Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Farmers’ use of information sources in British Columbia Shaw, Kenneth L. 1993

You don't seem to have a PDF reader installed, try download the pdf

Item Metadata


ubc_1993_spring_shaw_kenneth.pdf [ 8.93MB ]
JSON: 1.0064496.json
JSON-LD: 1.0064496+ld.json
RDF/XML (Pretty): 1.0064496.xml
RDF/JSON: 1.0064496+rdf.json
Turtle: 1.0064496+rdf-turtle.txt
N-Triples: 1.0064496+rdf-ntriples.txt
Original Record: 1.0064496 +original-record.json
Full Text

Full Text

FARMERS' USE OF INFORMATION SOURCES IN BRITISH COLUMBIAbyKENNETH LYNN SHAWB.A.Sc., The University of British Columbia, 1986A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIES(Department of Administrative, Adult, & Higher Education)(Faculty of Agricultural Sciences)We accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAMarch, 1993© Kenneth Lynn Shaw, 1993In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(Signature)Department of  (t rnirm ((rove, Ck 6,11-1 ) ck (-1^14 3 VIrr^4..)ccolorlThe University of British ColumbiaVancouver, CanadaDate  ap f‘ I^C), (993 DE-6 (2/88)ABSTRACTThis study is an investigation of the sources of information used by farmers inBritish Columbia. The study had four specific objectives: to determine what sources ofinformation farmers in British Columbia use and how much they value them, todetermine the relationships that exist between demographic characteristics and the useof information sources, to determine if there were significant differences indemographic characteristics of those who do or do not use British Columbiagovernment extension services, and to compare the level of contact districtagriculturists and horticulturists have with farmers with that measured in 1969.A survey was mailed to a stratified random sample of farmers. A total of 100farmers responded, and this forms a representative sample of agricultural producers inBritish Columbia.Out of the 10 groups of individuals who formally provide extension informationto farmers, agri-business sales representatives have the highest level of contact,followed by the district agriculturist and horticulturist.The most frequent method ofcontact between providers of extension information and farmers is through mail, fax, orcomputer. The least frequent method of contact is through farm visits. The mostfrequently used source of written information was general farm papers, followed byBritish Columbia Ministry of Agriculture publications. The number of farmersreporting that they obtained information from a visit to a British Columbia Ministry ofAgriculture demonstration site is the same as the number obtaining information from11visits to foreign countries. Visits to other farms was reported as being a significantsource of information.A strong consistent positive correlation was found against farm sales for bothsales representatives and financial advisors for several forms of contact. Farmers of alldemographic backgrounds are obtaining information at meetings and field days, as nocorrelations were found between this method and any demographic variable. Farmersplace increasing value on commercial supplier publications as the value of their farmsales increases.Farmers obtaining information from the British Columbia Ministry ofAgriculture were, on average, younger, more educated, and had higher off-farmincome and farm sales than those who did not. On a province wide basis, acomparison of the level of contact between farmers and district agriculturists andhorticulturists found that these contacts were at a higher level as compared with thoseobserved in 1969.The research conducted was not a diffusion/adoption study and no informationwas collected about how innovative the farmers were who responded to the survey. Inaddition, no information on how farmers made their judgements about the "value" ofvarious information sources was obtained. This study does not explain why farmersconsult the various sources, or what information they obtain from each one. Cautionmust be exercised in drawing conclusions that the Ministry of Agriculture is providinga better level of service than in 1969. These results simply report the status of contactduring those two time periods.iiiTABLE OF CONTENTSPageABSTRACT ^ iiLIST OF TABLES viLIST OF APPENDICES ^ viiiLIST OF FIGURES ixACKNOWLEDGMENTS ^1.0 INTRODUCTION 12.0 REVIEW OF PREVIOUS WORK^ 7British Columbia Studies 8Canadian Studies ^  10United States Studies  113.0 RESEARCH DESIGN ^ 17Development of the Instrument ^ 17Survey Design ^ 18Sampling Procedures 23Drawing the Sample^ 23Sample Size 264.0 RESEARCH RESULTS ^ 30Questionnaire Response 30Questionnaire Results ^ 36Demographic Characteristics ^ 36Frequency of Information Use 43Value of Various Sources of Information ^ 62ivV5.0 ANALYSIS AND DISCUSSION ^ 67Demographic Characteristics and Information Use ^ 67Extension Providers ^ 71Publications 76Miscellaneous Sources ^ 77Value of Information Sources 78British Columbia Government Extension Users ^ 79Extension Contact, 1991 Compared To 1969 856.0 SUMMARY AND CONCLUSIONS ^ 87REFERENCES ^ 97APPENDICES 100LIST OF TABLESPageTable 1:^Selected Sample by Commodity Group ^ 25Table 2:^Estimated Sample Sizes Required 28Table 3:^Survey Respondents by Commodity Group ^ 31Table 4:^Comparison of Survey Responses to Sample Selected ^ 33Table 5:^Adjusted Comparison of Survey Responses to Sample Selected ^ 34Table 6:^Age Distribution of Sample ^ 37Table 7:^Sex Distribution of Sample 37Table 8:^Marital Status Distribution of Sample ^ 37Table 9:^Mother Tongue Distribution of Sample 38Table 10:^Distribution of the Number of Children of Respondents^ 39Table 11:^Highest Level of Formal Education of Sample ^ 39Table 12:^Distribution of Membership in Farm Organizations ^ 40Table 13:^Number of Years on Present Farm ^ 41Table 14:^Number of Years as a Farmer ' 41Table 15:^Total Family Income Earned off Farm ^ 42Table 16:^Total Farm Sales^ 43Table 17:^Frequency of Farm Visits ^ 44Table 18:^Frequency of Phone Calls 45Table 19:^Frequency of Office Visits ^ 46Table 20:^Frequency of Talks at Meetings and Field Days ^ 47Table 21:^Frequency of Information Received by Mail, Fax, or Computer ^ 48Table 22:^Frequency of All forms of Contact ^ 50viTable 23:Table 24:Table 25:Table 26:Table 27:Table 28:Table 29:Table 30:Table 31:Table 32:Table 33:Table 34:Table 35:Table 36:Table 37:Table 38:Table 39:Table 40:Table 41:Table 42:Table 43:Table 44:Table 45:Table 46:Table 47:Frequency of Contact with District Agriculturist or Horticulturist 51Frequency of Contact with Other Provincial Specialists ^ 51Frequency of Contact with University or College Staff ^ 52Frequency of Contact with Agriculture Canada Staff ^ 53Frequency of Contact with Sales Representatives 53Frequency of Contact with Bank Manager or Financial Advisor ^ 54Frequency of Contact with Packing House or Processor Fieldman 54Frequency of Contact with Veterinarian ^ 55Frequency of Contact with Independent Consultant ^ 56Frequency of Contact with other Miscellaneous People ^ 56Total Number of Contacts with All Sources ^ 57Frequency of Information Use ^ 59Source of Video Tapes 61Visits to Various Sites ^ 62Value by rank of All Information Sources ^ 64Interpretation of Canonical Statistics 71Independent Variables used for Canonical Analysis ^ 72Dependent Variables used for Canonical Analysis 72Canonical Correlation Results Forms of Contact by Individuals- 73Canonical Correlation Results - Use of Publications ^ 76Canonical Correlation Results - Use of Miscellaneous Sources ^ 77Canonical Correlation Results - Value of Information Sources ^ 78Results of t-test Analysis Significant Demographic t-test Probabilities at 95%Extension Contacts 1969 vs. 1991vii828487LIST OF APPENDICESAppendix 1: Detailed Questionnaire Results Page^ 100^ 111Appendix 2: Interpretation of Canonical Analysis Computer Outputvii'LIST OF FIGURESPageFigure 1: Agricultural Regions of British Columbia ^ 5Figure 2: Frequency of All Forms of Contact 49Figure 3: Total Number of Contacts with All Sources ^ 58Figure 4: Frequency of Information Use ^ 60Figure 5: Visits to Various Sites  63Figure 6: Value by Rank of All Information Sources ^ 65Figure 7: Extension Contacts 1969 vs 1991 ^  88ixACKNOWLEDGMENTSI wish to thank the following for their support and contributions on this project.The Ministry of Agriculture, Fisheries, and FOod for providing funding under the"1991 Extension Program Review" for the costs of printing and mailing the surveys.Tom Sork for his advice and assistance throughout my master's program.Dr. Boldt of the Faculty of Education for providing assistance with the statisticalinterpretation of the results.Wayne Wickens of the Ministry of Agriculture for his comments and suggestions instarting with the project.The Canadian Society of Extension for a scholarship in 1990.My wife Barbara for her comments and suggestions.CHAPTER 1INTRODUCTIONFarmers use a variety of sources from which to obtain information to answer awide range of technical and financial questions. These sources range from the nextdoor neighbor to specialized consultants. Each of these is used with varying degrees offrequency depending on a number of factors such as availability and cost. The natureof the source of information, the frequency with which it is used, and the preferenceexhibited by the farmer for each type are important considerations in evaluating theeffectiveness of existing extension methods and the design of new ones. For example,if farmers are obtaining most of their information on pesticides from a company salesrepresentative, promoting the safe handling of pesticides by having brochures ondisplay at a British Columbia Ministry of Agriculture ) office may not be an effectiveway of reaching them. Providing training to sales representatives and giving them thebrochures to leave with farmers could be a more efficient and effective way to promotethe adoption of those practices.The purpose of the research project reported in this thesis was to survey farmersin British Columbia about the sources from which they obtain technical information andto determine how the use of these information sources is related to their demographiccharacteristics. Specifically, there were four objectives of this study.I The Ministry of Agriculture has had several different names during the past two decades. It waspreviously known as the Department of Agriculture and has had the addition of "Food" and "Fisheries"over the past several years. For simplification, the term Ministry of Agriculture will be used throughoutthe text.11. To determine what sources of information farmers are currently using inBritish Columbia and the relative preference they have for each type.2. To determine if the preference and use of information sources can becorrelated to demographic characteristics.3. To determine if the demographic characteristics of farmers who use theBritish Columbia government extension services differ from those who do not.4. To determine if the level of contact that farmers have with their districtagriculturist or horticulturist has changed over time.While a study of this nature is not new or unique, there are several reasons whycurrent research would be of value. Work of this nature has not been published aboutBritish Columbia for over twenty years. All of the work previously published wasconducted by graduate students between 1965 and 1969 under the direction of ProfessorCoolie Verner of the University of British Columbia.The evolution of the global trading village places increased demands uponfarmers to be more efficient in their business. The Ministry of Agriculture in a missionstatement defines one of their six operating principles that "British Columbiaagriculture, fish, and food industries will compete in a global economy" (Ministry ofAgriculture, 1989, p.3). The way information is used has transformed the way inwhich business is conducted. Information is seen as the key to innovation andeconomic success. Driving this development is the technology of informationacquisition and processing. Satellite communication, micro-computers, fax machines,databases, computer bulletin boards, and video equipment are readily available2technology that was not present or was limited in the nineteen sixties. Consequently,there is more information available and more choices in how to get it.The study of the use of information sources is related to that of the adoption ofinnovations.The adoption of innovations is a critical factor in the development and economicviability of British Columbia's agricultural industry. One of the Ministry ofAgriculture's strategic priorities is to "enhance the competitiveness of the agriculture,fish, and food industries by assisting in effectively transferring technology to producersand processors" (Ministry of Agriculture, 1989, p.11). Different types of informationsources are used at each stage of the adoption process. Previous research has foundrelationships between the type and frequency of use of information sources (Alleyne &Verner, 1969).Information produced by studies such as this assist government in evaluatingand understanding their role in the provision of information to farmers. For example,the 1979 British Columbia Legislative Assembly, Select Standing Committee onAgriculture (1979) used the research results from Akinbode & Dorling (1969) asmaterial for evaluating and comparing agricultural extension systems in BritishColumbia, Alberta, and Oregon. Akinbode's work in 1969 involved a study of thenature and frequency of contact farmers had with the District Agriculturists in BritishColumbia. The report produced by the Select Standing Committee concluded withrecommendations on the provision of extension services in British Columbia.Government funding for all programs is harder to come by and there is anincreased emphasis on justifying all expenditures. This has contributed to changes in34how extension programs are carried out. In general, greater emphasis is placed onprograms that reach farmers in larger groups as opposed to the traditional personal farmvisit. How this has affected the role the provincial extension service has in solvingfarmers' technical and financial problems is not known.The agri-food industry is British Columbia's 3rd largest industry, ranking onlybehind forestry and mining. It is an $11 billion industry and employs 210,000 people.In 1990, British Columbia produced more than 60 percent of the province's total foodrequirement and exported $1.3 billion of agricultural products (BCMAF, no date).The agricultural sector is constrained by a limited land base that is comprised offertile valleys located between several mountain ranges. The province can be dividedinto eight distinctive agricultural regions on the basis of climate, geography. Theseregions as shown in Figure 1 are: 1. Vancouver Island, 2. Fraser Valley, 3.Thompson/Okanagan, 4. Kootenays, 5. Cariboo, 6. North Coast, 7. Nechako, and the8. Peace River. Vancouver Island has a moist climate suited for long-season specialtycrops. Vegetables, berries, nursery stock, and specialty crops such as kiwifruit can begrown. Dairy is the predominate livestock, however swine and poultry are important.The second region is the Fraser Valley which has a similar climate to VancouverIsland. These two regions have the highest number of frost-free days in the provinceand the most rainfall. Dairy is again the most predominate livestock industry howevera significant poultry and swine industry is also present. Vegetables, berries, forages,and legumes are common. While a small greenhouse industry is located on VancouverIsland, this industry is mainly concentrated in the Fraser Valley. Extensive operationsproduce lettuce, flowers, peppers, cucumbers, and tomatoes. The third region, theThompson/Okanagan, is known primarily for tree fruit production, however wineries,dairy, and beef are also important industries. The climate is mild with low annual5precipitation. The fourth region, the Kootenays, has a moderate climate and is locatedin small valleys between various mountain ranges in the south-eastern part of theprovince. While a variety of products are produced, including vegetables, tree fruits,and honey, the cattle industry is most important. Area number five, the Cariboo, isFigure 1: Agricultural Regions of British Columbia6known as the heart of the ranching industry. A significant amount of forages isproduced to support the cattle industry. Irrigated alfalfa, some root vegetables andpotatoes are produced along the Fraser River benches. The growing season isrelatively short with moderate rainfall. The sixth region, the North Coast includes theQueen Charlotte Islands and moves inland as far as Terrace. The climate variessignificantly with significant rainfall on the coast and the Queen Charlottes to semi-aridareas near Terrace. The range of commodities that can be grown is limited by a shortfrost-free period. Agriculture here is limited to ranching. Further east lies the seventhregion, known as the Nechako, which is the area from Prince George to Smithers. Thegrowing season is short, (53 days), and there is moderate rainfall. Forage productionfor the dairy and cattle industry is widespread. Some grain is grown in the Vanderhoofarea. Region eight, known as the Central Peace River region, produces 86% of theprovince's grain. Some beef and vegetables are grown for local markets. Honeyproduction is a million dollar industry.Formal agricultural extension activities are primarily carried out by theprovincial Ministry of Agriculture, although many other government agencies and non-governmental organizations play a role. The ministry has commodity specialists anddistrict extension staff located in 22 different offices around the province. More than200 regional extension staff provide the "front line" contact with producers. The staffengage in many types of extension activities, including field trials and varietyevaluation, publication of technical bulletins and other informational materials,production of audio-visual materials, and seminars, short courses and workshops.CHAPTER 2REVIEW OF PREVIOUS WORKThis chapter provides an overview of the current literature on the relationshipsbetween demographic factors and use of information sources by farmers. It firstdescribes how the literature search was conducted, and then describes the previouswork in three sections: British Columbia studies, Canadian work, and United Statesresearch.Three database programs were available through the University of BritishColumbia's libraries to survey the body of literature. The first database is known bythe term "Agricola" which is an acronym for Agricultural OnLine Access. It isavailable on CD-ROM disk and extends from 1970 to the present. "Agricola" is aservice provided by the National Agricultural Library of the United States Departmentof Agriculture. The database indexes citations from 2120 journals, monographs,theses, software, audiovisual material, and technical reports.The second database used was the "Current Index to Journals in Education"(CIJE). This database encompasses 750 periodicals from 1969 to the present in thefield of education. The material is primarily American with some British and Canadianreferences.The third database utilized was the "Resources in Education" (RIE) which isorganized into two separate databases. RIE1 covers Educational Resources InformationCenter (ERIC) microfiche from 1980 to the present while RIE2 covers microfiche from1966 to 1979.78An extensive literature search quickly found that information available on thissubject was limited and obscure. Many articles published in the field of extension inCanada are not widely distributed and available in the University of British Columbialibrary system. It proved to be easier to find information about agricultural innovatorsin Ohio in 1961 because of the monthly journals produced by agricultural experimentalstations in the United States, than it was to locate work done on farmer's use ofinformation sources in Canada. Fortunately, most of the work pertaining to the BritishColumbia situation was done through the Department of Administrative, Adult, andHigher Education at the University of British Columbia and is available in thedepartmental library. Dr. Coolie Verner and his graduate students conducted a numberof studies from 1965 to 1969 (Akinbode, 1969), (Alleyne, 1968), (Millerd, 1965),(Verner & Gubbels, 1967). There has been no work published on the use ofinformation sources in British Columbia since that time.The literature on farmer's use of information sources can be broken into threecategories. The first group includes published studies done on British Columbia. Thesecond set of studies includes all other Canadian studies, and the final group describeswork conducted in the United States.British Columbia StudiesFour studies have been published which include data on the use of informationsources in British Columbia. These were all done prior to 1969, and were conductedby University of British Columbia graduate students under the direction of ProfessorCoolie Verner.A study by Verner & Gubbels (1967) looked at the adoption of innovationsthrough a random sample of 100 dairy farmers in the Lower Fraser Valley. Theyfound that dairy farmers used different sources of information at different stages in theadoption process. Mass media sources were the most important at the awareness stage,with personal and individual instruction sources being important at the interest stage.Alleyne (1968) interviewed 100 strawberry growers in the Lower Fraser Valley.He looked at the information sources that were used at each stage of the adoption ofinnovation process. The adoption of innovation process categorizes farmers by thelength of time it takes for them to adopt a new method or technique of farming. Thefour stages are: laggard, late majority, early majority, innovator. Friends andneighbors was the most referred to source for all stages of the adoption process. Thisaccounted for 23.5% to 28.7% of the farmers. Sales representatives, observations onother farms, the District Horticulturist, agricultural meetings along with personalexperience and foreign travel were the other most important information sourcesconsulted. The rank of importance of the preceding sources varied depending on theadoption stage. The District Horticulturist ranked second for all adoption stages exceptfor the laggard group.Millerd (1965) interviewed Okanagan Valley orchardists to determine thesources of information used in each of the five stages of the adoption process. Thegroup studied had been served by the 1964 television Chautauqua program. Thisprogram was widely viewed and introduced a number of innovations to orchardists. Itwas one of the earliest uses of the electronic media for educational purposes in theOkanagan. Prior to this program, innovations were introduced to orchardists throughmeetings in district halls. Millerd found that the following five sources were the mostused overall in the following order: District horticulturist, other orchardists,9Summerland Research Station (Agriculture Canada), the television Chautauquaprogram, and magazines.Akinbode (1969) conducted personal interviews with 265 farmers throughoutBritish Columbia about their contact with District Agriculturists. He looked at thedifferent ways in which a District Agriculturist may make contact with a farmer andbroadly categorized them into two groups, personal and impersonal methods. Personalcontact methods ranged from a high of 35% for those who visited the DistrictAgriculturist at their office, to a low of 16% for farm visits. Impersonal contactmethods ranged from a high of 93 % for articles written by the District Agriculturist infarm newspapers to a low of 81 % for mail sent from the office. This was a BritishColumbia wide study and is used later in this report to compare with the current levelof contact with District Agriculturists.Canadian StudiesDent (1968) conducted personal interviews of 147 farm operators in Two Hills,Alberta. Farmers reported that their top five most frequently used sources ofinformation were their own experience, farm papers, magazines, family, and