from django.core.management.base import BaseCommand from django.contrib.auth import get_user_model from django.utils import timezone from datetime import date, timedelta import random from decimal import Decimal from crop_management.models import Crop, CropVariety, YieldData, FarmingPractice, CropResearch from farm.models import Farm User = get_user_model() class Command(BaseCommand): help = 'Populate the database with test data for crop management' def add_arguments(self, parser): parser.add_argument( '--clear', action='store_true', help='Clear existing data before adding new test data', ) def handle(self, *args, **options): if options['clear']: self.stdout.write('Clearing existing data...') CropResearch.objects.all().delete() FarmingPractice.objects.all().delete() YieldData.objects.all().delete() CropVariety.objects.all().delete() Crop.objects.all().delete() self.stdout.write(self.style.SUCCESS('Existing data cleared.')) # Get or create a default user try: user = User.objects.get(username='admin') except User.DoesNotExist: try: user = User.objects.create_user( username='admin', email='admin@cropmanagement.com', password='admin123', first_name='Admin', last_name='User', is_staff=True, is_superuser=True ) except Exception: # If admin user creation fails, use the first available superuser user = User.objects.filter(is_superuser=True).first() if not user: self.stdout.write(self.style.ERROR('No superuser found. Please create one first.')) return # Create sample farms if they don't exist farms = [] farm_data = [ {'name': 'Green Valley Farm', 'address': 'Punjab, Pakistan', 'size': Decimal('50.5'), 'soil_type': 'loamy'}, {'name': 'Sunrise Agriculture', 'address': 'Sindh, Pakistan', 'size': Decimal('75.2'), 'soil_type': 'clay'}, {'name': 'Golden Fields', 'address': 'KPK, Pakistan', 'size': Decimal('120.8'), 'soil_type': 'sandy'}, {'name': 'River Side Farm', 'address': 'Balochistan, Pakistan', 'size': Decimal('95.3'), 'soil_type': 'loamy'}, ] for farm_info in farm_data: farm, created = Farm.objects.get_or_create( name=farm_info['name'], defaults={ 'address': farm_info['address'], 'size': farm_info['size'], 'soil_type': farm_info['soil_type'], 'owner': user, 'geolocation': '31.5204,74.3587' } ) farms.append(farm) self.stdout.write('Creating crops...') crops_data = [ { 'name': 'Wheat', 'scientific_name': 'Triticum aestivum', 'category': 'cereal', 'growth_season': 'rabi', 'growth_cycle_days': 120, 'water_requirement': 'medium', 'soil_type_preference': 'Well-drained loamy soil', 'climate_requirement': 'Cool, dry climate during growing season', 'description': 'Major staple crop grown extensively in Pakistan', 'nutritional_value': {'protein': '12-15%', 'carbohydrates': '70-75%', 'fiber': '2-3%'}, 'market_availability': 'Year-round', 'economic_importance': 'Primary food grain and export commodity' }, { 'name': 'Rice', 'scientific_name': 'Oryza sativa', 'category': 'cereal', 'growth_season': 'kharif', 'growth_cycle_days': 150, 'water_requirement': 'high', 'soil_type_preference': 'Clay loam with good water retention', 'climate_requirement': 'Hot, humid climate with abundant water', 'description': 'Second most important cereal crop in Pakistan', 'nutritional_value': {'protein': '7-8%', 'carbohydrates': '78-80%', 'fiber': '0.5%'}, 'market_availability': 'Post-harvest season', 'economic_importance': 'Major export crop and food security' }, { 'name': 'Cotton', 'scientific_name': 'Gossypium hirsutum', 'category': 'fiber', 'growth_season': 'kharif', 'growth_cycle_days': 180, 'water_requirement': 'high', 'soil_type_preference': 'Deep, well-drained fertile soil', 'climate_requirement': 'Hot climate with moderate rainfall', 'description': 'White gold of Pakistan, major cash crop', 'nutritional_value': {}, 'market_availability': 'October to February', 'economic_importance': 'Backbone of textile industry' }, { 'name': 'Sugarcane', 'scientific_name': 'Saccharum officinarum', 'category': 'other', 'growth_season': 'perennial', 'growth_cycle_days': 365, 'water_requirement': 'high', 'soil_type_preference': 'Rich, well-drained soil', 'climate_requirement': 'Tropical and subtropical climate', 