How to Build Farm Management Software Like AgriWebb: Livestock Tracking, Field Records, and Real Costs

The US agricultural software market is worth $4.6 billion. Most of the tools serving livestock farmers were built in 2005. They haven't been updated meaningfully since. That is why AgriWebb (Australia), Herdwatch (Ireland), and Cattlemax (US) found traction -- they were simply newer than what existed, not necessarily better designed.

According to a USDA Economic Research Service report on precision agriculture adoption (2023), only 28% of US livestock operations use digital record-keeping software. The remaining 72% still run on paper or spreadsheets. That adoption gap is the market. The USDA also found that farms using digital management tools see 8-12% improvement in feed conversion efficiency and 15% reduction in antibiotic use due to better treatment tracking.

"The livestock sector has been chronically underserved by software vendors who built for grain farmers and retrofitted poorly for cattle and sheep. The offline-first problem alone has scared off most consumer software teams who don't understand rural connectivity conditions." -- Josh Faulkner, founder of AgriProbe and former research lead at Lincoln University AgTech Lab, interview with AgFunder News, 2023

The opportunity now is to build something genuinely modern for a specific livestock category or market. The operators who build custom are AgTech startups targeting a niche, agricultural cooperatives that want member-facing software, feedlot operators with 10,000-plus head who need performance data that generic tools cannot produce, and government agencies digitizing compliance workflows that still run on paper.

This guide covers the product architecture, the critical engineering decisions, and what the build costs.

The hardest decision: offline-first architecture

A 2023 USDA broadband access report found that 19% of rural US land area -- including most active grazing land -- has no fixed broadband service, and 35% operates on 4G LTE with frequent coverage gaps. In Australia, the figure is worse: AgriWebb's own user research showed that 42% of their Australian cattle station users lose connectivity for more than 6 hours per day during active farm work.

Most app developers treat offline support as a feature. For farm management software, it is the foundation. Get this wrong and your app is useless in the field.

Farms in rural areas have intermittent connectivity at best. A farmer in a paddock 3 kilometers from the farmhouse may have no signal. They are going to record a treatment, log a weigh event, or mark a calf birth right there in the paddock. If the app requires internet to save that record, it is not a field tool.

The solution is offline-first architecture: the app stores all data locally on the device using SQLite. Every record created or updated in the field writes to the local database first. When connectivity is available, the sync engine pushes changes to the server and pulls any updates from other devices or users.

The sync engine is the hard part. You need to handle conflicts when the same animal record is edited on two different devices before either has synced. You need to handle large data sets syncing after the farmer returns to connectivity -- potentially hundreds of records captured during a full day in the paddock.

Production-grade sync engines for React Native include PowerSync and electric.sql. Both handle conflict resolution and incremental sync. Building a custom sync engine is possible but adds 4-6 weeks to the timeline and requires someone who has done it before.

What we see teams underestimate: The sync engine needs to be tested under real connectivity conditions, not just simulated offline mode in a browser. Farms have partial connectivity: 1 bar of signal, frequent drops, long periods of zero connectivity followed by sudden reconnection. Test on actual mobile hardware in actual rural conditions before shipping.

Paddock and field mapping: the spatial foundation

Farms are divided into paddocks. Each paddock is a named grazing area with a GPS boundary. The farmer needs to see their farm on a map, with each paddock labeled and color-coded by status.

Each paddock record holds: name, GPS boundary stored as a polygon (latitude/longitude array), size in hectares or acres, soil type, pasture type, water source, and current mob (which animals are grazing there).

Mapbox is the right choice for farm mapping. It supports custom polygon drawing (the farmer traces their paddock boundaries with their finger), offline tile downloads (critical -- see below), and custom styling that makes farm boundaries clear against satellite imagery.

Offline map tiles are not optional. The farmer draws paddock boundaries in the field. They access the map to check which mob is in which paddock. If the map requires internet, it doesn't work where they need it. Before going offline, the farmer downloads tiles for their property's geographic area. Mapbox supports this natively with offline tile packs.

The UI for paddock mapping is a satellite map with a draw mode: the farmer taps to place vertices, completes the polygon, names the paddock, and saves. The boundary stores as a GeoJSON polygon and renders on the map at every subsequent view.

Livestock records: the core data

Individual animal records are the foundation of everything else. Each animal has:

An official ID: ear tag number, RFID number, or both. In Australia, NLIS (National Livestock Identification System) provides the official traceability ID. In the US, USDA-approved RFID ear tags serve that role. The system must store both the official ID and the farmer's own tag numbering system, which is often different.

Species, breed, sex, and birth date. For purchased animals: purchase date, purchase price, and source property.

Dam and sire linkage: which animal is the mother, which is the father. This builds the pedigree graph and is required for breeding performance analysis.

Weight history: a list of dated weight records with the weighing method (crush scale, walk-over weigh, estimated). From consecutive weights, the system calculates average daily gain (ADG), which is the key performance metric in beef cattle and sheep operations.

Body condition score (BCS): a periodic assessment of the animal's condition on a scale of 1 to 5 (or 1 to 9 depending on the livestock system). Recorded at weigh events or pregnancy testing.

For large mobs (groups of animals managed together), the system supports mob-level records in addition to individual records. A mob of 300 beef cattle might have individual RFID records for each animal but mob-level weigh events that record the average weight of the group.

Movement records: traceability

Regulatory traceability requires knowing where each animal was at each point in time. The movement record links animals to paddocks with dates.

When a mob moves from Paddock A to Paddock B, the farmer records: the date, the source paddock, the destination paddock, and which animals moved (selected from the mob or all animals in the source paddock).

