This data product presents the latest household income forecast and estimates for U.S. family farms.

Abstract

The Integrated Farm Household Survey (IFHS) supported the agricultural Research and Development Program in terms of benchmark data on the characteristics of farms and farmers. The IFHS results provided inputs for the development and/or improvement of the performance indicators system in agriculture. Further, the survey results could quantify the impact of agricultural policies of the government.

The survey gathered household level data on the following; Household Information, Farm Particulars, Inventory of Farm Investments, Household Income, Household Expenditures and Credit Information.

Specifically, the following data are generated: 1. Level, structure and/or sources of farm household income; 2. Characteristics of farms/farm enterprises and the farm households; 3. Access of farm households to agricultural support services; 4. Farm management such as input use and cultivation practices; 5. Expenditure patterns of the farm households; 6. Farm and households investments; and 7. Other socio-economic data.

Geographic coverage

National Coverage.

Analysis unit

Households

Universe

The survey covered farm households with farming/fishing operations.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IFHS utilized different sampling frames at the barangay and household levels. At the barangay level, the list of agricultural barangays covered in the 1999 Barangay Screening Survey (BSS) served as the sampling frame while at the household level, the listing of households generated from the 2000 Census of Population and Housing (CPH) of the National Statistics Office (NSO) was used as basis for drawing the samples. The 2000 CPH listing was utilized as sampling frame for the IFHS despite the limitation that households were not classified into farming and non-farming categories for two major considerations. First, the 2000 CPH provided the most updated lists of households by barangay. Second, budgetary constraints precluded the conduct of household screening in the selected sample barangays for the survey.

The domain of the survey was the province. A two-stage stratified sampling design was adopted with the barangay as primary sampling unit and the farming household as secondary sampling unit. The number of farming households was used as the stratification variable. Primary and secondary sampling units were both drawn using simple random sampling.

In getting the number of barangays as representative of the domain (province) level, the total number of agricultural barangays in the province reported in the 1999 Barangay Screening Survey (BSS) was used in proportionately allocating the target sample size of around 600 barangays to the Integrated Farm Household Survey (IFHS) provinces. Due to budgetary consideration, the total number of barangays included for small and large agricultural sampling of households with at least one member engaged in agricultural activity. provinces was set at six (6) and nine (9) barangays, respectively, depending on the computed total sample size for the province, that is,

        n' = 6 if n < 6, and
        n' = 9 otherwise.

Ten (10) sample households were allocated for each sample barangay. This procedure resulted in total sample size of 592 barangays and 5,920 households for the entire country.

A general feature of the design was the division of the primary sampling units into strata of approximately equal sizes relative to the number of farming households reported in the 1999 BSS. The division of the barangays within the province and the drawing of sample was done as follows:

The barangays were arrayed in descending order based on the total number of farming households. These barangays were then divided into three (3) strata such that the cumulative total number of farming households of all the barangays in any one stratum was approximately of the same magnitude as the rest of the individual strata. Thus, Stratum 1 barangays constitute all "large barangays", Stratum 2 barangays constitute all "medium barangays", and Stratum 3 barangays constitute all "small barangays"; with respect to total number of farming households.

Equal sample sizes were allocated and drawn from the three strata, resulting in two (2) and three (3) sample barangays, respectively, per stratum depending on the sample size for the province. Selection of sample barangays wss done at the BAS Central Office using simple random sampling. The generated lists of sample barangays were then submitted to NSO for the drawing of sample households and for the photocopying of corresponding barangay maps.

Drawing of sample households was made at the NSO field offices using simple random sampling of households with at least one member engaged in agricultural activity. The generated lists of samples were sent back to BAS Central Office for control and distribution to concerned Provincial Operations Centers (POCs).

Sampling deviation

As in any survey, there were cases wherein samples need to be substituted or replaced. Following were the guidelines in replacing sample barangays and/or households:

Sample Barangays - Only two general reasons were considered valid for substituting barangays: 1. Transportation costs were way above the allocated budget for operations; or 2. Unfavorable peace and order situation in the area.

The list of replacement barangays served as the only source of substitute barangays. It was emphasized that a replacement barangay should be taken only from the list of replacement barangays in the same stratum.