'description': 'Important cash crop for sugar production', 'nutritional_value': {'sucrose': '12-18%', 'fiber': '12-16%'}, 'market_availability': 'November to April', 'economic_importance': 'Sugar industry and ethanol production' }, { 'name': 'Maize', 'scientific_name': 'Zea mays', 'category': 'cereal', 'growth_season': 'kharif', 'growth_cycle_days': 100, 'water_requirement': 'medium', 'soil_type_preference': 'Well-drained fertile soil', 'climate_requirement': 'Warm climate with adequate rainfall', 'description': 'Versatile crop used for food, feed, and industry', 'nutritional_value': {'protein': '9-10%', 'carbohydrates': '74-75%', 'fat': '4-5%'}, 'market_availability': 'Year-round', 'economic_importance': 'Food security and livestock feed' } ] crops = [] for crop_data in crops_data: crop, created = Crop.objects.get_or_create( name=crop_data['name'], defaults={**crop_data, 'created_by': user} ) crops.append(crop) if created: self.stdout.write(f'Created crop: {crop.name}') self.stdout.write('Creating crop varieties...') varieties_data = [ # Wheat varieties { 'crop': 'Wheat', 'name': 'Punjab-2011', 'variety_code': 'WH-PB-2011', 'yield_potential': Decimal('4500'), 'maturity_days': 115, 'disease_resistance': 'Rust resistant, moderate stripe rust resistance', 'pest_resistance': 'Aphid tolerant', 'quality_attributes': {'protein': '12.5%', 'gluten': 'High', 'test_weight': '78 kg/hl'}, 'special_requirements': 'Requires timely irrigation and balanced fertilization', 'recommended_regions': 'Punjab, Sindh irrigated areas', 'seed_availability': 'available', 'description': 'High yielding wheat variety suitable for irrigated conditions', 'developed_by': 'Punjab Agricultural Research Institute', 'release_year': 2011 }, { 'crop': 'Wheat', 'name': 'Sindh-81', 'variety_code': 'WH-SD-81', 'yield_potential': Decimal('4200'), 'maturity_days': 120, 'disease_resistance': 'Yellow rust resistant', 'pest_resistance': 'Moderate pest resistance', 'quality_attributes': {'protein': '11.8%', 'gluten': 'Medium', 'test_weight': '76 kg/hl'}, 'special_requirements': 'Drought tolerant, suitable for rainfed areas', 'recommended_regions': 'Sindh, Balochistan', 'seed_availability': 'available', 'description': 'Drought tolerant wheat variety for arid regions', 'developed_by': 'Sindh Agricultural University', 'release_year': 1981 }, # Rice varieties { 'crop': 'Rice', 'name': 'Basmati-385', 'variety_code': 'RC-BS-385', 'yield_potential': Decimal('3200'), 'maturity_days': 145, 'disease_resistance': 'Blast resistant, bacterial blight tolerant', 'pest_resistance': 'Brown plant hopper resistant', 'quality_attributes': {'aroma': 'Strong', 'grain_length': '6.5mm', 'cooking_quality': 'Excellent'}, 'special_requirements': 'Requires puddled soil and standing water', 'recommended_regions': 'Punjab, Sindh rice belt', 'seed_availability': 'available', 'description': 'Premium aromatic rice variety for export', 'developed_by': 'Rice Research Institute Kala Shah Kaku', 'release_year': 1985 }, { 'crop': 'Rice', 'name': 'IRRI-6', 'variety_code': 'RC-IR-6', 'yield_potential': Decimal('4500'), 'maturity_days': 135, 'disease_resistance': 'Blast tolerant', 'pest_resistance': 'Stem borer tolerant', 'quality_attributes': {'grain_type': 'Medium', 'milling_recovery': '68%', 'cooking_quality': 'Good'}, 'special_requirements': 'High water requirement, needs fertile soil', 'recommended_regions': 'Punjab, Sindh', 'seed_availability': 'available', 'description': 'High yielding non-aromatic rice variety', 'developed_by': 'International Rice Research Institute', 'release_year': 1975 }, # Cotton varieties { 'crop': 'Cotton', 'name': 'BT-121', 'variety_code': 'CT-BT-121', 'yield_potential': Decimal('2800'), 'maturity_days': 175, 'disease_resistance': 'Cotton leaf curl virus resistant', 'pest_resistance': 'Bollworm resistant (Bt gene)', 'quality_attributes': {'fiber_length': '28-30mm', 'micronaire': '3.5-4.5', 'strength': 'High'}, 'special_requirements': 'Requires warm climate and adequate moisture', 'recommended_regions': 'Punjab, Sindh cotton belt', 'seed_availability': 'available', 'description': 'Bt cotton variety with bollworm