Crop production (apro_cp)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: NATIONAL INSTITUTE OF STATISTICS


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

NATIONAL INSTITUTE OF STATISTICS
1.2. Contact organisation unit

DEPARTMENT OF AGRICULTURE AND ENVIRONMENTAL STATISTICS
1.5. Contact mail address

BD. LIBERTATII NO. 16, ZIP CODE 050706, BUCHAREST-ROMANIA


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops and production and yield levels. The statistics are collected from following sources: surveys, administrative sources and expert estimates. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).
2.2. Classification system

Hierarchical crop classification system
2.3. Coverage - sector

Growing of non-perennial crops, perennial crops and plant propagation (NACE A01.1-01.3)
2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook
2.5. Statistical unit

The agricultural holding that used the agricultural area.
2.6. Statistical population

All agricultural holdings that used the agricultural area.
2.7. Reference area

The entire territory of the country.
2.8. Coverage - Time

Crop year 2021-2022.
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous quality report?  NO      
If yes, which new data sources have been introduced since the previous quality report?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous quality report? NO      
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments


Data sources: Please indicate the data sources which were used for the reference year on which is reported

  Type Name(s) of the sources If other type, which kind of data source?
Table 1: Crops from arable land      
Early estimates for areas Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts

  
Final area under cultivation Survey

Main crop production
Production Survey
Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts

Main crop production
Yield Survey
Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts

Main crop production
Non-existing and non-significant crops Survey

Main crop production
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts
Final harvested area Survey

Main crop production
Production Survey
Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts

Main crop production
Non-existing and non-significant crops Survey

Main crop production
Table 3: Permanent crops      
Early estimates for production area Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts
Final production area Survey

Main crop production
Production Survey
Expert estimate

Estimates of the Ministry of Agriculture and Rural Development experts

Main crop production
Non-existing and non-significant crops Survey

Main crop production
Table 4: Agricultural land use      
Main area Survey

Main crop production
Non-existing and non-significant crops Survey

Main crop production.
Total number of different data sources

2

  
Additional comments

Data source for the humidity

Put x, if used

Surveyed: farmers report the humidity

 

Surveyed: farmers convert the production/yield into standard humidity     

 

Surveyed: whole sale purchasers report the humidity

 

Surveyed: whole sale purchasers convert the production/yield into standard humidity     

 

Surveyed by experts (e.g. test areas harvested and measured)

 

Estimated by experts

 x

Other type

 

If other type, please explain

 

Additional information

 
   


Which method is used for calculating the yield for main arable crops? Production divided by sown area
If another method, describe it.




 
3.2. Frequency of data collection

  Source 1 Source 2 Source 3 Source 4 Source 5 Source 6 Source 7 Source 8 Source 9
Name of data source

Main crop production

Estimates of the Ministry of Agriculture and Rural Development experts
Planning (month-month/year)

January-March 2022
Preparation (month-month/year)

April-October 2022
Data collection (month-month/year)

November 2022

January-November 2022
Quality control (month-month/year)

December 2022-January 2023

January-November 2022
Data analysis (month-month/year)

February-May 2023

January-November 2022
Dissemination (month-month/year)

31 March 2023 press release; 31 May 2023 publication

January-November 2022
If there were delays, what were the reasons?






 
3.3. Data collection

Definitions Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption? NO
Are special estimation/calculation methods used for main land use? NO
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? NO  
In case yes, how do they differ? ( list all items and explanations)
In case data are delivered for one of the items below, describe the crop species included in the item: C1900 - Other cereals n.e.c. (buckwheat, millet, etc.)
P9000 - Other dry pulses and protein crops n.e.c.
R9000 - Other root crops n.e.c.
I1190 -Other oilseed crops n.e.c
I9000 - Other industrial crops n.e.c.
G2900 - Other leguminous plants harvested green n.e.c.
G9100- Other cereals harvested green (excluding green maize)
G9900 - Other plants harvested green from arable land n.e.c.
V1900 - Other brassicas n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
V4900 - Other root, tuber and bulb vegetables n.e.c.
V5900 - Other fresh pulses n.e.c.
V9000 - Other fresh vegetables n.e.c.
U1900 - Other cultivated mushrooms n.e.c.
F1190 - Other pome fruits n.e.c.
F3900 - Other berries n.e.c.

