AN ANALYSIS OF wen AND AVERAGE MILK» PRODUCUON DAIRY FARMS , Thesis for the Degree of Ph. D. MICHIGAN STATE UNWERSITY' , ‘ RUSSEL WENTON ERICKSQN‘ , 19:12 “ This is to certify that the thesis entitled AN ANALYSIS OF HIGH AND AVERAGE MILK PRODUCTION DAIRY FARMS presented by RUSSEL WINTON ERICKSON has been accepted towards fulfillment of the requirements for Ph-D- degree mmflflce fl 2% Major professor Date November 7, 1972 0-7639 ABSTRACT AN ANALYSIS OF HIGH AND AVERAGE MILK PRODUCTION DAIRY FARMS By Russel Winton Erickson Average pounds of milk per cow has been increasing in Michigan over the last decade. There has also been a steady increase in the average pounds of milk per cow for those herds enrolled in the Dairy Herd Improvement Program. The average production of all herds on official test in this pro- gram is 12.973 pounds of milk. However. within this group of herds on test there is a wide variation in average produc- tion. Some herds produce less than 9,000 pounds of milk per cow and others produce in excess of 19,000 pounds of milk. The basic question, which this survey of 80 dairy farms attempted to answer, was what are the differences in management practices and other variables between average pro- duction herds and top production herds when paired according to herd size and County. The specific objectives of the study were to examine certain management factors known to influence milk production and to try to identify some unknown factors such as motivation. Russel Winton Erickson A personal survey was conducted which included the top no herds based on average pounds of milk per cow and their pairmate which had approximately average DHI produc- tion. The questionnaire was divided into six sections. demographic data. feeding, breeding, reproduction and herd health. calves and young stock, and motivation. The data were analyzed using a two sample T-test to determine signi- ficant differences between strata. The data were then sub- jected to a multiple regression. by least squares procedures. to circumvent the problem of univariate analysis. since most of the factors involved are essentially multivariate. The procedure of least squares allows one to make specific in- dependent tests of significance on the direct effect of the various factors. however. this also permits one to ascertain which combination of variables is the most reasonable predic- tor of the dependent variable. The owners of high producing herds when compared to average producing herds would: 1. Not use breeding and dry dates on herd report 2. Be less likely to be a h-H leader 3. Be less active in lodges and other non-related farm activities a. Be more likely to have a will 5. Be less likely to rent more land in the future 6. Be less likely to have his children substitute for milking and chores 7. Be more inclined to make a county milk record with a little extra effort 8. Have a dry cow mastitis treatment 9. 10. ll. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. Russel Winton Erickson Vaccinate his calves for P13. IBR and BV D Have more of his cows identified by sire number. Have a higher percentage of his cows registered Milk earlier in the morning in the summer Have higher producing cows Have higher low producing cows Would have heavier cows Have a higher single milk weight Feeds more pounds of grain to average cows Have a lower grade protein percent in the hay Feed more grain to his top cow Have more cows with milk fever and a higher veterinary bill Have higher milk production goals. higher percent drop in milk, higher value of product, higher number of cows with sires who have a predicted difference. Have a higher predicted difference of the sires of the cows in his herd This research project has shown there are signifi- cant differences between high producing herds and average production herds of about the same size. located in the same.county. It must be impressed upon the dairymen that some of the decisions he makes today will have there effect in six or seven years. This is evident in the difference between the two strata regarding the predicted difference for milk of the sires of the cows in the herd, these decisions were made at a minimum of four years ago. Russel Winton Erickson Time spent using management tools such as the DHI report appear to increase milk production per cow. Every dairyman is an individual. with certain habits. personal preferences. drives. capabilities. and certain ideas. The dairymen's routine is not always the same from day to day. therefore. it is difficult to estab- lish patterns of behavior. The area of motivation is a very difficult subject on which to question a dairyman, because he may give you what is a sociably acceptable answer to a question rather than his true feeling. There are many other variables and factors which were not included in this survey which may have an influ- ence on high milk production. AN ANALYSIS OF HIGH AND AVERAGE MILK PRODUCTION DAIRY FARMS By Russel Winton Erickson A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Dairy Science 1972 ACKNOWLEDGEMENTS I wish to express my gratitude to Dr. Clinton E. Meadows for his guidance and encouragement during the preparation of this thesis. The valuable assistance provided by Drs. Peter Spike, John L. Gill. John Speicher. William Thomas and Stephen B. harsh. both in and out of the classroom. during the period of my graduate study is also appreciated. I would also like to acknowledge with thanks the encouragement offered by Dr. Clarence L. Cole. Appreciation is expressed to the Department of Dairy Science for the financial support during the period of my graduate study. I am greatly appreciative to my family for the con- stant support, understanding and encouragement; especially to my wife. Geraldine and my children Lori Jo and Jeffrey Alan. without whose help this would not have been possible. ii TABLE OF CONTENTS INTRODUCTION . . . . . . . . . . . . . . . REVIEW OF LITERATURE . . . . . . . . . . . METHODS Genetic variations 0 O O O O O O O Non-Genetic Variations . . . . . . Impact of Artificial Insemination Industry . . . . . . . . . . . . Herd Management . . . . . . . . . Nutritive Requirements . . . .l. . Health . . . . . . . . . . . . . . Milking Systems . . . . . . . . . Housing Systems . . . . . . . . . Feeding Systems . . . . . . . . . Manure Handling Systems . . . . . Farm Management . . . . . . . . . Motivation . . . . . . . . . . . . AND PROCEDURES . . . . . . . . . . Sample Size and Sampling Frame . . The Questionnaire . . . . . . . . Interview Procedure . . . . . . . Method of Analysis . . . . . . . . Data 0 O O O O O O O O O O O O O 0 iii 12 13 15 l6 17 18 18 22 2h 25 25 26 27 29 TABLE OF CONTENTS (Cont'd). . . RESULTS AND DISCUSSION . . . . . Demographic Data . . . . Feeding 0 O O I O O O 0 Breeding . . . . . . . . Reproduction and Herd Health Calves and Young Stock . Supplemental Information Motivation . . . . . . . Multiple Correlation . . SUMMARY . . . . . . . . . . . . BIBLIOGRAPHY . . . . . . . . . . APPENDIX . . . . . . . . . . . . iv 5. 6. 7. 9. 10. 11. 12. 13. 1h. 15. LIST OF TABLES Time Spent Studying the Dairy Herd Improvement Report . . . . . . . . . . . Rank of Selected Variables on Dairy Herd Improvement Raport o o o o o o o o o o 0 Percentage Use of Selected Variables on DHI Report 0 O O O O O O O O O O O O O 0 Percentage of Farmers in Each Strata that were Leaders in “'H o o o o o o o 0 Percent Readership of Selected Magazine for each Strata . . . . . . . . . . . . Percentage of Respondents in each Strata that Participate in off-the-farm ACtiVitiESooooooococoon.- What Would Happen to the Farm at Retire- ment or if some Catastrophe Happened? . Extent of Wives' Participation in Farm ACtiVitieSooooooooooooooo Do you Plan to Rent More Land in Future? Type of Dairy Cattle Housing . . . . . . Percent of each Stratum who Utilize Different Methods of Milk Movement From Milking Area to Milk House . . . . ... . Percent of Cows Registered . . . . . . . Starting Time of Milking . . . . . . . . Vacuum Line Size and Percent of Respondents withthatSize............. Average Bacteria Count for Dairymen in eaChStratumooo00000000000 Page 32 33 34 35 36 37 39 39 #2 43 #3 an 1+5 #6 b6 LIST OF TABLES (Cont'd). . . Table 16. l7. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. The Percentage of Dairymen in each Stratum who would Obtain Different Substitutes for Milking and Chores . . . . . M8 Ranking of Items used to Cull Cows by Farmers in each Stratum . . . . . . . . . . #9 Percentage of Each Strata Who Would Make a Record with a Little Effort . . . . . . . 52 Pounds of Concentrates to Average Cow per Day by Stratum . . . . . . . . . . . . . 56 Average Pound of Concentrates Fed to Top Cow in Each Stratum . . . . . . . . . . 58 Rank of Dairymen's Criteria for Selection of Sires . . . . . . . . . . . . . 60 Average Number of Cows in Each Stratum with Milk Fever 0 O O O O O O O O O O O O 0 6“ Average Cost of Veterinary Services by Stratum O O O O O O O O O O O O O O O O O O 65 Percent of Dairymen in Each Stratum Who Use Dry Cow Mastitis Treatment . . . . . . . 67 Percent of Dairymen in Each Stratum Who Vaccinate Calves for IBR-PI3-BVD . . . . . . 68 Average Number 0. Jw‘h null Per Herd by Stratum o o o o o z u o o o o o o o o o o o 69 Average Percent Days in Milk . . . . . . . 70 Percent Distribution by Stratum of Personnel Who Feed Calves . . . . . . . . . 71 Percentage of Dairymen in Each Stratum Who Indicate the Calves are Weaned by the Various Factors . . . . . . . . . . . . 72 Percent of Cows in Each Stratum with Sire Numbers Reported . . . . . . . . . . . 75 Percent of Cows in Each Stratum Whose Sire had Predicted Differences Information .75 vi LIST OF TABLES (Cont'd). . . Table 32. 33. 3“- Average Predicted Difference of Sires of Cows in Herd at Present Time . . . . . . . 76 Percentage of the Most Enjoyed Activity byStratumoooooooooo000000078 Significant Variable in Multiple Regression Equation with Regression Coefficients and Partial Correlation Coefficients . . . . . . . . . . . . . . . . 8O vii Table 2. 6. 7. 8. APPENDIX LIST OF TABLES Herd pairs . . . . . . . . . . . . An analysis of high and average milk production dairy farms. personal interview questionnaire . . . . . . . . . . Card format . . . . . . . . . . . Variable u and 6'. . . . . . . . . Percent positive and negative responses by stratum . . . . . . . . . . . . . Ranking of selected variable by stratum Simple correlations.>'t 0.50 . . . Simple correlation with average pounds of milk per cow and selected variables viii Page 93 95 109 126 132 1&5 146 1&9 INTRODUCTION High milk production per cow depends upon many vari- ables within a herd. According to Michigan Dairy Herd Improvement information and Michigan Telefarm data there is considerable variation among Michigan dairy farmers in the average production per cow. At the present time there are herds that have an average production per cow of 20,000 pounds of milk; yet. the average production of all cows in the Michigan Dairy Herd Improvement program in 1971 was 12.973 pounds of milk. and the average for all dairy cows in Michigan in 1971 was 9,700 pounds of milk. Hoglund and McBride (15) in an analysis of Michigan dairy farms state that as the pounds of milk sold per cow increase. there is a positive increase in the gross value of sales from the farm. Therefore. there is not only a difference between pounds of milk per cow produced, but also in the income generated from this increase in production. There continues to be a variation in production per cow on farms of similar size. similar labor supply. similar location. similar feeds and similar breeds of cows. Some production differences are difficult to detect: others are identifiable. 2 Some of these differences can be assumed to be a function of resources. but even more they may be the result of differences in management abilities and practices. The success of a dairy farmer is determined by his ability to acquire and use his resources to achieve both monetary and non-monetary objectives. Success is usually measured by monetary objectives since these are quantifiable. Since many of the non-monetary goals cannot be measured accurately. success in dairy farm management is equated with maximizing income. Management is a part of production which develops within the lives of men. It is a mental process. a con- centration of desires and of will power. Dairy management is concerned with the decisions that affect the profitability of the dairy business. Management functions when a farmer is (l) observing and conceiving ideas. (2) analyzing and making further observations. (3) making decisions on the basis of analysis. (A) taking action and (5) accepting res- ponsibilities. Obviously. the decisions that are made are reflected in the process of assembling and coordinating the factors of production: land. labor. cattle. equipment, credit and capital. A problem of dairy extension specialists and research personnel alike. is the answer to the farmer ques- tion of "How can I increase the pounds of milk per cow?” This problem stems from a lack of information on the reasons for some dairymen having high milk production and other 3 farmers not being able to obtain these high levels. The objectives of this research proposal are to try to identify the differences between high and average produc- ing dairy herds. A second objective would be to single out these variables and factors which have a significant influence on high milk production. A third objective is to relay to the average and below.average dairymen the information obtained and to possibly motivate them to higher milk production. REVIEW OF LITERATURE Performance of an animal is determined by genetic and environmental circumstances. In the attainment of high milk production per cow there is interplay between heredity and environment. If the only goal is milk produc- tion per cow. the environment should provide an opportunity for each cow to produce up to inherited potential. When the goal is profit. then the environment should be one that maximizes profit. This review will deal with the genetic and manage- ment decisions that influence level of production. Even though environmental variation is not transmitted. this should not lessen the breeders' efforts to provide a favor- able environment that will permit the attainment of the animals' inherited potential. In many herds there must be animals that have a much higher potential performance than the environment permits them to express. This literature review will be restricted to those items thought to be. important and for which information exists about their in- fluence on level of milk production. 5 GENETIC VARIATIONS Variation refers to the observable or measurable differences in individuals for a particular trait. If there were no variation between individuals. there would be no need to select and there would be no progress made through selection. All phenotypic variations in dairy cattle are due to heredity. environment. or the interaction of both. Hereditary variation in a dairy animal is due to differences in the kinds of genes with which the individual begins life. Environmental variations are those factors such as disease. feeding. temperature effects. and all ex- ternal influences which the individual encounters from the time of conception until death. Plowman (31) has stated that genetics accounted for approximately 20 percent of the mean differences between herd averages. Feeding and manage- ment account for the other 80 percent. This does not mean that any two specific herds are only 20 percent different genetically. Expectations are that variation between herds should decrease with the increased use of artificial in- semination and frozen semen. Gaunt (12) has stated that in New York D.H.I.A. Al- sired Holsteins' genetic improvement was 103 lbs. and environmental improvement was 283 lbs. per year for the years 1956 to 1962. There not only exists genetic variations between herds. but also genetic variation within a herd. Most of this variation within a herd is due to low repeatability of 6 records within cows. Johanson and Rendel (17) state that the variation within cows of age-corrected lactation yield of milk or butterfat is about 60 percent of the total variation within a herd. Artificial insemination and progeny testing of bulls has had an impact on the genetic improvement which is poss- ible per year. Robertson and Rendel (32) state that by natural service. selection of cows by culling the poorest. and saving sons from only the best cows the maximum possible genetic improvement per year would be one percent of the mean production. Through the use of artificial insemination and progeny testing it is possible to have a maximum improve- ment per year of 1.69 percent of the mean. This mean in- crease is obtained by making optimum use of artificial in- semination and progeny testing within a population of 2.000 cows. Lush (23) has stated that their value may be too high because improvement possible by using sib-tests and pedigree information other than the records of the dam and paternal granddam with natural service and selection of cows only is omitted from that calculation. The AI industry has the potential of increasing genetic progress per year by intensification of selection. Lush (23) has stated that if one-tenth of one percent of the best bulls are saved. this, would make the progress something like twice that by natural service. Artificial insemination can also change the genetic improvement per year through the use of more accurate progeny tests in two ways. A bull can have more 7 daughters. thereby reducing random errors. Secondly. the bull studs can be sure those daughters are scattered through many herds. thereby reducing environmental correlations. McDaniels (25) holds that if selection is for milk production then the upper limit of genetic progress is approximately 2.3 percent per year. AI can permit special matings to a greater extent than can be done under natural service. thereby intensifying selection of dams of bulls. However. AI can be detrimental to genetic progress. Lush (23) contends that artificial insemination will tend to lengthen the generation interval from sire to son and from sire to daughter. especially if the proven sires are used for a long length of time. Johannson and Rendel (17) found when semen of different bulls was largely distributed at random to herds associated with the bull stud variation in the average geno- type of the herds almost completely disappeared during the course of a few cow generations. However application of different selection intensities among the females in the different herds is possible. but the possibilities of thereby differentiating the herds is relatively small. since the greater portion of the heifer calves born must be reared for herd replacements. The authors go on to write that after an AI unit has been in operation for some ten years the associated herds are to a large extent genetic- ally similar except from random variation. If certain herds are consistently selected to receive the semen from 8 the best of the progeny-tested bulls. then the results can be quite different genetically. If the current estimates of genetic parameters are approximately correct. any breeding system cannot be ex- pected to improve milk production more than two percent a year. Even a much lower rate