Orchard (orch)

National Reference Metadata in ESS Standard for Orchard survey Quality Report Structure (esqrsor)

Compiling agency: ISTAT


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

ISTAT
1.2. Contact organisation unit

DIPS/DCAT/ATC
1.5. Contact mail address

Viale Civiltà del lavoro, 50

00144 Rome

Italy


2. Statistical presentation Top
2.1. Data description

Main characteristics

2.1.1 Describe shortly the main characteristics of the statistics  

Structural orchard statistics provide data on the area, age and density for the species of Regulation (EU) N.1337/2011. For national interest also data on actinidia and cherry-trees are collected.

The statistics are collected through a sample survey at NUTS2 level.
2.1.2 Which of the fruit tree types are covered by the data collection?  Dessert pear trees
Dessert apple trees
Apricot trees
Dessert peach and nectarine trees
Orange trees
Small citrus fruit trees
Lemon trees
Olive trees
Table grape vines
Other
2.1.3 If Other, please specify

Actinidia, Cherry-trees.


Reference period

2.1.4 Reference period of the data collection 

2017
2.1.5 When was the data collection done (month(s) and year)

October 2017 - March 2018


National legislation

2.1.6 Is there a national legislation covering these statistics?  Yes
If Yes, please answer all the following questions.   
2.1.7 Name of the national legislation 

National Statistical Plan 2017-2019 (code IST-02680).
2.1.8 Link to the national legislation 

https://www.sistan.it/fileadmin/Repository/Home/PSN/Programma_statistico_nazionale/Psn_2017-2019/Elenco_ObbligoRisposta.pdf
2.1.9 Responsible organisation for the national legislation 

Istat
2.1.10 Year of entry into force of the national legislation 

2017
2.1.11 Please indicate which variables required under EU regulation are not covered by national legislation, if any.

None
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any. 

None
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?

Quality products, treatment of permanent crop residues, sales channels.
2.1.14 Is there a legal obligation for respondents to reply?  Yes


Additional comments on data description
2.2. Classification system

Species and variety group classification, age and density classifications available in RAMON
2.3. Coverage - sector

Growing of perennial crops (NACE A01.2)
2.4. Statistical concepts and definitions

See: Orchard statistics Handbook

2.4.1 Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? No
2.4.2 If Yes, please specify the items and the differences
2.4.3 If the fruits for industrial processing are not separately surveyed, are they included in dessert fruit categories? Yes
2.4.4 If yes, for which fruit tree types? Apple trees
Pear trees
Peach and nectarine trees
In case data are delivered for one of the items below, describe the 10 biggest varieties included in the item:   
2.4.5 Other dessert apples n.e.c.

The following varieties are included in Other dessert apples n.e.c..

We are not able to indicate the 10 biggest of them.

Rubens, Sansa, Summerfree, Gaia, Gemini, Abbondanza, Ambrosia, Delbarestivale , Gloster, Crimson Crisp, Opal, Primiera, Renoir, Topaz, Campanino, Delbar jubilé, Hapke, Harmione, Lavinia, Modì, Fujion, Smeralda
2.4.6 Other dessert pears n.e.c.

The following varieties are included in Other dessert pears n.e.c..

We are not able to indicate the 10 biggest of them.

 Nashi, Spadoncina, Volpina, Angelica, Angelys, Cascade, Harrow Sweet, Madernassa, Rosada, Rosired Bartlett, Sweet Sensation, Canal Red, Falstaff
2.4.7 Other oranges n.e.c.

We are not able to indicate the 10 biggest varieties included in Other oranges n.e.c..
2.4.8 Other small citrus fruits, including hybrids

We are not able to indicate the 10 biggest varieties included in Other small citrus fruits, including hybrids n.e.c..
2.5. Statistical unit

Utilised agricultural area used for the cultivation of permanent crops mentioned in point 2.1, cultivated by an agricultural holding producing entirely or mainly for the market. 
2.6. Statistical population

All agricultural holdings growing entirely or mainly for the market permanent crops mentioned in point 2.1.
2.7. Reference area
2.7.1 Geographical area covered

The entire territory of the country (Nuts 0).
2.7.2 Which special Member State territories are included?