C1900 includes (millet, buckwheat, canary seed);

P9000 includes (chick peas, broad bean, lentils and vetches);

R9000 includes (fodder beet, fodder carrots, turnips);

I1190 includes (mustard, poppy, castor oil plant, sesame seed, safflower);

I9000 includes sorghum for brooms;

G2900 includes (clover and other perennial forage);

G9100 includes cereals harvested green (excluding green maize) used as feed or to produce biomass;

G9900 includes (meslin, Sudan grass, sorghum);

V1900 includes kohlrabi;

V2900 includes (green onions, green garlic, parsley, dill, lovage);

V3900 includes okra;

V4900 includes (horse-radish, parsnips, parsley root);

V5900 includes green beans;

V9000 includes unspecified vegetables that are grown individually in Romania;

U1900 includes mushrooms (Pleurotus);

F1190 includes quinces;

F3900 includes blackberries;

ARA99 includes other arable land grown individually;

PECR9 includes plaiting and weaving plants.

 


Population

Which measures were taken in order to make sure that the requirement stipulated in Art. 3.2 are met?
(Statistics shall be representative of at least 95 % of the areas of each table in the Regulation).

Romania observed the provisions art.3.2 of Regulation No 543/2009 of the European Parliament and of the Council.
Is the data collection based on holdings? YES
If yes, how the holdings were identified? Unique statistical farm identifier
If not, on which unit the data collection is based on?
When was last update of the holding register? (month/year)

June/2022
Was a threshold applied? NO
If yes, size of the excluded area  Area excluded on the basis of the threshold in % of the total area for that crop
Cereals for the production of grain (in %)
Dried pulses and protein crops (in %)
Root crops (in %)
Oilseeds (in %)
Other industrial crops (included all industrial crops besides oilseeds)  (in %)
Plants harvested green from arable land (in %)
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)


Survey method (only for census and surveys)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Main crop production
Which survey method was used? Face-to-face interview
Other
If 'other', please specify

Based on self-registration for the legal units
Please provide a link to the questionnaire

http://www.insse.ro
Data entry method, if paper questionnaires? Manual


Administrative data (This question block is only for administrative data)

  Admin source 1 Admin source 2 Admin source 3 Admin source 4 Admin source 5 Admin source 6
Name of the register
Description
Data owner (organisation)
Update frequency
Reference date (month/year)
Legal basis
Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
Were the data used for sample frame?
Data used for other purposes, which?
Which variables were taken from administrative sources?
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?
What were the possible limitations, drawbacks of using the data from administrative source(s)?


Expert estimations (This question block is only for expert estimates)

  Expert estimate 1 Expert estimate 2 Expert estimate 3 Expert estimate 4 Expert estimate 5 Expert estimate 6
Name of the estimation

Cultivated/harvested areas, yields and main crop productions.
Data owner (organisation)

Ministry of Agriculture and Rural Development
Update frequency (e.g. 1 year or 6 months) Monthly
Reference date (Month/Year  e.g. 1/22 - 8/22)

1/2022-11/2022
Legal basis

Regulation No 543/2009.
Use purpose of the estimates?

The early estimates data were only used for transmission to Eurostat.
What kind of expertise the experts have?

Specialised technical expertise in agriculture.
What kind of estimation methods were used?

1.The cereal production evaluation is carried out in the ripening stage by means of a metric frame with 1m sides. The plants within the frame are cut, the ears are shaken and the grains are weighed. Several samples are taken and the grains of all samples are weighed, the result being divided to the number of samples and we get the production in sq.m., which is then multiplied by 10000 sq.m. and we find out the production per hectare.

2.The pasture production was evaluated by the direct method (repeated cuttings method). The direct method relies on the cutting of test areas at every grazing cycle and th related production weighing.

3.The vegetable production evaluation is dependent upon: minimal area for evaluation, density (number of plants per hectare), number of fruits per plant, average weight per fruit, variety production potential, plant health status and application of a suitable technology. To evaluate the production diagonal samples are taken.s The number and area of the samples are determined according to the species, variety and cultivated area. We calculate the average production of the samples and the production per hectare is reported.

4.The fruits are evaluated depending on: species, variety, cropping system (classical,intensive,superintensive). The minimum numer of fruit trees examined is of 2% for intensive and superintensive plantations and of 5% in the classical system. In order to have evaluations as real as possible, the selected trees should be very representative in vigour, health status and fruit load arranged on the parcel diagonal avoiding the side rows. The evaluation is made by numbering the fruits of the trees selected as an examination test. We calculate the average production per fruit tree. Average production/hectare = average production/tree*number of trees/ha.
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO
If yes, please describe the differences
What measures were taken to eliminate the differences?

Comparisons with previous year estimates and final data.
How were the reliability, accuracy and coherence (comparison to other available data) of the data originated from experts' estimates (ante- and/or ex-post)checked?

The data obtained by early estimates have an acceptable overall accuracy.
What were the possible limitations, drawbacks of using the data from expert estimate(s)?

