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 non-household level. The documentation also provides useful information for understanding the non-household holdings anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Non-household agricultural holdings in Liberia, to include agricultural cooperatives, concessions, communal farms, private farms, farmer base organizations and other institutional farms.

Universe

All non-household agricultural holdings in Liberia, to include agricultural cooperatives, concessions, communal farms, private farms, farmer based organizations and other institutional farms engaged in agricultural activities during the 2024 farming season.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A full census of all non-household holdings in Liberia was envisioned. The LAC-2024 technical team used fifteen (15) days to develop a complete list of all non-household holdings in Liberia. This llist was used by 30 county inspectors for the non-household holdings enumeration during the census.

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 non-household questionnaire include type of agricultural activities practiced by non-household holdings, characteristics of non-household holders and holdings, 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 non-household questionnaire was administered to the non-household holding head or any member who had vast knowledge of the holding and its agricultural activities. The primary respondent (i.e., the non- household member that provided most of the information for the questionnaire or a given module) 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.

For financial year 2024, the East Indian state of Bihar received the highest budget allocation of **** million Indian rupees for the agriculture census. This was followed by Uttar Pradesh with a value of around ** million rupees during the same period.

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 community level. The documentation also provides useful information on the community anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural Communities

Universe

The universe for the Liberia Agriculture Census 2024 community operations is: all communities (localities) in Liberia that are located within an agricultural enumeration area.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were conducted in communities in the EAs selected for the sample census. A sampled community had the same probability of selection and sample weight as the EA. If a community was linked to many EAs, additional adjustment for multiplicity was performed. The LAC-2024 community operations engaged 61,600 respondents across 7,193 sampled communities. Nationally, the distribution of respondents shows that males were 66.1% of the total 61,600 participants, while females were 33.9%.

Mode of data collection

Focus Group [foc]

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 MACsby 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 community questionnaire included the following sections:

1-respondents characteristics; 2- production and processing activities in the community; 3- land characteristics and irrigation in the community; 4- markets to sell agriculture products; 5- access to agricultural inputs, services and credits in the community; 6- social cohesion; 7- difficulties in agricultural activities; 8- livestock and Poultry Production; 9- environment; 10- disasters and shocks; 11- community infrastructure and transportation; 12- community organizations; 13- community resource management; 14- land prices and credit; 15- community key events; 16- labour and producer prices.

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

100%

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 community level. The documentation also provides useful information on the community anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural Communities

Universe

The universe for the Liberia Agriculture Census 2024 community operations is: all communities (localities) in Liberia that are located within an agricultural enumeration area.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were conducted in communities in the EAs selected for the sample census. A sampled community had the same probability of selection and sample weight as the EA. If a community was linked to many EAs, additional adjustment for multiplicity was performed. The LAC-2024 community operations engaged 61,600 respondents across 7,193 sampled communities. Nationally, the distribution of respondents shows that males were 66.1% of the total 61,600 participants, while females were 33.9%.

Mode of data collection

Focus Group [foc]

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 MACsby 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 community questionnaire included the following sections: 1- respondents characteristics; 2- production and processing activities in the community; 3- land characteristics and irrigation in the community; 4- markets to sell agriculture products; 5- access to agricultural inputs, services and credits in the community; 6- social cohesion; 7- difficulties in agricultural activities; 8- livestock and Poultry Production; 9- environment; 10- disasters and shocks; 11- community infrastructure and transportation; 12- community organizations; 13- community resource management; 14- land prices and credit; 15- community key events; 16- labour and producer prices.

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

100%

Exports - Other Agricultural Materials (Census Basis) in the United States increased to 1240.39 USD Million in February from 1174.49 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Other Agricultural Materials.

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.

Imports - Agricultural Products, Ism (Census Basis) in the United States increased to 1572.46 USD Million in February from 1551.70 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Agricultural Products, Ism.

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across 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 AGREGATED 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 ACESS CONDITIONS ARE PROVIDED IN THE DATA PROCESSING AND DATA ACESS 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 recorded the

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

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

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

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

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

Data available from: 2000

Status of the figures: The figures are final.

