Jennifer L. Howell Pierce Cedar Creek Institute 701 W. Cloverdale Rd Hastings, MI 49058 269-721-3819 voice 269-721-4402 fax howellj@cedarcreekinstitute.org and Geoffrey B. Habron* Department of Fisheries and Wildlife Department of Sociology Michigan State University 13 Natural Resources East Lansing, MI 48824-1222 517-432-8086 voice 517-432-1699 fax habrong@msu.edu Feature Article Submitted to Journal of Extension (3000 word text max) Submission Instructions: http://www.joe.org/sub1.html Background Given the increase in Internet use among many different segments of U.S. society (U.S. Department of Commerce 2002), extension professionals and agricultural educators express an increasing desire to inform farmers about improved management practices and other issues via the Internet (Hall et al. 2003; O'Neil 1999). In the 1990s, research indicated limited experience and perception of the Internet for educational communication purposes. For example, a three-year longitudinal study determined that while the percentage of respondents who used the Web to gain Extension-related information increased from 1.4% to 10%, the vast majority of respondents did not rely on that information source (Suvedi et al., 1999). Farmers rated Internet-delivered instructional technologies much lower than traditional instructional techniques (Trede and Whitaker, 1998). Gloy et al., (2002, p.) suggests that, At this point, it appears that the Internet might be a compliment rather than a substitute for traditional information sources.” Recent trends suggest that the Internet may now provide a more useful communication strategy. In 2001 an estimated 54% of U.S. population utilized the Internet, with children and teen-agers comprising the most frequent users (U.S. Department of Commerce 2002). Rural Internet use grew 24% annually between 1998-2001 equalizing the level of urban use at 53% (U.S. Department of Commerce 2002). However, rural users often lack choices of service providers (Malecki 2003) and access to high speed connections (Malecki 2003; U.S. Department of Commerce 2002). Between 1998-2001, Internet use increased 25% annually for homes with less than $15,000 annual income (U.S. Department of Commerce 2002) suggesting that even limited income homeowners continue to overcome such economic constraints. Farmers that utilize precision agriculture and other technologically driven production strategies may not view the Internet as a hurdle, but may view the Internet as the best way to obtain cutting edge information (Ferguson 2002). Therefore, evidence suggests that Cooperative Extension needs to continue to embrace the use of the Internet (Hall et al. 2003; O'Neill 1999; Tennessen et al., 1997). Methods In order to obtain valuable information about the role of communication preferences of Michigan’s agricultural landowners with respect to watershed conservation, a random sample of residents from four agricultural watersheds was asked to complete a survey instrument entitled: A Survey of Landowner Watershed Information Needs.” In the Spring of 2001, 922 survey instruments were mailed to landowners in four agricultural watersheds within the state of Michigan: the Lake Macatawa, the Gun River, the North Branch Flint River, and the Upper Thornapple. Watersheds were chosen based on level of watershed conservation activity and existing Extension contacts. The Lake Macatawa and Gun River included Total Maximum Daily Load (TMDL) and Clean Water Act Section 319 planning and implementation activities. Both watersheds also included Extension staff who participated actively in watershed activities. In contrast, few watershed conservation activities occurred in the Upper Thornapple and North Branch North Branch Flint River watersheds. The design enables longitudinal comparison where more changes in landowner attitude and behavior are expected in active watersheds than less active watersheds. Names and addresses of landowners were retrieved from county geographic information systems (GIS) or Equalization offices for each of these watersheds. The survey, including both open- and closed- ended questions, was developed using many question items derived from previous, peer-reviewed and field-tested studies from agricultural communication professionals in order to ensure validity and reliability. Once the survey questions were formulated, the survey instrument was peer reviewed by a number of Extension agents and water quality professionals before mailing the survey instrument to agricultural landowners. In the questionnaire, participants were asked to report demographic information such as age, education level, income, farm operation, farming status, and farm size. Respondents also identified how often they participated in Extension programs and which communication strategies they preferred to learn about watershed conservation issues. In addition, respondents