This survey is conducted by Michigan State University (MSU) in collaboration with University of Ibadan and ABU. The survey is to study the maize farm segment of the poultry value chain in order to make recommendations to policymakers and the private sector.

Abstract

The Sri Lanka Paddy Farmers Survey 2022-2024 collects panel data on paddy farmers in Sri Lanka, covering comprehensive information on demographics, risk management strategies, agricultural production, market engagement, and food security.

Geographic coverage

Rural Areas in Sri Lanka

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

This survey includes 2200 paddy farmers enrolled in 220 farmer organizations in Sri Lanka. The sample is nationally representative of the paddy farmers enrolled in the farmer organizations (more than 90% of the total paddy farmers in the country). The sampling strategy is a multi-stage stratified cluster sampling: - First Stage: The total of 25 districts in Sri Lanka are stratified based on agro-ecological zones and randomly sampled with a probability proportional to the share of land devoted to paddy fields over the total extent of the district. - Second Stage: Within each of the 10 sampled districts, farmer organizations are stratified based on irrigation scheme, and then 220 are randomly selected. - Third Stage: For each farmer organization, paddy farmers are randomly selected with the sampling weight proportional to the number of farmers in each farmer organization. The final stage produces 2200 households in total.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data collection process for the Sri Lanka Paddy Farmers Survey 2022-2024 involved a comprehensive Household Survey Questionnaire, meticulously crafted by the Environmental and Water Resources Management Division of HARTI in collaboration with FAO. Drawing from previous surveys, the questionnaire was specifically tailored to meet the project's objectives. The design process included stakeholder consultations, ensuring that all relevant topics, such as household demographics, land use, crop management, and income, were comprehensively addressed.

Cleaning operations

The microdata files for the Sri Lanka Paddy Farmers Survey 2022-2024 were carefully processed to ensure they met several critical data quality and privacy standards. Specifically, the files were stripped of any variables that could directly identify a data subject, such as names, phone numbers, ID numbers, addresses, or geo-references. Sensitive information that could potentially cause harm, such as HIV status, was also excluded. All categorical variables were properly labeled, and missing values were clearly coded and labeled. Each dataset included a unique identifier or a combination of variables that uniquely identified every record. Numerical variables were checked to ensure they fell within realistic thresholds, and any variables with all missing values were removed. Additionally, if the dataset had a hierarchical structure, the relationships between datasets were clarified with unique identification variables to facilitate accurate data merging. If any weighting factors were required but missing, an explanation was provided along with guidance on the appropriate use of the dataset.

The STRIVE project, funded by USAID's Displaced Children and Orphans Fund (DCOF) and managed by FHI 360, used market-led economic strengthening initiatives to improve the well-being of vulnerable children. Through STRIVE, ACDI/VOCA implemented the Agriculture for Children’s Empowerment (ACE) Project in Liberia, which is founded on the premise that increased household economic security will stimulate more consistent investments in children’s well being via longer term social investments in education and nutrition. ACE’s primary focus was on the horticulture value chain (VC) — the production and marketing of vegetables by smallholder farmers in Montserrado, Bong, and Nimba counties of Liberia. ACE also strengthened smallholder rice farming to increase household food security using a market-sensitive approach to rice seed lending and cultivation. This dataset contains endline information about each plot the household owns.

This table contains 664 series, with data for years 2009 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (8 items: Canada; Atlantic provinces; Quebec; Ontario; ...) Statistical variables (2 items: Total, all farms; Average per farm) Agriculture balance sheets, revenue and expenses (42 items: Number of farms; Total assets; Current assets; Cash and short term investments, current assets; ...).

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

This date gives the results of the Farmer Intentions Survey for England. This data covered farmers’ aspirations and plans for the future towards the whole business and for individual enterprises, the strength of these intentions and the reasons behind them, as well as any innovations adopted on farm.

Source agency: Environment, Food and Rural Affairs Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Farm Business Survey Farmers Intentions

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

The web survey was completed in 2017 by over 900 hosts, implementing staff and volunteers engaged in the current Farmer-to-Farmer program cycle (FY14-FY18). The sample for this web survey was created in collaboration with the program implementation team, as no central database of contacts existed. In total, over 1,800 contacts were shared by the implementing partners. In order to receive the web survey, individuals were required to have a unique and valid email address. In the event that an email address was shared by more than one individual, only one copy of the web survey was distributed so as to prevent any individual from responding more than once. The web survey was available to respondents in English, French and Spanish and the data was collected to supplement a program evaluation of the project.

Data collected from the French farmers’ survey with in addition to the pan-european questions, the choice experiment data. The objective is to better understand their expectations for the implementation of payments for environmental services contracts (PES). The CE was included as a section of a pan-EU survey on the acceptability of agri-environmental-climate contract solutions, conducted in spring 2021 among 130 farmers located in Brittany, Normandy and Pays de La Loire. Voluntary farmers were contacted to organize a face-to-face interview after being recommended by intermediaries (farmers union, organizations of milk producers, farmers associations…). The first section of the survey includes general information on farmer and farm characteristics, and the second section on the impact of contract characteristics on the willingness to adopt contractual solutions. In particular, farmers were asked to state from a scale from 1 to 5 how much would the possibility to collectively agree on environmental targets and measures at landscape-level together with other land managers, and to receive a common payment to be distributed among participants, increase or decrease their willingness to enroll? In this third section dedicated to the CE, respondents were introduced to the context, objective and rules of the game of the CE, and to the contract parameters (those fixed and those varying from one alternative to another). Preliminary questions were included to help the respondents estimating their current levels of management requirements (individual status-quo). The current soil cover duration was calculated from the stated hectares of permanent grasslands, arable crops, permanent crops and total utilised agricultural area (UAA), as well as the average number of days with bare soil on arable lands and proportion of grass cover on the permanent crops surfaces. The current hedgerows density was calculated from the total UAA and total meters of multispecies multilayer hedgerows currently present on the farmland. Farmers were then asked 9 times to choose the preferred option among 2 contract alternatives and the status-quo.

