December 2002
Volume 40 Number 6

Research in Brief

Communicating the Handling of Nonresponse Error in Journal of Extension Research in Brief Articles

James R. Lindner
Internet Address: j-lindner@tamu.edu

Gary J. Wingenbach
Internet Address: g-wingenbach@tamu.edu

Department of Agricultural Education
Texas A&M University
College Station, Texas

Introduction

How can social science researchers improve the criteria, standards, and level of rigor of scholarship reported in the Journal of Extension (Norman, 2001)? Scholarship. A single word that strikes fear or revere in the hearts of many agricultural and Extension professionals when communicating their research to peers and the public.

Social science professionals realize the very nature of reporting quality-laden research lies in the "equality" of said research when viewed by our colleagues in the hard sciences. Social scientists must strive to assure our peers that research conducted within our discipline is characterized by similar methods and protocols as practiced in the hard sciences. One important step in achieving this task is to confront the issue of nonresponse error in social science survey research.

Scholarship in the Journal of Extension (JOE) is elucidated further as the creative work that is validated by peers and communicated to the profession and the general public (Weiser, 1996, 1998). In a study by Weiser, five forms of scholarship expanded upon the earlier work by Boyer (1990). Weiser included Boyer's original scholarship forms (discovery, integration, and application), changed the teaching form to learning and teaching, and added creative artistry as the fifth type of scholarship.

These forms of scholarship cannot adequately address what constitutes scholarship for Extension professionals; if they do, then most all faculty members' activities can be considered scholarly endeavors. Unfortunately, what constitute scholarly works are the criteria, standards, and level of rigor (Norman, 2001) when reviewed and evaluated by peers in the hard sciences, especially when these scholarly works are being assessed for promotion and tenure decisions. Social science researchers therefore, must reconsider addressing at least one aspect of their research methodology, the issue of handling nonresponse error in survey research.

Nearly 20 years ago, Miller and Smith (1983) published the bellwether article regarding the treatment of nonresponse error in survey research. The article, published in JOE, illustrated five generally accepted methods for handling nonresponse error that threaten the external validity of studies employing sampling techniques. Such efforts to improve our research methods are necessary to ensure the objectivity and vigor of research. Miller (1998) noted that "numerous improvements can be made in our research" (p.10) and suggested that the profession continue to devote personal time to renewing, maintaining, and improving our ability to use appropriate research methods and techniques.

Improving research in agricultural and Extension education requires a periodic examination of research methods and techniques. In taking a step forward with this critical review of handling nonresponse error, it behooves us to recall the scholarship questions posed by Miller and Sandman (2000): "How do we assure scholarly standards? " and "How can we assure that new entrants to the field are professionally socialized to contribute to scholarship?" (p. 39).

As JOE board members rethink and reconsider the journal's criteria, standards, and level of rigor to redefine scholarship for Extension (Norman, 2001), a need exists to demonstrate research relevance to both higher education and the public. The results of this study provide information that may be useful in this debate.

Purpose

The purpose of the inquiry whose results are reported here was to explore and describe the treatment of nonresponse error in the Journal of Extension Research in Brief articles for the years 1995 through 1999.

Specific objectives included describing:

1. The types of sampling procedures used in JOE Research in Brief articles.
2. Response rates.
3. How often nonresponse error was mentioned, how it was controlled, and results from attempts to control.
4. Literature cited in handling nonresponse error.

Methods

All Research in Brief articles (N = 83) published in the Journal of Extension from 1995 through 1999 were analyzed using content analysis techniques (Fraenkel & Wallen, 1999). Data were analyzed using SPSS. The instrument, developed by Lindner, Murphy, and Briers (2001), used seven coding categories to gather data.

Article types were coded as sampling procedures (presence or absence), while response rate was coded as actual rate achieved. Mentioning of nonresponse error as a possible threat to external validity was coded as mentioned nonresponse, did not mention nonresponse, and a 100% response rate achieved. How nonresponse error was handled was coded into categories proposed by Miller and Smith (1983). Literature cited was coded by actual reference to the literature. Efforts to control for nonresponse errors were coded as no differences found, differences found, or did not indicate results. Sampling procedures used were coded in one of nine categories.

Each article was independently read and analyzed by two of the researchers. Researcher analyses of the data were entered onto the data collection instrument. To establish reliability of the instrument, results between researchers were compared to determine discrepancies between researchers. Less than one discrepancy per issue existed. When discrepancies existed, the two researchers, working together, reanalyzed the data and agreed on the correct code.

Findings

Objective One

Eighty-two Research in Brief articles were published in JOE during 1995-1999. Approximately 74% (N = 61) of these articles used sampling procedures. As revealed in Table 1, sampling procedures used most often were census (29.5%), convenience (23.0%), and purposive (16.4%). Sampling procedures used the least were cluster (4.9%) and Delphi (1.6%). Three articles did not report their sampling procedures.

Objective Two

Table 2 shows response rates of studies whose results were published. The average response rate was 71.5% (SD = 22.9), with a minimum response rate of 14% and a maximum of 100%. Approximately 18% of the studies reported that 100% response rate was achieved, while about 15% of the studies reported response rates of less than 50%. Almost 20% of the studies did not report a response rate.

Objective Three

Table 3 shows that about 20% of JOE articles mentioned nonresponse error as a potential threat to external validity. For almost 20% of these articles, nonresponse error was not a threat to external validity because of a 100% response rate. About 60% of JOE articles did not mention nonresponse error as a potential threat to external validity. Of the 50 articles, nonresponse was a threat to external validity in 82% of the studies.

No attempts were made to control for nonresponse error in 40 of the 50 articles. In six of these articles, JOE authors handled nonresponse error by comparing early to late respondents. In the remaining four articles, authors attempted to control for nonresponse error by following up with nonrespondents. In the 10 articles where nonresponse was handled, no differences between respondents and nonrespondents or differences in early/late responses or respondents/nonrespondents were reported in any of the articles.

Objective Four

A reference citation for the appropriate handling of nonresponse error was not provided in 47 of the 50 articles where nonresponse error was a potential threat to external validity. Three articles (6.0%) cited Miller and Smith (1983) as a source for handling nonresponse error.

Conclusions

Based on the results of this study, the following conclusions are drawn. To ensure the external validity or generalizability of research findings to the target population, researchers must satisfactorily answer the question of whether the results of the survey would have been the same even if a 100% response rate had been achieved (Richardson, 2000).

Seven different general sampling procedures were used to collect data for the 61 Research in Brief articles published in the Journal of Extension. Nonresponse error can be a threat to the external validity of a study when any of these sampling procedures are used and less than 100% response rate is achieved. A 100% response rate was achieved in 11 of the articles published in JOE. Nonresponse, therefore, was a potential threat to external validity in 50 articles. In approximately 60% of these 50 articles, nonresponse error, as a potential threat to external validity, was not mentioned. In 80% of these 50 articles, no attempts to control for nonresponse were mentioned. The external validity of those findings is, therefore, unknown.

