electricsheepafrica/africa-rwanda-season-a-screening-crops-78e9b0ea

Season A: Screening Crops | Africa (Rwanda Data Sharing Platform - NISR)

52,487 rows - 1 Africa country/area - 2021-09-01-2022-02-28 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 52,487 rows from Rwanda Data Sharing Platform - NISR, covering Season A: Screening Crops. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Agriculture datasets help analysts examine production, prices, inputs, land use, food systems, and rural economic activity.

Source-provided context: Summary: This table presents plot-level crop screening information collected during Season A (September 2021 – February 2022) of the 2021/2022 Seasonal Agricultural Survey conducted by the National Institute of Statistics of Rwanda (NISR). Each record corresponds to an agricultural plot and captures detailed information on land use and crop presence during the reference season. The dataset includes plot identification and size, land use type, cropping system, number of main crops per plot, and detailed crop-level characteristics such as crop name and category, proportion and density of plot coverage, banana plant counts where applicable, planting status, expected harvest status, and harvesting period. Geographic Coverage: National coverage across Rwanda, including all provinces and districts, covering both rural and peri-urban agricultural areas. Time Period: Season A of the 2021/2022 agricultural year (September 2021 – February 2022).

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: not detected.
• Time coverage basis: source metadata period.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	52,487
Countries/areas	1
First period	2021-09-01
Last period	2022-02-28
Indicators	0
Columns	42
Source format	CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
RWA	52,487	2021-09-01	2022-02-28	Rwanda