Abstract

Beyond causing immediate hardship and triggering a large exodus of displaced people, Russia’s military invasion of Ukraine and the blockade of its Black Sea export routes have also led to sharp increases in grain prices and raised concern about global food security. To provide information to the government for developing policies and programs to support the agricultural sector in Ukraine, the World Bank launched a nationwide survey of post-invasion farmers in cooperation with the Ministry of Agricultural Policy and Food (MAPF), with financial support from the EU, in areas controlled by Ukraine from October to December of 2022. The survey objective is to obtain information on changes in welfare, production, and productivity in the small and medium farm sector between 2021 and 2022 and to identify ways on how farmers could be most effectively supported. Data was collected via phone by the Kyiv International Institute of Sociology (KIIS) under the monitoring of World Bank research team.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame consisted of 63,374 registered farms. The distribution of farms by size and program participation shows that most of the farms are small (85%) with farm size less than 50 ha (35,264 PSG non-applicants vs. 18,605 PSG applicants) followed by farms with 50-120 ha (7.5% with 1,634 PSG non-applicants and 3,107 PSG applicants) and farms that are not eligible for PSG participation with size greater than 120 ha (7.5% with 2,743 less than 500 ha and 2,021 greater than 500 ha).

The expected sample size for the phone survey was 2,500 farms with 10% each in the small size category from PSG applicants and non-applicants, 20% each in the farm category of 50-120 ha from PSG applicants and non-applicants, 20% from the farm size category of 120-500 ha and 20% from the farm size category of greater than 500 ha. Given the expected high non-response rate of phone interviews, all the farms with size greater than 50 ha were included in the sample and then 1,125 and 1,126 farms were randomly selected from PSG non-participants and participants in the less than 50 ha category. The final response rate was about 20% with the lowest in the greater than 500 ha category (15%) and the highest in the 50-120 ha PSG non-applicant category (28%). The survey initially targets 2, 500 farms, and eventually collected data for 2, 251 farms.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire comprised four sections namely: • Screener & Background • Household Roster • Agricultural Production • Property and Finance

Cleaning operations

Data have been collected electronically. Survey logic has been incorporated into the instrument. After data collection, mainly general data completeness and outliers have been checked. Also, all text responses to open-ended questions have been analyzed and coded if necessary.

The final data file contains data from 2,251 interviews. It was provided to the World Bank team in SPSS formats.

Response rate

The overall and cooperation response rates were 21.3% and 37.3% respectively.

This study contains yield data of the two crops from tow representative sections of a field on plots 10m by 10m. Data taken on each of the plots included agronomic practices undertaken including plant spacing, pest and disease control, organic/inorganic fertilizer applications and field history according to a protocol implemented in AfSIS. Each field was geo-referenced. About the project Project title: Identification of the Key Biophysical Production Constraints to Crops and Livestock at Farm and Landscape Levels Project abstract This project will undertake soil survey to characterize two sentinel sites (Long and Matufa) and agronomic survey to estimate farmers' actual yield. Project website: http://africa-rising.net Project start date: 01/11/2012 Project end date : 01/10/2013</p Citation APA Harvard MLA Vancouver Chicago IEEE CSE AMA NLM Turabian

Survey data collected from Smartphone farmers.

In order to better understand how the FARM II project reached farmer-based organizations (FBOs) and farmers across the Greenbelt region and how the project may have influenced beneficiaries to use improved technologies or management practices, FARM II conducted two process evaluations in March 2016. The evaluations were conducted in 28 payams across eight counties of Central, Eastern, and Western Equatoria states. This first evaluation, the FBO survey, assessed the technical and managerial capacity of elected leaders of farmer-based organizations that received assistance from the FARM II project. The second evaluation, the farmer survey, measured knowledge, attitudes, and the application of improved technologies and management practices by targeted farmer beneficiaries.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

The 2019/20 and 2020/21 FBS survey samples are slightly smaller than normal, as Covid-19 restrictions impacted data collection.

For the second edition (December 2022) data for sections F3, K, N and R have been added to the study, and the documentation has been updated.

The Survey of Small Business and Farm Lending (SSBFL) is currently comprised of the Survey of Terms of Bank Lending to Farmers (FR 2028B), Prime Rate Supplement of the Survey of Terms of Lending (FR 2028S), and the Small Business Lending Survey (FR 2028D). The FR 2028B collects information on farm loans made by commercial banks during a representative week. The collected data include price and nonprice terms. The respondents provide information on the stated rate of interest on the loan and the frequency with which interest is compounded, and other important loan terms, including loan size, commitment status, maturity, collateralization, the purpose of the loan and loan risk ratings. The FR 2028S, a companion report, collects institutions' prime interest rate for the days reported. The FR 2028D collects information on the availability and cost of loans to small businesses from domestically chartered commercial banks during each quarter. The survey provides unique quantitative and qualitative information on small business commercial and industrial (C&I) loans, including amounts, interest rates, terms, bank credit standards, applications and quality of applicants, and loan demand. The Survey of Terms of Business Lending (FR 2028A) has been discontinued. The final data collection for the FR 2028A was for the May 2017 survey week.

Abstract

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

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

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

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

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

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

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

Southwest Michigan Farmers were surveyed about their perception and responses to climate...