provided information about their Internet access location and how often they use the Internet for management decisions. Survey methodology followed Dillman’s Total Design Method (Salant and Dillman 1994). The survey instrument was initially mailed to the sample of agricultural landowners in May of 2001. A reminder postcard was sent to the sample population approximately three weeks later. About four weeks following the second mailing, non-respondents were mailed a second copy of the questionnaire. Respondents completed and returned 403 of the 922 survey instruments providing an overall response rate of 43.7%. Survey Data Analysis Data were analyzed using SPSS 10.0.7 statistical software for social statistics (SPSS, 2000). Statistical analysis consisted of Pearson’s correlation (r), Pearson’s Chi-square test of independence (c2), and One-way Analysis of Variance (F-test) depending on the nature of the variables tested. Relationships between two ordinal variables were analyzed using Pearson’s correlation. Comparisons between means were examined using ANOVA while differences between proportions were assessed using Pearson’s Chi-square test of independence. The homogeneity of variance was then tested using Levene’s statistic. In all cases, Levene’s statistic was greater than 0.05 indicating that one would fail to reject the null hypothesis that the variances are equal and that ANOVA could be used. If differences between groups were detected using ANOVA, Bonferroni’s Post Hoc test was used to determine which means differed significantly. Bonferroni's Post Hoc test uses a more stringent confidence level for each interval than other multiple comparison procedures ensuring the overall confidence level is acceptably high. Non-response Analysis Since this study did not obtain a 100% response rate, differences between respondents and non-respondents could threaten external validity. To address representativeness, the research team specifically compared early and late respondents on Likert-type scale items and demographic information. (Lindner, et al., 2001). Since late respondents tend to be similar to non-respondents (Miller and Smith, 1983; Pace, 1939), demographic data and responses to Likert-type scale questions from early respondents were compared to data from late respondents. If no differences are found, then respondents are said to adequately represent the sample (Miller and Smith, 1983). Results Of the 29 variables tested for non-response bias, only 2 came out significant between early and late respondents. Compared to non-respondents, respondents implement higher cover crop use and less frequent manure application on the same field (r=0.245, p=0.005 and r=0.195, p=0.028, respectively). Overall, the most preferred communication strategies were written methods such as newsletters, printed bulletins, and fact sheets; while, the least preferred communication strategies were computer and Internet methods such as software, e-mail, and World Wide Web pages (Figure 2). Of all the communication strategies presented to respondents, 76.6% of respondents preferred written communication strategies such as newsletters, printed bulletins, and fact sheets to learn more about watershed conservation. Most (57%) of respondents preferred personal, face-to-face communication strategies such as farm meetings, workshops, field days, demonstration tours, visits to resource offices (Extension or conservation district), personal visits to their homes by resource persons, and visits to a university to learn more about watershed conservation. In addition, 39% of respondents preferred media sources such as newspapers, televisions, radios, and video tapes to learn more about watershed conservation; while, .7% of respondents preferred computer or Internet sources such as software packages, e-mail, and World Wide Web pages to learn more about watershed conservation. Figure 2. Survey respondents’ preference for traditional or technological communication strategies to learn about watershed conservation practices. \s Note: Percentages add up to more than 100% because respondents were asked to indicate all communication strategies that applied. Watershed Results Results indicate that watershed residence had no significant effect on agricultural landowners’ preference for communication strategies. Overall, respondents from all four watersheds had a higher preference for written materials than all other communication strategies. There is no statistical difference (Table 1) in preference for communication strategies among watersheds (written communication strategies, X2=0.997, p=0.802; personal communication strategies, X2=4.503, p=0.212; media, X2=2.401, p=0.493; and computer/Internet, X2=5.480, p=0.140). Table 1. The effect of watershed residence on respondents’ preference for communication strategies. Watersheds Statistics Communication