Abstract

The AAS is an integrated modular survey aiming to provide high quality and timely data on the performance of the Ugandan agricultural sector, as well as core indicators on crop and livestock for better agricultural policy making.

Data collection for the AAS is implemented in two waves, corresponding to the first (January-June) and second (July-December) seasons of the Ugandan agricultural year. For each season, households in the survey's sample are interviewed twice, during the Post-Planting (PP) period and the post-harvesting (PH) period. This results in a total of four visits during the agricultural year. For what concerns the AAS 2018, due to a change in the methodology and questionnaire in between seasons, data collected during the first and second season are not perfectly comparable and have been treated separately. Hence, this DDI only refers to microdata collected during the second season of 2018.

Among information collected with the AAS there is data on: - The use of agricultural land along with the health and quality of soils in Uganda; - The quantity and value of agricultural production; - The access to extension services, market information and agricultural facility; - Food security of agricultural households; - Livestock keeping and animal products production; - The socio-demographic characteristics of agricultural household members.

The collected data is used to produce a set of tables and indicators for tracking and evaluating the impacts of government and development programs on agriculture, and to compute SDG and CAADP indicators related to food and agriculture. For the main findings from the AAS 2018, see the Executive Summary of the AAS 2018 Report.

Geographic coverage

Regional Coverage

Analysis unit

Households

Universe

Agricultural households (i.e. agricultural holdings in the household sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the AAS 2018 was constituted by a complete list of census enumeration areas (EAs) covering the entire national territory of Uganda, for a total of 80183 EAs. An EA represents the smallest ground area portion, mapped with definite boundaries. EAs should not be intended as administrative area categories, but only as ground area portions defined to facilitate the selection of the sample and ease data collection activities. As of 2014, Uganda is divided into 112 administrative districts. In each district, the following hierarchical administrative division is in place: 1) County, 2) Sub county, 3) Parish, 4) Village, 5) Local council area. The frame file contains the administrative affiliation for each EA and number of households at the time of the census. Each EA has also a designated residence type: urban or rural.

The sampling design adopted is a two-stage sampling design. In order to increase the efficiency of the sampling design for the AAS, the sampling frame is divided into 10 Zonal Agricultural Research and Development Institutes (ZARDIs). At the first stage of selection, a sample of EAs (Primary Sampling Units) was drawn. At the second stage, a sample of agricultural households in the selected EAs was drawn (Secondary Sampling Units). The determination of the required number of EAs is based on the approach of Probabilities Proportional to Size (PPS), using the systematic sampling algorithm. The measure of size to be used in selecting the sample is the number of agricultural households resulting from the 2014 Population and Households Census (PHC). The employed sampling procedure led to the production of representative estimates at the region, sub region, and zardi level. Hence, the zardi is the maximum level of geographical disaggregation for which representative estimates can be computed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AAS 2018 was conducted using two main questionnaires i.e. the Post-Planting, and Post-harvesting questionnaires. For each season, agricultural households are interviewed twice: during the post-planting and the post-harvesting visit.

The questionnaire used during the post-planting season is called "Form 4 - Crop Area Module" and is organized as follows:

• Section 4.1: Cover Page;
• Section 4.2: Household Roster;
• Section 4.3: Enterprise Identification;
• Section 4.4: Parcel Roster;
• Section 4.5: Plot Roster;
• Section 4.6: Crop Roster.

The questionnaire used for the post-harvesting visit is called "Form 52- Crop Production, Household and Holding Characteristics Module" and is organized as follows: - Section 5.1: Cover Page; - Section 5.2: Household Roster; - Section 5.3: Production and Dispositions of Crops; - Section 5.4: Agricultural Inputs; - Section 5.5: Production Activities and their Costs; - Section 5.6: Labour Input on the Holding; - Section 5.7.1: Cattle and Pack Animals; - Section 5.7.2: Small Ruminants; - Section 5.7.3: Poultry; - Section 5.8.1: Cattle and Pack Animals: Input Costs; - Section 5.8.2: Small Ruminants: Input Costs; - Section 5.8.3: Poultry: Input Costs; - Section 5.9.1: Cattle Meat; - Section 5.9.2: Small Ruminants Meat; - Section 5.9.3: Poultry Meat; - Section 5.9.4: Cattle Milk; - Section 5.9.5: Small Ruminants Milk; - Section 5.9.6: Eggs Production; - Section 5.9.7: Other Animal Products; - Section 5.10: Sources of Agricultural Information; - Section 5.11: Access to facilities; - Section 5.12: Transport Means; - Section 5.13: Storage Facilities; - Section 5.14: Access to Credit; - Section 5.15: Fixed Costs; - Section 5.16: Shocks and Food Security; - Section 5.17: Extension Services; - Section 5.18: Land Disputes.

Cleaning operations

All data cleaning and editing operations were performed using the statistical software Stata.

Response rate

The response rate was about the 86% during the post-planting visit, and the 83% during the post-harvesting visit.

Sampling error estimates

The accuracy of a survey results depends on both sampling and non-sampling errors. The AAS 2018 had a large enough and representative sample hence limiting errors due to sampling. On the other hand, the non-sampling errors usually resulting from errors occurring during data collection, were controlled thorough training of the data collectors, field supervision by the headquarter team, and a well-developed CAPI program. The standard errors and Coefficients of Variations (CVs) for selected indicators at national, ZARDI & sub-regional levels are presented in an Appendix of the final Survey Report.

