FACTORS AFFECTING ADOPTION OF RECOMMENDED MANAGEMENT PRACTICES IN STOCKER CATTLE PRODUCTION

Rachel J. Johnson Former graduate student, Oklahoma State University and now at USDA/ERS

Damona Doye

Department of Agricultural Economics Oklahoma State University

Stillwater, OK 74078 Phone: 405-744-9813

Email: damona.doye@okstate.edu

David L. Lalman Department of Animal Science

Oklahoma State University

Derrell S. Peel Department of Agricultural Economics

Oklahoma State University

Kellie Curry Raper Department of Agricultural Economics

Oklahoma State University

Chanjin Chung Department of Agricultural Economics

Oklahoma State University Copyright 2008 by Rachel J. Johnson, Damona Doye, David L. Lalman, Derrell S. Peel, and Kellie Curry Raper and Chanjin Chung. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

1

Factors Affecting Adoption of Recommended Management Practices in

Stocker Cattle Production

Abstract Binary logit regression models were used to estimate factors affecting adoption of recommended

management practices (RMPs). Variables analyzed include aspects of farm structure, human

capital, farm objectives, and production system employed by the producer. Results reveal that

operation size and dependency upon income generated from the stocker operation, in particular,

influence the adoption of RMPs. Older producers and those pursuing a year-round production

strategy were found to lag behind in RMP adoption.

Keywords Beef production, logit, management practices, stocker cattle

JEL Classifications: Q12, Q16

2

Factors Affecting Adoption of Recommended Management Practices in Stocker Cattle Production

Three phases typically comprise U.S. beef production: cow-calf, growing, and finishing.

Most calves go through a post-weaning growing program, although specific programs vary in

structure, type, and nomenclature. Calves that have been weaned and are intended for sale as

commercial feeder cattle, but have not yet been placed in the feedlot, are commonly referred to

as stocker cattle. Stocker calves represent an important segment of the beef production and

marketing chain and typically weigh from 300-800 lbs. Inventory of stocker cattle in a specific

geographic area at a point in time are not easily captured in the USDA data collection system.

However, the National Agricultural Statistic Service (NASS) national cattle inventory reports

reveal that there were 1.75 million stocker calves grazing small grain pasture in Kansas,

Oklahoma, and Texas as of January 1, 2008 (USDA-NASS 2008).

Stocker cattle represent an economically viable enterprise characterized by inexpensive

weight gain relative to the cow-calf and finishing phases of production (Peel 2003). A cow-calf

producer may retain ownership of weaned calves for growing as a preliminary phase before

cattle feeding. Alternatively, beef cattle producers may choose to engage in stocker cattle

production as an independent commercial enterprise.

In the stocker phase, emphasis is placed on animal growth versus fattening and on the use

of forage/ grazing-based systems versus concentrate feeds. Some stockers are grazed throughout

the summer (season-long), while others may be double-stocked and removed from summer

pasture in mid-summer (early intensive strategy). Winter production systems typically employ

either annual cool season forage, such as small grains pasture, or perennial cool season forages.

Stockers may also be completely confined where they are fed harvested forages. Mineral,

3

protein and/or energy supplementation is generally practiced, depending on forage conditions

(Peel 2003).

Numerous technologies and management practices are available to improve biological

and economic efficiency of stocker operations. Examples include anabolic implanting, setting

proper stocking rates, correctly administering intramuscular (IM) injections, marketing cattle in

uniform lots, using risk management tools, and drafting a long-term business plan (Hart et al.

1988; Dexter et al. 1994; USDA-APHIS 2000; Schmitz, Moss, and Schmitz 2003; Avent, Ward,

Lalman 2005; Doye 2005; Reuter, Highfill, and Lalman 2005). Little research has been

undertaken to identify the current production and management practices of stocker producers.

Core components of stocker production include nutrition, pasture management, quality assurance

and animal health, marketing and risk management, genetics, and business management. Each

management area offers opportunities to add value to the product and/ or reduce costs of

production.

Determining factors affecting producer adoption of recommended management practices

(RMPs)1 is of interest. Why are recommended production and management practices not being

implemented in certain cases? Is there a definable category of producers who are not adopting

new information and technology to whom educational programs could be targeted? The

objectives of this research are to document the degree of adoption for selected management

practices and to identify factors that influence adoption of RMPs in Oklahoma stocker

operations. Findings will enable researchers and Extension staff to determine how to best direct,

or perhaps redirect, research and educational programs to achieve the goal of high adoption

levels of RMPs within various production systems.

4

Stocker Cattle Recommended Management Practices

Technologies and management practices that increase either economic or biological

efficiency are frequently recommended to producers for adoption by Extension educator, for

example, using implants, setting stocking rates to match forage resources, using proper injection

sites, selling in uniform lots, utilizing risk management and business planning tools. Research

has shown that anabolic implants are one of the most cost-effective technologies available to

cattle producers as producers can expect a 10-15% improvement in average daily gain (ADG)

over non-implanted controls (Reuter, Highfill, and Lalman 2005). Implants increase the rate of

growth measured by ADG as well as the metabolic and economic efficiency of growth.

Implanting calves provides the capacity to increase weight gains by 8-20% during the grazing

season (Selk, Reuter, Kuhl 2006).

Since forage utilization represents a critical cost factor in stocker production, knowing

how to set a proper stocking rate is key to stocker profitability. Setting proper stocking rates

ensures that plants will recover from grazing during the growing season, the quality of the

available forage will be maintained, and animal performance will be optimized (Hart et al. 1988).

Manipulation of stocking rates and duration of grazing is necessary to optimize range

management and ensure profitability.

Injection site lesions arise from the administration of intramuscular (IM) injections. The

incidence of blemishes in top sirloin beef is approximately 11% of carcasses and results in a

substantial loss to the beef industry (Dexter et al. 1994). Blemishes result not only in visual

defects but also require further processing, resulting in increased toughness in the end product

and therefore, an undesirable consumer eating experience. State and national industry leaders and

educators have made an effort to inform beef producers of ideal injection practices, namely

5

administering intramuscular and subcutaneous injections in the neck region of the animal. Losses

to the beef industry from injection site blemishes are thought to primarily originate from the

cow-calf and stocker levels, or early in the finishing period (USDA-APHIS 2000).

