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A Precision Ag Primer from Delta Data Systems Our goal here is to present some basic Precision Ag concepts. We believe that Precision Ag can be simple. We also believe that if you are comfortable with some very basic concepts, you can become an effective user of Precision Ag equipment and software. Even if you are not interested in becoming a hands-on user of Precision Ag, you certainly can become an informed consumer of Precision Ag services. A Short Definition of Precision Ag Precision Ag is (1) an application of new technologies to (2) agricultural production challenges that (3) result from field variability.
The tools of precision Ag come from the integration of Global Positioning (GPS), Geo-processing (GIS), certain sensor technologies and machine control systems. These really are not new or unusual technologies. You meet up with them almost everywhere. They are not necessarily ‘agricultural technologies’. They were not developed by the ag equipment companies, the ag chemical industry or at a USDA lab. Some of the technologies in Precision Ag involve complex systems. For example, GPS really is rocket science. You only need to accept that complex systems often do very simple things. (2) Agricultural Production Challenges While growers give serious consideration to good stewardship (of the environment) and personal satisfaction (in a way of life), we believe that the ultimate production challenge is profitability. Here’s the question: At the most basic level, doesn’t profitability mean growing more and spending less doing it? Much of Precision Ag is directed toward managing inputs. This does not necessarily mean reducing inputs across the board. It means directing inputs toward areas of greatest opportunity. Precision Ag is about optimization as a contributor to profitability. (3) Field Variability Most growers believe that variations in soil type, soil fertility, topography and numerous other factors affect the production potential of different areas within a single field. Precision Ag is simply proposing that you manage your fields from a standpoint that you have always accepted. You have probably heard the old saw: "If you can measure it, you can manage it." A lot of Precision Ag is about the mapping and measurement of "within field variability" in order to make sound "within field production decisions". Now that we have a basic definition, let’s look at some of the key concepts behind this "integration of technologies". GPS and Coordinates Precision Ag depends on a very old science and art- navigation. The time line for navigation finding its way to the farm is epic: About two thousand years ago one of Cleopatra’s relatives devised a global coordinate system of latitudes and longitudes. Less than three hundred years ago an Englishman built a clock that allowed us to navigate in this system with reasonable accuracy (+/- half a mile). At the tail end of the 20th century, your tax dollars completed the constellation of the Global Positioning System. This latest step in the evolution of navigation enabled anyone with the price of a GPS receiver and batteries to outperform the greatest navigator who ever lived. So, 2000 years of discovery and innovation have converged to enable a spinner truck to apply differing rates of fertilizer on selected areas of your field. You can make a very strong case that GPS made Precision Ag possible. The rocket science behind GPS combines to do something very simple. A GPS receiver supplies two numbers, a latitude and a longitude that define your current position in a global coordinate system. Latitude defines your position north or south of the equator. Longitude defines your position east or west of a line that runs from pole to pole through Greenwich, England (the prime meridian). GPS also supplies very accurate time. We won’t focus on time. However, you can imagine that associating a time with a position is important in a number of calculations and processes. A coordinate pair is a unique geographic address. Understanding coordinates and the significance of "geographic addressing" is key to building your knowledge of Precision Ag. Everything in Precision Ag comes back to coordinates. Is GPS Perfect? No. The fact is that GPS positions have to be corrected. The term you will hear is differential correction and the result of differential correction is a D (ifferentially corrected) GPS position. You must use DGPS for Precision Ag. This means that a $150 GPS receiver from a sporting goods store will not work for you. Even after differential correction, absolute accuracy of a DGPS position can range from +/- 16 to +/- 3 feet. To understand absolute accuracy, let’s assume that a survey team came to your farm and, sparing no expense in time or money, determined the exact (absolute) location of a corner of one of your fields. Then, they put a permanent survey monument on the location. Let’s say that the absolutely accurate survey coordinates were 34 deg 15 min 00.00 sec North and 89 deg 45 min 00.00 sec West. Now you put your DGPS antenna directly on the center of the survey monument and watch the GPS receiver display. You will see the seconds of latitude and longitude change by hundredths or more every clock second or so. One tenth of a second of latitude is approximately 10 feet. As you move along in your combine, on your ATV or on foot, the absolute accuracy of your position would be affected by the same "wiggle" that you noted as you stood on the monument.
