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

Communities (in Enumerated Areas).

Analysis unit

Community

Universe

The population units are communities encompassing the designated enumeration areas, where household listing was performed.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were performed in communities overlapping with in the EAs selected for the extended listing operation. Accordingly, a focus group discussion in a total of 26,555 communities were undertaken to administer the community level questionnaire. It is important to note here that the results from the community survey are unweighted results and all the tables produced from the community level data are only from the 26,555 communities interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC community listing questionnaire served as a meticulously designed instrument administered within every community selected to gather comprehensive data. It encompassed various aspects such as agricultural activities in the community, infrastructures, disaster, etc. The questionnaire was structured into the following sections:

• Identification of the community • Respondent Characteristics (Name, Sex, age) • Agricultural Activities in the Community • Disasters and Shocks • Community Infrastructure and Transportation • Community Organizations • Community Resources Management • Land Prices and Credit • Community Key Events • Labour

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.

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 the post-war period (insert dates) , 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 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 programmes.

· Enable LISGIS to establish an agriculture master sampling frame for 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 on 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 provided a wealth of information on the state of agriculture in Liberia. This documentation provides information on how data was collected at the household level. The documentation also provides useful information on the household anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The universe for the Liberia Agriculture Census 2024 household level data collection is all households in Liberia having at least one member engaged in agricultural 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 sample with enumeration areas (EAs) as clusters and farming households as units of interest. In line 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 by strata (districts, urban/rural) proportional to the numbers of farming households 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 (available in the downloads tab).

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, agencies and commissions(termed MACs by LISGIS), nongovernmental and international organizations as well as academic institutions researching agricultural issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, to ease administration. The household questionnaire included type of agricultural activities practiced, household members characteristics, housing conditions, hired labour practices, agricultural parcels and plots characteristics, types of crops and methods of crop cultivation, inputs, tools and equipment used, type and number of livestock and poultry. The household questionnaire was administered to the household head or an adult member of the household with 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 varied across modules.

Cleaning operations

The data was edited using CSpro software, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In a few cases, manual editing was applied to recode the “other specify” category. The SPSS software was used for this purpose.

Response rate

92.8%

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors and removing implausible values. The study team linked the 2023 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located.Study date(s) and durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of further questions.Study spatial scale (size of replicates and spatial scale of study area)Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC.Level of true replicationUnknownSampling precision (within-replicate sampling or pseudoreplication)No sampling was involved in the collection of this data.Level of subsampling (number and repeat or within-replicate sampling)No sampling was involved in the collection of this data.Study design (before–after, control–impacts, time series, before–after-control–impacts)None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2023 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.)In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2023 Farm to School Census Report.The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone

Abstract

The National Statistics Office, previously known as the Central Bureau of Statistics, conducted the National Sample Census of Agriculture 2021/22 (NSCA 2021/22) covering all parts of the country. Nepal has a glorious history of taking the agriculture census once every ten years, with the first one taking place in 1961/62 and subsequent ones in 1971/72, 1981/82, 1991/92, 2001/02, 2011/12, and 2021/22. The NSCA 2021/22 is the seventh census in this cycle and the first one after the new federal setup of the country. Its primary purpose is to provide data on the tructural aspects of agriculture that change slowly over time, such as farm size, land use, crop areas, and number of livestock, up to the local level (municipality). The census also includes the basic data on the organizational structure of agricultural holdings, including land tenure, irrigation, livestock numbers, labor, and use of machinery and other agricultural inputs. Furthermore, the census content has been broadened to encompass current areas of concern that vary annually, including the production of major crops. The census provides benchmark data on agriculture which is essential for monitoring and evaluating the impact of development policies and programs and addressing emerging social, economic, and environmental policy issues in agriculture. Regarding the content of the census, including statistical concepts, definitions, classifications, and output, the census has adhered to the guidelines set forth by the World Program for the Census of Agriculture 2020 (WCA 2020) developed by the FAO.

The main objectives of the agriculture census 2021/22 are as following :

1. To provide basic data on the structure of agriculture and characteristics of holdings for small geographical area (municipality),

2. To assist in planning and policy-making for agricultural development across the three tiers of government and monitoring the progress achieved,

3. To provide reliable data for benchmarking and reconciliation of current agriculture statistics,

4. To design frame for other agricultural surveys,

5. To avail core data for compilation and monitoring of some agriculture-related SDG indicators.

Geographic coverage

The seventh census of agriculture 2021/22 also covers the entire country including all districts and local levels (Urban and Rural Municipalities).

Analysis unit

Agriculture Holding

Universe

The census covers individual agriculture holdings of the country.

Kind of data

Census data [cen]

Sampling procedure

Sampling design

1. Domain of estimation Nepal is divided into seven provinces, 77 districts, and 753 municipalities for administrative purposes. The NSCA 2021/22 provides accurate estimates at the municipality level, making the 753 municipalities as domains of estimation for the sampling design.

2 Sampling method The sampling method for estimation of various parameters of interest at municipality level is one of strati?ied two-stage sampling. Within a municipality the enumeration areas (EAs) are the primary stage units (PSUs) of sampling and within the selected enumeration area the agricultural households are the second stage units (SSUs) of sampling. The enumeration areas are selected by probability proportional to size (PPS) systematic sampling (the number of holdings in the enumeration area is the size variable). The SSUs are selected by equal probability systematic sampling with implicit stratification.

3 Sampling frame In line with the proposed sampling design, there are two types of sampling frame used for the agriculture census 2021/22: the frame for selecting the PSUs and the frame for the selection of agricultural holdings. The sampling frame for PSUs was prepared from the list of enumeration areas (EAs) from the National Population and Housing Census 2021 (NPHC 2021). Following FAO recommendations an agricultural module was incorporated in the NPHC collecting basic agriculture related information from all households in the country including total area of operational holding, number of livestock, and number of poultry birds The frame of PSUs consisted of the list of enumeration areas along with the number of households and agricultural households.The frame for SSUs was developed through listing operations in the sampled EAs. All households are interviewed in each EA in order to develop an updated list of agricultural households as sample frame of SSUs in the selected EA.

