Abstract

Agriculture plays an important role in India's economy. It provides gainful employment to a large section of population of the country, particularly, the rural population. It contributes to the socio-cultural development of the farming community. The land holding provides them the confidence and strength to stay and survive in the society. In view of the importance of agriculture, Government of India has been conducting comprehensive Agriculture Censuses for collection of data on structure and characteristics of agricultural holdings, as part of World Census of Agriculture Programme since 1970-71. Operational holding, being the basic unit of decision-making in agriculture, detailed data on structure of agricultural holdings and its characteristics are necessary for formulation of any meaningful and effective strategy for agricultural development.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the operational holding, defined as an entity comprising all land that is used wholly or partly for agricultural production and is operated as one technical unit by one person alone or with others, without regard to the title, legal form, size or location. A technical unit was defined as the unit that is under the same management and has the same means of production, such as labour force, machinery, animals, credit, etc. The operated area includes both cultivated and uncultivated area, provided that a part of it is put to agricultural production during the reference period.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Sampling design For the collection of data in the Agriculture Census, an approach of Census-cum-sample survey has been adopted. Various States in the country have been grouped in to two categories i.e. land record States and non-land record States. Those States where comprehensive land records are maintained giving information on land and its utilization, cropping pattern etc are called land record States and those States where such information is not maintained in the form of land-records are called nonland record States. In land record States data on Agriculture Census is pooled for all the parcels of an operational holding irrespective of its location. However, for operational convenience the outer limit for pooling is restricted to taluka. This pooling is done for each operational holder in the village of his residence. In the non-land record States the data is collected through sample survey following household enquiry approach in 20% of villages in each block. In these selected villages, all the operational holdings are enumerated following household enquiry approach.Thus in land record States no sampling is resorted to for data collection for the number and area of operational holdings and in nonland record States sampling of villages in each block/taluka is resorted to

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used, one for each of the three phases of the census:

· Phase I questionnaire, for collecting data on number and area of operational holdings, according to the prescribed size classes2 for different social groups,3 types of holdings' and gender.

· Phase II questionnaire, for collecting data on: (i) dispersal of holdings; (ii) tenancy and terms of leasing; (iii) land utilization; (iv) irrigation status and source-wise area irrigated; (v) cropping pattern

· Phase III questionnaire, for collecting additional data.

The AC 2011 questionnaires covered 12 items of the 16 core items recommended for the WCA 2010 round. The exceptions were: (i) "Presence of aquaculture on the holding" (ii) "Other economic production activities of the holding's enterprise" (iii) "Number of animals on the holding for each livestock type" (iv) "Presence of forests and other woodland on the holding"

See questionnaire in external materials.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING In-house software was developed for data entry and processing of census data. Data entry, data validation and error correction, the generation of trial tables, and the generation of final tables and their examination by states or UTs took place according to the three phases of the census. All questionnaires were manually scrutinized by the statistical staff before they were submitted for data entry. Data are archived at tehsil level and are available in the public domain. The data entry and processing software included checks of census data for inconsistencies and mismatch.

Data appraisal

Census data are compiled at the national and tehsil level. The All India Report of Agriculture Census 2010-2011 is based on the data collected during Phase-II of the Census. The detailed data of AC 2010/2011 results are available on the website of the Department of Agriculture, Cooperation & Farmers' Welfare.

Abstract

The Agricultural Census aims to provide data on the structure of the agricultural sector as the basis for the efficient utilization of agricultural resources and the projection of related indicators to develop and make optimal use of agricultural resources. In addition, data provide a benchmark for setting estimates for subsequent years and to build a sampling frame as the basis for future agriculture-related surveys on different holdings in the Palestinian Territory. These could include periodic surveys of agricultural holdings, livestock, gardening, and farm management to provide basic and detailed data on the characteristics of the agricultural sector to meet the needs of ministries for planning and monitoring. Such data also contribute to regional planning, best distribution of resources, and meeting the needs of the private sector

Geographic coverage

The Agricultural Census is a comprehensive enumeration that should cover a specific geographical area accurately. The census covered all of the Palestinian Territory, including rural and urban areas and refugee camps

Analysis unit

The agricultural holding

Universe

The frame of the Agricultural Census 2010 includes a complete record of holdings by households and collaborative institutions. The frame was prepared by listing all holders through visiting every household, using maps to reach all addresses

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable

Sampling deviation

Not applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were designed to collect data covered by the census. The first questionnaire was designed to list households and agricultural holdings, while the second was related to the enumeration of the agricultural holdings. Items and variables were as follows:

1. Household and agricultural holdings questionnaire: This included data of households and agricultural holdings, in addition to identification data, building name or owner, type of building, current use of the building, the total number of housing units in the building, current use of housing unit, the name of the householder, number of household members (males, females), and number of holdings of the household.
2. Agricultural holdings enumeration questionnaire: The enumeration questionnaire of the agricultural holdings included the following:

Part One: Identification data: Identification data included the enumeration area number, building number, housing unit number in the building, in addition to identification data on the holder and the respondent.

Part Two: Holders and holding data: Holders and holding data included data on the holder, such as the legal status, age, sex, main occupation, holder's relation to the householder, number of holder's household members, educational level, specialization, and data about the holding, including the holding type, the holding management method and main purpose of production.

Part Three: Land use: This included the unit's address, total area, uncultivated area (buildings used for holding's purposes, building not used for holding's purposes, permanent meadows and pastures, other). Cultivated areas include areas of permanent and temporary crops, forests, land that is temporarily fallow, nurseries, sources of irrigation, and utility rights.

Part Four: Crops /field crops, vegetables, horticultural trees: This included the following:

• Field crops: Questions related to the cultivation of field crops during the agricultural year: crop name, agricultural session, crop status, rainfed area, irrigated area, method of irrigation, and harvested area. • Vegetable crops: Questions related to the cultivation of vegetable crops during the agricultural year: crop name, agricultural session, crop status, open air area, method of irrigation, protected area, type of protection, irrigation method, and harvested area. • Horticultural trees: Questions related to the cultivation of horticultural trees during the agricultural year: crop name, method of farming, crop status, number and area of bearing trees and method of irrigation, area and number of nonbearing trees and method of irrigation, area and number of protected bearing trees and method of irrigation, area and number of protected nonbearing trees and method of irrigation.

Part Five: Farm animals: This included the following:

• Raising farm animals (sheep, goats and cows): type and species, address, type of rearing, the number according to sex and age group, the main purpose of raising the animals. • Poultry farming: type, address, number of barns, area of barns, maximum production capacity, actual number on the enumeration day of the first of October 2010, average number of barn cycles per year, total number of poultry raised during 2009/2010. • Domestic poultry breeding: type, number, beekeeping, and other livestock.

Part Six: Agricultural labor force: It included data on the agricultural labor force in the agricultural holding: employment status, sex, age, number and temporary employment. Part Seven: Agricultural machinery and equipment: It included questions on the use of agricultural machinery and equipment during the agricultural year.

Part Eight: Agricultural practices during the agricultural year: This section included questions related to the use of agricultural practices; the availability of brooders or fish breeding; benefits from land reclamation projects; the construction of agricultural roads or any other agricultural projects.

Cleaning operations

Data processing included all activities that followed the field work, such as office editing of questionnaires, coding, data entry and computer editing. This process started on 15 December 2010 according to the plan, which included training the editors and coders and hiring 100 personnel in addition to the supervisory team.

Special data processing programs were developed and tested to capture the census data. The computer was used to enter the data of the households and holdings listing and enumeration questionnaires.

