Methodology and Quality Update

Latest Update on Methodology and Quality

04/12/2025

Statistical Presentation

Data description

The Agricultural statistics publication is an annual publication that provides up-to-date data for beneficiaries, decision-makers, and researchers. It includes data on cultivated areas by crop group, organic farming of agricultural crops, imports and exports of agricultural products, and loans granted to the agricultural sector at the administrative-region level in the Kingdom. The publication also presents international indicators, all displayed in statistical tables and analytical reports.
The Agricultural statistics publication includes the following key characteristics:
•    Cultivated area of grains, fodder, open-field vegetables, cut flowers, and the production volume.
•    Cultivated area of protected vegetables, number of greenhouses, and the production volume.
•    Numbers of permanent trees and palm trees.
•    Area and production of organic agriculture.
•    Value of exports and imports of agricultural products.
•    Development loans to the agricultural sector.
•    Bank credit granted by banks and finance companies.

Classifications

The following classifications are applied in Agricultural statistics:
The National Classification for Economic Activities (ISIC4):
The statistical classification based on the International Standard of Industrial Classification of 
All Economic Activities (ISIC4) is used to describe productive activities of an establishment.
The National Classification for Economic Activities (ISIC4)
The Harmonized System  (H.S.2022) 
The merchandise exports and imports statistics are based on classification issued by the World
Customs Organization (WCO), which is a table for describing and classifying merchandise that
includes sub-items and their numeric codes, sections, and chapters, in accordance with the
International Convention on Nomenclature for the Classification of Goods in Customs Tariffs,
done at Brussels.
The International Harmonized System for the Classification and Coding of Goods (H.S. 2022)
Central Product Classification (CPC 2.1):
A coherent product classification, covering both goods and services, issued by the United Nations Statistical Commission. It is based on a set of internationally agreed-upon concepts, definitions, principles, and classification rules. This classification serves as an international standard for compiling and categorizing all types of data that require product-level detail. It also provides a comprehensive framework for collecting and presenting product data in a way that enables economic analysis to support decision-making and policy development at both the national and international levels
Central Product Classification (2.1CPC)
Metadata is collected through interviews, so that outputs can be produces in accordance with all relevant classifications. Classifications are available on the GASTAT website:
 Classifications - General Authority for Statistics

