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

Abstract

The main purpose of the Survey of Agricultural Holdings is to produce official indicators in line with agricultural sector. The survey allows the compilation of statistics on crops and animal husbandry, of which information annual and permanent crops, sown area, average yield of annual crops and etc. Statistical tables are accessible through the following link: https:// www.geostat.ge/en/modules/categories/196/agriculture. One round of the survey (reference year) includes 5 inquiries: The Inception interview is carried out using the inception questionnaire during the period of January-February of the reference year. During this interview the sampled holdings are identified and situation existing at the holding as of first January is recorded. I, II and III quarter interviews are conducted by means of quarterly questionnaire at the beginning of the following month of the corresponding quarter of the reference year. Based on these surveys, the information about agricultural activities during the corresponding quarter is collected. The final interview is conducted by means of final questionnaire in January of the following year of the reference year. During this interview, the information about agricultural activities at the holding during IV quarter of the reference year and the summery information about agricultural activities at the holding during the whole reference year (from 1 January to 31 December of the previous year) are collected. During all five interviews, the same agricultural holdings (about 12 000) are interviewed which are selected by a two-stage stratified cluster random sampling procedure out of about 642 000 agricultural holdings operated in Georgia. On the first stage, clusters (settlements) are selected. On the second stage, holdings are selected within the selected clusters.

The survey completely covers the territory of Georgia, excluding the occupied territories of Autonomous Republic of Abkhazia and Tskhinvali region. Each year a new sample is selected based on a rotational design (on a 3-year basis). In particular, every year approximately 4 000 holdings out of the 12 000 sampled holdings are replaced by new holdings. Sampled holdings participate in the survey for 3 years. Large agricultural holdings are sampled every year with complete coverage. The statistical unit of the survey is the agricultural holding (family holdings and agricultural enterprises) - which is defined as an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size. Agricultural activities are conducted under the supervision of a holder (in case of households - a member of household, in case of agricultural enterprises - director or authorized person), who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities. More than 270 interviewers participate in the survey fieldwork. For the Data collection, computer-assisted personal interviewing method (CAPI) is used in the family holdings. In case of agricultural enterprises, the authorized persons of the enterprises (respondent) fill the electronic (online) questionnaires by themselves (CAWI). Coordination of the interviewers and the primary control of the collected data during the field is carried out by coordinators. Their working area covers several municipalities. The function of the coordinators also includes consultation for agricultural enterprises on methodological and technical issues related to the survey.

Geographic coverage

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

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

Cleaning operations

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

Response rate

In the 2021 fourth quarter, 963 holdings were not responded to due to refusing to be interviewed or would not be found during the fieldwork despite its existence. It is about 7.7% of the total Sampled holdings 12,436 holdings involved in the sample 2021 fourth quarter.

Abstract

The Sierra Leone Annual Agricultural Survey (SLAASS 2023) is a key component of Stats SL's efforts to provide up-to-date information on the agricultural sector. The 2023 SLAASS builds upon the successes of previous surveys and aligns with the best international practices. The primary objective of the SLAASS was to collect comprehensive data on crop and livestock production, as well as other relevant agricultural indicators. This information is essential for policymakers, researchers, and other stakeholders to assess the performance of the agricultural sector, identify opportunities for improvement, and inform evidence-based interventions. Specifically, it involved: · Collection of timely data on agricultural production and productivity at both national regional and district levels; · Gathering core data to help develop and review agricultural policies and to guide the implementation of agricultural plans at national and regional levels between agricultural sub-sectors; · Compilation of fundamental statistics that facilitate comparisons in the development of the agriculture sector across the country.

Geographic coverage

National coverage, with the exception of the Western Urban district.

Analysis unit

Agricultural households

Universe

Households involved in agricultural production and livestock rearing, in all the fifteen agricultural districts of the country, were considered for this study.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a stratified random sampling technique to ensure a representative sample of agricultural households across all five regions and fifteen districts of Sierra Leone with the exception of the Western Urban district. A two-stage sampling method was employed to select households.Both stages of sampling employed probabilistic methods.

The country was divided into districts and within each district, areas called Enumeration Areas (EAs) were identified. A sample of EAs was then selected, followed by a sample of agricultural households (Ag HHs) within each chosen EA. The total number of EAs selected for the survey was 520, with 5,200 households interviewed in total. For each EA, the field team had a list of 10 households.

The survey included households engaged in crop cultivation and/or livestock rearing, regardless of the scale of their operations. However, it did not cover non-household holdings, such as large-scale commercial farms, or sectors like aquaculture, forestry, and fisheries.

The survey generated national, regional, and sub-regional estimates.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

For this survey, two questionnaires were used: the Post Planting (PP) questionnaire and the Post Harvesting (PH) questionnaire. They were administered in each household, preferably to the head of household. They cover two modules, the CORE module and the ILP (Income, Labor and Productivity) module, split into several topics such as household demographics, land ownership, agricultural activities, livestock rearing, labor force composition, and participation in off-farm activities.

The questionnaires are provided as external resources.