4 Sample size The municipality is the sample domain of the census, therefore the sample size was determined ensuring reliable estimations of key variables of interest at municipality level. As recommended by FAO, agricultural area is a suitable variable that is considered in calculating the sample size. The target number of holdings sampled from each selected EA was set at 25. The actual number sampled varied between 20 and 30 and was determined in such a way to ensure equal probability of selection for all holdings in a municipality. Altogether, a sample of 330,112 holdings for the whole country (8% of all holdings) were selected from 13,576 EAs in the NSCA 2022.

5 Sample selection

The sample of PSUs was selected with a systematic probability proportional to sizemethod considering the number of agricultural households as measure of size.Selection of SSUs (agricultural households) were carried out in the field. The selection was done by using usual equal probability linear systematic sampling. However, before selection, an implicit stratification for Tarai and Hill/Mountain was used by making four implicit strata as follows: • Less than 1 Bigha (0.68Ha)/10 Ropani (0.51Ha) • 1 to 3 Bigha (0.68 to 2.03 Ha)/10 to 20 Ropani (0.51 to 1.01 Ha) • More than 3 Bigha (2.03 Ha)/ 20 Ropani (1.01 Ha) • Only having livestock.

Sampling deviation

No need to derive sample design

Mode of data collection

Face-to-face f2f

Research instrument

The questionnaires implemented in the National Sample Census of Agriculture 2021/22 to collect data are as follows: 1. Holding listing form (Form 1) Form 1 is a holding listing form that has been used to list all the agriculture holdings (within the cut-off threshold) in the selected enumeration area. It has been used as a frame to select the holdings (SSUs).

2 Selected holding listing form (Form 1A) The Form 1A is used to prepare a list of selected holdings that is used to fill out the main questionnaire (Form 2).

3 Agriculture holding questionnaire (Form 2) Form 2 is the main questionnaire implemented in the census to collect the agricultural data in detail from the selected holdings.

4 Community questionnaire (Form 3) Form 3 is used to collect community-level data from the ward office of the municipality.

Cleaning operations

The completed questionnaires collected from the various census offices were safely stored in the central storage building. Data processing for the census was done within the NSO premises. The data processing center of the NSO was equipped with basic facilities and functionalities like laptops, a local server, a local area network (LAN), security cameras, furniture, and air conditioners.The coding and editing of the questionnaires were accomplished by the temporarily recruited 50 coders and editors from November, 2022 to January, 2023. Likewise, the data entry of the hardcopy questionnaire were accomplished by the temporarily recruited 100 entry operators from November, 2022 to January, 2023.

Response rate

100%

Sampling error estimates

The NSO was highly focused on ensuring the accuracy of census data by implementing various measures to minimize non-sampling errors. To reduce sampling errors, an appropriate sampling design was prepared modifying the designs used in previous agriculture sample censuses. Quality control mechanisms for the data included training, supervision, completeness checks, verification of data entry, and consistency checks.

Data appraisal

Census estimates given in the tables are subject to sampling errors, standard error, relative standard error because the data are based on a sample of holdings rather than the entire population of holdings.The size of the SE,SE, RSR are estimated for major outputs. It is presented seperately in a technical report. The technical report provided more detailed information about how the errors are calculated. Therefore,in interpreting the tables, the figures should be suitably rounded off.

Abstract

The agricultural survey in its current form covers all regions of the country and all 45 departments of Senegal. The agricultural survey is an annual statistical operation whose general objective is to estimate the level of the main agricultural output of family-type agricultural holdings. It also makes it possible to provide information on the physical characteristics of cultivated plots (geo-location, area) and major investments made at their level (agricultural inputs, cultivation operations, soil management and restoration). The main indicators relate to yield levels, areas sown, production and means of production.

Following a modular approach, the 2021-2022 edition of the EAA is characterized by the integration of the ILP (Revenue, Labor and Productivity) module. The introduction of this module makes it possible to collect the information necessary for the calculation of SDGs 2.3.1 and 2.3.2. In addition, the basic module of the 50x2030 questionnaire allows the collection of data for the calculation of SDG 5.a.1 and CAADP indicators (3.1i, 3.1ii, 3.2i, 3.2ii, 3.2iii and 4.1i) .

Geographic coverage

The annual agricultural survey covers all 45 departments of Senegal. However, for reasons related to anonymization, the variable "Department" has been replaced by the variable "Agroecological Zone" which constitutes groupings in relation to the departments. The variable "Region" remains in the anonymized version of the data.

Analysis unit

Households

Universe

The agricultural survey covers all households and plots in the 45 departments of Senegal.

Kind of data

Sample survey data [ssd]

Sampling procedure

The EAA was built on a two-stage survey, with enumeration districts (DRs) as primary units (PU) and agricultural households as secondary units (US), as defined during the general census of population and l'Habitat, de l'Agriculture et de l'Élevage (RGPHAE) of 2013. In line with the broadening of the scope of the survey recommended by the AGRIS approach, the sampling plan has integrated from this campaign , a first-degree stratification, induced by that of the second degree, to better reflect the different agricultural activities and improve the efficiency of the estimates. The choice of a first-degree stratification induced by that of the second degree, although less efficient than an independent first-degree stratification. The stratification took into account the relative importance of the main agricultural activities (in terms of household size) identified during the 2013 RGPHAE, namely rainfed agriculture, livestock and horticulture.

