Friday, August 19, 2016

Proving Damages from Loss of Yield Monitor Data: Forensic Economics and Farm Data Valuation Part 3

This is the final part of my 3-part series on valuing farm data in the event that data were lost. Part 1 provided the background information on the farm scenario and Part 2 described how consequential damages were estimated.

Estimating Speculative Damages
Speculative damages are damages claimed by a plaintiff for losses that may occur in the future, but are highly improbable. Here, I discuss four potential speculative damages with respect to 1) foregone opportunity to participate in ‘big data’ communities, 2) the increased risk of damaging field equipment, 3) inability to negotiate with landowners, and 4) lack of information to base improved drainage structures.

Speculative damages #1: big data systems
Farm data has an inherent value in ‘big data’ systems; and the loss of data reduced the value of the farm’s database for participation these systems. It has been speculated that farm data are worth several dollars per acre-year. Even moderate sized farms have substantial value of data; or several thousand dollars in damages from inability to participate in community analytics.

Speculative damages #2: risk to field equipment
The yield monitor was used to flag large stones and foundation pieces that need to be removed from the field to prevent equipment damage. Given that the stones and foundation pieces are not going to move on their own, there is opportunity to locate these in upcoming seasons; however there is increased risk that combine heads, planters, and tillage equipment may be damaged prior to removal. Farmers paid $14.30 per acre on average for rocks to be picked out of their fields; therefore we can assume that farmers value this service at least $14.30 or higher per acre in perpetuity. By applying this per acre value to all acres of the farm gives a conservative estimate of any repair costs from equipment damages and an adequate risk adjusted value. Even on small areas of 100 acres, damages of $1,430 are reasonable ($14.30 per ac x 100 acres = $1,430). However the $14.30 per acre expense has expectation that the rocks were removed in perpetuity and therefore the value accumulates over time such that the entire $14.30 per acre was not enjoyed in the first year. Therefore the value must be discounted.

Speculative damages #3: leasing arrangements with landowners
Some landowners expect scale tickets to prove yield in crop share agreements while other landowners rely upon yield monitor data or maps for proving yield and negotiating future rental rates or improvements. For flex-cash and flexing based on yield, the yield monitor has become a standard source of yield data for calculating current rents and negotiating future rental arrangements. Irreparable harm may result in the relationship between the farmer and landowners that could impact the farm acreage structure. A 3,000 acre farm typically leases half of the total farmland; and even a loss of 500 acres forces the farm to a 2,500 acre farm in a market fiercely competing for farmland. The 2,500 acre may now be over equipped and not able to support the fixed costs of current equipment complement; therefore now at financial risk.    

Speculative damages #4: drainage improvements
Yield monitor data are used to calculate yield loss due to unimproved drainage. Without adequate data, improved drainage structures were not installed in proper locations for at least another year. Yield losses were estimated for one year by comparing yields from with and without improved drainage structures. This is especially important when negotiating with landowners in their decision to make drainage improvements.

Limitations of these analyses
It can be argued that if the farmer valued the data on the yield monitor that they would have downloaded the data prior to the combine being serviced or at the very least downloaded data periodically such as weekly or after each field or on-farm experiment were harvested. This limitation provides credence to the recommendation of downloading data frequently and to make redundant backup copies. Therefore if data from several on-farm experiments existed on the yield monitor then that implies that data were not downloaded on a periodic basis, and could be interpreted as the farmer placing relatively low value on that data. Similar arguments can be made for other types of precision agricultural data. Newer technology that wirelessly transmits data to the cloud alleviates some of this concern and could indicate the farmer placing more value on the data if they actively pay for services that securely archive the data.

One area of analysis omitted here is the value foregone from lost data for other entities besides the farmer. Seed company representatives may have been relying upon that data as part of a larger on-farm research program. Local retailers often rely upon farm-level data to populate their data systems for community analysis; and would be at some level of disadvantage especially early on in the lifecycle of their system. Landowners often expect yield monitor data as part of their indication of yields and some use to  reminisce from nostalgic purposes or use as conversation piece with their friends.

Farm data has a value to farmers (and others), although specific values have not been estimated that can globally be applied across farming operations. The value of data lies in how that data are converted to information for farm management decision making. Using yield monitor data and other spatial technologies to conduct on-farm research has been a leading example of monetizing data. In this specific case the greatest value estimated was from consequential damages while speculative damages were the most difficult to estimate a reasonable value. In the near future it is expected that the value of farm data will increase for farmers and others if that data are combined across farms into a community, i.e. big data. Estimating consequential and speculative damages from foregone data is one of the first steps in valuation of farm data.

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