Of the articles attempting to do so, nonresponse error was treated primarily by comparing early to late respondents or by comparing respondents with a sample of nonrespondents. A total of three reference citations were provided in explaining how nonresponse error was handled. During the 5 years of JOE Research in Brief articles addressed in this article, no differences were found to exist between early and late respondents or between respondents and nonrespondents. Early respondents were similar to late respondent, and respondents were similar to nonrespondents.

As noted throughout this article, not mentioning nonresponse error as a threat to external validity of a study, not attempting to control for nonresponse error, or not providing a reference to the literature were unfortunately the norm and not the exception. To ensure external validity of research findings, statistically sound and professionally acceptable procedures and protocols for handling nonresponse error are needed and should be reported. The authors recommend a follow-up study of the handling of nonresponse error in the Journal of Extension in 5 years to describe the reliability and validity of the recommended procedures. Also recommended is a replication of this study for articles published in other scholarly publications and with other professions to describe the generalizability of these findings to other populations and the applicability of recommendations.

Recommendations for Handling Nonresponse

Future Research in Brief articles reported in JOE should, when applicable, include how nonresponse error was handled. Based on the findings of this study and the review of literature, the authors conclude a need exists for Extension researchers to better address nonresponse error when it is a threat to the external validity of a study. Three methods for handling nonresponse errors proposed by Lindner, Murphy, and Briers (2001) are:

1. Comparison of early to late respondents,
2. Using "days to respond" as a regression variable, and
3. Comparison of respondents to nonrespondents.

Lindner, Murphy, and Briers suggested that procedures for handling nonresponse issues be implemented when less than an 85% response rate is achieved. To further reduce the threat of nonresponse error, it is recommended that a minimum response rate of 50% be achieved (L. E. Miller, personal communication, December 12, 2001; Fowler, 2001; Babbie, 1990).

Method 1--Comparison of Early to Late Respondents . . . . One technique to operationally define late respondents is based on responses generated by "successive waves of a questionnaire . . . . So, we recommend that late respondents should be defined operationally as those who respond in the last wave of respondents in successive follow-ups to a questionnaire . . . . If the last stimulus does not generate 30 or more responses, the researcher should "back up" and use responses to the last two stimuli as his or her late respondents. Comparison, then, would be made between early and late respondents on primary variables of interest. Only if no differences are found should results be generalized to the target population . . . . If respondents cannot be categorized by successive waves or if a wave of 30 respondents cannot be defined by successive stimuli, then we recommend that late respondents be defined operationally and arbitrarily as the later 50% of the respondents.

Method 2--Using "Days to Respond" as a Regression Variable . . . ."Days to respond" is coded as a continuous variable, and used as an independent variable in regression equations in which primary variables of interest are regressed on the variable "days to respond . . . ." If the regression model does not yield statistically significant results, it can be assumed that nonrespondents do not differ from respondents.

Method 3--Compare Respondents to Nonrespondents . . . . Comparisons between respondents and nonrespondents and differences found should be handled by sampling nonrespondents, working extra diligently to get their responses, and then comparing their responses to other previous respondents. A minimum of 20 responses from a random sample of nonrespondents should be obtained. If fewer than 20 nonrespondents are obtained, their responses could be combined with other respondents and used in conjunction with method 1 or 2. (p. 51-52)

Extension professionals who diligently adhere to one of the aforementioned methods for handling nonresponse error in their future social science surveys will contribute to improving the criteria, standards, and level of research rigor in our profession. Eventually, our colleagues in the hard sciences will realize that our collective creative works are truly scholastic, contribute new knowledge, and provide valuable information to society. Due diligence in addressing nonresponse error is a necessary component of reporting quality-laden research and is something all current and future social scientists in Extension must pay attention to if they want their efforts to be viewed as scholarly.

References

Babbie, E. R. (1990). Survey research methods (2nd ed.). Belmont, CA: Wadsworth.

Boyer, E. L. (1990). Scholarship reconsidered--Priorities of the professorate. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching.

Fowler, F. J., Jr. (2001). Survey research methods (3rd ed.). Thousand Oaks, CA: Sage.

Fraenkel, J. R., & Wallen, N. E. (1999). How to design and evaluate research in education (3rd ed.). New York: McGraw-Hill

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42(4), 43-53.

Miller, L. E. (1998). Appropriate analysis. Journal of Agricultural Education, 39(2), 1-10.

Miller, L. E., & Sandman, L. (2000). A coming of age: Revisiting AIAEE scholarship. Journal of International Agricultural and Extension Education, 7(2), 38-44.

Miller, L. E., & Smith, K. L. (1983). Handling nonresponse issues. Journal of Extension [On-line], 21(5). Available at: http://www.joe.org/joe/1983september/83-5-a7.pdf (pdf)

Norman, C. L. (2001). The challenge of Extension scholarship. Journal of Extension [On-line], 39(1). Available at: http://www.joe.org/joe/2001february/comm1.html

Richardson, A. J. (2000). Behavioral mechanisms of non-response in mailback travel surveys. Paper presented at the 79th Annual Meeting of the Transportation Research Board, Washington, DC.

Weiser, C. J. (1996). The value of a university--Rethinking scholarship. Oregon State University [On-line]. Available at: http://www.adec.edu/clemson/papers/weiser.html

Weiser, C. J., & Houglum, L. (1998).Scholarship unbound for the 21st century. Journal of Extension [On-line], 36(4). Available at: http://www.joe.org/joe/1998august/a1.html

Measuring the Ethical Cognition Effects of a Videotape Livestock Show Ethics Education Program

Jeff L. Goodwin
Extension Professor/4-H & Youth Specialist
University of Idaho
Moscow, Idaho
Internet Address: jgoodwin@uidaho.edu

Tim H. Murphy
Assistant Professor - Department of Agricultural Education
Texas A&M University
College Station, Texas
Internet Address: tmurphy@tamu.edu

Gary Briers
Professor - Department of Agricultural Education
Texas A&M University
College Station, Texas
Internet Address: gbriers@tamu.edu

Introduction

The issue of livestock show ethics gained public attention in 1994 as residues of clenbuterol were discovered in several major livestock shows in the United States. The Food and Drug Administration (FDA) acted on concerns about possible adverse effects of clenbuterol residues on public health (Rodrigquez, 1995).

In a provocative 1990 study of 1,945 participants of the Houston Livestock Show and Rodeo, Murphy, Norwood, and Dubes (1992) found that 25% of the respondents had knowingly used illegal drugs in preparing market animals for show ring competition. Even though "steroids" are contraband in this country, 7.9% of respondents indicated they had given these substances to market animals. Of those responding, 42.5% had illegally used tranquilizers in their animals, and 37.5% admitted to falsification of data on livestock registration certificates. The authors of this article noted that Murphy, Norwood, and Dubes (1992) referred to the compound clenbuterol as a "steroid" even though it is not actually classified as a steroid, but as a beta-agonist. Also, while clenbuterol was cleared for use in the United States for the treatment of horses in 1998, at the time of the study the drug was a contraband substance.