than this would be highly important in the long run because genetic improvement may be likened to compound interest. McDaniels (25) indicates that during the period 1953 to 1968, the American dairy cow population has been gaining genetically about 0.58 percent of the mean per year. NON-GENETIC VARIATIONS Milk production of cows is influenced by many factors. some of which are randomly distributed among different members of the population. whereas others are systematically distributed and so affect certain groups of individuals more than others. Causes of non-genetic variations are the age of cows. calving interval. and length of dry period. The age of the cow is an important non-genetic variation in yield. Production capacity of cows increases at a declining rate until the body is fully developed at six to eight years of age. After this. the capacity decreases at an increasing rate as aging of the body proceeds. Length of the calving interval as determined by Johannson and Rendel (17) has an influence on milk yield. A shorter calving interval leads to a lower milk yield in 9 both the current and the succeeding lactations; the longer calving interval operates in the opposite direction. Speicher and Meadows (35) in their study of 4,285 Holstein cows indicate a reduction in daily yield of milk by three pounds as calving interval increased from less than 366 days to over 425 days. when cows were grouped by four pro- ductive ability categories. Other factors which affect yield are season of calving. persistency of yield. and management. Johannson (16) states that most of the vari- ation in milk and butterfat yield between herds of the same breed is apparently due to differences in the level of nutrition and management. These changes may be temporary or they may follow a certain trend. Actual yield of a cow is the manifestation of her genotype under a given set of environmental conditions. Under another set of conditions yield may be more or less. Absolute maximum yield which may be obtained when all en- vironmental factors are present in the correct quantities may be rare. IMPACT OF ARTIFICIAL INSEMINATION INDUSTRY One of the management tools available to dairyman is artificial insemination. McDaniel (25) summarized ten different studies on the superiority of cows sired by AI bulls over their naturally-sired herdmates for milk yield and reported a range of from -l73 to +536 with most values positive. McDaniel further reviewed five studies that 10 dealt with the expected daughter superiority from pedigree information on bulls put into AI use. These five studies done in the late 1960's showed an expected daughter superiority ranging from -37 lbs. of milk to a +317. In 1966 only 50 percent of the cows bred were to sires with a plus for daughter's milk. At the same time one cow in nine was bred to bulls that had a minus predicted difference. McDaniel (25) reported over 3.5 million services to Holstein proven bulls. the expected daughter superiority was +257 lbs. of milk and +7 lbs. of fat. Dairymen not now using AI would make initial gains of approximately +500 to +600 lb if he used the best bulls now available to dairymen. Genetic gain possible through AI in five years with selec- tion strictly for milk production in the Holstein breed would be 1.536 1b of milk according to McDaniel. if the increase were 2.3 percent per year. Thus use of top proven bulls available through an AI unit would be a very important management tool. McDaniel (25) reports that there may be ways to increase the pro- duction through AI without using the top proven bulls since the latter may be priced too high for the average dairyman. Substantial improvement can be made through careful selection of sires and dams of young sires. If the sires of the young bulls are +1000 and the dams are in the top one percent of the breed. and if the top ten percent of the young sires are saved. the approximate daughter superiority would be +1925 lbs. If all the young ll bulls were saved. the daughter superiority would be +1179 lbs. of milk. Dickinson (9) has stated that bulls with a range in predicted difference of 0-199 lbs. will increase the dollars change in income over feed cost by $3.74 per cow. However. if the range is +800 to +999 the dollar change in income over feed cost would be $28.85 per lactation. On the other hand. any bull with a negative predicted differ- ence will decrease the dollar change in income over feed cost. The increase in income over feed cost which a dairy- man with a 50-cow milking herd. and which the industry as a whole would receive annually from the use of AI bulls under present conditions and the three suggested levels of selec- tion among these bulls were given by Dickinson and are reproduced below. Increase Received Received by the Level of Selection from a 50 cow Industry as a Milking Herd in Whole (6.500.000 income over feed Cows) __ cost Dollars Dollars Present condition 243 31.5 million Not use bulls below -400 lbs. PD for milk 391 50.8 million Not use bulls with negative PD for milk 641 83.3 million Not use bulls below +400 PD for milk 1090 141.7 million PD = predicted difference 12 HERD MANAGEMENT Bradford and Johnson (5) list five steps in manage- ment: they are: 1. Getting the ideas and making observations. 2. Analysis of observation. including formulation and reformulation of problems and ideas concern- ing their solution. 3. Decision-making 4. Action 5. Acceptance of responsibility for action "Management" may be short range or long range depending on the problems involved and may be a daily job or extend over a long period of time. The animals in a herd at the present time are con- sequences of previous action and past decision-making. However. the environment in which a cow is performing at the present time is a function of the type of management which is now employed. There are many known management factors that must be considered when herds are being analyzed to determine differences in milk production per cow. These include level of feeding. health of animals. conception rates. and milking systems as well as housing and feeding systems. size of operation labor quality, and availability. NUTRITIVE REQUIREMENTS A dairy cow's nutritive requirements must be met. in order to achieve high milk production. The cow must 13 have the proper balance and supply of energy. protein and other nutrients from the feed materials for maximum produc- tion. According to Foley et a1. (10) when feed is restricted. a dairy cow will use the available energy for maintenance and reproduction at the expense of growth and lactation. Cows should have enough protein to adequately supply the necessary amino acids for incorporation into protein that are needed for maintenance. growth. reproduc- tion and lactation. If the energyjprotein and other nutrients are not in the correct amounts. production will be suboptimal. Adequate supplies of water must be provided. Recommendations and requirements for dairy cattle are available from the National Research Council and dairymen are kept informed about feeding rates and problems through research and extension activities. HEALTH Health of a herd is very important in relation to high milk production. and is a function of the management of the herd. There are many health problems that severely reduce milk production. Probably the most common of these is mastitis and the dollar loss from this problem amounts to from $30 to $50 per cow per year (10). Mastitis.reduces milk production per cow and severely affects milk quality. It is one of the most frequent reasons for culling cows and according to Bath and Bratton (3) ranks third behind 14 low production and infertility as reasons for culling. Bath and Bratton (3) report the second most import- ant reason for culling cows from herds is infertility. For maximum production per cow,dairymen would like one calf per cow per year. Unfortunately this is not the case. Anatomical. physiological. pathological, and certain management factors affect the efficiency of reproduction. Management factors which may affect reproduction listed by Foley et al (10) are proper nutrition to eliminate the causes of infertility. breeding cows at the right period in the estrus cycle. observing cows for signs of heat. keeping breeding records. eliminating the chance of infection and the use of artificial insemination. There are many pathological and physiological causes of in- fertility. Some of the pathological causes may be eliminated from a herd through vaccination or cleanliness of the maternity areas. In a study by Gangwar et a1. (11) environ- mental factors such as season. temperature. humidity and light interact to affect reproduction. High temperatures shorten the duration and lower the behavioral expression of estrus in the cow. Thus there is more missed heat in summer than during the other seasons of the year (11). There are also other health and nutritional dis- orders that should be reduced to increase milk production. Two of the most common disorders are acetonemia and milk fever. Incidence of milk fever may be reduced through certain feeding regime. 15 MILKING SYSTEMS Most of the income in a dairy operation is from the sale of milk. Characteristics of good milking according to Foley et a1. (10) include milking at regular intervals. fast. gentle. and complete milking. good sanitation pro- cedures. and efficient use of labor. The authors state that persistent use of these procedures will result in in- creased milk yields. less mastitis. longer life in the herd and. ultimately. more profit per cow estimated. MacLachlan(24) reports that the milking operation requires more labor than any other single chore in the dairy barn. The milking equipment is sometimes badly neglected. MacLachlan (24) states that every milking system should be maintenance checked at least twice a year under a full operating load to assure maximum efficiency. Many times such factors as too many machines. inadequate vacuum, and poor maintenance are the cause of udder problems and reduced milk production. A system to obtain rapid. efficient removal of milk from the udder should be the goal of every milking program because it is the largest single user of labor on the dairy farm. according to Foley et a1. (10) There are many different types of milking systems available. One of the major management decisions is the choice of a milking system. since the initial.investment in either a pipeline system for a stanchion barn or for a milking parlor requires a large amount of capital according 16 to Foley et a1. (10) There are many styles and designs of milking parlors which must be considered--each type has its advantages and disadvantages. However. many of the present dairymen in the Lake States still use a.bucket- type milker and carry the milk by hand. Buxton and Hays (7) in their study of the Lake States found that 90 percent of the herds were housed in conventional barns with 80 per- cent of the Operators using 2 or 3 standard milking machines and carrying the milk by hand to the milk room. HOUSING SYSTEMS The type of housing for a particular farm are in- fluenced by many factors. such as climate. cost. size of herd. personal preference and the condition and layout of present buildings. The principal functions of any housing system for dairy cattle are to provide good working con- ditions for the personnel. to comply with sanitary codes. to provide a healthy. comfortable environment for the dairy animals and to integrate the housing facility with the milk- ing. feeding and manure handling systems. according to Light (22). Most of the housing in the Lake States in 1958 was of the conventional stanchion barns type with only 5 percent being loose housing and less than one percent being a combination. according to Csorba and Butler (8). In Michigan in January 1970. 68.1 percent of the dairy farmers were housing their dairy cattle in stanchion barns. and 17 5.6 percent had stanchion barns with switch-type milking. This would be considered a stanchion.and loose housing or stanchion and free stalls as reported by Hogland and McBride (15). The type of housing is related to the size of the herd. These same authors state that 88 percent of the herds of less than 30 cows were housed in stanchion or stanchion switch barns. This drops to 70 percent when herd size increases from 30 to 49 cows. Open lot and free stalls make up the majority of housing for herds of about 75 cows. Hogland and McBride (15) expect the trends in the future will be toward free stall systems with milking parlors because of the less amount of labor required per cow in these types of operations. FEEDING SYSTEMS The objective of any system of feeding dairy cows is to reduce the hours of manual labor required for feeding and to provide the animals with the proper amount of feed at the right time and place. according to Foley et a1. (10) The system involves the arrangement of the entire facility. types of storage. physical form of the feed materials. and the farmer's preference for feed materials. Hogland and McBride (15) indicate a trend away from grazing and hay to harvesting forages as silage.and haylage. They state that in the future most dairymen will feed largely corn silage and/or a haylage. as these practices lend themselves to highly mechanized harvesting and feeding l8 equipment. They indicate that a highly mechanized system will increase overhead expenses and reduce profit. It should further be noted that mechanical devices do fail. so an alternative method of feeding or auxiliary equipment should be available. The feed-handling system should be an integral part of the total dairy facilities in order to reduce the movement of feed from storage to ultimate con- sumption by the cow. MANURE HANDLING SYSTEM According to Light (21) the method of handling manure depends on the size of the herd. the housing system. available labor. and other management factors. as well as the location of the farm and health and sanitary regula- tions. Buxton and Hays (7) in their study of 567 Michigan dairy farms in 1967 indicate that 43 percent of the farms were hand loading materials on litter-carrier or were loaded directly into the spreader by driving through barns. Forty-eight percent tractor-loaded their manure and 29 per- cent used gutter cleaners. with one percent having liquid -manure. Some farms in the survey had two different types of handling. Presently there is a definite trend toward liquid manure systems as the herds increase in size over 75 cows. FARM MANAGEMENT Nielson (28) with reference to management states: "I believe that a set of variables that may explain a large part of the variation in management outcome is managerial behavior or process-~how the manager carries out the process_ 19 of management." Nielson further states that "we must also give attention to the personal characteristics of the managers. with the characteristics classified under the heading of Drives and Capabilities." Drives include moti- vation and variables which are likely to be related to it such as needs. goals. interest and attitude. Capabilities include such things as basic intelligence and various skills and abilities. In studying managerial performance we may include elements such as demographic information. drives. capabilities and processes for predicting managerial abilities. Travis (39) writes that managerial performance appears to be the result of a complex interaction of vari- ables and cannot generally be explained by means of the correlation of a few items of information about the manager with managerial performance. Boettinger (4) compares managers to artists. and states: "They observe the world. conceive visions of how it can be changed. gather people and resources. develop deployment strategies and inspire their followers to turn their visions into reality." Suter (38) defines a skillful manager as ”one who conducts his business. financial. per- sonal and family affairs with economy. making whatever resources he has available go as far.as possible toward achieving those ends he most desires." In this respect management abilities could be a very important aspect of obtaining high milk production per cow. 20 Willett and Albright (40) stated that maintenance of high production per cow is of prime importance in deter- mining the success of a large dairy operation. Albright et a1. (1) in a study of 440 large commercial dairy farmers. studied seven management factors. After adjusting for the overall size of the dairy operation. milk produc- tion per cow emerges as the most important factor which significantly influences economic performance over a five year period. However Speicher and Lassiter (36) stated "the assumption of a constant relationship between level of production and profits is erroneous." In reference to the 1966 Michigan Telfarm Summary, the range in labor income for milk sold per cow with production at less than 11.500 lbs. of milk was ~$6.318 to $14,911. With 13.500 lbs. of milk sold per cow the labor income ranged from $8,416 to $41,683. Speicher further affirmed that the milk sales per cow is one of the important factors influencing income. but that it is only one of several. In a study of dairy farm income (36) Speicher states that milk production per cow is positively correlated to net income. He also found a linear trend between milk produced per cow and net income. The literature contains many references to milk production per cow and the influence of different sources of variation affecting the milk production. Miller (26) in his analysis of 8,048 Dairy Herd Improvement Associations Holstein herds in 23 states from 1960 to 1964 stated that percent days in milk was a major factor influencing average 21 production. The variation in percent days in milk can be attributed to at least three sources. First of all. there is an automatic correlation with yield level because high producing cows dry off later than low producers. Secondly. differences in the length of time milk is pro- duced are influenced by the level of breeding efficiency. Herds with a low conception rate will tend to have a larger number of average dry days. Finally. dairymen may vary their drying-off policies with respect to the level of pro- duction at which milking is halted.. Miller further states that there is a strong relationship between percent days in milk and the level of production of herds. In analysis of herd average summaries there is an indication of steadily widening margin of return per-cow above feed costs as the herd's average production increases. McDaniel (25) found a correlation of .97 between income over feed cost and milk yield with prices.constant. Speicher (36) found that differences in amounts ofgrain fed per cow were associated with 20.4 percent of the variation in milk pro- duction per cow. Knisley (19) in a study of milking.time in Michigan DHIA herds found that the high milk production group aver- aged one more cow milked per hour than the low production group. and that they had more milk per cow per milking than the low group. Knisley. in looking at manufacturers.of milking machines. found a difference of 2.2 cow per hour between companies. This may be a factor in the number of 22 cows milked per man-hour and the amount of milk produced per man-hour. MOTIVATION Travis (39) in his study of 14 award-winning farm managers (the top three percent of all Telfarm partici- pants) found that the winner sold 500 pounds of milk more per cow than 14 non-winners. with production of 12.800 and 12.300 lbs. respectively. He concluded there was only a slightly more feed disappearance per cow ($50) for the non- award group. There was a difference in housing between groups. in that the non-award group had more stanchion barns than the award group. This was reversed when it came to free stalls. Travis further stated that the milking system reflected the type of barn utilized. He questioned the two groups on the use of veterinarians for pregnancy examination and post-calving examinations on the reproductive tract; he found no difference. Further in his study. Travis states that there is a tendency for the award group to use some natural service bulls in these herds. In this study there was some tendency for the award winners to use.more artificial insemination and to depend less upon the inseminator to select the bulls used. However, the differences between groups was slight. Fifty-seven percent of the non-award. group used predicted differences. while 78 percent of the award winners used predicted differences as the main.item in selecting sires to use. The remainder of the herds in the two groups left the choice of sires up to the inseminator. 