ICP Forests Manual 2021 QAQC
Source: environment.data.gov.uk (2026-01-05)

NFI+ Soil Condition & Health. This dataset contains comprehensive soil chemical and physical indicators collected as part of the NFI+ (National Forest Inventory Plus) programme and the BioSoil forest soil and biodiversity monitoring network. The dataset covers forest soil sampling sites across Great Britain and provides baseline and monitoring data on soil condition and health in forest ecosystems.

Chemical indicators measured include: soil pH (measured in water and CaCl2), organic carbon content (%), total nitrogen (%), available phosphorus (mg/kg), exchangeable cations (Ca, Mg, K, Na in cmol/kg), cation exchange capacity (cmol/kg), base saturation (%), and heavy metal concentrations where applicable.

Physical indicators measured include: soil texture (% sand, silt, clay), bulk density (g/cm³), porosity (%), aggregate stability, moisture content at sampling, and soil depth profiles.

Samples were collected at multiple depth intervals (0-10cm, 10-20cm, 20-40cm) using standardised protocols aligned with ICP Forests and BioSoil methodologies.  Each sampling location includes associated metadata on forest type, soil type, elevation and forest origin (plantation or natural).  The data include chemical and physical soil properties.

Quality assurance procedures included duplicate sampling, laboratory reference standards, and inter-laboratory comparisons.

Limitations:  Data coverage is limited to forest soils only. Some historical samples may have incomplete heavy metal analysis. Seasonal variation should be considered when interpreting results.

Data are georeferenced using a delocalised grid. The bounding coordinates for each grid square are included in the dataset. Further information about the delocalisation process is provided in the dataset evidence pack.

The 'NFI+ Soil Condition & Health' is a project funded by the UK Government through Defra’s Natural Capital and Ecosystem Assessment programme (NCEA), England sites, and the Department for Energy, Security and Net Zero (DESNZ), Scotland sites.  Users should ensure they have read the supporting documentation before viewing or making use of the data. This includes the NCEA Analysis Ready Data Limitations Statement and dataset specific limitations/caveats outlined in the supporting documentation, please refer to the 'Metadata' tab on the 'Soil Condition Data 2022-24' dataset  Attribution statement: © Forestry Commission copyright and/or database right 2025. Includes data from Ordnance Survey, British Geological Survey, and the Met Office. All rights reserved.

United Nations Economic Commission for Europe (UNECE) 
Convention on Long-range Transboundary Air Pollution (CLRTAP) 
International Co-operative Programme on Assessment and  
Monitoring of Air Pollution Effects on Forests (ICP Forests)  
MANUAL 
on 
methods and criteria for harmonized sampling, assessment, 
monitoring and analysis of the effects of air pollution on forests 
Part III 
Quality Assurance within the  
ICP Forests Monitoring Programme 
Version 2021-1  
 

 
 
Prepared by: 
Quality Assurance Committee of ICP Forests 
 
Ferretti M, König N, Granke O, Nicolas M, 2021: Part III: Quality Assurance within the ICP 
Forests monitoring programme. Version 2021-1. In: UNECE ICP Forests Programme Co-
ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, 
monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest 
Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://www.icp-forests.org/Manual.htm] 
ISBN: 978-3-86576-162-0 
 
Acknowledgements: The authors are grateful to Roberto Canullo, Nathalie Cools, Bruno 
Devos, Stefan Fleck, Alfred Fürst, Elena Gottardini, Karin Hansen, Morten Ingerslev, Anna 
Kowalska, Till Kirchner, Tom Levanič, Aldo Marchetto, Alexa Michel, Tiina Maileena Nieminen, 
Diana Pitar, Nenad Potočić, Anne-Katrin Prescher, Stephan Raspe, Pasi Rautio, Tanja 
Sanders, Marcus Schaub, Kai Schwärzel, Volkmar Timmermann, Liisa Ukonmaanaho Arne 
Verstraeten, Lars Vesterdal for providing written comments and/or participating to the 
discussions within the Quality Assurance Committee. 
The compilation of the first edition of this Manual part was co-financed by the European 
Commission under the LIFE Regulation and by Corpo Forestale dello Stato (Rome/Italy).  
All rights reserved. Reproduction and dissemination of material in this information product for 
educational or other non-commercial purposes are authorized without any prior written 
permission from the copyright holders provided the source is fully acknowledged. Reproduction 
of material in this information product for resale or other commercial purposes is prohibited 
without written permission of the copyright holder. 
Application for such permission should be addressed to: 
Programme Co-ordinating Centre of ICP Forests 
Thünen Institute of Forest Ecosystems 
Alfred-Möller-Str. 1, Haus 41/42 
16225 Eberswalde 
Germany 
Email: xxx@xxx.xx 
Eberswalde, 2021 

