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Agriculture Data

What Is Agriculture Data?

Agriculture data is essentially information that improves crop and livestock production. This includes soil quality, biome, local laws, plant and animal identification, food consumption, natural disaster, and economic data.

Where Does Agriculture Data Come From?

Most agriculture data still comes from governmental, intergovernmental sources, and private research organizations. However, over time there has been a growing movement toward working with open-source databases, where individual farms equipped with sensors update crop and livestock data in real-time. Due to this, the connections between agriculture and IoT data have only strengthened and should continue to do so.

What Types of Columns/Attributes Should I Expect When Working with This Data?

There is a wide range of attributes of this data. You may, for example, explore data on the type of crop or animal per nation over time. Or, you may explore the percentage of a nation’s population working in agriculture to the percentage suffering from food scarcity. Essentially, because this data category is so complex and vast, with so many effects on human, animal, and plant life, you can find an enormous amount of information to explore from almost any angle.

What Is This Data Used For?

Ranchers, farmers, conservationists, and policy-makers in government all use this data for a variety of reasons. First and foremost, they use the data to increase the amount and quality of food is produced. Secondary uses include combating world hunger and contributing to environmental conservation.

How Should I Test the Quality of Agriculture Data?

While most agriculture data is of good quality, coming from national and international researchers, one of the best means of improving data quality is using multiple sources. For example, a more accurate crop use dataset uses government land use data as well as crop production and trade data.

Additionally, as ever, the data must be recent and updated and as complete as possible. Open-sources databases updated by sensors in real-time are, therefore, excellent resources.

Interesting Case Studies and Blogs to Look Into Agriculture Datasets
FAO: Statistics

Tangible Examples of Impact

A major part of Mineral’s plan revolves around a four-wheeled robotic prototype that somewhat resembles a moon rover. The team appropriately calls it a plant buggy. Thanks to a suite of cameras and sensors. it can study crops, soil, and other characteristics of the environment over a large area. Those findings are then compared with satellite photos and weather data, according to Nick Statt of The Verge.

The Burn-In: Alphabet’s crop-inspecting robot connects agriculture with big data

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