1

Historical Evolution and Recent
Advances in Precision FarmingDavid Mulla and Raj Khosla

CONTENTS
1.1 Introduction and Scope of Chapter............................................................................................ 1
1.2 Soil Sampling............................................................................................................................2
1.3 Geostatistics and GIS................................................................................................................3
1.4 Farming by Soil.........................................................................................................................4
1.5 Variable Rate Fertilizer.............................................................................................................5
1.6 Site-Specific Farming and Management Zones.........................................................................6
1.7 GPS............................................................................................................................................ 7
1.8 Automated Tractor Navigation and Robots...............................................................................8
1.9 Yield Mapping...........................................................................................................................8
1.10 Variable Rate Herbicide Application....................................................................................... 10
1.11 Variable Rate Irrigation........................................................................................................... 12
1.12 Remote Sensing....................................................................................................................... 13
1.13 Proximal Sensing of Soils and Crops...................................................................................... 15
1.14 Environmental Benefits of Precision Farming........................................................................20
1.15 Profitability of Precision Farming........................................................................................... 21
1.16 Adoption of Precision Farming............................................................................................... 22
1.17 Summary and Future Trends...................................................................................................24
References.........................................................................................................................................25

1.1 INTRODUCTION AND SCOPE OF CHAPTER
Precision farming is one of the top 10 innovations in modern agriculture (Crookston 2006). Precision
farming is generally defined as doing the right practice at the right location and time at the rightintensity. Since its inception in the early 1980s, precision farming has been adopted on millionsof hectares of agricultural cropland around the world. The objective of this chapter is to review
the history of precision farming and the factors that led to its widespread popularity. The specificfocus is on the following aspects of precision farming: soil sampling, geostatistics and Geographic
Information Systems (GIS), farming by soil, variable rate fertilizer, site-specific farming, management zones, Global Positioning System (GPS), yield mapping, variable rate herbicides, variable rate
irrigation, remote sensing, automatic tractor navigation and robotics, proximal sensing of soils andcrops, and profitability and adoption of precision farming. For each topic, reference to key groups ofresearchers and the breakthroughs that helped propel precision farming onward are identified. The
chapter concludes with a vision for the future of precision farming.

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Soil-Specific Farming

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1.2 SOIL SAMPLING
Spot applications of fertilizer were advocated as early as the 1920s (Linsley and Bauer 1929), butcheap fertilizer and labor combined with the increasing area of farms caused most farmers to shift
to uniform applications (Franzen and Peck 1994) until the revolution in precision farming took
place during the 1980s. Between the 1920s and 1970s, interest in the variability of soil fertility
was primarily motivated by the need to accurately determine a field average soil fertilizer recommendation (Kunkel et al. 1971; Franzen 2007). Many scientists (e.g., Sig Melsted and Ted Peckfrom the University of Illinois) recognized that variability in soil fertility was large and that sparse
soil sampling was likely to be a poor representation of average fertilizer requirements (Melsted
1967). Melsted and Peck designed an intensive grid sampling study (at spacings of 24.3 m) for the
Mansfield field near Urbana, Illinois in 1961 with a view toward designing sampling strategies thatminimized the cost of determining average soil fertility (Franzen 2007). The variability in soil test
values prompted Melsted to suggest philosophically that customized fertilizer requirements weremore efficient than a single uniform recommendation (Melsted 1967). Intensive grid sampling wascontinued in the same field at regular time intervals until about 1994 (Figure 1.1). However, there
was little practical application of this concept until several decades later.
In Washington State, Irv Dow and colleagues conducted over 70 field trials on irrigated farms
during the period from 1963–1970 in which variations in soil fertility were quantified using intensive soil sampling (Dow et al. 1973a,b). They concluded that “soil test variation is not random andmay lend itself to mapping and differential fertilization.” They opined that “fertilizing according
to information from one composite sample results in erroneous fertility programs.” Their solution

(a)

(b)

FIGURE 1.1 Interpolated soil pH values at Mansfield, IL from 1961 (a) to 1994 (b) based on intensive grid
sampling by Melsted (1967) and his colleague Peck at 24.3 m intervals. (Courtesy of David Franzen.)Downloaded by [Canadian Agriculture Library, Agriculture and Agri-Food Canada] at 12:12 27 September 2017

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was to use “precision fertilization based on precision soil sampling.” As with research conductedby Peck in Illinois, this idea languished for a decade because of the lack of technology to implement variable rate fertilization. Beginning in 1984, Mulla at Washington State University conducted
intensive sampling for soil phosphorus across the eroded hilltops of the Palouse region (as reported
in Veseth 1986). He found a good relationship between slope position and soil test phosphorus (P)values, modeled these relationships using geostatistics, and produced computerized contour andthree-dimensional (3-D) maps of the relationships. He applied geostatistics and mapping techniques
to this data as well as data collected previously by Dow from irrigated potato (Solanum tuberosum)
farms in central Washington to show that applying variable rates of fertilizer was more efficient andcost-effective than applying uniform rates (Veseth 1986; Mulla and Hammond 1988; Hammond andMulla 1989). Further, Mulla suggested that for accurate representation of spatial patterns in fertility,
soil samples should be collected on a regular grid at spacings of between 30 and 60 m (Veseth 1986).
Wollenhaupt et al. (1994) compared traditional sampling strategies with those that involved estimating composite sample grid cell averages or using individual grid point estimates on some fields
in Wisconsin and sampled at spacings of 32.3, 64.6, or 69.9 m. Grid-point sampling was the mostaccurate strategy for making variable rate P or potassium (K) fertilizer recommendations, followed
by grid cell compositing. Traditional sampling for field average soil fertility was inadequate. They
also compared different methods for interpolation of soil fertility data, including Delaunay triangulation, inverse distance weighting, and kriging. The most accurate sample spacing was 32.3 m,
similar to results found by Mulla in Washington State. Soil fertility map accuracy was significantly
degraded at sample spacings of 69.9 m. Several excellent summaries of soil sampling techniques
are provided in the literature for those who wish to learn more about this topic (Wollenhaupt et al.
1997; Mulla and McBratney 2000).

1.3 GEOSTATISTICS AND GIS
The seeds for quantifying soil spatial variability were sown by soil scientists during the 1970s and1980s. Soil physicists, led by Don Nielsen, studied the spatial variability of soil moisture and soil
hydraulic properties (Nielsen et al. 1973). The Nielsen group was interested in quantifying the
spatial variability of water and solute transport at the field scale, and promoted the use of geostatistics as a tool for doing so (Vieira et al. 1981). On the other hand, soil pedologists, led by Richard
Webster, were interested in using geostatistics to quantify the spatial variability of soil properties
that could be used to improve the precision of soil mapping (Burgess and Webster 1980). While bothgroups quantified soil spatial variability using geostatistics, neither group was particularly interested in studying practical issues such as variable rate fertilizer management. The Webster groupstudied soil sampling strategies for estimating soil properties that could be used for soil classification (McBratney et al. 1981; Webster and Burgess 1984), and later became interested in strategies for
accurate estimation of the semivariogram (Webster and Oliver 1992) and interpolation by kriging(Oliver and Webster 1990).
Influenced by Nielsen’s studies of field scale variability, during 1985 David Mulla became
interested in the relationship between soil fertility and landscape position for rainfed wheat(Triticum aestivum) farms in eastern Washington state and irrigated potato farms in central
Washington state (as reported by Veseth 1986). He used geostatistics to map soil test P levels,and showed that soil fertility varied significantly from bottom slope to hill crest positions inwheat farms, and that P fertilizer recommendations for a field could be mapped into different
zones (Table 1.1). Parallel research on the spatial variability of soil P was also conducted by
Assmus et al. (1985). Mulla’s research caught the attention of Max Hammond, a crop consultant working for CENEX Land O’Lakes and Soil Teq, and in 1986 Soil Teq from Waconia,
Minnesota hired Mulla as a consultant to write software that automatically reclassified andmapped soil fertility sampling data into fertilizer recommendation zones, which Mulla called“management zones.” This was the first combined use of geostatistics and GIS for precision

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TABLE 1.1
Early Advances in Research on Soil Sampling, Geostatistics, and GIS in Precision Farming
Research Area

Nature of Contribution

Soil sampling

Grid sampling recommendations

Geostatistics and GIS

Map interpolation and reclassification
for soil fertility data

Key References
Melsted (1967), Dow et al. (1973b), Mulla andHammond (1988), Wollenhaupt et al. (1994)
Mulla and Hammond (1988), Mulla(1989, 1991, 1993)

farming (Mulla 1988; Mulla and Hammond 1988; Hammond et al. 1988). Fertilizer recommendation maps were burned onto an E-Prom device by Soil Teq, fitted into a computer in the cabof a fertilizer spreader, and used to guide the delivery of variable rate fertilizer applications
starting in the late 1980s. The combined use of geostatistics and GIS for precision farming was
detailed in a series of papers by Mulla (1989, 1991, 1993). The use of geostatistics in precision
agriculture is extensively documented by Oliver (2010).

