precision farming adoption and use in ohio: case studies of six leading-edge adopters

Precision farming adoption and use in Ohio: casestudies of six leading-edge adopters

Marvin T. Batte *, Michael W. Arnholt

Department of Agricultural Environmental and Development Economics, The Ohio State University, 2120

Fyffe Road, Columbus, OH 43210-1067, USA

Received 7 February 2002; received in revised form 9 September 2002; accepted 25 September 2002

Abstract

Precision farming (PF) has the potential to help farmers improve input allocation decisions,

thereby lowering production costs or increasing outputs, and, potentially, increasing profits.

However, little is known about how farmers use PF technologies to support managerial

decision-making, or about the relative magnitude of benefits and costs of PF technologies on

individual farms. An embedded, multiple-case study approach was used to collect information

about PF from six farms. The objective was to collect information about adoption and use of

PF from early adopters to glean information that would be useful to those considering

adoption of this farming system. Results suggest that farmers credit benefits to PF for a wide

variety of decision types. The case study farmers appear to derive more value from

information gathering technologies (e.g. yield monitors and mapping) than from variable

rate application technologies.

# 2002 Elsevier Science B.V. All rights reserved.

Keywords: Precision farming; Technology adoption; Case study research

1. Introduction

Precision farming (PF) is an emerging technology with substantial promise to aid

both farmers and society by improving production efficiency and/or environmental

stewardship. It is an information technology that allows the manager to ‘tailor soil

and crop management to fit the specific conditions found within a field’ (Erickson

* Corresponding author. Tel.: �/1-614-292-6406; fax: �/1-614-292-4749

E-mail address: [email protected] (M.T. Batte).

Computers and Electronics in Agriculture 38 (2003) 125�/139

www.elsevier.com/locate/compag

0168-1699/02/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 8 - 1 6 9 9 ( 0 2 ) 0 0 1 4 3 - 6

mailto:[email protected]

and Lowenberg-DeBoer, 2000). The National Research Council (1997) refers to

precision agriculture ‘as a management strategy that uses information technologies

to bring data from multiple sources to bear on decisions associated with crop

production’. PF differs substantially from previous farming technologies in that it is

comprised of numerous component technologies that farmers may adopt as a system.

Some farmers may adopt a few components while others may adopt several. PF

component technologies include yield monitors, georeference grid soil sampling,georeferenced variable rate technology (VRT) for lime, fertilizer, and pesticide

application, global positioning systems (GPSs), and detailed field maps created from

geographic information systems (GIS), to name just a few.

By making more informed management decisions and improving input allocation

through use of PF, farmers can become more efficient, lower production costs, and,

potentially, increase profits. However, little is currently known about how farmers

use PF technologies to support managerial decision making, or about the relative

magnitude of benefits and costs of PF technologies on individual farms. Additionalresearch on PF technology is needed to assist the agricultural community in finding

answers to questions surrounding the adoption, uses, and the potential management

benefits of PF technology.

The objective of this study was to collect information about adoption and use of

PF from early adopters of this technology and to glean from this information that

can be useful to those considering adoption. Specifically, the objectives were (1) to

perform exploratory and explanatory case study interviews with six progressive,

early adopters of PF technology in Ohio to understand why these farmers adoptedPF technology, (2) to understand which technology components have been the most

beneficial to the adopting farmers, and (3) to evaluate how management practices

have changed as a result of adopting PF components.

2. Literature review

According to early research by Swinton and Lowenberg-DeBoer (1998), PF

technology adoption has been rapid but uneven. A more recent study conducted inIllinois, Indiana, Iowa, and Wisconsin (Khanna et al., 1999) shows only 20% of

growers have adopted an advanced PF system. They found that adopters of PF tend

to be younger, more educated, full-time farmers, and operate larger sized farms. The

study also suggested that adoption of advanced PF systems is path dependent: 69%

of the studied farmers had chosen ‘a limited adoption strategy by adopting a

diagnostic technology but preferring to wait before adopting a variable-rate

application technology or a yield monitor’ (Khanna et al., 1999). The authors

further suggest that low rates of adoption are due to ‘uncertainty in returns due toadoption, high fixed costs of investment and information acquisition, and lack of

demonstrated effects of these technologies on yields, input-use, and environmental

performance’ (Khanna et al., 1999).