friendsand neighbors.Blackburn et al. (1983) surveyed 731 farmers selected at random and a secondgroup of 452 farmers known as agri-leaders chosen by the Ontario Ministry ofAgriculture. Farm papers and magazines, Ontario Ministry of Agriculturepublications, and ministry office programs were the most highly rated. All of the10public and private agency programs investigated were considered important by morethan one-half of the farmers.Alberta Agriculture (1983) conducted a telephone survey of 2312 Albertafarmers who had annual sales of at least $2500 to determine their information needs. Atotal of 39 questions were asked, eight of which related to demographic information.The remaining questions asked about the types of information they required, the bestsource for certain types of information, and about the types of information AlbertaAgriculture should be offering. The survey did not ask where they were currentlygetting their information. The sources that were rated most useful by Alberta farmerswere:1. Neighbors and friends2. Radio3. Alberta Department of Agriculture4. Farm magazines and newspapers5. District AgriculturistUnited States StudiesNolan & Lasley (1979) surveyed 691 farmers during the spring of 1978 inMissouri to determine who was using agricultural extension services. He investigatedthe use of government extension publications, visits to the extension office, attendanceat extension meetings, and the frequency of visits by extension specialists to the farm.Younger pork farmers with large amounts of land were the heaviest users of extensionpublications, and visited the extension office the most. Overall 55% of the farmers had1112been to the office at least once during the past year, and 44% had been to an extensionmeeting. The characteristic with the strongest positive correlation with attendance wasfarm size. Farm visits proved to be the least frequent source of contact, with only 23 %reporting a visit during the past year.Warner & Christenson (1981) surveyed the general Kentucky population todetermine a profile of the users and non-users of extension services. They found nostatistical difference in the age groups reached, and the educational level of users andnon-users was the same. They found that extension served a slightly larger proportionof those with lower incomes.Gross (1977) researched farmers' attitudes towards extension to see if therewere differences based on demographic characteristics. Farmers were asked to selectfrom a list of 20 statements, five that he agreed with. These statements ranged fromthe favorable to the unfavorable and had been previously ranked on a scale of 1 to 11.The median score became the attitude score. Gross (1977) found that the youngerfarmers (26-35) and older farmers (56+) had the highest attitude scores, with middleaged farmers scoring less. The higher the attitude score, the more favorably the farmerviewed the extension service. Attitude scores increased with level of education,frequency of contact with the extension service, and with participation in farmorganizations. Attitude scores for meetings, mailed information, and mass media werehigher than for office visits and phone calls. Gross (1977) interpreted this to mean thatthere was a greater certainty that farmers would get the information they were lookingfor from meetings, mailed information, and mass media methods, whereas if theyvisited the office or tried to phone the extension agent, there was a good opportunitythat staff members were out of the office and delays were was incurred in getting theinformation.13Warner & Christenson (1984) conducted a national survey of the United Statespopulation to discover the demographic characteristics of those who do and do not useextension services, along with a measurement of the awareness, support, andsatisfaction people have of the United States Cooperative Extension Service. A 101item questionnaire was administered through a telephone survey of 1048 people. Theyfound that extension clientele were predominately middle class. They had middle toupper incomes, a high school or college education, were white, married, employed,and homeowners.Coughenour (1959) studied the use 285 farmers made of five agriculturalagencies in Kentucky from 1950 to 1955. The single most important characteristic inthe use of agencies was socio-economic status. Socio-economic status was measuredthrough their participation in farm organizations, value of farm sales, and thefavorability of the social climate of the farmer's neighborhood. Therefore as farmsales, participation in farm organizations, and the favorability of the social climateincreased, so did the farmers' use of the agricultural agencies. The extent of thefarmer's formal education was the second most important factor. The farmers' age,years in farming, and attitude towards scientific farming were the least associated withwhether or not they would obtain information from various agricultural agencies.Iddings and Apps (1990) looked at the factors that influenced farmers' use ofcomputers. They referred to a 1987 Successful Farming article, which reported on aMichigan State University study of Michigan farmers in which 21 % of farmers eitherowned, leased, or shared a computer, while an additional 24% planned to obtain one inthe next three years. In their study, they worked closely with 18 farmers in Wisconsinand Kansas to determine how much the farmers used their computers. They found that  good sources of information such as user groups, newsletters, and software reviews,along with a wide network of other users, were important factors in increasing thefrequency of use.Many of the previous studies such as Dent, (1968), Verner & Gubbels, (1967),Alleyne, (1968), etc. have relied upon less sophisticated techniques of statisticalanalysis because of the limitations or accessibility of computer software. Themathematics involved in computing statistical results from a survey with a number ofquestions can only be reasonably dealt with through computer analysis. The types ofstatistical analyses used by those researchers were much more limited than thatavailable today.Extension in the United States is delivered quite differently than in Canada.Extension was created by the Smith-Lever Act in 1914 as part of the land-grant systemfor transmitting agricultural information from the colleges to the local people. InCanada extension is mainly under the jurisdiction of the provincial ministries ofagriculture. United States extension programs tend to be broader in nature. Theyinteract with a significantly larger urban clientele, and can involve communitydevelopment programs. The purpose in reviewing the Canadian and United Statesstudies was to show some of the similiarities that exist amongst farmers' use ofextension programs in other areas. The review also illustrates that studies such asIddings & Apps (1990) and Gross (1977) are very dependent on the type of extensionprograms that are offered and how they are conducted along with the cultural milieu athand. Therefore it is difficult relate some the findings from studies such as these to theBritish Columbia situation without fully understanding the context within which thoseextension programs are carried out.1415Several of the studies reviewed looked at information sources in view of theprocess of adoption of new innovations (Alleyne, 1968),(Millerd, 1965), (Verner &Gubbels, 1967). The adoption of innovation process describes how new ideas andpractices are communicated to farmers and how they decide to adopt or reject thoseinnovations. Farmers can be classified into "adoptor" categories based on the "degreeto which an individual is relatively earlier in adopting new ideas than other members ofthe system" (Lamble, 1984). These categories are know as: innovators, early adoptors,early majority, late majority, and laggards. Innovators are noted as being veryadventuresome and are eager to try out new ideas. This group represents 2-3% of thepopulation. Early adoptors represent the next 10 to 15%,. and unlike innovators whoseinterests lead them out of their local circle of peers, tend to be regarded with a greatdeal of esteem. "Potential adoptors look to early adoptors for advice and informationabout the innovation" (Rogers, 1983). The early majority is describe as being"deliberate" as a result of their long innovation-decision period. This group representsabout a third of the population. "Although they rarely hold leadership positions, theyinteract frequenctly with with peers and provide an important link in the diffusionprocess between the early adoptors and the late majority" (Lamble, 1984). The latemajority presents another third of the population who adopt new ideas just after theaverage person. "Pressure of peers is necessary to motivate adoption" (Rogers, 1983).The laggards are the last 15% to adopt. Laggards tend to be the most "traditional" andmake decisions in terms of what was done in the past. "Laggards tend to be franklysuspicious of innovations and change agents" (Rogers, 1983).As can be seen from the above discussion, the type of information source afarmer may use is related to some degree to the adoptor category they are in. Whilethis study did not attempt to relate sources of information to the farmer's adoptorcategory, it is important to remember that different groups of farmers prefer differentsources for obtaining information. Categorization of farmers into these groups is bestdone through examining specific examples of the adoption of an innovation for aspecific commodity group and by determining how the farmer learned about theinnovation.16CHAPTER 3RESEARCH DESIGNDevelopment of the InstrumentThe information required to satisfy the objectives of the research could becollected through personal interviews, telephone interviews, or through a mailedsurvey. A number of the previous studies on use of information sources by farmerscollected the information through personal interviews. For example, studies for theCanada Land Inventory (Verner, 1967) were conducted over a period of two summersduring 1966 and 1967 by hired staff. Each staff member was able to interview between3.0 and 5.1 people per day, which included time spent in the evenings (Verner, 1967).Dent (1968) took between August and December of 1965 to personally interview 158farmers in the county of Two Hills, Alberta. It took Verner & Gubbels (1967) 194farm visits to complete 100 personal interviews in the Lower Fraser Valley. It isapparent that this method of collecting information is costly.A second method of collecting the required information would be by telephoneinterview. It is a policy of University of British Columbia to discourages initial contactby telephone for research involving human subjects. To conduct telephone interviews,each farmer would have to be mailed a letter informing them about the study andadvising that they would be contacted by telephone for an interview. In addition to thisexpense would be that of long distance phone calls, as the survey group was scatteredthroughout the province. This method would also remove the anonymity of theresponses, and would make it difficult to collect information on sensitive demographicinformation, such as income from outside the farm and total farm sales. In addition,1718the questions require the respondent to think and reflect over who they may have talkedto in the past year, and some of the information such as farm sales may have to belooked up.Mailed questionnaires are widely used for many types of surveys, and permitwide coverage at minimal expense (Charach, 1975, p. 1). Mailed questionnaires allowthe survey to be applied uniformly without any influence from an interviewer. Theyalso provide a greater sense of privacy and anonymity, which is beneficial when askingpersonal questions such as income.The greatest concern with mailed questionnaires is the response rate. Is there adifference between those who completed the questionnaire and the non-respondents?Mailed surveys also limit the number of questions that can be asked, and theircomplexity.As a result of these considerations, and the very limited amount of fundsavailable to carry out the information gathering, a mailed survey proved to be the bestmethod. Significant consideration went into the design of the survey in order to dealwith the negative aspects of mailed questionnaires.Survey DesignThis section describes how the survey was developed and carried out. Anumber of key circumstances dictated the number of surveys sent out and the timeframe available.19In spring of 1991, the author's advisor, Thomas J. Sork, was asked by theBritish Columbia Ministry of Agriculture to prepare a comprehensive description ofBritish Columbia's extension programs and services since 1983. In addition, he wasasked to propose recommendations on the future development of these programs andservices. Several of the questions asked in the mandate of the review were: Who iscurrently being served by extension; Which aspects of extension work are best carriedout by the Ministry?; Which are best carried out by non-Ministry agencies? In orderto answer questions like this, basic information about the current extension services andthe information sources farmers use had to be gathered. The extension review wasgiven a small budget and a mandate to report back by August 30, 1991. The finalreport was based on information gathered from the survey used in this thesis, a secondsurvey on different aspects of extension, and interviews of many ministry staff. Thereport only utilized the raw survey results from this thesis and did not contain any ofthe statistical analysis.The intent and design of the survey was to collect information in three major areas.a) Frequency of use of different information sourcesb) Opinions on the value of different information sourcesc) Demographic data on the respondentThe survey, which can be found in Appendix One, was divided into three mainsections. In order to obtain a clear picture of the various sources a farmer may consult,section one of the survey contained an exhaustive list of possible sources a farmermight consult. Questions one through five asked about contact with a list of 10individuals who are generally considered to be in the business of providing informationto farmers. The objective of these questions was to explore the different ways in which20farmers interact with these individuals. In addition, this question format parallels thatof questions asked by Verner (1967) in 1966 and 1967 while conducting the CanadaLand Inventory Demographic Surveys. This allows direct comparison of those resultswith the information gathered in this survey. Question six asked farmers about avariety of publications that may contain information useful in making farm managementdecisions. Questions 7 through 16 contain all the remaining questions about sources thefarmer may have consulted that did not fit into any of the previous categories.Section two consisted of question 17 and asked farmers how valuable they foundeach source even if they have not had an opportunity to use them in the last 12 months.Section three consisted of questions 18 through 30 which pertain to demographicinformation.The survey only asked whether or not a farmer used or valued a particuliarsource of information. The questions do not attempt to determine why a farmer chosethat particuliar source or how reliable or trustworthy the source may be. To determinethe answers to these questions would make the questionnaire much longer and wouldmake it difficult to report on all of the information sources farmers are using.Questions of this nature would be more appropriate when investigating particuliarsources of information in more detail.Due to the large number of questions asked on the survey, they were organizedinto similar categories that could be answered by simply checking one of the boxesprovided. The length, appearance, and complexity of the survey was of majorconsideration. Charach (1975, p. 6) cites a number of studies on the effect of thelength of a survey. He states that the evidence suggests that a reduction in the amountof time required to complete a survey may increase the response rate, however this hasnot been proven. In fact, increasing the length can be beneficial if it improves theformat.Discussions with various individuals suggested that 20 minutes was an idealtime length to complete a survey. A forced choice questionnaire made it easier to fillout. The structure of the questions was such that forced choices would not obscure thetrue situation.Pilot testing of the survey was done on two farmers prior to mailing out thesurvey. One was a beekeeper and the second was a nursery grower. Verbal feedbackresulted in several minor changes to the instructions in order to better explain how tocomplete the questionnaire.The final questionnaire format along with the cover letter was submitted to, andapproved by, The University of British Columbia Behavioral Sciences ScreeningCommittee For Research and Other Studies Involving Human Subjects. The review bythe committee ensures that research conducted under the university's name meets thestandards approved by the University.The survey was also submitted to the 1991 Extension Program Review SteeringCommittee of the Ministry of Agriculture. They approved the use of the survey andprovided funds for it to be conducted as part of the 1991 Extension Review. Since thereview was not public, approval was also given for the publication of the survey resultsfor this thesis.21As described earlier, Dr. Thomas J. Sork of the University of British Columbiawas appointed Director of the 1991 Extension Review. As the author works with a22well known agricultural supply company, cover letters for the survey were sent onUniversity of British Columbia letterhead under Dr. Thomas J. Sork's signature. Itwas felt that this would lend additional credibility to the survey and increase theresponse rate, as the results were going directly to the Ministry of Agriculture.Farmers could have been disinclined to respond if they felt the survey was related to aparticular agricultural business rather than an impartial institution, such as theUniversity of British Columbia.Return envelopes included with the questionnaire were addressed and stamped.Regular postage stamps were used for the return envelopes. Charach (1975, p. 7)suggests that a stamp increases the sense of obligation of subjects to respond becausethe sender will be out the price of postage if they do not. In addition, the use of stampsavoids the survey being associated with junk mail.Time was a factor affecting how the survey could be carried out. Funding fromthe 1991 Extension Program Review project only became available in late April. Thesurvey had to be constructed, pilot tested, carried out, and a final report to the Ministryof Agriculture completed by August 31, 1991. For this reason, there was insufficienttime to carry out follow-ups or reminder letters to people in order to increase theresponse rate. Follow ups to mailed surveys can significantly increase response rates.Two and three follow-up letters followed by a telephone call, can in some instances,increase the response rate to over 90% (Orlich, 1978, p. 97). According to theliterature, one could expect at least a 10% increase in the response rate. However,since the survey was anonymous, it would not have been possible to determine whoreplied. A follow-up could be conducted by sending every individual a reminder,while thanking them if they had responded already. A copy of the survey would haveto be included with the reminder in case they had lost or misplaced the first one.23Conducting a follow-up of this nature therefore would have doubled the costs howeverfunds were not available to do this.Sampling ProceduresThis section describes how the sample was drawn and the statistical significanceof the sample size. The 1991 Extension Program Review included another survey thatwas sent to a different group of farmers. The samples for the two surveys were drawnfrom the same set of producer addresses, so the following discussion includesreferences to the second survey.Drawing the SampleThe main objective in developing a sampling procedure is to draw a sample thatis representative of the total population. Consequently, if a different sample was drawnfrom the same population, the results would be similar. To distinguish between thetwo surveys, the survey used in this thesis on farmer information sources is referred toas the "long questionnaire", and the other is referred to as the "short questionnaire".Sufficient funds were available to mail 1200 questionnaires in total. As the longquestionnaire was an addition to the questions being posed by the Ministry ofAgriculture in the 1991 Extension Program Review, only 400 of the longquestionnaires were sent out, with the remaining 800 receiving the short questionnaire.24Mailing lists for farmers in British Columbia are difficult to obtain, as many ofthem are confidential. Lists are maintained by various farm organizations, privatecompanies who supply products and services, and the Ministry of Agriculture. As theMinistry had requested the study, and their mailing lists are the most complete,Ministry-supplied mailing lists formed the basis for defining the survey population.Two types of lists were available. The commodity specialists maintained lists that werespecific to their specialty. District agriculturists and horticulturists maintained moregeneral lists. Each name on the list was categorized by the commodity the individualwas involved with. Individuals on the mailing lists could get on them in a variety ofways. Ministry staff attempt to keep accurate lists of individuals in their area, but onecould get on the list by simply requesting it. While Ministry mailing lists could beconsidered biased in favor of farmers using Ministry services, it is expected that due tothe fact they have been maintained for a number of years, that they are most likely tobe the most complete.On the basis of these mailing lists, 1200 names were drawn using a weightedaverage which combined the contribution each commodity group made in farm cashreceipts with the estimated number of producers in each group (Wiersma, 1986). Thiswas calculated by taking the mean value of the percentage of producers in eachcommodity group and farm cash receipts. The results are presented in Table 1 below:25Table 1Selected Sample by Commodity GroupCommodityGroupProducers Farm CashReceiptsa(millions)Weighted Sample(#)b voc (%)d (%)e ($) (%) (#) (%)Beef 2524 2524 27.6 28 190.5 18 276 23.0Grains & 800 800 8.8 9 31.8 3 72 6.0OilseedsDairy 950 950 10.4 10 242.9 22 192 16.0Poultry 443 443 4.9 5 200.1 18 138 11.5Swine 240 240 2.6 3 45.3 4 42 3.5Tree Fruits 1600 1600 17.5 17 50.5 5 132 11.0Berries 1200 103 1.1 1 54.0 5 10 1.0Vegetables 600 600 6.6 7 100.7 9 96 8.0Floriculture 380 380 4.2 4 111.8 10 84 7.0& NurseryOther 400 1497 16.4 16 60.3 6 160 13.0Totals 9137 9137 100.1f 100 1087.9 100 1200 100.0a 1989-90 British Columbia Ministry of Agriculture and Fisheries Annual Reportb Estimated number of farmers in each commodity groupc Number of farmers used to draw the sampled Actual percentage of each categorye Percentage of farmers used to draw the samplef Difference due to round-off errorColumn 1 and 2 describes the number of farmers in each commodity group assupplied by Terry Dever (1991) of the Ministry of Agriculture. Column 3 lists thenumber of farmers in each commodity group that were used to draw the sample.Column 4 presents the percentage of farmers in each commodity group. In calculatingthe weighted average, these percentages had been rounded off and these values arepresented in column 5. A small round-off error was made for the tree fruit category,however this has little effect on the actual sample.A mailing list for berry growers was not available at the time of the survey. Atime deadline for the final report to the Ministry of Agriculture meant that the survey26had to be conducted without this information. As the mailing lists from the districtoffices contained all the farmers in a district, it