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

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

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

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

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

In 2022, equidae are not part of the Agricultural Census. This affects the farm type and the total number of farms in the Agricultural Census. Farms with horses, ponies and donkeys that were previously classified as ‘specialist grazing livestock' could be classified, according to their dominant activity, as another farm type in 2022.

From 2020 onwards, the SO2017, based on the years 2015 to 2019, will apply (see also the explanation for SO: Standard Output).

From 2018 onwards the number of calves for fattening, pigs for fattening, chicken and turkey are adjusted in the case of temporary breaks in the production cycle (e.g. sanitary cleaning). The agricultural census is a structural survey, in which adjustment for temporary breaks in the production cycle is a.o. relevant for the calculation of the economic size of the holding, and its farm type. In the livestock surveys the number of animals on the reference day is relevant, therefore no adjustment for temporary breaks in the production cycle are made. This means that the number of animals in the tables of the agricultural census may differ from those in the livestock tables (see ‘links to relevant tables and relevant articles).

From 2017 onwards, animal numbers are increasingly derived from I&R registers (Identification and Registration of animals), instead of by means of the combined data collection. The I&R registers are the responsibility of RVO (Netherlands Enterprise Agency). Since 2017, cattle numbers are derived from I&R cattle, and from 2018 sheep, goats and poultry are also derived from the relevant I&R registers. The registration of cattle, sheep and goats takes place directly at RVO. Poultry data is collected via the designated database Poultry Information System Poultry (KIP) from Avined. Avined is a branch organization for the egg and poultry meat sectors. Avined passes the data on to the central database of RVO. Due to the transition to the use of I&R registers, a change in classification will occur for sheep and goats from 2018 onwards.

Since 2016, information of the Dutch Business Register is used to define the agricultural census. Registration in the Business Register with an agricultural standard industrial classification code (SIC), related to NACE/ISIC, (in Dutch SBI: ‘Standaard BedrijfsIndeling’) is leading to determine whether there is an agricultural holding. This aligns the agricultural census as closely as possible to the statistical regulations of Eurostat and the (Dutch) implementation of the definition of 'active farmer' as described in the common agricultural policy.

The definition of the agricultural census based on information from the Dutch Business Register mainly affects the number of holdings, a clear deviation of the trend occurs. The impact on areas (except for other land and rough grazing) and the number of animals (except for sheep, horses and ponies) is limited. This is mainly due to the holdings that are excluded as a result of the new delimitation of agricultural holdings (such as equestrian centres, city farms and organisations in nature management).

In 2011 there were changes in geographic assignment of holdings with a foreign main seat. This may influence regional figures, mainly in border regions.

Until 2010 the economic size of agricultural holdings was expressed in Dutch size units (in Dutch NGE: 'Nederlandse Grootte Eenheid'). From 2010 onwards this has become Standard Output (SO). This means that the threshold for holdings in the agricultural census has changed from 3 NGE to 3000 euro SO. For comparable time series the figures for 2000 up to and including 2009 have been recalculated, based on SO coefficients and SO typology. The latest update took place in 2016.

Data available from: 2000

Status of the figures: The figures are final.

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

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

This dataset contains the most up to date version of GLW 4 for the reference year 2020 for the following species: buffalo, cattle, sheep, goats, pigs and chicken. The individual species datasets are available at global extent and 5 minutes of arc resolution (approx. 10 km at the equator).

The fourth version of GLW, compared to the previous ones, reflects the most recently compiled and harmonized subnational livestock distribution data and much more detailed metadata.

The layers contain the density of animals per km², with weight estimated by the Random Forest model. The livestock species modelled include: buffaloes, cattle, chickens, goats, pigs and sheep.

All datasets are licensed through a Creative Commons Attribution 4.0 International License.

References

• Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010

• Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs

• Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Data publication: 2024-07-15

Supplemental Information:

Unit: head/pixel or birds/pixel

Data type: Float64

No data value: No data

Spatial resolution: Approximately 10km (0.08333 degrees)

Spatial extent: World

Spatial Reference System (SRS): EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Contact points:

Resource Contact: Dominik Wisser (FAO-NSAL)

Metadata Contact: Giuseppina Cinardi (FAO-NSAL)

Data lineage:

Recommentations on data representation

The standard lat/long visualisation of the global raster datasets tends to visually over-represent animal densities in pixels located in northern latitudes as they cover a much lower surface on earth than those close to the equator. Thus, altough the data files are distributed in lat/long, we recommend the use of an equal-area projection for a proper representation of densities of our livestock data.

Resource constraints:

Public-use data under the CC BY-NC-SA 3.0 IGO license.

