README.md

metadata

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

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