Chapter 1 Introduction.
differential GPS (DGPS)
digital soil maps
Geographic Information Systems (GIS)
Global Positioning System (GPS)
variable rate application
yield monitor system
This chapter will cover the basic principles of precision agriculture. Specific sections in the chapter will discuss the definition of precision agriculture, the tools of precision agriculture (i.e., GPS, GIS, remote sensing, and computers), and the processes of precision agriculture (i.e., data collection, data analysis, and information implementation).
Definition of Precision Agriculture
People have defined precision agriculture in many different ways. When a group of people, whether students or farmers, are asked to develop their own definition of precision agriculture, there are usually as many definitions as there are people. Instead of suggesting yet another definition, there is merit in reviewing similarities within and differences between the current definitions. In reviewing similarities, we can take note of the commonalties to get an accurate picture of precision agriculture.
Precision agriculture has been called site-specific farming or farming by the foot. These designations represent the same thing: the ability to collect data and make decisions on an area smaller than an entire field. In the past, decisions were based on data collected and averaged for an entire field. Yield was calculated on an average bushel or tons of product per acre for the entire field. Nutrients in the soil were estimated by collecting soil samples, combining them, and calculating an average nutrient level for the entire field. Even though these values were calculated on a per acre basis, it was assumed that every acre in the field had the same value.
The ability to mark off or identify a small area of a field (a subfield) for data collection and analysis allows us to be more precise and accurate with our decision making. These subfields may be a grid of squares that arbitrarily divide the field, or they may be a series of homogenous areas that have been determined to be significantly different from the surrounding areas. By collecting and analyzing data from that subfield area, the user makes decisions based on information from just that area, decisions that might not be appropriate for other areas of the field. (See Fig. 1-1.)
Spatial variability is the driving force of precision agriculture. Without variability, there is no need to divide a field into subfield areas. The practice of applying the same amount of fertilizer, using the same variety of seed, or other similar management decisions is made much easier if it applies to the entire field.
Variability occurs throughout the natural environment. Because of the way soils developed under prairie grass, forests, glaciers, or floods, there is going to be variability across an agricultural crop field. By using subfield areas, the producer can identify this variability and implement management practices specific to each subfield. It is because of variability that management decisions applied to one subfield area may not apply to another subfield area.
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Soil type defines much of the variability within an agricultural field. Each soil type represents a homogenous area of unique characteristics such as slope, permeability, drainage, depth of topsoil, and many other attributes. Many of these attributes, such as sand content, permeability, and drainage, are of major importance in cropping decisions. Management practices and other physical or environmental effects will also impact the level of nutrients, compaction, or acidity of the soil. This then adds to the variability of the field. Being able to record and map this variability allows the producer to analyze it and make use of it in decision making. (See Fig. 1-2.)
Most definitions of precision agriculture include a statement about efficiency, either in regards to management, decision making, or crop inputs. Management efficiency refers to the ability of the producer to use precision agriculture technology for fundamental management of the operation. Storing financial or production records on a spatial basis, manure management plans, and inventory are all examples of fundamental business management practices that precision agriculture technology can make more efficient. Many precision agriculture software packages now include these management functions.
Decision-making efficiency refers to the ability to use those financial and production records and the farmer's knowledge base to make an objective decision. Many decisions start as a question: Is no-till more cost effective than conventional tillage? Which crop provides the highest return on a specific soil type? Where is soil compaction a concern for yield and profitability? Which plants/trees need to be replaced because of productivity? The common thread within these questions is that they have a spatial component. Precision agriculture technology is very efficient at spatial-based questions.
The most common case for increased efficiency is in the variable application of fertilizer or chemicals. Broadcasting a constant amount of fertilizer is inherently inefficient. Because of the variability discussed previously, some of the areas of the field will receive inadequate amounts, some areas will receive excess amounts, and only a fraction will receive the correct amount of the product. If only 25 percent of the field is getting the right amount of product, we are not being efficient. Product is either being wasted by over application or an area produces a lower yield because of under-application of a product. Variable rate applications allow the farmer to place a specified amount of product on a particular area of a field. It is much more efficient to place the amount of an input that is needed rather than placing an average rate amount. Placing a smaller amount of an input, or even none, on those areas of the field unlikely to produce a crop response is more efficient. Placing a larger amount on those areas of the same field that have the potential for a crop response is more efficient. The goal of many producers would be to apply product so that 100 percent of the field has the right amount, although 80 percent to 90 percent would be reasonable.
Take note of the statement in the last paragraph: the farmer can place a specified amount ... Using a variable rate applicator, a farmer can decide on an amount and the system will place whatever amount the farmer wants--right or wrong. It still takes an informed decision-making process to make the final determination of what is the right amount. The use of precision agriculture tools can be used to analyze data to make this informed decision.
