 
DITHIANON 

83657-24-3 
106325-08-0 
2593-15-9 
114369-43-6 
136426-54-5 
85509-19-9 
76674-21-0 
79983-71-4 
58594-72-2 
125116-23-6 
88671-89-0 
66246-88-6 
60207-90-1 
107534-96-3 
112281-77-3 
55219-65-3 
41814-78-2 
99387-89-0 
131983-72-7 
120116-88-3 
161326-34-7 
72459-58-6 

110488-70-5 
1593-77-7 
67564-91-4 

57966-95-7 

2439-10-3 
108173-90-6 
71626-11-4 
188425-85-6 
66332-96-5 
55814-41-0 
57837-19-1 
70630-17-0 
67747-09-5 
175217-20-6 
731-27-1 
156052-68-5 
5234-68-4 
126833-17-8 

6980-18-3 

11113-80-7 
57-92-1 
1897-45-6 
99-30-9 
36734-19-7 
32809-16-8 
79622-59-6 
39300-45-3 

15845-66-6 

57018-04-9 
10004-44-1 
131807-57-3 
50471-44-8 
131341-86-1 
133-06-2 
133-07-3 
41483-43-6 
121552-61-2 
60168-88-9 
110235-47-7 
53112-28-0 
124495-18-7 

134-31-6 

3347-22-6 

690 
609 
518 
694 
474 
435 
436 
465 
335 
706 
442 
446 
408 
494 
726 
398 
547 
730 
652 
653 
650 
729 

483 
300 
427 

419 

101 
361 
416 
673 
524 
533 
365 
580 
407 
635 
275 
640 
273 
603 

703 

710 
312 
288 
150 
278 
383 
521 
98 

384 

479 
528 
594 
280 
522 
40 
75 
261 
511 
380 
611 
714 
566 

677 

153 

F6.14 

QUINOLINE FUNGICIDES 

F6.15 

QUINONE FUNGICIDES 

Definitions and classification 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
F6.16 
F6.16 
F6.16 
F6.16 
F6.16 
F6.16 
F6.17 
F6.18 
F6.18 
F6.18 
F6.18 
F6.18 
F6.18 
F6.19 

H0 

H0 

H1 

H1.1 
H1.1 
H1.1 
H1.1 
H1.1 
H1.1 
H1.1 

H2 

H2.1 

H2.2 
H2.2 
H2.3 
H2.3 

H3 

H3.1 
H3.1 
H3.1 
H3.1 
H3.1 
H3.2 
H3.2 
H3.2 
H3.2 
H3.2 
H3.2 

H3.3 

H3.3 
H3.3 
H3.3 

H4 

H4.1 
H4.1 
H4.1 
H4.2 
H4.2 

H5 

Herbicides, Haulm 
Destructors and 
Moss Killers: total. 
Herbicides, Haulm 
Destructors and 
Moss Killers: total 
excluding sulfuric 
acid. 

Herbicides based 
on phenoxy-
phytohormones 

Herbicides based 
on triazines and 
triazinones 

Herbicides based 
on amides and 
anilides 

Herbicides based 
on carbamates 
and bis-
carbamates 

Herbicides based 
on dinitroaniline 
derivatives 

STROBILURINE FUNGICIDES 
STROBILURINE FUNGICIDES 
STROBILURINE FUNGICIDES 
STROBILURINE FUNGICIDES 
STROBILURINE FUNGICIDES 
STROBILURINE FUNGICIDES 
UREA FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
UNCLASSIFIED FUNGICIDES 
OTHER-FUNGICIDES 

AZOXYSTROBIN 
DIMOXYSTROBIN 
KRESOXIM-METHYL 
PICOXYSTROBINE 
PYRACLOSTROBINE 
TRIFLOXYSTROBINE 
PENCYCURON 
ACIBENZOLAR 
BENZOIC ACID 
DICHLOROPHEN 
FENPROPIDIN 
2-PHENYPHENOL 
SPIROXAMINE 
ZZ-OTHER-FUNGICIDES 

131860-33-8 
149961-52-4 
143390-89-0 
117428-22-5 
175013-18-0 
141517-21-7 
66063-05-6 
126448-41-7 
65-85-0 
97-23-4 
67306-00-7 
90-43-7 
118134-30-8 

571 
739 
568 
628 
657 
617 
402 
597 
622 
325 
520 
246 
572 

PHENOXY HERBICIDES 
PHENOXY HERBICIDES 
PHENOXY HERBICIDES 
PHENOXY HERBICIDES 
PHENOXY HERBICIDES 
PHENOXY HERBICIDES 
PHENOXY HERBICIDES 