23 Travis (39) also looked at some other factors. such as size of operation. The non-award groups operated an average of 249 acres and rented 71 acres. while the award-group operated 376 acres and rented an average of 79 acres. The award winners had 2.47 times as many cows as the non-award winners. Travis further studied some effi- ciency measures. such as milk sold per man, which was 287.100 lbs. for the non-award and 400,700 lbs. for the award winners. The average age of farmers in the Travis study was 46 years for the non-award and 51 years for the award winners. There was a six year difference in the years spent farming, with the winners averaging more. There was a one year difference in formal education with the award winners averaging 13 years. .When the farmers were asked the factors contributing to their success as managers a high producing herd ranked third. In a study by Hess and Miller (13) of 151 farms. 102 of these farms adjusted the protein level of grain ration during the year. These Pennsylvania researchers.also studied some sociological factors influencing dairy farmers. They state that a dairyman's concept of a good level of milk production may serve as-a source of motivation to improve his herd production. Twenty-eight operators.who, considered 11,000 lbs. or better to be a good herd had herds averaging over 9,000 pounds of milk per cow while those whose concept of good production.was less.than 9,500 had an average herd production of 7,700 lbs. This merely shows 24 that one's version of good production is 2.000 lbs. above present level of production - a level that the operator could achieve. Hess and Miller in studying motivation found that a dairyman's self-rating of herd production in comparison to his neighbors may serve as a source of motivation to improve his herd's performance. Those operators who seriously underrate their herd's performance were actually operating herds with unusually high production.and returns over feed cost. 0n the other hand, those who tended to overrate their herd performance were operating dairies with lower producing herds which yielded only fair returns above feed costs. To move to a more profitable position, the operator must be motivated to make use of knowledge and capital to improve his economic position. Insofar as motivation is lacking, the removal of capital and knowledge limitations may not produce the expected improvement in the farm's operation. This suggests that the source and nature of farmers' motivation constitutes a general problem area of considerable importance. METHODS AND PROCEDURES This survey was designed to personally interview dairymen whose herds were enrolled in the Dairy Herd Improve- ment Program in Michigan that had the highest average pounds of milk per cow in October 1971. These herds were then matched with a herd of similar size--on DHI--in the same county. with the same breed of cows. and having approximately 25 the average DHI production of 12.973 pounds of milk. Appendix Table 1. shows the number of cows and the average pounds of milk for each pair of herds. Also shown in this Table is the mean and standard deviation for each of the strata. Sample Size and Samplinngrame Sample size was primarily determined by the time available and the financial budget allocation for the survey. Considering these two factors the maximum number of dairy farms that could be visited was set at 80. A listing of herds enrolled in the Dairy Herd Improvement Program. on official test. was obtained from Michigan DHIA Inc. The herds were listed by milk production in descending order. The 80 herds were then chosen and matched. Information was also obtained on farm location by township and section. There were 19 counties represented in the survey. The Questionnaire The questionnaire was designed with a one and one half hour interview time in mind. In the construction of the questionnaire. c0pies of instruments used by.Kucker (20) and Roche (33) were consulted. The guidelines suggested by Backstrom and Hursh (2) and Kerlinger (18) were also followed in designing the questionnaire and in structuring the questions. The questionnaire was divided into six separate sections: demographic data, feeding, breeding, 26 reproduction and herd health. calves and young stock, and motivation. The initial questionnaire contained l30.ques- tions with some of them having multiple answers. It was then pretested on dairy department faculty members and graduate students. as well as four dairymen with herds on test who were not included in the sampling frame. The ques- tionnaire was then reduced to 123 questions, with the same sections as previously mentioned. A copy of the question- naire is included in Appendix Table 2. Interview Procedure One half day was set aside to interview a dairyman. A letter of introduction was sent to each dairyman, which included a return postcard with which to reply whether the date and time for the interview was satisfactory. County maps. with township and section numbers, were obtained to use in locating the farm. The dairyman was given a copy of the questionnaire and a tape recorder was used to record the answers. This was found to be a most effective tool and no farmer objected to its use. However. each was assured that the material on the tapes would be destroyed after completion of the project. The tapes were reviewed so there was no misinterpretation. The interviews were begun in January 1972 and were completed in April 1972. Of the original 80 farmers chosen, six were dropped for various reasons. The final number of completed questionnaires was 74 or 37 pairs of herds. There were two substitutions to the original 27 list of herd owners with average production. Method of Analysis The collection of data is a very integral part of any research project and is generally very time-consuming. However. only through accurate and appropriate analysis is such data transformed into useful information. In review of the objectives of this research project and the factors involved in this survey, the data are essentially multi-variate. because many of the factors questioned are interrelated and several may co-vary together. Thus the method chosen for analysis was the multiple linear regression by least squares analysis. However, not all of the variables considered would have a linear relationship. Speicher et a1. (36) obtained curvilinear relationship with some management factors as they relate to net income. The model chosen for analysis was. y = a + blxl + b2x2 + ... +bnxn where y is milk production in pounds. the dependent variable. a is a constant, bl is the regression coefficient, and x1 is the value for a variable. Analysis was done using the least square addition model, where the variables that con- tribute the most to the total variation is added first: the least square deletion model was used for the final 35 vari- ables. This model removes variables one at a time. starting with the variable that contributes the least to the total variation. Three separate computer outputs for the least squares addition program were necessary because of matrix 28 capacity of the computer. The variables selected, for each separate output, were those with a high simple correlation. In the least squares addition procedure if two-variables are highly correlated only the variable that contributes the most to the multiple regression equation will be included. The variables included in the least squares are those which had 74 observations for each variable. The variables which were significant in the initial computer outputs, were then analyzed in a least squares deletion program. The least squares addition program does not give the regression coefficients or the standard partial regression coefficients as does the deletion procedure. The procedures for least squares permits a specific independent test of significance on the direct effect of the factors. In this example, this procedure permits one to ascertain which factors or combination of variables are the most reliable predictors of high milk production per cow. There are some limitations to this type of analysis which should not be overlooked. Some variables that may have an influence on milk production may not have been in- cluded. Secondly, in multiple regression analysis, there is an assumption that all independent variables are not representative of the population, but are fixed. Most in- dependent variables in this survey are random or represent- ative of the population and thus estimates of partial regression coefficients may be biased and the usual variance- covariance matrix will not be correct. 29 The mean difference between each of the strata for each variable was analyzed using a two-sample t-test with paired observation according to the procedure shown by Steele and Torrie (37). Qa_t§._ The data were transferred from the questionnaires to 80-column computer cards. The first 11 columns of each card contained the card number. herd code number, the strata of the herd and the pair number. There were nine cards necessary for each herd. The variables and card columns are listed in Appendix Table 3. One question asked in the survey was not tabulated or analyzed. There were three questions where the dairymen were asked to rank given answers. When they did not rank all the answers. an aver- age of the unassigned ranks was used for the missing values. The data for 11 variables obtained from the DHI master file tape on May 1. 1972. were the number of cows. average pounds of milk per cow, average pounds of milk fat, fat test. average percent days in milk. average gross value of product, total cows in herd for past year. the number of cows with sires reported. the sire number of each cow and the number of calves with sires reported. as well as the sire number of these calves. Predicted differences for milk for the sires of the cows was obtained from the U.S.D.A. sire summary approximately one year prior to the birth of the cow. If no information was available at that time. the 30 earliest proof was used. The same procedure was used to determine the predicted differences.for milk for the sires of the calves. Items on the questionnaire were coded either as a rank. yes and no type answer. or numeric answers. Yes answers were coded as 1 and no answers as 0. If a question was not answered it was coded as blank. Open-ended questions were not analyzed because of the variation in responses. Simple correlations were obtained on variables thought to be associated and were analyzed to determine which variables should be included in the initial least squares addition program. Herds with high average milk production will be referred to as stratum 1. while those with average produc- tion will be referred to as stratum 2. RESULTS AND DISCUSSION Demographic Data This section of the survey dealt with the farm and the farm family in general. There were 56 questions asked, some of which required multiple answers. There was no significant difference between the age groups in the two strata. The average age being 44.1 years. This does not differ significantly from the 44.4 years obtained by Kucker (20) on production testing adopters in Michigan. A positive simple correlation coefficient of +0.90 was obtained between age and years of farming. Age of the operator did not contribute significantly to the variation in milk production. The average years of educa- tion of surveyed dairyman was 11.7 years. with no significant difference between strata. Education was positively corre- lated with number of acres operated and number of acres Owned with coefficients of +0.32 and +0.31 respectively. The respondents had farmed for 21.8 years. with no signifi- cant difference between strata. Number of years in farming was positively correlated with years on test and reading the Michigan Farmer magazine. but was negatively correlated with number of children under 16, number of acres rented. 31 32 and plans to buy or rent more land. Ninety-five percent of the respondents were married. 18 percent of the wives of dairymen in stratum one worked full time. while 31 percent of the wives in stratum two worked full time. Years on test averaged 16 and there was no.differ- ence between strata. This variable was positively corre- lated with number of boys over 16. and years of age. Nega- tively correlated with this variance was renting more land. Years on test did not account for significant variation in the pounds of milk produced per cow. Information on time spent studying the monthly dairy herd improvement report was obtained. The hypothesis that dairymen having higher average milk production would spend more time studying their report was supported (Table l) by data obtained. TABLE 1. Time Spent Studying the Dairy Herd Improvement Report W Minutes per month .12 i 9’? Stratum 1 37 258.6* 305.6 Stratum 2 37 , 1250 3 129 e 1 ——— N = Number of observations in each stratum * Significant at p‘< .05 u = mean 0': standard deviation 33 There was a positive simple correlation between this variable and pounds of milk per cow,.with a value of 0.28. When the 35 most significant variables were analyzed by a least squares deletion model procedure, time spent studying the report was not included in the final regression equation. Dairymen were asked.to rate.fourwitems.which appear on the DHI report in order of.importance and their rankings are given in Table 2. There was no difference in the rank- ing between strata. i w — ‘— ———— TABLE 2. Rank of Selected Variables on Dairy Herd Improvement Report . Variable Egg; Individual cow test day production 1 Rolling herd average production 2 Lactation to date information 3 Herd test day average information 4 Dairymen were asked whether they used the dollar value expected in 305 days. pounds of grain,.1ast bred. and due date and the action needed sections of the Dairy Herd Improvement report and the percentages are given in Table 3. The significant difference in use of last bred and due date may have been due to dairymen in stratum one.using another set of records for that purpose. Those dairymen in stratum one use the selected variable less than the respondents in stratum two. 34 TABLE 3. Percentage Use of Selected Variables on DHI Report Variable Stratum l Stratum 2 Percent Yes Percent Yes Dollar value expected in 305 days p 35.1 45.9 Pounds of grain 21.6 24.3 Last bred and due date 32.4 54.1* Action Needed 29.7 35.1 *Significant at p‘<'.05. Eighty-seven percent and 76 percent of those res- ponding in strata one and two. respectively, were raised on dairy farms. The number of children on a dairy farm is an import- ant source of additional labor. as well as having an in- fluence on the parent's participation in off-the-farm activities. Number of children had a positive correlation with the operators' activities in church and school. but not with the wives' participation in their off-the-farm activities. The only exception to this was in the case of girls under 16 and the mothers' activity in school affairs. where the simple correlation coefficient was +0.31. If there were boys in the family. there was a positive correla- tion with the plans to rent more land. If the children participated in the record-keeping program on the farm. there was a high correlation with participation involving finances. consulting and planning. with coefficients of 35 +0.73. +0.87 and +0.84, respectively. Children participat- ing with chores had a high positive correlation with field work activity. the coefficient being +0.75. These data indicate that in some families children participate in chores and field work while in other families there is participation with records. finances. consulting and planning. There was a positive simple correlation between children's participation with farm activities. the number of children at milking and the pounds of milk per cow. The hypothesis that farmers with high average milk production would be leaders in 4-H clubs was not supported by the data: actually the reverse was true (Table 4). In the multiple regression. this variable accounted for,48 percent of the total variation in the dependent variable. TABLE 4. Percentage of Farmers in each Strata that were Leaders in 4-H a. Zea m. Strata l 37 29.7 70.3* Strata 2 37 56.8 43.2 *Significant at p < .05. The average number of magazines read was 5.3. with no significant difference between strata. Percent reader- ship of selected magazines by strata is shown in Table 5. 36 A TABLE 5. Percent Readership of Selected Magazines for each Strata Magazine Strata l Strata 2 z Readership % Readerghip Hoard's Dairyman 97.3 94.6 Farm Journal 83.8 83.8 Successful Farming 56.8 67.6 Dairy Herd Management 27.0 24.3 Michigan Farmer 78.4 81.1 Prairie Farmer 18.9 18.9 Reader's Digest 10.8 13.5 Farmers Advance 5.4 8.1 Holstein World 21.6 21.6 Michigan-Indiana Holstein World 18.9 16.2 Canadian Holstein World 0 2.? Farm Quarterly 8.1 16.2 Top Operator 24.3 29.7 Big Farmer) 8.1 8.1 Other Magazines 35.1 48.6 Number of magazines read was positively correlated with other magazines, with a coefficient of +0.59 and had no significantly influence on pounds of milk produced. There was a slight negative correlation between number of magazines read and pounds of milk produced per cow with a coefficient of -0.04. 37 There was no significant difference between the two strata for church activity, recreational activity. school and scout activity thus not confirming the hypo- thesis that farmers with average production were more in- volved in off-the-farm activities. —— — TABLE 6. Percentage of Respondents in each Strata that participate in off-the-farm Activities. Operators Political Activities N Yes No _ Percent Percent Stratum l 37 2.7 97.3* Stratum 2 37 16.2 83.8 Operators Lodge Activities Stratum l 37 0 100.0** Stratum 2 37 19.4 80.6 Operators Other off-the-farm Activities Stratum 1 37 o . 100.0 .... Stratum 2 37 16.2 383.8 Wives' Lodge Activities Stratum l 2.7 97.3* Stratum 2 21.6 ‘78.4 *Significant at p.