was possible to put together a list of103 berry growers. As names for the remaining 1097 were not available, this total wasadded to the 'other' category. This means that berry growers are under-represented inthe survey, while 'other' producers may be slightly over-represented. From thisinformation a weighted sample of 1200 names was drawn.Sample SizeFinancial considerations dictated the number of surveys that could be sent out.However, for the results from the survey to be interpreted as being significant, it isimportant to know how many surveys must be completed and returned in order toprovide a statistically representative sample. A basic assumption to this is that thesurvey is not self-selecting, so that certain demographic groups are not less likely torespond than others, and that the return of surveys is random, i.e. the reasons for notresponding are random.To determine if the 400 names drawn for the long questionnaire was largeenough to be statistically representative of the population, the following equation(Scheaffer, Mendenhall, & Ott, 1986) can be used. For a stratified random sample, theapproximate sample size (n), required to estimate the mean (m), with a bound B, on theerror of the estimated size of n is given by Equation 1.n —ioEN,' 0-2; / w;i=i N2D + EN, csEqn (1)27wheren = sample sizeN = total population sizeNi = population of each stratumwi = fraction of N used for each stratificationo-i2 = population variance of each stratumD = B2/4B = size of allowable error in estimating sample size nThe mean (m) referred to above can refer to a variety of information such as theaverage value of age, income, or number of farm visits. The population of eachstratum refers to the number of dairy farmers, beef farmers, et cetera. There are atotal of 10 stratums0. The population variance refers to how much individual scores ofthe item being measured differ from the average value of that item. For example, ifthe average age of farmers is 50 years, and the total population varied between 40 and60 years of age then the variance would be much lower than if the total populationvaried between 18 and 82 years of age. Since the variance of the total population isunknown, it can be estimated by the use of Tchebysheff's Theorem and themathematical principle of the normal distribution (Scheaffer, Mendenhall, & Ott,1986).This theorem states that the range will be between four to six standard deviations of themean. Therefore:a2 = [range/(4 to 6)12^Eqn (2)28The range refers to how accurate the values for the total population andpopulation stratums are thought to be. Since the population variance and the allowableerror must be estimated, the most suitable technique for using this equation is tocalculate a range of values of the sample size (n) to see if reasonable sample sizesresult. The results of these calculations are listed in Table 2 below.Table 2Estimated Sample Sizes RequiredRange Ba Db Number ofStandardDeviationsSample Size(n)5% 10 25.00 4 1810% 10 25.00 4 6910% 5 6.25 4 27020% 10 25.00 4 27020% 20 100.00 4 695% 10 25.00 6 810% 10 25.00 6 3110% 5 6.25 6 12220% 10 25.00 6 12220% 20 100.00 6 31a size of allowable error in estimating sample size nb D = B2/4Values for the range and "B" were picked to see the resulting sample size "n" thatwould result. The assumption is made that since the Ministry of Agriculture has beenmaintaining the mailing lists for many years, that any degree of error that exists mustbe less than 20%. Using these values in equation (1) gives a range of sample sizesfrom 8 to 270. This range indicates the size the sample should be based on the degreeof error estimated. As the degree of error is not known, and the sample size range of 8to 270 represents a broad range of possible errors. A total of 400 questionnaires weremailed with 100 responses. Given the range of values presented in Table 2, a surveyresponse of 100 appears to be large enough to minimize the possibility of making anerror when generalizing the results of this survey to the total population of farmers inBritish Columbia.29CHAPTER 4RESEARCH RESULTSQuestionnaire ResponseThis section describes and compares the response rate to the surveys that weresent as part of the 1991 Extension Program Review. As mentioned previously, thequestionnaire that forms the basis of this thesis is referred to as the 'longquestionnaire', and the other questionnaire the 'short questionnaire'.A total of 100 completed questionnaires were returned out of the 400 longquestionnaires that were sent out. In addition to the 100 responses, three were returnedby the post office indicating that the individuals had moved, and one was returned witha letter explaining that the individual had retired and was no longer farming.Therefore, the overall response was 25% of the surveys sent out, but as the actual totalsample was 396, the true response rate was 25.25%. The questionnaires were mailedon July 5 and a reply was requested by July 15. The response rate could have beenhigher if more time had been available for people to respond to the survey, or if it hadbeen conducted at a time of year when farmers were not busy. Some of the surveyswere held up by the post office and were not mailed until July 9. Given that mail cantake up to 10 days to reach more remote areas of the province, it is clear thatinsufficient time was allowed for a response. The results to be presented will show thatthe goals of the thesis were not compromised. Respondents made several writtencomments on the returned questionnaires about the lack of time they were given torespond. One respondent, postmarked Victoria, noted that they had received thesurvey on July 13 and another, from an unknown location, indicated that they hadreceived it on July 19. At the time the report was written, only 86 surveys had been3031received. Surveys continued to trickle in until early October. This demonstrates thatthe survey itself was viewed positively by farmers, as they took the trouble to respondlong after the given deadline. On the basis of the values presented in Table 2, itappears that the response rate of 100 is large enough to represent the total population offarmers, on the assumption that the estimates of the number of farmers in eachcommodity groups is accurate to within about 20%.A total of 120 completed questionnaires were returned out of the 800 shortquestionnaires sent out. The response rate for this questionnaire was 15.0%. Theresponse rates for each questionnaire are presented by commodity group in Table 3below. Comparison of the response rate by commodity group show variances on themagnitude of 5% to 70%.Table 3Survey Respondents by Commodity GroupCommodity Group Long Questionnaire Short Questionnaire(%) (%)Sample Size (n=100) (n=120)Beef 23.0 14.2Dairy 17.0 17.5Swine 5.0 1.7Poultry 5.0 6.7Grains & Oilseeds 4.0 4.2Bee Products 0.0 0.8Vegetables 4.0 9.2Berries 1.0 2.5Tree Fruits 5.0 13.3Sheep 5.0 5.0Grapes 4.0 2.5Forage 2.0 1.7Floriculture 2.0 2.5Nursery 8.0 5.8Other 4.0 6.7Multiple Products 11.0 5.8Totals 100.0 100.1aa Difference due to round-off error32Of the four respondents in the long questionnaire classified as 'Other', tworaised horses, one raised fallow deer, and the last one was a turf farmer. More choicesof commodity groups were given on the questionnaire than the categories used to drawthe sample. This allowed the respondents to find their commodity reflected in thesurvey. In addition, it provides a better picture of the characteristics of those whoreplied and allows flexibility when conducting the data analysis. It is always easier tocollapse categories later than to try and expand them to fit the analysis beingperformed. An additional category of 'Multiple Products' was created as a result of thesignificant number of respondents who checked more than one commodity andindicated that neither commodity took precedence over the other. This occurred eventhough this question clearly asked for only one commodity to be checked.Comparison of the response rate by commodity group to the sample drawn isdone by collapsing the responses by commodity group down into the same categoriesThe 'multiple' products' category has been added to the 'other' category. These resultsare presented in Table 4 below.33Table 4Comparison of Survey Responses to the Sample SelectedCommodity Group Long Short SamplebQuestionnaire Questionnaire(%) (%) (%)Beef 23.0 14.2 23.0Grains & Oilseeds 4.0 4.2 6.0Dairy 17.0 17.5 16.0Poultry 5.0 6.7 11.5Swine 5.0 1.7 3.5Tree Fruits 5.0 13.3 11.0Berries 1.0 2.5 1.0Vegetables 4.0 9.2 8.0Floriculture & Nursery 10.0 8.3 7.0Other 26.0 22.5 13.0Totals 100.0 100.0 100.1 aa Difference due to round-off errorb From Table 1A review of these results indicates that the 'other' category consists of a largepercentage of the sample due to the multiple category being added. The originalsample was drawn by selecting names from commodity lists maintained by governmentspecialists. Since a category for farmers producing multiple products was not used toselect the sample, it is necessary to eliminate it in order to make a better comparison.The only possible way to do this is to provide a frequency count in every category thata producer indicated a response and then dividing by the total. The 11 producers in themultiple category on the long questionnaire indicated a total of 29 frequency counts.Recalculating the percentages provides the following results in Table 5. The multipleproduct category for the short questionnaire was not broken down as the originalquestionnaires were unavailable.34Table 5Adjusted Comparison of Survey Responses to the Sample SelectedCommodity Group Long Questionnaire Short Questionnaire Sampleb(%) (%) (%)Beef 21.2 14.2 23.0Grains & Oilseeds 4.2 4.2 6.0Dairy 16.1 17.5 16.0Poultry 8.5 6.7 11.5Swine 5.9 1.7 3.5Tree Fruits 6.8 13.3 11.0Berries 4.2 2.5 1.0Vegetables 5.9 9.2 8.0Floriculture & Nursery 10.2 2.5 7.0Other 17.0 7.2 13.0Multiple Products 0.0 5.8 0.0Totals 100.0 100.1 a 100.0a Difference due to round-off errorb From Table 1Comparing the distribution of responses for the long questionnaire to thedistribution for the sample shows a fairly similar distribution. The chi-squaredtechnique is used to make this comparison mathematically. The method compares thesurvey responses (observed frequencies), to the sample (expected frequencies) that wasselected. The null hypothesis is that there is no difference between the observedfrequencies and the expected frequencies. The chi-squared statistic, as shown inEquation 3, is calculated by finding the difference between the observed and expectedfrequencies and dividing the square of that difference by the value of the expectedfrequency. The sum of each of the commodity groups gives the chi-squared value.X22^(0 - E) 2Ei Eqn (3)35Calculation of the chi-squared statistic (x 2) gives a value of 18.2. Evaluation ofthe chi-squared statistic is done by referring to a chi-squared table. At the 5% level,the chi-squared value is 16.92, and at the 1% level, the chi-squared value is 21.67.Since the calculated value falls between these two tabulated values, one can concludethat the probability of getting a chi-squared value as large as 18.2 is greater than 1 %,but less than 5 %. The conclusion is that the response to the questionnaire bycommodity group is not quite the same as was expected. The commodity groupcontributing the largest amount of variance between the observed and expectedfrequencies is the berry growers. Slightly more berry growers returned surveys thanwere expected. Since the survey was underestimating berry growers in the first placedue to lack of a mailing list, this helps to mitigate the low representation thiscommodity group has in the survey.In conclusion, the results of statistical analysis give a strong degree of certaintythat the surveys are representative of the British Columbia population of farmers.Therefore, it can be concluded that the farmers responding to the survey arerepresentative of all farmers in British Columbia and that the information derived fromthe survey accurately reflects their opinions and actions.Factoring out the livestock producers from the 'other' category and adding upall other livestock categories indicates that 55.1 % of the farms produce animal oranimal products of some nature. This figure becomes important later on whenanalyzing contact rates by individuals who may be crop or livestock oriented such asveterinarians.Questionnaire ResultsThe questionnaire results are divided down into three sections. Section onereports the demographic characteristics of the survey group. Section two summarizesthe information obtained on the frequency of use of different information sources, andthe third section deals with the opinions expressed by the farmers surveyed on the valueof each information source. As not all of the farmers returning questionnairescompleted every section of it, each results section indicates how many answered thatpart of the questionnaires out of the 100 returnedDemographic CharacteristicsBased on the statistical analysis presented previously, the followingdemographic characteristics can be considered representative of British Columbiafarmers with the exception of berry growers. The information is presented in tabularform by each demographic characteristic with comments on important aspects of eachone.The age distribution of the farmers surveyed is heavily weighted towards olderindividuals as indicated in Table 6. The mean age is 49.5 years and 54% of thefarmers are aged 50 years or greater.3637Table 6Age Distribution of Sample Age Category^Percentage of^Farmersa(years) (%) 1 to 9^ 010 to 19 120 to 29 230 to 39^ 1640 to 49 2750 to 59 3660 to 69^ 1270 to 79 6a Based on 100 casesAs can be seen in Table 7 below, the majority of the respondents were male.The questionnaire and cover letter did not contain instructions as to who should fill outthe questionnaire should both a husband and wife consider themselves to be farmers. Itis assumed that the individual who is involved in the day to day making of farmmanagement decisions would be the respondent.Table 7Sex Distribution of Sample Sex Category^Percentage ofFarmersa(%)Male^ 91Female ^9a Based on 100 casesTable 8 shows that over 90% of the respondents are married.Table 8Marital Status Distribution of SampleMarital Status Percentage of Farmersa(%)Married^ 93Widowed, Divorced, Single^ 4Never Married 3a Based on 99 cases38Seventy percent of the farmers surveyed spoke English as their first language,while the remaining 30% are divided between 9 other categories as shown in Table 9.The predominant language/ethnic backgrounds, other than English, were German at12% and Dutch at 9%. It is not clear from the survey results if any ethnic group isunder-represented because of language difficulties in reading and completing thequestionnaire. In particular, those of East Indian ancestry who speak Punjabi are notrepresented at all. It is possible that those farmers who have difficulty with English asa second language had older sons or daughters who spoke English as a first languagecomplete the questionnaire for them, particularly if they are involved in the day to dayfarm activities. If this was the case they may have indicated English as their firstlanguage. More probable is the fact that many Punjabi speaking farmers are berrygrowers and berry farmers were the one commodity group under-represented in thesurvey.Table 9Mother Tongue of SampleMother Tongue Percentage ofFarmersa(%)English^ 70French 1Chinese 1Italian^ 1Portuguese 1Dutch 9German^ 12Native Indian 1Scandinavian 2Other^ 2a Based on 100 casesTable 10 indicates that over 90% of the farmers surveyed have children.Table 10Distribution of the Number of Children of Respondents Number of Children^Percentage of Farmersa(%)None^ 9.1One 8.1Two 29.3Three^ 28.3Four 12.1Five or more 13.1 a Based on 99 casesTable 11 shows the distribution of farmers by the level of their formaleducation. A total of 38% of the respondents have some form of post-secondaryeducation. Most of the post secondary education (60.5% of the 38%) is at the collegeor technical diploma level.Table 11Highest Level of Formal Education of Sample Level of Education^ Percentage of Farmersa(%)Less than Five Years 2Five to Eight Years^ 10Nine to Eleven Years 20High School Diploma 30College or Technical School^ 23Bachelors Degree^ 9Masters Degree 4Doctorate^ 1Other 1a Based on 100 casesMembership in farm organizations is presented in Table 12. It was clear fromthe way respondents answered this question that they did not fully understand it. Forexample, several people wrote out the name of the B.C. Cattleman's Association under3940the 'other' category rather than checking the box for 'Breed Organization'.Membership in many farm organizations automatically gives a farmer membership inthe B.C. Federation of Agriculture (B.C.F.A.). Some people recognized that theybelonged to the B.C.F.A. either directly as members or through another group andchecked that box. Others did not. A person indicating membership in the B.C.Cattleman's Association was not given a score for the B.C.F.A. if they did not indicateit, although membership in the cattleman's group gives automatic membership in theB.C.F.A. The results of this question are presented in Table 12.Table 12Farm Organization MembershipFarm Organization Membersa(%)B.C. Federation of Agriculture 69.0A Farmer's or Women's Institute 11.5Alliance of B.C. Organic Producers' Association 1.2B.C. Fair Association 6.9Horse Council of B.C. 3.5Commodity marketing board 26.4Breed organization 41.4Packing house or crop marketing co-op 17.2A farm or rural women's group 5.8Other, please specify 18.4a Based on 87 casesTable 6 showed that the 54% of the farmers are aged 50 years or more.Considering this in relationship to the information presented in Tables 13 and 14, it canbe seen that many farmers have spent most of their life farming. Over 50% have beenfarming for at least 20 years. In addition, over 40% of them have been on their presentfarm for more than 20 years.Table 13Number of Years on Present FarmCategory(years)Percentage of Farmersa(%)1 to 9 30.310 to 19 28.320 to 29 18.230 to 39 14.140 to 49 3.050 to 59 4.060 to 69 1.070 to 79 1.0a Based on 99 casesTable 14Number of Years as a FarmerCategory^Percentage of Farmersa(years) ^(%)1 to 9 14.310 to 19^ 32.720 to 29 23.530 to 39 16.340 to 49^ 6.150 to 59 6.160 to 69 1.070 to 79^ 0.0a Based on 98 casesFarmers were asked to report how much income they and their spouse earnedoutside the farm during the past year. Most respondents (71.9%) reported earningsome income. The distribution is shown in Table 15 below. The less than $5000category and the $30,001 to $40,000 were the two largest groups reporting income at12.5% each.4142Table 15Total Family Income Earned Off-Farm Income Category^ Percentage of Farmersa(dollars) (%) None^ 28.1Less than 5,000^ 12.55,000 to 10,000 9.410,001 to 20,000 6.320,001 to 30,000^ 8.330,001 to 40,000 12.540,001 to 50,000 7.350,001 to 60,000^ 4.260,001 to 70,000 5.270,001 and over 6.3a Based on 96 casesFarmers were also asked to report their total farm sales dollars. As seen inTable 16 below, 28% of respondents reported earning less than $19,999 from theiroperation. The rest of the farmers are divided amongst all the other categories with thenext largest group (14%) falling in the $200,000 to $299,999 range.43Table 16Total Farm SalesSales Category(dollars)Percentage ofFarmersa(%)0-19,999 2820,000 to 39,999 840,000 to 59,999 960,000 to 79,999 880,000 to 99,999 6100,000 to 149,999 3150,000 to 199,999 7200,000 to 299,999 14300,000 to 499,999 4500,000 to 749,999 7750,000 to 999,999 21 Million to 1,999,999 22 Million to 3,999,999 14 Million and over 1a Based on 90 casesFrequency of Information UseAs a large amount of information was collected on the survey, the followingresults are listed in summary form in order to facilitate the presentation andinterpretation of the results. For example, the use of different sources of information ispresented in a yes/no format as opposed to reporting the various levels of use. A moredetailed and complete summary of the survey results in the form that the questions wereasked is available in Appendix 1. The results presented in each table have been sortedso that the frequencies are presented in descending order of use. The question that wasasked on the survey appears before each table so that the results can be interpreted inview of the wording that was used.Questions 1 through 5 list ten categories of individuals who are either in thebusiness of providing information to farmers, or the results of their work producesinformation that could be of use to a farmer. Each question asks about different waysin which contact between the farmer and these individuals can occur.QUESTION 1Please put a check in the box to the right of each information source that best indicateshow often during the past 12 months each person visited your farm and provided youwith information pertaining to a farm matter.Table 17 indicates that over half of all farmers were visited by a salesrepresentative and a veterinarian. Considering that 55.1 % (Table 5) of the farmersraise some form of livestock, the fact that 52% of all farmers had a veterinarian visitthem on their farm and provide information pertaining to a farm matter, is worthy ofattention. In addition, reference to Appendix 1, will indicate that the averagefrequency of those visits is 3 or 4 times.Table 17Frequency of Farm VisitsInformation Source Farm Visitsa(%)Sales Representative^ 58Veterinarian^ 52Other Provincial Specialist 25Bank Manager/Financial Advisor^ 25District Agriculturist/ Horticulturist 23Packing house or Processor Field Representative^22Independent Consultant^ 14Agriculture Canada staff 12Other^ 0.5University or College Staff^ 0.244Note: Based on 100 casesa Refers to a minimum of one visit45QUESTION 2Please put a check in the box to the right of each information source that best indicateshow often during the past 12 months you obtained information relating to a farm matterby talking to each person on the telephone. Table 18 reports the frequency with which the farmers used each informationsource. The level of contact between farmers and veterinarians has increased 10% ascompared to farm visits. The level of contact for the Bank Manager/Financial Advisorand District Agriculturist is almost double what it was for farm visits. The relativeranking of the different individuals remains very similar to that of farm visits exceptthat the category "other provincial specialists", which was in third place, has switchedplaces with the district agriculturist/horticulturist which was previously in fifth place.Table 18Frequency of Phone CallsInformation Source Phone Callsa(%)Sales Representative^ 61Veterinarian^ 61District Agriculturist/ Horticulturist^ 47Bank Manager/Financial Advisor 47Other Provincial Specialist^ 37Packing house or Processor Field Representative^34Agriculture Canada Staff 22Independent Consultant^ 20Other^ 8University or College Staff 7Note: Based on 100 casesa Refers to a minimum of one phone call46QUESTION 3Please put a check in the box to the right of each information source that best indicateshow often during the past 12 months you visited each person at their office to obtaininformation relating to a farm matter.Table 19 lists the level of contact farmers had with the various individuals attheir office. The most important change in the ranking of contact frequency ascompared with the previous sources, is with the bank manager/financial advisor whoranks the highest in office visits up from fourth place in both of the previous forms ofcontact. A notable difference can also be seen in the comparison of districtagriculturist or horticulturist with provincial specialists. The level of contact betweenthese two categories differs by 50%. This is probably due to the physical accessibilityof provincial specialists, as most of them are concentrated in a few offices, while adistrict agriculturist or horticulturist is located in every district office in the province.Table 19Frequency of Office VisitsInformation Source Office Visitsa(%)Bank Manager/Financial Advisor^ 