These unscrupulous practices not only threaten the future existence of 4-H and FFA youth development programs involving livestock, they also threaten consumer confidence in a safe and wholesome food supply. As a result, ethics educational efforts have been implemented in many states nationwide. The essence of livestock show ethics education is expressed as Coffey and Goodwin (1995) stress the importance of breaking the "curtain of silence" that the unethical few work behind.

Many states have aggressively implemented ethics educational efforts to 4-H and FFA audiences. The question at hand, then, is does the effort of presenting such educational programs result in a positive difference in the actions of individuals at youth livestock shows?

Purpose and Objective

The purpose of the study reported here was to determine the effectiveness of a videotaped ethics educational effort directed to individuals involved in youth livestock shows (e.g., 4-H and FFA members, parents, FFA Advisors, and Extension Educators).

To accomplish this purpose, the research objective was to determine livestock show participants' ability to correctly sort a list of livestock showing practices as either ethical or unethical. This ability was assessed both before and after exposure to a livestock show ethics education video program.

Methods and Procedures

Three questions were used to determine if a particular practice was ethical or unethical.

1. Does the practice violate FDA law? An example is the use of a substance not cleared for food animal use (e.g., certain diuretics, tranquilizers, anti-inflammatory agents, and feed additives).
2. Is it a fraudulent misrepresentation of the animal? Or, more succinctly stated, is it fraud? Examples include false ownership, falsified birth dates and ownership dates, and surgical manipulation of the animal.
3. Does the practice compromise the welfare of the animal? Examples include excessive short docking of lambs resulting in higher incidence of rectal prolapse, or severe restriction of feed and water to control weight.

If any of the three questions are answered yes, the practice in question is ethically unacceptable based on the constructs proposed in this study.

These questions were developed through an analysis of the available literature for use in the ethics education video "Line in the Sand" (Goodwin, 1996). This video has been adopted for use in all 50 states since its introduction in the fall of 1996. The ethical test offered in the video is a three-step test composed of three questions to assist individuals in discerning whether or not a particular practice involved in the showing of livestock is acceptable.

This presumption, that the construct offered above is a valid test of the ethical or unethical nature of a practice, has been scrutinized by a wide variety of audiences in many states. There has never been a valid argument against the three "Line in the Sand" questions reported by a presenter of the information in the United States to the producer of the video. The producer of the video has presented the three-question test to over 5,000 people in 10 states and has never had an audience member contest the validity of the three-question ethical test. As stated by Ann Swinker, Extension Equine Specialist at Colorado State University, "The three-question ethical test in the 'Line in the Sand' video has become the standard on which livestock showing practices are now measured in the state of Colorado, where the video is in use in every county in the state" (personal communication, April 30, 1997).

During the first nine months of 1997, 918 individuals involved in youth livestock shows in six states participated in this study. Data were collected from 4-H and FFA members, parents, FFA Advisors, and Extension Educators in Oklahoma, Idaho, Alabama, Washington, Oregon, and Ohio.

Sampling

A posttest-only control group experimental design was utilized in the study (Borg & Gall, 1996). Participants in the study were randomly selected into the treatment or the control group based on where they sat in the rooms used to deliver the ethics education programs. In half the cases, those sitting on the left-hand side of the presenter were selected as the Treatment group, while in the other half, those on the right were so selected. This selection procedure has been criticized. The most valid of these criticisms has been that two people, entering the room together, would not have the same probability distribution surrounding their selection into the Treatment group. (If one is chosen, and the other was bound to sit beside her, he would almost certainly be chosen). This is certainly true, and in the strictest of interpretations may well invalidate the principles of random assignment.

The reader may want to consider this fact a limitation of this study and use caution when generalizing to other populations. The authors contend, however, that the reader should consider the following when judging the reliability of this sampling technique. Many of the rooms used in this study were livestock show arenas--they were round. There are a few situations in which the side of the room is chosen for reasons other than random assignment. At weddings, for example, the choice of sitting on one side or another has underlying meaning. If there is no underlying reason to sit on one side or another, then two people entering together may well have different probability distributions for being chosen into the Treatment group, but the two other people entering right behind them would not. Finally, these were relatively large samples.

Treatment

Control group participants were asked to sort a list of eight livestock showing practices as either ethical or unethical prior to the audience being exposed to the experimental treatment (viewing the "Line in the Sand" videotape). The treatment group participants sorted the same list of practices after exposure to the treatment.

The eight livestock showing practices included on the instrument to be sorted as either ethical or unethical included the following.

1. Twine glued to cattle in order to exhibit the animal at its best advantage.
2. Clipping, fitting, and grooming an animal in order to exhibit the animal at its best natural advantage.
3. Changing the color pattern of an animal so that it can be shown in another breed classification.
4. False ownership, showing an animal that really doesn't belong to you.
5. Drenching an animal with water in order to meet a minimum weight requirement.
6. Cleaning or polishing the hooves of an animal (if not against the rules of the show).
7. Drenching an animal with water in order to express capacity and volume in a breeding animal.
8. The use of a diuretic (such as Lasix) in order to meet a weight requirement.

According to the three "Line in the Sand" questions proposed as a guide to determine the ethical or unethical nature of a livestock showing practice, situations 2 and 6 are ethical, and situations 1, 3, 4, 5, 7, and 8 are unethical. These were considered the "correct" responses to the instrument for the purposes of this study.

As an indication of internal consistency, Cronbach's Alpha was calculated for the eight questions included on the instrument and found to be .64. This would indicate that measures of correlation between items on the instrument were in the acceptable range.

Findings: Effectiveness of the Video Program

While the treatment was randomly assigned, this study involved intact groups who were, in some manner, self-selected through their participation in an ethics education presentation. Caution is warranted in making inferences beyond the sample population described here.

When analyzed together (n=918), the participants did quite well on the test. The mean score of the control group was 91.75% (7.34 of 8.0), while that of the treatment group was 95% (7.64 of 8.0). There was a statistically significant difference between the control and treatment groups' ability to correctly sort the eight livestock showing practices.

While the treatment was clearly effective in increasing the mean score achieved on the instrument by the participants, in the judgment of the researchers, only a perfect score could indicate the presence of the desired level of ethical cognition. In this view, one either possesses the necessary level of ethical reasoning, or one does not. The degree to which a given respondent lacked the prerequisite cognitive ability to behave ethically was deemed unimportant.

To investigate this hypothesis, a new variable was created. A "0" was assigned to those individuals who missed even one of the eight questions, and a 100 to those who correctly sorted all eight livestock showing practices. Additional tests were conducted to determine the number of perfect scores in the sample populations before and after the treatment and the probability that any differences in the sample populations occurred by chance.

