Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

The Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This data set is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN).

Abstract

The main target of the FSS 2007 was to obtain information about structure and typology of the agricultural farms and their agricultural activities in Latvia in accordance with EU and national requirements.

Geographic coverage

National

Analysis unit

Farms

Universe

All economically active farms - farms, which produce agricultural production, were involved in the target population for the FSS 2007. The definition of a holding is in line with the EU Farm Structure Survey definition. Agricultural holding is a single unit both technically and economically, which has a single management and the output of which is agricultural production. The holding may also provide other supplementary (non-agricultural) products and services.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Latvian farm structure survey 2007 was made as combination of exhaustive enumeration and sample. All units were sampled in the part of sampling frame where exhaustive enumeration was done. Stratified simple random sampling was done in the sampling part of the frame. For more details see 3.3.2 of the Methodological Report available as external resources.. For each farm structure survey new sample is drawn. Procedure for sample selection is self-made using SPSS®. In 2007 total sample size comprised 58.0 thousand holdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire form of FSS 2007 was developed in co-operation with the Ministry of Agriculture and other State institutions concerned. The list of characteristics included in the survey was compliant with EU requirements concerning the Farm Structure Survey 2007 (Commission Regulation (EC) No 204/2006 of February 6, 2006 adapting Council Regulation (EEC) No 571/88 and amending Commission Decision No 2000/115/EC with a view to the organization of Community surveys on the structure of agriculture holdings in 2007).

For all types of farms (private farms, state farms and statutory companies) Latvia has only one type of questionnaire form. The questionnaire form of FSS 2007 was developed in co-operation with the Ministry of Agriculture and other State institutions concerned.The questionnaire form was designed so that later it can easily be processed on scanners. The size of the questionnaire form is 8 pages. The following parts are included: · General description of the farm and holder (user) · Land use · Utilisation of arable land · Number of livestock and poultry · Stock of agricultural machines · Farm storage facilities of manure and irrigation devices · Farm labour force, permanent and temporary · Rural development

Cleaning operations

Data Control of the FSS 2007 was carried out as follows: Manual Control: The first visual control of questionnaire forms was done in regional offices. Regional supervisory stuff and other staff in regional offices carried out a preliminary verification to see if the forms were filled in correctly and completely. Verification and Logical Control: For data entering scanners were used. After scanning the verification of the logical and arithmetical control was done in the CSB in accordance with specially developed verification programme. There were approximately 200 different logical and arithmetical controls. After interviewers or farmers were contacted by phone the re-addressing of errors was done. Due to the error shown by logical control program, if necessary, land users were contacted by phone in, e.g., to find out volume of sown areas, number of livestock, etc. thus needed information was obtained, and there non-response in such cases does not exist. Comparison of the FSS with other data sources: After logical control was finished, the FSS data were compared with information from Statistical Farm Register (information on holder (user) of farm, land areas belonging to farm and other), with information from other statistical surveys (previous livestock survey), with Animal Register information (Agricultural Data Centre) on June 1, 2007, and with the list of Organic farms received from Ministry of Agriculture and Integrated Administration an Control System – IACS (Rural Support Service)

Response rate

Details on non-response are available in section 3.4.5 of the Methodological Report available as external resources.

Sampling error estimates

Please see section 3.5.2 of the Methodological Report (available as external resources) for a detailed explanation procedure used to estimate sampling errors.

Data appraisal

Comparison of the FSS with other data sources: After logical control was finished, the FSS data were compared with information from Statistical Farm Register (information on holder (user) of farm, land areas belonging to farm and other), with information from other statistical surveys (previous livestock survey), with Animal Register information (Agricultural Data Centre) on June 1, 2007, and with the list of Organic farms received from Ministry of Agriculture and Integrated Administration an Control System - IACS (Rural Support Service).

International comparability Eurostat Statistical Office of the European Union (Eurostat) on its homepage published information on agriculture on EU-27 and on each country separately. Main indicators are available in section: Main tables/ Agriculture, forestry and fisheries/ Agriculture/ Structure of agricultural holdings. More detailed Farm Structure Data: Database/ Agriculture, forestry and fisheries/ Agriculture/ Structure of agricultural holdings. Eurostat has published reports on agriculture in EU countries on its webpage: Publications/ Collections/ Statistics in focus.

