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 2018 covered three agricultural seasons:
- Agricultural Season A: starts from September 2017 to February 2018;
- Agricultural Season B: starts from March to June 2018;
- Agricultural Season C: starts from July to August 2018.
The main objective of the Seasonal Agriculture Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock to monitor current agricultural and food supply conditions and to facilitate evidence based decision making for the development of Agriculture sector. In this regard, the National Institute of Statistics of Rwanda conducted the Seasonal Agriculture Survey (SAS) from November 2017 to October 2018 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Second Economic Development and Poverty Reduction Strategy (EDPRS II) and Vision 2020. This 2018 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipment's, use of crop production by agricultural operators and by large scale farmers.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The scope of 2018 Seasonal Agriculture Survey concerned farm characteristics ( Area, yield and production; agricultural practices; small agricultural equipments; and use of crop production).
Agriculture & Rural Development
Food (production, crisis)
Land (policy, resource management)
The RSAS 2018 targeted potential agricultural land and large scale farmers.
Producers and sponsors
National Institute of Statistics of Rwanda
Ministry of Finance and Economic Planning
Ministry of Agriculture and Animal Resources
Government of Rwanda
National Agriculture Export Board
Government of Rwanda
Rwanda Agricultural Board
Government of Rwanda
Rwanda Environmental Management Authority
Government of Rwanda
Government of Rwanda
In order to provide the basis for conducting probability surveys based on complete coverage of the farm level, and as a better way of collecting agricultural data and finding better precise survey estimates, SAS used a Multiple-Frame Sampling (MFS) methodology by which, area frame was constructed and survey sample was drawn from it. Apart from that, a list frame of large-scale farmers (LSF), with at least 10 hectares of agricultural holdings, was done to complement the area frame just to cover crops mostly grown by large scale farmers and that cannot be easily covered in area frame. For detailed information regarding the sampling procedures, refer to the component of methodology in the report.
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.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
Data collection consists of 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 2018 the NISR employed around 151 field workers in the form of two-person teams to conduct the fieldwork. The fieldwork consisted of a Phase 1 for segment screening and a Phase 2 for plot data collection. 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.
There were two types of questionnaires used for this survey namely Screening questionnaire and plot questionnaires. 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
- inputs (seeds, fertilizers, labour…)
- agricultural practices
- crop production
- use of production.
All the surveys questionnaires used were published in English.
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.
All Farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. And in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the Team leader.
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.
1. 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 .
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 the National l Institute of Statistics of Rwanda .
4. 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 2018, December 2018
Disclaimer and copyrights
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