<..05 **Significant at p <:.01 38 There were significant differences in percentage of respondents in the two strata that participated.in politics. lodge. or off-the-farm activities (Table.6). Wives' activities off-the-farm were not significantly different between the two strata except for the participation in lodge activities (Table 6). In the initial analysis of the variables. Operator's political activities. wives' recreational.activities and children's scout activities entered into the multiple regression equation. Sixty-five percent of the respondents listen to agriculture programs on radio or television. There was no significant difference between strata. and this variable did not significantly influence pounds of milk produced but had a negative simple correlation coefficient of -0.04 with pounds of milk produced. The question of what would happen to the farm if the operator retired or if something happened was analyzed (Table 7). Many of the dairymen surveyed regarding.a will did not have specific plans but indicated that the farm would be transferred to wife or family. There was no legal arrangement. 39 TABLE 7. What would happen to the farm at retirement or if some catastrophe happened? Variable Stratum 1 Stratum 2 Percent Percent Sold 8.1 . EState 2.7 801 Will 18.9 2.7* Wife 16.2 16.2 Family 48.6 43.2 Other 8.5 18.9 *Significant at p4 .05 On many farms wives are a very important part of the dairy operation. They are often sources of additional labor in peak labor demand periods. These wives are also very involved with record-keeping as well as with other duties. Table 8 gives the extent of wives participation in different areas of the dairy operation. TABLE 8. Extent of Wives‘ Participation in Farm Activities ~—— — _— Variable Stratum l Stratum 2 Participation Participation % 1% __ Records 48.6 54.1 Chores 45.9 59-5 Finances A 30.6 40.5 Field Work 32.4 27.0 Planning _ 40.5 35.1 Consulting 37.8 32.4 Other 2.7 2.7 ——————~ 40 As can be seen from this table.wives were very involved in records and chores. There was no significant difference between strata. On many farms the wives were involved in the calf-raising program. In this survey. 22 percent of the wives fed the calves both winter and summer. There was no difference between strata. The simple correlation coefficients were small and ranged be- tween -0.08 and +0.06 between wives' different farm activi- ties and the pounds of milk produced. Children on the farm are an important source of additional labor as previously mentioned. Many times' children receive the responsibility of feeding of the calves. In strata one 41 percent of the respondents indi- cated that children fed the calves in the winter. and 43 percent indicated the children did this chore in the summer. In strata two. 31 percent of the dairymen indicated that the children fed the calves in the winter. while 39 percent indicated this was the children's job in the summer. Heat detection is also one of the responsibilities.sometimes given children. In analysis of the data, 19 percent of the dairymen in Strata one indicated their children did this in the winter. while 30 percent of those in strata two indi- cated this same practice. These percentages are increased somewhat during the summer months to 24 percent for strata one and remain the same for strata two. There was no significant difference between strata for these categories. Children were also cited as a substitute for milking if the need arose. 41 Michigan dairymen have followed the practice.of raising crops and marketing these crops through their live- stock. therefore amount of land farmed becomes a very important item. One of the.reasons for pairing.herds on a county basis was to eliminate the differences which may exist in the quality of land area involved. Herds were paired within a county on the assumption that such herds would have a similar feed supply for the dairy herd. .If herds had been assigned randomly. the variation.in land quality and quantity as well as feed supplies may have been evident between the two strata. There was no significant difference between strata in the number of acresxcperated. number of acres owned. (220.5),number of acres rented. sale price per acre of land ($539.40) or investment in the farm ($118.937). Dairymen in strata one rented 99 acres of land. while those in strata two rented 87 acres of.land. The number of acres operated, owned and rented had small posi- tive correlations with pounds of milk. Thirty-five percent of the respondents in both strata indicated they plan to buy more land in the future. however. this is not the situation when questioned about plans to rent more land. (Table 9) Information on amount of gross income from.differ- ent enterprises on the farm was obtained by the questionnaire. Ninety-six percent of the respondents indicated that they received most of their gross income from the dairy 42 TABLE 9. Do you plan to rent more land in the future? N Percentage Percentage _ Yes No Stratum one 37 24.3 75-7* Stratum two 37 51.4 48.6 *Significant at p ‘1 .05 enterprise. There was a negative simple correlation between pounds of milk per cow and gross income from crop and hogs. with coefficients of -O.l3 and -0.12 respectively., Most of the farms surveyed were either family farms or partnerships. and therefore most of the labor was family labor. Labor hired amounted to 9 percent. This value was slightly lower than the 17 percent obtained by Kucker (20). Labor from wife and children was 24 percent and 67 percent was considered the operator's labor. There was no significant difference between strata. Percentage of labor hired was negatively correlated with pounds of milk (-0.20). Percent of labor from wife and children had a positive correlation coefficient (+0.18) with pounds of milk. Percent labor hired accounted for .88 percent of the total variation in the dependent variable. An initial hypothesis was that the higher-producing herds housed their dairy animals in stanchion barns but there was no significant difference between the types of housing in the two strata. Table 10 gives the percentage in 43 each strata by type of housing. __a_ TABLE 10. Types of Dairy Cattle Housing Variable Stratum l Stratum 2 Percent Percent Stanchion barn 64.9 48.6 Open lot-free stalls 5.4 5.4 Cold-covered free stalls 18.9 24.3 Loose housing 0 ‘ 2.7 Stanchion barn and free stalls 5.4 13.5 Stanchion barn and loose housing 5.4 5.4 lo'o'.‘o 1m Different methods of milk movement from the milking area to the milk house was analyzed. Table 11 gives the percentage in each strata that utilize different methods of movement. TABLE 11. Percent of each stratum who utilize different methods of milk movement from milking area to milk house Variable Stratum l Stratum 2 Percent Percent Pipeline 52.4 43.2 Portable pipeline 29.3 43.2 Carried 18.5 13.5 44 There was no significant difference in the various.maans for the two strata. Portable pipeline milk movement system accounted for 10.1 percent of the total variation in the dependent variable. Percent of cows registered had a positive correla- tion with milk produced per cow. with a coefficient of +0.28 and the distribution for the 2 strata is given in Table 12. TABLE 12. Percent of Cows Registered .11 ’2‘ .3.“- Stratum one 37 66.3* 36.7 Stratum two 37 40.2 42.2 *Significant at p < .05 There is a trend toward dry lot feeding of the 'dairy cows throughout the year (15). The hypothesis that herds with a higher average level of milk production would have a higher incidence of dry lot feeding during the year than in average herds was not noted in the study with.94‘ percent of the respondents in both strata feeding their cows in dry lot. Other feeding regimes found in this atudy.were strip-grazing, green-chopping and partial dry lot combined with pasture. Time spent feeding per day in.the winter and summer may be used as a measure of feeding practice. ’45 There were no significant differences between the two strata or between summer and winter feeding. Neither dry lot feeding, time spent feeding in the winter nor time spent feeding in the summer had a significant influence on the pounds of milk produced per cow (22.53 0.0). Milking time was investigated to obtain information on the hypothesis that higher producing herds would milk on a more regular basis than average producing herds. Strata differed significantly between the starting times of milking in the morning both in the summer and.winter. TABLE 13. Starting Time of Milking Mgrnigngintgr Morning-Summgg Stratum one 6:01* 5:43** Stratum two 6:20 6:18 *Significant at p.< .05 **Significant at p 4 .01 Milking time in the morning in the winter accounted for 0.40 percent of the total variation in the dependent vari- able. Vaccum line size was investigated and the hypo- thesis was that the average producing herds would.have a smaller vacuum line and therefore more problems with udder infection (Table 14). One trend in the dairy industry is toward larger vacuum lines. 46 TABLE 14. Vacuum line size and percent of respondents with that size . , . . ~ variable Description Strat3m_l Stratum 2_ Percent Percent 0.75" line 2.7 10.8 1.0” line 10.8 8.1 1.25" line 45.9 45.9 1.50" line 32.4 29.7 1.75" line 0 0 2“ line 8.1 5.4 2.0" line 0 2.7 There was no significant difference in the percentage at any line size between the strata. However there was.a differ- ence between strata in the average bacteria count (Table 15). —~ —— —— TABLE 15. Average Bacteria Count for Dairymen in each . Stratum .11 E Zr Stratum one 37 9500* 9504 Stratum two 37 14543 ' 14051 *Significant at p < .05 47 The brand of milking machine was determined to ascertain if relationships to milk production or milk produced per cow existed. There was no significant difference detected between strata. The time spent milking per day was not significantly different between the stratum. nor was the time milking per cow with stratum one herds spending 2.1 minutes per cow and strata two herds taking 2.3 minutes per cow. The correlation between pounds of milk produced per cow and the time required to milk had a simple correlation coefficient of +0.19. The average number of men at milking in stratum one was 1.4 and for stratum two this variable was 1.2. When women and children over 16 are included in the milking labor the value for stratum one is 1.78 persons. while for stratum two this value is 1.48 persons.. There was no significant difference between the.strata for number of men. women or children milking. Number of women at milking accounted for 0.07 percent of the total variation in milk production per cow. Milking requires more labor on the dairy farm than any other operation. One of thesproblems whichiariae on many dairy farms is that of whom the farmer would.obtain for a substitute if he were unable to milk.. Tabulation of whom farmers. would obtain as substitutes is listed in Table 16.. There was a significant.difference in the mean percentage for children as substitutes between stratum. However. there is not a significant difference in the number of children in each of the strata. In stratum one herds #8 there is more reliance on other farm labor for shbstitute. This category may include.parents of operator and parents TABLE 16. The percentage of dairymen in each stratum who would obtain different substitutes for milking and chores Variable Description Stratum l Stratum 2 _'_%7'"_' _.__%____ Neighbor 18.9 8.1 Wife 8.1 5.h Neighbor boy 8.1 13.5 Children 10.8 37.8* Other farm labor “3.2 29.? Don' t know __1§_._2 _§_._l 100.0 100.0 *Significant at p < .05 of wife as well as hired labor. Nineteen percent of the owners of high producing herds do not have anyone they could contact for substitute milkers in case of illness or injury to themselves. The hypothesis that herds of high producing ability have more problems with their cows than.those.of.average production was tested. There was no significant difference between strata regarding the percentage of.dairymen who. indicated.they had more problems with cows going off feed. reproduction problems. mastitis and udder problems. ketosis 1+9 and milk fever. The most noticeable difference was-in milk fever problems. with 36 percent of the dairymen.in strata one indicating there was more problem in this area. compared to 16 percent of the dairymen in stratum two. This difference was not statistically significant. Dairymen were questioned concerning the order of importance of items for culling cows. The following table gives the ranking of items used in culling. TABLE 17. Ranking of items used to cull cows by farmers in each stratum Variable Description Strata l Strata 2 Rank Rank Milk production 1 1 Fat production 5 5 Feet and legs 6 6 Mastitis and udder 3 3 Reproduction 2 2 Type 7 8 Disposition 4 a Other 8 7 There was no difference in the ranking except for different ranking of the two least important items; type and other criteria. A herd with a high average of pounds of milk per cow must contain some high producing cows. The respondents were asked to state the production level of their highest 50 producing cow (305 day—record) in 1971. The highest.pro- ducing cows in stratum one averaged 20,050 pounds of milk with a standard deviation of 2851 pounds. .High cows.in stratum two averaged 18.798 pounds of milk with a standard deviation of 1713 pounds. There was a significant.differ- ence between strata. p <'.01. .Relationship of milk pro- duction of the highest producing cow to other items was studied and found to be positively correlated with lowest producing cow (+0.55) as well as with highest pounds of milk per day (+0.66) and averagepounds of milk per cow (0.79). It accounts for the largest amount of total variation in average pounds of milk per cow. The amount of variation contributed by this variable is automatic because of the definition of the strata. In the multiple regression the variable of highest producing cow had a multiple corre- lation coefficient of 0.852. Another variable was also investigated--that of the lowest producing cow which completed a 305 day record within each herd. There was a significant difference between strata. p <-.01. Low producing cows averaged 12.302 pounds in strata one and 9.477 lbs. in strata two. .This.was.the second most important variable accounting for the differ- ence in milk produced per cow. The value for lowest pro- ducing cow was positively correlated with that.for.high. producing cows and average pounds of milk produced.per cow. with correlation coefficients of 0.55 and 0.73. respectively. 51 There was a significant difference between strata in the value for highest pounds ofamilkiprodueedflperqoow. . per day (p.<'.01).. The average highest one day production in stratum one was 113.1 pounds compared.to.93.# pounds for stratum two. This variable was correlated with average production per cow (r 2 +0.67). There was a significant difference between strata in the average body weight of the cows. p.< .01. Cows in stratum one averaged 1.378 pounds. while those in strata two averaged 1.311 pounds. Individual cows were not taped and the value used was the dairyman's estimate.of the aver- age weight of cows in his herd.m Average body weight.had a positive simple correlation with the dependent variable (0.30). Data were gathered to test the hypothesis that dairyman with a high producing herd would be more interested in making a national. state. or a county milk record than would his pairmate in stratum two. Thefollowing table 18 gives the percentage in stratum one and stratum two who would attempt making records with a little effort.- The table illustrates that there was a higher percentage for all three variables in stratum one.. There was a high simple correlation between these three variables. with coefficients from +0.72 to +0.87. Making national. state and/or county records are all.positively.correlated with average pounds of milk per cow. with.coefficients of 0.25. 0.26 and 0.29. respectively. Correlation measures 52 TABLE 18. Percentage of each strata who would make a. record with a little effort Variable Description Stratum l Stratum 2 Yes Yes % % National record 83.8 62.2* State record 89.2 70.3* County record 99.6 73.0* *Significant at p 4 .05 the degree of association between two variables independent of there units. The average calving interval was investigated to determine if this variable accounted for any of the variation in milk production. The breeding records were not consulted. so the data represent the dairyman's estimate of his calving interval. There was only one-tenth of a month difference between the means of the two strata. a difference which was not significant. In retrospect. the records of each herd should have been examined to determine the.actual calving interval. estimate may have been inaccurate. Oxender (30) in a study of 37 Michigan herds on test. found a calving interval of approximately 13 months which compares to the value of 12.8 months found in this study. This item may have been expected to be different between strata since both strata indicated more prohdems with reproduction in high producers than in average cows. There is also no 53 significant difference between strata when the variable. days after calving to breeding. was analyzed. .The data presented here indicates that the variables of calving in- terval or calving to breeding interval had little or no influence on pounds of milk produced per cow. However data from actual breeding records should be used in any sub- sequent survey to more accurately determine this relation- Ship. Feeding The feeds and feeding systems on a dairy farm are very important in obtaining high milk production per cow. The correct qualities. quantities and amount of feed must be delivered to the animals at the proper place and time. Farmers indicate the ingredients used in grain mix in order to determine the percent protein and therms of net energy in the grain mixture being fed. Ingredients were stated in amounts per ton or quantity per batch. To deter- mine the percent protein and the therms of energy. values for feeds were obtained from numerous sources (l4),(27). (29). When dairymen were feeding additional supplements proper values were included in the calculations for the protein and energy. The percent of protein and therms of net energy per hundred weight of concentrate did not differ significantly between the strata. Stratum one herds had a 14.5 percent protein and 72.2 therms of net energy per hundred weight of concentrate ration compared to 13.9 per- cent and 73.8 therms for stratum two. These variables were 54 not included in the multiple.regression model. however. percent protein had a positive correlation_with,pounds of milk produced. Energy content.of grain was negatively correlated with pounds of milk produced per cow (-0.24). Miller and Hess (13) noted that 67 percent of the dairymen in Pennsylvania changed the protein control of their grain ration during the year. This item was investigated in the present study. There was no significant difference between strata. Thirty-eight percent of the dairymen in stratum one and 49 percent of the dairymen in stratum two changed the protein content of their grain mix during the year. This variable was not included in the least squares multiple regression equation. This variable had a positive correlation coefficient of +0.08 with pounds of milk pro- duced per cow. Supplemental feed was fed by 41 percent of the dairymen in strata one and 32 percent of the respond- ents in strata two. Most of this supplement was added as a top-dressing as compared to being mixed with the feed or being fed in some other way. Level of protein in the grain ration should be determined by the protein content of the roughage. .Twenty- two percent of the dairymen in stratum one and 14 percent of those herd-owners in stratum two knew the percent crude protein in their corn silage. When asked if their hay.had been tested for protein or fiber.32 percent of the respond- ents in stratum one and only 14 percent of those in stratum two gave an affirmative answer. There was no significant 55 difference between strata on the percentage of positive answers. There was a simple correlation between knowing the crude protein of the corn silage and having their hay tested. with a coefficient of +0.51. The correlation be- tween having hay tested and average production per cow was +0.29. The protein percentage of the corn silage and hay is given in Appendix Table 4. There was no significant difference between strata in the percent protein of the hay for those dairymen who had their hay tested. The averages were 15.0 percent and 15.9 percent for strata one and two. respectively. If first and second cutting tests were taken. an average of the two values was obtained for the herd average. The res- pondents in both strata indicated that they cut their first cutting of hay approximately on June 1. with a mean differ- ence between strata of 1.1 f 0.27 days which was not signi- ficant. Additives to corn silage to increase the protein content have been made available to the dairymen. Twenty- two percent of the dairymen in both strata used some type of additive to their corn silage. Sixty-three percent of those adding material to their silage added urea. The re- mainder added some commercial mixture. There was a significant difference between strata on the average of concentrate fed per cow per day. 56 TABLE 19. Pounds of Concentrate to Average Cow per day by Stratum .11 i 3- Stratum one 37 20.6** 5.1 Stratum two 37 17.6 4.3 **Significant at pt< .01 When included in the final least squares deletion method for multiple regression. it accounted for .07 percent of the total variation in average milk produced per cow. This variable has a positive correlation with milk production per cow (+0.38). The majority of the dairymen in both strata fed according to milk production. however. 