Looking for data set to put into practice some knowledge about Agroanalytics, I find some interesting challenges: There are a lot of courses about it, for example, at Wageningen University (In fact, there are some exciting courses in @edXOnline) But, I’ve had some problems in getting valuable Agro Analytics datasets to work or play in the internet.

The principal issue is the overwhelming quantity of types and categories of datasets I’ve found, for example, Kaggle. And the other is the impossibility of understanding how to work correctly despite the fact I need some different types. 

One day, the typical case that came to me was understanding if some climatological data allowed me to predict the harvest. But, I needed some images, something widely used to understand the future of the eventual returns.

By the time I am looking again at Kaggle, I found:

• Agro_data – Factors affecting crop yield: – Link to Kaggle Data
• Credit to agriculture in Brazil and the world – Link to Kaggle Data
• Cenagro – No description – Link to the Kaggle Data

One of the challenges about the incompleteness of the information is how to work with it in a proper way to make good predictions one time we start modeling or creating a simple dashboard. It’s a challenge we have to face and resolve before starting our journey. It has a name in the industry: it’s called: data preparation or data wrangling.