Census of Agriculture 2008-2009
Uganda, 2008 - 2009
Created on
Apr 25, 2019
Last modified
Apr 25, 2019
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1696
Identification
Survey ID Number
UGA_2008_AgCENS_v01_M
Title
Census of Agriculture 2008-2009
Country
| Name | Country code |
|---|---|
| Uganda | UGA |
Study type
Agricultural Survey [ag/oth]
Series Information
The first Census of Agriculture (CA) in Uganda was conducted during the period of 1963/65 with the assistance of the Food and Agriculture Organizations of the United Nations (FAO) and the then Department of Technical Cooperation of Britain. The second Census of Agriculture (National Census of Agriculture and Livestock) was conducted during 1990/91 although preparatory arrangements had started earlier. That census was funded by the United Nations Development Programme and executed by FAO. The Ministry then responsible for agriculture was the implementing agency. The third/latest in series is the Uganda Census of Agriculture (UCA 2008/09). Its main/long-term objective was to establish a system of Food and Agriculture Statistics (FAS), for providing information and data to inform policy and planning timely and sustainably.
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.
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.
Kind of Data
Census/enumeration data [cen]
Unit of Analysis
Agricultural households, Agricultural holdings
Version
Version Description
v1.2 Edited data, second version, for internal use only.
Version Date
2010-11
Version Notes
This version contains the final datasets
Scope
Notes
AGRICULTURAL HOUSEHOLD
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Household Member Characteristics
- Holder Identification
- Legal Status of Agricultural Holder
- Utilisation of Land on the Holding
- Agricultural Technology and Extension Services
- Sources of Agricultural Information
- Access to Facilities
- Means of Transportation
- Storage Facilities
- Access to Credit
- Irrigation System
- Other Types of Water Management Practices
- Holding Exposure for Flooding/ Water Retention
- Land Tenure System of Parcels
- Number of Tree Crops
- Agricultural Inputs
- Agricultural Equipment
- Labour Input
- Livestock
- Aquaculture
- Apiary (Bee keeping)
- Forestry
- Household Food Security
- Other Economic Production Activities
- Sources of Household Income
CROP AREA
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Area Characteristics
CROP PRODUCTION
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Production and Disposition of Crops (within EA)
- Production and Disposition of Crops (outside EA)
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Household Member Characteristics
- Holder Identification
- Legal Status of Agricultural Holder
- Utilisation of Land on the Holding
- Agricultural Technology and Extension Services
- Sources of Agricultural Information
- Access to Facilities
- Means of Transportation
- Storage Facilities
- Access to Credit
- Irrigation System
- Other Types of Water Management Practices
- Holding Exposure for Flooding/ Water Retention
- Land Tenure System of Parcels
- Number of Tree Crops
- Agricultural Inputs
- Agricultural Equipment
- Labour Input
- Livestock
- Aquaculture
- Apiary (Bee keeping)
- Forestry
- Household Food Security
- Other Economic Production Activities
- Sources of Household Income
CROP AREA
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Area Characteristics
CROP PRODUCTION
- Identification Particulars
o Holding Particulars
o Enumeration Particulars
- Production and Disposition of Crops (within EA)
- Production and Disposition of Crops (outside EA)
Topics
| Topic | Vocabulary | URI |
|---|---|---|
| agricultural, forestry and rural industry [2.1] | CESSDA | http://www.nesstar.org/rdf/common |
Keywords
| Keyword | Vocabulary |
|---|---|
| agricultural, forestry and rural industry [2.1] | CESSDA |
Coverage
Geographic Coverage
The Uganda Census of Agriculture 2008/09 covered all the 80 districts in the country as of July 2007.
Geographic Unit
A district was planned to be the domain of study i.e. census data was planned to be disaggregated at district level.
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.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Uganda Bureau of Statistics (UBOS) | Ministry of Finance, Planning and Economic Development |
| Ministry of Agriculture, Animal Industry and Fisheries | Government of Uganda |
Producers
| Name | Role |
|---|---|
| Food and Agriculture Organization of the United Nations | Technical Assistance |
| Department for International Development | Technical Assistance |
| Norwegian Agency for Development | Technical Assistance |
| World Bank | Technical Assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Uganda | UGA GOV | Funding |
Sampling
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.
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.
Deviations from the Sample Design
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.
Response Rate
The UCA2008/9 had several forms namely; Agricultural Households and holding Characteristics Module; Crop Area Module; Crop Production Module and PLS & IFs Module. Overall, the response rate was 93.5 percent.
Weighting
The estimates presented in this report were derived from a scientifically selected sample.