2,4-D 
2,4-DB 
DICHLORPROP-P 
MCPA 
MCPB 
MECOPROP 
MECOPROP-P 

METHYLTHIOTRIAZINE 
HERBICIDES 
TRIAZINE HERBICIDES 
TRIAZINE HERBICIDES 
TRIAZINONE HERBICIDES 
TRIAZINONE HERBICIDES 

METHOPROTRYNE 

SIMETRYN 
TERBUTHYLAZINE 
METAMITRON 
METRIBUZIN 

AMIDE HERBICIDES 
AMIDE HERBICIDES 
AMIDE HERBICIDES 
AMIDE HERBICIDES 
AMIDE HERBICIDES 
ANILIDE HERBICIDES 
ANILIDE HERBICIDES 
ANILIDE HERBICIDES 
ANILIDE HERBICIDES 
ANILIDE HERBICIDES 
ANILIDE HERBICIDES 
CHLOROACETANILIDE 
HERBICIDES 

DIMETHENAMID 
FLUPOXAM 
ISOXABEN 
NAPROPAMIDE 
PROPYZAMIDE 
DIFLUFENICAN 
FLORASULAM 
FLUFENACET 
METOSULAM 
METAZACHLOR 
PROPANIL 

ACETOCHLOR 

DIMETHACHLOR 
PRETILACHLOR 
PROPACHLOR 

94-75-7 
94-82-6 
15165-67-0 
94-74-6 
94-81-5 
7085-19-0 
16484-77-8 

841-06-5 

1014-70-6 
5915-41-3 
41394-05-2 
21087-64-9 

87674-68-8 
119126-15-7 
82558-50-7 
15299-99-7 
23950-58-5 
83164-33-4 
145701-23-1 
142459-58-3 
139528-85-1 
67129-08-2 
709-98-8 

34256-82-1 

50563-36-5 
51218-49-6 
1918-16-7 

1 
83 
476 
2 
50 
51 
475 

94 

179 
234 
381 
283 

638 
8158 
701 
271 
315 
462 
616 
588 
707 
411 
205 

496 

688 
711 
176 

BIS-CARBAMATE HERBICIDES 
BIS-CARBAMATE HERBICIDES 
BIS-CARBAMATE HERBICIDES 
CARBAMATE HERBICIDES 
CARBAMATE HERBICIDES 

CHLORPROPHAM 
DESMEDIPHAM 
PHENMEDIPHAM 
ASULAM 
CARBETAMIDE 

101-21-3 
13684-56-5 
13684-63-4 
3337-71-1 
16118-49-3 

43 
477 
77 
240 
95 

H5.1 

DINITROANILINE HERBICIDES 

BENFLURALIN 

1861-40-1 

285 

Definitions and classification 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
  
  
  
  
 
 
 
Herbicides based 
on derivatives of 
urea, of uracil or 
of sulphonylurea 

H5.1 
H5.1 
H5.1 
H5.1 
H5.1 

H6 

H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.1 
H6.2 
H6.3 
H6.3 
H6.3 
H6.3 
H6.3 
H6.3 
H6.3 
H6.3 

Other herbicides  H7 

Other herbicides 

Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 

Other herbicides 

Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 

H7.1 

H7.1 
H7.1 
H7.1 
H7.1 
H7.1 
H7.1 
H7.1 
H7.1 
H7.1 
H7.2 
H7.3 
H7.3 
H7.4 
H7.4 

H7.5 

H7.5 
H7.5 
H7.5 
H7.6 
H7.7 
H7.7 
H7.8 
H7.8 
H7.8 
H7.8 
H7.9 

DINITROANILINE HERBICIDES 
DINITROANILINE HERBICIDES 
DINITROANILINE HERBICIDES 
DINITROANILINE HERBICIDES 
DINITROANILINE HERBICIDES 

BUTRALIN 
ETHALFLURALIN 
ORYZALIN 
PENDIMETHALIN 
TRIFLURALIN 

SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
SULFONYLUREA HERBICIDES 
URACIL HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 
UREA HERBICIDES 

AMIDOSULFURON 
AZIMSULFURON 
BENSULFURON 
CHLORSULFURON 
CINOSULFURON 
ETHOXYSULFURON 
FLAZASULFURON 
FLUPYRSULFURON 
FORAMSULFURON 
IMAZOSULFURON 
IODOSULFURON 
MESOSULFURON 
METSULFURON 
NICOSULFURON 
OXASULFURON 
PRIMISULFURON 
PROSULFURON 
RIMSULFURON 
SULFOSULFURON 
THIFENSULFURON 
TRIASULFURON 
TRIBENURON 
TRIFLUSULFURON 
TRITOSULFURON 
LENACIL 
CHLORTOLURON 
DIURON 
FLUOMETURON 
ISOPROTURON 
LINURON 
METHABENZTHIAZURON 
METOBROMURON 
METOXURON 

ARYLOXYPHENOXYPROPIONIC 
HERBICIDES 

CLODINAFOP 

CYHALOFOP 
DICLOFOP 
FENOXAPROP-P 
FLUAZIFOP-P-BUTYL 
HALOXYFOP 
HALOXYFOP-R 
PROPAQUIZAFOP 
QUIZALOFOP 
QUIZALOFOP-P 
ETHOFUMESATE 
CHLORTHAL 
DICAMBA 
DIQUAT 
PARAQUAT 