Strategies North Branch Flint River (%) Gun River (%) Lake Macatawa (%) Upper Thornapple (%) X2 p-value Written 78.4 75.0 78.0 70.0 0.997 0.802 Personal/Face-to-Face 62.2 39.3 57.3 60.0 4.503 0.212 Media 41.9 28.6 42.7 33.3 2.401 0.493 Computer/Internet 12.2 32.1 19.5 20.0 5.480 0.140 Demographic Explanatory Factors Age Table 2 demonstrates the influence of age on communication strategy preference. There is a statistical difference between age groups and preference for written communication strategies, media, and computer or Internet methods of learning about watershed conservation issues. Results specifically indicate that age has a significant effect on respondents’ preference for computers and Internet for learning about watershed conservation issues. Younger age groups have a higher preference for computer-based resources than older age groups. Table 2. The effect age has on respondents’ preference for communication strategies. The Effect of Age on Respondents’ Preference for Communication Strategies Statistics Communication Strategies 20-40 years old (%) 41-60 years old (%) 61+ years old (%) X2 p-value Written 75.0 84.8 68.2 7.306 0.026* Personal or Face-to-Face 62.5 57.0 56.6 0.295 0.863 Media 58.3 42.4 30.7 6.787 0.034* Computer or Internet 41.7 24.2 5.7 20.312 0.000** *=Statistically significant result at the p=0.05 level **=Statistically significant result at the p=0.01 level Education Level Table 3 demonstrates the influence of respondents’ education level on respondents’ preference for communication strategies to learn about watershed conservation issues. A statistical relationship exists between respondents’ levels of education and preference for computers or Internet as communication strategies (r=0.303, p=0.000). As level of education increases, so does respondents’ preference for computers and Internet as a communication strategy. Table 3. The effect education level has on respondents’ preference for communication strategies. 90 The Effect of Education Level on Respondents’ Preference for Communication Strategies Statistics Strategies Grade school (%) Some high school (%) High school graduate (%) Vocational or trade school (%) Some college (%) College graduate (%) Post Graduate Degree or Work (%) Pearson’s correlation (r) p-value Written 60.0 82.4 81.1 71.4 71.4 85.7 80.8 0.027 0.702 Personal or Face-to-Face 60.0 58.8 58.1 35.7 73.5 47.6 46.2 -0.040 0.567 Media 60.0 47.1 41.9 35.7 22.4 66.7 30.8 -0.082 0.235 Computer or Internet 0.0 11.8 9.5 14.3 20.4 38.1 42.3 0.303 0.000** **=Statistically significant result at the p=0.01 level. Gross Annual Income Level Table 4 demonstrates the effect income level has on respondents’ preference for communication strategies to learn about watershed conservation issues. There is a statistically significant difference between level of income and respondents’ preference for computers and the Internet as communication strategies. Specifically, as respondents’ gross annual income level increases, so does their preference for computers and the Internet to learn about watershed conservation issues. Table 4. The effect gross annual income level has on respondents’ preference for communication strategies. The Effect of Gross Annual Income on Respondents’ Preference for Communication Strategies Statistics Strategies $15,000-$25,000 per year (%) $25,001-$35,000 per year (%) $35,001-$50,000 per year (%) $50,000-$75,000 per year (%) >$75,000 per year (%) Pearson’s Correlation (r) p-value Written 65.5 80.5 94.3 82.5 69.8 0.007 0.925 Personal or Face-to-Face 51.7 56.1 57.1 65.0 58.1 0.057 0.439 Media 44.8 39.0 34.3 42.5 32.6 -0.058 0.432 Computer or Internet 6.9 14.6 14.3 22.5 27.9 0.0 0.014* Role of Internet Access 32.2% of respondents did not have Internet access. Of all respondents with Internet access, 47.4% of them had Internet access in their home, 23.2% of respondents had Internet access at their business, 17.5% of respondents had Internet access at a local school or library, and 13.6% of respondents had Internet access at a friend’s or relative’s home (Figure 3). Regardless of Internet access, the majority of respondents (74.6% of respondents with Internet access and 77.8% of respondents without Internet access) still preferred written materials such as newsletters/mailers and printed bulletins/fact sheets than the other communication strategies. Figure 3. Internet access locations \s * Note: Percentages do not add up to 100% because respondents were requested to indicate all locations where they had Internet access. However, access to the Internet significantly impacts respondents’ preference for computers and the Internet. Survey respondents with Internet access expressed a significantly higher preference (27.5%) for computers and the Internet than did landowners without Internet access (1.6%, X2=.607, p=0.000) (Table 5). In addition, results indicate that