A statistical summary of census farm related data. This data is based on the Census of Agriculture, and includes:

• number and area of census farms in Canada and the Provinces
• Ontario census farms, broken down by industry, capital value and total farm capital, total gross farm receipts, cropland, and total farm area
• number of census farms and farm operators in Canada and the Provinces
• number of census farms and farm operators by county
• number of census farms by county
• classification of Ontario census farms, broken down by size of operation, economic class and total value of sales, industry, and capital value
• farmland area classified by use of land and by county
• farm capital value by county

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Abstract

Food security has become a burring issue in Ethiopia since it is an absolute prerequisite for political and social stability. It received national prominence in the aftermath of the recurring drought and famine and obviously became an immediate domestic policy concern. The gap between the dire need for food supply is compounded by rapidly increasing population, depletion of natural resources and the existing traditional way of farming. It even requires sacrifice to provide adequate supply of food in such a situation where natural and human factors have negatively impacted in the agricultural production and resulted in recurrent droughts and sometimes in catastrophe. Pressed by these problems and other economic factors, the Ethiopian government has centered its agricultural policy on ensuring food security by allocating more resources to increase agricultural production so as to ward off food shortage and ensure continuous adequate supply of food. To monitor and evaluate the performance of the policy and the trends in the charging patterns in agricultural, statistical information on agriculture is required as an input since agriculture is a primary activity connected with food availability. The Central Statistical Agency (CSA) has been generating statistical information used as inputs in the formulation of agricultural policies by collecting processing and summarizing reliable, comprehensive and timely data on the country's agriculture. As part of this mission the 2003-2004 (1996 E.C) Annual Agricultural Sample Survey was conducted to furnish data on cropland area and production of crops within the private peasant holdings for Main (“Meher”) season of the quoted year.

The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, food security, etc. The survey is composed of four components: Crop production forecast survey. Main (“Meher”) season survey, Livestock survey and “Belg” season survey.

The specific objectives of Main (“Meher”) season survey are: - To estimate the total cultivated area, production and yield of crops. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2003-2004 annual Agricultural Sample Survey covered the entire rural parts of the country except all zones of Gambella region, and the non-sedentary population of three zones of Afar and six zones of Somali regions.

Note: The crop cutting exercise part of the survey from November 2003 up to January 2004 was not done in Gambela regional state, therefore no production estimates for the region was computed for Meher (main) season.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2001/02 Ethiopian Agricultural Sample Enumeration (EASE) was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. Sample Design A stratified two-stage cluster sample design was used to select the sample. Enumeration Areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. Sample enumeration areas from each stratum were sub-samples of the 2001/02 (1994 E.C) Ethiopian Agricultural Sample Enumeration. They were selected using probability proportional to size systematic sampling; size being number of agricultural households obtained from the 1994 Population & Housing Census and adjusted for the sub-sampling effect. Within each sample EA a fresh list of households was prepared and 25 agricultural households from each sample EA were systematically selected at the second stage. The survey questionnaire was finally administered to the 25 agricultural households selected at the second stage. Information on area under crops and Meher season production of crops was obtained from the 25 households that were ultimately selected. It is important to note, however, that data on crop cutting were obtained only from fifteen sampled households (the 11th - 25th households selected).

The sample size for the 2003-04 agricultural sample survey was determined by taking into account both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non- sampling errors, manageability of the survey in terms of quality and operational capability was also considered. Except Harari, Addis Ababa and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2003-2004 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 96/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 96/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 96/3A: Used to list fields under temporary crops and farm management practice. - AgSS Form 96/3B: Used to list fields under permanent crops and farm management practice. - AgSS Form 96/3C: Used to list fields under mixed crops and farm management practice. - AgSS Form 96/3D: Used to collect information about other land use type and area and other agricultural related questions. - AgSS Form 96/5: Used to list temporary crop fields for selecting crop fields for crop cutting. - AgSS Form 96/6: Used to collect information about temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field , hence the need for data editing, and verification. An editing, coding and verification instruction manual was perpared and reproduced. Then 65 editors-coders and verifiers were trained for two days in editing , coding and.verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 1OO % basis before the questioners were passed over to the data entry unit. The editlng, coding and verification exercise of all questionnaires took 40 days.

