Abstract

The Agriculture Census is envisioned with the following objectives: · To provide data on the structure of agriculture as well as forestry and fisheries in Vanuatu; · To provide data that will be used as benchmark for current agricultural statistics; and · To provide sampling frame for surveys on agriculture (crops and livestock), fisheries and forestry.

Specifically, the Agriculture Census Phase II aims: · To determine the structure and characteristics of the agricultural activities of the households in Vanuatu such as crop gardening, coconut/cocoa/ coffee/kava/vanilla/pepper farming, tending of cattle and other livestock activities, forestry-related activities and fishing operations; · To determine the number and distribution of household engaged in crop gardening, coconut/cocoa/coffee/kava/vanilla/pepper farming, tending of cattle and other livestock activities, forestry-related activities and fishing operations at the island level; and · To provide data on the farm/holding/sub-holding area, quantity of the crops grown/sold, number of cattle and other livestock kept as of the day of enumeration, quantity of fisheries species gathered/caught, etc.

Geographic coverage

The 18 major islands were classified as: 1. Small - number of households engaged in agricultural activities less than 500 (Torres, Paama, Erromango, Aniwa, Aneityum and Futuna); 2. Medium - number of households engaged in agricultural activities 500-1,999 (Banks, Malo, Maewo, Ambrym,Epi and Shepherds); and 3. Large - number of households operating agricultural activities 2,000 or more (Efate, Malekula, Ambae, Pentecost and Tanna).

Analysis unit

Households and individuals

Universe

The Survey covers all rural households

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling method The 18 major islands were classified as: • Small - number of households engaged in agricultural activities less than 500 (Torres, Paama, Erromango, Aniwa, Aneityum and Futuna); • Medium - number of households engaged in agricultural activities 500-1,999 (Banks, Malo, Maewo, Ambrym, Epi and Shepherds); and • Large - number of households operating agricultural activities 2,000 or more (Efate, Malekula, Ambae, Pentecost and Tanna).

In determining the number of households to be interviewed in each island and in each enumeration area (EA): - For small islands, all households were listed and the identified households engaged in agricultural activities were enumerated; - For medium-sized islands, one-third of the sample EAs in these islands were selected and all households were listed and those found to be engaged in agricultural activities were interviewed; and - For large islands, one-third of the total EAs were selected in each island and all households listed. Of households found to have a crop garden, coconut sub-holding or kava sub-holding, one-third were selected to be further interviewed. In addition, all households listed and involved in the subholding of cattle and cash crops like cocoa, coffee (for Tanna only), vanilla and pepper (10 or more plants) were also enumerated.

Sampling deviation

No information mentioned about the sample deviation from the sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

Phase I: Census Listing

Phase II: Surveys Form 1.1 - Household Form 1.2 - Crop Garden Form 1.2A - Gardener's Form Form 1.3 - Kava Form 1.4 - Coconut Form 2 - Cocoa Form 3 - Coffee Form 4 - Vanilla Form 5 - Pepper Form 6 - Cattle Form 7 - Commercial Farm Form A - List of Activities Form B1 - Control Sheet for all small and medium sized islands Form B2 - Control Sheet for Santo, Pentecost and Ambae Form B3 - Control Sheet for Ambrym and Malekula Form B4 - Control Sheet for Efate and Tanna

Cleaning operations

Eight data entry operators were hired by the project to do the data encoding of the Phase I of the project. This was the first-hands on as far as the software is concerned for all the data entry operators. Before the actual data entry, the data processing expert had all eight operators plus the supervisors on a training session for a few days. At the end of the training session, they were familiar with the software and then started the actual data encoding. The processing of data for Phase I of the project took the entire month of June 2006 to be completed. During the Phase II of the project, the expert set up the system and trained the local staff on system operation for two weeks and then left for his home country. Since the project staff and the data entry operators who were hired were already familiar with CsPro, the whole data processing was done without the presence of the consultant. The expert later came for his final mission to prepare the data for tabulation and generate the required tables using the table specifications for that purpose.

The machine data processing of the forms was done using CsPro. Data encoding, data cleaning and tabulation were done using data entry, batch edit and cross tab applications respectively. Control and management of the data entry of the forms and data cleaning of the batch files were done using SCIPS (Survey / Census Integrated Processing System), a Visual Basic 6 (VB6) program developed by the expert designed to integrate the different phases of data capture and data cleaning of any survey/census. The program facilitates the assignment of folios to keyers that resulted to automatic recording of the data capture status of each batch/folio and eliminated errors in the encoding of the geographic identification codes. It also made the data cleaning easier since SCIPS enabled the users to correct errors found by the data consistency and completeness check programs without printing the generated error list.

Response rate

100%

Sampling error estimates

The number of households to be interviewed is based on the sampling methodology that is used in the census. The 15 major islands were classified as:

1. Small - if the number of households engaged in agricultural activities is less than 500; in this case, Torres, Paama and Erromango are under this category.
2. Medium - if the number of households engaged in agricultural activities is between 500 - 1,999; Banks, Malo, Maewo, Ambrym, Epi and Shepherds belong to this group.
3. Large - if the number of households operating agricultural activities is 2,000 or more; Santo, Efate, Malekula, Ambae, Pentecost and Tanna were considered to be large islands.

In selecting the number of households to be interviewed in each island, the following was carried out:

a. For Erromango, Torres and Paama, all households were listed and those households engaged in agricultural activities were enumerated; b. For Banks, Malo, Maewo, Ambrym, Epi and Shepherds, 1/3 of the sample EAs in these islands were selected and all households were listed and those engaged in agricultural activities were interviewed for their involvement in these activities; and c. For Santo, Efate, Malekula, Ambae, Pentecost and Tanna, 1/3 of the total EAs were also selected in each island and all households were listed in these islands, after which only 1/3 of the households engaged in agricultural activities were further interviewed if they were involved in crop garden, coconut sub-holding and kava sub-holding. In addition to this, all households in the selected EAs of these islands that were involved in the sub-holding of cattle and cash crops (with 10 trees or more) like cocoa, coffee (for Tanna only), vanilla and pepper were enumerated.

Data appraisal

Consultants have not provided documents regarding this aspect of data quality.

Abstract

The Agricultural Census aims in general to establish an updated, detailed and accurate holdings database to assist in planning and policy making at all levels related to the agricultural sector. It also aims in specific to provide data on the structure of agriculture, especially for small administrative and geographical units, rare items, and to enable detailed cross-tabulations, and to provide data that can be used as a benchmark for reconciliation of current agricultural statistics; and for setting estimates for subsequent years, in addition to provide frames for agricultural sampling surveys.

Geographic coverage

The census also covered all geographical levels in the West Bank and Gaza Strip, so that: 1. Implementation of a comprehensive listing in Gaza Strip that enumeration areas represent more than 5% of households that practice agricultural activity, according to the Population, Housing and Establishments Census, 2017 data. 2. Visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census, 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4%. 3. Implementation of a comprehensive listing in the West Bank for all localities except camps and city centers in the following governorates (Nablus, Ramallah & Al-Bireh, Hebron and J2 in Jerusalem Governorate). 4. Implementation of a comprehensive listing in the enumeration areas of camps and city centers in the following governorates (Nablus, Ramallah, Al-Bireh, Hebron and J2 of Jerusalem Governorate), for households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017, more than 5%, and visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4% in the same locality mentioned above. 5. About Jerusalem J1, a different methodology is applied in two phases. In the first phase, research and investigation are carried out in cooperation with responsible and dignitaries in Jerusalem J1 on agricultural holdings and holders, and in the second phase, enumeration of the holdings that were monitored in the first phase.