resistance', 'developed_by': 'Central Cotton Research Institute', 'release_year': 2010 } ] varieties = [] for variety_data in varieties_data: crop = next(c for c in crops if c.name == variety_data['crop']) variety_data['crop'] = crop variety, created = CropVariety.objects.get_or_create( name=variety_data['name'], crop=crop, defaults=variety_data ) varieties.append(variety) if created: self.stdout.write(f'Created variety: {variety.name}') self.stdout.write('Creating yield data...') # Generate yield data for the last 3 years current_year = timezone.now().year for year in range(current_year - 2, current_year + 1): for variety in varieties[:8]: # Use first 8 varieties for farm in farms[:3]: # Use first 3 farms # Determine season based on crop if variety.crop.growth_season == 'rabi': season = 'rabi' elif variety.crop.growth_season == 'kharif': season = 'kharif' else: season = random.choice(['kharif', 'rabi']) # Generate realistic yield data base_yield = float(variety.yield_potential or 3000) variation = random.uniform(0.7, 1.2) # 70% to 120% of potential yield_per_ha = Decimal(str(round(base_yield * variation, 2))) area = Decimal(str(random.uniform(5, 25))) # 5-25 hectares total_yield = yield_per_ha * area YieldData.objects.get_or_create( crop=variety.crop, variety=variety, farm=farm, harvest_year=year, harvest_season=season, defaults={ 'area_cultivated': area, 'total_yield': total_yield, 'yield_per_hectare': yield_per_ha, 'quality_grade': random.choice(['A', 'B', 'C']), 'rainfall_mm': Decimal(str(random.uniform(200, 800))), 'temperature_avg': Decimal(str(random.uniform(15, 35))), 'input_cost': Decimal(str(random.uniform(50000, 150000))), 'market_price': Decimal(str(random.uniform(30, 80))), 'notes': f'Good harvest in {year} season', 'data_source': 'Field Survey', 'recorded_by': user } ) self.stdout.write('Creating farming practices...') practices_data = [ { 'crop': 'Wheat', 'title': 'Land Preparation for Wheat', 'practice_type': 'soil_preparation', 'description': 'Proper land preparation is crucial for wheat cultivation', 'implementation_steps': '1. Deep plowing 2. Harrowing 3. Leveling 4. Making irrigation channels', 'timing_description': 'Start 2-3 weeks before sowing', 'days_after_planting': None, 'required_materials': 'Tractor, plow, harrow, leveler', 'estimated_cost': Decimal('15000'), 'labor_requirement': 'medium', 'expected_impact': 'Better seed germination and root development', 'success_indicators': 'Uniform field surface, good soil structure', 'applicable_regions': 'All wheat growing areas', 'climate_suitability': 'Suitable for all climates', 'research_source': 'Agricultural Extension Department', 'validation_status': 'proven', 'priority_level': 'high' }, { 'crop': 'Rice', 'title': 'Transplanting Method for Rice', 'practice_type': 'planting', 'description': 'Proper transplanting technique for rice seedlings', 'implementation_steps': '1. Prepare nursery 2. Raise seedlings 3. Prepare main field 4. Transplant at 25-30 days', 'timing_description': 'Transplant when seedlings are 25-30 days old', 'days_after_planting': 25, 'required_materials': 'Healthy seedlings, transplanting tools', 'estimated_cost': Decimal('25000'), 'labor_requirement': 'high', 'expected_impact': 'Higher yield and better plant establishment', 'success_indicators': 'Good plant stand, minimal transplanting shock', 'applicable_regions': 'Rice growing areas of Punjab and Sindh', 'climate_suitability': 'Hot, humid climate', 'research_source': 'Rice Research Institute', 'validation_status': 'proven', 'priority_level': 'high' }, { 'crop': 'Cotton', 'title': 'Integrated Pest Management for Cotton', 'practice_type': 'pest_control', 'description': 'Comprehensive approach to manage cotton pests', 'implementation_steps': '1. Monitor pest levels 2. Use beneficial insects 3. Apply selective pesticides 4. Crop rotation', 'timing_description': 'Throughout the growing season', 'days_after_planting': 30, 'required_materials': 'Monitoring tools, beneficial insects, selective pesticides', 'estimated_cost': Decimal('35000'), 'labor_requirement': 'medium', 'expected_impact': 'Reduced pest damage and pesticide use', 'success_indicators': 'Low pest incidence, healthy beneficial insects', 'applicable_regions': 'Cotton belt of Punjab and Sindh', 'climate_suitability': 'Hot, dry climate', 'research_source': 'Central Cotton Research Institute', 'validation_status': 'recommended', 'priority_level': 'high' } ] for practice_data in practices_data: crop = next(c for c in crops if c.name == practice_data['crop']) practice_data['crop'] = crop practice_data['created_by'] = user practice, created = FarmingPractice.objects.get_or_create( title=practice_data['title'], crop=crop, defaults=practice_data ) if created: self.stdout.write(f'Created practice: {practice.title}') self.stdout.write('Creating crop research data...') research_data = [ { 'crop': 'Wheat', 'title': 'Development of Drought Tolerant Wheat Varieties', 'research_type': 'climate_adaptation', 'objective': 'To develop wheat varieties that can withstand water stress conditions', 'methodology': 'Field trials under controlled drought conditions, molecular marker analysis', 'findings': 'Identified 5 promising lines with 20% better drought tolerance', 'conclusions': 'New varieties can maintain yield under 30% reduced irrigation', 'research_institution': 'National Agricultural Research Centre', 'principal_investigator': 'Dr. Ahmad Ali', 'research_period_start': date(2020, 1, 1), 'research_period_end': date(2023, 12, 31), 'publication_status': 'published', 'publication_reference': 'Journal of Arid Agriculture, Vol 15, 2023', 'doi': '10.1234/jaa.2023.001', 'practical_applications': 'Varieties suitable for water-scarce regions', 'impact_assessment': 'Potential to increase wheat production in arid areas by 15%' }, { 'crop': 'Rice', 'title': 'Nutrient Management in Basmati Rice', 'research_type': 'nutrition_study', 'objective': 'Optimize fertilizer application for maximum yield and quality in Basmati rice', 'methodology': 'Split plot design with different NPK combinations over 3 seasons', 'findings': 'Balanced NPK at 120:60:60 kg/ha gave best results', 'conclusions': 'Proper nutrient management increases yield by 25% and improves grain quality', 'research_institution': 'Rice Research Institute Kala Shah Kaku', 'principal_investigator': 'Dr. Fatima Khan', 'research_period_start': date(2021, 6, 1), 'research_period_end': date(2023, 5, 31), 'publication_status': 'peer_reviewed', 'publication_reference': 'Pakistan Journal of Agricultural Sciences, 2023', 'doi': '10.1234/pjas.2023.002', 'practical_applications': 'Fertilizer recommendations for Basmati cultivation', 'impact_assessment': 'Improved farmer income through better yield and quality' }, { 'crop': 'Cotton', 'title': 'Biological Control of Cotton Bollworm', 'research_type': 'pest_management', 'objective': 'Evaluate effectiveness of biological agents against cotton bollworm', 'methodology': 'Field trials using Trichogramma wasps and Bt sprays', 'findings': 'Biological control reduced bollworm damage by 60%', 'conclusions': 'Integrated approach with biological agents is highly effective', 'research_institution': 'Central Cotton Research Institute', 'principal_investigator': 'Dr. Muhammad Hassan', 'research_period_start': date(2022, 4, 1), 'research_period_end': date(2023, 10, 31), 'publication_status': 'completed', 'publication_reference': '', 'doi': '', 'practical_applications': 'Reduced pesticide use and cost-effective pest control', 'impact_assessment': 'Environmental benefits and reduced production costs' } ] for research_info in research_data: crop = next(c for c in crops if c.name == research_info['crop']) research_info['crop'] = crop research_info['added_by'] = user research, created = CropResearch.objects.get_or_create( title=research_info['title'], crop=crop, defaults=research_info ) if created: self.stdout.write(f'Created research: {research.title}') self.stdout.write( self.style.SUCCESS( f'Successfully populated test data:\n' f'- {Crop.objects.count()} crops\n' f'- {CropVariety.objects.count()} varieties\n' f'- {YieldData.objects.count()} yield records\n' f'- {FarmingPractice.objects.count()} farming practices\n' f'- {CropResearch.objects.count()} research records' ) )