Big Data, Ag Finance, and Risk Management

I recently was reading an article on AgWeb, How the feds interest rate decision affects farmers; and the following remarks stood out to me:

“You need to plan for higher rates. Yellen said in her remarks that the expectation is that the federal funds rate will rise to 1.5% by late 2016, 2.5% in late 2017, and 3.5% in 2018, so increases are coming. You can manage those hits by improving your efficiency and productivity in your fields and in your financials, which will allow you so to provide detailed cost projections and yield estimates to your banker. “Those farmers who are dialing those numbers in will be able to negotiate a better interest rate, simply by virtue of all that information,” Barron says.”


So to me this brings up some interesting questions. How interested are lenders in knowing about farmers data management and how they are leveraging their data generated across their enterprise? Does your farm need an IoT strategy? Or will these things work their way out in the financials lenders already look at regardless?

Regardless of what lenders are after, it would make sense to me that producers would want to make the most of their data to manage productivity and efficiency in both good and bad times. Firms like FarmLink come to mind.

From a research perspective, I would have some additional questions:

1.  Is there a causal relationship between producers that leverage IoT and Big Data analytics applications and farm output/performance/productivity
2. How do we quantify the outcome-is it some measure of efficiency or some financial ratio?
3. If we find improvements in this measure-is it simply a matter of selection? Are great producers likely to be productive anyway, with or without the technology?
4. Among the best producers, is there still a marginal impact (i.e. treatment effect) for those that adopt a technology/analytics based strategy?
5. Can we segment producers based on the kinds of data collected by IoT devices on equipment, aps, financial records, GPS etc.?  (maybe this is not that much different than the TrueHarvest benchmarking done at FarmLink) and are there differentials in outcomes, farming practices, product use patterns etc. by segment
6. 
   

See also:
Big Ag Meets Big Data (Part 1 & Part 2)
Big Data- Causality and Local Expertise are Key in Agronomic Applications
Big Ag and Big Data-Marc Bellemare
Other Big Data and Agricultural related Application Posts at 
Causal Inference and Experimental Design Roundup