Crop production (apro_cp)

National Reference Metadata in ESS Standard for CROPS Reports Structure (ESQRSCP)

Compiling agency: Statistics Sweden

Time Dimension: 2016-A0

Data Provider: SE1

Data Flow: CROPROD_ESQRSCP_A


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: EUROPEAN STATISTICAL DATA SUPPORT

Download


1. Contact Top
1.1. Contact organisation

Statistics Sweden

1.2. Contact organisation unit

Agriculture and Energy Statistics Unit

Regions and Environment Department

1.5. Contact mail address

Statistics Sweden

Agriculture and Energy Statistics Unit

Regions and Environment Department

SE-701 89 ÖREBRO

SWEDEN


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops, as well as production and yield levels. The statistics are collected from a wide variety of sources: censuses, surveys, administrative sources, models, wholesale purcharsers and other data providers. The data collection covers early estimates (before the harvest), preliminary data and final statistics. Data are collected at a national level, but for most of the crops regional data exist as well (NUTS1/2/3).

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

Utilised agricultural area cultivated by an agricultural holding.

2.6. Statistical population

All agricultural holdings growing crops.

2.7. Reference area

The entire territory of the country.

2.8. Coverage - Time

Crop year, that is the calendar year in which the harvest begins

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 (2014)?  NO      
If yes, which new data sources have been introduced since the previous quality report (2014)?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous Quality Report (2014)? NO      
Which data sources have been dropped since the previous quality report (2014)?
Type of source?
Why have they been dropped?
Additional comments


Data sources: Please indicate the data sources which were used for the reference year 2016

  Type Name(s) of the sources If other type, which kind of data source?
Table 1: crops from arable land      
Early estimates for areas Census
Survey
Administrative data

The Swedish Farm Register

The survey on areas of cereals and oilseed crops sown in the autumn

IACS

 

Final area under cultivation Census
Survey
Administrative data

The Swedish Farm Register

The crop statistics survey on cereals, dried pulses and oil seed crops

IACS

 

Production Census
Survey
Administrative data
Other

The Swedish Farm Register

The crop statistics survey on cereals, dried pulses and oil seed crops, The crop statistics survey on table potatoes, The crop statistics survey on potatoes for production of starch. 

IACS

 

 

Crop production forecast for cereals and oilseed crops, a regression model

Standard yields, a regression model

Nordic Sugar, sugar beet purchaser

Kalmar Öland Garden Products (Kalmar Ölands Trädgårdsprodukter), purchaser of kidney beans 

Yield Survey
Other

The crop statistics survey on cereals, dried pulses and oil seed crops, The crop statistics survey on table potatoes, The crop statistics survey on potatoes for production of starch 

 

 

Crop production forecast for cereals and oilseed crops, a regression model

Standard yields, a regression model

Nordic Sugar, sugar beet purchaser

Kalmar Ölands Trädgårdsprodukter, purchaser of kidney beans 

Non-existing and non-significant crops Administrative data

IACS

Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Expert estimate
Final harvested area Census
Survey

Horticultural Production 2016

The 2014 Horticultural Census

Production Census
Survey

Horticultural Production 2016

The 2014 Horticultural Census

Non-existing and non-significant crops Census

The 2014 Horticultural Census

Table 3: Permanent crops      
Early estimates for production area Expert estimate
Final production area Census
Survey

Horticultural Production 2016

The 2014 Horticultural Census

Production Census
Survey

Horticultural Production 2016

The 2014 Horticultural Census

Non-existing and non-significant crops Census

The 2014 Horticultural Census

Table 4: Agricultural land use      
Main area Census
Survey
Administrative data

The Swedish Farm Register

The crop statistics survey on cereals, dried pulses and oil seed crops, Horticultural production 2013

IACS

 

Non-existing and non-significant crops Census
Administrative data

The 2014 Horticultural Census

IACS

Total number of different data sources

12

   
Additional comments

Data source for the humidity

Put x, if used

Surveyed: farmers report the humidity

 x

Surveyed: farmers convert the production/yield into standard humidity     

 

Surveyed: whole sale purchasers report the humidity

 x

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

 

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

 

Estimated by experts

 

Other type

 

If other type, please explain

 

Additional information

 

 

Every third year, a horticultural census is performed in order to cover all crops and all horticultural holdings. The census - in this case "The 2014 Horticultural Census" - is the basis for determining non-significant crops in the years in-between the censuses. 

