electricsheepafrica/african-crop-suitability-shift

African Crop Suitability Shift Dataset

A comprehensive dataset projecting climate change impacts on crop suitability across 20 African nations under three climate scenarios through 2040-2060.

Dataset Overview

Metric	Value
Total Records	32,000
Countries	20
Crops	10
Scenarios	3
Time Slice	2040-2060
Baseline Period	1981-2010

Scenario Configurations

rcp45_moderate

• Emissions pathway: RCP 4.5 (intermediate)
• Projected warming: ~1.5°C by 2050
• Records: ~10,667
• File: rcp45_moderate.csv

rcp85_high

• Emissions pathway: RCP 8.5 (high emissions)
• Projected warming: ~2.8°C by 2050
• Records: ~10,667
• File: rcp85_high.csv

adaptation_success

• Emissions pathway: RCP 4.5 + successful adaptation
• Projected warming: ~1.5°C with enhanced resilience
• Records: ~10,667
• File: adaptation_success.csv

Countries Covered

South Africa, Nigeria, Kenya, Egypt, Morocco, Ghana, Ethiopia, Tanzania, Uganda, Ivory Coast, Senegal, Mali, Burkina Faso, Niger, Zambia, Zimbabwe, Mozambique, Cameroon, Malawi, Rwanda

Crops Tracked

Crop	Type	Climate Sensitivity
Maize	Cereal (C4)	High (-6.0%/°C)
Sorghum	Cereal (C4)	Moderate (-3.5%/°C)
Millet	Cereal (C4)	Low (-3.0%/°C)
Rice	Cereal (C3)	Moderate (-3.2%/°C)
Wheat	Cereal (C3)	High (-6.0%/°C)
Cassava	Root/Tuber (C3)	Low (-2.5%/°C)
Teff	Cereal (C3)	Moderate (-4.0%/°C)
Coffee	Perennial (C3)	Very High (-8.0%/°C)
Cocoa	Perennial (C3)	High (-5.0%/°C)
Cotton	Fiber (C3)	Moderate (-4.5%/°C)

Columns

Identification

• record_id — Unique identifier (format: ACSS-{SCENARIO}-{ID})
• scenario — Climate scenario name
• country — Country name
• crop — Crop name
• latitude / longitude — Geographic coordinates
• elevation_m — Elevation in meters
• agro_ecological_zone — GAEZ agro-ecological classification
• region — African sub-region

Baseline Climate (1981-2010)

• baseline_temp_c — Mean annual temperature (°C)
• baseline_rainfall_mm — Mean annual rainfall (mm)
• baseline_rainfall_cv — Rainfall coefficient of variation
• baseline_growing_season_days — Growing season length (days)
• baseline_frost_days — Annual frost days
• baseline_extreme_events_per_decade — Drought/flood frequency

Projected Climate (2040-2060)

• projected_temp_c — Projected mean annual temperature (°C)
• temp_change_c — Temperature change (°C)
• projected_rainfall_mm — Projected mean annual rainfall (mm)
• rainfall_change_pct — Rainfall change (%)
• rainfall_variability_increase_pct — Rainfall CV increase (percentage points)
• projected_rainfall_reliability — Rainfall reliability index (0-1)

Growing Season

• projected_growing_season_days — Projected growing season length
• growing_season_change_days — Change in growing season (days)
• temperature_stress_days_increase — Additional days >35°C
• projected_frost_days — Projected annual frost days

Water & Irrigation

• baseline_irrigation_pct — Baseline irrigated area (%)
• projected_irrigation_pct — Projected irrigated area (%)
• irrigation_requirement_change_pct — Irrigation demand change (%)
• baseline_soil_moisture_deficit_days — Baseline moisture deficit days
• projected_soil_moisture_deficit_days — Projected moisture deficit days
• baseline_crop_water_requirement_mm — Baseline crop water need (mm/season)
• projected_crop_water_requirement_mm — Projected crop water need (mm/season)

Suitability & Yield

• baseline_suitability_index — Baseline crop suitability (0-1)
• projected_suitability_index — Projected crop suitability (0-1)
• suitability_change — Suitability index change
• baseline_yield_t_ha — Baseline yield (tonnes/ha)
• projected_yield_t_ha — Projected yield (tonnes/ha)
• yield_change_pct — Yield change (%)

Pest & Disease

• baseline_pest_pressure_index — Baseline pest/disease pressure (0-1)
• projected_pest_pressure_index — Projected pest/disease pressure (0-1)
• pest_pressure_change — Pest pressure index change

Extreme Events & Adaptation

• projected_extreme_events_per_decade — Projected extreme event frequency
• adaptation_potential_score — Country-level adaptation capacity (0-1)
• market_access_index — Market access indicator (0-1)

Metadata

• time_slice — Projection period (2040-2060)
• baseline_period — Reference period (1981-2010)
• data_source — Source attribution
• generation_date — Generation date (YYYY-MM-DD)

Parameter Calibration

All parameters are calibrated against peer-reviewed literature:

Parameter	Source
Temperature sensitivity	Zhao et al. (2017) Nature; IPCC AR6 Table 5.3
Rainfall sensitivity	FAO GAEZ v4; CGIAR CCAFS crop modeling
CO2 fertilization	Deryng et al. (2016); Webber et al. (2018)
Baseline yields	FAOSTAT 2020-2023; CGIAR yield gap analyses
Suitability indices	GAEZ v4; FAO Ecocrop
Pest pressure	Deutsch et al. (2018) Science; IPCC AR6 Ch.5
Adaptation potential	ND-GAIN Index; CGIAR adaptation assessments
Growing season changes	IPCC AR6 WGII Ch.5, Ch.9

Key References

1. IPCC AR6 WGII Chapter 5 — Food, Fibre, and other Ecosystem Products
2. IPCC AR6 WGII Chapter 9 — Africa
3. Zhao et al. (2017) — Temperature effects on global crop yields. Nature 547:73-77
4. Lobell et al. (2011) — Climate trends and crop yields. Science 333:616-620
5. Schlenker & Lobell (2010) — African crop responses. PNAS 107:10661-10666
6. Deutsch et al. (2018) — Insect metabolism and pest pressure. Science 361:916-919
7. FAO GAEZ v4 — Global Agro-Ecological Zones
8. CGIAR CCAFS — Climate Change, Agriculture and Food Security
9. Deryng et al. (2016) — CO2 fertilization and crop yields. Nature Climate Change 6:799-804
10. Webber et al. (2018) — CO2 effects on wheat. Nature Climate Change 8:323-328

Usage

from datasets import load_dataset

# Load specific scenario
ds = load_dataset("electricsheepafrica/african-crop-suitability-shift", "rcp45_moderate")
df = ds["train"].to_pandas()

# Load all scenarios
ds = load_dataset("electricsheepafrica/african-crop-suitability-shift")
for config in ds.keys():
    df = ds[config].to_pandas()
    print(f"{config}: {len(df)} records")

License

CC-BY-4.0

Citation

@dataset{african_crop_suitability_shift_2026,
  title={African Crop Suitability Shift Dataset},
  author={Electric Sheep Africa},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/african-crop-suitability-shift},
  note={Calibrated against IPCC AR6, CGIAR, FAO GAEZ, and peer-reviewed crop modeling literature}
}