The Chi-Square statistic did exceed the tabulated value at the alpha established apriori (p < .05), thus the null hypothesis (that the populations would show a homogeneity of distribution) was rejected. The treatment resulted in a higher-than-expected percentage of perfect scores. In the control group, 64.1% of the subjects (268 of 418) achieved a perfect score. On the other hand, 79.6% (398 of 500) of the subjects exposed to the treatment achieved a perfect score. Subjects in the treatment group were better able to correctly identify all eight of the livestock showing practices as either ethical or unethical. So the treatment was deemed effective in altering the subjects' knowledge about acceptable and unacceptable practices.

The fact that 64.1% of the control population achieved a perfect score tends to support Coffey and Goodwin's (1995) supposition that the majority of livestock show participants behave ethically. For some people, this could lead to a conclusion that the comparatively small number of people involved reduces the significance of the problem. Unfortunately, history has demonstrated that a small number, or even one unethical act, can trigger an overwhelming response in the form of public outcry and governmental regulation.

Conclusions

No claims are made regarding the change of unethical behavior at youth livestock shows as a result of exposure to the educational program serving as the experimental treatment in this study. However, the authors contend that a change in ethical cognition did occur in the treatment group and that this change was due to the treatment. The authors also contend that this change in ethical cognition or knowledge is essential and prerequisite to positive changes in attitude and, finally, behavior. Because ethical behavior is the desired outcome of all ethics education efforts, additional research is needed to determine the relationship between ethical cognition and ethical behavior regarding the particular issue of livestock show ethics.

A complete reporting of this study can be found in the Proceedings of the 26th Annual National Agricultural Education Research Meeting in Orlando, Florida (Goodwin, 1999).

References

Gall. M., Borg, W., & Gall J. (1996). Educational research: An introduction. White Plains, New York: Longman Publishers.

Coffey, D. M., & Goodwin, J. L. (1995). Ethics in exhibiting and showing livestock--Facing reality. The Agricultural Education Magazine, 68, 10-11.

Goodwin, J. L. (1996). The line in the sand [Videotape]. Available from Instructional Materials Service, Mail Stop 2588, Texas A&M University, College Station, Texas 77843-2588, (409) 845-6601.

Goodwin, J., Briers, G., & Murphy, T. (1999). Measuring the ethical cognition effect of a videotape livestock show ethics education program. Proceedings of the 26th Annual National Agricultural Education Research Meeting, 26, pp. 95-107.

Murphy, T. E., Norwood, S. N., & Dubes, R. (1992). Unethical fitting and showing practices in junior livestock shows. Texas Journal of Agriculture and Natural Resources, 5, 99-106.

Rodrigquez, R. R. (1995). The role of USDA in residue avoidance. National Youth Livestock Program Ethics Symposium Conference Proceedings. Pages 114-117. Livestock Conservation Institute, 1910 Lyda Drive, Bowling Green, Kentucky 42104-5809.

Electronic Identification of 4-H Livestock Projects

Clinton P. Rusk
Assistant Professor
4-H Youth Department
Purdue University
West Lafayette, Indiana
Internet Address: cr@four-h.purdue.edu

Introduction

In 1999, more than 24,000 Indiana 4-H members enrolled more than 48,000 animals in beef, sheep, and swine projects. A reliable form of livestock identification is required to ensure the integrity of the program by verifying that enrolled animals are the same individuals being exhibited at county and state fairs. Currently, beef and sheep projects are nose printed to verify animal identity. Nose prints are good for individually identifying animals, but they require a certain skill level and proper conditions in order to ensure reliable prints are obtained at the beginning of the project.

Five-digit ear tags are currently used in 4-H beef and sheep projects for visual identification. In swine, ear notches are used to individually identify each pig. Swine producers have relied upon ear notches to identify hogs for many years, but their use in a verification program is limited. Recent challenges with swine ear notches at the Indiana State Fair and various Indiana county fairs offer further proof that a more reliable verification system is needed.

Livestock shows in Oklahoma and Texas are currently using an electronic identification system for the nomination and verification of market steers and market lambs. A tiny electronic device, called a "transponder," is injected into animals with a syringe similar to those used to deliver vaccines to animals. The device remains with the animal for life, where it provides the animal's unique ID number any time it is scanned by a compatible electronic ID reader. The Tulsa State Fair uses biological ID to back up its electronic identification system. Four drops of blood are taken from each animal and stored on a card. Using an antibody profile assay, similar to DNA fingerprinting, blood samples can be collected from animals at a later date to verify they are the same individuals enrolled in the program to start with.

The objective of the study reported here was to test the effectiveness of electronic ear tags in 4-H sheep and swine projects.

Materials and Methods

The State 4-H Department at Purdue University entered into a contractual agreement with AgInfoLink, a global company specializing in individual animal identification, data collection, and livestock information management. AgInfoLink provided Purdue with electronic identification tags (EID) manufactured by the Allflex Company. These were ISO-compliant, tamper-evident tags with laser-printed numbers. AgInfoLink also provided a software program called "FairTracks" to serve as the database to manage the individual identification of 4-H sheep and swine projects in five Indiana counties.

Data Collection

In early May, 508 4-H pigs in Knox County, Indiana were electronically identified with tamper-evident ear tags. A blood sample was collected from the anterior vena cava of the hogs by licensed veterinarians, placed on a specially designed card, and mailed to a DNA lab for storage until needed for animal identity verification at the county fair. In addition, the following information was entered into the FairTracks software program: the 4-H member's name and address, and the tag number, breed, sex and blood sample number of each animal.

This same procedure was applied to 625 4-H sheep projects in the following Indiana counties: Adams (52 animals), Hendricks (219 animals), Knox (94 animals), Lawrence (84 animals), and White (176 animals). Jugular blood samples were collected from lambs, placed on specially designed cards, and mailed to a DNA lab for storage. Following tagging, an Excel spreadsheet was developed for each county to assist them in the management of the data collected on their 4-H animals.

Later that summer, the author attended the Adams, Hendricks, Knox, Lawrence, and White county fairs to assist with the weigh-in of the 4-H sheep and swine projects. The author used an electronic tag reading device to scan the electronic identification tags previously placed in the 4-H sheep in each of the above-mentioned counties and the 4-H swine in Knox County.

As animals were unloaded and placed on the scale for weighing, the scanner was waived over their ear tag to pick up the animal's identification number. A signal was then transmitted through an antenna connected to a laptop computer, where each animal's data had been stored in the FairTracks data base, since the animals were enrolled in the 4-H program in early May. Once the ear tag was scanned, the animal's identification number signaled the computer to bring that animal's record up on the screen, so the animal's weight could be added to the record.