Quality Assurance within the ICP Forests Monitoring Programme  
Part III 
Version 2021-1 
CONTENTS 
 
1 
INTRODUCTION ............................................................................................................................ 5 
2 
SCOPE AND APPLICATION ......................................................................................................... 5 
3 
OBJECTIVES ................................................................................................................................. 5 
4 
THE QA TOOLKIT.......................................................................................................................... 6 
4.1 
THE ICP FORESTS MANUAL .................................................................................................................. 6 
4.1.1 
Manual structure and organization ................................................................................................... 6 
4.1.2 
Update and revision of the Manual .................................................................................................. 7 
4.2 
DATA QUALITY INDICATORS ................................................................................................................. 7 
4.3 
METHODS TO ENSURE AND EVALUATE DATA QUALITY ................................................................... 8 
4.3.1 
Requirements in the preparation of each survey ............................................................................. 9 
4.3.2 
Training courses .............................................................................................................................. 9 
4.3.3 
Intercalibration courses .................................................................................................................... 9 
4.3.4 
Testing the effect of using different methods ................................................................................... 9 
4.3.5 
Intercomparison rounds ................................................................................................................. 10 
4.3.6 
Control assessments ..................................................................................................................... 10 
4.4 
RESPONSE ACTIONS ........................................................................................................................... 10 
4.5 
REPORTING OF DATA QUALITY .......................................................................................................... 11 
4.5.1 
Evaluating the quality of surveys ................................................................................................... 11 
4.5.2 
Specific procedure for evaluating the quality in labs ...................................................................... 12 
5 
RESPONSIBILITIES FOR DATA QUALITY ................................................................................ 12 
6 
REFERENCES AND FURTHER READING ................................................................................ 13 
ANNEX I – MINOR CHANGES AFTER 2021 ....................................................................................... 15 
 
 
 

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1 Introduction 
Quality Assurance (QA) is essential in forest monitoring to promote, achieve and maintain high 
enough Data Quality (DQ). High DQ results from a process in which each step of the survey 
of concern is properly addressed, from the definition of the objectives to the comparability of 
the data in space and time, data storage, processing and reporting. QA is a cross-cutting issue 
as it is of major importance for all the surveys in ICP Forests, including all the various steps 
within a survey.  
Several QA related activities have been carried out in ICP Forests since the beginning of the 
programme. The development of a Manual and the crown condition intercalibration exercises 
both already started in 1987, the soil inter-laboratory comparisons started in the early 1990s. 
Later, the activities were extended, with particular emphasis on the analytical aspects and 
laboratory inter-comparisons, while field sampling long received less attention (Ferretti et al., 
2009).  
At the meeting of the Programme Co-ordinating Group (PCG) in Hamburg in 2003, the issue 
of a common approach to some aspects of QA was first discussed and a decision made to put 
forward a set of Quality indicators and a QA reporting. Some Expert Panels (EPs) submitted 
proposals, but no common reporting was developed. The issue was brought up again at the 
PCG meeting in 2006 and a Quality Assurance Committee (QAC) with members of the PCG 
was established by the ICP Forests Task Force (TF) at its 22nd meeting held in Zvolen, 
Slovakia, in May 2007 (see QAC documents at http://www.icp-forests.org/QAC.htm ). 
2 Scope and application 
This Manual Part III presents the overall QA approach within the ICP Forests. It is not a formal 
Quality Assurance Plan because it does not concern all its typical issues (EPA, 2002). Rather 
it serves as a reference document for the Expert Panels (EPs) and Working Groups (WGs) 
active within the ICP Forests to design and implement their own “Quality Assurance and 
Quality Control” (QA/QC) procedures.  
The major aim is (i) to prevent mistakes in data collection (“Quality assurance” as meant within 
the “QA/QC” term) and (ii) to control and evaluate the fulfillment of defined quality requirements 
(“Quality control”). It is also useful for external data users to understand the procedures 
adopted to improve the ICP Forests DQ and to understand the actual confidence that can be 
placed on the data generated by the Programme. Details about specific QA/QC procedures 
are described in each of the individual parts of the Manual and compiled in Part XVI for all 
surveys depending on analyses in laboratories1. 
3 Objectives 
The objective of this Manual Part is to describe the elements of the Quality Assurance 
programme and the QA/QC procedures that EPs and WGs should develop and implement 
within their own field of application. 
                                               
