Research-Grade XLSForms, Ready for Deployment.

'- 1. Automatic Categorization - Ammonia (safe / warning / danger) - Oxygen (safe / warning / danger) - pH (safe / danger) - Temperature (safe / warning) - Turbidity (safe / warning)

- It includes: - Multi-product, multi-batch storage - Temperature & humidity logging - Overcapacity alerts - GPS-verified facility location

'- Actor identification: Captures type of respondent (farmer, trader, processor, etc.) - Storage type & age: Records type of cold storage and age of units - Equipment-level data: Allows multiple entries per equipment (name, refrigerant, energy, temperature) - Refrigerant type & leakage: Records refrigerant type and monitors leakage for emissions estimation - Energy source & consumption: Tracks monthly energy usage per equipment - Temperature monitoring: Captures average storage temperature for quality assessment

'- Tracks multiple activities per actor - Computes water footprint per unit and total water use - Aggregates per-actor and regional water footprint - Conditional and calculated fields ensure clean data - Ready for reporting and cross-tab analysis

'- 1. Multi-Item Weighted Scoring - Weighted scoring for each subsidy type to reflect importance and impact - Produces per-farmer and total subsidy efficiency scores - 2. Barriers & Facilitators Mapping - Captures factors hindering or helping subsidy delivery - Counts barriers and facilitators for analytical insights

'- Urban-focused: Designed specifically for constraints in urban agriculture contexts. - Multi-sector applicability: Relevant to urban farmers, beekeepers, fish farmers, and seed producers. - Zoning & policy mapping: Captures zoning and permit restriction patterns. - Spatial constraint analysis: Collects space availability, production area, and infrastructure gaps. - Pollution impact assessment: Includes environmental stress sources and severity scoring. - Resource access evaluation: Tracks availability of water, inputs, and essential farm resources.

- LPCD Calculation: Automatically calculates Liters Per Capita per Day (LPCD) by dividing total water collected by household size. - Observation Verification: Includes an enumerator observation field for soap to validate self-reported hygiene practices. - Composite Scoring: Generates a 3-point WASH score based on improved water source, latrine access, and hygiene knowledge/availability. - Demographic Logic: Cross-checks number of children under 5 against total household size and conditionally displays child feces disposal questions. - Standardized Indicators: Uses standard UNHCR/UNICEF WASH categories for water sources and latrine types to ensure data comparability.

'- Expanded bee health metrics: Includes stressors, pests, hive strength scoring, CCD indicators. - Environmental monitoring: Adds rainfall, temperature, and climate impact data for correlation analysis. - Multi-product yield tracking: Tracks honey, wax, propolis, and pollen yields. - Management practice insights: Collects hive inspection frequency, queen breeding method, and replacement rate. - Behavioral/seasonal variables: Captures forage availability and supplemental feeding practices. - GPS-enabled mapping: Allows geographic mapping of apiaries.

'- Equipment ownership tracking: Captures which precision agriculture equipment is used - Basic ROI calculation: Computes ROI = monthly savings / equipment cost - Cost capture: Records purchase and operational costs - Benefit identification: Determines main realized benefit from equipment - Barrier identification: Records key challenges faced - Targeted respondents: Ag-Tech Startups, Early Adopters, Machinery Dealers included

'- Automated Performance Scoring: Calculates real-time scores for both practice adoption and AESA technical skills, allowing immediate feedback to facilitators. - Attendance Rate Calculation: Automatically generates an attendance percentage, flagging farmers who fall below participation thresholds. - AESA Skill Matrix: Uses a tiered skill-level scale (Beginner to Advanced) to track the qualitative growth of farmers in ecological observation. - Logical Constraints: Prevents data entry errors by ensuring "attended sessions" cannot exceed the "total sessions" held by the school. - Practice Adoption Tracking: Focuses on verifiable behavioral changes (IPM, composting, mulching) rather than just theoretical knowledge.

'- Transaction type breakdown: Separates B2B vs B2C digital adoption - Penetration metrics: Calculates % digital adoption overall and per transaction type - Platform usage: Captures which mobile money platforms are in use and the main platform - Adoption barriers: Identifies key constraints to mobile money adoption - Basic comments: Allows optional qualitative feedback - Targeted respondents: Only Ag-Tech Startups, Early Adopters, Machinery Dealers included

'- Targeted users: Relevant for Urban Farmers and Urban Fish Farmers. - Detailed system metrics: Captures system type, area, vertical layers, crop types, and planting density. - Energy, water & nutrient tracking: Monitors energy source, monthly energy cost, water usage, and nutrient management. - Yield & ROI analysis: Records monthly yield, ROI, and operational automation level. - Waste & environmental management: Tracks waste management methods and environmental risk factors. - Risk assessment: Includes power reliability, pest/disease, market, and environmental risks.