I am currently in the middle of data collection, digging through Crunchbase finding Ag Data and Precision Ag companies. I have just about wrapped up California companies, finding and coding for 24 different companies. However, three of these companies were no longer active or difficult to find data for and four companies focused on something other than industrial agriculture such as aquaculture or livestock. This leaves 17 fully-coded and functional companies. I was hoping for a few more, but I think this will still be enough for some interesting analysis. The following are interesting trends I noticed in data collection:
- Unexpectedly, there are a lot of companies that discuss empowering farmers and reducing inequalities. This goes against many concerns in the research I have done about big data technologies increasing inequalities, in agriculture and elsewhere. Some of these comments discuss national inequalities in Farmers (Farmers Business Network) and some discus international inequalities between farmers (Slantrange, Harvesting Inc). I will have to think more about the implications of the comments presented to see if they really address equality concerns.
- With a couple of exceptions, all of the companies I have looked at so far have been founded in the past 5 years, with 2014 being the most common year. This surprised me with just how recent a trend this is.
- Another interesting trend is how companies advertise a reduction in labor needs as a result of these technologies. Some write about the labor costs one saves: “Save up to 4 hours/month by eliminating manual data entry for utility bills or manually reading water usage” (OnFarm). Others talk about how this makes your farm “scalable” without adding additional labor: “Scale your labor force without adding boots on the ground” (Mavrx). Obviously, none of these companies go into the impacts on the actual workers, many with reduced hours or out of a job.
- In general, it seems that bigger companies focus more on sustainable development values while smaller companies focus more on their technology. It would be interesting to test this during data analysis.
I hope to explore all of the above during data analysis to see if these are quantifiable trends and could lead to interesting conclusions. Next, I will do the same procedure for all Ag Data and Precision Ag companies I can find in India. Then, I will choose a company from each location for a deeper narrative analysis. I am trying to finish this in the next several days and then be able to run statistical analysis.