58Veterinarian^ 49Sales Representative^ 47District Agriculturist/ Horticulturist^ 38Packing house or Processor Field Representative^24Other Provincial Specialist^ 18Agriculture Canada Staff 15Independent Consultant 11Other^ 5University or College Staff^ 1Note: based on 100 casesa Refers to a minimum of one office visit47QUESTION 4Please put a check in the box to the right of each information source that best indicateshow often during the past 12 months you have heard each person make a presentationor speak at a meeting or field day on an agricultural topic.B.C. Ministry of Agriculture staff lead the way over all other sources inproviding information in the workshop or field day format, as shown in Table 20.Well over half of the farmers surveyed attended a workshop or field day. AgricultureCanada staff often serve as guest speakers at these types of meetings. It is interestingto observe that sales representatives rank higher than Agriculture Canada staff. It isexpected that the reason for this level of contact is that many companies put on theirown demonstrations, field days, or meeting presentations where specific products andservices are being marketed.Table 20Frequency of Talks at Meetings or Field Days Information Source^ Presentationa(%)Other Provincial Specialist 55District Agriculturist/ Horticulturist^ 54Sales Representative^ 40Agriculture Canada Staff 36Veterinarian^ 35University or College Staff^ 19Packing house or Processor Field Representative^18Bank Manager/Financial Advisor 17Independent Consultant^ 16Other^ 9a Based on 100 cases and refers to a minimum of one presentation48QUESTION 5Please put a check in the box to the right of each information source that best indicateshow often during the past 12 months you have received informationfrorn each personby mail, fax, or computer. Since the selection of farmers for this survey was done through Ministry ofAgriculture mailing lists, it should be no surprise that Ministry of Agriculture staff topthe list, as shown in Table 21.Table 21Frequency of Information Received by Mail, Fax, or Computer Information Source^ Informationa(%)District Agriculturist/ Horticulturist^ 80Other Provincial Specialist^ 60Sales Representative 60Agriculture Canada Staff 50Bank Manager/Financial Advisor^ 43Veterinarian^ 31Packing house or Processor Field Representative^23Independent Consultant^ 15University or College Staff 14Other^ 5Note: based on 100 casesa Refers to a minimum of one piece of information receivedTo determine which of the previous five types of contact is the most used, allthe positive responses can be added for each question. The totals are listed in Table 22below and are graphically presented in Figure 2.50Table 22Frequency of All Forms of ContactType of Contact^ Total PositiveResponsesaInformation by Mail, Fax, or Computer^381Information from Telephone Call 344Presentation^ 299Office Visit 266Farm Visit 240a Out of possible 500Looking at the order in which these forms of contact are ranked, it can be seenthat the highest number of contacts between farmers and extension providers occur withinexpensive mass distribution methods and decreases as the form of contact becomesmore and more personalized. The lowest level of contact is through farm visits, with48% of the farmers reporting that someone visited them on their farm. The highestlevel of contact is through mail, fax, or computer, with 76% reporting receivinginformation.Tables 23 through 32 list the frequency of contact for each individualinformation provider by the method of contact. Table 23 below lists the results for thefive different forms of contact with district agriculturists and horticulturists.Most farmers (80%) are receiving information from their district agriculturist orhorticulturist by mail as indicated in Table 23. Over half of all farmers are obtaininginformation in the workshop/meeting format. A quarter to half of the farmers areobtaining information in one-on-one situations, such as phone calls or individualmeetings on the farm or at the office.51Table 23Frequency of Contact with District Agriculturist or HorticulturistMethod of Contact Yes(%)Information Sent by Mail, Fax, or Computer 80Presentations 54Telephone Calls 47Office Visits 38Farm Visits 23a Based on 100 cases and refers to a minimum of one contact/yearTable 24 indicates that the results for the provincial specialists are very similarto that of Table 23 except that the level of contact is about 10 to 20% less.Presentations are the only exception. Evidently, the specialists, as individuals withspecific commodity information, are very involved with presentations, although theyare not as widely available geographically throughout the province.Table 24Frequency of Contact with Other Provincial SpecialistsMethod of Contact Yesa(%)Information Sent by Mail, Fax, or Computer 60Presentations 55Telephone Calls 37Farm Visits 25Office Visits 18a Based on 100 cases and refers to a minimum of one contact/yearBritish Columbia universities and colleges do not have active extensionprograms designed to reach out to the farming community. There are linkages betweenthe university and the Ministry of Agriculture which are generally research oriented,such as the various Science Lead Committees. These joint committees between theMinistry and the University of British Columbia identify and publish a list of researchpriorities. The most common involvement of university staff is to act as an expert52resource for workshops, field days, or meetings. Thus, it is not surprising that thisform of contact ranks the highest as shown in Table 25. Given the location of BritishColumbia's universities in South Coastal B.C., a 19% contact rate during the past yearis better than might be expected. It is not clear what information the universities orcolleges have sent the 14% of farmers.Table 25Frequency of Contact with University or College Staff Method of Contact^ Yesa(%)Presentations 19Information Sent by Mail, Fax, or Computer^14Telephone Calls^ 7Farm Visits 2Office Visits 1 a Based on 100 cases and refers to a minimum of one contact/yearMuch of Agriculture Canada's activities are research and regulatory oriented.Table 26 indicates that Agriculture Canada does make an important contribution in theprovision of information to farmers. A comparison can be made between AgricultureCanada staff and British Columbia Ministry of Agriculture specialists, as they both canbe considered experts in their respective fields of specialization. The level of contactwith Agriculture Canada staff ranges from a low of 12% for farm visits to a high of50% for information sent by mail, fax, or computer. The level of contact withprovincial specialists indicated in Table 24 ranges from a low of 18% for office visitsto a high of 60% for information sent by mail, fax, or computer. These levels ofcontact appear to be quite similar to each other.Table 26Frequency of Contact with Agriculture Canada StaffMethod of Contact Yesa(%)Information Sent by Mail, Fax, or Computer 50Presentations 36Telephone Calls 22Office Visits 15Farm Visits 12a Based on 100 cases and refers to a minimum of one contact/yearSales Representatives have the highest overall level of contact with farmers ascompared to all the others surveyed. The level of contact shown in Table 27 for eachtype is quite similar for telephone calls (61 %), information sent by mail, fax, orcomputer (60%),and farm visits (58%). This is consistent with the role of the salesrepresentatives, as they are phoning, visiting, and generally pursuing farmers in orderto convince them to purchase their products. In addition, farmers are also active ingoing to the sales representative's place of business to seek information.Table 27Frequency of Contact with Sales RepresentativesMethod of Contact Yesa(%)Telephone Calls 61Information Sent by Mail, Fax, or Computer 60Farm Visits 58Office Visits 47Presentations 40a Based on 100 cases and refers to a minimum of one contact/yearThe role of financing in today's agricultural operations is evident from therelatively high level of contact between farmers and their financial advisors or bankmanagers, as seen in Table 28. These people are more active than one would expectwith 25 % of the farmers having been visited at their farm.5354Table 28Frequency of Contact with Bank Manager or Financial AdvisorMethod of Contact Yesa(%)Office Visits 58Telephone Calls 47Information Sent by Mail, Fax, or Computer 43Farm Visits 25Presentations 17a Based on 100 cases and refers to a minimum of one contact/yearPacking house and processor field representatives are individuals who representthe companies purchasing the farmer's crop and provide a variety of services. Theseindividuals typically work for organizations purchasing tree fruit products and certainvegetable crops. When Table 5 was discussed, it was noted that 55.1 % of the farmersproduced livestock products. Conversely, 44.9% of the farmers are involved with non-livestock crops, such as vegetables, forages, and tree fruits. Interpretation of the levelof contact with packing house or processor field representative should be made on thissmaller group. Therefore, when looking at Table 29, the level of contact by a packinghouse or processor field representative should be based on the 44.9% of the farmers notraising livestock crops. Thus, the level of contact for phone calls would then be 75.5%rather than 34%.Table 29Frequency of Contact with Packing House or Processor Field RepresentativeMethod of Contact Yesa(%)Telephone Calls 34Office Visits 24Information Sent by Mail, Fax, or Computer 23Farm Visits 22Presentations 18a Based on 100 cases and refers to a minimum of one contact/year55Table 30 shows the level of contact reported with veterinarians. Using the logicpresented with Table 29, it can be seen that the level of contact with veterinarians isvery high. In fact, more people (61 %) reported that they obtained information from aveterinarian over the phone than reported having livestock (55.1%). Apparently somefarmers who are not livestock producers have some contact with veterinarians. Thismay result because of inquiries relating to domestic pets.Table 30Frequency of Contact with Veterinarian Method of Contact^ Yesa(%) Telephone Calls 61Farm Visits^ 52Office Visits 49Presentations 35Information Sent by Mail, Fax, or Computer ^31a Based on 100 cases and refers to a minimum of one contact/yearOver the past several years, the British Columbia Ministry of Agriculture hasstopped supplying a number of services, such as rangeland seeding, preparation ofplans for farm buildings, and irrigation and drainage system design. These functionshave been picked up by various consultants or other companies. Independentconsultants, shown in Table 31, have the lowest overall level of contact with farmers,with the exception of the miscellaneous category shown next in Table 32.56Table 31Frequency of Contact with Independent ConsultantMethod of Contact Yesa(%)Telephone Calls 20Presentations 16Information Sent by Mail, Fax, or Computer 15Farm Visits 14Office Visits 11a Based on 100 cases and refers to a minimum of one contact/yearThe miscellaneous category was included in the survey should the previous ninegroups used not cover all the possible groups of people offering extension services. Avery low level of contact is reported in Table 32, indicating that the other ninecategories did represent the groups of extension providers quite well. Respondentswere asked to indicate who the 'other' was, but most failed to write anything down.Some of the individuals reported were breed stock company representatives, hatcherysales representatives, the Western Indian Agricultural Corporation, other farmers, cattlebuyers, a retired commercial sheep breeder, British Columbia Hydro, AgriculturalResearch & Development Corporation (ARDCORP) book-keeping program, andCustoms and Excise Canada.Table 32Frequency of Contact with Other Miscellaneous People Method of Contact^ Yesa(%)Presentations 9Telephone Calls^ 8Farm Visits 5Office Visits 5Information Sent by Mail, Fax, or Computer ^5 a Based on 100 cases and refers to a minimum of one contact/year57Totaling all forms of contact for each group provides the results shown in Table33 and Figure 3. Sales representatives have the highest overall level of contact,however the district agriculturist or horticulturist is close behind.Table 33Total Number of Contacts with all SourcesNature of Contact Total Number of AllContactsSales Representatives^ 266District Agriculturist/ Horticulturist^ 242Veterinarian^ 228Provincial Specialists^ 195Bank Manager or Financial Advisor^ 190Agriculture Canada Staff 135Packing House or Processor Field Representative^121Independent Consultant^ 76University or College Staff 43Other^ 32a Out of Possible 500The rest of the survey (questions 6 through 16, excepting 9(a) and 11) asked thesame question regarding the frequency at which the farmer obtained information from avariety of sources. The categories ranged from never to once per day, as can been seenby referring to Appendix 1. The responses are presented in Table 34 and Figure 4, andhave been ranked in descending order by lumping all the positive responses to the useof the information sources together into one category, labeled "sometimes".LA0059Table 34Frequency of Information UseInformation Source Sometimesa(%)Information from neighbors or friends 90Information from Spouse 78General Farm Paper or magazines 74B.C. Ministry of Agriculture publication 71Newsletter by farm organization 65Radio Reports 65Newsletter by commercial supplier 62Television Program 61Specialized farm paper or magazine 60Agriculture Canada publication 48Provincial or Local Newspaper 45Information from Parents and Relatives 41Video Tape 40United States publication 37Information from Employees 32Scientific Journal 15Computer Bulletin Board 13Other 9a Refers to a minimum use of once per yearNinety percent of the farmers surveyed indicated that they had obtainedinformation useful in making a farm management decision from neighbors or friends.The three least used sources of information were the 'Other' category, along withcomputer bulletin boards and scientific journals.Farmers were asked in question 9 whether or not they had received informationrelating to a farm matter from watching a video tape. A total of 40% of therespondents had received information from a video tape at least once during the pastyear. The tapes were obtained from the sources listed in Table 35 below. As someON0individuals had seen video tapes from more than one source, a frequency count isprovided for each box they checked. Of the 40 individuals indicating they had seen avideo tape, 39 answered the second part of this question.Table 35Source of Video TapesTape Source^Number of Timesa Percentage(%)B.C. Ministry of Agriculture 14 27.5Commercial Supplier 18 35.3University or College 2 3.9Agriculture Canada 4 7.8Other 13 25.5Total 51 100.0a Based on 39 of the 40 users reportingQuestion 11 asked farmers if they had taken any courses in agriculture or farmbusiness management during the past 12 months. Fifteen farmers indicated that theyhad taken a such a course.Question 16 asked farmers if they had obtained information about a farm matterwhile visiting any of a list of places. Table 36 indicates that 81 % of farmers had foundinformation useful to them while visiting another farm. This is shown quitedramatically in Figure 5.6162Table 36Visits to Various Sites Location^ Percentage of Visits(%)Another Farm 81Agriculture Canada Experimental Station^19B.C. Ministry of Agriculture Demonstration Site ^23Travel to a Foreign Country^ 23Other^ 3None of the Above^ 11a based on 100 casesValue of Various Sources of InformationQuestion 17 of the questionnaire asked the following:We would like your opinion on the value of all the information sources that areavailable to you, whether or not you have used them in the past 12 months. Please puta check mark in the box to the right of each information source that best indicates howvaluable you feel each source is. If you are not familiar with the source, having neverused it before, please check "DOES NOT APPLY".In addition to "Does Not Apply", five other categories were available forchoice. These were: "Of No Value"; Of Little Value"; "Undecided"; "Valuable";"Highly Valuable". There were 32 different information sources to be rated. Theresults of this are presented in Table 37. To enable the interpretation of all the variousscores, a weighted average is used to reduce the choices down to a single value whichcould be compared against other values. Table 37 and Figure 6 rank all theinformation sources in descending order of the weighted average value that farmersplaced on each source. The weighted average is calculated by assigning a value of 1 toTable 37Value by Rank of All Information Sources Information Source^ WeightedAverage Neighbors, friends, other farmers^ 3.93Visit to another farm^ 3.83Sales representative (feed, fertilizer, equipment, etc.)^3.33General farm papers or magazines (Country Life, B.C. Farmer, etc.)^3.30B.C. Ministry of Agriculture publications ^ 3.23Spouse or Children^ 3.21Veterinarian 3.15District Agriculturist or Horticulturist^ 3.04Newsletter published by farm organization (B.C. Blueberry Co-op,^2.87B.C. Cattleman's Association, etc.)Newsletter published by commercial supplier (feed, fertilizer,^2.80equipment, etc.)Agriculture Canada Publications^ 2.76Specialized farm papers or magazines (Greenhouse Manager, B.C.^2.73Dairy Digest, etc.)Courses on agriculture^ 2.66Visit to a B.C. Ministry of Agriculture demonstration site ^2.57Relatives, including parents 2.54Other Provincial government specialists^ 2.50Bank Manager or financial advisor 2.44Radio programs or announcements 2.35Visit to Agriculture Canada Experimental Station^ 2.29Farm Employees^ 2.25Television programs 2.23Agriculture Canada staff 2.07Video tapes^ 2.02Foreign Travel 2.01Provincial or local newspapers (Vancouver Sun, Similkameen^1.91Spotlight, etc..)Packing house or processor field representative^ 1.83Publication from a United States government or university source^1.57Independent Consultant^ 1.57University or college staff 1.33Scientific Journals (Journal of Plant Science, etc.)^ 1.31Computerized bulletin board^ 0.72Other^ 0.1464the response "Of No Value", a 2 to "Of Little Value", and so on, finishing with a 5 for"Highly Valuable". The percentage of responses in each column is multiplied as itsCi■til66decimal value times the value assigned to that column. The sum of the six calculationsis summed resulting in a single value.Neighbors and friends ranked as the most valued source of information followedclosely by visits to other farms and sales representatives. The category 'Other' rankedlast along with computerized bulletin boards and scientific journals. Comparison of thesources farmers value corresponds closely to the acutual sources they use. Visits toother farms ranked as the second most valuable source and 81% of farmers reportedthat they obtained information during a farm visit. Comparison of Table 33 with Table37 shows that of the individuals who are formal extension providers, salesrepresentatives rank the highest. Frequency of use of publications and that of extensionproviders all parallel in ranking that reported in the value of the sources. On the basisof the ranking procedures used, there are no sources of information that farmers reportas being of value that they are not using to the same extent. It can be generallyconcluded that if a farmer uses as source of information it is because he values it, notbecause he has no other alternative.This chapter concludes the presentation of the questionnaire results. Thefollowing section will analyze the results to determine what significance they have.CHAPTER 5ANALYSIS AND DISCUSSIONThe previous chapter reported on the types of information sources farmers arecurrently using and the sources they value. The following sections of chapter five willattempt to determine and analyze any trends that exist in why certain groups do or donot use certain kinds of information sources. The first section will utilize correlationmethods to determine if the use of certain information sources can be predicted bydemographic characteristics. Section two will use the techniques of hypothesis testingto determine if there are any significant differences in the demographic characteristicsof those who do and do not use British Columbia government extension services.Finally, section three will compare the level of contact between district agriculturistsand horticulturists in 1969 and 1991 to see if there have been any changes.Demographic Characteristics and Information UseThe second objective of the thesis was to determine if the use of differentinformation sources can be correlated to demographic characteristics. There are anumber of techniques available.The simplest method of determining relationships between sets of data is withthe Pearson correlation coefficient, abbreviated as "r". This coefficient is ameasurement of how linear two variables are when plotted against each other on an x-yaxis. Studies such as Dent (1968), Alleyne (1968), and Akinbode (1969), based muchof their analysis on the use of Pearson partial correlation coefficients. The Pearson6768correlation method has a number of limitations. Partial correlation coefficients provideonly limited information as each factor can only be looked at in isolation with anotherfactor, making it difficult to draw generalized conclusions. Frequently more than onefactor is responsible for the behavior of a particular item of interest. Many variablessuch as age or number of years as a farmer are highly correlated with each other. Itcan be difficult to make interpretations when the relationships between the predictorvariables are unknown.The use of a large data set will result in a large number of correlations. Thismakes it difficult to provide any meaningful interpretation. The previously mentionedstudies reported on every statistically significant correlation they found. As such, thesereports contained a number of comments about correlations such as the relationshipbetween a farmer's age and the number of children he or she had. This information isnot very useful in understanding a farmer's use of information sources.Pearson correlations were calculated between the frequency of use of eachinformation source and the demographic data. A total of 292 correlation coefficientswere found to be significant at the 95% confidence level. These coefficients are notpresented, as techniques providing more meaningful results were available.Multiple correlation methods provide a better look at the interaction betweenquestionnaire results and demographic data. The terms "independent variables" will beused for frequency and values of information sources, and "dependent variables" forthe demographic data, in order to simplify the discussion, however none of thevariables are truely dependent or independent of the others. When multiple correlationtechniques are used, the ability to make predictions is improved. This occurs becausethe use of a number of different variables, i.e. demographic, for the prediction of69another, i.e. use of newspapers, uses the different dimensions of each variable, such asage, income, etc., to better predict the use of an information source. A multiplecorrelation coefficient will never be less than the highest correlation between just twoof the values. For example, if the reason a farmer refers to an Agriculture Canadapublication is related to his age and farm size, the inclusion of farm size to thecorrelation coefficient of age, will increase its value as the two combined betterpredicts the use of the publication than either one by itself.The one drawback with some multiple correlation methods is that they do nottake into account the effect that internal correlations have on the outcome of multiplecorrelation coefficients. What is the effect on the correlation coefficient in the aboveexample if age and farm size have some relationship of their own? For this reason,Canonical Analysis was chosen as the "multiple correlation" method