Nuts 1 and Nuts 2 level.
2.8. Coverage - Time

2002-2017
2.9. Base period


3. Statistical processing Top
3.1. Source data

Overall summary

3.1.1 Total number of different data sources used

1
The breakdown is as follows: 
3.1.2 Total number of sources of the type "Census"

0
3.1.3 Total number of sources of the type "Sample survey"

1
3.1.4 Total number of sources of the type "Administrative source"

0
3.1.5 Total number of sources of the type "Experts"

0
3.1.6 Total number of sources of the type "Other sources"

0


Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.1 of the annexed Excel file 
3.1.7 Name/Title
3.1.8 Name of Organisation responsible
3.1.9 Main scope
3.1.10 Target fruit tree types
3.1.11 List used to build the frame
3.1.12 Any possible threshold values
3.1.13 Population size
3.1.14 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.1 of the annexed Excel file 
3.1.15 Name/Title

Survey on permanent crops - 2017
3.1.16 Name of Organisation responsible

Istat
3.1.17 Main scope

The survey on permamnent crops provide data on the area, age and density for the species of Regulation (EU) N.1337/2011. For national interest also data on actinidia and cherry-trees are collected.
3.1.18 Target fruit tree types Dessert apple trees
Dessert pear trees
Apricot trees
Dessert peach and nectarine trees
Orange trees
Small citrus fruit trees
Lemon trees
Olive trees
Table grape vines
Other
3.1.19 List used to build the frame

The frame was built by combining Census 2010's and IACS 2014's lists.
3.1.20 Any possible threshold values

For each species a threshold of 0.20 hectar was fixed in order to cut off very small holdings.
3.1.21 Population size

The population size was: 904.099 holdings.
3.1.22 Sample size

The sample size was: 25.965 holdings.
3.1.23 Sampling basis List
3.1.24 If Other, please specify
3.1.25 Type of sample design Stratified
3.1.26 If Other, please specify
3.1.27 If Stratified, number of strata

Initial number of strata: 918
3.1.28 If Stratified, stratification criteria Unit location
Unit specialization
3.1.29 If Other, please specify
3.1.30 Additional comments


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.1 of the annexed Excel file 
3.1.31 Name/Title
3.1.32 Name of Organisation responsible
3.1.33 Contact information (email and phone)
3.1.34 Main administrative scope
3.1.35 Target fruit tree types 
3.1.36 Geospatial Coverage
3.1.37 Update frequency
3.1.38 Legal basis
3.1.39 Are you able to access directly to the micro data?
3.1.40 Are you able to check the plausibility of the data, namely by contacting directly the units?
3.1.41 How would you assess the proximity of the definitions and concepts (including statistical units) used in the administrative source with those required in the EU regulation?
3.1.42 Please list the main differences between the administrative source and the statistical definitions and concepts
3.1.43 Is a different threshold used in the administrative source and statistical data?
3.1.44 If Yes, please specify
3.1.45 Additional comments


Experts

If there is more than one Expert source, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.46 Name/Title
3.1.47 Primary purpose
3.1.48 Target fruit tree types
3.1.49 Legal basis
3.1.50 Update frequency
3.1.51 Expert data supplier
3.1.52 If Other, please specify
3.1.53 How would you assess the quality of those data?
3.1.54 Additional comments


Other sources

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.55 Name/Title
3.1.56 Name of Organisation
3.1.57 Primary purpose
3.1.58 Target fruit tree types 
3.1.59 Data type
3.1.60 If Other, please specify
3.1.61 How would you assess the quality of those data?
3.1.62 Additional comments
3.2. Frequency of data collection