CLETHODIM 

BENZOFURANE HERBICIDES 
BENZOIC-ACID HERBICIDES 
BENZOIC-ACID HERBICIDES 
BIPYRIDYLIUM HERBICIDES 
BIPYRIDYLIUM HERBICIDES 
CYCLOHEXANEDIONE 
HERBICIDES 

CYCLOXYDIM 
TEPRALOXYDIM 
TRALKOXYDIM 
PYRIDATE 
CINIDON-ETHYL 
FLUMIOXAZIN 

DIAZINE HERBICIDES 
DICARBOXIMIDE HERBICIDES 
DICARBOXIMIDE HERBICIDES 
DIPHENYL ETHER HERBICIDES  ACLONIFEN 
DIPHENYL ETHER HERBICIDES  BIFENOX 
DIPHENYL ETHER HERBICIDES  NITROFEN 
DIPHENYL ETHER HERBICIDES  OXYFLUORFEN 
IMIDAZOLINONE HERBICIDES 

IMAZAMETHABENZ 

33629-47-9 
55283-68-6 
19044-88-3 
40487-42-1 
2582-09-8 

120923-37-7 
120162-55-2 
99283-01-9 
64902-72-3 
94593-91-6 
126801-58-9 
104040-78-0 
150315-10-9 
173159-57-4 
122548-33-8 
185119-76-0 
400852-66-6 
74223-64-6 
111991-09-4 
144651-06-9 
113036-87-6 
94125-34-5 
122931-48-0 
141776-32-1 
79277-67-1 
82097-50-5 
106040-48-6 
135990-29-3 
142469-14-5 
2164-08-1 
15545-48-9 
330-54-1 
2164-17-2 
34123-59-6 
330-55-2 
18691-97-9 
3060-89-7 
19937-59-8 

114420-56-3 

122008-85-9 
40843-25-2 
113158-40-0 
79241-46-6 
69806-34-4 
72619-32-0 
111479-05-1 
76578-12-6 
94051-08-8 
26225-79-6 
2136-79-0 
1918-00-9 
85-00-7 
4685-14-7 

99129-21-2 

101205-02-1 
149979-41-9 
87820-88-0 
55512-33-9 
142891-20-1 
103361-09-7 
74070-46-5 
42576-02-3 
1836-75-5 
42874-03-3 
100728-84-5 

504 
516 
537 
357 
183 

515 
584 
502 
391 
507 
591 
595 
577 
659 
590 
634 
663 
441 
709 
626 
712 
579 
716 
601 
452 
480 
546 
731 
735 
163 
217 
100 
159 
336 
76 
201 
168 
219 

683 

596 
358 
484 
395 
438 
526 
713 
429 
641 
233 
328 
85 
55 
56 

508 

510 
608 
544 
447 
598 
578 
498 
413 
170 
538 
529 

Definitions and classification 

59 

 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 

H7.9 
H7.9 
H7.10 
H7.10 
H7.11 
H7.12 
H7.13 
H7.13 
H7.13 

Other herbicides 

H7.14 

Other herbicides 

H7.14 

Other herbicides 

H7.15 

Other herbicides 
Other herbicides 

H7.16 
H7.16 

Other herbicides 

H7.17 

Other herbicides 

H7.18 

Other herbicides 

H7.18 

Other herbicides 

H7.19 

Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 
Other herbicides 

H7.19 
H7.20 
H7.20 
H7.21 
H7.22 
H7.22 
H7.22 
H7.22 
H7.22 
H7.23 
H7.24 
H7.25 
H7.26 
H7.26 
H7.27 
H7.27 
H7.27 
H7.27 
H7.27 
H7.27 

Other herbicides 

H7.28 

Insecticides and 
Acaricides, total: 
Insecticides based 
on pyrethroids 

Insecticides based 
on chlorinated 
hydrocarbons 

Insecticides based 
on carbamates 

I0 

I1 

I1.1 
I1.1 

I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 
I1.1 

I2 

I2.1 

I2.1 

I3 

IMIDAZOLINONE HERBICIDES 
IMIDAZOLINONE HERBICIDES 
INORGANIC HERBICIDES 
INORGANIC HERBICIDES 
ISOXAZOLE HERBICIDES 
MORPHACTIN HERBICIDES 
NITRILE HERBICIDES 
NITRILE HERBICIDES 
NITRILE HERBICIDES 
ORGANOPHOSPHORUS 
HERBICIDES 

PHENYLPYRAZOLE 
HERBICIDES 
PYRIDAZINONE HERBICIDES 
PYRIDAZINONE HERBICIDES 
PYRIDINECARBOXAMIDE 
HERBICIDES 
PYRIDINECARBOXYLIC-ACID 
HERBICIDES 
ES 
PYRIDYLOXYACETIC-ACID 
HERBICIDES  