to use. CanonicalAnalysis is a multivariate technique which analyzes and takes into account thecorrelations that exist within the dependent and independent variables. The techniquealso contains a number of checks to ensure the data is suitable for this type of analysis.Canonical Analysis is not available on personal computers due to the size andcomplexity of the software program. In addition, the number of mathematicalcalculations required to conduct the analysis would mean that a personal computerwould involve significant processing times . Canonical Analysis is available on theUniversity of British Columbia mainframe computer system, through a statisticalpackage known as BMDP6M. The survey data which had been summarized inSPSS/PC+ format was transferred onto the mainframe computer, and proved to beusable without any major alterations.70Canonical correlation analysis is a full statistical analysis package. In additionto producing canonical variates, a number of statistical values are calculated includingkurtosis, skewness, standardized scores, multicollinearity, and F-values. The purposeof calculating these values is to allow the data to be evaluated for its suitability forcanonical analysis and to ensure that no assumptions fundamental to the mathematicsare violated. An example of how the data is interpreted is provided in Appendix 2.Application of the canonical analysis procedure to the questionnaire data wasdone by breaking the survey up into eight sections. This allowed the analysis to beperformed on sets of data that formed complete units. These units are:1. Frequency of obtaining information from farm visits2. Frequency of obtaining information from phone calls3. Frequency of obtaining information from visits to their office4. Frequency of obtaining information from presentations or talks at meetings and fielddays5. Frequency of receiving information by mail, fax, or computer6. Frequency of obtaining information from the use of publications7. Frequency of finding information from a number of miscellaneous sources8. Opinions held on the value of various possible sources of informationA total of 13 different questions were asked concerning demographic data aboutthe respondents. Only eleven were used for the canonical correlation analysis.Question #28 regarding farm size, proved to be too difficult to summarize in consistentquantifiable terms. Question #24 asked about membership in various farmorganizations. This was excluded for two reasons. Canonical analysis does not workproperly if data are missing. Only 88 individuals responded to this question out of the100 questionnaires returned. The second reason, as outlined earlier, was that it wasapparent that people did not fully understand the question.Table 38 provides a guide for assessing the significance of canonical correlationscores. "As a rule, "loadings" in excess of 0.30 are eligible for interpretation, whereaslower ones are not. A correlation of 0.30 indicates that there is a 9% overlap in thevariance between the variable being examined and the demographic factor responsible.Choice of the cutoff of size of loading to be interpreted is a matter of researcherpreference" (Tabachnick & Fidel!, 1983, p.411).Table 38Interpretation of Canonical Statistics Canonical^Variance^Magnitude ofCorrelation Loadings Variance ^0.71 50%^Excellent0.63^40% Very Good0.55 30% Good0.45 20%^Fair0.32^10% PoorExtension ProvidersQuestions one through five in the survey asked farmers about the frequency withwhich they obtained information from a list of 10 different groups or types of people inthe business of providing extension information. Each one of the five questions askedabout a different type of contact with these people. Since this set of questions wasasked in a similar format, the results and interpretation are done together. Asmentioned previously, a separate analysis was conducted on each data set.7172Because of the amount of information generated from this analysis and thedifficulty in presenting the information, the following procedure is used. The symbolsused to report the results of the analysis are presented in Tables 39 and 40. Table 39lists the independent variables used for canonical analysis.Table 39Independent Variables Used for Canonical AnalysisSymbol Independent Variable Symbol Independent VariableY1 District Agriculturist orHorticulturistY6 Bank manager or financialadvisorY2 Other provincial agriculturalspecialistY7 Packing house orprocessor field rep.Y3 University or College staff Y8 VeterinarianY4 Agriculture Canada staff Y9 Independent ConsultantY5 Sales rep (feed, fertilizer,etc..)Y10 OtherTable 40 lists the dependent variables used. These symbols are used for all of theindividual canonical analyses used.Table 40Dependent Variables for Canonical AnalysisSymbolX1X2X3X4X5X6X7X8X9X10X11Dependent Variable AgeSexMarital statusMother tongueNumber of childrenHighest level of EducationYears on current farmYears as a farmerIncome earned off-farmTotal farm salesFarm typeTable 41 reports the results of the canonical correlations for the five forms of contact.73Table 41Canonical Correlation Results - Forms of Contact by IndividualsNature of Significant Correlation Variance Y Valuesa DemographicContact Pairs ValuesbFarm Visits one 0.750 0.562 Y1=0.538 X9 =-0.505Y2=0.470 X10=0.822Y5 =0.881Y6=0.629Phone Calls one 0.694 0.482 Y2 =0.585 X1=0.491Y4=0.574 X10=0.798Y5=0.775Y6 =0.747Office Visits one 0.691 0.477 Y5=0.665 X9 = -0.456Y6=0.844 X10=0.891Field Days none none none none noneMail, Fax,Computerone 0.710 0.505 Y3=0.480Y5=0.901X1=-0.614X10 = O. 802Y6=0.633a Refer to Table 39b Refer to Table 40Table 41 is interpreted in the following manner. Column two reports thenumber of statistically significant pairs of canonical variates that exist between eachform of contact with the farmer and the demographic data. The statistically significantpair of canonical variates are values, one which represents all the different types ofpeople who may have contacted the farmer, i.e. all the "x" values, and the second partthat represents all of the demographic variables, i.e. all the "y" values. More than onepair of canonical variates can exist if there is more than one statistically significant linkbetween the data. For example a significant link may be found between salesrepresentatives and a combination of farm size and the farmer's age, while a secondsignificant link may be found between the number of children a farmer has andAgriculture Canada and university staff.74The first form of contact "farm visits" shown in Table 41 is interpreted in thefollowing manner. Only one statistically significant link was found between theindividuals who visited a farmer and the farmer's demographic characteristics. Thislink is expressed by a pair of canonical variates. This pair has a correlation coefficientof 0.75. The variance (56.2%) is the square of the correlation coefficient. Thiscoefficient suggests that there is a strong correlation between how often certain peoplevisited a farm and certain demographic characteristics of the farmer. The Y-valuesand X-values are the individual components of that correlation that significantlycontributed to the linkage. The percentage given for each one is a measure of howstrongly each of the original variables is correlated to the canonical variate. Thus, theDistrict Agriculturist/Horticulturist, provincial specialists, sales representatives, andbank managers are the original independent variables that are strongly correlated to the"x" part of the pair of canonical variates, while farm sales and income earned off-farmare the demographic variables that are strongly correlated to the other half of the pairof canonical variates. The interpretation of these statistics would be that districtagriculturists/horticulturists, provincial specialists, sales representatives, and bankmanagers pay more farm visits to farmers with higher farm sales. In addition, becauseincome earned off-farm is a negative correlation, their farm visits decrease as off-farmincome rises.The remaining four types of contact can now be easily interpreted. Phone callsfrom farmers show similar results. The provincial specialists, Agriculture Canadastaff, sales representatives, and bank managers tend have increased contact with thefarmer as the size of farm sales increases. Also correlated, but to a lessor extent, isthat this contact increases with the farmer's age.A correlation for office visits only exists for sales representatives and bankmanagers. The level of contact with the farmer through an office visit increases withfarm size and decreases as off-farm income increases.No correlations were obtained by presentations and talks by individuals at fielddays and workshops. This can be interpreted positively as it says that farmers of alldemographic characteristics are obtaining information equally from the field day ormeeting method.Information received by mail, fax, or computer is significantly correlated touniversity or college staff, sales representatives, and bank managers. The amount ofinformation received from these individuals increases with farm sales and decreaseswith increasing age.The results of these five categories indicate that sales representatives, bankmanagers, and, to a lesser extent, provincial government extension staff, have morecontact with farmers with larger farms as indicated by their farm sales. The magnitudeof the canonical correlations indicate that this conclusion is quite strong. In the caseswhere off-farm income was significant, increases in off-farm income had a negativeeffect on the amount of information farmers received. The affect of the farmer's agehad a positive correlation in one case, and a negative one in another. It could beconcluded that the older the farmers were, the more likely they were to phonesomeone for information while the younger farmers were more likely than the olderfarmers to get information by mail, fax, or computer.75Comparing this information with that in Tables 17 through 21, it can be seenthat sales representatives, veterinarians, provincial specialists, district agriculturists and76horticulturists, and bank managers have the most frequent contacts with farmers ascompared to the other five individuals listed. Through canonical analysis, all of theseindividuals tend to favor the bigger farmer except the veterinarian. There are nocorrelations between the frequency at which information is obtained from a veterinarianand the farm size. As discovered earlier, virtually every farmer with livestock hadobtained information from a veterinarian during the past year.PublicationsFarmers were asked to indicate how often, on average, during the past 12months, they received information useful in making a farm management decision fromeach publication. Canonical analysis found the results shown in Table 42.Table 42Canonical Correlation Results - Use of PublicationsNature of^Significant Correlation Variance Y Valuesa^DemographicContact Pairs^ Valuesb Publications^one^0.658^0.433^Y7a=0.781^Xl0b=0.723a Y7 = Newsletter or magazine from a commercial supplierb X10 = Total farm salesCanonical correlation of different publications with the demographiccharacteristics of farmers indicates only one significant pair of canonical variates.Variables highly correlated with the pair of canonical variates are newsletters andmagazines from commercial suppliers and farm sales. The canonical correlationcoefficient and the correlation of the individual values with the canonical variate are allquite strong. This indicates that the use of newsletters and magazines from acommercial supplier increase as the value of farm sales increases.Miscellaneous SourcesCanonical analysis of all the remaining sources of information is shown in Table43.Table 43Canonical Correlation Results - Use of Miscellaneous SourcesNature ofContactSignificantpairsCorrelation Variance Y Values DemographicVariousmethodstwo 0.665 0.442 Y5a = 0.891 X10b = 0.7570.663 0.440 Y7c = 0.834 Xld = -0.732a Y5 = Farm employeesb X10 = Total Farm Salesc Y7 = Parents or relativesd X1 = AgeTwo sets of canonical variates were discovered in this category. The larger thefarm in terms of farm sales, the more they utilized their employees for making farmmanagement decisions. In addition, the older the farmers, the less they obtainedinformation from parents or relatives. Given that the mean age of farmers in thissurvey was 49.5 years, these farmer's parents may not be available for consultation.7778Value of Information SourcesFarmers were asked to give their opinion on the value of different sources ofinformation, whether or not they had used those sources during the past twelve months.Since demographic data had been collected in the survey, it is possible to use thecanonical analysis procedure on this information to see if there are any connectionsbetween the opinions people have and their demographic characteristics. Table 44presents the results of the canonical analysis.Table 44 Canonical Correlation Results - Value of Information SourcesNature of Contact Numberof linksCorrelation Variance Y Values DemographicValue of differentsourcestwo 0.847 0.718 Y6a=0.625YlOb = 0.520XlOe=0.745Xlf=-0.448Y19c = 0.5990.798 0.636 Ylld = 0.507 X1=-0.432a Y6 = Sales Representativeb Y10 = Relatives including parentsc Yll = Farm employeesd Y19 = Newsletter from commercial suppliere X10 = Total Farm Salesf X1 = AgeTwo significant linkages were observed between demographic characteristicsand the farmers' opinions of different sources of information. In the first link, salesrepresentatives, relatives, and newsletters from commercial suppliers were theinformation sources strongly correlated to the significant canonical variate. Farm saleswas the most significant demographic characteristic while age was also moderatelycorrelated. These correlations suggest that as the magnitude of farm sales increases,the more the farmer values information obtained from sales people, relatives, andnewsletters from commercial suppliers more. The negative correlation for age indicates79that the value farmers place on the previous three groups of people decreases as the ageof the farmer increases.The second canonical linkage is quite strong as well. However, demographicvariables correlating with the canonical variate are not as strong as in the first link.This linkage suggests that older farmers value farm employees less than youngerfarmers.British Columbia Government Extension UsersObjective #3 of this project was to determine if there were any significantdifferences in the demographic characteristics of farmers who use B.C. Ministry ofAgriculture Extension services and those who do not. Questions asked in the surveyabout use of government extension services were:1. Was the farmer visited by a district agriculturist or horticulturist?2. Was the farmer visited by some other provincial agricultural specialist?3. Did the farmer obtain information from a district agriculturist or horticulturist bytelephone?4. Did the farmer obtain information from a provincial agricultural specialist bytelephone?5. Did the farmer obtain information from a district agriculturist or horticulturist byvisiting at his/her office?6. Did the farmer obtain information from a provincial agricultural specialist byvisiting at his/her office?807. Did the farmer obtain information from a district agriculturist or horticulturist froma presentation made at a meeting or field day?8. Did the farmer obtain information from a provincial agricultural specialist from apresentation made at a meeting or field day?9. Did the farmer obtain information from a district agriculturist or horticulturist bymail, fax, or computer.10. Did the farmer obtain information from a provincial agricultural specialist by mail,fax, or computer.11. Did the farmer obtain information from a B.C. Ministry of Agriculture publication?12. Did the farmer obtain information while visiting a B.C. Ministry of Agriculturedemonstration site?Summarizing the data will result in two groups of farmers. Those who haveused B.C. Ministry of Agriculture extension services and those who have not. Withineach group, there will be an average value for the farmers' age, income, number ofchildren, et cetera. As it is not likely that each average value or mean is exactly thesame, it must be determined through statistical testing whether the differences in themeans is due to the natural variability of the sample, or because each group of farmersis different from another. The procedure for determining this is known as hypothesistesting.The null hypothesis states that farmers who use government extension serviceshave the same characteristics as those who do not. Testing the hypothesis is done byuse of the z-test or the t-test. Since the actual standard deviation of population meansfor various demographic characteristics is not known, the t-test must be used. The z-test assumes that the population standard deviation is known and that the population81distribution is normal. The t-test uses an estimate for the standard deviation of the totalpopulation.The t-test analysis consisted of the comparison of 11 selected demographicvariables, as outlined in the discussion associated with Table 40, against 12 forms ofcontact with the British Columbia Ministry of Agriculture. The t-test analysiscompares the mean demographic values for the user and non-user group and determinesthe probability that their differences is just due to random variations. Since thedifferences in the means is due to the variation of each of the individual valuesaveraged, it is necessary to compare the variance of each group as the t-test assumesthat there is equality of variance. This assumption is reasonable since the two groupsare sampled from the same population and should only have differences due to randomvariations. SPSS/PC+ provides two set of results from the t-test analysis: one is theprobability of there being no difference between the mean demographic values if thevariances are the same, and the other if the variance is not. The F-value, gives theratio of the variance of each group. Selection of the correct set of results depends onthe magnitude of the F-value. The closer this value is to one, the more similar thevariances are. SPSS/PC+ also calculates the probability of observing an F-value of atleast that size if the variances are equal. From the 132 t-test calculations, the followinglist, shown in Table 47, was selected for further examination on the basis ofprobabilities that fell within the 90% level of confidence. The 90% level has beenselected in this case to identify any trends that may lie just outside of the 95% level ofconfidence.82Table 45Results of t-test AnalysisForm of Contact Demographic F-Value F-Value Pooled Separate Level ofStatistic Probability Variance Variance SignificanceProbability Probability HypothesisRejected1. Visit by Sales 1.27 0.459 0.034 0.051 94.9%DistrictAgriculturist Education 1.41 0.274 0.065 0.099 90.1%2. Visit by Age 1.53 0.246 0.070 0.047 95.3%OtherProvincial Education 1.63 0.178 0.115 0.078 92.2%Specialist3. Phone Callfrom DistrictMarital Status 12.39 0.000 0.053 0.045 95.5%AgriculturistEducation 1.26 0.417 0.064 0.066 93.4%4. Phone Callfrom OtherChildren 1.79 0.395 0.049 0.034 96.3%ProvincialSpecialist Education 1.3 0.061 0.002 0.002 99.8%5. Office Visitsto DistrictEducation 1.48 0.176 0.035 0.045 99.5%Agriculturist Off-farm 1.03 0.927 0.015 0.015 98.5%Income6. Office Visitsto ProvincialYears as aFarmer1.72 0.223 0.072 0.038 96.2%SpecialistEducation 1.07 0.930 0.071 0.074 92.6%7. Presentationby ProvincialSales 1.44 0.245 0.008 0.007 99.3%Specialist Education 1.97 0.018 0.090 0.099 90.1%8. Mail from Marital Status 6.13 0.000 0.007 0.106 89.4%DistrictAgriculturist Years oncurrent farm 2.41 0.033 0.093 0.034 96.6%9. Mail from Years as a 1.13 0.661 0.008 0.092 90.8%Provincial FarmerSpecialistSelection of the appropriate t-test probability is based on the magnitude of theF-value and its probability of being the same as that shown if the variances are equal.83The criterion of the F-value being close to 1.0 and a probability of 95% that it will beclose to that value is used to determine if the variance is to be considered as "pooled"or "separate". "In general, it's a good idea to use the separate variance t-test wheneveryou suspect that the variances are unequal" (Norusis, 1988, p.211). This will thendetermine which of the above statistical values will be considered statisticallysignificant. Examination of the data presented in Table 45 indicates that the separatevariance probability should be used for all cases. The final column of Table 45indicates the level of significance that the hypothesis, that there is no difference, can berejected.Table 46 lists in more detail the forms of contact and details of thedemographics that meet the 95% confidence level criterion. Six different types ofcontact by extension individuals showed statistically significant differences in certaindemographic characteristics. A statistical difference was shown for marital status inTable 45, however because the choices of martial status given on the questionnaire donot represent a progressive scale, such as sales, the mean values of marital statuscannot be interpreted.Table 46Significant Demographic 1-test Probabilities at 95 %Type of Contact Demographic StatisticMean ValuesNon-User UserLevel ofSignificanceVisit by Other Provincial Age 50.6 years 45.9 years 95.3%SpecialistPhone Call from Other Children 2.92 2.32 96.6%Provincial SpecialistEducation Nine toEleven yearsMinimumof High99.8%Schoolwith somecollegeOffice Visits to District Education Nine to Minimum 95.5%Agriculturist or Eleven Years of HighHorticulturist Schoolwith somecollegeOff-farm $8,150 $21,100 98.5%IncomeOffice Visits to Provincial Years as a 23.4 years 17.0 years 96.2%Specialist FarmerPresentation by Provincial Sales $56,799 $97,199 99.3%SpecialistMail from DistrictAgriculturist orYears oncurrent farm14.0 years 20.4 years 96.6%HorticulturistThe t-test procedure calculated mean values for each of the demographiccharacteristics based on how they were categorized, as shown in Tables 6 through 16.These mean numerical values were then converted to the actual characteristics that theyrepresented. For example, the mean values of farm sales for presentations made by theprovincial specialist was 3.84 and 5.86. These values correspond to a range of farmsales. Category 3 referred to farm sales between $40,000 and $59,999 while category845 referred to the range $80,000 to $99,999. The value for each group was taken bycalculating the point indicated by the fractional part of the mean value.Farmers visited by provincial specialists are on average 4.7 years younger thanthose they do not visit. Those who phoned provincial specialists for information andvisited the district agriculturist at the office have a more education than the group thatdoes not. In addition, those who are phoning the provincial specialists have fewerchildren. Those who visited the district agriculturist or horticulturist at their officeearn more than double the amount of off-farm income than those who did not. Farmersvisiting the provincial specialist at their office have been farming 5.4 years less thanthose who do not. Farmers attending presentations by provincial specialists also haveclose to double the amount of farm sales. Farmers receiving information by mail, fax,or computer have been on the same farm for an average of 20 years while those thatdid not receive such information have been on their farm for an average of 14 years. Itis not certain what the significance of this is, hopefully this does not mean that it takes20 years to get onto the ministry's mailing list!While education was only present in two of the forms of contact at the 95 % level ofsignificance, it showed up in seven of the groups shown in Table 45 at the 90% level ofsignificance. This clearly indicates a trend that farmers making contact with theMinistry of Agriculture, have higher levels of education than those who do not.85Extension Contacts: 1991 Compared with 1969The fourth objective of the thesis project was to determine if the level