8.6 percent of the respondents in stratum one and 19.4 percent of the respondents in stratum two fed all cows alike...The prac- tice of feeding according to milk production had a low positive correlation with average production per cow (+0.16). Dry cows in both strata received an average of 3.7 pounds of grain per day. with no significant.difference.between strata. Sixty-eight percent of the respondents in stratum one and 60 percent in stratum two_increased the pounds of grain prior to freshening. .This is not a significant difference. The average cow in both strata received 10.3 pounds of grain prior to freshening. with no significant difference in means between the strata. 57 A significant difference was detected in.the crude protein of.the hay fed(12;1 percent vs. 12;] per- cent. p 4:.05). If a dairyman had his hay tested. this value was used. If the dairyman did not have his hay tested a constant of 17.7 percent crude protein was used. Those in stratum one had a lower percent protein in the hay and a higher percent had their hay tested. Therefore the difference in means may be related to the number of dairymen who had their hay tested. and to the fact that the actual average was lower than the constant used. None of the variables referring to dry matter. crude protein or energy of corn silage. hay or haylage were used in the least squares analysis since there were unequal observations in each strata. Over 75 Percent of the respondents in both strata indicated that salt and mineral supplements were available free choice. Based on the hypothesis that dairymen with high producing herds were giving their cows a maximum amount of concentrates information on the dairymen's concept of Whether their cows would eat more grain.was advanced. .. Forty-six percent of the herd owners in stratum one.and 49 percent of the herd owners in stratum two believed their... cows would eat more grain. There was no-significant differ- ence between strata. The variable had a negative correla- tion with milk production per cow with a.simple.coefficient of o0.06. The hypothesis was advanced that.dairymen with high producing herds fed their ”top" cows more concentrates 58 than did the average member on DHIA. -This hypothesis was supported and the average values for each stratum is given in Table 20. TABLE 20. Average Pounds of Concentrates Fed to Top Cow in each Stratum n ’12 E- Strata one 37 33-2** 7.9 Strata two 37 26.6 7.0 **Significant at p < .01 This variable was positively correlated with average pounds of milk per cow. with a coefficient of-+0.47. It was retained as one of the significant variables in the multiple regression equation and had a regression coefficient of +46.074 pounds of milk for each pound of grain. W The hypothesis was tested that those dairymen with high producing herds bred their heifers at a later age than did the owners of average production herds. since there is a positive correlation between age and stature.and weight up to maturity. In this investigation there was no differ- ence between strata in the breeding age or breeding weight of heifers. However the standard deviation for weight at breeding was about 20 pounds more for stratum two. Average 59 weight of the heifers at breeding has a very slight nega- tive correlation with average pounds of milk (r0.04). Artificial insemination.and.frozen semen.have increased the spread of genetic material. As mentioned previously there is a positive correlation between the predicted difference of a sire and the daughter's perform- ance and the assumption is made that any bull not the result of a special mating has a predicted difference of zero. Artificial insemination has been.avai1ab1e in Michigan for approximately 30 years. The average respond- ent in this survey had used A.I. for 17 years. There was no significant difference between strata. An average of 93 percent of the cows included in stratum one and 87 per- cent of those owned by respondents in stratum two were bred artificially. The value for heifers bred artificially was approximately 30 percent lower for both strata. Only 50.5 percent of the heifers owned by respondents in stratum two were bred artificially. while for stratum one this value was 63.6 percent. Both of these variables had a positive correlation with milk production. but were not included in the multiple regression model. Sire selection by a dairyman.is a very important decision that will affect future production and on an. individual basis. the sire and.dam are of equal importance. However. when the entire herd is considered. the sire or sires used are most important from.a genetic standpoint. There are many different criteria for selection of sires. 60 The respondents in this survey were questioned on how they would rank ten different criteria in making a decision about what bull to use. The following table gives the criteria and ranking for both strata. TABLE 21. Rank of Dairymen's Criteria for Selection of Sires Variable Description Stratum l Stratum 2 _, 4__ Rank Rank U.S.D.A. Predicted Difference l 1 Type traits of the sire's daughters 2 2 Repeatability factor 4 4 Dam's milk and fat production 3 3 Ancestry (pedigree) 5 7 Pleasing color markings 9 9 Price of semen 7 8 Conception rate 6 5 Price of sire 10 10 Other criteria 8 6 As has been previously mentioned. if.a farmer did not rank all the variables. an average of the.unassigned ranks was used for the missing values. These rankings are different than those obtained by Kucker (20). However. his survey was a sample of all Michigan.dairymen not only those on the DHI program. The U.S.D.A. predicted difference 61 information. which ranked first in this survey is made available by the extension service to herds on test. whereas it may not be available to all dairy herds in Michigan. This may account for some of the difference in rankings between the two investigations. The U.S.D.A.. predicted difference is the best estimate available of what a sire's daughters will produce in relation to their herd- mates. In this study. U.S.D.A. predicted difference ranked first as a criteria for sire selection. followed by type traits of the sires' daughters. dam's milk and fat produc- tion. and the repeatability factor. The other is an esti- mate of reliability of the predicted difference. There is no difference between strata on the first four rankings. A difference in ranking the use of pedigree information was found and those herd owners in stratum one ranked this variable higher than did those in stratum two. The cause for this may be the higher percentage of registered cows in stratum one. One goal of herd management is to have one offspring from every cow each year and thus an examination of the reproductive performance seemed important. It was hypo- thesized that the respondents in stratum one would have a post partum examination by a veterinarian and also a more regular pregnancy check than did dairymen in.stratum.two. There was no significant difference between strata in either item. However. there was a slightly higher percentage of the dairymen in stratum one who followed the practice of 62 examination prior to breeding and pregnancy checks (51.4 vs. 43.2 percent and 62.2 vs. 51.4 percent). Both of these variables were positively correlated with average pounds of milk per cow but neither entered the multiple regression equation accounting for any of the variation in the dependent variables. The decisions and selections which a dairyman makes today regarding the bulls used will not have any effect at the present time. However. they will have an effect in the future performance of his herd. The res- pondents were asked about the average predicted difference (for milk) of the sires presently used. The average P.D. in stratum one was 870.7 pounds of milk and 751.2 in stratum two and this difference was not significant. In order to ascertain if this value was near what the farmer was actually using. the sires of the calves born in the past year were identified. The information on P.D. for milk of those sires was obtained from the sire summary a year prior to the birth of the calf. The average P.D. for milk of sires of calves in stratum one herds averaged 807.8 pounds of milk. Those in stratum two averaged 694.1 pounds. The estimate given by the herd owner was slightly higher than the P.D. for the sires of calves. Estimates were 60 and 70 pounds higher. respectively. However. owners of herds in both strata may be using bulls at the at the present time which are on the average 60 to 70 pounds higher in P.D. than the bulls used the two previous years. 6'3 A hypothesis that the herd owners of high produc- ing herds would be more affirmative in the concept that a breeder with 50-100 cows would be rearing and proving his own bulls was examined. There was no significant differ- ence between strata. with 32 percent and 22 percent of herd owners in strata one and two. respectively. indicating that breeders should be rearing and proving own bulls. This variable had a slight positive correlation with milk production. Reproduction and Herd Health To maintain high milk production)cows in the herd must be healthy and able to reproduce on a regular schedule. The number of abortions in a herd can be used as an indi- cation of the health of the herd. Stratum one had an aver- age of 1.56 cows abort. while stratum two had .97 cows per herd abort (p > .05). One herd in the stratum one had 12 abortions due to infectious bovine rhinotracheitis. The number of abortions per herd had a small positive correla- tion with the dependent variable. but did not enter the multiple regression equation. The detection of estrus is very important for an efficient breeding program. The respondents were asked how many times a day in winter and in summer. were their cows observed for heat and by whom. Cows are observed for heat more frequently in summer than in winter with averages during summer of 3.0 and 3.6 times per day for strata one 64 and two respectively. Observations were most frequently done by the operator himself as compared to wife. children and/or hired labor. The length of time the cows were observed for heat was questioned but not analyzed. Dairy- man found difficulty in stating an exact time interval for this management practice. Most dairymen indicated they observed the cows while doing other chores. Only one dairy- man interviewed indicated he spent 15 minutes twice a day doing nothing but observing for signs of estrus. Because of the difficulty of the respondents to answering this question. it was not analyzed. Other common health-related problems are milk fever and ketosis and herds with higher average production were hypothesized to have a higher incidence of these prob- lems than average producing herds. There was a significant difference between strata in the average number of cows with milk fever. TABLE 22. Average Number of Cows in each Stratum with Milk Fever y. 3 E Stratum one 37 6.2* 5.3 Stratum two 37 2.7 2.5' *Significant at p <;.05 65 There was a higher average number of cows in both strata with milk fever than with ketosis. Both of these variables were positively correlated with average pounds of milk per cow. The simple correlation coefficient for milk fever was +0.41 and for ketosis was +0.08. The amount of money spent for veterinarian services was hypothesized to be more for dairymen in stratum one than for dairymen in stratum two. This was verified by the use of a two-sample T-test on the difference of two means. TABLE 23. Average Cost of Veterinary Services by Stratum a ’2‘ 3 Stratum one 35 $1012** $615 Stratum two 36 $ 488 $302 “*Significant at p <;.Ol This variable was not included in the initial least squares addition model since there was an unequal number of observa- tions in each strata. The relationship between the dairyman and his veterinarian is very important for a good herd health pro- gram. Answers to the question of whether a veterinarian visits a farm at times other than just when he is called gives some indication of the relationship which exists be- tween the individuals. It also may indicate whether the 66 dairyman has a contract. verbal or written. with the veterinarian about the services to be performed. even though the question was not worded this way. There is no significant difference in the percentage of affirmative answers to this question. Sixteen percent of the res- pondents in stratum one stated that the veterinarian visited the farm between times called. compared to approx- imately three percent of those in stratum two. The simple correlation coefficient between this variable and average pounds of milk per cow is +0.11. There appears to be a trend with those dairymen who have high milk production to have a closer relationship with their veterinarians. This variable was not included in the multiple regression equation. Mastitis control and prevention are very important to herd health and the production of a high quality milk. Some mastitis preventive measures are dry cow treatment and teat dip treatment. The detection of mastitis is also important to ascertain which cows may have abnormal milk. Answers to information about dry cow mastitis treatment. teat dip treatment and use of a strip cup were analyzed. There was a significant difference between strata in the percent of herd owners who used a dry cow mastitis treatment program. This variable had a positive simple correlation with average production per cow (+0.31). The use of teat dip was not significantly different between strata. nor was 6? TABLE 24. Percent of Dairymen in each Stratum who use Dry Cow Mastitis Treatment N Yes 59 Stratum one 37 89.2** 10.8 Stratum two 37 56.8 43.2 M*Significant at pt< .01 the use of a strip cup. Only 35 percent and 27 percent of the dairymen in strata one and two respectively used a strip cup. Another important aspect of herd health is the calf- hood vaccination program. Dairymen with high average pounds of milk per cow were hypothesized to vaccinate calves against more diseases than did dairymen with average DHI milk production. There was no significant difference be- tween strata for the vaccination against Brucella: both strata indicated 100 percent vaccination. There was a significant difference in the vaccination program for Infectious Bovine Rhinotrocheitis. and the Virus Diarrhea- Mucosal disease complex and this is shown in Table 25. This practice had a positive correlation with average pro- duction per cow (+0.40). The vaccine is usually given in a combined form. There was no difference between strata on vaccination for porterurella and/pr leptospirosis. with 24 percent and 14 percent of the respondents in strata one and two vaccinating for these diseases. 68 TABLE 25. Percent of Dairymen in each Stratum who Vaccinate Calves for IBR-PI3-BVD fl IBR-PIB-BVD Yes .N_° Stratum l 37 64.9** 35.1 Stratum 2 37 27.0 73.0 **Significant at p.< .01 Calf mortality is also an indicator of herd health. and it was hypothesized that more calves die at an early age in average producing herds than higher producing herds. The mean number of calf deaths in stratum one was 2.5 calves. while in stratum two the death loss was 5.6 calves (p :>.05). This variable has a negative correlation with the dependent variable. Calf losses were highest in the first three weeks of age. with 80 percent and 92 percent of the calves which died in strata one and two being lost at this time. The calf mortality rate in stratum two was slightly lower than the estimate obtained by Speicher and Hepp (34) of 13.5 percent in their analysis of 379 Michigan dairy farms. The estimate for stratum one of a 5 percent loss is much lower than the 13.5 percent less previously mentioned but stillbirths were counted in the 13.5 percent value and not in the present study. 69 The death loss of cows within a herd is.an indi- cation of level of management even though some losses may be accidental. others may be due to lack of sufficient observation. There was no significant difference between the mean death loss in the two strata. The number of cows sold during the year could have an influence on the average pounds of milk produced per cow as calculated in the DHI program. To obtain the average production per cow the cumulative pounds of milk produced by each cow is divided by the total number of cows. in- cluding dry cows. Therefore if the oows.sold are removed from the herd before a dry period starts. this would increase the percent days in milk as well as increase the average production of the herd. When the percent days in milk is approximately 85 percent. the average cow milked 305 days and had an average of 60days dry. If this value increases above 85 percent. then the dairyman may be removing cows from the herd at the end of lactation. There was a signi- ficant difference between the strata on the number of cows sold the previous year (Table 26). TABLE 26. Average Number of Cows Sold per Herd by Stratum .1! 