1.4 FARMING BY SOIL
Pierre Robert is often regarded as the father of precision farming because of his active promotionof the idea and organization of the first workshop, “Soil Specific Crop Management,” during theearly 1990s. In 1982, Robert defended his PhD dissertation under the direction of Richard Rust in
the University of Minnesota’s Department of Soil Science. The dissertation was titled “Evaluationof Some Remote Sensing Techniques for Soil and Crop Management” (Robert 1982). Robert’s
research on 15 Minnesota commercial corn (Zea mays)-soybean (Glycine max) farms showed thatcolor infrared (CIR) aerial photography could be used to detect “problems relating to drainage,erosion, germination, grass and weed control, crop stand and damage and machinery malfunction.”
Robert suggested that CIR data could be used to build a “farm information and management system
containing precisely located natural and cultural data to improve cost efficiency of future cultural
practices. Such improvement could come, for example, from adjusting seed density, herbicide control, or fertilization in response to detected field problems” (Robert 1982). In Robert’s dissertation,
he repeatedly notes that anomalous reflectance patterns from row-cropped fields were associatedwith soil series boundaries. He noted that “The important contribution of remote sensing in soil andcrop management is not as a real-time tool but as an input to a geographic soil and crop management data base system” (Robert 1982), indicating that farm management for the following cropping
season could be improved using CIR images from the previous year in association with soil seriesmaps. Robert spent the next 3 years developing a computerized soil mapping database in close
cooperation with Rust (Figure 1.2). The concept of farming by soil in Minnesota was formally introduced into scientific literature by Rust (1985), Larson and Robert (1991), and by Vetsch et al. (1993).
Carr et al. (1991) and Wibawa et al. (1993) conducted long transect trials to compare a farming bysoil fertilizer management strategy with a uniform strategy in Montana and North Dakota, respectively. Results from several fields in Montana showed that rainfed wheat grain yields differed significantly across soil types. However, there were no significant differences in economic returns forthe uniform versus the soil-based fertilizer management strategy in Montana. In North Dakota, theeconomically optimum strategy for growing rainfed barley (Hordeum vulgare) and wheat was eithera uniform nitrogen (N) fertilizer application based on composite soil samples or a variable rate N
strategy that involved compositing soil samples and yield goals by soil mapping unit. Variable rate
fertilizer applications based on grid soil sample spacings of 15.2 to 30.4 m were generally able toincrease crop yield in comparison with the uniform strategy, but they also incurred extra costs thatmade these strategies unprofitable.

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FIGURE 1.2 Pierre Robert explaining his computerized farming by soil map database (circa 1985) to Jim
Anderson at the University of Minnesota.

1.5 VARIABLE RATE FERTILIZER
The idea of a variable rate fertilizer spreader was studied by several scientists during the early to
mid-1980s, including John Hummel (Hummel 1985) working with the United States Departmentof Agriculture–Agricultural Research Service (USDA-ARS). Soil Teq of Waconia, Minnesota patented the first computer-controlled variable rate fertilizer spreading machine (Ortlip 1986; Schueller
1992). The system was apparently first tested in Minnesota using variable rate lime (Luellen 1985)
or fertilizer applications (Schmitt et al. 1986). Guidance was possible using either dead reckoning or
triangulation from radio beacons. Rates of fertilizer were varied according to digitized soil maps,
hence the initial appellation “farming by soil” (Larson and Robert 1991). After 1987, using software
written by Mulla from Washington State University, Soil Teq was able to vary rates of fertilizer
application according to digital maps (Figure 1.3) that were based on soil fertility data obtainedby grid sampling, hence the appellation “site-specific farming.” This software evolved into Soil
Geographic Information System (SGIS), which was marketed by SoilTeq and AgChem.
Gradually, the terms farming by soil and site-specific farming were replaced by variable ratetechnology (Sawyer 1994). Scientists at several U.S. universities started to investigate variable rate
fertilizer applications in the late 1980s (Reichenberger and Russnogle 1989), including Mulla at

FIGURE 1.3 An early 1990s variable rate fertilizer applicator control system.

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Soil-Specific Farming

Washington State University (as reported by Miller 1988; Mulla et al. 1992), Kachanoski at the
University of Guelph (Kachanoski et al. 1985) who had heard Mulla’s reports on spatial variabilityof soil fertility at annual meetings of the W-188 Western Regional Committee on Spatial Variability,
Searcy at Texas A&M (Borgelt et al. 1989, 1994) who used software written by Mulla, Robert at the
University of Minnesota (Robert et al. 1990), and Jacobsen and Nielsen at Montana State University(Carr et al. 1991; Wibawa et al. 1993). Variable rate fertilizer applications were perceived as being
both profitable and beneficial because they improved the efficiency of farm inputs, maintained or
improved crop yield and quality, and protected water quality.

1.6 SITE-SPECIFIC FARMING AND MANAGEMENT ZONES
The philosophy of site-specific farming was distinct from farming by soil. Farming by soil posited that fertilizer requirements varied across soil series but were homogeneous within a given
soil series. Site-specific farming posited that variability within soil series boundaries was significant, and the only way to identify fertilizer requirements was by grid or transect soil sampling
across soil series boundaries. Field investigations involving these two contrasting philosophies
were partially motivated by the need to document the profitability of variable rate fertilizer applications. To this end, Mulla and his colleagues from Cenex Land O’Lakes in Washington Stateestablished the first statistically rigorous field trials in 1987 comparing variable and uniform N
and P fertilizer applications on commercial wheat farms (Mulla et al. 1992). Without GPS, they
conducted long transect sampling at 15 m spacings across rolling landscapes, and used a manualcontroller to variably apply N and P according to a map developed by grouping and reclassification of soil fertility data from the transect sampling. While there were no statistically significant
differences in crop yield between the uniformly and variably fertilized strips, the profitability ofvariable rate fertilizer was better than uniform management due to cost savings in fertilizer andimproved protein content of wheat in the variably fertilized strips. Mulla introduced the conceptof management zones into precision farming as a result of these and other early field trials on
irrigated fields with variable rate fertilizer (Mulla 1991, 1993; Mulla et al. 1992). Managementzones were relatively homogeneous regions within a larger field that differed from one another in
fertilizer recommendations.
Site-specific farming was studied in irrigated agriculture beginning in 1986 where high-cashvalue crops such as potatoes were grown (Mulla and Hammond 1988; Hammond et al. 1988,
Hammond and Mulla 1989). The profitability of these irrigated crops justified intensive grid soil
sampling that could be used to define management zones for variable P and K fertilizer applications.
Through the use of geostatistics, it was determined that the optimum grid spacing for soil samplingwas ~61 m (Mulla and Hammond 1988; Mulla 1991).
The terms site-specific and precision farming were introduced into scientific literature by John
Schueller from the University of Florida (Schueller 1991, 1992). He helped organize an important
symposium on this topic at the 1991 Annual Meeting of the American Society of AgriculturalEngineers (ASAE) in Chicago. According to Schueller (1991), “the continuing advances in automation hardware and software technology have made possible what is variously known as spatiallyvariable, precision, prescription, or site-specific crop production.”
The use of management zones in precision farming has persisted to the present day, but theconcept and definition has shifted (Table 1.2). Mulla (1991) offered the first definition: “Eachmanagement zone should ideally represent portions of the field that are relatively similar andhomogeneous in soil fertility status so that a different uniform fertilizer recommendation canbe made for each zone.” Doerge (1999) broadened the concept by saying that managementzones “are sub-regions of a field that express a homogeneous combination of yield limiting factors for which a single crop input is appropriate.” Fraisse et al. (2001) introduced the k-meansclustering approach for delineation of management zones. Khosla et al. (2010) summarized an
extensive body of scientific literature for delineating management zones for precision farming.

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Historical Evolution and Recent Advances in Precision Farming

TABLE 1.2
Early Advances in the Concept and Testing of Farming by Soil, Variable Rate Fertilizer,
Management Zones, and Precision Farming

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Research Area

Nature of Contribution

Key References

Farming by soil
Variable rate fertilizer

Proposed conceptMachinery development andfield testingManagement zones

Proposed and tested concept

Precision farming

Proposed concept

Robert (1982), Rust (1985)
Hummel (1985), Luellen (1985), Ortlip (1986),
Schmitt et al. (1986), Borgelt et al. (1989, 1994),
Carr et al. (1991), Mulla et al. (1992)
Mulla (1991, 1993), Mulla et al. (1992),
Doerge (1999), Fraisse et al. (2001)
Schueller (1991, 1992)

They found that the most common approaches for delineating management zones were basedon soil properties such as soil texture and soil organic matter (SOM) content, followed bysensing technologies such as electrical conductivity mapping and remote sensing. Other lesscommon approaches for delineating management zones included yield mapping followed byelevation differences across a field.

1.7 GPS
Precise determination of location is essential for precision farming, especially for mapping thevariability in soil fertility or crop yield, and in locating farm machinery that can spread variable
rates of fertilizer relative to the information in these maps. When interest in precision farming firstdeveloped, there were two distinct philosophies about how to determine location within a field. The
first was to use radio-based triangulation with strategically placed beacons (Palmer 1991). The main
advantage of this approach was the ability to determine machinery position in real time at submeter
accuracy without postprocessing of data. The main disadvantages were loss of signal in rolling
topography and time and effort to establish beacon positions (Auernhammer and Muhr 1991). The
alternative was the GPS, which was established for military purposes in the late 1970s (Larsen et al.
1988; Tyler 1993). GPS accuracies of 3 m could be achieved by the military during the early 1990s
based on differential postprocessing of the P code, or 5 m accuracies based on processing of theC/A code (Tyler 1993). A GPS receiver in a fixed position was required for differential correction.
Civilian users without recourse to differential correction could only achieve accuracies of between
10–100m with GPS receivers before military selective availability (spoofing) was turned off in
2000. Differential correction became very popular with agricultural users during the 1990s as away to obtain acceptable accuracies before selective availability was turned off (Tyler et al. 1997).
Real-time differential correction became possible when the Coast Guard and several companies
such as Omnistar began to establish networks of GPS base stations whose real-time positions could
be broadcast to roving machines with an FM receiver (Tyler et al. 1997). This real-time differential
correction approach was preferred to real-time kinematic positioning, where the phase of signals
from at least four satellites were counted continuously at both the base and roving receivers. Lossof signal often occurred when the roving receiver passed behind a tree, building, or hill, causing
failure of the real-time kinematic approach.
The primary interest in GPS for precision farming was initially as a method for identifying thelocation of a combine that was collecting real-time data on spatial variability in crop grain yield(Auernhammer and Muhr 1991; Schueller and Wang 1994; Auernhammer et al. 1994). More information about yield mapping is provided in Section 1.7 below. Later, interest in GPS shifted to its use
in navigating agricultural machinery (Zhang et al. 1999) and autosteering (Keller et al. 2001). Moreinformation about these applications is described in Section 1.8 below.