Abstract

The Uganda Bureau of Statistics conducted the National Livestock Census (NLC) 2021 with an overall goal of providing information on the structure and organization of the livestock sector in Uganda. Specifically, the NLC 2021 provides inter alia a frame for livestock sample surveys; statistics on basic characteristics of livestock, farm infrastructure, farm equipment and machinery as well as aspects of the management of agricultural holdings disaggregated by sex. This information is used as benchmark data to validate and improve the reliability of livestock statistics generated from annual surveys and administrative sources.

The long-term objective of the NLC 2021 was to have information on the structure and organization of the livestock sector in Uganda and more specifically, the census was conducted to: 1. Generate statistics on basic characteristics of livestock, farm infrastructure, farm equipment and machinery; and aspects of the management of agricultural holdings disaggregated by sex. 2. Provide a frame for livestock sample surveys. 3. Provide benchmark data which will be used to validate and improve the reliability of livestock statistics generated from annual surveys and administrative sources. 4. Build national capacity for the development of the livestock sector

Geographic coverage

National coverage

Analysis unit

• Households

Universe

The NLC 2021 enumerated Household-based farms as well as Private Large Scale and Institutional Farms (PLS&IFs) in all the 135 districts of Uganda as of July 2019.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling Frame The Sampling Frame that was used to select the EAs in the non-cattle corridor was the 2014 National Population and Housing Census (NPHC 2014). The NPHC 2014 contained an Agricultural Module where a sampling frame was formed for censuses/surveys. About Four (4.3) million out of the 7.5 million households enumerated during the NPHC 2014 reported that one or more of their members engaged in an agricultural activity as of September 2014.

Sample Size The statistical unit of enumeration both in the cattle and non-cattle corridor was the livestock-keeping households. The total number of Enumeration Areas covered during the livestock census was 32,163 of which, 23,443 EAs about 73% were in the 559 SubCounties of the cattle corridor where complete enumeration was undertaken. Only 8,720 EAs, representing 27 percent were sampled from the 961 Sub-counties of the non-cattle corridor districts.

Sample Design A two-stage stratified cluster sampling method was used for selection of Livestock-keeping households. In the non-cattle corridor, the first stage sampling units were the sub-counties that were selected with certainty. The second stage sampling units were the Enumeration Areas (EAs). Where an EA was selected, all livestock-keeping households were enumerated. In the cattle corridor, all EAs in all Sub-counties (in the case of districts), or Divisions (in the case of Municipalities), were covered through a complete enumeration.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the NLC 2021 were structured questionnaires based on the UNHS and UHIS Model Questionnaires with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household head/holding including sex, age, No. of persons in Housheold, legal status of holding, production systems, livestock populations, and farm infrastructure, equipment and implements, access to veterinary services, land ownership, land tenure systems, ownership of livestock, labour used and sources of water.

All questionnaires and modules are provided as external resources.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of Stata data files

Detailed documentation of the editing of data can be found in the "Data Processing Guidelines" document provided as an external resource.

Response rate

Overall, the NLC 2021 targeted 32,163 EAs and 31,195 EAs were covered constituting a coverage rate of 97 percent. The response rate based on the expected number of households reported in National Population and Housing Census 2014 was 69 percent.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the NLC 2021 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Ethiopia had the highest number of cattle in Africa as of 2023, nearly ** million heads. United Republic of Tanzania possessed the second-highest bovine animal stock on the continent, with about ** million heads. In 2022, Africa had over *** million heads of cattle, one of the major species raised for livestock farming on the continent.

Abstract

The objective of the National Livestock Census was to establish Livestock and poultry numbers at national and district levels. Specific Objectives • To obtain data on basic characteristics of livestock such as age, sex, breed, use and livestock system. • To obtain information on farm infrastructure, equipment and implements. • To establish ownership and tenure regime for land used for livestock rearing. • To establish labour use of households that engage in livestock rearing.

Geographic coverage

The National Livestock Census was carried out in all the 80 districts of Uganda.

Analysis unit

The census had the following units of analysis: livestock/poultry, households and farms.

Universe

This census covered both household-based farms as well as institutional farms. While a complete enumeration of all institutional farms was conducted in all districts; a representative sample of household-based farms was enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A two-stage stratified cluster sampling design in which districts formed strata at the first stage was used in this census. At the second stage, Enumeration Areas (EAs)/villages were systematically selected from each selected sub-county. All households in the selected EAs were supposed to be completely enumerated. The sample of EAs for the National Livestock Census was selected using the 2002 PHC sampling frame. Countrywide, a total of 8,870 EAs was selected. These EAs were allocated to each district on the basis of the number of households with cattle. The use of households with cattle gave a representative spread of EAs by district. This sampling design resulted into a huge sample of 964,047 households representing 15.1% of the total number of households in Uganda as of 2008. Compared to other livestock censuses conducted in the past in this country and other developing countries; which usually consider sample sizes of 1%-5% of the total number of households; this census stands out as one of the most comprehensive livestock censuses.

Mode of data collection

Face-to-face [f2f]

Sampling error estimates

On the basis of the huge sample and the high precision of estimates as evidenced by the minimal coefficients of variation of almost all estimates (<20%); the results to be presented in the next section provide among other things; the most precise estimate of 6 the total number of livestock of their kind in this country as of 2008 and should be used as a benchmark for any future livestock surveys and censuses in Uganda.