1 http://icp-forests.net/page/icp-forests-manual 

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4 The QA Toolkit 
The various elements of the QA activities within the ICP Forests constitute the QA Toolkit. The 
QA Toolkit can be defined as the set of instruments and actions designed to ensure that 
methods are unambiguous, clearly presented, accepted and applied consistently across all 
participating countries. Within its own specific field, each EP and WG is asked to be compliant 
with the QA Toolkit. The QA Toolkit includes the following items: 
• 
ICP Forests Manual. It documents clear standard operating procedures (SOPs) 
formally approved by the ICP Forests TF (Chapter 4.1). 
• 
Indicators of DQ. It is important to develop and define a series of explicit, 
unambiguous indicators of DQ that prevent subjective statements on the level of 
data quality. They can be also used  to document the progress/maintenance of DQ 
(Chapter 4.2). 
• 
Methods to ensure and evaluate DQ. Continuous training, field, photo and 
laboratory intercomparison exercises and control assessments are central to 
ensure high DQ, and to collect data to document status and trends of DQ (Chapter 
4.3). 
• 
Response actions in case of below-threshold DQ. It is important to foresee a list of 
actions to improve DQ if results from DQ evaluation reveal DQ below the minimum 
acceptable level (Chapter 4.4). 
• 
DQ evaluation and reporting activities. Continuous evaluation of DQ and reporting 
about the uncertainties related to the data collected by the Programme (Chapter 
4.5). 
4.1 The ICP Forests Manual 
4.1.1 Manual structure and organization 
The Manual is the basis of the approach of the Quality within the ICP Forests. As a 
comprehensive document, the Manual describes the background of the Programme, its 
structure, design, and methods. The different parts of the Manual deal with specific surveys 
and provide clear and concise definitions of the scope and application, objectives, sampling, 
measurements, QA/QC procedures (including training and intercomparison exercises) and DQ 
requirements, reference materials and relevant bibliography. They are designed to provide all 
necessary details for every specific step of each survey while bearing in mind the final 
information need and avoiding redundancy and unjustified statements. The common structure 
of all the parts of the Manual includes: 
1 Introduction, where the nature of the survey is put in context of the whole monitoring 
programme; 
2 Scope and application of the described methods, with a table for quick reference; 
3 Objective for the survey of concern, in an operational format; 
4 Location of measurements/observations and sampling; 
5 Measurements/observations to be carried out mandatorily or optionally, reporting units and 
DQ requirements; 
6 Data handling; 
7 References: 
8 Annexes. 

