THESl S ‘ "VF-4‘55"}; This is to certify that the thesis entitled ANALYSIS OF THE INTERRELATIONSHIP OF NUTRITIONAL AND REPRODUCTIVE FACTORS IN DAIRY CATTLE presented by Luis Gonzales-Martinez has been accepted towards fulfillment of the requirements for M.S. degree in_Dain3L_S_cience Elm/m Major professor Date 2-26-82 0-7639 MSU LIBRARIES “ RETURNING MATERIALS: Place in book drop to remove this checkout from your record. FINES will be charged if book is returned after the date stamped below. ANALYSIS OF THE INTERRELATIONSHIP OF NUTRITIONAL AND REPRODUCTIVE FACTORS IN DAIRY CATTLE By Luis Gonzales-Martinez A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Dairy Science 1982 ABSTACT ANALYSIS OF THE INTERRELATIONSHIP OF NUTRITIONAL AND REPRODUCTIVE FACTORS IN DAIRY CATTLE by Luis Gonzalez-Martinez The purpose of this study was to determine inter- relationships between nutrient content of feeds, manage- ment and feeding practices that could be affecting re- productive performance of dairy cows and to establish basic guidelines for feeding and management practices in two areas of the State of Chihuahua, Mexico. Data were collected, from eight Mexican farms and nineteen American farms (used as a comparison), using a questionnaire and analyzed by multiple linear regression (least squares method), analysis of variance and Factor Analysis. Interactions between Protein, Net Energy, Crude Fiber, Calcium and Phosphorus contributed the most to the variation observed in reproductive parameters. The biological interpretation of these interaction proved to be difficult. Mexican dairy ration was consistently high in Calcium, Protein and Crude Fiber and deficient in Net Energy. From Factor Analysis, only the alfalfa hay-based ration in Mexico contributed significantly to explain the variation found in reproductive performance. Some practical recommendations for improvement of the Mexican dairy ration are given. ACKNOWLEDGMENTS The author wants to express his deepest gratitude to his wife Osvelia, to whom this work is dedicated, for her loving faith, understanding and many sacrifices throughout his graduate program, I would like to thank my academic adviser, Dr. Russel W. Erickson, for his support, encouragement and friendship during these two years. I also want to acknowledge the other members of my advisory committee, Dr. Roy Fogwell, Dr. Roy Emery and Dr. Dave Morrow for their guidance and advice. I wish to thank my beloved parents, Delfino and Maria Luisa Gonzalez, my brother Fernando and my sister Rosalba for their love and confidence in me. Thanks to my parents in-law, Antonio and Eva Galvan for their moral support during my endeavor. I appreciate the help of Dr. Clyde Anderson for valuable discussions and computer programming, Dr. John Gill for statistical guidance. The help of Cindy Coombs for typing this manuscript is greatly appreciated. A very special note of gratitude to Dr. Manuel Villarreal for his friendship and valuable contributions to the com: pletion of this study. ii. Thanks to my fellow graduate students, who were very supportive and giving of their time during my stay in the Dairy Science Department. Last, but not least, I wish to extend my sincere thanks to the Government of Mexico for the economic 'support provided through CONACYT during my graduate studies. iii TABLE OF CONTENTS List of Tables Introduction . Literature Review Effect of Protein on Reproduction . Calcium and Reproductive Function . Phosphorus and Reproductive Function Effect of Roughage level in the Ration and Reproductive Performance Energy and Reproductive Performance Materials and Methods Sampling Procedure Design of the Questionnaire . Data Methods of Analysis Results and Discussion . Conclusions Appendix . List of References iv (DD-9H ll . 13 16 . 27 . 28 . 29 . 30 . 32 38 . 83 . 85 .143 Table LIST OF TABLES Mean and Standard Errors for First Service Conception, Average Days from Calving to First Breeding, Days Open and Services per Conception Ration Nutrient Concentration Analysis of Variance for Protein, Net Energy, Calcium“ Phosphorus and Fiber Model for Days Open with the Variables Left After the Stepwise Backward Elimination Procedure Model for Days Open with the Variables Left After the Stepwise Backward Elimination Procedure Including COUNTRY as a Variable. Model for Average Days From Calving to First Service with the Variables Left After the Stepwise Backward Elimination Procedure Model Average Days from Caling to First Service with the Variables Left After the Stepwise Back- ward Elimination Procedure Including COUNTRY as a variable. Model for Services per Conception with the Variables Left After the Step- wise Backward Elimination Procedure. Model for Services per Conception with the Variables Left After the Stepwise Backward Elimination Procedure Including COUNTRY as a Variable. 