Cleaning operations

The PP and PH questionnaire were implemented using CAPI with CSPRO. During data collection, some validation controls were integrated into the app to minimize mistakes when typing households’ answers. After data collection, a processing program designed with SPSS software allowed for cleaning both cases and variables. Duplicated cases were deleted and then the sampling weights were adjusted to take the two non-covered EAs into account. Missing, illegal, unlike and incoherent values were detected and then locally imputed objectively in respecting filters. Finally, the necessary tabulation variables were created and then tables were produced according to the tabulation plan designed earlier.

Sampling error estimates

To appreciate the data quality, some tables were supported by sampling errors estimates. Especially, coefficients of variations and standard errors were estimated for a set of indicators for open data publishing purposes.

Time series data for the statistic Agriculture survey (Availability score over 10 years) and country Sierra Leone. Indicator Definition:Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census. These are surveys on agricultural land, production, crops and livestock, aquaculture, labor and cost, and time use. Some issues, such as gender and food security, are of interest to most agriculture surveys.

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

The National Institute of Statistics of Rwanda (NISR) has been conducting an annual agricultural survey since November 2012 for the estimation of the national agricultural crop area and production estimates. In 2019/2020 agricultural year, the NISR conducted the second edition of theUpgraded Seasonal Agricultural Survey (USAS) covering the three agricultural seasons. The USAS incorporated an increased sample size to provide more precise estimates. The USAS allows information for monitoring progress on agriculture programs and policies in Rwanda.

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

This seasonal agriculture survey focused on the following units of analysis: Small scale agricultural farms and large scale farms

Universe

The SAS 2020 targeted potential agricultural land and large scale farmers

Kind of data

Sample survey data [ssd]

Frequency of data collection

Seasonal

Sampling procedure

Out of 5 defined agricultural strata, only dominant hill crop land stratum, dominant wetland crops stratum, dominant rangeland stratum and mixed stratum were considered as land potential for agriculture. The remaining stratum is the non-agricultural land. Note that clusters covered by tea plantations were not considered in the area sample frame due to reasons stated above. Thus, SAS is conducted on 4 above mentioned strata to cover other major crops. In 2020 agricultural year, the sample of segments was increased in order to improve agriculture statistics where sample increased from 780 (sample used from 2018 to 2019) to 1200 segments. At first stage,1200 segments were selected and allocated at district level based on the power allocation approach (Bankier3, 1988). Sampled segments inside each district were distributed among strata with a proportional-to-area criterion.

At second stage, 25 sample points were systematically selected, following a special distance of 60 meters between points. Sample points are reporting units within each segment, where enumerators go to every point, locate and delineate plots in which the sample points fall, and collect records of land use and related information. The recorded information represents the characteristics of the whole segment which are extrapolated to the stratum level and hence the combination of strata within each district provides district area related statistics.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires used for this survey namely screening questionnaire and plot questionnaire. A Screening questionnaire was used to collect information that enabled identification of a plot and its land use using the plot questionnaire. For point-sampling, the plot questionnaire is concerned with the collection of data on characteristics of crop identification, crop production and use of production, inputs (seeds, fertilizers and pesticides), agricultural practices and land tenure. All the surveys questionnaires used were published in English.

Cleaning operations

The CAPI method of data collection allows the enumerators in the field to collect and enter data with their tablets and then synchronize to the server at headquarters where data are received by NISR staff, checked for consistency at NISR and thereafter transmitted to analysts for tabulation using STATA software, and reporting using office Excel and word as well.

Response rate

Data collection was done in 780 segments and 222 large scale farmers holdings for Season A, whereas in Season C data was collected in 232 segments, response rate was 100% of the sample.

Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census. 1 Point if 3 or more surveys done within past 10 years. 0.6 points if 2 surveys done within past 10 years. 0.3 points if 1 survey done within past 10 years. 0 points if none within past 10 years

Abstract

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

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

Geographic coverage

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

Analysis unit

Households and agricultural plots

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Thus, four strata were formed as follows:

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

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

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

Sampling deviation

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

Response rate

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

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

Abstract

The Annual 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 agriculture 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 term.

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 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 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 to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, 1,504 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 SSUs (agricultural households) in each selected EAs. A total sample of 18,048 agricultural holdings 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. 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 Questionnaire is attached as an external resource in the downloads tab.

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 is 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 was 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 was 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/ data 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 chicken 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 by do files (STATA 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 initiatives. - 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 a statistical unit, v) adding noise to continuous

Abstract

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

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

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

Geographic coverage

Communities (in Enumerated Areas).

Analysis unit

Community

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

Cleaning operations

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

Abstract

The main objective of the Pilot Survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The Pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by (FAO). Food and Agriculture Organization of the United Nations recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to Agricultural Census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named "National Agricultural Sample Census" derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding.

The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the Governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the Pilot Survey. The Pilot Survey implementation started with the first level training (Training of Trainers) at the NBS Headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at Headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the Pilot Survey commenced on the 9th July and ended on the 13th of July 07. The CSpro and SPSS were the statistical packages used to develop the data entry programme. The results of the survey are presented in chapter three of this report.