Production and feeding efficiency is increased with larger, more uniform lots of cattle

and a premium is often paid when purchased cattle are pooled into uniform lots (Avent, Ward,

and Lalman 2005). Uniform lots may consist of cattle with a similar frame, muscling, weight,

and breeding. Jones et al. (1992) and Schroeder et al. (1988) found feeder cattle transaction price

differentials to significantly differ between uniform and mixed cattle lots. Using 2001-2003 data,

Ward, Ratcliff, and Lalman (2004) found that average sale price increased $1.91/cwt for cattle

sold in uniform lots.

Feeder cattle prices are among the most difficult to predict due to a constantly changing

demand for slaughter cattle attributed to changing feed prices and shifting demand in both

domestic and international markets. Utilizing futures and options contracts are among the risk

management strategies available to producers when marketing cattle. Selective hedging

strategies in live cattle markets have been found to decrease the volatility of returns while

increasing profitability (Shafer, Griffin, and Johnston 1978; Schroeder and Hayenga 1988).

The stocker enterprise is in essence a margin business with highly variable input and

output prices, primarily reflected in stocker calf purchasing prices and feeder cattle market

fluctuations. Business planning for a stocker operation is particularly important, yet often

neglected by producers. A business plan defines the operation’s goals, identifies limitations, and

includes financial plans. Livestock are realistically matched to land resources, appropriate

markets are targeted, and financial resources are identified. A business plan can be particularly

useful for stocker operators since it can serve as an important reference for producers seeking

6

financing. The ultimate goal of business planning is to move the enterprise in a direction so that a

producer’s goals and objectives will be fulfilled and to provide a feasible operational/ financial

plan for fulfilling those goals (Doye 2005).

Adoption of Technologies and Management Practices

Examining the factors affecting the adoption of technologies has long been a focus of

agricultural economics research (Griliches 1957; Rogers 1983). Griliches (1957) was one of the

first economists to analyze the adoption and diffusion of technological innovations from an

economic perspective. In his analysis, profitability was found to be the largest determinant of

adoption in the case of hybrid corn. Rogers (1983) examined how various characteristics, either

real or perceived, of a certain technology affected its adoption. In his analysis, profitability

comprised only one component of adoption. Other attributes positively influencing technology

adoption included relative advantage, compatibility, complexity, trialability, and observability.

Trialability was explained as the potential to experiment with the practice on a smaller scale and

observability related to the degree to which the producer had the ability to see the results of the

implemented practice.

Farm size has frequently been identified as a significant determining factor in the

adoption of agricultural innovations (Just and Zilberman 1983; Popp, Faminow, and Parsch

1999; Diederen, et al. 2003; Gillespie, Basarir, and Schupp 2004; Rahelizatovo and Gillespie

2004; Ward, et al. 2008; Banerjee, et al. 2008). Just and Zilberman (1983) analyzed land-use

allocation and technology adoption while considering various risk preferences. The role of farm

size in technology adoption was determined by the risk attitudes and stochastic relationship of

land returns. When relative risk aversion was constant, the allocation of land devoted to modern

7

technology was proportional to farm size. If absolute risk aversion was constant, larger farms

were found to devote more land to newer technology than smaller farms.

Diederen et al. (2003) used a nested logit model to analyze determinants of adoption of

innovations in stages. Farm size and market position significantly and positively influenced

whether a producer was an innovator or early adopter of a technology. Rahelizatovo and

Gillespie (2004) examined size relative to adoption of environmental stewardship practices by

dairy producers. Results of the study clearly found that larger dairy operations were more likely

to adopt best management practices (BMPs). Gillespie, Basarir, and Schupp (2004) found larger,

commercialized beef operations to be more likely to utilize alternative cattle marketing channels.

Popp, Faminow, and Parsch 1999 analyzed factors affecting adoption of value-added

production on cow-calf farms. Although the study found farm size to be a significant factor for

adoption, farm size and scale of the operation had minimal impact on the decision to invest in

cattle backgrounding. However, the producer’s perceptions towards risk and profitability were

factors significantly impacting adoption of value-added production components into cow-calf

operations.

Caswell et al. (2001) examined how technology adoption can be driven by unquantifiable

factors by studying the relationship between farm and off-farm work and farm economic

performance. The amount of off-farm work undertaken by producers was significantly related to

the adoption of technologies that economized on managerial time. Alternatively, operators of

large farms, more dependent upon on-farm revenues and pursuing off-farm work to a lesser

extent, were more likely to adopt managerially intensive technologies such as precision

agriculture. Technology adoption was shown to be related to unquantifiable factors such as

simplicity and flexibility which translate to reduced management time. Reinforcing these

8

findings, Daberkow and McBride (2003) noted a positive relationship between full-time farming

and adoption of precision farming technologies.

Technology adoption has also been found to be contingent upon the degree to which a

producer’s net household income is generated from the operation. A greater concern for

economic efficiency exists when the total percent of household income from the cattle operation

is high. Non-adopters of BMPs tend to be less dependent upon the operation as a generator of

household income (Gillespie, Kim, and Paudel 2007). Vestal (2005) examined management

practice adoption among Oklahoma cow-calf producers using chi-square analysis. Producer

groups consisted of large, income dependent, and small, non-income dependent producers.

Numerous statistically significant differences were identified between the two producer groups

pertaining to nutrition, herd health, marketing and risk, and business management practices with

large, income dependent producers more likely to adopt recommended practices. Net household

income was a factor in adopting precision farming technologies in the cotton industry examined

by Banerjee et al. (2008), where income levels greater than $50,000 were significant in the

adoption of GPS guidance systems by cotton producers. Gillespie, Kim, and Paudel (2007)

analyzed adoption rates of 16 BMPs related to beef production in a study that focused on reasons

for non-adoption and factors influencing non-adoption. Most frequently adopted BMPs were

those that resulted in immediate economic benefits, such as grazing management practices and

mortality plus nutrient and pesticide management. Non-applicability and unfamiliarity were the

most commonly cited reasons for lack of BMP adoption.