Sample Households - Only the reasons enumerated below are considered valid for replacing households. 1.Household was not a qualified IFHS sample: a. For regions except NCR: Candidate household was not a farming household; b. For NCR: Candidate household was not into agricultural activities, or into agricultural activities but produce was not intended to generate income for the household; c. Conditions (a) and (b) were satisfied but there was no agricultural operation during the reference period (July 2002 to June 2003); 2. Household was a qualified IFHS sample but any of the following situations arose during visit: a. No qualified respondent was available for interview during the entire survey period; b. Qualified respondent refused to be interviewed; c. Interview was terminated;

It was emphasized that reasons for substituting sample households should be validated first by the field supervisor before replacement is allowed. Replacement households should be taken only from the list of replacements for the barangay.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Consistencies of data items within and across record types were first verified and checked according to the Data Processing Guidelines of the study. First stage of the editing was done manualy. A second stage consistency check was a component of the Computerized Processing System.

Initial editing of data was done by the Contractual Data Collectors (CDCs) on every filled up questionaire. These questionnaires were turned over to their supervisors for checking. Editing/Checking for consistencies of data items in particular record types and accross record types were done.

Second stage of editing was done at the Central Office. The Data Processing System (DPS) was equipped with a customized editing program to filter out-of-range data items to generate an errorlist. The errorlist is a compilation of errors on specific data item that did not pass the specification. The errorlist list was checked based on the information in the questionnaire. The correction was reflected to the data file using the the CENTRY module of the Integrated Micro-computer Processing System (IMPS).

Response rate

From 5920 sample households, 5448 sample units were successfuly interviewed for a response rate of 92.03%.

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England

If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk

Background and guidance on the statistics

Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers.

Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry.

This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B).

This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals.

Survey content and methodology

The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria.

Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years.

The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice.

For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey

Data analysis

The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income).

Accuracy and reliability of the results

We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calcula

In 2024, there were approximately 7.7 million agricultural households in Thailand. In that year, Nakhon Ratchasima province had the highest number of farm households, accounting for around 358,000 households.

** Article & Dataset Currently Under Review **

Dataset Overview

Our household dataset is associated with a pre-print article "Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania". The household survey is designed for the purposes of policy evaluation with selection of households based on proximity to large-scale land acquisitions (treatment) and a set of households in similar socio-ecological contexts with no association to large-scale land acquisitions (control). Households were selected as a random sample in 35 villages surrounding LSLAs who provided responses to a questionnaire covering household income, assets, farming practices, health, food-security, and energy-use.

Two datasets are provided. First, the "hh_dataset_rep.csv" providing household responses for variables used in this study. Second, the "hh_crops_rep.csv" provides detail on crops cultivated by each household, self-reported yields and farm-gate prices. Each variable is described in the "variable_descriptoin.xlsx". In addition to the datasets, we provide replication code for this study "lsla_mechanisms_rep.Rmd" as an R-Markdown file.

Article Abstract

Improving agricultural productivity is a major sustainability challenge of the 21st century. Large-scale land acquisitions (LSLAs) have important effects on both well-being and the environment in the Global South, but their impacts on agricultural productivity and subsequent effects on farm incomes or food-security are under-investigated. Prior studies lack data or methods to investigate the mechanistic nature of household change in agricultural practices that may vary due to LSLA conditions. To overcome this challenge, we use a novel household dataset and a quasi-experimental design to estimate household level changes in agricultural value driven by LSLAs in Tanzania. In addition, we use a causal mediation analysis to assess how contract farming arrangements, land loss, and adoption of new farming technologies around LSLAs influence agricultural productivity. We find that households near LSLAs produced 19.2% (95% CI: 3.5 – 37.2%) higher agricultural value, primarily due to increased crop prices and farmer selection of high-value crops. Importantly, effect sizes are positively and negatively mediated by different mechanisms. The presence of contract farming explains 18.1% (95% CI: 0.56%, 47%) of the effect size in agricultural value, whereas land loss reduces agricultural value by 26.8% (95% CI: -71.3%, -4.0%). We also estimate whether improvements in food-security and household incomes occur in proximity to LSLAs, as anticipated with higher agricultural value. However, we do not find increases in agricultural income and food security, which may be due to higher crop prices in proximity to LSLAs. Our results stand in contrast to assumptions that technological spillovers occur through LSLAs and are principal drivers of agrarian change, holding important implications for agricultural transformations. Instead access to output markets through contract farming enables greater agricultural value whereas land loss negatively affects the agricultural value of households. Governance strategies should focus on limiting negative impacts related to the loss of smallholder land rights enabling greater access to contract farming.