Uganda UG: Production Index: 2014-2016: Livestock data was reported at 99.020 2014-2016=100 in 2022. This records a decrease from the previous number of 99.680 2014-2016=100 for 2021. Uganda UG: Production Index: 2014-2016: Livestock data is updated yearly, averaging 43.915 2014-2016=100 from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 112.050 2014-2016=100 in 2018 and a record low of 19.090 2014-2016=100 in 1964. Uganda UG: Production Index: 2014-2016: Livestock data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Agricultural Production Index. Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on sheep dairy production intensification potential, defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, dairy distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Dairy Intensification” * 0.4) + ("Crop Production" * 0.3) + (“Major Cities Accessibility” * 0.2) + (“Population Density” * 0.1)

Abstract

The AAS is an integrated modular survey aiming to provide high quality and timely data on the performance of the Ugandan agricultural sector, as well as core indicators on crop and livestock for better agricultural policy making.

Data collection for the AAS is implemented in two waves, corresponding to the first (January-June) and second (July-December) seasons of the Ugandan agricultural year. For each season, households in the survey's sample are interviewed twice, during the Post-Planting (PP) period and the Post-Harvesting (PH) period. This results in a total of four visits during the agricultural year.

Among information collected with the AAS there is data on: - The quantity and value of agricultural production; - The access to extension services, market information and agricultural facility; - Livestock keeping and animal products production; - The socio-demographic characteristics of agricultural household members.

The collected data is used to produce a set of tables and indicators for tracking and evaluating the impacts of government and development programs on agriculture, and to compute SDG and CAADP indicators related to food and agriculture. For the main findings from the AAS 2019, see the Executive Summary of the AAS 2019 Report (see external resources/downloads section).

Geographic coverage

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone includes districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS:

1) Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; 2) Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; 3) Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; 4) Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; 5) Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; 6) Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu; 7) Nabuin: districts included are Moroto, Nakapiripirit, Kotido, Napak, Amudat, Kaabong and Abim; 8) Serere: districts included are Serere, Kumi, Bukedea Amuria, Ngora, Katakwi, Soroti and Kaberamaido; 9) Mbarara: districts included are Mbarara, Ntungamo, Bushenyi, Kiruhura, Lyantonde, Sheema, Rubirizi, Mitoma, Isingiro,Ibanda, Buhweju, Sembabule, and Rakai; 10) Rwebitaba: districts included are Bundubugyo, Kabarole, Kamwenge, Kasese, Kyegegwa, Kyenjojo and Ntoroko. Being an urban area, Kampala has been excluded from the survey. Also Ntoroko district was not included in the sample.

Analysis unit

Agricultural households (i.e. agricultural holdings in the household sector)

Universe

Agricultural households (i.e. agricultural holdings in the household sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling design was adopted for the AAS 2019. To increase the efficiency of the sample design, the sampling frame was stratified into 10 ZARDIs. In each stratum, the first stage was the selection of the Primary Sampling Unit (PSU), which is the EA (enumerator area) and the second stage was the selection of the Secondary Sampling Unit (SSU), which are the Ag HHs. The survey covered households cultivating crops and/or raising livestock, including households that were cultivating a few crops or raising a limited number of animals. No minimum threshold on the amount of land cultivated or animals raised was set nor did the survey aim to generate estimates concerning aquaculture, forestry and fisheries.

Sample size The survey generated national, regional and sub-regional level estimates. A sample of 593 EAs and an average of 12 Ag HHs were selected from each EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AAS 2019 implemented two main questionnaires i.e. the Post-Planting, and Post-harvesting questionnaires. For each season, agricultural households are interviewed twice: during the post-planting and the post-harvesting visit. The questionnaire used during the post-planting season is called "Form 4 - Crop Area Module" and collects information on:

1) Household member socio-demographic characteristics; 2) Agricultural enterprises undertaken by the household in the current agricultural season; 3) Land use (Parcel and plots used by the agricultural households) i.e. Access to land, land use rights, decision making, land area, seed/seedlings utilization, etc. The main objective of this questionnaire is to estimate land areas for crops planted. This is done combining objective measurement (i.e., GPS) on plots and parcels and then collecting the share of land area covered by each crop on each plot (based on farmer's assessment). In addition, the questionnaire collects information on land tenure and use of agricultural inputs. This questionnaire contains a roster of household members, a roster of parcels, a roster of plots for each parcel and a list of crops by plot.

The questionnaire used for the post-harvesting visit is called "Form 52- Crop Production, Household and Holding Characteristics Module" and collects information on:

1) Household member socio-demographic characteristics (only for new household members) 2) Crop production and disposals 3) Use of agricultural inputs for crop production 4) Cost of labour used for crop production 5) Labour input used on the agricultural household 6) Animal raised on the holding 7) Inputs used for livestock production 8) Livestock production and dispositions 9) Access to agricultural information 10) Access to means of transportation 11) Access to storage facilities 12) Access to agricultural credit 13) Fixed costs of the agricultural household 14) Shocks and food security of the agricultural household 15) Access to extension services 16) Land disputes

Information 1-5 are collected in both first and second season while 6-16 is asked during the second season only. The main objective of this questionnaire is to collect data on crops harvested by agricultural households, based on farm declarations. In addition, the questionnaire collects information concerning the disposition of crops, labour input and use of inputs such as chemicals. Furthermore, it aims to collect livestock capital, animal production and inputs over a 12- month reference period, thus covering the entire agricultural year. The post-harvesting questionnaire also collects information concerning household and holding characteristics, such as access to market and agricultural information, household food security, shocks and their impact on food security etc.