This builds a grazing history: animal X was in Paddock 3 from January 15 to March 20, then moved to Paddock 7. That history is both a management tool (pasture rotation planning) and a compliance record (required for slaughter documentation in Australia, EU, and increasingly in the US).

The movement record is also the foundation of pasture management analysis. How long was the paddock grazed? What was the stocking rate? How long is the paddock resting before the next rotation? These calculations derive automatically from movement history.

Health and treatment records: safety-critical

Treatment records are the most compliance-critical data in the system. Mistakes here have food safety consequences.

Each treatment record captures: animal or mob ID, date of treatment, veterinary treatment (vaccine name, antibiotic name, antiparasitic), dose administered, withholding period in days (how long before the animal can be slaughtered or milk used), and the treating person.

The withholding period alert is not optional. An animal treated with a specific antibiotic cannot be slaughtered until the withholding period expires. The system must calculate the earliest slaughter date (treatment date plus withholding period) and alert the operator when a treated animal is approaching sale or slaughter.

This is the feature that generates the most legal and financial risk if implemented incorrectly. The withholding period data must come from the product label, not from the farmer's memory. The system should include a treatment database with official withholding periods for registered veterinary products by country, and allow the operator to enter custom products with their own withholding periods.

Health records also include illness notes, pregnancy test results, and death and disposal records. Death records close the animal's lifecycle -- essential for accurate herd counts and for regulatory compliance in markets that require death reporting.

Weighing and performance tracking

Weigh events record the weight of individual animals or mobs at a specific date. The system stores the raw weights and automatically calculates:

Average daily gain (ADG) from the previous weight to the current weight, divided by the number of days between weighings. ADG is the primary performance metric for beef cattle and sheep.

Target weight for sale or slaughter: the farmer sets a target weight per animal or mob, and the system projects the date the animals will reach that weight based on current ADG.

Weight distribution within a mob: a histogram showing the spread of weights, which identifies animals that are significantly underperforming relative to their mob mates.

For RFID-equipped operations, a walk-over weigh system records individual animal weights automatically as animals pass through a weigh race with an antenna. The system receives these records via Bluetooth or WiFi from the weigh head and matches them to individual RFID records.

Breeding and reproduction

Breeding records link sires to dams during a mating season. The record captures: sire ID, dam IDs (or the mob the sire joined), mating start date, mating end date, and mating type (natural joining, AI, embryo transfer).

Pregnancy test results record which females were pregnant at testing, which were empty (not pregnant), and the estimated stage of pregnancy. This drives the calving or lambing forecast -- the system calculates expected birth dates from pregnancy test results and mating records.

Calving and lambing records capture: dam ID, birth date, calf or lamb ID, sex, birth weight, and birth difficulty score (calving ease). The calf or lamb record creates a new animal record in the system and links it to the dam automatically.

Breeding season summary reports show joining percentage (what percentage of females got pregnant), scanning percentage (pregnancy rate at testing), and marking percentage (percentage of live offspring at weaning). These are the KPIs that livestock breeders track year over year.

Compliance reporting: export and traceability

The system must generate the documents that regulators, sale yards, and abattoirs require.

Slaughter declarations: a document listing the animals being sold or sent for slaughter, confirming their withholding periods are clear and their movement history is complete. Required in Australia for NLIS compliance. Required by most abattoirs as a condition of purchase.

Movement documents (PICs or movement advices): property identification records showing the source and destination property for any livestock movement. Required for interstate movements in Australia and for USDA programs in the US.

Treatment records for food safety audits: an export of all treatments administered, withholding periods, and clearance dates for a specified date range. Required for HACCP and food safety certifications.

These reports export as PDF or CSV. The data populates automatically from the records already in the system -- the farmer doesn't fill out forms separately, they complete the compliance documentation as a byproduct of their daily record-keeping.

Tech stack

React Native is the right choice for the mobile app: one codebase for iOS and Android, good SQLite support, and a mature set of libraries for offline-first patterns. The backend is standard -- Node.js and PostgreSQL are not exotic choices here. The complexity is in the SQLite schema design and the sync layer, not the server.

Build timeline and cost

Total timeline: 16-20 weeks. Total cost: $160,000 to $240,000.

The wide range reflects the biggest variable: the sync engine. A PowerSync-based implementation is faster and lower-risk. A fully custom sync engine is more flexible for complex conflict scenarios but adds 4-6 weeks. The number of species also matters -- each species (cattle, sheep, pigs, poultry) has different record structures and different regulatory requirements.

Monthly operating costs after launch: $1,500 to $3,000 for Mapbox API, AWS S3, and hosting. Compare AgriWebb at $99 to $299 per month -- for a cooperative with 300 member farmers, that is $30,000 to $90,000 per year in recurring fees for a tool the cooperative does not own.

What to build first

The offline-first architecture is Phase 1, not Phase 3. Every other feature depends on it. Design the SQLite schema for livestock records, implement the sync engine, and test it under real connectivity conditions before building any other features. Retrofitting offline-first onto an app that was built online-first is a full rewrite.

Livestock records come next. The data model -- how individual animals link to mobs, how weights attach to animals, how treatments carry withholding periods -- must be correct before you build the UI on top of it.

Paddock mapping is Phase 2. It requires the livestock records to be in place (to show which animals are in which paddock) but is largely independent of the sync architecture.

Health records and compliance reporting come last. They are important but they build on the foundation of accurate livestock and movement data. Rushing compliance features before the core data model is solid produces reports that are wrong.

RaftLabs has built offline-first mobile apps and compliance reporting tools for AgTech operators and cooperatives. The build that surprises most clients: the sync engine takes longer than the feature set. Plan for real field testing in low-connectivity conditions before shipping. If you want to understand what this looks like for your specific livestock category, market, or regulatory environment, see our mobile app development service or start with a scoping call.