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1.8 AUTOMATED TRACTOR NAVIGATION AND ROBOTS
Precise automated navigation has been one of the most intense areas of research and implementation over the last three decades. The advantages of this approach include reduced operator fatigue,
elimination of machinery overlaps and skips, and improved efficiency in fuel usage and product application. Navigation of agricultural machinery has been studied for at least 75 years sinceAndrew (1941) patented a method for automated plowing of circular fields based on the distanceto center using a cable spool system. Reid and Searcy (1987) used near-infrared computer vision
to distinguish straight rows of crop from bare soil that could be used for straight-line navigation.
Dust and vibration of the camera were the main limitations of computer vision (Reid et al. 2000;
Wilson 2000). Triangulation of agricultural machinery positions using radio beacons (Palmer 1991,
1995) or microwave signals (Searcy et al. 1989b) required good line of sight and significant setupof equipment in the field.
The most frequent approach to precise automated navigation involves GPS, first proposed byLarsen et al. (1988). O’Connor et al. (1996) pioneered the use of real-time kinematic (RTK) GPS
for automatic steering of a tractor along straight lines. This system initially involved four separate
GPS receivers mounted on a tractor as well as a nearby GPS base station. An electrohydraulic steering unit on the tractor was automatically guided by the GPS. Accuracy of the RTK GPS method
was better than a 2.5-cm standard deviation, which is better than accuracy (15–33 cm std. dev.) of
the U.S. Federal Aviation Agency’s Wide Area Augmentation System (WAAS), or the accuracy(5–12.5 cm std. dev.) of the commercial OmniStar system. A series of patents were subsequently
issued for GPS-based navigation (Greatline and Greatline 1999; Keller et al. 2001; McClure 2005;
Collins et al. 2006; McKay and Anderson 2007), leading to rapid commercialization and adoptionof autosteer technology in agriculture, including the Beeline Navigator, which first appeared inAustralia. Thuilot et al. (2002) used RTK GPS to guide a tractor along curved paths, which is moredifficult than navigating along straight lines. Their accuracy was generally better than 2m deviations from prescribed pathways. Recent attempts to improve accuracy of navigation often involve
multiple sensors, including GPS, geomagnetic direction sensors, and machine vision (Zhang et al.
1999). Japan has been a leader in adoption of all types of autosteer navigation technology, particularly on the small agricultural fields of Hokkaido Island (Torii 2000). Automated navigationof tractors can take various forms, including manual guidance with a lightbar, assisted steering, or
autosteer (Berglund and Buick 2005), depending on the monetary investment.

1.9 YIELD MAPPINGThe concept of detailed spatial mapping of crop yield was initially developed and tested by Schueller
and Bae (1987) before the availability of GPS. They used variations in engine speed as a surrogate
for grain flow to the combine, which was operated under constant throttle and cutting head height.
Position of the combine was determined using real-time microwave ranging, with between 0.5 and6 m accuracy. Variations in crop yield were aggregated to blocks of 10 × 10 m to reduce variability
in measurements. Because measuring variations in engine speed could be inaccurate due to slippageof wheels and other factors, Bae et al. (1987) and Searcy et al. (1989a) also studied yield monitorsthat were based on measurements of the volume of grain leaving the grain auger. Grain volume flowin the combine could be estimated based on the rate of revolution of a rotating paddle in the auger.
Each paddle had a fixed volume capacity for grain.
More sophisticated indirect techniques for measuring grain flow in the combine were developed by Vanischen and Baerdemaeker (1991), who used measurements of deflection in a curved
plate caused by the impact of grain flow mass. Accuracy of this approach required sensitive measurements of swath width and combine speed, as well as filtering of the raw data signal from thedeflection of the curved plate to overcome vibration and distortion effects. Most commercial yieldmonitoring devices are based on deflection of plates or fingers or on impact of grain on impact plates

9(Pierce et al. 1997). Stafford et al. (1991, 1996) evaluated two other methods for yield mapping(Figure 1.4); namely; gamma ray detectors whose signal is attenuated based on the amount of grain
flow, and a capacitative sensor whose response varies with the dielectric constant of the air/grain
mixture. Neither technique was entirely satisfactory.
Errors in measuring grain yield with yield monitors are generally less than 5% (Pierce et al.
1997). The most common errors arise from inaccurate estimates of cutting swath width or combine
travel distance; the latter is estimated from combine speed. Cutting swath width can be in error
when overlap occurs between successive passes of the combine so that the effective cutting swath
is less than the width of the cutting head. Use of centimeter-accuracy GPS systems on the combine
can significantly reduce errors in estimating travel distance and can prevent cutting head overlap on
successive passes of the combine. The primary error that persists in yield monitor data occurs when
the combine turns around at the edge of a field. Because of a time lag between the measurement ofgrain yield relative to the location where the crop was harvested, the first few data points for crop
yield after a combine turns will generally be too low.
Attempts to explain spatial variability of crop yield were initially based on the hypothesis thatpatterns in yield were determined by relationships with soil series mapping units. To test thishypothesis, Karlen et al. (1990) produced a detailed soil series map in an 8-ha field by intensive soil
coring and profile descriptions in the Coastal Plains region of South Carolina (Karlen et al. 1990).
They then studied spatial variations in corn, wheat, and sorghum (Sorghum bicolor) yield for thisfield from 1985–1988. Results showed that spatial variations in crop yield were extensive, and weresignificantly different across soil mapping units (Table 1.3). The best indicator of spatial patterns
in crop yield was depth to argillic horizon, but spatial variation within soil map units was nearly aslarge as the variation in yield among map units. Sudduth et al. (1996) studied spatial relationships
between crop yields and soil or topographic factors on two fields in central Missouri from 1993 to
1995. Using data from over 300 sampling locations, they found that advanced regression techniques
could explain from 51%–77% of the variability in crop yield. The most important factor affecting
crop yield was elevation, followed by topsoil depth, organic matter content, and soil test phosphorus.
Both Karlen et al. (1990) and Sudduth et al. (1996) noted that there was significant variation in crop
yield from one year to the next in response to variations in precipitation, but the primary focus of
their analysis was on spatial variation.
Lamb et al. (1997) were the first to quantitatively compare spatial and temporal variations in continuous corn crop yield over a 5-year period for a 1.8-ha field in Minnesota. The field was divided
into 60 grid cells of area 279 m2 each. The highest and lowest crop yields for a given year differed
between 2762 and 4519 kg/ha from one grid cell to another depending on the year, but spatial
Smoothed cluster map
235,600
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Historical Evolution and Recent Advances in Precision Farming

235,400

Cluster
no.
1
2
3
4

508,500

Easting (m)508,700

FIGURE 1.4 Clustered yield map for winter barley based on 3 years of data (1993–1995) in Cashmore field,
UK. (Courtesy of John Stafford.)

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TABLE 1.3
Early Advances in Research on GPS, Machinery Navigation, and Yield Mappingfor Precision Farming

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Research Area
GPS
Machinery navigation andautosteer
Yield mapping

Nature of Contribution

Key References

Technology adaptation to farming andtesting
Technology development and testing

Larsen et al. (1988), Auernhammer and Muhr
(1991), Tyler (1993)
Reid and Searcy (1987), Palmer (1991),
O’Connor et al. (1996), Greatline andGreatline (1999), Keller et al. (2001)
Bae et al. (1987), Schueller and Bae (1987),
Searcy et al. (1989a), Karlen et al. (1990),
Stafford et al. (1991)

Technology development and fieldtesting

patterns in crop yield were not temporally stable. Yield maps for the first 4 years of the study could
only explain half of the spatial variability in grain yield for the last year of the study. These results
showed that the magnitudes of both spatial and temporal variability were important, but temporalvariability was not predictable from one year to the next.
McBratney and Whelan (1999) studied spatial and temporal variability in wheat crop yield infour fields located in Australia from 1995–1996. They noted that temporal variations across years
were larger in magnitude than spatial variations within a year. This led them to propose the concept
of using uniform field management techniques when yield stability across years was poor and variable management when yield was stable across years. Their null hypothesis was stated as: “given thelarge temporal variation evident in crop scale relative to the scale of a single field, then the optimalrisk aversion strategy is uniform management.” Blackmore et al. (2003) found that wheat yield variability across four fields in England for 6 years was unpredictable from one year to another, despite
significant spatial variability in yield during any single year. They suggested that managing for the
spatial variability that exists in a given year is better than trying to predict management needs fromthe previous year(s) yield map(s).
The focus on spatial and temporal variation in crop yield tends to overlook an important issue.
For variable rate management, what is really critical is the crop response rather than the crop yield.
Mamo et al. (2003) studied spatial and temporal variations in the response of a corn crop to variable rates of N fertilizer from 1995 to 1999 in Minnesota. Half of the field responded to N fertilizer
in a given year, while 60% of the responsive areas were stable across years. A variable rate fertilizer strategy would have reduced N rates overall by 69 to 75 kg/ha in comparison to a uniform
application of fertilizer. This study showed that crop yield by itself is a poor indicator of the crop
response to N fertilizer. The implication is that defining management zones based on differences incrop yield is not generally an efficient approach for developing a variable rate fertilizer application
strategy. Similar conclusions were reached by Scharf et al. (2006) in Missouri, who showed that
economically optimum N rates were more strongly controlled by soil N supply than yield-controlled
N uptake patterns.