Analysis unit

Agricultural Holding

Universe

Includes agricultural holdings in Palestine in 2021

Kind of data

Census/enumeration data [cen]

Sampling procedure

The frame of the Agriculture Census includes a complete record of households and non-household agricultural holdings, where all households are enumerated and the household agricultural holdings are identified, in addition to a list of non-households holdings that is obtained by listing all buildings as well as a list from the Ministry of Agriculture which includes cooperative societies/charity societies, companies, and government and private holdings…etc.

The census also covered all geographical levels in the West Bank and Gaza Strip, so that: 1. Implementation of a comprehensive listing in Gaza Strip that enumeration areas represent more than 5% of households that practice agricultural activity, according to the Population, Housing and Establishments Census, 2017 data. 2. Visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census, 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4%. 3. Implementation of a comprehensive listing in the West Bank for all localities except camps and city centers in the following governorates (Nablus, Ramallah & Al-Bireh, Hebron and J2 in Jerusalem Governorate). 4. Implementation of a comprehensive listing in the enumeration areas of camps and city centers in the following governorates (Nablus, Ramallah, Al-Bireh, Hebron and J2 of Jerusalem Governorate), for households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017, more than 5%, and visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4% in the same locality mentioned above. 5. About Jerusalem J1, a different methodology is applied in two phases. In the first phase, research and investigation are carried out in cooperation with responsible and dignitaries in Jerusalem J1 on agricultural holdings and holders, and in the second phase, enumeration of the holdings that were monitored in the first phase.

Sampling deviation

Not applicable

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Computerized program

Cleaning operations

Post enumeration data processing phase was limited to final examination and cleaning of Agricultural Census databases, with documentation of examinations on all topics of Agricultural Census 2021 questions. Data processing phase focused on the following: 1. Checking the allowed transfers and values. 2. Checking the consistency between different questions of the census questionnaire based on logical relationships. 3. Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, reviewed and identified the source of the error case by case, and if such errors were immediately modified and corrected based on the source of the error3. Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, reviewed and identified the source of the error case by case, and if such errors were immediately modified and corrected based on the source of the error.

Response rate

Not Applicable.

Sampling error estimates

The sampling errors occur during the sample-based surveys but not in censuses as it is a comprehensive inventory of all agricultural holdings. These errors are easy to measure with the error point estimate also, since it is considered as an error in the sample.

Data appraisal

The non-sampling errors occur at any stage during the implementation of censuses and surveys. Therefore, it is necessary to provide for a data quality control system to ensure maximum accuracy. Many of these stages were used during the agriculture census planning and implementation where are-interview was carried out as follows:

• There are two models that were used to collect data and were uploaded to tablets. The first model is to enumerate households in all enumeration areas; in which the percentage of households that practiced an agricultural activity (according to the data of the Population, Housing and Establishments Census, 2017) is 5% or more, and the second model was used if the household had agricultural holdings.

• The enumerator visited Palestinian households in the enumeration areas in which the percentage of households that practiced agricultural activity (according to the data of the Population, Housing and Establishments Census, 2017) is less than 5%, so that the inventory model and the model prepared for agricultural holdings were if the tenure conditions were met.

Abstract

The Government of Liberia and its Development Partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since post-war, the Government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the requisite data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census. The Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to: · Reduce the existing data gap in Liberia's agriculture sector. · Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programs. · Enable LISGIS to establish an agriculture master sampling frame for the conduct of future agricultural surveys and research. · Identify the structural changes in the agriculture sector over time. · Provide information on crop, livestock, poultry, and aquaculture activities. · Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings. · Generate disaggregated agriculture statistics. · Provide statistics for advocacy in Liberia's agriculture sector. · Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data collected at these three levels provide a wealth of information for understanding the state of agriculture in Liberia. This documentation provides a catalogue of information necessary for understanding how data was collected at the household level. The documentation also provides useful information for understanding the household anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural households

Universe

The universe for the Liberia Agriculture Census 2024 household level data collection encompasses: All households in Liberia having atleast one member engaged in agriculture activity during the 2022/2023 farming season.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Liberia Agriculture Census 2024 (LAC-2024) was a sampled census conducted in all 15 counties of Liberia. The sampling frame used for the LAC-2024 is based on the 2022 National Population and Housing Census (2022-NPHC), conducted by the LISGIS. The sample design for the census was a stratified cluster sampling with enumeration areas (EAs) as clusters and farming households as units of interest. In adequacy with budget availability, a large sample of 4,800 EAs was considered for the LAC-2024. These EAs had a total of 269,652 agricultural households in the frame. The sample was allocated in strata (districts, urban/rural) proportionally to the numbers of farming households computed in the frame. In total, about 78.8% of the sample was allocated to rural areas. The stratified sample of EAs was selected with a probability proportional to the number of farming households at EA level. A complete listing of all households (both agricultural and non-agricultural) was carried out in the selected EAs and detailed questions were addressed to all households that practiced agricultural activities during the 2022/2023 farming season. The results of the LAC-2024 are representative at the district level.

For more information on the LAC-2024 sampling methodology, see the methodology section of the Liberia Agriculture Census 2024 Household Report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, commissions and agencies (MACs), nongovernmental and international organizations as well as accademic institutions involved with agriculture issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, for the purpose of easy administration. The household questionnaire include type of agricultural activities practice, household members characteristics, housing conditions, hired labor practice, agricultural parcels and plots characteristics, types of crops and methods of crop cultivation, inputs, tools and equipment use, type and number of livestock and poultry. The household questionnaire was administered to the household head or an adult member of the household who had vast knowledge of the household and its agricultural activities. The primary respondent (i.e., the household member that provided most of the information for the questionnaire or a given module, household member, or crop) sometimes varies across modules.

Cleaning operations

The data was edited using CSpro programs, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In few cases, manual editing techniques were applied to recode responses generated from other specify options. The SPSS software was used for this purpose.

Response rate

92.8%.

The dataset contains the details of farm holdings in India by Size Class. The Size class include - Below 0.5 hectares 0.5-01. hectares 1-2 hectares 2-3 hectares 3-4 hectares 4-5 hectares 5-7.5 hectares 7.5-10 hectares 10-20 hectares Above 20 hectares The categorization is also done on social grouping with further categorization of gender wise holdings and categorization by Individual or joint holding

Abstract

The current India Agriculture Census with reference year 2010-11 is ninth in the series.

The Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India conducts Agriculture Census, quinquennially, to collect data on operational holdings in the country. The reference period for Agriculture Census is the Agricultural year (July-June). Being the ultimate unit for taking agriculture-related decisions, operational holding has been taken as statistical unit at micro-level for data collection.

The Agriculture Census was conducted in three distinct Phases. The provisional results for first Phase of the current Census were released at State and all India level in October, 2012. After, scrutinizing the results at District/Tehsil level, this database has now been finalized and is being published in the form of an All India Report on number and area of operational holdings.