Four strata were thus formed as follows: - the “rain-fed only” stratum which groups together all the households practicing only rain-fed crops; - the “livestock only” stratum for households that practice animal husbandry only; - the “Horticulture and other crops” stratum, which includes households that mainly practice horticulture and secondarily other crops (forestry, fruit growing, etc.); - the “Rain-fed-breeding” stratum made up of households that practice both rain-fed agriculture and livestock breeding.

The size of the sample of agricultural households to be surveyed was calculated by department (area of study) by setting a relative error of 10% on the variable of interest. The distribution of the sample of each department in the strata was made using the method of Bankier (1988) developed in the methodological guide on the Practices of Master Sampling Bases (pp. 79-81) of the Global Strategy (GSARS ).

At the national level, the total theoretical sample is equal to 7,450 households, spread over 1,460 physical CDs, with 5 households per CD. At the end of the enumeration operation carried out in the physical sample CDs, adjustments were made to take into account the actual updated size of the CDs, which led to a final size of 7,378 households, or 1,382 CDs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The first questionnaire collected information on census and characteristics of agricultural household plots. The second questionnaire collected information on agricultural production, labor and agricultural productivity.

Response rate

The overall response rate is 94% for the first phase of the survey while it is 89% for the second phase.

In the United States, agroforestry is commonly defined as a suite of land management practices that intentionally integrate woody plants (trees, shrubs, vines, etc.) with crop and/or animal production systems. Understanding agroforestry adoption in the United States is critical to serve as a baseline of existing agroforestry systems and for future planning purposes. There is growing interest in identifying where future systems are most likely to occur. Since 2017, the Census of Agriculture (COA) from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has asked whether farm operations have agroforestry. While the COA does not differentiate the type of agroforestry used (e.g., windbreak, silvopasture, forest farming, alley cropping, riparian forest buffer) it does provide county-level numbers of farm operations practicing agroforestry. These raw numbers, available from the NASS website in tabular format, can then be joined to county-level geospatial data to provide thematic maps. This data publication includes vector polygon spatial data in multiple formats that includes the number of farm operations reporting agroforestry, the total number of farms, and the percentage of farm operations reporting agroforestry for each county in the U.S. in 2017 and 2022. The change in the proportion of farms reporting agroforestry from 2017 to 2022 is also included.The raw data were produced by the USDA National Agricultural Statistics Survey (NASS) Census of Agriculture (COA.) The COA is completed every 5 years and is a count of U.S. farms and ranches from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year. It also looks at land use, ownership, production practices, income, and other characteristics. The 2017 COA was the first census to ask if producers have any of the five common agroforestry practices (windbreak, silvopasture, forest farming, alley cropping, riparian forest buffer.) NASS included the same agroforestry question in the 2022 COA, allowing for the first national-level trend analysis for agroforestry extent in the United States. The National Agroforestry Center published the first maps depicting the agroforestry results from the COA in 2017 and have now created a new series of maps to reflect newly published agroforestry data from the 2022 COA. In addition, maps showing change in agroforestry at the national scale have been created, using data from the 2017 and 2022 COA. The purpose of this project was to use the raw census numbers to create a spatial layer for visualization, mapping, and analysis purposes.For more information about these data, see Kellerman et al. (2025) and Smith et al. (2022).

The first edition of these data, Kellerman (2023, https://doi.org/10.2737/RDS-2023-0044) contains 2017 data. This second edition includes the same 2017 data, but a different source for county boundaries was used (more details below), as well as the addition to 2022 data.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Rio Grande Watershed Ag Census 2018Item Type: CSVSummary:NM Agriculture Census download - Detailed commodity and value information for Rio Grande Watershed in 2018Notes: Highly encouraged to perform your own search on the Quickstats agricultural database: https://quickstats.nass.usda.gov/ which will allow you to query data for sectors (animal and products, crop, demographics, economics), group (further ag categories), commodity (detailed list of commodities), by the geographic location (American Indian Reservation, County, National, Region, state, watershed, zip codes), for various years at the annual or point in time levelPrepared by: Uploaded by EMcRae_NMCDCSource: Agricultural Census https://quickstats.nass.usda.gov/Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=44bf4fe6be4040c399788076a754a311#UID: 26Data Requested: Ag CensusMethod of Acquisition: Queried with Search parameters: CENSUS-STATE-NEW MEXICO-2019-ANNUAL-YEAR and downloadedDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

Abstract

The agricultural survey in its current form covers all regions of the country and all 45 departments of Senegal. The agricultural survey is an annual statistical operation whose general objective is to estimate the level of the main agricultural output of family-type agricultural holdings. It also provides information on the physical characteristics of cultivated plots (geo-location, area) and major investments made in them (agricultural inputs, cultivation operations, soil management and restoration). The main indicators relate to yield levels, areas sown, production and means of production.

Following a modular approach, the 2022-2023 edition of the annual agricultural survey is characterized by the integration of the MEA module (Machines, Equipment and other Agricultural Assets). In addition, the basic module of the 50x2030 questionnaire allows the collection of data for the calculation of SDG 5.a.1.

Geographic coverage

The annual agricultural survey covers all 45 departments of Senegal. However, for reasons related to anonymization, the variable "Department" has been replaced by the variable "Agroecological Zone" which constitutes groupings in relation to the departments. The variable "Region" remains in the anonymized version of the data.

Analysis unit

Households and agricultural plots

Universe

The agricultural survey covers all households and plots in the 45 departments of Senegal.

