Private Farmer Livestock-Poultry 2006
Nigeria, 2007
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Reference ID
NGA_2006_PFLP_v01_EN_M_v01_A_OCS
Producer(s)
National Bureau of Statistics (NBS)
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Metadata
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Jul 17, 2019
Last modified
Jul 17, 2019
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Identification
Survey ID Number
NGA_2006_PFLP_v01_EN_M_v01_A_OCS
Title
Private Farmer Livestock-Poultry 2006
Country
| Name | Country code |
|---|---|
| Nigeria | NGA |
Study type
Agricultural Survey [ag/oth]
Series Information
This Private Farmer Livestock/Poultry is the 3rd in the series of collaborative effort of the National Bureau of Statistics (NBS), Central Bank of Nigeria (CBN) and the Nigeria Communications Commission (NCC) previously conducted in 2004, 2005 and 2006 being the current one. However the Private Farmer Livestock/Poultry is a regular survey of the National Bureau of Statistics conducted on quarterly basis before the collaboration was initiated.
Abstract
The Private farmer-Livestock/Poultry is a brainchild of the National Bureau of Statistics (NBS) and is often referred to as Regular survey carried out on quarterly basis by the NBS over the years. In recent times, starting from 2004 to be precise, there is a collaborative effort between the NBS and the CBN in 2004 and 2005 and in 2006 the collaboration incorporated Nigerian Communications Commission (NCC). The main reason of for conducting the survey was to enable the collaborating agencies fulfil their mandate in the production of current and credible statistics, to monitor and evaluate the status of the economy and the various government programmes such as the National Economic Empowerment and Development Strategy (NEEDS) and the Millennium Development Goals (MDGs). The collaborative survey also assured the elimination of conflicts in data generated by the different agencies and ensured a reliable, authentic national statistics for the country.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households
Scope
Notes
The section in the questionaire
· Identification
. Type of livestock kept
. Type of Poultry kept
. Sources livestock input
. Sources Poultry input
. Input Utilization(Livestock)
. Input Utilization(Poultry)
. Stocks and Changes in Stocks
. Stocks and Changes in Stock of Poultry/Dairy Products
. Loss as a result of bird flu
. Sales of Livestock
. Sales of Poultry
. Sources of funds
· Livestock
· Poultry
· Processing facilities
· Subsidy
· Stock
· Preservation methods
. Sales of produce
. Export your produce
. Compare the current livestock farming season with the previous
. Expectations for livestock activities in the next farming season?
. Problems do you encounter when purchasing livestock/poultry inputs?
. Problems do you encounter when purchasing livestock/poultry tools?
. Problems do you encounter during production process?
. Problems do you have during processing and storage?
. Improving livestock/poultry farming activities in the Country?
. Government assisttance
. Access to any of the following ICT facilities?
· Identification
. Type of livestock kept
. Type of Poultry kept
. Sources livestock input
. Sources Poultry input
. Input Utilization(Livestock)
. Input Utilization(Poultry)
. Stocks and Changes in Stocks
. Stocks and Changes in Stock of Poultry/Dairy Products
. Loss as a result of bird flu
. Sales of Livestock
. Sales of Poultry
. Sources of funds
· Livestock
· Poultry
· Processing facilities
· Subsidy
· Stock
· Preservation methods
. Sales of produce
. Export your produce
. Compare the current livestock farming season with the previous
. Expectations for livestock activities in the next farming season?
. Problems do you encounter when purchasing livestock/poultry inputs?
. Problems do you encounter when purchasing livestock/poultry tools?
. Problems do you encounter during production process?
. Problems do you have during processing and storage?
. Improving livestock/poultry farming activities in the Country?
. Government assisttance
. Access to any of the following ICT facilities?