Cleaning operations

Supervision

Data collection for the AAS 2019 was performed by 15 teams constituted by, on average, three enumerators and 1 supervisor. After recruitment, both supervisors and enumerators received two trainings, one on the post-planting (PP) and one on the post-harvesting (PH) questionnaires. During these trainings, the CAPI PP and PH applications to be used for data collection were tested and refined. During the data collection stage, after completing a CAPI interview, enumerators submitted the electronic interview to their supervisors through Survey Solutions. Then, Supervisor checked the quality of data collected and decided on whether accepting or rejecting the completed case. When a supervisor rejected an interview, the interview was sent back to the interviewer tablet in order to be corrected as requested. On the other hand, when the supervisor accepted an interview, this was sent to the headquarter for final validation. This process continued until the quality of collected data was considered as satisfactory.

Response rate

The response rate was about the 84%.

Sampling error estimates

The accuracy of the survey results depends on the sampling and the non-sampling errors. The AAS 2019 had a large enough and representative sample to limit sampling errors. On the other hand, the non-sampling errors, usually errors that arise during data collection, were controlled through thorough training of the data collectors, field supervision by the headquarters team, and a well-developed CAPI programme. The Coefficients of Variations (CVs) and Confidence Intervals (CIs) for selected indicators at national, ZARDI and sub-regional levels are presented in the Annex tables.

Uganda Agriculture Market size was valued at USD 4.07 Billion in 2023 and is projected to reach USD 6.21 Billion by 2031, growing at a CAGR of 4.20% from 2024 to 2031.

Key Market Drivers:

Government Support and Policies: The Ugandan government actively promotes the agriculture industry through a variety of programs and policies aimed at improving food security and increasing export profits. Initiatives such as the National Agricultural Advisory Services (NAADS) and the Plan for Modernization of Agriculture (PMA) have promoted the use of modern farming practices while also providing farmers with subsidies and support services.

Increasing Demand for Food and Agricultural Products: Uganda's rapidly rising population, along with urbanization, has resulted in increased demand for both food and agricultural products. The growing middle class and rising living standards are driving up demand for processed foods and higher-value agricultural items.

Abstract

The Global Strategy to improve Agriculture and Rural Statistics (GSARS) and Uganda Bureau of Statistics (UBOS) administered a pilot study to test questions around (a) intra-household decision-making process in the operation and management of agricultural holdings and (b) remunerated and non-remunerated work in agricultural households. The study sample consists of agricultural households in the districts of Bukedea, Kamelia, and Buikwe in the Eastern Region. The field tests consisted of two questionnaires: (1) a brief holding questionnaire and (2) an individual questionnaire. The holding questionnaire asked for the holder of the holding as is traditionally done in agricultural censuses and national surveys, where the holding is defined as an economic unit of agricultural production under single management comprising of all livestock kept and all land used for agricultural production purposes, and the holder is a person who manages or has control over the holding and makes the major decisions regarding the use of the holding. From the holding questionnaire, the enumerators selected two respondents from the agricultural household holding for the individual questionnaire. When possible, the holder was designated as the first respondent of the individual questionnaire. The second respondent was spouse or partner of holder if he or she lives in the household and is engaged in agriculture on the holding.

Geographic coverage

Not representative

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample consisted of 512 agricultural households from 32 randomly selected enumeration areas (EAs) in the districts of Bukedea, Kamelia, Buikwe in the Eastern Region with 16 systematically selected households per EA. It is not representative at the district level as this was cost prohibitive and some EAs needed to be dropped from the population prior to EA selection. A complete listing of the selected EAs was done prior to the survey implementation and sampling. In 21 households, the surveys were not completed resulting in a non-response rate of four percent and a final sample of 491 with 169 households from Bukedea, 161 from Kamelia, and 161 from Buikwe. For 318 households, there were two respondents.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Variables with all missing observations were deleted.

Response rate

Response rate was 96%.

Uganda Exports of residues, wastes of food industry, animal fodder to India was US$99.26 Thousand during 2022, according to the United Nations COMTRADE database on international trade. Uganda Exports of residues, wastes of food industry, animal fodder to India - data, historical chart and statistics - was last updated on December of 2025.

Abstract

The Annual Agricultural Survey (AAS) is an integrated modular survey aiming to provide high quality and timely data on the performance of the Ugandan agricultural sector, as well as core indicators on crop and livestock for better agricultural policy making. Data collection for the AAS is implemented in two waves, corresponding to the first (January-June) and second (July-December) seasons of the Ugandan agricultural year. For each visit, households in the survey's sample are interviewed twice, during the visit1 period and visit2. This results in a total of two visits during the agricultural year. The data collection activities were delayed by the pandemic. Among information collected with the AAS there is data on: The quantity and value of agricultural production; The access to extension services, market information and agricultural facility; Livestock keeping and animal products production; The socio-demographic characteristics of agricultural household members. The collected data is used to produce a set of tables and indicators for tracking and evaluating the impacts of government and development programs on agriculture, and to compute SDG and CAADP indicators related to food and agriculture. For the main findings from the AAS 2020, see the Executive Summary of the AAS 2020 Report (see external resources/downloads section).

Geographic coverage

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone includes districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS: Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu;

Analysis unit

Agricultural households (i.e. agricultural holdings in the household sector)

Universe

Agricultural households (i.e. agricultural holdings in the household sector).