IMAZAMOX 
IMAZETHAPYR 
AMMONIUM SULFAMATE 
CHLORATES 
ISOXAFLUTOLE 
FLURENOL 
BROMOXYNIL 
DICHLOBENIL 
IOXYNIL 

GLUFOSINATE 

GLYPHOSATE 

PYRAFLUFEN 

CHLORIDAZON 
FLURTAMONE 

PICOLINAFEN 

CLOPYRALID 

PICLORAM 

FLUROXYPYR 

TRICLOPYR 
QUINCLORAC 
QUINMERAC 
BENTAZONE 

QUINOLINE HERBICIDES 
QUINOLINE HERBICIDES 
THIADIAZINE HERBICIDES 
THIOCARBAMATE HERBICIDES  EPTC 
THIOCARBAMATE HERBICIDES  MOLINATE 
THIOCARBAMATE HERBICIDES  PROSULFOCARB 
THIOCARBAMATE HERBICIDES 
THIOCARBAMATE HERBICIDES 
TRIAZOLE HERBICIDES 
TRIAZOLINONE HERBICIDES 
TRIAZOLONE HERBICIDES 
TRIKETONE HERBICIDES 
TRIKETONE HERBICIDES 
UNCLASSIFIED HERBICIDES 
UNCLASSIFIED HERBICIDES 
UNCLASSIFIED HERBICIDES 
UNCLASSIFIED HERBICIDES 
UNCLASSIFIED HERBICIDES 
UNCLASSIFIED HERBICIDES 
OTHER HERBICIDES-HAULM 
DESTRUCTOR-MOSS KILLER 

THIOBENCARB 
TRI-ALLATE 
AMITROL 
CARFENTRAZONE 
PROPOXYCARBAZONE 
MESOTRIONE 
SULCOTRIONE 
CLOMAZONE 
FLUROCHLORIDONE 
QUINOCLAMINE 
METHAZOLE 
OXADIARGYL 
OXADIAZON 
ZZ-OTHER-HERBICIDES-HAULM 
DESTRUCTOR-MOSS KILLER 

PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 
PYRETHROID INSECTICIDES 

ACRINATHRIN 
ALPHA-CYPERMETHRIN 
BETA-CYFLUTHRIN 
BETA-CYPERMETHRIN 
BIFENTHRIN 
CYFLUTHRIN 
CYPERMETHRIN 
DELTAMETHRIN 
ESFENVALERATE 
ETOFENPROX 
GAMMA-CYHALOTHRIN 
LAMBDA-CYHALOTHRIN 
TAU-FLUVALINATE 
TEFLUTHRIN 
ZETA-CYPERMETHRIN 

ORGANOCHLORINE 
INSECTICIDES 

DICOFOL 

TETRASUL 

114311-32-9 
81335-77-5 
7773-06-0 
7775-09-9 
141112-29-0 
467-69-6 
1689-84-5 
1194-65-6 
1689-83-4 

51276-47-2 

1071-83-6 

129630-19-9 

1698-60-8 
96525-23-4 

137641-05-5 

1702-17-6 

1918-02-1 

69377-81-7 

55335-06-3 
84087-01-4 
90717-03-6 
25057-89-0 
759-94-4 
2212-67-1 
52888-80-9 
28249-77-6 
2303-17-5 
61-82-5 
128639-02-1 
145026-81-9 
104206-82-8 
99105-77-8 
81777-89-1 
61213-25-0 
2797-51-5 
20354-26-1 
39807-15-3 
19666-30-9 

101007-06-1 
67375-30-8 
68359-37-5 
65731-84-2 
82657-04-3 
68359-37-5 
52315-07-8 
52918-63-5 
66230-04-4 
80844-07-1 
76703-62-3 
91465-08-6 
102851-06-9 
79538-32-2 
52315-07-8 

115-32-2 

2227-13-6 

619 
700 
679 
7 
575 
304 
87 
73 
86 

437 

284 

605 

111 
569 

639 

455 

174 

431 

376 
493 
563 
366 
155 
235 
539 
388 
97 
90 
587 
655 
625 
723 
509 
430 
648 
369 
604 
213 

678 
454 
482 
632 
415 
385 
332 
333 
481 
471 
768 
463 
432 
451 
733 

123 

114 

Definitions and classification 

60 

 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
  
  
  
  
 
 
and oxime-
carbamate 

Insecticides based 
on 
organophosphates 

Biological and 
botanical product 
based Insecticides 

Other insecticides 

I3.1 

I3.1 

I3.2 
I3.2 
I3.2 
I3.2 
I3.2 
I3.2 
I3.2 
I3.2 

I4 

I4.1 

I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 
I4.1 

I5 

I5.1 
I5.1 
I5.1 
I5.1 
I6 
I6.1 

I6.1 

I6.1 
I6.3 
I6.3 
I6.3 
I6.3 
I6.3 
I6.3 
I6.3 
I6.4 

I6.5 

I6.5 

I6.6 

I6.6 
I6.6 
I6.7 

I6.8 

I6.8 
I6.9 

METHOMYL 

16752-77-5 

264 

OXIME-CARBAMATE 
INSECTICIDES 
OXIME-CARBAMATE 
INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 
CARBAMATE INSECTICIDES 