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4.1.2 Update and revision of the Manual 
Survey methods, variables to be measured and QA/QC procedures are under continuous 
screening by the EPs and WGs. This continuous process provides the basis for two kinds of 
amendments, the Manual update and the more comprehensive Manual revision.  
The Manual update consists of minor changes like e.g. the clarification of wording, correction 
of obvious mistakes, and the introduction of missing codes for unconsidered cases. Minor 
changes can be implemented at any time, but need to be discussed among the respective 
EPs/WGs and the PCC. All minor changes will be listed at the end of the respective Manual 
part and be made available on the ICP Forests website.  
The Manual revision by contrast is a more comprehensive process, when all individual Manual 
parts are subject to in depth review and more substantial modifications. All major Manual 
changes during a Manual revision need to be accompanied by a rationale document 
summarising the significant modifications and the motivation for each change. Revisions are 
carried out on a 5-year basis to allow methodological consistency as well as concentrated 
implementation and planning ability during interim periods. Each Manual revision must be 
formally approved by the Task Force. 
4.2 Data Quality Indicators 
DQ indicators in ICP Forests are standardized, evidence-based measures of DQ developed 
for each survey and facilitate the documentation and monitoring of the programme’s quality. 
They need to be explicit and rigorous, although reasonable and understandable. They are 
defined for selected variables and designed to be consistent with the importance and the 
expected precision/accuracy of the concerned survey. 
Four DQ indicators can be applied. They are specific for each survey: 
• 
Data Quality Objectives (DQOs) define the expected level of precision/accuracy for 
individual measurement/observations. 
• 
Data Quality Limits (DQLs) provide the minimum acceptable proportion of 
measurement/observations within the DQOs. 
• 
Plausibility 
Limits 
(PLs) 
set 
the 
range 
of 
acceptable 
values 
for 
measurements/observations. They have to be regularly updated. 
• 
Data Completeness Limits (DCLs) define the minimum acceptable proportion of 
data within the PLs.. 
 
 

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4.3 Methods to ensure and evaluate Data Quality 
Table 1: Quick reference of activities to be organized to ensure and evaluate Data Quality by 
survey type (o – optional, m – mandatory). All activities defined as mandatory are to be 
reported in the database. 
Survey type 
(Manual part) 
Activities at international level 
Activities at national level 
Experi-
mental 
compa-
risons of 
methods 
Field 
inter-
compa-
rison of 
observers 
Photo 
inter-
compa-
rison of 
observers 
Labora-
tory 
ringtests 
Preparation effort 
as required for 
each survey 
(including needs 
for training and/or 
intercalibration) 
Control 
assess-
ments 
Crown condition and 
damaging agents 
(Part IV) 
o 
m 
m 
- 
m 
m 
Tree growth (Part V) 
o 
o 
- 
- 
m 
o 
Tree phenology 
(Part VI) 
o 
m* 
m* 
- 
m 
o 
Ground vegetation 
(Part VII) 
o 
o 
- 
- 
m 
o 
Ozone injury on 
plants (Part VIII) 
o 
m 
m 
- 
m 
m 
Meteorological 
measurements 
(Part IX) 
o 
- 
- 
- 
m 
- 
Soil sampling and 
analyses (chemistry) 
(Part X) 
o 
- 
- 
m 
m 
m 
Soil sampling and 
analyses (physics) 
(Part X) 
o 
- 
- 
o 
m 
- 
Soil description 
(Part X) 
o 
o 
- 
- 
m 
o 
Soil solution sampling 
and analysis (Part XI) 
o 
- 
- 
m 
m 
m 
Foliar sampling and 
analysis (Part XII) 
o 
- 
- 
m 
m 
m 
Canopy Leaf Area 
(Part XVII) 
o 
o 
- 
- 
m 
- 
Litterfall sampling and 
analysis (Part XIII) 
o 
- 
- 
m 
m 
m 
Deposition sampling 
and analysis 
(Part XIV) 
o 
- 
- 
m 
m 
m 
Ambient air quality 
(Part XV) 
o 
- 
- 
o 
m 
o 
*: mandatory only for assessments by individual trees 