To many people, this is the single most important aspect of precision agriculture; in other words, technology is precision agriculture. This may be the result of a general consensus that variable rate is the prime benefit of precision agriculture, and variable rate requires technology. A closer look at some definitions of precision agriculture would show that technology is not necessarily a part of all of them. The concept that technology is not required to do precision agriculture is important because without technology within the definition of precision agriculture, the emphasis is on the processes and end results. A farmer that has a GPS receiver on his tractor to create a map and thinks he is doing precision agriculture needs to recognize that he is not there yet. Following through the entire process (data collection, data analysis, and implementation) on a subfield basis, regardless of the technology used, is what precision agriculture is about.
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Handheld computers, controllers, monitors, sensors, GPS, and communication devices are examples of technology that can be used as tools for management and decision making within a precision agriculture system. Variable rate applications can also make use of this technology to vary the amount of crop input on a field.
Some farmers will argue that they have been doing precision agriculture for years by manually adjusting the amount of manure or fertilizer on different areas of the field. These farmers have an intimate knowledge of their land, which makes decision making an art form that the science of computer programming can't match. However, for those farmers who don't have the historical background or intimate knowledge of every acre, technology can be useful. Although technology is not absolutely necessary to do site-specific farming, it has made decision making more logical, objective, and possible on a larger scale. (See Fig. 1-3.)
Most definitions of precision agriculture provide some statement of benefit. These benefits can usually be summarized as two types--environmental and economic. Environmental benefits include the ability to reduce or strategically place inputs or make management decisions that will reduce the impact on natural resources. It might be the application of a pest control product at a lower rate on sandy soil to reduce the amount of product that leaches. It could be placing a terrace or buffer strip where it will reduce erosion. It might be the ability to turn off all chemical applications when within a specific distance of a water source. Another example of an environmental benefit of precision agriculture technology is the data collection needed to determine the water quality of a lake and the possible sources of contamination.
Economic benefits are those decisions that result in higher monetary return, more income, or operating at a lower cost. Examples may be making a decision that results in higher yield or reducing the use of lime or fertilizer that results in lower operating costs. It could be an analysis of the efficiency of a tractor for tillage and planting operations. Determining the profitability of specific plants within a grape stand and the determination of when they should be replaced results in an economic benefit. Although there may not be a direct monetary benefit, the documentation of specific crops or inputs for federal regulations is helpful. Applications of products to the soil will require a higher degree of documentation in the future. Making this process easier and more secure will be a definite benefit of precision agriculture.
Producers need to look at environmental as well as economic benefits. Agricultural decisions require a balancing act between environment impacts and economic impacts. Sustainability is the concept of balancing environment and economic needs in order to maintain our natural resources and business. Technology has the ability to incorporate economic and environmental parameters to make a balanced decision, which creates sustainability.
Tools of Precision Agriculture
The new and emerging technologies that are being used in precision agriculture will be discussed as tools, recognizing that these technologies are a means to the end. The end result of using these tools should be profitability or environmental benefits. Just as a wrench is a tool and does not generate cash flow by itself, the value of these tools for precision agriculture is determined by how they are used.
The tools of precision agriculture include the Global Positioning System (GPS), Geographic Information Systems (GIS), remote sensing, Intelligent Devices and Implements (IDI), and computers. In order to provide background information on each of these technologies, a short summary is provided for each. It should be recognized that this is not meant to be a complete reference on GPS, GIS, or remote sensing. There are many other resources that provide information on these technologies in more detail. This chapter is meant to give the reader a basic understanding of these tools and their use in spatial analysis.
The Global Positioning System (GPS) is the United States' satellite navigation system. It consists of a minimum operational capability of 24 NAVSTAR satellites, in 12-hour orbits, 10,600 miles above the earth. Each of these satellites broadcasts a unique one-way coded signal toward earth. To use the system, it is necessary to have a GPS antenna/receiver that can receive and track the coded signals from at least three satellites. Each NAVSTAR satellite is also transmitting an almanac file that gives the satellite's exact position. It is also important to note that each of these satellites has two or three atomic clocks on which time is kept to the billionth of a second. (See Fig. 1-4.)
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How Does It Work?
Using the coded signal and the almanac, the earth-based receiver can calculate the time (in billionths of a second) that it took each signal to travel from each satellite. This time is used to calculate the exact distance from each of the three satellites to the receiver. There will be only one logical point at which all three distances meet. This process is called trilateration and is used by the GPS unit's receiver to calculate the latitude and longitude position of this point. If the signal from a fourth satellite is available, the receiver can calculate an elevation in addition to the latitude and longitude.
Positional accuracy of this system is important for making decisions. Whether determining the placement of a terrace in a 500-acre field or the placement of a herbicide within a 26-inch row, GPS must be able to provide the locational capabilities to accomplish either.
Accuracy will range from 10 to 30 yards for autonomous GPS (use of GPS without any correction) to sub-inch for a differentially corrected GPS signal. For most applications in agriculture, correction is needed.
Differential correction of a location calculated by a GPS field unit (referred to as a rover) relies on a second GPS receiver, referred to as the base, which is at a location with a known latitude/longitude. Since the base receiver knows its correct location, any difference in the latitude and longitude as calculated by the GPS can be considered as error. As an example, the base GPS unit calculates its position as position A, but the actual known position is B. The difference between point A and point B is known as the error differential.