The main objectives of the Agriculture Census are: i) To describe structure and characteristics of agriculture by providing statistical data on operational holdings, including land utilization, irrigation, source of irrigation, irrigated and unirrigated area under different crops, live-stock, agricultural machinery and implements, use of fertilizers, seeds, agricultural credit etc. ii) To provide benchmark data needed for formulating new agricultural development programmes and for evaluating their progress. iii) To provide basic frame of operational holdings for carrying out future agricultural surveys and, iv) To lay a basis for developing an integrated programme for current agricultural statistics.

Geographic coverage

National

Analysis unit

Agricultural household, individual

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Agriculture Census data is collected following two broad approaches; in States where comprehensive land records exist (Land Record States), for Phase-I of the Census, the data on primary characteristics of operational holdings are collected and compiled on complete enumeration basis through re-tabulation of information available in the Village Land Records. For other States (Non-Land record States), this data is collected on sample basis following household enquiry.

In land record States,data on Agriculture Census is pooled for all the parcels of an operational holding irrespective of its location. However, for operational convenience, the outer limit for pooling is restricted to taluka. This pooling is done for each operational holder in the village of his residence. In the non-land record States, the data is collected through sample survey in 20 per cent of villages in each block. These villages are selected through simple random sampling method and all the operational holdings in the selected villagesare enumerated following household enquiry approach.

In smaller UTs, like Lakshadweep, Daman & Diu etc., no sampling is done. i.e. all holdings in all the villages are surveyed for collection of data.

Mode of data collection

Face-to-face [f2f]

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

Households who are rearing livestock or kept poultry

Universe

Livestock or poultry 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 3 Livestock/poultry farming housing units were systematically selected and canvassed.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC livestock and poultry questionnaire was divided into the following sections: - Identification/description of holdings - Funds, employment and earnings/wages - Livestock - Poultry - Fixed assets - Sales - Stock - Subsidy

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 enterd 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 99.3 percent was recorded at 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.

This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org

Abstract

The main objective of the census of agriculture of Nepal is to publish data at district level on the following: 1. Structure and characteristics of the holding such as size, agricultural land use, land tenure, land fragmentation, area planted to crops, number of livestock, and others; 2. To provide benchmark data for improving the reliability of estimates from current agricultural survey; and 3. To provide basic data for national, ecological belts and development regions levels for national as well as sub-national policy, planning and decision making purposes.

Geographic coverage

National coverage - the agricultural census covered the whole of Nepal including urban areas. However, only agricultural holdings operated by households were included. urban and rural areas Ecological belt Development Region District

Analysis unit

Agricultural household, individual

Universe

All agriculture households having a minimum specified agricultural land area operated by holding (for hill and mountain region 4 anna and 8 dhure in terai) or having a specified minimum number of livestocks or poultry.

Agricultural activities undertaken by government organizations, businesses like corporations and other juridical persons were not covered by the NCA.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A. Complete Enumeration of Holdings, Area and Livestock The census methodology in undertaking the 2001/02 National Census of Agriculture of Nepal is a combination of complete enumeration and sampling.

The Census of Agriculture was implemented in two phases. The first phase was the complete enumeration of all holdings, their area and the number of livestock and poultry raised by all households in the country as an integral activity in the listing operations of the Census of Population 2001. Two questions were asked concerning agricultural activities of households, namely: total area of the agriculture holding and total number of livestock/poultry kept by the households. All households listed in the Population Census 2011 that possessed the characteristics of the holding as defined in the agriculture census were identified as agricultural holdings. These holdings and their corresponding areas and number of livestock kept including their household population were compiled by ward for all the 75 districts of Nepal from which the sampling frame was constructed.

B. Sampling Design A two-stage stratified sampling was employed in the selection of the samples for enumeration to obtain the characteristics of the holdings for the 2011/12, NCA. This design is almost similar to that of the 2001/02 sampling design, which is a self-weighting sample.

Construction of the sampling frame The listing of the wards in each district with the summarized data of the number of holdings and area was used to form enumeration areas (EA's). However, wards containing less than 30 holdings were combined to form one EA. The EAs in each district were stratified according to the number of holdings enumerated, arranged from the highest to the lowest.

Selection of samples The first stage : selection of the primary sampling units (PSUs), where sample enumeration areas (EAs) were selected with probability proportional to size (PPS), with power allocation 0.4. The measure of size is the number of holdings enumerated in the EAs during the Census of Population 2011 listing operations and to measure the importance of each district, the total area under 8 major crops (paddy, wheat, maize, millet, barley, sugarcane, oilseed and potato) was determined.

The second stage : selection of sample holdings systematically in each sample EA. Before the sample selection was done, a listing of holdings in each sample PSU was conducted to update the listing during the Population Census. The target number of holdings for enumeration in each sample EA was 25.

Approximately 5,200 enumeration areas were selected in the 74 districts and about 130,000 agricultural holdings were selected for enumeration. One district was completely covered in the second phase of the census of agriculture because of the few number of enumeration areas and holdings. This is the district of Manang.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the National Sample Census of Agriculture were structured questionnaires based on FAO recommendation with some modifications and additions. The main questionnaire was Schedule-2 which contains the following topics: Identification Information Part 1: Information on Holder and respondent Part 2: General Information Part 3: Description of Population Part 4: Description for Land and Water Part 5: Crops Part 6: Livestock and Poultry Part 7: Agriculture Machinery by Source and Use Part 8: Non-Residential Building Part 9: Forest and Fishery Part 10: Agricultural Loan Part 11: Miscellaneous

Community Questionnaire (Schedule 3)

The Community Questionnaire: This community questionnaire is adapted first time in Nepal. The aim of the questionnaire is to collect information of the community i.e. selected enumeration area. It contains four sections given below: 0. Information on Enumeration 1. Land used and Other Information of the ward 2. Social and Economic Situation of the ward 3. Community Structure and Facility of the ward 4. Ongoing Development Program in the ward

The total area of the holding was reported in the district where the holder resides, regardless of the physical location of the parcels comprising the holding. Although in Nepal a holding is usually equivalent to the household and it is very rare where a household operates a holding where some parcels are located in other districts.

Cleaning operations

The data will be subjected to the following editing process: 1. Manual editing and coding were done at the head office after collecting the filled questionnaires. 2. Completeness check after data entry done by a completeness checking computer program. 3. Machine editting by machine editing program.

Data appraisal

The previous series of Agriculture Census data as well as population data will be used for evaluating the quality of the data.

Abstract

The CAS 2020 was a comprehensive statistical undertaking for the collection and compilation of information on crop cultivation, raising livestock and poultry, and aquaculture and capture fishing operations.

The main objective of the CAS 2020 is to provide data on the current agricultural situation in the country that can be utilized by the planners and policy-makers. Specifically, the survey data is useful for: 1. Providing an updated sampling frame in the conduct of agricultural surveys; 2. Providing data at the country and regional level, with some items available at the province level; 3. Providing data on the current structure of the country’s agricultural holdings, including cropping, raising livestock and poultry, and aquaculture and capture fishing activities.

The data collected and generated from this survey effort will help reflect progress towards the 2030 Sustainable Development goals for the agricultural sector, focusing on: - Goal 1: End poverty in all forms everywhere. - Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. - Goal 5: Achieve gender equality and empower all women and girls. - Goal 6: Ensure availability and sustainable management of water and sanitation for all.

The questionnaire collected data on several aspects of the agricultural holding, including demographic information about the holder and the household members, crop production, livestock and poultry raising, aquaculture, capture fishing, and labour used by the holding. New for the CAS 2020 were additional questions on the impact of the COVID-19 pandemic on agricultural activities and a household-based Food Insecurity Experience Scale.

