After a largely dry May, it’s raining in southeastern Pennsylvania, just in time to give everyone’s newly planted fields a boost. The new hay fields my husband planted over a month ago have grown 3-4 inches in a few days. Corn that was planted late due to a cold Spring is getting knee high in some fields. In this part of the country, we don’t aim for “knee high by the 4th of July”, we aim to have corn to eat for the July 4 holiday.
Watching and hoping for rain over the past few weeks and watching the Texas floods has made me think about crop insurance, the farmer’s way of mitigating the risk of drought, flood, and other weather mishaps. Federal crop insurance in the US is controlled by the United States Department of Agriculture Risk Management Agency, which determines what crops can be insured in what parts of the country and at what rates. The agency also designates specific private companies (there are currently 19, of which Monsanto-owned Climate Corporation is one) to sell what is called Multi Peril Crop Insurance. Such crop insurance pays out if a farmer’s yield drops below the farm’s historical yield for an insurable cause, like flood or drought. There is also coverage for loss of revenue due to changes in market prices over the growing season. Last year, for example, corn prices plummeted due to an oversupply. Farmers fortunate enough to have purchased revenue protection crop insurance with the right terms and conditions were compensated.
As you might imagine, a huge amount of historical yield data, commodity pricing data, and weather data must be analyzed to design and price the multitude of crop insurance policies that are available. This makes crop insurance a classic big data problem. It is no surprise then to find insurance companies using the SAP HANA platform for actuarial analysis.
One of the most interesting of these companies is Meteo Protect, who provides weather risk management solutions to various industries, including agriculture. The distinguishing feature of their insurance products, and one that requires sophisticated predictive analytics, is the wide range of indices their policies can support. For US Federal crop insurance, the two measures that determine whether a farmer is compensated for loss are yield and revenue (determined by commodity price indices). But what if a farmer knows that the quality of his crop will be compromised if the soil temperature is above or below a specific range for a specific part of the growing season? Or if the number of “growing degree days” falls below a threshold number? It takes some serious computing power to be able to offer index-based policies based on one or more customized indices selected by a farmer or farm co-operative. This is exactly what SAP HANA enables Meteo Protect to do. They can work with a customer to create a weather index that best reflects the customer’s risk and then design a compensation model that meets the customer’s financial objectives. Voila…. Customized crop insurance.
Although we have never purchased crop insurance for the hay fields on our farm, a failure of our hay crop would mean we’d have to buy feed for our livestock, which would effectively wipe out the profit of our farm. So maybe we should think about this. Some indices I might pick apart from the obvious rainfall and temperature ones:
- Number of days the tractor that pulls the seeder and hay baler is in the shop
- Number of days the seeder and/or hay baler are in the shop
- Number of days we have to wait for parts for above equipment
- Number of times we run out of baler twine when the Farm Store is closed and it’s about to rain
- Number of times the neighbor’s herd of dairy cows breaks out and spends the night in one of the hay fields
- Number of ground hog holes in the hay fields (never a good thing for tires)
- Number of flat tires on the hay wagons
I’d be happy to provide historical data (complete with stories) around these metrics for any insurance company willing to cover us.