stockbreeder-expert

name stockbreeder-expert version 1.0.0 description Expert-level livestock management, animal health monitoring, breeding programs, and ranch management category domains tags ["livestock","animal-husbandry","breeding","ranch-management","veterinary"] allowed-tools ["Read","Write","Edit"] Stockbreeder Expert Expert guidance for livestock management, animal health monitoring, breeding programs, feed optimization, and ranch operations. Core Concepts Livestock Management Herd/flock management Animal identification and tracking Health monitoring Nutrition and feed management Breeding and genetics Facility management Animal Health Disease prevention and control Vaccination schedules Biosecurity protocols Health records Veterinary care coordination Early warning systems Technologies RFID ear tags Automated feeding systems Wearable sensors Milking automation Genetic analysis Precision livestock farming Livestock Management System from dataclasses import dataclass from typing import List , Optional from datetime import datetime, timedelta from enum import Enum class AnimalType ( Enum ): CATTLE = "cattle" SHEEP = "sheep" GOAT = "goat" PIG = "pig" POULTRY = "poultry" class HealthStatus ( Enum ): HEALTHY = "healthy" OBSERVATION = "observation" SICK = "sick" QUARANTINE = "quarantine" DECEASED = "deceased" @dataclass class Animal : animal_id: str tag_number: str type : AnimalType breed: str sex: str birth_date: datetime weight_kg: float sire_id: Optional [ str ] dam_id: Optional [ str ] health_status: HealthStatus location: str vaccinations: List [ dict ] treatments: List [ dict ] @dataclass class HealthRecord : record_id: str animal_id: str date: datetime type : str # 'vaccination', 'treatment', 'check-up' diagnosis: Optional [ str ] treatment: Optional [ str ] veterinarian_id: Optional [ str ] notes: str follow_up_date: Optional [datetime] class LivestockManagement : """Livestock management system""" def __init__ ( self, db ): self .db = db def register_animal ( self, animal_data ): """Register new animal in system""" animal = Animal(**animal_data) # Generate unique tag if not provided if not animal.tag_number: animal.tag_number = self .generate_tag_number(animal. type ) # Create initial health record health_record = HealthRecord( record_id=generate_id(), animal_id=animal.animal_id, date=datetime.now(), type = 'registration' , diagnosis= None , treatment= None , veterinarian_id= None , notes= 'Initial registration' , follow_up_date= None ) self .db.save_animal(animal) self .db.save_health_record(health_record) return animal def monitor_animal_health ( self, animal_id ): """Monitor individual animal health""" animal = self .db.get_animal(animal_id) sensor_data = self .get_sensor_data(animal_id) health_indicators = { 'temperature' : sensor_data.get( 'temperature' ), 'activity_level' : sensor_data.get( 'activity_score' ), 'rumination_time' : sensor_data.get( 'rumination_minutes' ), # For ruminants 'feeding_behavior' : self .analyze_feeding_pattern(animal_id), 'weight_change' : self .calculate_weight_trend(animal_id) } # Detect health issues alerts = [] if health_indicators[ 'temperature' ] > 39.5 : # Cattle normal: 38.5-39.5°C alerts.append({ 'severity' : 'high' , 'issue' : 'Elevated temperature - possible fever' , 'recommendation' : 'Veterinary examination recommended' }) if health_indicators[ 'activity_level' ] < 0.5 : # Below 50% of normal alerts.append({ 'severity' : 'medium' , 'issue' : 'Reduced activity' , 'recommendation' : 'Monitor closely, check for injury or illness' }) return { 'animal_id' : animal_id, 'tag_number' : animal.tag_number, 'health_indicators' : health_indicators, 'alerts' : alerts, 'health_score' : self .calculate_health_score(health_indicators) } def schedule_vaccinations ( self, herd_id ): """Generate vaccination schedule for herd""" animals = self .db.get_herd_animals(herd_id) vaccination_schedule = [] for animal in animals: # Check vaccination history last_vaccinations = self .db.get_vaccinations(animal.animal_id) # Required vaccinations based on animal type and age required_vaccines = self .get_required_vaccines(animal) for vaccine in required_vaccines: last_admin = next ( (v for v in last_vaccinations if v[ 'vaccine' ] == vaccine[ 'name' ]), None ) # Check if due if not last_admin or self .is_vaccine_due(last_admin, vaccine): vaccination_schedule.append({ 'animal_id' : animal.animal_id, 'tag_number' : animal.tag_number, 'vaccine' : vaccine[ 'name' ], 'due_date' : self .calculate_vaccine_due_date(last_admin, vaccine), 'priority' : vaccine[ 'priority' ] }) # Sort by priority and due date vaccination_schedule.sort(key= lambda x: (x[ 'priority' ], x[ 'due_date' ])) return vaccination_schedule Breeding Management class BreedingManagement : """Breeding program management""" def select_breeding_pairs ( self, herd_id, breeding_goals ): """Select optimal breeding pairs""" eligible_males = self .db.get_breeding_males(herd_id) eligible_females = self .db.get_breeding_females(herd_id) # Score each potential pairing breeding_recommendations = [] for female in eligible_females: scores = [] for male in eligible_males: # Check genetic compatibility if self .are_related(male, female, max_generations= 3 ): continue # Skip closely related animals # Calculate breeding value score = self .calculate_breeding_value( male, female, breeding_goals ) scores.append({ 'male_id' : male.animal_id, 'male_tag' : male.tag_number, 'score' : score, 