---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- agriculture
- africa
- synthetic-data
- sub-saharan-africa
- irrigation
- water-management
- synthetic
size_categories:
- 10K<n<100K
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Irrigation Access and Efficiency - Sub-Saharan Africa

Synthetic dataset capturing irrigation infrastructure, access, and water use efficiency among smallholder farmers. Covers irrigation types, water sources, costs, and productivity impacts across diverse farming systems.

## Dataset Statistics

| Scenario | Records |
|----------|---------|
| Low Burden | 4,000 |
| Moderate Burden | 5,000 |
| High Burden | 6,000 |
| **Total** | **15,000** |

**Key Metrics:**
- 10 countries with varying irrigation rates
- Years: 2018-2025
- 55 columns covering infrastructure, efficiency, and economics
- Irrigated cropland: 3-6% of total
- Water use efficiency: 30-50% below potential

## Column Descriptions

| Column | Description |
|--------|-------------|
| `record_id` | Unique record identifier |
| `irrigation_id` | Unique irrigation record identifier |
| `country` | Country name |
| `year` | Year of record |
| `farm_size_ha` | Farm size in hectares |
| `farm_type` | Farm classification |
| `has_irrigation` | Has irrigation (boolean) |
| `annual_rainfall_mm` | Annual rainfall (mm) |
| `rainfall_variability_pct` | Rainfall variability (%) |
| `drought_frequency` | Drought frequency |
| `water_stress_months` | Water stress months per year |
| `irrigation_type` | Irrigation system type |
| `water_source` | Water source |
| `energy_source` | Energy source for pumping |
| `ownership` | Ownership type |
| `area_irrigated_ha` | Area irrigated (ha) |
| `rainfed_area_ha` | Rainfed area (ha) |
| `irrigation_pct` | Percentage of farm irrigated |
| `irrigation_capacity_m3_day` | System capacity (m³/day) |
| `system_age_years` | System age (years) |
| `system_condition` | System condition |
| `installation_cost_usd` | Installation cost (USD) |
| `annual_maintenance_usd` | Annual maintenance (USD) |
| `subsidy_received` | Subsidy received (boolean) |
| `subsidy_amount_usd` | Subsidy amount (USD) |
| `financing_access` | Financing access (boolean) |
| `water_applied_mm` | Water applied (mm) |
| `crop_water_requirement_mm` | Crop water requirement (mm) |
| `application_efficiency_pct` | Application efficiency (%) |
| `effective_water_mm` | Effective water (mm) |
| `water_productivity_kg_m3` | Water productivity (kg/m³) |
| `conveyance_efficiency_pct` | Conveyance efficiency (%) |
| `distribution_uniformity_pct` | Distribution uniformity (%) |
| `irrigation_efficiency_index` | Overall efficiency index |
| `energy_cost_usd_season` | Energy cost per season (USD) |
| `labor_hours_season` | Labor hours per season |
| `irrigation_frequency` | Irrigation frequency |
| `scheduling_method` | Scheduling method |
| `primary_crop` | Primary irrigated crop |
| `yield_increase_pct` | Yield increase (%) |
| `cropping_intensity` | Cropping intensity |
| `seasons_irrigated` | Seasons irrigated per year |
| `total_water_use_m3` | Total water use (m³) |
| `water_withdrawal_m3_ha` | Water withdrawal (m³/ha) |
| `water_user_association` | WUA member (boolean) |
| `permit_obtained` | Water permit obtained (boolean) |
| `water_conflicts` | Water conflicts (boolean) |
| `groundwater_depth_m` | Groundwater depth (m) |
| `pump_capacity_hp` | Pump capacity (HP) |
| `maintenance_quality` | Maintenance quality |
| `technology_level` | Technology level |
| `technical_support` | Technical support (boolean) |
| `water_scarcity_impact` | Water scarcity impact (boolean) |
| `expansion_potential` | Expansion potential (boolean) |
| `investment_return_years` | Investment return period (years) |
| `irrigation_category` | Irrigation category |
| `scenario` | Burden scenario |

## Usage Example

```python
import pandas as pd

# Load the dataset
df = pd.read_csv('irrigation_access_efficiency_africa_moderate_burden.csv')

# Irrigation access by country
access = df.groupby('country')['has_irrigation'].mean() * 100
print(f"Irrigation access by country:\n{access}")

# Efficiency by irrigation type
efficiency = df[df['has_irrigation']].groupby('irrigation_type')['irrigation_efficiency_index'].mean()
print(efficiency)

# Compare yields: irrigated vs rainfed
yield_comparison = df.groupby('has_irrigation')['yield_increase_pct'].mean()
print(yield_comparison)
```

## Research Sources

- FAO 2024: Only 3-6% of cropland under irrigation in SSA
- World Bank 2023: Water use efficiency 30-50% below potential
- IWMI 2023: Small-scale irrigation expanding 2-3% annually
- AfDB 2023: Irrigation potential utilization 20-40%
- AGRA 2023: Solar-powered irrigation growing 15% annually

**Author:** Electric Sheep Africa