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

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

Abstract

The Annual Agriculture Sample Survey (AASS 2023/24) was conducted to generate up-to-date and precise data on crops, livestock and aquaculture activities. Accurate crop production figures are essential for a wide range of stakeholders in the agricultural sector. The data from this survey will provide critical insights for farmers, agricultural businesses, government policymakers, and other key players to inform their decisions in both the short and long terms.

The specific objectives of the AASS 2023/24 include:

1. To collect timely data on agricultural production and productivity at both national and regional levels;

2. To gather core data to help develop and review agricultural policies and to guide the implementation of agricultural plans at national and regional levels between agricultural census periods;

3. To compile fundamental statistics that facilitate comparisons in the development of the agriculture sector across the country; and

4. To collect data on agricultural machinery, equipment, and structures, as well as information on women’s empowerment and nutrition.

The Women's Empowerment and Nutrition was an additional module that was integrated into the AASS 2023/24 to generate nationally representative statistics on empowerment and women's dietary diversity among agricultural households. This module is useful in generating the Women Empowerment Metric for National Statistical Systems (WEMNS) indicator (https://weai.ifpri.info/wemns/) and the Women's Dietary Diversity (MDD-W) indicator.

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 1000 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. 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 2023/24) 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 2023/24 used a stratified two-stage sampling design which allows for reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, 1504 EAs were 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 District and Council codes 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 without replacement was conducted, for the selection of 12 Secondary Sampling Units (SSUs),i.e. agricultural households in each selected EA. In total, 18 048 agricultural holdings were selected across 1504 EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2023/24 Annual Agricultural Survey used two main questionnaires, Smallholder Farmers and Large-Scale Farms Questionnaire, consolidated into a single questionnaire within the CAPI System. The Smallholder Farmers questionnaire captured information for households while the Large Scale Farms questionnaire captured information for establishments/holdings. These questionnaires were used for data collection covering topics on core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons.

   The questionnaire in the Download section covers both of the questionnaires mentioned above.

Cleaning operations

The data processing and data editing phases were critical components of the Annual Agriculture Sample Survey for the agricultural year 2023/24. These phases ensure that the collected data are of high quality, consistent, coherent, and ready for analysis and reporting. The technical team responsible for these tasks included members from the National Bureau of Statistics (NBS), the Office of the Chief Government Statistician (OCGS), Agricultural Sector Lead Ministries (ASLMs), and academia, with technical support from FAO experts at various levels.

A. Data Processing

A.1. Data Entry: - Enumerators entered data directly into tablets during interviews, eliminating the need for a separate data entry activity. This method minimized errors associated with manual data entry. Data collected in the field were periodically synchronized with a central database, ensuring that the information was securely stored and readily accessible for processing.

A.2. Data Cleaning: - Upon synchronization, the data underwent initial automated checks to identify and flag obvious errors, such as missing values, out-of-range responses, and inconsistencies. - Technical staff conducted a manual review of flagged entries, correcting errors based on predefined rules and protocols. This step ensured that all data were accurate and complete before further processing.

A.3. Data Integration: - Data from different sections of the questionnaire (e.g., household information, crop production, livestock data) were integrated into a unified dataset. This process involved matching and merging records to ensure consistency across all sections by data scientists and programmers. - The technical team harmonized data formats and units of measurement to ensure consistency. This step was important for maintaining coherence in subsequent analyses.

B. Data Editing

B.1. Consistency Checks: - The data editing phase included rigorous checks for internal consistency within the dataset. This involved ensuring that related variables were logically consistent (e.g., the number of chickens reported matched the eggs production data). - The team conducted cross-sectional checks to verify consistency across different sections of the questionnaire. For example, crop production data were cross-referenced with input use and labor data to identify and correct discrepancies.

