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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
+  - livestock
+  - animal-health
+size_categories:
+  - 10K<n<100K
+---
+
+# Livestock Health and Productivity - Sub-Saharan Africa
+
+Synthetic dataset capturing livestock health, productivity, and management practices across smallholder farms in Sub-Saharan Africa. Covers cattle, goats, sheep, poultry, pigs, and donkeys with production and health metrics.
+
+## Dataset Statistics
+
+| Scenario | Records |
+|----------|---------|
+| Low Burden | 4,000 |
+| Moderate Burden | 5,000 |
+| High Burden | 6,000 |
+| **Total** | **15,000** |
+
+**Key Metrics:**
+- 10 countries with diverse livestock systems
+- Years: 2018-2025
+- 52 columns covering health, productivity, and economics
+- Cattle mortality: 8-15% in smallholder systems
+- Milk yields: 2-5L/day vs 15-25L potential
+
+## Column Descriptions
+
+| Column | Description |
+|--------|-------------|
+| `record_id` | Unique record identifier |
+| `livestock_id` | Unique livestock record identifier |
+| `country` | Country name |
+| `year` | Year of record |
+| `farm_size_ha` | Farm size in hectares |
+| `production_system` | Production system type |
+| `primary_species` | Primary livestock species |
+| `herd_size` | Herd/flock size |
+| `breed_type` | Breed type (local/crossbreed/exotic) |
+| `feed_source` | Primary feed source |
+| `water_access` | Water access quality |
+| `housing_type` | Housing type |
+| `mortality_rate_pct` | Mortality rate percentage |
+| `animals_died` | Number of animals died |
+| `disease_outbreak` | Disease outbreak occurred (boolean) |
+| `primary_disease` | Primary disease type |
+| `disease_severity` | Disease severity level |
+| `vaccination_coverage_pct` | Vaccination coverage (%) |
+| `animals_vaccinated` | Number vaccinated |
+| `deworming_frequency` | Deworming frequency |
+| `tick_control` | Tick control practiced (boolean) |
+| `veterinary_access` | Veterinary access (boolean) |
+| `vet_visits_per_year` | Veterinary visits per year |
+| `health_insurance` | Health insurance (boolean) |
+| `milk_yield_l_day` | Milk yield (L/day) |
+| `lactation_length_days` | Lactation length (days) |
+| `annual_milk_l` | Annual milk production (L) |
+| `weight_gain_kg_day` | Weight gain (kg/day) |
+| `live_weight_kg` | Live weight (kg) |
+| `reproductive_rate` | Reproductive rate |
+| `reproductive_health` | Reproductive health status (boolean) |
+| `fertility_issues` | Fertility issues (boolean) |
+| `body_condition_score` | Body condition score (1-5) |
+| `nutrition_status` | Nutrition status |
+| `supplementation` | Feed supplementation (boolean) |
+| `mineral_supplement` | Mineral supplement (boolean) |
+| `forage_quality` | Forage quality |
+| `breeding_method` | Breeding method |
+| `record_keeping` | Record keeping (boolean) |
+| `market_access` | Market access level |
+| `price_per_head_usd` | Price per head (USD) |
+| `annual_revenue_usd` | Annual revenue (USD) |
+| `feed_cost_usd` | Feed cost (USD) |
+| `health_cost_usd` | Health cost (USD) |
+| `labor_cost_usd` | Labor cost (USD) |
+| `total_cost_usd` | Total cost (USD) |
+| `net_income_usd` | Net income (USD) |
+| `productivity_index` | Productivity index (0-100) |
+| `health_index` | Health index (0-100) |
+| `mortality_category` | Mortality category |
+| `intervention_priority` | Intervention priority level |
+| `scenario` | Burden scenario |
+
+## Usage Example
+
+```python
+import pandas as pd
+
+# Load the dataset
+df = pd.read_csv('livestock_health_productivity_africa_moderate_burden.csv')
+
+# Mortality by species
+mortality = df.groupby('primary_species')['mortality_rate_pct'].mean()
+print(f"Mortality by species:\n{mortality}")
