Inspiration: USDA CIO Joyce Hunter and CDO Bobby Jones

How it works: USDA Farm Dashboard NASS Quick Stats and ARMS Big Data Sets are imported into Spotfire and a Data Science Guided Interface for Analytics and Visualizations was created.

Challenges I ran into: Trying to understand and use the USDA Farm Data Dashboard Documentation.

Accomplishments that I'm proud of: That I was able to submit on the first day!

What I learned: That our Federal Big Data Working Group Meetups attracted USDA senior officials and the dialogue led to the USDA Data Science MOOC, the Data Driven Farming Online Class, and the Big Data Science for Precision Farming Business work.

What's next for Farm Data Dashboards: Data Driven Farming Online Class Student Decision Dashboards during October 26-December 18, 2015.

Built With

  • mindtouch-wiki
  • excel
  • tibco-spotfire
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Updates

Brand Niemann posted an update

Second Call for Papers for 13th ICPA: http://www.ispag.org/ICPA/

Big Data Science for Precision Farming Business

The USDA and HeatSpring helped me develop an online course entitled Data-Driven Farming.

This online course provides Five Data Points You Need to Know for Farming:

Farming has become more efficient and productive through precision agriculture practices. The next evolution in precision agriculture is data science. Data science for precision farming requires the integration and interpretation of many data sources using what is called a data science platform in the cloud. I have pioneered the use of a data science platform in the cloud for the integration and interpretation of many data sources for the USDA. I have now extended that work for the USDA by adding many more national and local data sources, specifically for my boyhood family farm in Nebraska City, Nebraska, to show how you can do this yourself without the help of a service provider unless you want to turn your proprietary farm data over to others. This presentation will provid highlights of the upcoming online course (January 25-March 18, 2016) and includes the use of the IPNI AgriStats Data Sets which were imported from multiple spreadsheets into a Spotfire Dashboard, like the other USDA, etc. data sets.

The results are (not earth shattering) at: http://semanticommunity.info/Data_Science/Big_Data_Science_for_Precision_Farming_Business/Week_2__Data_Understanding#AgriStats

And are:

Population has increased while lands (Agricultural, Arable, Forested, and Irrigated) have remained the same or even decreased!

Nutrient Use for our main crops (Cotton Lint and Seed, Maize, Potatoes, Rice, paddy, and Wheat) exceeds Nutrient Removal from the Best Fit Line, but not for some individual crops and years.

But collecting and using data to improve profitability is the future of farming, but it can be overwhelming to farmers, students, and even data science professionals. This overview of the hands-on course, developed in partnership with the USDA, will help farmers implement a data-driven approach to decision making.

The presentation also includes a live demo of the author's USDA-Microsoft Innovation Challenge entry: http://devpost.com/software/farm-data-dashboards

Keywords: Data-Driven Farming, USDA, Data Science, IPNI AgriStats, Spotfire Dashboards

Thank you for your ICPA abstract submission, #1753.

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Brand Niemann posted an update

I added the IPNI AgriStats data to my Big Data Science for Precision Farming Business online course since I just received access to the data.

The results are (not earth shattering) at: http://semanticommunity.info/Data_Science/Big_Data_Science_for_Precision_Farming_Business/Week_2__Data_Understanding#AgriStats

And are:

Population has increased while lands (Agricultural, Arable, Forested, and Irrigated) have remained the same or even decreased!

Nutrient Use for our main crops (Cotton Lint and Seed, Maize, Potatoes, Rice, paddy, and Wheat) exceeds Nutrient Removal from the Best Fit Line, but not for some individual crops and years.

Log in or sign up for Devpost to join the conversation.