Big Data Webinars from PMN

In Nov. 2014, I posted a blog entitled “Privacy of Farm/Farmer Data” which deals with the issues surrounding the accumulation of “Big Data” sets that are being used by Agriculture Technology Providers (ATP’s). In this article, a link to “Privacy and Security Principles for Farm Data” is provided. This document was published by a coalition of farm groups, and provides principles and basic tenets that should be adopted and adhered to by each ATP.

To provide further information on the subject of Big Data use in agriculture, the Plant Management Network posted a webinar entitled “Big Data and Implications at the Farm” presented by Dr. John Fulton, Associate Professor at the Ohio State University. Dr. Fulton describes the current state of Big Data in agriculture, and provides recommendations to soybean producers and service providers to consider in their determination of how to use the options that are now available or soon will be available to them. He discusses:

         Using big data to promote sustainability, which involves farm profitability, accountability for water and nutrient applications, and crop yield

         Areas of investment and growth for digital agriculture

         Example application using planter technology that generates data to provide feedback to the operator

         Connectivity at the farm level to simplify data transfer and viewing

         Ensuring data quality and proper data cleaning and backup so that collected data are available and correct for use in making management decisions

         Generation of plant and machine data

Dr. Fulton has produced another webcast entitled “Advances in Prescriptive Agriculture and Big Data” that is now available for viewing on PMN’s Focus on Soybean site. In this presentation, he discusses:

         Using digital agriculture to measure farm success

         Segments of digital agriculture–Precision Agriculture, Enterprise Agriculture, and Prescriptive Agriculture

         Use of bi-directional data exchange to obtain accurate prescription applications

         Public and private level Big Data

         Using Big Data to improve input efficiency and asset management

         Types of Big Data–Agronomic, Machine, and Production

         Bridging agronomic and machine data

         Lack of data quality and a list of common errors that contribute to it

         Yield data quality–mainly historical

In the webcasts, Dr. Fulton provides example companies for data exchange, as well as website resources for additional detailed information.

I encourage you to view these webcasts to learn about the Big Data subject.

Compiled by Larry G. Heatherly, July 2016, larryheatherly@bellsouth.net