Data editing, coding, entry, checking and cleaning were finalized on 16 June 2011 in the West Bank and on 31 August 2011 in the Gaza Strip.

The technical team followed up the data processing, testing its accuracy and quality and comparing it with the preliminary results and other data resources, in addition to preparing the tables and the report of the final results of the census in the Palestinian Territory.

Response rate

100%.

Sampling error estimates

There are two types of error: statistical errors and non-statistical errors. Statistical errors occur in survey samples and not in censuses. These errors can easily be measured and the error rate estimated since it is an error in sampling. Non-statistical errors occur at any stage of the implementation of a survey or census. Therefore, a data quality system had to be established when conducting the Agricultural Census 2010 to achieve the highest level of data coverage and accuracy for the statistics produced in order for them to be utilized for planning, decision making, and research purposes. The impact of errors on data quality was minimized due to the high level of competency and professional performance of the well-trained field work team, and also due to the existence of a quality control program to prevent or minimize errors as much as possible, find these errors when they occurred, and take the relevant procedures to correct them. A strict quality control system was established at all stages of the census, from the preparatory stage to the data processing and dissemination stage, to ensure that highly accurate data would be obtained. Quality control in the preparatory stage is crucial as it is succeeded by all census stages. Therefore, adequate time and appropriate procedures were taken into consideration at each stage to ensure high quality and authentic census data

Abstract

The Census of Agriculture & Fisheries (AGC 2011) is a national government operation geared towards the collection and compilation of statistics in the agriculture sector of the country. The collected data will constitute the bases from which policymakers and planners will formulate plans for the country's development.

The first Census of Agriculture (CoA) in the Cook Islands was conducted in 1988 and the second in 2000. Both censuses were supported technically by FAO. The Cook Islands also has a long history of population census taking at 5-yearly intervals in years ending in 1 and 6. Traditionally the Census of Population and Dwellings (CoPD) has included questions on agricultural activity at the household level, types of crops grown, livestock numbers, farm machinery and involvement in fishing and pearl farming activities. Section 3 of this report looks at data collected in the CoPD 2011 related to agricultural, fishing and pearl farming activities

Geographic coverage

National coverage.

Analysis unit

Household; Holding; Parcel; Individual.

Universe

The census covered all households, agricultural operators, agricultural establishments, fishing operators and pearl farmers.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The census of population and dwellings had 4 categories of agricultural activity, namely: subsistence only, commercial only, subsistence and commercial and no agriculture. For those engaged in agricultural activity a further breakdown was collected, namely: vegetables, fruit, flowers and other. The census of agriculture also had 4 categories but for crop growing only, namely, non-agricultural, minor agricultural, subsistence and commercial. The differences in these classifications and the types of agriculture included make comparisons difficult, however, it is useful to evaluate the two sets of data and draw conclusions as to the extent of agricultural activity in the cook islands from these two sources.

The questionnaires used for the census of agriculture 2000 and the census of population and dwellings 2006, related to agriculture, were reviewed and efforts made to avoid duplication. In particular, the question on the numbers of livestock kept by the household was dropped from the census of population and dwellings as this data was being collected in the census of agriculture. Likewise, information on machinery and equipment was dropped from the census of agriculture as this was being collected in the census of population and dwelling. Questions on the extent of involvement in agricultural activity at the household level were maintained in both censuses as was the extent of involvement in fishing and pearl farming. This provided a useful coverage check for the census of agriculture, in particular, although it was noted that there were definitional differences between the two censuses especially related to flower cultivation which was considered an agricultural activity in the census of population and dwellings but not in the census of agriculture. At the individual level, data on labour inputs was recorded in the census of agriculture by age and sex but other data at the individual level has then to be obtained through linkages to the census of population and dwellings through the person and household number.

The household questionnaire was administered in each household, which collected various information on levels of agricultural activity, holdings detail (including name of operator, total area, number of separate parcels, location), crops currently growing and/or harvested (including crops currently growing, total area, number of plants,crops planted and/or harvested, total area, number of plants), proportion of income from agriculture, loans for agriculture purposes, fertilizers, agricultural chemicals, improved varieties, other selected activities during the last 12 months (including bee keeping, hydroponic, floriculture, handicrafts), traditional methods on food storage and planting, travelling with locally grown food, water usage

In addition to a household questionnaire, questions were administered in each household for holding which collected various information on holding iidentification, parcel details during the lasts 12 months (including location, area, land tenure, land use, months used), scattered plants/trees (including number of plants), labour input for persons 15 years and over working during the last month (including sex, age, status, type, average hours worked per week, wages per month, benefits and other paid job)

In addition to a holding questionnaire, questions were administered for parcels which collected various information (during the last 12 months) on plot details (including proportion to parcel area, crops grown, method of planting, number of plants and proportion for sale), crops planted and harvested (including area harvested, number of plants and proportion for sale)

In addition to a household questionnaire, questions were administered in each household for livestock which collected various information on type and number of livestock, type of operation, nature of disposal during the last 12 months (including kind of livestock, number disposed (including home use, feast/gifts, sold, slaughtered, live)

In addition to a household questionnaire, questions were administered in each household for fishing which collected various information on household members engaged, main purpose of fishing activity, household members (including average hours spent per week), details of fishing activities (including forms of fishing, number of people fishing, location, average number of fishing trips, average hours per fishing trip), boat details (including type of boat, length, engine), proportion of fish caught/collected and sold, proportion consumed

In addition to a household questionnaire, questions were administered in each household for pearl farming which collected various information (during the last 12 months) on farming details (including farm lines, spat collector lines, spat details, number of farm shells, labour input (including person number, sex, age, status, type, average hours worked per week, wages per month, benefits received, other paid job) , boat operation (including times used per week), type of equipment and facility, number of times per week, number owned, hired, borrowed), shelling details, proportion of income, loan details

The questionnnaires, that were developed in English, contain was divided into 5 forms: -Household Form: Levels of agricultural activity, List of agricultural holdings, Crops, Income from agricultural activities, Loans, Fertilizers, Other relevant questions. -Holding Form: Parcel details, Scattered plants/trees, Labour inputs. -Parcel Form: Number of sepearate plots, Plot details, Crops. -Livestock Form: Livestock details, Type of operation, Nature of disposal. -Fishing & Pearl Farming Form: Fisheries activities details, Pearl farm information, Labour inputs, Boats and other equipment used, Other relevant information.

Cleaning operations

The length and complexity of the census of agriculture forms made the exercise much more time consuming and virtually all records had to be edited. The data capture and data cleaning exercise for the census of agriculture took the best part of 12 months, including the adjustments following the re-enumeration of Aitutaki. Tabulation also proved to be challenging because of the need for considerable internal computation of areas and numbers of plants. The final database was then split up into a number of smaller databases designed for each set of tables. The tabulation was done using Microsoft EXCEL and ACCESS

In interpreting the results of the census of agriculture, account needs to be taken of the fact that households classified as having no agricultural or fishing activities in the census of population and dwellings were excluded from the census of agriculture, especially on Rarotonga. Other definitional differences between the two censuses should also be noted. The census of population and dwellings defined agricultural activity as crops, livestock and floriculture whereas the ensus of agriculture definition was primarily crops. Livestock and poultry raising was treated separately in the census of agriculture and flower growing was only included in the census of agriculture if it was a commercial activity or was carried out in conjunction with food crop activities.

