Agricultural Data: Best Agricultural Datasets & Databases

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.

Where Can I Buy Agricultural Data?

You can explore our data marketplace to find a variety of Agricultural Data tailored to different use cases. Our verified providers offer a range of solutions, and you can contact them directly to discuss your specific needs.

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.