Corrections to Improve Accuracy
This error differential can be sent by radio transmitter to one or more rover GPS units, which must have a corresponding receiver. The rover uses the error differential to correct its own GPS calculated position, thus resulting in a higher accuracy for field data collection.
Government agencies have developed a network of transmitting towers that provide a free differential correction signal, the most popular being the Coast Guard (CG) NAVCEN (Navigation Center) system. To support navigation on the nation's waterways, the CG provides a network of error differential correction towers that transmit a free correction signal. The user must purchase a beacon receiver that can receive this free signal. When connected to the user's GPS unit, it will correct the position based on the error differential. (See Fig. 1-5.)
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However, these towers are not accessible for most of the country's interior that is not within 300 miles of a navigable waterway. The FAA WAAS (Federal Aviation Administration's Wide Area Augmentation System) is another source of correction. Designed for use by airplanes, it is available to agriculture and other civilian uses. WAAS is also free with nationwide coverage as long as the user has a GPS unit that is WAAS-enabled. LAAS (Local Area Augmentation System) is under development and will soon be available nationwide. Also based at airports for use by airplanes, its potential use by civilians could result in GPS positions with high accuracy.
Private companies have set up their own differential correction systems, such as Deere and Co.'s StarFire. Deere leases time on NavCom, privately held satellites. They are in geosynchronous orbit (a satellite that follows the orbit of the earth and therefore stays centered above one specific area of the earth as the earth rotates) and provide an error differential correction signal to customers who pay a subscription fee. There are different levels of accuracy available with this system, from 2 feet (referred to as S1) to 10 inches (referred to as S2) and even sub-inch with the StarFire Real-Time Kinematic (RTK).
The level of accuracy in an autonomous GPS is an important factor, depending on how the location will be used.
For hunters and the recreational user, autonomous GPS is an excellent tool. With an accuracy of 15 to 30 feet, users should be able to get back to their camp or fishing spot. This is not accurate enough for most business applications. To determine the cause of a crop failure that is 30 yards across, autonomous GPS isn't accurate enough to compare factors within that space. An event or characteristic, such as weed infestation, might be 30 yards from a low yield area, but because of the positional error it could be attributed to the low yield area. Conversely, an event within the low yield area could be ignored because error places it outside of that area. In order to make precise decisions, precision agriculture requires sub-meter accuracy.
When considering the use of GPS for parallel swathing, an even higher degree of accuracy is needed. (See Fig. 1-6.) To make sure that chemicals or fertilizers from an applicator don't overlap or leave gaps, an accuracy of 2 to 10 inches is needed. In high-value vegetable crops, such as lettuce, sub-inch accuracy is needed.
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Being able to make a decision based on sub-meter accuracy or assuring that chemicals are applied in parallel swaths based on sub-inch accuracy is a valuable use of differential correction GPS. However, are these levels of accuracy needed to apply chemicals when the size of most custom sprayers is 60 to 90 feet? Does it do any good to identify a 10-foot patch of weeds that is at an economic threshold to be sprayed, if we have a 60-foot boom?
If the only reason we are collecting data and analyzing the data is for the purpose of variable rate applications, then all maps and decisions can be based on 90 feet. However, a major premise of this book is that variable rate is not the only purpose of precision agriculture. Spatial analysis and record-keeping objectives are types of decision making that require a more precise coordinate.
Data Storage in a GPS Unit
Data storage is another important aspect to consider. What is the difference between a $150 GPS unit purchased from a discount store and a $2500 GPS unit purchased from a manufacturer? Besides the $2350 in price, most people assume that it is in the accuracy of the unit. If both units use autonomous GPS, there is only a small improvement in accuracy with the more expensive unit. The main difference in price is due to the storage capacity and the format in which the data can be stored. A $150 unit can usually store 500 waypoints denoted by various icons, such as a fish. The $2500 unit has a data dictionary that can not only store more than 500 waypoints; it can also store several hundred pieces of information about each waypoint. A data dictionary is an outline of the objects that will be mapped and the characteristics or attributes that will be collected as data for each object. This information can be transferred to a database for analysis by a computer.
Uses of a GPS
From a business perspective, autonomous recreational GPS units are a limited-use tool. Data, on the other hand, are very useful! Spatial data, data about a specific location, is even more valuable. The value of GPS to a business is in its ability to associate pieces of information about an object at a calculated position. A specific latitude/longitude may mark the location of a tree. For the hiker or deer hunter, the location of the tree may be all that is important. For the arborist whose duty it is to care for that tree, other pieces of information are also important.
Now we don't have to use marking tape to divide a field into subfield areas. Using a GPS data-logging system allows attribute data to be collected and associated with a specific location or subfield, and it is determined digitally. This ability to collect spatial data is why GPS is so important in spatial analysis.