Data Entry, Cleaning and Tabulation: Before data entry, the Natural resource and Agricultural Statistics Department prepared edit specification for the survey for use on personal computers for data consistency checking purposes . The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 64 data encoders and it took 50 days to finsh the job. Finally, tabulation was done on personal computers to produce statistical tables as per the tabulation plan.

Response rate

A total of 2,072 enumeration areas were initially selected to be covered by the survey, however, due to various reasons 16 EA's were not covered and the survey was successfully carried out in 2,056 (99.23 %) EAs. As regards the ultimate sampling unit, it was planned to conduct the survey on 51,800 agricultural households and 51,300 (99.03 %) households were actually covered by the Meher season Agricultural Sample Survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II of 2003-2004 Agricultural Sample Survey, Volume I report.

Data appraisal

As it was explained in the response rate under sampling section, the non response rate was minimal. There is no testing for bias made in this survey.

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and housed animals, at regional level, by general farm type.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Reference date for livestock is 1 April and for crops 15 May.

Data available from: 2000

Status of the figures: The figures are final.

Changes as of March 28, 2025: the final figures for 2024 have been added.

When will new figures be published? According to regular planning provisional figures are published in November and the definite figures will follow in March of the following year.

Abstract

The objective of the GAPS is to strengthen the Multi-Round Annual Crop and Livestock Surveys (MRACLS) that the ministry implements through SRID. The MRACLS is the national agricultural survey on the basis of which SRID releases information on agricultural production and yields of important crops. The ultimate goal of GAPS is to provide more accurate and timely agricultural production estimates at the district, regional, and national levels. The survey is also to offer an opportunity for SRID to experiment with a number of potential improvements with a view to developing the required skills and competencies before scaling up, over time, to all the districts in the country.

As part of the terms of implementing GAPS, MoFA agreed to assign four Agriculture Extension Agents (AEAs) per district for data collection. The Agents were relieved from all extension duties. To distinguish these field data collection officers from other extension agents, they were referred to as District Agricultural Statistical Assistants (DASAs). One officer per district was designated as a District Management Information System (MIS) officer and was given additional responsibility as field supervisor and referred to as District Agricultural Statistical Officer (DASO). A total of 100 DASAs and DASOs were successfully trained and deployed to their districts for GAPS implementation and given the task of collecting and processing datafrom the field.

Geographic coverage

National Level Regions Districts

Analysis unit

Household

Universe

Agricultural household and holder

Kind of data

Census/enumeration data [cen]

Sampling procedure

The GAPS employed a three stage multi-sampling design in response to the Government of Ghana's requirement for reliable agricultural statistics at the national, regional and district levels.

· First Stage Sampling- Selection of 2 Districts from each of the 10 Regions. A total of 20 districts, 2 from each of the 10 regions were randomly selected with probability proportional to size, using districts' population in year 2000 as a measure of size.1. Eleven Metropolitan and Municipal Assemblies (Kumasi, Sunyani, Cape Coast, New Juaben, Accra, Tema, Tamale, Bolgatanga, Wa, Ho and Shama Ahanta East) were excluded from the study, given their urban predominance.

· Second Stage Sampling - Selection of 40 Enumeration Areas (EAs) from each of the 20 Districts. A total of 800 EAs was selected; 40 EAs were randomly selected with probability proportional to size in each district, using the list of EAs compiled by the 2010 Census as a sample frame, and projected total population as a measure of size.2 In the Kassena-Nankana East district, 53 of the 187 EAs compiled by the 2010 census were excluded from the study because of the land disputes prevalent in the area earlier in 2011.

· Third Stage Sampling - Selection of 5 holders At the third stage, five holders were randomly chosen in each EA, using as a sample frame; the full list of all holders, compiled from the Household and Holders Listing questionnaire. This provides a total sample of 4000 holders, consisting of 200 holders per district.