The owner-like possession was the most common system nationwide with a figure of 2,083,503 (holding) followed by family land 962,233 (holding) while squatter was the least system used 40,473 (holding). Distribution of holding by type of land showed that three types of land-upland, lowland and irrigated were mostly used with irrigated land being the highest 5,825,531 holding followed by lowland 5,320,782 holding and upland 3,070,911 holdings with the highest holding within the age group of 25-44 years. In all states, 2,392,725 males were involved in crop farming while 540,070 females were also paticipating. Out of the 11 major crops reported, cassava recorded the highest number of farms 2,649,098 farms, next was maize 2,199,352 and yam 2,042,440 farms while the least was cotton 46,287 farms. Other crops were Beans, Cocoyam, Groundnut, Guinea corn, melon, Millet and Rice.

Geographic coverage

State

Analysis unit

Household based

Universe

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

12 states were purposely selected in the country. 2 states from each of the 6 geo-political zones. 2 LGAs per selected state were studied. 2 Rural EAs per LGA were covered and 5 Housing Units were systematically selected and canvassed for GHS data.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Private Farmers (Holding) is a structured questionnaire based on household characteristics with some modifications and additions. The questionnaire contains the following sections. Holding identification Holding Characteristics Access to Land Access to Credit and Funds Used Production input utilization; quantity and cost Sources of inputs/equipment Area Harvested. Agric Machinery. Production. Farm Expenditure. Processing Facilities. Storage Facilities. Employment in Agric. Farm Expenditure. Sales. Consumption. Market Channels. Livestock Farming. Fish Farming.

Cleaning operations

The data editing is in 2 phases namely manual editing before the questionnaires were scanned. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already scanned data. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise.

The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collated and edited manually

(a) Office editing and coding were done by the editor using visul contro of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

On state basis, 100 percent response rate was acheived at EA level .

While 99.6 percent was recorded at housing units level.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

Abstract

This survey gathered information at the household level about basic agriculture indicators related to demographic household characteristics, farm characteristics, livelihood activities, crop information, livestock production, level of inputs use, agricultural practices, extension services, level of implementation of agricultural programs, the financial aspect of agricultural households, and other agriculture-related indicators. The information on crop production and productivity was not captured here as it is well recorded in the Seasonal Agricultural Survey (SAS) also conducted by NISR. Agriculture statistics are useful for monitoring progress on agriculture programs and policies in Rwanda. The government of Rwanda needs updated information on agricultural household in order to assist in addressing key agricultural issues and information needs that will inform policy makers and other stakeholders and allow more effective identification of priority intervention needs and to facilitate evidence-based decision making for the development of Agriculture sector

Geographic coverage

National coverage

Analysis unit

This seasonal agriculture survey focused on the following units of analysis: Agricultural Households for both household and individual level.

Universe

All household members

Kind of data

Sample survey data [ssd]

Sampling procedure

To ensure a good representation of data at the district level, the survey was conducted using a sample size of 900 Enumeration areas (EAs). A sample frame used was composed of a list of enumeration areas (EAs) retrieved from the 2012 Rwanda General Population and Housing Census (RGPHC-2012). A stratified two-stages sample design was used.

The first stage focused on a stratified sample of enumeration areas from the latest RGPHC-2012. Given that rural areas are dominated by many households practicing agriculture, the frame of EAs was sorted by urban and rural areas within districts. This provides an implicit stratification of the households by urban and rural areas. Out of 900 EAs, 860 were selected from rural EAs while the rest was selected from urban EAs, where there are fewer but not negligible numbers of households practicing agriculture.

To ensure adequate geographical distribution of the sample and given that the results were analysed up to district level, the sample of 860 rural EAs was allocated equally among 30 districts, while a sample of 40 urban EAs was allocated proportionally throughout the country. At this first stage, the sampled EAs in each district were selected systematically with a probability proportional to size (PPS) measured in terms of the total number of households in each EA from the RGPHC-2012.

The second stage looked at a random selection of a fixed number of 12 households who only did agriculture in each sampled EA. 12 households were selected from an updated number of households listed in each sampled EA, in the first phase of AHS 2020 data collection.

It is important to note that there are EAs with less than 12 agricultural households and these have been directly taken as a sample at the second stage. Finally, the sampling process conveyed a total sample of 10,666 agricultural households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed in CSPro software and android tablets were used to facilitate electronic data collection. The survey questionnaire was designed with a common set of core modules on household composition, household members' characteristics, land use and ownership, crops planted during the agricultural year 2019/2020, agriculture extension services, agricultural programs, access to savings and credits, access to inputs, livestock numbers, livestock production (milk, eggs and honey) and other agricultural related information. Moreover, GPS was used for locating sampled households at the same time used as a monitoring tool for field staff.