Specialization has been found to be a significant variable affecting technology adoption

in the dairy industry. El-Osta and Morehart (2000) found specialization increased the likelihood

of dairy producers having increased technical efficiency. Furthermore, specialization and use of

9

management-intensive technologies were among the factors affecting the likelihood of a farmer

being a top performer in the industry. However, diversification in both beef and dairy production

has also been shown to influence technology adoption (Gillespie, Basarir, and Schupp 2004;

Gillespie, Kim, and Paudel 2007).

Human capital characteristics such as age, education, and experience represent another

frequently identified factor influencing technology adoption (Traoré, Landry, and Amara 1998;

Caswell et al. 2001; Daberkow and McBride 2003; Diederen et al. 2003; Gillespie, Basarir, and

Schupp 2004; Rahelizatovo and Gillespie 2004; Vestal 2005; Gillespie, Kim, and Paudel 2007;

Banerjee, et al. 2008). Education, in particular, was often demonstrated to have a strong positive

effect on the adoption of information-intensive technologies as exemplified by Caswell et al.

(2001), a study analyzing the adoption of agricultural production practices specifically relating to

nutrient, pest, soil, and water management across differing natural resource regions. Gillespie,

Basarir, and Schupp (2004) found that education increased the likelihood that Louisiana beef

producers chose alternative marketing arrangements such as private treaty or strategic alliances.

The study also found that younger producers who had greater contact with county extension

educators were more likely to retain ownership of their cattle. Similarly, age had a negative

effect on adoption of precision farming technologies in a study conducted by Daberkow and

McBride (2003). However, in the study conducted by Banerjee et al. (2008) which analyzed

factors affecting adoption of GPS guidance systems, adoption of other precision farming

technologies was found to have a larger impact on adoption probabilities than age and education

variables. Analysis of steps in the BMP adoption decision by Lousiana dairy producers were

examined by Paudel et al. (2008) where visits between producers and the U.S. Department of

Agriculture Natural Resource Conservation Service was found to increase BMP adoption

10

probabilities. In addition to operation size and income dependency, Ward et al. (2008) also used

logit models in which age, education, and farm objectives were identified as positively impacting

adoption.

When considering the adoption of BMPs, the entire farm production system must be

considered since the profitability of various technologies can be influenced between varying

production locations (Fernandez-Cornejo 2007; Caswell et al. 2001). Likewise, heterogeneity of

the resource base has been shown to influence technology adoption and profitability (Green et al.

1996; Thrikawala et al. 1999). Subsequently, climate, soil fertility, pest infestations, distance to

markets or availability of information can all serve as factors which affect the profitability and

adoption of certain technologies and management practices. Site-specific data was used by

Caswell et al. (2001) accounting for such factors.

Studies thus far have not investigated the implementation of specific management

practices in the stocker industry. Furthermore and of notable importance, RMPs have not been

evaluated in specific stocker production systems. This consideration is essential to stocker

management due to the diversified nature of the stocker industry.

Data Source and Summary

The Oklahoma Beef Cattle Manual (Lalman and Doye 2005), written by sixteen lead

authors from six academic disciplines, was distributed through local Extension offices, producer

meetings, and by e-mail request from an Oklahoma State University (OSU) website

(http://agecon.okstate.edu/cattleman/). Producers who received a copy of the Oklahoma Beef

Cattle Manual through 2006 were asked to complete a “Beef Cattle Management Practices

Assessment.” Two surveys were distributed: one for beef producers with only stockers and a

second for those also with a cow-calf operation.

11

The survey documented current management practices of Oklahoma stocker producers in

the areas of production, forage and introduced pasture, quality assurance and animal health,

marketing and risk, genetics, and business planning management. The survey asked 54 questions

with the majority of the questions being presented in 1-7 Likert scale. Other questions asked

respondents to fill in blanks with percentages and numerical values. Producers were also

questioned regarding operational characteristics, importance of specific farm objectives, extent

of off-farm work, dependency upon income generated from the stocker operation, and aspects of

human capital in addition to questions examining other demographic variables. For this study,

surveys from 186 beef producers specializing in stocker production were the focus.

Methodology

For further examination of the impact of socioeconomic and operation characteristics on

producers’ likelihood of adopting RMPs, a qualitative choice model was required. Given a set of

independent attributes and behavioral responses that are qualitative, as is often the case with

many assessment studies on farm management practices, non-linear iterative methods were

required to adequately measure likelihood estimates.

A binary logit model is used for estimation since its distribution function is bounded by 0

and 1 and it has computational advantages relative to the linear probability model (Amemiya

1981). The decision to adopt each of the designated management practices was modeled by the

logit equation as:

(1) Pi=F(Zi) = ezi / (1+ezi) = 1/(1+ e-zi), where Zi=∑ Xi'∑ βk

j=1ni=1 j

where Pi is the probability that the ith producer adopts the management practice and is regressed

against the explanatory variables (Xi). Xi is the ith row of the n x k matrix of explanatory

12

variables, βj is the k x 1 vector of parameter coefficients, n is the number of observations, and k

is the number of coefficients.

The logit model differs from multiple regression models in interpretation of the estimated

coefficients. As in the multiple regression model, the sign of the coefficient determines the

effect; however, with the logit model, the magnitude of the effect of the explanatory variable on

the dependent variable changes with the values of the explanatory variable. The coefficient

measures a one unit change in the explanatory variable based on the logarithm of the probability

ratio, or Ln[Pi/1-Pi)], of the producer choosing to adopt the management practice, Xi=1 (adopt)

or Xi=0 (not adopt), and measures the likelihood that the producer adopts the management

practice (Cox 1958). Coefficient estimates are obtained using maximum likelihood estimation by

finding the value of β that maximizes the log likelihood. The amount of increase in probability,

or “likelihood,” depends on the original values of the independent variables. The change in P

with respect to a change in X measured changes in probability:

(2) dPi/ dXi = [ezi /(1+ ezi)2]·βj = βj· Pi·(1- Pi)

Independent variables used to measure the likelihood of management practice adoption

were socioeconomic and structural characteristics of the stocker producer and operation (Table

1). The explanatory variables included operation size (MEDIUM and LARGE), dependency

upon operation income (DEPINCOME), producer age (AGE2), education (EDU2, EDU3, and

EDU4), extent of off-farm work (PART and FULL), and the value that producers placed on

operation objectives such as generating income to reduce off-farm work (INCOMELOW and

INCOMEHIGH) and choosing management practices that reduced labor (LABORLOW and

LABORHIGH). The type of production system employed by the producer was also considered

and variables were generated to categorize grazing time periods (WINTERSP and YRROUND)

13

and forage bases (WSGRASSES and CSGRASSES). The independent variables were identified

with dummy variables (either 0 or 1). The variables SMALL, NONINCOME, AGE1, NOOFF,

INCOMEMED, REDUCEMED, SUMMER, and SMGRAINS were excluded from the estimated

models to avoid perfect collinearity. Most variables were hypothesized to have positive signs on

their estimated coefficients. However, assigning low importance to generating income to reduce

off-farm work or to choosing management practices to reduce labor were hypothesized to have

negative signs as was producer age. Detailed definitions of the independent variables are

provided in Table 1.