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4.3.1 Requirements in the preparation of each survey 
To prevent mistakes or inhomogeneity in data collection, the National focal centres (NFCs) 
must thoroughly organize and prepare each survey. The minimum preparation effort needed 
for each survey type must be explicitly defined in the respective Manual part, including: 
• 
Requirements for skills from crew leaders or all operators (either as a previous 
background, or to be taught in training courses). 
• 
Requirements for transcription of the ICP Forests method into written field guidelines 
adapted to the specific national monitoring design, and applicable by all operators 
involved with no or minimum room left to their interpretation.  
• 
Requirements for training courses, if skills are to be taught or updated by NFCs, or if 
field guidelines and remote technical assistance provided to operators are not sufficient 
to ensure an adequate implementation of the survey.  
• 
Requirements for intercalibration courses, if needed to harmonize field observations 
among operators. 
The implementation of the minimum preparation effort defined for each survey type must be 
confirmed by NFCs when they transmit their data to the ICP Forests database. 
4.3.2 Training courses 
At training courses operators (i) are familiarized with the methods to be applied; (ii) receive 
instructions for the implementation of new methods; (iii) receive training for enhancing 
accuracy and precision, and for handling situations where accurate measurements are difficult 
to obtain. Training courses should be developed for each survey. Guidelines for organizing 
training courses are given in respective parts of the Manual. 
4.3.3 Intercalibration courses 
Intercalibration courses are training courses organized in order to harmonize field observations 
among observers, at national or international level. Reference teams, which are designated by 
countries to participate to international calibration courses, can act as a link to other teams 
involved in national or infra-national intercalibration courses.  
Intercalibration courses can be also used for intercomparison purposes (cf. Chapter 4.3.5) if 
they are designed and conducted accordingly. After the same subjects have been assessed 
independently by different observer teams and these assessments have been recorded, the 
results are compared between teams. Reciprocally, the discussion of the results of field 
intercomparison courses among participants is also useful for intercalibration purposes. 
4.3.4 Testing the effect of using different methods 
In some cases, the Manual allows different options in methods to be used for sampling, 
processing samples, measurements or observations. Differences in methods that could lead 
to inconsistencies in data, should be systematically reported during data submission and be 
evaluated by the related EP on the basis of existing literature and, if needed, by experimental 
comparison tests.  
During intercomparison courses, which are primarily regularly organized to evaluate the effect 
of observers/labs, the impact of using different methods can also be evaluated. In addition, 
other experiments can be specifically organized to compare methods. If possible, bridging 
functions are determined to harmonize the data collected with the different methodological 
options identified. 

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4.3.5 Intercomparison rounds 
At intercomparison rounds the results of individual observer teams or laboratories are 
compared against a defined standard. The standard is in most cases defined as closeness of 
agreement between the arithmetic mean of a large number of test results and the true or 
accepted reference value (‘trueness’). Since in most cases, the ‘true’ value is not known, the 
intercomparison exercise usually compares the results of individual entities (laboratories, 
observers) with the median or the outlier-free mean value across all entities. 
Intercomparison rounds should be organized on a regular basis, according to defined 
procedures and under the responsibility to be determined within each EP.  
The two ways of data collection, (i) by direct assessments in the field and (ii) by collection of 
sample material and analysis in laboratory, are considered with the following actions: 
• 
The aim of intercomparison courses (ICCs) for field surveys is to compare the results of 
different observers using the methods they usually use, but they can be designed to 
experimentally test the effect of different methods in addition (see chapter 4.3.4). They 
apply to assessments of tree condition, tree growth, tree phenology, biodiversity, ozone 
symptoms, and soil description. Intercomparison courses for some of these surveys can 
also be organized by means of photographic material.  
• 
The aim of ring tests for laboratories is to compare the results obtained by laboratories with 
the analytical methods they usually use on ICP Forests samples. They apply to the surveys 
of soil and soil solution, foliar chemistry, deposition, soil physics and gaseous air pollutants. 
After their successful participation in ring tests, laboratories receive qualification reports. 
Laboratories with unsatisfactory ring test results have to requalify. 
4.3.6 Control assessments 
Control assessments are organized under the responsibility of the National Focal Centres 
(NFCs) to evaluate the results of field crews/laboratories in real conditions all along data 
collection campaigns. 
• 
During field assessments of tree condition, tree growth and ground vegetation, 
independent controls by a different team of observers are organized on a defined 
percentage of the surveyed plots, and close to the date of their actual survey in order to 
minimize possible sources of variations in the assessment conditions. 
• 
During the assessment of ozone symptoms, observers should take pictures of symptomatic 
and non-symptomatic leaves/needles for control by international reference experts. It is 
also recommended to take samples for further validation by microscopic analysis. 
• 
During ambient air quality surveys based on passive samplers, the measurements of some 
samplers are compared with reference measurements made by active stations.  
• 
For chemical analyses of soil, plant material and solutions, laboratories must control the 
consistency of their measurements over time, by regularly analysing reference materials 
and producing control charts. 
4.4 Response actions 
Results from quality evaluation activities should be followed by response actions, to support 
the continuous fulfilment of quality requirements and to call for improvements when problems 
are detected. Different response actions should be foreseen according to the severity of the 
problems encountered and the survey concerned. They are defined by EPs in their respective 
Manual parts. In general, problems are encountered during data submission to the ICP Forests 
database and during the intercomparison rounds.  