What this means is that you will have to accept a certain amount of "positional error" in the mapping that you do with Precision Ag. The good news is that the error is consistently small. Combining GPS and a computer If you had a GPS in your hand and you walked the boundary of your field, you would notice that the display of your position (as a latitude and longitude) would change continuously as you moved. You could write down selected positions as you walked the boundary and later, you could try to plot those positions on a map. Of course this is not practical. First, you could not write down enough points fast enough to adequately define the shape of your line of travel. Secondly, unless you had in-depth knowledge of the mathematics of coordinate transformations, you would have great difficulty plotting the few coordinates you copied down on a map sheet. If you connect a GPS receiver to a computer, these problems get solved. A GPS receiver can send out coordinates in a standard message format. This message format can be interpreted by software residing on a computer. By the way, a computer can be any device with a processor and storage capacity. We are not necessarily talking about what may be on your desk. Computers can fit in the palm of your hand. We are talking about a theoretical setup like this.
 (2) -93.2860530610, 33.7114992913 (3) -93.2859536340, 33.7109064712 (4) -93.2891366336, 33.7118574122 (5) -93.2861067250, 33.7117156416 Each pair is a location on earth. The fact that the longitude has a minus sign means that it is West of the prime meridian. The latitude, being without a minus sign, means that it is north of the equator. You may be familiar with longitudes and latitudes being expressed in degrees, minutes and seconds, as 93 degrees 17 minutes 9.9 seconds West and 33 minutes 42 minutes 42.1 seconds North. The numbers in the list above are longitudes and latitudes given in decimal degrees. Converting to decimal degrees simply makes the arithmetic easier. Notice that the first pair in the sequence (1) and the last pair in the sequence (5) have the same values. These coordinates are describing a closed feature, an area in which the starting point and the ending point are the same and there is at least one additional point.
These coordinates would describe a line: (1) -93.2861067250, 33.7117156416 (2) -93.2860530610, 33.7114992913 (3) -93.2859536340, 33.7109064712 (4) -93.2891366336, 33.7118574122 The starting pair (1) and the ending pair (4) do not have the same values. You could have as many coordinates as computer storage would hold. So long as the first and last coordinate did not have the same value you would have an open (line) as opposed to a closed (area) feature.
These coordinates would describe a point: (1) -93.2861067250, 33.7117156416 A single coordinate pair is a single point. There is only one location on earth that has this "address". You can see that with these three "models" you could describe many things on your farm- the boundary of a field, the run of a drainage line, the location of a soil sample site, etc. Combining GPS, a computer and sensors A GPS receiver connected to a computer is only capable of collecting and plotting GPS coordinates. Things get much more interesting if you collect other information as you collect coordinates. Here is a theoretical integration of GPS, a computer and a yield sensor. Mount this on your combine and you can simultaneously collect latitude, longitude and grain flow. The technologies and engineering are complex but the results are easily understood. Remember, in Precision Ag we are talking about complex systems doing simple things.
This sensor system continuously records the flow of grain. Software on the computer merges the recorded flows with GPS messages to create an expanded coordinate data file. There is no standard format for an expanded coordinate data file. In the end however, the files all contain at least, a latitude, a longitude and a flow. Again, an expanded coordinate file is simply a sequence of numbers:
-93.2861067250, 33.7117156416, 125 -93.2860530610, 33.7114992913, 132 -93.2859536340, 33.7109064712, 121 -93.2891366336, 33.7118574122, 133
As you now know, the first two numbers give us position as a unique global address. The third number is an event occurring at the global address. In this case, the event is yield (125 bu/ac, 132 bu/ac, 121 bu/ac and 133 bu/ac). The expanded coordinate data file could be large. Imagine that you harvest continuously for 10 hours. A one point per second, the file would contain 36,000 geo-referenced events. (1 hour = 3600 seconds, 10 hours = 36,000 seconds). Of course this is nothing for a computer to handle. An event could be anything. The sensor describes the event being mapped. In precision Ag we can sense the electrical conductivity of the soil, the moisture of the grain, plant stand densities, soil compaction, the energy reflected or emitted from plant leaves or the soil. The fact is that there are new sensors being developed all the time. In almost every case, the result of integrating GPS, a computer and a sensor is an expanded coordinate data file. Sometimes, a sensor is only indirectly connected to GPS and a computer. A familiar and important example is soil sampling. In this case, the sensors are in the lab. As soil samples are analyzed the results are integrated with the coordinate locations from the field. A typical expanded coordinate data file from a soil lab looks like this: -93.2861067250, 33.7117156416, 6.5, 3.2, 220, 100, 8.1 -93.2860530610, 33.7114992913, 6.1, 2.9, 190, 80, 7.5 -93.2859536340, 33.7109064712, 5.9, 2.7, 200, 95, 9.0 -93.2891366336, 33.7118574122, 6.5, 2.4, 140, 60, 5.5 Longitude and latitude provide position. The remaining values are "sensed" in the lab. They are, respectively, pH, Organic Matter, K2O, P2O5 and CEC. Each chemical property is an event. It should be clear now that a latitude and longitude define a unique address in a global coordinate system. When sensor information is added to a unique address we have the record of an event at a location. By visiting the same address with a number of sensors we can establish a series of events at a single location. Mapping these events and discovering the significance of a combination of events at single locations is the focus of Geo-processing. Mapping Events at Locations: Geo-processing Integrating DGPS and sensors makes geo-referenced data collection possible. The result of these data collection systems is an expanded coordinate data file that contains coordinates (geographic locations) and one or more events. Again, an event could be a measure of soil electrical conductivity, crop yield, K2O level, pH level, elevation, plant chlorophyll, seed population, etc. A number of different events could be associated with a single coordinate. Let’s look at how we could take a collection of pH values and build a map. First let’s accept that if you took a single sample from your field or if you mixed several samples to create an average level, a ‘pH map’ of your field could only look like this:
The only view backed up by evidence is that the entire field has the same pH level. You probably do not believe that this is the case. The solution is to take more samples from different areas of the field.