The empirical model used for the analysis was:

(4) Recommended Management Practice = β0 + β1 MEDIUM + β2LARGE + β3DEPINCOME + β4AGE2+ β5EDU2 + β6EDU3 + β7EDU4 + β8PART + β9FULL + β10INCOMELOW + β11INCOMEHIGH + β12LABORLOW + β13LABORHIGH + β14WINTERSP + β15YRROUND + β16WSGRASSES + β17CSGRASSES + et

Two categories were created for the dependent variables, or RMPs, to represent the

dichotomous choice in qualitative response. Dummy variables were created referring to each

RMP with 1=adopt (or nearly always) and 0=not adopt (or rarely, if ever). Specific management

practices were chosen pertaining to each area of stocker management including production,

forages, quality assurance and animal health, marketing and risk management, and business

planning management. Recommended management practices analyzed were implants,

maintenance of a proper stocking rate, administration of IM injections, marketing lot type, use of

risk management tools, and the presence of a long-term business plan for the stocker operation.

Recommended management practices, or dependent variables, are further identified and defined

in Table 2.

Results

14

Estimated coefficients from the logit analysis and percentage changes in probability for

each RMP are presented in Tables 3 and 4. Mean values of the explanatory variables, referring to

the proportion of producers taking on the particular qualitative attribute, were used in the logit

equation to calculate the changes in probability.

Implanting

Operation size, education, and production systems based on winter/ spring grazing of

small grains pasture were found to positively impact the probability that cattle are implanted

(Table 3). Both producer age and warm season grass production systems had statistically

significant negative impacts on the probability that cattle are implanted. Medium and large

operations were 11.4% and 13.6% more likely to implant cattle, respectively. As noted earlier,

implanting calves increases growth rates and the economic efficiency of growth; furthermore,

research confirms that profitability is a factor driving technology adoption by larger operators

(Griliches 1957). For operators with more cattle, additional cattle weight gain from implanting

likely equated to greater profits. In addition, the probability of implanting increased by 8.9% for

producers who indicated that they primarily use a winter or spring production system based on

small grains pasture. Using the same dataset, Johnson (2008) determined that a greater number of

large stocker operators were pursuing a small grain based production system, a factor which

might contribute to the likelihood that small grain stocker producers implant cattle. Additionally,

a small grain based production system would typically be more management intensive than a

summer pasture based system.

Producers who primarily graze cattle on warm season grasses were 23.5% less likely to

implant cattle. Consistent with our hypothesis, producers over age 50 were 6.2% less likely to

implant cattle. Despite perhaps greater years of experience, older producers were often reluctant

15

to adopt new technology (Gillespie, Basarir, and Schupp 2004). Post-graduate training or a

professional degree increased the probability by 8.1% that cattle were implanted.

Stocking Rate

Operation size, income dependency, and warm season grass production systems

significantly affected the likelihood that a producer knew how to set stocking rates (Table 3).

Producers dependent upon income generated from the stocker cattle operation were 6.8% more

likely to know how to set accurate stocking rates. These results were consistent with earlier

findings that producers were more likely to adopt technologies with immediate economic

benefits, such as grazing management practices, and that producers dependent upon stocker

income were implementing management practices which reduce costs and/ or increase

profitability. Large operations were also 4.9% more likely to know how to set proper stocking

rates. Due to economies of size, the economic benefits realized from increased plant and animal

efficiency will be greater for larger operations. Warm season production, however, negatively

impacted this likelihood by 21.1%. Producers pursuing the wheat-stocker enterprise face

complex decisions when producing both grain and beef gains from forage. Thus, producers

grazing cattle on warm season grasses do not perceive setting accurate stocking rates as critically

as do producers with dual purpose wheat.

Intramuscular Injections

Medium size operations were more likely to correctly administer IM injections in the

neck region (Table 3). Interestingly, Hoag, Ascough, and Frasier (1999) identified mid-sized

producers as most likely to adopt specific management practices, resulting in an inverted U-

shaped adoption pattern, as is demonstrated with the case of IM injections. No other statistical

16

difference was determined concerning IM injection sites and percentage changes in probability

were minimal.

Marketing Lot Type

Income dependency, producer age, and production systems based on grazing winter or

spring small grains pasture as well as warm season forage affected marketing lot types (Table 3).

Producer age was the only statistically significant factor that negatively impacted the probability

that cattle were marketed in uniform lots. Producers above age 50 were 7.9% less likely to

market cattle in uniform lots. Such findings corroborate with the results of Gillespie, Basarir, and

Schupp (2004) where younger producers utilized a greater variety of alternative marketing

arrangements. Producers dependent upon income generated from the stocker operation were

4.8% more likely to market cattle in uniform lots. Considering the additional and immediate

economic gains that can be realized from marketing cattle in uniform lots, such results were not

surprising. Production systems based both on small grains pasture and warm season grasses, the

two most common seasonal approaches to stocker production, positively impacted the adoption

of this marketing management practice. Producers engaged in production during the winter or

spring and producers grazing cattle on warm season grasses were 9.8% and 20.3%, respectively,

more likely to pool cattle together into uniform lots at time of sale. Seasonal stocker producers

had greater total herd numbers at specific points of the year and generally marketed cattle during

a designated time frame; thus, seasonal producers had an increased herd stock to assemble

uniform lots and appear to do so in a concerted effort when marketing cattle.

17

Risk Management Tools

Operation size and part-time off-farm work were factors which positively affected the use

of risk management tools such as futures, options, and/ or cash contracts (Table 3). Medium and

large operations were 0.6% and 1.7% more likely to use risk management tools, respectively.