ORGANOPHOSPHORUS 
INSECTICIDES 

OXAMYL 

BENFURACARB 
CARBARYL 
CARBOFURAN 
CARBOSULFAN 
FENOXYCARB 
FORMETANATE 
METHIOCARB 
PIRIMICARB 

AZINPHOS-METHYL 

CADUSAFOS 
CHLORPYRIFOS 
CHLORPYRIFOS-METHYL 
COUMAPHOS 
DIAZINON 
DICHLORVOS 
DIMETHOATE 
ETHOPROPHOS 
FENAMIPHOS 
FENITROTHION 
FOSTHIAZATE 
ISOFENPHOS 
MALATHION 
METHAMIDOPHOS 
NALED 
OXYDEMETON-METHYL 
PHOSALONE 
PHOSMET 
PHOXIM 
PIRIMIPHOS-METHYL 
TRICHLORFON 

BIOLOGICAL INSECTICIDES 
BIOLOGICAL INSECTICIDES 
BIOLOGICAL INSECTICIDES 
BIOLOGICAL INSECTICIDES 

AZADIRACHTIN 
NICOTINE 
PYRETHRINS 
ROTENONE 

ANTIBIOTIC INSECTICIDES 

ABAMECTIN 

ANTIBIOTIC INSECTICIDES 

MILBEMECTIN 

ANTIBIOTIC INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
BENZOYLUREA INSECTICIDES 
CARBAZATE INSECTICIDES 
DIAZYLHYDRAZINE 
INSECTICIDES 

INSECT GROWTH 
REGULATORS 

SPINOSAD 
DIFLUBENZURON 
FLUFENOXURON 
HEXAFLUMURON 
LUFENURON 
NOVALURON 
TEFLUBENZURON 
TRIFLUMURON 
BIFENAZATE 

METHOXYFENOZIDE 

TEBUFENOZIDE 

BUPROFEZIN 

INSECT PHEROMONES 
NITROGUANIDINE 
INSECTICIDES 

ORGANOTIN INSECTICIDES 

CYROMAZINE 
HEXYTHIAZOX 
(E,Z)-9-DODECENYL ACETATE 

CLOTHIANIDIN 

THIAMETHOXAM 
AZOCYCLOTIN 

Definitions and classification 

61 

23135-22-0 

82560-54-1 
63-25-2 
1563-66-2 
55285-14-8 
79127-80-3 
22259-30-9 
2032-65-7 
23103-98-2 

86-50-0 

95465-99-9 
2921-88-2 
5589-13-0 
56-72-4 
333-41-5 
62-73-7 
60-51-5 
13194-48-4 
22224-92-6 
122-14-5 
98886-44-3 
25311-71-1 
121-75-5 
10265-92-6 
300-76-5 
301-12-2 
2310-17-0 
732-11-6 
14816-18-3 
29232-93-7 
52-68-6 

11141-17-6 
54-11-5 
8003-34-7 
83-79-4 

71751-41-2 
51596-10-2 
51596-11-3 
168316-95-8 
35367-38-5 
101463-69-8 
86479-06-3 
103055-07-8 
116714-46-6 
83121-18-0 
64628-44-0 
149877-41-8 

161050-58-4 

112410-23-8 

69327-76-0 

66215-27-8 
78587-05-0 
35148-19-7 

210880-92-5 

153719-23-4 
41083-11-8 

342 

501 
26 
276 
417 
425 
697 
165 
231 

37 

682 
221 
486 
121 
15 
11 
59 
218 
692 
35 
585 
412 
12 
355 
195 
171 
109 
318 
364 
239 
68 

627 
8 
32 
38 

495 

660 

636 
339 
470 
698 
704 
672 
450 
548 
736 

656 

724 

681 

420 
439 
422 

738 

637 
404 

 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
I6.9 
I6.9 
I6.10 
I6.11 

I6.12 

I6.12 
I6.12 
I6.13 

I6.14 

I6.14 
I6.14 
I6.15 
I6.16 
I6.17 

I6.18 

I6.19 
I6.20 
I6.20 
I6.20 

I6.21 

M0 

Molluscicides, 
total:  

Molluscicides  M1 

M1.1 

M1.2 

M1.2 
M1.3 

PGR0 

PGR1 

PGR1.1 

PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 
PGR1.1 

PGR2 

Plant Growth 
Regulators, total: 

Physiological 
Plant growth 
regulators 

Anti-sprouting 
products 

ORGANOTIN INSECTICIDES 
ORGANOTIN INSECTICIDES 
OXADIAZINE INSECTICIDES 
PHENYL-ETHER INSECTICIDES  PYRIPROXYFEN 
PYRAZOLE (PHENYL-) 
INSECTICIDES 

