Systems Analysis for Designing Reproductive Management Programs to Increase Production and Profit in Dairy Herds

Systems Analysis for Designing Reproductive Management Programs to Increase Production and Profit in Dairy Herds P. A. O L T E N A C U , 1 T. R. R O U N S A V I L L E , l R. A. M I L L I G A N , 2 and R. H. F O O T E 1 Departments of Animal Science I and Agricultural Economics 2 Cornell University Ithaca, N Y 14853

ABSTRACT

Relative economic merits of three heat detection rates and three conception rates were evaluated by mathematical modeling and dynamic simulation. Three heat detection programs evaluated were: a) poor, with no specific time set aside for detection and detection rate of .35; b) average, with two 45-min observations each day and detection rate of .55; c) good, with three 45-min observations each day and detection rate of .75. Changing heat detection program from poor to average and average to good decreased days open from 136 to 119 and t o 105. Corresponding increases in net return per cow per year were $60 and $4. Three breeding programs evaluated were: a) poor, direct service by an inexperienced inseminator and conception rate of .42; b) average, professional artificial inseminator servicing the cows with a single insemination at each service and conception rate of .50; c ) g o o d , professional artificial inseminator using two inseminations during each service period and conception rate of .58. Changing breeding program from poor to average and average to good decreased days open from 123 to 119 and to 115. Corresponding changes in net return per cow per year were an increase of $39 and a decrease of $7. INTRODUCTION

Reproductive performance is determined by the interplay of a large number of management factors that can be controlled by the farmer

Received October 20, 1980. t Department of Animal Science. 2Department of Agricultural Economics. 1981 J Dairy Sci 64:2096-2104

and biological factors intrinsic to the cow in a given environment. Reproductive performance is an important factor in determining productivity and profitabili W of the dairy enterprise. The continuing trend of increased herd size, higher milk production per cow, and decreased hours of labor per cow per year have made the system more intensive and enhanced greatly the importance of management in operating the system. These trends also are associated with a decrease in herd reproductive performance, and many dairy farmers consider low fertility to be their most important herd management problem. The relationship between reproductive performance and profitability, as well as the role of various management factors in determining herd reproductive performance, is important in design of management programs. From the economic standpoint designing management programs to achieve optimal reproductive performance in a given environment has great practical implications. Two general approaches can be used to study these problems. One is a direct experimental approach in which the relationship between various management factors, reproductive performance, and profit is estimated from data of a sample of dairy herds. Data from a set of experimentally controlled herds are not available and would be expensive. In commercial herds there are many factors which could not be isolated and controlled, so it is doubtful if this approach would be successful. The approach used in our study is to develop a mathematical model which describes reproduction and milk production of a cow and then to use this to simulate dynamically a dairy herd. Rounsaville et al. (11) found that the two major factors affecting reproductive performance were heat detection and conception rate. Consequently, our analysis were restricted to evaluating the role of these two management

2096

2097

SYMPOSIUM: SYSTEMS ANALYSIS TABLE 1. The b, c, d, and g in the function y = A(DIM)b ecDIM(DP+I)d egDP to predict daily milk yield (kg) for three lactation ages. Lactation number

b

c

d

g

1 2 ~3

.11 .12 .16

-.002 -.004 -.0o5

.o2 .04 .o4

-.OOl -.oo2 -.002

were estimated using Holstein lactation records from New York Dairy Records Processing Laboratory; the d and g were chosen to generate differences in milk yield associated with different numbers of days open equivalent to those estimated by Ohenacu et al. (9). The b, c, d, and g are in Table 1. To simulate individual lactations A was generated for each cow from a truncated normal distribution with the following parameters: Lactation 1: A%N (# = 15.33; u = .90); 12.6 < A < 18.0

factors on reproductive profitability in dairy herds.

performance

and Lactation 2: A'X,N (/a = 20.59); a = 1.13); 17.2 < A < 24.0

M A T E R I A L S A N D METHODS

Lactation >3: A'X,N (/j = 21.23; o = 1.36); 17.2 < A < 25.3

The Simulation Model

The model developed by Oltenacu et al. (10) to describe the reproductive process in a herd of dairy cattle was the basis for this study. The reproductive process in a cow was viewed as a sequence of events beginning with parturition, followed by ovulation (which occurs with each estrus or heat), detection of heat, and subsequent insemination, conception, possible embryonic loss or culling, and again parturition. The most important management, environmental, and biological factors affecting this sequence of events and th.e time between events were included in the model (10). Feed consumption and milk yield have been added to the model to enable economic analysis of the reproductive process. Factors influencing individual milk production records were the cow's individual producing ability, age, and season of freshening. The model also accounted for the change in milk yield associated with the cow's reproductive status. The relationship between cow's milk yield and her days open estimated by Oltenacu et al. (9) was used for this purpose. Daily milk yield of a cow as a function of her days open was predicted by:

Total days in milk (TDIM) as a function of days open was determined for each lactation age by regression equations estimated by Oltenacu et al. (9): Lactation 1: TDIM = 242 + .81 (DO) Lactation 2: TD1M = 238 + .77 (DO) Lactation ~3: TDIM = 237 + .76 (DO) For average A, and b, c, d, and g in Table 1, lactation curves were generated for first, second, and third or greater lactations of

ca

2s,

i a

io,

y y DIM DP DO e

= = = = = =

A(DIM)b ecDIM(DP+l)deg DP where daily milk yield (kg) day in lactation (milk) day in gestation = DIM - DO days open the base of natural logarithm.

The b and c, which shape the lactation curve,

o

WEEKS IN LACTATION

Figure 1. Lactation curves generated by the function y = A(DIM)b ecDIM(DP+l)degDP for first, second, and third or greater lactation cows open 40, 100, and 160 days. Journal of Dairy Science Vol. 64, No. 10, 1981

2098

OLTENACU ET AL.

TABLE 2. Cumulative milk yield (kg) at various times in lactation and total days in milk for first, second, and third or greater lactation a cows open 40, 100, and 160 days. Lactation number 1 2 ~3

Days open

210 days

40 100 160 40 100 160 40 100 160

3902 4022 4061 4751 4886 4908 5351 5479 5492

Cumulative milk yield (kg) at 301 days Complete 4726 5260 5401 5467 6082 6235 6109 6737 6882

4726 5498 6195 5467 6221 6794 6109 6876

7440

Total days in milk 274 323 372 269 315 361 267

315 559

aThe milk yields are generated by the function y = A(DIM)b ecDIM(DP+I)d egDP with average A and b, c, d, and g for respective lactation age.

cows open 40, 100, and 160 days and are illustrated in Figure 1. Cumulative milk yield at various times in the lactation and total days in milk for these curves are in Table 2. Differences in milk yield from differences in days open generated with this function also agree with differences e~timated by Auran (1), Bar-Anan and Soller (2), and Olds et al. (8). The relationship between milk yield and fertility was estimated by Rounsaville and Oltenacu (12). The linear regression of conception rate at first service as a function of average daily milk yield for the first 60 days of lactation for first lactation and second or

,r

c ~ eso~. m a ~ M ~:T*laON ~*

8

AVERAGE DAILY MILK YIELD (Kg)

Figure 2. Regression of conception rate at first service (%) on average daily milk yield (kg) for first 60 days of lactation for first and second or greater lactation cows. Journal of Dairy Science Vol. 64, No. 10, 1981

greater lactations are in Figure 2. With this relationship, the difference in conception rate between cows one standard deviation above and below the average daily milk yield for the first 60 days is about 10 percentage units. This difference compares well with 7 percentage units found by Olds et al. (7) for 120 day milk yield as the dependent variable but is much lower than 22.5 percentage units found by Spalding et al. (13) for 305-day milk. Feed costs are based on feeding a total mixed ration meeting requirements determined by (6) and balanced with a least cost dairy ration program. Four diets were formulated: three diets for milking cows with high, medium, and low milk production and one diet for dry cows. Cows producing up to 19.00 kg milk per day were included in the low production group, and the diet fed was balanced for 16.8 kg milk per day. Cows producing from 19.01 to 25.00 kg milk per day were included in the medium production group, and the diet fed was balanced for 23.60 kg milk per day. All cows for the first 34 days of lactation and cows producing more than 25.01 kg milk per day were included in the high production group, and the diet fed was balanced for 31.80 kg milk per day. Assignment of cows to production groups and evaluation of amount of feed consumed are based on adding 4 kg to the daily milk of first lactation and 2 kg to the second lactation cows to account for growth. Feed prices, storage and feeding losses, and cost per kg of dry matter consumed for six feed ingredients in the diet

SYMPOSIUM: SYSTEMS ANALYSIS

2099

TABLE 3. Characteristics of feed ingredients in diets.