GIS (Geographic Information Systems) is more than a computerized map. It is a software system capable of displaying a digital map associated with an underlying database. (See Fig. 1-7.) The underlying database stores attribute data about each of the objects found on the map. Each record (row) in the database represents a mapped feature; each field (column) represents an attribute of the feature. Within each of the cells is the attribute's data value for each feature. These data can be sorted, retrieved based on a query of the data, or retrieved based on a query of the map. The true value of a GIS resides in the statistics and spatial analysis of the data. Relationships can be established attributes based on their spatial location. Chapter 2 concentrates more fully on GIS.
Sensors and Controllers
Known also as Intelligent Devices and Implements (IDI), a term coined by Dr. Joseph Berry, these tools of precision agriculture allow the user to collect data on various events or physical characteristics of an object or to control some piece of equipment based on those characteristics. Several sensors and controllers are listed here as examples.
Yield Monitor System
A yield monitor system is a good example of an IDI because it has both sensors and controllers. First, it has a sensor to measure the amount of grain, cotton, sugar beets, or other product that is passing through the harvest machinery. Depending on the product, the most common sensor is a load cell. This device has been around for many years and is used in scales to measure weight. A load cell in a yield monitor system measures the weight of the produce. More details of this process are discussed below in the section on data collection.
Second, most yield monitor systems have a moisture sensor. Since the weight of the crop is dependent upon the moisture and density, there needs to be a method of measuring this. In a grain combine, a moisture sensor measures the moisture of the grain and uses that to adjust the weight of the grain.
Third is a simple controller called a header switch. This type of controller is found on many yield monitor grain combines. At the end of the row, when the head of the combine (the part that cuts the plant's stem and pulls the grain into the combine) is lifted up, the header switch automatically turns off the collection of data within the yield monitor system. Without this device, the area that the harvesting device covers while turning around would be associated with 0 yield.
Evaporative Control Weather System
Another example of a system that includes both sensors and controllers is an evaporative control weather system to sense the surrounding microclimate conditions, using that data to control the amount of water being used to irrigate the area.
Sensors are used to record rainfall, wind speed, and humidity. These sensors are usually mounted on a single pole in a strategic location where information on the amount of water on the surrounding area is critical. This information is sent either through a cable or a wireless connection to a computer. The computer, with the use of a formula, can calculate, based on the information provided by the sensors, the amount of evaporation that is occurring.
The amount of evaporation can have a large impact on vegetable crops and turf grasses. Since moisture is critical, irrigation is a major element of operations growing vegetable crops or maintaining turf grass. Therefore, another important component of an evaporative control weather system is a controller that turns the irrigation system on and off based on evaporation and moisture requirements.
Remote Sensing/Digital Imagery
Another tool of precision agriculture is remote sensing or digital imagery. Remote sensing is the use of sensors to capture reflected light wavelengths to create a digital image. Although it is a type of sensor, the value it adds to precision agriculture is such that it deserves a full discussion here.
If key concepts within the definition of precision agriculture include subfield and variability, then digital imagery applies to both of these. It can be used to identify variability of features within a field and it uses that variability to determine appropriate homogenous management of subfield groups along with other analysis techniques.
How It Works
The use of sensors that need to make contact with the object being measured is common. The load cell or moisture sensors mentioned earlier rely on the crop product coming into contact with the sensor to measure the amount of force or moisture. Remote sensing relies on reflected light wavelengths and therefore does not come into contact with the object, thus it is remote. To understand how this works, the electromagnetic spectrum and light wavelengths must be described.
The sun produces an infinite range of electromagnetic (EM) waves. These waves of light and energy range from X-ray waves to visible light waves to radio waves. The difference between these waves is the length of the wave measured from peak to peak. This wavelength impacts the effect and potential application that the wave has. Most of these electromagnetic waves don't even reach the ground, thanks to the ozone layer. Visible light waves and a few invisible light waves, such as infrared and ultraviolet, do reach the earth's surface and provide the light by which we see. However, humans have recreated some of the other waves to be of use, such as X-rays in medicine and radio waves to transmit sound. (See Fig. 1-8.)
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Four things happen when the visible and invisible light waves hit an object. (See Fig. 1-9.) They are reflected (wave is reflected off of the surface), absorbed (wave is absorbed into the material of the object), scattered (wave is bounced in random directions), or transmitted (wavelength goes through the material). All materials will reflect, absorb, scatter, and transmit wavelengths to different degrees. The reflected and absorbed waves are most useful in digital imagery for precision agriculture.
Using sensors we can record the percentage of EM waves that are reflected; this percentage is called the reflectance. Reflectance value will be unique to different types of plant material or even the condition of the plant material. This means that because a wavelength will be reflected or absorbed at different rates, reflectance can be used to identify different crops, and possibly the condition or health of that crop.
Reflectance can be measured for one specific wavelength or for a series of wavelengths. If a single wavelength is known to be different between two crops of interest, then the reflectance value for that single wavelength could be used to differentiate between them. Most likely it will take a series of wavelengths to differentiate between plant materials. By comparing the reflectance for a wide range of wavelengths, we end up with a spectral signature for a specific object. Using a spectral signature can be more descriptive than comparing the reflectance value for one wavelength. (See Fig. 1-10.)