Sampling deviation

Not reported

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires used in the minor season survey include the followings:-

(a) The Household and Holding Inquiry - Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry - Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry - Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Cleaning operations

The set of questionnaires used in the minor season survey include:-

(a) The Household and Holding Inquiry – Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry – Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry – Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Response rate

The repond rate reported was 70%

Sampling error estimates

No estimates of sampling error given

Data appraisal

District information and communication infrastructure was upgraded in the 20 districts to improve data collection and management. Each office was provided with a computer, printer, voltage stabilizers, an internet modem, 5 GPS units, and other field equipment. Motorbikes were also provided to the DASAs to enhance mobility.

Similarly, the SRID head office was also upgraded with ICT equipment to facilitate work.To oversee the implementation of the pilot survey a cross-sectoral steering committee was established.

At the end of each phase of implementation, a team was put together to assess the institutional and financial feasibility of scaling up GAPS, and both assessment reports are available at SRID.

Abstract

The Government of Sierra Leone has made agriculture its top priority. Yet, a lack of information about the status of the sector can inhibit effective policy formulation, planning, implementation and performance evaluation. In view of this, in August 2009, the Government of Sierra Leone requested and supported an independent exercise, the Agricultural Household Tracking Survey (AHTS), to obtain accurate and credible agricultural data that can serve as a baseline for longitudinal analyses of Sierra Leone's progress in agricultural development over the next several years.

The AHTS focused on a subset of eight "core crops" comprised of five food crops (rice, cassava, maize, groundnut and sweet potato) and three tree cash crops (cacao, coffee and oil palm). Detailed data on these crops was collected in the eight "crop specific" sections of the AHTS and are provided in this AHTS Report. In addition, some basic data was collected across all crops as well as basic data on household characteristics. In addition, the AHTS collected some data on revenues, access to seeds from the formal sector and other aspects of agriculture that is designed to allow the Government of Sierra Leone to track whether subsistence farmers are becoming more linked to commercial systems over time.

In general, agricultural household surveys, such as the AHTS, are used to provide a representative picture of the status of farming households in a country. The random selection of the sample and its large size allow for a high level of confidence in the final results, even after taking into account issues such as survey measurement error. The AHTS was designed with an exceptionally large sample size so the impact of measurement error on the country level aggregates would be minimal.

The AHTS covered a final sample of 8,840 households, the largest household-level sample for an agricultural survey in Sierra Leone. However, since comparable agricultural data have not been collected before this, the AHTS results do not allow any comparisons over time. The AHTS questionnaire was designed to capture the decisions farmers make, the yields and production levels achieved by the average household, as well as the access to services and technology, food security and other dimensions of agricultural households in Sierra Leone. The survey design followed international standards, with adaptations to local context through extensive field testing. The target sample size was chosen following rigorous power calculations based on SSL data available prior to the AHTS.

This report covers the most salient features of household agriculture in Sierra Leone from the AHTS. The results also highlight areas for policy interventions that could improve productivity, food security and livelihoods of farming households more generally in Sierra Leone. These areas include:- i. Improvements in inputs use and planting practices could dramatically improve productivity a. Levels of fertilizer use are low, particularly for the main staples, rice and cassava; b. The adoption of improved seed varieties remains low - for example, only 2% of rice farming households have ever cultivated one of the NERICA varieties; c. Planting practices often involve broadcasting on upland farms and tree crops are often intercropped (not just with other tree crops for shading purposes); ii. Households' access to and interaction with markets remains low - for example, 92% of sampled households reported that their main point of sale for threshed rice was at farm gate (64% for clean rice). AHTS communities reported an average distance of 6.6 miles to the nearest market and 8.8 miles to the nearest permanent market;

iii. There is substantial scope to improve rural infrastructure. This includes both very localized infrastructure such as drying floors and storage facilities, as well as larger scale infrastructure such as roads. For example, 49% of farmers harvesting cereals stored their cereals in a room inhabited by the household; only 13% of households used a cement floor for drying. Out of 880 communities surveyed as part the AHTS community module, 25% reported a walking distance to the nearest motorable road of more than 30 minutes during the dry season.