Context

Abstract: Surveys for more than 9,500 households were conducted in the growing seasons 2002/2003 or 2003/2004 in eleven African countries: Burkina Faso, Cameroon, Ghana, Niger and Senegal in western Africa; Egypt in northern Africa; Ethiopia and Kenya in eastern Africa; South Africa, Zambia and Zimbabwe in southern Africa. Households were chosen randomly in districts that are representative for key agro-climatic zones and farming systems. The data set specifies farming systems characteristics that can help inform about the importance of each system for a country’s agricultural production and its ability to cope with short- and long-term climate changes or extreme weather events. Further it informs about the location of smallholders and vulnerable systems and permits benchmarking agricultural systems characteristics.

Content

The data file contains survey data collected from different families and has 9597 rows that represent the households and 1753 columns with details about the households. The questionnaire was organized into seven sections and respondents were asked to relate the information provided to the previous 12 months’ farming season. There are too many columns to describe here, however they are described in detail in this paper: https://www.nature.com/articles/sdata201620?WT.ec_id=SDATA-201605

• Questionnaire.pdf: This file contains the questionnaire used, a description for each variable name and the question ID.

• SurveyManual.pdf: This file gives further information on the household questionnaire, the research design and surveying. It was produced for the team leaders and interviewers in the World Bank/GEF project.

• AdaptationCoding.pdf: This file describes codes for variables ‘ad711’ to ‘ad7625’ from section VII of the questionnaire on adaptation options.

There is also some description in how the data was collected in Survey.pdf.

Acknowledgements

Waha, Katharina; Zipf, Birgit; Kurukulasuriya, Pradeep; Hassan, Rashid (2016): An agricultural survey for more than 9,500 African households. figshare. https://doi.org/10.6084/m9.figshare.c.1574094

https://www.nature.com/articles/sdata201620?WT.ec_id=SDATA-201605

The original DTA file was converted to CSV

Inspiration

This dataset contains a huge amount of information related to farming households in Africa. Data like these are important for studying the impact of global warming on African agriculture and farming families.

Abstract

Ethiopia's economy is predominantly agrarian and the majority of the population in the country is engaged in agriculture. In this respect, the collection of reliable, comprehensive and timely statistical information on agriculture is very essential. The information is expected to be used for the formulation of agricultural policy. The Central Statistical Authority (CSA) has been conducting Agricultural Sample Surveys on annual basis since 1980-1981 (1973 E.C.) to produce some of the statistical data that can be used in planning and policy making activities. The survey was interrupted in 1992-1993 (1985 E.C.) and 1993-1994 (1986 E.C.) because during these two years the CSA was fully engaged in undertaking the preparatory activities for the 1994 Population and Housing Census. However, after, undertaking the 1994 Population and Housing Census, the annual agricultural survey was resumed in 1994-1995 (1987 E.C.), and also conducted for the year 1995-1996 (1988 E.C.).

The general objective of the Agricultural Sample Survey was: - To estimate the total cultivated land; total production and yield of major crops per hectare; cropland uses (temporary and permanent); quantity and cost of agricultural inputs by type; number of livestock and poultry by type , purpose, sex and age; number of beehives and honey production in the private peasant holdings at national and different reporting levels which are regions or group of zones.

Geographic coverage

The 1995-1996 (1988 E.C.) annual Agricultural Sample Survey was designed to cover sedentary rural agricultural population in all regions of the country except urban and nomadic areas of the country.

Analysis unit

Agricultural household/ Holder/Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design: A two stage stratified sample design was used for the 1995-1996 (1988 E.C) annual Agricultural Sample Survey. In three regions, namely in Amhara, Oromiya and Southern Nationals and Nationalities Peoples' Region, group of contiguous zones were treated as strata/reporting levels of the survey results. In the remaining regions, the reporting levels were the regions themselves. The primary sampling units (PSUs) in all strata were enumeration areas (EAs). Agricultural households were the ultimate sampling units. The survey questionnires were administered to all agricultural holders in the sampled agricultural households. A fixed number of sample EAs was determined for each stratum/reporting level based on precision of estimates, household size of the stratum and cost considerations. The overall sample number of EAs in a stratum was proportionately allocated to zones/special wereds within the stratum to their household size. From within each zones/special weredas sample EAs were selected with probability proportional to size, size being the total number of households of EAs as obtained form the 1994 census map work. From each sample EA, 25 agricultural households were sampled systematically without replacement from a fresh list of agricultural households. All information were collected form these households except for crop cutting exercise, for which data were collected only from the last 15 agricultural households starting from the 11th selected agricultural households. Moreover, holders within these households were enumerated and the required data were collected form these holders.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1995-1996 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 88/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 88/1: Used to list sampled households and agricultural holders. - AgSS Form 88/2: Used to record crop condition. - AgSS Form 88/3A: Used to list fields and agricultural practices for, pure stand crops and mixed crops; list of permanent crops and number of tress. - AgSS Form 88/3B: Used to record quantity of improved seed, fertilizers and crop protection chemicals and price. - AgSS Form 88/4: Used to record results of area measurements. - AgSS Form 88/5: Used to list fields and selection of fields for crop cutting and details of record of crop cutting.