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Humans see reflected visible wavelengths as color. When we see a green plant, that plant is actually reflecting the wavelength we see as green and is absorbing all wavelengths of the other colors. The plant uses those other light waves that are absorbed and not reflected in photosynthesis for carbohydrate production. An unhealthy plant will not absorb all of these wavelengths. As a result of these colors being reflected, the plant may appear to be brown or a yellow color. Therefore, a green plant is seen as healthy.
When that healthy green color is reflected back to our eyes, it actually is a range of wavelengths and a range of shades of green. Each species of plant will reflect these ranges of wavelengths at different rates. Our eyes are not precise enough to see many of the differences in the shades of green, thus we just see the color green. This makes it difficult for the human eye to tell the difference between plants since they all look green to us.
Even though we might not be able to see a difference between plants based on color, there are sensors that can sense the degree of reflectance of this range of wavelengths. To the computer, which analyzes not the color but the wavelength reflectance, there are large differences. A corn plant will reflect green wavelengths differently than a cotton plant. A plant that has a water deficiency will reflect wavelengths differently than one that has adequate water. A plant that has aphids will reflect differently than a plant that has no aphids. A computer can differentiate between these "greens" by looking at the spectral signature. Knowing the spectral signature of different plant tissues or plants with abnormal conditions allows us to identify specific plant types or problems.
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One specific example of an application for remote sensing is the development of a vegetative index. Certain wavelengths, in this case infrared, will bounce off of healthy tissue, i.e., the waves reflect at a higher percentage. However, stressed tissue allows some of the infrared wavelength to be absorbed. Another wavelength, red, is absorbed at a higher value in healthy or unhealthy tissue. The ratio between the two wavelengths provides a comparison between two plants to determine which one is healthy and which one is stressed. This particular ratio is the Normalized Difference Vegetative Index, also referred to as the NDVI.
Another important concept to note is that these reflectance values can be captured in a digital file. Like a picture that has millions of color dots creating the image, the sensor records millions of reflectance values to create a band. Each 10 meter by 10 meter area (or 1m by 1m, depending on the resolution of the sensor) is being scanned for its reflectance value. In this way, every 10 meters can result in a different reflectance value for each pixel based on the object that is within that 10 meter area. All of the pixels viewed together create a picture of the producer's field.
If the sensor has a GPS attached, each pixel will also have a location associated with it; in other words, it is georeferenced. This means that it is not only an image--it is a map. The NDVI as a map is available from several sources, including the National Oceanic and Atmospheric Administration meteorological satellites. Including this as a map provides additional data for analysis and interpretation.
The last important concept that must be recognized about remotely sensed digital images is that the sensor can record several reflectance values into bands. This is known as multispectral imagery; in other words, multiple bands of reflectance values. One band may be an infrared wavelength, another one a red wavelength, another one a blue wavelength, and a final one a green wavelength. Each band creates its own picture and image, but they can also be combined to create a new image. Combining the red, blue, and green bands creates a natural color image. Combining the infrared, red, and green bands creates a false color image in which plant stress can be interpreted.
The resulting map of reflectance values provides several bands of raw data. The manipulation and analysis of this raw data can lead to an interpretation that will assist in making a decision. It should be noted that there is a difference in the way a computer interprets digital images and how a human interprets them. The human eye uses the color images created from the reflectance values, while computers use the numerical reflectance values.
Processes of Precision Agriculture
Now that we have some tools, i.e., GPS, GIS, handheld computers, data, and maps, the important question for precision agriculture is how we use those tools to make a decision. There are no "one fits all" set of specific steps that can be used, but there are several general processes that are used in making a decision. However, before getting to the processes, there needs to be a discussion of goals and the association between decision making and the processes of precision agriculture.
Goal of Precision Agriculture
Minimally, the tools of precision agriculture can be used to collect data and create maps, often referred to as pretty maps. The term pretty maps has a negative connotation, indicating maps that are displayed without being applied to decision making. However, the goal of precision agriculture is to use these tools for decision making for some type of benefit. Two types of benefits, discussed earlier, are greater profitability and reduced environmental impact. Both of these benefits influence many producers' decisions, although for some there may be a trade-off, where one of these is sacrificed for the other. For objective decision making and to balance the economic and environmental benefits, a producer needs analytical and interpretive maps instead of pretty maps.
There may not be much difference in the appearance of an interpretive map and a pretty map. In fact it may be the same map! Pretty maps can be created in the same way that analytical or interpretative maps are; the difference is in what they are used for after they have been created.
Analytical maps take raw data that has been collected and analyze it for information that is more useful. For example, yield data can be mapped to show individual points of low and high yield, but further analysis called neighborhood summary and reclassification can identify patterns of low and high yields in the field.