In 2023, most of the households in the agriculture industry in South Korea were farming households. That year, the country's total number of agricultural households was about one million.

This dataset holds survey data of individual farming households in the Eastern region in Bhutan relating to their nitrogen use. The survey was conducted in 2022 and the questions covered two seasons (2022 and 2021 farming seasons) asked at a single visit in the 2022 season. The questions on the winter season were based on recall. The data cover the following topics: household characteristics, general farm characteristics, plot characteristics, crop production and harvest, synthetic and organic fertiliser use and compost production, labour, irrigation, pesticides, livestock, information sources, drivers of and barriers to adoption of sustainable practices, attitude, behaviour, perception and opinion, household expenditure and income, household asset and wealth, subsidies. The data were collected primarily to assess differences in nitrogen use efficiency (NUE) and sustainable nitrogen practices between households. The data also aim to enhance understanding of farmers’ attitudes, opinion and decision making affecting NUE in crop production and farm related factors which enable adoption of sustainable practices. The data are part of a wider SANH (South Asian Nitrogen Hub) harmonised household survey covering Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka. Full details about this dataset can be found at https://doi.org/10.5285/cd35ca67-8121-4a0d-81c9-c4a7fae25117

Abstract

Data on agriculture is necessary to draw up policies and plans for the future development of this sector. Agriculture plays a vital role and represents a significant share of the Palestinian Gross Domestic Product (GDP), and of the Palestinian labour force. Statistical data on Household Farming is a components of agricultural sector and with direct to the economy; PCBS has henceforth implemented a survey on Household Farming.

Household Farming Survey 2015 conducted from 24 March 2015 to 31 May 2015 aims to provide data on the structure of Household Farming sector to inform future policies and plans for development.

Data include availability of a garden, use of garden in agricultural activity and the reasons of unused arable area, as well as the numbers of reared livestock (domestic), distribution patterns of the production from garden and Livestock (Domestic).

Geographic coverage

State of Palestine

Analysis unit

Household

Universe

All Palestinian households who reside normally in Palestine during 2015.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The sampling frame was based on a master sample which was updated in 2013-2014 for Expenditure and Consumption Survey (PECS) and Multiple Indicator Cluster Survey (MICS)) surveys, and the frame consists from enumeration areas. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size: The sample size is 7,690 households for Palestine, of which 6,609 households responded.

Sampling Design: Two stage stratified cluster (PPS) sample as following:

First stage: selection of a PPS random sample of 370 enumeration areas.

Second stage: A random systematic sample of 20 households from each enumeration area selected in the first stage.

Sample strata: The population was divided by: 1- Governorate 2- locality type (urban, rural, camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Household Farming Survey 2015 was designed based on the recommendations of the United Nations, and the questionnaire used for Survey of the Impact of the Israeli Unilateral Measures on the Social, Economic, and Environmental Conditions of the Palestinian Households. The specific situation of Palestine was taken into account, in addition to the requirements of the technical phase of field work and of data processing and analysis.

Cleaning operations

The data processing stage consisted of the following operations: 1. Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

1. Data entry: The Household Farming Survey questionnaire was programmed and the data were entered into the computer in the offices in Nablus, Hebron, Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements:
2. To prevent the duplication of questionnaires during data entry.
3. To apply checks on the integrity and consistency of entered data.
4. To handle errors in a user-friendly manner.
5. The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

86.0%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample rather than a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators and the variance table is attached with the final report.

Data appraisal

The non-sampling errors are possible to occur at all phases of implementing the project, through data collection and entry which could be summarized as non-response errors, and responding errors (respondents), and interview errors (fieldworkers) and data-entry errors. To avoid errors and reduce the impact, fieldworkers received intensive training on how to conduct interviews, interview tips, things that should be avoided. The training had, practical and theoretical exercises. Fieldworkers also received a guide which contained a private key questions of questionnaire, mechanism to fill questionnaire and methods of dealing with respondents to reduce refusal rates and providing correct and non-based data. Also, data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Abstract

The ECVM/A is an integrated multi-topic household survey done for the purpose of evaluating poverty and living conditions in Niger.