Abstract

Côte d'Ivoire has just carried out its third agricultural census called Census of Farmers and Agricultural Holdings i.e. Recensement des Exploitants et Exploitations Agricoles (REEA) 2015/2016. The special feature of this census is the exhaustive and systematic survey of all the farms in the country, agricultural households, rural villages, professional agricultural or livestock organisations (OPA/OPE) and modern farms in the country using new technologies. The method CAPI (computer-assisted personal interview) coupled with contact details using the Global Positioning System (GPS) have made it possible to carry out this census. The implementation of the REEA followed the modular approach recommended by the World Programme for the Census of Agriculture (PMRA) 2010.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as an economic unit of agricultural production under single management, comprising all land used wholly or partly for agricultural production and all livestock kept, without regard to title, legal form or size. The following types of agricultural holdings were covered in the census: (i) family farm ("agricultural household") (ii) crop/livestock production organization (OPA/OPE).

Rural villages were statistical units for the community survey conducted together with the REEA.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census Although a modular approach was planned for conducting the REEA, only the core census module comprising the agricultural households and the OPA/OPE were implemented, because of budget constraints. A community survey was implemented along with the core module.

ii. Frame A listing operation was conducted during census enumeration to identify the agricultural households. The EA maps from the Population and Housing Census (PHC) 2014 were used for this operation. For the frame of the OPA/OPE and modern agricultural holdings, different sources were used: information available at the National Institute of Statistics (NIS), and information provided by the regional directorates of the ministries involved in the REEA. In addition, local administrations provided a list of new modern agricultural holdings.

iii. Complete and/or sample enumeration methods The core module was conducted on a complete enumeration basis.

iv. Sample design Not applied

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The collection of data was done using electronic questionnaires. Specific questionnaires were administered for the REEA 2015/2016:

(i) a core module questionnaire for family farms; (ii) an OPA/OPE questionnaire; (iii) a community survey questionnaire.

The census questionnaires covered all 16 core items recommended in the WCA 2010, namely;

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING Two modes of data transmission were adopted under the REEA: (i) the routing of data via the Internet (specifically, via Dropbox) and (ii) physical transmission (on hard disk, USB key and paper supports). The data processing plan covered the following aspects: (i) equipment preparation; (ii) file clearance; (iii) data validation; and (iv) tabulation. Computer processing was done centrally, in Abidjan. The software used to process the REEA data was CSPro version 6.1. The clearance phase was an iterative process of cleaning up the database and producing clean files. To ensure undistorted data after clearance, the tabulation2 was done under CSPro on both versions of the database: with both raw and clean data. The analysis of census results was undertaken from March to April 2017. All data collected and documents produced (reports, methodology, manuals) were archived in a database the administration of which was entrusted to the Directorate of Statistics, Documentation and Informatics (DSDI) of MINAGRI and a backup copy was made.

(b) CENSUS DATA QUALITY Technical arrangements were made at different levels to ensure the quality of field data collection. The first provision was to incorporate consistency checks into the data entry programme for the different questionnaires, to minimize data entry errors, inconsistencies, and incomplete data. Two key programmes were designed and used: the Data Consistency Control Programme and the Team Tracking Programme. The latter programme made it possible to monitor the mobility of teams in the field.

Abstract

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

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

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

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

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

4. To design frame for other agricultural surveys,

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

Geographic coverage

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

Analysis unit

Agriculture Holding

Universe

The census covers individual agriculture holdings of the country.

Kind of data

Census data [cen]

Sampling procedure

Sampling design

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

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

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

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

5 Sample selection

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

Sampling deviation

No need to derive sample design

Mode of data collection

Face-to-face f2f

Research instrument

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

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

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

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

Cleaning operations

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

Response rate

100%

Sampling error estimates

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

Data appraisal

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

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 2002 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for the U.S. Virgin Islands is the second census in the U.S. Virgin Islands conducted by NASS. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years. The 2007 census is the 14th census of agriculture of the U.S. Virgin Islands. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in the U.S. Virgin Islands. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with the 1982 Economic Censuses covering manufacturing, mining, construction, retail trade, wholesale trade, service industries, and selected transportation activities. After 1982, the agriculture census reverted to a 5-year cycle. Data in this publication are for the calendar year 2007, and inventory data reflect what was on hand on December 31, 2007. This is the same reference period used in the 2002 census. Prior to the 2002 census, data was collected in the summer for the previous 12 months, with inventory items counted as what was on hand as of July 1 of the year the data collection was done.

Objectives: The census of agriculture is the leading source of statistics about the U.S. Virgin Islands’s agricultural production and the only source of consistent, comparable data at the island level. Census statistics are used to measure agricultural production and to identify trends in an ever changing agricultural sector. Many local programs use census data as a benchmark for designing and evaluating surveys. Private industry uses census statistics to provide a more effective production and distribution system for the agricultural community.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was a farm, defined as "any place from which USD 500 or more of agricultural products were produced and sold, or normally would had been sold, during the calendar year 2007". According to the census definition, a farm is essentially an operating unit, not an ownership tract. All land operated or managed by one person or partnership represents one farm. In the case of tenants, the land assigned to each tenant is considered a separate farm, even though the landlord may consider the entire landholding to be one unit rather than several separate units.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Method of Enumeration As in the previous censuses of the U.S. Virgin Islands, a direct enumeration procedure was used in the 2007 Census of Agriculture. Enumeration was based on a list of farm operators compiled by the U.S. Virgin Islands Department of Agriculture. This list was compiled with the help of the USDA Farm Services Agency located in St. Croix. The statistics in this report were collected from farm operators beginning in January of 2003. Each enumerator was assigned a list of individuals or farm operations from a master enumeration list. The enumerators contacted persons or operations on their list and completed a census report form for all farm operations. If the person on the list was not operating a farm, the enumerator recorded whether the land had been sold or rented to someone else and was still being used for agriculture. If land was sold or rented out, the enumerator got the name of the new operator and contacted that person to ensure that he or she was included in the census.

(b) Frame The census frame consisted of a list of farm operators compiled by the U.S. Virgin Islands DA. This list was compiled with the help of the USDA Farm Services Agency, located in St. Croix.

(c) Complete and/or sample enumeration methods The census was a complete enumeration of all farm operators registered in the list compiled by the United States of America in the CA 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire (report form) for the CA 2007 was prepared by NASS, in cooperation with the DA of the U.S. Virgin Islands. Only one questionnaire was used for data collection covering topics on:

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The questionnaire of the 2007 CA covered 12 of the 16 core items' recommended for the WCA 2010 round.

Cleaning operations

DATA PROCESSING The processing of the 2007 Census of Agriculture for the U.S. Virgin Islands was done in St. Croix. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for clarity and completeness. Reporting errors in units of measures, illegible entries, and misplaced entries were corrected. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies identified, but not corrected by the computer, were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and any inconsistencies or potential problems were then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by making corrections to individual data records.

Sampling error estimates

The accuracy of these tabulated data is determined by the joint effects of the various nonsampling errors. No direct measures of these effects have been obtained; however, precautionary steps were taken in all phases of data collection, processing, and tabulation of the data in an effort to minimize the effects of nonsampling errors.

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

The AGRIS Ghana Pilot test was implemented in 4 districts of the Ashanti Region (Ahafo Ano South, Asante Akim North, Ejura Sekye Dumase, and Sekyere Afram Plains) in February 2018, to collect information on: - Crop and livestock production as well as data on farm characteristics, diversification and structures; - Farm revenues and expenses; - Type of labour used by the agricultural holding; - Farming practices and their linkages with the natural environment; - Farm machinery, equipment and assets.