Note: The questionnaires are presented in the Appendix I of the 1995-1996 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

After the completion of the fieldwork the filled-in questionnaires were retrieved from the branch statistical offices for data processing. The first stage of data processing activity was begun by training data editors and coders at the head office by subject matter department professional staffs. About 55 editors-coders and 8 verifiers took part in the manual editing, coding and verification activities, which lasted for about a month. Edited and coded questionnaires then captured into computer using data entry program which was develop by data processing department. The data entry activities took about 15 days using 28 computers and as many data encoders. Computer data cleaning, attaching weighting coefficients to the data and tabulation activities were carried out procedurally by the professional staff from involved departments at the head office. The Integrated Microcomputer Processing System (IMPS) software was used for data entry, consistency checks and tabulation of survey results.

Response rate

A total of 620 enumeration areas (1.1% of the total agricultural EAs) were selected to be covered in all regions. Nevertheless, 8 of them were closed due to various reasons and the survey succeeded to cover only 612 enumeration areas (EAs). The response rate of enumeration area was 98.71%.

Sampling error estimates

Estimation procedures of total, ratio and sampling error is given in Appendix V of the 1995-1996 annual Agricultural Sample Survey, Volume I report which is provided as external resource.

Abstract

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

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

Geographic coverage

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

Analysis unit

Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

Response rate

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

The December survey of Agriculture provides information and evidence on the condition of the agricultural industry in England. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: December Survey of Agriculture, England If you require the data in a more accessible format, please contact farming-statistics@defra.gsi.gov.uk Commercial holdings are defined as those that exceed at least one of the thresholds detailed below. Thresholds for the EU Farm Structure Survey Characteristics Threshold Utilised agricultural area Arable land, kitchen gardens, permanent grassland, permanent crops >5 ha Permanent outdoor crops Fruit, berry, citrus and olive plantations, vineyards and nurseries >1 ha Outdoor intensive production Hops >0.5 ha Tobacco >0.5 ha Cotton >0.5 ha Fresh vegetables, melons and strawberries, which are outdoors or under low (not accessible) protective cover >0.5 ha Crops under glass or other (accessible) protective cover Fresh vegetables, melons and strawberries >0.1 ha Flowers and ornamental plants (excluding nurseries) >0.1 ha Bovine animals All >10 Head Pigs All >50 Head Breeding sows >10 Head Sheep All >20 Head Goats All >20 Head Poultry All >1,000 Head Hardy nursery stock >1 ha Mushrooms All mushroom holdings to be included >0 Note: The UK have also re-included holdings with >5ha temporary let out land or temporarily empty pig or poultry sheds. Further information Further details on all of the data sources used in this workbook can be found on our survey notes and guidance webpage via the link below: https://www.gov.uk/structure-of-the-agricultural-industry-survey-notes-and-guidance Revisions to the 2009 June Survey data The 2009 June Survey figures were revised on 16 September 2010. The 2009 figures were revised for two reasons. Firstly, the new methodology for 2010 employed thresholds to exclude holdings with very small amounts of activity, so revised 2009 figures were required to permit like-for-like comparisons. Secondly, the census prompted a register cleaning exercise that removed inactive holdings from the register. Revisions to the 2009 December Survey data The 2009 December Survey figures were revised on 1 March 2011. The December 2009 figures were revised in line with June 2009 figures for the reasons stated above. From 2010 onwards the December figures only relate to commercial holdings as defined by the EU Farm Structure Survey thresholds. Changes to the source of December sheep data "A review of the sources of sheep data as at 1 December was conducted in autumn 2010 to investigate the feasibility of using the Sheep and Goat (SAG) Inventory as the source of sheep data for the February returns to Eurostat and Defra publications of December data. Previously, sheep figures were collected in the annual December Survey of Agriculture at the same time that the SAG Inventory was held in England. This switch of sources would yield internal efficiencies and savings, avoid duplication of effort in data collection and reduce the burden of paperwork on farmers caused by Defra surveys. It would also improve the accuracy of the results, as over 56,000 SAG forms were sent out in England in the 2009 exercise compared with the corresponding December Survey’s sample of 15,000 holdings. The review recommended the SAG Inventory should be the source of English data on sheep at December and therefore questions about sheep were removed from the 2010 December Survey form. Results cover all sheep holdings in England. Data on sheep numbers will continue to be collected through the June Survey (for commercial holdings only)."

Abstract

The availability of statistical 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 also of the Palestinian labour force. There is a pressing need for specialized surveys to be conducted that will complement the first Agricultural Census in the Palestinian Territory that was conducted in 2010.

Geographic coverage

Palestinian Territory

Analysis unit

Agricultural Holding

Universe

All agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a one-stage stratified simple random sample due to: 1 - An updated framework. 2 - Strata depend on the type of holdings. 3 - Strata depend on the size of holdings. 4 - Deal directly with the holdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The agricultural statistics questionnaire was designed based on the recommendations of the Food and Agriculture Organization of the United Nations (FAO) and the questionnaire used for the Agricultural Census of 2010. The special situation of the Palestinian Territory was taken into account, in addition to the specific requirements of the technical phase of field work and of data processing and analysis.