+
+# Milk yield by production system
+milk = df[df['primary_species'] == 'cattle'].groupby('production_system')['milk_yield_l_day'].mean()
+print(milk)
+
+# Economic analysis by breed type
+economics = df.groupby('breed_type')[['net_income_usd', 'productivity_index']].mean()
+print(economics)
+```
+
+## Research Sources
+
+- FAO 2024: Cattle mortality 8-15% in smallholder systems
+- ILRI 2023: Milk yields 2-5L/day vs 15-25L potential
+- World Bank 2023: Poultry mortality 15-30% in traditional systems
+- AU-IBAR 2023: Disease prevalence 20-40% for endemic diseases
+- GALVmed 2023: Vaccination coverage 10-30% for priority diseases
+
+**Author:** Electric Sheep Africa

generate_dataset.py

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+"""
+Livestock Health and Productivity - Sub-Saharan Africa
+========================================================
+Based on research:
+- FAO 2024: Cattle mortality 8-15% in smallholder systems
+- ILRI 2023: Milk yields 2-5L/day vs 15-25L potential
+- World Bank 2023: Poultry mortality 15-30% in traditional systems
+- AU-IBAR 2023: Disease prevalence 20-40% for endemic diseases
+- GALVmed 2023: Vaccination coverage 10-30% for priority diseases
+
+PARAMETER EVIDENCE TABLE
+─────────────────────────────────────────────────────────────────────────
+Parameter              │ Value Used       │ Source                    │ Year
+───────────────────────┼──────────────────┼───────────────────────────┼──────
+Cattle mortality       │ 8-15%            │ FAO 2024                  │ 2024
+Milk yields            │ 2-5L/day         │ ILRI 2023                 │ 2023
+Poultry mortality      │ 15-30%           │ World Bank 2023           │ 2023
+Disease prevalence     │ 20-40%           │ AU-IBAR 2023              │ 2023
+Vaccination coverage   │ 10-30%           │ GALVmed 2023              │ 2023
+Goat mortality         │ 10-20%           │ FAO 2024                  │ 2024
+
+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']
+SPECIES = ['cattle', 'goats', 'sheep', 'poultry', 'pigs', 'donkeys']
+PRODUCTION_SYSTEMS = ['extensive', 'semi_intensive', 'intensive', 'pastoral', 'agropastoral']
+BREED_TYPES = ['local', 'crossbreed', 'exotic']
+FEED_SOURCES = ['grazing', 'crop_residues', 'concentrates', 'fodder_crops', 'mixed']
+HEALTH_ISSUES = ['tick_borne', 'respiratory', 'digestive', 'parasitic', 'metabolic', 'reproductive']
+YEARS = list(range(2018, 2026))
+
+SPECIES_BASE_MORTALITY = {
+    'cattle': 0.12, 'goats': 0.15, 'sheep': 0.14, 'poultry': 0.22, 'pigs': 0.18, 'donkeys': 0.08
+}
+
+SPECIES_BASE_PRODUCTIVITY = {
+    'cattle': {'milk_l_day': 3.5, 'weight_gain_kg_day': 0.4, 'calving_rate': 0.55},
+    'goats': {'milk_l_day': 0.8, 'weight_gain_kg_day': 0.06, 'kidding_rate': 1.2},
+    'sheep': {'milk_l_day': 0.5, 'weight_gain_kg_day': 0.08, 'lambing_rate': 1.1},
+    'poultry': {'eggs_year': 80, 'weight_gain_kg_week': 0.15},
+    'pigs': {'litter_size': 8, 'weight_gain_kg_day': 0.35},
+    'donkeys': {'work_hours_day': 4, 'weight_gain_kg_day': 0.1}
+}
+
+DISEASE_PREVALENCE = {
+    'tick_borne': 0.23, 'respiratory': 0.17, 'digestive': 0.14,
+    'parasitic': 0.28, 'metabolic': 0.07, 'reproductive': 0.11
+}
+
+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"LVS-{country[:3].upper()}-{year}-{i+1:05d}"
+        
+        farm_size = rng.lognormal(0.2, 0.6)
+        farm_size = np.clip(farm_size, 0.2, 30.0)
+        
+        production_system = sc({
+            'extensive': 0.35, 'semi_intensive': 0.25, 'intensive': 0.10,
+            'pastoral': 0.15, 'agropastoral': 0.15
+        }, rng)
+        
+        primary_species = rng.choice(SPECIES, p=[0.30, 0.25, 0.15, 0.20, 0.07, 0.03])
+        
