The Precision Farming Primer
Smart Farmers and Dumb Maps — identifies differences between descriptive and
GIS Quality Data — describes a field's geographic patterns as an organized set
Four Basic Steps in Precision Farming — describes the basic steps of the precision
Putting Yield in Its Place — discusses differences in mapping yield as discrete points
or continuous surfaces
Putting Yield in Its Proper Place — describes the major factors affecting geo-referencing of "raw" yield data
Connecting the Dots — discusses the implications of point, swath, and grid formats for mapping yield data
Is Precision Farming Accurate? — investigates differences between precision and accuracy
Resolving Yield Mapping Issues — describes the "accuracy advantage" of grid formatted data
(Back to the Table of Contents)
Smart Farmers and Dumb Maps (return to top of Topic 1)
We have been mapping for more than 8,000 years and farming even longer. So what makes the merger of maps and fields in precision farming so radically new and confusing? The precision farming revolution involves a new perspective of mapped data from one of description of the precise placement of physical features to one of prescription of appropriate actions based on spatial analysis. This transition involves extending the familiar paper map composed of inked lines and shadings to the somewhat intimidating digital map world of organized sets of numbers. In terms of mapped data it involves recognition of the differences between cartographic and geographic information systems (GIS) data quality. Cartographic quality uses the stored numbers simply as surrogates for pen colors of traditional map features. Computer-assisted design (CAD) drawings and graphical representations of maps use this approach and often are referred to as "dumb maps" because they merely automate descriptive mapping. The objective of these systems is drafting traditional map products, not map analysis for management action. Cartographic quality numbers also can serve as an index to feature attributes stored in a database enabling rapid access to database information and map displays of the data. The GIS quality numbers, on the other hand, respect all of the "map-ematical" rights, privileges and responsibilities needed for spatial statistics, analysis and modeling required for the prescriptive maps in precision farming.
GIS Quality Data (return to top of Topic 1)
Cartographic quality data simply use the stored numbers as surrogates for pen colors in drafting traditional map features and "geo-query" of the data. The numbers in GIS quality data, on the other hand, respect all of the map-ematical rights, privileges and responsibilities you fondly recall from high school math. At the heart of precision farming is the recognition that maps are organized sets of numbers poised for analysis.
Consider the derivation of a map of soil phosphorous levels from a set of soil samples depicted in figure 1.1. Inset (a) shows the level of phosphorous (P) for 16 samples taken from a field. The normalized values range from almost 0 to 87, with the higher levels depicted as the taller points. Note the lowest levels occur on the left side of the field. A "first-order" estimate of the spatial distribution of the data in inset (b) assigns the P-level of the closest sample for each location throughout the field (discrete sample points to continuous map surface). In techy terms the blocks are called Thiessen polygons with sharp boundaries formed by the perpendicular bisectors among neighboring samples.
Fig. 1.1. Deriving geographic distributions.
Inset (c) in the figure was derived by moving a smoothing window around the nearest-neighbor surface. When the window is centered near one of the sharp boundaries, it has a mixture of big and small values, resulting in an average somewhere in between—a whack off the top and a fill-in at the bottom. The remaining insets (d) through (f) continue to smooth the phosphorous surface. Eventually the surface will be completely eroded to a flat plane in inset (f) that approximates the average of the whole field—the ultimate in spatial aggregation.
That brings us to the crux of precision farming and variable rate technology (VRT). In the past you set the fertilizer rate for the average level of P in the field and let ‘er rip—everywhere the same based on the field average. But a lot of information locked in the field samples was thrown away. In precision farming/VRT the onboard computer gets a reading from the GPS unit, positions itself on the phosphorous surface, checks the P value for that part of the field, then sets the rate so just the right amount is applied. As the tractor moves through the field, the rate is continuously varied based on the spatial calculations. How well the system works has a great deal to do with the data quality and the spatial stat/math used in the.
Four Basic Steps in Precision Farming (return to top of Topic 1)
In the introduction we noted three basic technologies driving precision farming:
- global positioning systems (GPS) for positioning,
- geographic information systems (GIS) for analyses, and
- intelligent devices and implements (IDI)¾ such as yield monitors and variable-rate fertilizer rigs¾ for "on-the-fly" data collection and variable-rate control of field inputs.