This system proved to be fast (34 seconds/lamb in Hendricks County) and reduced the potential for human error. Blood samples were also collected on sheep that lost their electronic tag during the summer and on the champion and reserve champion market lamb at each of the five counties in the project. Knox county swine with missing or non-readable ear tags were re-tagged, but they were not bled for positive identification due to the large number of animals in this category.

Results

There were no missing ear tags in the 4-H lambs in Lawrence County. In Adams and Hendricks County, one lamb/county was found to have a split ear and was missing its ear tag. In each case, a parent had caused the tag to split the ear as they held the animal for their child to shear the lamb. In Knox County, two sheep tags were missing (one from a sheep with a split ear), and two tags would not produce a signal for the computer to read. In White County, the ear tags were placed upside down so that the numbers on the tag were visible to the people working with the sheep. This placement of the tags proved to be less successful than the tag placement in the other four counties in the study. Ten White county sheep tags were removed when they caused swelling of the animal's ear a few days (3-5) after the original tagging (substantiated by a Purdue veterinarian to be caused by the tags placed too close to the animal's head). Four tags were caught on the fence and pulled apart. Two tags pulled out of their respective animal's ears (but were still intact), and two additional tags would not produce a signal for the computer to read. These results produced a 90% success rate on tags in White County, while the other four counties had at least a 96% success rate on sheep ear tags.

In Knox County, 242 of the 362 hogs that came to the county fair had "readable" tags (67% success rate). One hundred five hogs were missing their ear tag, and 15 tags would not produce a signal for the computer to read. The Knox county swine tags were placed in the animals' ear in the same manner as the White county sheep tags (with the numbered side showing on the back of the animal's ear). This orientation of the tags made them easier for other hogs to grab and chew on, which appears to account for part of the tag failure. On the other hand, there is no data to substantiate tag placement as the sole reason for the low success rate. Additional data should be collected to help explain the low retention rate of electronic ear tags in swine.

Discussion and Conclusions

The use of electronic ear tags increased the speed and efficiency of weighing and checking in animals at the county fair and reduced the potential for human error in transposing numbers. Electronic ear tags provide a high-quality identification system, but they should not be relied upon for animal verification purposes (i.e., to replace nose printing or DNA finger printing).

Electronic ear tags worked well (>98% retention) in lambs when the electronic portion of the tags was placed on the inside of the animal's ear. Placing the electronic portion of the tag on the backside of the animal's ear increased the rate of tags being pulled out. A 33% failure of the tags placed in Knox county swine is too high to justify their use without further research.

Researchers found that Knox county 4-H swine members had a difficult time visually reading the number on the electronic tags. Some 4-H members became frustrated trying to determine which one of their hogs was supposed to be in a certain class during the swine show. Other brands of electronic ear tags should be tested, and further research should be conducted in swine to determine if placing the electronic portion of the ear tag on the inside of the hog's ear will improve ear tag retention.

The researchers would also recommend that tag manufacturers investigate the possibility of placing the electronic transponder in the traditional (non-electronic) swine tag that is rectangular in shape and displays a number on the outside of the tag that is from 1.25 - .75 inches in height. This would create an electronic swine tag with the added feature of a visual and readable number.

Collecting blood samples for a biological ID worked well in sheep. Researchers were able to collect jugular blood samples quickly and easily, as long as the lamb's neck had been sheared prior to collecting samples.

Collecting swine blood samples was more time consuming, but the blood was also used to test for pseudorabies, which is a required test for each of the animals to be exhibited at the county and/or state fair. Thus, collecting the swine blood samples saved the veterinarians from going to each 4-H member's farm later in the summer and stressing hogs at heavier weights and at hotter temperatures. The only negative issue resulting from using blood samples for biological ID in this project was that it took 2 weeks to get results back from the DNA lab, instead of the 48 hours that had been promised.

The information learned from electronic and biological identification of animals in five Indiana counties will allow the State 4-H staff to be more efficient as they implement the program on a statewide basis and will provide valuable information to Extension personnel and livestock show managers in other states who are trying to decide whether they want to use electronic ID. Having animals electronically identified and the demographic information of the 4-H members entered into a computer software program will save valuable time for Extension personnel and volunteer leaders during check-in at county and state fairs across the country. Having 4-H animals electronically identified will also place 4-H families in a positive position, if the federal government implements a mandatory animal ID program in future years.

Biologically identifying beef, sheep, and swine projects, using blood or hair samples, will improve the integrity of the 4-H livestock program nationwide by deterring the swapping of animals that has occurred in several instances in past years. The confusion from reading swine ear notches will have been eliminated, and the inconvenience of collecting beef and sheep nose prints will be a thing of the past. Although this research found blood samples to be easily obtained from 4-H sheep and swine projects, the DNA lab reported a preference for hair samples for future DNA testing. Using hair samples for animal verification may also be preferred by animal rights groups, as well as by the general public.

References

Ishmael, W. (2001, April). To ID, or not to ID? BEEF, pp. 3-12.

Johnson, J. (1999, February). An identity crisis. Beef Today, pp. 7-11.

Olson, C. (2000, October). USDA wants mandatory ID. National Cattleman, pp. 3-4.

Shlachter, B. (2001, February). U.S. debates tagging, ranchers worry about costs, but some say advantages make traceability inevitable. The Fort Worth Star-Telegram, pp. 7-9.

Wilcox, J. (1998, May/June). Positive ID. Successful Farming, pp. 4-7.

Measuring the Perceived Effectiveness of Training for the Dairy Option Pilot Program

Gregory Ibendahl
Assistant Extension Professor
Internet Address: gibendah@uky.edu

Leigh Maynard
Assistant Professor
Internet Address: lmaynard@uky.edu

Andrea Branstetter
Graduate Assistant

Agricultural Economics
University of Kentucky
Lexington, Kentucky

Background

The Risk Management Agency (RMA) of the United States Department of Agriculture (USDA) was established as part of the provisions of the Federal Agriculture Improvement and Reform Act of 1996. One of the RMA's main responsibilities is to help administer the crop insurance program. However, the RMA is also charged with providing risk management training to farmers. Some of this training and education is conducted jointly with the Cooperative Extension Service.

One of the new programs being developed by the RMA is the Dairy Option Pilot Program (DOPP). Most new programs such as DOPP are tested for 2 or 3 years before they are made broadly available. The current DOPP program requires a significant time commitment from Cooperative Extension Service personnel. However, no studies have been conducted to determine if DOPP will help farmers' perception of risk reduction or if DOPP training will improve farmers' knowledge and understanding of some of the available risk reduction tools.

This article presents the results of a survey designed to assess the perceived effectiveness of DOPP training. Specifically, the survey addresses the issue of whether farmers feel DOPP training increases a dairy farmer's knowledge and understanding of put options.