Kind of data

Sample survey data [ssd]

Sampling procedure

The AAS was built on a two-stage survey, with census districts (CDs) as primary units (PUs) and agricultural households as secondary units (SUs), as defined during the general census of population and l'Habitat, de l'Agriculture et de l'Élevage (RGPHAE) of 2013. In line with the broadening of the scope of the survey recommended by the AGRIS approach, from this campaign onwards the sample design incorporated a first-stage stratification, induced by the second-stage stratification, to better reflect the various agricultural activities and improve the efficiency of the estimates. The choice of a first-degree stratification induced by that of the second degree, although less efficient than an independent first-degree stratification, was guided by the constraint of non-existence of relevant variables of interest in the sampling frame of the RGPHAE to discriminate against the CDs. The stratification took into account the relative importance of the main agricultural activities (in terms of household size) identified during the 2013 RGPHAE, namely rainfed agriculture, livestock and horticulture.

Thus, four strata were formed as follows: - the "rainfed only" stratum which groups together all the households practicing only rainfed crops; - the "livestock only" stratum for households that practice animal husbandry only; - the "Horticulture and other crops" stratum, which includes households that mainly practice horticulture and secondarily other crops (forestry, fruit growing, etc.); - the "Rainfed-livestock" stratum made up of households that practice both rainfed agriculture and livestock breeding.

The size of the sample of agricultural households to be surveyed was calculated by department (area of study) by setting a relative error of 10% on the variable of interest. The distribution of the sample of each department in the strata was made using the Bankier method (1988) developed in the methodological guide to the main sampling frame practices (pp. 79-81) of the Global Strategy for Agricultural and Rural Statistics (GSARS).

At the national level, the total theoretical sample is equal to 7,450 households, spread over 1,460 physical CDs, with 5 households per CD. At the end of the enumeration operation carried out in the physical sample CDs, adjustments were made to take into account the actual updated size of the CDs, which led to a final size of 7,378 households, or 1,382 CDs.

Sampling deviation

Compared to the survey plan, adjustments were made based on the response rate at each phase.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The first questionnaire collected information on census and characteristics of agricultural household plots. The second questionnaire collected information on agricultural production, machinery, equipment and agricultural productivity.

Response rate

First phase: sample of 7378 households, including 6360 surveyed, i.e. a coverage rate of 86%.

Second phase: sample of 7218 households, including 6,834 surveyed, i.e. a coverage rate of 95%.

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across the United Republic of Tanzania by collecting comprehensive data on various aspects of the agricultural sector. This survey is crucial for policy formulation, development planning, and service delivery, providing reliable data to monitor and evaluate national and international development frameworks.

The 2022/23 survey is particularly significant as it informs the monitoring and evaluation of key agricultural development strategies and frameworks. The collected data will contribute to the Tanzania Development Vision 2025, Zanzibar Development Vision 2020, the Five-Year Development Plan 2021/22–2025/26, the National Strategy for Growth and Reduction of Poverty (NSGRP) known as MKUKUTA, and the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) known as MKUZA. The survey data also supports the evaluation of Sustainable Development Goals (SDGs) and Comprehensive Africa Agriculture Development Programme (CAADP). Key indicators for agricultural performance and poverty monitoring are directly measured from the survey data.

The 2022/23 AASS provides a detailed descriptive analysis and related tables on the main thematic areas. These areas include household members and holder identification, field roster, seasonal plot and crop rosters (Vuli, Masika, and Dry Season), permanent crop production, crop harvest use, seed and seedling acquisition, input use and acquisition (fertilizers and pesticides), livestock inventory and changes, livestock production costs, milk and eggs production, other livestock products, aquaculture production, and labor dynamics. The 2022/23 AASS offers an extensive dataset essential for understanding the current state of agriculture in Tanzania. The insights gained will support the development of policies and interventions aimed at enhancing agricultural productivity, sustainability, and the livelihoods of farming communities. This data is indispensable for stakeholders addressing challenges in the agricultural sector and promoting sustainable agricultural development.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individual data collected. Users must be aware that these anonymization or SDC methods modify the data, including suppression of some data points. This affects the aggregated values derived from the anonymized microdata, and may have other unwanted consequences, such as sampling error and bias. Additional details about the SDC methods and data access conditions are provided in the data processing and data access conditions below.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS-2022/23) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2022/23 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, the EAs (primary sampling units) were stratified into 2-3 strata within each region and then selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the codes of District and Council which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection was conducted . In hamlets with more than 200 households, twelve (12) agricultural households were drawn from the PHC 2022 list with a simple random sampling without replacement procedure in each sampled hamlet. In hamlets with 200 households or less, a listing exercise was carried out in each sampled hamlet, and twelve (12) agricultural households were selected with a simple random sampling without replacement procedure. A total of 1,352 PSUs were selected from the 2022 Population and Housing Census frame, of which 1,234 PSUs were from Mainland Tanzania and 118 from Zanzibar. A total number of 16,224 agricultural households were sampled (14,808 households from Mainland Tanzania and 1,416 from Zanzibar).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022/23 Annual Agricultural Survey used two main questionnaires consolidated into a single questionnaire within the CAPIthe CAPI System, Smallholder Farmers and Large-Scale Farms Questionnaire. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The 2022/23 AASS questionnaire covered 23 sections which are:

