README.md

---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- agriculture
- africa
- synthetic-data
- sub-saharan-africa
- livestock
- animal-health
- 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.

# 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