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling design was adopted for the AAS 2020. To increase the efficiency of the sample design, the sampling frame was stratified into 10 ZARDIs. In each stratum, the first stage was the selection of the Primary Sampling Unit (PSU), which is the EA (enumerator area) and the second stage was the selection of the Secondary Sampling Unit (SSU), which are the Ag HHs. The survey covered households cultivating crops and/or raising livestock, including households that were cultivating a few crops or raising a limited number of animals. No minimum threshold on the amount of land cultivated or animals raised was set nor did the survey aim to generate estimates concerning aquaculture, forestry and fisheries. Sample size The survey generated national, regional and sub-regional level estimates. A sample of 593 EAs and an average of 12 Ag HHs were selected from each EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Annual Agricultural Survey (AAS 2020) adopted three main questionnaires: the post-planting (PP), the post-harvest (PH) and the livestock and holding questionnaires. Normally, the PP and PH questionnaires are administered each season, while the livestock and holding questionnaire is administered at the end of the second season and covers the entire agricultural year. Nonetheless, in the AAS 2020, a different survey calendar was adopted due to movement limitations imposed as a result of the COVID-19 pandemic.

Cleaning operations

All the data captured from the field were stored in the cloud with a local backup. Editing and validation was done electronically using STATA software.

Response rate

The response rate was about the 94.5 %.

Sampling error estimates

The accuracy of the survey results depends on the sampling and the non-sampling errors. The AAS 2020 had a large enough and representative sample to limit sampling errors. On the other hand, the non-sampling errors, usually errors that arise during data collection, were controlled through thorough training of the data collectors, field supervision by the headquarters team, and a well-developed CAPI programme. The Coefficients of Variations (CVs) and Confidence Intervals (CIs) for selected indicators at national, ZARDI and sub-regional levels are presented in the Annex tables.

Abstract

The agricultural sector is the most important sector of the Ugandan economy. Empirical evidence attests to this; for example the share of the agricultural sector to Gross Domestic Product (GDP) is about 21 percent (at the then current prices). According to the Agricultural Module of the 2002 Population and Housing Census, the agricultural sector accounted for 73 percent of the total employment for the persons aged 10 years and above. In addition, 74 percent of the households had an agricultural holding. The long term vision of the Government of Uganda is to eradicate poverty and the strategies for this vision are defined in the then Poverty Eradication Action Plan (PEAP) which has been transformed into the National Development Plan (NDP).

The vision of PMA was to eradicate poverty through transforming subsistence agriculture to commercial agriculture. The whole process of transformation requires accurate and reliable agricultural data to monitor the progress made and inform policy and planning processes

Further, countries are focusing on the need to monitor progress towards the Millennium Development Goals (MDGs) through their National Statistical systems. The World Census of Agriculture (WCA), 2010 was formulated with this in mind and specifically to monitor eradication of extreme poverty and hunger, achievement of Universal Primary Education, Promotion of gender equality and empowerment of women and ensuring environmental sustainability.

Within the framework of the FAO/World Bank Agricultural Statistics Assistance to Uganda, a Data Needs Assessment Study was undertaken in August 1999. One of the major findings was that the Agricultural Statistics System was fragile, vulnerable, un-sustainable and above all, unable to meet the data needs of users. A Census of Agriculture (CA) is major source to meet these demands.

Census taking in Uganda Prior to the conducting of the Uganda Census of Agriculture (UCA), 2008/09 two (2) other censuses had been conducted. The first CA was conducted during 1963/65. The Government of Uganda was assisted by FAO and the then Department for Technical Cooperation of the United Kingdom both of which provided international and census equipment to a varying degree.

The second CA called the National Census of Agriculture and Livestock (NCAL) was conducted during 1990/91. It was funded by United Nations Development Programme (UNDP) and executed by FAO. Therefore the UCA 2008/09 formed the third CA in the history of census taking in Uganda.

Preparatory activities An Agricultural Module was included in the Population and Housing Census 2002, to collect the data that would form a basis for constructing an up-to-date and appropriate sampling frame for a Uganda Census of Agriculture (UCA), 2004/05. A Pre-Test was conducted in 2002 followed by a pilot Census of Agriculture (PCA) which was conducted in 2003.

Lack of financial resources militated against conducting the UCA, 2004/05. During the Financial Year (FY) 2007/08 Government made a budgetary provision for conducting a census of agriculture.

The FY 2007/08 was mainly a preparatory year. As mentioned earlier, the plan had been to conduct a UCA during 2004/05, which did not take place. By 2008/09 (the census reference year), many changes had taken place and needed to be addressed. To this end, another Pre -Test was conducted in May 2008. Based on the findings from the Pre-Test, the UCA instruments had to be revised. Another very important factor for the instruments' revision was an input from the International Consultants (like FAO Statisticians). Other preparatory activities included arrangements to procure census equipment and transport as well as recruiting and training of Field Staff.

Objectives of the UCA.2008/09 While the long-term objective of the UCA, 2008/09 was to have a system of Food and Agriculture Statistics (FAS) in place, the immediate objective was to collect and generate benchmark data needed for monitoring and evaluation of the agricultural sector at all levels, through a nation-wide CA.

Geographic coverage

The Uganda Census of Agriculture 2008/09 covered all the 80 districts in the country as of July 2007.

Analysis unit

Agricultural households, Agricultural holdings

Universe

The Uganda Census of Agriculture 2008/09 was therefore planned to cover all the 80 districts at the time and collect data on various structural characteristics of agricultural holdings. Limited data on livestock variables was planned to be collected because comprehensive livestock data was to be collected in a Livestock Census, 2008.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A stratified two-stage sample design was used for the small and medium-scale household-based agricultural holdings. At the first stage Enumeration Areas (EAs) were selected with Probability Proportional to Size (PPS), and at the second stage, households which were the ultimate sampling units were selected using systematic sampling.