+        herd_size_base = {'cattle': 8, 'goats': 15, 'sheep': 12, 'poultry': 35, 'pigs': 6, 'donkeys': 2}[primary_species]
+        herd_size = int(rng.lognormal(np.log(herd_size_base), 0.8))
+        herd_size = np.clip(herd_size, 1, {'cattle': 100, 'goats': 200, 'sheep': 150, 'poultry': 500, 'pigs': 50, 'donkeys': 10}[primary_species])
+        
+        breed_type = sc({
+            'local': 0.60, 'crossbreed': 0.30, 'exotic': 0.10
+        }, rng)
+        
+        feed_source = sc({
+            'grazing': 0.45, 'crop_residues': 0.20, 'concentrates': 0.10,
+            'fodder_crops': 0.10, 'mixed': 0.15
+        }, rng)
+        
+        if production_system in ['pastoral', 'extensive']:
+            feed_source = 'grazing'
+        elif production_system == 'intensive':
+            feed_source = rng.choice(['concentrates', 'mixed'], p=[0.5, 0.5])
+        
+        water_access = rng.choice(['adequate', 'seasonal', 'limited', 'poor'], p=[0.35, 0.30, 0.25, 0.10])
+        
+        housing_type = rng.choice(['none', 'simple_shelter', 'improved_shelter', 'modern'], p=[0.35, 0.35, 0.20, 0.10])
+        
+        base_mortality = SPECIES_BASE_MORTALITY[primary_species]
+        mortality_modifier = 1.0
+        mortality_modifier *= 0.7 if production_system == 'intensive' else 1.2 if production_system in ['pastoral', 'extensive'] else 1.0
+        mortality_modifier *= 0.8 if breed_type == 'local' else 1.1 if breed_type == 'exotic' else 1.0
+        mortality_modifier *= 0.9 if housing_type in ['improved_shelter', 'modern'] else 1.1 if housing_type == 'none' else 1.0
+        
+        mortality_rate = base_mortality * mortality_modifier * rng.uniform(0.8, 1.2)
+        mortality_rate = np.clip(mortality_rate, 0.03, 0.45)
+        
+        animals_died = int(herd_size * mortality_rate)
+        
+        disease_outbreak = rng.random() < 0.20
+        
+        primary_disease = None
+        if disease_outbreak:
+            primary_disease = rng.choice(list(DISEASE_PREVALENCE.keys()), 
+                                        p=list(DISEASE_PREVALENCE.values()))
+        
+        disease_severity = rng.choice(['mild', 'moderate', 'severe'], p=[0.40, 0.40, 0.20]) if disease_outbreak else 'none'
+        
+        vaccination_coverage = rng.lognormal(np.log(0.20), 0.8)
+        vaccination_coverage = np.clip(vaccination_coverage, 0.02, 0.60)
+        
+        animals_vaccinated = int(herd_size * vaccination_coverage)
+        
+        deworming_frequency = rng.choice(['never', 'yearly', 'biannual', 'quarterly'], p=[0.30, 0.35, 0.25, 0.10])
+        
+        tick_control = rng.random() < 0.45
+        
+        veterinary_access = rng.random() < 0.35
+        
+        vet_visits_year = rng.integers(0, 6) if veterinary_access else 0
+        
+        health_insurance = rng.random() < 0.05
+        
+        base_prod = SPECIES_BASE_PRODUCTIVITY[primary_species]
+        
+        prod_modifier = 1.0
+        prod_modifier *= 1.3 if breed_type == 'exotic' else 1.1 if breed_type == 'crossbreed' else 1.0
+        prod_modifier *= 0.85 if disease_outbreak else 1.0
+        prod_modifier *= 0.9 if water_access in ['limited', 'poor'] else 1.0
+        prod_modifier *= 1.15 if production_system == 'intensive' else 0.9 if production_system == 'pastoral' else 1.0
+        prod_modifier *= 1.1 if feed_source in ['concentrates', 'mixed'] else 0.95 if feed_source == 'grazing' else 1.0
+        
+        if primary_species == 'cattle':
+            milk_yield_l_day = base_prod['milk_l_day'] * prod_modifier * rng.uniform(0.8, 1.2)
+            milk_yield_l_day = np.clip(milk_yield_l_day, 0.5, 20)
+            lactation_length_days = int(rng.normal(250, 40))
