We extract used tractor listings, combine harvester specifications, heavy equipment pricing, and dealer intelligence from Agriaffaires. Delivered as clean JSON, CSV, or Parquet to your warehouse.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Machinery Listings objects from agriaffaires.com. All fields typed and schema-versioned.
"listing_id": "8934102", "title": "John Deere 6155R", "make": "John Deere", "model": "6155R", "year": 2018, "hours": 4250, "price": 78500.0, "currency": "EUR"
| # | listing_id | title | category | make | model | year |
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Complete list of extractable fields for Technical Specifications objects from agriaffaires.com. All fields typed and schema-versioned.
"listing_id": "8934102", "engine_power_hp": 155, "transmission_type": "AutoPowr", "drive_type": "4WD", "front_tire_wear_pct": 30, "rear_tire_wear_pct": 40, "gps_ready": true
| # | listing_id | engine_power_hp | transmission_type | drive_type | front_tire_size | front_tire_wear_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dealer Data objects from agriaffaires.com. All fields typed and schema-versioned.
"dealer_id": "PRO-4921", "dealer_name": "Agri Sud Ouest", "dealer_type": "Professional", "city": "Toulouse", "country": "France", "phone_number": "+33 5 61 00 00 00", "inventory_count": 142
| # | dealer_id | dealer_name | dealer_type | address | postal_code | city |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Valuation objects from agriaffaires.com. All fields typed and schema-versioned.
"listing_id": "8934102", "listed_price": 94200.0, "price_excl_vat": 78500.0, "vat_rate": 20.0, "currency": "EUR", "price_drop_indicator": false, "days_on_market": 14
| # | listing_id | listed_price | price_excl_vat | vat_rate | currency | price_drop_indicator |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Search Results objects from agriaffaires.com. All fields typed and schema-versioned.
"keyword": "claas lexion", "category_filter": "combine-harvester", "position": 3, "listing_id": "9012345", "price": 145000.0, "highlighted_ad": true, "pro_seller": true
| # | keyword | category_filter | page_number | position | listing_id | title |
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Our Agriaffaires scraper handles complex technical specifications, multi-language listings, dynamic contact details, and pagination limits — delivering normalised machinery datasets ready for analysis.
Extract engine power, transmission types, operating hours, tire wear percentages, and attachment compatibility across thousands of categories.
Capture listed prices, prices excluding VAT, currency, and price drop indicators to build accurate valuation models.
Execute JavaScript interactions to reveal obfuscated dealer phone numbers and contact forms across listings.
Map entire professional seller catalogues, tracking inventory turnover, time-on-market, and regional market share.
Extract data from agriaffaires.co.uk, agriaffaires.de, agriaffaires.fr, and other regional domains with language normalisation.
Capture high-resolution image URLs, inspection reports, and technical PDF brochures attached to listings.
Isolate critical valuation metrics like engine hours and drum hours for heavy machinery and harvesters.
Run continuous pipelines that only emit records when prices drop, hours increase, or listings are removed.
Map Agriaffaires' complex category taxonomy (e.g., Tillage > Ploughs > Reversible Ploughs) into clean, queryable hierarchies.
Brief in. Clean data out.
Select target categories (e.g., tractors, harvesters), regions, and specific data fields. We map the schema.
We configure Scrapy/Playwright crawlers, proxy rotation, and normalisation rules for Agriaffaires' multi-language DOM.
Schema validation, checking null rates on critical fields like operating hours and price, before full deployment.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your chosen cadence.
Extracting heavy machinery data at scale requires overcoming regional variations, obfuscated data, and strict pagination limits.
Agriaffaires hides dealer phone numbers behind 'Show Number' buttons to prevent scraping. We use Playwright to simulate human interaction, clicking elements and waiting for network responses to capture the full contact details.
A tractor listed on the French site has different field labels ('Heures' vs 'Hours', 'Puissance' vs 'Power') than the UK site. We map these variations to a single, normalised English schema in the pipeline.
Large categories like 'Used Tractors' exceed standard pagination limits. We implement deep-crawl strategies, intersecting categories with granular filters (make, year, region) to extract the complete catalogue without hitting display caps.
The technical specifications for a combine harvester differ entirely from a reversible plough. Our extraction logic adapts to the equipment category, dynamically capturing category-specific attributes.
We route requests through EU-based residential proxies, matching the geographic origin to the target domain (e.g., French IPs for agriaffaires.fr), preventing IP bans and CAPTCHA walls.
Financiers and insurers build depreciation curves based on make, model, year, and operating hours.
Machinery dealerships monitor competitor inventory levels, pricing strategies, and time-on-market metrics.
Manufacturers track the secondary market volume of their equipment versus competitors across different European regions.
Traders identify price discrepancies for identical machinery models across different countries to facilitate cross-border sales.
Large agricultural cooperatives automate the sourcing of specific machinery configurations matching their fleet requirements.
Actuaries use historical pricing and condition data to determine replacement values for heavy agricultural equipment.
"Agriaffaires holds the definitive dataset for secondary agricultural machinery markets in Europe — but extracting normalised technical specifications requires heavy lifting."
Most teams underestimate the investment required: reliable Agriaffaires scraping requires residential proxies, full JavaScript execution to reveal contact details, and complex normalisation logic for machinery specifications across 15+ languages. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our agriaffaires.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy manages crawl orchestration and deduplication. Playwright handles JavaScript execution for revealing phone numbers and interacting with galleries.
We maintain pools of European residential ISP proxies to match the target domain's region, preventing blockades and ensuring accurate local pricing.
Pipelines run on AWS infrastructure. Airflow handles scheduling for daily inventory updates, with all state stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About agriaffaires.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available machinery listings and dealer information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or circumvent authentication walls. Clients should review Agriaffaires' ToS and consult legal counsel for specific use cases.
Agriaffaires operates multiple regional domains (e.g., .fr, .de, .co.uk). We configure the pipeline to target the specific regions you need, and our normalisation layer maps the various languages into a single, unified English schema.
Yes. We use Playwright to simulate the necessary click events on the 'Show Number' buttons, capturing the network responses to extract the full contact details for dealers and sellers.
We can configure pipelines for daily, weekly, or monthly cadences. A full category refresh (e.g., all used tractors in France) typically completes within 4-8 hours depending on category size.
Yes. Using our change-detection pipeline, we maintain a state of known listings. Subsequent runs compare current prices against historical records, flagging price drops and calculating time-on-market.
Our smallest packages start at defined category extractions (e.g., all combine harvesters in the UK) with weekly delivery. For full-site extractions across multiple regions, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of tractor specifications or a daily feed of dealer inventory changes — we scope, build, and operate the pipeline. Tell us what you need.