CYHEXATIN 
FENBUTATIN OXIDE 
INDOXACARB 

FENPYROXIMATE 

PYRIDINE INSECTICIDES 
PYRIDYLMETHYLAMINE 
INSECTICIDES 

FIPRONIL 
TEBUFENPYRAD 
PYMETROZINE 

ACETAMIPRID 

CLOFENTEZINE 

IMIDACLOPRID 
THIACLOPRID 
SULFITE ESTER INSECTICIDES  PROPARGITE 
TETRAZINE INSECTICIDES 
TETRONIC ACID INSECTICIDES  SPIRODICLOFEN 
(CARBAMOYL-) TRIAZOLE 
INSECTICIDES 
UREA INSECTICIDES 
UNCLASSIFIED INSECTICIDES 
UNCLASSIFIED INSECTICIDES 
UNCLASSIFIED INSECTICIDES 
OTHER INSECTICIDES-
ACARICIDES 

DIAFENTHIURON 
ETOXAZOLE 
FENAZAQUIN 
PYRIDABEN 
ZZ-OTHER-INSECTICIDES-
ACARICIDES 

TRIAZAMATE 

13121-70-5 
13356-08-6 
173584-44-6 
95737-68-1 

134098-61-6 

120068-37-3 
119168-77-3 
123312-89-0 

135410-20-7 

138261-41-3 
111988-49-9 
2312-35-8 
74115-24-5 
148477-71-8 

112143-82-5 

80060-09-9 
153233-91-1 
120928-09-8 
96489-71-3 

289 
359 
612 
715 

695 

581 
725 
593 

649 

582 
631 
216 
418 
737 

728 

8097 
623 
693 
583 

CARBAMATE MOLLUSCICIDE 
UNCLASSIFIED 
MOLLUSCICIDES 
OTHER MOLLUSCICIDES 
OTHER MOLLUSCICIDES 

THIODICARB 

FERRIC PHOSPHATE 

METALDEHYDE 
ZZ-OTHER-MOLLUSCICIDES 

59669-26-0 

10045-86-0 

108-62-3 

543 

629 

62 

PHYSIOLOGICAL PLANT 
GROWTH REGULATORS 

CHLORMEQUAT 

CYCLANILIDE 
DAMINOZIDE 
DIMETHIPIN 
DIPHENYLAMINE 
ETHEPHON 
ETHOXYQUIN 
FLORCHLORFENURON 
FLURPRIMIDOL 
IMAZAQUIN 
MALEIC HYDRAZIDE  
MEPIQUAT 
1-METHYLCYCLOPROPENE 
PACLOBUTRAZOL 
PROHEXADIONE-CALCIUM 
SODIUM 5-NITROGUAIACOLATE 
SODIUM O-NITROPHENOLATE 
TRINEXAPAC-ETHYL 

999-81-5 

113136-77-9 
1596-84-5 
55290-64-7 
122-39-4 
16672-87-0 
91-53-2 
68157-60-8 
56425-91-3 
81335-37-7 
51542-52-0 
24307-26-4 
3100-04-7 
76738-62-0 
127277-53-6 
67233-85-6 
824-39-5 
95266-40-3 

143 

586 
330 
689 
460 
373 
517 
633 
696 
699 
310 
440 
767 
445 
567 
718 
720 
8349 

602 
43 

PGR2.2  ANTISPROUTING PRODUCTS 
PGR2.2  ANTISPROUTING PRODUCTS 

CARVONE 
CHLORPROPHAM 

99-49-0 
101-21-3 

Other plant growth 
regulators 
Other plant growth 
regulators 
Other Plant 
Protection 
Products, total: 

PGR3 

PGR3.1 

ZR0 

Mineral oils  ZR1 
Mineral oils  ZR1.1 
Vegetal oils  ZR2 
Vegetal oils  ZR2.1 

Soil sterilants  ZR3 

OTHER PLANT GROWTH 
REGULATORS 

ZZ-OTHER-PGR 

MINERAL OIL 

PETROLEUM OILS 

64742-55-8 

VEGETAL OIL 

TAR OILS 

Definitions and classification 

29 

30 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
(incl. Nematicides) 

  ZR3.1 

  ZR3.2 

  ZR3.2 
  ZR3.2 
  ZR3.2 
  ZR3.3 

METHYL BROMIDE 
UNCLASSIFIED SOIL 
STERILANTS 
OTHER SOIL STERILANTS 
OTHER SOIL STERILANTS 
OTHER SOIL STERILANTS 
OTHER SOIL STERILANTS 

METHYL BROMIDE 

CHLOROPICRIN 

DAZOMET 
1,3-DICHLOROPROPENE 
METAM-SODIUM 
ZZ-OTHER-SOIL-STERILANTS 

74-83-9 

76-06-2 

533-74-4 
542-75-6 
137-42-8 

Rodenticides  ZR4 

  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.1 
  ZR4.2 

RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
RODENTICIDES 
OTHER RODENTICIDES 

