FlNAbéCIAL SEASONALBTY 0F BARRY FARMING AND ITS RELATION TO RISK AND UNCERTAINTY Thesis for the Degree 0‘ M. S. MICHLGAN STATE UNEVERSITY John Ronald Brake 1956 FINANCIAL SEASONALITY OF DAIRY FARMING AND ITS RELATION TO RISK AND UNCERTAINTY by John Ronald greke AN ABSTRACT Submitted to the College of Agriculture of Michigan State University of Agriculture and Applied Science in partial fulfillment of the requirements for the degree of MASTER.OF SCIENCE Department of Agricultural Economics ‘\ 1956 ‘ 5 Approved WW MW ABSTRACT The purpose of the study was to attempt to reduce risk and uncertainty concerning the financial flow pattern of the dairy farm business by analyzing the monthly flow of cash receipts and expenses. In keeping with this purpose the study attempted: (1) to determine the extent to which definite and reliable monthly variations in income and expense items existed on the sample farms; (2) to reduceLummrtaintyassociated with income and expense flow by presenting a description of the flow pattern, and; (3) to make some suggestions based on the seasonality pattern of income and eXpenses which could, be beneficial to farmers. The prime hypothesis of the study was that many of the farm eXpenses were seasonal in nature. Thus,if the pattern were found to be seasonal, there would seem to be applications not only for farmers and farm planners, but also for extension men, farm credit agencies, and teachers of agriculture and farm management. The study was made on a sample of forty Ingham and Jackson County dairy farms. These farms were in the Michi- gan State University Mail-In Account project in which the cooperators were chosen on a volunteer basis. Each of the forty farms had more than fifty per cent of its 1955 income from the sale of dairy products and dairy cattle. 2 The statistical procedure was to determine the median for specific categories although the mean figures were in- cluded. The selection of the sample made it inapprOpriate to set confidence limits for the mean. Three approaches to the financial seasonality were taken. The first was to analyze each expense and income item individually to show the per cent of its total which occurred in each month. The second was to explore the changing relative importance of the various income and ex- pense items in each month of the year. The third approach was to present the per cent of all expenses and all income which occurred in each month. A majority of eXpenses were found to have a strong seasonal pattern. The eXpenses exhibiting this pattern were hired labor, seeds and plants, fertilizer and lime, machin- cry repair and maintenance, supplies, improvement repair and maintenance, machine hire,breeding fees, veterinary and med- icine, fuel and oil, and taxes. Others exhibiting a mild seasonality or months of high expense percentagewise were feed purchases, insurance, interest, rent, auto upkeep, and other expenses. Telephone eXpense and other livestock ex- pense had little indication of seasonality. ‘Electricity was the most constant of all exnenses. Considering all eXpenses together, April and December were the two highest months while the three months following April were higher than others. 3 Dairy products income was very definite and constant. Dairy cattle sales and crop sales were seasonal in nature. All monthly income together was high in July and the last three months of the year. Suggestions were made largely on the basis of the total expense-total income pattern. Indications were that in April and May, eXpenses were high relative to income while in July and October, expenses were low relative to in- come. Applications could be made not only by farmers, but also by extension workers, farm planners, and credit agencies. For example, extension workers might be more effective in urging special purchases in July or October while in April and May, credit problems may be more appropriate. FINANCIAL SEASONALITY OF DAIRY FARMING AND ITS RELATION TO RISK AND UNCERTAINTY by John Ronald Brake A THESIS Submitted to the College of Agriculture of Michigan State University of Agriculture and Applied Science in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Economics 1956 ACKNOWLEDGMENT To name the many people who have in one way or an- other made this thesis a reality would be impossible. Specific thanks are due Dr. Warren H. Vincent under whose direction this study was accomplished. His patience and many important suggestions served to make a difficult task considerably easier. The author wishes to express his appreciation to Dr. L. L. Boger, Head of the Department of Agricultural Economics, for the financial assistance via a graduate assistantship. Miss Joyce Malcho aided the author with her knowl— edge of the Main-In Account Project and by her fine Job of typing on the original manuscript. Mrs. Ann Brown com- pleted the final manuscript and also did an excellent job. The author wishes to thank his wife, Betty, and daughter, Susan, for their understanding during the diffi- Cult days when the manuscript was being written. Thanks are due my father also. His sturdy faith and confidence in the author and his work have always been sources of strength and encouragement. The author assumes responsibility for any errors in the thesis. ii LIST OF LIST OF CHAPTER I. II. III. IV. TABLE OF CONTENTS TABLES. . . . . . '. . . . . . . FIGURES . . . . . I . . . . . INTRODUCTION 0 C O C O O C O O O The Problem . . . Need for the Study. . objectives . . . . Hypothesis . . . . Assumptions . . . Definition of Terms Review of Literature Farm Accounts. . Income Variations . . . Risk and Uncertainty . . DESCRIPTION OF THE SAMPLE . . . . . . History of the Project . . . . . . Selection and Size of the Sample . . . Description of the Sample Farms . . . MONTHLY VARIATION IN THE FLOW OF INDIVIDUAL INCOME AND EXPENSE ITEMS . . . . . Statistic Used for Analysis. . . . . Analytical Approach . . . . . . Organization of Tables . . . . . Seasonality of EXpenses . . . . . . Seasonality of Income. . Differences in Seasonality of Certain Expenses Due to Herd Size . . . . . PERCENTAGE OF THE MONTHLY INCOME OR EXPENSE ACCOUNTED FOR BY EACH ITEM. . . . . . Introduction. . . . . . . . . . Seasonality of Expenses . . . . . Seasonality of Income. . . . . . . MONTHLY VARIATION OF TOTAL EXPENSES IXND INCOME O O I O O O O O I O 0 Introduction. . . . . . Seasonality of Total Monthly Expenses and Income. . . . . Per Cent of Each Month's Income Needed for Expenses . . . . . . . . . iii Page .vii [.4 w o .25 . 27 . 28 . 30 .36 ,w .55 CHAPTER Page VI. SUMMARY AND CONCLUSIONS. . . . . . . . 70 Summary . . . . . . 70 Purpose and Approach of the Study . . . 70 Results of the Study . . . . . . . 75 Conclusions . . . . . . . . . 80 Shortcomings Of the Study . . . . . . 86 Suggestions for Future Work. . . . . . 88 BIBLIOGRAPHY . . . . . . . . . . . . . . 91 APPENDICES O O O O I O O O C I O I O O O 92 APPENDIX A. Farm Expense Blank Used by Cooperators . . 93 Farm Income Blank Used by Cooperators . . 9A Reminder Letter Sent to Cooperators Tardy with Accounts. . . . 95 Form Letter Sent to Cooperator Indicating Receipt of Accounts. . . . 96 Productivity Ratings for Soil Types Used in Classifying Cooperator' 8 Land . . . 97 APPENDIX B. Table B: 96.2% Confidence Limits on the Median Per Cent of Total Annual Ex- penses in Each Month for A0 Dairy Farmsin 1955 o o o o o o o o o o o o o 98 APPENDIX C. Table 0: 96.2% Confidence Limits on the Median for the Per Cent of Annual In- come from Each Item of Income in Each Month for 40 Dairy Farms in 1955 . . . . . . 99 APPENDIX D. Table D: 98.1% Confidence Limits on the Median for Selected EXpenses as a Per Cent of Annual Expense in Each Month for 19 Farms with Less than 23 Cow Herds in 1955 . 100 APPENDIX E. Table E: 97.3% Confidence Limits on the Median for Selected Exoenses as a Per Cent of Annual Expenses in Each Month for 21 Farms with 23 or Larger Cow Herds in 1955 . 101 APPENDIX F. Table F: 96.2% Confidence Limits for Median Per Cent that Each Item of Expense Was of the Month's EXpenses for A0 Michigan Dairy Farms in 1955 . . . . . . . . 102 APPENDIX G. Table G: 96.2% Confidence Limits on the Median for the Per Cent of Monthly Income from Each of Four Sources for MC Mich- igan Dairy Farms in 1955. . . . . . . 103 iv APPENDICES Page APPENDIX H. Table H: Confidence Limits for. the Per Cent of Total Income and EXpenses in Each Month for 40 Central Michigan Dairy Farms for 1955. . . . . . . . . . 10A LIST OF TABLES Table Page 1. The Estimate of Median Monthly Cash Expenses as a Per Cent of Each Annual Total Expense on 40 Central Michigan Dairy Farms for 1955 . 31 2. Mean Monthly Cash Expenses as a Per Cent of Annual Total EXpenses for Each Month for #0 Central Michigan Dairy Farms for 1955 . . . 32 3. Monthly Cash Income from Each Source as a Per Cent of Total Annual Income for #0 Central Michigan Dairy Farms for 1955. . . . . . 37 A. Monthly Cash Expenses from Selected Sources as a Per Cent of Total Annual Expense for 19 Central Michigan Dairy Farms with Less Than 23 Cow Herds in 1955 . . . . . . . #0 5. ionthly Cash Expenses from Selected Sources as a Per Cent of Total Annual Expense for 21 Central Michigan Dairy Farms with More than 23 Cow Herds in 1955 . . . . . . . Al 6. The Median Estimate of Each Cash Expense as a Per Cent of the Monthly Cash Expenses for #0 Central Michigan Dairy Farms in 1955 . . 46 7. The Mean from Each Cash Expense Item Expressed as a Per Cent of the Monthly Cash Expenses for 40 Central Michigan Dairy Farms in 1955 . A7 8. The Importance of the Various Income Items in Each Month Expressed as a Per Cent of the Monthly Income for #0 Central Michigan Dairy Farms in 1955 . . . . . . . . . . . 56 9. Flow of All Income and All EXpenses in Each Month Shown as a Per Cent of Total Annual Income and Expenses for #0 Central Michigan Farms in 1955 . . . . . . . . . . . 61 vi FIGURES l. 2. LIST OF FIGURES Location of the #0 Farms in the Study. . . The Proportion of Each Month's Income Needed for That Month's Expenses Expressed as a Percentage for #0 Central Michigan Dairy Farms for 1955. . . . . . . . . . The Proportion of Each Months' Income Needed for That Month's EXpenses Expressed as a ' Percentage for 19 Central Michigan Dairy Farms with Herds of Less than 23 Cows in 1955 O 0 O O O O I O O I o O 0 The Proportion of Each Month's Income Needed for That Month's Expenses Expressed as a Percentage for 21 Central Michigan Dairy Farms with Herds of 23 or More Cows in 1955 vii Page . 20 . 65 . 67 CHAPTER I INTRODUCTION The Problem Farming involves a large degree of risk and uncer- tainty. In such a business with lack of knowledge and many types of uncertainty, decision making becomes very important and difficult. This study is concerned with improving knowl- edge in the area of uncertainty of monthly income and expense flow throughout the year. It might be well at the outset to attempt a short ex- planation of what is meant by risk and uncertainty. Frank Knight makes the following differentiation between risk and uncertainty: The practical difference between the two categories, risk and uncertainty. is that in the former the dis- tribution of the outcome in a group of instances is known (either through calculation a_priori or from statistics of past experience). while in the case of uncertainty this is not true. the reason being in general that it is impossible to form a group of in- stances, because thf situation dealt with is in a high degree unique. A risk, then, is a situation which can'be reduced to a probability. It is then possible to protect oneself from 1Frank R. Knight, Risk. UncertaintyI and Profit (Boston and New York: Houghton Mifflin Company, 1921), p. 233. 2 a risk situation by insurance. For example, a probability distribution exists and is known for the number of barns that will burn out of a given total in a year. It is possi- ble to protect oneself from most of the loss through fire insurance. With uncertainty, the situations are uniQue, or enough so, that a probability distribution is not known. Knight says, ”the fundamental uncertainties of economic life are the errors in predicting the future and in making present adjustments to fit future conditions."2 The manager is not able to refer to enough numbers or experiences to calculate a probability. Weather, for instance, stands as an uncer- tainty. The farmer cannot refer to experiences or tables to predict the chance that certain weather conditions will prevail at any particular time. There are many other areas of uncertainty in addition to weather. Uncertainty concerning institutional changes is another that is often mentioned. Changes in government pol- icies, laws, or interpretations of policies by administrators can have a profound influence on the way farm managers react. Marketing quotas on wheat have made changes in the decisions of many farmers. The chance that the same might occur with respect to corn acreage could easily influence some farmers to overplant in order to build a larger base acreage. 2 Ibid., p. 259. 3 Farmers are faced with uncertainty concerning tech- nological changes. New methods and eQuipment are constantly being introduced. Farmers realize it is ”the first one in" that makes the most gain, but if it is not a good idea, he is also the one to suffer the most loss. Farmers often de- velop their own "strategy" for coping with such uncertainty. Uncertainty exists in the area of human relations. Quarrels, deaths, and divorces can have much to do with manager's decisions. It is likely that a farmer might make an entirely different decision if his only son is planning to remain on the farm than he would if the son planned to leave. Price uncertainty is probably the one most often mentioned. A major change in prices may put a farmer out of business, particularly if he is specialized and short on reserve credit. Farmers often use a "strategy" of diver— sificaticntx>protect themselves. If a farm is best fitted for producing one crop and the farmer diversifies to protect himself against price changes, he will have cut down his pro- duction efficiency each time that the unfavorable price change does not occur. Uncertainty nearly always results in less than maximum use of resources. Because he does not know, the farmer may use restraint even in specialized enterprises. The preceding discussion was meant to review some of the concepts of risk and uncertainty as an orientation for the immediate study. Uncertainty is responsible for poor ii use of resources or at least less than optimum use of re- sources. If research in the area of uncertainty situations can bring them more into the realm of risk or improve knowl- edge for COping with uncertainties; then that research can make a definite contribution. Need for the Study Farm production is usually thought of as being sea- sonal in nature. Empirical evidence of this is found in such phenomena as the harvesting of wheat each July. or corn each autumn, and the seasonal fluctuations in numbers of hogs marketed. Many others could be cited. However, when it comes to the question of income and eXpense fluctuations from month to month, little is known of the variations which may exist. If income and eXpenses are based on the pro- duction cycles, it could be eXpected that there would be a fairly definite and reliable seasonal fluctuation in the in- come and eXpense items associated with a type of farming enterprise. To the extent that these variations in income and ex- pense items exist, limitations on our present method of farm planning may be imposed. For it may well be that farm man- agement peOple are merely balancing the budget at the end of the year and ignoring the possibility that more expenses need to be paid early in the year than there is income to cover them--in which case the farmers would need additional liquid reserves or obtainable credit. Conversely, it may 5 be that the farmer can eXpect more income early in the year than he will need for eXpenses, so that he could better utilize his liquid reserves or credit instead of keeping more than needed on hand. Short run planning is of great importance in the farm business. Schickele was emphasizing this short run problem when he said, A farm family-~any family--lives in the short run. Mouths have to be fed every day, bills have to be met every month, taxes have to be paid every year. In face of great uncertainties, the farmer's first concern is survival in the short run, of reducing the probasility of risk loss so heavy as to bank- rupt him. It is this short run income and eXpense aspect of risk and uncertaintywwith which this study is concerned. Probably one of the best statements that could be added at this point is one made by Williams which points up the need in this area: Extension agents have been left to their own de- vices to develop principles by which they can take account of the aspects of farm management which relate to uncertainty and the farmer's response to it ” Extension workers also feel a definite need for in- formation which would aid them in timing their releases and. tOpics of instruction. They would like to know when farmers 3Rainer Schickele, "Farmers Adaptations to Income Uncertainty," Journal of Farm Ecgngmics, Vol. 32, p.367. Li D. B. Williams, "Price Expectations and Reactions to Uncertainty by Farmers in Illinois," Journal of Farm 1300110111108, V01. 33’ pe 20. 6 rent and buy land, when they buy machinery, and when they make other major purchases. This knowledge would enable them to be more effective in their work and perform a greater service to farmers. The study is important with reference to farm plan- ning. In many cases planning is done on a short run basis so that accurate, up-to-date figures are needed. For pur- poses of linear programming and budgeting, it is important to know if these menthly variations in income and expenses impose a restriction that should be taken into consideration. If such a limitation does exist, it is important that it be brought to light. Research is needed in this area with reference to farm credit. Monthly variations are important to a credit agency when they set up a repayment schedule for farmers. Data on income and eXpense Variation would aid them in set- ting up a more satisfactory repayment schedule. Farmers would like any information they could get that would help them in planning and running their business. With the present agriculture problem, it is important that any work which might be done to reduce uncertainty be done as soon as possible. Dairy is a logical enterprise with which to start this type of study in Michigan, for it is Michigan's chief source of agricultural income. In 1955. 