The main objectives of the ECVMA are to: - Gauge the progress made with achievement of the Millennium Development Goals (MDGs); - Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population; - Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ; - Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

The ECVM/A involves two visits, which means that each household is visited twice. The first visit takes place during the planting season. The second visit takes place during the harvest season. The household and agriculture/livestock, as well as the community/price questionnaire are administered during the first visit. During the second visit, only the household and agriculture/livestock questionnaires are administered.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The ECVM/A 2011 has been designed to have national coverage, including both urban and rural areas in all the regions of the country. The domains are defined as the entire country, the city of Niamey; and other urban areas, rural areas, and in the rural areas, agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 26 explicit sampling strata were selected: Niamey, and urban, agriculture, agro-pastoral and pastoral zones of the seven regions other than Niamey.

The target population is drawn from households in all 8 regions of the country with the exception of certain strata found in Arlit (Agadez Region) because of difficulties in going there, the very low population density, and collective housing. The portion of the population excluded from the sample represents less than 0.4% of the total population of Niger. Of a total of 36,000 people not included in the sample design, about 29,000 live in Arlit and 7,000 in collective housing.

The sample was chosen through a random two stage process:

• In the first stage a certain number of Enumeration Areas (known as Zones de Dénombrement or ZDs) will be selected with Probability Proportional to Size (PPS) using the 2001 General Census of Population and Housing as the base for the sample, and the number of households as a measure of size.
• In the second stage, 12 or 18 households will be selected with equal probability in each urban or rural ZD respectively. The base for the sample will be an exhaustive listing of households that will be done before the start of the survey.

The total estimated size of the sample is 4,074 households. The fact that this is the first survey with panel households to be revisited in the future was taken into account in the design and therefore it is possible to lose households between the two surveys with minimal adverse effects on the analyses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire comprises 13 sections, not including the cover page which covers information of a general nature (identity, name of household head) and Section 0 which covers detailed information on household identification and the results of the survey. The second visit household questionnaire is a reduced version of the version used in the first round. It includes information to determine if members who were in the household in the first visit are still in the household and if there are any new members. When there are new members, the questionnaire is used to collect basic information on their socio-demographic.

Like the household questionnaire, the agriculture/livestock questionnaire is divided into sections and sub-sections. The different sections, numbering 8 in all, address the issues of access to land, rainy season agriculture, "contre-saison" agriculture (dry season), livestock, forestry, agricultural equipment, access to agricultural extension services, and climate change. The agriculture and livestock questionnaire, second visit, collects information on harvests from the recently completed season and information on livestock rearing and production. In addition, information was collected on tree crops, agricultural extension, and climate change.

The community questionnaire has 7 sections. In addition, the cover pages collects general information (identification information, etc.) and section 0 provides the names of the respondents. In the second visit, the community questionnaire was used only to collect local prices.

Cleaning operations

The data entry was done in the field simultaneously with the data collection. Each data collection team included a data entry operator who key entered the data soon after it was collected. The data entry program was designed in CSPro, a data entry package developed by the US Census Bureau. This program allows three types of data checks: (1) range checks; (2) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data entry from the first passage was completed in September 2011 and data cleaning was completed in December. The data cleaning process took longer than expected because it was done simultaneously with preparing for the second visit. Data entry from the second visit was completed in January 2012 and the data cleaning for both rounds was completed in August 2012.

Household surveys:

Subnational information from different available household surveys of farmers and smallholders in developing and emerging countries.

Household data provides an overview on farmer households livelihoods, decisions, constraints, among other dimensions. One of the main purposes of this suite of data is to provide farmer information disaggregated at different subnational levels, as well as georeferenced information, when available.

The surveys available in this section provide information divided in ten main dimensions: Production, Consumption, Income, Capital, Inputs, Access to markets, Labor, Technology adoption, Infrastructure, and Social.

Currently available is the Data Portrait of Small Family Farms, more will be added.

Data Portrait:

The Data Portrait of Small Family Farms is a project developed by FAO with the objective to set the ground for a standardized definition of smallholders across countries as well as provide consistent measures of inputs, production, sociodemographic characteristics of smallholder farmers across the world. It generates an image on how small family farmers in developing and emerging countries live their lives, putting in numbers the constraints they face, and the choices they make so that policies can be informed by evidence to meet the challenge of agricultural development.