Interestingly, higher education levels significantly reduced the probability that a producer used

futures, options, and/ or cash contracts in managing risk. Producers with some college were 5.8%

less likely to use risk management tools. The likelihood also decreased by 7.4% for college

graduates. The field of study or degree attained by the producer was not captured in the data set;

thus, education levels may not specifically relate to areas of agricultural study. Additional

variables capturing agricultural related education and/ or participation in extension educational

programs would have perhaps shed more light on such counter-intuitive results. Part-time off-

farm work, however, positively influenced the use of risk management tools by 0.9%. According

to Harwood et al. (1999), the riskiness of farm income is positively related to working off the

farm; thus, producers working off the farm may be more risk averse, leading such producers to

be more attentive to risk management tools.

Business Plan

Numerous statistically significant factors were identified regarding producers’ probability

of having a long-term business plan for their operation (Table 3). Income dependency, some

college education, full-time off-farm work, and the use of cool season forages positively

impacted this probability, while producer age and the use of warm season grasses had a negative

impact. Producers dependent upon income generated from the stocker operation were 7.2% more

likely to have a long-term business plan. Vestal (2005) also found income dependent cow-calf

producers to be more likely to have a business plan. Such results were not surprising as

producers who derive a greater percentage of net income from their cattle operation have a

18

greater incentive to maximize profit. Furthermore, the business plan aids in efficiently allocating

financial resources to achieve operational objectives. Producers who indicated they had at least

some college education were 5.0% more likely to have a business plan. The probability increased

by 6.1% for producers engaged in full-time off-farm work. Producers over age 50 were 9.8% less

likely to have a long-term business plan, demonstrating that older producers may be less

concerned with expanding and improving the operation. Vestal (2005) also noted the same trend

regarding long-term business plans with older cow-calf producers. Producers grazing cattle on

cool season grasses were 10.1% more likely to have a long-term business plan. The probability

decreased by 33.9% for producers primarily utilizing warm season grasses. Results specifically

related to production system are particularly important for targeting producer groups for future

extension education programs.

Conclusions and Implications

Few studies have analyzed technology and recommended management practice adoption

in the stocker cattle industry. Previously, knowledge of practices in this segment remained

somewhat ambiguous due to the large variety of production strategies and systems. We analyzed

the probability of adoption of six recommended management practices, specifically implanting,

stocking rates, IM injections site, marketing lot type, use of risk management tools, and long-

term business planning. Binomial logit models were used to model adoption behavior using

variables relating to farm structure, human capital, producer evaluation of certain farm

objectives, and production system.

Results demonstrated a clear disparity between producer groups regarding management

practice adoption behavior. Numerous statistically significant variables were identified; however,

operational characteristics had the most impact upon adoption probabilities. Operation size was

significant in four of six management practices modeled and positively affected adoption of each

19

practice analyzed. Income dependency was also statistically significant for half of the practices

analyzed. Despite differing production strategies for the stocker and cow-calf beef enterprises,

the impacts of size, income dependency, age, and education on adoption rates were nonetheless

evident.

Extension educational programs such as the Oklahoma State University Master

Cattleman program seek to enhance the profitability of beef cattle operations and the quality of

life of beef cattle producers through education. However, our research results suggest that if

large and small, income dependent and non-income dependent producer groups become

increasingly differentiated from one another with growing disparity between rates of adoption,

such programs will become increasingly advantageous to the small, income dependent producer.

This also suggests that when educational resources are limited, efforts could be targeted to the

groups with the highest return on investment.

Education levels had a positive impact on adoption probabilities. Interestingly, education

levels beyond a high school education negatively influenced the use of futures, options, and/ or

cash contracts in most instances. Future research which differentiates between fields of education

related to agriculture as opposed to non-related fields and which accounts for extension

education might yield informative results. Likewise, knowledge about producer attitudes towards

risk would be helpful.

Similar to previous studies, a common finding was the negative impact that producer age

has on adoption rates. Producers over age 50 are simply less likely to adopt recommended

practices without special incentives. Younger producers have a longer time horizon over which

to recoup any costs associated with technology adoption. If age is consistently identified as a

20

factor negatively impacting adoption of technology which is beneficial to society, incentive

programs for older producers may need to be considered.

Educational programs can encourage technology adoption. Considering Oklahoma’s role

in beef production, high levels of technology adoption in the stocker sector have the potential to

result in sizable economic impacts for the state. Thus, it is essential that producers engaged in

specific areas of beef production, such as stocker production and systems with a specific forage

base, are identified and targeted for extension educational programs. Until this study, technology

adoption by stocker producers had not been examined in detail, nor had differing production

systems been considered. Results revealed that seasonal stocker producers, primarily producers

engaged in the wheat-stocker enterprise, were more likely to adopt recommended practices while

year-round producers lagged behind in adoption. A better understanding of producer groups and

their characteristics should enable extension educators to identify producer groups that would

benefit from educational programs. While conferences targeted to wheat-stocker producers are

ongoing in Oklahoma, other stocker systems have received less emphasis and could be

beneficial.

In addition to having implications for educators, this analysis could interest producers. As

producers gain a better understanding of the stocker industry as a whole, it is foreseeable that

smaller, income dependent producers, for example, will be made aware of their status as laggards

in adoption. This could spur adoption as the practices analyzed in this study should not be

limited to larger operators. Economies of scale, for example, should not hinder producers from

implanting cattle, being knowledgeable in setting proper stocking rates, knowing where to

properly administer injections, marketing cattle in uniform lots, using risk management tools, or

21

having a long-term business plan. The same is true for producers pursuing differing production

strategies.

Limitations of this research should be mentioned. Data generated from the survey

instrument does not represent a random sample. Many producers who requested or received a

copy of the Beef Cattle Management Practice Assessment were interested in becoming part of

the Master Cattleman program. Therefore, findings may not be extrapolated to the stocker

producer population unconditionally. Furthermore, more detailed analysis could have been

conducted if the sample size were larger.