Statistical concepts and definitions

Terms and concepts of Agricultural Statistics:
•    Holding:
It is an economic unit of agricultural production, in both crop and livestock, under a single management. It includes all animals on the holding and all land used, whether fully or partially, regardless of ownership, legal status, or size. The holding may be managed by a single individual, by members of a family, or jointly by two or more individuals or families. Management may also be carried out by a legal entity such as a company, cooperative, government agency, or other organization. The land of the holding may consist of one or more parcels located within a single locality, provided that all parts share the same production resources—such as labor, machinery, and agricultural equipment. This shared use of resources must be evident for the holding to be considered a single economic unit.
•    Holding Type: 
There are two types of holdings: 
-    Traditional (unregulated):
It is the most common type of agricultural holding in the Kingdom. Traditional holdings 
do not require previous approval or licenses from concerned authorities, and their 
production activities may be crops based, livestock based, or both (Mixed).
-    Specialized (regulated):
This type of holding includes the preapproved holdings (projects) that have acquired 
licenses from the concerned authorities after submitting technical and economic studies 
for the establishment of such holdings, whether they are holdings specialized in crops, 
livestock, poultry or fishery production, or projects that mainly adopt modern 
unconventional irrigation methods, the use of agricultural mechanization and modern 
technology in agricultural production and specialization in production with regard to 
open or protected agriculture, raising cows to produce milk or fattening calves, sheep 
and poultry.
•    Main activity of holding:
The main activity of a holding is the prevailing activity carried out by the holding which is consistent with its economic revenues, such that it represents more than 50% of the annual income of that holding.
For the purposes of this agricultural census, the main activities of holdings have been divided into five categories:
-    Crop holding:
A holding in which the agricultural activity represents more than 50% of its annual income during an agricultural year, depending on various permanent and temporary winter and summer crops.
-    Livestock holding:
A holding in which the livestock activity represents more than 50% of its annual income during an agricultural year, depending on various types of animal resources and livestock farmed on the holding (lamb, sheep, camel, cow, riding and draught animals) and on beehives.
-    Poultry Holding:
A holding in which the poultry production activity represents more than 50% of its annual income during an agricultural year, depending on various types of poultry (boiler chicken, laying chicken, parent chicken, grandparent chicken, free range chicken, other birds such as ostrich, duck, goose, or quail). Rabbits are also considered poultry.
-    Fish holding:
A holding in which fish production activity represents more than 50% of its annual income during an agricultural year, depending on the farming and aquaculture of fish in internal ponds such as farms dedicated to fish or shrimp production.
-    Mixed holding:
When the activity at the holding is mixed and none of its components represent more than 50% of the holding’s annual income during an agricultural year, provided that the holding practices three or more different activities from the following: (crops, livestock, poultry, or fish activities) and that each component represents less than 50% of the holding’s activity.
•    Agricultural year:
For the purposes of agricultural surveys, the crop production agricultural year starts on (1st March) and ends on (28th February) of the following year.
•    Harvested Area:
The actual area of crop production. In the case of sequential cropping on the same land within the same year, the area is recorded as many times as it is cultivated to yield the total harvested area.
•    opened agriculture:
It is the lands directly cultivated with various agricultural crops, with no barrier between them and various weather conditions.
•    Protected agriculture:
The cultivation of crops in greenhouses areas covered in glass, plastic or another material to protect them from weather conditions, such as: (light, temperatures, air and relative humidity). This creates a favorable environment for different kinds of plants to grow throughout the year. This method is usually used for cultivating vegetables and protected cut flowers.
Greenhouses have several types:
-    Regular plastic:
They are houses made of several materials, such as (polyethylene) covers, which do not last more than a year. They are the most common type of greenhouses, and one of the regular types of double-sided (polyethylene) greenhouses covered with two layers of (polyethylene) covers and separated by a layer of air. 
-    Air-conditioned plastic:
The only difference between this type and the regular plastic greenhouses is that it is equipped with artificial air-conditioning.
-    Glass:
As a covering material, glass allows good light transmission (88%) and lasts the longest among greenhouse types. 
-    Fiberglass:
Also called glass-reinforced plastic (GRP), it is made from plastic, glass fibers and a binding material. This type withstands impacts better than glass houses and it is more durable and lasts longer than plastic houses.
-    Other:
Greenhouses made from other materials.
•    Agricultural production:
The plant production of an agricultural holding, such as crops, vegetables, and evergreen trees; as well as all animal and livestock production. It includes grains, fodder, open-field and protected vegetables, cut flowers, fruits (including dates), as well as sheep, goat, cow and camel count; milk and dairy products, number of boiler chicken, table eggs, chicks, hatchery eggs, fish, ostrich and quail eggs and meat, and amount of rabbit meat and honey, in addition to secondary products, such as organic fertilizers and ostrich feathers and skin.
•    Agricultural products:
it is category of products that includes crops grown in a field area for harvesting, intended for use as food, fodder, oil extraction, fiber, sugar, or any other medicinal or industrial materials.
•    Organic agriculture:
It is a comprehensive agricultural production management system that promotes and improves the integrity
of the agroecosystem, including biodiversity, biological cycles, and soil bioactivity. It emphasizes the use of management methods rather than non-agricultural inputs, while taking regional conditions into account, which necessitate systems that are tailored to local conditions. This is accomplished by employing agricultural and mechanical methods rather than synthetic materials such as fertilizers, pesticides, veterinary drugs, genetically modified seeds and strains, preservatives, and additives.
•    Agricultural loans:
These are loans that are provided to finance agricultural areas for the purpose of growing crops of all
kinds, fruit farms, apiaries, fishing boats, agricultural tourism loans, veterinary clinics and pharmacies
and vegetable carts.
•    Agricultural exports:
According to the criteria of Foreign Trade Statistics, these are all agricultural goods (agricultural crops
and livestock) that have been fully produced or manufactured locally or on which an industrial process
has changed their form and value for export outside Saudi Arabia.
•    Value of agricultural exports:
The value of exported goods is determined by the value of agricultural goods in addition to other costs
until they are delivered using a shipment method or include the value of the goods including all the
expenses as well as the export office.
•    Agricultural imports:
According to foreign trade statistics standards, it refers to all agricultural goods and products (agricultural crops) imported into the country to meet domestic needs, subject to all customary procedures required to complete the importation of a commodity.
•    Value of agricultural imports:
The value of agricultural imports is defined as the cost of the imported product plus the costs of
shipping, insurance, transportation, and other expenses incurred until the goods arrive at the port of
entry.