In this case we divide the field into blocs and we take a soil sample in the middle of each one. Normally, the bloc dimension is one or two acres. You may have heard this referred to as grid sampling. It has been a common practice in Precision Ag. Because we use DGPS to mark the location of each sample, we end up with an expanded coordinate data file containing latitude, longitude and pH. We have 140 entries in the expanded coordinate data file. There are 140 latitudes and longitudes and 140 associated pH readings. What we want is a continuous picture of pH over the entire field. Here is an expanded view of the problem:
We took samples #27, #34 and #35. You see that #35 has a pH of 5.4, #34 has a pH of 5.2 and #27 has a pH of 6.0. No samples were taken in areas 1-6. What are the pH levels in these areas? How do we determine them? The answer is estimation and it involves some math (what computers were made for). There are many approaches to estimation. Most methods say that an estimate can be made by taking adjacent and surrounding values into consideration. Look at area 4. Is a reasonable estimate for a pH level in this area 5.4 (the value of sample #35) or 5.2 (the value of sample #34) or 6.0 (the value of sample #27)? There is no absolutely correct answer. There are only more or less intelligent estimates. We often use a method of estimation that says since the center of area 4 is, by distance, closest to sample #27, the pH level in this area is probably closer to 6.0 than to 5.2 or 5.4. However, the method also says that because samples #34 and #35 are in reasonable proximity to area 4, these samples contribute to the estimate as well. So an intelligent estimate for the pH level of area 4 is probably going to be 5.7 or 5.8 and not 6.0. We do not want to get buried in a discussion of spatial estimators. We want to make the point that pH levels in your field probably look like this:
Where low pH levels are reds and oranges and higher pH levels are yellows to greens;
instead of this:
Where all values are the same based on a single test or an average of a small number of tests.
This process of spatial estimation can be applied to all expanded coordinate data files. Maps of yield data, conductivity, elevation, soil chemistry, etc. are all built this way to produce continuous surfaces of specific events. (1)
(1) Yield, (2) Conductivity, (3) Elevation, (4) P2O5 Levels Before we go any further, let’s look at the continuous surface of pH and take note of some important ideas.
First, you are looking at a map. Every position on this surface has a unique global address and a pH value associated with each address. You may have heard the term, geo-referenced data. This map of pH variability is what is meant by geo-referenced data. It means that if you know the address of any location (and you do now) you can use GPS to navigate to that location. Next, you are looking at a map of within field variability and this is the focus of Precision Ag. The fact that pH values change from region to region within the field means that pH levels are variable. You can see that an application of lime to this field would probably not be made using a single rate. Lastly, this map is in electronic (digital) form. It and any maps derived from it can be transferred from computer to computer. Specifically, it can be transferred from the computer on your desk to a computer on a spreader truck. Making New Information from a Map: Variable Rate Prescriptions Up to this point we have implied that computer software plays a role in Precision Ag. In fact, geo-processing software is an essential component of the integration of new technologies that is Precision Ag. We just finished discussing the application of geo-processing in creating digital maps from field-collected data and we hinted that other maps could be derived from these maps. The derivation of new information is important. This is how you can turn observations into instructions. A theoretical design of a variable rate application system looks like this:
Building variable rate instructions involves a geo-processing tool called map algebra. If the word ‘algebra’ immediately depresses you, don’t let it. We are really talking about basic logic that you use all the time. For example, you know this:
You think like this:
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