The Data Portrait of Small Family Farms makes use of household surveys developed by national statistical offices in conjunction with the World Bank as part of its Living Standards Measurement Study (LSMS).

The Data Portrait of Small Family Farms collected data for 19 countries across the world, and for some of them data was reported for more than one round, resulting in a total of 29 surveys. The following table shows the sources of the data. Country and year available information is also presented. Find the link to the table here

Abstract

The survey on Land and Livestock holdings of Households and Situation Assessment of Agricultural Households using an integrated schedule will be conducted in the rural areas of the country. The survey is aimed at generating different indicators of ownership and operational holdings of rural households, including their ownership of livestock and various estimates related to the situation of agricultural households such as indicators of (i) economic well-being as measured by their consumption expenditure, income, productive assets and indebtedness, (ii) their farming practices and (iii) awareness and access to various technological developments and welfare schemes in the field of agriculture. The survey will collect detailed information on receipts and expenditure of the agricultural households’ farm and non-farm businesses and receipts from all other economic activities pursued by their members so as to arrive at an estimate of average monthly income per agricultural household.

Geographic coverage

The survey will cover the whole of the Indian Union except the villages in Andaman and Nicobar Islands which are difficult to access.

In 2023, the number of commercial farm households operating in the agricultural sector in Japan amounted to around 1.03 million. The figure decreased continuously since 2015, when the number of commercial farm households stood at around 1.33 million.  Japan's agricultural work force is shrinking Japan's aging population and the low birth rate have produced a labor shortage in many industries. As the age of people who work in the farming industry increased significantly over the past decades, the overall number of people engaged in farming keeps declining. The farming industry is further dominated by male farmers, while female farmers represent a minority.  Farming industry in Japan Even though only about 20 percent of the mountainous archipelago is suitable for cultivation, the Japanese farmland is highly cultivated. Vegetables and rice generate the highest output within the farming industry, while livestock farming only plays a minor role. As Japan's self-sufficiency ratio remains low, the country is dependent on agricultural imports to feed the nation.

Abstract

The survey on Land and Livestock holdings of Households and Situation Assessment of Agricultural Households using an integrated schedule will be conducted in the rural areas of the country. The survey is aimed at generating different indicators of ownership and operational holdings of rural households, including their ownership of livestock and various estimates related to the situation of agricultural households such as indicators of (i) economic well-being as measured by their consumption expenditure, income, productive assets and indebtedness, (ii) their farming practices and (iii) awareness and access to various technological developments and welfare schemes in the field of agriculture. The survey will collect detailed information on receipts and expenditure of the agricultural households’ farm and non-farm businesses and receipts from all other economic activities pursued by their members so as to arrive at an estimate of average monthly income per agricultural household.

Geographic coverage

The survey will cover the whole of the Indian Union except the villages in Andaman and Nicobar Islands which are difficult to access.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

All the States and Union Territories except Andaman & Nicobar Islands, Dadra & Nagar Haveli and Lakshadweep participated. Formation of sub-units (SUs) are the following:

Rural areas: A rural village is notionally divided into a number of sub-units (SU) of more or less equal population during the preparation of frame. Census 2011 population of villages was projected by applying suitable growth rates and the number of SUs formed in a village was determined apriori.

The above procedure of SU formation was implemented in the villages with population more than or equal to 1000 as per Census 2011. In the remaining villages, no SU was formed.

Urban areas: SUs were formed in urban sector also. The procedure was similar to that adopted in rural areas except that SUs were formed on the basis of households in the UFS frame instead of population, since UFS frame does not have population. Each UFS block with number of households more than or equal to 250 was divided into a number of SUs. In the remaining UFS blocks, no SU was formed.

Outline of sample design: A stratified two stage design has been adopted for the 77th round survey. The first stage units (FSU) are villages/UFS blocks/sub-units (SUs) as per the situation. The ultimate stage units (USU) are households in both the sectors.

Mode of data collection

Face-to-face [f2f]

Abstract

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

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

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

Geographic coverage

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

Analysis unit

Agricultural Households.