Interpretive maps combine data and analysis from several maps to help answer a question, i.e., it provides an interpretation of spatial information. Many interpretive maps are not actually maps; they could be graphs or charts that can be used to show an interpretation of the information. Examples of each of these will be demonstrated in later chapters. Regardless of the form, an interpretive map should clearly show the solution to the question. However, they often lead to additional questions.
How do we move toward interpretive maps? Any map can be interpreted; the problem occurs when people try to go beyond the information that is in the map. For example, when creating a yield map, we can assign a color to each yield point based on the specific yield value (red for low yields, yellow for medium yields, and green for high yields is a logical color assignment). The resulting map can be used in three ways: hung on the office bulletin board (true use of a pretty map), interpreted for areas of high yield and low yield, or interpreted for lack (or excess) of nutrients and moisture. The second use is really the only correct one. Putting the map on your bulletin board may impress your friends, but it doesn't improve sustainability. The third use is a favorite activity of people viewing a yield map. However, since nutrient and soil information is not included in a yield map, this becomes guessing instead of interpretation. Yield maps, by themselves, can only be used to quantify spatial variability in yield and not for assessing the potential factors influencing yields. In other words, the most we can do with a yield map is to interpret the patterns of high and low yield.
To create a fully interpretive map means not only collecting yield data, but also collecting other information that might be related. Using techniques to compare, summarize, and analyze data creates interpretive maps that show the producer the information needed to understand relationships and make a decision.
Uses of Precision Agriculture Technology
Related to the decision-making process and the benefits of precision agriculture are how technologies can be used. Farmers make decisions every day. The majority of these decisions are spatial because they deal with the land. To make these decisions, the farmer requires data. He must keep records of crop inputs and fields. He must be able to review and analyze these data to make a decision. All of these concepts--spatial information, data, and analysis--represent precision agriculture tools. The result is a threefold use of precision agriculture technology: record keeping, spatial analysis, and variable rate application. Each has value by itself but each builds upon the previous use.
Using precision agriculture tools, records can contain a spatial component. Financial records can be attributed and mapped to a specific field. Knowing the specific cost of the fertilizer product put on the field and the cost of that product per acre is valuable. Production records can be kept on a subfield basis. Knowing the specific amount of seed, the seeding rate, the plant count, and the final yield are all valuable pieces of production information for the producer. Of course, some producers may already keep these records for individual fields, but the ability to store this data in a database, automatically collect it in the field using sensors and handheld devices, and mapping the resulting data allows the producer to connect, visualize, and analyze the financial information with the production records.
Spatial Analysis for Decision Making
Records in themselves are important, but they are most valuable when used to make a decision. The analysis capabilities of precision agriculture technology for the purpose of making decisions are the main topic of this text. It is important to note here that using GIS techniques to retrieve, manipulate, and analyze data is one part of a complete precision agriculture system.
Variable Rate Application
As previously mentioned, variable rate application is commonly thought of as the main purpose of precision agriculture. Although it is a very important part of precision agriculture, it is only one part of a complete precision agriculture system. For this reason, variable rate application is mentioned last, because it is only after maintaining accurate spatial records and analyzing the data from those records that an effective variable rate prescription can be created.
Benefits Within Uses
It should be noted that within each of these uses there are environmental and economic benefits. In record keeping, the ability to keep an accurate accounting of crop costs has economic benefits and storing data about conditions during pesticide spraying has environmental benefits. In spatial analysis, deciding on the most cost effective crop for a specific field is an economic factor while deciding where a buffer should be placed is an environmental concern. In variable rate, the application of appropriate amounts of crop input is economic and applying variable rates of fertilizers based on runoff potential is environmental.
Farming has always required management, but with tighter profit margins, the importance of making informed decisions has increased. For example, what variety of seed, chemical, or fertilizer yields the greatest amount of product in a specific subfield area? Where will the drainage tiles have the greatest effect? Is no-till more profitable? Did planting at a later date hurt the yield? These are everyday decisions that can affect a farmer's profitability and can be better made by having the correct information available and a way to interpret it. Geospatial tools, such as GPS, GIS, and remote sensing, are tools that can help farmers do this.
Process of Decision Making
Decision making is a recognized process composed of five steps: recognition of the problem, collecting information, determining solutions, selecting and engaging a solution, and evaluating the solution. This decision-making process has many different forms, but these basic steps remain the same.
The steps of a precision agriculture system duplicate this process. The process of precision agriculture can be broken down to data collection, data analysis, and information implementation. Data collection is obviously the collecting information part of decision making. The main difference is that most producers start the data-collection process without first recognizing what the problem, goal, or question is. Collecting yield data, soil type, and soil nutrient data is pretty typical, but this may not be good enough if the question the producer has is about compaction impact on yield. The recognition of a problem drives collection of data in decision making and a question should drive the data collection in a precision agriculture system.
Data analysis relates to the identification of the problem and an evaluation that will lead to the best solution. Selection of the best solution and engaging the solution is the implementation process within a precision agriculture system. Taken together, these three elements create a process for decision making in precision agriculture.
Now that the whys are understood a little better, the three processes can be discussed.