Every 5 years
3.3. Data collection

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.3 of the annexed Excel file 
3.3.1 Name/Title
3.3.2 Methods of data collection
3.3.3 If Other, please specify
3.3.4 If face-to-face or telephone interview, which method is used?
3.3.5 Data entry method, if paper questionnaires?
3.3.6 Please annex the questionnaire used (if very long: please provide the hyperlink)
3.3.7 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.3 of the annexed Excel file 
3.3.8 Name/Title

Survey on permanent crops 2017
3.3.9 Methods of data collection Face-to-face interview
3.3.10 If Other, please specify
3.3.11 If face-to-face or telephone interview, which method is used? Paper questionnaire
3.3.12 Data entry method, if paper questionnaires? Manual
3.3.13 Please annex the questionnaire used (if very long: please provide the hyperlink)

 
3.3.14 Additional comments

Data were collected through a paper questionnaire; at a later time data collected were entered by the enumerator in an electronic questionnaire.


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.3 of the annexed Excel file 
3.3.15 Name/Title
3.3.16 Extraction date
3.3.17 How easy is it to get access to the data?
3.3.18 Data transfer method
3.3.19 Additional comments


Experts

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.3 of the annexed Excel file 
3.3.20 Name/Title
3.3.21 Methods of data collection
3.3.22 Additional comments


Annexes:
Italian questionnaire Survey on permanent crops 2017
3.4. Data validation
3.4.1 Which kind of data validation measures are in place? Manual
Automatic
3.4.2 What do they target? Completeness
Aggregates
Consistency
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process
The electronic questionnaire used to enter the data collected implemented the algorithms of accounting and logical control. At the same time, the applications did not allow the omission of the questions which were required to be completed on certain “paths” of the interview. As a consequence, row data were of good quality as regards internal coherence of the questionnaire.
To detect critical units with potentially influential errors a selective editing procedure was applied (through an R package developed by Istat called SeleMix).
Imputation (by interactive treatment) was limited to a very few cases (clearly identified as potential errors). We consider negligible the effects of imputation on the estimates.
 
The sample design is a probability design: one-stage stratified random sampling of holdings.  Simple random sampling was used to draw units in each stratum. Stratum and initial weights were identified by the iterative genetic algorithm used to extract the sample (through the R package: Sampling strata).
 
Design weights were adjusted for non response by multiplying by the inverse of the response rate on each stratum (in few cases the factor has been computed collapsing two similar strata).
 
Final weights were obtained through calibration to include auxiliary information in order to achieve the consistency of sample estimates with respect to some known totals of the population (most recent and reliable sources available).
 
 
3.5.2 Additional comments
3.6. Adjustment

None


4. Quality management Top
4.1. Quality assurance
4.1.1 Is there a quality management system used in the organisation? No
4.1.2 If yes, how is it implemented?
4.1.3 Has a peer review been carried out? No
4.1.4 If Yes, which were the main conclusions?
4.1.5 What quality improvements are foreseen? Improve data validation
4.1.6 If Other, please specify
4.1.7 Additional comments
4.2. Quality management - assessment

Development since the last quality report

4.2.1 Overall quality Stable
4.2.2 Relevance Stable
4.2.3 Accuracy and reliability Stable
4.2.4 Timeliness and punctuality Improvement
4.2.5 Comparability Stable
4.2.6 Coherence Stable
4.2.7 Additional comments


5. Relevance Top
5.1. Relevance - User Needs
5.1.1 If certain user needs are not met, please specify which and why

No unmet user needs known.
5.1.2 Please specify any plans to satisfy needs more completely in the future
5.1.3 Additional comments
5.2. Relevance - User Satisfaction
5.2.1 Has a user satisfaction survey been conducted? No
If Yes, please answer all the following questions 
5.2.2 Year of the user satisfaction survey
5.2.3 How satisfied were the users?
5.2.4 Additional comments
5.3. Completeness
5.3.1 Data completeness - rate

100%
5.3.2 If not complete, which characteristics are missing?
5.3.3 Additional comments