For each of the sampled EAs, listing took place in the field and a number of filter questions (using Listing Module) were administered to determine eligibility (i.e., only the Households with Agricultural Activity would be eligible). Further, the eligible households were stratified into two strata namely, the small/medium holdings stratum and the Private Large-Scale holdings stratum.

On the other hand, district supervisors compiled separate lists of Institutional Farms and Private Large Scale Farms. These were to be covered on a complete enumeration basis.

During sampling, two (2) lists namely for EAs and PLS&IFs were used to identify possibilities of duplication and address them. If a PLS&IF was in both lists, it was deleted from the EA frame. However, if it was found only in the EA frame, it was left as part of the frame from which to sample. In other words, the List was not updated based on the information collected from the EAs sampled from the Area Frame.

The UCA2008/09 estimates were planned to be generated at national, regional and district levels. To achieve this, a sampling scheme of 3,606 EAs and 10 agricultural households in each selected EA, leading to 36,060 households was adopted.

In this design, an optimum number of households to be sampled per EA was determined on the basis of a suitable cost ratio (ratio of the cost per PSU to cost per SSU) and intra-class correlation, calculated from the Agricultural Module data from PHC 2002. For a cost ratio of 40 and intra-class correlation as 0.29, optimum number of households to be selected was obtained as 10.

The required sample size of EAs was selected from each district with probabilities proportional to size (PPS), using the systematic sampling algorithm described in Hansen, Hurwitz, and Madow (1953) while Agricultural Households were selected with equal probability systematic sampling procedure. The measure of Size (MOS) which was used for sample selection was the number of Agricultural Households determined from the 2002 PHC.

Sampling deviation

EAs where there was no enumerations due to insecurity: There were EAs which could not be listed or even enumerated due to insecurity , resistance by residents or nonexistent etc. These were in Moroto, Nakapiririt, Mubende, Kampala etc. Since there were no replicate EAs, the number of sampled EAs in those districts was lowered reducing the estimated number of EAs expected to give good results in those respective districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The principles of validity, optimization and efficiency which refer to ability for the questionnaires to yield more reliable information per unit cost; measured as a reciprocal of the variance of the estimate and enables objective interpretation of the results was followed. While costs involved man hours and money expended for data collection from sampled units, the design of questionnaires had to collect a minimum set of internationally comparable core data(indices) for Uganda, as enshrined in the pillars of FAO.

Cleaning operations

Data Processing monitored the data quality parameters and data quality team could continuously report to the field operations team who could make feed back to the DSs for improvement. Returned questionnaires were subjected to the following steps Coding, Data capture, Editing, Secondary Editing and Quality control.

Coding This involved making sure that all forms/questionnaires had correct geographical identification information and correct crop codes. The coding team reviewed the sampling of holdings within an enumeration area to see that only eligible/sampled holdings were actually enumerated.

Editing This involved the process of identifying inconsistencies within the data and removing them. At the beginning of UCA data processing, a set of editing rules and guidelines where developed by the data processing team with technical guidance from the subject matter specialists. Many of these were incorporated into the data entry application and others were left for the secondary editing stage.

Secondary Editing Errors that passed the data entry stage were subjected to the editing stage. This stage was meant to find inconsistencies within the data. It brought out problems that required subject matter specialists to resolve. To resolve most of such errors, consultations were made with the national supervisors, district supervisors, UBOS and MAAIF technical teams.

Response rate

The UCA2008/9 had several forms namely; Agricultural Households and holding Characteristics Module; Crop Area Module; Crop Production Module

Lebanon Imports from Uganda of Residues, wastes of food industry, animal fodder was US$18 during 2023, according to the United Nations COMTRADE database on international trade. Lebanon Imports from Uganda of Residues, wastes of food industry, animal fodder - data, historical chart and statistics - was last updated on December of 2025.

Uganda Exports of residues, wastes of food industry, animal fodder to United States was US$2.83 Million during 2024, according to the United Nations COMTRADE database on international trade. Uganda Exports of residues, wastes of food industry, animal fodder to United States - data, historical chart and statistics - was last updated on November of 2025.

The cattle industry contributes to Uganda’s agricultural output. It however faces challenges that include theft and parentage ascertainment. These challenges can benefit from recent developments in genomics and bioinformatics technologies. In the current study, we provided a proof-of-concept conflict resolution method based on the available bovine genomic tools. Briefly, two farmers we refer to as A and B contested for ownership of a group of 9 cattle. To offer objective and data-based guidance to resolving this conflict, we sampled hair samples of the 9 contested cattle as well as 7 and 2 cattle from farmers A and B respectively that were identified by these farmers as the closest relatives of the contested animals. DNA was extracted from each of the collected hair samples and subsequently genotyped on the Illumina BovineSNP50-24 version 3 BeadChip for 53218 DNA Single Nucleotide Polymorphisms (SNP) variants. Upon genotype data cleaning we retained ~30,000 SNPs which were used for different genomic analyses including principal component analysis, identity by state (IBS) analysis, and hierarchical clustering analysis, to establish the genetic relationships within and between the investigated animal groups. Methods In this study, genetic analyses of 18 indigenous Ugandan cattle were performed to establish genetic relationships between cattle from 3 different groups. Hair samples of the 9 contested cattle as well as 7 and 2 cattle from farmers A and B respectively that were identified by these farmers as the closest relatives of the contested animals were collected. DNA was extracted from each of the collected hair samples and subsequently genotyped on the Illumina BovineSNP50-24 version 3 BeadChip for 53218 DNA Single Nucleotide Polymorphisms (SNP) variants. Upon genotype data cleaning we retained ~30,000 SNPs which were used for different genomic analyses including principal component analysis, identity by state (IBS) analysis, and hierarchical clustering analysis, to establish the genetic relationships within and between the investigated animal groups.