The general objective of the pilot was to customize AGRIS instruments and methodologies for adoption as a standard tool to efficiently gather relevant and reliable agricultural data for policy making and monitoring the Sustainable Development Goals (SDGs).

The specific objectives of the AGRIS Ghana pilot were as follows: - Elaborate the overall set up of AGRIS in Ghana; - Customize the content of the AGRIS questionnaire to the Ghanaian context; - Assess the overall efficiency of the customized, integrated questionnaires and their feasibility in terms of length, flow, use of Computer Assisted Personal Interviewing (CAPI), and integration of core and rotating modules; - Assess the difficulty and relevance of each question, each section and each generic questionnaire for different types of holdings; - Test the use of Survey Solutions software to implement CAPI data collection, and the current version of the CAPI questionnaires; - Assess the relevance of the training material developed to train survey enumerators and supervisors.

Geographic coverage

District level coverage. The 4 district covered by the survey were: - Ahafo Ano South (CORE+PME) - Asante Akim North (CORE+MEA) - Ejura Sekye Dumase (CORE+LABOUR) - Sekyere Afram Plains (CORE+ECO)

Analysis unit

Agricultural holdings in the household sector

Universe

All households, agricultural or not, in the 4 surveyed districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

Definition of Agricultural Holding As stated in the manual of the World Programme for the Census of Agriculture (FAO, 2015), an agricultural holding is defined as an economic unit of agricultural production under single management comprising all livestock and poultry kept, and all land used wholly or partly for agricultural production purposes, without regards to title, legal form, or size. Single management may be exercised by an individual or household, jointly by two or more individuals or households, by a clan or tribe, or by a juridical person such as a corporation, cooperative or government agency (FAO, 2015).

1. The Sampling Frame The initial plan for the pilot survey was to consider as statistical units, agricultural holdings covering both the household and the non-household sectors, as proposed in the AGRIS methodology. For holdings in the household sector, no updated list of agricultural households in the country was available, and therefore a sampling frame needed to be established. To do so, the 2010 Population and Housing Census (PHC) was used to build a frame of EAs which were the primary sampling units (PSUs) of the adopted sampling design. After selecting the sample of PSUs in the four districts of interest, a complete list of holdings in the selected EAs was built. All households, agricultural or not, present in the selected EAs were listed.

Holdings in the non-household sector are by definition, economic units such as commercial farms and government institutions engaged in agricultural production. GSS and MoFA provided a list of these holdings to be used as sampling frame. Therefore, the plan was to use as the overall sampling frame a multiple frame composed of the two lists described above (one for the household sector and one for the non-household sector). However, after further discussion and evaluation, it was determined that the list of holdings in the non-household sector could not be considered as a reliable sampling frame for the targeted units. As a consequence, the data collected for the 80 non-household units could not be analysed to represent holdings in the nonhousehold sector.

1. The Sampling design A stratified two-stage sampling design was used for the holdings in the household sector. The PSUs were the EAs and the secondary sampling units (SSU) were the agricultural households.

2. The Sampling Size For holdings in the household sector, the calculation of sample size was performed fixing the minimum degree of precision required for the final estimates of main variables of interest. The variable considered to determine the sample size was the area of the agricultural land owned by the households. This information had been collected during the 2012-2013 Ghana Living Standards Survey 6 (GLSS6). Therefore, data from this survey was used to estimate the coefficient of variation (CV) of the variable of interest in the chosen four districts. It should be noted that the estimation domain of the GLSS6 was the region. For that survey, a two-stage sampling design was used and the PSUs (EAs) were selected in each region with the probability proportional to size (PPS). The measure of size was given by the number of individuals in each region, provided for the chosen districts for the AGRIS-Ghana pilot survey by the GLSS6. For the estimation of the CV of the households' agricultural land, it was assumed that the EAs sampled in GLSS6 and located in the target districts were selected in these districts with the same method of selection (PPS). Thus, the households included in the sample were supposed to have been selected with a two-stage sampling design.

The formula for the computation of the sampling size can be consulted in the final report of the survey.

The number of households to be surveyed in each PSU is fixed to 10. Therefore, the size of the sample of PSU is the size of the sample of the households divided by 10.

1. Agricultural holding definition As stated in the manual of the World Programme for the Census of Agriculture (FAO, 2015), an agricultural holding is defined as an economic unit of agricultural production under single management comprising all livestock and poultry kept, and all land used wholly or partly for agricultural production purposes, without regards to title, legal form, or size. Single management may be exercised by an individual or household, jointly by two or more individuals or households, by a clan or tribe, or by a juridical person such as a corporation, cooperative or government agency (FAO, 2015).

Sampling deviation

As mentioned in the sampling procedure section, holdings in the non-household sector were not included in the survey, as per initial plan, due to a problem in the listing frame provided by the Ghana Statistical Service.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AGRIS Core module integrated with the Machinery and Equipment module (Core+ Mea) collected information on household and holding characteristics, agricultural production and agricultural assets and machinery of agricultural holdigs.

A full appraisal of the contents of the questionnaires can be get by downloading the questionnaires in the documentation section.

Response rate

Out of 370 households planned for interview, 366 were interviewed (98.91% response rate).

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

Abstract

Agriculture Census involves the collecting of key structural data by complete enumeration of all agricultural holdings using sampling frames. The Agriculture Census is a government activity, undertaken by the Ministry of Agriculture which will include the enumeration of all farming programmes by Village, Tikina and Province regarding land tenure, livestock and day to day activities by the household.

Objectives of Agriculture Census are: i) To provide objective criteria for planners and decision makers regarding agricultural and rural development ii) Re-strengthening the On-Going Agriculture Statistics program

Geographic coverage

National

Analysis unit

Farm

Universe

All farms existing in Fiji on the census day were covered; the urban sector (cities and towns) and lands totally under forests or one hundred percent not suitable for agriculture, were excluded from the coverage.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Area Sampling Frame methodology was used. The Segment (SM) was the primary sampling unit. The complete list of SMs was used as frame.

Methods: A stratified two-stage sample survey was carried out. The 895 Enumeration Areas (EAs), 646 rural and 249 urban, used for the 1986 Population Census, were classified into 9 main strata and 21 sub-strata, according to importance, intensity and type of agriculture (land use). The EAs were divided into segments (SMs), i.e. pieces of land with physically defined boundaries of different sizes. 1220 SMs out of 14413 in rural sector, and 50 SMs out of 1933 in peri-urban areas, were systematically sampled all over the country; all farms (reporting units) in the sample SMs were investigated, following a weighted-segment method, by enumerators through direct interview.

Mode of data collection

Face-to-face [f2f]

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2012 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2012.

ii. Frame NASS maintains a list of farmers and ranchers from which the CML is compiled.

iii. Complete and/or sample enumeration methods The CA 2012 was an enumeration of all known agricultural holdings meeting the USDA definition of a farm.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Seven regionalized versions of the main report form (questionnaire) were used for the CA 2012. The report form versions were designed to facilitate reporting on the crops most commonly grown within each report form region. Additionally, an American Indian report form was developed to facilitate reporting for operations on reservations in Arizona, New Mexico and Utah. All of the forms allowed respondents to write in specific commodities that were not listed on their form.

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The CA 2012 covered all 16 core items recommended to be collected in the WCA 2010. See questionnaire in external materials.

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2012 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

Abstract

The Fiji National Agricultural Census 2009 is the fourth agricultural census. After a lapse of 18 years, National Agricultural Census 2009 was carried out in Fiji beginning in October 2009; data collection was interrupted by two cyclones and not completed until March of 2010.