+            annual_milk_l = milk_yield_l_day * lactation_length_days
+            weight_gain_kg_day = base_prod['weight_gain_kg_day'] * prod_modifier * rng.uniform(0.7, 1.3)
+            calving_rate = base_prod['calving_rate'] * prod_modifier * rng.uniform(0.8, 1.1)
+            calving_rate = np.clip(calving_rate, 0.3, 0.9)
+            live_weight_kg = rng.normal(320, 60) if breed_type == 'local' else rng.normal(400, 70)
+        elif primary_species == 'goats':
+            milk_yield_l_day = base_prod['milk_l_day'] * prod_modifier * rng.uniform(0.7, 1.3)
+            weight_gain_kg_day = base_prod['weight_gain_kg_day'] * prod_modifier
+            kidding_rate = base_prod['kidding_rate'] * prod_modifier
+            annual_milk_l = milk_yield_l_day * 180
+            calving_rate = kidding_rate
+            lactation_length_days = 180
+            live_weight_kg = rng.normal(28, 8)
+        elif primary_species == 'sheep':
+            milk_yield_l_day = base_prod['milk_l_day']
+            weight_gain_kg_day = base_prod['weight_gain_kg_day'] * prod_modifier
+            annual_milk_l = 0
+            lambing_rate = base_prod['lambing_rate']
+            calving_rate = lambing_rate
+            lactation_length_days = 0
+            live_weight_kg = rng.normal(32, 7)
+        elif primary_species == 'poultry':
+            eggs_per_year = int(base_prod['eggs_year'] * prod_modifier * rng.uniform(0.7, 1.3))
+            weight_gain_kg_week = base_prod['weight_gain_kg_week'] * prod_modifier
+            weight_gain_kg_day = weight_gain_kg_week / 7
+            milk_yield_l_day = 0
+            annual_milk_l = 0
+            calving_rate = 0
+            lactation_length_days = 0
+            live_weight_kg = rng.normal(1.8, 0.5)
+        elif primary_species == 'pigs':
+            litter_size = int(base_prod['litter_size'] * prod_modifier * rng.uniform(0.8, 1.2))
+            weight_gain_kg_day = base_prod['weight_gain_kg_day'] * prod_modifier
+            litters_per_year = rng.uniform(1.8, 2.3)
+            milk_yield_l_day = 0
+            annual_milk_l = 0
+            calving_rate = litter_size
+            lactation_length_days = 0
+            live_weight_kg = rng.normal(80, 20)
+        else:
+            milk_yield_l_day = 0
+            annual_milk_l = 0
+            calving_rate = 0
+            lactation_length_days = 0
+            weight_gain_kg_day = base_prod['weight_gain_kg_day']
+            live_weight_kg = rng.normal(140, 25)
+        
+        reproductive_health = rng.random() < 0.75
+        fertility_issues = not reproductive_health and rng.random() < 0.25
+        
+        body_condition_score = rng.integers(1, 6)
+        
+        nutrition_status = 'adequate' if body_condition_score >= 3 else 'poor'
+        
+        supplementation = rng.random() < 0.30
+        
+        mineral_supplement = rng.random() < 0.20
+        
+        forage_quality = rng.choice(['poor', 'moderate', 'good'], p=[0.30, 0.45, 0.25])
+        
+        breeding_method = rng.choice(['natural', 'artificial_insemination', 'both'], p=[0.75, 0.10, 0.15])
+        
+        record_keeping = rng.random() < 0.20
+        
+        market_access = rng.choice(['farm_gate', 'local_market', 'regional', 'export'], p=[0.40, 0.35, 0.20, 0.05])
+        
+        price_per_head = live_weight_kg * rng.lognormal(np.log(2.5), 0.3)
+        
+        annual_revenue = herd_size * price_per_head * 0.3
+        
+        feed_cost = herd_size * rng.uniform(20, 80) if feed_source in ['concentrates', 'mixed'] else herd_size * rng.uniform(5, 20)
+        health_cost = animals_vaccinated * 2 + vet_visits_year * 25
+        labor_cost = herd_size * rng.uniform(5, 15)
+        