1.10 VARIABLE RATE HERBICIDE APPLICATION
Weeds tend to occur in patches rather than in uniform coverage across fields, and if these patches
exceed threshold populations, they can reduce crop yield and vigor (Coble and Mortensen 1992).
Before the advent of Roundup Ready crops, there was significant interest in variable rate applications of preemergent or postemergent herbicides (Wiles et al. 1992).
Haggar et al. (1983) fitted an optical sensor to a handheld weed sprayer to test the concept of
optically activated spot spraying. The optical sensor estimated the ratio of red to near-infrared

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reflectance of weeds on a background of bare soil. Guyer et al. (1986) and Thompson et al. (1990,
1991) suggested the concept of mapping weed locations using either machine vision, remote sensing, video cameras on tractors, or manual counts, and then spraying the field at a uniformly low
rate with a higher dosage of herbicide in areas with weed patches. Thompson et al. (1990, 1991)
believed that this approach would work well in fields where weed patches tend to occur in the same
locations from one year to another. Thompson et al. (1991) discussed the potential for real-time
mapping of weed populations in a growing crop, but this approach was rejected due to the difficultyof discriminating weeds from crop and low spatial resolution of aerial imagery. Felton and McCloy(1992) proposed a spot herbicide sprayer that was based on detection of weeds using red and nearinfrared reflectance. This research led to the development of a commercial spot sprayer in Australia
known as DetectSpray. Stafford and Miller (1993) built a variable rate herbicide sprayer that applieda low uniform rate of herbicide throughout the field and a higher rate where weed patches had beenpreviously mapped (Figure 1.5). Sprayer position relative to the weed map was determined using
differential GPS techniques. Weed patches were mapped using a model airplane equipped with a
35-mm color photography camera; this was the first use of an unmanned aerial vehicle for precision
farming. Further development of the map-based approach to variable herbicide spraying in Europe
was subsequently restricted as a result of legal rulings relating to an infringement of the SoilTeq
patent for map-based variable rate applications.
Johnson et al. (1995a) mapped weed density in 12 corn or soybean fields in Nebraska for a single
year. This research showed that weeds tended to occur in patches, and many areas of the field tended
to be weed-free. They suggested that variable rate herbicide spray could be targeted to weed patches
if the density of weeds in those patches exceeded an economic threshold. Research by Johnson etal. (1995b) then mapped weed patches for 2 years in 18 Nebraska corn and soybean fields. Resultsof this research showed that locations of weed patches were not stable from one year to the next.
As a result of research by Johnson et al. (1995b), interest in weed mapping soon turned to realtime mapping of weeds using photodetectors and variable herbicide spraying (Beck and Kinter1998a,b) with what became known as WeedSeeker. Hanks and Beck (1998) evaluated two commercial sensors, DetectSpray and WeedSeeker, for their ability to identify and spray weeds; the former
used passive reflectance, while the latter had an active sensor consisting of gallium-based lightemitting diodes. DetectSpray and WeedSeeker were both initially designed to sense green vegetation on a background of bare soil and were not appropriate for use in fields where crops and weeds
were mixed together. Weeds were sprayed whenever detected by either system. When used in fields

FIGURE 1.5 Variable rate herbicide applicator developed by Stafford and Miller (1993).

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Soil-Specific Farming

where crops had already germinated, a spray hood was installed over the weed sensors and sprayer
nozzles to prevent the application of glyphosate herbicide to growing crops. WeedSeeker performed
better than DetectSpray in variable ambient lighting conditions because of its active sensor system.
Sensor-based weed control reduced the volume of herbicide applied by 63%–85% relative to a uniform spray application (Hanks and Beck 1998). Using machine vision, Giles and Slaughter (1997)
found that variable herbicide spray reduced application rates by 66%–80% in vegetable crops relative to a uniform spray application. Tian et al. (2000) developed a variable rate herbicide sprayerthat used a low-resolution color video camera to identify clusters of weeds growing between rows ofcorn or soybean. Herbicide spray could be varied nozzle by nozzle depending on the weed density.
For low-density weed cover, tests of this system showed that herbicide application amounts could be
reduced by 71% relative to a uniform application rate (Tian 2002).

1.11 VARIABLE RATE IRRIGATION
Water conservation is a pressing issue in the face of drought and competition for water resources
by agricultural, municipal, and industrial users. Overirrigation wastes water and leads to leaching
and runoff losses that carry soluble pollutants such as nitrate-N and pesticides to ground or surface
waters. McCann and Stark (1993) patented a method for variable application of irrigation waterand chemicals applied through center pivot irrigation systems (Table 1.4). Aerial photography orsoil sampling was used to identify management zones requiring different amounts of irrigation.
Each nozzle on the irrigation spray boom could be independently controlled using a solenoid valve.
A microprocessor was used to determine the location of each nozzle relative to mapped irrigation
zones, and a control program then turned the nozzle on or off in order to deliver the required amountof water for that location. Variable rate irrigation was field-tested by King et al. (1996), who foundthat this method was able to accurately deliver recommended variations in irrigation water depth
and associated nitrogen fertilizer requirements. Variable rate irrigation through linear move systems was developed by Fraisse (1994) at Colorado State University in parallel with the Washington
State research by McCann and Stark (1993). In South Carolina, Omary et al. (1997) and Camp et al.
(1998) placed multiple manifolds on a center pivot in order to have the flexibility of delivering oneof eight possible rates of irrigation to any portion of the field.
Evans et al. (1996) collaborated with the Nelson Irrigation company in Walla Walla, Washington
to install variable rate irrigation controllers on a center pivot system in a commercial farm. Thirtyzones having two to four nozzles were cycled on and off by a master controller on an RS485 bus toachieve desired rates of water application. Water demands could be calculated using a potato growthsimulation model based on landscape, soil, and climatic information. They found that the performance of the variable rate irrigation system was excellent, and that the main limitation in implementing the system “lies in the ability to interpret spatially variable data and develop rational and

TABLE 1.4
Early Research Advances in Variable Rate Herbicide for Weed Control
and Variable Rate Irrigation
Research Area

Nature of Contribution

Key References

Variable rate herbicide

Technology development and testing

Variable rate irrigation

Technology development and testing

Haggar et al. (1983), Guyer et al. (1986),
Beck and Kinter (1988a,b), Thompson et al.
(1990, 1991), McCloy and Felton (1992),
Stafford et al. (1993)
McCann and Stark (1993), Fraisse (1994),
Evans et al. (1996)

Historical Evolution and Recent Advances in Precision Farming

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coherent site-specific crop management prescriptions” (Evans et al. 1996). Distortion of irrigation
spray patterns by wind was also a particularly vexing problem.

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Remote sensing applications in precision agriculture are primarily based on reflectance of the sun’svisible and near-infrared light by soils or crops. Remote sensing does not require contact between
the sensor and the soil or crop and is usually achieved using cameras mounted on satellites, airplanes, towers, or unmanned aerial vehicles. Proximal sensing, discussed in Section 1.13 below,
differs from the traditional definition of remote sensing in that proximal sensing involves sensors
placed on ground vehicles rather than aerial platforms.
The earliest applications of remote sensing in agriculture were primarily focused on estimating
crop yield (Pinter et al. 1981; Wiegand et al. 1991), although Al-Abbas et al. (1974) conducted laboratory studies of the spectral properties of corn leaves with various levels of nutrient stress. Robert(1982) used color infrared aerial photography in Minnesota for diagnosis of “problems related todrainage, erosion, germination, grass and weed control, crop stand and damage, and machinery
malfunction.”
Landsat imagery was investigated for diagnosis of agricultural problems by Robert (1982), butdifficulties in processing satellite remote sensing data at that time prevented meaningful results.
Zheng and Schreier (1988) and Bhatti et al. (1991) were the first to use aerial and satellite imagery,respectively, for the specific purpose of estimating spatial patterns in soil fertility that could be
used to guide variable rate fertilizer applications. Zheng and Schreier (1988) found that potassium
fertilizer recommendations for a bare field in British Columbia could be reduced relative to uniform
applications if rates were varied according to spatial patterns in soil organic matter content identified using color aerial photographs. Bhatti et al. (1991) found that spatial patterns in soil organic
matter from Landsat satellite imagery for bare soil on a commercial farm in Washington State werestrongly related to patterns in soil phosphorus and wheat yield. They proposed that areas with loworganic matter content and low crop productivity “could be managed with customized fertilizer
and tillage practices” for environmental protection. During this early period in precision farming,
satellite remote sensing imagery with Landsat was limited to 30 m spatial resolution with a return
frequency of no better than 15 days. These factors, coupled with the problem of acquiring satellite
imagery during cloudy days, limited the application of satellite imagery to precision farming during
the 1990s.
Attention soon turned to using remote sensing to detect nitrogen deficiency in corn and othercrops. Blackmer et al. (1995) used canopy reflectance measurements of single leaves with a spectroradiometer to confirm previous research by Walburg et al. (1982) that showed an increase inreflectance in the green spectrum (550 nm) with nitrogen stress. Blackmer et al. (1995, 1996a) thenused black and white aerial photography of stressed and unstressed corn plants in Nebraska with acamera that filtered all light except green. Results showed an excellent relationship between canopy
reflectance and grain yield across a large range of crop nitrogen stress. In a companion paper,Blackmer and Schepers (1996b) showed that crop nitrogen stress was accurately detected using
the brightness of red in a digitized aerial color photograph of a cornfield in Nebraska. Bausch andDuke (1996) developed a green vegetation index (GVI) defined by the ratio of near-infrared to green(NIR/G) reflectance that accurately predicted differences in nitrogen stress for irrigated corn inColorado. Research results showing that remote sensing could accurately identify areas of nitrogenstress in crops led directly to the development of proximal sensors (described in Section 1.13) for
precision management of crop nutrient deficiencies.
Until launch of the commercial Ikonos satellite in 1999, there were few instances where satellite
remote sensing was used for precision farming applications (Mulla 2013). Ikonos collected reflectance data using blue, green, red, and near-infrared bands at 1–4 m spatial resolution with a return
frequency of 3 days, leading to immediate applications in precision farming such as diagnosis ofDownloaded by [Canadian Agriculture Library, Agriculture and Agri-Food Canada] at 12:12 27 September 2017