4O 41 45 46 48 49 51 52 Table 10 11 12 l3 14 15’ 16 17 18 19 20 vi Model for First Service Conception with the Variables Left AFter the Stepwise Backward Elimination Procedure. Model for First Service Conception with the Variables Left After the Stepwise Backward Elimination Procedure Including COUNTRY as a Variable. Factor Matrix Using Principal Factor (Correlation Coefficients) Eigenvalues Regression Model for Days Open with the Variables Derived from Factor Analysis. Regression Model for Average Days From Calving to First Service with the Variables Derived From Factor Analysis. Regression Model for Services per Conception with the Variables Derived from Factor Analysis. Regression Model for First Service per Conception with the Variables Derived from Factor Analysis. Correlation Coefficients Between Average Protein, Average Net Energy, Average Calcium, Average Phosphorus and Average Fiber. Effect of the Interaction of Net Energy and Fiber on Days Open Interval. Effect of the Interaction of Calcium and Phosphorus on Days Open Interval. 55 61 63 64 65 66 67 72 75 75 Table 21 22 23 24 25 26 27 28 29 30 31 32 33 vii Effect of the Interaction of Protein and Net Energy on Days Open Interval. Effect of the Interaction of Net Energy and Calcium on Days Open Interval. Effect of the Interaction of Protein and Fiber on Days Open Interval. Effect of the Interaction of Fiber and Calcium on Days Open Interval. Effect of the Interaction of Calcium and Phosphorus on Average Days from Calving to First Service. Effect of the Interaction of Fiber and Ca1cium.on Average Days from Calving to First Service. Effect of the Interaction of Net Energy and Phosphorus on Average Days from Calving to First Service. Effect of the Interaction of Net Energy and Phosphorus on Average Days from Calving to First Service. Effect of the Interaction of Protein and Calcium on Average Days from Calving to First Service. Effect of the Interaction of Protein and Fiber on Average Days from Calving to First Service. Effect of the Interaction of Net Energy and Phosphorus on Services Per Conception. Effect of the Interaction of Calcium and Phosphorus on First Service Conception. Effect of the Interaction of Fiber and Phosphorus on First Service Conception. 77 77 78 78 79 79 80 8O 81 81 viii IEELE. Page 34 Effect of the Interaction of Fiber 82 and Phosphorus on First Service Conception. 35 Alphabetical List of Variables 85 36 Card Format 97 37 Means and Stadard Errors of Reproductive Variables. 110 38 Mean and Standard Errors of Reproductive Variables. 112 39 Criteria Used for Selection of Sires 117 40 Most Common Disorders Cited in High Producing Cows 118 41 Rank for Culling Cows 119 42 Comparison of A.I. and Bull In Terms of Conception Rate 120 43 Percentage of Respondents That Use Grain Feeding Guide 121 44 Uterine Medication After Parturition 122 45 Criteria For Cutting Alfalfa Hay 123 46 Availability of Trace Mineral Salt 124 47 Management of Cows in Estrus 125 48 Schedule for Observing Cows for Estrus 126 49 Season to Which Better Conception Rate is Obtained (% of Respondents) 127 50 Use of Clean-Up Bull on the Farm 128 51 Artificial Insemination Variables 129 52 Heat Detection Variables 131 53 Reproductive Management of Cows After Calving 133 54 Use of Heat Detection Aids 135 INTRODUCTION The effect of nutrition upon reproductive perfor- mance of cattle has been a topic of concern for many years. With the trends for higher milk-yielding cows, the accuracy of providing an adequate plane of nutrition and the implementation of improved reproductive manage- ment schemes becomes more critical. Mexico has not been able to meet the internal de- mand of milk, and with a growing population (3.27. per year), the problem will tend to increase in the near future. Studies of the interrelationship between nutrition and reproduction on Holstein-Friesian cattle in Mexico are very scarce. Pathological alterations and diseases always have been linked to impaired performance in dairy cattle and, in most cases, when nutrient deficien- cies have been suggested, the assumption has been based on a very minimum.amount of research data. The Northern states of Mexico are one of the most significant areas for milk production, as well as the area with the most increase in production over the last 20 years. The climatic pattern of this region (low and non-well distributed annual rainfall) has restricted the feeding of dairy cows to hays and grains basically due to limited availability and cost of protein supple- ments. As a result of scarce feed stuffs, Mexican dairy- men should pay increased attention to the nutritional aspect of dairy cows and its impact on reproduction performance and milk production. For these reasons, veterinarians, animal scientiest, dairymen and feed suppliers in Mexico need to increase the efficiency of dairy husbandry factors and take into account differences in management and climate from populations previously studied. Since dairy technology in Mexico is in the stage of development, most technology is imported from the USA. In this study American herds were used as models for comparisons with the goal to study the differences in management and