of contactbetween district agriculturists and horticulturists and farmers had changed over time.86The only published information about previous levels of contact on a provincial basisdates back to the 1967 Agricultural Regional Development Agreement (ARDA) socio-economic research project conducted by Dr. Coolie Verner and reported in Akinbode(1969). This was based on personal interviews conducted with 256 farmers throughoutBritish Columbia during the summer of 1967. Alleyne (1968) conducted similarresearch but only on farmers in the Lower Fraser Valley.The survey used for this thesis project was designed to ask similar questionsabout levels of different types of contact with district agriculturists and horticulturists,as was done by Verner in 1967. The questions in 1967 read as follows (Verner, 1967):1. Have you visited your District Agriculturist in his office during the past year?2.Have you consulted your District Agriculturist about a farm matter over the telephoneduring the past year?3. Did your District Agriculturist visit you during the past year about a farm matter?4. Have you attended local meetings or field days sponsored by the DistrictAgriculturist during the past year?The only other difference in the wording was that the 1991 questionnaireconsidered the District Horticulturist to be the same type of contact as the DistrictAgriculturist.The levels of contact as reported by Akinbode (1969) and Alleyne (1968) canthen be compared with the results of 1991, as presented in Table 47 and Figure 7.87Table 47Extension Contacts 1969 vs. 1991Level of ContactaAlleyne Akinbode 1991(1968) (1969)Visits to Office 43% 35% 38%Telephone Calls 63% 17% 47%Visits to Farm 56% 16% 23%Attendance at Meetings 34% 54%Average 54% 26% 41%a Refers to a minimum of one contact/yearThe use of hypothesis testing to determine if a statistical difference betweenresults exists cannot easily be used in this case. Each of the individual questionnaireresponses would have to be set up in SPSS/PC+ format to conduct the analysis. As aresult, conclusions drawn from the comparison can only be drawn by inference.On a provincial basis, these statistics indicate that there has been little change inthe level of contact in visits to the district agriculturist's or horticulturists office.However, all other categories show increases. The number of telephone calls to theoffice have more than doubled. Farm visits have a modest increase of 7%, from 16%to 23 %. The number of farmers reporting that they obtained information frommeetings and field days has increased 20%. Averaging out all forms of contactindicates that about 15% more farmers are obtaining information from their districtagriculturist or horticulturist than they were in 1969.Comparison of Alleyne (1968) to the 1991 results show declines in allcategories. Averaging out all forms of contacts indicates a 13 % drop in contact. Thedifferences in the results is likely due to the fact the Akinbode study was doneprovince-wide, while the Alleyne study was just of strawberry growers in the LowerFraser Valley. The Ministry of Agriculture may be doing a better job of servicing allfarmers in general, while back in 1968 only those farmers close to the major centerswere getting a high level of service. It is difficult to draw conclusions from acomparison between the Alleyne study and the findings of this research because onestudy covers the general population, while the Alleyne study was on a sub-group.89CHAPTER 6SUMMARY AND CONCLUSIONSThe focus of this study has been on the collection and analysis of informationabout farmers who do or do not value as well as use or do not use various sources ofinformation. The result of this research provides a very detailed "snapshot" of farmersin British Columbia. The purpose of this chapter is to summarize some of the majorfindings and to delve into the significance of them.Detailed statistical analysis of the response to the survey has shown that theinformation gathered meets the tests of being statistically significant and representativeof British Columbia farmers. This is stated with one qualification: berry growers werenot well represented in the survey due to the lack of a mailing list.The farmers surveyed were predominately male (91 %) and had an average ageof 49.5 years. A significant number are married (93%) and have at least two children.Seventy per cent speak English as their first language and only 30% have less than ahigh school education. Another 30% have some form of post-secondary training.Over 70% have been farming at the same location for at least 10 years and over 85%have been farmers for at least 10 years. Many of the farm families earn some portionof their income off the farm. Only 28.1 % reported not earning any of their incomeoff-farm. Many of the farmers run small operations, with 28% reporting less than$19,999 in farm sales.The most frequent method that farmers obtain information from individuals inthe business of providing information is by mail, fax, or computer. The least frequent9091was the "one on one" farm visit. In general, the more "personal" the form of contactbetween the farmer and the extension provider, the lower the level of contact. Thismeets general expectations as it is easier to contact more farmers in a shorter period oftime by mailing them a newsletter as compared to visiting them individually. Whenlooking at the total number of contacts between extension providers and farmers, had,sales representatives have the highest overall level of contact at a rate of 53.2%. Thedistrict agriculturist/horticulturist is the second choice of farmers at a rate 4.8% less.This position is due in part to the relatively high level of contact these ministry staffhave through the mail, fax, or computer method. Veterinarians, provincial specialists,and bank managers or financial advisors are the next three highest levels of contact.Non-formal extension providers are also of importance to farmers. Ninetypercent of farmers reported obtaining information from neighbors and friends with theirspouse being of secondary importance at a level of 78%. Published materials mostlyfrequently referred to were general farm papers or magazines (74%) , British ColumbiaMinistry of Agriculture publications (71 %), and farm organization newsletters (65%).A large number of farmers reported that they obtained information from visits to otherfarms (81 %). Video tapes were also utilized with 40% of farmers reporting that theyhad obtained information from one.When asked to rate the value of all information sources, farmers reported thatneighbors, friends, and other farmers were the most valuable. Other valuable sources,in order, were visits to other farms, sales representatives, general farm papers andmagazines, and Ministry of Agriculture publications.Canonical analysis, a multiple correlation technique, was used to identifysignificant demographic factors that are strongly linked with the use of the information92sources. The value of farm sales was positively correlated with the use the informationsources in almost all cases. Age and off-farm income were also found to be importantpredictors of use for some circumstances. The level of contact between salesrepresentatives, provincial extension agents, Agriculture Canada staff, and bank orfinancial advisors increased with farm sales. As farm sales increase, greater use ismade of newsletters and magazines from commercial suppliers. No correlations werefound between extension providers and those that obtained information at field days ormeetings. It would appear that while other methods of contact that extension providershave with farmers tends to favor the larger producer, the field day/ meeting method issuccessful in reaching all demographic groups. As many farmers reported that theyfound information from visiting other farms, field days that involve tours to otherfarms and discussion of the techniques being used would appear to be a valuabletechnique. The study has also illustrated the effectiveness of utilizing a multiplecorrelation technique to isolate the factors of importance. This technique is suitable foruse with many forms of survey research not isolated to that of agriculture.The data was analyzed to determine if British Columbia Ministry of Agricultureextension staff are serving all farmers equally. Statistically significant differences werefound in the average demographic statistics of those who use extension services andthose who do not. In general, the farmers using extension services were younger, hadmore education, higher off-farm income, higher farm sales, and had been farming for ashorter period of time.Comparison of the level of contact between farmers and the provincial extensionservice was made between a 1969 survey (Akinbode) and the survey results. On aprovincial basis, the level of contact is higher in 1991 for farm visits, phone calls, andvisits to the office as was observed in 1969. Caution must be exercised in interpreting93a straight line trend between these two dates. The level of extension staffing hasfluctuated significantly over the years and the nature and type of extension programsdelivered has varied. It is difficult to determine whether or not the increase in the levelof contact between Ministry extension staff and farmers is adequate to meet the needsof the farmer in the 1990's or whether the level of contact is too high. The answer tothis question depends on the values held by the respondent. On one hand farmers arefaced with an increasingly competitive global marketplace and are facing increasedenvironmental and economic pressures. On the other hand, farmers are bettereducated and have more resources available to them to solve their own problems. The1991 Extension Program Review conducted by Sork (1991) deals with this question inmore detail.The study has shown that farmers obtain information from a variety of sourcesand that the Ministry of Agriculture is one of the more important sources. It is alsoevident that commercial suppliers play a major role in the provision of information tofarmers. Canonical analysis showed that farmers obtain information more frequentlyand place greater value on information obtained from commercial suppliers as farmsales increase. The implication of this to commercial suppliers is that their customersconsider them to be valuable sources of information, and that information can be animportant marketing tool. The supplier that does a good job of providing qualityinformation and linking that to the supply of their product will earn that farmer'sbusiness. Given the overall level of contact these suppliers have with farmers, it wouldappear that an opportunity exists for Ministry of Agriculture extension staff to utilizethis in some circumstances. If the Ministry is attempting to improve certain practicesof farmers that are related to the products that commercial suppliers provide, thendirecting some of their extension efforts towards those suppliers may prove to be ofbenefit. For example, nitrates in the groundwater in the Abbotsford area of British94Columbia are of concern to the Ministry. The source of the nitrates is speculated to berelated to the handling and disposal of manure as well as the application of commercialfertilizer. Commercial fertilizer sales personnel work closely with their customers indeveloping fertilizer recommendations. Extension efforts directed toward that salesperson would have an impact on what the farmer does in the field as that salesmanwould have considerable influence and contact with the farmer. This suggestion doesnot mean that the Ministry should refocus extension efforts toward commericialsuppliers, but that the Ministry might better achieve some of its objectives byconsidering its influence on other persons providing extension information.The fact that commercial suppliers tend to spend more time with larger farmersmeans that smaller farmers tend to get overlooked. The data indicates that Ministry ofAgriculture extension staff also tend to spend more time with the larger farmers. Whoshould the Ministry be serving? Should they apply the 80/20 rule as commercialsupplier do, or do they define their clientele in a different fashion. While smallerfarmers may not be making a large contribution to the economy, they do hold andmaintain land in an agricultural state. This land could be considered as an importantinventory for future food production or in providing greenbelt space. This studycannot provide these answers, however they make interesting points which could beelaborated upon in future studies.As this study has covered the use of all types of information for all groups offarmers, further research in this area should be more specific. The study was not adiffusion/adoption study and there is no information about how innovative the farmerswere who responded to the survey. Future work could look at which informationsources are utilized at each of the different stages of the adoption process. Forexample, are farmers learning about new techniques at the Lower Mainland95Horticultural Improvement Association short course held in Abbotsford everyFebruary, or are a large number of farmers learning them from a farm visit by acommercial supplier who attended the lectures at that course? Specific research of thisnature will provide answers to extension providers as to the effectiveness of theirprograms or how best to target them.The study has a number of important limitations that must be taken into accountwhen drawing conclusions about how farmers use information. The sample was under-represented by berry growers due to a lack of a mailing list. The accuracy of theMinistry of Agriculture mailing list is not known and it was not possible to preciselyidentify how large the sample had to be to represent the whole population. Thequestionnaire only asked farmers to indicate which sources of information they use andvalue. No information collected that would give insight into why farmers wereconsulting the various sources or how they made their judgements of the "value" ofsources. Because farmers frequently get information from commercial suppliers doesnot mean that the Ministry services are redundant or should be re-oriented to includethose suppliers. Each information source has its own characteristics as to availability,cost, reliability, and appropriateness. It is not likely that farmers would look to anindependant consultant to keep them up to date on technological advances, however itis more likely that a consultant would be called in to provide specific services such asthe design of a new structure. No information was collected as to the adoptor groupthat the farmer may have belonged to. It is not known if the farmers who replied to thesurvey were evenly distributed amongst the adoptor groups or skewed in somedirection.A considerable amount of data was collected in this project which could beanalyzed further to answer additional questions. While the study attempted to look at96all the information that was gathered, it was analyzed in view of the original fourobjectives. For example, detailed analysis could be performed on the differencesbetween those farmers who use video tapes and those who don't. A follow-up studycould be performed to determine why just as many farmers reported obtaininginformation from a British Columbia Ministry of Agriculture demonstration site, asthose who travelled to foreign countries. Work could be performed on the type ofinformation or stage of adoption the Ministry should be involved in and what should beturned over to other individuals. In conclusion, the findings of this study raises asmany questions as it answers and sets the stage for the next study.ReferencesAkinbode I. A. (1969,April). The Relationship Between the Socio-economicCharacteristics of Farmers in B.C. and their Contacts with District Agriculturists.Unpublished master's thesis, University of British Columbia, Vancouver B.C.Akinbode, 1. A. & Dorling,.M.J. (1969). Farmer Contacts with District Agriculturistsin Three Areas in British Columbia. Rural Sociology Monography #5. Department ofAgricultural Economics, University of British Columbia.Alberta Agriculture. (1983, December).  Information Needs of Alberta Farmers andFarm Families: Provincial Results. Alleyne, P.E. (1968, April). Interpersonal Communication and the Adoption ofInnovations among Strawberry Growers in the Lower Fraser Valley. Unpublishedmaster's thesis, University of British Columbia, Vancouver, B.C.Alleyne, P. E. & Verner, C.(1969). The Adoption and Rejection of Innovations byStrawberry Growers in the Lower Fraser Valley. Rural Sociology Monography #3,Department of Agricultural Economics, University of British Columbia, VancouverB.C.Blackburn, D.J. Young, W. S., Sanderson, L., & Pletsch, D.H. (1983, January).Farm Information Sources Important to Ontario Farmers.  School of AgriculturalEconomics and Extension Education, Ontario Agricultural College, University ofGuelph.British Columbia Legislative Assembly, Select Standing Committee on Agriculture.(1979). Agricultural Extension Services in British Columbia, Alberta, andOregon.(Phase II Research Report). Queen's Printer, Victoria, B.C.British Columbia Ministry of Agriculture and Fisheries. (1990) Annual Report 1989-90. Ministry of Agriculture & Fisheries. Victoria, B.C.British Columbia Ministry of Agriculture, Fisheries and Food. (no date) Fast FactsBrochure.British Columbia Ministry of Agriculture and Fisheries. 1989. Strategic Planning forthe 1990's, Victoria, B.C.9798Charach, L.(1975, April). Using Mail Questionnaires: The Optimal Methodology andan Example (Report No. 75:34). Vancouver, B.C. Educational Research Institute ofBritish Columbia and the Institute of Industrial Relations at the University of BritishColumbia.Coughenour, C.M. (1959, November). Agricultural Agencies as Information Sourcesfor Farmers in a Kentucky County. 1950-1955. Kentucky Agricultural ExperimentStation. Progress Report 82.Dent, W. J. (1968, May). The Sources of Agricultural Information Used by Farmers ofDiffering Socio-economic Characteristics. Unpublished master's thesis, University ofBritish Columbia, Vancouver B.C.Dever, T. (1991, May 8). Agricultural Producers by Sector. Computer mail message toThomas J. Sork. Gross, J. G. (1977, March/April) Farmers Attitudes Toward Extension. Journal ofExtension, pp 13-19.Iddings, R.K. & Apps, J.W. (1990, Spring). What Influences Farmers' Computer Use?Journal of Extension. pp 19-21.Lamble, W.. (1984). Diffusion and Adoption of Innovations. In D. J. Blackburn(Ed.), Extension Handbook.(pp. 32-41). University of GuelphMillerd, F. W. (1965, May) An Analysis of the Adoption of Innovations by OkanaganOrchardists. Unpublished master's thesis, University of British Columbia, Vancouver,B.C.Nolan M. & Lasley, P. (1979, September/October). Agriculture Extension: Who UsesIt? Journal of Extension, pp 21-27.Norusis, M.J. (1988). The SPSS Guide to Data Analysis for SPSS/PC+. Rush-Presbyterian-St Luke's Medical Center. SPSS Inc.Orlich, D. C.(1978). Designing Sensible Surveys. Redgrave Publishing Company.Pleasantville, New York.Rogers, E.M. (1983). Diffusion of Innovations (Third Edition). New Yor.: The FreePress.Scheaffer, R. L., Mendenhall, W. & Ott, L. (1986). Elementary Survey Sampling, 3rdEdition. PWS Publishers. Boston, Massachusetts.Sork, T.J., Palacios A. & Dunlop C. (1991). 1991 Extension Program Review: FinalReport. Unpublished Report.Tabachnick, B. G. & Fidel!, L.S. (1983). Using Multivariate Statistics. CaliforniaState University, Northridge. Harper & Row, Publishers, New York.Verner, C. (1967, October). Planning and Conducting a Survey: A Case Study.Canada Land Inventory, Project No. 16018. Rural Development Branch, Departmentof Forestry and Rural Development.Verner, C. & Gubbels, P.M. (1967, June). The Adoption or Rejection of Innovationsby Dairy Farm Operators in the Lower Fraser Valley. Agricultural EconomicsResearch Council of Canada. Publication No.11.Warner, P. D. & Christenson, J.A. (1984). The Cooperative Extension Service: Anational Assessment. Westview Press Inc. Boulder Colorado. 1984.Warner, P. D. & Christenson, J.A. (1981). Who is Extension Serving? Journal ofExtension, pp 22-28.Wiersma, W. (1986). Research Methods in Education. Newston, MA: Allyn andBacon, INc.99Appendix OneINFORMATION SOURCES IN BC AGRICULTURE:A PRODUCERS' SURVEYINSTRUCTIONSPart I of the survey consists of questions about the different sources of information thatfarmers use to solve problems and make decisions. The questions will ask you how oftenyou have used various information sources. When thinking about how you may havereceived information relating to a farm matter, remember that it can be anything that helpsyou make a decision about your farm business. Examples are: fertilizer recommendations,methods to improve the ventilation in your barn, or even how to take the GST intoconsideration in your financial accounts.Part H of the survey includes several questions asking for general information such asyour age and what type of farm you have. The purpose of these questions is to categorizeyour answers with other farmers in British Columbia so that the different informationrequirements of different groups of farmers can be determined. Answering both parts ofthe questionnaire is very important to give us a clear picture of what information sourcesare used by different types of farmers throughout the Province.Thank you for investing the time to help improve British Columbia's extension service.Please mail the completed survey in the enclosed preaddressed, stamped envelopebyJuly 15, 19911991 Extension Program Reviewdo Dr. Thomas J. SorkUniversity of British ColumbiaAdult Education Research Centre5760 Toronto RoadVancouver, BC V6T 1L2Phone: (604) 822-5702FAX: (604) 822-6679100101PART I1. Please put a check in the box to the right of each information source that best indicates how oftenduring the past 12 months each person visited your farm and provided you with information pertainingto a farm matter.INFORMATION SOURCENO FARMVISITS1 OR 2VISITS3 OR 4VISITS5 OR MOREVISITSa. District Agriculturist orHorticulturist77% 11% 7% 5%b. Other provincial agriculturalspecialist75% 18% 3% 4%c. University or college staff 98% 1% 0% 1%d. Agriculture Canada staff 88% 7% 1% 4%e. Sales rep. (feed, fertilizer, etc.) 42% 22% 8% 28%f. Bank manager or financial advisor 75% 19% 2% 4%g. Packing house or processor fieldrepresentative.78% 10% 3% 9%h. Veterinarian 48% 18% 12% 22%i. Independent consultant 86% 10% 2% 2%j. Other, please specify: 95% 2% 1% 2%2. Please put a check in the box to the right of each information source that best indicates how oftenduring the past 12 months you obtained information relating to a farm matter by talking to each personon the telephone.INFORMATION SOURCENO PHONECALLS1 OR 2CALLS3 OR 4CALLS5 OR MORECALLSa. District Agriculturist orHorticulturist53% 27% 7% 13%b. Other provincial agriculturalspecialist63% 17% 11% 9%c. University or college staff 93% 5% 1% 1%d. Agriculture Canada staff 78% 15% 4% 3%e. Sales rep. (feed, fertilizer, etc.) 39% 18% 14% 29%f. Bank manager of financial advisor 53% 20% 10% 17%g. Packing house or processor fieldrepresentative66% 10% 6% 6%h. Veterinarian 39% 24% 13% 24%i. Independent consultant 80% 9% 7% 4%j. Other, please specify: 92% 1% 2% 5%1023. Please put a check in the box to the right of each information source that best indicates how oftenduring the past 12 months you visited each person at their office to obtain information relating to a farmmatter.INFORMATION SOURCENO OFFICEVISITS1 OR 2VISITS3 OR 4VISITS5 OR MOREVISITSa. District Agriculturist orHorticulturist62% 24% 9% 5%b. Other provincial agriculturalspecialist82% 12% 3% 3%c. University or college staff 99% 1% 0% 0%d. Agriculture Canada staff 85% 12% 1% 2%e. Sales rep. (feed, fertilizer, etc.) 53% 26% 6% 15%f. Bank manager or financial advisor 42% 30% 13% 15%g. Packing house or processor fieldrepresentative76% 14% 2% 8%h. Veterinarian 51% 31% 6% 4%i. Independent consultant 89% 10% 0% 1%j. Other, please specify: 95% 0% 3% 2%4. Please put a check in the box to the right of each information source that best indicates