32.5 per cent of 5 Michigan's cash farm receipts came from dairy sources. 5Michigan Crop Reporting Service. Objectives The specific objectivescfi‘this study are as follows: 1. To determine the extent to which a definite and reliable monthly variation in certain income and eXpense items exists on a sample of forty central Michigan dairy farms. 2. To attempt to reduce short run risk and uncer- tainty associated with income flow and expense flow by pre- senting a description of income and eXpense flow. 3. To suggest ways farmers might plan their finan- cial practices to take advantage of the seasonality of farming. Hypothesis In addition to the accepted belief that certain ex- penses such as fertilizer, are closely tied to the produc- tion cycle, it is felt by the author that several other eXpenses are highly seasonal in nature also. The prime hy— pothesis of this thesis is that these expenses have a de— finite pattern of monthly distribution and that this pat- tern may be used in more effective farm planning. Assumptions Conclusions drawn from this study must be tempered by the following assumptions: 8 First, it is assumed that planning is desirable in farming as in any business. Without planning, the farm business Operations tend to be haphazard, poorly organized, and inefficient. It is obvious, too, that farmers do plan, though it may be a very informal or even mental plan. If planning is desirable, then figures which make planning easier or more accurate, serve a worthwhile purpose. Secondly, it is assumed that averages may be useful for certain purposes if they are reliable. These averages may be used advantageously at times in farm planning. Then again, they may be useful merely as an aid in understanding the farm business more fully. The usefulness of the aver- ages for farm planning purposes depends on the reliability of the statistic in question. Thirdly, it is assumed that for purposes of short run planning, the time that expenses are paid is more im- pcrtant than the time at which expenses are incurred, since it is the actual payment which has to be planned fOr in the short run. Fourthly, it is assumed that 1955 was a normal year. This assumption includes weather as well as other aspects. The accuracy and applicability of the results depend on the year under consideration. To the extent that 1955 was not normal, the results would need to be modified for use in other years. Finally, it is assumed that the decisions made and actions taken by the cooperators in the study were rational 9 and that the results in most cases can serve as guides to planning. Definitiongof Terms Following is a list of terms that may need clarifi- cation. Other terms used in this thesis are as defined in Heady and Jensen's Farm Management Economics.6 Exgense refers to the payment of a bill or debt rather than to the time a debt is incurred. Operating expenses are those variable expenses tied in with the Operation of the farm business. Included in these are hired labor, feed, crops expense, machinery main- tenance eXpense, livestock eXpense, improvement maintenance eXpense, and others of a monthly or annual nature such as telephone, electricity, taxes, etc. §h2££ 323 is a length of time shorter than one year. In general, it shall mean from one to six months. Dairy farms in this study are farms which received more than fifty per cent of their 1955 income from sales of dairy products and dairy cattle. .Eiflguéflfl uggertainty were differentiated earlier. However, to review, risk refers to a situation in which a probability can be calculated. Uncertainty refers to a situation in which a probability cannot be calculated and which cannot be guarded against by formal insurance. 6Heady and Jensen, Farm Management Economics (New York: Prentic-Hall, Inc., 19555, paesim. 10 Review of Literggure Farm Accounts It might be apprOpriate first to review literature dealing with farm accounts. The present study was based on figures supplied by the Mail-In farm account project being carried on at Michigan State University. The Mail-In pro- ject itself is a new application of the older farm accounts, and will be explained further in Chapter II. Older farm accounts were wholly inadequate to give seasonal figures. Although there are many farm account projects carried on in the various land grant colleges and universities, there has been little literature written dealing specifi- cally with them. Many text books make a reference to farm accounts but do not go beyond this to any extent. However, one source of information was a report by Pond in the Journal of Farm Economics. He wrote: On January 1, 1902, W. M. Hays and Andrew Boss started some cost accounts on Minnesota farms. This was the first organized continuing project in farm management research in the United States. It was also the first research project in the general field of agricultural economics in the country.' He goes on to say that the accounts came about be- cause it was found that eXperimental plots did not give 7George A. Pond, ”Fifty Years of Farm Records in Minnesota,” Journal of Farm Economics, Vol. 35. p. 2u9. ll adequate cost figures. It was then decided to try and get the figures from farmers themselves. Continuing, Pond states that the fifty years of records in Minnesota have "supplied a continuous flow of information on farm costs, farm returns, and factors affecting farm financial success."8 Income Variations Work on income variation has come about largely with- in the last decade or so. Heady has several interesting tables in his Economics of Agricultural Production and Re~ source Use related to income variation.9 One of these shows income distribution and variation data for Iowa livestock enterprises in Which the coefficient of yearly variation ranged from fourteen per cent in dairy to thirty-nine per cent for feeder lambs. Another approach Heady took was that of combining two enterprisestnydetermine which combinations provide the steadiest flow of income year after year. Later in the chapter, he stated: Income variability can be lessened through diversi- fication only if the prices or yields of the products bear the proper correlations. If the correlation co- efficient is -l.O, the 2 enterprises serve optimally as an uncertainty precaution. A correlation coeffic- ient of zero is preferable to greater (+) correlation coefficients.1 81bid. 9E. O. Heady, Economics of Agricultural Progggtion and Resource Use (New York: Prentice Hall, Inc., 19527] p. O lOIbid. 12 D, Gale Johnson summed up the problem very well when he wrote: A firm is confronted not only with the necessity of considering the expected value of the income stream but also with the desirability or necessity of maintaining within limits the capital value of the firm as a going concern. If expectations are certain, knowing the eXpected (discounted) value of the income stream Specifically denotes the capital value of the firm as a going concern. If not . . . there arises the possibility that events may force the liquidation of the firm.1 Risk and Uncertainty In the area of risk and uncertainty, the literature deals more with bringing to light what the problems are. However, some attempts have been made to actually reduce risk as in crop insurance, fire insurance,emc. Some other studies have been made to check farmer's reaction toward uncertainty. One such study was made by Williams in Illinois.12 When he asked 161 farmers to choose between a sure $5,000 a year income or a chance of $7,500 or $2,500, 111 chose the sure $5,000 income. When the question was asked as 31,000 sure or flipping a coin as to a chance of $1,500 or $500, 127 of the 161 chose the sure $1,000. Evidently farmers are typically interested in the “sure“ thing. 11D. G. Johnson, Forward Pricesfor riculture (Chicago: University of Chicago Press, l9h75, p. 39. 12 Williams, op, cit., p. 30. 13 Williams found that farmers tended to react similarly ac- cording to (l) the year they started farming, (2) education, and (3) age. D. G. Johnson listed four ways Of limiting uncer- tainty.13 They are (l) diversification, (2) flexibility, (3) liquidity, and (u) risk aversion and combination of factors. These are the factors most often mentioned in other references on the subject in one combination or an- other. Some authors break them down more, but for present purposes, this is adequate. A. G. Hart came closer to the problem Of this study when he listed three phases of enterprise planning.14 The first phase, he said, was planning a set of production sched— ules. The second phase was the marketing plan which was made up ofbmying schedules for productive services or goods, and Of sales schedules for saleable output. The third phase he listed was the “finance plan." This is made up Of outlay schedules for paying for purchases, and receipts schedules for sales proceeds. It is this third phase that is of prime concern to this study. Most references on this subject are cited in North Dakota Experiment Station Bulletin #00, the proceedings 13Johnson, Op, cit.. p. 50. 14 A. G. Hart, Anticipations, Uncertainty, and Plan- ning (Chicago: University Of Chicago Press,‘19u8). p. 11. lu issue of the Research Conference on Risk and Uncertainty in Agriculture.15 The conclusions of this review are several. First, research in the risk and uncertainty area has only begun. Further work is needed. In regard to the financial season- ality with which this study is particularly concerned, the author found no indications of previous research. The enter- prise combination work of Heady which was mentioned above is the closest to financial seasonality in the general area of risk and uncertainty that has been undertaken. lsAgricultural Experiment Station Bulletin #00, Fargo, North Dakota: North Dakota Agricultural College, August, 1953. CHAPTER II DESCRIPTION OF THE SAMPLE History Of the Project The data studied were obtained from farm records kept by Michigan farmers in cooperation with Michigan State Univ- ersity. SO, it seems apprOpriate to describe briefly this farm accounting project. Since 1929, Michigan farmers have been keeping re- cords under supervision of extension specialists in farm management at Michigan State University. The farmers have used Special farm account books obtained through the Univ- ersity. Generally, the accounts include complete itemizing of the various eXpense and income items, crop and livestock production figures, and inventories on land, machinery, feed, livestock, and improvements. At the end Of the year, the extension specialist Would meet with the farmer to check and close his books for that year. Later, the books were brought to the University to be used for extension and research purposes. An area report for several areas in the state was one of the more extensive uses of the data Obtained in this way. After 19h9, a part of the information was put on IBM cards for ease and speed of operation. Other than this, the calculations and summarizing were done completely by 15 16 the staff and clerical help in the agricultural economics department. Since the books were Often used for income tax purposes, this manual aspect placed a great seasonal burden on the extension specialist staff and its clerical help. The extra burden at one time of year also magnified the possibility of error. Late in 1954, Dr. Warren H. Vincent, Extension Specialist in Agricultural Economics,1flunmfln:of the possib- ility of carrying on a Mail-In account project which wOuld be handled entirely on IBM cards. The farmers would send in their reports every month so that the first Of the year rush could be somewhat relieved. The IBM machines would speed the process and save on labor. In December, Dr. Vincent contacted Kenneth Swanson and Kenneth Brown, the assistant county agents of Jackson and Ingham Counties, respectively, to see if they could get enough farmers interested to carry out the project in 1955. There were fifty farmers in Jackson County and thirty in Ingham County who indicated a willingness to try the new system of farm accounts. Of the eighty who started the project, seventy-five actually completed the year. As indicated above, the reports were to be sent in at the end of every month (see Appendix A). If they were not received by a certain date the following month, one, tWO, or three reminders were sent to the farmer (See Appen- dix A). When the reports were received, they were stamped with.the date received, checked, totaled, and the data was 16 'the staff and clerical help in the agricultural economics (department. Since the books were Often used for income tax 'purposes, this manual aspect placed a great seasonal burden on the extension specialist staff and its clerical help. The extra burden at one time Of year also magnified the possibility of error. Late in 1954, Dr. Warren H. Vincent, Extension Specialist in Agricultural Economics,thoughtof the possib- ility Of carrying on a Mail-In account project which would be handled entirely on IBM cards. The farmers would send in their reports every month so that the first of the year rush could be somewhat relieved. The IBM machines would speed the process and save on labor. In December, Dr. Vincent contacted Kenneth Swanson and Kenneth Brown, the assistant county agents of Jackson and Ingham Counties, respectively, to see if they could get enough farmers interested to carry out the project in 1955. There were fifty farmers in Jackson County and thirty in Ingham County who indicated a willingness to try the new system of farm accounts. Of the eighty who started the project, seventy-five actually completed the year. As indicated above, the reports were to be sent in at the end of every month (see Appendix A). If they were not received by a certain date the following month, one, twO, or three reminders were sent to the farmer (See Appen— dix A). When the reports were received, they were stamped with the date received, checked, totaled, and the data was 1? put on IBM cards. Next, a check sheet (see Appendix A) was sent back to the farmer indicating whether his report was all right or what additional data was needed. Along with the check sheet was sent the envelope to be returned the next month. The farmers were invited to ask questions on anything they did not understand when they sent their monthly reports in. Answers to these questions were put in a space provided on the check sheet. The Mail-In project has several desirable features over the annual method. First, it makes information avail- able about what farmers are doing much sooner than the annual accounts. It can be set up to give new information with a minimum of effort and cost. Besides being up to date, the Mail-In project gives monthly information on expense and income items such as is used in this study. The project was expanded in 1956 to include about twice as many farms. The "regular" account cooperators from Jackson and Ingham Counties were absorbed into the project, and about twenty new cooperators were taken in from both Ionia and Calhoun Counties. In 1957. plane are to eXpand the project tozlbsorb all the "regular" account OOOperators. By 1959, it is hOped to increase the project until approxi- mately thirty farms from each Of seventy-five counties are included. 18 Selection and Size Of the Sample The Mail-In project was originally set up in Ingham and Jackson Counties. Both of these counties are in Michi- gan's Area Five which is classifed as a dairy and general farming area. It was quite likely then, that of the orig- inal cooperators in the project, a large preportion would classify as dairy farmers. An examination of the farms showed this to be the case. In drawing a sample for this study, it became neces- sary to define a dairy farm. For this purpose, the census definition was used. A farm is a dairy farm when fifty per cent or more Of total sales were from dairy products, or if; 1. Milk and other dairy products accounted for 30 percent or more Of the total value of products, and 2. Milk cows represented 50 percent or more of all cows, and 3. Sales of dairy products, together with the sales Of cattle, amougted to 50 percent or more of total sales.1 Each of the farms in the sample used for this study had greater than fifty per cent Of their income from dairy product sales and dairy cattle sales in 1955. There were forty-two of the farms in the Mail-In project which met this definition. TWO of these forty-two were thrown out when an examination revealed they had "sold out” during the year. This fact seemed sufficient to take 16United States Bureau Of the Census. 1950 United States Census Of Agriculture (Washington: Government rinting Office, 19 l p. xix. l9 them out of the sample. The remaining forty farms were usd in the present study. Several things should be kept in mind concerning the Sample selection. First Of all, no attempt was made to get a. random or representative sample. Any farmers who indicated a Willingness to Mr. Swanson or Mr. Brown to be in the pro- Sect were given the Opportunity to do so. This in itself, Probably indicates that the farms would tend to be better than average. Evenif this were not so, a year of keeping the cords with the University and special help from the assis- tant county agents would probably tend to bias the sample. While this might be a rather serious fault in many studies, 11: is much less of a limitation with the statistical analy- sis to be used in this study since the analysis does not assume a random sample nor a representative one. Description Of the Sample Farms The forty farms selected for the sample are in Ingham and Jackson Counties. There are fifteen in Ingham and twenty-five in Jackson. Figure 1 indicates the loca- tion of Ingham and Jackson Counties in Michigan as well as the loCation of the forty farms within the two counties. In size, the per farm average was 256 acres which in- c33Ludes an average rented acreage of seventy-four acres per 1tVarm. An average of 170 out of the 256 acres were tillable. The mean rented acreage amounted to twenty-nine per cent. The median farm in total acres was 231; in tillable acres, 20 Michigan Fig. 1. i fl _i_ i I; 1 ' . o . :ZVTS’HIITI , . J «lo/<50» __ p 1 . ' ) hr:L1. I , -———.1-.