Tag Archives: precision ag

Terry Griffin: “Agricultural big data has evolved out of precision ag technology”

Terry Griffin, PhD
Terry Griffin, PhD
Dr. Terry Griffin (@SpacePlowboy) is the cropping systems economist specializing in big data and precision agriculture at Kansas State University. He earned his bachelor’s degree in agronomy and master’s degree in agricultural economics from the University of Arkansas, where he began using commercial GIS products in the late 1990s. While serving as a precision agriculture specialist for University of Illinois Extension, Terry expanded his GIS skills by adding open source software. He earned his Ph.D. in Agricultural Economics with emphasis in spatial econometrics from Purdue University. His doctoral research developed methods to analyze site-specific crop yield data from landscape-scale experiments using spatial statistical techniques, ultimately resulting in two patents regarding the automation of community data analysis, i.e. agricultural big data analytics. He has received the 2014 Pierre C. Robert International Precision Agriculture Young Scientist Award, the 2012 Conservation Systems Precision Ag Researcher of the Year, and the 2010 Precision Ag Awards of Excellence for Researchers/Educators. Terry is a member of the Site-Specific Agriculture Committees for the American Society of Agricultural and Biological Engineers. Currently Dr. Griffin serves as an advisor on the board of the Kansas Agricultural Research and Technology Association (KARTA). Terry and Dana have three wonderful children.

Q: Your background is in Agronomy and Agricultural Economics. When along this path did you discover spatial/GIS technologies, and how did you apply them for the first time?

A: During graduate school my thesis topic was in precision agriculture, or what could be described as information technology applied to production of crops. GPS was an enabling technology along with GIS and site-specific sensors. I was first exposed to GIS in the late 1990s when I mapped data from GPS-equipped yield monitors. I dived into GIS in the early 2000s as a tool to help manage and analyze the geo-spatial data generated from agricultural equipment and farm fields.

Q: Precision Agriculture is a huge market for all sorts of devices. How do you see spatial playing a role in the overall Precision Agriculture sandbox?

A: Precision Ag is a broad term, and many aspects of spatial technology have become common use on many farms. Some technology automates the steering of farm equipment in the field, and similar technology automatically shuts off sections of planter and sprayers to prevent overlap when the equipment has already performed its task. Other forms of precision ag seem to do the opposite — rather than automate a task they gather data that are not immediately usable until processed into decision-making information. These information-intensive technologies that are inseparable from GIS and spatial analysis have the greatest potential for increased utilization.

Q: What do you see as hurdles for spatial/data analytics firms who want to enter the Precision Agriculture space, and what advice would you give them?

A: One of the greatest hurdles, at least in the short run, is data privacy issues as it relates to ‘big data’ or aggregating farm-level data across regions. A tertiary obstacle is lack of wireless connectivity such as broadband internet via cellular technology in rural areas; without this technology agricultural big data is at a disadvantage.

Q: While there have been attempts at an open data standard for agriculture (agxml, and most recently SPADE), none have seemed to catch on.  Do you think this lack of a standard holds Precision Agriculture back, or does it really even need an open standard?

A: Data must have some sort of standardization, or at least a translation system such that each player in the industry can maintain their own system. Considerable work has been conducted in this area, and progress is being made; we can think of the MODUS project as the leading example. Standards have always been important even when precision ag technology was isolated to an individual farm; but now with the big data movement, the need for standardization has been put toward the front burner. Big data analytics relies on the network effect, specifically what economists refer to as network externalities; the value of participating in the system is a function of the number of participants. Therefore, the systems must be welcoming to all potential participants, but must also minimize the barriers to increase participation rates.

Q: What is your preferred spatial software, or programming language?

A: All my spatial econometric analysis and modeling is in R, and R is also where a considerable amount of GIS work is conducted. However, I use and recommend to many agricultural clients QGIS due to being more affordable when they are uncertain if they are ready to make a financial investment. For teaching I use Esri ArcGIS and GeoDa in addition to R.

Q: If money wasn’t an issue, what would be your dream Spatial/Big Data project?

A: Oddly enough I think I already am doing those things. I am fortunate to be working on several aspects of different projects that I hope will make a positive difference for agriculturalists. Many of the tools that I am building or have built are very data-hungry, requiring much more data than has been available. I am anxious for these tools to become useful when the ag data industry matures.

Q: You tend to speak at a number of Precision Agriculture conferences, you have spoken at a regional GIS group, have you ever considered speaking at one of the national conferences?

A: I’m always open to speaking at national or international conferences.

Q: Lastly, explain to our audience of geohipsters what is so hip about Precision Ag, Big Data and Spatial.

A: Agricultural big data has evolved out of precision ag technology, and in its fully functional form is likely to be one of the largest global networks of data collection, processing, archiving, and automated recommendation systems the world has ever known.