These communities are the most likely to lack access to markets for agricultural inputs and outputs. 67% of communities were listed as motorable during the dry season; iv. Financial access - much as improved practices are important for farmers, the cost associated with some of these improved practices increases the need for financial services. A lack of these services can create a bottleneck for yield growth and productivity improvements. The AHTS data show that 68% of respondent households who did not borrow money said they had nobody to apply to for a loan and the majority of farmers with existing loans borrowed on the informal market; v. There is need for continued dissemination of better, more sustainable cultivation practices, via continued investments in extension and investments in developing new cultivation techniques and new seed varieties.

Geographic coverage

The survey coverage is at: - National level - District Level

Analysis unit

The unit of analysis is the Agriculture practices within the household and Communities

Universe

All farmers within the Household and the Community as a whole

Kind of data

Sample survey data [ssd]

Sampling procedure

The Agricultural Household Trading Survey follows a two-level sampling methodology, which is the standard for household level surveys.
First, 920 Enumeration Areas (EAs) were sampled for the Agricultural Household Trading Survey out of the 9,671 Enumeration Areas (EAs) from the 2004 census. The sampling was stratified by district to ensure that the results are representative at this level, allowing presentation of district averages. Second, within each sampled EA, a sample of up to 10 agricultural households was drawn using information collected during the Survey Listing Exercise, conducted in October and November 2009. The total target sample size for the AHTS Survey was 9,030 agricultural households. For each EA, 5 additional replacement households were also drawn.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The Agricultural Household Tracking Survey (AHTS) has two questionnaire which were designed to provide a representative picture of the status of farming household and the community in a country.

A Household Questionnaire was administered in each household, which captured the decision farmers make:- - The yields and production levels achieved by the average household - Access to services and technology, food security in the household - Financial access to the household

In addition to the household questionnaire, a Community Questionniare was also administered for a particular community - Insight into farmers household's agricultural and commercial activies prevailing ecological and crop condition and the services they access in their communities.

Cleaning operations

Manually editing was done on questionnaire, and computer editing was also done as a way of validating the data as the software provided automatic data checks for acceptable values for the variables and checks between different components of the questionnaires

Response rate

The final Agricultural Household Trading Survey sample has data on 8,840 households. The target sample size was 9,030 households. Of these, 9,006 households were reached in 917 Enumeration Areas (24 households were not reached and not replaced, over 3 different EAs).

Sampling error estimates

No estimates of sampling error

Data appraisal

Technical members comprising MAFFS and SSL team monitored the progress of fieldwork and resolved any issues arising.

Big Data Analytics in Agriculture Market Outlook

The global big data analytics in agriculture market is anticipated to witness substantial growth from 2024 to 2032. In 2023, the market size was valued at approximately USD 2.5 billion, and it is projected to reach around USD 8.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 14.1%. Several factors are driving this impressive growth, including the increasing adoption of precision farming techniques and the heightened need for sustainable agricultural practices to meet the rising global food demand. As the agriculture industry shifts towards more data-driven methodologies, big data analytics emerges as a critical tool for enhancing productivity and efficiency.

One of the significant growth factors propelling the big data analytics in agriculture market is the rise in global population, which has resulted in an increased demand for food. To cope with this demand, farmers and agribusinesses are turning to technology-driven solutions such as big data analytics to optimize production processes and maximize yield. Big data analytics provides insights into various agricultural practices, helping to improve crop management and resource utilization. Additionally, the pressure to adopt environmentally friendly practices is encouraging the use of analytics to minimize waste and optimize resource usage, thereby supporting sustainable agriculture.

Technological advancements in data processing and analysis are also playing a crucial role in the market's expansion. The integration of the Internet of Things (IoT) with big data analytics allows for real-time data gathering from various agricultural equipment and sensors. This capability enables the precise monitoring of farm conditions, leading to data-driven decision-making processes that optimize crop growth, pest control, and harvesting schedules. Furthermore, advancements in machine learning and artificial intelligence are enhancing the predictive capabilities of big data analytics, allowing for better anticipation of weather patterns, disease outbreaks, and market trends, which are vital for strategic planning and risk management in agriculture.

Another significant growth factor is the increased investment in agricultural technology by both government and private sectors. Governments around the world are recognizing the importance of agricultural technology in ensuring food security and are therefore investing in research and development initiatives. Additionally, venture capitalists and private firms are funding startups that specialize in agricultural analytics, further propelling market growth. The collaboration between technology companies and agricultural stakeholders is resulting in the development of innovative solutions that are tailored to the specific needs of the agricultural sector, thereby enhancing the market uptake of big data analytics.