33 £- Stratum one 37 15.4* 10.1 Stratum two 37 11.9 7.5 *Significant at p 4:.05 70 The number of cows sold has a positive simple correlation with the dependent variable (+0.23)., There was a.signi- ficant difference between strata in the percent days in milk (Table 27). TABLE 27. Average Percent Days in Milk .11 ii. @- Stratum one 37 88.6* 2.3 Stratum two 37 86.5 2.8 *Significant at p <..05 The simple correlation between percent days in milk and average production per cow was +0.39. This is in contrast to the previously mentioned work of Miller where it was stated that percent days in milk is a major factor influ- encing average production. The present study has less variation in the percent days in milk. as well as a much smaller sample size than the previous study by Miller. Calves and Young Stock The future of a dairy herd begins with the raising or purchase of the herd replacements. Calves must be properly raised to obtain heifers which are of sufficient size for breeding at the recommended age of 14-16 months. Differences in raising calves and young stock between the two strata were hypothesized. Seventy-six percent and 81 71 percent of the respondents in strata one and two indicated they fed a milk replacer starting.an.average of 7.3 and 5.8 days after birth for strata one and two respectively. Neither of these variables entered the multiple regression equation. Most of the feeding of the calves is done by the operator. wife and children. with the children doing a higher percentage of the feeding of calves in stratum one than in stratum two. Table 28 gives the percentage of the respondents and other persons who feed calves at different seasons. TABLE 28. Percent Distribution by Stratum of Personnel Who Feed Calves Variable Description Stratum l Stratum 2 ‘ Percent Percent Operator-winter 37.84 56.76 Operator-summer 35.14 45.95 Wife-winter 21.62 21.62 Wife-summer 21.62 21.62 Children-winter 40.54 30.56 Children-summer 43.24 38.89 Hired labor-winter 5.41 8.33 Hired labor-summer 5.41 11.11 Other-winter 2.70 2.78 Other-summer 2.70 2.78 72 There is no significant difference in these percentages. Calves are weaned at an average age of 58.5 days by the respondents in stratum one and 57.1 days of age by the res- pondents in stratum two. Items used to determine time of weaning varied from farm to farm. therefore. factors which may determine weaning were investigated. Table 29 gives the factors and the percentages of owners in stratum one and two who use those factors to determine weaning time. TABLE 29. Percentage of Dairymen in each Stratum who Indicate the Calves are Weaned by the Various Factors_ Variable Description §tratum one Stratum two Percentage Percentage Age 29.7 21.6 Amount of grain and hay consumed 24.3 32.4 Condition of calf 32.4 45.9 Combination of above factors 32.4 24.3 Percentage in each stratum using any one item was not significantly different. Seventy percent of the farmers in both strata purchased a calf starter: the remainder fed calves herd ration or a special calf ration mixed on the farm. Contracting to rear heifers is becoming popular in some parts of the country. As a matter of interest. we 73 investigated how many dairymen would contract their heifer raising from three days of age to the springing.heifers stage for $300.00. Eleven percent of the respondents in stratum one and 31 percent of the respondents in stratum two indicated they would. Twenty-two percent.and 14 per- cent of strata one and two respectively indicated.they didn't know. or had not thought about it. .Sixty-eight percent and 56 percent of owners in strata one and two. respectively. indicated that they were not interested in a contract arrangement. Most dairymen in both strata sell bull calves rather than rear them. The majority of the bull calves were sold at seven days of age with a range in price from $40.00 to $50.00. Forty-three percent of the dairymen in stratum one indicated they sold an average of five bred heifers a year and received an average of $546.25. The eight respondents in stratum two who sold bred heifers averaged 4.4 heifers and received $478.13 each for these heifers. Four dairymen in each of the strata indicated they sold an average of 2.0 and 3.3 open heifers per year and received an average of $231.25 and $222.50 for owners in stratum one and in stratum two respectively. Nineteen percent of the dairymen in stratum one indicated they purchased some heifers compared to 30 per- cent of those respondents in stratum two who indicated they purchased heifers. 7a Colostrum is the first milk secreted by the mammary gland after parturition. It is higher in energy andother milk constituents than normal milk. The excess colostrum not utilized by the calf can successfully be fed to other calves if diluted with water. rather than being wasted. There was no significant difference between strata in the percentage of owners that utilize the colostrum for feeding other calves. Supplemental Infogmatigg Additional information on each herd in both strata was obtained from the DHI program master file on May 1. 1972. This information was the rolling herd average infor- mation. such as average number of cows. average pounds of milk per cow. pounds of milk fat per cow. average percent fat of milk. percent days in milk and gross value of the product. As would be expected. there was a significant difference between strata on the average pounds of milk per cow. the average pounds of milk fat per cow. the percent days in milk and the gross value of the product (p < .01). The average pounds of milk per cow was used as the depend- ent variable. There was a high positive multiple correla- tion between the variables mentioned and the average pounds of milk per cow. Average pounds of milk fat and gross value of the product were removed from the final least squares addition and deletion models because of the high single correlations obtained. 75 Information was obtained on the number of cows in the herd with a sire number reported. There was a signi- ficant difference between strata in the number of sires so reported (Table 30). TABLE 30. Percent of Cows in each Stratum with Sire Numbers Reported N Percentage Stratum one 37 91.6** Stratum two 37 62.9 **Significant at p < .01 The number of cows in a herd with sire reported. who had a predicted difference. was significantly different between strata (Table 31). TABLE 31. Percent of Cows in each Stratum whose Sire had Predicted Differences Information N Percentage Stratum one 37 73.8** Stratum two 37 ‘ 49.5 **Significant at p < .01 This variable was not included in the multiple regression equation because of unequal observations but had a positive 76 simple correlation with average pounds of milk per cow (+0.38). There was a significant difference between strata in the average predicted difference of the sires of the cows in the herd at the present time (Table 32). TABLE 32. Average Predicted Difference of Sires of Cows in Herd at Present Time .11 i? is" Stratum one 37 374.6** 189.4 Stratum two 35 21501 17801 **Significant at p.< .01 This variable was not entered into the multiple regression equation because of unequal numbers in each strata. The number of calves with sires reported was also analyzed. There was no significant difference between strata. nor was there a significant difference in the aver- age predicted difference of the sires of the calves in the two strata. Motivation ‘ This is the most difficult section of the survey to obtain meaningful data and to analyze because of the lack of quantitative measures. There has been very little research done on the motivations that a farmer has for be- ing in the dairy profession. Many of the questions asked are of an open ended nature giving a wide variety of answers. 77 The only significant difference detected in.ana1ysis of this section cf the survey was on the.milk production goals of the dairyman (p < .01). Those dairymen in stratum one have as their average milk production goal 19.185 pounds of milk. while the dairymen in stratum 2 had a goal of 16.333 pounds of milk produced per cow. Not all the farmers had a goal-~only 88 percent of those in stratum one and 81 percent of the respondents in stratum two gave a definite goal. Most dairymen in both strata did not give any other overall goals for the future. They either did not have them formulated in their minds or they did not want to disclose this information. The farmers in both strata belong to an average of 3.4 organizations. the majority of the dairymen attending over 75 percent of the meetings of these organizations. Most of the respondents in both strata received help from their parents or wives' parents through a partnership arrangement. However. 22 percent of the dairymen in stratum one and 16 percent of those in stratum two did not receive help from anyone. Most of the decisions on purchases for the farm or house are made between the operator and his wife. -The next most important decision-making group is the total. family. followed by the operator and.another.member of the family. The decisions made by the operator and other members of the family are those decisions which are made in partnership-type operations. 78 Dairymen in both strata were asked which they enjoyed doing the most. field work or dairying. Table 33 gives the percentage in each stratum of the most enjoyed activity. TABLE 33. Percentage of the most enjoyed activity by stratum Variable Description Stratum ong Stggtum two Percentage Percentage Dairy 54.05 29.73 Field work 8.11 29.73 Combination 37.84 40.54 There was no significant difference between strata for either of the three items when analyzed using the two sample t-test. Most dairymen surveyed indicated that if they were not in the farming business. they would be in a profession which was oriented toward agriculture. The majority of the respondents indicated that they would not be happy working for someone else. Multiple Cgrrelation The data were summarized using a CD0 3600 Computer and utilizing the least squares addition of variables to a multiple regression equation. The initial least squares addition of variables was divided into three separate analyses. This was necessary because of the number of 79 variables and the number of observations involved as well as the limits of the computer. Variables which had a simple correlation greater than .50 were included in the same initial computer output. The least squares addition procedure was used initially since it could not be deter- mined which variables would influence the variation in pounds of milk produced. The variables which were added to the multiple regression equation with the stopping criterion that if a variable. under consideration for addition to the regression. is entered into the regression the probability of type I error for the F statistic for that variable will be less than or equal to .10. This stopping criterion was used throughout the entire analysis. By the method of least squares addition.a.numher of vari- ables were retained from each of the initial analysis for inclusion in a final analysis. There are limitations which are involved in combining variables from three separate results into a final least squares deletion procedure for multiple correlation and regression coefficients. One of the problems involved may be the correlation of‘variablee not in the same computer results. Another problem.involved is the number of total observations (74) and the number.of. variables involved (344). In survey.information..an attempt should be made to limit the variable to less than the number of observations. This will help to circumvent some of the problems involved. 80 Table 34 gives the final ten variables. which are the result of the least squares deletion solution for a multiple regression. The final multiple correlation co- efficient for these variables is .9279. This coefficient measures the closeness with which the regression plane fits the observations. Listed in Table 34 is the variable description. regression coefficients and partial correla- tion coefficients. TABLE 34. Significant Variables in Multiple Regression Equation with Regression Coefficients and Partial Correlation Coefficients *See Appendix Table Three for variable description Variable Regression Coefficient Partial Correla- tion Coefficient Constant -6388.65 - HPC 0.30 0.70 RML - 440.49 -0.23 PDAYM 128.22 0.34 PPIP ~951.50 —0.45 UN -1221.50 -0.28 ’ PLH .30.43 .0.40 TOCG 46.07 0.33 LD4H -556.22 -0.28 LBSG 46.10 0.21 LPC 0.28 0.42 gun- . ...-A.I. 9391's,... . 81 In analysis of the variables. the two of the three most important variables are high producing cow and low producing cow in the herd for 305 days. These two vari- ables would be quite automatic. when the definition of the stratum was by highest producing herds and average pro- duction herds. There was a significant difference between the strata for both these variables. The second variable of importance was having a portable pipeline system. There was not a significant difference between strata. however there were more users of portable pipelines in the average strata. Percent labor hired (PLH) has a negative effect on the average production per cow. There was a difference between stratum for this variable which was not significant (6.8 percent vs. 11.0 percent). The intention of renting more land (RML) in the future was a significant variable. There was also a significant difference (p <:.05) between the strata for this variable. Twenty-four percent of the dairymen in stratum one and 51.4 percent of the respondents in stratum two indicated they planned to rent more land in the future. The other variables which have a positive effect on average production are the percent days in milk (PDAYM). pounds of grain to top cow (T000). and pounds of grain to average cows (LBSG) in the herd. Negative effects not previously mentioned were the use of a universal (UN) milking machine and being a leader in the 4-H program (LD4H). The multiple regression coefficients would be used as the 82 coefficients of the variables included in the prediction of milk production. SUMMARY The dairy business is a very important segment of the Agriculture economy in Michigan. In 1970. the Dairy products sold accounted for 29 percent of the total agri- culture farm marketing in the state. Average pounds of milk per cow has been increas- ing in Michigan over the last decade. There has also been a steady increase in the average pounds of milk per cow for those herds enrolled in the Dairy Herd Improvement Program. The average production of all herds on official test in this program is 12.973 pounds of milk. Hewever. within this group of herds on test there is a wide varie ation in average production. Some herds produce less than 9.000 pounds of milk per cow and others produce in excess of 19.000 pounds of milk. The basic question. which this survey of 80 dairy farms attempted to answer. was.what'are the differences in management practices and other variables between average production herds and top production herds when paired according to herd size and County. The specific objec- tives of the study were to examine certain management factors known to influence milk production and to try to 83 84 identify some unknown factors such as motivation. A personal survey was conducted which included the top 40 herds based on average pounds of milk per cow and their pairmate which had approximately average DHI production. The questionnaire was divided into six sections. demographic data. feeding. breeding, reproduc- tion and herd health. calves and young stock. and motiva- tion. The data were analyzed using a two sample T-test to determine significant differences between strata. The data were then subjected to a multiple regression. by least squares procedures. to circumvent the problem of univariate analysis. since most of the factors involved are essentially multivariate. The procedure of least squares allows one to make specific independent tests of significance on the direct effect of the various factors. however. this also permits one to ascertain which combina- tion of variables is the most reasonable predictor of the dependent variable. The owners of high producing herds when compared to average producing herds would: 1. Not use breeding and dry dates on herd report 2. Be less likely to be a 4-H leader 3. Be less active in lodges and other non-related farm activities 4. Be more likely to have a will 5. Be less likely to rent more land in the future 6. Be less likely to have his children substitute for milking and chores 7. 8. 9. 10. ll. 12. 13. 14. 15. l6. 17. 18. 19. 20. 21. 22. 85 Be more inclined to make a county milk record with a little extra effort Have a dry cow mastitis treatment Vaccinate his calves for P13. IBR and BVD Have more of his cows identified by sire number Have a higher percentage of his cows registered Milk earlier in the morning in the summer Have higher producing cows Have higher low producing cows Have heavier cows Have a higher single milk weight Feeds more pounds of grain to average cows Have a lower grade protein percent in the hay Feed more grain to his top cow Have more cows with milk fever and a higher veterinary bill Have higher milk production goals. higher per- cent drop in milk. higher value of product. higher number of cows with sires who have a predicted difference. Have a higher predicted difference of the sires of the cows in his herd. In the least squares deletion solution to a multiple regression equation, all factors are included in the initial equation and are then deleted one at a time until a specified stopping criterion. The following are the final variables which were included in the equation in order of importance. 1. Highest producing cow 2. Lowest producing cow 86 3. Use of portable pipeline 4. Percent of labor hired 5. Pounds of grain to top cow 6. Percent days in milk 7. Universal milking machine 8. Leader in 4-H 9. Pounds of grain to average cow 10. Rent more land The foregoing variables have a final multiple correlation coefficient of .9279. This research project has shown there are signifi- cant differences between high producing herds and average production herds of about the same size. located in the same county. It must be impressed upon the dairymen that some of the decisions he makes today will have their effect in six or seven years. This is evident in the difference be- tween the two strata regarding the predicted difference for milk of the sires of the cows in the herd. these decisions were made at a minimum of four years ago. Time spent using management tools such as the DHI report appear to increase milk production per cow. Every dairyman is an individual. with certain habits. personal preferences. drives. capabilities. and certain ideas. The dairymen's routine is not always the same from day to day. therefore. it is difficult to establish pat- terns of behavior. The area of motivation is a very diffi- cult subject on which to question a dairyman. because he 87 may give you what is a sociably acceptable answer to a question rather than his true feeling. This area of motivation will require additional research and investiga- tion to determine why a dairyman strives for high milk production per cow. is it prestige. recognition. status symbol. increased income. additional security. a goal or a desire to do the best he is capable of doing or is it to prove something to himself or others. It is evident from this survey that not all dairymen are interested in making or breaking records. There are many other variables and factors which V were not included in this survey which may have an influ- ence on high milk production. I am sure many of these variables are difficult to measure or determine. A more indepth study would be needed to determine all the factors related to milk production. 