Gelb et al. (2001) summarized a questionnaire administered to attendees of the

2001 European Federation for Information Technology in Agriculture (EFITA)

M.T. Batte, M.W. Arnholt / Computers and Electronics in Agriculture 38 (2003) 125�/139126

conference. Attendees included PF professionals from academia, Extension and

private industry representing 25 countries. The questionnaire asked attendees to

evaluate those factors that limited adoption of information and communication

technologies (ICT) by farmers. Because there were no practicing farmers among the

respondents, the results should be ‘‘interpreted as ‘non-farmers’ perceptions of what

motivated ‘farmers’’’ (Gelb et al., 1999).

In response to the question do you think that there are problems with the uptake of

ICT in agriculture? , 52.3% indicated in the affirmative (Gelb et al., 2001). When

asked specifically about PF, 47.6% felt that this technology had unique character-

istics that restricted adoption by farmers. Sixty percent of the countries in attendance

had at least one representative who felt that there were characteristics unique to PF

that restricted its adoption. When asked to identify those factors limiting the use of

ICT by farmers, the factors suggested most frequently (in decreasing order of

incidence) were cost of technology, too hard to use/unfriendly, no perceived economic

or other benefits, do not understand the value of ICT , and lack of training .

Profitability of PF continues to be difficult to predict (Atherton et al., 1999). A

study of nine field research sites by Swinton and Lowenberg-DeBoer (1998), found

variable rate fertilizer application to be unprofitable on wheat and barley, sometimes

profitable on corn, and profitable on sugarbeets. They concluded that because PF

practices are site-specific, their profitability potential too is site-specific. Other

studies have recognized that the profitability of PF depends heavily on the degree of

spatial variability of soil attributes (e.g. soil types, fertility and organic matter) and

yield response (Roberts et al., 2000). These researchers conclude that economic

returns of variable rate nitrogen application can only be determined on a field-by-

field basis because returns depend on the specific attributes of each field.

Farmers also may derive value from record keeping and documentation functions

of PF. For instance, yield monitors, GPS receivers, and GIS mapping are useful to

maintain precise records of the location, hectares planted, and yields of crops and

may be a facilitating technology for identity preservation (Erickson and Lowenberg-

DeBoer, 2000). It is difficult to attach a monetary value to this benefit of PF, but it

may be substantial for some farmers.

PF methods may also have implications regarding risk management. Lowenberg-

Deboer (1999) presented a simple theoretical model that suggested there were

circumstances when site-specific farming (SSF) could reduce whole-field yield

variability. Empirical evidence from on-farm tests of site-specific fertilizer manage-

ment supported the hypothesis that SSF can have risk-reducing benefits. The data

suggest that SSF can reduce the probability of profits falling into the lower profit

distribution level. However, he also recognized that SSF may increase some risks,

including business, financial, human, and technological risks.

Some studies considered the potential of environmental benefits that may arise

from the use of PF, especially variable rate application technology. Environmental

benefits of VRT are thought to increase with increased fertility variability due to the

relative increases in fertilizer use efficiency as compared with the traditional single

rate application method (Thrikawala et al., 1999). As with the profitability of PF,

M.T. Batte, M.W. Arnholt / Computers and Electronics in Agriculture 38 (2003) 125�/139 127

environmental benefits of PF my also be site-specific and could play a role in the

motivation and adoption of PF.

3. Procedures

Case studies have been and continue to be used extensively in various socialsciences. A case study is ‘an empirical inquiry that investigates a contemporary

phenomenon within its real-life context, especially when the boundaries between

phenomenon and context are not clearly evident’ (Yin, 1994). Case studies are

becoming more and more accepted, ‘not as a replacement for theoretical or statistical

approaches, but as complements that enhance understanding’ (Kennedy and Luzar,

1999, pp. 582). The goal of the case study research reported here is to explain,

understand, and answer the ‘how’ and ‘why’ questions regarding adoption

motivation and the changed management practices resulting from PF adoption.The six cases studied are not a random sample of Ohio farmers. County Extension

agents from various regions within the state were contacted and asked to nominate

precision farmers who are innovative, open to experimentation, have experience with

a broad range of PF component technologies, and who are known to be excellent

managers.

