The Ultimate Guide to Agricultural Data 2021
What is Agricultural Data?
Agricultural data is a subsection of Industry data. It can be used to understand crop production and to cater to the growing number of people in the world. Due to the increase of urbanisation worldwide, agricultural data is needed to maximize the production potential of farmland through understanding weather patterns or managing areas of land.
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 maxmise crop planting or animal rearing to ensure the most fruitful produce. Information on weather features, such as air temperature or humidty, 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 sucess. 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.
Who are the best Agricultural Data providers?
Finding the right Agricultural Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Agricultural Data providers that you might want to buy Agricultural Data from are Automaton AI, Ambee, Meteomatics, HSAT, and CropProphet.
Where can I buy Agricultural Data?
Data providers and vendors listed on Datarade sell Agricultural Data products and samples. Popular Agricultural Data products and datasets available on our platform are Automaton AI Agriculture Image Data (raw, annotated) by Automaton AI, CropProphet Daily Weather Data: Weather & Agricultural Data for USA (county-level) by CropProphet, and HSAT Agricultural Data: Sugarcane - Calculating Area, Health and Harvest Predictions in 46 Countries by HSAT.
How can I get Agricultural Data?
You can get Agricultural Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Agricultural Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Agricultural Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Agricultural Data?
Agricultural Data is similar to Telecom Data, AI & ML Training Data, Automotive Data, Research Data, and Cyber Risk Data. These data categories are commonly used for Agriculture Management and Agricultural Data analytics.