Future research might compare results with those from the recent National Stocker

Survey analysis. The National Stocker Survey was conducted by Elanco and Beef Magazine, in

conjunction with Kansas State University in 2007-2008. Comparisons with the national survey

might reveal interesting results. An analysis could also be conducted examining the economic

impact resulting from the disparity in adoption probabilities between producer groups. Future

research might also include cost-benefit analysis for certain practices and for particular groups of

producers. Considering the importance of the beef industry to Oklahoma, and the role that

Oklahoma plays as a stocker producing state, an analysis of this scope would have regional and

national implications.

22

1 Best management practices are often associated with natural and environmental resource

management practices. This study analyzes management practices recommended by extension

educators and researchers; thus the term recommended management practices (RMPs) is used.

23

References

Amemiya, T. 1981. “Qualitative Response Models: A Survey.” Journal of Economic Literature

19(1): 1483-1536.

Avent, R. K., C. E. Ward, and D. L. Lalman. 2004. “Market Valuation of Preconditioning Feeder

Calves.” Journal of Agricultural and Applied Economics 36(1): 173-83.

Banerjee, S., S.W. Martin, R. K. Roberts, S. L. Larkin, J. A. Larson, K. W. Paxton, B. C.

English, M. C. Marra, and J. M. Reeves. 2008. “A Binary Logit Estimation of Factors

Affecting Adoption of GPS Guidance Systems by Cotton Producers.” Journal of

Agricultural and Applied Economics 40(1): 345-355.

Caswell, M., K. Fuglie, C. Ingram, S. Jans, and C. Kascak. 2001. Adoption of Agricultural

Production Practices: Lessons Learned from the U.S. Department of Agriculture Area

Studies Project. Washington, D.C.: U. S. Department of Agriculture, Economic Research

Service, Agricultural Economic Report No. 792.

Cox, D. R. 1958. “The Regression Analysis of Binary Sequences.” Journal of the Royal

Statistical Society 20(2): 215-242.

Daberkow, S. G. and W. D. McBride. 2003. “Farm and Operator Characteristics Affecting the

Awareness and Adoption of Precision Agriculture Technologies in the US.” Precision

Agriculture 4(2): 163-177.

Dexter, D.R., G. L. Cowman, J. B. Morgan, R. P. Clayton, J. D. Tatum, J. N. Sofos, G. R.

Schmidt, R. D. Glock, and G. C. Smith. 1994. “Incidence of Injection-Site Blemishes in

Beef Top Sirloin Butts.” Journal of Animal Science 72(4): 824-827.

24

Diederen, P., H van Meijl, A. Wolters, and K. Bijak. 2003. “Innovation Adoption in Agriculture:

Innovators, Early Adopters and Laggards.” Cahiers d’Economie et Sociologie Rurales

67(2): 29-50.

Doye, D. 2005. “Ranch Business Planning and Management.” Beef Cattle Manual 5th ed. D.

Lalman and D. Doye, eds. Oklahoma Cooperative Extension Service, Oklahoma State

University.

El-Osta, H. S. and M. J. Morehart. 2000. “Technology Adoption and Its Impact on Production

Performance of Dairy Operations.” Review of Agricultural Economics 22(2): 477-498.

Fernandez-Cornejo, J. 2007. Off-Farm Income, Technology Adoption, and Farm Economic

Performance. Washington, D.C.: U. S. Department of Agriculture, Economic Research

Service, Agricultural Economic Report No. 36.

Gillespie, J., A. Basarir, and A. Schupp. 2004. “Beef Producer Choice in Cattle Marketing.”

Journal of Agribusiness 22(2): 149-161.

Gillespie, J., S. Kim, and K. Paudel. 2007. “Why don’t producers adopt best management

practices? An analysis of the beef cattle industry.” Agricultural Economics 36(1): 89-102.

Green, G., D. Sunding, D. Zilberman, and D. Parker. 1996. “Explaining Irrigation Technology

Choices: A Microparameter Approach.” American Journal of Agricultural Economics

78(4): 1064-1072.

Griliches, Z. 1957. “Hybrid Corn: An explanation in the Economics of Technological Change.”

Econometrica 25(4): 501-522.

Hart, R.H., M. J. Samuel, P. S. Test, and M. A. Smith. 1988. “Cattle, Vegetation, and Economic

Responses to Grazing Systems and Grazing Pressure.” Journal of Range Management

41(4): 282-286.

25

Harwood, J., R. Heifner, K. Coble, J. Perry, and A. Somwaru. 1999. Managing Risk in Farming:

Concepts, Research, Analysis. Washington, D.C.: U. S. Department of Agriculture,

Economic Research Service, Agricultural Economic Report No. 774.

Hoag, D. L., J. C. Ascough, and W. M. Frasier. 1999. "Farm Computer Adoption in the Great

Plains." Journal of Agricultural and Applied Economics 31(1): 57-67.

Johnson, R. J. 2008. “Adoption of Recommended Management Practices in Stocker Cattle

Production.” Master’s Thesis. Department of Agricultural Economics, Oklahoma State

University, Stillwater, Oklahoma.

Jones, R., T. Schroeder, J. Mintert, and F. Brazle. 1992. “The Impacts of Quality on Cash Fed

Cattle Prices.” Southern Journal of Agricultural Economics 24(2): 149-162.

Just, R. E., and D. Zilberman. 1983. “Stochastic Structure, Farm Size, and Technology Adoption

in Developing Agriculture.” Oxford Economic Papers 35: 307-328.

Lalman, D., and D. Doye. 2005. Beef Cattle Manual, 5th ed. Oklahoma Cooperative Extension

Service, Oklahoma State University.

Paudel, K., W. Gauthier, J. Westra, and L. Hall. 2008. “Factors Influencing and Steps Leading to

the Adoption of Best Management Practices by Louisiana Dairy Farmers.” Journal of

Agricultural and Applied Economics 40(1): 203-222.

Peel, D. S. 2003. “Beef Cattle Growing and Backgrounding Programs.” The Veterinary Clinics:

Food Animal Practice. 19(2): 365-385.

Popp, M. P., M. D. Faminow, and L. D. Parsch. 1999. “Factors Affecting the Adoption of Value-

Added Production on Cow-Calf Farms.” Journal of Agricultural and Applied Economics

31(1): 97-108.

26

Rahelizatovo, N. C., and J. M. Gillespie. 2004. “The Adoption of Best-Management Practices

by Louisiana Dairy Producers.” Journal of Agricultural and Applied Economics 36(1):

229-240.