——-1L e 4 Q - R3W azw RlW RlE RZE Location of the #0 Farms in the Study ThN T3N T2N TlN T13 T28 TBS Tbs 21 172; and with respect to rented land, 17%. Total acres ranged from 100 to 730, and tillable acres ranged from 62 to 298. One farmer rented all the land he worked. The average size figures are similar to the 1954 area 5 report.17o The 133 farms which kept records in the regular project in 195M had an average of 254 total acres per farm 'with 194 tillable. The mean rented acreage was 27%. The farms could be classified according to herd size. There were 999 cows on the forty farms to give an average of 25.0 per farm. The farms ranged from 10 cows to 53 with the median being 23. One could divide the sample into large herds and small herds. Such a division gives a mean of l7.b cows and a median of 18 for the small division. A mean Of 31.9 and a median of 30 are Obtained in the large herd division. Some figures on receipts and expenses may be worth- while for descriptive purposes. Average cash receipts per farm for the whole sample were $11,663. Average eXpense per farm.amounted to $5.87“. Sorting the sample again into large herds and small herds, the average income for large herds was $14,668, and for small herds it was $8,3h2. Aver- age eXpenses were 37.339 and $4,25h respectively. The sample description with respect to source of income provides important information. Dairy product sales, as expected, were the chief income source. These sales amounted to 65.1% of the income. Crop sales were next in 17"Farming Today," Area 5 Report, Michigan State University, Department of Agricultural Economics. 22 importance with 11.8% of income from this source. Dairy cattle sales were 8.9% so that the combination of dairy product and dairy cattle sales accounted for 75% of the in- come on the sample farms. Other income sources were as follows: swine, 4.7%; poultry, 2.5%; off farm labor, 2.0%; custom work, 1.5%; and other sources, 3.5%. Other sources include income from sheep sales, beef sales, machinery sales, dividends, and refunds. The farms were ranked on soil productivity. The var- ious soil types were given an index rating according to a chart compiled by Schneider and Engberg (see Appendix A). Soil maps of thezarea indicated the soil types on various parts Of each farm. The index numbers were weighted and a soil production index number was assigned to each farm. The numbers assigned to sample farms ranged from thirty- four to ninety-two. The average of all farms was 58.8,and the median farm index was fifty-eight. The cropping system is an important consideration on livestock farms. Percentagewise, the farms in the sample had an average Of 48% of their tillable land in sod, 23% in small grains, and 27% in cultivated crops. The median farm in each category was 44%, 21%, and 27% in the three uses. There was a wide range however. The per cent in sod varied from 12% to 97%. The per cent in small grains varied from 0 to 40%. The cultivated crop percentage varied from 0 to 69%. 23 Again, these figures fall fairly close to the area 5 report in which the 133 farms averaged 41% of their land in sod crops, 31% in small grains, and 24% in cultivated crOps. The sample farms had about 2% of their tillable land idle while the area 5 farms averaged 4%. Farm investment data may be helpful in a description. The average total investment per farm was $41,503. This figure includes an average investment Of $14,992 in land, $9,143 in farm improvements, $7,469 in machinery and equip- ment, S3,847 in feed and crOps, $5,690 in dairy cattle, $33 in beef cattle, 387 in sheep, $200 in swine, and $42 in poultry. The middle farm in each category was below the mean. The median farms in beef, swine, sheep, and poultry were zero since so few farms had investments in these cat- egories. It is interesting to note the range in investments. Total investment varied from $16,205 up to $82,379. The land investment ranged from 35,457 to $32,319. Farm improve— ments had a wide range of from $1,915 to $24,257. Machinery and equipment investments varied from 32,068 up to 315,134. The feed and crops investment had a low figure Of $852 and ranged up to $10,431. The low in dairy cattle investment was $2,185 while the high farm had $16,325 invested. The investments in beef, swine, sheep, and poultry went as high as $800, $1,970, $850, and $450 respectively. The forty farm operators averaged 40.2 years of age. The youngest operator was twenty-five and the Oldest was 24 sixty. The middle farmer in this reapect Was forty-two. there were eighty-two children on the sample farms. Two Operators had five children in their families while eight had no children. The median family size was two children. CHAPTER III MONTHLY VARIATION IN THE FLOW OF INDIVIDUAL INCOME AND EXPENSE ITEMS _§tatistic Used for Analysis When analyzing data, it is important to consider the purpose of the study and the use that is to be made of the results. With respect to the present study, the analysis should furnish figures of use to farm planners and managers in helping to reduce the uncertainty on the financial side of the business. With this purpose in mind, it seemed that more use- ful figures could be obtained by using percentages rather than absolute amounts. If the flow of eXpenses and income is seasonal for the items, then percentage figures would be more applicable for a larger number Of farms than would absOlute figures. It is for this reason that the analysis is on a percentage basis. As mentioned in Chapter II, the selection of the sample farms was voluntary rather than by chance. With a relatively small size of sample and no random or chance selection, it becomes rather difficult to assume that the sample includes a normal distributiOn of farms. Indeed, judgment would suggest in many cases that other than a normal distribution would be desirable. Certainly such is 25 26 the case in an analysis of cause and effect factors. How— ever, without the normality assumption, the arithmetic mean becomes difficult tO use since setting a confidence limit on the mean requires the calculation of Variance which in itself assumes normality in the sample. .For planning pur- poses, it seemed important to use some measure of central tendency. Without the necessary assumptions about the sample for the mean, the median was considered. With this statistic, no assumption of normality is needed. Since it is a non-parametric statistic useful for small samples, it was chosen as the statistic for analysis. The median has several other desirable features. It eliminates the unusually large values which influence the mean. Thus the median may in reality be more apprOpriate to a larger number of farms than the mean. A further advan- tage is that the confidence limits on the median are both actual observations and very nearly divide the sample into thirds. Nair was referring to this statistic when he wrote: This is important work in view of the fact that in small samples it is not easy to‘Egst whether the assumption Of normality holds good. The median will also be very close to the mean in a normal distribution. In a perfectly normal distribution it will be the same as the mean. Even without the normality assumption, the median will be fairly close to the mean and 1 . 8K. R. Nair, "Table of Confidence Interval for the Median in Samples From Any Continuous Population," Sankhya. V01. Ll" 19140, p. 5510 27 may actually be a more usable measure of central tendency since it will have as many Observations on one side as on the other. When setting confidence limits on this statistic, the median of the universe can be eXpected to fall between the 14th and 27th observations 96.2% of the time when they are arranged from highest to lowest.19 The middle thirty per cent of the Observations are between the two limits. In several cases, it was felt that more apprOpriate and meaningful estimates could be Obtained by dividing the sample on the basis of herd size into farms with larger herds and farms with smaller herds. The dividing line was between twenty-two and twenty-three cows. This gave a large herd sample of twenty-one farms and a small herd sample of nineteen. In the sample Of twenty-one, the median can be expected to fall between the 6th and 16th Observations 97.3% of the time. In the nineteen farm sample, the median could be expected to.fa11 between the 5th and 15th Observa- tions 98.1% of the time.20 Analytical.Approagh There are several possible approaches which may give indications of financial seasonality. There are three 1?Ibid.. pp. 551—558. 28 approaches that will be taken in the present study. The one taken in this chapter is to analyze each expense and \income item individually to estimate the per cent of the item that occurs each month. This would mean, for example, to take items like hired labor expense and figure the per cent of the year's labor expense that occurs each month. This analysis shows the seasonality throughout the year for each individual item. A second approach is used in Chapter IV. That ap- proach will analyze the relative importance of the monthly eXpense and income items for the various months of the year. This would mean, for example, estimating the per cent of January expenses which go for feed, taxes, hired labor, etc. The third approach is used in Chapter V. In that chapter, an attempt is made to measure seasonality by ana- lyzing the percentage Of the year's total eXpenses and in- come which occur each month. This approach is important in comparing the total income flow by months with the total expense flow in those months. Organization of Tables Since the study is oriented in some degree toward planning of the farm business and especially planning as a means of reducing uncertainty,it seemed that a single estimate of the median might be more apprOpriate than pre- senting a range of estimates. This has been done in the tables by using the midpoint between the confidence limits 29 as the single estimate of the median. The confidence limits that were used in arriving at this estimate can be found in the Appendix in Tables B, C, D, and E. Asterisks will be found in the tables alongside some of the percentages. This is an indication that the two con- fidence limits from which the estimate came were less than 4.0% apart. Estimates with narrow confidence limits could be expected to be more reliable for planning purposes than estimates with wide limits. For instance,¢a figure of 5% in the table with confidence limits of less than 2.0% (”1 either side cOuld be eXpected to be more nearly correct when . used a number~of times than a figure Of5% with confidence limits of 5% on either side. The tables also include the arithmetic mean. Mean percentages are useful in a descriptive way as well as for items which may not have very definite or reliable median estimates. It is also interesting to compare the median estimates with the mean for disparity and similarity. In addition, the mean percentages should always total 100 per cent so that all the item is accounted for.) This is not true Of the median. For present purposes, it is necessary only to note that this is not true for the median since the median farm in each month will probably not be the median farm the next month, and certainly will not be the median farm in each month. 30 Seasonality of Expenses Tables 1 and 2 give definite indications of season— ality in some of the expense items. Many of these, such as the planting expenses, would be expected. Others, like insurance and interest perhaps would not have been expected. Still other expenses like electricity, are fairly constant throughout the year. Hired labor expense is one Of the more important ex- penses that have a seasonal pattern. Both the median esti— mate and the mean percentages indicate the seasonal flow with the high in the spring and summer and the low in the late fall and winter months. It is interesting to note that the median estimate indicates August as the month of highest eXpense for the median farm while the mean per cent is largest in July. Table B in the Appendix shows that August was the month when the most farms had hired labor expense. Feed purchases had less of a seasonal pattern than might be expected. Both the median estimate and mean in- dicated that feed expenses would be high in November, Dec- ember, February, and March. Though thisis somewhat Of a seasonal pattern, it is rather surprising that the January feed expense did not remain high also. The median estimate showed fairly large figures for feed purchases in April and October while the mean per cent for June was higher than other summer months. TABLE 1 THE ESTIMATE OF MEDIAN MONTHLY CASH EXPENSES As A PER.CENT OF EACH ANNUAL TOTAL EXPENSE ON 40 CENTRAL MICHIGAN DAIRY FARMS FOR 1955 a CONGO 31 * * 0\O \fi “Jr-400550003 e .Hcpb- Q) .000 one 000 000000000 a owoo mom om a eeomamdoHn I s *__ g mmoo mum mm m Hwommmdomm 2 H500 mmm mm A dzdzfifiiédfi . a II: 0 3 mooo doc Hm m mdoooomomm 0000 000 00 0 0000000000 0 nmo: woo Mo m mmooowwoom . *7 s s t t a Hmwm now on d mmoommmowu 0000 000 00 0 0000000000 (% dMMN no: 0H m :mooommo3H st & NMOH wmm mo m mmOOOHHomd 0000 000 00 0 000000000. g 5000 BNO do a woooomdomm [—4 s w '7 0 e e s ': mama no: mm m MHooommoww 0000 000 00 0 0000000000 5 mnHN com 0H H dwooomuomH h H H a g :de mo: Hm m omooommoom g sees 06m MN N whoéddfiomm W e s s a 7*0 3 wam mom on a mzoomomom: 00 0 000 00 0 0000000000 z mdfim :OH cm H mgOOOBQOHH . e~ e t a mmoo moo :m m mwooomNomN 0000 000 00 0 0000000000 d mum: mom NH m mwooowm0HN HH - s t s a t g 0000 con mm m wdoomwooow 00.0 000 00 0 0000000000 : Hmdo 30H 0: m moooowuomm - t n Ndoo don OH m mcooomHomH Q) 0000 000 00 0 0000000000 h NBOO 30H om m mmooomaomm . *0 e s t g H000 Ho: 0: o GawOHamon 0000 000 00 0 0000000000 6 Hmoo HOH o: w WNROHmoodm U m on m as no a H Ho 0 OHM on R p H as M N madam on p O m m omega mom: 0 e m mHmmco How u H t m 8 Add mos 9:0 mm H a Q: a) 0.3:: $4 CH :chmcooo 49 mm +9 3060:5051 mm AH>m 0 Ho 00. H est-taut”: £96:dech 0430!: xx damn-402000 ace-1.40:: cmHo 91m HCHchcHHv as moan D 'd mHHdHHocrdth-ax cont-«4:9. t4 ocupnzsahmmmzmmHoamoouom $402090 09.9.. 0+3 .2: oxupchpn Hmmmd 095 no P SdCCHQmSP mama: zmH m> O hBHHmBm40 Indicates range of 96.2% confidence limits was less than 2.0% on both sides of the median 0 zero On either side. All zero estimates indicate 96.2% confidence limits were **Confidence limits were within one per cent on either side for all items. estimate. TABLE 2 EACH MONTH FOR 40 CENTRAL MICHIGAN DAIRY FARMS FOR 1955 MEAN MONTHLY CASH EXPENSES AS A PER CENT OF ANNUAL TOTAL EXPENSES FOR H d p 000 o 000 00 o 0 000000000 o 000 o 000 00 o 0 000000000 B HHH H HHH HH H H HHHHHHHHH 8 duox x0 mmox mox Ox m O\®(\\OO\0HV\N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 O “HO :7 mHox mm N o \OL\\CO\(Dr-h—Hl\m H H H NH HN H r0 NHo in :rNN :hn Ox Ox oomooaxncxNLxOx 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 non) N 000x: Nm o Co \ommchc-tndcx- H H HH H NN H 0 4; OxOxoo x0 omoo fixo H H (\oxovxdeONH 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 O \ocxn Ox O\ON mxc x0 (x COOHuxoomH-zio H H H H a‘mHoo H \nxom MH xc :r (\oomooxuva: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o COIxOx H Comm 00x0 Ox co O\Ot\\0(\~U\L\C\-C"\. m H HH 'moufi 0 H00 N N30 0‘“ O\ \O 0 0'0 chi—'mnm was. e 000 so 0 0HONm00000 :3 exam :I Oxtno NN x0 Ox H commHoo <1: H HH H H 3‘. onm 00 m50x No H H \oooommd'txom 0 I 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 :3 NLClx co HHvx mm xo L\ HON3C\O\O\O\\O *3 H HHH N H g in: \o xoxom Coo Ox N OOO\ON:IV\O\O 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 :3 OOOH H Home mm m 00 O\OL\NCDU\\O(\(D r: H H H H H H a. [\J-fiN H HNL\ (\m as Ox HNO\N\O(\L\€'\® 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2‘. COMN x0 How mm tx- Co HNNNl\O\-‘:Yl\-O\ N H H H H a. ona cx'duxN cCc: Co H M\O[\O\\ONOMM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Q. 000: 0 (\NW Corn l\ a) oozNuxOxOMxOH 4: H N N H H H g xoxnxn \n xovax mm H 00 (\MQHNOOWGD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2: (\OH :r \AHM \DO\ 0 d) (\0HL\COOxmoocx HH H . i .3 Como Ox mono oH Ox CD NdeHOO-trlxoxxo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 t2. (\OxN o \ONB- Nox Ox xo mlzmnooxooxowx 0 Fm H 00 o 0‘ V0 000 00 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 50:4 \n mom HOx \o (x max-OxnCOOchxoo *3 m H “to Tim on $40 do +9 HS: in: M and aid! as rd 0 m oer: Dar: game 0 m 0H6 «1:00 comm +9 H to 7420. (3199 ppm to H be CM: 0 $4 C-H saber-moo O u (on) .oo'ua) th: mH hubs) a) 0 HO on. ushers so memmeHerm 090:: MN game-«oomoomzccogccc Gun-Ho 94:1 a Hat: :H> HHH onto moan D I H mHHHrdHHo'd'dh'US-cn. Olaf-than. $4 mdcpdncsahcmommxHXm50004-300) hoot-o omoanmovxnmmmxmpchvn Home) as 45:35 no +3 :5 MECHoozu-a mama S zmH m> o h BHHmBmwn .m ooa nn 0.0 5.H H.5H o.ma, 0.0 :.oa H.NH nu H.HH m.am 5.0 new; aspasom Am ooa nu H.0 . nu nn m.m w un 5.NH m.m 0.00 nu 0.0H nn moamm ammnm .5 ooa 0.0 5.0a m.0 m.m 0.: m.0 . m.HH m.m m.¢a 5.0 H.@ “ 0.N madam mom .0 ooa nu o.m uu nn 5.0m nn nu 0.0m o.oa m.m :.ma nu moasm moom .0 ooH o.HH 0.m n.HH 5.0 m.m m.0 0.0 0.0 0.5 m.0 0.0 0.5 moaam wwm .: OOH 5.mH 0.0 m.oa N.: m.m m.mm 3.: m.a 0.: a 0.: m.0 0.5 upcmsaam w newscam>ow w i one madam mono .m ooa 0.0 m.m 0.0 H.0 0.0 m.0 H.m m.0 m.w 3.5 3.5 5.5 upospoam madam .m ooa 0.5 m.5 0.m 0.HH N.0 0.0 0.5 2.0 0.0 m.ma 0.0 m.m mappeo madam .H :sozn. 0.0 0.0 0.0 0.0 t0.o 0.0 0.0 o0.H To.a a0.H .w.a o.o modem mwm .: 0.0 0.0 0.0 0.0 0.0 0.0m 0.0 0.0 0.0 , 0.0 .o 0.0 upcmshwm , i _ newscao>o¢ use moamm mono .m m.m 0.0 5.0 m.0 0.0 N.0 0.0 H.0 3.0 m.5 0.5 5.5 mwospoam madam .N 5.m 2.0 0.: m.0 H.m 1N.H o5.H H.m H.N N.m m.: 0.: «prwo madam .H stumpmefipmm smacmz Hence .ooo .>oz .poo .paom .w:¢ masn mean as: .aa4 .amz .nom .cwn sopH r1 all mmma mom msmea NmHaa zauHmOHz mamazmo o: mom mzoozH a<=zz< 44909 ao azmo man a me momsom moem zoma axoozH mmao wheeze: n @492. 