Data collection is the process of determining objects to be mapped and collecting data about those objects. Examples of data typically collected in precision agriculture are yield mapping (collecting yields on a point-by-point basis throughout a field), soil sampling (collecting soil samples and marking their location with a GPS receiver), and crop scouting (collecting evidence of abnormalities within the crop and marking their locations with GPS). Searching, finding, and downloading base maps, aerial photography, and topographical maps from the Internet are also a part of data collection. This all results in, of course, data.
Yield, as a measure of the end product of most production enterprises, is one of the most important pieces of data a producer can have. Grain growers have had access to a yield monitor for many years. Growers of other crops, such as cotton or hay, have not had access to yield monitors because of the difficulty with accurately weighing or measuring the moisture of the crop. Research continues on processes and equipment for monitoring the yield of these and other crops.
Originally, the grain yield monitor was designed to provide a real-time display of yield data and to estimate the weight of grain for a specified harvest area. With the advent of GPS, the real-time yields were able to be georeferenced and saved to a yield map. Thus we have yield mapping instead of just yield monitoring.
A grain combine with a yield mapping system estimates the amount of grain harvested from a specific area of the field, which is then attributed to a central point calculated by a differential GPS (DGPS). Sounds simple enough, but a more detailed description follows.
First, to estimate the amount of grain harvested, most yield mapping systems rely on a force plate or other method of measuring mass and a moisture sensor. The force plate is placed in the clean grain elevator of a combine, just before the grain is dumped into the combine's grain tank. The force plate registers the amount of pressure placed on it by the grain flowing past it on a second-by-second or possibly every-other-second basis. These pressure values are stored in memory.
Since computers have no insight or intelligence, this pressure value has no meaning concerning the yield. A combine must be calibrated to match the amount of grain that flowed past the force plate with the pressure values. The typical process includes harvesting a full grain tank, weighing that amount, and entering the weight into the yield mapping system. The computer, based on its human-created programming, can divide the total weight between the pressure values that were stored in memory. A larger value is assigned a larger weight value. This process is repeated three or four times, depending on the system used. By the fourth time, the yield monitor should be predicting with relative accuracy the weight of the grain as it is harvested. When the system can estimate the total amount in the grain tank, it can associate the amount of grain to the amount of pressure placed on the force plate. This allows the system to estimate the yield on a second-by-second basis.
In addition, a moisture sensor calculates the moisture of the grain. This is important because the moisture will affect the weight and density of the crop. By measuring the moisture, a dry yield can be calculated.
The area that was harvested is defined by the swath (the width of the harvester) and the length of travel (distance covered during the time the load was measured). As an example, the width of a soybean combine may be 15 feet and the distance traveled by the combine in the two seconds between force plate readings is 15 feet. This means that a 15 foot by 15 foot area is created and it will have a yield value associated with it.
Second, the DGPS unit calculates the latitude/longitude of the area that was harvested. Actually, a point in the center of the harvested area is calculated. Most yield mapping systems require a differentially corrected position.
One problem with this system is that if a location is calculated while the grain is being harvested, the system will have to wait about 12 seconds before the grain from that spot actually gets measured. This is known as lag time and is necessary to make sure that the yield amount is attributed to the proper location.
Third, all of the information discussed here is sent to and collected in a digital storage unit or module. This unit usually has a computer and firmware that joins the appropriate latitude/longitude position with the information from the force plate and the moisture sensor. The computer can then combine the information into one yield map file.
Characteristics of a soil account for much of the variability that occurs in a field. To analyze this variability and to use it in making decisions, we must collect data on as much of this variability as possible. Two sources of spatial soil data are readily available, digital soil maps and soil samples.
Digital soil maps include maps of all soil types with an underlying database containing over 40 attributes. Most of these digital maps were created from paper soil surveys and contain the same information found in the soil manuals. Creation of the digital files has been done at the state level. Many of the land-grant universities have overseen the process. Thus, many of these maps are accessible to the public on the Internet in several formats.
Soil samples provide the nutrient and pH information for a field. Samples taken and averaged result in whole field nutrient values. With GPS, soil samples can be georeferenced and variability within a field can be calculated and displayed as a map.
To take georeferenced soil samples, the producer uses a soil sample probe along with a GPS data collection system to navigate through a regular pattern of random soil sample points; these samples are then analyzed and the information is entered into the GIS.
First, points from which soil samples will be taken must be determined for the field. GPS is used to create a boundary of the field within the data collection software. Most data collection software includes the ability to divide a field into grids that serve as subfields. A variety of random or systematic methods of placing sampling points within each subfield grid is available.
Once placed, sample points are referred to as waypoints and GPS can be used to navigate to each one in turn. A soil probe is used to take a composite sample that is identified with a waypoint ID. The sample is sent into a soil test lab for analysis. The producer usually has a choice of the nutrients for which the sample is tested. Phosphorus, potassium, and pH are common nutrients for a soil test, with calcium, magnesium, and manganese as other possibilities.
