Information that improves crop and livestock production makes up agriculture data. This includes soil quality, biome, local laws, plant and animal identification, food consumption, natural disaster, and economic data.
Most agriculture data still comes from governmental, intergovernmental sources, and private research organizations. However, there is 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 with time, and should continue to do so.
There are a wide range of attributes of this data. You may, for example, explore data on 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, with so many effects on human, animal, and plant life, you can find an enormous amount of information to explore from almost any angle.
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 production. Secondary uses include combating world hunger and contributing to environmental conservation.
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 recently updated and as complete as possible. Open-sources databases updated by sensors in real-time are, therefore, excellent resources.
Data.gov: Agriculture Datasets
FAO: Statistics
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