how oftenduring the past 12 months you have heard each person make a presentation or speak at a meeting orfield day on an agricultural topic.INFORMATION SOURCE NEVER1 OR 2TIMES3 OR 4TIMES5 OR MORETIMESa. District Agriculturist orHorticulturist46% 41% 8% 5%b. Other provincial agriculturalspecialist45% 47% 5% 3%c. University or college staff 81% 15% 4% 0%d. Agriculture Canada staff 64% 34% 1% 1%e. Sales rep. (feed, fertilizer, etc.) 60% 32% 5% 3%f. Bank manager of financial advisor 83% 15% 1% 1%g. Packing house or processor fieldrepresentative82% 12% 4% 2%h. Veterinarian 65% 31% 3% 1%i. Independent consultant 84% 13% 2% 1%j. Other, please specify: 91% 5% 1% 3%1035. Please put a check in the box to the right of each information source that best indicates how oftenduring the past 12 months you have received information from each person by mail, fax or computer.INFORMATION SOURCE NEVER1 OR 2TIMES3 OR 4TIMES5 OR MORETIMESa. District Agriculturist orHorticulturist20% 18% 36% 26%b. Other provincial agriculturalspecialist40% 28% 17% 15%c. University or college staff 86% 11% 3% 0%d. Agriculture Canada staff 50% 26% 14% 10%e. Sales rep. (feed, fertilizer, etc.) 40% 23% 16% 21%f. Bank manager or financial advisor 57% 22% 9% 12%g. Packing house or processor fieldrepresentative77% 9% 5% 9%h. Veterinarian 69% 17% 9% 5%i. Independent consultant 85% 12% 1% 2%j. Other, please specify: 95% 2% 1% 2%[PLEASE CONTINUE TO THE NEXT PAGE]1046. Please put a check mark in the box to the right of each information source to indicate how often on average,during the past 12 months, you have received information useful in making a farm management decision fromeach publication. PLEASE CHECK ONE BOX FOR EACH INFORMATION SOURCE.INFORMATIONSOURCE NEVERONCE AYEARONCEEVERY 6MONTHSONCEEVERY 3MONTHSONCEAMONTHONCEAWEEKEVERYDAYa. B.C. Ministry ofAgriculture publications29% 19% 14% 25% 9% 3% 1%b. Agriculture Canadapublications52% 15% 12% 13% 8% 0% 0%c. General farm paper ormagazine (Country Life,B.C. Farmer, etc.)26% 11% 12% 13% 30% 8% 0%d. Specialized farm paper ormagazine (GreenhouseManager, B.C. DairyDigest, Vegetable Grower,etc.)40% 10% 7% 14% 24% 5% 0%e. Scientific Journal(Journal of Plant Science,Journal ofAnimal Science,etc.)85% 8% 3% 2% 2% 0% 0%f. Provincial or Localnewspaper (Vancouver Sun,Similkameen Spotlight, etc.)55% 12% 8% 5% 5% 8% 7%g. Newsletter or magazinepublished by a commercialsupplier (feed, fertilizer,equipment, etc.)38% 11% 17% 18% 15% 1% 0%h. Newsletter or magazinepublished by a farmorganization (B.C.Blueberry Coop, B.C.Cattlemen's Assoc., etc.)35% 14% 15% 18% 16% 2% 0%i. Publication from a UnitedStates government oruniversity source63% 19% 5% 3% 10% 0% 0%j. Other, please specify: 91% 1% 2% 3% 1% 2% 0%105PLEASE CHECK THE ANSWER WHICH BEST CORRESPONDS TO HOW OFTEN YOU HAVEUSED AN INFORMATION SOURCE7. On average, how often during the past 12 months have you received information relating to a farmmatter, other than the weather report, from a radio program or announcement?35% never^ 18% once per month8% once during the last year^9%^once per week12% once every six months 6%^every day12% once every three months8. On average, how often during the past 12 months have you received information relating to a farmmatter, other than the weather report, from a television program?39% never^ 19% once per month6% once during the last year^7%^once per week14% once every six months 1%^every day14% once every three months9. On average, how often during the past 12 months have you received information relating to a farmmatter from watching a video tape?60% never^ 1% once per month24% once during the last year^1%^once per week8% once every six months 0% every day6% once every three months9(a). If you have watched a video tape, what was the source of the tape? Please check all thatapply.35.9% Ministry of Agriculture46.2% Commercial supplier5.1% University or college10.3% Agriculture Canada33.3% Other, please specify:^10. On average, how often during the past 12 months have you received information related to a farmmatter from a computerized bulletin board?87% never^ 3% once per month3% once during the last year^0%^once per week5% once every six months 0% every day2% once every three months11. Have you taken any courses in agriculture or farm business management during the past 12 months?15% yes85% no11(a). If you answered "yes" above, please indicate who offered the course(s):10612. On average, how often during the past 12 months have you received information relating to a farmmatter from an employee before making a farm management decision?68% never^ 3%^once per month6% once during the last year^3%^once per week4% once every six months 7%^every day9% once every three months13. On average, how often during the past 12 months have you received information relating to a farmmatter from your spouse or children before making a farm management decision?22% never^ 17%^once per month3% once during the last year^17%^once per week10% once every six months^20%^every day11% once every three months14. On average, how often during the past 12 months have you received information relating to a farmmatter from your parents or other relatives before making a farm management decision?59% never^ 3%^once per month8% once during the last year^5%^once per week10% once every six months 4%^every day11% once every three months15. On average, how often during the past 12 months have you received information relating to a farmmatter from a neighbour, friend or other farmer?10% never^ 33%^once per month10% once during the last year^7%^once per week14% once every six months 1%^every day25% once every three months16. Have you obtained information about a farm matter while visiting any of the following places duringthe past 12 months? Please check all that apply.81% another farm19% Agriculture Canada Experimental Station23% B.C. Ministry of Agriculture demonstration site23% travel to a foreign country3%^other, please specify: ^11% none of the above[PLEASE CONTINUE TO NEXT PAGE]10717. We would like your opinion on the value of all the information sources that are available to you, whether or not youhave used them in the past 12 months. Please put a check mark in the box to the right of each information source thatbest indicates how valuable you feel each source is. If you are not familiar with the source, having never used it before,please check "DOES NOT APPLY."INFORMATION SOURCEDOESNOTAPPLYOF NOVALUEOFLITTLEVALUE UNDECIDED VALUABLEHIGHLYVALUABLEa. District Agriculturist orHorticulturist15% 5% 13% 12% 38% 17%b. Other Provincial governmentspecialists29% 4% 12% 8% 37% 10%c. University or college staff 54% 7% 9% 12% 18% 0%d. Agriculture Canada staff 35% 8% 9% 16% 27% 5%e. Neighbours, friends, otherfarmers3% 0% 4% 7% 66% 20%f. Sales rep. (feed, fertilizer,equipment, etc.)16% 1% 5% 8% 52% 18%g. Bank manager or financialadvisor22% 12% 15% 12% 29% 10%h. Packing house or processorfield rep.46% 6% 10% 6% 21% 11%i. Veterinarian 30% 1% 0% 0% 31% 38%j. Relatives, including parents 31% 6% 8% 2% 39% 14%k. Farm employees 38% 3% 7% 9% 34% 9%1. Spouse or children 18% 2% 11% 3% 42% 24%m. B.C. Ministry of Agriculturepublications11% 1% 15% 9% 55% 9%n. Agriculture Canadapublications19% 3% 17% 12% 42% 7%o. General farm papers ormagazines (Country Life, B.C.Farmer, etc.)11% 2% 10% 14% 49% 14%p. Specialized farm papers ormagazines (GreenhouseManager, B.C. Dairy Digest,etc.)29% 3% 6% 7% 38% 17%q. Scientific journals (Journal ofPlant Science, etc.)57% 5% 8% 12% 16% 2%r. Provincial or Localnewspapers (Vancouver Sun,Similkameen Spotlight, etc.)25% 17% 25% 12% 17% 4%108INFORMATION SOURCEDOESNOTAPPLYOF NOVALUEOFLITTLEVALUE UNDECIDED VALUABLEHIGHLYVALUABLEs. Newsletter published bycommercial supplier (feed,fertilizer, equipment, etc.)21% 2% 14% 8% 49% 6%t. Newsletter published by afarm organization (B.C.Blueberry Coop, B.C.Cattlemen's Assoc., etc.)24% 3% 6% 3% 57% 7%u. Publication from a UnitedStates government or universitysource51% 5% 11% 8% 19% 6%v. Radio programs orannouncements23% 6% 23% 13% 31% 4%w. Television programs 27% 7% 20% 12% 30% 4%x. Video tapes 41% 2% 11% 13% 26% 7%y. Computerized bulletin board 72% 3% 9% 13% 3% 0%z. Courses on agriculture 34% 0% 3% 5% 45% 13%A. Visit to an AgricultureCanada Experimental Station37% 1% 11% 6% 37% 8%B. Visit to a B.C. Ministry ofAgriculture demonstration site33% 0% 5% 8% 47% 7%C. Foreign travel 46% 2% 7% 4% 32% 9%D. Independent Consultant 54% 3% 8% 10% 17% 8%E. Visit to another farm 9% 0% 5% 0% 57% 29%F. Other, please specify: 97% 0% 0% 0% 1% 2%[PLEASE CONTINUE TO THE NEXT PAGE]109PART IIIn order to categorize your answers with those of other farmers across B.C., we would like to ask you somegeneral questions.18. In what year were you born?^see Table 619. Please indicate your sex.^91%^Male9%^Female20. What is your marital status?92%^Married (including common-law marriages)4%^Widowed, divorced, separated3%^Never been married21. What is your Mother tongue, that is the first language you learned which you still understand?70% English 9% Dutch1% French 12% GermanI% Chinese 0% Punjabi0% Japanese 1% A Native language (North American Native or Inuit)0% Korean 2% Scandinavian language0% Spanish 0% Ukrainian1% Italian 2% Other, please1% Portuguese specify:^22. How many children do you have?9%^None^28% Three8%^One 12% Four29%^Two 13% Five or more23. What is the highest level of education that you have completed and received credit for?2%^less that 5 years10%^5-8 years20%^9-11 years30%^High school diploma (Grade 12 or 13)23%^College or technical diploma (1-2 year program)9%^Bachelor's degree4%^Master's degree1%^Doctorate1%^Other, pleasespecify:11024. Are you a member or is a group you belong to a member of any of the following farm organizations?Please check all that apply.60% B.C. Federation of Agriculture10% A Farmer's or Women's Institute1% Alliance of B.C. Organic Producers' Association6^B.C. Fair Association3% Horse Council of B.C.23% Commodity marketing board36% Breed organization15% Packing house or crop marketing co-op5% A farm or rural women's group16% Others, pleasespecify:^25. How many years have you been on this farm? ^see Table 1326. For how many years have you been a farmer? ^see Table 1427. How much income did you and your spouse together earn outside the farm last year?27% none 12% $30,001-40,00012% less than $5,000 7% $40,001-50,0009% $ 5,001-10,000 4% $50,001-60,0006% $10,001-20,000 5% $60-001-70,0008% $20,001-30,000 6% $70,000 plus28. What is the size of your farm? Please report the unit of measurement that is most appropriate foryour type of operation. For example, the number of acres if you grow crops, the number of cattle ifyou have a feed lot, the size of your egg quota, etc.29. What was the total value of sales from all your agricultural operations last year? $30. What is the principal agricultural product sold? Please check one only.BeefDairySwinePoultryGrain and oilseedsBee productsVegetablesBerriesTree fruitsSheepGrapesForageFloricultureNurseryOther, pleasespecify:^— THANK YOU AGAIN FOR TAKING THE TIME TO COMPLETE THIS SURVEY —Appendix TwoAppendix two presents an example of the data output of a canonical analysis andinterprets the output line by line. This example is based on the correlation donebetween the frequency with which farmers talked to extension providers on thetelephone with the farmers' demographic data.Lines 19 through 29 contain the command lines which tell BMDP6M whichvariables to use and correlation against. In this example there are a total of 21variables, 10 of which are Y1 to y10, and eleven of which are X1 to X11. The Y's arethe 10 different possible information sources to whom a farmer might call on thetelephone. The X's are the eleven categories of demographic data used. The formatstatement and variables to be used are checked to ensure that everything matches. Thenext part of interest begins at line 133 titled Univariate Summary Statistics.Information reported in this category provides checks that the data is correctthrough statistics such as the mean, smallest value, and largest value. In addition, thekurtosis and skewness evaluates the shape of the curve with respect to the normaldistribution. Canonical correlation does not require that the variables be normallydistributed, however the analysis is enhanced when it is (Tabachnick & Fide11, 1983, p.149). Results from multivariate analysis cannot be tested for normality, however thelikelihood of it being so is much greater when the independent and dependent variablesare normally distributed. Data can be used even if it is skewed providing that it is notbadly skewed and the sample size is large. There must be at least more cases analyzedthan there are dependent variables. As there are 100 cases with the survey and only 11111dependent variables, the sample size can therefore be considered quite large. TheCentral Limit Theorem can also be used to justify the use of skewed variables.The normality of the data is assessed by the value of the skewness coefficient.If the skewness ss =0 then there is perfect symmetry about the mean. Ideally values ofskewness should be less than 3.Line 167 list univariate correlation coefficients between the variables.Line 341 list the squared multiple correlation coefficients of each variable withall the other variables. This indicates how much of the variance of each variable isaccounted for by all the other variables. Squared multiple correlations check for thecondition of multicollinearity and singularity. Multicollinearity occurs when twovariables are highly correlated with each other. Singularity occurs when one score is alinear or almost linear combination of the others. Since the mathematics of canonicalcorrelation involve matrix algebra and the inversion of matrices, variables that arehighly correlated with each other mean that the discriminate of their matrices is almostzero. Since matrix inversion is the mathematical equivalent of division, the result ishuge fluctuations with only minor changes in the correlation. Any values of squaredmultiple correlations greater than 0.95 will indicate that two variables are highlycorrelated and therefore one of them is redundant. For example, if the demographicdata included a question about age and another about how long that person had beenmale or female, it would be expected that the results would be highly correlated. Interms of using this information for predicting the value of something else, the goal ofcanonical correlation, it would only be necessary to use one of them. It is alsodesirable to have the variance between variables to be greater than 10% so that there issome connection between them.112113Barletts test calculates the eigenvalues of each matrix and the canonicalcorrelation is calculated by taking the square root. The number of significant links thatwill exist between the independent and dependent variables is given by the number ofeigenvalues with a statistically low possibility of making a type 1 error. That is there isa very low probability of being overly optimistic that a link exists. The first eigenvalueindicates that the probability is 0.05 % while the probability of making a mistake inassuming that a second link exists is 10.58%. Using the standard significance tests of95% we would thus conclude that there is only one significant link. As the eigenvalueswere calculated by taking a linear composite of both the independent and dependentvariables there thus exists a pair of canonical variates. As the value of the eigenvalueis 48% we can then state that these two canonical variates share 48% of the variancebetween them. More simply put, 48% of the variance in who farmer phoned forinformation (y set) can be accounted by the demographic data (x set). In this exampleit can be seen that the one link that exists between the demographic data and phonecalls to people is a very good link.Interpretation of the canonical variates proceeds best by looking at the full canonicalcorrelations. The standardized canonical coefficients are only partial coefficients.Proceeding to line 886 titled Canonical Variable Loadings, only CNVRF11 can beused for interpretation as it has been found that only one significant link exists. Use ofCNVRF22 means that interpretations drawn are subject to a high degree of error. Todetermine how strongly each of the original variables is correlated to the canonicalvariate, the values of the full correlations of the independent and dependent variablesare examined as seen at line 886.The next section at line 909 lists squared multiple correlations. These valuedescribe which of the correlations of the original variables can be considered to bestatistically significant as given by the P-value. In addition the proportion of thevariance of each of the independent variables that can be accounted for by thedemographic data is given.114^L1st1,^I -A at 20:44:24 on FEB 12, 1992 for CCW=KENS on G2^1PAGE^1 BMDP6M34^BMDP6M - CANONICAL CORRELATION ANALYSIS5 BMDP STATISTICAL SOFTWARE, INC.6^1964 WESTWOOD BLVD. SUITE 2027 (213) 475-57008^PROGRAM REVISED OCTOBER 19839 MANUAL REVISED -- 198310^COPYRIGHT (C) 1983 REGENTS OF UNIVERSITY OF CALIFORNIA1112^TO SEE REMARKS AND A SUMMARY OF NEW FEATURES FOR13 THIS PROGRAM, STATE NEWS. IN THE PRINT PARAGRAPH.1415^FEB 12, 1992^AT 20:29:141617^PROGRAM CONTROL INFORMATION1819^PROBLEM TITLE IS 'CA ANALYSIS1'./20 INPUT VARIABLES=21.21 FORMAT IS '(136,10F1.0/55X,F2.0,2F1.0/F2.0,2F1.0,10X,3F2.0,22^1X,2F2.0)'.23 UNIT=8./24^VARIABLE NAMES ARE Y1,Y2,Y3,Y4,Y5,Y8,Y7,Y8,Y9,Y10,X1,X2.)(3,X4,X5,X6,X725 ,X8,99,110,X11./26^CANONICAL FIRST ARE Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10.27 SECOND ARE X1,X2,X3,X4,X5,X6,X7,X8,X9,X10.X11./28^PRINT MATRICES ARE CORR,CANV,COEFF,LOAD./29 END/3031^PROBLEM TITLE IS32 CA ANALYSIS13334^NUMBER OF VARIABLES TO READ IN. . . . . . .^.^2135 NUMBER OF VARIABLES ADDED BY TRANSFORMATIONS.  036^TOTAL NUMBER OF VARIABLES  ^2137 NUMBER OF CASES TO READ IN ^  TO END38^CASE LABELING VARIABLES^. ^39 MISSING VALUES CHECKED BEFORE OR AFTER TRANS. ^ NEITHER40^BLANKS ARE ^  MISSING41 INPUT UNIT NUMBER . . . . . . . . . .  ^842^REWIND INPUT UNIT PRIOR TO READING. . DATA. .  YES43 NUMBER OF WORDS OF DYNAMIC STORAGE ^1499844^NUMBER OF CASES DESCRIBED BY INPUT FORMAT . .  14548^VARIABLES TO BE USED47 1 Y1^2 Y2^3 Y3^4 Y4^5 Y548 6 Y6 7 Y7 8 Y8 9 Y9 10 Y1049^11 X1^12 12^13 X3^14 14^15 X550 16 X6 17 X7 18 X8 19 X9 20 X1051 21 X1152^1PAGE^2 BMDP6M CA ANALYSIS15354^OINPUT FORMAT IS55 (13X,10F1.0/55X,F2.0,2F1.0/F2.0,2F1.0,10X,3F2.0,^1X,2F2.0)5657^MAXIMUM LENGTH DATA RECORD IS^59 CHARACTERS.58 1PAGE^3 BMDP6M CA ANALYSIS1Listing of5960616283-A at^20:44:24 on FEB^12,^1992^for CCId=KENS on GOINPUT^VARIABLES ^^VARIABLE^RECORD^COLUMNS^FIELD^TYPEINDEX^NAME^NO.^BEGIN^END^WIDTH VARIABLEINDEX^NAME RECORD^COLUMNSNO.^BEGIN^END FIELDWIDTH TYPE64 1^Y1 14^14 F 12 X2 2 58 58 1 F65 2 Y2 15^15 F 13 X3 2 59 59 1 F66 3 Y3 16^16 F 14 X4 3 1 2 2 F67 4 Y4 17^17 F 15 X5 3 3 3 1 F68 5 Y5 18^18 F 16 X6 3 4 4 1 F69 8 Y6 19^19 F 17 X7 3 15 18 2 F70 7^Y720^20 F 18 18 3 17 18 2 F71 8 Y821^21 F 19 X9 3 19 20 2 F72 9 re 22^22 F 20 X10 3 22 23 2 F7310 Y10 23^23 F 21 111 3 24 25 2 F7411^XI 2^56^57^F7576 FIRST SET OF VARIABLES777879 1^Y1 2 Y2^3 Y3^4 Y4 5 Y580 6 Y6 7 Y7 8 Y8 9 Y9 10 Y108182 SECOND SET OF VARIABLES838485 11^X1 12^X2 13^X3 14^X4 15^1586 16^X6 17^X7 le^xe 19^19 20 X1087 21^XII8889 NUMBER OF VARIABLES IN FIRST SET ^1090 NUMBER OF VARIABLES IN SECOND SET  1191 TOTAL NUMBER OF VARIABLES USED ^2192 MAXIMUM NUMBER OF CANONICAL VARIABLES  1093MINIMUM CANONICAL CORRELATION TO BE USED.^.^.^0.00094 CASE WEIGHT VARIABLE ^85 PRECISION^.^.^.^.^.^.  ^DOUBLE98 TOLERANCE FOR MATRIX INVERSION ^ 0.000100097 EIGENVALUE^LIMIT,^.^.^.^.^........^.^.^0.00000098 OBASED ON INPUT FORMAT SUPPLIED^3 RECORDS READ PER CASE.99 1PAGE^4^BMDP6M CA ANALYSIS1100101102 DATA AFTER TRANSFORMATIONS FOR FIRST^5 CASES103 CASES WITH ZERO WEIGHTS AND MISSING DATA NOT INCLUDED.104105108OCA SE^1^2^3^4 5 6 7 8 9 10107 NO.^LABEL^Y1^Y2^Y3^Y4 Y5 Y6 Y7 Y8 Y9 Y10108 11 12 13 14 15 16 17 18 19 20109X1^12^X3^X4 15 X6 X7 111 19 X1011021112113 1^1^2^1^1 1 1 2 2 2L1stIn -A^at^20:44:24 on FEB412.^1992 for CC1d=KENS on G114 48 1 12 1 5 6 28 41115 12116 2 1 1 1 4 4 4 4 11117 29 1 9 2 5 2 2 68118 4119 3 1 3 1 1 1 1 1 11120 55 1 1 5 3 0 0 51121 1122 4 3 1 1 2 2 1 4 14123 38 1 9 8 4 5 4 51124 2125 5 3 2 1 2 1 1 4 43128 42 2 1 4 5 17 30 67127 1128129 NUMBER OF CASES READ ^ 100130 1PAGE^5^BM0P6M CA ANALYSIS1131132133 UNIVARIATE SUMMARY STATISTICS134135136 SMALLEST LARGEST137 STANDARD COEFFICIENT SMALLEST LARGEST STANDARD STANDARD138 VARIABLE MEAN DEVIATION OF VARIATION VALUE VALUE SCORE SCORE SKEWNESSKURTOSIS139140 1^Y1 1.80000 1.04447 0.580259 1.00000 4.00000 -0.77 2.11 1.08-0.14141 2 Y2 1.66000 0.99717 0.600703 1.00000 4.00000 -0.66 2.35 1.250.21142 3 Y3 1.10000 0.41439 0.376718 1.00000 4.00000 -0.24 7.00 4.8928.52143 4 Y4 1.32000 0.69457 .^0.526188 1.00000 4.00000 -0.48 3.86, 2.355.19144 5 Y5 2.33000 1.26375 0.542383 1.00000 4.00000 -1.05 1.32 0.23-1.63145 8 Y6 1.91000 1.14676 0.600396 1.00000 4.00000 -0.79 1.82 0.85-0.83146 7^Y7 1.76000 1.18168 0.671408 1.00000 4.00000 -0.64 1.90 1.12-0.49147 8 Y8 2.22000 1.20252 0.541877 1.00000 4.00000 -1.01 1.48 0.41-1.41148 9 Y9 1.35000 0.78335 0.580259 1.00000 4.00000 -0.45 3.38 2.183.89149 10 Y10 1.20000 0.71067 0.592224 1.00000 4.00000 -0.28 3.94 3.389.89150 11^X1 49.45000 11.25048 0.227512 14.00000 74.00000 -3.15 2.18 -0.14Listing of -A^at^20:44:24 on FEB^12.^1992^for CCid=KENS on 00.10151 12 X2^ 1.09000^0.28762^0.283875 1.00000 2.00000 -0.31 3.16 2.826.03152 13 X3 1.09000 0.40440 0.371005 0.00000 3.00000 -2.70 4.72 3.4113.48153 14^X4 3.59000^4.22617 . 1.177206 1.00000 15.00000 -0.61 2.70 1.16-0.30154 15 X5 3.70000 1.46680^0.396434 1.00000 8.00000 -1.84 2.93 0.12-0.21155 18^X6 4.16000^1.46142^0.351304 1.00000 9.00000 -2.18 3.31 0.440.50158 17^X7 18.89000^15.18588 0.802842 0.00000 70.00000 -1.25 3.37 1.111.09157 18^18 21.85000^13.57313^0.821198 0.00000 81.00000 -1.61 2.88 0.65-0.14158 19^X9 4.08000 2.99387 0.733791 0.00000 10.00000 -1.38 1.98 0.48-1.05159 20 110 4.49000^3.75915^0.837227 0.00000 14.00000 -1.19 2.53 0.53-0.95160 21^XII 8.93000 5.72828 0.826592 1.00000 18.00000 -1.04 1.58 0.40-1.50161162 VALUES FOR KURTOSIS GREATER THAN ZERO INDICATE DISTRIBUTIONS163 WITH HEAVIER TAILS THAN THE NORMAL DISTRIBUTION.164 1PAGE^8^BM0P6M CA ANALYSISI165166167 CORRELATIONS168169170171 YI^Y2^Y3^Y4^Y5^Y8 Y7^Y8 Y9 Y10 XI X2X3172 1^2^3^4^5^8 7 8^9 10 11 1213173174 YI^1^1.000175 Y2 2^0.681^1.000176 Y3^3^0.327^0.328^1.000177 Y4 4^0.368^0.275^0.414^1.000178 Y5^5^0.425^0.459^0.149^0.247^1.000179 Y6 8^0.167^0.282^0.210^0.290^0.578^1.000180 Y7^7^0.264^0.359^0.153^0.427^0.338^0.409 1.000181 Y8 8^0.027^0.071^0.158^-0.097^0.344^0.403 -0.048 1.000182 yg^9^0.247^0.298^0.358^0.163^0.311^0.395 0.179 0.411^1.000183 Y10 10^0.150^0.054^0.103^-0.131^-0.108^-0.114 0.010 0.137^0.145 1.000184 XI^11^-0.123^-0.174^-0.125^-0.134^-0.359^-0.208 -0.201 -0.084^-0.130 -0.147 1.000185 X2 12^0.061^-0.033^-0.076^-0.044^-0.083^-0.098 -0.203 0.088^0.083 0.010 -0.069 1.000186 X3^13^-0.100^-0.074^-0.054^-0.104^-0.098^-0.135 -0.039 0.042^-0.037 0.042 -0.073 0.2771.000187 14^14^-0.158^-0.184^-0.115^-0.189^-0.082^-0.001 -0.058 0.044^0.083 0.017 0.160 -0.119-0.020188 15^15^-0.059^-0.195^-0.100^-0.123^-0.022^-0.010 -0.071 0.055^-0.040 -0.048 0.234 -0.247-0.295189 X8^18^0.240^0.294^0.073^0.307^0.168^0.099 0.104 0.031^0.083 0.037 -0.118 0.0370.044190 X7^17^-0.023^0.042^0.040^-0.030^-0.077^0.097 -0.012 0.069^0.012 -0.151 0.341 -0.134-0.007 I.-.1.-,00LIettr- -A at 20:44:24 on FEB^12.^1992 for CC1d.KENS on 0191 X8^18^-0.104^-0.104^-0.075^-0.100 -0.055 -0.051 -0.158 0.177 0.021 0.018 0.369 -0.144-0.038192 X9^19^0.089^0.043^0.042^-0.017 -0.108 -0.151 -0.189 0.015 0.009 0.140 0.065 0.1680.002193 X10^20^0.221^0.250^0.288^0.260 0.450 0.528 0.231 0.235 0.181 -0.082 -0.115 -0.181-0.038194 XII^21^0.212^0.187^0.224^0.153 -0.037 -0.178 0.138 -0.259 0.22e 0.100 -0.077 0.0770.020195198187 X4^X5^X8^X7 X8 X9 X10 XII198 14^15^18^17 18 19 20 21199200 X4^14^1.000201 X5 15^0.138^1.000202 XB^18^-0.153^-0.088^1.000203 X7 17^-0.064^0.098^-0.237^1.000204 X8^18^0.005^0.217^-0.208^0.683 1.000205 X9 19^-0.135^-0.031^0.366^-0.287 -0.157 1.000208 X10^20^0.114^0.058^0.189^0.285 0.137 -0.280 1.000207 XII 21^-0.069^-0.197^0.075^-0.188 -0.330 0.048 -0.218 1.000208 IPAGE^7^8MDPBM CA ANALYSIS!209210211212213 ABSOLUTE VALUES OF CORRELATIONS IN SHADED FORM214215216 1^Y1^X217 0218219220 +^2 Y2^XX221 00222  223224 +^3 Y3^++X225  0228  227228 +^4 Y4^X+XX229 * 0230  231232 +^5^Y5^XX.