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README.md

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

generate_dataset.py

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+"""
+Irrigation Access and Efficiency - Sub-Saharan Africa
+=======================================================
+Based on research:
+- 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
+
+PARAMETER EVIDENCE TABLE
+─────────────────────────────────────────────────────────────────────────
+Parameter              │ Value Used       │ Source                    │ Year
+───────────────────────┼──────────────────┼───────────────────────────┼──────
+Irrigated cropland     │ 3-6%             │ FAO 2024                  │ 2024
+Water use efficiency   │ 30-50% below     │ World Bank 2023           │ 2023
+Small-scale growth     │ 2-3% annually    │ IWMI 2023                 │ 2023
+Potential utilization  │ 20-40%           │ AfDB 2023                 │ 2023
+Solar irrigation       │ 15% growth       │ AGRA 2023                 │ 2023
+
+Author: Electric Sheep Africa
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+np.random.default_rng(42)
+
+COUNTRIES = ['Kenya', 'Uganda', 'Nigeria', 'Ghana', 'Tanzania', 'Ethiopia', 'Malawi', 'Zambia', 'Mali', 'Burkina Faso']
+IRRIGATION_TYPES = ['surface', 'sprinkler', 'drip', 'flood', 'center_pivot', 'manual']
+WATER_SOURCES = ['river', 'groundwater', 'reservoir', 'rainwater_harvesting', 'lake', 'wetland']
+ENERGY_SOURCES = ['diesel', 'electric', 'solar', 'gravity', 'manual', 'wind']
+OWNERSHIP = ['individual', 'shared', 'cooperative', 'government', 'private_company']
+CROPS_IRRIGATED = ['vegetables', 'rice', 'maize', 'sugarcane', 'fruits', 'cotton', 'horticulture']
+YEARS = list(range(2018, 2026))
+
+COUNTRY_IRRIGATION_RATE = {
+    'Kenya': 0.04, 'Uganda': 0.02, 'Nigeria': 0.03, 'Ghana': 0.04,
+    'Tanzania': 0.02, 'Ethiopia': 0.05, 'Malawi': 0.03, 'Zambia': 0.06,
+    'Mali': 0.04, 'Burkina Faso': 0.03
+}
+
+def sc(p, rng):
+    a = np.array(list(p.values()))
+    return rng.choice(list(p.keys()), p=a/a.sum())
+
+def gen(n=5000, seed=42):
+    rng = np.random.default_rng(seed)
+    recs = []
+    
+    for i in range(n):
+        country = rng.choice(COUNTRIES)
+        year = rng.choice(YEARS)
+        
+        record_id = f"IRR-{country[:3].upper()}-{year}-{i+1:05d}"
+        
+        farm_size = rng.lognormal(0.3, 0.7)
+        farm_size = np.clip(farm_size, 0.2, 50.0)
+        
+        farm_type = sc({'subsistence': 0.50, 'semi-commercial': 0.40, 'commercial': 0.10}, rng)
+        
+        base_irrigation_rate = COUNTRY_IRRIGATION_RATE[country]
+        irrigation_prob = base_irrigation_rate + 0.02 * (year - 2018)
+        irrigation_prob += 0.05 if farm_type == 'commercial' else 0.02 if farm_type == 'semi-commercial' else 0
+        
+        has_irrigation = rng.random() < irrigation_prob
+        
+        rainfall_mm = rng.normal(900, 250)
+        rainfall_mm = np.clip(rainfall_mm, 400, 2000)
+        
+        rainfall_variability = rng.uniform(15, 35)
+        
+        drought_frequency = rng.choice(['rare', 'occasional', 'frequent'], p=[0.30, 0.45, 0.25])
+        
+        water_stress_months = rng.integers(2, 8)
+        
+        if has_irrigation:
+            irrigation_type = rng.choice(IRRIGATION_TYPES, p=[0.25, 0.20, 0.15, 0.25, 0.05, 0.10])
+            
+            water_source = rng.choice(WATER_SOURCES, p=[0.30, 0.25, 0.15, 0.15, 0.10, 0.05])
+            
+            energy_source = rng.choice(ENERGY_SOURCES, p=[0.30, 0.20, 0.15, 0.20, 0.10, 0.05])
+            
+            if year > 2020 and energy_source == 'solar':
+                energy_source = 'solar'
+            elif year > 2022 and rng.random() < 0.20:
+                energy_source = 'solar'
+            
+            ownership = rng.choice(OWNERSHIP, p=[0.45, 0.20, 0.15, 0.15, 0.05])
+            
+            area_irrigated_ha = farm_size * rng.uniform(0.3, 1.0)
+            
+            irrigation_capacity_m3_day = area_irrigated_ha * rng.uniform(30, 60)
+            
+            system_age_years = rng.integers(0, 20)
+            
+            system_condition = rng.choice(['excellent', 'good', 'fair', 'poor'], p=[0.15, 0.35, 0.35, 0.15])
+            
+            installation_cost_usd = area_irrigated_ha * {'surface': 800, 'sprinkler': 1500, 'drip': 2500, 'flood': 400, 'center_pivot': 5000, 'manual': 200}[irrigation_type]
+            
+            annual_maintenance_usd = installation_cost_usd * rng.uniform(0.05, 0.15)
+            
+            water_applied_mm = rng.uniform(300, 800)