Cleaning operations

This phase included the following operations: - Preparation of Data Entry Program. The data entry program was prepared using ACCESS software and data entry screens were designed. Rules of data entry were established to guarantee successful entry of questionnaires and queries checked data after each entry. These queries examined variables on the questionnaire. - Data Entry Having designed the data entry program and tested it to verify readiness, and after training staff on data entry programs, data entry began on 12 February 2012 and finished on 20 May 2012 with 12 staff engaged in the data entry process. - Editing of Entered Data Special rules were formulated for editing the stored data to guarantee reliability and ensure accurate and clean data.

Response rate

93.3%

Sampling error estimates

Survey data may be affected by statistical errors due to the use of the sample. Therefore, certain differences may emerge from the true values anticipated through censuses. The variation of the most important indicators was calculated and dissemination levels of the data were particularized at governorate level in the West Bank and Gaza Strip, according to the sample design and the variance calculations for the different indicators.

Abstract

The 2007/08 Agricultural Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmers' organizations, and others. The dataset is both more numerous in its sample and detailed in its scope and coverage so as to meet the user demand.

The census was carried out in order to:

• Identify any structural changes,in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in the rural infrastructure and the level of agricultural households living conditions;

-Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stakeholders; and

• Obtain data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing and service delivery.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

Community, Household, Individual

Universe

Small scale farmers, Large Scale Farmers, Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mainland sample consisted of 3,192 villages. The total Mainland sample was 47,880 agricultural households while in Zanzibar, a total of 317 EAs were selected and 4,755 agricultural households were covered.

The villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the previous 2002 Population and Housing Census.

The numbers of villages/Enumeration Areas (EAs) were selected for the first stage with a probability proportional to the number of villages/EAs in each district. In the second stage, 15 households were selected from a list of agricultural households in each village/EA using systematic random sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census used three different questionnaires: - Small scale farm questionnaire - Community level questionnaire - Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it included questions related to crop and livestock production and practices; population demographics; access to services, community resources and infrastructure; issues on poverty and gender. The main topics covered were:

• Household demographics and activities of the household members
• Land access, ownership, tenure and use
• Crop and livestock production and productivity
• Access to inputs and farming implements
• Access and use of credit
• Access to infrastructure (roads, district and regional headquarters, markets, advisory services, schools, hospitals).
• Crop marketing, storage and agro processing
• Tree farming, agro-forestry, and fish farming
• Access and use of communal resources (grazing land, communal forests, water for humans and livestock, beekeeping)
• Investment activities ( irrigation structures, water harvesting, erosion control, fencing)
• Off farm income and non agricultural related activities
• Households living conditions (housing, sanitary facilities )
• Livelihood constraints
• Poverty Indicators

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The Large Scale Farm questionnaire was administered to large farms either privately or corporately managed.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: - Manual cleaning exercisePrior to scanning. (Questionnaires found dirty or damaged and generally unsuitable for scanning were put aside for manual data entry ) - CSPro was used for data entry of all Large Scale Farms and Community based questionnaires - Scanning and ICR data capture technology for the smallholder questionnaire - There was an Interactive validation during the ICR extraction process. - The use of a batch validation program developed in CSPro. This was used in order to identify inconsistencies within a questionnaire. - Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations - Microsoft Excel was used to organize the tables, charts and compute additional indicators -Arc GIS (Geographical Information System) was used in producing the maps. - Microsoft Word was used in compiling and writing up the reports

Abstract

CAS 2023 was a comprehensive statistical undertaking for the collection and compilation of information on crop cultivation, livestock and poultry raising, aquaculture and capture fishing, agricultural economy, adaptation strategies of the holding to shocks, and the Food Insecurity Experience Scale. The National Institute of Statistics (NIS) of the Ministry of Planning (MOP), and the Ministry of Agriculture, Forestry and Fisheries (MAFF), were the responsible government ministries authorized to undertake the CAS 2023. While NIS had the census and survey mandate, the MAFF was the primary user of the data produced from the survey. Technical support was also provided by the Food and Agriculture Organization of the United Nations (FAO).

The main objective of the CAS was to provide data on the agricultural situation in the Kingdom of Cambodia, to be utilized by planners and policy-makers. Specifically, the survey data are useful in:

1.Providing an updated sampling frame in the conduct of agricultural surveys; 2.Providing data at the country and regional level, with some items available at the province level; 3.Providing data on the current structure of the country's agricultural holdings, including cropping, raising livestock and poultry, and aquaculture and capture fishing activities.

The data collected and generated from this survey effort will help reflect progress towards the 2030 Sustainable Development goals for the agricultural sector, focusing on:

-Goal 1: End poverty in all forms everywhere. -Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. -Goal 5: Achieve gender equality and empower all women and girls.

Geographic coverage

The CAS 2023 provides national coverage.

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

Analysis unit

Household agricultural holdings

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling approach for the CAS 2023 relied fully upon the sampling procedure of CAS 2022 and CAS 2021 before it, utilizing a panel approach. The CAS 2021 had used statistical methods to select a representative sample of enumeration areas (EAs) throughout Cambodia from the 2019 General Population Census of Cambodia Sampling Frame. Households within these EAs were then screened for any agricultural activity. Using this basic information, the agricultural households were stratified and sampled for additional data collection.