   


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

The crop statistics survey on cereals, dried pulses and oil seed crops

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016 

The 2014 Horticultural Census

Crop statistics received from Nordic Sugar

Standard yields

Crop production forecast for cereals and oliseed crops

Planning (month-month/year)

10-12/2015

10-12/2014

10-12/2015

10-12/2015

09-12/2016 

03-10/2014

No information available

03-04/2016

05-06/2016

Preparation (month-month/year)

01-09/2016

01-09/2015

01-09/2016

01-09/2016

09-12/2016 

10-12/2014

No information available

03-05/2016

05-06/2016

Data collection (month-month/year)

09-04/2016-2017

09-11/2015

09-03/2016-2017

09-03/2016-2017

01-03/2017

01-03/2015

No information available 

No data collection

06-08/2016

Collection of weather data from the Swedish Meteorological and Hydrological Institute. 

Quality control (month-month/year)

09-04/2016-2017

09-11/2015

09-03/2016-2017

09-03/2016-2017

02-03/2017 

02-04/2015

No information available

03-06/2016

08/2016

Data analysis (month-month/year)

10-04/2016-2017

10-11/2015

10-04/2016-2017

10-04/2016-2017

02-03/2017

02-04/2015

03-04/2017

05-06/2016

08/2016

Dissemination (month-month/year)

11-04/2016-2017

11/2015

12-04/2016-2017

12-04/2016-2017

03/2017

06/2015

30/06/2017

06/2016

08/2016

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? YES

Unharvested areas are included in the crop areas.

Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? YES

The submitted data on C1420 Spring cereals mixtures includes also mixtures of spring cereals and dried pulses. The submitted data on "Energy crops n.e.c." refer to Reed canary grass. This crop is used both for energy purposes and for litter, and to some extent also for fodder. Temporary Leguminous plants harvested green are not collected separately but together with Temporary grasses. Annual Leguminous crops harvested green are insignificant and are submitted together with Other plants harvested green from arable land n.e.c. Production from grazed areas is not collected, instead 60% of the yield from Temporary grasses is used as a substitute.

Are special estimation/calculation methods used for main crops from arable land? YES

The area under cultivation corresponds to the sown area, also after the harvest, even if parts of the sown area have been ruined e.g. due to natural disasters. However, areas recorded as cereals, dried pulses and oil seed crops harvested green, according to information from the farmers in the yield survey, are withdrawn and added to "Plants harvested green". Thus, the area of cereals, dried pulses and oil seed crops can be reduced during the crop year.

The yield is calculated per sown area. If some areas were ruined during the growing period, this will lead to reduced yields instead of reduced crop areas. But information on unharvested areas are collected for most of the crops and statistics on unharvested areas are published in Sweden.

Are special estimation/calculation methods used for vegetables or strawberries? YES

For minor crops, a regression model was used to calculate area and production based on data from The Horticultural Censuses which were conducted every third year during the period 1999-2014

Are special estimation/calculation methods used for permanent crops for human consumption? YES

For minor crops, a regression model was used to calculate area and production based on data from The Horticultural Censuses which were conducted every third year during the period 1999-2014

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)? YES  
In case yes, how do they differ? ( list all items and explanations)

Temporary Leguminous plants harvested green are not collected separately but together with Temporary grasses. Annual Leguminous crops harvested green are insignificant and are submitted together with Other plants harvested green from arable land n.e.c.

In case data are delivered for one of the items below, describe the crop species included in the item: P9000 - Other dry pulses and protein crops n.e.c.
G9100- Other cereals harvested green (excluding green maize)

Other dry pulses and protein crops n.e.c.:  Kidney bean, Phaseolus vulgaris L.

Other plants harvested green from arable land n.e.c.: All cereals (exept maize), all field peas, broad and field beans and all oilseeds harvested green according to the crop yield survey, a crop code in IACS named "Cereals for green fodder/silage" and another crop code in IACS named "Green fodder". 