Topics
| Topic |
|---|
| consumption/consumer behaviour |
| rural economics |
| agricultural, forestry and rural industry |
| business/industrial management and organisation |
| employment |
| working conditions |
| basic skills education |
| plant and animal distribution |
| land use and planning |
| transport, travel and mobility |
| gender and gender roles |
| children |
| elderly |
| youth |
| community, urban and rural life |
| information technology |
Keywords
| Keyword |
|---|
| products |
| employment |
| sales |
| market |
| storage |
| problems |
| production |
| livestock |
| poultry |
| sources of funds |
| Sheep |
| Goat |
| Cattle |
| Dog |
| Donkey |
| Camel |
| Rabbit |
| Horse |
| Pig |
| Chicken |
| Duck |
| Ostrish |
| Turkey |
| Geese |
| Guinea Pig |
| Pigeon |
Coverage
Geographic Coverage
National Coverage
Universe
Livestock and Poultry Household Members
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Federal Government of Nigeria |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Central Bank of Nigeria | Federal Government of Nigeria | Collaboration |
| Nigerian Communications Commission | Federal Government of Nigeria | Collaboration |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| National Bureau of Statistics | NBS | Funding |
| Central Bank of Nigeria | CBN | Funding |
Sampling
Sampling Procedure
Total sample sizes of 32,850 Farming Housing Units (FHUs) were drawn from 2,190 EAs. In each state, 900 FHUs drawn from 60 EAs were studied. Four hundred and fifty (450) FHUs from 30 EAs were studied in (FCT), Abuja. The listings of housing units in the selected EAs were updated before they were stratified into farming and non-farming housing units. The farming housing units were further stratified into Crop Farming Housing Units (CFHU), Livestock Farming Housing Units (LFHU) and Fishing Farming Housing Units (FFHUs). In each EA, 5 FHUs were studied for crop farming, 5 FHUs were covered for livestock and 5 FHUs for fishery. At each level of selection, housing units were systematically selected using different random start.
All households in the HUs that qualified as farming households were served with relevant private farmers questionnaires.
Out of 2,190 EAs to be covered, 2010 Livestock/Poultry EAs were actually studied. So also out of 10,950 Livestock/Poultry Holders expected to be to be covered, 4,961 Livestock/Poultry Holders were actually studied.
All households in the HUs that qualified as farming households were served with relevant private farmers questionnaires.
Out of 2,190 EAs to be covered, 2010 Livestock/Poultry EAs were actually studied. So also out of 10,950 Livestock/Poultry Holders expected to be to be covered, 4,961 Livestock/Poultry Holders were actually studied.
Deviations from the Sample Design
Variance Estimate (Jackknife Method)
Estimating variances using the Jackknife method will require forming replicate from the full sample by randomly eliminating one sample cluster [Enumeration Area (EA) at a time from a state containing k EAs, k replicated estimates are formed by eliminating one of these, at a time, and increasing the weight of the remaining (k-1) EAs by a factor of k/(k-1). This process is repeated for each EA.
For a given state or reporting domain, the estimate of the variance of a rate, r, is given by
k
Var(r ) = (Se)2 = 1 S (ri - r)2
k(k-1) i=1
where (Se) is the standard error,
k is the number of EAs in the state or reporting domain.
r is the weighted estimate calculated from the entire sample of EAs in the state or reporting domain.
ri = kr - (k - 1)r(i), where
r(i) is the re-weighted estimate calculated from the reduced sample of k-1 EAs.
To obtain an estimate of the variance at a higher level, say, at the national level, the process is repeated over all states, with k redefined to refer to the total number of EAs (as opposed to the number in the states).
Estimating variances using the Jackknife method will require forming replicate from the full sample by randomly eliminating one sample cluster [Enumeration Area (EA) at a time from a state containing k EAs, k replicated estimates are formed by eliminating one of these, at a time, and increasing the weight of the remaining (k-1) EAs by a factor of k/(k-1). This process is repeated for each EA.
For a given state or reporting domain, the estimate of the variance of a rate, r, is given by
k
Var(r ) = (Se)2 = 1 S (ri - r)2
k(k-1) i=1
where (Se) is the standard error,
k is the number of EAs in the state or reporting domain.
r is the weighted estimate calculated from the entire sample of EAs in the state or reporting domain.
ri = kr - (k - 1)r(i), where
r(i) is the re-weighted estimate calculated from the reduced sample of k-1 EAs.
To obtain an estimate of the variance at a higher level, say, at the national level, the process is repeated over all states, with k redefined to refer to the total number of EAs (as opposed to the number in the states).