the location of Internet access has a significant effect on respondents’ preference for the Internet as a communication strategy. A significantly higher percentage of respondents preferring the Internet had Internet access in their homes (X2=16.948, p=0.000), their business (X2=9.502, p=0.002), or at a local library or school (X2=4.813, p=0.028) than did respondents who did not prefer the Internet as a communication strategy. Table 5. The effect Internet access has on respondents’ preference for the Internet as a communication strategy. The Effect of Internet Access on Respondents’ Preference for Communication Strategies Statistics Communication Strategies Respondents with Internet Access (%) Respondents without Internet Access (%) X2 p-value Written 74.6 77.8 0.232 0.630 Personal or Face-to-Face 59.2 50.8 1.242 0.265 Media 41.5 34.9 0.802 0.370 Computer or Internet 27.5 1.6 .607 0.000** **=Statistically significant result at the p=0.01 level Discussion Overall, survey respondents preferred traditional written communication strategies such as newsletters, printed bulletins and fact sheets. These findings are supported by research conducted by Gloy et al. (2000) that revealed the strong importance of farm publications as communication tools. In addition, respondents expressed the least amount of preference for technological communication strategies such as computers, e-mail, and the Internet. These findings mesh with results by Tavernier et al. (1996) which indicate the lack of preference by farmers for modern communication technology. Despite an overall lack of support for the Internet, it is important to know whether preference for innovative communication strategies is related to farmers’ demographic characteristics. Results indicate that respondents’ preference for computers and the Internet as communication strategies to learn about watershed conservation issues is related to respondents’ age, level of education, and gross annual income level. Younger, more educated farmers demonstrate a greater appreciation for modern sources of information (Hall et al. 2003; Riesenberg and Gor 1989). The youngest respondents in the current study indicated a significantly higher preference for computers and the Internet than older respondents. Because one would expect younger farmers to be more inclined to utilize modern technology (Kolodinsky et al. 2002; U.S. Department of Commerce 2002; Tavernier, et al., 1996), one could argue that while farmers currently prefer traditional written communication strategies over computers and the Internet to learn about watershed conservation issues, farmers may prefer technological communication strategies in the future. In support of these findings, Suvedi et al. (2000) illustrated that farmers’ use of Internet sources in Michigan increased from 1.4% to 10.0% between the years 1996 and 1999. Results also indicate that level of education is positively correlated to respondents’ preference for written materials and computers. According to Gloy et al. (2000), higher levels of education are expected to be positively related to the usefulness of information received from all information sources. In addition, higher levels of education should increase the usefulness of information received from the sources that deliver the most sophisticated information (Gloy et al., 2000). Results from this study resemble results from other studies (Richardson and Mustian 1994; Bowen and Escolme 1990). According to Richardson and Mustian (1994), college graduates were found to have a significantly higher preference for method demonstration and video tapes than did persons who have less than a college education. Bowen and Escolme (1990) discovered that three-fourths of farmers who used computers had at least some college education. Additionally, gross annual income levels are positively correlated with respondents’ preference for computers and the Internet. These results are consistent with previous research (Tavernier et al. 1996) where farmers with high gross annual incomes (more than $100,000/year) increasingly adopted computer technology. Further, those who adopt high technology precision agriculture are also more likely to utilize Internet communication (Ferguson 2002). This derives in part by the suggestion that more profitable farmers have a greater capacity to purchase the newest and most expensive technology (Tavernier, et al., 1996). Not only are farmers’ preferences for computers and Internet related to demographics such as income and education level; farmers have also been reluctant to adopt computers and innovative technologies due to lack of convenient Internet access (Hall et al. 2003; Samson 1998; Tavernier et al., 1996; Iddings and Apps, 1992). Regardless of whether respondents had Internet access, the majority of respondents still preferred written materials more than the Internet to