B.2. Outlier Detection and Treatment: - Statistical techniques were employed to identify outliers in the dataset. Outliers could indicate data entry errors or exceptional cases that required further investigation. - Identified outliers were validated through additional checks by using STATA program or, if necessary, follow-up with the respondents. This ensured that the outliers were genuine and not due to errors.

B.3. Imputation of Missing Data: - For instances where data was missing, the team used imputation techniques to estimate the missing values. Imputation methods included statistical techniques such as mean substitution, regression imputation, or hot-deck imputation, where necessary. All imputed values were documented in STATA do-files. This transparency ensured that subsequent analyses accounted for the imputed data appropriately.

B.4. Data Validation: - The dataset was validated against external data sources, such as previous surveys, administrative records, and satellite imagery (limited), to ensure accuracy and reliability. - The validation process included a feedback loop where any identified issues were communicated back to the data collection teams for clarification and correction. - Technical online meetings between FAO, NBS, OCGS and ASLMs related to data validation were conducted professionally to ensure accountability of data along the value chain.

C. Continuous Improvement - After the completion of the survey, the entire process was reviewed to identify areas for improvement. Feedback from all team members and stakeholders was gathered to refine the methodologies and protocols for future agriculture surveys in series under 50x20230 Initiative. - Detailed documentation of all processes, decisions, and methodologies was maintained. This documentation served as a reference for future surveys and contributed to the transparency and reproducibility of the survey process.

STATISTICAL DISCLOSURE CONTROL (SDC)

Microdata are disseminated as Public Use Files under the terms indicated in Appendix A of the NBS Dissemination and Pricing Policy (https://www.nbs.go.tz/publications/policies-and-legislations). These access conditions are also indicated in the "Data Access" section below.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individuals that data was collected from. These methods include: i) removal of information that may directly identify a respondent (name, address, etc.), ii) grouping values of some variables into categories (e.g. age), iii) limiting geographical information to the region level or higher, iv) suppression of some data points for variables that, in combination with others, may pose a relevant risk of identification of

About this dataset

Context

The dataset tabulates the Ridge Farm population by age. The dataset can be utilized to understand the age distribution and demographics of Ridge Farm.

Content

The dataset constitues the following three datasets

• Ridge Farm, IL Age Group Population Dataset: A complete breakdown of Ridge Farm age demographics from 0 to 85 years, distributed across 18 age groups
• Ridge Farm, IL Age Cohorts Dataset: Children, Working Adults, and Seniors in Ridge Farm - Population and Percentage Analysis
• Ridge Farm, IL Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Abstract

The Annual Agricultural Survey (AAS) is an integrated modular survey aiming to provide high quality and timely data on the performance of the Ugandan agricultural sector, as well as core indicators on crop and livestock for better agricultural policy making. Data collection for the AAS is implemented in two waves, corresponding to the first (January-June) and second (July-December) seasons of the Ugandan agricultural year. For each visit, households in the survey's sample are interviewed twice, during the visit1 period and visit2. This results in a total of two visits during the agricultural year. The data collection activities were delayed by the pandemic. Among information collected with the AAS there is data on: The quantity and value of agricultural production; The access to extension services, market information and agricultural facility; Livestock keeping and animal products production; The socio-demographic characteristics of agricultural household members. The collected data is used to produce a set of tables and indicators for tracking and evaluating the impacts of government and development programs on agriculture, and to compute SDG and CAADP indicators related to food and agriculture. For the main findings from the AAS 2020, see the Executive Summary of the AAS 2020 Report (see external resources/downloads section).

Geographic coverage

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone includes districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS: Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu;

Analysis unit

Agricultural households (i.e. agricultural holdings in the household sector)

Universe

Agricultural households (i.e. agricultural holdings in the household sector).