'expected_traits' : self .predict_offspring_traits(male, female) }) # Get best male for this female if scores: best_match = max (scores, key= lambda x: x[ 'score' ]) breeding_recommendations.append({ 'female_id' : female.animal_id, 'female_tag' : female.tag_number, 'recommended_male' : best_match, 'optimal_breeding_date' : self .calculate_optimal_breeding_date(female) }) return breeding_recommendations def calculate_breeding_value ( self, male, female, goals ): """Calculate breeding value for pair""" score = 0 # Evaluate based on breeding goals if 'milk_production' in goals: score += (male.genetics[ 'milk_yield' ] + female.genetics[ 'milk_yield' ]) * 0.3 if 'growth_rate' in goals: score += (male.genetics[ 'growth_rate' ] + female.genetics[ 'growth_rate' ]) * 0.3 if 'disease_resistance' in goals: score += (male.genetics[ 'disease_resistance' ] + female.genetics[ 'disease_resistance' ]) * 0.2 if 'fertility' in goals: score += (male.fertility_score + female.fertility_score) * 0.2 return score def track_pregnancy ( self, animal_id ): """Track pregnancy and predict due date""" animal = self .db.get_animal(animal_id) breeding_record = self .db.get_last_breeding(animal_id) if not breeding_record: return { 'status' : 'not_pregnant' } # Check pregnancy status pregnancy_check = self .db.get_latest_pregnancy_check(animal_id) if pregnancy_check and pregnancy_check[ 'confirmed' ]: gestation_period = self .get_gestation_period(animal. type ) due_date = breeding_record[ 'date' ] + timedelta(days=gestation_period) days_pregnant = (datetime.now() - breeding_record[ 'date' ]).days # Schedule checkups checkup_schedule = self .generate_pregnancy_checkups( breeding_record[ 'date' ], due_date ) return { 'status' : 'pregnant' , 'breeding_date' : breeding_record[ 'date' ], 'due_date' : due_date, 'days_pregnant' : days_pregnant, 'days_remaining' : (due_date - datetime.now()).days, 'checkup_schedule' : checkup_schedule } return { 'status' : 'unknown' , 'needs_pregnancy_check' : True } Feed Management class FeedManagement : """Feed optimization and management""" def calculate_feed_requirements ( self, animal_id ): """Calculate nutritional requirements""" animal = self .db.get_animal(animal_id) # Base requirements on: # - Weight # - Age # - Production status (lactating, pregnant, growing) # - Activity level requirements = { 'dry_matter_kg' : self .calculate_dm_requirement(animal), 'crude_protein_kg' : self .calculate_protein_requirement(animal), 'energy_mcal' : self .calculate_energy_requirement(animal), 'minerals' : self .calculate_mineral_requirements(animal) } return requirements def optimize_feed_ration ( self, herd_id ): """Optimize feed ration for herd""" animals = self .db.get_herd_animals(herd_id) available_feeds = self .db.get_available_feeds() # Group animals by similar requirements groups = self .group_animals_by_requirements(animals) ration_plans = [] for group in groups: # Linear programming for least-cost ration avg_requirements = self .calculate_group_requirements(group[ 'animals' ]) optimal_ration = self .solve_ration_optimization( avg_requirements, available_feeds ) ration_plans.append({ 'group_id' : group[ 'id' ], 'animal_count' : len (group[ 'animals' ]), 'ration' : optimal_ration, 'daily_cost_per_animal' : sum ( ingredient[ 'amount' ] * ingredient[ 'cost_per_kg' ] for ingredient in optimal_ration ), 'meets_requirements' : True }) return ration_plans def monitor_feeding_behavior ( self, animal_id ): """Monitor feeding patterns""" feeding_data = self .db.get_feeding_data(animal_id, days= 7 ) analysis = { 'avg_daily_intake_kg' : np.mean([d[ 'intake' ] for d in feeding_data]), 'feeding_frequency' : len (feeding_data) / 7 , 'intake_variation' : np.std([d[ 'intake' ] for d in feeding_data]), 'eating_time_minutes' : np.mean([d[ 'duration' ] for d in feeding_data]) } # Detect issues if analysis[ 'avg_daily_intake_kg' ] < expected_intake * 0.8 : analysis[ 'alert' ] = 'Low feed intake - possible health issue' return analysis Herd Analytics class HerdAnalytics : """Herd performance analytics""" def analyze_herd_performance ( self, herd_id ): """Comprehensive herd performance analysis""" animals = self .db.get_herd_animals(herd_id) metrics = { 'total_animals' : len (animals), 'avg_weight' : np.mean([a.weight_kg for a in animals]), 'avg_age_months' : np.mean([ (datetime.now() - a.birth_date).days / 30 for a in animals ]), 'health_status_distribution' : self .get_health_distribution(animals), 'mortality_rate' : self .calculate_mortality_rate(herd_id), 'reproduction_rate' : self .calculate_reproduction_rate(herd_id), 'avg_daily_gain' : self .calculate_avg_daily_gain(herd_id), 'feed_conversion_ratio' : self .calculate_fcr(herd_id) } # Financial metrics metrics[ 'production_value' ] = self .calculate_production_value(herd_id) metrics[ 'feed_cost' ] = self .calculate_total_feed_cost(herd_id) metrics[ 'veterinary_cost' ] = self .calculate_vet_costs(herd_id) metrics[ 'profit_per_animal' ] = ( metrics[ 'production_value' ] - metrics[ 'feed_cost' ] - metrics[ 'veterinary_cost' ] ) / metrics[ 'total_animals' ] return metrics def predict_production ( self, animal_id, days_ahead= 30 ): """Predict animal production (milk, eggs, etc.)""" animal = self .db.get_animal(animal_id) historical_production = self .db.get_production_history(animal_id, days= 90 ) # Use time series model forecast = self .production_model.forecast( historical_production, periods=days_ahead ) return { 'animal_id' : animal_id,