learn about watershed conservation issues. These results suggest that even if agricultural landowners have Internet access, they will likely still express a higher preference for more traditional or written communication strategies. However, having access to the Internet at home or work does significantly increase one’s preference for the Internet as a communication strategy. Extension Implications Based on previous direct experience research such as the Technology Acceptance Model (TAM) and user acceptance studies focusing on individual differences (Irani, 2000), subjects with greater prior experience with a technology will more likely use it than those who lack experience (Figure 1). Previous research indicates that Internet experience and perceived usefulness were the strongest predictors of behavioral intent to use Internet communication tools (Irani, 2000). Therefore, understanding the factors which influence attitude and user perceptions toward technology is a critical need (Irani, 2000). The Technology Acceptance Model states that increased perceptions of ease of use and technology usefulness lead to increased use (See Figure 1). SHAPE \* MERGEFORMAT External Variables Perceived Usefulness Perceived Ease of Use Behavioral Intention To Use Attitude Toward Use System Usage Figure 1. The Technology Acceptance Model (Hubona & Geitz, 1999). If information technology and telecommunications are to satisfy the informational needs and extend the capabilities of the farmer, both the technology and the dissemination strategy must be sufficiently flexible to adapt themselves to the farmers’ way of working (Wilde and Swatman, 1996). Extension should organize seminars, institutes, and workshops to train farmers in computer applications for agriculture (Bamka 2000; O'Neill 1999; Findlay et al, 1993). For example, incorporating youth to work with senior citizens significantly improved the seniors’ perceptions of their comfort and skill levels regarding Internet use up to six months after training (Kolodinsky et al. 2002). However, a need exists to determine the actual effectiveness of web sites both with and without training sessions to help guide participants through the program. Technical training (Bamka 2000; O'Neill 1999) and application to real needs emerge as crucial aspects to reach beyond the innovators and early adopters (Hall et al. 2003; Ferguson 2002; Carr 1999). If farmers perceive technology as difficult to learn, too time consuming to use, or in some way presenting a threat, they probably will not use it (Carr 1999). Therefore, in addition to providing training sessions to introduce farmers to the benefits of using the Internet as a communication strategy, educators must specifically address reasons why farmers are hesitant to utilize the Internet as a communication strategy on an individual needs basis (Hall et al. 2003). This is particularly important if a strong desire exists among specialists to provide data via web sites, as they prove to be more time and cost efficient than newsletters and brochures. Acknowledgements The United States Department of Agriculture, Cooperative States Research, Extension and Education Service, National Integrated Water Quality Program provided funding for the research. The Survey Research Center at Michigan State University processed our mail surveys. We appreciate the helpful comments of Murari Suvedi at Michigan State University and three anonymous reviewers. References Bamka, W.J. (2000). Using the Internet as a farm-marketing tool. Journal of Extension [On-line], 36(4). Available at: http://www.joe.org/joe/2000april/tt1.html Bowen, B. E., & Escolme, K. M. (1990). Computer education of farmers. Journal of Agricultural Education, 31 (1), 7-11. Carr, V. H., Jr. (1999). Technology Adoption and Diffusion. The Learning Center for Interactive Technology. Retrieved June 21, 2001, from http://www.au.af.mil/au/awc/awcgate/innovation/adoptiondiffusion.htm Findlay, H. J., Zabawa, R., Morris, C. E. & Oben, M. (1993). Computer awareness among limited-resource farmers. Journal of Extension, [On-line], 31 (1), 22-23. Available at: http://www.joe.org/joe/1993spring/a8.html Ferguson, R.B. 2002. Educational resources for precision agriculture. Precision Agriculture 3 (4):359-371. Gloy, B. A., Akridge, J. T. & Whipker, L. D. (2002, August). The usefulness and influence of information sources on commercial farms. Paper presented at the 2002 AAEA Annual Meeting, Tampa, FL. Hall, L., Dunkelberger, J., Ferreira, W., Prevatt, J., Martin, N.R. 2003. Diffusion-adoption of personal computers and the Internet in farm business decisions: southeastern beef and peanut farmers. Journal of Extension [On-line], 41(3). Available at: http://www.joe.org/joe/2003june/a6.shtml Iddings, R. K., & Apps, J. W. (1990). What influences farmers’ computer use? Journal of Extension [On-line], 28 (1), 19-21. Available