further to incorporate those practices that can be beneficial to the Mexican dairy industry. The specific objectives of this study include the following: - To establish interrelationships among the nutrient content of feeds and reproductive performance of dairy cattle in various areas of the state of Chihuahua, Mexico. To detect possible interactions between management and feeding practices that could be affecting re- production of dairy cows in the two areas of study. To establish basic guidelines for feeding and manage- ment practices in the two selected areas and make projections that may be useful for other Mexican areas . LITERATURE REVIEW ” EFFECT OF PROTEIN ON REPRODUCTION Amino acids absorbed from the small intestine of ruminant animals are supplied from microbial protein synthesized in the rumen, undergraded or protected food protein, amino acids which bypass the rumen, and endo- genous secretions (Chalupa, 1975). The massive intervention of microorganisms at the start of the digestive process in ruminants has a pro- found influence on the amino acids supplied to the small intestine of ruminants (Satter and Roffler, 1975). There- fore, the extent of dietary protein breakdown and the synthesis of mircobial protein result in marked alter— ations in the quantity and pattern of amino acids absorbed from the gut of ruminants, compared to the amino acid composition of the diet (Clark, 1975). As a result of the complexity of ruminant amino acid nutrition, dairy cows can at times suffer an amino acid imbalance which affects body maintenance, milk synthesis and reproductive performance. Chandler 3E 31. (1976) conducted an experiment adding methionine hydroxy analog to diets of 12.5% and 15% crude protein. It reduced services per conception from 2.54 to 1.90 and decreased days open from 149 to 116. The authors suggested that this indirectly make available to the animal increased quantities of energy in the form of acetate and propionate. It also increased quantities of microbial protein for postruminal utili- zation by stimulating rumen fermentation. A protein—deficient ration in heifers prolongs the onset of puberty (Palmer 33 31., 1941, Wiltbank 33 31., 1965), has detrimental effects on estrous behavior (Guilbert, 1942, Bedrak 33 31., 1964), increases the length of the estrous cycles and reduced fertility rate (Durrell, 1951, Hill 33 31., 1970). Protein deficient diets (.32 kg. per day) prior and after parturition are associated with clinically severe uterine infections in primiparous beef heifers (Ruder 33,31,, 1981). The authors suggested that a reduced crude protein intake had a negative effect on utérine antibody production. Israeli researchers (Davidson 33 31., 1978, Mayer 33 31., 1978 and Francos 3E 31., 1978) found significant differences between low and high fertility herds with respect to the amount of digestible protein in the daily ration. In low fertility herds, protein requirements were will below recommended levels. Julien 33 31. (1976) obtained evidence that deffi- ciencies of protein along with Selenium could be involved in the etiology of retained placenta in dairy cows. Excess of crude protein has been suggested as a cause of increased anestrus, lowered peak milk production (Gould, 1969), lengthened interval between parturition and first service (Sonderegger and Schurch, 1977), as well as decreased conception rate and more services per conception (Maree, 1981). Gibson (1969) noted that although excess protein intake may be related to these problems, such excess is only relative to low energy. Energy for microbial growth is derived frmm the fermentation of dietary carbohydrate since the nitrogenous constituents provide the nitrogen require- ments of the micro organisms, nitrogen-carbohydrate interrelationships occurring within the rumen are of considerable importance to overall rumen metabolism (McMeniman 3E 31., 1976). Hewitt (1971), using data from Swedish farms, reported that the fertility of herds, were not higher when the highest levels of protein were fed. However, the higher the level of protein of energy for a cow milking 25 liters or more, the better was the herd's fertility. Huber and Kung, Jr. (1981) summarized the effects of protein on reproduction of dairy cattle and they pointed out that excess protein might impair reproductive performance in cows. Physiologically, one would suspect that overfeeding of protein to ruminants was more likely to cause meta- bolic stresses. A possible toxic effect is the libera- tion of large quantities of ammonia from an easily soluble protein (Huntgate, 1966). This condition may cause cellular damage throughout the body, resulting in a suboptimal, uterine or ovarian environment and thereby reducing reproductive efficiency (Jordan and Swanson, 1979). Feeding excess protein appears to be wasteful in that it is expensive and also reproductive parameters tend to increase as protein concentration increases without significant milk yield increment (Jordan and Swanson, 1979, Edwards 33 