BRODIFACOUM 
BROMADIOLONE 
CHLORALOSE 
CHLOROPHACINONE 
COUMATETRALYL 
DIFENACOUM 
DIFETHIALONE 
FLOCOUMAFEN 
WARFARIN 
ZZ-OTHER-RODENTICIDES 

56073-10-0 
28772-56-7 
15879-93-3 
3691-35-8 
5836-29-3 
56073-07-5 
104653-34-1 
90035-08-8 
81-81-2 

128 

298 

146 
675 
20 

370 
371 
249 
208 
189 
514 
549 
453 
70 

All other plant 
protection 
products 

Total Plant 
Protection 
Products: 

ZR5 

ZR5.1 

ZR5.2 

ZZ0 

DISINFECTANTS 
OTHER PLANT PROTECTION 
PRODUCTS 

ZZ-OTHER-DISINFECTANTS 

ZZ-OTHER-PPP 

Definitions and classification 

63 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
 
  
  
  
  
 
 
 
 
 
2. Methodological notes 

Details of data sources 
The  guidelines  for  the  Collection  of  Pesticide  Usage  Statistics  within  Agriculture  and 
Horticulture published by Eurostat in 1999 as an internal working document, and the results 
of the pilot surveys on the use of pesticides carried out in the framework of the 2005 Multi-
Beneficiary  and  Transition  Facility  Statistical  Co-operation  Programmes  on  which  this 
methodological  paper  is  based,  can  be  found  on  the  CIRCA  website  dedicated  to  pesticide 
statistics: http://circa.europa.eu/Public/irc/dsis/pip/library?l=/indicators_pesticides 

the 

title  AGRICULTURE  AND  ENVIRONMENT. 

The main results of these surveys in terms of quantities of active substances used per crop and 
main categories of products are also available in the domain dedicated to ENVIRONMENT 
STATISTICS  under 
  Data  on 
consumption  and  sales  of  pesticides  are  currently  collected  from  Member  States  and  EEA 
Countries on a voluntary basis. As a consequence, data are still very fragmentary and difficult 
to  compare  between  the  different  countries.    This  project  was  a  first  attempt  to  collect 
representative data on pesticide use at national level in the various countries on the basis of a 
harmonised methodology. 
In order to cope with this lack of information, the Commission adopted on 11 December 2006 
a  proposal  for  a  Regulation  of  the  European  Parliament  and  of  the  Council  concerning 
statistics  on  plant  protection  products  (COM(2006)  778  final),  which  is  currently  being 
discussed in the Council and the European Parliament. 
This proposal for a Regulation aims to provide the data necessary to measure the progress of 
the thematic strategy for the sustainable use of pesticides as presented by the Commission to 
the  European  Parliament  and  Council  in  a  Communication  (COM(2006)  372  final)  and  a 
proposal    for  Directive  of  the  European  Parliament  and  of  the  Council  establishing  a 
framework for Community action to achieve a sustainable use of pesticides (COM(2006) 373 
final). 

Definitions of variables and indicators 
Pesticide  is  a  broad  concept  covering  plant  protection  products  which  are  mainly  used  by 
farmers to protect their crops (covered by Directive 91/414/EEC) and biocidal products which 
are  used  for  a  very  large  diversity  of  purposes  (covered  by  Directive  98/8/EC).  This  paper, 
like  the  statistics  published  by  Eurostat  on  this  issue,  concerns  plant  protection  products 
exclusively. The data refer to amounts of active substances (in kg), which are the components 
in a commercial product that cause the desired effect on target organisms (fungi, weeds, pests, 
etc.)  
Individual  active  substances  can  be  grouped  in  different  categories  according  to  their  main 
function  and  to  their  chemical  properties.    The  major  functional  groups  covered  by  this 
publication  are  fungicides,  herbicides,  insecticides,  and  growth  regulators.    Within  these 
groups, active substances can also be grouped in categories of products and chemical classes 
according  to  a  harmonised  classification  presented  in  Annex  III  of  the  proposal  for  a 
Regulation concerning statistics on plant protection products  (COM(2006) 778).   
The areas referred to in this paper correspond to the physical areas in which the relevant crops 
are  cultivated  (essentially  wheat,  except  for  Malta  where  the  area  concerns  all  the  crops 
covered). In general, the area cultivated was grossed up at national level except when the size 
of the survey was insufficient to allow such an extrapolation. 

Methodological notes 

64 

 
 
 
 
 
The main indicator presented in this paper is the average intensity of treatment, which is the 
ratio between the quantity of active substance (in kg) and the total area under wheat. 