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling design was adopted for the AAS 2020. To increase the efficiency of the sample design, the sampling frame was stratified into 10 ZARDIs. In each stratum, the first stage was the selection of the Primary Sampling Unit (PSU), which is the EA (enumerator area) and the second stage was the selection of the Secondary Sampling Unit (SSU), which are the Ag HHs. The survey covered households cultivating crops and/or raising livestock, including households that were cultivating a few crops or raising a limited number of animals. No minimum threshold on the amount of land cultivated or animals raised was set nor did the survey aim to generate estimates concerning aquaculture, forestry and fisheries. Sample size The survey generated national, regional and sub-regional level estimates. A sample of 593 EAs and an average of 12 Ag HHs were selected from each EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Annual Agricultural Survey (AAS 2020) adopted three main questionnaires: the post-planting (PP), the post-harvest (PH) and the livestock and holding questionnaires. Normally, the PP and PH questionnaires are administered each season, while the livestock and holding questionnaire is administered at the end of the second season and covers the entire agricultural year. Nonetheless, in the AAS 2020, a different survey calendar was adopted due to movement limitations imposed as a result of the COVID-19 pandemic.

Cleaning operations

All the data captured from the field were stored in the cloud with a local backup. Editing and validation was done electronically using STATA software.

Response rate

The response rate was about the 94.5 %.

Sampling error estimates

The accuracy of the survey results depends on the sampling and the non-sampling errors. The AAS 2020 had a large enough and representative sample to limit sampling errors. On the other hand, the non-sampling errors, usually errors that arise during data collection, were controlled through thorough training of the data collectors, field supervision by the headquarters team, and a well-developed CAPI programme. The Coefficients of Variations (CVs) and Confidence Intervals (CIs) for selected indicators at national, ZARDI and sub-regional levels are presented in the Annex tables.

This data release provides preliminary estimates of annual agricultural use of pesticide compounds in counties of the conterminous United States, for the year 2018, 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, 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. 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. California Department of Pesticide Regulation, 2020, Pesticide use reporting (PUR): California Department of Pesticide Regulation Pesticide Use Reporting (PUR), Pesticide Use Report Data (PUR archives pur2018.zip), accessed December 29, 2020, at http://www.cdpr.ca.gov/docs/pur/purmain.htm. 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/.

Imports - Agricultural Foods, Feeds & Beverages (Census Basis) in the United States increased to 14991.82 USD Million in February from 13946.88 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Agricultural Foods, Feeds & Beverages.

About this dataset

Context

The dataset tabulates the Meadowbrook Farm population by age. The dataset can be utilized to understand the age distribution and demographics of Meadowbrook Farm.

Content

The dataset constitues the following three datasets

• Meadowbrook Farm, KY Age Group Population Dataset: A complete breakdown of Meadowbrook Farm age demographics from 0 to 85 years, distributed across 18 age groups
• Meadowbrook Farm, KY Age Cohorts Dataset: Children, Working Adults, and Seniors in Meadowbrook Farm - Population and Percentage Analysis
• Meadowbrook Farm, KY Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Imports - Non Agricultural Products Total (Census Basis) in the United States increased to 3291.50 USD Million in February from 3036.73 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Non Agricultural Products Total.

Abstract

Overview of the Census The National Population and Housing Census 2024 was conducted in line with international best practices and guided by the need to produce accurate, relevant, and timely data. Covering all households and individuals across the country, this census marks a significant milestone in Uganda’s journey towards data-driven development. The specific objectives of the NPHC 2024 were: i) To ascertain size, structure and distribution of the population ii) To gather data on housing conditions and access to basic services iii) To monitor changes in key social and economic indicators since the previous Census iv) To update census maps and lists of Enumeration Areas for effective execution of the census, construction of efficient area sampling frames for subsequent surveys and geographical maps at the lowest level. v) To establish the statistical infrastructure for future operations at the lowest Local Government level. vi) To further enhance the capacity of UBOS staff to undertake future censuses and large-scale sample surveys. vii) Inform policies and programmes aimed at improving the quality of life of all Ugandans

Uses of National Population and Housing Censuses The findings of the 2024 Census will be instrumental in shaping Uganda’s development agenda. They provide a basis for: a) Planning: Facilitating evidence based National and Local Government planning processes. b) Resource Allocation: Enabling equitable distribution of resources across programmes and Local Governments. c) Program Design: Informing interventions in social services such as health, education, infrastructure, and housing, to mention a few. d) Monitoring Progress: Tracking Uganda’s advancements towards achieving socio-economic transformation as envisioned in Vision 2040, the National Development Plans, as well as regional, continental and global development initiatives.