6. Accuracy and reliability Top
6.1. Accuracy - overall
6.1.1 How good is the accuracy? Good
6.1.2 What are the main factors lowering the accuracy? Sampling error
6.1.3 If Other, please specify
6.1.4 Additional comments
6.2. Sampling error

Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.2 of the annexed Excel file 
6.2.1 Name/Title

Survey on permanent crops 2017
6.2.2 Methods used to assess the sampling error Relative standard error
6.2.3 If Other, please specify
6.2.4 Methods used to derive the extrapolation factor Basic weight
Non-response
6.2.5 If Other, please specify
6.2.6 If coefficients of variation are calculated, please describe the calculation methods and formulas

The variance estimator for national estimates is defined by formula 5.14 of Estevao, Hidiroglou and Särndal (1995) “Methodological Principles for a Generalized Estimation System at Statistics Canada”, Journal of Official Statistics, vol. 11, n.2, pp181-204 that is implemented in the software ReGenesees (available on Istat’s web site:
https://www.istat.it/it/metodi-e-strumenti/metodi-e-strumenti-it/elaborazione/strumenti-di-elaborazione/regenesees)

 
6.2.7 Sampling error - indicators

Please provide the coefficients of variation in % 

  CV (%)
Dessert apple trees  0,9
Apple trees for industrial processing  not applicable
Dessert pear trees 1,7
Pear trees for industrial processing  not applicable
Apricot trees 2,8
Dessert peach and nectarine trees 1,0
Peach and nectarine trees for industrial processing (including group of Pavie)  not applicable
Orange trees 1,8
Small citrus fruit trees 4,4
Lemon trees 4,9
Olive trees 0,2
Table grape vines 5,4
6.2.8 Additional comments
6.3. Non-sampling error

See sections below.
6.3.1. Coverage error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.1 Name/Title
Over-coverage
6.3.1.2 Does the sample frame include wrongly classified units that are out of scope?
6.3.1.3 What methods are used to detect the out-of scope units?
6.3.1.4 Does the sample frame include units that do not exist in practice?
6.3.1.5 Over-coverage - rate
6.3.1.6 Impact on the data quality
Under-coverage
6.3.1.7 Does the sample frame include all units falling within the scope of this survey?
6.3.1.8 If Not, which units are not included?
6.3.1.9 How large do you estimate the proportion of those units? (%)
6.3.1.10 Impact on the data quality
Misclassification
6.3.1.11 Impact on the data quality
Common units
6.3.1.12 Common units - proportion
6.3.1.13 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.14 Name/Title

Survey on permanent crops 2017
Over-coverage
6.3.1.15 Does the sample frame include wrongly classified units that are out of scope? Yes
6.3.1.16 What methods are used to detect the out-of scope units?

During the interview the presence of area cultivated in at least one of the species of interest was verified. If not, the interview was interrupted and the unit classified as out of target.
6.3.1.17 Does the sample frame include units that do not exist in practice? Yes
6.3.1.18 Over-coverage - rate

The estimate of over coverage- based on the results of the survey- is about 7%, considering both the out of target and the ceased units.
6.3.1.19 Impact on the data quality Low
Under-coverage 
6.3.1.20 Does the sample frame include all units falling within the scope of this survey? No
6.3.1.21 If Not, which units are not included?

The units not included in IACS 2014 register (the frame).
6.3.1.22 How large do you estimate the proportion of those units? (%)

The rate of undercoverage is not known but it is supposed to be low.
6.3.1.23 Impact on the data quality Low
Misclassification
6.3.1.24 Impact on the data quality Low
Common units 
6.3.1.25 Common units - proportion

Not applicable
6.3.1.26 Additional comments

The reference period of the frame was different from that of the survey. This leads to under/over coverage errors. In the near future we expect the timing of the frame updates to improve.