+        total_cost = feed_cost + health_cost + labor_cost
+        
+        net_income = annual_revenue - total_cost
+        
+        productivity_index = 50 + (body_condition_score - 2) * 10 + (10 if reproductive_health else -5)
+        productivity_index = np.clip(productivity_index, 10, 100)
+        
+        health_index = 100 - mortality_rate * 200 - (15 if disease_outbreak else 0) + vaccination_coverage * 50
+        health_index = np.clip(health_index, 10, 100)
+        
+        recs.append({
+            'record_id': i + 1,
+            'livestock_id': record_id,
+            'country': country,
+            'year': year,
+            'farm_size_ha': round(farm_size, 2),
+            'production_system': production_system,
+            'primary_species': primary_species,
+            'herd_size': herd_size,
+            'breed_type': breed_type,
+            'feed_source': feed_source,
+            'water_access': water_access,
+            'housing_type': housing_type,
+            'mortality_rate_pct': round(mortality_rate * 100, 1),
+            'animals_died': animals_died,
+            'disease_outbreak': disease_outbreak,
+            'primary_disease': primary_disease if primary_disease else 'none',
+            'disease_severity': disease_severity,
+            'vaccination_coverage_pct': round(vaccination_coverage * 100, 1),
+            'animals_vaccinated': animals_vaccinated,
+            'deworming_frequency': deworming_frequency,
+            'tick_control': tick_control,
+            'veterinary_access': veterinary_access,
+            'vet_visits_per_year': vet_visits_year,
+            'health_insurance': health_insurance,
+            'milk_yield_l_day': round(milk_yield_l_day, 2) if milk_yield_l_day > 0 else 0,
+            'lactation_length_days': lactation_length_days if lactation_length_days > 0 else 0,
+            'annual_milk_l': round(annual_milk_l, 0) if annual_milk_l > 0 else 0,
+            'weight_gain_kg_day': round(weight_gain_kg_day, 3) if primary_species in ['cattle', 'goats', 'sheep', 'pigs'] else 0,
+            'live_weight_kg': round(live_weight_kg, 1),
+            'reproductive_rate': round(calving_rate, 2) if calving_rate > 0 else 0,
+            'reproductive_health': reproductive_health,
+            'fertility_issues': fertility_issues,
+            'body_condition_score': body_condition_score,
+            'nutrition_status': nutrition_status,
+            'supplementation': supplementation,
+            'mineral_supplement': mineral_supplement,
+            'forage_quality': forage_quality,
+            'breeding_method': breeding_method,
+            'record_keeping': record_keeping,
+            'market_access': market_access,
+            'price_per_head_usd': round(price_per_head, 2),
+            'annual_revenue_usd': round(annual_revenue, 2),
+            'feed_cost_usd': round(feed_cost, 2),
+            'health_cost_usd': round(health_cost, 2),
+            'labor_cost_usd': round(labor_cost, 2),
+            'total_cost_usd': round(total_cost, 2),
+            'net_income_usd': round(net_income, 2),
+            'productivity_index': round(productivity_index, 1),
+            'health_index': round(health_index, 1),
+            'mortality_category': 'low' if mortality_rate < 0.10 else 'moderate' if mortality_rate < 0.20 else 'high',
+            'intervention_priority': 'high' if mortality_rate > 0.20 or disease_outbreak else 'moderate' if mortality_rate > 0.12 else 'low'
+        })
+    
+    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'livestock_health_productivity_africa_{sn}.csv'), index=False)
+        print(f"Saved: livestock_health_productivity_africa_{sn}.csv, n={len(d)}")