Now that the basic elements are in place four basic processing steps in precision farming tie it all together:
- continuous data logging
- point sampling
- mapped data analysis
- spatial modeling
The data logging and point sampling steps provide the base maps of yield and field conditions, such as soil properties and nutrients. The mapped data analysis step uses the computer to establish the relationships between the output (yield map) and the inputs (soil properties and nutrient maps). This step is analogous to the ag researcher’s derivation of the familiar crop production functions published in texts and reports. The main difference is that the analysis generates a production curve for your own backyard—not for a research field 70 miles away on a different soil and using different varieties. The spatial modeling step uses your tailored "backyard" relationships to determine appropriate management actions and, as Fran Pierce of Michigan State puts it, the "if <condition> then <action>" guidance a farmer needs. The difference from traditional research and guidance is that precision farming’s recommendations respond to the unique conditions and variability within your field and is reported in the form of a map. Admittedly, this process might seem a bit overwhelming at first¾ and more appropriate for a "techno-farmer"¾ but it could be heralding the next revolution in agriculture.
Putting Yield in Its Place (return to top of Topic 1)
The yield monitoring step in the
above-mentioned process is rapidly becoming commonplace and seems to be fading
as high tech talk at the coffee shop. The new buzz is about yield mapping, which hooks a yield monitor’s output to a
computer. The GPS "stamps" these data with earth coordinates,
thereby enabling the computer to map them. There has been a fair amount
of discussion about the accuracy of the GPS stamp, but less discussion about
the process and accuracy involved in generating a yield map—the art and science
of creating "pretty maps." The data logging of yield results in
thousands of point
measurements over a field.
Some mapping systems simply place a small colored dot at each location, with
the color indicating relative yield. The assembled mass of dots forms a point map with color patterns tracking the differences in yield
throughout a field. Although point mapping generates acceptable map
images, it doesn’t provide the consistent data structure needed for map
analysis. An alternative approach uses a rectangular grid to summarize
yield for a continuous
map by averaging the measurements
falling in each grid cell (see fig. 1.2). Many of these systems use
smoothing windows that weight-average numerous points around a cell as a means
to remove measurement
noise and get a better generalization
of the data’s spatial distribution. Also, the summary for each cell
provides statistics about localized yield variation, such as a standard deviation map, which directs attention to areas with highly
variable yields—salt-and-pepper patterns of high and low yields. More
importantly, the grid forms a consistent structure for relating different maps,
such as the percentage of change in yield between two years or the correlation
among yield and soil nutrient maps (mapped data analysis).
However, there are several technical issues that, if ignored, turn the science
of yield mapping into a large part art. These issues will be addressed
throughout the book.
Note:A short paper by Tom Doerge discussing the benefits of yield monitoring and mapping is available in appendix A, part 1, "Yield Mapping Considerations, Yield Monitors Create On- and Off-Farm Profit Opportunities."
Putting Yield in Its Proper Place (return to top of Topic 1)
Precision farming presumes you have good data to analyze. Before we forge ahead, it might be prudent to investigate the issues that can turn good data bad, specifically, the precise placement of yield measurements.
Fig. 1-2. Aggregating yield into grid cells.
Your GPS may be accurate to a meter, but does that same precision hold for the yield data? Three factors beyond the GPS signal influence the precise placement of yield measurements: 1) antenna offset. 2) mass flow adjustment, and 3) synchronization of computer clocks.
Antenna offset is the easiest to deal with because it simply adjusts for the fixed distance from the harvesting point to where you anchored the antenna. Simply plug in the offset and correct for the point of harvest¼ sort of. That's the current point (inset T2 in fig. 1.3), but the material at the yield sensor at that moment was collected somewhere further back (inset T1). A simple fixed distance offset to the yield monitor won't do, as the path of the material doesn't conform to simple geography.
Fig, 1.3. Mass flow delay repositions yield measurements.
Several things contribute to the dynamics of mass flow through the harvester, and ultimately the calculation of the physical distance needed to put the yield measurement in its proper place. First, the mechanical design of the harvester follows a contorted internal path to the yield monitor. The twisted path coupled with the speed of the conveyor belts results in a processing time delay as the material moves through the system.
Under constant conditions the delay can be measured and the physical distance calculated by multiplying the standard delay by the harvester's speed. For example, a delay of 16 seconds at 4 mph repositions the current yield measurement about 70 feet behind—kind of like a trolling a lure behind a boat. However, things aren't that simple. What happens on turns? Harvesting pauses, speed changes, the line bends, the processing path clears out, then harvesting resumes; all of which makes repositioning the yield measurement a lot tougher.