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Soil-Specific Farming

crop nitrogen stress, fungal infestations, and soil drainage problems (Seelan et al. 2003). A second
commercial satellite, Quickbird, launched in 2001, collected reflectance in the blue, green, red, andnear-infrared bands at 0.6–2.4 m spatial resolution with a return frequency of 1–4 days. Quickbird
normalized green normalized difference vegetation index (NGNDVI) data were used by Bauschand Khosla (2010) to identify locations experiencing crop N stress.
High-resolution, high-return-frequency commercial satellites launched from 2008–2009 included
RapidEye, GeoEye1, and WorldView2. These satellites have spatial resolutions ranging from 6.5 to
0.5 m (Mulla 2013) and return frequencies ranging from 5.5 days to 1.1 days. Thus, they are highly
suitable for applications in precision farming. More interestingly, these satellites offer additional
spectral bands in comparison with earlier satellites. RapidEye collects reflectance data in the blue,
green, red, red-edge, and near-infrared bands. Red-edge reflectance is highly sensitive to the chlorophyll status of growing crops. GeoEye1 collects reflectance data in the blue, green, red, and twonear-infrared bands. The images in Google Earth are commonly obtained using GeoEye1 imagery. WorldView2 collects imagery in purple, blue, green, yellow, red, red-edge, and near-infrared
bands. Despite the improvement in return frequencies for commercial satellites, difficulties persist
in acquiring satellite imagery when needed due to cloud cover and competition for imagery among
civilian and military users.
Remote sensing has been used in precision farming for a variety of purposes, including estimating spatial variability in soil organic matter (Bhatti et al. 1991; Mulla 1997; Fleming et al. 2004), in
crop yield (Yang et al. 2000; Boydell and McBratney 2002; Garcia Torres et al. 2008), in crop water
stress (Barnes et al. 1996; Meron et al. 2010; Rud et al. 2014), in insect infestations (Franke andMenz 2007; Prabhakar et al. 2011), in crop disease (Muhammad 2005; Huang et al. 2007; Mirik et
al. 2011), and in weed infestations (Zwiggelaar 1998; Lamb and Brown 2001; Thorp and Tian 2004;
Lopez-Granados 2011). By far the most common application of remote sensing in precision farming,
however, is for detection of spatial and temporal patterns in crop nutrient deficiencies (Bausch andDuke 1996; Haboudane et al. 2002; Miao et al. 2007, 2009; Tremblay et al. 2012; Nigon et al. 2014).
In many of these applications, various combinations of spectral bands known as spectral indices
are used to detect the property of interest (Haboudane et al. 2002, 2004; Thenkabail 2003; Mulla2013). The spectral index most commonly used when a growing crop is present is the normalized difference vegetative index (NDVI) (Rouse et al. 1973), which is based on the sharp contrast in reflectance between the red and near-infrared portions of the spectrum. Plant pigments
absorb radiation in narrow wavelength bands centered around 430 nm (blue or B) and 650 nm(red or R) for chlorophyll a and 450 nm (B) and 650 nm (R) for chlorophyll b. Wavelengths withlow absorption characteristics conversely have high reflectance, particularly in the green (550 nm)
wavelength. Remote sensing of crops in the near-infrared spectrum (particularly at 780, 800, and880 nm) responds to crop canopy biomass and leaf area index (LAI), leaf orientation, and leaf
size and geometry. NDVI has been used to detect crop nutrient deficiencies, patterns in crop yield,
insect and weed infestations, and crop diseases (Mulla and Miao 2015). NDVI values are often nota good indicator of crop status due to either interference from bare soil reflectance or to insensitivity to changes in leaf chlorophyll in closed canopy crops when leaf area index values exceed 2 or 3
(Thenkabail et al. 2000).
As a result, there has been significant research effort devoted to finding broadband multispectral
indices that can be used as an alternative to NDVI (Sripada et al. 2006, 2008; Miao et al. 2009). In
general, there are three classes of broadband multispectral indices used in precision farming. These
include soil-adjusted vegetation indices, ratios of green and near-infrared reflectance bands, andratios of red and near-infrared reflectance bands (Thenkabail 2003; Mulla 2013). Soil-adjusted vegetation indices reduce reflectance from bare soil that interferes with the interpretation of reflectancefrom a growing crop before canopy closure. Red ratio indices typically are sensitive to absorption ofradiation by leaf chlorophyll, while green ratio indices are sensitive to leaf pigments other than chlorophyll. In commonly used red and green ratio indices, either the red or green or the near-infrared
reflectance can appear in the numerator of the ratio.

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Hyperspectral remote sensing data involves the collection of reflectance data over the entire visible and near-infrared spectra in narrowbands typically of 10 nm or narrower width. In contrast,
multispectral data typically involves reflectance in broadbands, 50 nm or wider, centered in theblue, green, red, and lower near-infrared portion of the spectrum. All of the broadband spectral
indices calculated with multispectral data can be calculated as narrowband spectral indices using
hyperspectral imaging. The advantage of doing this is that specific plant or soil responses thatwould be obscured by other plant or soil reflectance characteristics using broadband multispectral
imagery become clear with narrowband hyperspectral imagery. For example, plants contain different pigments whose light absorption peaks at specific narrow wavelengths. Plant pigments such as
chlorophyll strongly absorb radiation, particularly at wavelengths such as 430 (blue or B) and 660(red or R) nm for chlorophyll a and 450 (B) and 650 (R) nm for chlorophyll b (Pinter et al. 2003).
Other plant pigments such as anthocyanins and carotenoids absorb strongly at different wavelengths(Blackburn 2007). Crop reflectance also responds to changes in crop biomass, LAI, canopy structure, and leaf density in the red and near-infrared wavelengths. A narrow red band centered at 687
nm is sensitive to crop LAI and biomass, while a narrow near-infrared band centered at 970 nm
is sensitive to crop moisture status (Thenkabail et al. 2010). Further examples of linking specificsoil and crop characteristics with narrowband reflectance are given by Thenkabail et al. (2010).
Hyperspectral imaging can be used to estimate narrowband spectral indices that have no analog in
multispectral imagery. For example, several researchers have used red-edge reflectance in the spectral region between 700 and 740 nm to construct spectral indices that are sensitive to crop nitrogenstatus (Guyot et al. 1988; Datt 1999; Clarke et al. 2001; Haboudane et al. 2002; Gitelson et al. 2005;
Fitzgerald et al. 2010; Shiratsuchi et al. 2011).
Interest is growing in the use of low-altitude unmanned aerial vehicles (UAVs) as a platform forremote sensing in precision farming (Zhang and Kovacs 2012). Imagery collected with UAVs can behigh enough in resolution to view individual plants and leaves, although images at such high resolution have to be mosaicked to obtain complete coverage of a field. UAVs have been used to assess
crop LAI, biomass, plant height, nitrogen status, water stress, weed infestation, and yield and grain
protein content (Berni et al. 2009; Swain et al. 2010; Samseemoung et al. 2012; Bendig et al. 2013).
Current limitations to using UAVs include governmental restrictions on their usage, light payloads,
low power, and limited flight times.

1.13 PROXIMAL SENSING OF SOILS AND CROPS
Proximal sensing has been widely used in precision farming to map spatial patterns in soil or crop
properties. Early advances in proximal soil sensing were initially based on geophysical prospecting
techniques that were used to discover mineral reserves buried deep in the earth (Parasnis 1973).
Two categories of geophysical prospecting techniques have been adapted for proximal sensing ofsoil in precision farming: electrical resistivity/conductivity methods and electromagnetic induction methods. Halvorson and Rhoades (1974) adapted electrical resistivity mapping methods to the
problem of mapping soil salinity in agricultural fields based on the four-probe Wenner array developed in the mining industry. Wenner array probes were simply metal spikes inserted in soil along a
straight line at fixed spacing. A battery supplied current to the soil through two of the spikes, while
the other two served as voltage probes. The depth of measurement could be controlled by varying
the spacing between metal electrodes. Carter et al. (1993) built on the research by Halvorson andRhoades (1974) to pioneer continuous mobile electrical conductivity measuring equipment for soil
salinity mapping. The mobile apparatus consisted of a battery attached to four equally spaced chisel
blades mounted on a tractor. This apparatus was the inspiration for the Veris electrical conductivity
mapping system (Christy and Lund 1998) based on equally spaced electrode disks that is widelyused in precision farming. Colburn (1991) patented a device for a resistivity-based sensor mounted
behind a moving fertilizer spreader that was claimed to accurately vary fertilizer rate in response todifferences in soil nitrate-N concentrations, soil cation exchange capacity, organic matter content,

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Soil-Specific Farming

and soil moisture. This device, called Soil Doctor, was widely marketed for applications in precision
farming, although many scientists were skeptical of its accuracy in the absence of rigorous scientifictesting.
There were several drawbacks of the Wenner array of electrodes for soil salinity mapping. Firstwas the difficulty of ensuring good contact between electrodes and dry soil, and second was theneed to eliminate site-specific calibration of electrical resistivity measurements with soil samples
that were analyzed in the laboratory. To overcome these limitations, Rhoades and Corwin (1981)
began working with Geonics Ltd. of Canada, who were commercial suppliers of electromagnetic
induction probes for geophysical prospecting in the mining industry. Rhoades and Corwin (1981)
suggested the development of a noncontacting electromagnetic induction probe that could be used
specifically for shallow sensing of soil materials, and this suggestion led to the development of the
EM-38 electromagnetic induction probe (Figure 1.6) that is widely used in precision farming (Leschet al. 1992; Doolittle et al. 1994; Sudduth et al. 1995; Kitchen et al. 1999, 2003). Electromagnetic
induction is a process where an electrical field generated above ground induces current loops inthe soil that are proportional in magnitude to the soil’s electrical conductivity. The primary current
loops in the soil induce a secondary electromagnetic field whose strength is proportional to thecurrent flowing in the loops. This secondary field is then measured by a receiver above ground to
estimate soil electrical conductivity.
The commercially available EM-38 electromagnetic induction unit from Geonics Ltd. ofCanada became a preferred tool to map soil salinity (Corwin and Lesch 2003). Lesch et al. (1992)
reported the advantage of using an EM-38 unit that enhanced their ability to accurately predictspatial soil salinity patterns with 60% to 90% fewer soil samples. They concluded that EM-38
readings were a more practical and cost-effective tool for accurate mapping of spatial salinity
patterns at the field scale than soil sampling. Likewise, Doolittle et al. (1994) in Missouri wereable to quantify and map variations in the depth to claypan soils that restrict infiltration, influence the lateral movement of soil water and agrichemicals, and limit crop production. They foundthat EM techniques were noninvasive, less labor-intensive, more economical, and could produce
large quantities of data in a relatively short period of time. Sudduth et al. (1995) continued thework of Doolittle et al. (1994) and found that by automating the process of collecting EM-38
data, they could map variations in soil properties over large areas for site-specific nutrient management. Kitchen et al. (1999) studied the relationships in spatial maps from EM-38 data andgrain yield monitors. They found a significant relationship between yield maps and apparent
electrical conductivity maps in nine out of 13 site years of data. Later, Kitchen et al. (2003) measured apparent soil electrical conductivity using a Veris electrical conductivity unit developed by

FIGURE 1.6 First commercial unit (circa 1980) of the Geonics EM-38 single dipole electromagnetic induction conductivity meter. (Courtesy of Dennis Corwin.)