The results can be used by policy makers and educators to help address the issues of risk training for farmers. Often farmers may be reluctant to participate in a program because they feel overwhelmed by the materials and concepts. The success of DOPP has implications beyond the dairy industry, as DOPP was initially conceived as the forerunner in a potential series of option pilot programs. DOPP fits into the broader context of efforts to encourage use of private risk management tools, as an alternative to reliance on government loss assistance.

Background of Options and DOPP

Until the 1980's, dairy farmers did not face much price risk because government price supports were so high (Plourd, 1997). Starting in the 1980's, the government reduced its price supports. This has resulted in much greater variation in milk prices. The last few years have seen the largest month to month price drops in history. These price changes mean dairy farmers are now operating in a much riskier environment.

Put options are one of the main tools dairy farmers can use to manage price risk. These options give dairy farmers the right, but not the obligation, to sell their milk at a predetermined price. Farmers are basically buying price insurance when they purchase a put option. Like other types of insurance, buyers must pay a premium for the protection and can choose among several levels of protection. A study by Wolf and Berwald (1999) found that the dairy futures market is an efficient hedging tool.

There are several factors that discourage farmer use of dairy put options. One of the main obstacles with purchasing put options is their cost. Costs vary but can often be 5% of the milk price (Chicago Mercantile Exchange, 2002). Another potential problem is the lack of farmer knowledge about futures and options. Terms like "basis," "strike price," and "premium" are probably unfamiliar to the typical dairy farmer. In addition, the farmers usually must deal with a broker whom they likely have never met.

The DOPP program is designed to help farmers determine if options contracts can provide useful risk reduction on their farms. DOPP provides a financial incentive toward purchasing put options as well as providing education about futures and options. Under the DOPP rules, eligible farmers only have to pay 20% of the cost of a put option (RMA, 2001). The USDA pays for the other 80% as well $30 of the broker fee per option. Additional restrictions control who is eligible and when the options can be purchased and sold. However, the biggest requirement is that dairy farmers attend a 4-hour training session.

Methods

This article presents the results of a survey designed to gauge the perceived effectiveness of DOPP training in Kentucky. During June 2001, 41 farmers from 12 counties participated in one of four training sessions. This represents 4.5% of the 917 eligible farmers. DOPP training began in 2000 in Kentucky in two counties and was expanded in 2001 to include 12 counties. Because counties included in 2000 were also part of the current training, there were a few participants who had already been through the training.

All of the participating farmers were given a two-part survey. Pre-training questions were asked to gauge the farmers' perceived knowledge of put options and risk management. Post-training questions asked many of the same questions, along with some others about the overall usefulness of the training. Differences between pre- and post-training responses were used to determine if the DOPP training increased farmers' knowledge of put options. Pre-training surveys were given at the start of the day's training, and post-training questions were asked at the end of the day's training. The training usually lasted 4 hours. The authors developed the questions used in the survey instrument.

Table 1 presents descriptive statistics of dairy farmers participating in the DOPP training. The average farmer was 42 years old with some college education. These farmers were milking over 100 cows, and the typical cow produced over 18,000 pounds of milk per year. Only three farmers had ever tried using put options before, and at least two of the three had done so because of DOPP training the previous year.

According to Kentucky Agricultural Statistics (1999-2000), the average Kentucky dairy farm is smaller and has lower milk production per cow than farms participating in DOPP training. In 1999, the average milk production per cow for Kentucky was 12,368 pounds, while the average farm had fewer than 42 cows. In total, Kentucky has 133,000 milk cows on 3,200 dairy farms.

Findings

Perceived Learning Outcomes from Training

Table 2 presents results of the survey instrument about perceived learning outcomes from training. The first column lists the question that was asked in both the pre- and post-training parts. The second column shows the mean pre-training score, and the third column shows the mean post-training score. The fourth column tests whether there was a significant change from pre- to post-training response by using a paired t-test. If the t-value in column four is greater than two, then the change in the responses was deemed statistically significant at approximately the 0.05 level.

The first five questions of Table 2 use a five-point scale to qualify responses. If farmers strongly agree with a statement, then it is scored as a one, while strongly disagreeing is scored a five. Agree, indifferent, and disagree are scored a two, three, and four, respectively.

The last four questions of Table 2 are not designed to qualify responses. These questions have a correct and an incorrect response. A farmer's response is scored a one for choosing the correct answer and is scored a zero for picking the incorrect answer.

Question one is probably the best indication that the training encouraged farmers to participate in the program. Before the training began, most were not comfortable with options and how they worked. Training increased their comfort level so that many farmers would agree that they would be comfortable using options in their operations. Assuming increased confidence in a tool increases the usage of the tool, then the training achieved its objective.

Questions two and three indicate that the training may have helped farmers understand the terms involved with purchasing an option. What is somewhat surprising is that farmers feel slightly more comfortable using options than they do explaining the option terms. This difference appears both in the pre- and post-training responses. However, the differences are very small, especially for the post-training responses.

The training was less successful in convincing farmers that using put options is not gambling. Question four indicates that farmers started the training indifferent about whether options are gambling. By the end of the training, they only slightly disagreed with the gambling statement. A preferred result would have more farmers disagreeing that options are gambling. When options are compared to either gambling or insurance, as in question 6, farmers did a better job of correctly describing options as insurance. By the end of the training, 94% of the participants thought the insurance analogy better described hedging with options.

Part of the explanation of why farmers may have compared options to gambling may be a factor of the training methods used. Farmers played a simulated game involving whether and when to purchase options. In the game, timing was critical to payoffs, and some farmers even made more money not purchasing options. The fact that options helped reduce income variation may have been overlooked by farmers.

Questions seven, eight, and nine indicate the training helped farmers understand the mechanics behind options. The correct response was picked by more participants at the end of the training than at the beginning. For questions seven and nine, farmers started with correct responses in pre-training above 70% and finished with correct responses above 90%.

Another weak area of the training appears to be about the function of brokers. Question five indicates some wariness among farmers about whether to trust brokers. Training did not significantly change this perception. Question eight indicates farmers do not fully understand how brokers make money, although the training did improve the correct response rate. Outcomes on questions 5 and 8 may be related to broker participation. During the Kentucky training, only one broker attended and for only two of the four sessions.

Perceived Usefulness of Training

Table 3 shows the results of questions only asked during the post-training. They concern perceived usefulness of training.

Question one of Table 3 corresponds closely to question one of Table 2. Most farmers agree they will purchase a put option. A similar response from Table 2 indicates most farmers feel comfortable using put options. Thus, comfort level can be used as an indication of whether the training was successful at encouraging farmers to buy put options. However, an unanswered question is whether farmers will buy put options outside the DOPP program.

The rest of the questions provide a guide about sufficiency of the training. Question two and three show that farmers agree the training was helpful and useful to dairy farmers. Farmers stated that the information covered was just right (question five). However, there was slight agreement that farmers need to learn more (question four).