38 Dairy cattle sales varied a great deal during the year. Two months seemed to have large percentages of the sales. In May 12.9% of this income was received, and in September, 11.8% was received. The median estimate was high in these two months although not as high as the mean. Dairy products income was very constant with a slight seasonal rise toward the end of the year. The median esti— mate and the mean percentages were within 0.2% of each other every month of the year. The confidence limits also were very narrow on either side of the estimate. It would appear that dairy farming does indeed provide a constant and re— liable flow of income into the business. Crop sales and government payments provided a fairly large proportion of the cash receipts on.ttw sample farms. The largest part of this comes into the business in July with the harvest of wheat. The median estimate for July was 28.5% which was very close to the mean per cent of 29.2%. Less than one-third of the farms sold crOps in other months. However, there was a large percentage of the total crop sales which occurred in September. Egg sales are not very important as a source of in- come on dairy farms. Still, enough of the sample farms had this income to give estimates for the median farm other than zero. It appears that the median farm would be most apt to have egg sales from February to May though the mean per cents indicate that most of the money from this source came in from October to December. 39 Other sources were relatively unimportant as sources of income. Less than one-third of the sample farms received income from any one of these sources in any one month so that no median estimates were obtained other than zero. Differences in Seasonality of Certain Expenses Due to Herd Size As mentioned earlier in the chapter, a division of the sample based on herd size was made in hopes of getting more usable figures. There were some differences brought out by the division. Hired labor expense appeared to be much more constant on the larger herd farms as indicated in Table 5. This may be due to many of the larger farms having labor in several months of the year. The smaller herd farms (Table h) fluc- tuate more between months with the median estimate for August nearly double that of the larger herds. The seasonal nature of the expense appears in both sub-samples, though a greater seasonality is found on smaller herd farms. In general, there is little difference between the two sub-samples in feed purchases eXpense. Large herd farms appear to have a greater prOportion of this eXpense in Feb- ruary and smaller herds appear to have a little more in December. There is little in the way of a definite seasonal pattern in either group for this item of eXpense. There are several differences between the two groups with respect to machinery repairs and maintenance expense. .omeapmo cmficos no mdam Amanda co Rm cmnu mama mam mafia“ OCH OCH 00a OCH OCH OCH OCH ("n O G) H H H NO\ 03B- \0 m (““0 [\-0‘~(\.‘ 0"! «mm shoox H mm when 0\ C\ H.0H a.5 0.0H H.5 .0 .m .o (\qu H I I O C 0\ 00 0:0 m w «)5. «Meow \o H m.ma 0.0H m.5 0.0 :.oa n.m w.NH 0.0a .0 :im Pin—1CD 0 0m W30 r-l *w.o H C“ m.m 0a” :.m a :.HH m.HHl ON COB-N l\ O O (”\\O 3HCD O\ (\- H rt? WL¢41 -3 3'0 (DWW \H (N H 0 (DOWN \n \O :13 .d\AU\ \n ru \60- H m.ma 0.0H 3.: 0.m «.3 m.ma ~.: tN.H Hence .00Q .paom hash ha: nmma 2H mammm 300 MN z amok wcduooam mouaaaom oocmcopCAoz can «wnom maocfizomx momenoasm coca sons; coafim mucoo pom coo: D-o\ :rrho c. e» H HL\ L\O\O O\ CO H and' carnn O V\ (\n\0 HO\O O N ON mm nqcvcw :rvvo D~ Ado use Hash ocfioaooz can mascaaopo> womb waddooam moaaagsm mocmcopcdmz was mammom hamcdsomx momonoasm doom mono; cmadm nonmedpmm cmapoz dwc muncw \0 o— :roue .3 m) nacur§ :fvuo c. . w r4v\ (new: ux us qu -dc\o~ 3' cu O " TABLE 5 MONTHLY CASH EXPENSES FROM SELECTED SOURCES AS A PER CENT OF TOTAL ANNUAL EXPENSES FOR 21 CENTRAL MICHIGAN DAIRY FARMS WITH MORE THAN 23 cow HERBS IN 1955 1+1 H 00 000 00 a 00 000 00 8 HH HHH HH B 5 mm mum NH mm d¢m HN I. 0.. I O O O .0 O O O 0 mm mma o: “H 0cm :0 G H H H H Q 00 m5n m5 NN own mm 0 m5 050 dd m5 $36 H0 2 H H I 8 3 mm 5mm mm H5 0mm mm .0 O O. O O O O .0. O. 0 m0 5H: mm 55 mm5 0m é 3H Boo mm :5 5Hm 0m 0 m0 000 m5 mm wié 6& m H H & mH 300 “H mm mH: ON .0 O O O O O I O .0. 0 O s 50 wdo 5m 00 mmH mm 4 H t t t 3 mm who m5 om mmw mm .0 00 O O. o o 0.0 o. 3 m: OOH Ho N0 HmN :0 0 HH HH H a t 2 mm 55: 00 mm mmw mH 5 50 mmo 05 ow &m: m5iH h H HH '3 0m 5m5 mm H0 mmm m: on o 0 o o o O O o o. o. 2 an mNm N3 mm 005 QH a mH moo do 5N 0:0 mm 14 65 0MO 0w mm.5mfi 5w 5 5m 000 NH No MM5 :0 I O O O. O I O I O. O I. a Mm 3H0 00 5H 0HN Hm x H H H t 5 «W 0m5 m0 mm 030 HH 0. .0. O O O O 0.. Q. g do 3H5 0: 5H m0o on 8 t 5 mm dm5 Hm m5 mwo 0m 0. O I O O. 0 O 0.. O. 6 3m Hmox 0m 00 mm: mm t: H 0 “0 mas o mflo U m mac n: was an o In m mm H a: as was H pang: m H mhggc 0 H «03 0 bk 0 no m M» O epohu $40 CDO>5~P $40 8 H maacmwocu o shacmwwso 0 PnJSQHOCCHC 0.33mHocch +9 In maHHHoa mcmHHHom H mu HZHUHH Aw HzHUhH 0213:: DnOO'dH a: (DUI: va'dn—i cameraman-=00 mhmo'dnnopqo dqammcnhmss «womanhozs :3mhzomm> h gmmzwmm> E Q) o no .0 o 0 Q) e on cos 0 EHNM :TUMO [\- SHNM 41-me 5 ’97.3% confidence limits are less than 2% on either side of median estimate. 42 'The smaller herds had an average of 15.1% of the year's ex- ‘pense in May. The median estimate was also high with a figure of 15.9%. The larger herd farms had their highest month of expense in June when the mean was 12.5% and the median estimate was 12.7%. There was a large difference in December, also, when the smaller herd operators paid 12.6% of the year's expenses as opposed to 6.h% for the larger herd farms. The median estimate for December was 11.4% for the smaller herd farms. This may be due to the small herd operators paying bills in December rather than when the bills are incurred. There were large differences between the two groups on months of high supplies expense. The smaller herd farms had large expenditures in February, March, June and August with the latter month accounting for 18.#% of the year's expense. On the larger herd farms, the large expenditures were in June, July, September, and December with June the month of largest proportional expense. The median estimate was higher for July than June on the larger herd farms. There was no indication in the data as to why a difference existed between the two divisions. The larger herds had a more seasonal pattern of breeding fees expense than the smaller herds. There was a definite seasonal for large herds with the high expenses in the months from November through March. November and Dec- ember wére high months for the smaller herds, but more cows were bred in the late spring and summer months than in the 43 other group. The data would tend to indicate that the large herds were more uniform in breeding for later summer or early fall freshening whereas the smaller herd operators were more varied in their production pattern. There were some differences in veterinary and medi— cine expenses between the two groups. The small herd farms had a more seasonal pattern than the larger herds in that the expenses were high from November through February. July was the month of high expense in the summer. The winter months were high on the larger herds too, except that the January figure Was quite low. The December and March expenses were considerably higher for larger herds than for the smaller ones. Fuel and oil expense had a strong seasonal pattern for both groups. This was indicated more by the mean fig- ures than the median estimate for both groups. July was the month of highest eXpense for the smaller herds as in- dicated by both the median and mean. The large herds had the highest expense in August rather than July. There is reason to doubt whether the differences in this expense item were significant. There may have been an influence from the smaller herd operators paying bills at different times than the larger Operators, or the differences may be random differences. CHAPTER IV PERCENTAGE OF THE MONTHLY INCOME OR EXPENSE ACCOUNTED FOR BY EACH ITEM Introduction Chapter III described the seasonal variation of sel- ected individual items over a twelve month period. This chapter will include an analysis month by month of the var- ious items making up the month's expenses or income. This will also deal with seasonality by showing the changing per- centage that any one item is of the total in variOus mOnths. Again as in the previous chapter, the statistic of interest Willtxzthe median and its appropriate confidence interval. Using this statistic for a sample of forty farms, one can have 96.2% confidence that the median farm will fall between the luth and 27th observations when the obser- Vations are ranked from highest to lowest. Thus the lhth and 27th observations in each category are the upper and lower estimates respectively, between which the universe median should fall 96.2% of the time. It is assumed, for purposes of planning, that it is usually more difficult to use a range than an individual number. For this reason, a mid-point between the lhth and 27th observations has been taken as the One most appropriate figure. This mid-point is used as an estimate of the median nu 45 farm and for each such estimate in the chapter, there can be found a table in the appendix with the appropriate con- fidence interval for that estimate of the median. For those who might prefer to lay plans on a range of possible out— comes, Tables F and G in the Appendix have the confidence intervals for the data presented in this chapter. Even though the median is of chief interest, the mean will also be shown. The mean can aid in the description of the farms and in comparing with the median figures. In many cases the mean percentages indicate the same trend as the median thus tending to bear out the median estimates. Tables 6 and 7 indicate the changing relative impor- tance of the various items in the twelve months of the year. The median estimates in Table 6 tend to leave a large pro— portion of each month's eXpenses unaccounted for. The one exception is in April when the median estimates total 100.2%. This tendency exists largely because the same farm will prob— ably not be the median farm for more than a few items or months and the largest percentages are ruled out by the median. Seasonality of Expenses Taxes and feed purchases are indicated as the two im- portant expenses in January. The median estimate of 15.7% for feed purchases was the largest single item while taxes were estimated at 12.0%. The confidence interval for taxes was very wide, however, as shown by Table F in the Appendix. A6 Rooa Am>o cams nonmedpmo schema Hope» pcnp mendedccHtt .opeedpmo cmacms esp mo ocdm Amanda no RN cdnpdz undead mocmvamcoo RN.mme 00a 00a 00a _ 00a 00a 00H 00a 00a 00H 00H 00H 00H Hence N.0m 3.0: m.mz* 0.N: N.H: m.wm 0.0m. m.mn t: 0.3: m.n: 0.0: pom Umpcsoooecw s0.H *m.0 *®.0 em.0 «0.0 ¢N.c tm.0 sm.0 em.0 *m.0 *0.0 tH.H momcmaxm pomp *m.0 *H.N tm.0 $0.0 #m.H $0.0 *m.0 tn.0 $3.0 t0.H cm.N *@.H Amman: on: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0:00 *:.0 em.0 $0.0 sm.o *:.0 e0.o *N.0 s:.0 ea.0 s0.0 $0.0 $0.0 sconasaoe *m.m tm.m tn.m #®.N *m.m *m.N *H.m *M.N #:.m cm.m *0.m *H.3 hpuouhpooam .0.0 e~.H _ 0.0 *0.0 0.0 0.0 0.0 .n.0 0.0 0.0 0.0 sa.a esseeesH t0.H m.m 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 monogamCH 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.NH moxwe H.m 0.0 0.0H m.ma n.0H 0.0a 0.NH 0.:H m.aa m.ma 0.0 _ m.0 ado 0cm Hosm *m.H tm.H tN.H tH.H *0.H t0.H t®.0 tH.H $0.0 *0.H *w.H t0.N mmcmmxm accumo>aq umspo *0.H *m.H tH.H t1.H *H.H *m.0 em.0 tm.0 $0.0 em.H tm.H cm.H mcdoaUmz can zaacdaopm> 0.0 0.0 0.m m.HH *0.H e.m :.m 0.0a 0.ma 0.0 0.0 0.0 osaq 0cm macadaapaeh t0.H *0.H ¢N.0 tN.0 0.0 #N.0 $0.0 *m.0 *m.0 *H.H *B.H *H.H mock mcavmuhm 0.0 $3.0 c0.0 em.0 t0.H em.H $0.0 0.0 t0.0 ¢H.0 0.0 0.0 mucmso>oacsH e.u 0.0 m.a m.a a.m 0.0a s.0H 0.0a a.m 0.0 ~.0 w.0.H essencesasz use madcaom .hamcfinoez es.0 es.0 0.0 *0.H .0.H *H.H ss.a *N.0 0.0 .0.0 3.0 0.0 esaasssm 0.0 e:.H 0.0 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 spam weaned: 0.0 0.0 0.0 0.N 0.0 em.0 0.: 0.0 0.0 tw.a 0.0 0.0 upceam 0cm mcoom 3.0H 3.0m m.ma :.NH 0.:H m.ma 0.:H H.HH m.ma 0.:N m.HN u.ma mmmwnoazm comm t0.0 $0.0 1:.H N.N o.m m.m 0.: m.m 0.: e0.H m.m tw.0 honed coham .omn .002 .900 .9000 .w:< mach mesa he: .AQ< .awz .nom .ceh omcoaxm nmma zH mzm «DUN—loo wF‘AO\ \0 <13 0M\ow\oxoxoocxm8 o 0.... 00. o c coo-000.0. z MHHw wdH m N HOMMHNOHaHH N H H 3 NH: 0 0NH m m 0000NN0mm08 o 0.000 on. o o coo-coo... o (DO\N€'\O OMH W H HWOMMMONHH H H H H H é.0:oom H00 4 m mHommmimmwg 00000 000 0 0 0000000000 3 0mmmN 0N0 m N HWOHMNQHNQH HH H H & 0000M mm: 0 0 moomHqung 00000 000 0 0 0000000000 5 NmHmN 0:0 0 N N000MMOH¢HH 4: HH H H h oomdo 000 m : incomdmmmmg 00000 0 0 0 0 00 000000 3 Nmmmm H60 H H dedNNOHNOH HKHH H H H m mHamd Nmm m 3 woomumaNMNg c 0000. so. 00 so... a. : OBmNm HHO m H HNonN NHH 0 HH H H H : mmmHH wm0 N m mammmzmmng 00000 000 0 0 0000000000 .2 CDOHOH OHO H H_ HWOOMNOONH H HH H N H d wdm00 mmm 0 0 dmmdommmmmg 00000 0.0 0 0 0000000000 3 OWHOO 0N0 m H HOHNNNO0HHH HH N H L: “\thom H\C\0 O\ H NHde F‘ICDLNC'Vng é éfifidd NNH 0 m N30030000HH HN H 0 o0m0H N00 0 d 0000NH000N8 0 000 00 0 0 0000000000 £ NdHON 00H H m H000N30N¢Hr4 HN HH 5 mHNN0 N00 0 0 HHmdm00©N08 o. o. .0. o 0 0000000000 g 00H00 00H 0 N NNNHmMHNMHH HH H N a he 9 Ho 0 M ma dc G U o 0 mm as we a o m m mHo mcmm c p H m c: $.me man-20cc In «H >. as 0 on H pdbh h m 0 a mo 0.000m ham 0 fin”, 0 Ho mp y,- qstec: (ah-Hews! (nan-{GU Opes: 04K m Aawmomdocd 2'4qu Gin-HO QMH :ch>HrIrHJ ca 6095 :3 a u mHHH ochhHhm manna hp mTJ'USQfl'UhmpEm'UmQHmsooopom o SaccoQOQthHpmnxoxmpchpflE—4 Homesewshogmzpmsccchm mmmzmz Hmk > O MBHHHB 48 The mean per cents in Table 7 showed that for the whole sample 23.8% of the January expenses were for taxes. Feed was next with 18.1%. There are several differences between Tables 6 and 7. Hired labor was a minor expense as shown by the median esti- mate. The mean percentage indicates that it was a rather important expense in January. Several farms with full time men would tend to give a disparity in the tables. The fer- tilizer expense was of a similar nature. Table F indicates that more than two-thirds of the farms had no fertilizer purchases, yet the mean percentage fortshe total sample Was 10.0% for this expense in the month. Feed eXpense became even more important percentage- wise in February. The median estimate was over one-fifth of the month's eXpense while the mean amount was actually 26.9%. The median estimate indicated fuel and oil and mach- inery repairs as the next largest expenses for the month of February. The mean percentages in Table 7 tend to bear this out. TaXes andhired labor are also important when all the sample is put together to get averages. Electricity and auto upkeep seemed to be of relatively more importance in the second month among the minor items of expense.. In March, feed purchases were the largest per cent 0f the month's expenses of any month in the year. The med— ian estimate was 20.8%‘while the mean percentage for all sample farms was but 2.3% larger. #9 Fuel and oil eXpense continued to rise with a 12.2% median estimate for March. The mean percentage also rose. Machinery repairs decreased slightly in relative importance in March. Table 7 indicates that some of the farms were pre- paring for the spring planting with early purchases of seeds, plants, and fertilizer. The mean percentages for several of the minor items of eXpense such as auto upkeep, and the livestock expenses were relatively important in the first three months of the year. There was little indication, how- ever, that this relationship was necessarily true for the median farm. Taxes, insurance, and machine hire were relatively unimportant in March. The planting eXpenses became important in April. Table 7 showed that 25% of the April expenses for all sample farms was for fertilizer while 11.5% of the month's eXpenses went toward seeds and plants. The median estimate tended to eliminate the weight of the very large purchases to give an estimate of 13.0% for fertilizer and 9.0% for ‘ seeds. The confidence interval for the two eXpenses was wide apart, however. Feed purchases while relatively less important in April than in March, were still a major item of expense. Both tables indicated slightly over 15% of the month's ex- pense would be for feed. 50 Machinery repairs and fuel and oil eXpense remained at about the same relative importance in April. The median estimate for labor rose to 0% as some extra labor may have been needed on more farms to help with planting. In May, the fuel and oil expense is the largest ex- pense according to Table 6. Both tables indicate that ap- proximately 15% of the May eXpense is for fuel and oil. The mean per cent for fertilizer and lime expense was larger than for fuel and oil. The median estimate for fertilizer and lime was 10.9% of the May eXpenditure. Feed purchases were an important eXpenditure in the month with over 10% of the month's eXpenses. Machinery re- pairs were also important in the month and the two tables indicated about 10.5%.of the month's eXpenses for this item. The machinery repair expense as well as the fuel and oil eXpense was the largest yet for either item in May. Seeds and plants were still a fairly important ex- pense with a median estimate of 9.0%. Interest eXpense was relatively more important than the previous month as shown by Table 7. In June, the median estimates show feed purchases, machinery repairs and maintenance eXpense,and fuel and oil purchases as the three largest items of expense. Feed pur- chases had a median estimate of 14.0%. Machinery repairs remained at 10.0% which was the high percentage for the year. Fuel and oil eXpense dropped slightly percentagewise to 12.6% of the month's eXpenses. Table 7 also indicated 51 that the three above mentioned eXpenses were important although the per cents were different. In addition, the total sample spent 15.5% of its June expense money for fer- tilizer and lime. Supplies were relatively more important in June than in previous months. The median estimate was 1.h% of the month's eXpenses while the average for all the sample farms was 3.“% of the month's expenses. Hired labor was relatively more important in June than in previous months according to the median estimate. The planting eXpenses were still taking some of the June eXpense money. Fuel and oil was the largest single item in the July expenses. The median estimate was 18.0% of the month's expenses. On the sample farms, the mean percentage was 16.“% of the expense. Feed purchases were relatively low in July but were still an important item of eXpense with a median estimate of 13.7% and a mean of 15.0%. Machinery repairs and maintenance expense was esti- mated at 10% for the median farm. The comparable mean per cent was 11%. Hired labor was a more important part of the July expense than in previous months. The median estimate of 5.6% was the largest of any preceding month but was still considerably below the 12.6% average for all farms. 