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For detecting poor data quality during data submission, tests are implemented as described in 
the ICP Forests online documentation2.  
Response actions for quality issues detected during intercomparison rounds are decided 
among the person responsible for the intercomparison round, the concerned EP and the 
Programme Coordinating Center (PCC)). These include:  
• 
Warnings: the observer/lab is notified about the unsatisfactory results, and asked 
to check procedures and equipment, and repeat measurements.  
• 
Further training and assistance: if the re-measurements are of unsatisfactory 
quality, the observer/lab is provided with further training and will enter a 
requalification stage. 
• 
Requalification: after additional training, the observer/lab participates in a new 
intercomparison round with the possibility to produce new results and to document 
improvements made to meet the quality requirements. 
• 
Flagging of data: Data may be flagged in the database and reports for e.g. the 
following reasons:  
o A requalification was unsuccessful and/or the cause of unsatisfactory DQ 
cannot be addressed without hampering the comparability with existing time 
series at country level.  
o A country did not participate in a mandatory intercomparison round or did 
not conduct mandatory control assessments.  
o A country that fulfilled the respective quality requirements can be 
highlighted and acknowledged this way. 
• 
Exclusion of data from international data evaluations: when the problem is very 
severe and it may hamper the outcome of data analysis, the data can be excluded 
from data processing and evaluations. 
4.5 Reporting of Data Quality  
4.5.1 Evaluating the quality of surveys 
NFCs must confirm the implementation of the minimum survey preparation effort as defined 
for each survey type, by year and plot, during data submission to the ICP Forests database. 
EPs should evaluate and report regularly (from every year to every 5 years, depending on the 
frequency of the data collection) about the fulfilment of at least some of the main quality 
indicators defined for each survey, and about their progression. To contribute to this purpose:  
• 
International inter-comparison rounds and tests of methods must be systematically 
documented in reports. At least synthetic results by country must be saved in the ICP 
Forests database so that they can be provided to data users. Detailed results must be kept 
in a safe and proper way by the concerned EP so that they can be reused in further 
analyses.  
• 
Results from mandatory and recommended control assessments in the field should be 
transmitted by NFCs and stored in the ICP Forests database to contribute to the evaluation 
of the quality indicators at the international level.  
                                               
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Data submission forms need to be adapted for a regular transmission of all quality information 
required from NFCs and their storage in the ICP Forests database.  
4.5.2 Specific procedure for evaluating the quality in labs 
Specific forms for quality information (QA/QC forms) were developed to monitor laboratory 
quality indicators and quantification limits. QA/QC forms must be submitted to the ICP Forests 
database together with the respective monitoring data. They are presently available for the 
surveys of soil and soil solution, foliar chemistry, deposition, and ground vegetation biomass 
and nutrients.  
The results of the corresponding interlaboratory comparison tests (i.e. ring tests) are submitted 
to the ICP Forests database by the ring test organizers and can be linked to QA/QC forms. 
Each measured value for each variable can thus be linked to the respective laboratory quality 
indicator and ringtest result. For each single dataset this provides information on the quality 
and the uncertainties of the data. 
The quantification limit of each variable enables the assignment of the code “-1” for values 
below the quantification limit in the data files. 
5 Responsibilities for data quality 
Within the limits of the resources at their disposal (but keeping in mind the fundamental 
importance of the implementation of quality procedures for the relevance of any investment 
made in long-term monitoring): 
- 
NFCs are responsible for the proper application of the methods and QA/QC procedures 
defined in the ICP Forests Manual for collecting and reporting data of required quality 
from their respective country; 
- 
EPs and WGs are responsible, within their respective fields of application and with the 
support of the PCC, for: 
o the definition and update of proper methods in the ICP Forests Manual including 
QA/QC requirements, procedures, and response actions following the Quality 
Assurance programme described in this Manual part, 
o the organization and reporting of international training courses, international 
intercomparison courses, and international tests of methods,  
o the regular evaluation of the implementation of the QA/QC procedures, and of 
the quality indicators in their respective survey, 
o the validation of the data consistency, and the consideration of the data quality 
in the interpretation of results in ICP Forests publications and in the 
documentation of the data; 
- 
The QAC is responsible for the definition and update of the overall Quality Assurance 
programme described in this ICP Forests Manual part; 
- 
The PCC is responsible for: 
o the administration and development of the database, and its related forms and 
routines, in consistency with the methods and QA/QC procedures defined in the 
ICP Forests Manual,  