1. COVER; The cover page included the title of the survey, survey year (2022/23), general instructions for both the interviewers and respondents. It sets the context for the survey and also it shows the survey covers the United Republic of Tanzania.
2. SCREENING: Included preliminary questions designed to determine if the respondent or household is eligible to participate in the survey. It checks for core criteria such as involvement in agricultural activities.
3. START INTERVIEW: The introductory section where basic details about the interview are recorded, such as the date, location, and interviewer’s information. This helped in the identification and tracking of the interview process.
4. HOUSEHOLD MEMBERS AND HOLDER IDENTIFICATION: Collected information about all household members, including age, gender, relationship to the household head, and the identification of the main agricultural holder. This section helped in understanding the demographic composition of the agriculture household.
5. FIELD ROSTER: Provided the details of the various agricultural fields operated by the agriculture household. Information includes the size, location, and identification of each field. This section provided a comprehensive overview of the land resources available to the household.
6. VULI PLOT ROSTER: Focused on plots used during the Vuli season (short rainy season). It includes details on the crops planted, plot sizes, and any specific characteristics of these plots. This helps in assessing seasonal agricultural activities.
7. VULI CROP ROSTER: Provided detailed information on the types of crops grown during the Vuli season, including quantities produced and intended use (e.g., consumption, sale, storage). This section captures the output of short rainy season farming.
8. MASIKA PLOT ROSTER: Similar to Section 4 but focuses on the Masika season (long rainy season). It collects data on plot usage, crop types, and sizes. This helps in understanding the agricultural practices during the primary growing season.
9. MASIKA CROP ROSTER: Provided detailed information on crops grown during the Masika season, including production quantities and uses. This section captures the output from the main agricultural season.
10. PERMANENT CROP PRODUCTION: Focuses on perennial or permanent crops (e.g., fruit trees, tea, coffee). It includes data on the types of permanent crops, area under cultivation, production volumes, and uses. This section tracks long-term agricultural investments.
11. CROP HARVEST USE: In this, provided the details how harvested crops are utilized within the household. Categories included consumption, sale, storage, and other uses. This section helps in understanding food security and market engagement.
12. SEED AND SEEDLINGS ACQUISITION: Collected information on how the agriculture household acquires seeds and seedlings, including sources (e.g., purchased, saved, gifted) and types (local, improved, etc). This section provided insights into input supply chains and planting decisions based on the households, or head.
13. INPUT USE AND ACQUISITION (FERTILIZERS AND PESTICIDES): It provided the details of the use and acquisition of agricultural inputs such as fertilizers and pesticides. It included information on quantities used, sources, and types of inputs. This section assessed the input dependency and agricultural practices.
14. LIVESTOCK IN STOCK AND CHANGE IN STOCK: The questionnaire

This table contains data at national level on land use, arable farming, horticulture, grassland, grazing livestock, penned animals and labour. For land use, crops and animals, the surface area and the number of animals and the corresponding number of farms are presented respectively. For labor force, the number of persons, the annual work units (aje) and the number of companies are presented for the different types of labor force. Areas are rounded to 10 hectares, the number of grazing animals and pigs to 10, the number of poultry, rabbits and precious fur animals to 100, workers and annual work units to hundreds and the number of farms to tens. This table is therefore less suitable for observing small changes between different years. It is better to use the regional table for this (see chapter 3). The data for this table come from the agricultural census. The agricultural census is part of the combined task, which is used, among other things, for the implementation of agricultural policy and enforcement of the Fertilizers Act. The reference date for the number of animals is 1 April; the reference date for the crops is 15 May. In 2022, horses, ponies and donkeys will not be part of the Agricultural Census. This affects the type of farm and the total number of farms in the Agricultural Census. Farms with horses and ponies that were previously classified as 'horse and pony farms' will be classified under another business type in 2022, if there are also agricultural activities in addition to keeping horses and ponies. This has a particular effect on grazing livestock farms and 'arable farms with mainly fodder crops', where there is a clear trend break. As of 2018, the number of veal calves, fattening pigs, chickens and turkeys will be adjusted in case of temporary vacancy on the reference date. The statement from the previous year is used for the adjustment. The Agricultural Census is a structure survey, in which an adjustment in case of temporary vacancy is important, among other things, for determining the type of business and the economic size of the businesses. The number of animals on the reference date is important for the size of the livestock, which is why the animal numbers in the livestock tables are not adjusted in the event of temporary vacancy. As a result, differences may occur between the animal numbers in the Agricultural Census Tables and the Livestock Tables (see 'link to relevant tables and articles'). As of 2017, the animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of data. direct request in the Combined Statement. The I&R registers are the responsibility of RVO (Netherlands Enterprise Agency). Since 2017, cattle numbers have been derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly with RVO. Poultry data is collected via Avined's designated database Flock Information System Poultry (KIP). Avined is a trade association for the egg and poultry meat sector. Avined passes the data on to the central database of RVO.nl. Due to the transition to the use of I&R registers, there will be a change in the classification for sheep and goats from 2018. As of 2016, information from the Commercial Register is used to delineate the Agricultural Census. Registration in the Trade Register with an agricultural SBI (Standaard BedrijfsIndeling) is leading in determining whether there is an agricultural company. This demarcation is in line as closely as possible with the statistical regulations of Eurostat and the (Dutch) implementation of the term 'active farmer' from the Common Agricultural Policy (CAP). The demarcation of the Agricultural Census on the basis of information from the Trade Register mainly affects the number of companies, a clear trend break here. The influence on areas (except for non-cultivated land and natural grassland) and animal numbers (except for sheep, horses and ponies) are limited. This is mainly due to the type of companies that are excluded from the new demarcation (such as riding schools, children's farms and nature conservation organisations). As of 2010, a new standard for the economic size of companies and a new type of company will be used. Up to and including 2009, the economic size of agricultural companies was expressed in nge (Dutch size unit). As of 2010 this has been replaced by SO (Standaard Yield). As a result, the lower limit for including companies in the publication of the Agricultural Census changes from 3 nge to 3000 euros SO. For comparability over time, the data from 2000 to 2009 have been recalculated on the basis of SO standards and classifications. SO standards are changing updated every three years. The most recent update took place in 2016; the SO standards from 2010 were used in the recalculation. Data available from: 2000 Status of the figures: The figures are final. Changes as of March 17, 2023: the final figures for 2022 have been added. In addition, the structure of the table has been changed because new categories for seed onions have been added (yellow and red seed onions). When will new numbers come out? According to schedule, the first provisional figures ('quick figures') will be published at the end of June. At that time, not all statements have been received and/or fully processed, and only the most important plausibility checks have taken place. An estimate for non-response has been made on the basis of last year's statement. The data collection will be closed in September, then an estimate will be made again and further analyzes and plausibility checks will take place. Adjusted provisional figures are published at the end of September and in November, followed by the definitive figures in March of the following year.