Data sources

Agricultural statistics data are based on two sources:
First source: The 2024 Agricultural Survey was conducted using an electronic field questionnaire, and its data are quantitative in nature (numbers, areas, and quantities).
Second source: Register-based data from the following government entities:
•    Ministry of Environment, Water and Agriculture:
The data were sourced from the ministry, analyzed, and incorporated into the publication. The data are quantitative in nature (numbers, areas, and quantities).
•    Agricultural Development Fund.
Data on loans granted for 2024, with the data being quantitative in nature (numbers and percentages).
•    Saudi Central Bank:
Bank credit data granted by economic activity (agriculture), with the data being quantitative in nature (numbers).

Designing the data collection tool

A data collection tool was designed for two sources:
Agricultural survey data: 
An electronic questionnaire (CAPI) was designed to ensure ease of use by field researchers, along with a telephone version (CATI) for contacting respondents when needed, and a web-based version (CAWI) sent via email link for self-completion. The tool’s design went through several steps, including identifying the key variables and constructing the questions based on the concepts, definitions, and classifications provided by the Ministry of Environment, Water, and Agriculture, which are aligned with the Food and Agriculture Organization (FAO) standards.
The agricultural production form the Agricultural Survey consists of 10 sections, which are:
•    Section one: Basic data:
•    Section two: Identification data:
•    Section three: Crops
•    Section four: Livestock (data not used in the Agricultural statistics publication).
•    Section five: Commodity production requirements
•    Section six: Data on land use of holdings.
•    Section seven: Change in assets
•    Section eight: Number of employees and their due compensations
•    Section nine: Income, expenses, and subsidies.
•    Section Ten: Methods of waste and mortality disposal.
Methodology for calculating key Indicators of the collection tool:

Indicator

Review and Correction Rules:
To ensure the quality of agricultural survey data, four types of review and correction rules were established, as follows:
•    Automated adjustment rules:
These rules are applied for the automatic calculation of certain fields or automatic adjustment of responses in specific fields to align with some questionnaires, totaling approximately 80 rules.
•    Navigation rules between sections and fields:
Special rules were programmed to organize automatic navigation between sections and fields, as well as between agricultural survey questionnaires, based on the respondent’s input, totaling approximately 30 rules.
•    Error rules: 
These rules cannot be bypassed during data entry. The field researcher must correct the data by referring back to the respondent to ensure accuracy. There are approximately 320 such rules.
•    Alert (Warning) Rules: 
These rules are designed to verify the correctness of the data entered by the researcher. The field researcher may override them if the data accuracy is confirmed, with a total of approximately 75 rules.
Administrative Data (organic and transitioning agriculture, agricultural loans, and bank credit):
A data collection tool was designed based on standardized data request tables sent to relevant data providers, aiming to obtain periodic, coordinated, and verified data on organic and transitioning agriculture, agricultural loans, and bank credit.

Questionnaire test (cognitive test)

A pilot test of the survey questionnaire was conducted to assess respondents’ understanding of the questions. The questions were asked to respondents, and their answers were recorded in a cognitive test questionnaire designed by specialists at GASTAT.

Statistical population

The statistical population for agricultural statistics consists of a sample of agricultural holdings across the 13 administrative regions of the Kingdom of Saudi Arabia, classified by holding type, main activity, holding area, and crop distribution by type.

Sample Design

Traditional Holdings samples:
•    Sample type Stratified
•    Sampling frame: Comprehensive Agricultural Survey frame 2023
•    Basic unit: Agricultural holding
•    Size of sample: 30,000 agricultural holdings.
The sample was designed using a stratified cluster random sampling method. The total sample size reached 30,000 agricultural holdings, distributed across the administrative regions as follows:
Table1: Distribution of the sample at the level of administrative regions:

Statistical unit (sampling unit)

The statistical unit in agricultural statistics is the agricultural holding.