This dataset and code are part of supplementary data for a manuscript Muwonge, A., Kakooza, T., Johnson, P.C.D., Kisuule, L., Kimaanga, M., Kankya, C., de Clare Bronsvoort, B.M., Lembo, T., 'Production system drivers of antibiotic resistance at the human-animal interface in Uganda', The Lancet Planetary Health (in submission). They explore the role of livestock production systems in the epidemiology of antibacterial resistance (ABR) in sympatric human and livestock populations, which is poorly understood. Here, they examine ABR at the farmer-pig interface of Uganda, where the pig sector is rapidly growing, to quantify rates of resistance, understand associated human- and livestock-related factors, and investigate cross-species transmission. The motivation of this is to improve our understanding of the role of livestock production systems in the emergence and transmission of AMR, this paper uses phenotypic resistance profile from sentinel bacteria E.coli and Klebsiella, recovered from a faecal sample collected from farmers and their pigs across a one-year longitudinal study. This is mapped to AMR gene carriage of four selected genes measured using QPCR. Using the Metadata, they examine drivers of resistance in this setting, and also use the prevalence and sharing of MDR profiles to infer transmission.

Abstract

The objectives of the Smallholder Household Survey in Uganda were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Uganda according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CGAP smallholder household survey in Uganda is a nationally-representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level and for the following administrative four regions: Central, Eastern, Northern, and Western regions. The Central region includes central metro (i.e., four municipalities surrounding Kampala), the parishes in Kampala with poultry activity but it excludes Kampala city which is entirely urban.

Sampling Frame

The sampling frame for the smallholder household survey is the list of enumeration areas (EAs) created for the 2014 Uganda National Population and Housing Census. Uganda is divided into 112 districts with each district comprised of counties/municipalities. Each county/municipality consists of sub-counties/town councils with each of them being further divided into parishes/wards and villages/cells.

For the 2014 population census, each village and cell was further divided into EAs. Information on the number of agricultural households at the EA level will be available in December 2015, and thus not on time for the smallholder survey. As a result, the sample allocation of the survey was based on the distribution of households per region and urban and rural according to the 2014 Census.

Sample Allocation and Selection

In order to take non-response into account, the target sample size was increased to 3,158 households assuming a household non-response rate of 5% observed in similar national households. The total sample size was first allocated to the four regions proportionally to their number of households. Within each region, the resulting sample was then distributed to urban and rural areas proportionally to their size.

The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each region into urban and rural areas. The urban/rural classification is based on the 2014 population census. Therefore, eight strata were created and the sample was selected independently in each stratum. Prior to the sample selection, the sampling frame was sorted by the nine agricultural zones called Zardi (Zonal Agriculture Research Development Institute).

In the first stage, 216 EAs were selected as primary sampling units with probability proportional to size, the size being the number of households in the EAs. A household listing operation was carried out in all selected EAs to identify smallholder households according to the definition used in the survey, and to provide a frame for the selection of smallholder households to be included in the sample.

In the second stage, 15 smallholder households were selected in each EA with equal probability. Due to rounding, this yielded a total of 3,240 smallholder households. In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviors and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Building on secondary research on the smallholder sector and discussions with stakeholders, the design process for the survey instrument began. This process involved defining the end goal of the research by: • Drawing from existing survey instruments; • Considering the objectives and needs of the project; • Accounting for stakeholder interests and feedback; • Learning from the ongoing financial diaries in country; and, • Building from a series of focus groups conducted early on in the study.

Using this foundation, a framework for the survey instrument was developed to share with stakeholders and capture all the necessary elements of a smallholder household. The framework consisted of five main subject areas: (i) demographics, (ii) household economics, (iii) agricultural practices, (iv) mobile phones, and (v) financial services.

In order to capture the complexity inside smallholder households, the smallholder household survey was divided into three questionnaires: the household questionnaire, the multiple respondent questionnaire and the single respondent questionnaire. In addition to English, the questionnaires were translated into nine local languages: Lugishu, Luganda, Ateso, Lugbara, Runyakole, Lutooro, Ngakaaramojong, Langi, and Acholi.

The household questionnaire collected information on: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head) • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires.
• Household assets and dwelling characteristics

Both the Multiple and Single Respondent questionnaires collected different information on: • Agricultural practices: farm information such as size, crop types, livestock, decision-making, farming associations and markets • Household economics: employment, income, expenses, shocks, borrowing and saving habits, and investments

In addition, the Single respondent questionnaire collected information on: • Mobile phones: attitudes toward phones, usage, access, ownership, desire and importance • Financial services: attitudes towards financial products and services such as banking and mobile money, including ownership, usage, access and importance.

Following the finalization of questionnaires, a script was developed to support data collection on mobile phones. The script was tested and validated before its use in the field.