Weights were computed for each holding within the EA in a particular district.
WHi= [1/(Prob1 * Prob2)]* A*B
Where Prob 1: Is the probability of selecting an EA j from District K.
Prob2: Is the probability of selecting HH i from EA j
A : Is the coverage rate at EA level within the district
B: Is the coverage rate of sampled hhs within EA j
WHi: Is the final weight of selecting HHi in EA j from district K
Weights were computed for each holding within the EA in a particular district.
WHi= [1/(Prob1 * Prob2)]* A*B
Where Prob 1: Is the probability of selecting an EA j from District K.
Prob2: Is the probability of selecting HH i from EA j
A : Is the coverage rate at EA level within the district
B: Is the coverage rate of sampled hhs within EA j
WHi: Is the final weight of selecting HHi in EA j from district K
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2008 | 2009 |
Time periods
| Start date | End date |
|---|---|
| 2008-08 | 2009-05 |
Data Collection Mode
Face-to-face [f2f]
Supervision
On the average, an Enumerator was allocated four (4) EAs for purposes of having equal workload. The allocation of EAs to an Enumerator was carried out in such a way that distances covered could be minimized thus reducing on time loss and costs.
It was a requirement that for the four (4) EAs assigned/allocated to an Enumerator, he/she was a resident I n one of them. This was intended not only to ensure proximity to the assigned EAs but also take advantage of the enumerator's familiarity with an EA. In addition, his/her being known to the respondents in the locality was a prerequisite for securing their cooperation.
The large districts like Masaka, Bushenyi, etc. had two (2) DSs, while the small ones like Bukwo, Busia, etc. had one (1). In the case of the former, the two (2) DSs apportioned work based on number of EAs, enumerators and the terrain to ensure equal workload.
Enumerators were trained to probe the respondents until they were satisfied with the response given before they recorded them in the questionnaire. The first check of the questionnaires was done by enumerators in the field during enumeration. The second check was done by the district supervisors followed by National Supervisors. Supervisory visits at all levels of supervision focused on consistency checking of the questionnaires. Inconsistencies encountered were corrected, and where necessary a return visit to the respondent was made by the enumerator to obtain the correct information.
It was a requirement that for the four (4) EAs assigned/allocated to an Enumerator, he/she was a resident I n one of them. This was intended not only to ensure proximity to the assigned EAs but also take advantage of the enumerator's familiarity with an EA. In addition, his/her being known to the respondents in the locality was a prerequisite for securing their cooperation.
The large districts like Masaka, Bushenyi, etc. had two (2) DSs, while the small ones like Bukwo, Busia, etc. had one (1). In the case of the former, the two (2) DSs apportioned work based on number of EAs, enumerators and the terrain to ensure equal workload.
Enumerators were trained to probe the respondents until they were satisfied with the response given before they recorded them in the questionnaire. The first check of the questionnaires was done by enumerators in the field during enumeration. The second check was done by the district supervisors followed by National Supervisors. Supervisory visits at all levels of supervision focused on consistency checking of the questionnaires. Inconsistencies encountered were corrected, and where necessary a return visit to the respondent was made by the enumerator to obtain the correct information.
Data Collection Notes
The headquarter staff involved in the training was drawn mainly from the Agricultural Statistics Sections of UBOS and MAAIF. Training was carried out in three (3) phases namely:
Training of trainers: This entailed headquarter staff to ensure that the trainers when dispatched to various training centers would issue similar instructions.
Training of District Supervisors (DSs): The main reason for carrying out a separate training for the DSs, was to ensure that when different centers of training for the Enumerators were established, the trained DSs would support the headquarter staff in the training. Due to their large number, the DSs were trained in two sessions each of about 60 participants to keep the class size manageable.
Training of Enumerators: This training was mainly facilitated by National Supervisors (NSs) assisted by DSs at eight (8) regional centers namely; Mukono, Jinja, Mbale, Soroti, Lira, Arua, Mbarara, and Kabarole. Five (5) teams were constituted and some trained at more than one venue. The training had an advantage in that those DSs who had missed out or had not completely understood some aspects on the previous training were given another chance to catch up.
Each team of field staff was trained for a period of seven (7) days. Five (5) were mainly for classroom work and the remaining two (2) days were spent on practical demonstrations on field activities, probing techniques and the use of GPS. The training was based on the questionnaires and Instructions Manuals.
Forms trained on had been designed according to various modules for data collection and included the following: The Listing Module, Agricultural Household and Holding Characteristics Module, Crop Area and Production Module and Private Large Scale and Institutional Farms Module.