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

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

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

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

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

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

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

The questionnaire was structured into the following sections:

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

Cleaning operations

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

Sampling error estimates

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

About this dataset

Context

The dataset presents median household incomes for various household sizes in Farming Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Farming township did not include 7-person households. Across the different household sizes in Farming township the mean income is $90,391, and the standard deviation is $24,751. The coefficient of variation (CV) is 27.38%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $48,641. It then further increased to $95,255 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/farming-township-mn-median-household-income-by-household-size.jpeg" alt="Farming Township, Minnesota median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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/.

Recommended for further research

This dataset is a part of the main dataset for Farming township median household income. You can refer the same here

Abstract

The study includes a merged core data file from the 7 country RuralStruc surveys conducted in 2007-2008.

Geographic coverage

Areas covered in the data are selected rural areas in the following regions:

• in Kenya: Bungoma, Nakuru North, Nyando

• in Madagascar: Alaotra, Antsirabe, Itasy, Morondava

• in Mali: Diema, Koutiala, Macina, Tominian

• in Mexico: Tequisquiapan (Queretaro), Sotavento (Veracruz)

• in Morocco: Chaouia, Saiss, Souss

• in Nicaragua: El Cua, El Viejo, La Libertad, Muy Muy, Terrabona

• in Senegal: Casamance, Mekhe, Nioro, Senegal River Delta.

For more detailed information on geographic coverage, data users can refer to the RuralStruc National Reports.

Analysis unit

The basic unit of observation and analysis that the study describes is the rural household, with the exception of Mali.The preference for rural and not only farm households was justified by the objective of identifying more precisely agriculture's role with respect to other rural activities and sources of income. This option was not neutral, as it refers to analytical categories whose definition are more complicated than one may believe a priori, like the definition of what “rural” is, its characterization varying between countries. The Program National teams considered the following definitions for rural housholds:

-Kenya: "The household was defined as a family living together, eating together, and making farming and other household decisions as a unit"'

-Madagascar :" Le ménage est un ensemble de personnes avec ou sans lien de parenté, vivant sous le même toit ou dans la même concession, prenant leur repas ensemble ou par petits groupes, mettant une partie ou la totalité de leurs revenus en commun pour la bonne marche du groupe, et dépendant du point de vue des dépenses d'une même autorité appelée chef de ménage », le chef de ménage étant la personne reconnue comme tel par l’ensemble des membres du ménage".

-Mali : "La Loi d’Orientation Agricole (LOA), dans ses articles 10 à 28, définit ce que sont les exploitations agricoles au Mali. « L’exploitation agricole est une unité de production dans laquelle l’exploitant et/ou ses associés mettent en oeuvre un système de production agricole. Elles sont classées en deux catégories : l’exploitation agricole familiale et l’entreprise agricole. L’exploitation agricole familiale est constituée d’un ou de plusieurs membres unis librement par des liens de parenté ou des us et coutumes et exploitant en commun les facteurs de production en vue de générer des ressources sous la direction d’un des membres, désigné chef d’exploitation, qu’il soit de sexe masculin ou féminin. Le chef d’exploitation assure la maîtrise d’oeuvre et veille à l’exploitation optimale des facteurs de production. Il exerce cette activité à titre principal et représente l’exploitation dans tous les actes de la vie civile. Sont reconnus comme exerçant un métier Agricole, notamment, les agriculteurs, éleveurs, pêcheurs, exploitants forestiers".

-Maroc : "L’unité ménage renvoie au groupe domestique qui est défini comme une unité de résidence, de production et de consommation. Le plus souvent, le groupe domestique a pour noyau une famille, à laquelle peuvent s’ajouter des parents éloignés ou des « étrangers ». Il peut aussi se composer de plusieurs familles nucléaires comme il peut rassembler des personnes sans aucun lien de parenté".

-Mexico : "El Instituto Nacional de Estadística Geografía e Informática (INEGI) usa el concepto de localidad que define como “todo lugar ocupado por una vivienda o conjunto de viviendas, de las cuales al menos una está habitada. El lugar es reconocido comúnmente por un nombre dado por la ley o la costumbre”, y por otro considera que una localidad es rural cuando tiene menos de 2 500 habitantes. El INEGI define también en concepto de hogar como una “unidad doméstica [que] hace referencia a una organización estructurada a partir de lazos o redes sociales establecidas entre personas unidas o no por relaciones de parentesco, que comparten una misma vivienda y organizan en común la reproducción de la vida cotidiana a partir de un presupuesto común para la alimentación, independientemente de que se dividan otros gastos”.