The classical diffusion model depicts the development and distribution of

technology as a multistage process: invention, innovation, adoption, diffusion, and

structural reorganization. Inventions are ideas perceived as new by an individual.Schumpeter (1939) identified innovation as the incorporation of invention into

technology, thereby successfully establishing the superiority of the new technology in

practice. Adoption occurs when the entrepreneur accepts a new idea full-scale and

continues its use. Diffusion is the process by which innovation is transferred among

producers.

The Social Interaction Perspective (S-I) assumes the existence of a diffusible

innovation as a precondition for any analysis of the diffusion process (Havelock,

1971). The flow of adoption through time is the primary concern in S-I, and theuser’s position in the social system is emphasized. For instance, the ‘innovator’, who

tends to be on the fringe of his home system because he has so many links with the

other outside systems, the source of inventions for innovation; the ‘early adopter’,

who often is viewed as an opinion leader in the local social system and who is very

influential in influencing adoption by other members of the system; and the

‘laggard,’ who is isolated and is peripheral to the main streams of interpersonal

relations.

An alternative definition of adopter roles is common in the literature and employsa definition of adopter categories based on the timing of adoption, generally

expressed relative to the cumulative adoption curve. As Rogers (1983) observes,

innovativeness, ‘the degree to which an individual is relatively earlier in adopting

new ideas. . . is a continuous variable, and partitioning it into discrete categories is

only a conceptual device’ (Rogers, 1983, pp. 245). Rogers arbitrarily defines


Table 1

Farmer and farm business characteristics for the six case farms

Ted Jones Andy Smith Mike Gate Jim King Todd Hall Steve Brown

Age 42 51 55 44 55 20

Family members

involved in farm

operation

None None Wife and son Brother Wife, son, and

daughter in-law

Father, mother, and

two brothers

Total years inter-

viewee actively

farming

17 18 37 23 38 4

Gross sales per year

$ range

$250 001�/

500 000

$500 001�/1 000 000 Over $1 000 000 $250 001�/500 000 $500 001�/1 000 000 Over $1 000 000

Highest level of

education achieved

High school di-

ploma and 2

years of college

High school diploma High school diploma High school diploma

and 1 year of college

High school diplo-

ma

2 year Associates

degree

Total hectares

farmed

688 809 1376 546 1093 931

Total hectares

owned

89 283 607 486 279 283

Location of within

Ohio

East Southwest Northwest South Southwest Southwest

Crops grown and

hectares

#2 yellow corn

344 ha, soy-

beans 344 ha

#2 yellow corn 364 ha,

soybeans 364 ha,

wheat 162 ha

High oil corn 445 ha,

#2 yellow corn 162 ha,

soybeans 607 ha,

wheat 162 ha

# 2 yellow corn 243 ha,

soybeans 243 ha, and

wheat 40 ha

#2 yellow corn 526

ha and soybeans 526

ha

#2 yellow corn 445

ha, soybeans 445 ha,

alfalfa 20 ha, wheat

20 ha

Livestock None None 4000 sows None None None

Identity preserved

crops grown

No Yes: seed soybeans,

tofu soybeans, non-

GMO soybeans, and

GMO soybeans

Yes: high oil corn Yes: non-GMO soy-

beans and GMO soy-

beans (in the past grew

high oil and waxy corn)

Yes: tofu soybeans,

STS soybeans, non-

GMO corn and

GMO corn

Yes: seed soybeans

Own computer(s)

that are used in

farm management

Yes Yes Yes No Yes Yes

M.T

.B

atte,

M.W

.A

rnh

olt

/C

om

pu

tersa

nd

Electro

nics

inA

gricu

lture

38

(2

00

3)

12

5�

/13

91

29

innovators as the earliest 2.5% of adopting producers, and early adopters as the next

13.5% of adopters.

The case farmers studied probably should be considered early adopters rather than

innovators. Although they are among the earliest adopters of PF in their

communities, most are adopting commercially available systems, and thus are not

part of the invention process. Generally, they are well-respected farm managers, and

typically are viewed as opinion leaders in the local farm community. Certainly, thecase farmers should not be thought of as representative of all Ohio precision farmers.

Still, the things learned from these case studies will be instructive for that larger

group.

The data collection protocol for the case studies included the following: (1) the

case study questionnaire was administered by the same researcher during each

interview, (2) an open ended question and discussion format was used by the

researcher during the interviews, (3) careful attention was made to avoid any

‘leading’ of farmers being interviewed, and (4) standardized lists of options were usedto capture ranking information on specific questions. These rules helped limit

potential for bias and aided in the collection of qualitative, multiple-case study data.