Data was collected from household agricultural holdings and juridical agricultural holdings. Only the household agricultural holdings are included in the released microdata.

Geographic coverage

The CAS 2020 provides national coverage. The national territory is divided in four Regions or Zones (Coastal Region, Plains Region, Plateau and Mountain Region, and Tonle Sap Region) and 25 Provinces (Banteay Meanchey, Battambang, Kampong Cham, Kampong Chhnang, Kampong Speu, Kampong Thom, Kampot, Kandal, Kep, Koh Kong, Kratie, Mondul Kiri, Otdar Meanchey, Pailin, Phnom Penh, Preah Sihanouk, Preah Vihear, Prey Veng, Pursat, Ratanak Kiri, Siem Reap, Stung Treng, Svay Rieng, Takeo, and Tboung Khmum.).

Analysis unit

Household agricultural holdings and juridical agricultural holdings. NOTE: the juridical agricultural holdings are not included in the released microdata

Universe

Agricultural households, i.e. holdings in the household sector that are involved in agricultural activities, including the growing of crops, raising of livestock or poultry, and aquaculture or capture fishing activities. It was not considered a minimum threshold to determine a household's engagement in the above mentioned activities.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CAS 2020 utilized the same sample from the CIAS 2019, which was the 2013 Agriculture Census Sampling Frame. This frame consisted of around 14,000 villages and 35,000 Enumeration Areas (EAs).

The target population comprised the households that were engaged in agriculture, fishery and/or aquaculture. Given their low number of rural villages, the following districts were excluded from the frame: Krong Preah Sihanouk (province Preah Sihanouk), Krong Siem Reab (province Siemreap). Khan Chamkar Mon, Khan Doun Penh, Khan Prampir Meakkakra, Khan Tuol Kouk, Khan Ruessei Kaev, and Khan Chhbar Ampov (province Phnom Penh).

Since the number of rural households per EA was not known, in order to calculate the number of rural households in each province the sum of the households in the villages that were classified as rural was computed. The listing operation in each sampled EA was conducted for the CIAS 2019 with the aim of identifying the target population, i.e., the households engaged in agricultural activities.

The CAS 2020 used a panel approach utilizing the CIAS 2019 household holding sample, which was extracted through a two-stage stratified sampling procedure, with EAs as primary units and households engaged in agriculture as secondary units. In the CIAS 2019, 1,350 EAs and 12 agricultural households for each EA were selected, for a total planned sample size of 16,000 households. The 1,350 EAs were allocated to the provinces (statistical domains) proportionally to the number of rural households. Since there are no rural villages in Phnom Penh, 50 EAs (corresponding to 60,000 rural households) were allocated to Phnom Penh Province by default and 1,320 EAs were allocated to the other provinces. In order to select the EAs within each province, the villages were ordered by district, then by commune, then by type of village (Rural-Urban) and a systematic sampling was performed, with probability proportional to size (number of households). The total effective sample size of the survey was 14,722 agricultural households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data editing for CAS 2020 took place throughout all the survey implementation, particularly during the following stages: - data entry, thanks to consistency checks included in the CAPI tool that made use of Survey Solutions software; - data approval by Data Supervisors, who checked the interviews sent by enumerators through the Survey Solutions software and, in case of errors or suspicious data detected, returned the record to the enumerator to address the issues with the respondent if needed; - data approval by Headquarter Supervisors, who double-checked the completed questionnaires after being approved by Data Supervisors. If any issues or suspicious data were discovered, the records were returned back to the enumerators for verification or correction. - data-cleaning phase, where approved data were cleaned in Rstudio through automatic detection of outliers and suspicious records, using validation rules that also took into account validity ranges and comparison with similar data from other sources. In this phase, some respondents were also recontacted to fix possibly wrong responses; - data-imputation phase, where some values coming from item non-response or systematic errors were imputed.

Abstract

The Ministry of Agriculture and Forestry (MAF) was entrusted with the responsibility to lead the implementation of the agricultural census project with the assistance of the Statistics Department (SD). The Census was conducted under the National Statistics Act 1978 which provides for obligation of the citizens to provide information, confidentiality of information provided and the duties of the census staff. A National Agriculture Census Committee was constituted to guide and supervise the entire census exercise. Technical and financial assistance for undertaking the census was provided by the FAO of the UN. The undertaking of the AC 2001 was envisioned to:

1. provide benchmark or basic data on the structure of agricultural holdings and their main characteristics;
2. use this information to develop a regular system of agricultural statistics;
3. build up some important village and regional level statistics;
4. establish a technical and organizational foundation on which to build up a comprehensive and integrated system of food and agricultural statistics; and
5. provide a frame from which samples can be drawn to study certain aspects of agricultural activities in greater depth.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

The Agricultural Census 2001 was conducted at the household level and a complete enumeration of all households residing in Tonga during the period of the census enumeration. However, households that left for abroad and came back after the period of the census taking and those that were permanently living in other countries were no longer included.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Data appraisal

Data Limitations:

The AC 2001, as stated in one of its objectives, provides basic information on the structure of agricultural holdings in the Kingdom and its characteristics that do not change over a certain period of time. Like any other census and surveys, it has limitations which are enumerated in the following: 1. No data on crop production was included in this census since this information would be better asked in a follow up survey specifically designed for major crops. 2. The level of agricultural activity of a household was determined only through its involvement in the cultivation/growing of crops and size of its agricultural land which should be more than 1/8 of an acre. A household having only livestock or poultry was not considered to have an agricultural holding for this census. 3. The size of the agricultural holding, to be considered as an agricultural holder, was more than 1/8 of an acre. 4. The economic characteristics of the household members were asked for member 15 years old and over as recommended and being done internationally and in other local surveys. 5. Apart from Livestock animals, only information on dogs was included in the Livestock Section of this census. Other domesticated animals such as cats and birds like parrots were excluded. 6. Fisheries Section was asked for only few data items such as main purpose of fishing, type of fishing method engaged in, use of fishing boats and proportion of fish/other sea products sold. 7. The holding and parcels included only those agricultural lands cultivated by the holder whether owned or leased from other households. On the other hand, all lands owned by the holder but rented out to other households whether for a fee or for free were excluded in the Holding Questionnaire and Parcel Questionnaire. 8. Sections on Agricultural Income and Loan, Agro-Forestry on the Holding and Handicraft Making were asked only for agriculturally active households. The nonagricultural and minor agricultural households did not have such information. 9. Questions on the use of fertilizers were answerable only by “Yes” or “No” and questions on agricultural chemicals were the name of chemical and crops it was used on. Quantities on these agricultural inputs were not taken for it is best to include these items in a follow-up survey. 10. Small implements such as knife, spade and other gardening tools were not included in this census since it is assumed that almost all agriculturally active households owned and used such small tools. 11. The section on Agro-forestry was limited to the name of the trees/shrubs and its uses, not on the number of trees for this would create problems on data processing. Hence, a follow-up survey using this information as a frame can be done to get additional information on agro-forestry. 12. The section on Crops Planted and Already Harvested of the Parcel Questionnaire was supposed to be answered by all agricultural holders who answered the section on Parcel Details of the Holding Questionnaire. However, due to memory recall of the respondents, information on this section might have been under reported. 13. The quality of the data collected can be affected by many factors. For example, the complete enumeration and coverage limits the completeness of data, the concepts and interpretation of questionnaires may not be fully understood by enumerators, inadequate supervision and others.