Data collection

Data collection from the survey:
Agricultural statistics data are collected through the Agricultural Survey via telephone interviews (CATI), online questionnaires (CAWI), or face-to-face interviews (CAPI).
Data collection from administrative records:
Administrative data for agricultural statistics are obtained from the Ministry of Environment, Water and Agriculture, the Agricultural Development Fund, and the Saudi Central Bank. The data are stored in GASTAT databases after verification and review according to approved statistical methods and recognized quality standards. Data sources are consulted if errors or discrepancies are detected. Both administrative and survey data are checked for consistency, completeness, logical coherence, and to ensure no duplication.
The data is stored in the authority's databases after undergoing auditing and review processes following approved statistical methods and recognized quality standards. If errors or discrepancies are discovered, the data is cross-referenced with the data source for correction or clarification.

Data collection frequency 

The agricultural statistics data collection is conducted on an annual basis. 

Reference area

Agricultural statistics cover data from the 13 administrative regions of the Kingdom of Saudi Arabia.

Reference period (time reference)

Agricultural statistics data are based on the years 2023–2024.

Base period

Not applicable.

Measurement unit

•    Ton: For quantities such as production, exports, and imports.
•    Hectare, e.g.: Cultivated and harvested area.
•    Numbers such as: Number of trees and number of greenhouses.
•    Saudi Riyal: Value of exports and imports.

Time coverage

Data are available from the year 2015 to 2024. 

Publication frequency

Agricultural statistics results are published annually according to the approved statistical plan.

Statistical processing

Error detection

First: Agricultural statistics survey data:
Accurate procedures are carried out to detect errors in the data collected during the field survey and stored in the data lake. This is achieved through the automation of the data collection tool and the implementation of necessary constraints and procedures to control and manage the entered data, ensuring quality, accuracy, and consistency. Additionally, supportive methods are used to measure quality indicators, such as the survey response rate. These procedures include:
•    Identifying illogical or out-of-range values, such as inconsistent quantities or numbers.
•    Detecting missing values.
•    Reviewing internal consistency between questionnaire responses to verify logical relationships and data accuracy.
•    Comparing survey data with previous data to ensure validity and prepare for data processing, results extraction, and review.
Second: Administrative data for agricultural statistics:
Verification and review procedures are conducted through: 
•    Identifying illogical or out-of-range values, such as inconsistent quantities or totals.
•    Categorizing data to verify accuracy, with reference to the primary data source when errors or quality issues are detected.
•    Reviewing internal consistency among the provided data to ensure logical coherence.
•    Comparing the provided data with previous records to ensure validity and prepare for data processing, results extraction, and review.

Data integration and matching from multiple sources 

Two main sources were used to produce the Agricultural statistics: Administrative data received from relevant authorities, and data collected through the field survey. These data are processed integratively to ensure comprehensiveness and accuracy of the statistical outputs.
The data from the two sources are matched and checked for consistency through several steps: 
•    Checking for duplication or variation in values.
•    Comparing common variables, such as the number of projects and production quantities.
•    Resolving discrepancies by giving priority to the most accurate and comprehensive data.
•    Reviewing with the data source to clarify discrepancies.
This procedure aims to ensure the reliability and accuracy of the final data used in preparing the statistical publication and to provide a clear and consistent picture of agricultural statistics.

Imputation and calibration

Handling missing values (Imputation):
In cases of missing or incomplete data, approved statistical imputation methods are applied, such as using central tendency measures or the hot deck method, where missing values are replaced based on a set of variables within the same dataset, or the cold deck method, where an external source is used based on a set of variables. This is done to minimize missing cases. Administrative records are also consulted to support data completion, ensuring completeness and consistency in the final results.
Procedures for calculating variables and aggregates:
•    By region:
 Total production by administrative region was calculated by summing the production quantities of all included crops (grains, fodder, open-field vegetables, protected vegetables, and palm trees).
•    By type: 
To calculate total production by crop type, the production quantities of that type were summed across all administrative regions.
•    By source: 
The area and production of organic agriculture were differentiated by source (organic, transitioning), with data for each category aggregated separately by administrative region and type.
Non-response:
If a response cannot be obtained from some holdings during the field visit, administrative data and available historical time series for those holdings are used to complete the data, ensuring the comprehensiveness and accuracy of the results.