5. 6. 7. 9. 10. BIBLIOGRAPHY Albright. J.L. 1962. Analysis of Land. Capital. Labor and Management in Large Commercial Dairy Herds in Los Angeles County. 16th Intern. Dairy Congress. Sect. 932s Backstrom. C.H. and Hursh. G.D. 1968. Survey Research. Northwestern University Press. Evanston. Illin018. Bath. D.L. and Bratton. C.A. 1970. A study of feed- ing and management practices on New York dairy farms. Animal Science Mimeograph Series No. 10. Boettinger. H.M. 1970. To Manage Tomorrow. Bell Tglephone Magazine. Nov.-Dec. Bradford. L.A.. and Johnson. G.L. 1967. Fagm Manage- ment Analysis. John Wiley and Sons Inc.. New York. Brown. R.W.. Blobel. H.G.. Pounder. W.D.. Scholm. O.W.. Slonetz. L.W.. and Spencer. G.R. 1963. Current con- cepts of bovine mastitis. The National Mastitis Council Inc.. Hinsdale. Illinois. Buxton. B.M. and Hay. M.J. 1969. Milk production practices on dairy farms in the Lake States. Mimeo Report. Department of Agriculture Economics. University of Minnesota. Csorba. J.J. and Butler. C.C. 1958. Dairy cows: Housing and methods of milking. Economic Research Service Publication No. 15. U.S.D.A. Dickinson. F. 1968. Dollars and cents values of utilizing sire summaries: Effective use of breeding values of dairy cows and sires for production traits. Proceedings: National Extension Seminar: Madison. Wisconsin. Foley. R.C.. Bath. D.L.. Dickinson. F.N.. and Tucker. H.A. 1972. Dairy Cattle: Principles. PracticesI Problems and Profits. Lea and Febiger. Phi adelph a. Penney vania. 88 11. 12. 13. l4. 15. 16. 17. 18. 19. 20. 21. 22. 89 Gangwar. P.C.. Bronton. C. and Evans. D.L. 1965. Reproductive and physiological responses of Holstein heifers to controlled and natural climatic conditions. Jo Dairy SCie “83222e Gaunt. S.N. 1968. Guidelines for establishing and implementing an effective educational program--use of visual aids: Effective use of breeding values of dairy cows and sires for production traits. Proceed- ings: National Extension Seminar. Madison. Wisconsin. Hess. C.V. and Miller. F. Some personal. economic and sociological factors influencing dairymen's action and success. Agri. Expt. Station Bulletin 577. Pennsylvania State University. Hillman. D.. Huber. J.T.. Emergy. R.S.. Thomas. J.W.. and Cook. R.M. 1970. Basic dairy cattle nutrition. Department of Dairy Science Mimeo D-239. Michigan State University. East Lansing. Hoglund. G.R. and McBride. G. 1970. Michigan's changing dairy farming. Research Report 96. Agri. Expt. Station. Michigan State University. East Lansing. Johansson. I. 1961. Genetic Aspect§_of Daigy Cattle Breeding. University of Illinois Press. Urbana. 11 inc Se Johansson. I. and Rendel. J. 1966. Genetics and Animal Breeding. W.H. Freeman and Company. San Francisco. California. Kerlinger. Fred N. 1964. Foundations of Behavioral Research. Holt. Rinehart and WinstOn Inc.. New York. Knisley. R.G. 1959. The variable affecting the milk- ing time in Michigan DHIA Herds. M.S. Thesis. Michigan State University. East Lansing. Kucker. W.L. 1970. Adoption of production testing and artificial insemination by Michigan dairy farmers. Ph.D. Thesis. Michigan State University. East Lansing. Light. R.G. 1970. Materials handling to expedite disposal of agriculture waste. Cooperative Extension Service. University of Massachusetts. Amherst. Light. R.G. 1966. Dairy systems development. Mimeo. Department of Agricultural Engineering. College of Agriculture. University of Massachusetts. Amherst. 23. 24. 25. 26. 27. 28. 29. 30- 31. 32. 33- 34. 9O Lush. J.L. 1968. Animal breeding research and appli- cation. present and future (North America): Animal breeding in the age of A.I. Symposium. College of Agriculture and Life Sciences. The University of Wisconsin. Madison. MacLachlan. D.L. 1967. A study of dairy chore labor under different systems of free stall housing. Ph.D. Thesis. Michigan State University. East Lansing. McDaniel. B. 1968. Production increases possible through use of USDA-DHIA sire summaries: Effective use of breeding values of dairy cows and sires for production traits. Proceedings: National Extension Seminar. Madison. Wisconsin. Miller. R.H. 1969. Dairy Herd Improvement Association herd averages. Characteristics of herds at different production levels. J. Dairy Sciences 52:3. Morrison. Frank B. 1954. Feeds and Feeding. The Morrison Publishing Co.. Ithaca. New York. Nielson. J. 1962. Aspects of management of concern to the basic researcher. Farm Management in the West - Problems in measuring management. Farm Management Research Committee of the Western Agricultural Economics Research Council - Conference Proceedings. Report No. 4 Denver. Colorado. Nutrient requirements of dairy cattle. 1971. National Academy of Science. Fourth revised edition. Oxender. W. 1972. Unpublished research. Plowman. R.D. 1968. Guidelines for establishing and implementing an effective educational program-- genetic tools available: Effective use of breeding values of dairy cows and sires for production traits. Proceedings: National Extension Seminar. Madison. Wisconsin. Robertson. A. and Rendel. J.M. 1950. The use of pro- geny testing with A.I. in dairy cattle. J. Genetics 50 8 21" 3’40 Roche. Frank. 1971. Personnel management guides for large scale dairy operations. Ph.D. Thesis. Michigan State University. East Lansing. Speicher. J.A. and Hepp. R.E. 1972. Calf mortality in Michigan dairy herds. Agri. Expt. Station Paper D 4968. Michigan State University. East Lansing. 35- 36- 37- 38- 39- 40. 91 Speicher. J.A. and Meadows. C.E. 1967. Milk pro- duction and costs associated with length of calving interval Holstein cows. Department of Dairy Science Mimeo D 154. Michigan State University. East Lansing. Speicher. J.A. and Lassiter. C.A. 1965. Influence of specified farm management factors on dairy farm net income. J. Dairy Science 48:1698. Steele. R.G.D. and Torrie. J.H. 1960. Princi Ice and Procedures of Statistics. McGraw-Hill Book Co. Inc.. New York. Suter. R.C. 1963. The management process. Journal of the American Society of Farm Managers and Rural Appraisers. October. 1963. Travis. V.C. 1971. A study of some personal and managerial traits of Southern Michigan Telefarm Dairy- men to determine their relationship to business success and form of business organization. M.S. Thesis. Michigan State University. East Lansing. Willett. L.B. and Albright. J.L. 1968. Dairy manage- ment in larger herds. J. Dairy Science 51:138. APPENDIX 92 93 TABLE lo Herd pairSe Strata l _Str_a_ta 2 ,_ p... {33?52‘53 it??? {33325: 2‘33???" 1 ' 90 17.349 78 13.223 2 26 16.917 34 13.016 3 41 18.964 ‘ 32 12.566 4 47 16.924 58 12.822 5 46 17.859 49 13.018 6 112 16.715 93 12.790 7 24 16.909 25 12.645 8 55 17.066 57 12.341 9 46 16.579 43 12.995 10 53 16.747 45 13.029 11 45 16.618 44 13.021 12 40 16.853 50 12.269 13 50 17.230 47 13.049 14 21 18.353 44 12.725 15 33 16.894 33 12.490 16 24 16.667 43 13.266 17 50 18.847 55 12.742 18 114 17.581 131 13.075 19 34 17.852 32 13.145 20 43 17.879 46 14.404 21 45 17.004 40 12.925 22 59 17.249 42 13.684 23 47 17.387 42 12.769 94 TABLE 1. Herd pairs (continued) Strata l Strata 2 2;... $3352.53 27.8%" 33525: 233%" 24 67 16.932 46 13.022 25 40 17.070 31 13.416 26 39 16.962 50 13.351 27 45 18.853 42 12.978 28 83 19.319 69 12.022 29 19 16.900 23 13.369 30 59 17.011 58 13.162 31 50 17.174 51 13.013 32 39 20.429 41 13.516 33 39 16.981 34 13.582 34 36 16.737 30 13.075 35 39 16.836 51 13.223 36 42 16.638 40 12.451 37 36 17.223 38 12.798 u = 48.05 17.392.1 47.75 12.999.6 0': 21.50 880.4 19.71 435.5 TABLE 2. 95 An analysis of high and average milk production dairy farms. personal interview questionnaire. Herd code Strata -- High Low A. Demographic data 1. 2. 3. 4. 5. 6. 7. 8. 9. Age of operator Years of education of operator after 8th grade Years of farming Marital status -- 1. married ____ 2. single Age of wife ____ Wife's education. Is she (or has she) worked for pay. full time ____. part time ____? Years on DHI test How much time a month do you spend studying the DHI report? Rate the listed items on DHIA monthly report in order of importance. 1. Test day average 2. Rolling herd average 3. Lactation to date 4. Test day production 5. $ value expected in 305 days 6. Pounds of grain 7. Last bred and due date 8.' Action needed 9. Other 10. Other 11. Total number of children at home 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 96 Boys over 16 at home Girls over 16 at home _____ Boys under 16 at home Girls under 16 at home ____ Have you ever been a leader or are you presently a leader in 4-H? Were you raised on a dairy farm? 1. Yes 20 NO What magazines do you read regularly? 1. 2. 3. 4. 5- 6. 7. 8. 9. 10. What activities do your children participate in out- side of agriculture related activities? 1. School 2. Church 3. Scouts 4. Sports 5 e Other What activities do you (the operator) participate in outside of agriculture related activities? 1. Political 2. Church 3. Lodges 4. Recreational 5. School 6. Scouts ‘ 7. Other What activities do you (the wife) participate in outside of agriculture related activities? 1. Political 2. Church 3. Lodges 4. Recreational 5. School 6. Scouts 7. Other 22. 23. 24. 125. 215. 2P7. 2E3. 259. 3CL. 131. 97 Do you listen to agriculture programs on the radio or television? 1. Yes 2. No What would happen to the farm when you retire or if something happened? 1. Sold 2. Estate 3. Will 4. Wife 5. Family 6. Other (Wife) How much do you participate in the dairy farm operation? 1. Records 2. Chores 3. Finances 4. Field work 5. Other 6. Plan 7. Consult on operation (Children) How much do they participate in the dairy farm operation? 1. Records ' 2. Chores 3. Finances 4. Field work 5. Other 6. Consult on operation 70 Plan Number of acres operated? ________ How many tillable acres do you own? How many tillable acres do you rent? In the future do you plan to buy more land? 1. Yes _____ 2. No In the future do you plan to rent more land? 1. Yes 2. No What would be the sale price per acre for your farm on today's market if it were sold? 32. 33- 34- 35- ENE. :17. 138. 98 What enterprise do you think you get the most gross income from on the farm? 1. Dairy 2. Crops 3. Hogs 4. Beef cattle‘ 5 e Other What percent of your labor is hired? What percent of your labor is wife and children? How would you describe your dairy operation-type of facilities? 1. Stanchion barn or tie stalls 2. Open lot-free stalls and parlor 3. Warm enclosed-free stalls and parlor 4. Cold covered-free stalls and parlor 5. Loose housing and parlor 6. Stanchion barn and parlor 7. Stanchion barn and free stalls 8. Stanchion barn and loose housing 9. Other 7 How is milk moved from milking area to milk house? 1. Pipeline 2. Portable pipeline 3. Small portable pull tank 4. Carried 5. Other If you have a parlor. what type of parlor? 1. Side open 2. Herringbone 3. Walk thru ’ 4. Home made 50 Other What percent of your cows are registered? 7"2‘ : A! ‘19! ‘. wad-mini} 39- 40. 41. 42. 43. 44. 45. 46. 99 Do you feed your cows in dry lot during the summer? 1. Yes 2. No What time in the morning do you usually start milking? Winter Summer What time in the afternoon do you usually start milking? Winter Summer What size of vacuum line do you have? 1. 3/4 inch 2. 1 inch 3. 1 1/4 inch 4. 1 1/2 inch 5. 1 3/4 inch 6. 2 inch 7. Over 2 inches What brand of milking machine are you using? 1. Surge ____ 2. DeLaval ____ 3. Bou-Matic ____ 4. Universal ____ 5. Zero ____ 6. Sta-rite ____ 7. Jamesway ____. 8. Condi _;__ 9. Combination ____ 10. Other ____ How long does it take to milk -- per milking? . How many cows? . How many persons do you have at milking time. washing udders and handling machines? Men Women Children Ages If the need arises for a substitute. who do you have do your milking and chores? 1. Neighbor 2. Wife 3. Neighbor boy 4. Children 5. Other farm labor 6. Don't know 47. 48. 49. 50- 51. 52. 53- 54- 55. 100 How much time do you spend excluding manure handling? Summer Winter Feeding What was your average bacteria count last month? What problems do you have with your high producing cows that you do not have with your average cows? 1. Going off feed ____ 2. Reproduction problems 3. Mastitis and udder problems 4. Ketosis _____ I 5. Milk fever ____ 6. Other ____ List the order of importance of the following items for culling cows 1. Milk production ____ 2. Fat production ____ 3. Feet and legs ____ 4. Mastitis and udder problems 5. Reproduction ____ 6. Type 7. Disposition ____y 8. Other ____ How many pounds of milk did your highest producing cow give last year in 305 days? How many pounds of milk did your lowest producing cow give last year in 305 days? What is the average weight of the last 5 cows you sold? What is the highest pounds of milk given by any of your cows in a single day? If you had a cow that could make a world record with a little effort. would you do it ? A state record 1. Yes 2. No A county record 1. .Yes 2. No 56. 57- 58. 59- 101 What is your average calving interval? FEEDING What has been the composition of your grain mix since January 1? 1. Shelled corn ____ Amount 2. Oats Amount 3. Wheat Amount 4. Beet pulp Amount 5. Protein supple.__ Amount 6. Salt Amount 7. Minerals Amount 8. Corn & cob Amount 9. H.M. Corn Amount 10. Vit. A Amount 11. Barley Amount 12. Molasses Amount 13. Linseed meal ____ Amount 14. Soybean meal Amount 15. Vit. D Amount 16. Other Amount 17. Amount 18. Amount Do you change your grain mix year? 1. Yes ____ 2. No If no supplement is included in the ration. is any additional supplement fed? 1. Yes 2. No PrOte Prot. Prot. Prot. Prot. Prot. PrOte Prot. Prot. Prot. Prot. Prot. Prot. PrOte ENE ENE ENE ENE ENE ENE ENE ENE ENE ENE ENE ENE ENE ENE composition during the 60. 61. 62. 63. 64. 65. 102 3. How much? 4. How Fed? a. Mixed with feed by hand b. Top dressed c. Other Do you know the crude protein percent of your corn silage? 1. Yes 2. No 3. If yes. what is it? Do you add any additive to your corn silage? 1. Yes 2. No If yes. what? 1. Urea . 2. Commercial additive 3. Anhydrous ammonia 4. Other How much per ton? Do you have your hay tested? 1. Yes 2. No If yes. what percent protein? 1st test 2nd test When do you cut your lst cutting of hay? After April 30 In your winter feeding program. how many pounds of grain per day was fed to your average producing cows? What is your feeding guide? 1. According to milk production ____ 2. According to fat production ____ 3. According to cows condition 4. Give all the cows the same 66. 67. 68. 69. 70. 71. 72. 73- 74. 76. 77. 78. 79. 80. 103 5. Other How many pounds of grain does the average dry cow get? Do you increase the pounds of grain to dry cows be- fore calving? le Yes 20 NO How many pounds of grain are they receiving prior to calving? How many pounds of corn silage per day do you feed your milking cows in an average year? How many pounds of hay is fed per day to your milking cows in an average year? How many pounds of haylage is fed per day to your milk- ing cows in an average year? If haylage is fed. what is the percent protein? Is salt and mineral available free choice? 1 How many pounds of grain a day did your top cow receive last year? Do you think your cows would eat more grain? 10 Yes 2e NO If yes. why don't you give them more? BREEDING At what age do you breed your heifers? At this age. how much do they weigh? What percent of the cows are bred artificially? What percent of the heifers are bred artificially? In what order do you rank the following in making your decision about what bull to use? 81. 82. 83. 84. 85. 86. 87. 104 l. Dam's milk and fat production 2. Ancestry (pedigree).____ 3. U.S.D.A. predicted difference ____ 4. Pleasing color markings 5. Type traits of sire's daughters ____ 6. Repeatability factor _____ 7. Price of sire 8. Price of semen 9. Conception rate 10. Other Are cows examined after calving to determine if they are ready to rebreed? - 1. Yes 2. No Are cows examined for pregnancy after breeding by a veterinarian? 1. Yes 2. No How long after calving are your cows bred for the first time? How many years have you used A.I.? What is the average predicted difference of bulls used now? Do you think the breeder with 50-100 cows should be trying to rear and prove his own bulls? 1. Yes 2. No REPRODUCTION AND HERD HEALTH How many cows aborted in the past year?’ How many times a day are your cows observed for heat in the winter? By whom? l. Operator 2. Wife 3. Children 4. Hired labor 88. 89. 90. 91. 92. 93. 94- 95- 96. 97- 98. 99. 100. 101. 105 How many times a day are your cows observed for heat in the summer? By whom? 1. Operator 2. Wife 3. Children 4. Hired labor How long a time each day are the cows observed for heat? How many cows had milk fever last year? How many cows had ketosis last year? What was your veterinarian bill last year? Does the veterinarian visit your farm in between the times you call him? 10 Yes 2. NO Do you have a dry cow mastitis treatment program? 1. Yes 2. No Do you use a teat dip? Do you use a strip cup? Are your calves vaccinated for l. Brucella 2. PI3 (shipping fever) 3. IBR (red nose) 4. BVD (virus diarrhea) 5. Porterurella (shipping fever) 6. Leptospirosis ____. How many calves died last year? 1. Under 3 weeks.____ 2. 3 weeks-3 months 3. 3 months-l year ____ How many cows were sold last year? How many cows died last year? CALVES AND YOUNG STOCK Do you feed your calves a milk replacer? 1. Yes 2. No 102. 103. 104. 105. 106. 107. 108. 109. 106 If yes. when do you start? Who usually does the feeding of the calves? Winter Summer Winter Summer 1. Operator 2. Wife 3. Children 4. Hired labor 5. Other How old are your calves when they are weaned? What determines when the calves are weaned? 1. Age 2. Amount of grain and hay consumed 3. Condition of calf'____ 4. Combination Do you feed a purchased calf starter? 1. Yes ____ 2. No ____ If no calf starter. what is fed? 1. herd ration 2. special calf ration -- mixed by farmer 3. Other ____ Would you contract your heifer raising from 3 days of age to springing heifers if someone would do it for $300? 1. Yes ____ 2. No 3. Undecided ____ What do you do with most of your bull calves? 1. Sell ____ 2. Raise ____ If sold. at what age 8 How much do you get at this age? Do you sell any bred heifers? 1. Yes ____ 2. No If yes. how many a year? 110. 111. 112. 113. 107 What do you usually receive? Do you sell any open heifers? 1. Yes ____ 2. No ____ How many a year? What do you usually receive? Do you feed calves all of your colostrum? 1. Yes 2. No Do you purchase any heifers? 1. Yes 2. No MOTIVATION When you started to farm did you receive any help from your parents or your wife's parents such as: 1. Land 2. Cattle 3. Money 4. Labor 5. Machinery 6. Was this a partnership before you became the operator? 7. Advise 8. Other 114. What organizations are you active in? Officer Pay Dues On Committee Attend 75% Meetings 1. 2. 3. 108 115. Which do you enjoy the most. field work or dairying? 1. Field work 2. Dairying 116. What goals regarding milk production do you have for the next five years? 117. What overall goals do you have for the future? Operator Wife 118a. Who is involved in making a decision on a purchase? 1. Operator only ____. 