The Ministry of Agriculture and Forestry (MAF) was entrusted with the responsibility to lead the implementation of the agricultural census project with the assistance of the Statistics Department (SD). The Census was conducted under the National Statistics Act 1978 which provides for obligation of the citizens to provide information, confidentiality of information provided and the duties of the census staff. A National Agriculture Census Committee was constituted to guide and supervise the entire census exercise. Technical and financial assistance for undertaking the census was provided by the FAO of the UN.

The undertaking of the AC 2001 was envisioned to: 1. provide benchmark or basic data on the structure of agricultural holdings and their main characteristics; 2. use this information to develop a regular system of agricultural statistics; 3. build up some important village and regional level statistics; 4. establish a technical and organizational foundation on which to build up a comprehensive and integrated system of food and agricultural statistics; and 5. provide a frame from which samples can be drawn to study certain aspects of agricultural activities in greater depth.

The 2001 Agricultural Census covered the following items:

HOUSEHOLD
1. Level of Agricultural Activity
• Non-agricultural
• Minor agricultural
• Subsistence only
• Subsistence with occasional selling
• Commercial crop producer

1. Ownership of Tax Allotment (‘Api Tukuhau)
   • Name of Owner(s)
   • Area
   • Present Status
   • Land Location

2. Agricultural Holdings and Method of Operation
   • Name of Operator(s)
   • Method of Operation
   • Holding Area
   • Number of Separate Parcels
   • Location of Holding

3. Crops and Trees Grown by Minor Agricultural Household Only
   • Crops/Trees Currently Growing/Still Growing

4. Name of Crops/Trees

5. Area

6. Number of Plants
   • Crops/Trees Planted and Harvested During the Last 12 Months

7. Name of Crops/Trees

8. Area

9. Number of Plants

10. Household Membership and Economic Characteristics
    • Name
    • Sex
    • Age
    • Main Activity
    • Employment Status
    • Occupation
    • Industry

11. Livestock Including Dogs
    • Number Kept as of the day of visit to the household
    • Number Disposed during the past twelve months prior to census enumeration

12. Fisheries
    • Main Purpose of Fishing Activity
    • Type of Fishing Method
    • Number of Trips during the past week
    • Number of Persons Engaged
    • Use of Boats whether Owned/Hired/Borrowed
    • Number of Boats Owned
    • Proportion of Fish/Other Sea Products Sold
    • Means of Disposal/Selling Fish/Other Sea Products

HOLDING
1. Parcel Details
• Location of Parcel
• Total Parcel Area
• Land Tenure
• Main Land Use
• Length of Use/Fallow

1. Agricultural Income and Loan
   • Proportion of Household Income Derived from Agricultural Activities
   • Availment of Loan for Agricultural Activities
   • Main Source of Loan(s)

2. Labour Inputs
   • Membership Status
   • Sex
   • Age
   • Type of Labor
   • Hours Worked in the Holding last week
   • Wages per Month if paid worker
   • Other Benefits Received if paid worker
   • Industry for Other Occupation
   • Status of Other Occupation
   • Number and Sex of Hired Laborers
   • Average Number of Days Worked of Hired Laborers (male and female)
   • Average Hours Worked/Day of Hired Laborers (male and female)

3. Use of Fertilizers and Agricultural Chemicals
   • Use of inorganic fertilizers
   • Use of organic fertilizers
   • Use of agricultural chemicals

4. Name of Crop

5. Chemical Used

6. Equipment Used
   • Type of Equipment used
   • Number Owned
   • Whether Hired/Borrowed

7. Agro-Forestry on the Holding
   • Name of Trees/Shrubs
   • Uses of Trees/Shrubs

8. Handicraft Making
   • Household Members Engaged
   • Proportion of Raw Materials Taken from the Holding
   • Proportion of Raw Materials Bought
   • Proportion of Raw Materials Sold

PARCEL
1. Number of Separate Parcels

1. Plot Details
   • Area of Plot
   • Crops Grown
   • Method of Sowing
   • Proportion of Mixed Crops
   • Number of Plants in Scattered Planting

2. Scattered/Boundary Crops/Trees Growing on this Parcel
   • Name of Scattered Crops/Trees
   • Number of Crops/Trees
   • Name of Boundary Crops/Trees
   • Number of Crops/Trees

3. Crops Planted and Already Harvested on this Parcel
   • Name of Crops/Trees Planted and Already Harvested
   • Area Harvested
   • Number of Plants if no area
   • Proportion Sold

• Collection start: 2001
• Collection end: 2001

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography. With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections:

Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through resampling approaches (Bootstrap/Jackknife)

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 summery 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 4 000 holdings out of the 12 000 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 participate in the survey fieldwork. For the Data collection, computer-assisted personal interviewing method (CAPI) is 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 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 2021 fourth quarter, 963 holdings were not responded to due to refusing to be interviewed or would not be found during the fieldwork despite its existence. It is about 7.7% of the total Sampled holdings 12,436 holdings involved in the sample 2021 fourth quarter.

Abstract

The Fiji National Agricultural Census 2009 is the fourth agricultural census. After a lapse of 18 years, National Agricultural Census 2009 was carried out in Fiji beginning in October 2009; data collection was interrupted by two cyclones and not completed until March of 2010.

The Agriculture Census is a national obligation conducted by the country to provide benchmark data for planning and policy decisions in sustainable agricultural and rural development; and to strengthen and improve the ongoing Fiji Agriculture Statistics System (FASS) to generate key agricultural data on a regular basis using the results of the 2009 NAC as the benchmark and the dissemination of this statistical information in the form of regular reports.

The 2009 National Agriculture Census (NAC) is the first census programme to be conducted in the country using Multiple Sample Frame (MSF) as the main methodology. Given the experiences of the previous census programmes in terms of funding and availability of resources, the 2009 agriculture census programme provides a platform for more diversification and improvement programmes within the agriculture sector thus ensuring compatible foreign exchange earnings as well as uplifting the living standards of rural populace.

Geographic coverage

National

Analysis unit

• Agricultural Holding and Holders

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey design used the multiple sampling frame methodology. This methodology combines the advantages of an area frame (complete coverage) and a list frame (rare commodities and large and special farms). In the 2009 NAC, it was expected to provide reliable results at district level for most tables, although results for smaller districts might not be possible. In addition, a small island strategy (SIS) was used where complete enumeration of villages occurred within some districts.

The underlying basis for an area frame sample is to select small areas (in this case, one square kilometer - 100 hectares) that represent the entire area of interest. To improve the efficiency of the sample, the entire country was stratified (or characterized) by the intensity of agriculture. The stratification split the country into areas of high intensity agriculture, medium intensity agriculture, low intensity agriculture, forest areas, peri-urban areas and urban areas/non agricultural areas. The overall sample size was limited by the resources available; it was determined to use a ten percent sample of "agricultural land" as determined during the stratification process.