Seasonal adjustments

Not applicable. The final results are published based on the available agricultural survey data and data provided by the relevant authorities.

Adjustment of preliminary results 

Not applicable. The results are published in their final form and are not released as preliminary results.

Used Resources

Quality dimensions

Suitability

A standard that measures the extent to which the product meets the needs of users.

User needs 

The agricultural statistics product aims to provide fundamental and structural data on crop activities and production, and to build a reliable information base that supports decision-makers and researchers. It also contributes to preparing studies and conducting local, regional, and international comparisons to develop this vital sector.
Key variables that users benefit from include:
•    Cultivated area of grains, fodder, and open vegetables.
•    Cultivated area, number of greenhouses, and quantity of protected vegetable production.
•    Cultivated area and production quantity of cut flowers.
•    Numbers of permanent trees and palm trees.
•    Area and production of organic agriculture.
•    Development loans to the agricultural sector.
•    Value of exports and imports of agricultural products.
•    Bank credit granted by banks and finance companies.
•    Exports, imports, and re-exports
•    Grain yield per hectare (tons/hectare), which is part of the Sustainable Development Solutions Network (SDSN) objectives.
•    Number of plant genetic resources for food and agriculture conserved in medium- or long-term storage facilities, which is part of the Sustainable Development Goals (SDGs) objectives.
Agricultural statistics data are also used by: 
Internal users within GASTAT: 
•    National accounts and price statistics.
•    International indicators. 
External users who rely heavily on Agricultural statistics, include, most notably:
•    Government entities.

•    Regional and international organizations.

•    Research institutions.
•    Media.
•    Individuals.

Completeness 

A comprehensive review of data from various sources was conducted to ensure its completeness and compliance with national requirements and international standards, including SDG indicators and other relevant metrics. This review aimed to guarantee the accuracy, comprehensiveness, and alignment of the data with international standards.
The publication includes the following key elements: 
•    Cultivated and harvested area and production quantities for grains and fodder crops.
•    Cultivated and harvested area and production quantities for open-field vegetables.
•    Cultivated and harvested area and production quantities for protected vegetables.
•    Cultivated area and production quantities of cut flowers.
•    Areas and production quantities for organic and transitioning farms.
•    Number of palm trees and fruit-bearing palms, and production quantities.
•    Number of permanent trees and fruit-bearing trees, and production quantities.
•    Quantities of fertilizers used in agricultural holdings by type.
•    Quantities of exports, imports, and re-exports of agricultural crops by product groups.
•    Granted Bank credit
•    Grain yield per hectare (tons/hectare), which is part of the Sustainable Development Solutions Network (SDSN) objectives.
•    Number of plant genetic resources for food and agriculture conserved in medium- or long-term storage facilities, which is part of the Sustainable Development Goals (SDGs) objectives.

Accuracy and reliability 

A standard that measures how close the calculations or estimates are to the exact or true values that reflect reality.

Overall accuracy 

Errors that may be detected and affect data accuracy:
•    Updating the Statistical Frame:
The agricultural framework is a key tool for defining samples in agricultural surveys. Therefore, failing to update the framework continuously affects its ability to accurately reflect the actual status of agricultural holdings.
•    Data aggregation errors:
Aggregated totals may not match the details by type due to differences in aggregation methods from the source.
Data accuracy and reliability are ensured through:
•    Using updated statistical frameworks.
•    Training and qualifying data specialists to enhance their efficiency.
•    Applying alert rules, error rules, and correction rules during data collection in the electronic questionnaire.
•    Comparing data with previous years to identify any significant changes.
•    Verifying internal consistency of the data, including coordination with the data-providing entity to standardize coverage and ensure consistency between totals.
•    Examining relationships between variables and ensuring consistency across time series of different datasets.

Timeliness and punctuality 

A standard that measures the time gap between the availability of information and the occurrence of the event.
However, timeliness reflects the time difference between the date of data publication and the target date when it is actually published.

Timeliness 

The General Authority for Statistics is committed to applying internationally recognized standards regarding the announcement, clarification of the time of publishing statistics on its official website, as outlined in the statistical calendar, as well as adhering to the announced time of publication. In the event of any delay, updates will be provided accordingly.