2. Wife only ____ 3. Operator & wife ____ 4. Operator & other member of the family 5. Wife and other member of the family ____ 6. Total family ____ 118b. What criteria do you use for making decisions? 119. From the standpoint of the family. what are the advent- ages of farm living as compared to city living? 120. What are the disadvantages to farm living? 121. What other profession would you like to be in. if you were not in farming? 122. Do you think you could be happy working for someone else? 123. What do you consider the best possible source of information on: . l. Cropping 2. Nutrition of dairy cattle 3. Breeding of dairy cattle 4. Dairy buildings 5. Dairy equipment TABLE 3. Variable Name St. PNO Age Ed. YDFM MOS YAge YWork YTest TMire TDA RHA LTD TDP DUAL LBGR BDDA ACTN OT NOCH 3016 'Card format. Variable JJBEEEE. \0 CD \‘I O\U'\ (TU N H H O H F‘ m P‘ 13 14 15 l6 17 18 19 20 21 22 109 Variable Description Strata Pair Number Age of Dairymen Education Years of Dairy Farming Marital Status Wife's Age Wife's Work Years on Test Card No. l +4 :a P‘ +4 :d Id h‘ ea Time Spent Studying DHIA Report (Monthly Minutes) H Rank on Test Day Average 1 Rank on Average Rank on Date Rank on Daily Production % Use of Dollar Value % Use of Lbs. of Grain % Use of Breeding and Rolling Herd Lactation to Dry Dates % Use of Action Needed Col. % Use of Other Parts of DHI Report % Use of Other Parts of DHI Report_ Number of Children Number of Boys Over 16 Col. 9 10-11 12-13 14-15‘ 16-17 18 19-20 21-22 23-24 25-29 30 31 32 33 34 35 36 37 38 39 40 41 TABLE 3. Variable Name G016 B06 G06 LD4H RDF MAGR HD FJ SF DHM MF PF RD FA HW MIHW CHW T0 BF 0TH CSC CSH 110 Card format (continued). Variable Number VariableQescription 23 Number of Girls Over 16 24 Number of Boys Under 16 25 Number of Girls Under 16 26 Leader in 4-H Program 27 Raised on a Dairy Farm 28 Number of Magazines Read 29 Hoard's Dairymen 30 Farm Journal 31 Successful Farming 32 Dairy Herd Management 33 Michigan Farmer 34 Prairie Farmer 35 Reader's Digest 36 Farmer's Advance 3? Holstein World 38 Michigan-Indiana .Holstein World 39 Canadian Holstein World 40 Farm Quarterly 41 Top Operator 42 Big Farmer 43 Other.Magazines 44 Children-School Activities 45 Children-Church Activities Card No. H +4 +4 +4 +4 :4 +4 +4 :4 +4 +4 +4 r4 +4 +4 +4 +4 44 45 46 47-48 49 50 51 52 S3 54 55 56 57 58 59 6O 61 62 63 64 65 TABLE 3. Variable Name CSCO CSP COT OPO OCH OLO ORE OSC OSCO OOT WPO WCH WLO WRE WSC WSCO WOT ROT SOL Variable Number 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 111 Card format (continued). Variable Description Children-Scouts Activities Children-Sports Activities Children-Other Activities Operator-Political Activity Operator-Church Activity Operator-Lodge Activity Operator-Recreational Activity Operator-School Activity Operator-Scout Activity Operator-Other Activities Wife-Political Activity Wife-Church Activity Wife-Lodge Activity Wife-Recreational Activity Wife-School Activity Wife-Scouts Activity Wife-Other Activities Listen Radio or TV Farm Sold Card No. ...a N NNNNNN 67 68 69 70 71 72 73 74 75 12 13 14 15 16 17 18 20 TABLE 3. Variable Name EST WIL WIF FAM 0TH WPR WPC WPF WPFW WPOT WPPL WPCO CPR CPC CPF CPFW CPOT CPCO CPP Variable Number 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 112 Card format (continued) Variable Descriptipp Estate Will Wife Family Other Arrangements Card NO. NNNNN Wife-Participate Records 2 Wife-Participate Chores 2 Wife-Participate Finances Wife-Participate Field Work Wife-Participate Other Wife-Participate Planning Wife-Participate Consulting Children-Participate Records Children-Participate Chores Children-Participate Finances Children-Participate . Field Work Children-Participate Other Children-Participate Consulting Children-Participate Planning 28 29 3O 31 32 33 34 35 36 37 38 39 TABLE 3. Variable Name NAOP NAOW NAR BML RML SAPA DA CR HO BC OTHE PLH PLWC PLO SB OLFS WEFS CCFS LH SBP SBFS 113 Card format (continued). Variable Number 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 Variable Description Number of Tillable Acres Operated Number of Tillable Acres Owned Number of Tillable Acres Rented Buy More Land Rent More Land Sale Price Per Acre Gross Income - Dairy Gross Income - Crops Gross Income - Hogs Gross Income - Beef Cattle Gross Income - Other Percent Labor Hired Percent Labor Wife & Children Percent Labor Operator Stanchion Barn Open Lot-Free Stalls Warm Enclosed-Free Stalls Card N00 NNNNNNN N 2 Cold Covered-Free Stalls 2 Loose Hbusing 2 Stanchion Barn & Parlor 2 (Stanchion Barn & Free Stalls 40-43 44-47 48-51 52 53 54-5? 58 59 6o 61 62 63-65 66-68 69-21 72 73 TABLE 3.. Variable Name SBLH Other PIP PPIP SPT CA Ther PSO PHE PWT PHM POT PGR DL 30 PDPP GC DOT MTMW MTMS MTAW MTAS Variable Number 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 114 Card format (continued). Variable Description Stanchion Barn & Loose Housing Other Housing Pipeline Portable Pipeline Small Pull Tank Carried Other Parlor Side Open Parlor Herringbone Parlor Walk Thru Parlor Home Made Parlor Other Percent Cows Registered Feed in Dry Lot Strip Graze Part Dry Lot-Part Pasture Green Chop Other Milking Time A.M. Winter. Milking Time AeMe Summer Milking Time P.M. Winter Milking Time P.M. Summer Card No. UUUWWWWWKJWUUUNN U 22-24 25 26 27 28 29 30-32 33-35 36-38 39-41 TABLE 3. Variable Name VACl VAC2 VAC3 VAC4 VACS VAC6 VAC? SU DEL BM UN ZE STU JA CON COM MMO MMILK NOCO NMM NWM NCM 115 Card format (continued). Variable Number 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 variable_Dg§cription 3/4" Vacuum Line 1" Vacuum Line 1 1/4" Vacuum Line 1 1/2" Vacuum Line 1 3/4" Vacuum Line 2" Vacuum Line 2+" Vacuum Line Surge Machine DeLaval Machine Bou-Matic Machine Universal Machine Zero Machine Sta-rite Machine Jamesway Machine Condi Machine Combination Machine Other Milking Machine Minutes It Takes to Milk Number of Cows Number of Men Milking Number of Women Milking Number of Children Milking Card No. mwuuuuwwwuuwuuwwu wmuw 59-62 63-65 66 67 68 TABLE 3. Variable Name SN SW SNB SCH SOFL SDN TSFS TSFW AVBC HPOF HPRE HPMU HPKE HPMF HPOT RIMP RIFP RIFL RIMU RIRE Variable Number 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 116 Card Format (continued). Variable Descripticg Substitute-Neighbor Substitute-Wife Substitute-Neighbor Boy Substitute-Children Substitute-Other Farm Labor Substitute-Don't Know Time Feeding-Summer (Minutes) Time Feeding-Winter (Minutes) Average Bacteria Count High Producers-Off Feed High Producers- Reproduction High Producers- Mastitis & Udder High Producers-Ketosis High Producers-Milk Fever High Producers-Other Culling-Milk Prod. Culling-Fat Prod. Culling-Feet & Legs Culling-Mastitis & Udder Culling-Reproduction Card N00 #34??? r- 6010 69 70 71 72 73 74 75-78 12-15 16-21 22 23 24 25 26 27 28-30 31-33 34-36 37-39 ' 40-42 TABLE 3. Variable Name RITY. TIDI RIQ HPC LPC AWC HPM MWR MSR MCR ACI PPG TECWT CGM HMPD MWFE TD THERO CPCS CPCSP AAA 117 Card-format (continued). Variable Number 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 Variablquescription Culling-Type Culling-Disposition Culling-Other Highest Producing Cow Lowest Producing Cow Average wgt. of Cows Highest lbs.-Milk Per Day Make World Record Make State Record Make County Record Average Calving Interval Percent Protein-Grain Therms of Energy-Grain- CWte Change Grain Mix Add. Supplemental Feed Card No. c- 1r -: e- 4: .e .s c- 4: .e 5 5 5 Amt. of Add. Supplement/ Day Suppl. Mixed with Feed Suppl. Top Dressed Suppl.-Fed Other 5 5 5 Crude Prot. Corn Silage 5 Amte Cope in com Silage Add. Additive to C. Silage 92;. 43-45 46-48 49-51 52-56 57-61 62-65 66-70 71 72 73 74-77 12-15 16-19 20 21 22423 24 25 26 27 28-30 31 TABLE 3. Variable Name UREA COMM AMM HEROT ADPT WHT PERPRO DAYSH LBSG ATM ATF ATC GACS LBSGD ILBGC LBGPC LBCS DMCS CPCS ENECS LBSH CPH 118 Card format (continued). Variable Number 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 Vgriable Description Add Urea to C. Silage Add Commercial to C. Silage Add Ammonia to C. Silage Add Other to C.Silage Additive-How Much Per Ton Was Hay Tested Percent Protein-Hay First Cut-Hay-After 4-30 (Days) Lbs.-Grain-Average Cow Feeding-Guide-Milk Feeding-Guide-Fat Feeding-Guide-Condition Give All Cows Same Lbs.-Grain-Dry Cows Increase-Grain— Freshening Lbs.-Prior to Freshening Lbs.-Corn Silage-Day % D.M. of C. Silage C.Protein-Corn Silage ENE of Corn Silage Lbs. of Hay C.P. of Hay Card No. U1 \JIU‘lU’IU‘U‘U‘U‘ U't U'tU‘KAUtU‘U‘xU‘ 33 34 35 36-38 39 40-43 44-46 47-48 49 50 51 52 53-54 55 56-57 58-60 61-62 63-65 66-67 68-69 70-73 119 Rate ‘ TABLE 3. Card format (continued). Variable Variable Card Name Number Variable Description No. ENEH 213 ENE of Hay 5 LBSHA 21“ Lbs. of Haylage 5 DMHA 215 D.M. of Haylage 5 CPHA 216 C.P. of Haylage 6 ENEHA 217 ENE of Haylage 6 SMAF 218 Salt & Mineral-Free Choice 6 TOCG 219 Lbs.-Grain-Top Cow 6 ENG 220 Would Cows Eat More Grain 6 AGEH 221 Age to Breed Heifers 6 AWHF 222 Wgt. of Heifers at Breeding 6 PCAB 223 Percent of Cows Bred Artificially 6 PHAB 22h Percent of Heifers Bred Artificially 6 RBMF 225 Rank-Bulls-Dams M & F 6 RBA 226 Rank-Bulls-Ancestry 6 RBPD 227 Rank-Bulls-Predicted Difference 6 RBCM 228 Rank-Bulls-Color Mark- ing RBTT 229 Rank-Bulls—Type Traits 6 RBR 230 ‘Rank-Bulls-Repeatability 6 RBP 231 Rank-Bulls-Price of Sire 6 RBPS 232 Rank-Bulls-Price of Semen 6 RBCR 233 Rank-Bulls-Conception 99;. 74-75 76-78 79-80 12-15 16-17 18 19-20 21 22-25 26-29 30-32 33-35 36-38 39-u1 uz-uu 45-u7 48-50 51-53 5U-56 57-59 60-62 TABLE 3. Variable Name RBO CETR CEFP CADTB YAI APD SBRPO CA TCOHW TCO TCW TCC THL TCOHS TCOS TCWS TCCS THLS NCMF Variable Number 23h 235 236 237 238 239 240 241 242 243 24# 2H5 2A6 24? 248 2&9 250 251 252 120 Card format (continued). Variable Description Rank-Bulls-Other Cows Examined-Rebred Cows Examined-Pregnancy Days Aft. Calving to Breed Years in AI Ave. P.D. Of Bulls NOW Rear & Prove Bulls- Breeder Cows Aborted Time Cows Observed Heat-Winter Heat Observed by Operator Heat Observed by Wife Heat Observed by Children Heat Observed by Hired Labor Time Cows Observed Heat-Summer Heat Observed by Operator Heat Observed by Wife Heat Observed by Children Heat Observed by Hired Labor No. Cows-Milk Fever Card No. 92;. 63-65 66 67 68-70 71-72 73-77 78 79-80 12 13 1n 15 16 17 18 19 20 21 22-23 1L... TABLE 3. Variable Name NCKE VETB VVF DCMT UTD USC CVB DUPI CVIBR CVEVD CVSF CVLE CD CDU3 CD33 CD31 NCSOL NCDIE FMR MRST FCWO FCSO 121 Card format (continued). Variable ’ Number 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 Variable Description No. Cows-Ketosis Vet. Bill Does Vet. Visit Farm Dry Cow Mastitis Trt. Use Teat Dip Use Strip Cup Calves Calves Calves Calves Calves Fever Calves Number Number Number Number Number Number Vac.for Vac.for Vac.for Vac.for Vac.for Vac.for Brucella PI 3 IBR BVD Shipping Lepto of Calves Died under 3 Weeks 3 Weeks-3 Mos. 3 M080- 1 Yr. Cows Sold Cows Died Feed Milk Replacer Start Milk Replacer (DayS) Feed Calves-Winter- Operator Feed Calves-Summer- Operator Card N00 \1 \)-'\1 \‘l \J \l \1 \I \I \l «a -v so -q \J -q \J ‘Q, \J 991. 24-25 26-29 30 31 32 33 34 35 36 37 38 39 40.. 41 42-43 44-45 46-47 48-49 50-51 52 53-55 56 57 ~44 TABLE 3. Variable Name FCWW FCSW FECW FCCS FCHLW FCHLS FCSO FCWO CWEA WAGE WAHG WCOC WCOMB DPCST WIFH WIFS WIFO WYC Variable Number 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 122 Card format (continued). Variable Description Feed Calves-Winter- Wife Feed Calves-Summer- Wife Feed Calves-Winter- Children Feed Calves-Summer- Children Feed Calves-Winter- Hired Labor Feed Calves-Summer- Hired Labor Feed Calves-Winter- Other Feed Calves-Summer- Other Calves Weaned-Age Age Determines Weaning Grain & Hay Determines Weaning Condition of Calf Determines Weaning Combination Determines Weaning Purchase a Calf Starter Herd Ration Fed Calves Special Calf Ration Fed Calves Other Ration Fed Calves Would Contract Calf Raising Card No. \l 59 6O 61 62 63 64 65 66-68 69 70 71 72 73 74 75 76 77-73 TABLE 3. variable Name SBCA RBCA BSWA BSRP SBHEF HOWM PRICE SOHEF HWM PREE FCAC DYPH RHLA RHCAT RHMO RHLAB RHMAC RHPART RHAVE RHOTHE NONE NOFOR ENJOY Variable Number 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 123 Card format (continued). Variable Description Sell Bull Calves Raise Bull Calves Bulls Sold-What Age Price Received-Bulls Sell Bred Heifers How Many a Year Price Received-Bred Heifers Sell Open Heifers How Many a Year Price Received-Open Heifers Feed Calves All Colostrum Purchase Heifers Receive Help-Land Receive Help-Cattle Receive Help-Money Receive Help-Labor Receive Help-Machinery Receive Help-Partner- ship Receive Help-Advice Receive Help-Other Receive Help-None Number of Organizations Enjoy-Dairy-Field Work Card No. CDCDGJCDVV CD moooooooooo 21-24 25 26-27 28-31 32 33 34 35 36 37 38 39 4O 41 42 43 44-45 E I'- TABLE 3. Variable Name GOALS 00 W0 OW OOM WOM TF COWS MILK FATX TEST PDAYM GVP TCOWS NCSR NCPD NCASR NCAPD PDSC PDSCA SING F28 Variable Number 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 124 Card format (continued). Variable Description Goals-Milk Production Decisions-Operator Only Decisions-Wife Only Decisions-Operator & Wife Decisions-Operator & Other Decisions-Wife & Other Decisions-Total Family Ave.Number Cows-4-1 Ave. Milk-4-l Ave. Fat-4-l Ave. Test-4-l % Days in Milk-4-l Gross Value Product-4-l Total Cows-Herd-Year- 4-1 No.Cows-Sires Reported- 4-1 No.Cows-Sires P.D.-4-l No.Calves-Sires Reported-4-l No.CalveS-Sires P.D.- 4-1 Ave.P.D.-Sires-Cows Ave.P.D.-Sires-Calves Single Ownership Father. Son Father. 2 Sons Card NO. 000000 92;. 46-50 51 52 53 54 55 56 57-59 60-64 65-67 68-70 71-72 73-76 77-79 12-14 15-1? 18-20 21-23 24-28 29-33 34 35 36 125 TABLE 3. Card format (continued). Variable Name BROSZ B251 B282 B2N FSIL REHTO 330/329 323*324/ 146+147+148 323*328 Variable Number 339 340 341 342 343 344 359 361 362 Variable Description 2 Brothers 2 Brothers. 1 Son 2 Brothers. 2 Sons 2 Brothers, Nephew Father. 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92 -O.57 PLH 95 NOCO 145 0.51 PLWC 96 PLO 97 -O.81 SB 98 CCFS 101 -0.60 147 TABLE 7. (continued) Variable Variable Variable Variable Name Number Name Number Correlation SB 98 PHE 113 -O.53 CCFS lOl PHE 113 0.53 LH 102 N000 145 0.48 PHE 113 NOCO 145 0.56 VAC3 129 VAC4 130 -O.62 su 134 DEL 135 -o.7u MMILK 144 N000 145 0.60 TSFS 155 TSFW 156 0.86 RFC 172 LPC 173 0.55 HPC 172 HPM 175 0.66 HPC 172 MILK 325 0.79 LPC 173 MILK 324 0.73 HPM 175 MILK 324 0.67 MWR 176 MSR 177 0.83 MWR 176 MCR 178 0.72 MSR 177 MCR 178 0.87 CPCS 188 WHT 196 0.51 ILBGC 205 LBGPC 206 0.64 RBPD 227 RBMF 225 -O.5O RBCM 228 RBP 231 0.68 RBP 231 R80 234 0.53 CETR 235 CEFP 236 0.61 TCW 244 TCWS 249 0.95 Lida-L --- . "Hu- . "@- 148 TABLE 7. (continued) Variable Variable Variable Variable Name Number Name Number Correlation TCCS 250 TCC 245 0.86 THLS 251 THL 246 0.93 NCSR V 330 NCMF 252 0.54 DUPI 260 CUIBR 261 1.00 DUPI 260 CUBVD 262 1.00 CUIBR 261 CUBVD 262 1.00 CDU3 266 CD 265 0.99 FCSO 274 FCWO 273 0.87 FCSW 276 FCWW 275 0.92 wcoc 286 WCOMB ' 287 -o.51 RHPART 310 NONE 313 —0.54 0W 319 SING 336 0.54 00M 320 BROSZ 339 0.53 COWS 323 GUP 328 0.94 MILK 324 FATX 325 0.97 MILK 324 GUP 328 0.98 FATX 325 GUP 328 0.98 TCOWS 329 323*328 362 0.95 NCSR 330 NCPD 331 0.94 NCSR 330 323*328 362 0.55 NCPD 331 323*328 362 0.50 149 TABLE 8. Simple correlation with average pounds of milk per cow and selected variables. Variable Name Variable Number Correlation Coefficient Age 3 -.05 Ed 4 -.04 YDFM 5 .04 YTest 9 -0.08 TMIRE 10 0.28 TBA 11 -0.01 RHA 12 -0.10 LTD 13 0.04 TDP 14 0.12 DVAL 15 -0.15 LBGR 16 0.03 BDDA 17 -0.12 ACTN 18 -0.0 NOCH 21 -0.11 3016 22 0.10 0016 23 -0.07 B06 24 -0.18 006 25 -0.04 LD4H 26 -0.28 RDF 27 0.11 MAGR 28 -0.04 HD 29 0.12 FJ 30 -0003 150 TABLE 8. (continued) Variable Name Variable Number Correlation Coefficient SF 31 -0.07 DHM 32 -0.02 MF , 33 0.01 PF 34 0.03 5* RD 35 -0.03 FA 36 -0.03 HW 37 -0.03 _ . MIHW 38 0.01 i CHW 39 ~0.10 F0 40 -0.15 TO 41 -0.08 BF 42 0.08 OTH 43 -0.13 CSC 44 -0.15 CSH 45 -0.08 0500 46 -0.25 CSP 47 -0.09 COT 48 0.02 0P0 49 -0.28 OCH 50 . ~0.l9 ORE 52 0.06 OSC 53 0.02 0300 54 -0.09 OOT 55 -0.28 WPO 56 -0.23 TABLE 8 (Continued) Variable Name WCH WLO WRE WSC WSCO WOT ROT SOL EST WIL WIF FAM WPR WPC WPFW WPOT WPPL WPCO CPR CPC CPF CPFW CPCO CPP NAOP NAOW 151 Variable Number 57 58 59 60 61 62 63 64 65 66 67 68 7o 71 73 70 75 76 77 78 79 80 82 83 84 85 Correlation Coefficient -0.16 -0.26 -o.15 -0.02 -o.2o 0.08 -o.ou -o.ou -0.16 0.19 .0.01» 0.12 -0.03 -0.08 0.0 .0.05 0.05 0.06 0.16 0.00 0.08 0.02 0.03 0.02 0.0L» 0.02 152 TABLE 8. (continued) Variable Name Variable Number Correlation Coefficient NAR 86 0.02 BML 87 0.01 RML 88 -0.29 DA 90 0.10 CR 91 -0.13 H0 92 -0.12 PLH 95 -0.20 PLWC 96 0.18 PLO 97 -0.06 SB 98 0.14 OLFS 99 0.03 CCFS 101 -0.12 LH 102 -0.10 SBFS 104 -0.09 SBLH 105 0.06 PPIP 108 -0.18 CA 110 -0.10 P80 112 -0.03 PHE 113 -0.15 PWT 114 -0.15 PHM 115 0.16 PGR 117 0.28 MTMW 123 -0.38 MTMS 124 -0.39 MTAW 125 -0.14 TABLE 8. Variable Name 153 (continued) Variable Number MTAS VACl VACZ VAC3 VAC4 VAC6 VAC? SU DEL BM ZE CON MMO MMILK NOCO NMM NWM NCM SN sw SNB SCH SOFL SDN 126 127 128 129 130 132 133 134 135 136 137 138 141 143 140 145 146 147 148 149 150 151 152 153 154 Correlation Coefficient -0.19 -0.10 0.08 -0.06 0.02 0.09 -0.09 -0.02 0.03 0.05 -0.06 0.02 0.08 0.02 0.19 0.08 0.17 0.12 0.10 0.12 0.15 -0.07 -0.26 0.17 0.07 154 TABLE 8. (continued) Variable Name Variable Number Correlation ___ ‘__ Coefficient TSFS 155 0.16 TSFW’ 156 0.16 HPRE A 159 0.03 HPKE 161 -0.01 RIMP 164 -0.07 RIFP 165 -0.06 RIFL 166 -0.13 RIMU 167 0.05 RIRE 168 0.10 RITY 169 0.07 TIDI 170 -0.05 RIQ 171 0.03 HPC 172 0.79 LPC 173 0.73 AVG 174 0.30 HPM 175 0.67 MWR 176 0.25 MSR 177 0.26 MCR 178 0.29 A01 179 -0.09 PPG 180 0.03 TECWT 181 -0.24 CGM 182 -0.10 ASF 183 0.08 CPCS 188 0.14 155 TABLE 8. (continued) Variable Name Variable Number Correlation __ Coefficient AAA 190 0.03 WHT 196 0.29 LBSG 199 0.38 ATM 200 0.16 LBSGD 204 -0.07 ILBGC 205 0.08 LBGPC 206 0.08 SMAF 218 0.18 T000 219 0.47 EMG 220 -0.06 AWHF 222 -0.04 PCAB 223 0.06 PHAB 224 0.16 CETR 235 0.04 CEFP ' 236 0.17 CADTB 237 0.22 SBRPO 240 0.12 CA 241 0.16 TCOHW 242 0.02 TCOHS 247 0.19 NCMF 252 ' 0.41 NCKE 253 0.08 VVF 255 0.10 DCMT 256 0.31 VTD 257 0.04 USC 258 0.05 r , .F, “min.qu - ' ‘0: i; ‘ -‘ O r i d A" 4‘ .‘ 1.. 156 TABLE 8. (continued) Variable Name Variable Number Correlation Coefficient CVPI 260 0.40 CUIBR 261 0.40 CUBVD . 262 0.40 CUSF 263 0.00 __ CULE 264 0.21 E 3 CD 265 -o.22 I a" CDU3 266 40.23 CD33 267 -0.01 ' :1 “ CD31 268 o. 18 ii NCSOL 269 0.23 NCDIE 270 0.04 FMR 271 -0.12 MRST 272 0.05 WAGE 284 0.15 WAHG 285 -0.10 wcoc 286 -0.15 WCOMB 287 0.03 DPCST 288 0.0 SBCA 293 0.16 RBCA 294 -0.16 SBHEF 297 0.19 SOHEF 300 0.10 FCAC 303 -0.02 DYPH 304 -o.17. RHLA 305 -0.05 157 TABLE 8. (continued) Variable Name Variable Number Correlation Coefficient RHCAT 306 -0.12 RHMO 307 -0.03 RHLAB ‘ 308 -0.09 RHMAC ' 309 0.01 RHPART 310 0.05 RHAVE 311 0.04 RHOTHE 312 -0.11 NONE 313 0.04 NOFOR 314 -0.22 ENJOY 315 0.26 00 317 -0.27 OW 319 -0.05 OOM 320 - 0.10 TF 322 0.13 COWS 323 0.04 FATX 325 0-97 TEST 326 0.03 PDAYM 327 0-39 GVP 328 0.98 TCOW 329 0.09 NCSR 330 0-35 NCPD 331 0.38 SING 336 -0.08 F5 ‘ 337 0.01 158 TABLE 8. (continued)‘ Variable Name Variable Number Correlation 92222121221 F23 338 0-23 BROSZ 339 0.02 8281 340 -0.14 B282 341 0.19 BZN 342 0.07 330/329 359 0-40 323*328 362 0.36 mm I 9 4 o 2 6 5 o 3 0 3 9 2 1 3 " III II|| H T | "I! II I I'll l u " “I “I II”