Even on the straight-away, things are a lot more complex than simply trolling for data. There is a myriad of muddling factors. Consider moving down a slope in a dense part of the field¾ material accumulates so the processing delay is increased to 19 seconds and the speed is cut back to 3 mph to handle the flow. The physical distance under these conditions extends to nearly 84 feet (4.4 ft./sec. * 19 sec.).
The final issue involves synchronization of computer clocks. Like a metronome, the GPS and yield monitors are sampled at a given downbeat (e.g., each second). However, communications interference across the I/O bus in the hardware can stall things, particularly when recording conflicts with writing out data buffers or screen display updates. Like a lousy dancer, a dropped downbeat can really step on your toes.
All this seems to make an accurate yield map beyond reach and precision farming a pipe-dream. Not true; what it should do is alert you that "dumb maps" simply put a yield measurement at its initial GPS position. It should also arm you with some serious questions to look into about how "smart" your mapping software actually is. Many of these issues or questions are presented in appendix C, "Checklist for Yield Mapping Software."
Note:A short paper by Neil Havemale discussing several factors affecting spatial accuracy in yield mapping is available in appendix A, part 1, "Yield Mapping Considerations, Yield Mapping Sparks Precision Farming Success."
Connecting the Dots (return to top of Topic 1)
The sage advice of "what seems simple ain’t" certainly holds for precision farming. The precise placement of GPS yield records isn’t simply reflected in the accuracy of a GPS unit. A mass flow offset is required to place the measurement somewhere behind the instantaneous GPS location. The offset accounts for the time it takes for plant material to move from the harvest point to the yield monitor, and its calculation involves a complex interaction between mechanical design and amount of material flow. The delay multiplied by the intervening speed calculates the offset, which is used to reposition a yield record. The ability of yield mapping software to account for mass flow dynamics is the key to precise placement of yield measurements.
However, the geographic treatise of the precisely placed points determines, to a large extent, what you can do with the data. Figure 1.4 illustrates how yield options can be calculated. A point map, inset (a), is the simplest rendering of yield data. It uses a fixed symbol, such as a dot centered on a point’s x,y coordinates, which is assigned a color corresponding to an interval of yield such as dark green for 150 to 200 bushels of corn. When viewed, groups of similarly colored dots form patterns of yield variation. The display is visually effective but lacks the geographic structuring needed for data analysis. In the computer, the coordinates and yield values simply form a long list of independent numbers—your eye and brain assembles the data into patterns of neighboring points.
Fig. 1.4. Basic types of yield options.
A swath map, inset (b), provides a bit more data organization by linking points along a harvester’s path. The swath can be sequential pairs of points, but often is represented as a mathematical vector (line segment), plotted as a rectangle with a width of the harvester’s header and a length of a set number of consecutive points. The color fill of the rectangle (termed a swath element) is determined as the average of the yield points it contains. Swath elements can be plotted to align with the actual GPS coordinates forming a jagged path, or they can be "forced" to the straight lines characterizing adjoining harvester paths. The advantage of swaths over points is that the computer "sees" more of the relationships in the data—sequencing, length, alignment, and data smoothing. However, both data structures are too limited for data analysis as they are based on irregular geographic units (points and rectangles) of varying sizes and placement.
A grid map, inset (c), on the other hand, uses a regular grid to statistically summarize the data. Like the daily or weekly reports of commodity prices, averages are useful in determining trends from the volatility of moment-by-moment price fluctuations. In addition to a better representation of field trends, a regular grid provides the geographic consistency the computer needs to analyze data patterns within and among maps. Since the size and spacing of grid elements are consistent, the computer can easily locate neighboring cells for intramap analysis, such as average yield for different portions of a field. More importantly, the consistent partitioning into grid cells provides the structure for intermap analysis, such as relating a yield map to soil nutrient maps. Generally speaking, point and swath maps support graphical analysis (visualization), and grid maps support data analysis (map-ematical). Both perspectives are needed.
Is Precision Farming Accurate? (return to top of Topic 1)
What’s the difference between precision and accuracy? Is it merely semantics? Is a precise device always accurate? Or can precise measurements still produce inaccurate data?
Precision refers to the "exactness of a measurement"; accuracy refers to its "correctness." Precision is dependent upon
- measurement scale (e.g., eighth-of-an-inch tic marks of a first-grader’s
- straightedge versus thousands-of-a-foot marks on an engineer’s rule) and
- measurement procedure (e.g., sloppy versus careful alignment).