The results of the analysis will be provided to the producer in a digital spreadsheet or hard copy paper form. These results will need to be added or combined with the waypoint list, matching each soil test result with its respective waypoint. When added and displayed in a GIS, the user is able to see nutrient values based on spatial locations and can include them in spatial analysis.
Besides soil, other environmental factors will affect yield. The ability to document these factors on a map is important in trying to determine the cause of yield variation during spatial analysis.
Crop scouts are hired to search for weeds, insects, and other pests that may adversely affect yield. Most scouts will go beyond the pests, taking yield checks, looking at growth and maturity of the crop, and noticing abnormal conditions such as herbicide damage. If they use GPS, all of this information becomes part of a map and can be used for analysis and variable rate. It is possible to use the location and incidence of pests to spray those subfields determined to have threshold levels of the pest. For future reference, it is also possible to compare the incidence of pests with final yield to analyze the impact of the pest.
A crop scout using GPS would follow similar procedures to those without GPS. Taking random checks throughout the field, the types, density, and amounts of insects or weeds are recorded within the GPS data collection unit. The crop scout may also use tools, such as a potentiometer, that measure soil compaction. By taking random compaction samples throughout a field, a map showing patterns of compaction can be created. VERIS and EM-38 are two instruments used to measure soil conductivity. Soil conductivity is an indication of a soil's ability to hold and make available water and nutrients and therefore is a good predictor of yield.
Moisture is highly correlated with yield. Adequate soil moisture allows planted seeds to germinate, fertilizers to be absorbed, and pollination to take place. Excess moisture can limit root development and promote disease. Scouting those areas that are excessively wet or dry is also valuable in analyzing the soil's relationship with yield.
There are additional types of data that can be valuable for analysis. Much of this data has been collected by somebody else and is available on the Internet. One example of this is a Digital Elevation Model. This creates a map of elevation that can be used to create 3-D maps, calculate a slope map, or derive an aspect map. These can be used to determine the relationship between yield and topography.
Data analysis is the process of organizing, manipulating, querying, and summarizing the data in order to get more valuable information. Raw data can be a large amount of numbers and extremely hard to understand by itself. Using tools within GIS can help to summarize and identify relationships between variables that the farmer can use to make a decision. Data analysis will be discussed in detail in later chapters.
Information implementation is the process of creating a product to implement the information. This could also be called data utilization, since it is at this point that a decision is made and put into practice. This could be an interpretive map that recommends a specific action, a prescription map for a variable rate application, or a pesticide application report. Information implementation will be discussed in detail in the following chapters.
Before studying spatial analysis, it is necessary to be aware of the significance of georeferenced agricultural data in decision making and the problems associated in understanding it. Having the ability to establish or recognize relationships between cropping factors (such as nutrient level, moisture, or compaction) and crop yield can be helpful in making a decision. The process of decision making and the process of precision agriculture are very similar. The essential concept of this chapter is that the definition of precision agriculture isn't just about tools of technology, but rather the processes by which those tools are used to make decisions.
1. Summarize the discussion concerning a definition of precision agriculture by writing a 20-word definition.
2. Would precision agriculture technology be more useful in field conditions with greater variability or lesser variability?
3. Besides yield, soil, and pests, what other data is important for management decisions?
4. What was given as the two main benefits of precision agriculture technology?
5. What were the three uses of precision agriculture technology?
6. In what ways can record keeping, as a use of precision agriculture technology, have an environmental benefit?
7. What level of accuracy is needed for a decision on seed variety?
8. Research the costs of each type of differential correction.
9. What value does remote sensing have for the producer?
10. What types of decisions do farmers make on an annual and daily basis? Which ones can be best answered using geospatial technologies?
11. How can maps help with the interpretation of data?
TERRY A. BRASE
KIRKWOOD COMMUNITY COLLEGE
DEPARTMENT OF AGRICULTURAL SCIENCE
CEDAR RAPIDS, IOWA
Figure 1-7 Map and database in a GIS. The database component is associated with the map component. Each of the polygon shapes on the map has a row in the database which includes the attribute data for each polygon. SCSSOILS SOILNAME LCC PRIMELND LEAGFMLND IA0048 KENYON 2E P P1 IA0071 COLO 2W P5 P4 IA0071 COLO 2W P5 P4 IA0048 KENYON 3E S P3 XS0361 KLINGER-MAXFIELD SICL 2W P2 P1 IA0066 DINSDALE 2E P P1 IA0066 DINSDALE 2E S P3 SCSSOILS CSR CORNYLD SOYBNYLD OATYLD WHEATYLD IA0048 87 158 48 95 0 IA0071 80 136 46 82 0 IA0071 80 136 46 82 0 IA0048 72 153 47 92 0 XS0361 80 143 48 86 0 IA0066 90 160 54 96 0 IA0066 75 155 52 93 0
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|Author:||Brase, Terry A.|
|Article Type:||Work overview|
|Date:||Jan 1, 2006|
|Next Article:||Chapter 2 Basics of a GIS.|