 


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).

IACS, the farm register and FSS-results are used as benchmarks.

Is the data collection based on holdings? YES
If yes, how the holdings were identified? Other
If not, on which unit the data collection is based on?

 

When was last update of the holding register? (month/year)

March/2017

Was a threshold applied? YES
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 %)

0,11 %

Dried pulses and protein crops (in %)

0,01 %

Root crops (in %)

0,09 %

Oilseeds (in %)

0,05 %

Other industrial crops (included all industrial crops besides oilseeds)  (in %)
Plants harvested green from arable land (in %)

2,2 %

Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)

0.3 %

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

The crop statistics survey on cereals, dried pulses and oil seed crops

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016

The 2014 Horticultural Census 

Which survey method was used? On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire
On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire
On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire
On-line electronic questionnaire filled in by respondent
Telephone interview, electronic questionnaire
Postal questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent
If 'other', please specify

 

Please provide a link to the questionnaire

Not available

Not available

Not available

Not available

Not available

Not available

Data entry method, if paper questionnaires? Manual Optical


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

IACS

Swedish Farm register

Description

Based on on the farmers' applications for subsidies 

Based on on the farmers' applications for subsidies, Central livestock register, questionnaires etc. 

Data owner (organisation)

Swedish Board of Agriculture

Swedish Board of Agriculture

Update frequency Continuous Once per year or more often
Reference date (month/year)

06/2016

06/2016

Legal basis

European Parliament and Council Regulation (EU) No 1306/2013

SJVFS 2016-7 (regulation from the Swedish Board of Agriculture)

Reporting unit

Holding

Holding

Identification variable (e.g. address, unique code, etc.)

Unique code

unique code

Percentage of mismatches (%)

Unknown, low

unknown, low

How were the mismatches handled?

Searching in other data sources

Searching in other data sources

Degree of coverage (holdings, e.g. 80%)

97.6 %

100 %

Degree of completeness (variables, e.g. 60%)

95 %

100 %

If not complete, which other sources were used ?

Variables not covered are insignificant and data are thus not collected

http://www.jordbruksverket.se/download/18.37e9ac46144f41921cd4cb5/1396525701358/F%C3%B6rdjupad+dokumentation+av+statistiken+f%C3%B6r+arealer+slutliga+och+prelimin%C3%A4ra+2013.pdf

When it comes to degree of coverage of holdings, the 5 % outside the Farm register recorded above correspond with holdings under the thresholds for the Farm register. 

How were the data used?
Sample frame
Validation
Directly for estimates
Sample frame
Directly for estimates
Data used for other purposes, which?

For example analysing trends

For example analysing trends

Which variables were taken from administrative sources?

Crop areas, contact information to farmers

Final data on crop area

Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? YES YES
Please describe the differences

Crop areas harvested as green fodder are to some extent included in crop areas for cereals, dried pulses and oil seed crops

Crop areas harvested as green fodder are to some extent included in crop areas for cereals, dried pulses and oil seed crops.

What measures were taken to eliminate the differences?

Crop areas harvested as green fodder are collected in the crop yield survey

Crop areas harvested as green fodder are collected in the crop yield survey

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?

Preprinted crop areas can be corrected by the farmers in the crop yield survey.

Crop areas of cereals, dried pulses and oil seed crops from the Farm register are reduced by estimated areas harvested as green fodder according to the crop yield survey.

What were the possible limitations, drawbacks of using the data from administrative source(s)?

A single application for subsidies can sometimes cover different holdings in different regions, thus regional area distribution in the administrative source may differ slightly from the true area distribution.

The reference date 15 june has the effect that some farmers do not yet know if the crops will be threshed or harvested as green fodder.