at: http://www.joe.org/joe/1990spring/a4.html Iddings, R. K. & Apps, J. W. (1992). Learning preferences and farm computer use. Journal of Extension [On-line], 30, 16-17. Available at: http://www.joe.org/joe/1992fall/a4.html Irani, T. (2000). Prior experience, perceived usefulness and the web: factors influencing agricultural audiences’ adoption of Internet communication tools. Journal of Applied Communications, 84 (2), 49-63. Kolodinsky, J., M. Cranwell and E. Rowe. 2002. Bridging the generation gap across the digital divide: teens teaching internet skills to senior citizens. Journal of Extension [On-line], 40 (3), 19-21. Available at: http://www.joe.org/joe/2002june/rb2.html Lichtenberg, E. & Zimmerman, R. (1999). Information and farmers’ attitudes about pesticides, water quality, and related environmental effects. Agriculture, Ecosystems and Environment, 73, 227-236. Lindner, J. R., Murphy, T. H. & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42 (4), 43-53. Malecki, E.J. 2003. Digital development in rural areas: potentials and pitfalls. Journal of Rural Studies 19:201-14. Miller, L. E., & Smith, K. L. (1983). Handling nonresponse issues. Journal of Extension [On-line], 21 (5), 45-50. Available at: http://www.joe.org/joe/1983september/83-5-a7.pdf O'Neill, B. (1999). Teaching consumers to use the Internet to make consumer decisions. Journal of Extension [On-line], 37(3). Available at: http://www.joe.org/joe/1999june/iw4.html Pace, C. R. (1939). Factors influencing questionnaire returns from former university students. Journal of Applied Psychology, 23, 388-397. Richardson, J. G. (1993). Extension information delivery methods: detecting trends among users. ACE Quarterly, 72 (1), 23-27. Richardson, J. G., & Mustian, D. (1994). Delivery methods preferred by targeted Extension clientele for receiving specific information. Journal of Applied Communications, 78 (1), 23-33. Riesenberg, L. E., & Gor, C. O. (1989). Farmers’ preferences for methods of receiving information on new or innovative farming practices. Journal of Agricultural Education, 30, 7-13. Salant, P., & Dillman, D. A. (1994). How to conduct your own survey. New York, NY: John Wiley Co. Samson, S. (1998). Technological issues for improving access to Internet web sites for rural users. Journal of Extension [On-line], 36(4) Available at: http://www.joe.org/joe/1998august/tt2.html SPSS, Inc. (2000). SPSS for Windows, Release 10.0.7, standard version. SPSS, Inc., Chicago, Illinois, USA. Suvedi, M., Campo, S., & Lapinski, M. K. (1999). Trends in Michigan farmers’ information seeking behaviors and perspectives on the delivery of information. Journal of Applied Communications, 83 (3), 33-50. Suvedi, M., Lapinski, M.K., & Campo, S. (2000). Farmers’ perspectives of Michigan State University Extension: trends and lessons from 1996 and 1999. Journal of Extension [On-line], 38 (1). Available at: http://www.joe.org/joe/2000february/a4.html. Tavernier, E. M., Adeaja, A. O., Hartley, M. P., & Schilling, B. (1996). Information technologies and the delivery of Extension programs. Journal of Agricultural & Food Information, 3 (4), 75-85. Taylor, M. T., Hoag, D. L. & Owen, M. B. (1991). Computer literacy and use. Journal of Extension [On-line], 29 (4), 11-13. Available at: http://www.joe.org/joe/1991winter/a3.html Tennessen, D. J., Pon Tell, S., Romine, V. & Motheral, S. W. (1997). Opportunities for cooperative Extension and local communities in the information age. Journal of Extension [On-line], 35 (5). Available at: http://www.joe.org/joe/1997october/iw4.html. Trede, L. D., & Whitaker, S. (1998). Perceptions of Iowa beginning farmers toward the delivery of education. Journal of Applied Communications, 82 (4), 22-33. U.S. Department of Commerce. 2002. A Nation Online: How Americans Are Expanding Their Use Of the Internet. National Telecommunications and Information Administration. http://www.ntia.doc.gov/ntiahome/dn/index.html Wilde, W. D. & Swatman, P. A. (1996). Towards Virtual Communities in Rural Australia. Center for Information Systems Research, Swinburne University of Technology. Abstract Extension providers need to improve the communication of watershed conservation practices. In order to determine landowners’ communication preference a survey was mailed to a random sample of landowners from four selected watersheds in Michigan. 403 landowners from four agricultural watersheds completed the survey. A majority (77%) of landowners expressed support for written communication media, while a minority (19%) supported the Internet. Younger, more educated, more affluent landowners with home Internet access expressed more support for using the Internet. Results suggest that Extension staff need to provide more Internet training and experiences if the Internet is to contribute to watershed conservation.
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