| """ |
| 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)}") |
|
|