A Pilot Census of Agriculture (PCA) was conducted in 2003 in the ten districts of Central (Masaka and Wakiso), Eastern (Iganga and Mbale), Northern (Arua, Lira and Nakapiripirit); Western (Kabale, Kabarole, Masindi) followed by a pre-test in Mityana district during the month of May 2008. The objectives of the pre-test were to; further test the suitability of instruments following the revision after the PCA 2003, and determine, field staff workloads.
An objective method was used for area measurement using a Global Positioning System (GPS) device. For small plots of less than 100 square meters, it had been established during the Pre-Test and PCA, that the GPS device did not give accurate readings.
In light of this observation, a Pacing Method was used as an alternative method to address the problem of small plots. Each Enumerator was assisted to establish a Pacing Co-efficient which was used in the computation of area of a given small polygon.
Crop plots can be either in pure stand or in mixed stand. Determining the physical area occupied by each of the crops in mixed stand plot can be a challenge. In the UCA, an attempt to address this problem was done by instructing the field staff to determine the approximate percentage crop cover of each of the crops in the mixed stand and record it.
Training of trainers: This entailed headquarter staff to ensure that the trainers when dispatched to various training centers would issue similar instructions.
Training of District Supervisors (DSs): The main reason for carrying out a separate training for the DSs, was to ensure that when different centers of training for the Enumerators were established, the trained DSs would support the headquarter staff in the training. Due to their large number, the DSs were trained in two sessions each of about 60 participants to keep the class size manageable.
Training of Enumerators: This training was mainly facilitated by National Supervisors (NSs) assisted by DSs at eight (8) regional centers namely; Mukono, Jinja, Mbale, Soroti, Lira, Arua, Mbarara, and Kabarole. Five (5) teams were constituted and some trained at more than one venue. The training had an advantage in that those DSs who had missed out or had not completely understood some aspects on the previous training were given another chance to catch up.
Each team of field staff was trained for a period of seven (7) days. Five (5) were mainly for classroom work and the remaining two (2) days were spent on practical demonstrations on field activities, probing techniques and the use of GPS. The training was based on the questionnaires and Instructions Manuals.
Forms trained on had been designed according to various modules for data collection and included the following: The Listing Module, Agricultural Household and Holding Characteristics Module, Crop Area and Production Module and Private Large Scale and Institutional Farms Module.
A Pilot Census of Agriculture (PCA) was conducted in 2003 in the ten districts of Central (Masaka and Wakiso), Eastern (Iganga and Mbale), Northern (Arua, Lira and Nakapiripirit); Western (Kabale, Kabarole, Masindi) followed by a pre-test in Mityana district during the month of May 2008. The objectives of the pre-test were to; further test the suitability of instruments following the revision after the PCA 2003, and determine, field staff workloads.
An objective method was used for area measurement using a Global Positioning System (GPS) device. For small plots of less than 100 square meters, it had been established during the Pre-Test and PCA, that the GPS device did not give accurate readings.
In light of this observation, a Pacing Method was used as an alternative method to address the problem of small plots. Each Enumerator was assisted to establish a Pacing Co-efficient which was used in the computation of area of a given small polygon.
Crop plots can be either in pure stand or in mixed stand. Determining the physical area occupied by each of the crops in mixed stand plot can be a challenge. In the UCA, an attempt to address this problem was done by instructing the field staff to determine the approximate percentage crop cover of each of the crops in the mixed stand and record it.
Questionnaires
Questionnaires
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.
Data Processing
Data Editing
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.
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.
Other Processing
Hardware
This included Fifty three (53) desktop computers which were fully dedicated to data entry. They were distributed to various laboratories and each laboratory had a central computer “supervisor's machine” which was in charge of holding and distributing data entry application to the computers attached to it. It was also responsible for receiving data from the individual computers and this was automatically done.
Data Capture
Edited/coded forms were delivered to the data entry room(s) where actual data entry took place. The data entry rooms were managed by a data processing supervisor managing 16 data entry operators. The data processing supervisor was in charge of assigning and supervising work of the data entry operators. The operators were in charge of the actual data capture as well as cleaning of any data entry errors that they had generated.
To eliminate data entry errors, a double entry system was used. Each round of data capture was done by a different data entrant at different time. Data from both entries was continuously compared until there was no difference between the two. Batches that passed this check were considered free of any data entry errors. The first round of entry was referred to as "main entry" and the second "verification".