The Agriculture Census is a national obligation conducted by the country to provide benchmark data for planning and policy decisions in sustainable agricultural and rural development; and to strengthen and improve the ongoing Fiji Agriculture Statistics System (FASS) to generate key agricultural data on a regular basis using the results of the 2009 NAC as the benchmark and the dissemination of this statistical information in the form of regular reports.

The 2009 National Agriculture Census (NAC) is the first census programme to be conducted in the country using Multiple Sample Frame (MSF) as the main methodology. Given the experiences of the previous census programmes in terms of funding and availability of resources, the 2009 agriculture census programme provides a platform for more diversification and improvement programmes within the agriculture sector thus ensuring compatible foreign exchange earnings as well as uplifting the living standards of rural populace.

Geographic coverage

National

Analysis unit

• Agricultural Holding and Holders

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey design used the multiple sampling frame methodology. This methodology combines the advantages of an area frame (complete coverage) and a list frame (rare commodities and large and special farms). In the 2009 NAC, it was expected to provide reliable results at district level for most tables, although results for smaller districts might not be possible. In addition, a small island strategy (SIS) was used where complete enumeration of villages occurred within some districts.

The underlying basis for an area frame sample is to select small areas (in this case, one square kilometer - 100 hectares) that represent the entire area of interest. To improve the efficiency of the sample, the entire country was stratified (or characterized) by the intensity of agriculture. The stratification split the country into areas of high intensity agriculture, medium intensity agriculture, low intensity agriculture, forest areas, peri-urban areas and urban areas/non agricultural areas. The overall sample size was limited by the resources available; it was determined to use a ten percent sample of "agricultural land" as determined during the stratification process.

Initially the Fiji Bureau of Statistics (FIBOS) enumeration areas (EAs) for the 2007 Population and Housing Census were used for stratum identification. Subsequently it was determined that re- stratification of whole EAs and subdivision of other EAs would be more efficient. In many of the FIBOS EAs, farms were present only in small pockets; the uniformity of agriculture in the EA, one of the strengths of the stratification, did not exist. These EAs were, first, reviewed for the presence of natural pine forest and natural reserves. After these areas were removed, the remainder of the EA was divided into one square kilometer grids before the sampling process occurred. After the grids were selected, the Land Use Section of the DOA prepared maps using detectable boundaries "around the grid". It was not possible for segments to retain the gridlines as boundaries because they seldom were along recognizable boundaries; however, it was possible to approximate 100 hectares in that general area.

A farm can consist of land areas that are separated by physical boundaries or by land use patterns; these are called tracts. The method of data collection was to account for each tract inside the segment, but, also to collect information about areas outside the segment for farms with tracts both inside and outside. If a segment boundary splits an existing tract, it is divided into one tract inside the segment and one tract outside the segment. The percentage of the farmland inside the segment is used as a weighting factor for the farm in the expansions.

One of the limitations of area frame samples is the accurate expansion of rare or concentrated (non- uniform) variables - such as poultry houses or large dairy or beef farms. The list frame sample, developed from the knowledge and experience of DOA Animal Health and Production Division and Extension Division staff, was expanded as data collection occurred and there was better awareness of large and specialized farms. Data were collected from all of these farms. It should be noted that shortly before the beginning of data collection, a severe outbreak of brucellosis occurred and some culling took place.

Three levels of data presentation were identified for tabulation of the data of the National Agriculture Census 2009 (NAC 2009). The first is tables and expansions at district level; the second is tables and expansions at provincial and national level; the third is tables and (estimates) for special variables.

The census data were collected at farm level, at tract level, at crop level and at animal/poultry level. Information about households and their demographics were also collected. One priority area has been the role of gender in agriculture in Fiji. A special section of the census questionnaire was targeted at identifying these roles and highlighting any special differences. These data also have been broken out by age group.

Accurate land stratification for the 2009 NAC was essential; it was necessary to estimate the percentage of agriculture land use. Initially the stratification was made for each of the Fiji Islands Bureau of Statistics (FIBOS) enumeration areas (EAs).

The census estimates were requested at national, divisional, provincial and also tikina levels. The 15 provinces including Rotuma Island were the main focus of the tabulation. Consequently, the entire country was divided into strata according to the intensity of land use for agriculture. They were further subdivided into sub-strata according to specific land use. This sub-stratification technique guaranteed the sample allocation for priority and special crops. Another stratum was created for special farms including large commercial and freehold farms.

A total of 1,602 existing EAs from the 2007 population census were overlaid on the ASF topographic maps scale 1:50,000 in preparation for stratification activities according to land use. Each EA was classified into one of the strata keeping the same geographical identification codes as those used in the population census. The percentage of area under crops, pastures, forest, etc. (land use) of each EA was estimated by field observation to check that each EA was classified in the right stratum and sub-stratum.

The sampling procedures are more fully described in "National Agricultural Census 2009 - Final Report" pp.7-13.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires, NAC 1 and NAC 3, were used to record information about the segments from the sample. The NAC 1 itemized all tracts inside the segment and all associated farm tracts outside the segments. The NAC 3 documented the nonfarm tracts inside the segment. Enumerators were required to fill out these questionnaires; during the interview process the main questionnaire (NAC 2) was used. Neither the NAC 1 nor NAC 3 was necessary for List Frame farms.

The questionnaire was designed and tested by the staff of the Agricultural Statistics Unit and training manuals were prepared for supervisors and enumerators. A Pilot Census was carried out in several locations to evaluate the content and layout of the questionnaires and the completeness of the census documents. The questionnaire and training materials were updated as the result of the Pilot Census.

Cleaning operations

After a prioritized order of data collection from the provinces, the questionnaires were received at the Agricultural Statistics Unit in batches. Unique questionnaire numbers were assigned by the data processing administrator and recorded in a management system designed to prevent duplicate numbers and to coordinate the collection and processing of the three types of questionnaires. The questionnaire numbers consisted of province, district and a sequence number starting with an initial value assigned previously to each of the segments.

The editing and coding process for a total of 9,341 NAC 2 questionnaires containing farm data started in mid November 2009. Four persons managed the archives of census materials (questionnaires, cartography and photo-enlargements, etc.). Eleven coders were contracted and trained using the Field Team Manual and the Coding, Editing and Data Processing Manual. One table head checked the manual editing and coding. Data entry activities were conducted by ten data entry operators beginning in early December.

Consistency checks were also carried out in the ACCESS databases. Queries were designed to identify data entry and coding errors. Data were entered into 15 provincial databases (including Rotuma Island) which were combined into four divisional databases. The LSF database was kept separate, but combined in SPSS for tabulation and analysis.

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

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

Abstract

Ethiopian farming largely produces only enough food for the peasant holder and his family for consumption, leaving little to sell. This inadequate volume of production is ascribed to the tardy progress in the farming methods and scattered pieces of land holdings. Under this traditional sector, agriculture is practiced on public land and most of the produce is mainly for own consumption. The diverse climate of the country and the multiple utilizations of crops have prompted the vast majority of agricultural holders to grow various temporary and permanent crops. Despite the variation in the volume of production, the relative importance and pattern of growth of these crops are largely similar across many of the regions.

There is a general agreement that the performance of an agricultural system should achieve a steady supply of food to the people of a country. But, unless special attention is focused on agriculture, its performance can be impeded by vagaries of nature, population growth and scarcity and fragmentation of land, thus, affecting food supply and posing a challenge to the federal and regional governments. This situation calls for an overhaul of the agricultural system in the country or the regions.