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
Data owner (organisation)
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year  e.g. 1/16 - 8/16)
Legal basis
Use purpose of the estimates?
What kind of expertise the experts have?
What kind of estimation methods were used?
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?
If yes, please describe the differences
What measures were taken to eliminate the differences?
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?
What were the possible limitations, drawbacks of using the data from expert estimate(s)?
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
Other dataset
If other, please describe

Data from Farmers' associations

Occasionally statistics from neighboring countries

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 2014? Improvement Stable Improvement Stable Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Contents, accuracy, timeliness, comparability and coherence, availability and clarity

       
Is there a Quality Report available? YES        
If yes, please provide a link(s)

https://www.scb.se/contentassets/462f77b26e0d4febbbaab8b204d960f5/jo0601_kd_2016_gl_-170428.pdf

https://www.scb.se/contentassets/c9a882665ecf4a98a60f4495ed536a35/jo0110_bs_2016_gl_161130.pdf

https://www.scb.se/contentassets/5314c2bc702042bb81710a19ca22d97a/jo0603_kd_2016_gw_170428.pdf

Horticultural Production 2016

The 2014 Horticultural Census

 

       
To which data source(s) is it linked?

Documentation of the survey on Crop production:

Production of cereals dried pulses and oil seed crops

Areas of cereals and oil seed crops sown in the autumn 

Production of potatoes

 

Tables 2 and 3:

Horticultural Production 2016

The 2014 Horticultural Census

 

       
Has a peer-review been carried out for crop statistics? YES        
If, yes, which were the main conclusions?

An extended work-routine description should be written  

       
What quality improvement measures are planned for the next 3 years? Further automation
Other
       
If, other, please specify

Improvement of online and postal questionnaires for the 2017 Horticultural Census

       
Additional comments

A more user-friendly electronic questionnaire for Table 1 crop production was introduced in 2016.

       
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? YES
Describe the unmet needs

More regional data are often asked for.

Does the Regulation 543/2009 meet the national data needs? NO
Does the ESS agreement meet the national needs? NO
If not, which additional data are collected?

Unharvested areas, yields and production of Temporary grasses. 

Additional comments

In Nordic countries data on production of Temporary grasses is more important than for example data on production of Green maize.

5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? YES
If yes, how satisfied the users were? Not completely satisfied
Additional comments

Many data users want more regional data 

5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

See the European level Quality Report


6. Accuracy and reliability Top
6.1. Accuracy - overall

See the European level Quality Report.

6.2. Sampling error

Sampling method and sampling error

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

The crop statistics survey on cereals, dried pulses and oil seed crops 

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch 

Horticultural production 2016

Sampling basis? Area Area Area Area Area
If 'other', please specify
Sampling method? Stratified
Other
Stratified
Other
Random
Stratified
Random
Stratified
Random
Stratified
If stratified, number of strata?

101

90

21

9

26

If stratified, stratification basis? Location Location Location
Size
Location
Size
Size
Specialisation
If 'other', please specify
Size of total population

25 888

25 198

2 753

407 

3 180

Size of sample

4 397

3 982

739

191

697

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

RSE (standard error estimate / point estimate) was calculated with ETOS. A program, written in the SAS macro language, designed to compute estimates in sample surveys.

RSE (standard error estimate / point estimate) was calculated with ETOS. A program, written in the SAS macro language, designed to compute estimates in sample surveys.

RSE (standard error estimate / point estimate) was calculated with ETOS. A program, written in the SAS macro language, designed to compute estimates in sample surveys.

RSE (standard error estimate / point estimate) was calculated with ETOS. A program, written in the SAS macro language, designed to compute estimates in sample surveys.

Single strata variance according to: N(N-n)(SD^2/n).

Population RSE: Sqrt (sum of strata variances)/total sum

If the results were compared with other sources, please describe the results

Not relevant

Not relevant

Not relevant

Not relevant

Not relevant

Which were the main sources of errors?

Sample error

Sample error

Sample error

Sample error

Sample error


Sampling error - indicators

Not applicable


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

The crop statistics survey on cereals, dried pulses and oil seed crops

The survey on areas of cereals and oilseed crops sown in autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016

Cereals for the production of grain (in %)

0,2%

1.8%

Dried pulses and protein crops (in %)

0.4%

Root crops (in %)

0%

0%

Oilseeds (in %)

0%

2.5%

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

-

Plants harvested green from arable land (in %)

0.3%

Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)

9.9 % (apples)

Berries (in %)

6.0 % (strawberries)

Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)
Additional comments

Statstics on crop areas are based on IACS, but areas harvested as green fodder are estimated based on data from the sample surveys.