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Christy and Lund (1998) across three states (Missouri, Kansas, and Colorado), under four different crops (maize, wheat, soybean, and sorghum), and indicated that sensor-based soil information
can greatly assist farmers in understanding yield variations for planning management decisionsin precision farming.
Early proximal sensing in precision farming was also focused on the use of in situ reflectancemethods to assess spatial patterns in soil organic matter content (Shonk and Gaultney 1988; Sudduth
et al. 1991). Soil organic matter content is often correlated with other soil properties such as moisture content and nitrogen mineralization, each of which can affect potential crop yield. In addition,certain classes of crop protection chemicals are adsorbed by soil organic matter content. In either
case, varying the rate of fertilizer or pesticide according to levels of soil organic matter content was
the motivation for developing proximal sensing techniques for soil organic matter content. Gaultney
et al. (1991) patented a device to measure soil organic matter content in real time that was basedon laboratory calibration curves that depended on soil texture. The device consisted of an array of
red-light-emitting diodes surrounding a photodiode sensor, both of which could be mounted on achisel blade dragged by a tractor. McGrath et al. (1990) used the organic matter sensor to vary ratesof herbicide applied in Midwestern fields, with good success at controlling weeds. Sudduth et al.
(1991) developed an organic matter sensor that was based on near-infrared reflectance (Figure 1.7).
When tested in the laboratory, the sensor was able to accurately detect differences in SOM content
even when soil moisture content varied. However, field trials were initially unsatisfactory because
reflectance values were sensitive to soil roughness (Hummel et al. 1996).
Electrochemical sensing of soil chemical properties was an important emphasis in early precision farming research (Colburn 1991; Adsett and Zoerb 1991; Birrell and Hummel 1993). Adsett andZoerb (1991) adapted an ion-selective electrode that measured nitrate-N concentrations in soil solution to a real-time proximal sensor. A rather cumbersome apparatus was developed that sampled
soil, mixed and stirred it with water, and then used the ion-specific electrode to measure nitrateconcentrations on the go. The sample container was then dumped in the field and rinsed out automatically before taking another soil sample. Birrell and Hummel (1993) tested an ion-sensitive field
effect transistor (ISFET) for nitrate-N concentration measurements in the laboratory. They foundthat samples could be analyzed every 1.5 seconds through a cycle that involved sample injection andwashing of the sample container. ISFET sensors are smaller and have a faster response and higher
signal-to-noise ratio than ion-specific electrodes. However, they also have greater electronic drift,
requiring frequent recalibration. Flat surface ion-selective probes were developed by Adamchuk
et al. (1999) and patented (Adamchuk et al. 2002) for real-time automated mapping of soil pH,

FIGURE 1.7 Soil organic matter sensor based on NIR reflectance. (From Sudduth, K. A. et al., Soil organic
matter sensing: A developing science. In: G. A. Kranzler (ed.), Automated Agriculture for the 21st Century.
ASAE Publication 11–91, St. Joseph, MI, pp. 307–316, 1991.)

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Soil-Specific Farming

while Adamchuk et al. (2003) developed ion-selective probes for real-time automated mapping ofsoil nitrate and potassium levels.
Interest in proximal sensing of crops is not new. Scientists and farmers alike will visually inspect
crops to nondestructively estimate crop health status. Their visual inspection may often lead to a
management decision such as fertilizer, irrigation, or pesticide application. Human eyes are in fact apair of reflectance-based optical sensors. However, human eyes require experience to discern subtle
differences in crop appearance due to various biotic and abiotic stresses. Machine-based opticalsensors, on the other hand, can be used repeatedly without bias or need for experience. In the early
1970s, Leamer and his coworkers at the USDA-ARS unit in Weslaco, Texas, and his colleague
Silva from the electrical engineering department at Purdue University, recognized the need for an
instrument that would scan a specific field target through the visible and thermal infrared spectrum(Leamer et al. 1973). They designed and developed an instrument and provided specifications andplans to Exotech Inc. to build a sensor that could be used in fields for crop sensing. The instrument
was built by Exotech, who also contributed many engineering concepts and the electronic circuits
required to measure the reflected or emitted energy and to produce an electrical signal proportional
to the energy detected by the instrument (Leamer et al. 1973).
The instrument (Exotech model 20-B Spectroradiometer) consisted of two systems, each madeup of an optical unit and a control unit. One system covered the spectral range 0.37–2.52 μ; theother system covered the spectral range 2.76–13.88 μ. The optical units of the two systems were
mounted side by side on a tiltable base mounted on an aerial lift truck. Separation between theobjective lenses was about 30 cm to minimize parallax. The system was configured such that it canbe operated separately or in the tandem, boresighted mode (Leamer et al. 1973). The control units
were mounted in a camper-type equipment van and were connected to the optical units by 60 m ofarmored cable. Preamplifiers and auxiliary electronics in the optical units were designed to operate
without picking up interference over this length of cable. The entire system was designed to operate
in an outdoor environment.
The Laboratory for Applications of Remote Sensing (LARS) at Purdue University was among
the early pioneers and leaders in advancing the science of remote sensing and its applications inagriculture. Much of the early work in the area of crop sensing came from LARS under the leadership of Bauer, Baumgardner, and many of their graduate students (Al-Abbas et al. 1974; Bauer 1975;
Ahlrichs and Bauer 1978; Kumar et al. 1979; Daughtry et al. 1980; Walburg et al. 1982).
The 1980s witnessed the introduction of optical sensing into agriculture as a complementary
monitoring tool to estimate crop health, growth, abiotic stresses, and crop yield (Bell and Xiong2007). Daughtry et al. (1980), using an advanced version of Exotech Spectroradiometer (Exotech
100A), investigated the effects of various management practices (soil moisture, planting date, nitrogen fertilizer, and cultivar) on spring wheat crop canopies. They suggested that LAI, biomass, andpercent soil cover can potentially be monitored by crop canopy sensing. Likewise, Walburg et al.
(1982) studied the effects of N rates on growth, yield, and reflectance characteristics of corn canopies and reported that spectral reflectance differed significantly with N rate. Wanjura and Hatfield(1987) reported that vegetation indices had greater sensitivity to plant vegetation reflectance than
did the reflectance of a single wavelength. Early work that utilized crop canopy sensing was primarily done to understand cause and effect relationships and to identify particular wavebands, theirsimple ratios, or various vegetative indices that were most effective in distinguishing the treatments
of interest (Chappelle et al. 1992; Blackmer et al. 1994; Filella et al. 1995). Reflectance near the 550nm wavelength was found to be the best at distinguishing nitrogen treatments in soybean (Glycine
max [L.] Merr; Chappelle et al. 1992). Blackmer et al. (1994) reported similar findings for nitrogenin corn canopies. Around the same time, Filella et al. (1995) reported that reflectance at 550 and 680
nm was significantly correlated with canopy chlorophyll content across five N treatments in wheat
canopies.
Around the same time, engineers Solie and Stone and agronomist Raun at Oklahoma State
University were working on developing rapid ways of estimating leaf nitrogen concentrations and19

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crop biomass using sensing technology (Solie et al. 1996). Stone developed a sensor with two detectors working in synchrony; one measured the incoming radiance and the other faced the plant
canopy and measured the reflected radiance (Table 1.5). The irradiance reflected was divided by
the incoming irradiance to determine reflectance (Bell and Xiong 2007). One of the limitations
among the crop sensing devices of the 1980s and 1990s was that they were passive sensors (i.e.,
without their own sources of light energy). Hence, they were limited by the time of day when sensor readings were acquired in the field with and without cloud cover, and the need for constantwhite plate calibration (Rutto and Arnall 2009). To address this limitation, in 1998, the Oklahoma
team joined hands with Mayfields (future founders of N-Tech Industries and manufacturers of theGreenSeeker™ sensor), who owned much of the relevant intellectual properties for active opticalsensors, which they purchased from John Deere & Company, Moline, IL (Rutto and Arnall 2009).
The team felt that the development of active sensors (sensors that have their own source of lightenergy) was necessary for such sensing devices to be incorporated into in-field and in-season precision nutrient recommendations.
The year 2002 marked the commercial release of the GreenSeeker active sensing device (Raunet al. 2002) by N-Tech Industries (Ukiah, CA). Since then other proximal crop-sensing devices such
as Crop Circle active sensor (Holland Scientific, NE), the Yara N-Sensor ALS (active light sensor)
by Yara International, Norway; and Isaria Crop Sensor by CLAAS, Germany have become commercially available. These reflectance-based active sensors are being widely used to guide variable
rate nitrogen fertilizer applications around the world (Shanahan et al. 2008; Samborski et al. 2009;
Kitchen et al. 2010).
Initially, the majority of the commercially available active sensors were reflectance-based sensors
and were providing either NDVI or a modification of the NDVI readings. However, more recently
there are fluorescence-based active sensors that are commercially available such as the Multiplex
Fluorescence sensor by Force-A, France, and the MiniVeg laser-induced chlorophyll fluorescencesensor by Fritzmeier, Germany. Fluorescence sensors utilize either ultraviolet or laser light sources,
or both, to stimulate a plant’s chlorophyll to emit fluorescent light. Green plants emit fluorescencein the blue-green (440–520 nm) and in the red to far-red (690–740 nm) regions of the light spectrum
when excited with a potent light source (Buschmann et al. 2000). Different combinations of red andfar-red fluorescence ratios obtained with different excitation wavebands can be used to calculate amultitude of indices for the plant status (Tremblay et al. 2011). A fluorescence-based index called