In order to have a flourishing agriculture, which sustains reliable food supply, the federal and regional governments have to formulate and implement farm programs that ensure food security. The preparation, execution, monitoring and assessment of these programs entail statistics on agriculture particularly crop production since it is the prime target that national or regional agricultural policies aim at.

The collection of data on crop production should encompass all crop seasons in the agricultural calendar and farming activities in both rural and urban areas. It should also include the wide range of crops that are grown and embodied in the food security system, which are indispensable for a sustained provision of staple diet and other cash crops like coffee and Chat.

In view of this, crop production data for private peasant holdings for both “Meher” and “Belg” seasons in both rural and urban areas were collected in the census to provide the basis for decision making in the process of implementing timely food security measures and to make policy makers aware of the food situation in the country.

Geographic coverage

The 2001-2002 (1994 E.C) Agricultural Sample Enumeration was designed to cover the rural and urban parts of all districts (weredas) in the country on a large-scale sample basis excluding the pastoralist areas of the Afar and Somali regional states.

Analysis unit

Household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling Frame The list of enumeration areas for each wereda was compiled from the 1994 Ethiopian Population and Housing Census cartographic work and was used a frame for the selection of the Primary Sampling Units (PSU). The 1994 Population and Housing Census enumeration area maps of the region for the selected sample EA's were updated, and the EA boundaries and descriptions were further clarified to reflect the current physical situation. The sampling frame used for the selection of ultimate sampling units (agricultural households) was a fresh list of households, which was prepared by the enumerator assigned in the sampled EA's using a prescribed listing instruction at the beginning of the launching of the census enumeration.

Sample Design In order to meet the objectives and requirements of the EASE, a stratified two-stage cluster sample design was used for the selection of ultimate sampling units. Thus, in the regions each wereda was treated as stratum for which major findings of the sample census are reported. The primary sampling units are the enumeration areas and the agricultural households are secondary (ultimate) sampling units. Finally, after the selection of the sample agricultural households, the various census forms were administered to all agricultural holders within the sampled agricultural households.

For the private peasant holdings in the rural areas a fixed number (25) of sample EA's in each wereda and 30 agricultural households in each EA were randomly selected (determined). In urban areas, weredas with urban EA's of less than or equal to 25, all the EA's were covered. However, for weredas with greater than 25 urban EA's, sample size of 25 EA's was selected. In each sampled urban EA, 30 agricultural households were randomly selected for the census. The sampled size determination in each wereda and thereby in each EA was based upon the required precision level of the major estimates and the cost consideration. The pilot survey and the previous year annual agricultural sample survey results were used to determine the required sample sizes per wereda.

Sample Selection of Primary Sampling Units Within each wereda (stratum) in the region, the selection of EAs was carried out using probability proportional to size systematic sampling. In this case, size being total number of agricultural households in each EA obtained from the listing exercise undertaken in the 1994 Ethiopian Population and Housing Census of the region.

Listing of Households and Selection of Agricultural Households In each sampled enumeration area of the region, a complete and fresh listing of households was carried out by canvassing the households in the EA. After a complete listing of the households and screening of the agricultural households during the listing operation in the selected EA, the agricultural households were serially numbered. From this list, a total of 30 agricultural households were selected systematically using a random start from the pre-assigned column table of random numbers. The sampling interval for each EA was determined by dividing the total number of agricultural households by 30. For crop cutting exercise purposes (rural domain) a total of 20 agricultural households were randomly selected from the 30 sampled agricultural households. The systematical random sampling technique was employed in this case, because its application is simple and flexible, and it can easily yield a proportionate sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Forms and equipment are instrumental in gathering information from various sources. The census forms are the vehicle and basic document for collecting the desired data. These include general-purpose forms covering farm management practices, demographic and economic characteristics, area, and production of both temporary and permanent crops; livestock, poultry and beehives ... etc. These forms are formulated for recording data generated through interview as well as objective measurements. Although the planning, organization and execution of the census were the responsibilities that rested within the CSA, development of the census forms was a tedious task that involved the formation of a working group composed of members of government and non-governmental organizations who are major users of agricultural data. Members of the working group were given the opportunity to identify their data requirements, define the needs of others and determine the specific questions that the forms should contain. The working group included the staff of the organizations that are involved in agricultural planning, collection of agricultural statistics and the use of data within the agricultural sector. The working group designed different forms for the various data items on crop area, production, and other variables of interest to meet the needs of current data users and also considered the future expectations. Attempt was made to make the content of the forms of acceptable length by distributing the variables to be collected in the different census forms. The rural census questionnaires/forms included: - Forms 94/0 and 94/1 that are used to record all households in the enumeration area, identify the agricultural households and select the units to be covered by the census. - Form 94/2 is developed to list all the members of the sampled agricultural households and record the demographic and economic characteristics of each of the members. - Forms 94/3A, 94/3B, 94/3C and 94/3D are prepared to enumerate crop data through interview and objective measurement. - Form 94/5 is designed to record crop area data via the physical or objective measurement of crop fields. - Form 94/6 is used to list all the fields under crop and select a crop field for each type of crop randomly for crop cutting exercise. - Forms 94/7A, 94/7B, and 94/7C are developed for recording yield data on cereals, oil seeds, pulses, vegetables root crops and permanent crops by weighing their yields obtained from sub-plots and/or trees selected for crop-cuttings. - Form 94/8 is prepared to enumerate livestock, poultry and beehives data by type, age, sex and purpose including products through interview (subjective approach). - Forms 94/9, 94/10 and 94/11 are used to collect data on crop and livestock product usage; miscellaneous items and farm tools, implements, draught animals and storage facilities, in that order, by interviewing the sample holders.

“Belg” season questionnaires identified as: - Form 94/12A and 94/12B that are used to record data on farm management practices of the “Belg” season. - Form 94/4 was the questionnaire used for collecting data on crop production forecast for 2001-2002 and the data collected using this form was published in December 2001 subjectively, while 94/12C is for recording “Belg” season crop area through objective measurement and volume of production through

Abstract

The 2010 Census of Agriculture was the second Census of Agriculture undertaken since 2001. The 2001 was an enumeration of all known agricultural holdings. In 2003, 2005, and 2007 sample farm structure surveys were conducted. The 2010 Census of Agriculture was a combination of an enumeration of all households for the Farm Structure Survey data and a sample survey for the information collected for the Survey of Agricultural Production Methods.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding (farm), defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in annex Ito the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, as either its primary or secondary activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Statistical Register of the SOSR provided information on the registered units reporting agricultural activity to build the Register of Farms (RF). The RF was then reviewed, supplemented and updated with information from other available agriculture data sources, such as on orchards, vineyards, cattle, pigs, sheep, goats, organic farms, and applicants of the single area payment scheme (SAPS).

Mode of data collection

Mail Questionnaire [mail]

Research instrument

One single questionnaire was used for data collection of the AC and SAPM items. The census questionnaire covered all 16 core items recommended in the WCA 2010.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding’s enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Data entry was done by the SOSR regional offices using computer software and working in an Oracle environment. After the checking, editing and revision of data at regional offices, data files were created and saved in regional AC databases, from where they were incorporated into the databases of the SOSR. Nonresponse was followed up on by telephone. In case of item nonresponse, supplementary data from external sources or qualified estimates were used to handle missing data. Unit imputations were applied for 329 farms (1 percent of all farms). Final unit nonresponse rate was 2.1 percent. Item imputation was not performed because the missing data was re-surveyed during the data processing. Unit imputation was applied when, for the unit involved, there were relevant internal or external resources created for the reference period. Data archiving is secured in compliance with the archiving policy of the SOSR. Data anonymization for Eurostat was secured by replacing the identification number by a randomly assigned sequence number.

b. CENSUS DATA QUALITY The census data were compared with information from the FSS of previous years, and other statistical data. Close supervision, coordination and monitoring activities were undertaken during the field data collection operations by the regional SOSR offices; extensive quality checks were made once the census questionnaires were returned to those offices.