Initially the Fiji Bureau of Statistics (FIBOS) enumeration areas (EAs) for the 2007 Population and Housing Census were used for stratum identification. Subsequently it was determined that re- stratification of whole EAs and subdivision of other EAs would be more efficient. In many of the FIBOS EAs, farms were present only in small pockets; the uniformity of agriculture in the EA, one of the strengths of the stratification, did not exist. These EAs were, first, reviewed for the presence of natural pine forest and natural reserves. After these areas were removed, the remainder of the EA was divided into one square kilometer grids before the sampling process occurred. After the grids were selected, the Land Use Section of the DOA prepared maps using detectable boundaries "around the grid". It was not possible for segments to retain the gridlines as boundaries because they seldom were along recognizable boundaries; however, it was possible to approximate 100 hectares in that general area.

A farm can consist of land areas that are separated by physical boundaries or by land use patterns; these are called tracts. The method of data collection was to account for each tract inside the segment, but, also to collect information about areas outside the segment for farms with tracts both inside and outside. If a segment boundary splits an existing tract, it is divided into one tract inside the segment and one tract outside the segment. The percentage of the farmland inside the segment is used as a weighting factor for the farm in the expansions.

One of the limitations of area frame samples is the accurate expansion of rare or concentrated (non- uniform) variables - such as poultry houses or large dairy or beef farms. The list frame sample, developed from the knowledge and experience of DOA Animal Health and Production Division and Extension Division staff, was expanded as data collection occurred and there was better awareness of large and specialized farms. Data were collected from all of these farms. It should be noted that shortly before the beginning of data collection, a severe outbreak of brucellosis occurred and some culling took place.

Three levels of data presentation were identified for tabulation of the data of the National Agriculture Census 2009 (NAC 2009). The first is tables and expansions at district level; the second is tables and expansions at provincial and national level; the third is tables and (estimates) for special variables.

The census data were collected at farm level, at tract level, at crop level and at animal/poultry level. Information about households and their demographics were also collected. One priority area has been the role of gender in agriculture in Fiji. A special section of the census questionnaire was targeted at identifying these roles and highlighting any special differences. These data also have been broken out by age group.

Accurate land stratification for the 2009 NAC was essential; it was necessary to estimate the percentage of agriculture land use. Initially the stratification was made for each of the Fiji Islands Bureau of Statistics (FIBOS) enumeration areas (EAs).

The census estimates were requested at national, divisional, provincial and also tikina levels. The 15 provinces including Rotuma Island were the main focus of the tabulation. Consequently, the entire country was divided into strata according to the intensity of land use for agriculture. They were further subdivided into sub-strata according to specific land use. This sub-stratification technique guaranteed the sample allocation for priority and special crops. Another stratum was created for special farms including large commercial and freehold farms.

A total of 1,602 existing EAs from the 2007 population census were overlaid on the ASF topographic maps scale 1:50,000 in preparation for stratification activities according to land use. Each EA was classified into one of the strata keeping the same geographical identification codes as those used in the population census. The percentage of area under crops, pastures, forest, etc. (land use) of each EA was estimated by field observation to check that each EA was classified in the right stratum and sub-stratum.

The sampling procedures are more fully described in "National Agricultural Census 2009 - Final Report" pp.7-13.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires, NAC 1 and NAC 3, were used to record information about the segments from the sample. The NAC 1 itemized all tracts inside the segment and all associated farm tracts outside the segments. The NAC 3 documented the nonfarm tracts inside the segment. Enumerators were required to fill out these questionnaires; during the interview process the main questionnaire (NAC 2) was used. Neither the NAC 1 nor NAC 3 was necessary for List Frame farms.

The questionnaire was designed and tested by the staff of the Agricultural Statistics Unit and training manuals were prepared for supervisors and enumerators. A Pilot Census was carried out in several locations to evaluate the content and layout of the questionnaires and the completeness of the census documents. The questionnaire and training materials were updated as the result of the Pilot Census.

Cleaning operations

After a prioritized order of data collection from the provinces, the questionnaires were received at the Agricultural Statistics Unit in batches. Unique questionnaire numbers were assigned by the data processing administrator and recorded in a management system designed to prevent duplicate numbers and to coordinate the collection and processing of the three types of questionnaires. The questionnaire numbers consisted of province, district and a sequence number starting with an initial value assigned previously to each of the segments.

The editing and coding process for a total of 9,341 NAC 2 questionnaires containing farm data started in mid November 2009. Four persons managed the archives of census materials (questionnaires, cartography and photo-enlargements, etc.). Eleven coders were contracted and trained using the Field Team Manual and the Coding, Editing and Data Processing Manual. One table head checked the manual editing and coding. Data entry activities were conducted by ten data entry operators beginning in early December.

Consistency checks were also carried out in the ACCESS databases. Queries were designed to identify data entry and coding errors. Data were entered into 15 provincial databases (including Rotuma Island) which were combined into four divisional databases. The LSF database was kept separate, but combined in SPSS for tabulation and analysis.

Abstract

The Livestock Survey, 2013 aims to provide data on the structure of the livestock sector as the basis for formulating future policies and plans for development. It will also update existing data on agricultural holdings from the Agricultural Census of 2010 and build a database that will facilitate the collection of agricultural data in the future via administrative records

Geographic coverage

Palestine

Analysis unit

Agricultural holding

Universe

All animal and mixed holdings in Palestine during 2013.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The animal and mixed agricultural holdings frame was created from the agricultural census data of 2010 and extracted based on the following criteria: any number of cattle or camels, at least five sheep or goats, at least 50 poultry birds (layers and broilers), or 50 rabbits, or other poultry like turkeys, ducks, common quail, or a mixture of them, or at least three beehives controlled by the holder.

A master sample of 7,297 holdings from the animal and mixed holdings frame was updated prior to sample selection.

Sample Size The estimated sample size is 5,000 holdings.

Sample Design
The sample is a one-stage stratified systematic random sample.

Sample Strata The animal and mixed holdings are stratified into three levels, which are: 1. Governorates. 2. The main agricultural activities were identified by the highest holding size in the category: these activities are the raising cattle, raising sheep and goats, raising camels, poultry farming, beehives, mixed animals. The size of the holdings were classified into five categories

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Livestock Survey 2013 was designed based on the recommendations of the Food and Agriculture Organization of the United Nations (FAO) and the questionnaire used for the Agricultural Census of 2010. The special situation of Palestine was taken into account, in addition to the specific requirements of the technical phase of field work and of data processing and analysis The questionnaire consisted of the main items as follows: Identification data: Indicators about the holder, the holding and the respondent.

Data on holder: Included indicators on the sex, age, educational attainment, number in household, legal status of holder, and other indicators.

Holding data: Included indicators on the type of holding, tenure, main purpose of production, and other indicators.

Livestock data: Included indicators on the type, number, strain, age, sex, system of raising, main purpose of raising, number acquired or disposed of, quantity and value production, slaughtered in a holding, value of slaughtered, and other indicators.

Poultry data: Included indicators on the type, area of worked barns, average cycles per year, system of raising, quantity and value production, and other indicators.

Domestic poultry & equines data: Included indicators on type and number.

Beehive data: Included indicators such as the type, number, strain, quantity and value of production??.

Agricultural practices data: Included indicators on agricultural practices for livestock, poultry and bees.

Agricultural labor force data: Included indicators on the agricultural labor force in a holding such as the number, employment status, sex, age, average daily working hours, number of work days in an agricultural year and average daily wage.

Agricultural machinery and equipment: Included indicators on the number and source of machinery. Agricultural buildings data: Included indicators on the type and area of building.