Abstract

CAS 2022 was a comprehensive statistical undertaking for the collection and compilation of information on crop cultivation, livestock and poultry raising, aquaculture and capture fishing, agricultural economy and labour. The National Institute of Statistics (NIS) of the Ministry of Planning (MOP), and the Ministry of Agriculture, Forestry and Fisheries (MAFF), were the responsible government ministries authorized to undertake the CAS 2022. While NIS had the census and survey mandate, the MAFF was the primary user of the data produced from the survey. Technical support was also provided by the Food and Agriculture Organization of the United Nations (FAO).

The main objective of the CAS was to provide data on the agricultural situation in the Kingdom of Cambodia, to be utilized by planners and policy-makers. Specifically, the survey data are useful in: 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. Data was collected from household agricultural holdings and juridical agricultural holdings. Only the household agricultural holdings are included in the released microdata.

Statistical Disclosure Control (SDC) methods were applied to the microdata, to protect the confidentiality of the individual data collected. Users must be aware that these methods modify the data, including suppression of some data points. This affects the aggregated values derived from the anonymized microdata, and may have other unwanted consequences, such as sampling error and bias. Additional details about SDC methods and data access are provided in the sections on 'data processing' and 'access conditions' below.

Geographic coverage

The CAS 2022 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 sampling approach for the CAS 2022 relied fully upon the sampling of CAS 2021 utilising a panel approach. The CAS 2021 had used statistical methods to select a representative sample of enumeration areas throughout Cambodia from the 2019 General Population Census of Cambodia Sampling Frame. Households within these EAs were screened for any agricultural activity. Using this basic information, the agricultural households were stratified and sampled for additional data collection. Juridical holdings, which are farm enterprises operated by corporations or government institutions, were also surveyed based on listings provided by MAFF and other governmental offices with knowledge of agricultural juridical holdings.

For the CAS 2021, and therefore CAS 2022 using its panel approach, the 2019 General Population Census Sampling Frame was utilized. This frame consisted of around 14,500 villages and 38,000 Enumeration Areas (EAs). For each village, the following information was available: province, district, commune, type (rural/urban), number of EAs and number of households. 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: - Province Preah Sihanouk, District Krong Preah Sihanouk - Province Siemreap, District Krong Siem Reab - Province Phnom Penh, District Chamkar Mon - Province Phnom Penh, District Doun Penh - Province Phnom Penh, District Prampir Meakkakra - Province Phnom Penh, District Tuol Kouk - Province Phnom Penh, District Ruessei Kaev - Province Phnom Penh, District Chhbar Ampov

Since the number of rural households per EA was not known from the 2019 census, 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 CAS 2021 to identify the target population, i.e., the households engaged in agricultural activities.

For this survey, there was no minimum threshold set to determine a household's engagement in agricultural activities. This differs from the procedures used during the 2013 Agriculture Census (and that would be used in the 2023 Agriculture Census later), in which households were eligible for the survey if they grew crops on at least 0.03 hectares and/or had a minimum of 2 large livestock and/or 3 small livestock and/or 25 poultry. The procedure used in the CAS, which had no minimum land area or livestock or poultry inventory, allowed for smaller household agricultural holdings to have the potential to be selected for the survey. However, based on the sampling procedure indicated below, household agricultural holdings with larger land areas or more livestock or poultry were identified and associated with different sampling strata to ensure the selection of some of them.

The CAS 2021 and therefore CAS 2022 used a two-stage stratified sampling procedure, with EAs as primary units and households engaged in agriculture as secondary units. In the CAS 2021 and CAS 2022, 1,381 EAs and 12 agricultural households for each EA were selected, for a total planned sample size of 16,572 households. The 1,381 EAs were allocated to the provinces (statistical domains) proportionally to the number of rural households. To select the EAs within each province, the villages were ordered by district, commune, and then by type of village (Rural-Urban). Systematic sampling was then performed, with probability proportional to size (number of households). After attrition from the previous year, the total effective sample size of the survey was 15,751 agricultural households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Once the enumerators collected the survey data for an agricultural household, they submitted the completed questionnaire via Survey Solutions to their data supervisors who, in turn, carried out quality checks. If there errors or suspicious data were detected, the data supervisor would return the record to the enumerator to address the issues with the respondent if needed, and the corrected record would be re-submitted to the data supervisor. Once the records were validated by the data supervisors, they would approve them for final review by headquarters staff.

At the survey headquarters, the completed questionnaires were received after being approved by the data supervisors. If any issues or suspicious data were discovered during the headquarters review, the records could be returned to the enumerator for verification or correction if needed. Documentation on how to review questionnaire data for suspicious items or outliers was provided to both data supervisors and headquarters staff.

The data review and calculation of the survey estimates was undertaken using the RStudio software tool. Validation of the data began even when the questionnaires were being designed in the CAPI tool, as Survey Solutions allows for consistency checks to be built into the data collection tool. As soon as completed records were returned during the data collection stage, additional consistency checks were completed, evaluating the ranges for certain items, and verifying any outlier records with the enumerator and/or respondent. Moreover, when the data was cleaned, another step was conducted to impute the missing values derived from item non-response.

STATISTICAL DISCLOSURE CONTROL (SDC):

Microdata are disseminated as Public Use Files under the terms and conditions indicated at the NIS Microdata Catalog (https://microdata.nis.gov.kh/), as indicated in the section 'access conditions'.

In addition, anonymization methods have been applied to the microdata files before their dissemination, to protect the confidentiality of the statistical units (e.g. individuals) from which the data were collected.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and granivores, at national level by (main) farm type. For all subjects, both the counting data (area, number of animals) and the corresponding number of holdings are presented.