TABLE 1.5
Early Research Advances in Remote Sensing, Proximal Sensing of Soils, and Proximal
Sensing of Crops for Precision Farming
Research Area

Nature of Contribution

Aerial remote sensing

Ability to identify spatial
patterns, identification ofsensitive wavelengths

Proximal sensing of soils

Development and testing oftechnology

Proximal sensing of crops

Development and testing oftechnology

Key References
Robert (1982), Zheng and Schreier (1988),
Bhatti et al. (1991), Blackmer et al. (1995),
Barnes et al. (1996), Bausch and Duke (1996),
Blackmer and Schepers (1996b), Haboudane et al.
(2002), Thenkabail (2003)
Rhoades and Corwin (1981), Shonk and Gaultney(1988), Colburn (1991), Gaultney et al. (1991),
Sudduth et al. (1991), Lesch et al. (1992), Carter etal. (1993), Doolittle et al. (1994), Christy and Lund
(1998), Adamchuk et al. (1999)
Leamer et al. (1973), Daughtry et al. (1980),
Walburg et al. (1982), Chappelle et al. (1992),
Solie et al. (1996), Raun et al. (2002)

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Soil-Specific Farming

the nitrogen balance index (NBI) was developed to detect N variability by the ratio of chlorophyll
content to the flavonoid content (Cartelat et al. 2005). In a recent study, Longchamps and Khosla(2014) reported early detection of nitrogen variability at the five-leaf growth stage of maize using
the Multiplex 3 fluorescence sensor. They also reported that the fluorescence readings were notinfluenced by soil background noise when used at the recommended height of measurement. Suchfindings are significant for precision farming and are leading the way to enhance our ability to manage nutrients more efficiently. Interestingly, fluoresensing is not new. Lorenzen (1966) demonstrated
that chlorophyll fluorescence can be used to assess plant chlorophyll content and photosynthetic
activity through the state of photosystem II. However, such measurements were made in a darkchamber on a potted plant by inducing fluorescence with a laser beam. Today, similar measurements
can be acquired using commercially available fluorescence sensors in broad daylight and in motion
above the crop canopy. One can therefore envision that in the near future as technology and science
continue to progress, scientists and farmers will be working with many innovative proximal crop
sensors that are yet to be developed and commercialized to further the goals of precision farming.

1.14 ENVIRONMENTAL BENEFITS OF PRECISION FARMING
Precision farming allows for variation in the rate of applied fertilizer, manure, and pesticides tobetter match spatial patterns in soil fertility and pesticide adsorption, and to respond to changing
temporal patterns in crop nutrient stress and infestations of weeds, insects, and disease. In addition,with autosteer technology, precision farming reduces overapplication of chemical inputs due tooverlap between successive passes of chemical applicator machinery. All of these factors lead, conceptually, to improved environmental quality and sustainability (Larson et al. 1997; Bongiovanniand Lowenberg-DeBoer 2004).
The use of precision farming to reduce the risks of pesticide leaching to groundwater in sandy
soils were first studied by Mulla et al. (1996) at a field site in Washington State. Measured concentrations in carbofuran applied at 8.1 kg ha–1 a.i. were measured to a depth of 1.8 m at 57 locations
throughout the field and this data wad used to calibrate the convective-dispersive equation for porewater velocity, dispersion coefficient, and retardation factor. Results showed that there was significant spatial variability in pesticide leaching risks. Over half of the field was at high risk for leachinglosses, and these losses could be significantly reduced by applying low rates of carbofuran in highrisk leaching areas. Spatial patterns in leaching risk were controlled primarily by variations in water
movement rather than variations in soil organic matter content, making the a priori identification ofhigh-risk leaching areas challenging.
Khakural et al. (1994, 1999) and Mulla et al. (2002) measured the impact of variable rate herbicide applications on herbicide losses in runoff, tile drainage, and eroded sediment for a fine textured
soil in Minnesota. These studies showed that variable rate herbicide applications based on spatial
patterns in soil organic matter content, soil pH, or weed populations resulted in significant reductions of herbicide applied to soil and subsequent reductions in the amount lost to surface waters
relative to uniform herbicide applications. A number of other researchers showed that spatial variability in soil properties and weed populations could be used as the basis for variable rate herbicide
applications that could lead to significant reductions in the amount of herbicide applied to fields(Mortensen et al. 1995; Johnson et al. 1997).
Mixed results have been found by a number of researchers concerning the environmental benefits of variable rate nitrogen fertilizer applications using either assessments of residual soil N andnitrogen use efficiency (NUE) or simulation modeling (Bongiovanni and Lowenberg-Deboer 2004).
Hergert et al. (1996) compared uniform versus variable rate applications of N fertilizer in irrigatedcorn in Nebraska. They found that residual N concentrations in soil were significantly lower forvariable rate N applications than uniform applications, but there were no differences in NUE for
either strategy. On the other hand, Redulla et al. (1996) found no significant differences in residual
soil N or NUE between uniform and variable rate N applications in irrigated Kansas corn fields.

21

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Historical Evolution and Recent Advances in Precision Farming

Whitley et al. (2000) studied N leaching in irrigated potatoes grown in Washington State using
either uniform or variable rate N applications. They found that N leaching was reduced in lower
landscape positions with variable rate N application relative to uniform application.
A variety of simulation models have been used to assess the benefits of variable rate N applications on water quality. Larson et al. (1997) used Leaching Estimation and Chemistry Model(LEACHM) in rainfed corn produced in Minnesota to show that N leaching was reduced with variable rate N applications relative to uniform applications on a loamy sand, but not on a loamy soil.
The Environmental Policy Integrated Climate (EPIC) model was used by Rejesus and Hornbaker(1999) and by English et al. (1999) to show that N losses to surface waters were reduced using
variable rate N application in Illinois and Tennessee, respectively. Delgado et al. (2005) used the
Nitrogen Loss and Environmental Assessment Package (NLEAP) model in Colorado to show that
N leaching in irrigated corn could be reduced using variable rate N applications. Variable rate N
applications were particularly effective at reducing N leaching losses in management zones withlow crop productivity where crop uptake of N was limited.

1.15 PROFITABILITY OF PRECISION FARMING
Since the inception of precision farming, scientists, farmers, and practitioners alike have questioned
the economic feasibility of precision farming. The perceptions among the early users of precision
farming were (1) that it is technologically and resource-intensive and time consuming and hence
it would be cost-prohibitive, and (2) that it primarily increases system efficiencies and not necessarily output (Napier et al. 2000). These perceptions were further strengthened by the limited
number of early studies evaluating agronomic and economic gains of precision farming (Table 1.6).
Hammond (1993) reported that variable management is unlikely to cause increases in crop yield or
quality in high or intermediate fertility areas of the field. Furthermore, a majority of those earlystudies reported that precision farming was not profitable or that profitability was mixed at best
(Lowenberg-DeBoer and Boehlje 1996). Table 1.6, adopted from Lowenberg-DeBoer and Boehlje(1996), summarizes the findings of 11 early studies evaluating the economics of precision farming.
Five of the 11 studies reported precision farming as not being profitable, four studies reported mixed
or inconclusive results, and only two studies showed potential profitability. They attributed the

TABLE 1.6
Profitability Findings from Eleven Precision Farming Studies
Study and Year
Beuerlein and Schmidt (1993)
Carr et al. (1991)
Fiez et al. (1994)
Hammond (1993)
Hayes et al. (1994)
Hertz and Hibbard (1993)
Lowenberg-DeBoer et al. (1994)
Mahaman (1993)
Wibawa et al. (1993)
Wollenhaupt and Buchholz (1993)
Wollenhaupt and Wolkowski (1994)

Crop(s)Conclusion: Precision Farming
Profitable or Not?

Corn, soybean
Wheat, barley
Wheat
Potato
Corn
Corn
Corn
Corn
Wheat
Corn
Corn

No
Mixed
Yes, potentially
Inconclusive
Yes, potentially
No
No
Mixed
No
Mixed
Mixed

Source: Modified and adapted from Lowenberg-DeBoer, J., and M. Boehlje, Revolution, evolution or dead-end: Economic
perspectives on precision agriculture. Proc. 3rd Intl. Conf. Precision Agriculture, pp. 923–944, 1996.

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Soil-Specific Farming

failure to demonstrate economic gains to two primary reasons: (1) these studies compared wholefield gains to precision farming treatments, and (2) the findings depended heavily on the cost of sampling and variable rate applications that exceeded any gains made in yields. Lowenberg-DeBoer and
Boehlje (1996) argued that whole-field profit maximizing conditions differ from the site-­specificconditions and that there has to be a more economical, less expensive way of preparing variable
rate prescription maps for precision farming. In a follow-up study, Lowenberg-DeBoer and Swinton(1997) suggested that many previous studies failed to indicate whether problems with the technology or with its management were behind the apparent low profitability findings.
The limitations identified by Lowenberg-DeBoer and Boehlje (1996) and others (Khosla and Alley
1999) highlighted the need to develop techniques to replace expensive grid soil sampling for the purpose of variable rate management of crop and soil inputs. Khosla et al. (2002) reported significant
agronomic gains from using site-specific management zones to variably apply nitrogen in comparison
with conventional uniform applications of nitrogen across the entire field. A more detailed, in-depth
study on economic feasibility by Koch et al. (2004) clearly documented for the first time significant
economic gains from using precision farming. An increase in net return was reported ranging from
$18.21 to $29.57 ha–1 higher for variable-rate N application than for uniform N management (Koch etal. 2004). Interestingly, most studies in the late 1990s and early 2000s focused on the precision nutrient management aspects of precision farming, logically so, because this was the first precision farming
technology that was technically feasible (Lowenberg-DeBoer and Swinton 1997).
In the following years, novel technologies and improved management techniques continued to accelerate the adoption of precision farming, including, but not limited to, precision autoguidance, lightbar,
autopilot systems, active crop sensing, precision irrigation systems, and smart sampling (Jones 2004;
McClure 2005; Inman et al. 2007; Shaner et al. 2008). Griffin et al. (2005) investigated the economics
of using a lightbar and autoguidance GPS navigation technologies and found that a high-precision realtime kinematic (RTK) GPS system was less profitable to operate compared with no GPS use on a farm.
However, when a farmer begins using GPS, its profitability increases with the level of GPS precision(Griffin et al. 2005). They found that the lightbar was the most profitable navigation technology; however, as farm size increases, RTK-GPS becomes profitable and it outranks lightbar-GPS-based precision farming operations (Griffin et al. 2005). More recently, Shockley et al. (2011, 2012) demonstrated
that autosteer technologies can influence machinery selection and replacement decisions, increase net
returns, and reduce production risk. This is even more significant because their work incorporated over
500 real-world cropland fields from farms in Colorado, Kansas, and Nebraska.
The economics of precision farming continue to improve as technologies and management techniques improve. However, a major factor that previously has and will continue to greatly influence
the economics of precision farming is commodity prices. During the last decade we have witnessed
substantial fluctuations in commodity prices. For example, corn prices have gone from approximately $80 Mg–1 in early 2000 to over $200 Mg–1 recently (USDA-ERS 2014). Such increases incommodity prices are a welcome relief for farmers and have resulted in higher economic gains andinvestment of such gains back into innovative technologies and techniques, including precision
farming. However, recent declines in commodity prices may start to reverse the growing adoptionof precision farming.