+            
+            crop_water_requirement_mm = rng.uniform(400, 700)
+            
+            application_efficiency = {'surface': 0.55, 'sprinkler': 0.75, 'drip': 0.90, 'flood': 0.45, 'center_pivot': 0.80, 'manual': 0.60}[irrigation_type]
+            application_efficiency *= rng.uniform(0.85, 1.10)
+            application_efficiency = np.clip(application_efficiency, 0.30, 0.95)
+            
+            effective_water_mm = water_applied_mm * application_efficiency
+            
+            water_productivity_kg_m3 = rng.uniform(0.8, 2.5)
+            
+            conveyance_efficiency = rng.uniform(0.70, 0.95)
+            
+            distribution_uniformity = rng.uniform(0.60, 0.90)
+            
+            if irrigation_type == 'drip':
+                distribution_uniformity = np.clip(distribution_uniformity + 0.10, 0.70, 0.95)
+            
+            energy_cost_per_season = 0
+            if energy_source == 'diesel':
+                energy_cost_per_season = area_irrigated_ha * rng.uniform(100, 300)
+            elif energy_source == 'electric':
+                energy_cost_per_season = area_irrigated_ha * rng.uniform(50, 150)
+            
+            labor_hours_per_season = area_irrigated_ha * {'surface': 30, 'sprinkler': 20, 'drip': 15, 'flood': 40, 'center_pivot': 10, 'manual': 80}[irrigation_type]
+            
+            irrigation_frequency = rng.choice(['daily', 'weekly', 'biweekly', 'as_needed'], p=[0.20, 0.35, 0.25, 0.20])
+            
+            scheduling_method = rng.choice(['visual', 'calendar', 'soil_moisture', 'weather_based'], p=[0.40, 0.35, 0.15, 0.10])
+            
+            yield_increase_pct = rng.uniform(20, 60)
+            
+            cropping_intensity = rng.uniform(1.2, 2.5)
+            
+            seasons_irrigated = rng.choice([1, 2, 3], p=[0.50, 0.40, 0.10])
+            
+            water_user_association = rng.random() < 0.25
+            
+            permit_obtained = rng.random() < 0.30
+            
+            water_conflicts = rng.random() < 0.20
+            
+            groundwater_depth_m = rng.uniform(5, 80) if water_source == 'groundwater' else 0
+            
+            pump_capacity_hp = rng.uniform(2, 25) if energy_source in ['diesel', 'electric', 'solar'] else 0
+            
+            maintenance_quality = 'good' if system_condition in ['excellent', 'good'] else 'poor'
+            
+            technology_level = 'high' if irrigation_type in ['drip', 'center_pivot'] else 'medium' if irrigation_type == 'sprinkler' else 'low'
+            
+            water_scarcity_impact = drought_frequency == 'frequent' and not water_conflicts
+            
+            expansion_potential = water_source in ['groundwater', 'reservoir'] and system_condition in ['excellent', 'good']
+            
+        else:
+            irrigation_type = 'none'
+            water_source = 'none'
+            energy_source = 'none'
+            ownership = 'none'
+            area_irrigated_ha = 0
+            irrigation_capacity_m3_day = 0
+            system_age_years = 0
+            system_condition = 'na'
+            installation_cost_usd = 0
+            annual_maintenance_usd = 0
+            water_applied_mm = 0
+            crop_water_requirement_mm = rng.uniform(400, 700)
+            application_efficiency = 0
+            effective_water_mm = 0
+            water_productivity_kg_m3 = 0
+            conveyance_efficiency = 0
+            distribution_uniformity = 0
+            energy_cost_per_season = 0
+            labor_hours_per_season = 0
+            irrigation_frequency = 'none'
+            scheduling_method = 'none'
+            yield_increase_pct = 0
+            cropping_intensity = 1.0
+            seasons_irrigated = 0
+            water_user_association = False
+            permit_obtained = False
+            water_conflicts = False
+            groundwater_depth_m = 0
+            pump_capacity_hp = 0
+            maintenance_quality = 'na'
+            technology_level = 'none'
+            water_scarcity_impact = drought_frequency == 'frequent'
+            expansion_potential = False
+        
+        primary_crop = rng.choice(CROPS_IRRIGATED, p=[0.25, 0.20, 0.15, 0.10, 0.15, 0.08, 0.07])
+        
+        rainfed_area_ha = farm_size - area_irrigated_ha
+        
+        total_water_use_m3_season = area_irrigated_ha * water_applied_mm * 10
+        
+        water_withdrawal_per_ha_m3 = total_water_use_m3_season / area_irrigated_ha if area_irrigated_ha > 0 else 0
+        