Administrative data

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.27 Name/Title of the administrative source
Over-coverage
6.3.1.28 Does the administrative source include wrongly classified units that are out of scope?
6.3.1.29 What methods are used to detect the out-of scope units?
6.3.1.30 Does the administrative source include units that do not exist in practice?
6.3.1.31 Over-coverage - rate
6.3.1.32 Impact on the data quality
Under-coverage
6.3.1.33 Does the administrative source include all units falling within the scope of this survey?
6.3.1.34 If Not, which units are not included?
6.3.1.35 How large do you estimate the proportion of those units? (%)
6.3.1.36 Impact on the data quality
Misclassification 
6.3.1.37 Impact on the data quality
6.3.1.38 Additional comments
6.3.2. Measurement error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.1 Name/Title
6.3.2.2 Is the questionnaire based on usual concepts for respondents?
6.3.2.3 Number of censuses already performed with the current questionnaire?
6.3.2.4 Preparatory testing of the questionnaire?
6.3.2.5 Number of units participating in the tests? 
6.3.2.6 Explanatory notes/handbook for surveyors/respondents? 
6.3.2.7 On-line FAQ or Hot-line support for surveyors/respondents?
6.3.2.8 Are there pre-filled questions?
6.3.2.9 Percentage of pre-filled questions out of total number of questions
6.3.2.10 Other actions taken for reducing the measurement error?
6.3.2.11 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.12 Name/Title

Survey on permanent crops 2017.
6.3.2.13 Is the questionnaire based on usual concepts for respondents? Yes
6.3.2.14 Number of surveys already performed with the current questionnaire?

At least 3 previous occurences.
6.3.2.15 Preparatory testing of the questionnaire? No
6.3.2.16 Number of units participating in the tests? 
6.3.2.17 Explanatory notes/handbook for surveyors/respondents?  Yes
6.3.2.18 On-line FAQ or Hot-line support for surveyors/respondents? Yes
6.3.2.19 Are there pre-filled questions? No
6.3.2.20 Percentage of pre-filled questions out of total number of questions

 
6.3.2.21 Other actions taken for reducing the measurement error?

1. Training of the enumerators;

2. Checks (automatic sums, coherence controls) in the software tool used for data entry;

3. Correction of the data collected with a selective editing procedure.
6.3.2.22 Additional comments
6.3.3. Non response error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.1 Name/Title of the survey
6.3.3.2 Unit non-response - rate
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context?
6.3.3.4 Measures taken for minimising the unit non-response
6.3.3.5 If Other, please specify
6.3.3.6 Item non-response rate
6.3.3.7 Item non-response rate - Minimum
6.3.3.8 Item non-response rate - Maximum
6.3.3.9 Which items had a high item non-response rate? 
6.3.3.10 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.11 Name/Title of the survey

Survey on permanent crops 2017.
6.3.3.12 Unit non-response - rate

4.8% (refusals and failed contacts)
6.3.3.13 How do you evaluate the recorded unit non-response rate in the overall context? Low
6.3.3.14 Measures taken for minimising the unit non-response Weighting
6.3.3.15 If Other, please specify
6.3.3.16 Item non-response rate

No item non response since in the electronic questionnaire used to enter the data, the fields concerned all the data necessary to fulfill the Regulation were compulsory.

6.3.3.17 Item non-response rate - Minimum

 Not applicable
6.3.3.18 Item non-response rate - Maximum

 Not applicable
6.3.3.19 Which items had a high item non-response rate? 

Not applicable
6.3.3.20 Additional comments
6.3.4. Processing error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.1 Name/Title
6.3.4.2 Imputation - rate
6.3.4.3 Imputation - basis
6.3.4.4 If Other, please specify
6.3.4.5 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.6 Name/Title

Survey on permanent crops 2017.
6.3.4.7 Imputation - rate
The electronic questionnaire used to enter the data collected contained the algorithms of accounting and logical control. At the same time, the applications did not allow the omission of the questions which were required to be completed on certain “paths” of the interview. As a consequence, row data were of good quality and imputation (by interactive treatment) was limited to a very few cases, clearly identified as potential errors.
 