Precision is focused on the repeatability of measurements; accuracy seeks appropriate representation of a characteristic or condition.
One source of imprecision is termed random error. Sometimes measurements are more than and sometimes they’re less than without any discernible pattern. In most instances, this "noise" is easily remedied—simply take a bunch of readings and average them. However it’s impossible to repeatedly harvest and measure the same spot in a field so we assume random error is minimal in our yield monitors and choose to ignore it.
Another situation is termed biased error. Your bathroom scale might repeat time after time that you weight 201 pounds, but you know it’s always 10 pounds over so your weight is actually only 191. In precision farming you might have a precise yield monitor that repeatedly records differences within a bushel, but always 10 over. That’s easy to remedy—measure known amounts and calculate a calibration factor (subtract 10).
Things get a bit tougher if the bias changes with the level of measurements; say at least 10 more for high readings to 10 or less for low ones. Now you have to develop a calibration function whose adjustments change as the yield values change. Even more insidious is biased error that results from a complex interaction of several factors, such as the dynamic mass flow adjustments of yield discussed earlier (see "Putting Yield in Its Proper Place").
So what sort of error does a weigh-wagon calibration address? If the actual weight is 10 percent more than the yield monitor estimate (spatial integral for you techy-types) most folks simply adjust each yield record by 10 percent. Sure, the mathematics works as both the weigh-wagon and monitor values for the area are forced to be the same. But does a spatially aggregated benchmark make sense as a spatial calibration factor? Actually it doesn’t make any difference—spatially, that is. Every point simply is increased by 10 percent so the pattern (relative yield) hasn’t changed. What’s needed is research into the nature of yield map errors and effective calibration functions.
If "appropriate adjustment" of imprecise data is one way to make measurements more accurate (measurement accuracy), another way is to aggregate the data to characterize a larger unit (feature accuracy). For example, politicians do this all the time. They aggregate data on individuals into statistics about groups of people that best represent general characteristics of voters, then tailor their spiel to the nonexistent typical person representing each group. That’s sort of what a grid map does to yield records—it summarizes them into spatial groups (grid cells). As in most statistical applications, the summary tends to enhance trends by dampening individual fluctuations. It recognizes that site-specific management doesn’t operate at the individual plant level, but somewhere between that and whole-field management. What’s needed is a better understanding about how best to define the aggregation unit (grid cell) and determine the best mix of management actions (fertilizer blend, seeding rate, variety selection, etc.) for each of them. Bet you never knew you had so much in common with a politician.
Resolving Yield Mapping Issues (return to top of Topic 1)
There are two main advantages to this approach:
- It provides consistent geographic referencing among various mapped data layers.
- It smoothes out measurement noise while reporting statistics on localized yield variation.
However, there are a few of technical issues that need to be considered. First, the gridding resolution (cell size) can greatly affect the calculations. Insets (a) and (b) in figure 1.5 depict two gridding resolutions. Note that the number of points falling in the cells is significantly different between the two resolutions; hence, the average yield reported will most certainly differ. If the grid pattern is too large, some of the information in the data will be lost (averaged-over). If it is too small, undue credence might be attached to differences arising simply from measurement and positioning errors.
Fig. 1.5. Summarizing yield measurements.
Other technical issues involve the definition of the summary window and the summary procedure used. Insets (c), (d) and (e) show three different summary window designs—single-cell, inline filter and nearest-neighbors summary. The single-cell design uses only those measurements that actually fall within a cell for the summary calculations. The inline design uses the direction of travel to "filter" surrounding data. It can help in smoothing out measurement errors resulting from the "coughs and spits" due to uneven grain flows through a combine. The technique is analogous to moving averages routinely used in analyzing times series data, such as commodity prices or the stock market. Both the single and inline techniques can be used for "on-the-fly" data compression—simply keep the summary statistics and discard the pile of raw measurements.
The nearest-neighbors technique involves the post-processing of the raw data. It moves a window around the field sequentially centered on each grid cell. At each stop, it calculates a summary for the points falling within the window and assigns that value to that grid cell. In addition to window design, the summary procedure can vary—such as simple or weighted averaging. For example, a distance-weighted average is influenced more by nearby measurements than those that are farther away.
OK, where does all this leave us? It should instill a healthy skepticism when viewing a yield map because different approaches or different systems can create radically different looking "pretty maps" from the same data set.