Animal intermediate consumption: Included indicators on the type, quantity and value of animal intermediate consumption.

Cleaning operations

Preparation of Data Entry Program The data entry program was prepared using Oracle software and data entry screens were designed. Rules of data entry were established to guarantee successful entry of questionnaires and queries were used to check data after each entry. These queries examined variables on the questionnaire.

2.5.2 Data Entry Having designed the data entry program and tested it to verify readiness, and after training staff on data entry programs, data entry began on 4 November 2013 and finished on 8 January 2014 with 15 staff engaged in the data entry process.

2.5.3 Editing of Entered Data Special rules were formulated for editing the stored data to guarantee reliability and ensure accurate and clean data.

2.5.4 Results Extraction and Data Tabulation An SPSS program was used for extracting the results and empty tables were prepared in advance to facilitate the tabulation process. The report tables were formulated based on international recommendations, while taking the Palestinian situation into consideration in the data tabulation of the survey.

Response rate

Response rate was 94.3%

Sampling error estimates

Includes multiple aspects of data quality, beginning with the initial planning of the survey up to the final publication, plus how to understand and use the data. There are seven dimensions of statistical quality: relevance, accuracy, timeliness, accessibility, comparability, coherence, and completeness.

2.6.1 Data Accuracy
Includes checking the accuracy of data in multiple aspects, primarily statistical errors due to the use of a sample, as well as errors due to non-statistical staff and survey tools, in addition to response rates in the survey and the most important effects on estimates. This section includes the following:

Statistical Errors Survey data may be affected by sampling errors resulting from the use of a sample instead of a census. Variance estimation was carried out for the main estimates and the results were acceptable within the publishing domains as shown in the tables of variance estimation.

Data appraisal

Non-sampling Errors Non-statistical errors are probable in all stages of the project, during data collection and processing. These are referred to as non-response errors, interviewing errors, and data entry errors. To avoid and reduce the impact of these errors, efforts were exerted through intensive training on how to conduct interviews and factors to be followed and avoided during the interview, in addition to practical and theoretical exercises. Re-interview survey was conducted for 5% of the main survey and re-interview data proved that there is high level of consistency with the main indicators.

Abstract

The Agriculture Census 2007 was the third undertaken in Vanuatu for the purpose of compiling information on agriculture, forestry and fisheries resources. Data collected will be used to formulate plans, policies and programs for the development and improvement of these sectors. This census was conducted by the Vanuatu National Statistics Office (VNSO), a centralized organization mainly responsible for the collection and dissemination of important statistics in the country by means of censuses of population, agriculture, household income and expenditure surveys, business surveys, collection of imports and exports data, etc. However, owing to limited resources, the VNSO cannot accommodate requests for extra data needed by other government and private sectors: hence, these data may have to be collected independently. Agricultural statistics in Vanuatu are produced in two ways: through census/ survey and through administrative records. The census/survey provides baseline or benchmark information for planners and policymakers while the data from administrative records are used to keep track of changes and development in specific aspects of agriculture.

Geographic coverage

National coverage. The Agriculture Census (Phase I and II) was undertaken in eighteen main islands of Vanuatu which are: namely, Banks. Torres, Malo, Santo, Ambae, Maewo, Pentecost, Malekula, Ambrym, Paama, Epi, Shepherds, Efate, Erromango, Tanna, Aneityum, Aniwa and Futuna.

Analysis unit

Households and individuals about their agricultural involvements.

Universe

The Survey covers all rural households · Crop gardening · Kava sub-holding · Coconut sub-holding · Cocoa sub-holding · Coffee sub-holding · Vanilla sub-holding · Pepper sub-holding · Cattle sub-holding · Other livestock keeping · Household fishing · Household forestry-related activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling method The 18 major islands were classified as: • small - number of households engaged in agricultural activities less than 500 (Torres, Paama, Erromango, Aniwa, Aneityum and Futuna); • medium - number of households engaged in agricultural activities 500-1,999 (Banks, Malo, Maewo, Ambrym, Epi and Shepherds); and • large - number of households operating agricultural activities 2,000 or more (Efate, Malekula, Ambae, Pentecost and Tanna).

In determining the number of households to be interviewed in each island and in each enumeration area (EA):

-for small islands, all households were listed and the identified households engaged in agricultural activities were enumerated; -for medium-sized islands, one-third of the sample EAs in these islands were selected and all households were listed and those found to be engaged in agricultural activities were interviewed; and -for large islands, one-third of the total EAs were selected in each island and all households listed. Of households found to have a crop garden, coconut sub-holding or kava sub-holding, one-third were selected to be further interviewed. In addition, all households listed and involved in the subholding of cattle and cash crops like cocoa, coffee (for Tanna only), vanilla and pepper (10 or more plants) were also enumerated.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire of the 2006-2008 Agriculture Census of Vanuatu was published in English and divided into 9 forms.

Form 1.1: Household Form 1.2: Crop Garden Form 1.3: Kava Plants Sub-Holding Form 1.4: Coconut Farming Form 2: Cocoa Trees Sub-Holding Form 3: Coffee Trees Sub-Holding Form 4: Vanilla Plants Sub-Holding Form 5: Pepper Plants Sub-Holding Form 6: Cattle Sub-Holding Form 7: Commercial Farm/Holding Form A: List of Agriculture Activities Form B1: Control Sheet for all Small and Medium Sized-Islands.

Cleaning operations

Coding, processing and tabulation were completed by mid-September 2006.

Eight data entry operators were hired by the project to do the data encoding of the Phase I of the project. This was the first-hands on as far as the software is concerned for all the data entry operators. Before the actual data entry, the data processing expert had all eight operators plus the supervisors on a training session for a few days. At the end of the training session, they were familiar with the software and then started the actual data encoding. The processing of data for Phase I of the project took the entire month of June 2006 to be completed. During the Phase II of the project, the expert set up the system and trained the local staff on system operation for two weeks and then left for his home country. Since the project staff and the data entry operators who were hired were already familiar with CsPro, the whole data processing was done without the presence of the consultant. The expert later came for his final mission to prepare the data for tabulation and generate the required tables using the table specifications for that purpose.

The machine data processing of the forms was done using CsPro. Data encoding, data cleaning and tabulation were done using data entry, batch edit and cross tab applications respectively. Control and management of the data entry of the forms and data cleaning of the batch files were done using SCIPS (Survey / Census Integrated Processing System), a Visual Basic 6 (VB6) program developed by the expert designed to integrate the different phases of data capture and data cleaning of any survey/census. The program facilitates the assignment of folios to keyers that resulted to automatic recording of the data capture status of each batch/folio and eliminated errors in the encoding of the geographic identification codes. It also made the data cleaning easier since SCIPS enabled the users to correct errors found by the data consistency and completeness check programs without printing the generated error list.

Response rate

100%.

Sampling error estimates

The number of households to be interviewed is based on the sampling methodology that is used in the census. The 15 major islands were classified as: 1. small - if the number of households engaged in agricultural activities is less than 500; in this case, Torres, Paama and Erromango are under this category. 2. medium - if the number of households engaged in agricultural activities is between 500 - 1,999; Banks, Malo, Maewo, Ambrym, Epi and Shepherds belong to this group. 3. large - if the number of households operating agricultural activities is 2,000 or more; Santo, Efate, Malekula, Ambae, Pentecost and Tanna were considered to be large islands.