52 Table 7 indicates that a fairly large percentage of the month's eXpenses went for improvement repairs and main— tenance on the sample farms. The 8% figure is a larger pro- portion of the month's eXpense than any other month though the median estimate indicates August as a month of high im- provement eXpenses. In August, fuel and oil eXpense was still the largest item of expense. The median estimate of 17.3% was slightly under the July estimate, but the mean per cent from the whole sample of 19% was the largest proportion that fuel and oil was of any month's eXpense in the year. Feed purchases were a lesser prOportion of the month's expense than in the winter months, but it was still a large item of eXpense with an estimate of 14%for the median farm. Labor expense was at its seasonal high in August as shown by both tables. This was also brought out in Chapter III under the other approach. The median estimate was 5.6% of the month's expense for labor. Actually, of the expense on all forty farms, the labor was 12.7% of the months expense. Machinery repairs dropped slightly but still remained an important item of expense as shOWn by the estimate of 8.7% for the median farm. 0f the minor items, machine hire eXpense and auto upkeep were relatively high. While this is shown slightly by the median estimate, the mean per cents in Table 7 tend to emphasize it more. Machine hire was 5.5% and auto up- keep 4.1% of the month's expense. 53 September shows the planting expenses rising rather sharply again. The median estimate for fertilizer and lime rose to 11.5% while seeds and plants went up to 2.9%. The mean for fertilizer and lime was 16.0% while seeds and plants were 5.6% of the month's eXpenses. Fuel and oil was still an important item in September. The median estimate of 13.2% was fairly close to the actual mean of 15.1%. Machinery repairs and maintenance continued to parallel fuel and oil eXpense as it dropped slightly but still remained an important item of eXpense in the month. Feed purchases were at the fall low in September with a median estimate of 12.4%. Even at a low, however, feed is an important part of the monthly eXpense on dairy farms. Machine hire expense was 5% of the month's expenses for all farms thus indicating September as a fairly impor- tant month for machine hire eXpenses. In October, the two large expenses indicated by the median estimates are feed purchases and fuel and oil. The estimates for these two were 18.5% and 15.0% respectively. The correSponding mean per cents for the two items were 19.1% and 15.0%, both very close. Machinery repairs and maintenance expense was the third largest as shown by the median estimate of 7.8%. This estimate was below the average of 10.7 for the whole Sample. Table 7 indicated that fertilizer expense accounted for a large share of the month's eXpenses on the forty farms. 5h November's one large item of eXpense was feed. The item amounted to over one-fifth of the month's eXpense for all farms and the estimate for median farms was just about one-fifth of the month's eXpense. Machinery repairs and fuel and oil expense dropped considerably from the previous month. The estimates for the median farm were 6.9% and 6.6% reapectively. In both cases the mean per cents were higher. Interest eXpense became more important in November. While the median estimate of 1.2% was relatively unimportant, the mean per cent of 11.6% made it a major item of eXpense when considering all farms. Several of the smaller items of eXpense might be noted. Machine hire expense was at its highest for the year as shown by both tables. Insurance payments were a larger portion of this month's expense than any month in the year. Auto upkeep was more important relatively in November than in any other month. Machinery repairs and fuel and oil eXpense were the two next largest items with a median estimate of 7.4% and 5.1% respectively. The mean per cents indicated that while less than one-third of the farmers paid taxes in December, those who did, paid 12.2% of the total month's expenses for that item. Insurance, rent, and the livestock expenses were proportionally more important in December than in other months as shown by Table 7. 55 Seasonality of Income Table 8 gives the median estimates for four sources of income and the mean percentages from all sources. Only the four sources were included in the table with median estimates since the estimates for all other sources were zero. The estimate for median farm indicates that dairy cattle sales income Would likely be an important part of the monthly income in January, March, and September. In July, the income from this source is a very small part of the month's income. The mean for all farms tends to bear out the relationship above with one exception. In February, the median estimate wash.1%*while the sample mean showed 11.2% of the month's income from this source. Evidently a few farms made large sales which did not affect the median estimate. Dairy product sales were the important source of in- come throughout the year. With the approach used in this chapter, the results are a much less constant proportion of the month's income than in the Chapter III approach where the item was very steady month after month. The confidence interVal was quite wide also for the per cents in Table 8 as shown by Table G in the Appendix. April was indicated as the month when dairy products were the largest proportion of the month's income, and July was indicated as the month when this source was smallest TABLE 8 THE IMPORTANCE OF THE VARIOUS INCOME ITEMS IN EACH MONTH EXPRESSES AS A PER CENT OF 56 THE MONTHLY_INCOME FOR no CENTRAL MICHIGAN DAIRx FARMS IN 1955 fé 0Hmmd0mN omo N38 9> mmNHodo HHN 0HH W4 0 H 8 mHoo N0mm'm" m0N .08 0000 0000 0 000 0 £3 :MOO \mwwhrul 00N IMH A 0 0 H g 0000 Hmm330MN 3N0 mwg 0000 000.00.. 000 00 0 0:00 00N®0m00 HMN 00H 2 0 0 5 M000 0000 0 H NNm NW8 coo. cool-Io so. .0 0 :000 0:00:010 NHH 00H 0 0 H g N000 000H 0'“ 000 008 ' oooo 0000!. o so. .0 3 0000 Mdeldio NNN 00H 0 H0 o é VMJO 100:00N: N3: m8 000. .0000... so. I. 5 mmoo 00N0HN00 0HN IHH ¢ 0 0 h * fl N0om 0m30'w N m0: mNO one. 0000 0'. nos .00 5 H000 :HHNuNlo 0NH 0HH h m H m m a g 0H00 ommH'dmm 000 H08 0000 0000 000 000 00 5 Hmoo 00H0|000 0NH 0NH 0 0 m h t w mmNo 00Hmno0: 00: H08 0000 0000000. 000 0. 2 N000 0dNNH000 0HN 00H 0 0 - e 3‘ 00:0 mommmmN' H00 H08 0000 0000000 .00 00 < H300 00N0 00: 00N 0HH w m 3 dHoo m0mmnm : mHo :8 see. coco-0|. 00. lo 2 Hmoo MMNmomlo 00N IMH H0 H0 % H000 N0Hm0wN0 000 ’08 0... 0.00.... no. 0 h 0000 HNN0omH0 HON IOH 0 H g 0000 wddm 0 m m 08 a... 0 00 0" 0'. '. h wmoo HdNNuHII 0|N IOH 0 H0 H #7 mm mm o“ 00 .03? gins pram $8” a p p 0 E m 0 8030 4900 PC: .54 :5: 0 H H: CH5 one hwimod F u 90 m 000 mm mm omlmmo 5 m pomp ouomommHzm 3 chz .3 m waoH hdfiwHng on mass: omHm momHme hCUQHQmHH G aid) 94 4.6me hQCCOC Hui d >300) ‘ hhm campus-4 Hnop H as Q CHM QM mHHunxnmoo 'd HHhoo dHHwomwmsbmoF-«ouimE—i 0 some mommaoo£OHmwoe0H S ammo anamommmmn :3: S *96.2% confidence limits within 2% on either side of median estimate. 57 percentagewise. The average of all farms indicated that May, not April had the largest proportion of income from this source. In general the median estimate was higher than the mean percentage since it tends to disregard the several farmers who had income from other sources. Crop sales and government payments were the second largest source of income for the sample farms on the average. It was 11.8% of the year's income. The only month with a median estimate was July. Table G, in the Appendix, shows that the confidence limit went as high as 39.1% so that the 19.5% estimate may be rather inaccurate. Less than one- third of the farms had income from this source in other months. The mean percentages indicate that of the forty farms, there were some farms with income from this source _ in each month. Although egg sales income is relatively unimportant percentagewise, there were enough of the farms with this in- come to get median estimates greater than zero. There were more than one-third of the farms with income from thissource in April, May, and August. While other sources were not important enough to get median estimates of more than zero, the mean percentages in the lower portion of Table 8 serve to give some indication of the relative importance of certain sources in the various months. When one considers that less than two—thirds of the farms had these sources of income, it is very possible that some of these sources may have been much more important on indi vidu a1 farms . 58 In concluding this chapter, there are one or two things that may need to be said. First, the approach used in this chapter was in the author's opinion less useful than the Chapter III or Chapter V approach largely due to the wide confidence intervals and the small proportion of each month's eXpenses or income accounted for by the median estimate. While this approach is of some importance in showing the major items of eXpense for a month, it is open to a large degree of error. Secondly, it is difficult to see the applications of the chapter when such a large proportion of the month's ex- penses are unaccounted for. It may be difficult to see how the median estimate is worthwhile in such cases. It should be noted first that the median estimate will always disregard the extremely high or extremely low percentages. Next, it should be noted that the median estimate was presented as the ggg,most applicable figure for planning. The 96.2% confidence interval was in many cases double the median estimate so that one would need to consider the confidence intervals in the appendix also when planning. Thirdly, in trying to plan a farm that might approach the median farm, one needs to realize that the farm may not be in the con- fidence interval for Ell items. Thus a knCWledge of the farm is important in using any percentages found in this study. CHAPTER V MONTHLY VARIATION OF TOTAL EXPENSES AND INCOME Introduction There remains one important approach. That is to analyze the variation in the total eXpenses and income each month. This approach will be followed in this chapter. The two previous chapters have supplied information first on monthly variation of individual items and secondly on the changing relative importance of the various items in each month. The approach of the present chapter in analyzing the total month's eXpense and income is important to farm planners and managers in balancing the flow of expense-- primarily of the farm but also perhaps of living eXpenses-- against the flow of income in each month. The same statistical analysis will be used in this chapter as in Chapters III and IV. To review a moment, the statistic is the median with confidence limits for it. When the items from a sample of forty are arranged from highest to lowest, the median of the population can be expected to fall between the luth and 27th observations from the top 96.2% of the time. For descriptive purposes these two obser- vations divide the sample into thirds. 59 60 When dividing the sample into large herds and small herds, the median farm of large herds could be eXpected to fall between the 6th and 16th obserVations from the top 97.3% of the time. With the small herd sample, the median farm could be eXpected to fall between the 5th and 15th observations from the top 98.1% of the time. The tables are arranged similar to those in previous chapters. The median estimate is the sum of the two con- fidence limits divided by two. It is intended as the starting point for purposes of application. Mean percent— ages are included also for description and comparison. Seasonality of Total Monthly EXpenses and Income Table 9 indicates that there is a definite seasonal nature to the flow of total expenses. Income, however, has a more constant pattern with the exception of July and the last few months of the year. If one keeps in mind that there would be 8.3% of the expenses and income in each month if there were no monthly fluctuations, it appears that in the first three months of the year expenses are at a rela- tively low figure. From April through July, the mean ex- penses run at a seasonal high with.April being the month of highest expenses in the year. From August through October, expenses are average or just below, and then in the last month the expenses are again on the high side. Income had slightly less seasonality than eXpenses. In the first five months of the year, mean income Was below 61 TABLE 9 TOTAL ANNUAL INCOME AND EXPENSES FOR 40 CENTRAL MICHIGAN DAIRY FARMS IN 1955 THE FLOW OF ALL INCOME AND ALL EXPENSES IN EACH MONTH SHOWN AS A PER CENT OF EXpenses Income Jan.‘ Feb. Mar.[ Apr. May June July Aug. Sept. Oct. Nov.l Dec. Sample \nr4 000 our 00 00 00 00 0N mm 00 00 00 m0 0: 00 0'0' 0'0' 0'0“ 03 00 0H 00 00 0. 0m 00 0m 00 N0 m0 00 00 0. 00 00 00 Hm om H0 00 00 00 00 00 mm H mm 00 H: on a. 00 m0 m0 m0 Hm m0 m0 am\ 00 00 0 H0 00 00 00 00 HH HH 3N N0 mm \00 \0m 00 0““ 0m HCD 00 00 0 mm mm ‘06 m0 0m HN e. 00 .0 we 00- 00 m m 0 GP :9 up diet «so did HES His-13.45:: 0H0 0H 0H0 0pm mp0 mp0 in: 502 S”: m m m 0 H0 00 HE H0 we r-l 0mm HHS-t «m .480 New Ex. ma: ACE OO 3H \00\ 0 0 0 0 0 00 00 0:50 H H \OH (\5 CUM 0 0 0 0 0 0 000 coco (00 NB (\\0 map 0'06 «500' 060' # O\H 000 NW 0 0 I 0 O 0 (\CD O\O\ (\B N: \00 mm N0 0%: SN 41:? O‘fi HO\ 0 0 O 0 O 0 O'\O OH 00\ H HH H NW NH \05 0 0 O 0 0 0 mm (ECO COCO O\N HO OM: . 0 O O 0 0 ON (\5 \OC\- \OO (DON on 0 0 0 0 0 0 \OQ) \GB “(0| mm \CO (DOW 0 0 0 0 . C (\L\ (\4'0 l\(\ mm NOW (00“ 00-‘60 00 on O‘.O\ O\O 00 \oxo 0'0. 0 Q) a) 5+: :24: 0+2 d an do H85 H82 H82 '5"! CH“ Uri“ 090 04-30) mum E@2:Zm2 :0: (:3 [:3 [:1 II: He on Fig "V2 :00 H $3 HHH o H 0 N00 0 mm am 62 average. June and July were months of higher income pr0por- tionately, with July the month of highest income for the year. Crop sales probably account in large part for the high income in the month. The last five months exhibit a constant seasonal rise from 7.h% in August to 10% in Decem- ber. One could assume from information in Chapter III that this late seasonal rise results from the seasonal nature of dairy products income at this time of year. The estimate of the median bears out the mean rela- tionship except that in general it tends to even out the differences. The figures indicate that in the months of April, May, and June, the median farm could expect to have a higher proportion of the year's eXpenses occurring than the proportion of the year's income coming into the business. The median estimate indicates that December is also a month when expenses are high relative to income. The mean percent— ages did not indicate that this was so. The sample was again divided into large herds and small herds to see if there were differences between them. In general, there was little difference between the large and small herds. The confidence limits were slightly wider in most cases as would be eXpected. Table 9 also shows the results of the herd size divisions. The median estimates indicated that the smaller herds tended to be more variable percentagewise both with respect to income and expenses. The confidence limits are wider apart in many months as shown by Table H in the Appendix. 63 The upper limits tend to be higher and the lower limits lower for the small herds than for large herds. One-third of the small herds had 13.6% or more of the year's expense in April. The corresponding figure for large herds was 12.2%. They did agree, however, in that April was the month of greatest expense for both divisions. The median estimate is quite similar for the two divisions and for all farms. The notable exception with re— spect to expenses is the December percentage of total eXpense for large herds. The 7.9% figure is considerably below either the small herd division or the all farm percentage. The median estimate for income exhibited little of a sea— sonal pattern except in July when both divisions showed an estimate of about ten per cent. The mean figures show more of a seasonal pattern than the median estimate. On the eXpense side, both tables indicate a seasonal nature with the high in the late Spring and early summer months. September and December are also months of high expenses. Income appeared to be more constant on the large herd farms than on the smaller herd farms. The small herds had a smaller proportion of the year's income in the early months of the year and a larger proportion in July, Septem- ber, and December than the large herds. The upper confi— dence limit on the median (Table H in Appendix) brought this out especially well as shown by the 14.5, 12.“, and 13.7 percentage figures for the three months respectively. 60 Per Cent of Each Month's Income Needed for Egpenses For purposes of application the preceding information was developed into three graphs to show the proportion of each month's income flow needed to pay the eXpenses for that month. This again shows a seasonal nature and indicates several months in which large proportions of the income are- needed for expenses, and other months when relatively small proportions of the income are needed for eXpenses. The percentage figures were obtained by multiplying the monthly expense percentages by the annual total eXpense to get an absolute amount. Then the income percentages for each month were multiplied by the total annual income to get another absolute amount. Each month's income figure was then divided into the expense for the correSponding month to get the percentage of each month's income needed for ex- penses in that month. The mean figures indicate that close to two-thirds of the April and May income went to pay expenses in the two months. In June, slightly over 55% of the month's income went towards expenses. ‘February, July, and October were months in which less than forty-five per cent of the income was used to cover eXpenses. The median estimate percent- ages generally follow the mean. The most notable exception was in April when the median estimate indicated that 76.2% of the month's income would be needed for the month's 65 mm0H sou manna HnHmn ccthoH: Homecoo 0: you oweucmOANm m we commoamxm.momcomxm 0.30:0: pomp pom dooooz msoocH 0.20002 zoom no COHpaoQonm one .N .mHh Spec: 0 z 0 m .