Season Agriculture Survey 2019-2020
Rwanda, 2019 - 2020
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Reference ID
RWA_2019-2020_SAS_v01_EN_M_v01_A_OCS
Producer(s)
National Institute of Statistics of Rwanda
Collections
Metadata
Created on
Nov 14, 2022
Last modified
Nov 15, 2022
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Identification
Survey ID Number
RWA_2019-2020_SAS_v01_EN_M_v01_A_OCS
Title
Season Agriculture Survey 2019-2020
Country
| Name | Country code |
|---|---|
| Rwanda | RWA |
Study type
Agricultural Census [ag/census]
Series Information
The Seasonal Agriculture Survey (SAS) is a study conducted annually by the National Institute of Statistics of Rwanda from November to September of the following year to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda.
The SAS 2019 covered three agricultural seasons:
- Agricultural Season A: starts from September 2018 to February 2019;
- Agricultural Season B: starts from March to June 2019; and
- Agricultural Season C: starts from July to august 2019.
The SAS 2019 covered three agricultural seasons:
- Agricultural Season A: starts from September 2018 to February 2019;
- Agricultural Season B: starts from March to June 2019; and
- Agricultural Season C: starts from July to august 2019.
Abstract
The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.
The National Institute of Statistics of Rwanda (NISR) has been conducting an annual agricultural survey since November 2012 for the estimation of the national agricultural crop area and production estimates. In 2019/2020 agricultural year, the NISR conducted the second edition of theUpgraded Seasonal Agricultural Survey (USAS) covering the three agricultural seasons. The USAS incorporated an increased sample size to provide more precise estimates. The USAS allows information for monitoring progress on agriculture programs and policies in Rwanda.
The National Institute of Statistics of Rwanda (NISR) has been conducting an annual agricultural survey since November 2012 for the estimation of the national agricultural crop area and production estimates. In 2019/2020 agricultural year, the NISR conducted the second edition of theUpgraded Seasonal Agricultural Survey (USAS) covering the three agricultural seasons. The USAS incorporated an increased sample size to provide more precise estimates. The USAS allows information for monitoring progress on agriculture programs and policies in Rwanda.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Agricultural holdings
Scope
Notes
The scope of 2019 Seasonal Agriculture Survey concerned farm characteristics (Area, yield, production; use of production, agricultural practices; agriculture inputs and land tenure).
Coverage
Geographic Coverage
National coverage allowing district-level estimation of key indicators
Universe
The SAS 2020 targeted potential agricultural land and large scale farmers.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning |
Producers
| Name | Role |
|---|---|
| National Institute of Statistics of Rwanda | Main producer |
| Ministry of Agriculture and Animal Resources | Technical partner |
| Rwanda Agricultural Board | Technical partner |
| National Agriculture Export Board | Technical partner |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Rwanda | GoR | Funder |
Sampling
Sampling Procedure
The USAS used a Multiple-Frame Sampling (MFS) methodology providing the basis for conducting probability surveys. This approach is mainly based on complete coverage of the area and enables to avail agricultural data with precise survey estimates. The methodology encompasses two sample frames: the area frame from which the main survey sample is drawn and a list frame of large-scale farmers (LSF)to complement the area frame, with the aim to cover crops mostly grown by large scale farmers which are not easily covered in the area frame. The large-scale farmer should have at least 10 hectares of agricultural holdings to be included. The process of area frame construction is a three-pronged process involving the following steps: land cover classification, land stratification and sampling of segment.
Weighting
Based on the stratified two-stage sample design used with the new area frame, the first stage sampling probability for the sample segments in each stratum is calculated as:
p1h = nh/Nh
Where:
p1h = probability of selection of sample segments in stratum h (district by stratum)
nh =number of sample segments selected in stratum h
Nh = Total number of segments in the area frame for stratum h in each stratum
The second stage probability was calculated at the plot level based on the assumption that the plots within
each sample segment were implicitly selected with PPS using the area of the plot as the measure of size.
Therefore, the second stage probability of selection can be expressed as follows:
p2hi = Ghi x Ahij/Ahi x Ghij
Where:
p2h= Probability of selection of the plot in segment h
ghi = Number of grid squares selected in the i-th sample segment of stratum h;
Ahij = Area of the j-th sample plot selected in the i-th sample segment of stratum h
Ahi = Area of the i-th sample segment of stratum h;
ghij = Number of selected grid squares in the j-th sample plot of the i-th sample segment of stratum h
The weight of a sample plot is equal to the inverse of the first and second stage probabilities of selection:
Wphij = 1/p1h x p2hi = Nh x Ahi x G hij / Nh x Ghi x Ahij
Where:
WPhij =weight for the j-th sample plot in the i-th sample segment in stratum h
p1h = nh/Nh
Where:
p1h = probability of selection of sample segments in stratum h (district by stratum)
nh =number of sample segments selected in stratum h
Nh = Total number of segments in the area frame for stratum h in each stratum
The second stage probability was calculated at the plot level based on the assumption that the plots within
each sample segment were implicitly selected with PPS using the area of the plot as the measure of size.
Therefore, the second stage probability of selection can be expressed as follows:
p2hi = Ghi x Ahij/Ahi x Ghij
Where:
p2h= Probability of selection of the plot in segment h
ghi = Number of grid squares selected in the i-th sample segment of stratum h;
Ahij = Area of the j-th sample plot selected in the i-th sample segment of stratum h
Ahi = Area of the i-th sample segment of stratum h;
ghij = Number of selected grid squares in the j-th sample plot of the i-th sample segment of stratum h
The weight of a sample plot is equal to the inverse of the first and second stage probabilities of selection:
Wphij = 1/p1h x p2hi = Nh x Ahi x G hij / Nh x Ghi x Ahij
Where:
WPhij =weight for the j-th sample plot in the i-th sample segment in stratum h
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2019-09-15 | 2020-09-15 |
Data Collection Mode
Face-to-face [f2f]
Access policy
Confidentiality
The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO
Access conditions
Micro datasets disseminated by FAO shall only be allowed for research and statistical purposes. Any user which requests access working for a commercial company will not be granted access to any micro dataset regardless of their specified purpose. Users requesting access to any datasets must agree to the following minimal conditions:
- The micro dataset will only be used for statistical and/or research purposes;
- Any results derived from the micro dataset will be used solely for reporting aggregated information, and not for any specific individual entities or data subjects;
- The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO;
- The micro dataset cannot be re-disseminated by users or shared with anyone other than the individuals that are granted access to the micro dataset by FAO.
- The micro dataset will only be used for statistical and/or research purposes;
- Any results derived from the micro dataset will be used solely for reporting aggregated information, and not for any specific individual entities or data subjects;
- The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO;
- The micro dataset cannot be re-disseminated by users or shared with anyone other than the individuals that are granted access to the micro dataset by FAO.
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_RWA_2019-2020_SAS_v01_EN_M_v01_A_OCS
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| National Institute of Statistics of Rwanda | NISR | Ministry of Finance and Economic Planning | Metadata producer |
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Metadata adapted for FAM |
DDI Document version
RWA_2019-2020_SAS_v01_EN_M_v01_A_OCS_v01