In the following sections, the individual cases will be described. Names of farmers

have been changed to protect the identity of the individual. This is followed by a

cross-case summary where generalizations are made.

3.1. The Ted Jones Farm

Ted is a full-time farmer operating a 688-ha grain farm in east�/central Ohio(Table 1). Ted has farmed for 17 years, typically producing 344 ha each of corn and

soybeans with annual sales of $250 001�/500 000. Ted began using PF technologies in

1995 with the adoption of a yield monitor, GPS receiver, and GIS (Table 2). In 1997,

he added soil sampling by management zones. In 2000, he added infrared aerial

photography and a light-bar navigation system. Ted has had technical difficulties

with the light bar navigation system, but he is certain he will work the ‘bugs’ out.

Weather and scheduling issues have made it difficult to get infrared photographs

taken at appropriate times during the growing season.Ted and his crop consultant have identified soil management zones based on crop

yield results. His soil samples are taken by a crop consultant and analyzed by an

independent lab. Ted tests the soil for phosphorus, potassium, calcium, magnesium,

organic matter, pH level, and cation exchange capacity (CEC). Also, selected

management zones are tested for other macro and micronutrients. The crop

consultant makes the fertilizer recommendations.

Ted does not use VRT for fertilizers at this time, primarily because there is a lack

of local providers of this service. Ted mentioned that his local agricultural serviceproviders only sell VRT fertilizer applications in ‘packaged’ service agreements, and

will not sell the individual services Ted wants. However, Ted expects to begin using

map-based VRT of phosphorus, potassium, and lime in the next 1�/3 years.

Ted has not used map-based VRT of herbicides or pesticides, but has manually

varied the rate of herbicides. Ted has not used map-based variable rate seeding, but


Table 2

PF practices currently used on the six case farms

PF currently adopted Year adopted

The Ted Jones

Farm

Andy Smith

Farms

Mike and Joan Gate,

Inc

King

Farms

Todd and Rob

Hall

Brown

Farms

0.8�/1.2 ha soil grid sampling 1998 1996 1994 1996 1995

Management zone soil sampling 1997 1990

GPS VR application of fertilizers (N, P, or K) 1996 1997 1996

GPS VR application of lime 1999

GPS VR application of liquid manure 1998

GPS VR application of herbicides 1998

GPS VR planting 1999 1998

Yield monitor 1995 1999 1995 1995 1995 1994

GPS receiver 1995 1999 1998 1995 1995 1995

GIS mapping software 1995 1999 1999 1995 1995 1995

Infrared aerial photography 2000 1998 1994

Georeferenced scouting for weeds 2000

Field areas elevation measured with lasers and GPS

referenced

1998

Light-bar navigation system 2000 1998 1999

M.T

.B

atte,

M.W

.A

rnh

olt

/C

om

pu

tersa

nd

Electro

nics

inA

gricu

lture

38

(2

00

3)

12

5�

/13

91

31

he has manually varied the variety of soybean planted within the field. His crop

consultant makes the seed recommendations.

Ted’s biggest success with PF has been better overall input management that has

directly led to higher yields and increased profits. Ted’s biggest disappointment with

PF technologies has been the lack of technical and customer support for this

emerging technology, especially for his GPS receiver. Getting the ‘bugs’ out has been

a big challenge for Ted. However, he is mostly satisfied with his overall PF system.

3.2. Case study #2: Andy Smith Farms

Andy is a fulltime farmer who is in an equal partnership with a friend on an 809-

ha operation in Southwest Ohio (Table 1). Andy and his partner share all farm

management duties. In 1990, Smith Farms began using PF technologies with the

adoption of management zone sampling (zones defined by soil type) on the 809 ha.

One-hectare soil samples were drawn on about 162 ha in 1998 to compare results forthese grids with the management zone sampling results from 1990. In 1999, Andy

adopted a yield monitor, GPS, GIS, and georeferenced VRT of lime.

Soil samples are drawn by a crop consultant and analyzed by an independent lab.

Soil is tested for nitrogen, phosphorus, potassium, magnesium, calcium, pH level,

CEC, and organic matter. The crop consultant makes the fertilizer recommenda-

tions. Currently, Andy is using map-based VRT only for lime. Andy feels that there

is sufficient variability of pH across the field to make VRT application of lime

profitable, however, the variability of phosphorus and potassium are insufficient forprofitable VRT application of these nutrients. Andy does not variably apply

herbicides, insecticides, or pesticides, nor does he use variable rate seeding.