Evaluating the possibility use of administrative sources (Agricultural Register).
Additional comments


 
3.4. Data validation

Which kind of data validation measures are in place? Automatic and Manual
What do they target? Completeness
Outliers
Aggregate calculations
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
If other, please describe






 
3.5. Data compilation

Not applicable.
3.6. Adjustment

Not applicable.


4. Quality management Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since the previous quality report? Improvement Improvement Improvement Improvement Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Quality report

        
Is there a quality report available? NO        
If yes, please provide a link(s)

 

        
To which data source(s) is it linked?

 

        
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years? Systematic validation improvements
Quality report		        
If, other, please specify        
Additional comments        






 
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? NO
Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs? YES
Does the ESS agreement meet the national needs? YES
If not, which additional data are collected?
Additional comments






 
5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? NO
If yes, how satisfied the users were?
Additional comments






 
5.3. Completeness

97.7%

Please refer to the European level Quality Report for more information.
5.3.1. Data completeness - rate

97.7%

Please refer to the European level Quality Report for more information.


6. Accuracy and reliability Top
6.1. Accuracy - overall

The data obtained from the main crop production survey have a good overall accuracy in accordance with Regulation no 543/2009. The coefficient of variation shall not exceed, at national level, 3% for the area under cultivation for each of the following groups of main crops: cereals for the production of grain (including seed), dried pulses and protein crops for the production of grain (including seed and mixtures of cereals and pulses), root crops, industrial crops and plants harvested green.
6.2. Sampling error

Sampling method and sampling error

  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Main crop production survey
Sampling basis? List
If 'other', please specify
Sampling method? Stratified
If stratified, number of strata?

Cereals=361; Dry pulses=160; Oilseeds=146; Root crops=141; Plants harvested green from arable land=167; Industrial crops=137
If stratified, stratification basis? Location
Size
Legal status
If 'other', please specify
Size of total population

3636732
Size of sample

57431
Which methods were used to assess the sampling error?  Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
Non-response
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Calculated by SAS application.
If the results were compared with other sources, please describe the results

 
Which were the main sources of errors?

 Not applicable.


Sampling error - indicators

Relative standard error (RSE):

- Cereals=0.5%

- Dry pulses=0.3%

- Oilseeds=4.4%

- Root crops=0.3%

- Plants harvested green from arable land=1.1%

- Industrial crops=4.1%


Coefficient of variation (CV) for the area (on the MS level)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Main crop production
Cereals for the production of grain (in %)

0.4%
Dried pulses and protein crops (in %)

1.0%
Root crops (in %)

1.7%
Oilseeds (in %)

1.8%
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

1.5%
Plants harvested green from arable land (in %)

1.3%
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)
Additional comments            




 
6.3. Non-sampling error
6.3.1. Coverage error

Over-coverage - rate

1.1%


Common units - proportion

1.6%


  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Main crop production
Error type Contact errors
Degree of bias caused by coverage errors Low
What were the reasons for coverage errors?

The Register of Agricultural Holdings was made up of data from the General Agricultural Census 2010 and is updated annually with surveys data (which have a small share in the total holdings of 1.6%).
Which actions were taken for reducing the error or to correct the statistics?

Data corrected by the coordinators.

Included new units and recontacted units.
Additional comments




 
6.3.2. Measurement error

  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Main crop production
Was the questionnaire based on usual concepts for respondents? YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

14
Preparatory (field) testing of the questionnaire? YES
Number of units participating in the tests? 

50
Explanatory notes/handbook for surveyors/respondents?  YES
On-line FAQ or Hot-line support for surveyors/respondents? YES
Were pre-filled questionnaires used? NO
Percentage of pre-filled questions out of total number of questions

0
Were some actions taken for reducing the measurement error or to correct the statistics? YES
If yes, describe the actions and their impact

Correlations between the questionnaire (indicators, chapters, control totals) and the control tables.






 
6.3.3. Non response error

Unit non-response - rate

2.3%


Item non-response - rate

No


  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Main crop production
Unit level non-response rate (in %)

2.3%
Item level non-response rate (in %)              
               - Min% / item

 
               - Max% / item
               - Overall%
Was the non-response been treated ? YES
Which actions were taken to reduce the impact of non-response?

Recalculation of grossing-up coefficients
Which items had a high item-level non-response rate? 

None
Which methods were used for handling missing data?
(several answers allowed)		 Imputations
Weighting
In case of imputation which was the basis? Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

 2.3%
Estimated degree of bias caused by non-response? Insignificant
Which tools were used for correcting the data?

IT application module for correcting the grossing-up coefficients according to non-response.
Which organisation did the corrections?

