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September 26, 2017 Precision Ag and Technology Articles

2 Since farm data are non‐rival and digital (i.e. copies can be made at near‐zero cost), we must accept that farm data are “non‐excludable” once other users have access. Given that copies of data are considered identical to the original, the ability to exclude others from accessing data no longer exists once another entity has a copy (i.e. access). Most privately held goods typically are excludable; however the lack of excludability does not necessarily mean that farm data are a public good. Rather than consider data ownership, a better question regarding farm data may be ‘can I keep others from accessing data that only I control?’. The answer is ‘yes, but only until the data are shared with a third party or aggregated into community’. Privately held farm data may be considered excludable if and only if it is in the possession of the party that generated it (see our recent paper for discussion). Once the farm data has been shared with another party or aggregated into a big data community, excludability has essentially been eliminated. Therein lies the problem. To make the most use of farm data it typically needs to be aggregated into a community for big data analysis to provide useful insights (see forthcoming article on value of data isolated to the farm versus aggregated into community). The notion of 1) excluding others from accessing data and 2) making the most valuable use of data are at competing ends. I’m not going to discount the importance of excluding others from accessing data; the individual must make a benefit‐cost analysis decision at each decision step. First, if a landowner asks for a copy of data generated from their farmland then it is likely most farmers would opt to maintain a positive working relationship. Second, data remaining in ‘data tombs’ or ‘data silos’ isolated to an individual farm has essentially zero value until that data are converted to information suitable for making farm management decisions. When properly analyzed, farm data shared in a community has much greater value to not only the community but the individual farms (I’ll address this in a future blog post). So, when we discuss who ‘owns’ or who can ‘own’ farm data, we may be using that word as if it means something other than what we think it means. References Griffin, T.W., T.B. Mark, S. Ferrell, T. Janzen, G. Ibendahl, J.D. Bennett, J.L. Maurer, and A. Shanoyan. 2016. Big Data Considerations for Rural Property Professionals. Journal of American Society of Farm Managers and Rural Appraisers. pp 167‐180 http://www.asfmra.org/wp‐ content/uploads/2016/06/441‐Griffin.pdf …

Can Agricultural or Farm Data Be Considered Big Data?

September 25, 2017 Precision Ag and Technology Articles

1 Can Agricultural or Farm Data Be Considered Big Data? Terry Griffin (twgriffin@ksu.edu) Kansas State University Department of Agricultural Economics ‐ September 2017 I am often asked if farm data are truly ‘big data’. It’s a valid question, especially since the nature of agriculture does not fit neatly into existing categories for most any discussion. So, does farm data qualify as ‘big data’? In short, yes; here’s why. The consensus among agricultural data experts is that farm data are ‘big’ once data from individual farms are pooled together with farm data from other farms into a community. Even the largest American farms would still be considered ‘small data’ when in isolation (see forthcoming blog post for more detail). Applying the criteria suggested by Victor Mayer‐Schönberger and Kenneth Cukier to the farm data community, the three V’s of big data (Volume, Velocity, Variety, and Veracity) hold (OK, maybe four V’s; see Gartner for more details). Here, I build upon these discussions regarding how the Vs of big data apply to agriculture. Volume: a lot of farm data exists on office computers, cloud services, and more being collected daily. It has been estimated that 10 MB of data per acre may be collected from farm equipment for planting, spraying, and harvesting; and when only looking at the 90 million acres of corn in the United States 900 TB (that is terabytes; 1 TB is 1,000 GB) could be collected in just one season. Dr. Scott Shearer puts the volume of farm data into perspective by reporting the annual storage requirement not as TB per season but as 0.5 KB per corn plant. In 2017, the Ohio State University Precision Agriculture team are conducting the “Terra Byte” project; recording how much data has been collected. When considering multiple years of farm data, especially for all commodity crops not just corn, the community dataset is much too big to move around via broadband connection speeds or even with external hard drives; therefore the analytics must be moved to the data. In this agricultural example, the first V (Volume) has been met. Velocity: meaning changing quickly. Looking only at as‐planted data collected from planters via telematics, 5.5 MB of data on location, speed, cultivar, and other geo‐spatial and meta‐data are collected for each acre. During planting seasons, the size of the aggregated farm data community becomes much larger every day. Although agricultural operations are seasonal, it should be recognized that even for commodity crops like corn, cotton, soybean, rice, and wheat that peak planting times differ for each such that as‐planted data are collected during several months of the year rather than all at once. In addition to planting, other field operations such as tillage, spray applications, and harvest occur at other times during the season; each operation adding to the community of data. Again, this agricultural example meets the 2nd V (Velocity) of big data. Variety: the spectrum of data sources. In the previous examples of file sizes, each of those in‐field operations may be accomplished with different brands of equipment and thus different proprietary file formats. In addition, third party aftermarket telematics may have been added. In addition to the near‐ automated data collection and transfer from machine‐based sensors and telematics, farm data include Kansas State University Department Of Agricultural Economics Extension Publication …

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August 15, 2019 Grain Marketing Presentations

Extension Ag Economist Email: dobrien@ksu.edu Blog: www.ksugrains.wordpress.com KSUGrains …

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August 1, 2019 Breakout Sessions

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November 7, 2019

paper: http://jaysonlusk.com/blog/2019/8/8/consumer-preferences-for-labgrown-and-plant-based-meat …

April 13, 2020 Ag Law Issues

response. Last week, I devoted a blog article to the small business …

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