Key Findings 1. Population Size and Growth: Uganda’s population as of May 2024 was 45,905,417 persons, reflecting an average annual growth rate of 2.9 percent since the last Census in 2014. 2. Demographic Composition: A half of the population is under the age of 18. Five in every one hundred persons are aged 60 and above. 3. Housing and Living Conditions: i) Eight in ten (81.1%) households have access to improved water sources ii) Slightly over a half (53.4%) of households have access to electricity (25.3% from grid and 28.1% from solar). 4. Literacy: Seventy four percent of persons aged 10 and above were able to read and write meaningfully in any language. 5. Well-being and Health: i) One third (33.1%) of the households were in subsistence economy. ii) Twelve percent of persons aged 10 and above had experienced at least some form of probable general psychological distress. 6. Labour Force (15 years and above): i) The working age group was 25,494,490 persons (57.4% of the population). ii) The unemployment rate was 12.3 percent. iii) The share of Youth (15-24 years) Not in Employment, Education or Training (NEET) was 4,001,528 persons (42.6%)

Geographic coverage

National coverage

Analysis unit

The units of analysis for the NPHC 2024 include; - Individuals - Households - Housing

Universe

The census was done on a de facto basis i.e. every person was enumerated where he/she spent the Census Reference Night of 9th May 2024.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the National Population and Housing Census 2024 structured and included: - HOUSEHOLD: Characteristics of household members, housing and household characteristics, agriculture, deaths in the household, and information on physical address.

-INSTITUTION: Characteristics of institution members.

About this dataset

Context

The dataset tabulates the Prairie Farm population by age. The dataset can be utilized to understand the age distribution and demographics of Prairie Farm.

Content

The dataset constitues the following three datasets

• Prairie Farm, WI Age Group Population Dataset: A complete breakdown of Prairie Farm age demographics from 0 to 85 years, distributed across 18 age groups
• Prairie Farm, WI Age Cohorts Dataset: Children, Working Adults, and Seniors in Prairie Farm - Population and Percentage Analysis
• Prairie Farm, WI Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The Census of Agriculture, produced by the United States Department of Agriculture (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 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers. Dataset SummaryPhenomenon Mapped: Hay productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024 AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations. Commodities included in this layer:Hay - Acres HarvestedHay - Operations with Area HarvestedHay - Production, Measured in TonsHay, (Excl Alfalfa) - Acres HarvestedHay, (Excl Alfalfa) - Operations with Area HarvestedHay, (Excl Alfalfa) - Production, Measured in TonsHay, (Excl Alfalfa), Irrigated - Acres HarvestedHay, (Excl Alfalfa), Irrigated - Operations with Area HarvestedHay, Alfalfa - Acres HarvestedHay, Alfalfa - Operations with Area HarvestedHay, Alfalfa - Production, Measured in TonsHay, Alfalfa, Irrigated - Acres HarvestedHay, Alfalfa, Irrigated - Operations with Area HarvestedHay, Irrigated - Acres HarvestedHay, Irrigated - Operations with Area Harvested Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area. What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Agriculture Census: Average Size of Operational Land Holdings: Bihar: Size Group: 2 to 4 Hectares data was reported at 2.600 ha in 2016. This records an increase from the previous number of 2.590 ha for 2011. Agriculture Census: Average Size of Operational Land Holdings: Bihar: Size Group: 2 to 4 Hectares data is updated yearly, averaging 2.595 ha from Jun 2001 (Median) to 2016, with 4 observations. The data reached an all-time high of 2.620 ha in 2001 and a record low of 2.590 ha in 2011. Agriculture Census: Average Size of Operational Land Holdings: Bihar: Size Group: 2 to 4 Hectares data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIK003: Agriculture Census: Average Size of Operational Land Holdings: by Size Group.