Posted on June 13, 2017 @ 04:47:00 AM by Paul Meagher
In previous blogs (part 1, part 2, part 3), I have argued that the term yield is most useful as a measure of productivity per unit area. Some usages of the term yield are simply productivity measures without any accounting of the area involved (e.g., stock and bond yields). Here we will delve deeper into the spatial aspect of yield and talk about yield mapping. Yield maps are visual depictions of how yield varies as a function of GPS coordinates.
There is a convergence of technology in agriculture that enables on-the-fly calculation of yield as an operator is harvesting a field.
Yield mapping technology is built into some combine harvesters now so the operator can gauge or verify that a certain part of a field is yielding more than others and to compare to historical yields from that area.
The 4 ft x 8 ft garden I planted in my cold frame exhibits a similar variability in productivity per unit area with yield being quite high in most areas, but with a noticeable gap in one area where I have planted basil (at the same time as the other crops).
The power of yield mapping comes from comparing it with other maps that contain information about the presence of other variables.
A combine harvester might also contain sampling tools that record the level of nitrogen or moisture in the soil as it progresses through the
field enabling the operator to see how the yield map might be explained by the levels of nitrogen and moisture in those areas. The
yield maps might also be compared with maps produced by flyover drones doing multi-spectral imaging as a basis for measuring different
field characteristics. The point is that to increase yield we can't just measure yield itself, we also have to measure other characteristics that might explain the yield patterns and, in the case of farming, would allow us to make precise interventions to improve yield.
So the concept of yield mapping includes not just mapping the levels of productivity over an area but can also be extended to mapping associated variables that might be used to explain and improve yield (e.g., where it might be lacking in, say, nitrogen in a certain part of the field).
In a store front, we could measure yield per square foot or cubic foot of space. We could do yield mapping of each shelf in the store and
compute the relative yield derived from the different locations of the store. We might measure yield by computing the amount of income generated by a given area of shelf space. Perhaps we could optimize store front yield by co-relating the yield map to the presence of other variables that might co-vary with such yield. Yield in agriculture is also affected by ambient conditions like the weather. Similarly, yield in a store front would be affected by factors such as types and levels of traffic, socioeconomic status of the catchment area, and the competitive landscape. Something like yield mapping might be useful to do in bricks and mortar establishments.
The term yield mapping was briefly mentioned in the interesting book Push Button Agriculture: Robotics, Drones, Satellite-Guided Soil and Crop Management (2016) by K.R.Krishna. The author argues that the next level of productivity improvement in industrial agriculture is now happening but will become more pronounced as robotics, drones, and gps technology makes further inroads into farming. The level of productivity per unit area of land will increase because we have more precise control over what needs to be done to maintain or increase yields (via maps created using drones, gps, and onboard sensors) but also because robotic innovation will continue to reduce the need for repetitive work to be done by humans. A lesson from industrial agriculture is that precision and robotics are two major factors that are now being targeted to increase yields even further.