Reuter, R., G. Highfill, D. Lalman. 2005. “Implants and Their Use in Beef Cattle Production.”

Beef Cattle Manual 5th ed. D. Lalman and D. Doye, eds. Oklahoma Cooperative

Extension Service, Oklahoma State University.

Rogers, E. 1983. Diffusion of Innovations, 3rd Edition. Free Press, New York.

Schmitz, T. G., C. B. Moss, and A. Schmitz. 2003. “Marketing Channels Compete for U.S.

Stocker Cattle.” Journal of Agribusiness 21(2): 131-148.

Schroeder, T., and M. L. Hayenga. 1988. “Comparison of Selective Hedging and Options

Strategies in Cattle Feedlot Risk Management.” Journal of Futures Markets 8(2): 141-

156.

Schroeder, T.C., J. Minert, F. Brazle, and O. Grunewald. 1988. “Factors Affecting Feeder Cattle

Price Differentials.” Western Journal of Agricultural Economics 13(1): 71-81.

Selk, G. E., R. R. Reuter, and G. L. Kuhl. 2006. “Using Growth Promoting Implants in Stocker

Cattle.” Veterinary Clinics of North America: Food Animal Practice 22(2): 435-449.

Shafer, C. E., W. L. Griffin, and L. D. Johnston. 1978. “Integrated Cattle Feeding Hedging

Strategies,” Southern Journal Agricultural Economics. 10: 34-42.

Thrikawala, S., A. Weersink, G. Kachanoski, and G. Fox. 1999. “Economic Feasibility of

Varible-Rate Technology for Nitrogen on Corn.” American Journal of Agricultural

Economics 81: 914-92.

27

Traoré, N., R. Landry, and N. Amara. 1998. “On-Farm Adoption of Conservation Practices: The

Role of Farm and Farmer Characteristics, Perceptions, and Health Hazards.” Land

Economics 74(1): 114-127.

U. S. Department of Agriculture (USDA), Animal and Plant Health Inspection Services

(APHIS). 2000. “Injection Practices in U.S. Feedlots.” Washington, DC. Available at:

http://nahms.aphis.usda.gov/feedlot/

U. S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). 2008.

“Cattle.” Washington, DC. Available at:

http://usda.mannlib.cornell.edu/usda/nass/Catt//2000s/2008/Catt-02-01-2008.pdf

Vestal, M. K. 2005. “Production Practices and Management Intensity of Oklahoma Cow-Calf

Producers across Income and Herd Size.” Master’s Thesis. Department of Agricultural

Economics, Oklahoma State University, Stillwater, Oklahoma.

Ward, C.E., M. Vestal, D. Doye, and D. Lalman. “Factors Affecting Adoption of Cow-Calf

Production Practices in Oklahoma.” Journal of Agricultural and Applied Economics 40,

3 (2008):December.

Ward, C.E., C.D. Ratcliff, and D.L. Lalman. 2004. “Buyer Preferences for Feeder Calf Traits.”

Division of Agricultural Sciences and Natural Resources. AGEC-602, Oklahoma State

University.

28

Table 1. Description of Independent Variables Category/ Variables1 Description Mean

Farm Structure

SMALL Number of stocker/feeder cattle managed each year. (1=less than 100 hd, 0=otherwise) 0.359

MEDIUM Number of stocker/feeder cattle managed each year. (1=100-500 hd, 0=otherwise) 0.301

LARGE Number of stocker/feeder cattle managed each year. (1=greater than 500 hd, 0=otherwise) 0.280

NONINCOME Percent of net household income generated from the beef cattle operation. (1=1-40%, 0=otherwise) 0.582

DEPINCOME Percentage of net household income generated from the beef cattle operation. (1=41-100%, 0=otherwise) 0.349

Human Capital AGE1 Producer age. (1=less than 50 years, 0=otherwise) 0.449 AGE2 Producer age. (1=greater than 50 years, 0=otherwise) 0.502 EDU1 Highest level of education attained by the producer. (1=high school, 0=otherwise) 0.174

EDU2 Highest level of education attained by the producer. (1=some college, 0=otherwise) 0.280

EDU3 Highest level of education attained by the producer. (1=college graduate, 0=otherwise) 0.322

EDU4 Highest level of education attained by the producer. (1=some post graduate work or graduate/ professional degree, 0=otherwise) 0.185

NOOFF Extent of producer off-farm work. (1=no off-farm work, 0=otherwise) 0.465 PART Extent of producer off-farm work. (1=part-time, 0=otherwise) 0.179 FULL Extent of producer off-farm work. (1=full-time, 0=otherwise) 0.317 Farm Objectives

INCOMELOW Importance of generating enough farm income so that off-farm work is not necessary. (1=very unimportant, 0=otherwise) 0.089

INCOMEMED Importance of generating enough farm income so that off-farm work is not necessary. (1=medium importance, 0=otherwise) 0.211

INCOMEHIGH Importance of generating enough farm income so that off-farm work is not necessary. (1=very important, 0=otherwise) 0.661

LABORLOW Importance of choosing practices to reduce labor use. (1=very unimportant, 0=otherwise) 0.063

LABORMED Importance of choosing practices to reduce labor use. (1=medium importance, 0=otherwise) 0.195

LABORHIGH Importance of choosing practices to reduce labor use. (1=very important, 0=otherwise) 0.719

Production System WINTERSP Primary time period cattle are grazed. (1=winter, spring, or both 0=otherwise) 0.587 SUMMER Primary time period cattle are grazed. (1=summer, 0=otherwise) 0.412 YRROUND Primary time period cattle are grazed. (1=year round, 0=otherwise) 0.407

SMGRAINS Primary forage based used for grazing cattle. (1=small grains pasture, 0=otherwise) 0.566

WSGRASSES Primary forage base used for grazing cattle. (1=warm season grasses: Bermuda, Old World bluestem, weeping lovegrass, or native range, 0=otherwise) 0.857

CSGRASSES Primary forage base used for grazing cattle. (1=cool season grasses: fescue or smooth brome, 0=otherwise) 0.624

1Variables in bold are omitted from the analysis to avoid perfect collinearity and serve as the reference point.