-Nicaragua : "Se define hogar como el número de personas comparten una olla común. Un hogar puede estar compuesto de una o más familias. La definición oficial en Nicaragua de rural es aquel territorio que “comprenden los poblados de menos de 1000 habitantes que no reúnen las condiciones urbanísticas mínimas indicadas y la población dispersa.” INEC, 2007".

-Senegal : "Le rural se définit par opposition à l’urbain, constitué par les villes et les communes, même à dominance rurale. Au Sénégal, les populations d’une commune sont de facto considérées comme des urbains ; or, plusieurs communes sont composées à plus de la moitié par des agriculteurs. Le ménage rural se définit comme un groupe familial résidant en milieu rural au sein duquel s’organisent la production agricole et/ou non agricole, la préparation et la consommation des repas. Traditionnellement, le ménage rural se confond avec le ménage agricole ; toutefois, on note de plus en plus que la nourriture du ménage rural provient de moins en moins de la production ou des revenus tirés de l’agriculture au sens large : production agricole, élevage, pêche et foresterie. L’unité familiale de production et de consommation16 ne coïncide pas forcément avec l’unité de résidence, ker en wolof ou galle en pulaar".

For detailed information on the rationale corresponding to the definition of rural households, the data users can refer to the National Reports, available as External Resources.

Universe

The universe covered by the study includes rural households and all household members that were selected in the study areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

With the objective of 300 to 400 surveyed households per region (i.e. between 900 and 1,200 surveys per country),the Program National teams engaged in the sampling process in two steps. The first step was the selection of the localities to be surveyed, with consideration of regions' characteristics and national team expertise. The second step was the sampling itself, which was based on existing census lists or intentionally prepared locality household lists. Then, households were selected at random, targeting a sufficient number of households per locality allowing representativeness at local level.

In the seven countries, 8,061 rural households' surveys were selected for the sample in 26 regions and 167 localities (depending on the settlement structure), and 7,269 were successfully interviewed and kept for the analysis. In Mali, the 634 household surveys (at the family farm level) were completed by 643 interviews with dependent households and 749 interviews with women.

Mode of data collection

Face-to-face [f2f]

Research instrument

The merged dataset was constructed from variables extracted from national datasets.

For details on questions relating to these variables, see the attached questionnaires for each country survey. Each country questionnaire was derived and adapted from a questionnaire template which was designed collectively by the RuralStruc Program Coordination team and the national teams.

The original page and question numbers for each variable is included in the variable descriptions.

Cleaning operations

Secondary editing of the data in this core dataset included:

(i) Data in local currency units (for example, incomes, prices, sales of agricultural products) were converted to international dollars ($ PPP), for comparability across national surveys. Purchasing Power Parity conversion rates were calculated using the World Bank Development Data Platform. They refer to the period January 2007 to April 2008. The conversion rates between $1 PPP and local currency units are the following: - Kenya: 34 Kenyan Shilling - Madagascar: 758.7 Ariary - Mali: 239.6 CFA Franc - Mexico: 7.3 Mexican Peso - Morocco: 4.8 Dirham - Nicaragua: 6.7 Cordoba - Senegal: 258.6 CFA Franc

(ii) Data in local currency units were converted into kilo-calories, for comparability across national surveys. In all the studied zones, diets rely primarily on cereals - at least in terms of energy. Thus, the basic cereal of each zone (or basket of cereals in the case of Mali) was used as a reference. The conversion rates between Kg of cereals and Kcal are those provided by the FAO's Food Balance Sheets (FAO 2001). The prices of cereals are those used by the RuralStruc national teams to estimate the value of self-consumption. These prices correspond with the average producer sale prices (or the median in the case of Madagascar) for the surveyed year. One will note that, in general, the farm income for the poorest households largely consists of self-consumption of cereals, which are valued, therefore, at the producer sale price. The average cereal prices and kilocalorie ratios permitted calculation of a price for units of 1000 Kcal in $PPP and then to convert the estimated monetary incomes in incomes in kilocalories equivalent. For detailed information, data users can refer to the methodological annex of the synthesis report.

(iii) Recoding of the geographical component of the household identifier

For more details on data editing, the data user should refer to the variable descriptions.