-X233 N^0234  235236  8 Y8^-+-+XX237  00238  239240  7^Y7^+X.X+XX241  N^0242243244 +^8 Y8^-XX XListing of -A^at^20:44:24 on FEB^12, 1992 for CCId=KENS on G245 0246247248 9 Y9 -+X.+X-%X2490250251252 +^10 Y10253 0254255258 +^11^XI257 0258259260 :^12 X2 .-. X261 0262  263264 +^13^X3 +X2650266  267268 +^14^X4 ..^X269 0270271272 +^15 X5273 0274  275278  18^X8 ..X2770278279280 17^X7 .-X281  0282  283284 +^18 X8 --XX285  00288287288  19 xs .^X+.X289 0290291292 +^20 X10 --++XX--- .^.+.+X293 NO 0294295296 +^21^XII -^-+^-X297  0298299300301302^Usti'^f -A at 20:44:24 on FEB 12, 1992 for CC1d.KENS on G303^THE ABSOLUTE VALUES OF304 THE MATRIX ENTRIES HAVE BEEN PRINTED ABOVE IN SHADED FORM305^ACCORDING TO THE FOLLOWING SCHEME306307308309^LESS THAN OR EQUAL TO^0.085310311 0.085 TO AND INCLUDING^0.171312313314315^ 0.171 TO AND INCLUDING^0.256316317318319^ 0.256 TO AND INCLUDING^0.341320321322323^X^0.341 TO AND INCLUDING^0.427324325328327^X^0.427 TO AND INCLUDING^0.512328329330331^X^0.512 TO AND INCLUDING^0.5973320333334335^X^ GREATER THAN^0.5973360337338^1PAGE^8 BMDP6M CA ANALYSISI339340341^SQUARED MULTIPLE CORRELATIONS OF EACH VARIABLE IN342SECOND SET WITH ALL OTHER VARIABLES IN SECOND SET343344 VARIABLE345^NUMBER^NAME^R-SQUARED348347 11 X1 0.27870348 12 X2^0.15797349^ 13 X3 0.14094350 14 14 0.14294351 15 15^0.20251352 18 X8 0.28962353^ 17 X7 0.5724935418 X8^0.5405235519 X9 0.30040358 20 X10 0.31811357^ 21 XII^0.17123358359360^SQUARED MULTIPLE CORRELATIONS OF EACH VARIABLE INListing of381382383384385-A at^20:44:24 on FEB^12.^1992 for CCici.KENS onFIRST SET WITH ALL OTHER VARIABLES IN FIRST SETVARIABLENUMBER^NAME^R-SQUARED368 1^Y1 0.55408387 2 Y2 0.54203368 3 Y3 0.31940369 4 Y4^0.40872370 5 Y5 0.50500371 8 Y6 0.50260372 7^Y7 0.35897373 8 Y8 0.38080374 9 re 0.32871375 10 Y10^0.17201378 1PAGE^9^BMOP6M CA ANALYSIS1377378379380381 CANONICAL^NUMBER OF^BARTLETT'S TEST FOR382 EIGENVALUE^CORRELATION^EIGENVALUES^REMAINING EIGENVALUES383384 CHI-^TAIL385 SQUARE^D.F.^PROB.388387 184.99^110^0.0008388 0.4821u^0,89440^1^107.07 90^0.1058 389 0.31804 0.56395 2^73.39^72^0.4323390 0.25289^0.50288 3^47.73 58^0.7762391 0.20891 0.45487^4^27.33^42^0.9609392 0.15433^0.39284 5^12.58 30^0.9978393 0.08198 0.24898 6^8.95^20^0.9968394 0.04775^0.21852^7^2.65 12^0.9976395 0.02082 0.14428 8^0.80^6^0.9922398 0.00893^0.08325 9^0.18 2^0.9121397 0.00209 0.04569398399400 BARTLETT'S TEST ABOVE INDICATES THE NUMBER OF CANONICAL401 VARIABLES NECESSARY TO EXPRESS THE DEPENDENCY BETWEEN THE402 TWO SETS OF VARIABLES.^THE NECESSARY NUMBER OF CANONICAL403 VARIABLES IS THE SMALLEST NUMBER OF EIGENVALUES SUCH THAT404 THE TEST OF THE REMAINING EIGENVALUES IS NON-SIGNIFICANT.405 FOR EXAMPLE,^IF A TEST AT THE^.01 LEVEL WERE DESIRED,408 THEN^1 VARIABLES WOULD BE CONSIDERED NECESSARY.407 HOWEVER, THE NUMBER OF CANONICAL VARIABLES OF PRACTICAL408 VALUE IS LIKELY TO BE SMALLER.409 1PAGE^10^BM0P6M CA ANALYSIS1410411412 COEFFICIENTS FOR CANONICAL VARIABLES FOR FIRST SET OF VARIABLES413414415418417 CNVRF1^CNVRF2^CNVRF3^CNVRF4 CNVRF5 CNVRFB CNVRF7FeCNVRL1st1i418419f^-A^at820:44:24 on FEB^12,^1992 for CC1d=KENS on 0^1 2^3^4^5^6^7420 Y1E-021^-0.847574E-01^0.448147E-01^-0.201114^0.110307^0.801584^-0.225197^-0.830778 -0.251964421 Y2 2^0.186111^-0.141545^-0.471281^0.832127^-0.821537^0.389238^0.838411 -0.887793E-01422 Y3 3^0.829387^-0.195619^-0.458229^-1.51671^0.788922^1.86332^-0.265540 0.837376423 Y4 4^0.280169 -0.337391 0.183684 1.20852 -0.656682 -0.308533 -0.308752 0.589496E-01424 Y5 5^0.394264^-0.217087^0.713146^-0.310529^-0.220885^-0.473521E-01^-0.272625 -0.291718E-01425 Y8 8^0.396379^0.696658^-0.309953^-0.125036E-01^0.325874^-0.340023E-01^0.228684 -0.718975426 Y7 7^-0.106794E-01^-0.499574E-01^-0.153085 -0.569704^-0.303002 -0.294252^0.531329E-01 0.552055427 Y8 8^-0.138312^0.344614^0.262621^0.464482 -0.476397E-02^0.122940 0.103864 0.756152428 Y9E-019^-0.239187 -0.873990 0.828764E-01^-0.610415E-01^0.678783^-0.817052^0.810947 -0.426102429430Y10 10^0.183847^-0.407511^0.748881^0.900754E-02^-0.404408^0.514217^0.237706 -0.793041431432433 CNVRF9^CNVRF10434 9 10435436 Y1 1^0.714358^-0.316231437 Y2 2^0.171685 0.490870438 Y3 3^-0.665442^0.105029439 Y4 4^-0.956593 -0.516727440 Y5 5^-0.924447E-01^0.567998441 Y6 8^-0.648994E-01^-0.330881442 Y7 7^0.398325^-0.349190443 Y8 8^0.214311 -0.224141444 Y9 9^-0.279773^0.119487445 Y10 10^0.135879 -0.706247446447448449 STANDARDIZED COEFFICIENTS FOR CANONICAL VARIABLES FOR FIRST SET OF VARIABLES450451 (THESE ARE THE COEFFICIENTS FOR THE STANDARDIZED VARIABLES -452 MEAN ZERO,^STANDARD DEVIATION ONE.)453454455456 CNVRF1^CNVRF2^CNVRF3^CNVRF4^CNVRF5^CNVRF8^CNVRF7^CNVRF8^CNVRF9^CNVRF10457 1 2 3 4 5 6 7 6 9 10458459 Y1 1^-0.089^0.047^-0.210^0.115^0.837^-0.235^-0.868^-0.003^0.746^-0.330460 Y2 2^0.188^-0.141^-0.470^0.630^-0.620^0.388^0.836^-0.089^0.171^0.489481 Y3 3^0.344^-0.081^-0.190^-0.629^0.327^0.772^-0.110^0.347^-0.276^0.044462 Y4 4^0.195^-0.234^0.128^0.839^-0.456^-0.214^-0.214^0.041^-0.664^-0.359463 Y5 5^0.498^-0.274^0.901^-0.392^-0.279^-0.060^-0.345^-0.037^-0.117^0.718464 Y6 6^0.455^0.799^-0.355^-0.014^0.373^-0.039^0.262^-0.824^-0.074^-0.379465 Y7 7^-0.013^-0.059^-0.181^-0.673^-0.358^-0.348^0.063^0.652^0.471^-0.413466 Y8 8^-0.166^0.414^0.316^0.559^-0.008^0.148^0.125^0.909^0.258^-0.270487 Y0 9^-0.187^-0.685^0.065^-0.048^0.532^-0.640^0.419^-0.033^-0.219^0.094468 Y10 10^0.131^-0.290^0.532^0.008^-0.287^0.365^0.169^-0.564^0.098^-0.502469 'PAGE 11^BMDP6M CA ANALYSIS'Listing of470471472473474475476-A^et^20:44:24 on FEB^12,^1992^for^CCid.---.KENS on GCOEFFICIENTS FOR CANONICAL VARIABLES FOR SECOND SET OF VARIABLES477 CNVRS1^CNVRS2^CNVRS3^CNVRS4^CNVRSS CNVRS6 CNVRS7 CNVRSI1478 1 2 3 4 5 6 78479480 X1^11^-0.248645E-01^0.176673E-01^-0.479708E-01^0.144355E-01^0.701834E-02 0.299714E-02.-0.430244E-01 0.141826E-01481 X2^12^-0.497958^0.996118E-01^0.458377^1.78179^2.18412 -1.38498 -0.432249 -0.177180482 X3 13^-0.553802 0.402720E-01^0.329216 -0.619149 -0.380305 0.551678 0.181181 1.82227483 X4^14^-0.600340E-01^-0.997042E-02^0.331188E-01^-0.257982E-01^0.630947E-01 -0.826368E-01 0.174540 -0.837270E-01484 X5^15^-0.101621^0.492647E-01^0.129813^-0.142884^0.232188 -0.230808 -0.345933 0.202716485 18 18^0.476452E-01^-0.122634^-0.100158 0.503975 -0.338179 -0.394124 0.100398 0.504011E-01488 X7^17^-0.155817E-02^0.191802E-01^-0.822020E-01^0.372740E-02^0.151817E-01 -0.214843E-02 0.487461E-01 0.318045E-02487 X8^18^-0.894835E-02^-0.388778E-01^0.744352E-01^0.260708E-01^-0.110774E-02 0.850929E-02 0.107359E-01 0.278015E-01488 X9^19^0.1291382E-01^-0.300908E-01^0.209518E-01^0.201020E-01^0.176198 0.305801 0.448016E-01 -0.972885E-01489 X10^20^0.217469^0.353472E-01^0.347348E-01^-0.237330E-01^0.161002 0.571983E-01 -0.521883E-01 0.827245E-01490 21^0.215230E-01^-0.184288^-0.245235E-01^-0.418821E-01^0.640329E-01 -0.138058E-01 -0.569757E-02 0.452093E-01491492493494 CNVRS9^CNVRSIO495 9 10498497 X1^11^-0.826634E-01^-0.321857E-01498 X2 12^0.409156^1.46151499 X3^13^0.802200 -0.893465500 X4 14^0.529470E-02^-0.984943E-01501 15^15^0.416778^-0.154048502 86 16^0.147938 -0.250227503 17^17^0.523986E-01^0.143288E-01504 18 18^-0.472901E-01^0.980702E-02505 X9^19^0.958769E-01^-0.103951508 110 20^-0.958499E-01^-0.606277E-01507 X11^21^-0.175840E-01^-0.269248E-02508509510511 STANDARDIZED COEFFICIENTS FOR CANONICAL VARIABLES FOR SECOND SET OF VARIABLES512513 (THESE ARE THE COEFFICIENTS FOR THE STANDARDIZED VARIABLES -514 MEAN ZERO, STANDARD DEVIATION ONE.)515518517ListIr^ f518519520-A^at^20:44:24^on FEB^12,^1992 for CCId=KENS on GCNVRSI^CNVRS2^CNVRS3^CNVRS41 2 3 4CNVRS5^CNVRSB^CNVRS7^CNVRS85 8 7 8CNVRS99CNVRSIO10521 XI 11 -0.280^0.199^-0.540 0.162 0.079 0.034 -0.484^0.160 -0.705 -0.362522 X2 12 -0.143^0.029^0.132 0.507 0.628 -0.398 -0.124^-0.051 0.118 0.420523 X3 13 -0.224^0.018^0.133 -0.250 -0.154 0.223 0.085^0.737 0.324 -0.381524 X4 14 -0.254^-0.042^0.140 -0.109 0.267 -0.391 0.738^-0.354 0.022 -0.416525 X5 15 -0.149^0.072^0.190 -0.210 0.341 -0.339 -0.507^0.297 0.611 -0.226528 X8 18 0.070^-0.179^-0.148 0.737 -0.494 -0.576 0.147^0.074 0.216 -0.368527 X7 17 -0.024^0.291^-0.943 0.057 0.230 -0.033 0.709^0.048 0.795 0.217528 X8 18 -0.121^-0.528^1.010 0.354 -0.015 0.115 0.148^0.377 -0.842 0.133529 X9 19 0.039^-0.090^0.063 0.060 0.528 0.916 0.134^-0.291 0.287 -0.311530 X10 20 0.817^0.133^0.131 -0.089 0.605 0.215 -0.196^0.238 -0.360 -0.228531 XII 21 0.123^-0.941^-0.140 -0.240 0.387 -0.078 -0.033^0.259 -0.101 -0.015532 1PAGE^12 BMOPBM CA ANALYSIS1533534535 CANONICAL VARIABLE SCORES536537538 CASE539 LABEL NO. WEIGHT^CNVRFI CNVRF2 CNVRF3 CNVRF4 CNVRFS CNVRF8 CNVRF7 CNVRF8 CNVRF9540 CNVRS10541 CNVRS10542543 1^1.000^-0.539 -2.099 1.377 0.063 -1.487 1.130 2.282 -1.720 ,^0.288544 -1.374545 -1.374548 2^1.000^1.035 2.187 0.948 -1.715 -0.832 -0.572 0.302 1.183 0.742547 -0.715548 -0.715549 3^1.000^-0.514 -0.438 -1.533 0.834 -1.288 0.965 1.639 -0.695 -0.280550 -0.238551 -0.238552 4^1.000^-0.132 0.225 2.445 0.888 0.457 1.586 -0.718 -1.381 I.898553 -0.775554 -0.775555 5^1.000^-1.242 -2.827 1.784 1.340 1.951 -0.978 1.487 -0.086 0.959558 1.388557 1.368558 8^1.000^-0.335 -0.250 0.744 -1.046 0.029 -0.305 -1.256 0.730 0.519559 0.544560 0.544581 7^1.000^1.465 1.585 -0.458 -0.805 -1.069 -0.141 1.044 0.226 I.588582 1.138583 1.138564 8^1.000^0.770 -0.523 0.692 1.484 -0.651 -0.785 -1.802 -1.179 -2.072585 0.183568 0.163Listing of567-A^at^20:44:24 on FEB^12.^19929^1.000for CCid=KENS on G1.519^-1.772 1.009 -0.829 -1.942 1.162 0.269 -2.321 3.294568 0.157569 0.157 -0570 10 1.000 -0.941 -1.171 -0.979 0.141 0.034 -0.241 1.412 -0.649.732571 -1.038572573 11-1.0381.000 0.121 0.700 0.379 0.808 0.031 0.488 -0.139 0.126 0.441574 -0.117575576 12-0.1171.000 -0.800 -0.352 -1.589 -0.827 -0.449 -0.238 0.077 0.495 1.059577 0.868578579 '^130.8681.000 0.575 -0.230 0.710 0.145 -0.798 -0.251 -0.663 -1.238 -1.830580 1.687581 1.687 -0582 14 1.000 0.689 -1.090 0.607 -0.098 -1.929 0.823 0.822 -0.783.558583 1.030584 1.030 0585 15 1.000 0.169 -0.011 1.193 1.408 -1.141 0.968 0.302 1.483.800588 0.839587 0.839 -0588 18 1.000 -0.377 0.201 -0.259 1.843 -0.497 0.435 2.518 0.783.074589 -0.174590 -0.174 -0591 17 1.000 -0.236 1.583 -0.948 0.009 0.624 0.241 0.524 -1.199.539592 1.063593594 181.0631.000 1.377 -0.198 -0.103 -0.442 -1.031 -2.327 0.256 -1.001 -2.001595 -1.442596 -1.442 -0597 19 1.000 0.299 1.935 -1.520 -0.468 0.954 0.084 0.649 -2.674.818598 0.015599 0.015 0600 20 1.000 0.330 1.538 0.540 -0.037 0.979 0.044 -0.748 -0.504.?05801 0.063602603 210.0631.000 -0.787 . -0.252 -1.263 0.312 0.157 0.350 -0.030 -0.809 0.263604 -1.874605 -1.974 1608 22 1.000 0.781 1.436 0.415 0.896 2.739 -0.792 -0.901 -0.632.582607 -0.338608 -0.336 0609 23 1.000 -0.323 0.714 -0.128 -0.178 0.879 0.003 -0.808 -0.512.148liatil810611f^-A^at 20:44:24 on FEB^12,1.0981.0981992 for CCid.KENS on G612.49824 1.000 -0.080 -2.351 0.878 2.249 1.187 -1.550 -0.550 0.696 0613 -1.063614 -1.063615.27425 1.000 -0.845 -0.218 0.421 0.043 -0.432 0.824 0.304 -0.554 -0618 0.659617 0.659618.38826 1.000 0.773 0.440 -1.811 1.358 -0.192 -0.204 -0.093 -1.556 0619 0.100620 0.100621 27 1.000 0.062 -0.878 0.205 -2.494 0.148 2.387 -0.448 0.592 0.708622 -0.278623 -0.278824.17028 1.000 -0.393 -0.489 -0.550 0.002 -0.084 0.303 -0.302 -0.838 0825 -1.491628 -1.491827.15429 1.000 1.819 -1.420 -0.215 2.137 -2.077 -1.695 -2.209 0.324 -0828 -0.668629 -0.668630.32630 1.000 0.542 1.099 0.328 -1.182 -0.121 -0.148 0.032 -0.708 -0831 -0.809632 -0.809633 31 1.000 0.135 -0.594 -2.546 -1.032 0.823 2.083 -0.234 0.889.881834 -0.198835 -0.198838 32 1.000 -0.912 -0.255 -0.e9e -1.570 -0.629 -0.402 0.089 0.587 0.173837 -0.578638 -0.578639.35233 1.000 -0.378 1.014 0.338 0.176 0.072 0.351 0.128 0.247 -0840 -0.307641 -0.307642 34 1.000 -0.656 -0.128 0.079 -1.418 -0.855 -0.327 -0.100 1.314 0.295843 -0.225644 -0.225845 35 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.186 -0.037 -0.517 -0.824646 0.535847 0.535648 38 1.000 0.954 0.238 0.477 0.587 0.089 -0.839 3.094 -0.204 -0.270849 0.445650 0.445651 37 1.000 1.801 -0.368 -1.591 0.288 -0.055 -1.574 0.315 -1.363 1.521852 0.419653 0.419Listing of -A^at^20:44:24 on FEB^12,^1992 for CCid.-KENS on G654 38 1.000 -0.120 1.141 1.314 0.330 -0.153 0.426 -0.042 0.974 -0.230655 1.054658 1.054657 39 1.000 2.837 -0.449 -1.177 0.274 3.098 2.587 0.547 0.881 -0.642858 -0.082659 -0.082660 40 1.000 -0.512 -0.785 1.509 1.288 -1.088 2.139 0.891 -1.478 1.098661 0.527862 0.527663 41 1.000 -0.781 1.181 -0.528 -0.083 -0.010 0.104 0.452 0.828 0.139664 -0.018665 -0.018666 42 1.000 0.777 0.428 1.102 0.504 1.013 -0.934 2.203 -0.687 -0.840687 -0.742668 -0.742669 43 1.000 -1.029 0.189 -0.328 0.034 -0.028 0.309 0.087 0.239 -0.409670 -2.302671 -2.302672 44 1.000 -0.238 0.869 0.078 -0.289 0.077 0.228 0.023 -0.509 -0.587673 0.286674 0.288875 45 1.000 -0.842 -0.642 -0.886 -0.931 -1.589 -1.005 -0.187 1.198 -0.386678 0.982877 0.982678 48 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.186 -0.037 -0.517 -0.824879 -0.070680 -0.070681 47 1.000 -0.675 -0.110 -0.791 -0.320 0.779 -0.039 -0.888 -0.520 0.091882 0.250683 0.250884 48 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.624685 -0.052686 -0.052687 49 1.000 0.969 1.231 1.027 2.068 0.276 -0.025 -1.218 0.191 0.258688 -0.511689 -0.511890 50 1.000 -0A890 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.624891 -0.352692 -0.352693 1PAGE^13^BM0P6M CA ANALYSIS1694695698 CANONICAL VARIABLE SCORES697898L1atIn -A at 20:44:24 on FEB^12,^1992 for CC1d=KENS on G699 CASE700 LABEL NO. WEIGHT CNVRF1 CNVRF2 CNVRF3 CNVRF4 CNVRF5 CNVRFB CNVI1F7 CNVRFB CNVRF9701 CNVRSIO702 CNVRS10703704 51 1.000 -0.813 -0.477 -0.538 -2.340 -0.351 - 0.969 -0.961 1.107 1.193705 -0.829708 -0.829707 52 1.000 -1.187 0.534 -0.065 0.499 -0.033 0.432 0.170 0.995 -0.195708 -1.287709 -1.287710 53 1.000 0.484 -0.752 -0.205 1.330 -0.150 0.659 -0.728 -0.123 1.972711 0.338712 0.338713 54 1.000 1.514 1.788 -0.667 0.292 -1.895 0.371 1.986 0.894 1.972714 0.410715 0.410716 55 1.000 0.274 0.924 2.027 0.019 -0.374 0.379 -0.315 0.944 -0.323717 -2.439718 -2.439719 58 1.000 -0.205 1.186 1.113 0.440 0.848 0.201 -0.873 0.971 0.484720 -1.361721 -1.361722 57 1.000 0.317 -0.454 1.197 -1.898 1.404 -3.023 2.135 1.035 - 0.097723 -0.149724 -0.149725 58 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.824726 -0.399727 -0.399728 59 1.000 -0.378 0.100 1.361 -0.122 -0.474 0.338 -0.375 0.936 -0.380729 1.370730 1.370731 80 1.000 0.977 -0.330 1.305 -0.345 -0.320 -1.321 0.061 -0.718 -1.854732 -0.351733 -0.351734 81 1.000 1.311 1.478 0.423 -2.644 -0.822 -0.818 0.094 -0.350 0.314735 -0.045736 -0.045737 82 1.000 3.956 -2.344 -1.844 -0.595 2.341 2.134 0.435 3.450 -2.305738 0.749739 0.749740 83 1.000 -0.281 -0.512 -0.256 1.105 -1.028 -0.177 -0.454 0.729 -0.174741 0.378742 0.378Listing of -A^at 20:44:24 on FEB^12,^1992 for CCid=KENS on G743 64 1.000 -1.124 0.184 -0.682 -0.425 0.471 -0.210 -0.711 0.788 0.703744 0.079745 0.079746 85 1.000 -0.e90 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.824747 0.928748 0.928749 68 1.000 -0.447 -0.935 -0.883 0.388 -1.444 -1.539 -1.326 1.254 -0.827750 0.058751 0.058752 67 1.000 -0.816 -0.080 -0.488 0.576 0.733 0.201 -1.029 0.116 1.099753 1.190754 1.190755 88 1.000 0.072 0.497 0.588 -0.489 0.858 -0.045 -1.081 -0.541 0.055758 0.986757 0.986758 88 1.000 0.472 1.545 0.987 1.836 0.317 -0.141 -0.953 0.311 -0.538759 0.457760 0.457781 70 1.000 0.394 -0.252 -0.889 1.162 -1.965 -1.108 -0.428 1.173 -0.399782 0.439763 0.439764 71 1.000 -0.895 -0.448 -0.808 0.889 0.122 -0.347 -1.177 -0.461 -0.688785 0.839768 0.639767 72 1.000 -0.840 0.048 -0.799 0.668 -0.649 0.699 0.905 0.150 -0.238768 -0.709769 -0.709770 73 1.000 0.870 -0.778 0.824 -0.895 -0.337 1.769 -1.053 1.077 -2.218771 -1.600772 -1.800773 74 1.000 -1.389 -1.903 -0.425 -0.552 1.335 -1.448 1.185 -0.803 -1.183774 -0.311775 -0.311776 75 1.000 0.926 0.832 0.886 -2.062 -0.645 -0.489 -0.188 -0.183 -0.020777 1.138778 1.138779 78 1.000 -0.013 -1.888 0.557 -0.460 1.778 -1.059 -1.898 -0.744 1.134780 -0.283781 -0.263782 77 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.624783 1.593784 1.593785 78 1.000 -0.358 -0.455 0.233 -0.720 1.940 -0.584 73.075 -0.583 1.335Listin786787-A^at 20:44:24 on FEB^12,^1992-0.023-0.023for CC1d=KENS on G788 79 1.000 0.303 -1.763 2.354 -0.861 1.802 2.020 1.069 -0.627 "^-0.242789 -0.054790 -0.054791 80 1.000 -1.305 0.879 0.198 0.983 -0.037 0.555 0.274 1.751 0.019792 -2.127793 -2.127794 81 1.000 -1.167 0.534 -0.065 0.499 -0.033 0.432 0.170 0.995 -0.195795 1.126796 1.126797 82 1.000 -0.909 1.575 -0.112 0.951 0.288 0.521 0.503 1.032 -0.048798 -0.093799 -0.093800 83 1.000 -0.773 0.317 0.848 0.188 -0.253 0.385 -0.102 0.968 -0.287801 -2.149802 -2.149803 84 1.000 0.293 -0.808 1.549 -1.362 -0.688 0.044 -0.855 -0.605 -0.901804 2.152805 2.152806 85 1.000 0.432 -0.902 -0.665 -0.379 -1.059 -0.223 -0.569 1.533 3.187807 0.146808 0.146en88 1.000 -0.118 2.055 0.291 0.828 0.393 0.440 0.459 0.284 -0.203810 0.568811 0.568812 87 1.000 -0.634 -0.028 0.385 -0.276 -0.249 0.262 -0.206 0.210 -0.502813 0.206814 0.206815 88 1.000 -1.187 0.534 -0.065 0.499 -0.033 0.432 0.170 0.995 -0.195816 2.450817 2.450818 89 1.000 0.837 0.082 1.297 -0.829 0.089 -2.514 1.215 1.136 -0.774819 -0.487820 -0.487821 90 1.000 1.585 1.187 -0.053 -0.657 0.471 0.108 -0.182 -2.853 -0.210822 -0.934823 -0.934824 91 1.000 -1.029 0.189 -0.328 0.034 -0.028 0.309 0.067 0.239 -0.409825 -0.498828 -0.498827 92 1.000 -0.890 -0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.624828 -0.509829 -0.509Listing of -A at 20:44:24 on FEB^12.^1992 for CCid=KENS on G830 93 1.000^0.018^-0.117 2.074 -0.433 -0.895 0.290 -0.648 0.907 -0.472831 0.438832 0.438833 94 1.000^-0.556^1.105 -0.357 2.198 1.438 -0.864 0.203 0.327 -0.832834 2.063835 2.063838 95 1.000^3.430^-0.384 -1.784 1.228 -1.259 0.489 -1.179 -0.365 -0.778837 -0.238838 -0.236839 98 1.000^-0.890^-0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517.824840 0.256841 0.258842 97 1.000^0.366^-0.308 -1.654 0.801 -0.821 -0.464 -0.269 -0.285 0.831843 -1.642844 -1.642845 98 1.000^-0.579^0.588 -1.101 -0.332 1.104 -0.073 -0.839 -1.239 0.026848 -1.608847 -1.608848 99 1.000^-0A890^-0.155 -0.590 -0.430 -0.023 0.188 -0.037 -0.517 -0.624849 2.274850 2.274851 100 1.000^0.827^-0.745 -0.821 0.329 1.945 -2.531 2.158 0.781 2.581852 0..p1853 0.311854855 NUMERICAL CONSISTENCY CHECK858057858 THE FOLLOWING VARIANCES OF CANONICAL VARIABLES SHOULD ALL BE EOUAL TO ONE859660 CANONICAL VARIABLE VARIANCE^RELATIVE ERROR861862863 CNVRF1 0.100000E+01^-0.488498E-14864 CNVRF2 0.100000E+01^-0.777156E-14865 CNVRF3 0.100000E+01^-0.777156E-14866 CNVRF4 0.100000E+01^-0.333067E-14867 CNVRF5 0.100000E+01^-0.399680E-14868 CNVRF8 0.100000E+01^-0.577316E-14869 CNVRF7 0.100000E+01^-0.577316E-14870 CNVRF8 0.100000E+01^-0.333067E-14871 CNVRF9 0.100000E+01 0.874301E-15872 CNVRF10 0.100000E+01^-0.288658E-14873 CNVRSI 0.100000E+01^-0.466294E-14874 CNVRS2 0.100000E+01^-0.643929E-14875 CNVRS3 0.100000E+01^-0.444089E-14878 CNVRS4 0.100000E+01^-0.199840E-14877 CNVRS5 0.100000E+01^-0.399680E-14878 CNVRS6 0.100000E+01^-0.310862E-14879 CNVRS7 0.100000E+01^-0.244249E-14Usti!^-A at 20:44:24 on FEB 12. 1992 for CCid=KENS on 0CNVRS8^0.100000E+01^0.549560E-14CNVRS9^0.100000E+01 0.000000E+00CNVRSIO^0.100000E+01^0.299066E-131PAGE 14 BM0P6M CA ANALYSIS1CANONICAL VARIABLE LOADINGS(CORRELATIONS OF CANONICAL VARIABLES WITH ORIGINAL VARIABLES)FOR FIRST SET OF VARIABLES892893894895^CNVRFI^CNVRF2^,NVRF3^CNVRF4^CNVRF5^CNVRF61 2 3 4 5 8CNVRF7 '7CNVRF88CNVRF99CNVRFIO10ego Y1^1^0.488^-0.348^-0.172^0.305^0.299^-0.021 -0.347 -0.000 0.558 -0.049897 Y2 2^0.585^-0.311^-0.321^0.335^-0.078^0.100 0.248 0.044 0.459 0.235898 Y3^3^0.545^-0.300^-0.200^-0.129^0.351^0.495 0.013 0.329 -0.240 -0.150899 Y4 4^0.574^-0.265^-0.205^0.344^-0.172^-0.215 -0.287 0.189 -0.380 -0.329900 Y5^5^0.775^0.014^0.404^0.000^0.018^-0.248 -0.053 0.090 0.200 0.351901 Y6 6^0.70^0.429^0.009^-0.000^0.225^-0.285 0.297 -0.102 -0.006 -0.168902 Y7^7^11.497^-0.121^-0.219^-0.328^-0.353^-0.398 0.098 0.288 0.283 -0.359903 TO 8^0.17,8^0.325^0.509^0.298^0.319^0.080 0.368 0.487 0.142 -0.118904 TO^9^0.284^-0.434^0.141^0.062^0.550^-0.324 0.521 0.142 -0.053 -0.070905 Y10 10^-0:018^-0.372^0.433^-0.010^-0.042^0.394 0.198 -0.314 0.305 -0.535908907908909 SQUARED MULTIPLE CORRELATIONS OF EACH VARIABLE IN THE910 FIRST SET WITH ALL VARIABLES IN THE SECOND SET.911912913 ADJUSTED^F^DEGREES OF914 VARIABLE^R-SQUARED R-SOUARED STATISTIC^FREEDOM^P-VALUE915918 1^Y1^0.200123^0.100139^2.00 1^88^0.0425917 2 Y2 0.251345^0.157763 2.69 1^88^0.0064918 3 Y3^0.222132^0.124899^2.28 1^88^0.0197919 4^Y4 0.229454^0.133138 2.38 1^88^0.0150920 5 Y5^0.335520^0.252460^4.04 1^88^0.0001921 6 Y6 0.344820^0.262923 4.21 1^88 .0.0001922 7^Y7^167169965^0.088733^1.88 1^88^0.0591923 8 TO 0.160209^0.055235 1.53 1^88^0.1435924 9 TO^0.171254^0.067660^1.65 1^88^0.1049925 10 Y10 0.106786^-0.004868 0.98 1^68^0.4869928 1PAGE^15^BM0P8M CA ANALYSIS1927928929 CANONICAL VARIABLE LOADINGS930931 (CORRELATIONS OF CANONICAL VARIABLES WITH ORIGINAL VARIABLES)932 FOR SECOND SET OF VARIABLES933934935936 CNVRS1^CNVRS2^CNVRS3^CNVRS4^CNVRSS^CNVRS6 CNVRS7 CNVRS8 CNVRS9 CNVRSIO937 1 2 3 4 5 8 7 8 9 10880881882883884885886887888889890eelListing of -A at 20:44:24 on FEB^12,^1992 for CCid=XENS on G938939 XI^11^-0.491^0.182^-0.423^0.175^0.237^0.043 -0.168 0.203 -0.514 -0.310940 X2 12^-0,.228^-0.075^0.094^0.468^0.424^-0.134 -0.091 -0.019 0.202 0.376941 03^13^-0.213^-0.025^0.104^-0.041^-0.127^0.181 0.204 0.815 0.268 -0.159942 X4 14^-0.228^0.094^0.185^-0.283^0.287^-0.393 0.499 -0.280 -0.188 -0.488943 X5^15^-0.162^0.228^0.184^-0.178^0.293^-0.281 -0.429 0.133 0.236 -0.283944 86 16^0.332^-0.241^-0.082^0.882^-0.285^-0.147 -0.052 -0.024 0.244 -0.473945 X7^17^-0.014^0.284^-0.369^0.103^0.237^0.048 0.443 0.494 -0.054 0.253946 X8 18^-0.195^0.135^0.270^0.247^0.144^0.111 0.292 0.488 -0.363 0.148947 89^19^-0.137^-0.216^0.040^0.388^0.194^0.832 -0.121 -0.349 0.288 -0.356948 X10 20^0.798^0.306^0.067^0.008^0.286^-0.118 0.137 0.279 -0.175 -0.205949950XII^21^0.051^-0.892^-0.328^-0.219^0.139^-0.092 -0.076 0.031 0.017 0.033951952953 SQUARED MULTIPLE CORRELATIONS OF EACH VARIABLE IN 7HE954 SECOND SET WITH ALL VARIABLES IN THE FIRST SET.955956957 ADJUSTED^F^DEGREES OF958 VARIABLE^R-SQUARED R-SQUARED STATISTIC^FREEDOM^P-VALUE959960 11^X1^0.191498^0.100856^2.11^10^89^0.0318961 12^X2 0.104202^0.003551 1.04^10^89^0.4210962 13 X3^0.040155^-0.067692^0.37^10^69^0.9555963 14^14 0.087646^-0.014868 0.85^10^89^0.5778964 15 X5^0.071904^-0.032378^0.69^10^89^0.7318955 16^X8 0.178051^0.085897 1.93^10^89^0.0515968 17^X7^0.085925^-0.018780^0.84^10^89^0.5948967 18^X8 0.069130^-0.035463 0.66^10^89^0.7574968 19^X9^0.090021^-0.012224^0.88^10^89^0.5544969 20^X10 0.354181^0.281617 4.88^10^89^0.0000970 21^XII^15:n4850^0.215819^3.72^10^89^0.0003971972973974 AVERAGE^AV.^SQ.^AVERAGE^AV.^SO.975 SQUARED^LOADING^SQUARED^LOADING978 LOADING TIMES^LOADING TIMES977 FOR EACH^SQUARED^FOR EACH^SQUARED978 CANONICAL^CANON. CANONICAL^CANON.^SQUARED979 CANON.^VARIABLE^CORREL.^VARIABLE^CORREL. CANON.980 VAR.^(1ST SET)^(1ST SET)^(2ND SET)^(2ND SET)^CORREL.981982 1^0.27209^0.13120^0.11116^0.05360^0.48219983 2^0.10081^0.03206^0.10840^0.03448^0.31804984 3^0.08931^0.02258^0.05323^0.01346^0.25289985 4^0.05403^0.01118^0.09744^0.02018^0.20691986 5^0.08304^0.01281^0.06515^0.01005^0.15433987 6^0.08700^0.00539^0.06759^0.00419^0.06198988 7^0.08109^0.00387^0.07588^0.00362^0.04775989 8^0.05948^0.00124^0.10807^0.00225^0.02082990 9^0.09581^0.00068^0.07053^0.00049^0.00693991 10^0.07738^0.00018^0.09650^0.00020^0.00209992993 THE AVERAGE SQUARED LOADING TIMES THE SQUARED CANONICAL994 CORRELATION IS THE AVERAGE SQUARED CORRELATION OF A995 VARIABLE IN ONE SET WITH THE CANONICAL VARIABLE FROM


Citation Scheme:


Usage Statistics

Country Views Downloads
United States 5 0
Hong Kong 2 0
Tanzania 2 0
India 2 0
South Africa 1 0
Malaysia 1 1
Japan 1 0
Ukraine 1 0
City Views Downloads
Unknown 7 22
Ashburn 5 0
Central District 2 0
Mumbai 1 0

{[{ mDataHeader[type] }]} {[{ month[type] }]} {[{ tData[type] }]}
Download Stats



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items