Season Agriculture Survey 2020
Rwanda, 2019 - 2020
Get MicrodataIdentification
RWA_2020_SAS_v01_M
Season Agriculture Survey 2020
| Name | Country code |
|---|---|
| Rwanda | RWA |
Agricultural Census [ag/census]
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 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.
Sample survey data [ssd]
This seasonal agriculture survey focused on the following units of analysis: Small scale agricultural farms and large scale farms
Version
Version 01. Edited, anonymous dataset for public use.
2021-02-10
Scope
The scope of 2019 Seasonal Agriculture Survey included the following farm characteristics:
- Area, yield, production;
- Use of production, agricultural practices; and
- Agriculture inputs and land tenure).
Coverage
National coverage allowing district-level estimation of key indicators
National coverage allowing district-level estimation of key indicators
The SAS 2020 targeted potential agricultural land and large scale farmers
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning |
| Name | Affiliation | Role |
|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning | Main producer |
| Ministry of Agriculture and Animal Resources | Government of Rwanda | Technical partner |
| Rwanda Agricultural Board | Ministry of Agriculture and Animal Resources | Technical partner |
| National Agriculture Export Board | Ministry of Agriculture and Animal Resources | Technical partner |
| Name | Role |
|---|---|
| Government of Rwanda | Funder |
Sampling
Out of 5 defined agricultural strata, only dominant hill crop land stratum, dominant wetland crops stratum, dominant rangeland stratum and mixed stratum were considered as land potential for agriculture. The remaining stratum is the non-agricultural land. Note that clusters covered by tea plantations were not considered in the area sample frame due to reasons stated above. Thus, SAS is conducted on 4 above mentioned strata to cover other major crops. In 2020 agricultural year, the sample of segments was increased in order to improve agriculture statistics where sample increased from 780 (sample used from 2018 to 2019) to 1200 segments. At first stage,1200 segments were selected and allocated at district level based on the power allocation approach (Bankier3, 1988). Sampled segments inside each district were distributed among strata with a proportional-to-area criterion.
At second stage, 25 sample points were systematically selected, following a special distance of 60 meters between points. Sample points are reporting units within each segment, where enumerators go to every point, locate and delineate plots in which the sample points fall, and collect records of land use and related information. The recorded information represents the characteristics of the whole segment which are extrapolated to the stratum level and hence the combination of strata within each district provides district area related statistics.
Data collection was done in 780 segments and 222 large scale farmers holdings for Season A, whereas in Season C data was collected in 232 segments, response rate was 100% of the sample.
Sampling weights were calculated for each stratum in each district considering the total number of segments in the stratum and the sample size in the specific stratum.
Survey instrument
There were two types of questionnaires used for this survey namely screening questionnaire and plot questionnaire. A Screening questionnaire was used to collect information that enabled identification of a plot and its land use using the plot questionnaire. For point-sampling, the plot questionnaire is concerned with the collection of data on characteristics of crop identification, crop production and use of production, inputs (seeds, fertilizers and pesticides), agricultural practices and land tenure. All the surveys questionnaires used were published in English.
Data collection
| Start | End |
|---|---|
| 2019-09-01 | 2020-08-31 |
2019-2020
Seasonal
- Face-to-face [f2f]
| Name | Affiliation |
|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning |
The 2019 SAS used 153 fieldworkers and 22 team leaders. All fieldwork staff in 2019 held a degree in Agricultural Sciences and was trained by NISR headquarters staff before starting data collection. Higher level supervision of staff from NISR visited the field teams during each phase of data collection to ensure data quality control. At the bottom of the hierarchy, there are enumerators who would be assisted by a team leader also known as a controller. His/ her main function is to introduce the enumerators to the various key people from the sector to the villages leaders up to operators in the Secondary Sampling Unit (known as Segment), and assist enumerators during the whole course of the survey .
A higher level supervision staff from NISR visited the field teams during each phase of data collection to ensure quality control. Responsibilities of a Team Leader is to manage the interviewers to ensure successful completion and quality of data collected in a given time period for the fieldwork. He/she was expected to record information about the fieldwork, which tracks the status of completion of the work in the field, document problems in the field and solutions taken to resolve these problems. Specifically, his/her tasks included:
- Introduce the survey and interviewers at local level where the survey is administered;
- Monitor and attend some interviews and make comments on the worker's performance;
- Meet frequently with each member of the group to discuss, improve and organize work;
- Check the availability of all the necessary items before going on field;
- Help workers to solve the problems they encounter;
- Manage the team's work schedule;
- Make sure all the big farmers are identified and surveyed;
- Communicate with NISR, regarding field issues, as necessary.
Data collection is done in two distinct phases: The first Phase, known as screening activity, consists of visiting all sampled segments and delineating all plots in which the sampled grids points are fallen and thereafter recording the related information using screening questionnaire. The second phase consists of visiting the sub-sampled agricultural plots from screened plots in phase one as well as all Large- Scale Farmers having cultivated plots in the season the survey is being conducted. This phase is conducted in the period of harvesting where farmers are requested to provide information about sowing period and harvesting period, inputs used, agricultural practices done on the plots, the crop production and its use.
For SAS 2019 the NISR employed around 153 field workers and 22 team leaders. Training was provided to all fieldwork personnel on the data collection methodologies associated with the use of GPS for point-sampling and computer tablet questionnaires used for plot data collection and farmer interviews. The tablet computer assisted data collection and interview allowed for very fast and efficient uploading and transfer of the enumerated data from the field to NISR headquarters for processing. The tablet software instruments (electronic version of the paper questionnaires) allowed for instantaneous checking of the respondent data and automatically directed the enumerator questioning to reduce non-sampling errors within the data collection.
Data processing
The CAPI method of data collection allows the enumerators in the field to collect and enter data with their tablets and then synchronize to the server at headquarters where data are received by NISR staff, checked for consistency at NISR and thereafter transmitted to analysts for tabulation using STATA software, and reporting using office Excel and word as well.
Distributor information
| Organization name | Affiliation | URL |
|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning | http://statistics.gov.rw/datasource/seasonal-agricultural-survey-2020 |
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| National Institute of Statistics of Rwanda | MINECOFIN | www.statistics.gov.rw | info@statistics.gov.rw |
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | Confidentiality of respondents is guaranteed by low N° 45/2013 OF 16/06/2013 in it's article 17, 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 ny 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 ordiscussion, oranalysis. Such inadvertent identification revealed in her/his analysis will be immediate brought to the attention of the data. |
- The data and other materials provided by the National Institute of Statistics of Rwanda will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the Nationall Institute of Statistics of Rwanda .
- 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.
- 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 the National l Institute of Statistics of Rwanda .
- No attempt will be made to produce links among datasets provided by the National l Institute of Statistics of Rwanda.
National Institute of Statistics of Rwanda (NISR), Seasonal agriculture survey 2019y, December 2019.
Disclaimer and copyrights
(c) 2019, National Institute of Statistics of Rwanda
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| National Institute of Statistics of Rwanda | MINECOFIN | Info@statistics.gov.rw | www.statistics.gov.rw |
Metadata production
DDI_RWA_2020_SAS_v01_M
| Name | Affiliation | Role |
|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning | Producer of the study |
| Development Data Group | The World Bank | Metadata adapted for World Bank Microdata Library |
2023-08-02
Metadata version
Version 01 (August 2023): This metadata was downloaded from the Rwanda NISR catalog (https://microdata.statistics.gov.rw/index.php/catalog) and it is identical to Rwanda NISR version (RWA-NISR-SAS-2020-v0.1). The following two metadata fields were edited - Document ID and Survey ID.