 
6.3.4.8 Imputation - basis
6.3.4.9 If Other, please specify

 
6.3.4.10 How do you evaluate the impact of imputation on Coefficients of Variation? Not important
6.3.4.11 Additionnal comments
6.3.5. Model assumption error

No model assumption error
6.4. Seasonal adjustment

None
6.5. Data revision - policy

None
6.6. Data revision - practice
6.6.1 Data revision - average size

No data revision
6.6.2 Were data revisions due to conceptual changes (e.g. new definitions)  carried out since the last quality report?
6.6.3 What was the main reason for the revisions?
6.6.4 How do you evaluate the impact of the revisions?
6.6.5 Additional comments


7. Timeliness and punctuality Top
7.1. Timeliness
7.1.1 When were  the first  results for the reference period published?

February 2019
7.1.2 When were  the final results for the reference period published?

February 2019
7.1.3 Reasons for possible long production times?

 
7.2. Punctuality
7.2.1 Were data released nationally according to a pre-announced schedule (Release Calendar)? Yes
7.2.2 If Yes, were data released on the target date? No
7.2.3 If No, reasons for delays?

Technical problems encountered and overlapping of other activities.
7.2.4 Number of days between the national release date of data and the target date

30


8. Coherence and comparability Top
8.1. Comparability - geographical

To be assessed by Eurostat
8.1.1. Asymmetry for mirror flow statistics - coefficient
8.2. Comparability - over time
8.2.1 Length of comparable time series

From 2002
8.2.2 Have there been major breaks in the time series? No
8.2.3 If Yes, please specify the year of break and the reason
8.2.4 Additional comments
8.3. Coherence - cross domain
8.3.1 With which other national data sources have the data been compared? Annual crop statistics 2017
FSS 2016
Other
8.3.2 If Other, please specify

IACS 2015
8.3.3 Describe briefly the results of comparisons

Results should be expressed as percentage deviation from the corresponding areas in the orchard survey.

  Annual Crop Statistics (2017) FSS (2016) IACS 2015 Other source
Dessert apple trees  2,6  2,4  Not applicable  
Apple trees for industrial processing  Not applicable Not applicable  Not applicable  
Dessert pear trees  10,9  -8,4  -2,2  
Pear trees for industrial processing  Not applicable  Not applicable  Not applicable  
Apricot trees  0,5  -2,4  -5,5  
Dessert peach and nectarine trees  23,7 3,9 -1,4  
Peach and nectarine trees for industrial processing (including group of Pavie)  Not applicable  Not applicable  Not applicable  
Orange trees  6,9  0,9   Not applicable  
Small citrus fruit trees  32,7  9,8   Not applicable  
Lemon trees  50,4  -25,4   Not applicable  
Olive trees  9,3  -3,5  2,2  
Table grape vines  10,6  13,0  -1,2  

 

The differences between the results of the Permanent crops survey and other statistical
sources (ACS, FSS 2016, IACS) are mainly due to:

1- differences in definitions, reference period, statistical unit, etc.;

2- different technique of data collection;

3- different scope of data collection.

As regards ACS, the most important source of discrepancies is the difference between the data collection methods: the Orchard Survey is a sample survey based on direct interviews to the holdings, while ACS is based on experts elicitation and/or on the IACS data (on average, for the 50% of Italian Regions). Moreover, the Orchards data collection includes only the data on farms producing mostly for the market and very small holdings are cut off from the observation. Two different statistical surveys, even though referred to similar domains, cannot produce the same results. The use of calibration techniques (which as a matter of fact has not been adopted for Italian agriculture statistics with regards to ACS) would be misleading, because the use of ACS constraints for producing estimated totals derived from Orchard would solve problems only apparently. As already stated in other contexts, we do not consider a good practice the use of short-term surveys (e.g., infra-annual measurements) as ACS are for structural analysis purposes: that is not done in any other context – as business statistics – since it is well known that precision of structural surveys (as Orchard) should always be higher than short-term, which however are available in advance.