Cleaning operations

During data collection, InterMedia received a weekly partial SPSS data file from the field which was analyzed for quality control and used to provide timely feedback to field staff while they were still on the ground. The partial data files were also used to check and validate the structure of the data file.

The full data file was also checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide includes household and individual response rates for the CGAP smallholder household survey in Uganda.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

People in northern Uganda are currently rebuilding their lives after a lengthy period of conflict. To facilitate this, the Ugandan government and donors have promoted investment in pigs as an important strategy for generating income quickly and ensuring livelihood security. In this context, animal health issues are an acknowledged challenge, creating uncertainty for animal owners who risk losing both their animals and income. This paper draws on policy documents guiding the veterinary sector, interviews with faculty staff at Makerere University and with veterinarians and paraprofessionals in northern Uganda, and ethnographic fieldwork in smallholder communities. The aims of this study were to contribute to an understanding of the structure of veterinary support and its dominant development narratives in policy and veterinary education and of the way in which dominant discourses and practices affect smallholders' ability to treat sick animals. Particular attention was paid to the role of paraprofessionals, here referring to actors with varied levels of training who provide animal health services mainly in rural areas. The results suggest that veterinary researchers, field veterinarians and government officials in agricultural policy share a common discourse in which making smallholders more business-minded and commercializing smallholder production are important elements in reducing rural poverty in Uganda. This way of framing smallholder livestock production overlooks other important challenges faced by smallholders in their livestock production, as well as alternative views of agricultural development. The public veterinary sector is massively under-resourced; thus while inadequately trained paraprofessionals and insufficient veterinary support currently present a risks to animal health, paraprofessionals fulfill an important role for smallholders unable to access the public veterinary sector. The dominant discourse framing paraprofessionals as “quacks” tends to downplay how important they are to smallholders by mainly highlighting the negative outcomes for animal healthcare resulting from their lack of formalized training. The conclusions of this study are that both animal health and smallholders' livelihoods would benefit from closer collaboration between veterinarians and paraprofessionals and from a better understanding of smallholders' needs.

Some data were used in maps 4, 5, 6, 7, 8, 9, 10 of ""Mapping a Better Future: How Spatial Analysis Can Benefit Wetlands and Reduce Poverty in Uganda."" from Wetlands Management Department, Ministry of Water and Environment, Uganda; Uganda Bureau of Statistics; International Livestock Research Institute; and World Resources Institute. 2009. [RETURN] This dataset is based on the National Wetlands Information System (NWIS). NWIS, maintained by the Wetlands Management Department, contains detailed data on different wetland uses, the level of use, and the impact of these uses on wetland systems. It is based on a standardized inventory of wetlands carried out for approximately 5,000 wetland sample points between 1997 and 2001. Each sample point reflects the uses and impacts observed in the field of vision at that location. Field teams inventoried 37 different wetland products, which they aggregated to 12 different main activities. Here are the main uses and associated products inventoried: harvesting of natural herbaceous vegetation for food, fuel, building material, craft material, mulch material, and medicines; excavation of minerals for salt, clay, sand, gravel, gold/ gemstones/ minerals; cultivation for food and fibre; water collection and use for rural domestic water, urban domestic water, water for livestock, industrial water, irrigation water; harvesting of natural trees for food, fuel/ firewood, building poles/ timber, craft material, medicines; plantation tree cultivation/ harvesting for building/ fencing materials, food, fuel/ firewood, craft material, medicines; fisheries for food/ skin; livestock grazing; beekeeping; human settlement for housing, industrial development; hunting for meat, skins, craft material; tourism. Each product was characterized by their level of use (none, low, medium, and high) and impact of use (none, low, medium, and high). The level of use and impact of use were summarized at the wetland sample point by adding the level of use/ impact of use of each product weighted by 1 if low, 2 if medium, and 3 if high. Also inventoried are abuses/ threats such as artificial drainage, dam construction/ water diversion, virgin land clearing, burning of vegetation, propagation of exotic plant/ animal species, solid and liquid waste disposal. Cautions Dataset is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. Citation

To mitigate the development of antimicrobial resistance (AMR), antibiotic use (ABU) in the livestock sector needs to be reduced. In low- and middle-income countries, regulations have shown to be less successful in reducing ABU. Here, a bottom-up approach can complement legal frameworks which requires an understanding of the drivers for ABU. In this study, we investigated the influence of geographic and socioeconomic settings on determinants for ABU among pig farmers in Uganda. The data was collected through a questionnaire (containing 75 questions) in two districts, Lira and Mukono, and comparative statistical analyses were performed. Farmers in Lira had lower access to animal health services, applied disease preventive measures less and used antibiotics more. In Mukono, it was more common to consult a veterinarian in response to disease, while it in Lira was more common to consult an animal health worker. There was no difference in how many that followed treatment instructions from a veterinarian, but it was more common in Lira to follow instructions from pharmacies. The findings support the need for locally tailored AMR-reducing interventions to complement regulations. To accomplish this tailoring, systematic collection of knowledge of farm structures, farm practices and access to animal health services and veterinary drugs is necessary.

The questionnaire was administered and recorded electronically on tablets using Open Data Kit (ODK) (https://getodk.org), an open-source tool for smart devices (i.e., smartphone or tablet) that enables creation and use of electronic questionnaires. The data collection through ODK was backed up daily.

The dataset contains 72 columns and 463 rows.

For explanations of the variables, see the separate file "AMUSE_Uganda_2022_dataset_Explanations". For explanations of the answer options, see the separate file "AMUSE_Uganda_2022_Questionnaire".