In selecting the number of households to be interviewed in each island, the following was carried out: a. For Erromango, Torres and Paama, all households were listed and those households engaged in agricultural activities were enumerated; b. For Banks, Malo, Maewo, Ambrym, Epi and Shepherds, 1/3 of the sample EAs in these islands were selected and all households were listed and those engaged in agricultural activities were interviewed for their involvement in these activities; and c. For Santo, Efate, Malekula, Ambae, Pentecost and Tanna, 1/3 of the total EAs were also selected in each island and all households were listed in these islands, after which only 1/3 of the households engaged in agricultural activities were further interviewed if they were involved in crop garden, coconut sub-holding and kava sub-holding. In addition to this, all households in the selected EAs of these islands that were involved in the sub-holding of cattle and cash crops (with 10 trees or more) like cocoa, coffee (for Tanna only), vanilla and pepper were enumerated.

Data appraisal

Consultants have not provided documents regarding this aspect of data quality.

Nitrogen balances (N-balances) are an important tool for quantifying nitrogen emissions (nitrogen surpluses) from agriculture. For the calculation of the N area balance, the N-inflow (on the agricultural area) is compared to the N-exit: N feeder — N discharge = N balance

As part of the nationwide base emission monitoring, a N-area balance model is used, which was developed at the Johann Heinrich von Thünen Institute and adapted to the regional conditions in Lower Saxony. The result is nitrogen land balances at municipal level calculated on the basis of agricultural statistics, which can be recalculated with each appearance of the agricultural census or agricultural structure survey (every 3 to 4 years). The calculated N area balance shall be issued in [kg N/ha*a] in relation to the agricultural area (excluding set-aside areas).

The shown N-area balance balances2016 are an important basis for calculating the potential nitrate concentration in leachate. The potential nitrate concentration is used to estimate the leachate quality at the lower boundary of the root space.

It should be noted that the data of agricultural statistics on animal numbers and land use in the N-area balances have been collected according to the farm seat principle and therefore spatial shifts are possible.

Detailed method description see: Methodology_Base_Emission monitoring_LBEG.pdf

Abstract

The National Census of Agriculture and Livestock (NACAL) was conducted by the Agriculture Statistics Division of the National Statistical Office (NSO) in collaboration with the Ministry of Agriculture and Food Security (MoAFS) between October 2006 and October 2007. It was based on a random sample that covered 25,000 households drawn from all districts of the country. The NACAL is part of a concerted effort by the government to provide relevant information on the structure of agriculture in the country, especially in view of its importance to the economy. The census was designed to collect information on different aspects of small holder agriculture including crops grown, area planted and production, land husbandry practices, food security, marketing and structure of the small holder sector.

Geographic coverage

National

Analysis unit

• Household

Kind of data

Census/enumeration data [cen]

Sampling procedure

A two stage sample design was used where the first sampling units were the Enumeration Areas (EAs) and the second sampling units were farming households. Stratification was done at district level and at EA level. Each district was stratified by agro-ecological zones and each EA was stratified by land cultivated (small scale farmers and large scale farmers). This stratification was done to improve the precision of the estimates by reducing the variance between EA and within EA. The total sample size was 25,000 households nationwide. The sample size at EA level was 15 households. The livestock sample comprised two samples: the NACAL sample from all districts and an extra district sample to cover landless households.

Since non farming households had a zero chance of being selected for NACAL, an extra sample was drawn from the population, with the intention of selecting 5 landless households in rural areas and 10 landless for non farming households in urban areas. Hence, in urban areas 10 non farming households were drawn. This was done by systematic sampling from the list of non farming households.However, in rural areas most often there were no non-farming households in the EA. In such cases the 5 extra households were drawn from the farming households systematically. About 8,000 of these households were enumerated. In total therefore there were about 32,500 households in the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

NACAL had 9 modules and they are as follows:

• Module 1: Household composition
• Module 2: Land parcel
• Module 3: Plot details
• Module 4: Food security and HIV/AIDS
• Module 5: Marketing
• Module 6: Welfare Monitoring survey
• Module 7: Livestock survey
• Module 8: Village facilities
• Module 9: Estate survey

Cleaning operations

The data was entered using scanners, then cleaned and analyzed using the SPSS software.

Abstract

A farm structure survey was carried out in all EU Member States in 2003 (2002 in Poland). The detailed results are now available for 10 Member States: Belgium, Denmark, Ireland, Latvia, Luxembourg, Hungary, Malta, Finland, Sweden and the United Kingdom.The objective of Latvia FSS 2003 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 2003. 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 survey was a sample one. Prior full-scale agricultural census was conducted in 2001. The target population was all economically active farms which produce agricultural products; indeed some holdings may provide other supplementary (non-agricultural) products and services. These holdings are included in the statistical farm register, which is updated regularly (from statistical surveys and from administrative sources like Business and Population Registers). The frame was stratified by economic size (8 categories), by location (NUTS4 level), by type of farming (3 groups) and by special characteristics (active, non-active and new). For non-active farms owned by natural person a 20 hectare agricultural land threshold was applied. In practice all holdings have an economic size of at least 2 ESU were included, while from the other strata sample was drawn by simple random method. The sampling rate is bigger for the important strata. Altogether 49,7 thousand holdings were chosen to be surveyed.

Mode of data collection

Face-to-face [f2f]

Response rate

The unit non-response was 2%, re-weighting was used to adjust it.

Data appraisal

The data are multi-level checked. For each activity (enterprise) on a farm (for instance wheat, dairy cow or vineyard), a standard gross margin (SGM) is estimated, based on the area (or the number of heads) and a regional coefficient. The sum of such margins in a farm is its economic size, expressed in European Size Units (ESU). 1 ESU is equal to 1200 euros. Due to differences in the coverage of units of less than 1 ESU across Member States, these data are not comparable between countries. That is why the present analysis and graphs focus on the holdings of at least one ESU. Each farm is classified in the community typology by its economic size and its type of farming, depending on the share of each enterprise in its economic size. For instance, a farm where breeding sows account for more than 2/3 of the economic size is classified as specialist pig rearing (type 5011). Depending on the level of aggregation, farms are grouped into 8 to 70 types. Annual working unit (AWU) means the labour force working yearly like a worker employed on full time basis, it is 1800 hours (225 working days of 8 working hours per day).

The health of the farming community in Northern Ireland (NI) requires further research as previous mortality studies have reported contradictory results regarding farmers’ health outcomes compared against other occupations and the general population. This study collated the NINIS area-level farm census with the population census information across 582 non-overlapping wards of NI to compile three mortality datasets (2001, 2011, and pooled dataset) (NISRA 2019). These datasets allow future researchers to investigate the influence of demographic, farming, and economic predictors on all-cause mortality at the ward level. The 2001 and 2011 mortality datasets were compiled for cross-sectional analyses and subsequently pooled for longitudinal analyses. Findings from these datasets will provide evidence of the influence of Farming Intensity scores influence on death risk within the wards for future researchers to utilise. This data report will aid in the understanding of socio-ecological variables’ additive contribution to the risk of death at the ward level within NI. This data report is of interest to the One Health research community as it standardises the environment−human−animal data to pave the way towards a new One Health research paradigm. For example, future researchers can use this nationally representative data to investigate whether agriculturally saturated wards have a higher mortality risk than non-agriculturally based wards of NI.