From a regional perspective, North America holds a significant share of the big data analytics in agriculture market due to the presence of advanced agricultural practices and the early adoption of technology. Meanwhile, the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period. This growth can be attributed to the increasing population in countries like China and India, which is driving the demand for food and pushing the agricultural sector to adopt advanced technologies. Additionally, government initiatives in these regions to support technological integration in agriculture are further aiding market growth. Europe is also witnessing steady growth, with an increasing focus on sustainable farming practices and the utilization of analytics to enhance productivity.

Component Analysis

The component segment of the big data analytics in agriculture market comprises software, hardware, and services, each playing a vital role in the effective deployment and utilization of data analytics in agriculture. Software solutions in this market are particularly critical, as they provide the platforms and applications necessary for data collection, analysis, and visualization. These software applications range from farm management systems to predictive analytics tools that help farmers make informed decisions about crop planting, pest control, and resource management. With advancements in cloud computing and AI, software solutions are becoming more sophisticated, offering enhanced functionalities and user-friendly interfaces that cater to the specific needs of the agricultural sector.

Hardware components, such as sensors, drones, and IoT devices, are essential for the col

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

The National Institute of Statistics of Rwanda (NISR) has been conducting seasonal agricultural survey since 2012 for the estimation of the national agricultural crop area and production estimates. In 2022/2023 agricultural year, the NISR conducted Seasonal Agricultural Survey (SAS) covering the three agricultural seasons. The SAS provides information used as a tool to assist in addressing key agricultural issues and information needs that will inform policymakers and other stakeholders and allow more effective identification of priority intervention needs.

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

Small scale agricultural farms and large scale farms

Universe

The SAS 2023 targeted potential agricultural land and large-scale farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

The total country land was classified into five strata, of which four are agricultural, while the remaining stratum is designated for land not suitable for agriculture. The four agricultural strata are: dominant hill crop land, dominant wetland crops, dominant rangeland, and mixed stratum, all considered suitable for agriculture. The fifth stratum comprises non-agricultural land, including areas occupied by water bodies, forestry plantations, settlements, parks, and protected marshland not utilized for agriculture. The sampling frame excludes land areas covered by tea plantation farms. In 2023 agricultural year, the total sample used was 1200 segments. At first stage,1200 segments were selected and allocated at district level based on the power allocation approach (Bankier, 1988). Sampled segments inside each district were distributed among strata with a proportional-to-size criterion.

At the second stage, 25 sample points were systematically selected, following a special distance of 60 meters between points. For every sample point, a corresponding farm or plot is identified, and the operator is interviewed. The farms therefore constitute the sampling units within each segment. Enumerators locate every sample point, delineate plots in which the sample points fall using high accurate GPS devices and then collect information on land use and other related information. Sampling weights are calculated and applied to the sample data to obtain stratum-level estimates. District estimates are then derived by aggregating the estimates from all strata within the district.

Data collection was done in 1200 segments and 345 large scale farmers holdings for Season A and B, whereas in Season C data was collected in 1769 sites potential to grow season C crops in addition to 513 segments, response rate was 100% of the sample.

During the SAS 2023 exercise, data collection covered three main agricultural seasons A, B and C and was conducted into two separate phases in each season: A. The first phase, known as screening activity (post-planting phase), consists of visiting all sampled segments and demarcating all plots with sampled points with the aim of covering the information related to land area, planted crops and land use.

B. The second phase involves capturing of production data by visiting sampled agricultural plots identified from screening activity as well as all large-scale farmers. To ensure the smooth completion of the SAS workload, NISR employed 137 Enumerators and 23 Team Leaders. All fieldwork staff hold a degree in agriculture sciences and were consistently trained by NISR headquarter staff before starting data collection in each season. Moreover, higher-level supervision was organized and done by staff from NISR who frequently visited the field teams during each phase of data collection to ensure the quality of collected data. For Season A, data collection started on 4th December 2022 and ended on 16th February 2023. For Season B, data collection started on 2nd May 2023 and ended on 30th June 2023. For Season C, data collection started on 10th September 2023 and ended on 30th September 2023.