Abbreviations and symbols 
EU 

European Union 

BG 
CY  
LT 
HR 
PL 
SI 
TR 

Kg 
Ha 

AS  
F 
H 
I 
GR 

LC50 
LD50 
NOEC 

FADN 
FR 
FSS 
IACS 

EE 
LV 
HU  
MT 
RO 
SK 
UK 

Estonia 
Latvia 
Hungary 
Malta 
Romania 
Slovakia 
United Kingdom 

Bulgaria 
Cyprus 
Lithuania 
Croatia 
Poland 
Slovenia 
Turkey 

Kilogram 
Hectare 

Active substance 
Fungicide 
Herbicide 
Insecticide 
Growth regulator 

Lethal concentration for 50% of the exposed population 
Lethal doses for 50% of the exposed population 
No observed effect concentration 

Farm Accountancy Data Network 
Farm registers 
Farm Structure survey 
Integrated Administration and Control System 

References 

ADAS  International  LTD,  2005.  2002  Phare  Multi-Beneficiary  Programme  for  Statistical 
Co-operation with the PHARE Countries, Lot 3, Final report. Published on CIRCA. 
ASA Institute, 2008. Provision of Services for the Implementation of Sectoral Pilot Projects 
for the Multi-Beneficiary Statistical Co-operation Programme for Bulgaria, Croatia, Romania 
and Turkey 2005, Lot 2: Agricultural and environmental statistics, component 2.3. Pesticide 
Indicators. Final report. Published on CIRCA. 
BCPC, 2000.  The Pesticide Manual, Twelfth Edition.  The British Crop Protection Council.  
Editor: CDS Tomlin. 
HAIR, 2007. HArmonised environmental Indicators for pesticide Risk. Project supported by 
the  European  Commission  under  the  6th  framework  Program  (Project  number  SSPE-CT-
2003-501997).  
ICON Institute, 2008. Transition Facility Statistical Cooperation Programme 2005  
LOT 2: Pesticide Indicators.  Final report. Published on CIRCA. 
VAN  BOL  V.  et  al.,  2002.  Study  and  Analysis  of  Existing  Pesticide  Risk  Indicators; 
Veterinary and Agrochemical Research Center. Tervuren. 

Methodological notes 

65 

 
 
 
 
 
 
 
 
 
 
THOMAS,  M.  R.,  1989.  Guidelines  for  the  Collection  of  Pesticide  Usage  Statistics  within 
Agriculture and Horticulture, Eurostat internal working document published on behalf of the 
Eurostat Pesticide Statistics Task Force. Published on CIRCA. 
COM(2006) 372  final. Communication from the Commission to the Council, the European 
Parliament, the European Economic and Social Committee and the Committee of the Regions 
- A thematic strategy on the sustainable use of pesticides, 12.07.2006. 
COM(2006)  373  final.  Proposal  for  a  Directive  of  the  European  Parliament  and  of  the 
Council  establishing  a  framework  for  Community  action  to  achieve  a  sustainable  use  of 
pesticides, 12.07.2006.  
COM(2006)  778  final.    Proposal  for  a  Regulation  of  the  European  Parliament  and  of  the 
Council concerning statistics on plant protection products, 11.12.2006. 
COM(2006)  388  final.  Proposal  for  a  Regulation  of  the  European  Parliament  and  of  the 
Council concerning the placing of plant protection products on the market, 12.07.2006. 
Council  Directive  91/414/EEC  of  15  July  1991  concerning  the  placing  of  plant  protection 
products on the market. OJ L 230, 19.8.1991, p. 1. 
Directive  98/8/EC  of  the  European  Parliament  and  of  the  Council  of  16  February  1998 
concerning the placing of biocidal products on the market. OJ L 123, 24.4.1998, p. 1. 

Links 

ADAS: http://www.adas.co.uk 
HAIR: http://www.rivm.nl/rvs/overige/risbeoor/Modellen/HAIR.jsp 
FAO: http://www.fao.org/corp/statistics 
OECD: http://www.oecd.org/statsportal 
EUROPEAN COMMISSION, Directorate general for "Health and Consumers", Food Safety: 
http://ec.europa.eu/food/plant 
EUROPEAN COMMISSION, Directorate general "Environment", Sustainable Use of 
Pesticides: http://ec.europa.eu/environment/ppps 
UK Pesticides Forum: http://www.pesticides.gov.uk/pesticides_forum_home.asp 
Chemical Abstracts Service Registry Numbers: http://www.cas.org 
Collaborative International Pesticides Analytical Council: http://www.cipac.org 
Alan Wood Compendium of Pesticide Common Names: http://www.alanwood.net/pesticides 

Further information 

Pierre.Nadin@ec.europa.eu 

Methodological notes 

66 

 
 
 
 
 
 
 
 
 
 
European Commission

A common methodology for the collection of pesticide usage statistics within agriculture and horticulture

Luxembourg: Office for Official Publications of the European Communities

2008 — 66 pp. — 21 x 29.7 cm

ISBN 978-92-79-07848-4
ISSN 1977-0375