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.

The Agriculture in Uganda Market is Segmented by Type (Cereals and Grains, Oilseeds and Pulses, and Fruits and Vegetables). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Export Analysis (Value and Volume), Import Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

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 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 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%.

GDP from Agriculture in Uganda increased to 8683.38 UGX Billion in the second quarter of 2025 from 7404.86 UGX Billion in the first quarter of 2025. This dataset provides - Uganda Gdp From Agriculture- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

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 UG: Production Index: 2014-2016: Crop data was reported at 148.350 2014-2016=100 in 2022. This records a decrease from the previous number of 169.720 2014-2016=100 for 2021. Uganda UG: Production Index: 2014-2016: Crop data is updated yearly, averaging 89.090 2014-2016=100 from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 169.720 2014-2016=100 in 2021 and a record low of 43.360 2014-2016=100 in 1961. Uganda UG: Production Index: 2014-2016: Crop 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. Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

Actual value and historical data chart for Uganda Agriculture Value Added Us Dollar

The Uganda Agriculture Industry size was valued at USD 4.07 Million in 2023 and is projected to reach USD 5.43 Million by 2032, exhibiting a CAGR of 4.20 % during the forecast periods. Recent developments include: November 2022: The Government of Uganda, through the Ministry of Agriculture, Animal Industries, and Fisheries (MAAIF), together with development partners, launched and released biological control agents for the Mango mealybug (Rastococcus invaders)., August 2022: The government of Uganda has developed an ambitious yet detailed plan to increase its current production of 402,000 tons of coffee to 1.2 million tons annually by 2025. This will lead to the increased production and productivity of coffee in the country., November 2021: An association of farmers known as Hortifresh was formed in the country. The mission of this farmer's cooperative is to create a conducive environment for the long-term cultivation of Uganda's fresh fruits and vegetables. Ugandan farmers of fresh fruits and vegetables are expected to benefit greatly from the association's inception.. Key drivers for this market are: Rising Consumption of Cashew Nuts in the Country, Favorable Government Initiatives. Potential restraints include: Hazardous Climatic Condition Hinders Cashew Production, Stringent Regulations Related to Food Quality Standards. Notable trends are: Agriculture Contributes Highly to Uganda’s GDP.

Actual value and historical data chart for Uganda Employment In Agriculture Percent Of Total Employment

Uganda UG: Total Fisheries Production data was reported at 507,295.200 Metric Ton in 2016. This records a decrease from the previous number of 513,795.000 Metric Ton for 2015. Uganda UG: Total Fisheries Production data is updated yearly, averaging 212,331.000 Metric Ton from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 572,219.000 Metric Ton in 2014 and a record low of 61,200.000 Metric Ton in 1961. Uganda UG: Total Fisheries Production 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 and Consumption. Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included.; ; Food and Agriculture Organization.; Sum;

Actual value and historical data chart for Uganda Agriculture Value Added Percent Of GDP

Uganda UG: Agricultural Land data was reported at 144,150.000 sq km in 2015. This stayed constant from the previous number of 144,150.000 sq km for 2014. Uganda UG: Agricultural Land data is updated yearly, averaging 118,170.000 sq km from Dec 1961 (Median) to 2015, with 55 observations. The data reached an all-time high of 144,650.000 sq km in 2012 and a record low of 90,180.000 sq km in 1961. Uganda UG: Agricultural Land 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: Land Use, Protected Areas and National Wealth. Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

Uganda: Agriculture value added, billion USD: The latest value from 2024 is 13.24 billion U.S. dollars, an increase from 11.75 billion U.S. dollars in 2023. In comparison, the world average is 27.33 billion U.S. dollars, based on data from 150 countries. Historically, the average for Uganda from 1960 to 2024 is 3.32 billion U.S. dollars. The minimum value, 0.21 billion U.S. dollars, was reached in 1960 while the maximum of 13.24 billion U.S. dollars was recorded in 2024.

Comprehensive dataset containing 730 verified Agricultural production businesses in Uganda with complete contact information, ratings, reviews, and location data.

Uganda export data: Unveiling a budding economy with potent areas like tourism and manufacturing, driven by key exports coffee, tea, and gold.

Uganda: Agriculture value added per worker, constant USD: The latest value from is U.S. dollars, unavailable from U.S. dollars in . In comparison, the world average is 0.00 U.S. dollars, based on data from countries. Historically, the average for Uganda from to is U.S. dollars. The minimum value, U.S. dollars, was reached in while the maximum of U.S. dollars was recorded in .

Actual value and historical data chart for Uganda Agriculture Value Added Annual Percent Growth

The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this over arching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilisation of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)