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household crop farmers

Universe

Crop farming household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and
4 Crop farming housing units were systematically selected and canvassed .

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC crop questionnaire was divided into the following sections: - Holding identification - Holding characteristics - Access to land - Access to credit and funds used - Production input utilization, quantity and cost - Sources of inputs/equipment - Area harvested - Agric machinery - Production - Farm expenditure - Processing facilities - Storage facilities - Employment in agric. - Farm expenditure - Sales - Consumption - Market channels - Livestock farming - Fish farming

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already entered data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 98.44 percent was achieved at crop farming housing units level

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

Data collected at the end of 2018 to assess program progress in the far-west and mid-west regions of Nepal. The data set was generated by interviewing three samples of program participants in all 14 working districts: goat farmers, commercial vegetable farmers, and cereal farmers. Topics of the survey include agricultural commodities, farming seasons, area, production, applied technologies, sales, loss, and decision makers for technology and sales.

The United States Department of Agriculture-Agricultural Research Service (USDA-ARS) North Central Soil Conservation Research Laboratory - Soil Management Unit established a weather data collection system at the Swan Lake Research Farm in 1997. Weather data collected include wind speed and direction, barometric pressure, relative humidity, air temperature, soil temperatures, soil heat flux, solar radiation, photosynthetic active radiation, and precipitation. In 2015 the site became part of the Long Term Agroecosystem Research (LTAR) project. The Swan Lake Research Farm is located in Stevens County Minnesota, in the Upper Mississippi River Basin (UMRB) watershed. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ad80c14b-f4a0-41b2-8592-3a5b6bbebcc7

The Agricultural Management Dataset contains two variables which help describe management regimes within agricultural lands of China. The first variable, Irrigation Index, reports the fraction of cropland in a county which is under irrigation, excluding rice paddies. The Second variable, Nitrogen Fertilizer, reports the tonnes of nitrogen fertilizer applied in the county per year.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

The MilKey project aims at assessing the environmental, economic, and social sustainability of European dairy production systems, and at identifying ‘win-win’ farming practices for sustainable and greenhouse gas (GHG) optimised milk production. These data collection template were prepared to guide stakeholders wishing to conduct a sustainability assessment of dairy production systems. This template covers all the necessary data to assess the environmental, economic, and social sustainability dimensions of commercial dairy farms. Data requirements gathered in this template were deduced from the list of sustainability indicators presented in the DEXi-Dairy indicator handbook. The template is composed of 5 parts, i.e., Parts I-II-III for the environmental assessment, Part IV for the economic assessment, and Part V for the social assessment. The number of files to fill out depends on the number and nature of additional farming enterprises present on case study dairy farms. 1) Part I concerns the general information that must be collected on all commercial farms. 2) Part II focuses specifically on the dairy enterprise and must thus be completed for all commercial farms. 3) Part III records information about a potential beef enterprise and must thus be filled out for commercial farms that have an additional beef enterprise. 4) Part IV gathers all the economic data and must be filled out for all commercial case study farms. 5) Part V gathers all the social data and must be filled out for all commercial case study farms. Please refer to the guide for the collection of farm environmental and economic data for the detailed description of all variables included in Parts I-II-III-IV.

This table contains data at regional level on the number of persons employed on agricultural holdings, the corresponding annual work units (AWUs) and the number of holdings with workers.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Data on labour force refer to the period April to March of the year preceding the agricultural census.

Data available from: 2000

Status of the figures: The figures are final.

Changes as of March 28, 2025: the final figures for 2024 have been added.

When will new figures be published? According to regular planning provisional figures for the current year are published in November and the definite figures will follow in March of the following year.

Abstract

The main purpose of the Survey of Agricultural Holdings is to produce official indicators in line with agricultural sector. The survey allows the compilation of statistics on crops and animal husbandry, of which information annual and permanent crops, sown area, average yield of annual crops and etc. Statistical tables are accessible through the following link: https://www.geostat.ge/en/modules/categories/196/agriculture.