Punctuality 

Agricultural statistics are published according to the release dates specified in the statistical calendar on GASTAT website, following the schedule outlined in the calendar.

Coherence and comparability

Statistics should be consistent internally and over time, and logically interconnected across scope and statistical domains, meaning that data should be comparable across regions and countries as well as across different time periods for the same region, and data from diverse sources can be combined and used interchangeably.

Comparability - geographical

Agricultural statistics data are fully comparable geographically across the regions of the Kingdom, as well as at the regional and international levels, in accordance with the standards used in designing specialized agricultural project forms, based on the concepts, definitions, and classifications provided by the Ministry of Environment, Water and Agriculture and the Food and Agriculture Organization (FAO).
The geographic distribution of agricultural holdings has not changed within the administrative regions of the Kingdom, ensuring that the main indicators and their related variables remain unaffected. Concepts and definitions aligned with international standards have been adopted to ensure the accuracy and quality of the statistical Publication.

Comparability - over time 

The comparability of recent data with historical data has been maintained. Despite ongoing improvements in the survey and updates to the questionnaire, the results of the Agricultural statistics publication remain comparable over time.
The main changes to the publication are as follows:
•    2015:
The agricultural census includes data on crop production.
•    2017:
The first agricultural survey was conducted, and crop production data were published in the Agricultural production publication in 2018.
•    2018:
The second agricultural survey was conducted, and crop production data were published in the Agricultural Production Publication in 2019.
•    2019:
A data gap occurred as data collection was not conducted in 2020 due to the COVID-19 pandemic.
•    2020 - 2021:
The agricultural survey resumed in 2022, and agricultural production data for 2020 and 2021 were published.
•    2022:
A data gap occurred; the survey was not conducted in preparation for the comprehensive agricultural survey of 2023.
•    2024:
The comprehensive agricultural survey was conducted, and the 2023 agricultural statistics data were published.

Coherence- Cross domain

GASTAT has ensured the consistency of agricultural statistics data by aligning them with international trade statistics based on the Harmonized System for the classification and coding of goods (HS 2017). This includes verifying the consistency of export and import data for agricultural products with the publication results and ensuring that quantities remain consistent when aggregated at multiple levels (such as by product type and reference year). In case of any discrepancies between sources, their causes are systematically analyzed and addressed according to approved statistical quality standards. This aims to provide a consistent and reliable picture of agricultural statistics in the Kingdom, facilitating temporal comparisons and enhancing the alignment of results with national and international data sources. 

Coherence- Sub-annual and annual statistics 

Not applicable, as the Agricultural Statistics are published only as an annual publication.

Coherence- National Accounts 

Agricultural statistics data are integrated with national accounts requirements by adopting recognized economic classifications, such as the International Standard Industrial Classification of All Economic Activities (ISIC4). The publication results, including data on production quantities, changes in assets, number of employed persons and their compensation, as well as income, expenditure, and price data, are used as key inputs to estimate the contribution of the crop sector to the Gross Domestic Product within the national accounts framework. Continuous coordination with national accounts statistics ensures consistency between the publication's results and macroeconomic indicators.

Coherence- Internal 

The Agricultural Statistics Publication is internally consistent, with statistics within each dataset aligning logically and matching across different measures, such as totals, quantities, and percentages.
Internal consistency is verified through:
•    Ensuring that data logically align with one another within the overall context of the bulletin.
•    Matching totals with detailed data by type and administrative region.
•    Reviewing the relationships between indicators and variables, such as areas and production quantities.

Accessibility and clarity

The ability for users to access data, the availability of accurate or complete data, and the availability of a methodology and quality report.