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	6c25cf99-4399-4adb-b1e4-017a83b24116:0
country_iso3	string	ISO3 country or area code.	RWA
country_name	string	Country or area name.	Rwanda
id	int64	Row ID	1
segment_id	double	Segment identifier	12001.0
s1q1	int64	Numeric code (1–5) representing the administrative province of Rwanda where the survey respondent is located. 1 = Kigali City, 2 = Southern Province, 3 = Western Province, 4 = Northern Province, 5 = Eastern Province.	1
s1q2	int64	Numeric code (11–57) representing the respondent's district in Rwanda. The first digit corresponds to the province (1-5), and the second digit identifies the district within that province. 11 = Nyarugenge, 12 = Gasabo, 13 = Kicukiro, 21 = Nyanza, 22 = Gisagara, 23 = Nyaruguru, 24 = Huye, 25 = Nyamagabe, 26 = Ruhango, 27 = Muhanga, 28 = Kamonyi, 31 = Karongi, 32 = Rutsiro, 33 = Rubavu, 34 = Nyabihu, 35 = Ngororero, 36 = Rusizi, 37 = Nyamasheke, 41 = Rulindo, 42 = Gakenke, 43 = Musanze, 44 = Burera, 45 = Gicumbi, 51 = Rwamagana, 52 = Nyagatare, 53 = Gatsibo, 54 = Kayonza, 55 = Kirehe, 56 = Ngoma, 57 = Bugesera.	12
s1q3	int64	Numeric code (0–40) representing the stratum classification of the surveyed area: 0 = NA, 10 = Hillside cropland, 20 = Marshland cropland, 30 = Rangelands, 40 = Mixed.	0
s1q4	double	Numeric identifier representing the survey segment	``
s1q7	double	Number of grids sampled in the segment	``
s2q1	int64	Plot number	1
s2q2	double	Number of grid points that fall in this plot	``
s2q3	double	Grids fallling in the plot	``
s2q4	double	Plot size (m2)	193122.390625
s2q4_ha	double	Plot size(ha)	19.312238693237305
s2q5_2	double	Farmer ID	1.0
s2q6	int64	Plot land use: 96=Agricultural, 97=Pasture, 98=Fallow, 99=Non agricultural	97
s2q7	double	Non agricultural Land Type: 1=Buildings, 2=Road or Path, 3=Forest or Bush, 4=Bare or Rocky soil, 5=Unmanaged marshland, 6=Water body, 7=Other(specify)	``
s2q7_other	string	Other non agricultural Land Type	``
s2q17	double	Cropping system: 1=Pure Cropping, 2=Mixed Cropping	1.0
s2q18	double	Number of main crops in the plot	1.0
s3q1	double	Crop name 101=Maize, 102=Paddy rice, 103=Sorghum, 104=Wheat, 105=Other cereal (specify), 106=Bush bean, 107=Climbing bean, 108=Pea, 109=Other pulse, 110=Irish potato, 111=Sweet potato, 112=Taro, 113=Yams, 114=Other tubers, 115=Tomato, 116=Cabbage, 117=Cauliflower, 118=Onion, 119=Carrot, 120=Eggplant, 121=Other seasonal vegetables (specify), 122=Soybean, 123=Groundnut, 124=Sunflower, 125=Black eggplant, 126=Sweet pepper, 127=Amaranth, 128=Celery, 129=Spinach, 130=Small red bean, 131=Beetroot, 132=Garlic, 133=African cabbage, 134=Leek, 135=French beans, 136=Lettuce, 137=Broccoli, 138=Millet, 139=Cucumber, 140=Chia seeds, 141=Other seasonal crops, 201=Pyrethrum, 202=Pepper, 203=Pumpkin, 204=Napia grass, 205=Sugar cane, 206=Tobacco, 207=Other annual crops (specify), 300=Banana, 301=Cooking banana, 302=Dessert banana, 303=Banana for beer, 304=Coffee, 305=Cassava, 306=Mulberry, 307=Jatropha, 308=Stevia, 309=Macadamia, 310=Tea, 311=Other perennial crop (specify), 401	502.0
s3q1_o	string	Other Crop name	``
s3q2_1	double	Crop proportion (in %)	98.0
s3q2_2	double	Crop proportion code	9.0
s3q3_1	double	Crop density (in %)	100.0
s3q3_2	double	Crop Density code	9.0
s3q4	double	Number of banana plants	``
s3q5	double	Is this crop for this season? 1=Yes, 2=No	1.0
s3q6	double	Will this crop be harvested in this season? 1=Yes, 2=No	1.0
s3q7	double	What is the expected period for harvesting this crop 1=Before 01/12, 2=Between 01-15/12, 3=Between 16-31/12, 4=Between 01-15/01, 5=Between 16-31/01, 6=Between 01-28/02, 7=After Feb, 8=Other season (applicable for all crops), 9=Before 01/05, 10=Between 01-15/05, 11=Between 15-31/05, 12=Between 01-15/06, 13=Between 16-30/06, 14=Between 01-15/07, 15=Between 16-31/07, 16=Between 01-31/08, 17=After August, 18=Other season (applicable for all crops), 19=Before 01/08, 20=Between 01-15/08, 21=Between 16-31/08, 22=Between 01-15/09, 23=Between 16-30/09, 24=After 30/09	3.0
plot_weight	double	Plot weight	1.0
cropcategory	double	Crop category 1=Maize, 2=Sorghum, 3=Paddy rice, 4=Wheat, 5=Other cereals, 6=Sweet potato, 7=Irish potato, 8=Taro & Yams, 9=Cassava, 10=Bush bean, 11=Climbing bean, 12=Pea, 13=Groundnut, 14=Soybean, 15=Cooking banana, 16=Dessert banana, 17=Banana for beer, 18=Vegetables, 19=Fruits, 20=Fodder crops, 21=Other crops	20.0
crop_area	double	Crop area	18.925994873046875
source_provider	string	Publishing organization.	NISR
source_dataset	string	Source dataset or package title.	Season A: Screening Crops
source_resource	string	Source resource title, table name, or file name.	rwa_sas_seasona_screening_crops
source_package_id	string	Source package identifier.	6c25cf99-4399-4adb-b1e4-017a83b24116
source_resource_id	string	Source resource identifier.	6c25cf99-4399-4adb-b1e4-017a83b24116
source_url	string	Original source URL or download URL.	https://api.data.gov.rw/api/v1/datasets/public/6c25cf99-4399-4adb-b1e...
license_id	string	Source license identifier.	cc-by-4.0
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-07-18T11:48:51Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-season-a-screening-crops-78e9b0ea")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "RWA"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: Rwanda Data Sharing Platform - NISR
• Publisher: NISR
• Portal: https://data.gov.rw/
• Resource: rwa_sas_seasona_screening_crops
• License: CC BY 4.0
• Retrieved/generated: 2026-07-18T13:52:12Z
• Hugging Face repo: electricsheepafrica/africa-rwanda-season-a-screening-crops-78e9b0ea

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Track production or price movements
• Compare regions or commodities
• Join with climate and trade data
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_rwanda_season_a_screening_crops_78e9b0ea_2022,
  title        = {Season A: Screening Crops | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2022},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/6c25cf99-4399-4adb-b1e4-017a83b24116},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-season-a-screening-crops-78e9b0ea}}
}

License

Released under CC BY 4.0.

Original data is published by NISR. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://api.data.gov.rw/api/v1/datasets/public/6c25cf99-4399-4adb-b1e4-017a83b24116