RWA-NISR-SAS-2025-v01
Rwanda Seasonal Agricultural Survey 2025
| Name | Country code |
|---|---|
| RWANDA | RWA |
This is the annual report for the Seasonal Agricultural Survey (SAS) conducted by the National Institute of
Statistics of Rwanda (NISR) for the 2024/2025 agricultural year. It covers Rwanda’s three agricultural seasons.
The main agricultural seasons include Season A, starting from September 2024 to February 2025, Season B
starting from March to June 2025, and Season C starting from July to September 2025.
Key findings
A comprehensive summary of the primary indicators assessed during SAS 2025 is presented in Table 0. These
indicators include crop production, yield estimates, land use, use of agricultural inputs and agricultural practices.
Land use
In 2025 agricultural year, the country’s total land area was estimated at 2.376 million hectares. In season A,
1.399 million hectares (59%) were used for agricultural purposes, of which 1.019 million hectares were used
for Seasonal crops, 0.492 million hectares used for permanent crops, and 0.096 million hectares to permanent pasture.
In season B, the total land area remained 2.376 million hectares, with 1.423 million hectares
(60%) used for agricultural purposes. 1.022 million hectares were allocated to Seasonal crops, 0.524 million
hectares for crops, and 0.1 million hectares were allocated to permanent pasture.
Use of inputs
Agricultural inputs include improved seeds, organic and chemical fertilizers, as well as pesticides and fungicides.
In 2025, 37.3 % of farmers used improved seeds during season A, compared to 18% in season B, and
18.9% in season C. The organic fertilizers was used by 88% of farmers in season A, compared to 80.8% in
season B, and 83.5 % in season C. In contrast, the inorganic fertilizers was used by 63.2% of farmers in season
A, compared to 55.5% in season B, and 65.8 % in season C. Pesticides and fungicides was used by 41.9% of
farmers in season A, compared to 36.6% in season B, and 67 % in season C.
Agricultural practices
In season A of 2025, 13.4% of farmers practiced irrigation compared to 11.5% of farmers in season B and
58.9% in season C. In 2025 Season A, 90.3% of farmers practiced anti-erosion activities, compared to 89.9%
of farmers in season B and 94.7% of farmers in season C.
This seasonal agriculture survey focused on the following units of analysis: Small scale agricultural farms and large scale
farms
Version 0.1 Edited anonymized dataset for public use
National coverage allowing district-level estimation of key indicators
The SAS 2025 targeted potential agricultural land and large-scale farmers
| Name | Affiliation |
|---|---|
| National Institute of Statistics of Rwanda (NISR) | Ministry of Finance and Economic Planning (MINECOFIN) |
| Name | Affiliation | Role |
|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economic Planning (MINECOFIN) | Producer of the Survey |
| Name | Abbreviation | Role |
|---|---|---|
| Goverment of Rwanda | GoR | Funder of the survey |
To provide the basis for conducting probability-based surveys that comprehensively capture farm-level
data and to enhance the precision of survey estimates, the Seasonal Agricultural Survey employs a Multiple
Frame Sampling (MFS) methodology. This approach involves constructing an area frame from which the
survey sample is selected. In addition, a list frame of Large-Scale Farmers (LSF), defined as those operating
at least 10 hectares of agricultural land is done to complement the area frame. This ensures comprehensive
coverage of crops predominantly cultivated by large-scale farmers, which might not be adequately represented using an area frame alone.
The construction of the area frame involves several steps, including land
cover classification, land stratification and the sampling of segments.
Land classification serves as the first step in designing the sampling frame for the Seasonal Agriculture Survey.
This process involves categorizing the total available land in the country into distinct land use or land
cover types. The primary purpose is to enhance sampling precision by ensuring the appropriate targeting
of the adequate land. This classification was achieved through a combination of available national different
spatial layers with the photo-interpretation of a time series of high-resolution (50 to 30 cm) satellite images
spanning from 2010 to 2023.the country’s total land was divided into 14 distinct land cover
classes and Among 14 land cover classes, only 6 are related to agricultural activities include agricultural land on hillside,
non-rice agricultural Wetland, mixed rangeland, low-density built-up area, wetlands designated for paddy
rice and tea plantation.The subsequent step involves constructing the area frame which includes grouping the land cover classes
linked to agricultural activities into strata to identify agricultural strata to be considered in the sampling
frame.
The stratification is a result of a combination of sampling units (clusters) and land use/land cover.
The stratification assigns each cluster a stratum based on the predominant land class type. Among the fourteen land
cover classes, four are included in the agricultural survey frame, while the others are excluded.
The included land cover classes comprise hillside agricultural land, non-rice agricultural land, mixed rangeland,
and low-density built-up areas (which retain potential for agricultural production, such as kitchen gardens, fruit trees, and livestock).
However, specific agricultural land classes are excluded from the sampling
frame. For example, tea plantations are omitted due to regular monitoring by the National Agricultural Export
Development Board (NAEB). Similarly, wetlands designated for paddy rice cultivation are typically considered
under the Large-Scale Farmers component, thereby integrating them into the survey frame.
Moreover, beginning in 2024, a new land cover class called “Exclusive Rangeland” has been introduced specifically to
idintify areas used for pastoral activities. This class is also excluded from the sampling frame.
The stratified two-stage sample design used with the new area frame, the first stage sampling probability for the sample
segments in each stratum was calculated.
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.
2025
| Organization name | Abbreviation | Affiliation | URL |
|---|---|---|---|
| National Institute of Statistics of Rwanda | NISR | Ministry of Finance and Economic Planning (MINECOFIN) | https://www.statistics.gov.rw/sites/default/files/documents/2025-12/SAS%202025%20Final%20report.pdf |
RWA-NISR-SAS-2025-v01
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| National Institute of Statistics of Rwanda | NISR | Ministry of Finance and Economic Planning (MINECOFIN) | Producer of the Survey |