- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Agricultural Data?
Agricultural data refers to information collected and analyzed from various sources within the agricultural sector. It includes data related to crop yields, weather patterns, soil conditions, market prices, and other relevant factors. This data is used to make informed decisions, improve farming practices, optimize resource allocation, and enhance overall agricultural productivity. Agricultural data is used for various purposes such as improving crop productivity, optimizing resource allocation, predicting and managing risks, and making informed decisions in the agricultural sector. In this page, you’ll find the best data sources for agricultural data.
Best Agricultural Databases & Datasets
Here is our curated selection of top Agricultural Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Agricultural Data | Agriculture & Farming Leaders Worldwide | Verified Global Profiles from 700M+ Dataset | Best Price Guarantee
CustomWeather | 6-Month Agricultural Data Outlooks | Temperature Data | Rainfall Data|Precipitation Data | Global Weather Data | Weather Forecast Data
Agricultural | Product Data | Commodity Prices Data | Benchmarking | Region | Purchase Volume | Cost Planning | Market Trends | Supply Chain
Agriculture & Land Health Data | Satellite Data: Agriculture, Vegetation Data, Health Data, Land Productivity
LinkedIn Data | Professional Network Data | 50+ Industry Segmentation | Global Coverage | 121M+ Professionals | Verified Email, Phone | 20+ Attributes
Automaton AI Agriculture raw data
Geospatial Data | 164M+ Global Places
CustomWeather API | Real-Time Weather Data | Current Conditions For 12,500 Global Weather Stations | Temperature Data I Rainfall Data I Wind Data I
Agriculture stocks Price data 10yr history
Financial Derivatives EoD Pricing | Options & Futures Pricing Data on Commodities
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Top Agricultural Data Providers & Companies
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FarmersEdge
CoverageUSACanada+88We collect data from four primary sources: weather stations, satellite imagery, soil testing and machine telematics.3200Weather stations28000Machines3BData points -
Latitudo 40
CoverageUSAUK+248Latitudo 40 offers seamless access to multi-temporal geospatial data, turning satellite imagery into actionable insights for global climate resilience. Through our AI-powered platform, we help cities, real estate, and infrastructure sectors assess risks like heat islands, floods, and land changes.Coverage100% GLOBALData> 10 years of historyAccuracy99% -
CustomWeather
CoverageUSAUK+247EStrong data coverage and quality. Ended up licensing hourly and daily weather data in the United States. Strong historical coverage. Team was easy to work with and moved quickly to deliver the data in the format we needed.
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Ambios Network
CoverageUSAUK+18Ambios Network delivers hyper-local, real-time outdoor air quality data from a rapidly growing sensor network across the US and Europe. Backed by leading investors, our calibrated, high-resolution data powers smarter decisions for cities, researchers, and innovators.3,000+Sensors100%1st Party20Countries -
Automaton AI
CoverageUSAUK+238We are a full-stack AI company with a mission to democratize Data. Automaton AI is an AI industry expert who is Transforming how businesses see the world with end-to-end AI services ranging from Data sourcing to Application Development and Model Deployment. -
costdata
CoverageUSAUK+248costdata provides precise solutions for product costing and cost optimization. With a unique database and software, we combine industry expertise with cutting-edge technology to support businesses in making informed decisions in procurement, R&D, controlling, cost engineering, sales and management.33,000+Metals with Pricing Data14,000+Plastics with Pricing Data13,500+Machines with Simulation
Popular Use Cases for Agricultural Data
Agricultural Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for Agricultural Data.
Main Attributes of Agricultural Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| Float | The latitude of a point on earth's surface. Commonly abbreviated as "lat". | View 12 datasets | |
| String | Mobile Ad ID is a sequence of random symbols, given by the mobile device’s operating system. It’s shared with the servers of the apps that the user is using to track his customer journey and “remember” his or her choices. | View 12 datasets | |
| Float | The longitude of a point on earth's surface. Commonly abbreviated as "long". | View 10 datasets | |
| String | The first name of a contact. | View 9 datasets | |
| String | The name of a country. | View 9 datasets | |
| String | The last name (surname) of a contact. | View 8 datasets |
Agricultural Data Explained
How is Agricultural Data collected?
Agricultural data can be collected in many different ways. Weather data can be collected from satellites and sensors while land and crop data can be harvested by agricultural vehicles and drones. Laboratory analysis is also used for the collection of agricultural data, such as soil information or nutrient availability.
What are the typical attributes of Agricultural Data?
Lots of different information makes up an agricultural dataset.
Weather data - By understanding weather conditions and the forecast, you can maxmize crop planting or animal rearing to ensure the most fruitful produce. Information on weather features, such as air temperature or humidity, is an equally important part of this dataset.
Crop data - Data on crop yield is key for making the most of what the land can provide without over-using natural resources. This data also provides an insight into the condition of the land, such as the availability of nutrients or the amount of fertiliser used and compare that to yield level. Actual production can also be compared to forecasted yield with some datasets.
Machinery data - Following machinery patterns helping maximise effectiveness of large vehicles and gives an insight into how often different agricultural operations were conducted.
What is Agricultural Data used for?
Agricultural data is mainly used to maximize land yield to ensure that you are getting the best from your land. Weather forecasting data can be used to advise on planting, crop care or harvesting schedules. Actual yield data can be compared to forecasted yield data to highlight areas where production could be increased to give you the most success. It also allows you to track the world’s consumption as you can see areas where there has been more or less demand by consumers.
How can a user assess the quality of Agricultural Data?
For agricultural data to be the best it needs to be constantly and consistently updated due to the changing nature of the subjects it monitors, such as the weather. The best datasets will provide the most up to date information but will also contain historical information too so that you can analyze annual information and changes in trends. Before buying any agricultural data, make sure to check the data provider’s reviews and ask for a sample before you buy to ensure that their data meets your needs.
Frequently Asked Questions
How is the Quality of Agricultural Data Maintained?
The quality of Agricultural Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is Agricultural Data Updated?
The update frequency for Agricultural Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is Agricultural Data Secure?
The security of Agricultural Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is Agricultural Data Delivered?
Agricultural Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for Agricultural Data.
How Much Does Agricultural Data Cost?
The cost of Agricultural Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of Agricultural Data for your needs.
What Are Similar Data Types to Agricultural Data?
Agricultural Data is similar to other data types, such as Marketing Data, Education Industry Data, Insurance Data, and Food Data. These related categories are often used together for applications like Crop Monitoring.