In 2023, the population of farm households in South Korea amounted to around **** million, continuing the steady decrease. In South Korea, the number of agricultural households across farming, forestry, and fishery sectors is steadily decreasing and the aging of agricultural households is also an ongoing issue.

Abstract

Afrint intensification of food crops agriculture in sub-Saharan Africa Swedish-African Research Network Agricultural development and its relation to food security and poverty alleviation Primary research in nine sub-Saharan African countries. Afrint - three phases 200I-2016.

Afrint I - 2001-2005: The African Food Crisis - the Relevance of Asian Experiences

Afrint II - 2007-2010:The Millennium Development Goals and the African Food Crisis

Geographic coverage

Sub-Saharan Africa, (Ethiopia, Ghana, Keny, Malawi, Nigeria, Tanzania, Uganda, Zambia) Regions within selected countries

Analysis unit

Household

Universe

Farming Household

Kind of data

Aggregate data [agg]

Sampling procedure

Data collection for the first round of the Afrint project was made in 2002. The data collected as part of the second round are referred to as 2008 data, although in some cases collected in late 2007. From the outset the research team selected five case study countries: Ghana, Kenya, Malawi, Nigeria and Tanzania. Outside francophone Africa, these five countries were ideally suited, in the researchers' view, to charting progress in intensification, induced from below by farmers themselves, or state induced, as in the Asian Green Revolution. At the insistence of Sida, to the original five countries, four more were added: Ethiopia, Mozambique, Uganda and Zambia. Unlike the original five, the three last mentioned countries were deemed less constrained with respect to productive resources in agriculture. Ethiopia on the other hand is peculiar in an African context, with its long history of plough agriculture, and feudal-like social formation. In this project, the heterogeneous sample of countries has proved less cumbersome to work with than one might have expected.

Formally, the Afrint sample was drawn in four stages, of which the country selection described above was the first one. The next stage was regions within countries, followed by selection of villages within regions, and with selection of farm households as the last stage. All stages except the final one have been based on purposive sampling. Data collection was sought to be made at all four levels.The households sampled within these countries were selected with respect to the agricultural potential of the areas in which they reside.The intention was to capture the dynamism in the areas that are 'above average' in terms of ecological and market (infrastructure) endowments but excluding the most extreme cases in this regard.For logistical reasons we could not aim for a sample which is representative in a statistical sense. Instead we aimed at a sample which is illustrative of conditions in the maize-cassava belt, excluding both lowpotential dry and remote areas and extreme outliers at the other end of the scale.

Thus we used a four-stage sample design, with purposive sampling at all stages, except the last one, where households were sampled after having made up household lists. When we compare point estimates from the sample with those from other sources, for examples yields for the various crops with FAO statistics, no apparent sample bias has been detected.

In addition to household questionnaires we also used village questionnaires. Respondents to village interviews were key persons, like villageleaders and extension agents. Investigators were also instructed to conduct focus group interviews with representatives for various segments of the village population, including women farmers. When going for a second round and a panel in 2008, we went for a balanced panel design, i.e. constructing the 2008 sample so that in itself it would be representative of village populations in 2008. This also involved sampling descendants when a household had been partitioned since 2002. In case of sizeable in-migration to a village, we also provided for sampling from the newly arrived households. The 2002-2008 panel thus is a subset of the two cross sectional samples. In itself this subset is not statistically representative of the village population in any of the two years

Sampling deviation

20.6 Percent

Mode of data collection

Face-to-face [f2f]

Research instrument

Scope of Surey Round I (2001-2005)

Household demographic and socio-economic characteristics Farm and crop management Maize Cassava Cassava, marketing conditions Sorghum Rice Other food crops and vegetables Non-food cash crops Land resources Livestock Labour resources Institutional conditions Incomes and expenditures

Scope of survey II Household Demographic and Socio-Economic Characteristics Farm and Crop Management Crops Maize Cassava
Sorghum Rice
Rural - Urban and Rural - Rural Linkages (staple crops) Other food crops and vegetables (for local markets) Non-food cash crops (wholly or partly for export) Agricultural Techniques Land resources Livestock & Fish Livestock

Cleaning operations

No edting specification given

Response rate

79.4 Percent

Sampling error estimates

No sampling error estimates given

Data appraisal

No other froms of appraisal given.