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electricsheepafrica/africa-rwanda-season-c-screening-crops-14c8ff0d

Season C: Screening Crops | Africa (Rwanda Data Sharing Platform - NISR) 12,067 rows - 1 Africa country/area - 2022-07-01-2022-09-30 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 12,067 rows from Rwanda Data Sharing Platform - NISR, covering Season C: Screening Crops. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-season-c-screening-crops-14c8ff0d.

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Dataset Card

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

12,067 rows - 1 Africa country/area - 2022-07-01-2022-09-30 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 12,067 rows from Rwanda Data Sharing Platform - NISR, covering Season C: 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 C (July – September 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 C of the 2021/2022 agricultural year (July – September 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

DimensionValue
Rows12,067
Countries/areas1
First period2022-07-01
Last period2022-09-30
Indicators0
Columns40
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
RWA12,0672022-07-012022-09-30Rwanda

Indicators, Variables, Or Resource Contents

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

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.8d24a04e-4099-4d73-9a32-1fc9f6babe86:0
country_iso3stringISO3 country or area code.RWA
country_namestringCountry or area name.Rwanda
idint64Row ID1
segment_idint64Segment identifier112001
s1q1int64Numeric 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
s1q2int64Numeric 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.11
s1q3doubleNumeric code (0–40) representing the stratum classification of the surveyed area: 0 = NA, 10 = Hillside cropland, 20 = Marshland cropland, 30 = Rangelands, 40 = Mixed.20.0
s1q4doubleNumeric identifier representing the survey segment1.0
s1q7doubleNumber of grids sampled in the segment9.0
s2q1int64Plot number1
s2q2doubleNumber of grid points that fall in this plot1.0
s2q3stringGrids fallling in the plotA
s2q4doublePlot size (m2)1593.4
s2q4_hadoublePlot size(ha)0.1593399941921234
s2q5_2doubleFarmer ID1.0
s2q6doublePlot land use: 96=Agricultural, 97=Pasture, 98=Fallow, 99=Non agricultural96.0
s2q7doubleNon 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_otherstringOther non agricultural Land Type``
s2q17doubleCropping system: 1=Pure Cropping, 2=Mixed Cropping1.0
s2q18doubleNumber of main crops in the plot1.0
s3q1doubleCrop 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), 401205.0
s3q1_ostringOther Crop name``
s3q2_1doubleCrop proportion (in %)100.0
s3q2_2doubleCrop proportion code9.0
s3q3_1doubleCrop density (in %)100.0
s3q3_2doubleCrop Density code9.0
s3q5doubleIs this crop for this season? 1=Yes, 2=No2.0
s3q6doubleWill this crop be harvested in this season? 1=Yes, 2=No2.0
s3q7doubleWhat 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 30/0918.0
weight_plotdoublePlot weight90.4729939588482
crop_areadoubleCrop area0.1593399941921234
source_providerstringPublishing organization.NISR
source_datasetstringSource dataset or package title.Season C: Screening Crops
source_resourcestringSource resource title, table name, or file name.rwa_sas_seasonc_screening_crops
source_package_idstringSource package identifier.8d24a04e-4099-4d73-9a32-1fc9f6babe86
source_resource_idstringSource resource identifier.8d24a04e-4099-4d73-9a32-1fc9f6babe86
source_urlstringOriginal source URL or download URL.https://api.data.gov.rw/api/v1/datasets/public/8d24a04e-4099-4d73-9a3...
license_idstringSource license identifier.cc-by-4.0
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-18T11:48:51Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-season-c-screening-crops-14c8ff0d")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

Time-Series Pattern

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

Pivot For Analysis

python
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

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

bibtex
@misc{electric_sheep_africa_africa_rwanda_season_c_screening_crops_14c8ff0d_2022,
  title        = {Season C: Screening Crops | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2022},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/8d24a04e-4099-4d73-9a32-1fc9f6babe86},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-season-c-screening-crops-14c8ff0d}}
}

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/8d24a04e-4099-4d73-9a32-1fc9f6babe86