Response Rate
At Enumeration Area (EA) level the response rate was 91.78 per cent while at Livestock/Poultry holder level, the response rate was 45.31 per cent
Weighting
The formula adopted in calculating the design weights for the survey data (sample results) were as follows:
(i) The probability of selecting an EA within a state was obtained by dividing the total number of EAs sampled in a state by total number of EAs in that particular state. Let this be represented by fj. That is,
fj = (Total Number of EAs sampled in a state)/(Total Number of EAs in that particular State)
(ii) Likewise, the probability of selecting an housing unit (HU) within an EA was obtained by dividing the total number of housing units selected in an EA by the total number of housing units (HUs) listed in that particular EA. Let this be represented by fk. That is,
fk = (Total Number of HUs selected in an EA)/(Total Number of HUs listed in that particular EA)
Then the product (fj) x (fk) represented by f is the sampling fraction for each of the corresponding study unit (Enumeration Area) for all the 1,920EAs canvassed throughout the 36 states of the Federation and FCT, Abuja. The inverse of the sampling fraction is known as the design weight and was applied accordingly to all the study units.
Mathematically,
Design weight = ((Total number of EAs in a state)/(Total number of EAs sampled in that particular state)) X ((Total Number of HUs listed in an EA)/(Total Number of HUs selected in that particular EA))
The above value was obtained for each of the 2,190EAs canvassed throughout the 36 states of the Federation and FCT, Abuja. Thereafter, adjustment factors were applied to adjust for the non-responses.
(i) The probability of selecting an EA within a state was obtained by dividing the total number of EAs sampled in a state by total number of EAs in that particular state. Let this be represented by fj. That is,
fj = (Total Number of EAs sampled in a state)/(Total Number of EAs in that particular State)
(ii) Likewise, the probability of selecting an housing unit (HU) within an EA was obtained by dividing the total number of housing units selected in an EA by the total number of housing units (HUs) listed in that particular EA. Let this be represented by fk. That is,
fk = (Total Number of HUs selected in an EA)/(Total Number of HUs listed in that particular EA)
Then the product (fj) x (fk) represented by f is the sampling fraction for each of the corresponding study unit (Enumeration Area) for all the 1,920EAs canvassed throughout the 36 states of the Federation and FCT, Abuja. The inverse of the sampling fraction is known as the design weight and was applied accordingly to all the study units.
Mathematically,
Design weight = ((Total number of EAs in a state)/(Total number of EAs sampled in that particular state)) X ((Total Number of HUs listed in an EA)/(Total Number of HUs selected in that particular EA))
The above value was obtained for each of the 2,190EAs canvassed throughout the 36 states of the Federation and FCT, Abuja. Thereafter, adjustment factors were applied to adjust for the non-responses.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2007-03-03 | 2007-03-26 | 23 days |
Data Collection Mode
Face-to-face paper [f2f]
Data Processing
Data Editing
The data editing is in 2 phases namely manual editing before the questionnaires were scanned. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already scanned data.
Data Appraisal
Estimates of Sampling Error
The population of the country is large and due to limited fund, census enumeration of Livestock/Poultry farmers is not visible. To reduce Sampling Error, selection of Livestock/Poultry farmers was based on State of the Federation.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| G.O Adewoye | Director Census and Surveys | [email protected] | Link |
| A.N. Adewinmbi | Head (ICT) | [email protected] | Link |
| Biyi Fafunmi | Data Curator | [email protected] | Link |
| A.A Akinsanya | Data Archivist | [email protected] | Link |
| National Bureau of Statistics (NBS) | Fedral Government of Nigeria (FGN) | [email protected] | Link |
| Mr R.F. Busari | ICT | [email protected] | Link |
Confidentiality
The confidentiality of the individual respondent is protected by law (Statistical Act 2007).
This is published in the Official Gazette of the Federal Republic of Nigeria No. 60 vol. 94 of 11th June 2007. See section 26 para.2. Punitive measures for breeches of confidentiality are outlined in section 28 of the same Act.
Access conditions
A comprehensive data access policy is been developed by NBS, however section 27 of the Statistical Act 2007 outlines the data access obligation of data producers which includes the release of properly anonymized micro data.
Citation requirements
National Bureau of Statistics, Nigeria, Private Farmer Livestock-Poultry Survey (NGA) 2006-v.1.0
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_NGA_2006_PFLP_v01_EN_M_v01_A_OCS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Metadata adapted for FAM |
| National Bureau of Statistics | NBS | Federal Government of Nigeria | Metadata Producer |
DDI Document version
NGA_2006_PFLP_v01_EN_M_v01_A_OCS_v01