Sampling error estimates

The primary methodology for minimising non-sampling errors was rigorous controls and procedures for the data collection activity. Interviewers were carefully trained, as were their supervisors and once in the field they were provided with detailed procedures and questionnaire manuals. There was also close supervision and coordination of the field collection operation by the regional Offices and extensive checks on the data collected once it was returned to the Regional Office for data entry and checking.

Data appraisal

Part A of the technical project contains the checking rules - mandatory or informative. During the processing of data at the decentralized level (regional offices of the SO SR) binding errors must be eliminated because these indicate errors that cannot occur in the file, and errors of informative type must be verified. After saving data in a central database, we verify the completeness of the file, check out what informative errors have passed through, and in case on any doubt, the regional office is consulted. In the next step, the data file for Eurostat in the prescribed structure was compiled. After creating the data set we have used all control rules described in the manual (DSM).

The Farm Business Survey (FBS) provides information on the financial, physical and environmental performance of farm businesses in England to inform and evaluate policy decisions. The FBS is intended to serve the needs of farmers, farming and land management interest groups, government, government partners and researchers. Survey results typically give comparisons between groups of businesses, e.g between regions or between farm types. The results attracting most attention are on farm incomes and productivity. Some of the results are produced and published by the Department for Environment, Food and Rural Affairs (Defra), whilst others are produced and published by the Rural Business Research (RBR) team. The farm business survey produces a number of reports, the most influential of which are. Farm Accounts (1999-2015) in England is the primary publication from the Farm Business Survey. It provides information on farm incomes, outputs and costs for the various farm types, farm sizes, regions and economic performance. This report can also include information on weather, diversification and succession . Farm Business Income (2011-15) - Annual statistics on farm business income in England. Farm Household Income and Household Composition (2005-2015) - Data on farm household income, which comprises Farm Business Income (including that from diversified enterprises), the off-farm income of the principal farmer and their spouse/common law partner and income from other household members. Information also on household composition and farm net worth and assets. Farm Rents (1968-2015) - Annual statistics about average farm rents in England. Farmer’s intentions (2010-11, 2013-14) - Farmers’ aspirations and plans for the whole business and for individual enterprises. Water usage on farms (2009-10, 2013-15) - Estimates of water use on farms in England. Other analysis from the farm business survey include - Animal health and welfare (2005-6, 2011-12) - Extensive information on cattle, sheep, pigs and poultry. Balance sheet analysis and farm performance (2010-11, 2012-13) - This release presents the main results from an analysis of the profitability and resilience of farms in England. Countryside maintenance and management (2005-11) - Number of farms participating in countryside management and maintenance activity (CMMA) and costs involved. Participation in different types of CMMA also documented (e.g Soil and water protection) Farm business management practices (2010-11) - Information on farmers qualifications and relevant skills across farm types. Computer usage information also there. Farm diversification (1998-2010) - Number of holdings participating in some form of diversification, by type of diversification, farm size and farm type. Later reports include the contribution of diversification to farm income. This is now included under farm accounts. Farm energy use ( 2007-2008, 2011-2012) - Fuel use, fertiliser use, minimum tillage information, dairy feed intake, straw baling, contractor use Farm Succession (2013-14) - Data about the presence and nature of farm business succession arrangements. Fertiliser usage on farms (2012-15) - Data (by region) on the quantities of nutrients from manufactured fertilisers that were applied from a subset of farms within the main survey. The use of precision farming techniques, soil nutrient software, clover and legumes in grass swards, green manures and areas subject to fertiliser restrictions. Milk selling arrangements (2010) - Data on the milk selling arrangements of Dairy Farmers.

Agricultural land in India amounted to over *** million hectares in financial year 2023. This was a decrease in agricultural land when compared to the previous year. That year, Haryana and Punjab were the leading Indian states with land available for agricultural purposes.  Cultivation and farming Of all agricultural land in India, *** million hectares has been cultivated. This makes India one of the largest agricultural economies globally. Major land types include alluvial soil in the Indo-Gangetic plains, black soil in Deccan, and red and laterite soils in the southern and eastern regions – all supporting the cultivation of rice, wheat, pulses, cotton, and oilseeds across varying climatic zones. Digitalization in farming Of recent policy developments from the central government towards agriculture has been the promotion of natural farming and digital land records. Initiatives like PM-KISAN provide income support to farmers. Additionally, states including Madhya Pradesh and Uttar Pradesh are digitizing land ownership data to reduce disputes. While controversial, several other states have eased land leasing laws to attract private investment and improve land utilization.

Abstract

The records of agricultural statistics in Sweden date back to the beginning of the nineteenth century. In the first half of the twentieth century established statistical methods were introduced for production of statistics on agricultural holdings, crop areas, crop production livestock etc. In 1968, in order to improve the coordination of the statistics within the agricultural sector, Sweden established a farm register which was updated annually. The register covered all agricultural holdings with: more than 2 hectares of arable land; a large number of livestock but less than 2 hectares of arable land; and holdings with horticultural production. Since its establishment the farm register was used as a sample frame for both farm structure surveys and other agricultural statistical surveys.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the farm/agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes the agricultural activities listed in annex Ito the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, as either its primary or secondary activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The frame for the AC 2010 was the LBR. The frame consisted of holdings from the FSS 2007, updated with information from the Livestock Survey for 2008 and 2009, the Holdings Applying for Subsidies 2008, and the 2009 IACS. The frame was also updated with information from the Poultry, Sheep, and Pig Registers. In addition, a special Register Survey was sent out to 6 000 holdings that were found in the 2007 FSS population but that could not be found in the IACS system in 2009. The AC 2010 was carried out as a complete enumeration of all agricultural holdings in the "frame". Sampling was used for the OGA section, in accordance with the EU Regulation (EC) No. 1166/2008.

b. Sample design The SAPM and OGA used the same sample, based on a stratified random sample. The sampling frame was divided into 66 strata. The variables for stratification were divided into: (i) NUTS II regions; (ii) area of agricultural land; (iii) number of animals of different kinds; and (iv) new holdings.

Mode of data collection

Computer Assisted Web Interview (CAWI)

Research instrument

In total, four questionnaires were designed to collect the data requested; three questionnaires covering the AC variables and one covering the SAPM variables (SP). Two of the AC questionnaires were adopted for natural persons (one including OGA variables (SFK) and the other without OGA (SFE)), while the third one was designed for legal persons (SJ). The questionnaires covered all 16 core items recommended in the WCA 2010:

Page 1. 1. Land use 2. Cultivation of fruit and berries 3. Green houses 4. Nurseries 5. Client number at the Swedish Board of Agriculture 6. Irrigation 7. Client number in the Organic Farming Register (at the control body)

Page 2: 1. Production location number for livestock (bovine) according to the Bovine register (pre-printed) 2. Pigs broken down by category 3. Horses 4. Sheep broken down by category 5. Poultry broken down by category 6. Rural development/Other gainful activity 7. Renewable energy

Page 3: Labor Force, on Manager, and on the training of the Manager

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Optical scanning was used for data entry. An IT system was designed for the identification of all errors occurring when a questionnaire contained information that did not fulfil the validation criteria. Various methods were used to solve problems associated with missing data. The instructions, that were in force for the entire survey process, generally permitted the data to be corrected or completed by the staff directly on personal computers. The software used in this part of the process was SAS and Microsoft Excel.

b. CENSUS DATA QUALITY The calculated national statistical results for 2010 were compared with corresponding results from the ACs/FSSs of earlier years at the macro level. Some micro level results were also compared with data from earlier years.