Andy’s biggest success has been that he feels he can verify and confirm

performance of current management practices on his farm. The biggest disappoint-

ment with his PF system to date has been the lack of the ability to justify the cost of

the overall system. He shows positive returns with georeferenced VRT of lime, but

negative returns on the yield monitor and yield mapping. The net result continues to

be an overall negative net return for Smith Farms. Still, Andy plans continuedadoption of PF technologies.

3.3. Case study #3: Mike and Joan Gate, Inc

Mike and Joan, his wife, are the sole owners of their central Ohio farming

operation. Mike farms full time, managing the grain enterprise and feed mill. Joan is

the record keeper. Their son is an employee, the chief financial officer, and the swine

manager. The cropland is planted with 445 ha of high oil corn, 162 ha of number 2

yellow corn, 607 ha of soybeans, and 162 ha of wheat. The farm also includes a 4000sow farrow-to-finish operation.

The Gates began adopting PF technologies in 1995 with purchase of a yield

monitor, followed by 1-ha grid soil sampling and map-based variable rate

application of lime and potassium in 1996. VRT lime is applied by a local

agricultural service provider. Mike also variably-applies potassium with owned


equipment. Mike and Joan have variably-applied liquid manure since 1998. A light-

bar navigation system is also used when applying herbicides and other inputs on his

farm. They tried infrared aerial photography in 1998, but questioned its potential

and have discontinued the use of this technology.

Detailed soil grid sampling is an important part of the Gate’s PF system. The

uniform soil grids are 0.8�/1.0 ha in size. Farm employees draw the samples, with

analyses done by an independent lab. A farm employee makes the fertilizer

recommendations using the Tri-State recommendations as a guideline. Currently,

Mike and Joan use map-based variable rate seeding on about 20% of their corn

planting.

The Gate’s biggest success from the adoption and use of PF technologies has been

the personal gratification from becoming increasingly independent and having the

increased confidence in the management decisions made each day. The biggest

disappointment has been the tremendous learning curve associated with this

technology. The Gates plan to continue to purchase, adopt, and use new PF

technologies as they become available. They are especially interested in any future

technology that can perform on-the-go soil sampling/testing. They are satisfied with

their overall PF system. They feel that PF technologies have been instrumental in

helping accurately determine operating costs on the farm.

3.4. Case study #4: King Farms

King farms consists of two owner-operators, brothers Jim and Dave. They evenly

share most of the daily management responsibilities. The farm consists of 546 ha in

south�/central Ohio, 486 of which are owned by King Farms. The typical annual

cropland area is 243 ha in each of corn and soybeans, and 40 ha of wheat.

King Farms began PF in 1994 with adoption of grid soil sampling (1.1-ha grids)

and infrared aerial photography of about 121 ha. In 1995, the Kings began use of a

yield monitor, a GPS receiver, and GIS field mapping software. They adopted

georeferenced VRT of potassium and phosphorus in 1997, and had about 40 ha

mapped by elevation for drainage management purposes.

A local service provider draws soil samples. The soil analysis is done by an

independent lab, which tests for phosphorus, potassium, magnesium, calcium, pH,

organic matter, and CEC. The local service provider then makes the fertilizer

recommendations and applies phosphorus and potassium at variable rate. Lime is

applied by manually varying the rate based on grid soil test results.

King Farm’s biggest success with PF technologies arises from analysis of crop

yield maps to make better hybrid and variety selections. Their biggest disappoint-

ment has been the extreme variability in the results of grid soil samples taken 3 years

ago compared with the grid soil sample results taken recently. They are not sure why

so much nutrient level variation has occurred over time. They plan to continue to use

and adopt additional PF technologies as new components are developed.


3.5. Case study #5: Todd and Rob Hall

Todd has farmed for 38 years. The farm, which consists of 1093 ha of grain crops

(279 ha are owned), is located in southwest Ohio. Todd, and his son Rob, share the

daily responsibilities of operation and management on their grain farm. Their wives

also take an active role in the farm business, maintaining the GIS, yield records, and

accounting records. All four family members are employed full time on the farm.The Halls began using PF technologies in 1995 with the adoption of a yield

monitor, GPS receivers, and GIS software. They added 1.2-ha grid soil sampling in

1996 and a light-bar navigation system in 1998. They have manually varied the rate

of application of phosphorus, potassium, and lime since 1996 on approximately 109

ha and planned to use map-based VRT of phosphorus, potassium, and lime in the

spring of 2001. They planned to introduce georeferenced scouting for insects, weeds,

and other crop diseases in 2001.