For the CAS 2023, the 2019 General Population Census Sampling Frame was utilized, similarly to previous survey rounds. This frame consisted of around 14,500 villages and 38,000 Enumeration Areas (EAs). For each village, the following information was available: province, district, commune, type (rural/urban), number of EAs and number of households. The target population comprised the households that were engaged in agriculture, fishery and/or aquaculture. Given their low number of rural villages, the following districts were excluded from the frame: -Province Preah Sihanouk, District Krong Preah Sihanouk -Province Siem Reap, District Krong Siem Reab -Province Phnom Penh, District Chamkar Mon -Province Phnom Penh, District Doun Penh -Province Phnom Penh, District Prampir Meakkakra -Province Phnom Penh, District Tuol Kouk -Province Phnom Penh, District Ruessei Kaev -Province Phnom Penh, District Chhbar Ampov

Since the number of rural households per EA was not known from the 2019 census, to calculate the number of rural households in each province, the sum of the households in the villages that were classified as rural was computed. The listing operation in each sampled EA was conducted for the CAS 2021 to identify the target population, i.e., the households engaged in agricultural activities.

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

The CAS 2023 used a two-stage stratified sampling procedure, with EAs as primary units and households engaged in agriculture as secondary units. Overall, 1,381 EAs and 12 agricultural households per each EA were selected, for a total planned sample size of 16,572 households. The 1,381 EAs were allocated to the provinces (statistical domains) proportionally to the number of rural households. To select the EAs within each province, the villages were ordered by district, commune, and then by type of village (Rural-Urban). Systematic sampling was then performed, with probability proportional to size (number of households). After two years of attrition, the total effective sample size of the survey was 15,323 agricultural households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

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

At the survey headquarters, the completed questionnaires were received after being approved by the data supervisors. If any issues or suspicious data were discovered during the headquarters review, the records could be returned to the enumerator for verification or correction if needed. Documentation on how to review questionnaire data for suspicious items or outliers was provided to both data Supervisors and headquarters staff. The data review and calculation of the survey estimates was undertaken using the RStudio software tool. Validation of the data began even when the questionnaires were being designed in the CAPI tool, as Survey Solutions allows for consistency checks to be built into the data collection tool. As soon as completed records were returned during the data collection stage, additional consistency checks were completed, evaluating the ranges for certain items, and verifying any outlier records with the enumerator and/or respondent. Moreover, when the data was cleaned, another step was conducted to impute the missing values derived from item non-response.

STATISTICAL DISCLOSURE CONTROL (SDC)

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

In addition, anonymization methods have been applied to the microdata files before their dissemination, to protect the confidentiality of the statistical units (e.g. individuals) from which the data were collected. These methods include: i) removal of some variables contained in the survey (e.g. name, address, etc.), ii) grouping values of some variables into categories (e.g. age categories), iii) limiting geographical information to the province level, iv) removal of some records or specific data points, v) censoring the highest values in continuous variables (top-coding) by groups, replacing them with less extreme values from other respondents, or vi) rounding numerical values.

Users must therefore be aware that data protection with SDC methods involves perturbations in the microdata. This implies information loss and bias, and affects the resulting estimates and their parameters. In general, the smaller the subpopulation, the higher the potential impact derived from the anonymization process.

Abstract

NASS is an exercise designed to provide accurate and up-to-date agricultural statistics that allows policymakers, researchers, and development partners to make informed decisions that directly impact the well-being of farmers, rural communities, and the broader economy. These statistics are essential for enhancing food security, improving productivity, and addressing regional disparities in agricultural performance. Additionally, robust agricultural data is vital in supporting Nigeria’s efforts to diversify its economy from oil dependency. By identifying key areas for investment, such as crop production, livestock management, and agro-processing, data can guide both public and private sector investments to boost agricultural output and expand exports. Moreover, they help track progress toward national goals while supporting Nigeria's efforts to meet global commitments like the Sustainable Development Goals (SDGs). Hence, NASS provides useful data for understanding the state of the agricultural sector and offer essential production and structural data to support evidence-based planning and implementation of agricultural programs vital for addressing current economic challenges and enhancing the livelihood of many Nigerians. This survey is also essential for monitoring and evaluating the effectiveness of existing agricultural programs and ensuring that resources are allocated efficiently. Capturing detailed data on agriculture practices, outputs, and challenges, the survey supports the planning and implementation of initiatives aimed at improving productivity, enhancing food security, and adapting to challenges like climate change and market fluctuations.

The objectives of the survey are to; i. provide data on agricultural production in 2022/ 2023 and the structure of the sector as a whole to assist the government in policy formulation and programme planning; ii. effectively and efficiently provide appropriate agricultural information to increase public awareness; and iii. provide data that could be used to compute agricultural sector contribution to the Gross Domestic Product (GDP).

