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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)}")
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