< 0 h z < z m h w a a H Ti w u A l1 as H 4 com: . eessapsm ceases . u - . Anni 1.0: T on :I 00 v . . I ll 00 . ‘ a H < T 00 dues Jed 66 mama ea 0:00 mm no used no muaom the mason anamn cmeEOHz Hmapcoo QMH so“ omepcooamm a mo commoanxm momcoaxm m.£pcoz use» Hop 000002 osoocH m.£u:oz 30mm mo :oHpmoQoam one .0 .mHm - Anne: 0 z 0 m d h 0 z < z 0 0 A w .+. + w w 7% T w [A A A com: masseuse asses: - u u . on 00 00 00 dues Jed Per Cent 6? 80'F 70 4h- 60 db 50 v- ‘m c- 30 . Mean A - - - - Median 7 Estimate 5 1 l 1 1 I J_ i n n L n. V 1 1 1 I "I T 1' T T —1' J F M A M J J A. S O N D Month F18- 4. The Proportion of Each Month's Income Needed for that Month's Expenses Expressed as a Per- centage for 21 Central Michigan Dairy Farms with Herds of 23 or More Cows in 1955 68 expenses. In May, the median estimate dropped to 55% while the mean figures indicated 66%, Dividing the sample into larger herds and smaller herds, some interesting differences come to light. The smaller herd farms used a considerably larger proportion of the January income for eXpenses than the larger herd farms. In September, the reverse was true; the larger herd farms had almost ten per cent more of the month's income going to pay expenses than did the farms with the smaller herds. The median estimate closely approximates the mean in most months. In general, this estimate tends to rule out the unusually large or small percentages that influence the mean. It may be significant to note that this estimate is considerably higher than the mean in April thus indicating in both larger and smaller herd farms that the proportion of April income needed for expenses may be closer to three- fourths than two-thirds. Likewise, when the unusually large May expenditures are ruled out by this statistic, it would seem to indicate that three-fifths is a more accurate esti— mate of the May income needed for expenses than the two- thirds indicated by the mean. Assume that a farmer plans to have a constant flow of cash income for his farm eXpenses and living-costs in each month. In this respect, the foregoing figures are especially significant. For they indicate that in April, May, and perhaps June, there might need to be additional income from savings or other sources. February, March and 69 the late months of the year have low proportions of the in- come needed for expenses, so that savings might be made in these months. From the credit standpoint or extension standpoint, the graphs indicate that smaller herd operators will be most apt to have additional funds for purchases or repayment of loans in the months of February, March, July, September, or October. March, July, and October appear particularly appro- priate since they are months of near average or above average income in addition to having a smaller proportion of the in- come needed for eXpenses. With large herd Operators, it appears that the five months from October through February and the month of July are the time of low expense in proportion to income. July, October, November, and December would seem to be the most likely months for additional purchases or repayment of loans since more than the average income is received in each of those months. CHAPTER VI SUMMARY AND CONCLUSIONS Summary Purpose and Approach of the Study The study concerned itself with the problem of risk and uncertainty in the farm business. More specifically, it was concerned with the monthly flow of income and expenses into and out of the business. By analyzing the monthly flow pattern of income and expenses, it was hoped that uncertainty concerning the financial ascect of the dairy farm business could be reduced. A differentiation was made between risk and uncer- tainty. Risk was defined as a situation which could be re— duced to a probability status. Uncertainty cannot be re- duced to a probability status since either there are not enough numbers available, or the interested person does not have enough eXperience with the situation to calculate a probability. Such was believed to be the case with the financial flow pattern of the farm business. While many farmers do understand the financial seasonality of their business, others may not. However, many other people who deal with farmers were enough unaware of the exact nature of the 70 ——F . ram-1‘“-- 71 financial seasonality, that there was a need for work which would indicate if a definite pattern existed, and if so, what that pattern was. Among those other than farmers for whom more information was needed Were extension workers, farm credit people, farm planners, and teachers of farm management. With this need in mind, the three main objectives of the study were: 1. To determine the extent to which a definite and reliable monthly variation in certain income and expense items exists on a sample of forty central Michigan dairy farmS. 2. To attempt to reduce short run risk and uncer- tainty associated with income flow and expense flow by pre- senting a description of income and eXpense flow. 3. To suggest ways farmers might plan their finan- cial practices to take advantage of the seasonality of farming. The prime hypothesis of the study was that many of the farm expenses were seasonal in nature. If the season- ality pattern were rather definite, it could be used advan- tageously in more effective farm planning: . A review of literature indicated that little--if any-~previous work of this type had been undertaken. Prac- tically all previous work in farm accounts were on an annual basis. Some work had been done on income variation, but this had taken the form of enterprise combination rather 72 than seasonality analysis. In regard to the area of risk and uncertainty, work has only begun. The sample was selected from the farms in the Mail-In accounting project originally started at Michigan State Univ- ersity in January, 1955. There were forty farms that met the dairy farm definition with more than fifty per cent of their income from the sale of dairy products and dairy cattle. All of the farms are in Ingham and Jackson Counties. A compari— son of the sample farms with the farms in the "regular" Michigan State University account project in the same area showed the two samples to be similar in most respects. The main statistic used in the analysis was the median. This statistic was chosen for several reasons. For results that might be useful in planning, it seemed that some measure of central tendency was needed. The selection of the sample was voluntary rather than by chance. Thus an assumption of a normal distribution of farms was difficult to Justify. As confidence limits for the mean need an assumption of a normal distribution, the median statistic was considered. This statistic required no assumption of normality. Since it is a non-parametric statistic useful for small samples, it was chosen as the statistic for analysis. In setting confidence limits, the median of the univ- erse can be eXpected to fall between the lath and 27th ob- servations 96.2% of the time when forty obserVations are ranked from highest to lowest. In several cases the farms were divided into large herds and small herds to see if 73 there were differences. This division usually PLSulted in a wider confidence interval. The median is difficult to use and understand in some respects. In setting the confidence limits for this statistic, two actual observations are used as mentioned previously. These two, the 14th and 27th, very nearly divide the sample into thirds. In using the result, one needs to be rather cautious. Take, for example, the January median estimate for taxes in Chapter III. The estimate Was 31.6%. If the estimate were accurate, this Would mean that 31.6% of the median farm's taxes would be paid in January. How- ever, for all other months, the median estimate was zero. This, it would seem, is impossible. To make use of the results, it is important to under- stand more fully how this could come about. The lhth farm from the top in January paid 63.3% of the year's taxes in that one month. Thirteen of the farms paid more than 63.3% of the taxes in January. Some may have paid 100% of their taxes in January. The 27th farm from the top in the month paid no taxes. Thus the range of only the middle third of the sample was from O to 63.3% of the year's taxes in that one month. In all other months less than fourteen farms paid taxes so that both confidence limits were zero. One can begin to see at this point how the confidence interval for the median works. January is obviously the month in which the most farms paid taxes. In other months, there may have been several farms which paid large amounts 7A of taxes, but because less than one-third paid taxes, one can see that the median farm did in fact pay no taxes. For items which occur only a few times a year, it is not only possible, but probable, that the median farm will not have the expense in any one month. Yet any one farm Will have the expense at some time during the year. This is probably not a strong argument against the median since the eXpenses that are of this nature such as taxes, insurance, and inter- est are items that can be fairly reliabily anticipated on the individual farm. However, in using any of the median figures, it is important to realize that approximately one- third of the sample had percentages larger than the upper confidence limits and approximately one-third had percent- ages less than the lOWer confidence limits. With this realization, one may then use the1?esults cautiously-«and, in the author's Opinion--advantageously. In all cases, the mean per cents were presented also, even though confidence limits could not be calculated. This was done to aid in description and for purposes of compari- son with the median. There were three approaches used in an attempt to bring out the seasonality aspect. In Chapter III, each of the expense and income items were taken individually and arranged to show the per cent of the annual expense or in- come of that item which occurred in each month. In Chapter IV, each month's expenses and income were explored to bring out the changing relative importance of Various expense and 75 income items in each month of the year. Chapter V approached seasonality by lumping all the expenses and income items to- gether for each month to see what per cent of the total in- come and expense came in each month. On the basis of these figures, several graphs were presented in Chapter V indicat- ing the per cent of each month's income that was needed to pay that month's eXpenses. Results of the Study The results may be more meaningful if they are men- ioned in light of some of the applications and of the above mentioned purposes and objectives of the study. Therefore the following portion of the summary is intended to be more than Just a condensation of the previous three chapters. The hypothesis that many of the expenses of dairy farms are seasonal in nature seemed to be borne out by the study. There were eleven of the tWenty expenses that were shown to have a strong seasonal pattern by the median sta- tistic. These were generally substantiated by the mean. The eleven were hired labor, seeds and plants, fertilizer and lime, machinery repair and maintenance, supplies, im- provement repair and maintenance, machine hire, breeding fees, veterinary and medicine expense, fuel and oil expense, and taxes. Six others exhibited a mild seasonal influence or else had several months when the expense was quite high. These six were feed purchases, insurance, interest, rent, auto upkeep, and other expenses. Telephone expense and other 76 livestock expenses had no strong indications of seasonality, and electrCity was the most constant of all the expenses with no seasonal influence noticeable. Each of these items will be mentioned later under'conclusions of the study along with the indicated seasonal pattern. The first objective was met in Chapter III. That chapter indicated that definite monthly variations in cer— tain expenses did exist on the forty farms in thetsample. Some of the items were less definite than others, as would be expected. Those with a seasonal nature were mentioned above. Tables are included in the appendix which give in- dications of how definite and reliable the monthly varia- tions were. The one income item which proved to be most definite and reliable was the dairy product sales income. The middle one-third of the sample was within a range of 1.7% in every month of the year for this source of income. The second objective was met in Chapters III, IV, and V as each of these chapters presented information on the income and eXpense flow on the sample farms. In many cases this information was definite enough to be worth~ while in reducing some of the uncertainty of the financial pattern on dairy farms. This was especially true of Chapter V which indicated that April and May were months of high expenses relative to income. There was also indica- tion that February, July, and October were months of low expenses relative to income. 77 A consideration of the third objective is in order. The study indicates that farmers, as well as others, might plan to take advantage of the seasonality of farming. Such things as bulk purchases of feed or purchases of planting supplies in months when expenses are normally low relative to income would seem to be one of the more obvious applica-. tions. Chapter V indicated that perhaps February and March would be months in which purchases of fertilizer and seeds could be made to even out the high seasonal expenses normally occurring in April. If the study is representative of the population, it appears quite likely also that increased savings would result from such planning since other farmers do not make many purchases before the actual planting time. Chapter V indicates further that July or October might be months when bulk purchases of feed would be appro- priate since the proportion of income needed to pay expenses in those months is low relative to other months. These same months of low expenses relative to income may be important in planning purchases of equipment for the farm or home. For a farmer taking on additional insurance of some sort, the months of proportionally low expenses might well be considered as the most convenient times for premiums to come due. April and May, on the other hand, are indicated as months in which additional income might be most welcome. Perhaps there are sources of income that might be improved in these months. Storage and sale of commodities may be an 78 answer if a loss is not incurred due to a seasonal price drop. Minor enterprises such as pork or poultry may serve to supplement the income. In fact, there was some indica- tion in Chapter III that pork was being used in this way on some of the farms. Extension workers can make use of the results from the study. For there are indications of times when some subjects of instruction might be more fruitful than others. Insurance and interest expenses exhibited a pattern that was rather surprising. If they were representative of the cen- tral Michigan area, late fall Would be an ideal time for in- formation on these subjects to be presented. Though less than one-third of the farms paid rent in any one month, on the average almost one-third of the year's:rent was paid in December and January. Chapter V indicated that in July, October, November, and December, extension people might do well to call farmers' attention to important purchases that could be made. In- dications were that these four months would be most apt to be the months when farmers would have the largest pr0portion of "extra“cash on hand. Thus if a situation arose when protein supplement or poncentrate was a partiailarly good buy in the fall; extension men, with the seasonality pat- tern in mind, might make their efforts more effective. People in thezarea of farm credit could make use of the results of this study. Being acquainted with the nature of the financial seasonality pattern, they can better 79 understand a farmer's need for credit in the late spring months. At the same time, it is possible for them to set up a more appropriate repayment plan for the farmer Which will more nearly fit his circumstances. There were several differences in the farms associated with herd size that might be important considerations also. July and October would appear to be good months for repayment of loans for most dairy farmers. The statistics indicated that March might be a good time for small herd farms to make payments while the larger herd farms would seem to be in better fin- ancial position late in the year. Farm planners should be interested to note that in the first five months of the year about three per cent more of the year's expenses occurred than of the percentage of the year's income. Since the income is based on a much larger amount, this probably does not impose as great airestriction on annual budget methods as might have been the case. How- ever, the figures are close enough to indicate that a farm planner needs to exercise care in proposing a plan for in- dividual farmers. As a specific example, consider a farm withwa $6,000 net farm income. The study would indicate that an invest- ment of say $2,000 for a tractor in the spring would be an impossibility without drawing heavily on credit even though the farm plan shows the $6,000 net farm income for the whole year. Thus, the seasonal pattern of farming is an important consideration in farm planning or budgeting. 80 There are some applications of the study for teachers of farm management. Students taking courses in farm manage- ment are quite apt to be unaware of the nature of the finan- cial seasonality of farming. Though dairy farming is gen- erally considered as the type of farming with the most con- stant income and verified by this study, the seasonal nature of the majority of expenses can still serve to put great variation in the flow of cash income that is available for other than farm operating expenses. If dairying, the most steady typecaf farm business, fluctuates this much, students could soon come to recognize that farming does indeed have its financial seasonality. Conclusions The study indicated that a majority of the expense items did have a seasonal nature. The items with a short explanation of the seasonal pattern follow. The explanation is both in terms of the time of year when the major portion of that expense occurs and the relative importance of the expense in various months. 