As regards FSS: even if both Surveys on permanent crops and FSS are based on a sample survey, the constrains adopted to draw the sample (and the holdings) are very different, since two different Regulations rule the surveys. In particular, FSS constrains concern structural variables of the holding (UAA, UBA, and other aggregated variables), which are different from the target variables of permanent crops statistics, so that the accuracy level for permanent crops should be larger in Orchard than in FSS. Moreover, the reference period is different.

As regards the administrative data (IACS): at the moment data are not available for some species because they belong to very aggregated items (for example, the total area cultivated for Citrus tree is present, but it is not available the breakdown into the different species). Where the comparison is possible, the results appear consistent, considering the different scope of the data collections and the different reference periods.

 

 

2012: In FSS only main crop area has to be surveyed. In ACS also secondary and successive crop area are counted. But the most important source of discrepancies (explaining also the case of ACS data bigger than Orchard Survey) are the two different methods of data collection:
 Orchard Survey is a sample survey based on direct interviews to the holdings, while ACS is based on experts elicitation.

 
8.3.4 If no comparisons have been made, explain why
8.3.5 Additional comments
8.4. Coherence - sub annual and annual statistics

Not applicable
8.5. Coherence - National Accounts

Not applicable
8.6. Coherence - internal

Not applicable


9. Accessibility and clarity Top
9.1. Dissemination format - News release
9.1.1 Do you publish a news release? No
9.1.2 If Yes, please provide a link
9.2. Dissemination format - Publications
9.2.1 Do you produce a paper publication? No
9.2.2 If Yes, is there an English version?
9.2.3 Do you produce an electronic publication? No
9.2.4 If Yes, is there an English version?
9.2.5 Please provide a link
9.3. Dissemination format - online database
9.3.1 Data tables - consultations

Data tables are published on Istat's website in a specific area dedicated to Agricultural surveys.
9.3.2 Is an on-line database accessible to users? Yes
9.3.3 Please provide a link

agri.istat.it
9.4. Dissemination format - microdata access
9.4.1 Are micro-data accessible to users? Yes
9.4.2 Please provide a link

Microdata are accessible only for selected users and on justified demand. They are stored in ARMIDA, a storage of all validated microdata of Istat's surveys. 

Microdata are anonymous.
9.5. Dissemination format - other

Data are published at the following link: www.agri.istat.it
9.6. Documentation on methodology
9.6.1 Are national reference metadata files available? Yes
9.6.2 Please provide a link

siqual.istat.it
9.6.3 Are methodological papers available? No
9.6.4 Please provide a link
9.6.5 Is a handbook available? Yes
9.6.6 Please provide a link 
http://siqual.istat.it/SIQual/welcome.do


From English version:  Search documents, Documents, Year 2017, Operational documents (the second one: Guida per l'intervistatore (2017)).
9.7. Quality management - documentation
9.7.1 Metadata completeness - rate

Not applicable
9.7.2 Metadata - consultations

Not applicable
9.7.3 Is a quality report available? No
9.7.4 Please provide a link


10. Cost and Burden Top
10.1 Efficiency gains if compared to the previous quality report Other
10.2 If Other, please specify

web monitoring of data and on line data entry
10.3 Burden reduction measures since the previous quality report More user-friendly questionnaires
Easier data transmission
Other
10.4 If Other, please specify
The major efforts to reduce respondent concern:
a) data collected by the survey were limited to the information strictly necessary to comply the Regulation;
b) electronic questionnaire was used to facilitate the data collection;
c) best estimates and approximations were accepted when exact details were not readily available;
d) burden on individual respondents was limited to the extent possible by minimizing the overlap with other surveys (FSS 2016).


11. Confidentiality Top
11.1. Confidentiality - policy
11.1.1 Are confidential data transmitted to Eurostat? No
11.1.2 If yes, are they confidential in the sense of Reg. (EC) 223/2009?
11.1.3 Describe the data confidentiality policy in place
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

At least 3 observations for each cell.
11.2.2 Additional comments


12. Comment Top


Related metadata Top


Annexes Top
ESQRS_ANNEX_IT