           
6.3. Non-sampling error
6.3.1. Coverage error

Over-coverage - rate

Not applicable


Common units - proportion

Not applicable


  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

The crop statistics survey on cereals, dried pulses and oil seed crops

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016

Error type Under-coverage
Over-coverage
Under-coverage
Over-coverage
Under-coverage
Over-coverage
Under-coverage
Over-coverage
Under-coverage
Over-coverage
Degree of bias caused by coverage errors Low Low Low Low Low
What were the reasons for coverage errors?

Late changes of holders of the farms and farmers not applying for subsidies

Late changes of holders of the farms and farmers not applying for subsidies

Late changes of holders of the farms and farmers not applying for subsidies

Late changes of holders of the farms and farmers not applying for subsidies

Rapid turnover of horticultural holdings

Difficult to establish if some horticultural holdings actually produce horticultural crops ahead of survey

Which actions were taken for reducing the error or to correct the statistics?

weighing for over-coverage

weighing for over-coverage

weighing for over-coverage

weighing for over-coverage

Increased sample size used for strata containing holdings with uncertain cultivation activity

Additional comments




 

6.3.1.1. Over-coverage - rate

Not applicable

6.3.1.2. Common units - proportion

Not applicable

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

The crop statistics survey on cereals, dried pulses and oil seed crops

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016

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

12

12

11

11

9

Preparatory (field) testing of the questionnaire? YES YES NO NO NO
Number of units participating in the tests? 

Year 2003: 449 units, year 2004: 480 units

Year 2003: 449 units, year 2004: 480 units

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

10

10

10

10

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

In the web forms: Checks for missing items, outlier warnings, invalid values checks, relational checks, calculation systems help the farmers to register correct data. These actions are improving the data quality. 

In the web forms: Checks for missing items, outlier warnings, invalid values checks, relational checks, calculation systems help the farmers to register correct data. These actions are improving the data quality. 

In the web forms: Checks for missing items, outlier warnings, invalid values checks, relational checks, calculation systems help the farmers to register correct data. These actions are improving the data quality. 

In the web forms: Checks for missing items, outlier warnings, invalid values checks, relational checks, calculation systems help the farmers to register correct data. These actions are improving the data quality. 

Checks for outliers and invalid values and corrections for partially answered questions.

6.3.3. Non response error

Unit non-response - rate

Not applicable


Item non-response - rate

Not applicable


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

The crop statistics survey on cereals, dried pulses and oil seed crops 

The survey on areas of cereals and oilseed crops sown in the autumn

The crop statistics survey on table potatoes

The crop statistics survey on potatoes for producrion of starch

Horticultural production 2016

Unit level non-response rate (in %)

6.2

7.3

6.6

4.2

14.3

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

14.7

               - Max% / item

18.3

               - Overall%

Not measured, low

0

Not measured, low

Not measured, low

16.4

Was the non-response been treated ? YES NO YES YES YES
Which actions were taken to reduce the impact of non-response?

Reminders and phone contact.

Reminders and phone contact.

Reminders and phone contact.

Reminders and phone contact.

Reminders and phone contacts. Focus on strata with known large producers.

Which items had a high item-level non-response rate? 

None

None

None

None

Item-level non-response rate was similar across all strata

Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Reminders
Weighting
Follow-up interviews
Reminders
Weighting
Follow-up interviews
Reminders
Weighting
Follow-up interviews
Reminders
Weighting
Imputations
Weighting
In case of imputation which was the basis? Imputation based on the same unit in previous data
Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

Data was partially or completely imputated for 16.4 % of the holdings. The maximum imputation rate for any specifik item was 4.0 %.

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

Reminders and phone contact.

Reminders and phone contact.

Reminders and phone contact.

Reminders and phone contact.

Which organisation did the corrections?

Statistics Sweden

Statistics Sweden

Statistics Sweden

Statistics Sweden

Swedish Board of Agriculture

Additional comments

Item level non-response rate is very low and is not measured.

Item level non-response do not occur.

Item level non-response rate is very low and is not measured.

Item level non-response rate is very low and is not measured.

6.3.3.1. Unit non-response - rate

Not applicable

6.3.3.2. Item non-response - rate

Not applicable

6.3.4. Processing error

Not applicable.