1. Hired labor expense was high in the summer months and low in the winter with a fairly regular rise and fall between the two. It was an important part of the monthly expenses in each month, but more so in the summer months. 2. Feed purchases expense exhibited a mild season— ality with a high proportion of its expense in the late fall 81 and late winter months. The item Was.generally the largest expense in each month but was even more important in the months fromOctober through March. 3. Seeds andgplants expense had a definite seasonal pattern with the high in April and May and another slight rise in September. It was an important expense in each of these months. 4. Machine hirefiexpense was at a seasonal high in the last six months of the year with August and November the two months of highest expense. It was of less import- ance in the monthly expenses at that time of year than several other expenses, however. 5. Supplies expense had a strong seasonal pattern with the high in the three summer months. As an item of monthly expense, it was relatively unimportant percentagewise. 6. Machinery repairs and maintenance expense was at its seasonal high in the late spring and summer months with a rather slow drop in the fall. This was a rather important part of the monthly eXpense, particularly in the late spring and Summer. 7. limprovement repairs and maintenance expens§_had a seasonal rise with the high in July. After the decline, there was another rise of less magnitude in October and November. The expense was relatively unimportant as an item of monthly eXpense except perhaps in the month of its seasonal high. 82 8. Breeding fees expense was of a seasonal nature with the high occurring in November and December and a slow deline through the late winter months. It was relatively small as a proportion of the monthly expenses. 9. Fertilizer and lime expense exhibited a seasonal pattern very similar to seeds and plants with the largest proportion of the expense in the spring and a secondary rise in September. The item was a major portion of the monthly expenses when at its seasonal high. 10. Veterinary and medicine expense had a seasonal pattern with a high in November and December, and then a fairly constant, but lesser proportion in the next three months. For the median or mean farm, the item was of rather slight importance as a part of the monthly expense. ll. Other livestock eXpense was fairly constant throughout the year with little indication of a seasonal pattern. The item Was relatively unimportant percentagewise as a part or the monthly eXpense bill. 12. Fuel and oil expense exhibited a seasonal pattern much as would be expected with the high in the summer months preceded by a rise in the spring and fOIIOWed by a decline in the fall to the low in the winter months. The item is an important expense in nearly all months but proportionately more important in the late Spring and summer months. 13. $3593 eXpense has a seasonal nature with the high in January. December was also high as shown by the mean percentages though not as high as January. The item 83 is important as a part of monthly eXpenses in the winter months. In. Insurance expense exhibited somewhat of a seasonal pattern with November the month of highest expense and Decem- ber slightly lower but still high. The data did not indicate why this migh be so. This expense was of about average im- portance in these two months and relatively unimportant in other months. 15. Interest expense did not have a very definite seasonal pattern. There was some indication-~most1y by the mean per cents-~that there were two seasonal highs, one in May and June, and the other in November and December, The mean figures in Chapter IV indicated also that this was a fairly important part of the monthly expense bill in those four months. 16. Electricity expense was the most constant of all eXpenses with no seasonality indicated. The expense was not large percentagewise as a part of the monthly expense bills. 17. Telephone expense exhibited no noticeable sea- sonality pattern. What might appear as a slight seasonality influence was due largely to different payment plans on the farms. The item was relatively insignificant as a proportion of monthly eXpenses. 18. Rent expenSe had a seasonal nature according to the mean percentages with the high in December and January. Too few farms had the expense to get median figures. As an 8” average, the eXpense was relatively unimportant in relation to other monthly eXpenses. 19. Auto upkeeptexpense had a slight seasonality pattern. There were three times when the item had seasonal rises. They were mid-winder, late summery and late fall. The item averaged between four and five per cent of the monthly expenses at those times. 20. Other expense was slightly seasonal with the high point in the late fall and winter months. It was relatively unimportant as a percentage of the monthly expenses. 21. Dairy cattle sal§§_income was high in the two months of March and September with the winter months some- what higher than summer months. The item was an important source of income in these months as well as in several fall and winter months between these two. 22. Dairy product saleg income was remarkably defi- nite and constant with a slight rise occurring in June and the last four months of the year. In general this source of income was two-thirds to three-quarters of the monthly income. 23. Crap sales and government pgyments income had a seasonal pattern With the high in July. The item Was second only to dairy product sales as a source of income in July. It was an important source of monthly income yet in October and the winter months. 24. Egg sales income exhibited a slight seasonal pattern with more receipts in the late winter and spring 85 months. The item was relatively unimportant as a source of monthly income. There were not enough farms with the other sources of income to show the seasonality with the main statistic of analysis used in the study. When all eXpenses and income items were put together, there was a slight seasonality. Expenses were high in the late Spring and early summer and then again in December. In— come had its high in July and then in the last three months of the year. The combination of all the individual items resulted in a loss of some of the seasonality shown by the individual items. When the monthly expenses are put against the monthly income, there is a great deal of difference between the pro~ portion of each month's income needed to covereaxpenses. The median statistic indicated that in March and July less than forty-five per cent of the month's income would be needed to pay expenses. In the months of April, May, and August, more than fifty-five per cent of the inmame would be needed to pay the monthly expenses. The mean indicated Feb- ruary, July, and October as the three months of low expense relative to income while April, May, and June were the months of high expense relative to income. Several tentative conclusions can be drawn. Many of the individual items of eXpense and income are seasonal in nature. Only a few seem to have no seasonality influence. However, when the items are combined, the various seasonal 86 patterns tend somewhat to offset each other. Thus when con- sidering all expenses and income, a less marked seasonality is observed than with some of the individual items. The study has application in several areas of need. It can be useful to farmers, farm planners, extension men, farm credit peOple, teachers of farm management, and students interested in farming. Shortcomings of the Study The results of the study would have been improved had it not been for several shortcomings which were for one rea- son or another unavoidable. Keeping these shortcomings in mind, one can make more apprOpriate use of the results. In order to undertake the study, it was necessary to tassume things about the sample which either were not true or might not be true. For example, it was necessary to .assume that the time at which eXpenses were paid Wouli be Inore important than the time they were incurred. This may 'be a faulty assumption since, for one thing, the creditors of the farmers in this sample may be altogether different :from other areas. Further, the seasonality results may be ISomewhat different or even less definite than might have tbeen found if this assumption would not have been made. However, the assumption was necessary since the data did riot specify whether entries were made at the time the debt was incurred or when it was paid. To attempt to get the rlecessary additional information at the time this study was 87 made would have been prohibitive financially as well as being open to a large degree of human error. A second shortcoming has to do with the sample se- lection. The original project from which the sample came was set up on a voluntary basis. Had the sample been random or representative, more thorough--and quite likely, more useful-~statistical analyses could have been performed on the data. If, for instance, a chance selection had been used, an assumption of a normal distribution would have been in order and confidence limits for the mean could have been calculated. This alone might have made the study more useful. Thirdly, little information was known on the effici- ency of the farms in the sample. For most effective farm planning, one would expect (other things being equal) that figures taken from more efficient farms would be more appro- priate to use than figures from farms which might be rela- tively inefficient. At any rate, knowing something more of the efficiency of the sample farms would have increased the usefulness of the results. A further shortcoming was that thetaample was sel- ected before the studyxvas set up. The purpose of the pro- ject was primarily of an extension character rather than . research. Had it been possible to plan the study first and then select a sample with specific purposes in mind, the results probably Would have been more useful. Certainly some of the shortcomings could have been eliminated. How— ever, it often happens that this is not possible; so that 88 one needs to make the best use of the data as it is rather than incur great additional cost for the sake of some un— known increase in usefulness or accuracy. Suggestions for Future Work There are several suggestions that might be made for future work in this area. There is a question of how the money from the income items was used. Many of the farms had minor enterprises in addition to the dairy enterprise. There Was no indication in the data used in this study as to whether the income from crop sales or poultry, for instance, Was used for Special purposes. There is also the question of when investments are made. Do farmers plan their investments when they have a large income relative to eXpenses or do they buy When the "fancy" strikes them? Perhaps the equipment dealers have more influence on when farmers buy than the season of the year. At any rate, this is a fertile field for further work. Another question arises as to whether farmers may already be using practices that this study suggests. There was indication earlier that some dairy farmers raise pork along with their dairy operation and time the pork production cycle so that they are marketed in the Spring when the rela- tively high expenses occur. It would be interesting to know if farmers do plan to make major investments with the money from crop sales or whether outside purchases are plan— ned for months when the expenses are low relative to income. 89 Work is needed on other types of farming operations. Crop farming, livestock farming, or general farming are all important enough in some areas of Michigan to merit this type of work, and it would be surprising if they did not exhibit a greater seasonality pattern of expenses and in- come than that on dairy farms. There is a question raised as to what the equity position of the farmer may have to do with the way he oper- ates his business. Does the ability to get more credit have a major influence on the seasonal pattern of the farm or is there no essential difference? One might expect that this would have some influence on the farm operation, but the data obtained in this study did not give indications of the equity influence. There was no indication either as to whether payments were made in cash or by credit. If made by credit, informa— tion on the time of payment of the credit bill would be worthwhile in helping to understand more about the farm business. This study covered only a period of one year. Further work should be carried out which would cover a longer time period. Such work would provide more reliable and definite information about the nature of the financial seasonality on dairy farms. The central tendency is an important consideration and probably the one type of information most often used in planning. However, in working with data, one often notices 90 that certain farms are almost consistently on either one side or the other of the middle. Work needs to be done to find out why certain farms or farmers have this tendency. Information along this line would aid not only in under~ standing the farm operations but also in helping to make farm planning and extension Work more useful and more ade- quate for the individual farmer. BIBLIOGRAPHY Agricultural preriment Station Bulletin 400. North Dakota Agricultural College, August, 1953. "Farming Today," Area 5 Report. East Lansing, Michigan: Department of Agriculture, Michigan State University. Hart. A. G. AnticipationsLUncertaintykzgnd Planning. Chicago: University of Chicago Press, 1948. Ready, Earl 0. Economics of Agricultural Prodggtion and Resource Use. New York: Prentice-Hall, Inc., 1952. Heady and Jensen. Farm Management Economics. New York: Prentice-Hall, Inc., 193“. Johnson, D. Gale. Forward Prices for Agriculture. Chicago: University of Chicago Press, 1947. Knight, Frank H. figskL_Uncertainty and Profit. Boston and New York: Boughton-Mifflin Company, 1921. Nair, K. R. "Table of Confidence Interval for the Median in Samples From Any Continuous Population," Sankhya, Vol. 4, 1940. Pond, George A. "Fifty Years of Farm Records in Minnesota," Journal of Farm Economics, Vol. 35. P. 249. Schickele, Rainer. "Farmers Adaptations to Income Uncertainty," Journal of Farm Economics, Vol. 33. p. 367. Williams, D. B. "Price Expectations and Reactions to Un— certainty by Farmers in Illinois," Journal of Farg Economics, Vol. 33, p. 20. United States Bureau of the Census. 1950 United States Census of Agriculture. Washington: Government Printing Office, 1951. 91 APPENDICES APPENDIX A 93 County FARM EXPENSES HIRED LABOR Nome r Farm No. Description Doyd Rots Amount _ Labor by Day or Hour S S ; Hired Man by Month Cosh Cost of Board Insurance on Labor MONTHLY LABOR COSTS S CROPS EXPENSES Seeds and Plants Purchased and Seed Treatment Date Description Price ‘ Fertilizer and Lime I Machine Hire and Custom Work Y CROPS EXPENSES MACHINERY EXPENSES Other Fuel and Lubrication Item Auto Truck Tractor General S S and Maintenance S S Description For the month of 19.— Livestock Being Fed Amount Cost LIVESTOCK EXPENSES Kind of Livestock Expense Cost Breeding Fees: S D. H. I. A. Veterinary Fees and Medicine Livestock Insecticides Registrations Supplies: Strainer Pods, Soap, Etc. Other: MONTHLY LIVESTOCK EXPENSES S IMPROVEMENT EXPENSES Fire Insurance Wind Insurance Fencing Repairs: Building Repairs: OTHER EXPENSES Form Share ofTelephone 5 Form Share of Electricity Taxes Cash Farm Rent Farm Organization Dues Principal Payments on Form Debts Interest on Form Debts Advertising Form Papers and 0‘“ Supplies Other: eeOOelOO.eIOOIIOICIIaQQCUGC FARM INCOME Forthemonthof 19 Farm No. LIVESTOCK PRODUCTS CROPS A No. of Cows.__ Am't Income . Cream: 2 Date Pounds est Price S Date Dos. Price Receipt Date S Government TOTAL CROPS RECEIPTS MISCEANEOUS INCOME 3 Machinery Sold (not mad) MONTHLY EGG SALES TOTAL s E LIVESTOCK SALES Smworlk: . 5 Date Description No. Wt. Price Hauling Receipt Description A“ Days Rate 5 s s ‘ 1 Work off the iarm: Days S E Patronage Dividends 3 Woodland Products: Days 5 Insurance Claims for Building Losses 5 Feed Bags Rama 3 . . I Gas Tax Refund g 8.9. Additions ital End Losses 1060' Breeding Fees ; Bo't Born Sold Died Ate 0“,." TOTAL MISCELLANEOUS INCOME S TOTAL CASH INCOME S TOTAL CASH EXPENSES S FARM INVESTMENTS (If the items below are still on hand at the end of the year, then add them to inventory) AND TRY PURCHASED FARM Kind No. Wt. Price Hauling Net Cost Kind of Improvement (S) Material Ina-0...... rIOOle. Fencing: S -r vault—I" ‘ Citiel 00'. ‘rilr I. e 6 31'7": Building: .. 00' Item Bought Trade-in Allowance Difference S S oeeiiiiit‘?“ 95 APPENDIX A COOPERATIVE EXTENSION WORK In Agriculture and Home Economics State of Michigan Michigan State University Cooperative Extension U.S. Department of Agriculture Service Cooperating Agriculture Economics Dear "Mail-In" Account Cooperator: This is a gentle reminder to have you fill our your farm expense and farm income sheets and mail them to us in the self addressed envelope. Ten minutes now may save an hour or so later. As we work through this experiment together this first year I am alWays eager to hear your suggestions and ideas. Sincerely yours, Warren H. Vincent Extension Specialist in Agricultural Economics P. S. Please ignore this if it has already been sent. WHV:jlm 96 APPENDIX A COOPERATIVE EXTENSION WORK In Agriculture and Home Economics State of Michigan Michigan State College Cooperative Extension U.S.Department of Agriculture Service Cooperating Agricultural Economics Dear “Mail—In" Account Cooperator: Your report for the month of has been re— ceived. Thanks again for your cooperation. This is a form letter that you may eXpect each month and the space below will be used to discuss any details that may come up. If you have questions at any time don‘t be afraid to ask them. §OUR CASH RECEIPTS FOR THE MONTH: $ CASH EXPENSES: Find also a self addressed envelope (with extension enclosure slip) for use in mailing next month's report. Cordially yours, Warren H. Vincent -Extension Specialist in WHV:Jlm Agricultural Economics 88 one needs to make the best use of the data as it is rather than incur great additional cost for the sake of some un- known increase in usefulness or accuracy. Suggestions for Future Work There are several suggestions that might be made for future work in this area. There is a question of how the money from the income items was used. Many of the farms had minor enterprises in addition to the dairy enterprise. There was no indication in the data used in this study as to whether the income from crop sales or poultry, for instance, was used for Special purposes. There is also the question of when investments are made. Do farmers plan their investments when they have a large income relative to eXpenses or do they buy when the "fancy" strikes them? Perhaps the equipment dealers have more influence on when farmers buy than the season of the year. At any rate, this is a fertile field for further work. Another question arises as to whether farmers may already be using practices that this study suggests. There was indication earlier that some dairy farmers raise pork along with their dairy operation and time the pork production cycle so that they are marketed in the Spring when the rela- tively high eXpenses occur. It would be interesting to know if farmers do plan to make major investments with the money from crOp sales or whether outside purchases are plan- ned for months when the expenses are low relative to income. 