Online resources:

Buffalo: metadata

Chicken: metadata

Cattle: metadata

Goats: metadata

Pigs: metadata

Sheep: metadata

Data for download: All species density

Data for download: Buffalo density

Data for download: Chicken density

Data for download: Cattle density

Data for download: Goats density

Data for download: Pigs density

Data for download: Sheep density

Description:

Data on camer trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.
Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by the author in 2017.
Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances

Project: This dataset was collected as part of the following SAFE research project: Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.

XML metadata: GEMINI compliant metadata for this dataset is available here

Data worksheets: There are 2 data worksheets in this dataset:

1. Surveys Data (Worksheet SurveysData)

   Dimensions: 767 rows by 10 columns

   Description: Summary data generated from RecordData

   Fields:

   • Location: Camera placement location on SAFE project core grids (Field type: Location)
   • Start: Date survey started (Field type: Date)
   • End: Date survey ended (Field type: Date)
   • Trap.Nights: Length of survey (Field type: Numeric)
   • Land.Use: Land use type (Field type: Categorical)
   • Individuals: Number of individuals in survey (Field type: Abundance)
   • Adults: Number of adults in survey (Field type: Abundance)
   • Subadults: Number of subadults in survey (Field type: Abundance)
   • Juveniles: Number of juveniles in survey (Field type: Abundance)

2. Record Data (Worksheet RecordsData)

   Dimensions: 2621 rows by 15 columns

   Description: Data relating to all camera trap records of bearded pigs; data generated from individual camera trap images

   Fields:

   • Location: Camera placement location on SAFE project core grids (Field type: Location)
   • Code: Unique event code (Field type: ID)
   • Date: Date of photo capture (Field type: Date)
   • Time: Time of photo capture (Field type: Time)
   • Ambient.Temp: Temperature at the time of photo capture (Field type: Numeric)
   • Moon.Phase: Moonphase at time of photo capture (Field type: Categorical)
   • Species: Identity of the individual(s) (Field type: Taxa)
   • Soc.Str: Social structure (Field type: Categorical)
   • Sex: Sex of the individual (Field type: Categorical)
   • No.Individuals: Number of individuals in survey (Field type: Abundance)
   • No.Juveniles: Number of adults in survey (Field type: Abundance)
   • No.Subadults: Number of subadults in survey (Field type: Abundance)
   • No.Adults: Number of juveniles in survey (Field type: Abundance)
   • Land-use: Land use type (Field type: Categorical)

Date range: 2011-05-05 to 2017-03-16

Latitudinal extent: 4.6350 to 4.7538

Longitudinal extent: 116.9472 to 117.6253

Taxonomic coverage:
All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.

Animalia
 - Chordata
 -  - Mammalia
 -  -  - Artiodactyla
 -  -  -  - Suidae
 -  -  -  -  - Sus
 -  -  -  -  -  - Sus barbatus

This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized.

The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely

    variable
    description
    values


    farm id
    unique identifier of the farm
    characters, including country code based on ISO2


    breeding type
    type of pig entreprise on the found on the farm
    breeding, finishing or both


    entrerprise_x
    description of other entreprises found on the farm
    feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.


    number non-pig entreprise
    number of entreprise describes
    integer


    structure
    type of pig housing structure
    permanent, temporary, both


    outdoor area
    type of oudoor access for pig
    concrete, shifting arable land, permanent pasture


    LSU
    livstock standard units computed following Eurostat standards
    numeric


    pig/ha
    intensity of production as LSU/UAApig
    numeric


    self-sufficiency
    percentage of pig feed produced on farm
    numeric


    UAA pig
    utilized agricultural area for the pig production
    numeric


    UAA total
    utilized agricultural area of the farm
    numeric

The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information

    variables name
    description
    values


    farm id
    unique identifier of the farm
    characters, including country code based on ISO2


    country
    country code
    based on ISO2


    question related to shocks
    shocks to which the resilience narrative related to
    input cost, price, outbreak, climate, legislation, labour, general


    narratives (a= first, b=second)
    identifier of the narrative within a question
    a, b


    capacity
    resilience capacity following the Meuwissen et al (2019) framework
    robustness, adaptability, transformability, non-resilience


    resilience attribute type
    resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)

functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use)

    resilience attribute
    description of the attribute that led to the resilience attribute type classification

ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature

To compute the resilience capacity score (Cscore)

assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken.

Use following R code in dplyr

mydata<- ResilienceDataPreProcessed %>%

mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity "no resilience capacity",1000,ifelse(capacity"no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>%

group_by(farm, question)%>%

summarise(Ccode=sum(code))%>%

mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))

Resilience questionnaire

Farm number:

Farm name or ID:

Country:

System descriptors

Breeding or finishing (or both)

Indoor or outdoor (or a mix)

Organic or conventional

Number of years organic

1) Has your farm experienced significant challenges in the last 5 years?

Yes or no?

Yes / No

If "no", what factars (farm/external) created this resilience?