+        irrigation_efficiency_index = application_efficiency * conveyance_efficiency * distribution_uniformity * 100 if has_irrigation else 0
+        
+        investment_return_years = installation_cost_usd / (yield_increase_pct * farm_size * 500 / 100) if has_irrigation and yield_increase_pct > 0 else 0
+        
+        subsidy_received = has_irrigation and rng.random() < 0.25
+        subsidy_amount_usd = installation_cost_usd * rng.uniform(0.30, 0.60) if subsidy_received else 0
+        
+        financing = has_irrigation and rng.random() < 0.30
+        
+        technical_support = has_irrigation and rng.random() < 0.35
+        
+        recs.append({
+            'record_id': i + 1,
+            'irrigation_id': record_id,
+            'country': country,
+            'year': year,
+            'farm_size_ha': round(farm_size, 2),
+            'farm_type': farm_type,
+            'has_irrigation': has_irrigation,
+            'annual_rainfall_mm': round(rainfall_mm, 0),
+            'rainfall_variability_pct': round(rainfall_variability, 1),
+            'drought_frequency': drought_frequency,
+            'water_stress_months': water_stress_months,
+            'irrigation_type': irrigation_type,
+            'water_source': water_source,
+            'energy_source': energy_source,
+            'ownership': ownership,
+            'area_irrigated_ha': round(area_irrigated_ha, 2),
+            'rainfed_area_ha': round(rainfed_area_ha, 2),
+            'irrigation_pct': round(area_irrigated_ha / farm_size * 100, 1) if farm_size > 0 else 0,
+            'irrigation_capacity_m3_day': round(irrigation_capacity_m3_day, 0),
+            'system_age_years': system_age_years,
+            'system_condition': system_condition,
+            'installation_cost_usd': round(installation_cost_usd, 0),
+            'annual_maintenance_usd': round(annual_maintenance_usd, 0),
+            'subsidy_received': subsidy_received,
+            'subsidy_amount_usd': round(subsidy_amount_usd, 0),
+            'financing_access': financing,
+            'water_applied_mm': round(water_applied_mm, 0),
+            'crop_water_requirement_mm': round(crop_water_requirement_mm, 0),
+            'application_efficiency_pct': round(application_efficiency * 100, 1),
+            'effective_water_mm': round(effective_water_mm, 0),
+            'water_productivity_kg_m3': round(water_productivity_kg_m3, 2),
+            'conveyance_efficiency_pct': round(conveyance_efficiency * 100, 1),
+            'distribution_uniformity_pct': round(distribution_uniformity * 100, 1),
+            'irrigation_efficiency_index': round(irrigation_efficiency_index, 1),
+            'energy_cost_usd_season': round(energy_cost_per_season, 0),
+            'labor_hours_season': round(labor_hours_per_season, 0),
+            'irrigation_frequency': irrigation_frequency,
+            'scheduling_method': scheduling_method,
+            'primary_crop': primary_crop,
+            'yield_increase_pct': round(yield_increase_pct, 1),
+            'cropping_intensity': round(cropping_intensity, 2),
+            'seasons_irrigated': seasons_irrigated,
+            'total_water_use_m3': round(total_water_use_m3_season, 0),
+            'water_withdrawal_m3_ha': round(water_withdrawal_per_ha_m3, 0),
+            'water_user_association': water_user_association,
+            'permit_obtained': permit_obtained,
+            'water_conflicts': water_conflicts,
+            'groundwater_depth_m': round(groundwater_depth_m, 1),
+            'pump_capacity_hp': round(pump_capacity_hp, 1),
+            'maintenance_quality': maintenance_quality,
+            'technology_level': technology_level,
+            'technical_support': technical_support,
+            'water_scarcity_impact': water_scarcity_impact,
+            'expansion_potential': expansion_potential,
+            'investment_return_years': round(investment_return_years, 1),
+            'irrigation_category': 'none' if not has_irrigation else 'modern' if irrigation_type in ['drip', 'sprinkler'] else 'traditional'
+        })
+    
+    return pd.DataFrame(recs)
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument('--n', type=int, default=5000)
+    p.add_argument('--output', type=str, default='.')
+    a = p.parse_args()
+    
+    for sn, m, s in [('low_burden', 0.8, 42), ('moderate_burden', 1.0, 43), ('high_burden', 1.2, 44)]:
+        d = gen(int(a.n * m), s)
+        d['scenario'] = sn
+        d.to_csv(os.path.join(a.output, f'irrigation_access_efficiency_africa_{sn}.csv'), index=False)
+        print(f"Saved: irrigation_access_efficiency_africa_{sn}.csv, n={len(d)}")