'r .19." n ie.E 89 Work is needed on other types of farming operations. Crop farming, livestock farming, or general farming are all important enough in some areas of Michigan to merit this type of Work, and it would be surprising if they did not exhibit a greater seasonality pattern of eXpenses and in- come than that on dairy farms. There is a question raised as to what the equity position of the farmer may have to do with the way he oper- ates his business. Does the ability to get more credit have a major influence on the seasonal pattern of the farm or is there no essential difference? One might expect that this would have some influence on the farm operation, but the data obtained in this study did not give indications of the equity influence. There was no indication either as to whether payments were made in cash or by credit. If made by credit, informa— tion on the time of payment of the credit bill would be worthwhile in helping to understand more about the farm business. This study covered only a period of one year. Further work should be carried out which would cover a longer time period. Such work would provide more reliable and definite information about the nature of the financial seasonality on dairy farms. The central tendency is an important consideration and probably the one type of information most often used in planning. However. in working with data. one often notices 90 that certain farms are almost consistently on either one side or the other of the middle. Work needs to be done to find out why certain farms or farmers have this tendency. Information along this line would aid not only in under— standing the farm operations but also in helping to make farm planning and extension work more useful and more ade- quate for the individual farmer. BIBLIOGRAPHY Agricultural Experiment Station Bulletin #00. North Dakota Agricultural College, August, 1953. "Farming Today," Area 5 Report. East Lansing, Michigan: Department of Agriculture, Michigan State University. Hart. A. G. AnticipationsL,Uncertainty, and Planning. Chicago: University of Chicago Press, I938. Heady, Earl 0. Economics of Agricultural Prodgction and Resource Use. New York: Prentice-Hall, Inc., 1952. Heady and Jensen. Farm Management Economics. New York: Prentice-Hall, Inc., 195“. Johnson, D. Gale. Forward Prices for Agriculture. Chicago: University of Chicago Press, l9h7. Knight, Frank H. Risk, Uncertainty and Profit. Boston and New York: Boughton-Mifflin Company, 1921. Nair, K. R. "Table of Confidence Interval for the Median in Samples From Any Continuous Population," Sankhya, Vol. 4, 1940. Pond, George A. "Fifty Years of Farm Records in Minnesota," Jgurnal of Farm Economics, Vol. 35. p. 2&9. Schickele, Rainer. "Farmers Adaptations to Income Uncertainty," Journal of Farm Economics, Vol. 33, p. 367. Williams, D. B. "Price EXpectations and Reactions to Un~ certainty by Farmers in Illinois," Journal of Farm Economics, Vol. 33. p. 20. United States Bureau of the Census. 1950 United States Census of Agriculture. Washington: Government Printing Office, 1951. 91 APPENDICES Farm No. APPENDIX A 93 Name County FARM EXPENSES HIRED LABOR Description Days Rate Amount Labor by Day or Hour S S : Hired Man by Month . Cash Cost of Board ’ Insurance on Labor MONTHLY LABOR COSTS S CROPS EXPENSES Seeds and Plants Purchased and Seed Treatment Description Amount Date Price Fertilizer and Lime Machine Hire and Custom Work MONTHLY CROPS EXPENSES MACHINERY EXPENSES Other Fuel and Lubrication Item Auto Truck Tractor General S S Date Machinery Repair and Maintenance S S Description For the month of Livestock Being Fed Amount LIVESTOCK EXPENSES 19 Cost Kind of Livestock Expense Cost Breeding Fees: S D. H. I. A. Veterinary Fees and Medicine Livestock Insecticides Registrations Supplies: Strainer Pads, Soap, Etc. Other: MONTHLY LIVESTOCK EXPENSES S IMPROVEMENT EXPENSES Fire Insurance Wind Insurance Fencing Repairs: Building Repairs: OTHER EXPENSES Farm Share of Telephone S Farm Share of Electricity Taxes Cash Farm Rent Farm Organization Dues Principal Payments on Farm Debts Interest on Form Debts Advertising Form Papers and Office Supplies Other: FARM INCOME Forthemonthof 19 Farm No.___ LIVESTOCK PRODUCTS CROPS Cream: No. of Cows— Date Pounds est Price I..."-. s Income S Date Dos. Price Government TOTAL CROPS RECEIPTS DOOOOOOOOQOOOOOsoceoaeeee MISCELLANEOUS INCOME Machinery Sold (not traded) MONTHLY EGG SALES TOTAL S Date Description No. Wt. Price 0mm” AW DOW Rate S \OOOOC...O..C..OI..'IOI... Work off the farm: Days Patronage Dividends Woodland Products: Days Insurance Claims for Building Losses Feed Bags Returned Add't' L Gas Tax Refund Irons otaI End osses Total Breeding Fees eg. Bo't Born A Sold Died Ate B 0",." } TOTAL MISCELLANEOUS INCOME S I | ! LIVESTOCK SALES ,___Custo_m Work: I l TOTAL CASH INCOME S TOTAL CASH EXPENSES S FARM INVESTMENTS (If the items below are still on hand at the end of the year, then add them to inventory) LIVESTOCK AND TRY FARM Kind No. Wt. Price Hauling Net Cost Kind of Improvement (S) Material Fencing: Building: Item Bought Trade-in Allowance Difference S S COOeOroseeeeeee.eoee\eI 00400'IOOOOOII' rveeeeeeseeee 0.00.00-ovoe'eeeeeOeeoeeeOes 95 APPENDIX A COOPERATIVE EXTENSION WORK In Agriculture and Home Economics State of Michigan Michigan State University Cooperative Extension U.S. Department of Agriculture Service Cooperating Agriculture Economics Dear "Mail-In" Account Cooperator: This is a gentle reminder to have you fill our your farm expense and farm income sheets and mail them to us in the self addressed envelope. Ten minutes now may save an hour or so later. As we work through this experiment together this first year I am aIWays eager to hear your suggestions and ideas. Sincerely yours, Warren H. Vincent Extension Specialist in Agricultural Economics P. S. Please ignore this if it has already been sent. WHV:jlm 96 APPENDIX A COOPERATIVE EXTENSION WORK In Agriculture and Home Economics State of Michigan Michigan State College Cooperative Extension U.S.Department of Agriculture Service Cooperating Agricultural Economics Dear "Mail-In” Account Cooperator: Your report for the month of has been re- ceived. Thanks again for your cooperation. This is a form letter that you may eXpect each month and the space below will be used to discuss any details that may come up. If you have questions at any time don't be afraid to ask them. gens CASH RECEIPTS FOR THE MONTH: S CASH EXPENSES: Find also a self addressed envelope (with extension enclosure slip) for use in mailing next month's report. Cordially yours, Warren H. Vincent -Extension Specialist in WHV:Jlm Agricultural Economics mpwpm :wmfinoaz .mocofiom Hacm no pumapamama .mmcaaoo .wnmnwcm oocmpmao one pmcamcnom cm>H an omHfiQEoot smog paum commxcoum 0.00H smog mawomaafim o.mu Smog anon cosmsooam o.ooa smog paam manage 0.05 mecmm mawomaafim o.om Smog ufiam no>ocoo m.mo smog mwcwm mxooq m.mw smog hocwm Sumac: 0.0m Edoq ao>ocoo m.nm smog pthHHw n.50 smog zpcmm Mom 0.0m smog mafia cepmMoonm m.wm Ecoq mcfiwpcomaamm m.m© Ewoq pfiam gammapw 0.0m smog macaw smog xom m.mm awoq 7: mafia cdazwxzwx 0.0m smog scones m.mw mucmm mcdmpcomoaamm 0.0m o/ smog aewaz m.mm swag macaw smog mommm gasoom o.m: ccwm hemoq uxcanm m.mN smog edaoso m.nm weds easemaaam m.mo Amman onesmv A.Q.HV Edog naoowz m.mm Eooq mocdm humnm m.mm Edoq mocmm oEopnmo m.mm ewog mocmm escape o.om “swam oassmv smog aeeam egocado n.5m A.Q.HV scam meson msmppo m.sa smog neooaz n.5m A.Q.Hv Ucmm heme; campnmo m.mm ccmm hewog cepzmz o.mH smog defiamo m.mw ewoq accwm mops: m.mm smog mvcmm oacpnmo m.mm ccom hewoq «anoo m.NH smog paam panoam m.mw awoq mocom smamwo m.mm ccwm memo; cedanom 0.0m ocmm smog pdam awake: m.mw ecoA mvcom coucopm m.mn pawn mamoq campnmo n.um heme; oaoahcamam 0.0H some Haom o.coa 0» m.N® o.mn op m.mm 0.0m on m.mm m.mN 0» 0.0H mafipmm xmecH E d XHszmm¢ tmmmwe AHOm z¢wHEUH2 A¢DQH>HQZH m0& meHBHEoDQCmm APPENDIX B 96;2% CONFIDENCE LIMITS ON THE MEDIAN PER CENT OF TOTAL ANNUAL EXPENSES IN 'MEEB 98 \OONB-OOOOWFOOWOOOMOQNMONOOOCOO EACH MONTH FOR no DAIRI FARMS IN 1955 O U OO...OOOOIOIIIOOOOIOCCOOI... 0 HOHmoooommoomoooaommmo 00050 n H H H g wowmoooomwmooomofiodmmommocooa IOOOOOOIOIOOOOOIOO00......C z homooooommgozonofiogHwowooOno0 - ) g mOHHOooommoooomomomomoméooooE OO...CIIOOIOOIOOOOOIOOOOOIOO O \ooo\\0ooooooxmoooochHONONoovnoooOMa é o H u Non:HmmOHwoomomomomowonwoooo3 04 ococooooooooooooooooooooooooo m wowmmHmoodooaOHONoo moomOOOOH m H H H H H o (H . mmmmoOHozmmOBOQOOOHodommoooo b0 on...00000000000000.000000000 5 NHmmoOOOHamomomooomoomeooooS H H H H H H P C S MOQHJOmOONOOOWBONOBOWOHHOOOOfl O00......OIOOOOOOOOOOIOOOOOO 5 How:NomomooomHMOMOmowommoooom b H H H.H H g (D m mowmwomoHmoomomomomOOOHmooooU C: onooncoo-OOIQOoOQOQOOQOOOOQO'I—‘I S oomimomommoooozomOMOOOHmoooo% b H H H H H H C 8 3 dooo:mmoomoodOOOOOQOMHWJOOOOm O .00...OOOOOOOOOIOCOOOOOOOO z oommmo:ommoomooomomommmmooo P NHH H H d F 1 Cl; 3 mommdmcommoooowooomomooooooo3 o0000-0000-0000...0000000000 4 mommmzmommoomo:OMOmo 0H0 0000 N N H U *fl 0 g o0HmoooooooomomomdmomomoooooP O0.0....OOOOOIOOOOIOOOOIOOOO 2 NoSomooommoomooow0Homommoooog H m - :5 D zomooooomQOOHOOOMOOOBoNHoooo Q) ococoons-0000.00.00.00oo-ooOQJ m dowmoooomHoomoooSomo ooHoooon :1 g NomMOOOOHNOO®OOO®OJBN®BHQOOOg OOOOOCOOOOOOI‘COOOOOOIO.'00.?“ h NoxozroooONooomcooooo'C'KDCOH-trHfgooouD H L.» S 64H 4: 35 DHDHDHDHDHDADHDHDHDADADHDADAm $4 0A w g h E H. 0 m “hm mm H 9 4H0 90 M | m G can 0C 6 o I m a cam mm a: o H m H 5mm 0 Q on p H E h m m mm A um m u H M m o n a p H CH h» o o M P .0 0 U m»: m we hob m 0 H «5 :4 c: NAH one-H mmchum 0 EL H a a Hmd 0 mad :CHAWCW : F Has a H>z HHO can a D U a HH H Ho chHhm mm h ' m U 6 vac Q th omUWQHQm S J n co m Hog o and: mpmflxoxx m H H m m one: as SECS hmzptflfimw z: * m m m b2 2 MH m> O k B H ficmmnooooomo on... no... MOO\COIDOOONO-:o 99 y moNHmoooono O O O I O I O O C I I I wommwooogHmo OOO\NO\NOOO\OHO o o o o o o o o OOQNQJOOHOBO QOHONOOONOWO 0000000000.. HOO\(\(\OOOO\O€'\O OOCDWMOOOWOChO OOGDNCDOO (\O-‘ZT bomimdoomomo o o o o oowhmooomomo OOWQQOOOHOWC O 0 O O O I oommnooo3omo APPENDIX B-Continued mownmnooj’ 000 coo-0.000000 OOCDBCDJOOMONO OOO\O\(DL\OO\OOU\O o o O o I O o I o o o I oommmwoomozo monoxmmoomomo coo-000000.. HOQNCDMOONOWO Feb. Mar. Apr. May June July Aug. {Sept Oct1,N0V. Dec. OOQOMOOOd’fi-(‘KO 0.00.0000... OOO\(I)ru3 L ‘— \‘ \. nomgoomo o o o O o o o o. QDOCD§OOMO" ___ . \\ GuOCflNOOOD ‘ 0 0| d‘o‘ooo‘ mowsoooo‘ DADADADA FROM EACH ITEM OF INCOME IN EACH MONTH FOR no DAIRY FARMS IN 1955. Item Dairy Cattle Dairy Products Crop Sales Egg Sales APPENDIX D ANNUAL EXPENSE IN EACH MONTH FOR 195§ARMS WITH LESS THAN 23 COW HERDS ' INl9 98.1% CONFIDENCE LIMITS ON THE MEDIAN FOR SELECTED EXPENSES AS A PER CENT OF I I TABLE D 100 3 moomNmmomomONo I I I I I I I I I I I I I I Q HOQBONOOBOQOJO L H N N H H g Homommdomomomm I I I I I I I I I I I I I I z wowmwcmomomomo H H r-l G HOHdeoomodofim O o o o o o o o c o o o o o o O oomwOHoocomooo r1 r1 .4 a NomHmwmomooomo I I I I I I I I I I I I I I m ®O®33NOON00030 m H H H H H - mommmeOCOHomo m I I I I I I I I I I I I I I S OOHOMMBOWOHONO < m H H N H H : BOQQGNQOHONOOH I I I I I I I I I I I I I I 5 dOHMNNQOQONomm h H H H H N H g mommommom0Homm I I I I I I I I I I I I I I S OOHszHOQOQOHo b H H H N H : mommmmaomooomm I I I I I I I I I I I I I I S NOQHNmQOBOQOMQ N H a NOQNmOdOQOOONO I I I I I I I I I I I I I I < womMHomooomom H H H H E oommmomomomOHh I I I I I I I I I I I I I I z oomimeomomoaH H H H % OOHOHmdowodomo I I I I I I I I I I I I I I In. :rooxdmoooooomoo H H H g QOQWQOMOHONNWO I I I I I I I I I I I I I I h HomNmofiomonowo 0+3 NH 55 54345434343434 OH no Ho was U m m mg E cam 06 H o A «mm m H p o ,2: +3 [103% O H .0 ohm 90 m 99H m mocoo 4 god m cchm :2 H HHouc c H H ULH m a) 65:6 Q. mm'Ur-ifi- h mo: Q mumox H was : amzsm m m: m m> h APPENDIX E 97.3% CONFIDENCE LIMITS ON THE MEDIAN FOR SELECTED EXPENSES AS A PER CENT OF ANNUAL EXPENSES IN EACH MONTH FOR 21 FARMS WITH 23 OR LARGER COW HERDS IN 1955 TABLE E 101 o MOJHMMOOMOmomO Q) o o o o o o o o o o o o o 0 Q [\OO\O(DF\(\OO\OOOCDO H H N S ocamnnasmuflc>h43ruauxo I I I I I I I I I I I I I I o \OOOd'ONUNONOB mo 2 H H H H H p. (\omoxNNQOCOOBOHd' I I I I I I I I I I I I I I O (\ooxNHmmooxOHoz—d O H H H t; ocvrvirao<>anc>rn4 I I I I I I I I I I I I I I a) OOO‘\("\O QOOOBOOJ m H H H H H . mommxomoooooooooxn (JD 0 o o o o o o o o o o I o o :5 (DOOxNMMOOOOWOUVD <11 H H H H H: mommmHNHNOQDOH-tt I I I I I I I I I I I I I I :3 300MN3NHMOMOVN® '1 H H N H g 0100““de OO\O(\IO\0\O I I I I I I I I I 0 I I I I :3 WONJQWBOOOMONN '1 H H H H H 3 MO©WMH®omOHOHm I I I I I I I I I I I I I I 2 momcdmmouomomm H H a OONfimeOOOChOOCD I I I I I I I I I I I I I I <2: Hozf-fi’oxm\OOOONom H H H H g momMHomomomoom I I I I I I I I I I I I I I 2: [\OHNQNMOMOOOOH‘N H H N ,0 \OOHdmCfiHOd'OOO-dw Q) o o o o o o o o o o o o o I in CDON\OL\HC'\OU\ONO~o O H ,0 can no u d Lop-H w bounce) .4 &3®»d m 2::v4c:n :2: «a -H~4:)w£2 ru -H H 15341-1 (D a) «:1an Q. mo'dHQ. g mom a opomx r1 azwcn S A Ln APPENDIX E NCE LIMITS FOR MEDIAN PER CENT THAT EACH ITEM OF EXPENSE WAS OF THE .._5 Vs FOR #0 MICHIGAN DAIRY FARMS IN 1955 S MONTH'S EXPEN 6.2% CONFIDE C / TABLE F. Dec. 102 MOOQOOOOQOHNOOQOOOQMJOMOOO I I I II II II II I I I I I I II II I I III HOH‘TSI'OOO OOOQ’OOMOOOMONOOOOO H H \ Nov. O\O<"\\OO0000\OO\O\O\OHOOOL\-3‘C\OC\V\OO I I I I I II I I I I I I I I I I I I I I I I HOP‘HOONOOOONOONOOONONONHOO NH H H Oct. mommoooooom3003030NoiomHoo I I I I I I I I I I I I I I II I II I I II II I NOMMOOOOOOH-Q’HOOOHONO OOOOO NH H H NH Sept. WOQHMWOOHONafiomOOOQOMOQWCO I I I I I o I I I I O I I I I I o I I I I I I I I I domwmooomoHMOOOOMONONOBQO H H N H WRONGOONOOONNGOOOOOMOOQDNWOO I I I I I I I I I I I I I I I I I I I I I I O\HO\®OOHONOHWMOOONONOMOMHOO H H NH July Aug. NowmwOOOOMOHmofiomooooomuoo I I I I I I I I I I I I I I I I I I HomeooomommMOOOOOHomo:Hoo r4 r4 r4 r4 OJH dOOdQOOOO‘ONNBO-fiOCDOGJO\00\O\OOO I monomooo odoHOHOOOHOHowooo FHA r4 r4 r4 May June OQNHWOOOWOMUWOOOO‘DOOONONWOO I I I I I I I I I I I I I I I I U\O\f\l\C\J\I\CJCD(30("\(\OC)r-'IOF-‘UCDr-‘IOr-{C3t-4 CO H H H (\t N Apr. OOHUWB-fi‘COOOWONOOOHOQOCfiom-‘JOO I I I I I I I I I I I I I I I I I I I I I I I I I I COCOOWNOOOOQMHOHOCOHOHOOBOO NHH N H Mar. OONONOOOCDOUWCDNONOOOOOHOQQOO I I I I I I I I I I I I I I I I I I I I I I I I I I (\lOKSMOOOOOCONOONOOOMONOSWOO Feb. WOOHOOOO0OC’WNOOWOOONOMOOOOO I I I I I I I I I I I I I I I I I I I I I 3'O\O®OOOOQOWMOONOOOONBMMMHC I I I I I I I I I I I I I I I I I I I I I I I I I I Conf. Limit Jan. DHDADHDHDHDADADHDHDHDHDHDA EXpense and Maintenance Medicine Other Livestock Fertilizer and Lime Expense Hired Labor Feed Purchases Seeds and Plants Machine Hire Supplies Machinery Repair Improvements Breeding Fees Veterinary and Fuel and Oil Taxes APPENDIX F-Continued Dec. MD HONOCWOQOOONOOO I I IIIIIIIIIIII NOHOMC‘JOOOOOONO Nov. GO-fioCDCDNOOOOMOO I I I I I I I I I I I I I I [\ONOMHOOOO—TOHO Oct. OOOONNNC‘WOOOONO I I I I I o I I I Sept. OOO\OCDO\\OOOO(DO\OO IIIIIIIIIIIIII OOOOMHOOOOHOOO c>ow3caruiaao<3cn0o<3u%&<3cn3c>:war4 OONOQCWOC’WOOMONO I I I OONO‘HNHOOOMON “ 24945454242494 EXpense Insurance Interest Electricity Telephone Auto Upkeep Other Expenses Rent APPENDIX G 96.2% CONFIDENCE LIMITS ON THE MEDIAN FOR THE PER CENT OF TOTAL MONTHLY INCOME FROM EACH OF FOUR SOURCES FOR #0 MICHIGAN DAIRY FARMS IN 1955* TABLE G Dec. I O\ HW—‘J’QOO OCI)[\O com I O CO .I CC Nov. '. 'o H 'oncc30u3c3 OCDHO C0\C OO 00 Oct. l& H Hmmmoo “-300 63W 00 . I DO Sept. WOONOO I. \O H O\0HOO (DUN ,mmmamo KocadWfic>c> mun July Aug. Y I Luxcuhw>o<> I I I NOOJO hi I O ‘moomoo I m I O HO\O O\U\ May June modem-Jo .5 O\O O\OO ®\O Apr. QDOOVNQO MOWJIOO O\L\ lower conFTden e lib Mar. I N N I O CDC-11'CDOO I I I “NC [\UN I O Feb. (I) NONUNOO OCDO\OO we 0 O 3:- upper anc W‘W \C‘ H Od‘b— CD\O I C) BOBNOO O 00 c;o for’bot. Conf. Limit Jan. DADADA DA .ad 0% Source Dairy Cattle Dairy Products Egg Sales Crop Sales ‘AII other sources APPENDIX H FOR 140 CENTRAL MICHIGAN DAIRY FARMS FOR 1955* CONFIDENCE LIMITS FOR THE PER CENT OF TOTAL INCOME AND EXPENSES IN EACH MONTH TABLE H Dec. :” Nov. *— July]Aug. Sept. Apr. Maleune Mar. .v--m * Sample Expenses 104 ““1“de O C O O O I r4BJV(DO r4 .4 r4 [\NNUNCDN O O I I I I OvorH:Icmc r4 .4 OHNNN: O C O C O . O\\OOU\CD H “3030\CDB . O O O C O oomHnomo H HoxNNCOm O\\D O\O mm ,_| N O (\(‘W\0\O O O O\C\-NU\O\\O r-I HoodHN I O O O O O ccxrm3<3m> H H H ONW\00\N O O O O I C oxor4v\cwo r4 H r4 ommumm O O O O I . rfidDFVCCV H H H \C‘NNNN-fl' O O O O I O D-mwdoxm cmuwo:t«\o 053W564mn§ HO\\OO\l\\O (1)-3CD: com 243424 19 Small Herds 21 Large Herds All Fa .s Income \OJNNNV‘ o o o o o o ONMWO H H H MO\\OCDK\O\ o o o o o o O\(\O\OO\(\ H "\OUir4dwn O I O O O l O\bdDC~CHO r4 r4 (\Nd'OMN o o o o o o mfififiwo HMMQNO O O O C o o ”\O 0\U\d)\o co OWW\0\O OQJWNN H H H mNHmm-fl' o O\l\O\OO\ ~ H OOWBHQ) O I O O O O txxoaomcoxrx Duetnravpo C I C O 'e~wunuwmnn 0Lfi€VC>flL3 coxc O\\O O\\C O NHMQQ O C C O O O [\lfiNUWQ-d‘ N‘DBO’WV} oomocxnooxn =3F1:h4:3h3 19 Small Herds 21 Large Herds All Farms sure . confidence limits for 21 large herds are 97.3% 1% sure “Confidence limits for all farm sample are 96.27% sure; confidence limits for 19 small herds are 98. Ram-1 USE earn Pu! 253:: Date Due Aug ' D? If; I“ V ‘ .v Demco-293 ‘I-Dg-fin-o—m— *7 m -.. .. ”'TITI'ITHEIT’LISIMLWIMlijflfilfilil'lflflfliflleufifilfl“