The Halls biggest success has arisen from increased knowledge and awareness ofthe ‘details’, such as variability in soil pH levels, crop yields, weed pressure, and

drainage patterns. Also, they feel they have a better understanding of soil types and

fertility needs. There has been an overall gain of agronomic information that has

assisted them to make better management decisions.

The Halls single biggest disappointment with PF technologies has been lack of

affordable and compatible software. As the technology changes and is updated, they

feel that there are too many incompatibility problems with the software and

computers. They would like to see greater standardization among manufactures.Still, Todd and Rob plan to continue to adopt and use PF technologies.

3.6. Case study #6: Brown Farms

Steve Brown farms with his father, mother, and two brothers in western Ohio.

Steve’s father and mother are the owners of the farm business. The three sons each

contribute to management. Steve has primary responsibility for managing the PF

technologies used in the business. The family also provides custom spray and VRT

services to area farmers as well as selling seeds and herbicides to local farmers.Currently, the Browns use several PF technologies. Adoption began in 1994 with a

yield monitor. In 1995, four GPS receivers, a GIS system, and a 1-ha grid soil

sampling technique were added. Map-based VRT of fertilizers began in 1996 and

map-based variable planting of soybeans followed in 1998. Further adoption

included VRT of herbicides in 1998 and a light-bar navigation system in 1999.

Finally, georeferenced weed scouting was adopted in 2000. They have used Soil

DoctorTM for soil electric conductivity, but have decided to abandon this tool.

The biggest success in Steve’s opinion has been the precise and detailed GIS yieldmaps, which help answer questions and assist in making better management

decisions. Steve stated he is able to see where yields change within fields and look

at fertilizer, pH, and soil type map overlays to try to understand why the variability

may be occurring. The biggest disappointment for Steve has been the overall lack of

technical support on the PF equipment itself. He feels that the technology is not as


user-friendly as it could be and would like to see improvements made in this area.

Steve plans to continue to adopt and purchase additional PF technologies as they

become available. He is interested in GPS satellite imagery and would be very

interested in a technology that could measure soil nutrient levels on the go.

4. Cross-case summary

A look across the six cases studied reveals a number of similarities and a number

of unique factors. The case farmers are early adopters of this technology. Five

growers have been using at least one PF technology since 1995. The four most

commonly used PF technologies in this case study were yield monitors, GPS

receivers, GIS and mapping software, and georeferenced grid or zone management

soil sampling (Table 3). All six growers are currently using all four of these PF

technologies on nearly all the land they farm. Four of the six case farms began with

adoption of a yield monitor. The other two farmers began with adoption of grid orzone soil sampling. Although variable rate application of inputs is often thought of

as an important element of PF, only half of the case farms are using VRT application

of N, P or K nutrients, only one is doing VRT application of herbicides, and only

two are planting with variable seed populations or site-specific variety selection.

Many of the case farmers questioned the economics of these practices given the

current VRT service fees and soil sampling costs.

The case farmers were asked several questions focused on understanding their

motives for adopting PF technologies. When asked to identify the top reason theyhad adopted PF, five of six suggested a profit motivation*/either to earn profits now

or to better position their farm to be profitable in the future (Table 4). The sixth

farmer has a large hog enterprise, and cited environmental compliance as the most

important motive. This picture becomes more interesting when one looks at other

important motives. The rightmost column of Table 4 summarizes those motives that

were listed by the case farmers as the first, second, or third most important motive

for adoption. Profitability concerns clearly are most important, but farmers revealed

that on-farm experimentation, improved information regarding within-field varia-bility to support decisions, and the risk reduction potential were all motives for

adoption.