If "yes", please describe the 1st challenge

What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?

Did this change your management or farm structure subsequently (and how)?

If "yes", please describe a 2nd challenge

What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?

Did this change your management or farm structure subsequently (and how)?

2) In the future, how do you feel your pig system would cope with these challenges:

a) Decreasing or negative margins due to increased feed or other input costs?

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)

b). Decreasing or negative margins due to reduced pig prices?

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)

c) Wide spread disease outbreak such as African Swine Fever

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

How are you prepared for this potential challenge? (what are

The first version of this data base originally was set up for testing and validation of the so-called Integrated Model of the Greenhouse Effect (the IMAGE model; see Alcamo, 1994), developed at RIVM. The main aim of the model is to use state-of-the-art models to assist policy makers in the development and evaluation of future scenarios to mitigate the negative effects of global change. The modelling framework consists of several subsystems that cover the different aspects of the earth system.

    Many calculations in IMAGE and other models are performed on a 0.5o by 0.5o
    longitude/latitude grid. This is because nearly all potential impacts of
    climate change (impacts on ecosystems, agriculture and coastal flooding) have a
    strong spatial variability. Moreover, land use related greenhouse gas emissions
    depend on local environmental conditions and human activity. There are also
    other reasons for using grid-scale information. First, policy makers are
    interested in regional/national policies to address climate change. Secondly,
    grid-scale information makes model caluclations more testable against
    observations as compared to more aggregated models.

    Nevertheless, it is infeasable to perform grid-based calculations for economic
    models, because of the difficulty in specifying economic/demographic factors on
    a country scale for the entire world over the long horizon of the model.
    Therefore, the world has been divided into 19 world regions, according to
    economic and geographic similarity. This classification also takes into account
    the regional aggregations used by the IPCC, OECD, FAO, UN and IEA. It should be
    noted, however, that IMAGE has the additional requirement that countries within
    a region be adjacent or nearby because of the model's approach to global land
    cover simulation.

    An important initiative for the update the previous version of HYDE (Klein
    Goldewijk, 2001) was the publication of a new population density data base, the
    Gridded World Population v.3 (Balk et al, 2005), which is now used as a
    starting point for historical gridded population calculations. Because
    population data are important in many calculations, it resulted in modified
    land cover estimates, as well as estimates for GDP, value added, private
    consumption. Furthermore, numerous new data have been incorporated in many
    tables.

    Besides the testing of IMAGE, HYDE has already been used for integrated
    environmental assessents such as the Global Environmental Outlook (GEO) of the
    United Nations Enviromental Programme (UNEP, 1997), technical background
    reports for GEO (RIVM/UNEP, 1997), the TARGETS project (Rotmans and De Vries,
    1997), the Dutch National Environmental Outlook (RIVM, 1997) and the Mappae
    Mundi project (Goudsblom and De Vries, 2002). Also, HYDE has contributed to
    other research e.g. in the field of historical atmospheric trace gas
    inventories (e.g. Kroeze et al, 1999; den Elzen et al, 1999; van Aardenne et
    al, 2001; Pitman et al, 2000; Pielke et al, 2003 ), biological diversity (e.g.
    Gaston et al, 2003), and climate reconstructions (e.g. Matthews et al, 2003;
    Brovkin et al, 2004).

    Furthermore, this effort very much fits within the Land-Use and Land-Cover
    Change LUCC project, (activity 3; database development), part of the the
    International Human Dimensions Project (IHDP), and the PAGES (Human
    Interactions in Past Environmental Changes) - focus 3: Human Impacts on
    Terrestrial Ecosystems (HITE) initiative. PAGES is the International
    Geosphere-Biosphere Programme (IGBP) core project charged with providing a
    quantitative understanding of the Earth's past climate and environment.

    Please note that this data base is far from complete. Work is continuous in
    progress to update and extent the data series where possible.

    [Summary provided by MNP]

Estimates of annual volumes of manure produced by six broad farm livestock types for England and Wales at 10 km resolution, modelled with MANURES-GIS [1]. The farm livestock classes are: dairy cattle; beef cattle; pigs; sheep and other livestock; laying hens; broilers and other poultry. The quantities produced by each type are subsequently apportioned into managed and field-deposited manure. The managed manure sources are categorised as beef farmyard manure, beef slurry, dairy farmyard manure, dairy slurry, broiler litter, layer manure, pig farmyard manure, pig slurry and sheep farmyard manure. The destinations are recorded as grass, winter arable, spring arable and direct excreta when grazing. For each 10 km square, the quantity of manure going from each source to each destination is estimated. The values specify amount of excreta, in kilograms for solid manure and in litres for liquid manure. [1] ADAS (2008) The National Inventory and Map of Livestock Manure Loadings to Agricultural Land: MANURES-GIS. Final Report for Defra Project WQ0103

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