The data for this table are taken from the agricultural census. The agricultural census is part of the combined task, which is used, among other things, for the implementation of the agricultural policy and enforcement of the Fertilizers Act.

The reference date for the number of animals is 1 April; the reference date for the crops is 15 May.

In 2022, horses, ponies and donkeys will not be part of the Agricultural Census. This has an impact on farm typing and the total number of farms in the Agricultural Census. In 2022, farms with horses and ponies that were previously classified as 'horse and pony farms', if there are also agricultural activities in addition to keeping horses and ponies, will be classified under a different business type.This has a particular effect on grazing livestock farms and 'arable farms with mainly fodder crops', where there is a clear break in the trend.

From 2020 onwards, the SO2017, based on the years 2015 to 2019, will apply (see also the notes to SO: Standard Yield).

As of 2018, the number of veal calves, fattening pigs, chickens and turkeys will be adjusted in the event of temporary vacancy on the reference date. The adjustment shall be made on the basis of the previous year's statement. The Agricultural Census is a structure survey, in which an adjustment in case of temporary vacancy is important for the determination of the type of holding and the economic size of the holdings. The number of animals on the reference date is important for the size of the herds, therefore the animal numbers in the livestock tables are not adjusted in the event of temporary vacancy. As a result, there may be differences between the animal numbers in the Agricultural Census Tables and the herd tables (see ‘link to relevant tables and articles’).

As of 2017, animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of direct enquiries in the Combined Declaration. The I&R registers are the responsibility of RVO (Rijksdienst voor Ondernemend Nederland). Since 2017, the cattle numbers are derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly at RVO. Poultry data is collected via the designated database Link Information System Poultry (KIP) of Avined. Avined is a trade association for the egg and poultry meat sector.Avined transmits the data to the central database of RVO.nl. Due to the transition to the use of I&R registers, there will be a change in the classification for sheep and goats from 2018 onwards.

From 2016 onwards, information from the Trade Register will be used for the demarcation of the Agricultural Census. Registration in the Commercial Register with an agricultural SBI (Standard Business Classification) is leading in determining whether there is an agricultural holding. This demarcation is as close as possible to the statistical regulations of Eurostat and the (Dutch) implementation of the concept of 'active farmer' from the Common Agricultural Policy (CAP).

The demarcation of the Agricultural Census based on information from the Trade Register mainly affects the number of holdings, a clear break in trend occurs here. The impact on areas (except for non-cultivated land and natural grassland) and the number of animals (except for sheep, and horses and ponies) is limited. This is mainly due to the type of companies excluded by the new demarcation (such as riding schools, petting zoos and nature management organisations).

As of 2010, a new standard for the economic size of companies and a new type of company will be applied. Until 2009, the economic size of agricultural holdings was expressed in NGE (Dutch Size Unit). As of 2010, this has been replaced by SO (Standard Yield). As a result, the lower limit for inclusion of holdings in the publication of the Agricultural Census changes from 3 nge to 3000 euro SO. For comparability over time, data from 2000 to 2009 have been recalculated on the basis of SO standards and classifications. SO standards are updated every three years. The most recent update took place in 2016; The 2010 SO standards were used in the recalculation.

Data available from: 2000

Status of figures: The figures are final.

Changes as of 29 March 2024: the final figures for 2023 have been added.

When will there be new figures?
According to the regular schedule, the provisional figures will appear in November and the final figures will follow in March of the following year.

Abstract

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

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

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

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

Geographic coverage

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

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

Cleaning operations

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

Response rate

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

This map used in this dashboard summarizes payments made to producers by the Federal government from the 2017 Census of Agriculture at the county level.On the left, there is a list widget identifying the counties with the largest federal payments. Selecting a county in the list, zooms to the county on the map so that we can see what crops are grown in that area. For example, the number one county, Gaines County, Texas grows predominantly cotton. According to the chart showing total federal payments by state, Texas is also the state receiving the most federal payments. Charts are also selectable to filter the features shown in the map and county list for further exploration.The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides a in-depth look at the agricultural industry.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: Top 50 Counties Receiving Agriculture Federal PaymentsItem Type: Web Mapping Application URLSummary: This is a dashboard showing the top counties receiving payments made to producers by the Federal government from the 2017 Census of Agriculture.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: 2017 Census of Agriculture - Esri Living Atlas map applicationFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=edfbb46756b34c8f94ef0ed9d17734e2UID: 28Data Requested: Ag CensusMethod of Acquisition: Map found in Esri Living AtlasDate Acquired: 5/2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

This data release provides preliminary estimates of annual agricultural use of pesticide compounds in counties of the conterminous United States, for the year 2019, compiled by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). For all States except California, U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District-level pesticide-use data to estimate county-level pesticide use. Where Crop Reporting District data were not available or were incomplete, estimated pesticide-use values were calculated with two different methods, resulting in a low and a high estimate based on different assumptions about missing survey data (Thelin and Stone, 2013). Pesticide-use data for California were obtained from the California Department of Pesticide Regulation Pesticide Use Reporting (DPR–PUR) database (California Department of Pesticide Regulation, written commun., 2020). The California county data were appended after the estimation process was completed for the rest of the Nation. Preliminary estimates in this dataset may be revised upon availability of updated crop acreages in the 2022 Agricultural Census, expected to be published by the U.S. Department of Agriculture in 2024. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and amount in kilograms. Tables of annual agricultural pesticide-use estimates beginning in 1992 are available for download on the Pesticide National Synthesis Project webpage: https://doi.org/doi:10.5066/F7NP22KM. Beginning in 2019, estimates are reported for a reduced number of compounds. References cited: Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12: U.S. Geological Survey Data Series 907, 9 p., accessed July 12, 2015, at https://doi.org/10.3133/ds907. Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009, 54 p., accessed July 12, 2015, at http://pubs.usgs.gov/sir/2013/5009/.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and granivores, at national level by Standard Industrial Classification (SBI) 2008. For all subjects, both the counting data (area, number of animals) and the corresponding number of holdings are presented. The SBI in this table is based on the crops and animals present on the holding at the reference date. This may differ from the SBI under which the company is registered in the Trade Register (this is based on the activities specified at the time of registration).