Data appraisal

The first AC 2010 results were published in October 2010 and the final results in June 2011. The census results were disseminated in a series of statistical reports on the SBA website, and on the Statistics Sweden website. The data from the AC 2010 were also published through the online Statistical Database of the SBA.

Abstract

Ethiopia is endowed with abundant resources suitable for agriculture. As result of which the agricultural activity in Ethiopia is quite varied being conditioned by such factors as climate, soils topography, ... etc that have favored not only the employment of the majority of the country's population but also served as the main source of input (raw material) for the large and medium scale industries as well as the main generator of the country's foreign currency earnings.

Though agriculture is the backbone of Ethiopian economy it is characterized by low level of productivity and subsistence farming system that have resulted hand to mouth production. Nowadays the problem mentioned has become more acute as a result of two factors. First the number of people is increasing at a rate that doubles the present population of the country in about a generation. Secondly this is occurring at a time when the area of new land suitable for cultivation is rapidly diminishing.

Till recently traditional practice such as use of animal dung and crop residue crop rotation and expanding cultivable crop land had helped a lot to increase productivity, however, the problem mentioned above has become more acute and beyond the limits of the traditional practices which of course had already been exhausted. Hence, the scale of severity of the country's food and other related problems will be so great that a massive short and longrange innovative efforts will be required to solve it.

As a result, increasing productivity on various field crops is the only realistic option to raise the living standards of the rural population and to ensure food security and poverty alleviation. There are many modern techniques and technologies of achieving enhanced crop productivity. Accordingly, the major factors behind achieving high level of crop productivity increases are greater and more efficient use of fertilizers, wide spread uses of improved variety seeds, pesticides, expanded use of irrigation and effective extension services.

This section of the EASE is therefore, deals with the agricultural census data that indicates the type of inputs applied, quantity of inputs applied, the irrigated cropland area, estimates of cropland area damage, number of holders who applied different agricultural inputs and farm management practices, and number of holders covered by extension package programs

Geographic coverage

The 2001-2002 (1994 E.C) Agricultural Sample Enumeration was designed to cover the rural and urban parts of all districts ("weredas") in the country on a large-scale sample basis excluding the pastoralist areas of the Afar and Somali regional states.

Analysis unit

Household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling Frame The list of enumeration areas for each wereda was compiled from the 1994 Ethiopian Population and Housing Census cartographic work and was used a frame for the selection of the Primary Sampling Units (PSU). The 1994 Population and Housing Census enumeration area maps of the region for the selected sample EA's were updated, and the EA boundaries and descriptions were further clarified to reflect the current physical situation. The sampling frame used for the selection of ultimate sampling units (agricultural households) was a fresh list of households, which was prepared by the enumerator assigned in the sampled EA's using a prescribed listing instruction at the beginning of the launching of the census enumeration.

Sample Design In order to meet the objectives and requirements of the EASE, a stratified two-stage cluster sample design was used for the selection of ultimate sampling units. Thus, in the regions each wereda was treated as stratum for which major findings of the sample census are reported. The primary sampling units are the enumeration areas and the agricultural households are secondary (ultimate) sampling units. Finally, after the selection of the sample agricultural households, the various census forms were administered to all agricultural holders within the sampled agricultural households.

For the private peasant holdings in the rural areas a fixed number (25) of sample EA's in each wereda and 30 agricultural households in each EA were randomly selected (determined). In urban areas, weredas with urban EA's of less than or equal to 25, all the EA's were covered. However, for weredas with greater than 25 urban EA's, sample size of 25 EA's was selected. In each sampled urban EA, 30 agricultural households were randomly selected for the census. The sampled size determination in each wereda and thereby in each EA was based upon the required precision level of the major estimates and the cost consideration. The pilot survey and the previous year annual agricultural sample survey results were used to determine the required sample sizes per wereda.

Sample Selection of Primary Sampling Units Within each wereda (stratum) in the region, the selection of EAs was carried out using probability proportional to size systematic sampling. In this case, size being total number of agricultural households in each EA obtained from the listing exercise undertaken in the 1994 Ethiopian Population and Housing Census of the region.

Listing of Households and Selection of Agricultural Households In each sampled enumeration area of the region, a complete and fresh listing of households was carried out by canvassing the households in the EA. After a complete listing of the households and screening of the agricultural households during the listing operation in the selected EA, the agricultural households were serially numbered. From this list, a total of 30 agricultural households were selected systematically using a random start from the pre-assigned column table of random numbers. The sampling interval for each EA was determined by dividing the total number of agricultural households by 30. For crop cutting exercise purposes (rural domain) a total of 20 agricultural households were randomly selected from the 30 sampled agricultural households. The systematical random sampling technique was employed in this case, because its application is simple and flexible, and it can easily yield a proportionate sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Forms and equipment are instrumental in gathering information from various sources. The census forms are the vehicle and basic document for collecting the desired data. These include general-purpose forms covering farm management practices, demographic and economic characteristics, area, and production of both temporary and permanent crops; livestock, poultry and beehives ... etc. These forms are formulated for recording data generated through interview as well as objective measurements. Although the planning, organization and execution of the census were the responsibilities that rested within the CSA, development of the census forms was a tedious task that involved the formation of a working group composed of members of government and non-governmental organizations who are major users of agricultural data. Members of the working group were given the opportunity to identify their data requirements, define the needs of others and determine the specific questions that the forms should contain. The working group included the staff of the organizations that are involved in agricultural planning, collection of agricultural statistics and the use of data within the agricultural sector. The working group designed different forms for the various data items on crop area, production, and other variables of interest to meet the needs of current data users and also considered the future expectations. Attempt was made to make the content of the forms of acceptable length by distributing the variables to be collected in the different census forms.

The rural census questionnaires/forms included: - Forms 94/0 and 94/1 that are used to record all households in the enumeration area, identify the agricultural households and select the units to be covered by the census. - Form 94/2 is developed to list all the members of the sampled agricultural households and record the demographic and economic characteristics of each of the members. - Forms 94/3A, 94/3B, 94/3C and 94/3D are prepared to enumerate crop data through interview and objective measurement. - Form 94/5 is designed to record crop area data via the physical or objective measurement of crop fields. - Form 94/6 is used to list all the fields under crop and select a crop field for each type of crop randomly for crop cutting exercise. - Forms 94/7A, 94/7B, and 94/7C are developed for recording yield data on cereals, oil seeds, pulses, vegetables root crops and permanent crops by weighing their yields obtained from sub-plots and/or trees selected for crop-cuttings. - Form 94/8 is prepared to enumerate livestock, poultry and beehives data by type, age, sex and purpose including products through interview (subjective approach). - Forms 94/9, 94/10 and 94/11 are used to collect data on crop and livestock product usage; miscellaneous items and farm tools, implements, draught animals and storage facilities, in that order, by interviewing the sample holders.

"Belg" season questionnaires identified as: - Form 94/12A and 94/12B that are used to record data on farm management practices of the "Belg" season. - Form 94/4 was the questionnaire used for collecting data on crop production forecast for 2001-2002 and the data collected using this form was published in December 2001 subjectively, while 94/12C is for recording "Belg" season crop area through objective measurement and