Big Data, IoT, Ag Finance, and Causal Inference

Over at my applied economics blog, I recently discussed an article from AgWeb; How the feds interest rate decision affects farmers. This actually got me questioning some of the ramifications of leveraging data analysis in the context of ag lending (from both a farmer and lender perspective), which ultimately lead to me thinking about some interesting questions that would be exciting to investigate:

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

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 EconometricSense
Causal Inference and Experimental Design Roundup