29

Table 2. Description of Recommended Management Practices Category/ Management Practice Description MeanProduction Implanting Steers are implanted. (1=nearly always, 0=rarely, if ever) 0.362 Forages

Stocking Rate The producer has knowledge of setting and monitoring a proper stocking rate. (1=yes, 0=no, or not sure) 0.483

Quality Assurance and Animal Health

IM Injections Intramuscular injections are administered in the neck. (1=nearly always, 0=rarely, if ever) 0.111

Marketing and Risk Management Marketing Type Lot type used for marketing cattle. (1=uniform lots, 0=mixed lots) 0.257

Risk Management Tools Feeder cattle futures, options, and/or cash contracts are used to lock in expected fixed prices. (1=nearly always, 0=rarely, if ever) 0.895

Business Planning Business Plan The producer has a long-term business plan. (1=yes, 0=no) 0.497

30

Table 3. Results of Logit Models for Selected Recommended Practices

Implanting Stocking Rate Intramuscular Injections Category/ Variables Coefficient

Standard Error

Change in Probability Coefficient

Standard Error

Change in Probability Coefficient

Standard Error

Change in Probability

Intercept -0.2320 0.8359 - 0.4648 0.7305 - 13.9135 268.0000 - Farm Structure MEDIUM 1.5221* 0.4918 11.40% 0.4271 0.4170 3.19% 1.8500** 0.8867 1.43% LARGE 1.9480* 0.5874 13.55% 0.7083*** 0.4504 4.93% 1.0030 0.8560 0.81% DEPINCOME -0.0106 0.5235 -0.08% 0.7794** 0.4026 6.79% 0.0653 0.8662 0.03% Human Capital AGE2 -0.6037* 0.4428 -6.20% -0.0075 0.3498 -0.09% 0.3331 0.6327 0.27% EDU2 0.1202 0.5864 0.84% 0.4079 0.4901 2.83% 0.0099 0.8737 0.00% EDU3 0.8088 0.5594 6.39% 0.5354 0.4629 4.27% 0.5738 0.8882 0.30% EDU4 1.7844* 0.6818 8.05% 0.4439 0.5374 2.02% -0.0521 0.9344 -0.02% PART 0.6011 0.6096 2.28% -0.4595 0.4902 -2.05% -0.9831 0.8334 -0.22% FULL -0.3597 0.5458 -2.52% 0.0137 0.4347 0.11% -0.2827 0.8177 -0.11% Farm Objectives INCOMELOW 0.5032 0.7345 1.11% -0.7298 0.6670 -1.56% -0.5882 1.0718 -0.09% INCOMEHIGH 0.8076 0.5309 12.64% -0.4297 0.4346 -6.94% 0.3033 0.7205 0.31% LABORLOW 0.2022 0.9651 0.16% 0.6305 0.8060 0.58% -0.9968 1.3712 -0.04% LABORHIGH 0.0448 0.5601 0.71% 0.3595 0.4471 6.45% -0.5618 0.8779 -0.48% Production System WINTERSP 0.6833*** 0.4070 8.94% -0.0555 0.3468 -0.81% 0.3844 0.6012 0.30% YRROUND -0.2931 0.4313 -2.86% -0.1758 0.3431 -1.78% -0.5703 0.6415 -0.38% WSGRASSES -1.6198** 0.8434 -23.46% -1.1256*** 0.6630 -21.13% -11.2531 268.0000 -0.73% CSGRASSES 0.2628 0.4743 1.64% -0.2056 0.3854 -2.38% -1.1711 0.8685 0.00% *Indicates significance at .01 level. **Indicates significance at .05 level. ***Indicates significance at .10 level.

31

Table 3 (continued). Results of Logit Models for Selected Recommended Practices

Marketing Lot Type Risk Management Tools Business Plan Category/ Variables Coefficient

Standard Error

Change in Probability Coefficient

Standard Error

Change in Probability Coefficient

Standard Error

Change in Probability

Intercept -0.0837 0.9204 - -2.6331 1.6965 - 0.1772 0.8106 - Farm Structure MEDIUM 0.6109 0.5115 3.36% 2.0701*** 1.2400 0.58% 0.1528 0.4271 1.14% LARGE 0.5513 0.6068 2.84% 4.4570* 1.4257 1.66% 0.2901 0.4822 2.01% DEPINCOME 0.7943*** 0.5521 4.78% 0.1568 0.7605 0.22% 0.8346** 0.4222 7.16% Human Capital AGE2 -1.1943* 0.4733 -7.88% -0.0136 0.7354 -0.03% -0.7821** 0.3668 -9.76% EDU2 -0.0121 0.6189 -0.07% -1.8310*** 1.0154 -5.84% 0.7345*** 0.5169 5.01% EDU3 0.6634 0.6083 3.98% -2.1617*** 1.2179 -7.43% 0.0494 0.4918 0.38% EDU4 0.9907 0.7085 3.45% -1.3896 1.0610 -3.20% 0.5547 0.5622 2.48% PART 0.8249 0.6252 2.80% 2.0201*** 1.0754 0.92% 0.3296 0.4956 1.42% FULL 0.7661 0.5840 4.49% 1.0733 0.9686 0.85% 0.7810*** 0.4627 6.06% Farm Objectives INCOMELOW -0.8355 0.7723 -1.35% 2.0095 1.5445 0.78% -0.2785 0.6848 -0.62% INCOMEHIGH 0.408 0.5483 4.45% -0.2340 1.0185 -0.58% -0.0043 0.4461 -0.07% LABORLOW -0.8222 0.9789 -0.33% -1.9307 2.8520 -0.52% 1.3339 0.8416 1.20% LABORHIGH -0.7719 0.6074 -7.25% -0.8691 0.9203 -3.66% 0.2584 0.4448 4.57% Production System WINTERSP 1.1506* 0.4291 9.80% 0.1758 0.8801 0.39% -0.3472 0.3621 -5.09% YRROUND -1.20258 0.4775 -9.76% 0.0796 0.8298 0.12% 0.1249 0.3573 1.26% WSGRASSES 1.2362*** 0.7890 20.27% -0.9068 1.2670 -5.99% -1.6495* 0.3865 -33.89% CSGRASSES -0.2827 0.5225 -4.22% -0.5032 0.7729 -2.89% 0.8432** 0.4115 10.09% *Indicates significance at .01 level. **Indicates significance at .05 level. ***Indicates significance at .10 level.