The data for this table are taken from the agricultural census. The agricultural census is part of the combined task, which is used, among other things, for the implementation of the agricultural policy and enforcement of the Fertilizers Act. Companies in the Agricultural Census have an economic size >= 3000 euro SO (Standard Yield).

The reference date for the number of animals is 1 April; the reference date for the crops is 15 May.

In 2022, horses, ponies and donkeys will not be part of the Agricultural Census. This has an impact on farm typing and the total number of farms in the Agricultural Census. In 2022, farms with horses and ponies that were previously classified as 'horse and pony farms', if there are also agricultural activities in addition to keeping horses and ponies, will be classified under a different business type.This has a particular effect on grazing livestock farms and 'arable farms with mainly fodder crops', where there is a clear break in the trend.

As of 2018, the number of veal calves, fattening pigs, chickens and turkeys will be adjusted in the event of temporary vacancy on the reference date. The adjustment shall be made on the basis of the previous year's statement. The Agricultural Census is a structure survey, in which an adjustment in case of temporary vacancy is important for the determination of the type of holding and the economic size of the holdings. The number of animals on the reference date is important for the size of the herds, therefore the animal numbers in the livestock tables are not adjusted in the event of temporary vacancy. As a result, there may be differences between the animal numbers in the Agricultural Census Tables and the herd tables (see ‘link to relevant tables and articles’).

As of 2017, animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of direct enquiries in the Combined Declaration. The I&R registers are the responsibility of RVO (Rijksdienst voor Ondernemend Nederland). Since 2017, the cattle numbers are derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly at RVO. Poultry data is collected via the designated database Link Information System Poultry (KIP) of Avined. Avined is a trade association for the egg and poultry meat sector.Avined transmits the data to the central database of RVO.nl. Due to the transition to the use of I&R registers, there will be a change in the classification for sheep and goats from 2018 onwards.

From 2016 onwards, information from the Trade Register will be used for the demarcation of the Agricultural Census. Registration in the Commercial Register with an agricultural SBI (Standard Business Classification) is leading in determining whether there is an agricultural holding. This demarcation is as close as possible to the statistical regulations of Eurostat and the (Dutch) implementation of the concept of 'active farmer' from the Common Agricultural Policy (CAP).

Data available from: 2016

Status of figures: The figures are final.

Changes as of 29 March 2024: 2023 figures have been updated and are now final.

When will there be new figures?
Preliminary figures will be published in September and November and final figures will follow in March of the following year.

What?

A dataset containing 313 total variables from 33 secondary sources. There are 261 unique variables, and 52 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.

Why?

Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.

How?

Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo.

What is 'new' or corrected in version 2?

Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster.

Added: 55 variables, mostly from the 2022 Ag Census, and v2.1 added a .pdf file with descriptives of data sources and years, and a .sav file.

Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column.

CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K

Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

Abstract

This study is a test pilot to evaluate a set of questions to allow the recollection of information on SDG indicator 5.a.1 - (a) Percentage of people with ownership or secure rights over agricultural land (out of total agricultural population), by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure, in Panamá.

The main objective of the pilot test is to test a question set on SDG indicator 5.a.1 in agriculture census.

Geographic coverage

The data is not representative at national level.

Analysis unit

Agricultural holdings

Sampling procedure

The sample was selected until it reached at least 75 households. The selection was done considering easy access, adequate roads that allow rapid movement between districts, and important development of agricultural activity, at the level of large, medium and small household agriculture.

Mode of data collection

Other [oth]

Cleaning operations

The data collected underwent the following stages: 1. Field editing which consisted of checking of consistency, correctness and completeness of entries while in the field. 2. Manual processing of complete questionnaries where the following were done: verification of identificacion by family name and district of forms, checking elegibility of entries, coding.

Response rate

The response rate was 100%.

Data appraisal

Each survey corresponds to a household.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The Food and Agriculture Organization of the United Nations (FAO) conducted the fourth round of the Data in Emergencies household survey (DIEM-Monitoring) in Chad between 16 December 2022 and 10 January 2023 to assess agricultural livelihoods and food security. Data was collected through face-to-face surveys in the provinces of Kanem, Lac, Moyen-Chari, Logone Occidental, Moyen-Kebbi Est and Wadi Fira. A total of 5310 households were interviewed. Data collection took place after the rainy season, during the harvest period. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey for Phase 4 was developed in partnership with INSEED to achieve representation at the administrative level 2, drawing upon the 2009 General Census of Population and Housing (RGPH 2) and incorporating a 3.5% estimated annual growth rate. Selection criteria, aligned with FAO standards and in collaboration with SISAAP, prioritized vulnerability as identified in the Harmonized Framework outcome analysis, particularly for communities in levels 3 and 4 within Sahelian and Sudanian zones, and factored in the FAO's operational presence. This selection also considered regions significantly affected by the floods in 2022. The methodology employed a two-stage probability sampling, designating villages as the primary sampling units and households as the secondary units.

The methodology stipulated a cluster size of 12, necessitating a minimum of 22 village clusters, resulting in a sample size of 264 per stratum. Consequently, the survey encompassed 5,808 households across 22 departments, ensuring representativeness at the admin 2 level for the designated provinces. For more details on the sampling procedure, consult the methodology document attached in the documentations tab.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries.