A STUDY OF THE RELATIONSHIPS BETWEEN SOME SOIL PROPERTIES. ABILITY OF FARMERS, NUMBER OF ANIMAL UNITS CARRIED, AND CROP YIELDS ON ST. CLAIR COUNTY FARMS Thesis for the Degree of M. S. MICHIGAN STATE COLLEGE L-. W BuxIcn 1942 A STUDY OF THE RELATIONSHIPS BETUEEN SOXE SOIL PROPERTIES, BILITY OF FARUERS, NUHBER OF AKIXAL UNITS CARRIED, AND CROP YIELDS ON ST. CLAIR COUNTY FARJS THESIS RESPECTFL'LLY SUBLIIT'"ED IN PARTIAL FULFILLEITT FOR.THE DEGREE OF MASTER.OF SCIENCE AT HICHIGAU STATE COLLEGE OF AGRICULTURE AID APPLIED SCIEYCE LIV 3' I L. ‘Vflro well 1942 THESIS El” 0 C o E o :15..le KI, , LI" 0 C o T: o E :v'1L111"."<" 51 ‘V’ J. 1x The writer vie as to exgzer. I 4"“ 1 r ‘5 V . Q . "T r ‘-. lucn, aju J. F. [@915 for L‘s V: ‘ L Ed counsel offered during t. manuscrigt. tion to the Farm S Q 4 1- nu. “ ~~' ..‘, - r - Le hiIter ELSQ tiales to ex) of this manuscript. iéfiiia r; helpful 1e pro;aratlon of t“ (D O C H C+ &: :1" (A‘ 1 p. [G CA. H 9.. Cf. F° O b’ H) O H Ft h) (D H o H O O I A STUDY OF THE RELATIONSHIPS BETWEEN SOEE SOIL PROPERTIES, ABILITY OF FAREERS, I‘IUI’L'BER OF AI-IIWIL UNITS CARRIED, AND CROP YIELDS ON ST. CLAIR COUNTY FARMS L. W. BUXTON INTRODUCTION The purpose of this study is to determine the effect of soil type, ability of farmer, number of animal units, and percent of organic matter in the soil on crap yield, under conditions prevailing on the farms of the low income group of farmers. The data concerning crop yields and numbers of livestock have been compiled from farm records of farmers who have loans with the Farm Security Administration in St. Clair County. To find the relationship of soil type, ability of farmer, number of animal units and condition of buildings to crOp yields, it was necessary to have some definite rating as to the productivity of each farm. Therefore, the crop yields for corn, oats and wheat have been taken. As more than one crOp yield was used, it was necessary to place these crap yields on a ratio, or percentage basis. This is called a crap index. The crop indexes may be averaged to obtain a farm index. To study the value of the crOp index in this thesis, it was compared with other available farm ratings. Because the crop index is also used in farm appraisal and farm.management studies, it was thought that the comparisons might be of value in these fields. The crop indexes and farm ratings were used: To determine the effect of soil type and of the skill of the farmer on yields; to find if a certain type of farmer was located on a certain soil type; to study the effect of the number of animal units on the soil productivity; and to determine the effect that soil productivity has on the condition of buildings. For a further analysis of soil productivity, the percentage of soil organic matter and degree of soil acidity were obtained for the low-yield farms and the high-yield farms on certain soil types. From the data on soil organic matter content a comparison was made of the ignition and carbon-chain methods of determining this constituent. Also the relationship of the yield of cats to the percentage of soil organic matter and degree of soil acidity was considered, tOgether with the effect of the number of animal units on the percentage of organic matter in the soil. REVIEW OF LITERATURE Bousman (1) found that the type Of buildings gave a fair indication of the quality of soil, providing agriculture had been carried on in that area for a sufficient time to allow trial-and- error adjustments to take place. The study by Bonsteel, (2) is based on the assumption that farmers over a period of time will grow the crOp best suited to the kind of soil being used. In conclusion he states that the assumption generally was found to be true. An investigation carried on by Brown and Eke (3) in the Minidoka Irrigation Project led to the following conclusions: Soil types influenced the kind of crops grown; tenants tended to grow cash crOps more extensively and the return per acre was less than in the case of owner Operators; tenants Operated larger farms than owners, especially on poorer soil; the average yields obtained by tenants were lower than those of owner Operators on heavy soil by 8.4 percent, and on sandy soils by 12.6 percent; tenants had one third less livestock than owner Operators; where tenancy was relatively stable the yields were much higher in comparison to those of owner Operators than where tenancy was unstable; and owner Operators had a higher investment in farm equipment than tenants. Neither soil type nor soil texture affected the concentration of calcium or magnesium in alfalfa, green beans or peas in any definite manner according to Ponder (6,7,8). There was found to be a relationship between the calcium content Of the soil and calcium content Of the green bean plants. Gustafson (9) made a detailed study of the size of farms, crops grown, amount of pasture and woods, number and kind of live- stock and farm practices used on each main type of soil in Cayuga County, New York. The soil in Montgomery County, New York, was divided into four groups according to its present use and its best use in the future. Hill and Blanch (11) concluded that the poor classes I and II were better for forestry and recreation than for agriculture. The formulas used to calculate the coefficient Of correlation and coefficient Of contingency were taken from Love (12). Love stated that the coefficient of contingency may be used the same as the coefficient of correlation for practical purposes. It was shown by EOCOOI and Weldon (13) that the soil type affected the percent of phosphorous in the plant to a certain extent. The application of phosphorous to the soil also affected the percent of this element in the plant. According to Paden (15) the soil type does affect the number and activity of microorganisms in loessial Clyde clay loam as compared to Muscatine silt loam. Pasco (16) in studying the relationship between soil type and use of land in southern Richigan concluded that: Forest, brush and pasture were most common on Griffin loam, Carlisle muck and Rifle peat soils; that idle land was found most on sand soil especially Bridgman sand; alfalfa was largely limited to well drained soils regardless of fertility or texture; beans were associated with the more fertile soils as Brookston, Wiener, and Thomas types; beets were grown on the same soils as listed for beans but also included burned muck; truck and special crOps were associated with sandy, well drained soils and organic soils; wheat was grown mostly on Hillsdale loam, Miami loam and heavier soils; orchards were most common on the rolling, well-drained soils as the Coloma, Bridgman and Plainfield sands. In the bulletin ”Utilization of lands in West Virginia" (17) it was stated that the four main factors affecting the "Operators land-labor income" were type of soil, tOpography, size of farm and personal characteristics of the Operator. Yet if soil and tOpography were both unfavorable the ”Operator land-labor incomes” were, with few exceptions low, regardless of the personal characteristics of the farmer. Veatch and Schneider (18) give certain criteria for the rating of agricultural land as the net income from land, money value of agriculture products, measured yield Of crops, selling price of land, values assessed for taxation purposes, value of farm buildings, and physical character of the land. There are various major objections to each of these ratings when used alone, but the conclusion was, that the best rating could be arrived at by combining as many Of the criteria as possible. PROCEDURE In order to discover relationships between soil type, skill of farmer, number of animal units, kind and condition of buildings, per- cent of soil organic matter, degree of soil acidity and crOp yields, data relative to these matters were obtained from seventy-five farms in St. Clair County for the years of 1939 and 1940. The data on crOp yields and number of animal units were taken either from the account books kept by Farm Security Administration borrowers or Obtained directly from the farmer himself. The soil types Of each farm were Obtained from the soil survey map of St. Clair County (4). The types*of farmer and types of building were classified by the writer on the basis of observation and judgment. To determine the percent of soil organic matter and degree of soil acidity, a sample of soil was taken from the definite soil types in fields where oats had been raised in the summer of 1939. This study may be divided into three parts: First, to determine the correlation between various farm indexes; second, to compare these various indexes with the soil types, ratings of farmer, number of animal units and types of building; third, to compare the percent of soil or- ganic matter to yield of cats and to number of animal units, and also to compare the soil pH to the yield of oats. Farm indexes: Six different indexes were secured for each farm as recorded in table 3. * "Types of Farmer" is the same as "rating Of farmer" The 1939 and 1940 indexes were made by dividing the yield per acre on each farm by the average yield of the county* (10) for each of three crops; corn, oats, and wheat. These percentages or indexes were then added and divided by three, giving the index for the farm. The year of 1939 was dry, causing low corn yields; the year 1940 was the Opposite, being exceptionally wet. Approximately 5 percent of the crOps were not harvested in 1940 due to the wet season. It, therefore, seemed necessary that this condition should be considered in making the index, in order to bring out the poorly drained soils and poor managerial ability. Thus, a crap index of 31 was assigned to crOp failure and 50 to a crap with an apparently satisfactory yield, but not harvested on account of un- favorable weather conditions at harvest time. Corn oats, and wheat were used for the index, as almost every farmer raises these crOps and the yields may be secured much more accurately than those of many other craps. The yields of corn are the least accurate of the three, as some was fed in the bundle, and some placed in the silo, thus making an estimate of the corn yields necessary. Possibly corn yields should not have been used; yet, this crop may tend to show the quality of the soil and the managerial ability of the farmer better than wheat and oats, as these latter crOps receive the early spring moisture. The 1939-1940 index was made by an average of the 1939 and 1940 crOp indexes. This was done to balance the dry year against the wet year, making a more accurate index for the farm. The Agricultural Adjustment Administration index was taken directly from the (AAA) St. Clair County ratings for each farm. * The average yield for the county was taken from special bulletin 206, Michigan State College. Buxton's index was made by rating each farm either poor, fair, medium, good, high, or poor to fair, etc. This rating was made according to the observed productivity of the soil which included the kind of soil (clay, loam, or sand), the locality, and growth of craps. Even though each farm was rated without considering the recorded crop yields, it would be natural that the author would remember a farm having poor or excellent yields at the time visits were made to the farm. All farms of medium rating were given an index of 100, poor 75*, high 125; fair and good were given ratings equally in between the others adjacent to them. Due to this type of classification, many of the farms came out with the same index number. The all average index consists of an average of the 1939-1940 average, the AAA index, and Buxton's index. Correlation coefficients; The correlation coefficients of the various indexes were calculated1 (12) and presented in table 4. When r exceeds the one percent point (this is determined by reference to * 75 used as the lowest index listed by the Agricultural Adjustment Administration; thus, 125 was used as the high, since poor was 25 below 100 1 a: N ‘ (Cx Cy) ”I‘D?— -10- Fisher's table of values of r for different values of n) (S) the correlation is considered to be significant. Thus, the larger the correlation coefficient is, above the one per cent point, the greater is the correlation between the two values being correlated. To compare the indexes to soil types, types of farmer, number of animal units, and types of building, each index was divided as nearly as possible into the high one-third, medium one-third, and low one-third groups. Soil types: The type of soil that each farm was mostly composed of, was determined from the County Soil Survey Map of the year 1929 (4). The land description of each farm was marked out on the survey map. Then a transparent piece of celluloid which had been ruled off in squares of 1/16 inch was sized to cover an area of the farm. From this, the number of acres for each type of soil on the farm was determined. The number of acres for each type of soil was then divided by the total acres in the farm and the result multiplied by one hundred giving the percent of each type of soil. The farm was then placed under the type of soil having the largest percentage. If the farm was composed of several types of soil of about equal percentage, it was placed under the type of soil that the most crOps were grown on, or into the type of soil ‘Vhich seemed to fit the farm best. -11- St. Clair County (4) has 19% of its acreage mapped as Conover silt loam, 11% Brookston,1 10% Napanee silt loam, 9% Allendale fine sandy loam, 7fl Conover loam, 7% Berrien loamy fine sand, 4.7fl St. Clair silt loam, 1.7% Macomb loam, 1.7% Jeddoz. This variation in the acreage of types of soils accounts partially for the unequal distribution of number of farms for each type of soil. In general, the number of farms under each type of soil in this study tends to correlate with the percentage of that soil in the county. About 50% of the farms are on Conover silt loam. This large percentage may possibly be accounted for by: First, the large mapped acreage of this soil in the county; second, many of the farms have a fair percentage of Brookston soils, yet the per- centage of Conover silt loam is the larger and the farm is classed as Conover silt loam; third, perhaps the soil has become depleted to the extent that fair yields, or incomes can not be secured, thus, resulting in a low income family. In this study, there are few sand farms, due to the fact there are few farms loans in the sand area that lies adjacent to Port Huron. Much of this sand land will not support even a low income family. The Conover silt loam farms were divided into two classes. The farms under the Conover silt loam (C25) type have a heavy clay soil, light in color, (showing lack of organic matter) and are on the higher ground; therefore, these are more like a Napanee soil type than Conover silt loam. The farms under the Conover silt loam (Cs) type tend more towards a loam soil that is dark in color. 1 Includes Brookston loam, silt loam, and clay loam 2 Includes Jeddo silt loam and clay loam. Rating_pf farmers: Each farmer was rated either good, medium, or poor. The qualifications for a good farmer were as follows; prompt care of farm duties, a suitable knowledge of the prOper farm practices and the use of this knowledge, managerial ability and the ability to care for his family. The qualifications for a poor farmer were; not prompt in caring for farm duties, lack of knowledge of prOper farm practices, poor managerial ability, and possibly poor care of the family. The medium farmer was one that seemed to be between the high and poor group. Three separate ratings were made: August, 1939; January, 1941; and April, 1941; at the time each rating was made, no previous rating was reviewed. The farmer was then given a rating from the final average of these three ratings. The ratings were expressed by numbers: 1 represented high, 2 medium, and 3 low. To secure an average of the three ratings, 1-, 2+, 2-, and 3+ were used. For example, a farmer rated high twice and medium once was given a rating of 1-. In case a farmer rated high once and medium twice his rating was 2+. The number of farmers in the 1 and 2 ratings are about equal, but the 3 rating has a small number of farmers. The farmers were rated against each other, as low income farmers, not in comparison with other farmers. Possibly this accounts, partly, for the small number of farmers -13- rated as 3. (This means that all rated at 3 are very poor farmers.) Ratigg of farm buildings: Each set of farm buildings was graded as excellent, good, fair, poor, and very poor, according to the author's personal observation. Buildings considered excellent were well painted, in fine condition, large enough for farm needs, and were conveniently arranged. Good buildings were in fair repair, and suitable for the needs of the farm. Buildings classed as fair were suitable for the needs of the farm, but needed some repairs, such as a roof for the barn. Buildings considered poor were in need of repair and were not entirely suitable to the needs of the farm. Very poor buildings are simply shacks. In this study of farm buildings, it must be pointed out that only Farm Security Administration borrower‘s farms were used; therefore, this study cannot be used to show whether the type of building forecasts the productivity of soil, except for the 75 farms used. It must be noted that no Farm Security Administration borrowers were located on the poor sandy soils of St. Clair County. The opposite tendency is true that very few borrowers are on farms with excellent buildings. Therefore, this study tends to include only certain types of farm buildings and Can not be used as a study of the relationship of soil productivity to all types of buildings for St. Clair County. Determination of the percent of organic matter in the soil samples -14- tgggp: The percent of soil organic matter was determined by two methods - the ignition and carbon-chain. From these data, the soils were grouped according to the percent of organic matter to find the relation- ship of oat yields or number of animal units to soil organic matter content. To determine the percent of organic matter in the soil samples taken, five types or groups of soils were selected; namely, Conover loam, Conover silt loam, Brookston*, Napanee silt loam and Allendale, Berrien, and Newton sands. Five high yielding farms and five low yielding farms for each soil type or group were selected for this part of the investigationl. Samples of soil were collected from the type of soil given, and not from the farm which was classified under a type of soil as in the previous part of this thesis. The 1939 crop of oats was selected as the indicator of the soil fertility level of each field. The sample of soil was secured in 1940 from the field on which the oats were grown in 1939. In collecting the sample, a spade was used to dig out a small hole, with one straight side, to the depth of the surface soil (6-8 inches). Then a slice of soil about one inch thick, and to the depth of the surface soil was taken. This slice of soil was placed in a pail with five to six other slices from the field. The sample was then placed on a cloth for mixing. After a thorough mixing, a one-quart sample of it was taken andglgid out on paper to dgy. * Brookston includes both silt loam and clay loam It was possible to find only 3 Brookston and 4 Conover loam farms -15- After air drying, the soil was pulverized and screened. These samples were analyzed for organic matter content by the ignition method and the carbon-chain method as previously mentioned. Ignition method: The hygroscopic water was obtained by heating in an oven at 110° C for 24 hours. Then a sample of each soil was weighed and burned for 20 minutes in the muffle, electric furnace. The burned soil was again weighed. From these figures the percent of soil organic matter was determined.* If the duplicate samples did not check within .3 of a percent, the sample was run over until there were duplicates that checked within .3 of a percent. Carbon-chain method: A sample of approximately one gram of soil was weighed out. This was then mixed with aluminum oxide and manganese dioxide and the mixture placed in a heated tube which burned the organic matter, releasing the carbon or carbon dioxide. The carbon dioxide was absorbed by ascarite in an absorption tube which was weighed before and after the absorption of carbon dioxide. From these weights, the weight of carbon dioxide was obtained and percent of soil organic matter * weight of soil 1-+-(% of moisture s 100) Weight of moist soil - weight of oven dry soil g Grs. of hygrpsgopic H20 0' 3 'Weight of oven dry soil C) Wt. of soil before burning - fit. of hygroscopic water - Loss due to S ' organic matter Loss due to organic matter H . weight of oven dry soil x 100 - P of organic matter -16- determined.* The determinations were repeated until duplicates checked within .4 of a percent. (Five samples checked between .3 to .4 of a percent, all others checked within .3 of a percent or lower.) Method of testing soils for pH: The soil samples were tested for pH by the Soiltex method. Each sample of soil was tested twice to check against possible error. Coefficient of contingency: Table 28 presents the coefficients of contingency as calculated1 (12) and also r at the 1 per cent point (5). According to Love (12) the coefficient of contingency may be used the same as r as far as practical purposes are concerned. * wt. of 002 x Clatomic wt.) x 1,72 002 (atomic wt.) : i of organic matter “R. of sample 1 Cl : s - n s n - number of individuals 8: sum -17- DISCUSSION In this thesis it must be understood that more factors are usually involved than those actually given in a comparison. For instance, in finding the relationship of soil type to crOp indexes, these other factors also enter in: The skill of farmer, the weather, and many others. Therefore, it must be expected that the results obtained in many of the relationships studied will show only a tendency in a certain direction. Correlation of the various farm indexes: From Table 4 it is found that all indexes as compared to another are significant, except the 1939 crap index and 1939-1940 average crop index as compared with the AAA index. Buxton's index as compared with the 1939 crOp index, and 1939-1940 average crop index gives a fairly high correlation. Thus, the farms rated by Buxton's index* were more nearly rated like the crop indexes than any of the other indexes. The 1939 crOp index as compared to the 1940 crap index shows some correlation, even though the two seasons had Opposite weather conditions. The summer of 1939 was hot and dry, but 1940 was cool and exceptionally wet. The Buxton index and :iA index of these farms compared more closely than the AAA index and crOp indexes. The AAA index and Buxton index were averaged together and compared to the 1939 crOp index and the 1939-1940 average crop index to find if several indexes combined would give a better correlation. It is found that this * It must be noted that Buxton collected the data on yields, which may have influenced his farm index ratings. -18- method gives a correlation coefficient that is significant in all instances. Therefore, according to these results, a farm may be given a truer rating by using more than one index, since it lessens the chance of using an index that shows little correlation. A comparison of soil types with farm indexes: In classifying the farms as to productivity according to soil types in Tables 5 to 10, the Conover loam stands out as the best soil in every index. The Conover silt loam is divided about equally from high to poor, both for the Cs and C23 types. The Napanee silt loam shows a definite soil quality of medium to poor. The Allendale fine sandy loam is about medium in quality according to the tables. In the other soils, not enough farms are listed to give any weight to their classification. thmITable 28 it may be stated that a definite relationship exists between soil type and farm index. Comparison of rating of farmer with farm indexes: From this study, Tables 11 to 16, there is a tendency for the grade 1 farmers to be on the best farms and the grade 2 and 2- farmers to be on the medium to poor farms. The Coefficient of Contingency in Table 28 is quite similar for all indexes, tending to show that a correlation exists between the rating of farmer and farm index value. This may be the result of a farmer residing on a good farm, a good farmer selecting the best farm, or the good farmer may secure higher yields thus giving a better index rating. Comparison of rating of farmer with soil type 8 Tables 17 and 18 were prepared to find the relationship between soil type and rating of farmer. In this manner it might be shown if there were a difference between the relationship of farm index to rating of farmer, Tables ll to 16, or soil type to rating of farmer. According to the coefficient of contingency, Table 28, there is not nearly as great a correlation between farm index and rating of farmer as between soil type and rating of farmer. From the results in Tables 17 and 18 it may be stated that the best rated farmer tends to be on the better soil type. Relationship of number of animal units to farm indexes: The number of animal units (14) are about the same, according to the Tables 19-22, on farms in each of the high, medium, and low quality classes of soil as determined by farm indexes. However, there are a few more farms on high quality soil with a large number of animal units than there are on low quality soil. This is probably due to the high quality of soil being able to support more animal units per acre, and may not be the result of more animal units placing the farm in a higher class. Most of these farms are rented, which means a change of tenants every few years; thus, the present amount of livestock might not affect the present quality. Quite often, the livestock units on a farm are determined by the number of units the farmer owns. This may be shown in the 2 low quality farms, in the all -20- average index, having 21 to 24 units of livestock. Both farms have 120 acres. This means these farms are of only average size which does not warrant the large number of animal units in relation to its productivity. Yet, according to the values of the Coefficients of Contingency, Table 28, there is a tendency for the number of animal units to correlate with the index value of the farm. From Table 28, it may be stated that the soil type tends to affect the making of a higher farm index, or farm productivity rating, more than the type of farmer or number of animal units. Relationship of type of buildings to the alllaverage farm index: .The correlation of contingency as calculated for'Table 23 shows a small relationship between condition of building and farm index. Results obtained from the study of percent of organic matter in soils and soil types: According to the data presented in Table 24, the ignition method gave an average of 1.83 percent of organic matter higher than the carbon dioxide method. The Conover loam and Brookston* soils had a higher percentage of organic matter than the Conover silt loam, Napanee silt loam, and sand soil types. With the Conover silt loam the percent of soil organic matter for high and low yielding soils was approximately the same according to the * Brookston includes both silt loam and clay loam -21- results of the ignition method. iowever, with the carbon dioxide method the high yielding soils had the highest percentage of soil organic matter. The converse was true in the case of the Brookston soil. The difference in the percent of soil organic matter of the high and low yielding Napanee silt loam was too small to be of any significance regardless of the method used. The same situation was found in regard to the sandy soils. Relationship of percent of soil organic matter to yields: In Table 26 the 46 farms were divided according to the percent of soil organic matter into the high one-third, medium one-third, and low one-third groups. The groups were then classified according to crap yields. From this grouping there is a tendency for the soils with the highest percent of soil organic matter to correlate with the soils having the highest yields. Relationship between number of animal units and percent of soil organic mattgg: According to the coefficient of contingency,fmable 28, as worked out for Table 26, there is a high correlation between percent of soil organic matter and number of animal units. In other words, the larger the number of animal units, the higher the percentage of organic matter. Relationship of pH to soil productivity: The Conover loam and Brookston soils have the highest pH according to Table 27. The Conover silt loam, Napanee silt loam, and sandy soils are somewhat similar in pH values. The Conover silt loam, Conover loam, and sandy soils tend to have a higher pH on the high yielding soils. The Napanee and Brookston soils show little difference in pH between the high and low yielding soils. A relationship between yield and soil acidity is indicated by the coefficient of contingency. CONCLUSION 1." It was found that a higher correlation existed between Buxton's index and the crOp indexes than between crOp indexes and the AAA ratings. In general, the combination of several indexes may give a truer correlation than one index. The various farm indexes tend to correlate with each other, but not to an extent that any two indexes will prove that one farm may be measured as so much more productive than another farm. 2. Each index evaluated the Conover loam soil as the best. The Conover silt loams ranged from high to poor. The Allendale soil tended to show medium.quality. The Napanee soil was medium to poor. There were not enough samples of the other types of soil to give any evaluation. 3. There was a tendency for the best farmer to be on the better farm, but the medium farmer might be on either a medium or poor farm. 4. The correlation between number of animal units and quality of soil was small. 5. There is a low correlation between quality of soil and type of buildings. 6. The ignition method gave a higher percentage pf organic matter in the soils than did the carbon dioxide method. 7. The Conover loam and Brookston soils on the average gave the highest percent of organic matter by both methods. 8. The ignition method gave the high yielding soils of the Conover loam and Brookston types the larger percent of organic matter, the Con- over silt loams and sands had about the same amount of organic matter -24- for both high and low yielding soils. The Napanee low yielding soils had a slightly higher percent of organic matter than the high yield soils. 9. The carbon dioxide method gave the high yielding soils of the Conover silt loam, and Conover loam the greater percent of organic matter. The high and low yielding soils of the Brookston and sand soils are about the same in organic matter content. The Napanee low yielding soil had a little higher percent of organic matter than the high yielding soil, but probably not enough to be of significance. 10. There is some correlation between the soils having the highest percent of organic matter and those having the highest yields. 11. According to this study there is a fair correlation between the number of animal units and the percent of organic matter in the soil. 12. Some relationship was found between soil pH and crop yield. 1. 0'0 0 -25- REFERENCES Bausman, R. 0. An economic study of land utilization in New Castle County, Deleware. Agricultural Experiment Station, University of Delaware, bulletin number 228, 1941. Bonsteel, J. A. Soils of Southern New Jersey and their uses. U. S. D. A. bulletin, 1918. Browne, Harold F., and Eke, Paul A. Influence of tenancy on types of farming and agriculture; income by soil types (Iinidoka Irrigation Project). Agricultural Experiment Station, University of Idaho, bulletin number 222. Deeter, E. 8., Fulton, H. W., Musgrave, B. E., and Knapp, L. C. Soil survey of St. Clair County, Hichigan. 1929 Fisher, R. A. Statistical methods for research workers, 6th edition. Fonder, J. F. A critical study of the influence of soil type on the Ca. and Hg. content and other physiological characteristics of the alfalfa plant. Soil Science 278205-232, 192 Fender J. F. The relationship of soil type to the Ca. and Mg. content of green bean stems and leaves and of their expressed sap. Soil Science 27:415-431, 1929 9. 10. ll. 12. 13. 17. -26- Fender, J. F. Variations in the Ca. and Kg. contents of pea plants on different soil types. Soil Science 28:13-26, 1929. Gustafson, A. F. Soil and field cron management of Cayuga County, New York. Agriculture Experiment Station of Cornell University, 1932. Hill, E. B. Types of farming in Michigan. 1939 Hill, F. F. and Blanch, George T. An economic study of land utilization in montgomery County. Agriculture Experiment Station of Cornell University, 1932. Love, H. H. Application of statistical methods to agriculture research. 1937. McCool, K. K., and Weldon, h. P. The effect of soil type and fertilization on the composition of expressed sap of plants. Jourl. Amer. Soc. Agron. 20:778-792, 1928. Overton and Roberts. Farm management. Paden, William Reynolds, Effect of crop succession and soil type upon the number and activity of microorganisms in two types of soil. 1932. Pasco, Ray E. Some relationships between soil type and use of land in southern Kichigan. Thesis, michigan State College. Peck, Millard, Frank, Bernard, and Eke, Paul A. Utilization of lands in West Virginia. U. S. D. A. technical bulletin 303. -27- 18. Veatch, Jethro Otto and Schneider, Ivan F. Comparison of criteria for the rating of agricultural land. 1942 -28- ow Os 0 H o a NH ma O¢H * Hm mm mo ma mu mm mm Hm am am mm mm an ac So 0 cm Sm mm mm mm Md Essa hem mopo¢ mm {H- Hfiow .Epwm some :0 momma aflom mo omswzooaea ssh I .H manna memhe Hwou ms mm‘ oNH om * om m as so am ;; .x. o mH mm ow mN * a He omH mm X. OH ow mm s 1 mm s as mm .x. es @ oNH em .x. my mm om MN .x. m ms omH mm .x. H em oh ow * m- o mH om mH * H as s H osH wH * a m as Hm as as ex ea pm me He so He as «a so He so so cm as ea mm mm as Essa sass hem mo moped .oz Bush some so monks Hwom mo emspseUAog 0:9 I .H magma -30- , Hm, my OO me * b i OOH OO es Om ms “ OO ms * f .wH O es H OH Om ms .x. OH Hm O mm OO He .x. .wO N m mHH mm * OOH ONH mm * - * He Om OH [MN on m M Om Om .x. s HM] NOH mm .x. Om s OO em .x. mo Om ONH mm * _ OOH OO Hm * mm m as Hm as so a: s2 pm me HO sO HO MO as so He so HO om sm sm mm mm as Spam ssss _ gem Mo meuoa .oz mossy Hues .Easm some :0 memhp aflom Mo swapseoaom emu I .H mfipwh OOH OOH HO * mm may OO OO k. OO O OO Ow * 0.00 H. H O.m|‘ OOH O * OOO OOH Hull .x. OO OO OOH Om * OH Om Os OO mw1‘ * Om HH OH HH Hs OO em * OH ON ON my Om, OOH H * ms OOH Om * «u m “H mH N5 ”the * OH Om, NO, OOH Os .x. HO mm Om Os .x. OO O as.HO Om OO as s2 s2 we HO sO HO OO so HO so so 0O am sO Om sO Os sass sass pom mo menum .oz .Epsm mode so mommy Hwom Mo emOOSeopem may a .H wanes mamas HHOO OOH OOH OO Om, * ms OOH HO * mO OH OO OO * HO mH OOH OO O *OH OO wH OOH OOl‘ m * OO mO OOH OH O on ms mm Om OO O * OdH Om MO * up 0O ON ON * OO OO OO * HO IMO OOH HO sO OO * s OOH mO * OOH OO OO 11OO O O: HO Om OO OO O: OO mO HO sO HO me as HO HO so OO 0O sO sO mm mm OO sass sass an”. O“ .Spsm game :0 Omaha Hfiom mo ewwpsooumm one I .H eHan -33- .mmhp Hwom mfigp ca Ooosam was BOOM esp mmmoepsa o>wpwpdg§oo Ooh * mO OH OOH HOH * OOH \Om OO * OOH OO law a HO O OO OO * OO O OO OJ OO mm * Owl OO OO OO O O O OO OOO HO .x. OO OO O OO OO .x. O OO HH OOH OO Lkn OO OH OOH mO .x. OOH OO OO * OO OH OO MO .x. ‘OO O NO HO OO O: Oz pa OO HO sO HO OO OO OO HO 5O O om am sO OO 0O O. sssO atma pom mo meao¢ .oz momma Hwom .Easm Some :0 momma HOom mo emmpsoopem age I .H mange -34- Table 2. - Legend: hams of soil types and symbols used in Table l. Af Allendale fine sandy loam Be Berrien loamy fine sand Bf Berrien fine sandy loam Bn Bono clay Bm Brookston silt loam Bc Brookston clay loam Cy Clyde loam Cm Carlisle muck Cl Conover loam Cs Conover silt loam (Light phase) C28 Conover silt loam (Heavy phase) FF Fox fine sandy loam Gf Genesee fine sandy loam G1 Gilford loam J1 Jeddo silt loaf Js Jeddo silty clay loam ‘ Mb Uacomh loam Ns Napanee silt loam Nf hewton loamy fine sand Of Oshtemo loamy fine sand Pf Plainfield fine sand 81 St. Clair silt loam wy Wauseon fine sandy loam B Burned muck Gp Green wood peat Gn Griffin loam -35- sOsO O.HH Om‘ wO HO OO on On OO m_OO OH sHsO OH -O HOH HOH OOH HOH OOH OO mO OH OooO OH -H mm mm OOH HO HOH HO OO OH sHsO m.mH O HHH OHH OHH HHH HOH OOH HO OH sHsO OO .+O OOH OHH OHH OOH Om. HOH sO HH sHsO O.OH -H mm OOH “mm OOH OOH HHH mm OH poem O.OH IO OOH OOH OOH OHH OHH OHH aOO HO eooO O.OH -H HOH HHH OO OOH HOH OOH mm O sOsO m.OO O OO HOH OO Om OOH OO OO O OHOO IO.O -H OO OO mm OO mm OO OO O OOOO O.OO +m OO OO HO mO OO OO OOO In OooO O.OH H OOH OHH HO OHH OOO‘ OOH nmw O OooO OO -H OO HO OOI‘ IMO OO HO «O H 1 Fl A rll-LILFI .m cam mpwzs Aoahsh xovcH «dd me2H KeucH XOOQH vanH NovsH Shah Os HesOsO as O sopssO OOO m.soOst OOIOOOH OOOH OOOH mass Os OOOO mo .02 OsHOsO OOIOOOH mo .sO HOOO .02 mo .>< .uoavzvm mahsm esp Mo mwcflpfiwzp mo was» use .mpwns Hwafizw he hopes: .uoeawm mo mnfivsu .mwzwvsh News“ .momhp Hfiom mo nomfiusano < I .m wanna sssO O.OH O OO (‘OO OOH OO, OO OO mO mm sOsO O.HH -H OHH OOH OHH OOH OOH OHH mO Hm ssm IOOOO H OO OHH OOH OO OO OO OOO on -HHOsxO OssO OH -H OOH OOH OOH OOH OO OOH Om OO OssO O.HH O OO OOH OO HO OOH OHH HO OO OssO H.OH .OO HOH OO OO‘ OHH OOH OO mm OO sOsO OH H OHH OHH OOH HOH OOH OOH man. OO sOsO OH In Om OO OHH OO OOH OO sO OO IO tOsO O.OH O OHH HHH OOH OOH OOH OOH OOO OO 3 _ sHsO OH O OOH OOH OHH OOH OO OHH OO OO sOsO O.HO .+m OO OO OO OO HHH OO Oz OO OssO O.OH H OOH may OOH OOH OOH OOH MO OO sssO O.OH 4O OO HHH OOH OO I; OHH HO mO OH .mwvam mean: heapsh KOOCH 44¢ aoan XOOSH er2H er2H xoczH emxa sash Os HsaOsO Os O sspst OOO m.ssOst OO-OOOH OOOH OOOH HOsO Os sOOO Os .s: OsHOsO OOIOOOH Os .sO .sz Os .sO .Ouwwsvm OEOOM esp Mo mmswuawsp Mo Omzp Ons .mvwss answss He hopes: .aeahdm mo msfipmu .mmsflpsh xOOSO .monhv Hwom he nomwndaaoo ¢ I .m oHpeB -37- OOOO O.HO +O OOH OO OO OOH ”OOH HHH uO OO OssO 0.0H H OHH OO‘ OOH HOH OOH OHH uOO OO Oho> Ossm O.OH H OHH OOH OOH OOH OOH OOH OOO OO Ossm O.OH +O OHH OO OOH OOH OOH OOH ONO OO OssO O.HH H OOH OO OOH OOH OHH OO‘ HO OO OssO H.OH O OHH HHH OOH OOH HOH OHH mO OO OOOO 0.0H -O OOH HOH mm‘ OOH OOH OHH \mm HO OssO OH -O OOH OOH OOH OOH OOH OOH HO OOOO 0.0H H OO mm OO OO OOH OO mOO OO OOOO O.NH O OO OO HO WOO OO OO Oz On OssO 0.0H +O OO OHH HO oO OO Om mOO OO Opm> OssO 0.0H OO OOH HOH OOH OHH OHH OHH OO Om OOOO 0.0H H OO OOH HO OO‘ OO OO mOO Om ommvflm mpfifib hthwh NouEH 44¢ KOUSH vaGH KowflH NQUSH meQH OQhB Shah Os HOEOOO Os O :sOxam OOO u.OsOxOm OO-OOOH OOOH OOOH HOsO Os OOOO Os .sz OOOOOO OOnOOOH Os .>O .sz Os .>O .vmfiwzpm mcflwm opp Mo mmsflcawdn mo Omhv cum .mpand HOEOSO mo umpada .hoEme mo mnfipwh .mmnwku xOOzH .momhv Hfiom Mo nomwuwgsoo 4 I .m oHpua OHOO 93 O OOH HOH OOH SH OHH OHH % NO OssO OH H OOH OOH OOH HOH OOH Ow mOO FIOOOI OHOO. O.OH O OO OHH OOH OO OO OOO HO OOOO O.fl H OOH OOH O.H\H OOH HOH OOH HO OO OOOO HH O OO OHH OO OO MO Ow W2 OO 88 0.0H O OW «O OO OO OO HO mm On .3er OO.OH H OHH a OOH OOHI OOH OOH HO Ow OOOO O.OO +O OOH JOH OHH OO HOH OH 8 O OHOO O.HH ..O N? OO MOW OO OO OO OO OO OOOO O O HO OO HO OO OO OO OO. OO OOOO OH O OOH [Om OO OOH OOH OOH OO HO OssO O.OH H OOH OOH OO OHH OHH OOH HO fl OOOO 0.0H O «O OO QM OHH OOH OO W27 OO Ildmmmwm Opfins Omahwh xovnH <¢< xonzH mmmmMO KowszlwmmmlemmmmH omxa HOMO Os Hsfia Os O 5325 32 3:355 OO-OOOH OOOH OOOH HOsO Os 25. Os 62 OOOOOO OWOMOOOH. Os .3. .s2 .uwfivzvm OEOOM O39 Mo manavafisn mo omhp Onw .mvwzs Hmawzw Mo hopes: .OoSOOm ho mnfivmh .umzfiku Kong“ Omega» Hwom mo zomfiummsoo 4 u .m mamas —39_ OOOO O.O +O OO OO‘ OOH OO OHH OO Oml‘ OO OssO H.OH OO OO OOH mm OO OOH OO mOO OO OOOO O.OH H OHH OO OHH \OOH OOH OOH am OO OssO O.OH OO JON OOH OOH Imm OO‘ OHH mO HO OHOO O.OH H OOH OO OOH OOH OOH OOH am OO OssO O.OO O OHH HO OHH OOH OOH OHH mO OO OssO O.OH H OO \Om OO IIOO 1mm OO mO OO IIxmmsO OO H OOH OOH OOH OOH HOH OHH OO OO OOOO 0.0H -H \OOH OO OO OOH OOH OO OO OO\ O20 O.OH +O OOH OOH OOH OOH “OOH OOH OOO OH -HHssxm OOOO O.OO +O OOH OOH OOH OOH HOH OOH HO OO OOOO 0.0H +O OO HOH OOH OO HO‘ OO an OO OOOO \OOOH OO OOI HHH OOH OO HO OO mOO OO .II IIIIIL IIIIILI: IIIIIILrI-a. nmmwam mafia: Ooauwh ch:H 44¢ xmccH NOOQH xOOqH xovum xoczH cams Sham Os HOEOOO Os O :spxsm OOO O.OsOxsm OOIOOOH OOOH OOOH HOsO Os OOOO Os .sz OsOOOO OOIOOOH Os .>O .sz Os .>O .OOHOSOm magma msp no nmgfivawzn no Oahu Onm .mvass HOEOQO ma hopes: .Omeumh mo mnfiwwh .mmnwvmh XOOEO .momxa Hwow Mo :omwhdeoo i I .m oaade -40- OssO OO H OHH OHH OIOHH OOH HOH OOH HO ‘mmm: OssO OO.OH O OOH ILMHH OOO OOH OOO, OOH OO OO OOOO Nu; O OO OO OO OOO xmmmw OO OOO .OO Ossm ‘wH O OO1 HOH OOH JwO HO Ow pa OO OOOO OO H HOH OHH LOOH OO OO OO HO Om; OosO OH H HHH OHH OOH OHH OOH OOH Oz O OssO OH O OO OO OHH OO OOH NO .JOO .mw OssO OO H OOH OOH OOH OOH OOH OOH am OO OsoO [O.OO H OHH \OOH OHH \OOH OOH HOH OO OO OssO O.OH -H HHH Om OOH OOH “OOH OOH OOO OO samuam mpwza hoakuh xoqu <4¢ MovnH woqu KovzH KovnH KovzH omwa shah Os HOaOsO Os O OsOst OOO O.Ospxsm OOuOOOH OOOH OOOH HOsO Os sOOO Os .sz OOOOOO OWMOWMM Os .>O .sz .OOOOSOO wEOwO wgp mo mwzwcafisn mo mahp vzw .mpwns HOEOQO Oo hopsdq .Omauwm mo wswpmu .mmqflpwu xovnfi .mmmmp Hwom mo somOAwLSoo 4 I .m oHan Table 4. - Correlation coefficients between indexes Indexes r* 1939 Crop Index Conpared to the 1940 CrOp Index .3897 1939 CrOp Index Compared to the AAA Index .2862 1939 CrOp Index Compared to the Buxton Index .5527 1939 Crop Index Compared to the Average of AAA- Buxton'e Index .5249 1939-1940 Average Crop Index Compared to AAA Index .2070 1939-1940 Average Crop Index Compared to Buxton's Index .5088 1939-1940 Average Crop Index Compared to Average of AAA-Buxton's Index .4531 AAA Index Compared to Buxton's Index .3999 * r (1% point) r (Efl point) .3017 .2319 -42- O O O H O O HH OH OH O O O OO OEOOO Mo hmnfidz O H O H H H O O O O O OO ssg H O O O O O O OO OHOOOO H O O O O m H O O OO OOOO magma OO O .0.0.0 Om OO OOOO :sOaOz 92 HO Oz OOO mo OO 5O HO Os SOO Os amaze Hfiom nonadz osawb Xequ mogzp HOom op News“ coho mmma Mo QOOOOHOOOOQ I .m OHnt -43- N m w H m m HH wH OH m m w ms mEOOh Os OOOEOO O H H H O O O O H OO seq O O H O O O O H H O OO OHOOOO O O O O O O O OO OOOO OEOOO OO O .0.0.o Om OO Onem OsOaOz p2 HO Oz OOO mO OO am HO Os EmO Os momma Hwom hopesz osHm> KowcH .mmmz How 0 xe 2H douo . O . OOOH Oo csOOOHOOOOO . .O OHOOO -44- O O O H O O HH OH OH O O O OO 923 MO “@351...“ w H H m H m w m H H mm 30H m H H m o m O H N mm EdHnofl O O O O H O O OO OOE OEOOO «L O .H.m.o Om O< Omen copamz pm Hm m2 mmo mo mh Em H0 Mo ELMO Mo mmmwe HHcm hopasz 03HO> KOOSH .mmghp HHom op xmwzfl mono omwpm>m ovanmmmH 029 Mo SOHOOHOOOOQ I .O OHQOB N O H O O HH OH HH O O O 2. 35 mo gonadz N H O O N H H ON 23 N H O N N N O H H ON 8:302 H e m H N H N N :me mauam .E O .H.m.o mm 2:5 53% .5 HO mz m“No 8 E. 5 Ho ho ES .3 mogfiw HHom umnadz ost> xman .mmmap HHcm op mezH ¢<¢ mo 20waHogpoo I .w oHnwe -46- N O H O O HH OH OH O O O ON «spam Mo honadz N N H H N N N O ON sou H H O m H N H NN ssHOmm H H N OH H N O ON OOHO maumm mm O .H.O.o NO Oman 209302 as Hm m2 mNo mo ma am Ho No spam no madam HHom nopssz osz> mocsH nanny HHom op Xovzfi O.Qcpxsm Mo SOHPmeppoo ¢ .m mHan -47- N m o H m m HH wH ¢H m m w MN magma mo Ampazz N H H H N N m O O ON 30H N e H H O m N N H N «N echoz H N N O H N O m N SOHO ‘prmm mm £.H.m.o mm Md cnmm 209302 92 HO m2 ONO mo Oh Em Ho Mo Sham mo @0959 HHom Ampesz cus> NovnH .moqmp HHom op anzH omwgm>w HHd Mo QOHOOHohpoo s .OH oprB Table 11. - Correlation of 1939 croo index to rating of farmer -48- Index value Number Ratings of farmer of farm of farms 1 1- 2+ 2 2- ‘3? 93 High 25 12 3 3 5 2 middle 25 6 3 7 5 2 2 Low 25 4 3 7 5 l 3 2 Number of farms 75 22 9 17 15 5 3 4 Table 12. - Correlation of 1940 crop index to rating of farmer Index value Number Ratings of farmer of farm of farms 1 1- 2+ 2 2- 3f 3 High 25 ll 2 4 5 3 Medium 25 7 4 6 5 l l 1 Low 25 4 3 7 5 1 2 3 Number of farms 75 22 9 l7 l5 5 3 4 -49- Table 13. - Correlation of the 1939-1940 average crop index to rating of farmer Index value Number Ratings of farmer of farm of -___- 13;ng 1 1- 2+ 2 2- 3+ 3 High 25 12 3 4 4 2 Hedium 25 6 4 6 6 3 Low 25 4 2 7 5 3 4 Number of farms 75 22 9 l7 l5 5 3 4 Table 14. - Correlation of AAA index to rating of farmer Index value Number of farm of Ratings of farger farms 1 1- 2 j 2 2- 1+ 3 High 24 9 1 7 5 l 1 medium 26 8 4 6 4 3 1 Low 25 5 4 4 6 l 3 2 Number of farms 75 22 9 17 15 5 3 4 -50- Table 15. - Correlation of Buxton index to rating of farmer Index value Number Ratings of farmer of farm of farms 1 1- 2+ 2 2- __3+ 43 High 25 8 l 5 9 l 1 medium 22 9 3 6 l 2 1 Low 28 5 5 6 5 2 3 2 Number of farms 75 22 9 17 15 5 3 4 Table 16. - Correlation of all average index to rating of farmer Index value Number Ratings of farmer of farm of farms 1. 1- 2+ 2 2- 3} ,1 High 25 ll 2 4 6 2 Medium 24 7 4 8 2 2 1 Low 26 4 3 5 7 1 3 3 Number of farms 75 22 9 17 15 5 3 4 -51- maesuwm ON H N N O H N O .Ho amnesz O +O N H H IN O H O H H N e N H H +N N H H uH HH H N N H N O H 232 .E Oiwwd .5 .2 ES 5322 a...