Research-Grade XLSForms, Ready for Deployment.

'- Captures whether subsidies reached the intended recipients - Checks for quantity, timeliness, and quality of inputs - Allows qualitative notes on leakage - Compatible with Kobo/ODK for quick data collection and basic analysis

'- Measures transparency, democratic decision-making, trust, financial record-keeping, and meeting frequency - Captures general comments for qualitative insights - Compatible with Kobo/ODK for quick data collection and basic analytics

'- Actor identification: Captures type of respondent (farmer, trader, processor, etc.) - Storage type classification: Identifies type of cold storage used - Refrigerant type: Records refrigerant for environmental impact assessment - Storage capacity: Records size of the cold storage facility - Energy source & usage: Captures main energy source and monthly usage - Efficiency rating: Allows quick assessment of energy efficiency

'- Accounts for vulnerabilities (children, pregnant/lactating women, elderly) - Combines food shortage, coping measures, and access to assistance - Produces community_food_insecurity_index for each location - Supports rapid situational analysis - Identifies key obstacles and supporting factors for food access - Counts barriers and facilitators for analytics

'- FAO-validated methodology: Uses the official 8-question FIES developed by FAO Voices of the Hungry for SDG 2.1.2 monitoring with proven cross-cultural validity - Experience-based measurement: Captures direct experiences and behaviors related to food insecurity rather than proxy indicators, providing valid subjective assessment - 12-month recall period: Uses standard 12-month reference period to measure chronic/annual food insecurity patterns and enable SDG monitoring comparability - Dichotomous response format: Employs yes/no answers with don't know and refused options to capture clear occurrence of food insecurity experiences - Raw score calculation (0-8): Sums affirmative responses across 8 questions to generate preliminary raw score for field-level screening and data quality checks - Four-level severity classification: Provides indicative categorization into food secure (0), mild (1-3), moderate (4-6), or severe (7-8) food insecurity for preliminary assessment

'- Multi-commodity tracking per trip - % loss per commodity and per trip - Cause-specific analysis (spoilage, handling, pests, theft) - Analysis by storage/transport conditions, temperature, and humidity - Route-based insights for transit loss

'- Target Audience: Healthcare workers, nutritionists, researchers, NGOs - Global Scope: Includes all countries worldwide - Sections: Respondent info, Anthropometry, Dietary Intake, Health Status - Automated Calculations: BMI calculation and category, Dietary Diversity Score - Malnutrition Flags: MUAC for children 6-59 months, BMI risk categories - Dynamic Relevance: MUAC only for children, illness only when relevant

- Farmer and Field Identification: Captures farmer details, geospatial field boundaries, field location, and plot identifiers for precise traceability. - Crop and Field Profiling: Records crop type (with support for ‚other crops) and field size to contextualize agrochemical use. - Detailed Agrochemical Application Tracking: Collects information on agrochemical type, brand, and application frequency across multiple application events. - PPE Usage and Compliance Monitoring: Documents types of personal protective equipment used and whether PPE was fully complied with during application. - Visual Verification: Includes photo capture of agrochemical application or PPE use to support monitoring and audits. - Automated Risk Scoring: Calculates a labor risk score that increases when PPE compliance is low, enabling quick identification of high-risk practices.

'- Advanced respondent segmentation: Includes startup stage, years in operation, early adopter profiling - Drone operations module: Detailed insights into autonomy, brand, number of drones, operators, and coverage - Operator certification tracking: Repeat group gathers operator-level compliance indicators - Regulatory workflow measurement: Captures permit processes, wait time, permissions - Impact quantification: Productivity loss, cost per drone, compliance difficulty - Compliance cost analytics: Automatically computes cost-per-drone metric

'- Unique farmer identification: Captures farmer name and contact details for follow-up and traceability. - Geo-referencing: Uses geopoint to record the exact farm location, enabling spatial analysis and mapping of farms. - Farm characterization: Collects core farm attributes including farm size, farm type, and cropping systems. - Dynamic crop-specific logic: Fertilizer type only appears when fertilizer_use = 'yes', ensuring cleaner data. - Conditional agricultural practice assessment: Crop rotation question only appears when any crop is selected, reducing unnecessary responses. - Multi-crop and multi-practice selection: Uses multiple-selection lists for crops cultivated, soil health practices, and challenges faced.

'- Target audience: Urban Consumers, Environmentalists, Gig Workers - Mid-level complexity: GPS, photos, condition ratings - Decision logic: Conditional contamination description - Detailed hygiene scoring: Bag cleanliness, physical condition, odor rating - Driver hygiene: Simple PPE and cleanliness checks - Photo verification: Supports visual hygiene audit

• Target users: Veterinarians, Quality Auditors, Lab Technicians
• Simple design: Minimal but analysis-ready survey structure
• Commodity coverage: Maize, peanuts, and milk‚Äîthe primary aflatoxin-risk foods
• Risk scoring: Categorizes aflatoxin levels into low/medium/high for quick analysis
• Global applicability: Includes global country list for international use
• Clean logic: No complex calculations; simple mandatory fields only