Precision Ag Primer

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.

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.

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.

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:

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:

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.

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.

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;

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.

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.

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.

The question is:

Can you reasonably expect the value of input cost controls and increases in yields over a two to three year period to amount to more than $6.40 per acre? The experiences of many growers suggest that you can exceed these values in normal year-to-year cropping cycles.

You need to note that:

The same issues relate to the application of other inputs. Potash, phosphate, anhydrous ammonia and several micro-nutrients are routinely applied VRT (using Variable Rate Technology). A number of successful trials of variable planting rates have also demonstrated value. The processes and basic logic are the same for any variable rate application.

Using a reasonably powerful geo-processing system, there should be no limit as to the number of events at a location that can be processed through prescription logic. Prescription logic can be very simple, as in the Lime example or, it can involve a number of different layers and conditional statements. Regardless of the complexity or simplicity of prescription logic (map algebra) the outcome is always a set of geo-referenced instructions that say: "if you are at this coordinate, apply this amount."

Summary

We have taken you from some basic definitions to the creation of a prescription file. That is actually a long trip. There are obviously many detours we could have taken but those would have dealt with details that are beyond the scope of a primer. You have the basics now. If you meet up with providers of Precision Ag services and/or products you should have a better understanding of what they are trying to sell you. Hopefully, you will be able to separate the wheat from the chaff.

To conclude, let’s hit the main points again:

Precision Ag as an integration of technologies with a focus on managing variability field-by-field.

Precision Ag can involve complex systems. But, complex systems do simple things.

Precision Ag relies on the Global Positioning System (GPS) as the key technology in mapping and field navigation. GPS collects coordinates.

Coordinates are a pair of numbers that describe a unique address in a global coordinate system.

Precision Ag data collection is about matching coordinates with events. An event could be anything from corn yield (at a location) to soil conductivity (at a location).

Geo-processing is about the construction, organization and analysis of layers of events. Each layer of events is a map.

Because the maps all share the same coordinate system you can examine the significance of a series of events at a single location. For example: Do high yields always occur in conjunction with relatively high levels of soil conductivity?

You can use geo-processing to build new layers of information from existing layers. An important outcome of this process is a variable rate prescription. A variable rate prescription is a plan of action that directly addresses within field variability- the focus of Precision Ag.