“ HO Oz mNo 8 a. am He Sag umpmoHOnH Mo mumsumm amaze HHom mmanwm mo pmnssz XOOQH mmwpmbw HHw exp On OmnHaaome ma asoam qunwuoso :mH; mgp 2H posawm mo wspra op messy HHom Mo nOvaHmuaoo age 1 .NH OHQOB whoauwm ON N H H H N N m O O «0 Ampedz m H H H m O H N O H H IN O N H H H N H H N O H H N H N H H IH e m H H manwu mm O .H.m.o mm m4 vcwm 209903 4 HO ma mNo mo ma Sm Ho muosawm vopmochH Mo mmeawm amaze HHom mmsHpam mo Amped: .XQOQH Oweuo>w HHw map Op OOQHEampoO Ow msoam cangnmno 30H esp 2H amEpwm we msdea op mommy HOom Mo noHpmHmauoo One I .OH erwB -53.. Table 19. - Correlation of 1939 crap index to number of animal units Index value Kumber Number of animal units of farm of , farms 3 6 9 12 15 18 21 24 21 High 25 3 7 9 1 1 2 2 Medium 25 1 3 5 10 5 1 LOW 25 3 3 6 5 4 2 2 Number of farms 75 1 3 9 18 24 10 4 4 2 Table 20. - Correlation of 1940 crOp index to number of animal units Index value Number Kumber of animal units of farm of _w farms 3 6 9 12 15 18 21 24 21, High 25 3 7 9 2 2 l 1 Medium 25 l 3 4 10 3 l 2 1 Low 25 1 2 3 7 5 5 1 l Kumber of farms 75 l 3 9 18 24 10 4 4 2 -54- Table 21. - Correlation of the 1939-1940 average crOp index to the number of animal units Index value number Fumber of animal units ' of farm of farms 43- 6 9 12 15 18 21 24 _2] High 25 4 6 9 2 2 2 Medium 25 1 3 5 10 6 Low 25 1 2 2 7 5 4 2 2 Number of farms 75 1 3 9 18 24 10 4 4 2 Table 22. - Correlation of all average index to number of animal units 1W- Index value Number Humber of animal units of farm of arms 3 6 9 12 l5 18 21 24 27 __ High 25 3 6 9 2 1 2 2 Hedium 24 1 2 7 8 3 2 1 , Low 26 1 2 4 5 7 5 l 1 Number of farms 75 1 3 9 18 24 10 4 4 2 -55.. Table 23. - Correlation of all average index to types of buildings Index value Number .1 Types of buildings, ___ of farm of farms Excellent Good Fair Poor Very poor High 25 1 8 10 5 l Iedium 24 9 15 Low 26 1 8 l5 1 1 Number of 75 2 25 4O 6 2 farms QHO.4 ON0.0 0.0 oOO.4 OO4.0 0.0 OOO.O ooN.O 0.0 oH0.0 ONO.O NOO.O OON.O 0.0 OO0.0 oON.O 0.0 N4O.O ONH.O o.O OON.4 OOo.O 0.0 O44.O ONO.O OHO.N omN.O 0.0 OOH.O OOH.O O QNN.N CON.O xv O.4 Oom.H OOH.O OJ ON.O ONO.O OOo.O O.4 OO0.0 0Ho.O HON OoOOme Ooxpos :sto QOHOHQOH ccopaso LOPOOE smppmfi OHCOOLO oHsmwso 9:00pmm psoosem mEsmm OHOHO 30H 09 mm OOHMHmmmHo mapsm 2c mHHom Mo 0O 0O OH OO ON OO 4N ON ON 0.0H OH HN Aesow\snv mmmH OHOHO O40 HO O4 ON Nmopmxooam NOH Om NH O4 Smog pe>onmm <4 OO 4O OH O anH pHHm.ho>osco pmnssz OOO.4 O04.O O O4N.4 0H4.N 04 O OO0.0 ONO.O OO O OHN.O Om4.0 OO NOo.4 Ooo.O 0.0 oOO.N COO.O 0O O.N 004.O OHH.O 0O o.O 4OO.4 O4N.O 0.00 o.O OO0.0 ON0.0 O4 ON0.0 OOH.O O OON.O ONO.O OO O.4 OOH.O omN.4 04 0.0 omN.O OHO.4 N4 0.0 OOO.N ONO.O 0O o.O OOH.4 o4N.4 o4 Hmm cospoE Ooxvos noaoe\snv :Hmao =0HOH2OO OOOH -copaao poppas OHOHO hoppms oHsOmso POO owzwmpo pceogmm useosmm mapmm OHOHO gqu mmaNp HHom opHsHOOO emwsomw NO OO NN i ouwsm>4 HOH 0N NO OO 1 ebmsm>« NO O4 Om OO HO amnasz msHm> mm Ocm smegma UHSOMLO Mo vsmopog .cHeHh pmo I .ON oHnt -57.. EOOH OwHo an“ .EOCH PHHO .EOOH sovmxooam mowsHosH .N .onHH Mo #50 mpHSOOu ommpm>w exp oome OH503 »O OO omwaebw may :H 950 vmeH Owe NO * OoOOOa OOOHHOO H OON.O OOO.O H4409 mo OOOOO>O OO0.0 OOH.4 OOH.O O4H.4 OOOOOMO4 O OOO.OH OON.OH ON NO O OHN.N OOO.N OO N 0.0 O4N.O O4N.4 ON HO O.4 OOO.N ONO.O OH OOH 0.0 OON.O O4H.O OO OO ON.4 OON.O OON.4 NH O N OOO.N OO4.4 O4 ON 0.0 OO0.0 OOH.4 ON 4N O OO0.0 OOO.O OO O4 mawm 209502 cam NOsOO :eHhsmm «weHmucmHH¢ OOH.H O24.O OOH.M HOO.4 mmmmmwO 0.0 ONH.O ONO.4 OO ON 0.0 OOO.O OOO.4 ON OH 0.0 OOH.O ON0.0 O.HO 4O O.4 ONO.O OOH.O OOO44>OOO O04 OO 0.0 O4N.4 OO0.0 OO OO O.O ONN.4 ONO.4 ON O4 O.4 ONO.N O4N.4 OO H4 O.4 OHO.N ONO.4 HN NN O.4 OOH.N OO4.4 OO OOO O.O O40.0 OHO.4 ON 4 0.0 OO0.0 ONN.4 OO OO 8OoH pHHm mesmmwz O.4 OO.O ONO.O ON N4 O OH0.0 ONN.N OO OOO OOeoO Hma wonpme Oogpos Ampow\:pv amnesz Hmm wonpma wogpms Hopom\:nv Lenssz :HOOO OOHOHOOH OOOH :HOOO OOHOHOOH OOOH .Inopawo uopvsa OHOHO Inonawo hovpaa OHOHO seapas oHsOOAO pmo umpvme aflswwao pee oHsemso pzeopem OHQOmao pseoaom wcoosmm vacuumm isOm OHOHO Boa OELOM uHeOO anm AcoscHOSOOV mommy HHom epHsHmow op mm ueHmHmmeHo msumm so mHHom mo 05HO> mm ch heppme OHssmao mo pcmoamm .OHOOO 950 I .4N QHQOB Table 25. - Correlation of oat yields to percent of soil organic matter Soil rating Number Yield of oats 1939 in bushels on basis of of organic matter farms 10 15 2O 25 3O 35 40 45 5O 55 60+ content 15 20__25 30 435 40 45 450 455 60 High 15 l 1 2 3 3 2 l l 1 Medium 15 3 3 2 2 3 1 1 Low 16 2 4 1 4 1 l l 2 Number of farms 46 3 3 8 5 9 7 4 2 4 l -59.. Table 26. - Correlation of animal units to percent of soil organic matter I Rating Number based on of Percent of organic matter number of farms animal units 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5__5.0 3 6 1 1 9 6 2 3 l 12 9 1 l 3 1 2 1 15 17 l 3 4 4 1 2 2 18 4 l 2 1 21 3 l 2 24 2 l 1 27 2 2 Number of farms 44 1 2 7 13 10 4 3 4 Table 27. - Correlation of oat yield to soil acidity Rating based on Number Yielg'of oats 1939 in bushels pH of of 10 15 20 25 30 35 40 45 50 55 60+ soils farms 15 20 25 3O 35 4O 45 5O 55 60 High 11 1 1 1 3 1 2 l 1 Medium 16 1 1 4 1 5 l 1 2 Low 19 2 2 3 3 3 3 2 1 Number of farms 46 3 3 8 5 9 7 4 2 4 1 -61- Table 28. - The coefficients of contingency as calculated from the correlation of farm indexes to soil type, rating of farmer, number of animal units, type of buildings; of soil types to rating of farmer; percent of soil organic matter to oat yield, to number of animal units; soil acidity to oat yield. Number Correlation of farm Coefficient r of indexes to of table soil type contingency (15 point 5 1939 crOp index to soil types .4982 .3017 6 1940 crOp index to soil types .5140 .3017 7 1939-1940 average crop index to soil type .5454 .3017 8 A A A index to soil types .5674 .3017 9 Buxton index to soil types .6902 .3017 10 All average index to soil types .5522 .3017 Correlation of farm indexes to rating of farmer 11 1939 crOp index to rating of farmer .4089 .3017 12 1940 crop index to rating of farmer .3715 .3017 13 , 1939-1940 average crOp index to rating of farmer .4880 .3017 14 A A A index to rating of farmer .3777 .3017 15 Buxton index to rating of farmer .4248 .3017 16 All average index to rating of farmer .4405 .3017 Correlation of soil types to rating of farmer 17 The correlation of soil types to ratings of farmer in the high 1/3 of the all aVersge index .6309 .4869 18 The correlation of soil types to ratings of farmer in the low 1/3 of the all average index .7715 .4869 Correlation of farm indexes to number of animal units 19 1939 crop index to number of animal units .4514 .3017 20 1940 crap index to number of animal units .3465 .3017 21 1939-1940 crOp index to number of animal units .4630 .3017 22 All average index to number of ‘ animal units .3681 .3017 I C,’\ m I 23 All average index to type of buildings .3631 .3017 25 Correlation of oat yield to percent of soil organic matter .4516 .3721 26 Correlation of number of animal units to percent of soil organic matter .7163 .3721 27 Correlation of oat yield to soil acidity .5316 .3721 I , - \T T4 a ,3“. 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