Kentucky modified the original DOPP material to help emphasize the insurance aspects. This was done because of the lack of experience with futures and options of most farmers. These changes probably helped with the response to question five but may have lowered the responses to question four. However, using the original material probably would have increased the "too difficult" response for question five without increasing the understanding of most farmers.

Conclusions

The survey reported here indicates that DOPP training is useful in helping dairy farmers understand and use options. Most farmers in the training had little prior knowledge about put options and probably would not have considered this tool without the training. The training significantly improved the farmers' comfort level, and most agreed they would consider buying put options. Yet to be determined, however, is whether farmers will buy put options without the DOPP program subsidy.

A final point of concern is about the desire of dairy farmers to undertake risk management. The DOPP program provides significant financial incentives to help farmers learn about risk management. Despite the substantial financial incentives to attend the training, fewer than 5% of the eligible farmers elected to attend the training.

References

Chicago Mercantile Exchange (2002). [On-line]. Available at: http://www.cme.com/

Kentucky Agricultural Statistics (1999-2000). [On-line]. Available at: http://www.nass.usda.gov/ky/

Plourd, P. (1997) From price taker to price maker. New York: Coffee, Sugar & Cocoa Exchange, Inc.

Risk Management Agency (November, 2000) Risk Management Agency Strategic Plan for FY 2000-2005. USDA. Available at: http://www.rma.usda.gov/aboutrma/what/00-05_StratPlan.pdf (pdf)

Wolf, C., & Berwald, D. (1999, August) The potential of dairy futures contracts as risk management tools . Paper presented at AAEA meetings, Nashville, TN.

Impacts of Extension Education on Improving Residential Stormwater Quality: Monitoring Results

Michael E. Dietz
Department of Natural Resources Management and Engineering
Internet Address: michael.dietz@uconn.edu

John C. Clausen
Department of Natural Resources Management and Engineering
Internet Address: john.clausen@uconn.edu

Glenn S. Warner
Department of Natural Resources Management and Engineering
Internet Address: glenn.warner@uconn.edu

Karen K. Filchak
Department of Cooperative Extension

University of Connecticut
Storrs, Connecticut

Introduction

Nonpoint sources contribute nutrients, bacteria, and other contaminants to receiving water bodies (Chesters & Schierow, 1985). In Connecticut, both the Branford River and Branford Harbor in Long Island Sound are impaired due to low dissolved oxygen and eutrophication caused by excess nitrogen from stormflow (CT DEP, 1998). In addition, high bacteria levels have caused beach and shellfish bed closures on the Branford River and Branford Harbor.

Education is one tool available to foster adoption of best management practices (BMPs) in residential neighborhoods. The role of education in changing the actions of homeowners with respect to nonpoint source pollution has been researched in one study. Swann (2000) found that media campaigns and intensive training seemed to be the most effective method of producing change, with up to a 50% change in the use of BMPs. Other methods such as community newsletters, demonstration projects, and use of the Internet were not as effective as media campaigns and intensive education. However, the ultimate evaluation of nonpoint source education is an improvement in water quality, and education programs typically stop short of measuring a water quality response.

The objective of the project reported here was to determine if the quality of runoff from a suburban neighborhood would improve as a result of educating homeowners about residential BMPs. This project involved a collaboration of Extension educators and university researchers. Pollutants considered for this study were nitrate+nitrite-N (NO₃-N), ammonia-N (NH₃-N), total Kjeldahl-N (TKN), total nitrogen (TN), total phosphorus (TP), and fecal coliform bacteria.

Study Area

The project area was a residential neighborhood located near Long Island Sound in the town of Branford, CT. Two adjacent watersheds were studied (Figure 1). The control watershed was 5.4 ha in area and contained 22 homes, with an average lot size of 0.25 ha. The treatment watershed was 6.1 ha in size, and contained 34 homes, with an average lot size of 0.21 ha. Impervious area was 23% for both watersheds. Eight lots had property in both watersheds. These homes received the same treatment as the homes in the treatment watershed. Two new homes (numbers 20 and 37) were constructed in the control watershed during the study. 

Figure 1.
Study of Residential Neighborhoods in Branford, CT. Dashed lines represent watershed boundaries. Numbers represent street numbers. Triangles represent monitoring stations. Other symbols represent changes made after treatment.

Methods

The study design used was the paired watershed approach (Clausen & Spooner, 1993), using one control and one treatment watershed. The control watershed accounts for year-to-year differences such as climate. During the calibration period, no education was performed, and no BMPs were implemented. The purpose of the calibration period was to develop significant regressions between paired observations from both watersheds for the constituents measured. Water quality monitoring began in May 1998, and water quantity monitoring was added in November 1999. The treatment period began with the education of residents in July 2000. The calibration period was 25 months, and the treatment period was 13 months.

Education

A "train the trainer" approach was used to educate volunteers who instructed homeowners. Beginning in 1998, members of the University of Connecticut Departments of Cooperative Extension, Plant Science, and Natural Resource Management and Engineering provided a series of eight evening seminars.

The goal of the seminars was to educate project volunteers and other members of the community on how to properly evaluate home sites, care for lawns, collect soil samples, and educate homeowners. Volunteers learned how to identify structural features of lots and management practices of homeowners that contribute to nonpoint source pollution.

Trained volunteers then performed site assessments similar to Andrews, et al. (1997) on 24 lots in the treatment watershed. A soil test was also performed on each lawn in the treatment watershed. Volunteers recommended changes in homeowner practices based on information collected and reviewed by extension personnel. The recommendations focused on the following:

1. Redirecting runoff from impervious areas to pervious surfaces,
2. Applying fertilizer based on soil test results,
3. Leaving grass clippings on the lawn to reduce nitrogen input, and
4. Disposing pet waste to reduce sources of bacteria.

Several structural modifications were made in the treatment watershed. In November 2000, gutter downspouts were diverted on four houses so that roof runoff drained to the lawn and not on the driveway. In April 2001, a rain barrel and a rain garden were installed at one house. In May 2001, a rain barrel was installed at another house.

Survey

A resident survey was designed to collect data on homeowner management practices during the calibration and treatment periods (Jonna Kulokowich, personal communication, 1999). The survey consisted of questions regarding lawn care practices such as watering and fertilization, car washing, leaf disposal, and pet waste management. Residents of the treatment watershed received the survey by mail in March 1998.

A follow-up survey was given to residents of the treatment watershed in 2001. The results from the follow-up survey were compared to the results from the initial survey using contingency analysis and the x² statistic to determine if there was a significant change in surveyed behavior as a result of education.

Water Monitoring

Water monitoring sites were located where concrete stormwater pipes from each watershed discharged into small brooks. Stage data was recorded at each site by a solar/battery powered CR-10 datalogger and pressure transducer. Samples were analyzed for nitrate+nitrite-N (NO₃-N), ammonia-N (NH₃-N), total Kjeldahl-N (TKN), and total phosphorus (TP) on a Lachat colorimetric flow injection system using EPA approved methods (USEPA, 1983). Total nitrogen (TN) concentrations were calculated by adding TKN and NO₃-N concentrations. Grab samples were also obtained for 29 runoff events and were analyzed at an independent laboratory for fecal coliform bacteria.