Geographic coverage

The National Population Commission (NPC) provided the frame of Enumeration Areas (EAs), newly demarcated for the proposed 2023 Housing and Population Census. This was used as the primary sampling frame. Although data was collected across the 36 states and the Federal Capital Territory (FCT), some Local Government Areas (LGAs) were not covered due to insecurity. The LGAs covered during the survey were seven hundred and sixty-seven (767) out of the 774 LGAs in Nigeria due to security challenges. The affected states/LGAs are Borno state (Monguno, Kukawa and Abadam LGAs) and Orlu, Orsu, Oru East, and Njaba LGAs in Imo state. The number of EAs covered varied from state to state depending on the number of Agricultural EAs and LGAs. Nationally, a total of 15,591 EAs were selected across the 36 States of the Federation and FCT and a total of 152,485 households were designated to be covered.

Analysis unit

Agricultural Households.

Universe

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households selected in a subsample of EAs among the sample of EAs covered during the extensive listing survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households selected in a subsample of EAs among the sample of EAs covered during the extensive listing survey. The sampling method of NASS-household is a stratified three-phased sampling as follows: -First phase: Stratified Probability Proportional to Size (PPS) selection of 80 EAs Second phase: systematic sub-sampling of 40 EAs for the extended listing Third phase: two-stage sampling for NASS-household

i. First stage: Stratification of EAs into Agricultural and non-agricultural EAs drawn from the 40EAs listed in each LGA ii. Second stage: Systematic sampling of 10 farming households (crop/ livestock farming) and a systematic selection of complementary households practicing only fishery in fishery-intensive LGAs (18) up to a maximum of 12 households were interviewed in the concerned EAs. That selection was stratified by sorting the listed farming households by various agricultural-related information including farming activities practiced, number of plots, livestock numbers in tropical livestock units, as well as the gender of the household head.

Sample Size and Reallocation A total of 15,591 Enumeration Areas (EAs) were selected for the NASS household survey. The sample was distributed across Local Government Areas (LGAs) based on the estimated total number of plots per LGA. Within each LGA, the sample was further allocated between urban and rural areas in proportion to the estimated agricultural population. In the selected EAs, 152,485 households were finally sampled.

Sampling deviation

The probabilities of selecting EAs for NASS households were derived from two stages: the likelihood of their selection in the listing sample and the probability of selection from the subsample of EAs chosen for NASS households. These probabilities were then combined with the probabilities of selecting farming households within the EAs to determine the final selection probabilities for farming households. The design weights were calculated as the inverse of these selection probabilities. These weights were further adjusted to account for non-responses, resulting in final sampling weights used in estimating means, totals, proportions, and other statistics through standard Horvitz-Thompson estimators. Special consideration was given to fishery-related estimates, ensuring that data from the independent sample of households engaged solely in fishery activities were fully incorporated. Due to the complexity of the sampling design, sampling errors were estimated using resampling methods such as Bootstrap and Jackknife techniques.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASS household questionnaire served as a meticulously designed instrument administered within selected households to gather comprehensive data. The questionnaire was structured into the following sections:

0A. HOLDING IDENTIFICATION 0B. ROSTER OF HOUSEHOLD MEMBERS 0C. AGRICULTURAL ACTIVITIES 0D. AGRICULTURALACTIVITIES 2. PLOT ROSTER AND DETAILS 3. CROP ROSTER 1A: TEMPORARY (NON-VEGETABLE) CROP PRODUCTION 1H: TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) 1B: TEMPORARY CROP DESTINATION 2A: PERMANENT CROP PRODUCTION 2B: PERMANENT CROP DESTINATION 4: SEED AND PLANT USE 3C: INPUT USE 2(DS): PLOT ROSTER AND DETAILS 3(DS): CROP ROSTER 1A(DS): TEMPORARY (NON-VEGETABLE) CROP PRODUCTION - DRY SEASON 1H(DS): TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) - DRY SEASON 1B(DS): TEMPORARY CROP DESTINATION - DRY SEASON 4(DS): SEED AND PLANT USE - DRY SEASON 3C(DS): INPUT USE - DRY SEASON 4A: LIVESTOCK IN STOCK 4B: CHANGE IN STOCK- LARGE AND MEDIUM-SIZED ANIMALS 4C: CHANGE IN STOCK-POULTRY 4G: MILKPRODUCTION 4H: EGG PRODUCTION 4I: OTHERLIVESTOCKPRODUCTS 4J:APIARYPRODUCTION (BEEKEEPING) 5A: FISH FARMING/AQUACULTUREPRODUCTION 6A: FISH HUNTING/CAPTURE 7A: FORESTRYPRODUCTION 9: LABOUR 2_GPS.PLOT GPS MEASUREMENT 99. END OFTHE SURVEY

Cleaning operations

Data processing and analysis involved data cleaning, data analysis, data verification/validation, and table generation. World Food Programme (WFP), Food and Agricultural Organization (FAO), and NBS carried out the data processing and analysis for both the household and corporate farms questionnaires. The corporate farm questionnaire involved manual editing as well as data entry.

Sampling error estimates

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