6.3.4.1. Imputation - rate

Not applicable.

6.3.5. Model assumption error

Not applicable.

6.4. Seasonal adjustment

Not applicable.

6.5. Data revision - policy

Not applicable.

6.6. Data revision - practice

Not applicable.

6.6.1. Data revision - average size

Not applicable.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result

The time lag varies between different crops.


Time lag - final result

The time lag varies between different crops.


  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?

4

3

3

4

1

1

1

0.33

1

0.33

1

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

1

0

0

1

0

0

0

0

0

0

0

When was  the first  forecasting published for crop year 2016? (day/month/year) 19/08/2016 15/11/2016 07/12/2016 19/08/2016
When were the final results published for crop year 2016? (day/month/year) 28/04/2017 28/04/2017 28/04/2017 28/04/2017 28/04/2017 28/03/2017 28/03/2017 28/03/2017
Additional comments

The dates above refer to potatoes. The results for sugar beet were published only on June 30, 2017

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

Full statistics on horticultural crops are published every third year. Last publication was in 2015 (for the crop year 2014), the next is scheduled for 2018 (for the crop year 2017).

7.1.1. Time lag - first result

The time lag varies between different crops.

7.1.2. Time lag - final result

The time lag varies between different crops.

7.2. Punctuality

Please refer to the European level Quality Report

7.2.1. Punctuality - delivery and publication

Please refer to the European level Quality Report


8. Coherence and comparability Top
8.1. Comparability - geographical

Not applicable.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

8.2. Comparability - over time

Length of comparable time series

Not applicable


  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 since 2013? 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 Low Low
Additional comments
8.2.1. Length of comparable time series

Not applicable

8.3. Coherence - cross domain

  Data source
With which other data sources the crop statistics data have been compared?  IACS
Other
If others, which?

Data from Farmers' associations

If no comparisons have been made, why not?


Results of comparisons FSS 2016 (if available) Vineyard survey 2015 IACS Other source(s)  In case of other sources, which?
Cereals

 

 

 

+13 %

Forecast from Farmers' associations

Dried pulses and protein crops

 

 

 

 

 

Root crops

 

 

 

Oilseeds

 

 

 

-5 %

Data from Farmers' associations

Other industrial crops (than oilseeds)

 

 

 

Plants harvested green

 

 

 

 

 

Total vegetables, melons and strawberries  
Vegetables and melons  

+8.5 %

Strawberries  

+12.7 %

Cultivated mushrooms  
Total permanent crops  
Fruit trees  

-11.9 %

Berries  

+10.8 %

Nut trees  
Citrus fruit trees  
Vineyards
Olive trees  
If there were considerable differences, which factors explain them?

Slightly different definitions for included holdings for table 2 and 3 crops.

Concering cereals, the forecast from the Farmers' association is very early and rough. Concerning oilseeds, the data from the Farmers association corresponds predominantly with production from high yielding areas.       

 
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

9.2. Dissemination format - Publications

9.3. Dissemination format - online database

Data tables - consultations

Not applicable


9.3.1. Data tables - consultations

Not applicable

9.4. Dissemination format - microdata access

Availability Links
YES

http://www.scb.se/en/services/guidance-for-researchers-and-universities/mona--a-system-for-delivering-microdata/

http://www.jordbruksverket.se/omjordbruksverket/statistik.4.67e843d911ff9f551db80003060.html

 

 

 

 

 

9.5. Dissemination format - other

https://www.scb.se/en/finding-statistics/statistics-by-subject-area/agriculture-forestry-and-fishery/

http://www.jordbruksverket.se/omjordbruksverket/statistik.4.67e843d911ff9f551db80003060.html

9.6. Documentation on methodology

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 reference year (2013) Other
If other, which?

Improved functions and calculation tools for the respondents in Crop Production Survey.

Reduced sampling in the Horticultural Survey.

Burden reduction measures since the previous reference year  Less respondents
More user-friendly questionnaires
If other, which?

A minor reduction of sample size in the crop yield survey on potatoes. 


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


12. Comment Top


Related metadata Top


Annexes Top
SE_list of sources