One round of the survey (reference year) includes 5 inquiries: The Inception interview is carried out using the inception questionnaire during the period of January-February of the reference year. During this interview the sampled holdings are identified and situation existing at the holding as of first January is recorded. I, II and III quarter interviews are conducted by means of quarterly questionnaire at the beginning of the following month of the corresponding quarter of the reference year. Based on these surveys, the information about agricultural activities during the corresponding quarter is collected. The final interview is conducted by means of final questionnaire in January of the following year of the reference year. During this interview, the information about agricultural activities at the holding during IV quarter of the reference year and the summary information about agricultural activities at the holding during the whole reference year (from 1 January to 31 December of the previous year) are collected. During all five interviews, the same agricultural holdings (about 12 000) are interviewed which are selected by a two-stage stratified cluster random sampling procedure out of about 642 000 agricultural holdings operated in Georgia. On the first stage, clusters (settlements) are selected. On the second stage, holdings are selected within the selected clusters.

The survey completely covers the territory of Georgia, excluding the occupied territories of Autonomous Republic of Abkhazia and Tskhinvali region. Each year a new sample is selected based on a rotational design (on a 3-year basis). In particular, every year approximately 4000 holdings out of the 12000 sampled holdings are replaced by new holdings. Sampled holdings participate in the survey for 3 years. Large agricultural holdings are sampled every year with complete coverage. The statistical unit of the survey is the agricultural holding (family holdings and agricultural enterprises) – which is defined as an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size. Agricultural activities are conducted under the supervision of a holder (in case of households - a member of household, in case of agricultural enterprises - director or authorized person), who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities.

More than 270 interviewers participated in the survey fieldwork. For the Data collection, computer-assisted personal interviewing method (CAPI) was used in the family holdings. In case of agricultural enterprises, the authorized persons of the enterprises (respondent) fill the electronic (online) questionnaires by themselves (CAWI). Coordination of the interviewers and the primary control of the collected data during the field is carried out by coordinators. Their working area covers several municipalities. The function of the coordinators also includes consultation for agricultural enterprises on methodological and technical issues related to the survey.

Geographic coverage

Entire country (Georgia), excluding occupied regions (Abkhazia and Tskhinvali region)

Analysis unit

Agricultural holding – economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size in which agricultural activities are conducted under the supervision of a holder, who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities.

Universe

Survey sampling frame includes about 642,000 agriculture holdings (households and agricultural enterprises) operated in country. The Agricultural Census 2014 is the main source of the sample frame. Sampling frame is updated on a permanent basis in according to the results of survey of agricultural holdings, business register and different administrative sources.

Kind of data

Sample survey data [ssd]

Sampling procedure

• Main Source of the sample frame since 2016 - Agricultural Census 2014; • Sample frame contained 642,000 holding - sample size 12,000 (1.9%); • Sample Design: two-stage stratified cluster random sampling; - First stage - selection of cluster (Settlement); - Second stage - Selection of holdings within the selected clusters; • Each year a new sample is selected based on a rotational design; - Every year 1/3 of holdings (4,000) selected a year before are replaced (Sampled holdings participate in the survey during 3 years); • Extremely large agricultural holdings are sampled every year with complete coverage; • Additional Sources for updating sample frame: Sample Survey of Agricultural Holdings, Statistical Business Register, Administrative data existing in MEPA (large agricultural holdings); Sampling error of main indicators do not exceed 5% for a country level and 10% for a regional level.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Detailed information on structure, and sections of questionnaires used in the survey of agricultural holdings are available in following link: https://www.geostat.ge/en/modules/categories/564/questionnaires-Agricultural-Statistics

Cleaning operations

After the field work, cleaning and harmonization of all inquiries are established at the Geostat head office - logical and arithmetical inconsistencies, as well as non-typical and suspicious data are detected, checked and corrected. Verification of the data is performed by contacting the respondents by phone. If verification with respondent is impossible, different imputation methods are used. Finally, indicators are calculated using weighted data. The obtained results are compared with corresponding results of the previous periods. In case of significant differences, the possible causes are identified and analyzed.

Response rate

In the 2022 fourth quarter, 1,349 holdings were not surveyed, due to the fact that some holdings refused to be interviewed or were not found during the fieldwork despite its existence. This is about 10.7% of the total sampled holdings of 12,589 holdings involved in the sample 2022 fourth quarter.