Press releases

The announcements for each publication are available on the statistical calendar as mentioned in 10.1. The press releases can be viewed on the website of GASTAT on the link: 
Press release

Publications

GASTAT ensures that the Agricultural Statistics Publication results are published in a way that serves all users, including bulletins in various formats containing (data and indicator tables, charts, methodology and quality reports, and questionnaires used) in both Arabic and English.
The Agricultural Statistics Publication results are also available at the following link:
GASTAT official website - Agricultural Statistics Publication

Online database

The data is published on the statistical database on the link:
Statistical database

Microdata accessibility

Accurate data is unit-level disaggregated data obtained from multiple sources such as sample statistical surveys, general population and housing censuses, and administrative systems, providing detailed information about the characteristics of individuals, families, business entities, and geographical areas, supporting the construction and development of statistical indicators and scientific research.
Different types of microdata files to meet diverse information needs:
•    Public use:
It consists of sets of records containing information on individuals, households, or business entities anonymized in such a way that the respondent cannot be identified either directly such as: (name, address, contact number, identity number etc.) or indirectly (by combining different - especially rare - characteristics of respondents) such as: (age, occupation, education etc.).
•    Scientific use:
These files were established based on a specific methodology according to the data requester's requirements to extract the datasets with specific characteristics used for strategic studies, decision-making, and scientific research by individuals, households, and enterprises; while ensuring they contain no direct identifiers and are subject to confidentiality protection controls.
Qualified users who meet the confidentiality protection standards and procedures can access scientific-use files of detailed data through the “Etaha” platform of GASTAT, while more sensitive data are shared for use through visits to the Secure Data Lab, managed by GASTAT.

References and standards

The processing and classification of raw data for Agricultural statistics were based on the classification and coding inputs obtained during data collection. The data were organized according to the concepts, definitions, and classifications provided by the Ministry of Environment, Water, and Agriculture, in alignment with the Food and Agriculture Organization (FAO).
The Enumerator’s Guide was adopted as the primary reference for data collection teams and was officially used throughout the survey process. It provides clear instructions for completing the questionnaire, comprehensive explanations of the concepts and definitions used, and guidance for handling various field situations, ensuring data quality, accuracy, and consistency with the survey standards.
Agricultural statistics Framework:
GASTAT carries out all its statistical work according to a unified methodology that aligns with the nature of each statistical product. This methodology is based on the Generic Statistical Business Process Model (GSBPM), which is consistent with the operational procedures adopted by international organizations and harmonized with the practices of the relevant national entities.
For more details, please refer to the following link:
Generic Statistical Business Process Model(GSBPM)

Quality assurance

GASTAT declares that it considers the following principles: Impartiality, ensuring that the statistical product is user-oriented, maintaining the quality of processes and outputs, enhancing the effectiveness of statistical operations, and reducing the burden on respondents. 
Data is validated through procedures and quality controls that are applied during the process at various stages, such as: (data entry, data collection, and other final controls).

Quality assessment

GASTAT performs all statistical activities according to a national model (Generic Statistical Business Process Model – GSBPM). According to the GSBPM, the final phase of statistical activities is overall evaluation using information gathered in each phase or sub-process. This information is used to prepare the evaluation report, which outlines all the quality issues related to the specific statistical activity and serves as input for improvement actions.

Confidentiality

Confidentiality - Policy

According to Royal Decree No. 23 dated 07/12/1379, data must always be kept confidential and must be used by GASTAT for statistical purposes only.
Therefore, the data is protected in the data servers of GASTAT.

Confidentiality - Data Treatment

Data of SMEs survey are presented in right tables in order to summarize, understand, as well as extract their results. Moreover, to compare them with other data, and to obtain statistical significance about the selected study population. However, referring to such data indicated in tables is much easier than going back to check the original questionnaire that may include some data like: names and addresses of individuals, and names of data providers, which violates data confidentiality of statistical data.
“Anonymity of data” is one of the most important procedures. To keep data confidential,
GASTAT removed information on individual persons, households, or business entities such a way that the respondent cannot be identified either directly such as: (name, address, contact number, identity number etc.) or indirectly (by combining different - especially rare - characteristics of respondents) such as: (age, occupation, education etc.).

Dissemination policy

Statistical calendar

Agricultural statistics have been included in the statistical calendar.
Statistical Calendar

User access

One of the objectives of GASTAT is to better meet the needs of its users; therefore, the Agricultural Statistics Publication results are made available to all users immediately upon release.
Customer questions and inquiries about the publication and its results are also received through various communication channels, such as:
•    GASTAT official website: www.stats.gov.sa
•    GASTAT official email address:   info@stats.gov.sa
•    Official visits to GASTAT’s official head office in Riyadh or one of its branches in Saudi Arabia.
•    Official letters.
•    Statistical telephone: (199009).