1.16 ADOPTION OF PRECISION FARMING
Precision farming has been termed the most significant innovation introduced to U.S. agriculture in
the mid- to late 1980s (Napier et al. 2000). Yet its adoption rate, at least for the first 10 to 15 years,
did not meet expectations. Yapa and Mayfield (1978) indicated a number of reasons that could leadto nonadoption of an innovation. These include, but are not limited to, lack of sufficient information, lack of favorable attitude, lack of economic means to acquire technology, and lack of physicalavailability of technology. Nowak (1992) narrowed the reasons down to two major factors: willingness and ability to adopt. Rogers (1995) argued that adoption of technological innovations will notDownloaded by [Canadian Agriculture Library, Agriculture and Agri-Food Canada] at 12:12 27 September 2017

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23

occur unless potential adopters become aware that a problem exists that can be resolved in a more
efficient manner by new technologies. The early practitioners of precision farming highlighted the
problem they were trying to address. However, the perception that it amounts to mere improvement in efficiency of farming was not enough to attract adopters. Nowak (1992) emphasized thatthe willingness to adopt depends on access to information about the technology, confidence in that
information, and belief that the information provides a basis for favorable outcomes. Fairchild andDuffy (1993) suggested that profitability is one favorable outcome desired by virtually all producers
in a market economy and it is a necessary condition for extensive, voluntary technology adoption. It
is therefore no surprise that the pace of adoption of precision farming techniques and technologies
was slow at best until the mid- to late 1990s because there was no clear evidence of economic feasibility or profitability associated with precision farming (Lowenberg-DeBoer and Boehlje 1996).
By early 2000, it was widely recognized among the precision farming community that for adoptionof precision farming to occur, the technology needed to be relevant in addressing problems, be easy
to use, and above all be profitable to farming (Napier et al. 2000; Kitchen et al. 2002). Batte andArnholt (2003) examined case studies of six leading-edge adopters and found that profitability was
the biggest motivating factor in using precision agriculture tools.
The first decade of the twenty-first century saw accelerated adoption of precision farming. Overthe past decade, the adoption and use of different precision agricultural technologies among producers and commercial agricultural businesses increased steadily (Whipker and Akridge 2006). There
are a number of reasons that can be attributed to the change in pace of adoption of precision farming techniques and technologies over the past decade: (1) certain components of precision farming
such as precision nutrient management have been thoroughly tested and evaluated and were being
reported to enhance efficiency, productivity, and profitability in an environmentally responsible
manner (Franzen et al. 2000; Khosla et al. 2002; Koch et al. 2004), (2) positive change in commodity prices that increased multifold over the last 10 years (USDA-ERS 2014), which led to higher
net returns to farmers and their respective investment into advanced technologies, (3) introductionof new technologies such as autoguidance systems that in addition to improving efficiencies andprofitability, reduced farmers’ fatigue and allowed them to work longer hours (Griffin et al. 2005),
and (4) enhanced capacity and availability of a skilled labor force, trainers, and practitioners who
understood both technology and agriculture. While these may only serve as a partial list of factors
responsible for the enhanced level of adoption over the last decade, it does reflect on bridging the
gap in many areas as pointed out by Nowak (1992), Fairchild and Duffy (1993), and Kitchen et al.
(2002).
Unique partnerships among industries, the media, and academic institutions have enabled monitoring and documentation of change, progress, and adoption of precision farming among U.S. farmers. For the past 15 years, Purdue University in partnership with CropLife Media and recently
Trimble Inc., has employed a survey tool to quantify changes in precision technologies, their respective adoption rates, and predicted future trends by agricultural retailers, practitioners, and farmers.
Figure 1.8 reflects a summary of the most recent survey conducted in 2011 (Whipker and Erickson
2013). It lists the top 11 precision technologies that have gained popularity and adoption among
farmers, agricultural retailers, and practitioners. The top two are both related to precision guidance
that allow farmers to increase speed of field operations, work longer days, provide greater flexibility
in hiring labor, have a more appropriate placement of inputs, and reduce overlap or chemical use(Griffin et al. 2005). It is interesting to note that some of the latest innovations such as crop and soil
sensing devices that are still undergoing extensive research across academic institutions are being
adopted by very few farmers and practitioners. Overall it is encouraging that about 60% of the agricultural retailers surveyed are offering precision farming services to their clientele. Review of thesurvey reports from previous years (data not presented) indicates that precision farming technologies and its adoption continue to grow among farmers.
While some may term the adoption of precision farming as slow, it is still remarkable given thatprecision farming was a new concept only about 25 years ago. In contrast, machine harvesters and24Soil-Specific Farming
GPS guidance with manual control/lightbar

65.8%

GPS guidance with auto control/autosteer

63.3%

Precision services offered

58.7%

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GPS-enabled sprayer boom section or nozzle control

38.8%

Satellite/aerial imagery for internal use

30.4%

Field mapping (GIS)—legal/billing/insurance

30.0%

GPS for logistics

15.8%

Soil electrical conductivity mapping

13.3%

Telemetry for field to home office information

6.7%

Chlorophyll/greenness sensors

3.8%

Other vehicle-mounted soil sensors for mapping

2.5%

2011 Base: 240

0%

60%
20%
40%
% of respondents

80%

FIGURE 1.8 Summary of a nationwide survey reflecting the use of precision technologies across agricultural states in the United States. (Modified and adapted from Whipker, L. D., and B. Erickson, 2011 Precision
agriculture services dealership survey results. Staff paper, Department of Agricultural Economics, Purdue
University. W. Lafayette, IN, 2013.)

combines that are used today by most farmers in North America and elsewhere were invented in
the early 1800s, commercialized in the late 1800s, and it took over 50 years or more since commercialization before they were used in large numbers on agricultural farms (Wikipedia 2014).
Nevertheless, precision farming technologies continue to evolve and change and its impacts arereflected in overall production, efficiency, and environmental footprints of farming operations.

1.17 SUMMARY AND FUTURE TRENDS
The history of precision agriculture has shown that it is more strongly influenced by technologicalinnovations rather than innovations in information analysis and decision support. For example,
when first introduced, GPS and yield monitors were viewed as technological advances that could be
added to existing farm equipment to add value. Later, agribusiness began embedding both GPS andyield monitors onto farm combines as part of the standard sales package. This combination of technology is now widely adopted by farmers so that it is used by practitioners of precision farming as
much as by practitioners of conventional farming. The addition of GPS to farm equipment enabledmany other technological breakthroughs in precision farming, such as autosteer, and furthermore,machine location was essential for variable rate fertilizer application technology.
In contrast, information analysis and decision support systems for deriving management zones
or making variable rate recommendations have not largely been embedded in routine farm operations. In many cases these functions are performed by crop retailers, consultants, and agribusinessservice providers for a fee. There seems to be a trend toward more focus on information analysis anddecision support systems in precision agriculture (McBratney et al. 2005). In particular, large corporations and researchers are beginning to focus their attention on big data issues, involving combinations of spatially and temporally varying yield monitor, soil fertility, crop stress, and climate data.
This data is overlain from many separate farming operations with a view toward identifying andmodeling relationships with soil or landscape features that could be used to create knowledge that

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25

informs precision farming decisions. In general, the volume, variety, and value of large databases isincreasing, while the scale at which management decisions are being visualized and implemented
is becoming finer. Increasingly, there is likely to be a trend toward stronger reliance on forecasting
precision farming operations based on short-term weather forecasts and expert system simulation
models and delivering recommendations to farmers via the Internet and smartphones.
Within the technology realm, there is an increasing convergence between proximal sensing androbotics. Sensors mounted on aerial and ground robots are increasingly being used to scout forand mitigate damages caused by crop stress. Significant research efforts are being directed toward
improved software algorithms that are devoted to improved navigation and coordination between
swarms of aerial and ground robots deployed in large agricultural fields. However, this convergence
between robotics and proximal sensing will not be successful without increased emphasis on information analysis and decision support systems that allow massive amounts of data collected withthese technologies to be quickly and accurately turned into useful recommendations and management strategies. A wide range of analytic tools are increasingly being used for this purpose, including partial least squares analysis, neural network analysis, and computer vision.
The spatial and temporal resolution of remote sensing information has dramatically improved
since the inception of precision farming. In the early years of precision farming, spatial resolutionsof satellite data were on the order of 30 m, while temporal resolutions were on the order of weeks
to months. Today, spatial resolutions are as good as several centimeters, while temporal resolutions
are as good as a few days. With this level of spatial and temporal resolution, it is likely that precision
farming practitioners will be able in the near future to develop customized management recommendations on a weekly basis for every single plant growing in their field.

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