National Institute of Statistics
Additional comments




 
6.3.4. Processing error

Not applicable.
6.3.4.1. Imputation - rate

2.3%.
6.3.5. Model assumption error

Not applicable.
6.4. Seasonal adjustment

Not applicable.
6.5. Data revision - policy

According requirements Eurostat.
6.6. Data revision - practice

For years 2000-2015 the data was revised according requirement Eurostat.
6.6.1. Data revision - average size

For years 2000-2015 the data was revised according requirement Eurostat. (16 years)


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result


Time lag - final result


  Cereals Dried pulses and protein crops Root crops Oilseeds Other industrial crops Plants harvested green Vegetables and melons Strawberries Cultivated mushrooms Fruit trees Berries Nut trees Citrus fruit trees Vineyards Olive trees
How many main data releases there are yearly in the national crop statistics for the following types of crops?

9

7

7

9

6

7

2

1

1

3

1

1

0

1

0
How many of them are forecasts (releases before the harvest)?

8

6

6

8

5

6

3

0

0

2

0

0

0

0

0
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 31/01/2022 31/05/2022 31/05/2022 31/01/2022 30/06/2022 30/06/2022 31/05/2022 31/05/2022
When were the final results published for the crop year on which is reported? (day/month/year) 31/05/2023 31/05/2023 31/05/2023 31/05/2023 30/06/2023 31/05/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023 31/03/2023
Additional comments




 
7.2. Punctuality

See the European level Quality Report.
7.2.1. Punctuality - delivery and publication

See the European level Quality Report.

Press release=31.03.2023

Publication=31.05.2023

TEMPO online database – NIS=30.06.2023

 

 


8. Coherence and comparability Top
8.1. Comparability - geographical

The are no differences between national and european methodology.
8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.
8.2. Comparability - over time

Length of comparable time series

From 2000.


  Crops from arable land
(Table 1)		 Vegetables, melons and strawberries (Table 2) Permanent crops
(Table 3)		 Agricultural land use
(Table 4)
Have there been major breaks in the time series in the previous 5 years? NO NO NO NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)
Impact on comparability
Additional comments





 
8.2.1. Length of comparable time series

From 2000.
8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared? 
If others, which?

 
If no comparisons have been made, why not?


Differences between ACS and other data sources (%)

Results of comparisons IFS 2020 Vineyard survey 2020 IACS Other source(s)  In case of other sources, which?
Cereals    

 

 

 
Dried pulses and protein crops    

 

 

 
Root crops    

 

 

 
Oilseeds    

 

 

 
Other industrial crops (than oilseeds)    

 

 

 
Plants harvested green    

 

 

 
Total vegetables, melons and strawberries    

 

 

 
Vegetables and melons    

 

 

 
Strawberries    

 

 

 
Cultivated mushrooms  

 

 
Total permanent crops  

 

 

 
Fruit trees

Not applicable.

 

 

 
Berries  

 

 

 
Nut trees  

 

 

 
Citrus fruit trees

Not applicable.

 

 
Vineyards

 

 

 
Olive trees

Not applicable.

 

 
If there were considerable differences, which factors explain them?

 

 

  





 
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

Availability Links
YES

https://insse.ro/cms/ro/tags/comunicat-productia-vegetala-la-principalele-culturi






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://insse.ro/cms/sites/default/files/field/publicatii/productia_vegetala_la_principalele_culturi_in_anul_2022.pdf
Publications in English None






 
9.3. Dissemination format - online database

Data tables - consultations

TEMPO online database – NIS.


  Availability Links
On-line database accessible to users YES

B.5AGRICULTURE, 3.AREA AND CROP PRODUCTION: http://statistici.insse.ro:8077/tempo-online/#/pages/tables/insse-table
Website National language

http://statistici.insse.ro:8077/tempo-online/#/pages/tables/insse-table





 
9.4. Dissemination format - microdata access

Availability Links
NO






 
9.5. Dissemination format - other

 
9.6. Documentation on methodology

  Availability Links
Methodological report National language

Methodological documents: http://colectaredate.insse.ro/metadata/viewStatisticalResearch.htm?researchId=457

 
Quality Report

 
Metadata National language

Indicators clases: http://colectaredate.insse.ro/metadata/viewStatisticalResearch.htm?researchId=457
Additional comments  






 
9.7. Quality management - documentation

Not applicable.
9.7.1. Metadata completeness - rate

Not applicable.
9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous quality report? Further automation
Increased use of administrative data
If other, which?
Burden reduction measures since the previous reference year  Easier data transmission
Multiple use of the collected data
If other, which?






 


11. Confidentiality Top
11.1. Confidentiality - policy
Restricted from publication
11.2. Confidentiality - data treatment
Restricted from publication


12. Comment Top
Restricted from publication


Related metadata Top


Annexes Top