PF is not a single technology, but rather a suite of technologies that can be

assembled into a system. Farmers are expected to adopt various component

technologies, depending on their needs and other site-specific characteristics of the

business. The case farmers were asked to identify which of the adopted component

technologies were the most important in their PF system (Table 5). When asked to

identify the single most important technology, half of the case farmers pointed to theyield monitor and two identified georeferenced grid or zone soil sampling. The

rightmost column of Table 5 identifies those technology components that were

ranked by farmers as either first, second or third most important. The yield monitor

remains at the top of the list, but the global positioning receiver and GIS/mapping

capabilities are second and third most frequently cited. This is important in that all


three of these components are necessary to be able to produce visual summaries of

the site-specific data that is necessary to allow site-specific management of the field.

This underscores the importance of PF data as an input into the decision making

process.Along these lines, farmers were asked to evaluate the most important information

derived from the PF system (Table 6). When asked to identify the single most

important information output, five different categories were identified. Two farmers

cited information supporting the selection of crops and/or variety as most important.

Two farmers focused on variation in soil fertility or soil pH level. One farmer

pointed to information useful to support drainage problems, and one identified

information that quantified within-field yield variability. The rightmost column of

Table 6 summarizes the first, second or third most important information types. This

reinforces the information types just identified, but also identified as important

Table 3

Technology adoption summary for the six case farms

Technology Number of case farms that have adopted this technology

GPS receiver 6

Mapping software 6

Yield monitor 6

Grid soil sampling 5

Aerial/satellite imagery 3

Navigation systems 3

VRT of N, P or K 3

Management zone soil sampling 2

Variable rate planting 2

Elevation mapping 1

Georeferenced scouting for weeds 1

VRT of herbicides 1

VRT of lime 1

VRT of manure 1

Table 4

Primary motives for adoption of PF technologies

Motive Single most important

motive

First, second, or third most important

motive

To increase profits 5 6

To facilitate on-farm experimenta-

tion

0 4

To better understand field varia-

bility

0 4

To reduce risk 0 2

To facilitate environmental com-

pliance

1 2

Total 6 18


information useful for crop insurance records/needs, data useful to more efficiently

manage the grain drying process, and data useful to define appropriate managementzones for variable rate application of inputs.

Finally, farmers were asked to identify how their PF system has changed the way

they manage. Table 7 lists the most important management practices these farmers

have changed due to their use of PF technologies. The most commonly cited change

was in the rates of fertilization or lime application. Two farmers also cited field

drainage decision-making as the most important change. When the top three

management changes are considered, other decisions are also revealed as changed,

including decisions about tillage practices, timing of plantings, weed controlmethods, the scheduling of machinery, and aspects of the control function of

management.

The case study results are mixed when it comes to whether or not the benefits of

PF have exceeded costs. Two growers feel benefits clearly exceed costs, one is

uncertain, and three feel that benefits do not clearly exceed costs of PF technologies.

Still, all of the growers were optimistic about the future of PF technologies. None of

these growers plan to abandon current PF technologies, and all are eager to learn

more about new technologies that are emerging. Even though PF may not currentlybe profitable for all, they all expect that as the technology matures it will bring

profitability.

5. Conclusions

The results of the six cases studied here suggest that managers derive value fromdifferent components of the system, often focusing on distinct aspects of the

business. All of the studied managers had found important ways that the PF system

could improve their business. Although the case farmers were split on whether the

overall system was profitable, they all agreed that they would continue to adopt new

precision technologies as they become available.

Table 5

Most important components of the farm’s PF system

Component technology Single most important

component

First, second, or third most impor-

tant component

Yield monitor 3 6

GPS 1 5

GIS mapping 0 5

Georeferenced grid or zone soil sam-

pling

2 3

Variable application of fertilizer, lime

or manure

0 2

Total 6 21a

a The total exceeds 18 because two individuals cast two third-place votes.


There are a number of lessons that might be conveyed to other farmers

considering this technology. The site-specific nature of PF benefits is conspicuous.

The ‘killer application’ differs from farm to farm, depending on the unique problems

faced on that farm (e.g. soil pH, fertility, drainage) or perhaps with the analytical

style and managerial strengths of that individual. Also, the apparent importance of

PF as a data instrument is striking. Several farmers viewed the yield monitor, GPS

receiver and GIS/mapping software as the most important components of the

system, and these components as a set are useful primarily for yield data capture and

its visual depiction as a map.