All statistical analyses were performed using SAS Version 8.2 software (SAS Institute, Inc., 2001). Because most of the water quality data were found to be log-normally distributed, log-transformed data were used for statistical analysis. Mass export was calculated on an event basis by multiplying flow by concentration from the sample that represented that event. Regressions were performed on paired nutrient and bacteria concentration data, nutrient export data, and flow data for the calibration and treatment periods. The slopes and intercepts of the two regressions were compared using ANCOVA. Calibration regressions were used to predict treatment observations based on control observations during the treatment period. Treatment watershed predicted values were then compared to observed data and a percent change was calculated.

Results and Discussion

Stormflow

No significant change in event stormflow was found between calibration and treatment periods.

Nitrogen and Phosphorus

The concentration of NO₃-N in stormwater runoff significantly (p=0.001) decreased by 60% in the treatment watershed following education (Figure 2). This was observed as a change in intercepts for the calibration and treatment regressions for the paired NO₃-N samples (Figure 3). The percent change was based on the difference between predicted values using the calibration regression equations and observed values for the treatment watershed (Table 1). Concentrations of NH₃-N, TKN, TN, and TP in runoff were not significantly different due to the treatment.

Figure 2.
Stormflow Concentrations of NO₃-N from the Control and Treatment Watersheds in Branford, CT

Results from the Nationwide Urban Runoff Program (NURP) indicated that event mean concentrations in runoff from residential areas were 0.736 mg/L for NO₃-N, 1.9 mg/L for TKN, and 0.383 mg/L for TP (EPA, 1983b). The mean stormwater concentrations for both the control and treatment watersheds during the calibration and treatment periods were slightly higher than the NURP mean for NO₃-N, and slightly lower than the NURP mean for TKN. TP means for this study were lower than the NURP mean of 0.383 mg/L (Table 1).

Figure 3.
Calibration and Treatment Period Regressions Between Stormflow Concentrations of NO₃-N from the Control and Treatment Watersheds in Branford, CT (*** =significant to p=0.001)

Bacteria

During the calibration period, bacteria concentrations in both watersheds were similar. However, after treatment, bacteria concentrations in stormwater from the treatment watershed decreased (Figure 4). Using ANCOVA, a significant (F=20.06, p=0.01) change in regression slopes was detected. This change represented a 26% reduction in bacteria levels in stormwater runoff (Table 1). The reduction occurred mostly for high concentrations.

Figure 4.
Stormflow Concentrations of Fecal Coliform Bacteria from the Control and Treatment Watersheds in Branford, CT

Export

None of the regressions for nutrient export were found to be significant. For the treatment regressions, this may have been due to the fact that only eight-paired export values existed. More samples are needed to evaluate nutrient export during the treatment period. 

Survey Results

The initial survey was distributed to a total of 61 property owners in both the control and treatment watersheds. Of the 61 receiving the initial survey, 72% completed and returned it. Responses are analyzed based on a nominal scale, according to the classification in Davis (1971). Responses for the survey question regarding fertilization were grouped according to those who fertilized less than four times per year and those who fertilized four or more times per year. The survey question asked how many times per year they fertilized their lawn. This grouping was done to minimize low observed cell frequencies and to simplify presentation of results. Responses to the survey can be seen in Table 2.

Bracketed groupings represent responses to individual questions and the corresponding calculated x² statistic. A p-value of 0.05 or less would indicate that the response rate was significantly different for that question from the initial survey to the follow-up survey, for treatment watershed residents. Analysis of the survey results indicated that no significant changes in measured behavior occurred (Table 2).

The four residents who made changes in their lawn care fertilization practices all live close to the monitoring station in the watershed (Figure 1). It is possible that the impact of their change was greater due to the proximity of their property to the station, even though no significant behavior differences were detected by x²-analysis watershed-wide. It is also possible that other residents made subtle changes that were not reported on the survey. Part of the education included general housekeeping practices such as the impacts of over-spreading fertilizer on impervious areas.

Conclusions

Intensive education efforts appeared to produce a relatively small change in measured behavior in the first 13 months following treatment. However, bacteria counts in the treatment watershed decreased. Although there was a significant reduction in NO₃-N concentrations, TN concentrations did not significantly change due to treatment. Continued monitoring of water quality and quantity may show changes in nutrient concentrations or in runoff exports; however, at this time the only significant changes due to treatment was a reduction in bacteria counts and NO₃-N concentrations.

Future research might include more detailed survey questions, such as type of fertilizer used (organic vs. inorganic), amount of lime applied to lawns, and whether fertilizer was overspread on impervious areas. Also, the effectiveness of other innovative education methods could be researched. For example, an educational seminar or picnic could be held for residents, and BMPs such as a mulching lawn mower or rain barrels could be raffled off to those in attendance.

Acknowledgements

We would like to thank several members of the Branford River Project, Joan Merrick, Lil Sakai, Mariah Storm, and Don MacDonald, for all of their efforts in interactions with the homeowners in the study. This project was funded in part by the Connecticut Department of Environmental Protection through a US EPA nonpoint source grant under §319 Clean Water Act and by the Connecticut Cooperative Extension System. Storrs Agricultural Experiment Station Scientific Contribution # 2070.

References

Andrews, E., Bosmans, R., Castelnuovo, R., DuPoldt, C., Filchak, K., Johnson, C., et al. (1997). Home*A*Syst, an environmental risk-assessment for the home. Ithaca, NY. Northeast Regional Agricultural Engineering Service.

Chesters, G., & Scheirow, L. (1985). A primer on nonpoint pollution. Journal of Soil and Water Conservation, 40(1), pp. 9-13.

Clausen, J.C., & Spooner, J. (1993). Paired watershed study design. United States Environmental Protection Agency. EPA 841-F-93-009. Washington, D. C.

Connecticut Department of Environmental Protection. (1998). Connecticut waterbodies not meeting water quality standards (draft).

Davis, J. A. 1971. Elementary survey analysis. Prentice-Hall, Inc., Englewood Cliffs, NJ.

SAS Institute Inc. (2001). The SAS System for Windows (Release 8. 0). Cary, NC.

Swann, C. P. (2000). A survey of nutrient behavior among residents in the Chesapeake bay watershed. In EPA, Proceedings of National Conference on Tools for Urban Water Resource Management and Protection (pp. 230-237). Chicago, IL. EPA/625/R-00/001.

United States Environmental Protection Agency. (1983). Methods for chemical analysis of water and wastes. Cincinnati, OH. EPA 600/4-79-020.

United States Environmental Protection Agency. (1983b). Results of the nationwide urban runoff program. (NTIS No. PB 84-185552).

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