Feed ingredient

% Dry matter

Hay crop silage Corn silage Corn grain Soybean meal Di-Cal Salt

40 30 85 90

Price

Storage and feeding losses

Cost per kilogram dry matter consumed

(3) 25.50/t 20.O0/t .11/kg 6.22/kg .37/kg .12/kg

(%) 19.0 15.0 14.0 5.0 5.0 5.0

(3) .0772 .0784 .1505 .3626 .3895 .1263

are in Table 3. Prices used in the analysis of profitability are in Table 4. Expenses associated with reproductive management programs, such as breeding costs and heat detection costs, also are considered. They are discussed when the effect of changing management programs is evaluated. RESULTS AND DISCUSSION

Variables that are under complete or partia/ control of the dairy farmer and, therefore, can be used to manipulate performance of the system are defined as control or management variables. Five variables available to the dairy herd manager are specified in the model: time of first breeding after calving, reproductive culling, service sire selection, heat detection, and breeding. The first two are implemented directly by the herd manager as policies while the last three are management strategies. The

TABLE 4. Prices used in the analysis of profitability. Price/cost Income Milk sold Reproductive culls Bull calves sold Value of heifer calves Expense Cow replacement Milk marketing Livestock marketing Reproductive culls Bull calves sold

326.00/100 kg milk $625.00/head $75.00/calf $75.00/calf $950.00/heifer $.88/100 kg milk $40/head $10.00/calf

first three management variables were fixed while the last two were allowed to vary. First breeding policy (1BDG) defined as minimum accepted number of days from parturition to first breeding was set to 60 days. Culling policy for reproductively related problems (RCLG) is defined in terms of the criteria used by the manager to decide which cow leaves the herd because of unsatisfactory reproductive performance. In this study RCLG was set to cull cows open after four services or 280 days from parturition. Service sire selection (SSPM) affects reproductive performance in a herd when a measure of fertility and ease of calving are criteria in distinguishing among sires available to the breeding program. In this study SSPM selected sires with .65 fertility. Two control variables, heat detection (HDPM) and breeding (BDPM), were allowed to change. Heat detection includes management actions and activities to identify cows in heat. The program can be based on visual observation only or it could combine visual observation with various aids for heat detection such as heat m o u n t detectors, marking devices, and others. The measure of the program's performance is heat detection rate (HTDT) defined as proportion of observable ovulations detected. Three HDPM were considered, a) Poor HDPM in which no specific times are set aside to detect heat. Cows are observed for heat during daily chores resulting in HTDT = .35. This program cost nothing, b) Average HDPM consisted of checking signs of heat during two 45-min periods (morning and evening) each Journal of Dairy Science Vol. 64, No. 10, 1981

2100

OLTENACU ET AL.

TABLE 5. Effect of heat detection rate (HTDT) on various measures of reproductive performance of a dairy herd (CNRTa = .50; 1BDGb = 60 days). Reproductive measures

HTDT =. 35

Days to first breeding Days open No. service periods per cow Reproductive culls (%)

HTDT =. 55

HTDT =. 75

.X

SD

X

SD

X

SD

109 136

44 57

90 119

26 48

81 105

17 36

1.7 21.3

1.0

1.9 13.5

1.1

2.0 11.5

1.1

aconception rate. bMinimum days to first breeding.

day when the cows were in a holding area before milking. Such a program, according to (14, 3, 4, 5) generates a HTDT = .55. As this activity is during milking chores, only 30 rain of additional labor were charged to HDPM for each observational period, at a cost of $4/h. c) Good HDPM consisted of three 45-min observational periods each day at 8-h intervals when cows were not involved in the milking process. This program (3) generates a HTDT = .75 and its cost was $4/h of labor. Breeding (BDPM) includes all management actions and activities at time of insemination. Performance is measured by conception rate (CNRT) defined as the probability of an

insemination resulting in conception. Important factors in the program are skill of inseminator and timing of insemination with respect to optimal time for m ax i m u m conception rate. Three BDPM were considered, a) Poor BDPM was a direct service program by an inexperienced inseminator. This program generates an average C N R T = .42. The cost per service period was $6. b) Average BDPM used a professional artificial inseminator (AI) servicing the cows with a single insemination at each service period. This program generates an average CNRT = .50, and the cost per service period for this program was $10. c) Good BDPM uses a professional AI technician servicing the cows and two inseminations (morning and evening) during each service period. The program generates an average CNRT = .58. The cost per service period for this program was $18.

Role of Heat Detection Program (HDPM) .12= m

..

!il

,10, '~"

.081

< ~

06q

T

.041

35

.02=

.0(]

20

II 40

. 60

.

. 80

. 100

. . . 120 140

. 160

180

200

DAYS OPEN

Figure 3. Distribution of cows by days open in a herd with poor (heat detection rate = .35) and good heat detection program (heat detection rate = .75). Journal of Dairy Science Vol. 64, No. 10, 1981

With 1BDG, RCLG, and SSPM set as described and with an average BDPM (i.e., CNRT = .50), a herd of 120milking cows was simulated for each of the three HDPM. Selected measures of herd reproductive performance for three detection programs are in Table 5. Results in Table 5 indicate that improving HTDT reduces average number of days to first breeding and also average number of days open. The standard deviation with these averages is reduced by a relatively greater amount. Consequendy, the coefficient of variability of days open is decreasing as HTDT improves, 42%,

SYMPOSIUM: SYSTEMS ANALYSIS

2101

TABLE 6. Effect of heat detection rate (HTDT) on various measures of production in a herd of 120 milking cows (CNRT a = .50; 1BDG b = 60 days). Production measures Milk sold per herd per year (kg) Milk sold per cow per year (kg) Calves produced per herd per year (no.) Reprod. culls per herd per year (no.)

HTDT = .35

HTDT = .55

HTDT = .75

750,117

776,384

785,791

6,251

6,470

6,548

106.6

110.2

109.6

25.6

16.2

13.8

40%, and 34% for .35, .55, and .75 HTDT. Figure 3 shows the distribution o f cows by days open for e x t r e m e HTDT. When H T D T = .35 there are substantially m o r e cows with a long open period. A d d i t i o n a l c o n s e q u e n c e s of improving H T D T are the increase in n u m b e r of service periods per c o w per y r and a decrease in percentage o f cows culled for reproductive reasons (Table 5). The change in herd reproductive p e r f o r m a n c e brought a b o u t by changes in HDPM also affects p r o d u c t i v e p e r f o r m a n c e of the herd. Table 6 summarizes m a j o r changes in herd p r o d u c t i o n associated with changes in H T D T . I m p r o v e d H T D T results in m o r e milk sold per cow per

year and fewer culls per year for r e p r o d u c t i v e reasons. Changing f r o m p o o r to average H T D T also increases n u m b e r o f calves p r o d u c e d per year, b u t this change is negligible when H T D T subsequently is i m p r o v e d to good. This lack of response is p r o b a b l y the result of variability o f this measure. T o evaluate the e f f e c t o f i m p r o v i n g H T D T on profitability o f the dairy herd, m a j o r i n c o m e and expense items a f f e c t e d by change in H D P M were calculated for each H T D T and are in Table 7. The resulting measure of i n c o m e is i n c o m e minus affected expenses. This should be considered in relative terms only, as neither fixed costs nor variable costs n o t affected by re-

TABLE 7. Effect of heat detection rate (HTDT) on affected income and expense items in a dairy herd of 120 milking cows (CNRT a = .50; 1BDGb = 60 days).

Affected incomes ($) Milk sold Reproductive culls Value of calves Total Affected expenses ($) Feed Replacements Marketing Breedings Heat detection Total Income - expenses Return per cow per year ($)

HTDT = .35

HTDT = .55

HTDT = .75

195,138 16,000 7,995 219,133

201,971 10,125 8,265 220,361

204,418 8,625 8,220 221,263

77,026 24,320 8,172 2,076 .... 111,594 107,539 896

78,350 15,390 8,045 2,356 1,460 105,601 114,760 956

79,354 13,110 8,029 2,594 3,285 106,372 114,891 957

aConception rate. bMinimum days to first breeding.

of Journal

Dairy Science Vol. 64, No. 10, 1981

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OLTENACU ET AL.

TABLE 8. Effect of conception rate (CNRT) on various measures of reproductive performance of a dairy herd (HTDT a = .55 ; 1BDG b = 60 days). Reproductive measures

CNRT = .42

Days to first breeding Days open Number service periods per cow Reproductive culls (%)

90 123 2.1 16.7

CNRT = .50

CNRT = .58

SD

.X

SD

X

SD

25 48 1.2

90 119 1.9 13.5

26 48 1.1

90 115 1.7 10.8

26 47 1.0

aHeat detection rate. bMinirnum days to first breeding.

p r o d u c t i v e p e r f o r m a n c e are included in the analysis. F r o m - T a b l e 7, change in m a n a g e m e n t to increase H T D T f r o m .35 to .55 increases the return per c o w per year by $60. Because cost of this change in r e p r o d u c t i v e m a n a g e m e n t program (i.e., the additional breeding cost and cost o f h e a t detection) is $ 1 5 / c o w per year, the net return per dollar invested for this change is equal to $ 4 / c o w per year. F u r t h e r i m p r o v i n g m a n a g e m e n t to increase H T D T f r o m .55 to .75 increases the return per c o w per year by o n l y $1. The cost of this increase in H T D T is $ 1 7 / c o w per year; therefore, n e t return per dollar invested in this change is $ . 0 6 / c o w per year. Considering only the three h e a t d e t e c t i o n programs f r o m Table 7, changing f r o m p o o r to average HDPM is profitable ($60 additional return per c o w per year) whereas changing f r o m an average to good HDPM just pays for itself. Role of Breeding Program (BDPM)

With

1BDG, RCLG, and SSPM set as de-

scribed and with an average HDPM (i.e., H T D T = . 55), a herd o f 120 milking cows was simulated for each o f three BDPM. Selected measures o f herd r e p r o d u c t i v e p e r f o r m a n c e for three breeding programs are in Table 8. F r o m Table 8, increasing C N R T does n o t change the n u m b e r o f days to first breeding, an e x p e c t e d result as both h e a t d e t e c t i o n and first breeding policy were constant. But increasing C N R T reduces by a small a m o u n t the average n u m b e r o f days open. The standard deviations with these averages change little. Increasing C N R T also reduces n u m b e r o f service periods per c o w per y e a r and substantially decreases the percentage o f cows culled for r e p r o d u c t i v e reasons. The change in herd r e p r o d u c t i v e p e r f o r m a n c e brought a b o u t by changes in BDPM also affects productive p e r f o r m a n c e of the herd. Table 9 summarizes m a j o r changes in herd p r o d u c t i o n associated with changes in C N R T . F r o m Table 9 increasing C N R T f r o m .42 to .50 results in

TABLE 9. Effect of concel~tion rate (CNRT) on various measures of production in a dairy herd of 120 milking cows (HTDT a = .55; 1BDG o = 60 days). Production measures

CNRT = .42

CNRT = .50

CNRT = .58

Milk sold per herd per year (kg) Milk sold per cow per year (kg) Calves produced per herd per year (no.) Reproductive culls per herd per year (no.)

756,149 6,301

776,384 6,470

772,245 6,435

aHeat detection rate. bMinimum days to first breeding. Journal of Dairy Science Vol. 64, No. 10, 1981

106.4

110.2

110.6

20.0

16.2

13.0

SYMPOSIUM: SYSTEMS ANALYSIS more milk sold, more calves produced, and fewer cows culled for reproductive reasons per herd per year. Increasing CNRT from .50 to .58 has little effect on amount of milk sold and number o f calves produced per herd per year but does decrease number of cows culled for reproductive reasons. Table 10 gives major income and expense items affected by change in CNRT. From Table 10 a change in BDPM which increases CNRT from .42 to .50 also increases return per cow per year by $39. The cost for this change in reproductive management is $7/cow per year, yielding a net return per dollar invested of $5.57. Changing the BDPM to increase CNRT from .50 to .58 decreases the return per c o w per year by $7. The increase in CNRT to .58 was achieved by two inseminations per service (or heat) period at a cost of $18 per service period. From breeding expenses in Table 10, the increase in CNRT to .58 costs an additional $13/cow per year, and this cost is higher than savings resulting from replacing fewer cows. If this increase in CNRT was achieved by a skillful technician using a single insemination per service period at a cost of $10 per insemination, breeding expenses would decrease by approximately $1.65/cow per year, as fewer services per cow per year would be required, and

2103

the result would be an increase in net return per cow per year of approximately $7. For comparison with an extreme, a herd with a poor HDPM (HTDT = .35) and poor BDPM (CNRT = .42) also was simulated over 15 yr. Table 11 summarizes the six reproductive management programs in terms of herd reproductive performance and profit. From Table 11 the return per cow per year, excluding costs for reproductive management, increases as reproductive performance of a herd measured by average days open improves. When costs associated with reproductive management programs also are considered, the trend in return per cow per year does not increase continuously. The best strategy for improving reproductive performance of a herd with an average of 145 days open resulting from a poor HDPM and BDPM can be structured from the results from Table 11. The first step is to improve HTDT from .35 to .55 at a cost of $14/cow per year. This would yield an increase in net return per cow per year of $19. The second step is to improve CNRT from .42 to .50 at an additional cost of $7/cow per year, resulting in an increase of $39 in n et return per cow per year. The third step is again to improve HTDT from .55 to .75 at additional cost of $17/cow per year, resulting in an increase of $1 in net return per cow per year. This study provides quantitative estimates of

TABLE 10. Effect of conception rate (CNRT) on affected income and expense items in a dairy herd of 120 milking cows (HTDTa = .55; 1BDGb = 60 days.

Affected incomes ($) Milk sold Reproductive culls Value of calves Total Affected expenses ($) Feed Replacements Marketing Breeding Heat detection Total Income - expenses ($) Return per cow per year ($)

CNRT = .42

CNRT = .50

CNRT = .58

197,707 12,500 7,980 217,187

201,971 10,125 8,265 220,361

200,894 8,125 8,295 217,314

77,159 19,000 8,000 1,558 1,460 107,177 110,010 917

78,350 15,390 8,045 2,356 1,460 105,601 114,760 956

77,858 12,350 7,350 3,884 1,460 103,414 113,900 949

aHeat detection rate. bMinimum days to first breeding. Journal of Dairy Science Vol. 64, No. 10, 1981

2104

BROWN ET AL.

return to be gained by investments in different aspects of reproductive management. Other strategies which improve herd reproductive performance without major increases in cost also would increase net return.

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REFERENCES

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Journal of Dairy Science Vol. 64, No. 10, 1981

1 Auran, T. 1974. Studies on monthly and cumulative monthly milk yield records. II. The effect of calving interval and stage of pregnancy. Acta. Agric. Scand. 24:339. 2 Bar-Arian, R., and M. Soller. 1979. The effect of days-open on milk yield and on breeding policy post partum. Anim. Prod. 29:109. 3 Esslemont, R. J. 1974. Economic and husbandry aspects of the manifestation and detection of estrus in cows. III. The detection of estrus. Agric. Develop. Advisory Serv. Q. Rev. 15:83. 4'Foote, R. H. 1975. Estrus detection and esrrus detection aids. J. Dairy Sci. 58:248. 5 King, G. J., J. F. Hurnik, H. A. Robertson. 1976. Ovari/m function and estrus in dairy cows during early lactation. J. Anim. Sci. 42:688. National Research Council. 1971. Nutrient requirements of dairy cattle. 4th rev. ed. Nat. Acad. Sci., Washington, DC. 7 0 l d s , D., T. Cooper, and F. A. Thrift. 1979. Relationship between milk yield and fertility in dairy cattle. J. Dairy Sci. 62:1140. Olds, D., T. Cooper, and F. A• Thrift. 1979. Effect of days open on economic aspects of current lactation. J. Dairy Sci. 62:1167. Oltenacu, P. A., T. R. Rounsaville, R. A. Milligan, and R. L. Hintz. 1980. Relationship between days open and cumulative milk yield at various intervals from parturition for high and low producing cows. J. Dairy Sci. 63:1317. 10 Oltenacu, P. A., R. A. Milligan, T. R. Rounsaville, and R. H. Foote. 1980. Modelling reproduction in a herd of dairy cattle. Agric. Syst. 5:193. 11 Rounsaville, T. R., P. A. Oltenacu, R. A. Milligan, and R. H. Foote. 1979. Effects of heat detection, conception rate, and culling policy on reproductive performance in dairy herds. J. Dairy Sci. 62:1435. 12 Rounsaville, T• R., and P. A. Oltenacu. 1980. Production level effects on fertility in dairy cows. (Personal communication). 13 Spalding, R. W., R. W. Everett, and R. H. Foote. 1974. Fertility in New York artificially inseminated Holstein herds in dairy herd improvement• J. Dairy Sci• 58:718. 14 Williamson, N. B., R. S. Morris, C. D. Blood, and C. M. Cannon. 1972. A study of estrus behavior and estrus detection methods in a large commercial dairy herd. I. The relative efficiency of methods of estrus detection. Vet. Rec. 91 : 50.