The UCA hired an external consultant who had very high expertise in data processing. The sole purpose of this was to ensure a very high quality dataset. With the technical help of the consultant, the data processing team was able to develop very strict and efficient quality processing applications. These controlled the flow of questionnaires at the various stages of data processing and also eliminated outliers and inconsistencies from the final dataset as much as possible.
Software
- Census and Survey Processing (CsPro) application which was used for data capture and management of the information within a batch and data editing.
- Ms Access and Visual Basic which were used for the general data management
- STATA that was used for data editing, import, export and analysis
- Excel that was used for presentation of results from the analysis
This included Fifty three (53) desktop computers which were fully dedicated to data entry. They were distributed to various laboratories and each laboratory had a central computer “supervisor's machine” which was in charge of holding and distributing data entry application to the computers attached to it. It was also responsible for receiving data from the individual computers and this was automatically done.
Data Capture
Edited/coded forms were delivered to the data entry room(s) where actual data entry took place. The data entry rooms were managed by a data processing supervisor managing 16 data entry operators. The data processing supervisor was in charge of assigning and supervising work of the data entry operators. The operators were in charge of the actual data capture as well as cleaning of any data entry errors that they had generated.
To eliminate data entry errors, a double entry system was used. Each round of data capture was done by a different data entrant at different time. Data from both entries was continuously compared until there was no difference between the two. Batches that passed this check were considered free of any data entry errors. The first round of entry was referred to as "main entry" and the second "verification".
The UCA hired an external consultant who had very high expertise in data processing. The sole purpose of this was to ensure a very high quality dataset. With the technical help of the consultant, the data processing team was able to develop very strict and efficient quality processing applications. These controlled the flow of questionnaires at the various stages of data processing and also eliminated outliers and inconsistencies from the final dataset as much as possible.
Software
- Census and Survey Processing (CsPro) application which was used for data capture and management of the information within a batch and data editing.
- Ms Access and Visual Basic which were used for the general data management
- STATA that was used for data editing, import, export and analysis
- Excel that was used for presentation of results from the analysis
Data Appraisal
Data Appraisal
The estimated number of Ag HHs was 3.95 million. Out of this, the Western Region had the highest percentage (28.5 percent) closely followed by the Eastern Region with 28.1 percent while the Northern and the Central Region had the 22.9 and 20.5 percent respectively.
Out of about 3.6 million Ag HHs with information on the sex of HH head, 2.8 million (78.9%) and 754,000 (21.1%) were Male and Female respectively
Out of about 3.6 million Ag HHs with information on the sex of HH head, 2.8 million (78.9%) and 754,000 (21.1%) were Male and Female respectively
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Uganda Bureau of Statistics (UBOS) | Ministry of Finance, Planning and Economic Development | ubos@ubos.org | http://www.ubos.org |
Contacts
| Name | Affiliation | |
|---|---|---|
| Uganda Bureau of Statistics (UBOS) | Ministry of Finance, Planning and Economic Development | ubos@ubos.org |
Confidentiality
Confidentiality of respondents is guaranteed by Article 19 of The Uganda Bureau of Statistics Act, 1998.
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Access conditions
The dataset is available as a Public Use Dataset. It is accessible to all for statistical and research purposes only, under the following terms and conditions:
1.The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of Uganda Bureau of Statistics.
2.The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3.No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to Uganda Bureau of Statistics.
4. No attempt will be made to produce links among datasets provided by Uganda Bureau of Statistics, or among data from Uganda Bureau of Statistics and other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Uganda Bureau of Statistics will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Uganda Bureau of Statistics.
7. The original collector of the data, Uganda Bureau of Statistics, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
1.The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of Uganda Bureau of Statistics.
2.The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3.No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to Uganda Bureau of Statistics.
4. No attempt will be made to produce links among datasets provided by Uganda Bureau of Statistics, or among data from Uganda Bureau of Statistics and other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Uganda Bureau of Statistics will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Uganda Bureau of Statistics.
7. The original collector of the data, Uganda Bureau of Statistics, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Citation requirements
"Uganda Bureau of Statistics, Uganda Census of Agriculture (UCA - 2008-2009), Version 1.0 of the public use dataset (December 2010), provided by the Uganda National Data Archive. www.ubos.org/nada"
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the Uganda Bureau of Statistics and the relevant funding agency(ies) bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_UGA_2008_AgCENS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Lawrence Mugula Steven | UBOS | Ministry of Finance and Economic Development | Documentation of the Study |
Date of Metadata Production
2011-08-16
DDI Document version
Version 01: Adopted from "DDI-UGA-UBOS-UCA-2008/2009-v01" DDI that was done by metadata producer mentioned in "Metadata Production" section.