Finally, of importance to researchers is the observation that many of these highly

capable farmers expressed frustration with the hardware, software and procedures of

Table 6

Most important information derived from the farm’s PF system

Information use Single most impor-

tant type

First, second, or third most

important type

Precise soil nutrient levels in grids and/or man-

agement zones

1 4

Precise soil pH levels in grids and/or management

zones

1 3

Information relevant to selection of crops planted

(hybrid and variety)

2 3

Quantified yields by field location 1 3

To identify drainage problem areas 1 3

To identify/define appropriate management zones 0 1

For crop insurance records 0 1

Information to more efficiently manage grain

dryers

0 1

Total 6 19a

a The total exceeds 18 because one individual cast two third-place votes.

Table 7

Most important management practice changes that have occurred as a result of PF adoption

Management practice Single most impor-

tant change

First, second, or third most

important change

Fertilizer, lime or manure application rate 3 4

Hybrid and variety seed selection 1 4

Tillage practices performed 0 3

Field tiling and drainage system decisions 2 3

Planting timing 0 1

Weed control program 0 1

Control function of management*/to verify the

performance of management practices

0 1

Schedule machinery 0 1

Total 6 18


the system. It is clear that this is not a turn-key technology. There are many complex

relationships that must be understood before the system can be used successfully.

Before we have widespread adoption of these systems, research will be needed to

simplify the technology, to improve decision rules for key inputs, and to develop

new, lower-cost and more reliable sources of data to support PF decisions.

Acknowledgements

Salaries and research support provided by State and Federal funds appropriated

to the Ohio Agricultural Research and Development Center, The Ohio State

University, by the VanBuren Program of Farm Management, and by US

Department of Agriculture Grant No. 97-36200-5239, Economic and Environmental

Evaluation of Site Specific Farming Technologies.

References

Atherton, B.C., Morgan, M.T., Shearer, S.A., Stombaugh, T.S., Ward, A.D., 1999. Site-specific farming: a

perspective on information needs benefits and limitations. Journal of Soil and Water Conservation 54

(2), 455�/460.

Erickson, K., Lowenberg-DeBoer, J. (Eds.), Precision Farming Profitability. Purdue University, West

Lafayette, IN 2000, p. 1.

Gelb, E.M., Schiefer, G., Parker, C., Rosskopf, K., 1999. ‘Why is the IT adoption rate by farmers so

slow?’ (EFITA papers-http://www.efita.dk). October, p. 2.

Gelb, E., Parker, C.G., Wagner, P., Rosskopf, K., 2001. ‘Why is the ICT adoption rate by farmers still so

slow?’ Paper presented at the International Conference on Agricultural Science and Technology,

Beijing, PROC, November.

Havelock, R.G., 1971. Planning for Innovation through Dissemination and Utilization of Knowledge.

Center for Research on Utilization of Scientific Knowledge and the Institute for Social Research,

University of Michigan, Ann Arbor, MI.

Kennedy, P.L., Luzar, E.J., 1999. Toward methodological inclusivism: the case for case studies. Review of

Agricultural Economics 21 (2), 579�/591.

Khanna, M., Epouhe, O.F., Hornbaker, R., 1999. Site-specific crop management: adoption patterns and

incentives. Review of Agricultural Economics 21 (2), 455�/472.

Lowenberg-Deboer, J., 1999. Risk management potential of precision farming technologies. Journal of

Agricultural and Applied Economics 31 (2), 275�/285.

National Research Council, 1997. Precision Agriculture in the 21st Century: Geospatial and Information

Technologies in Crop Management. National Academy Press, Washington, p. 149.

Roberts, R.K., English, B.C., Mahajanashetti, S.B., 2000. Evaluating the returns to variable rate nitrogen

application. Journal of Agricultural and Applied Economics 32 (1), 133�/143.

Rogers, E.M., 1983. Diffusion of Innovations. McMillan Publishing Company, New York, p. 1983.

Schumpeter, J., 1939. Business Cycles. McGraw-Hill, p. 1939.

Thrikawala, S., Weersink, A., Gary, K., Fox, G., 1999. Economic Feasibility of Variable-Rate Technology

for Nitrogen on Corn. American Journal of Agricultural Economics 81, 914�/927.

Swinton, S.M., Lowenberg-DeBoer, J., 1998. Evaluating the profitability of site-specific farming. Journal

of Production Agriculture 11 (4), 439�/446.

Yin, R.K., 1994. Case Study Research: Design and Methods, second ed.. Sage Publications, Newbury

Park, p. 13.


http://www.efita.dk

precision farming adoption and use in ohio: case studies of six leading-edge adopters

Documents