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o the support of NFCs during the data submission/ uploading process including a 
set of automated consistency checks, and feedback to NFCs in case of quality 
problems detected in their data, 
o the provision of the data and their documentation to data users upon request 
following the ICP Forests data policy, 
o the edition and distribution of the ICP Forests Manual, the communication of 
Manual updates, and other ICP Forests publications with information on data 
quality issues,  
o the information of NFCs in case of continuing quality problems of their data or 
their failing of inter-comparison tests, and the moderation to overcome these 
problems together with the EPs and WGs. 
o supporting the coordinating bodies (EPs, WGs, QAC, TF) involved in the ICP 
Forests Quality Assurance programme. 
6 References and further reading 
Cline, S.P., Burkman, W.G., 1989. The role of quality assurance in ecological programs. In: Bucher J.B. 
and Bucher-Wallin I. (eds.), Air Pollution and Forest Decline, Proc. 14th Int. Meeting for 
Specialists on Air Pollution Effects on Forest Ecosystems, IUFRO P2.05, Interlaken, Switzerland, 
Oct. 2-8 (1988) Birmensdorf, pp.361-365. 
Cools N., Delanote V., Scheldeman X., Quataert P., De Vos B., Roskams P., 2004. Quality assurance 
and quality control in forest soil analyses: a comparison between European soil laboratories. 
Accred Qual Assur, 9:688–694 DOI 10.1007/s00769-004-0856-4 
Cools N., Mikkelsen J.H. and De Vos B., 2007. Quality assurance and quality control in forest soil 
analysis: 5th FSCC interlaboratory comparison, , Forest Soil Co-ordinating Centre / INBO, 
Geraardsbergen, 63 p. 
Cozzi A., Ferretti M. and Lorenz M., 2002. Quality Assurance for Crown Condition Assessment in 
Europe. Federal Research Centre for Forestry and Forest Products-LINNÆA ambiente Srl,111 p. 
EPA, 2002. Guidance for quality assurance project plans, EPA QA/G-5, EPA/240/R-02/009, United 
States Environmental Protection Agency, Washington, 111 p. (www.epa.gov/quality/qs-docs/g5-
final.pdf). 
Ferretti M., 2009. Quality Assurance in ecological monitoring—towards a unifying perspective. J. 
Environ. Monitor., 11, 726 – 729. 
Ferretti, M., 1997. Forest health assessment and monitoring. Issues for consideration, Environmental 
Monitoring and Assessment 48, 45-72. 
Ferretti M., König N., Rautio P., Sase H., 2009 Quality Assurance in international forest monitoring 
programmes: activity, problems and perspectives from East Asia and Europe Annals of Forest 
Sciences, 66: 403-415 
Ferretti M., Mues V.,  2002.  New Design of  International Cross-Calibration Courses of ICP Forests and 
the EU Scheme. Federal Research Centre for Forestry and Forest Products (BFH) -LINNÆA 
ambiente Srl, 21 p.  
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Quality Assurance within the ICP Forests Monitoring Programme  
Part III 
 
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