31., 1980). Bond and Wiltbank (1970), working with 54 beef heifers, pointed out that there was no effect on estrus cycle and conception rate when the animals were fed different levels of protein ranging frmm 4.1 to 28.1%. .Wohlt and Clark (1978) also found no significant differ- ences in reproductive performance in cows fed rations containing 9.2, 13.5 or 18.1% crude protein. Treacher 33 31., (1976) concluded that feeding 75% of protein requirements to dairy cows during the first 14 weeks of lactation does not have an adverse effect on fertility. Reproductive performance is not impaired when urea is added to rations of lactating dairy cows (Holter 33 31., 1968, Ryder 33 31., 1972, Erb 33 31., 1976a and 1976b and Treacher 33 31., 1979). The maximum dietary protein at which NPN additions benefit dairy cattle is probably not over 15% of the ration dry matter even at high energy concentrations (Huber and Kung, Jr., 1981). Low protein consumption results in ovarian atrophy in adults and to a failure of maturation of the repro- duction organs in young animals (Leathem, 1966). Pit- uitary LH and the response of the uterus to estrogen and progesterone are reduced in protein-deficient animals (Herbert, 1977). Rowlands 33 31. (1977) found an inverse relationship between albumin levels in blood and a direct relationship between globulin levels in blood when compared to number of services per conception. Calcium and Reproductive Function Considerable evidence has been accumulated indicating that mineral deficiencies may have an effect on reproduction. However, the interrelationships in the absorption and utilization of minerals makes it difficult to identify relationships between a specific mineral and reproduction (Jacobson 33 31., 1972). It has been suggested that the excess of Calcium can reduce fertility (Hignett, 1950, Hignett and Hignett, 1951, King, 1971). However, Ward 33 31., (1971) fed high levels of Ca (200 g. daily) and found that first ovulation occurred earlier in this group compared to low levels of Ca (100 g. daily) but there was no significant difference between treatments in services per conception. Ward and Call (1979) suggested that adequate calcium intake promotes rapid uterine involution and early ovulation in dairy cows. Breeding efficiency was not reduced when different levels of Ca (.12, .18, .32 and .64% of ration D.M.) were fed to dairy cows (Fitch 33 31., 1932, Palmer 33 31., 1935). These cows ranged in milk production frmm 2925 to 3351 kgs. per lactation. The majority of the research relating Ca levels to reproduction functions has centered on the effect of the Calcium-Phosphorus Ratio. Hignett (1959) showed that with a low Manganese consumption (40 mg. per 100 lb. body weight), fertility is high when Ca and P are in the correct proportions. However, when Ca is in excessive 10 relative to P or vice versa and Mn low, fertility is depressed. Littlejohn and Lewis (1960) obtained results that showed quite clearly that fertility was not affected by the Ca and P content of the experimental diet, regardless of the general level of fertility in the herd (Steevens 33 31., 1971) Carson, Caudle and Riddle (1978) examined a herd with a high incidence of dystocia, retained placenta and metritis. The milking herd's ration was .6% Ca and .5% P, at the same time mean serum Calcium was 8.98 mg% and mean serum.phosphorus 8.25 mg%. After supplementation with steamed bone meal, reproductive disorders decreased and serum concentrations were 10.26 mg% and 6.72 mg% for Ca and P respectively. They suggested that a narrow serum Ca:P ratio is one of many causes of reproductive problems which must be considered . when dealing with problem herds. The suggested calcium levels for cows in milk should contain 0.7% Ca on a dry matter basis in the ration (NRC, 1978). A high ratio of Ca to P in the diet is not critical for Ruminants (Smith 33 31., 1966), except for pre-partum rations (Jorgensen, 1974). 11 Phosphorus and Reproductive Function Phosphorus is the mineral most commonly associated with reproductive disorders in dairy cows. It has been noted that a low phosphorus ration was accompanied by a temporary disturbance or an entire cessation of the estrous cycle (Jordan 33 31., 1906, Eckles 33 31., 1935, Palmer 33 31., 1941, Alderman, 1963). Morrow (1969) reported a case in which infertility in 26 dairy heifers were attributed to phosphorus de- ficiency. Presence of this deficiency was verified when low blood P levels were found (3.9 mg./100 ml.), other blood metabolites were normal. The conception problem in this herd decreased from 3.7 services per conception before P supplementation to 1.3 services after P supple- mentation, and blood phosphorus levels returned to normal range (6.6 mg./100 ml.). Steevens 33 31. (1971) tested the effects of varying amounts of Ca and P in rations for dairy cows. In the lowest P group (.4% P of Ration D.M.) with a Ca:P ratio of 3:1, they found a higher incidence of ovarian dys- functions and a larger number of services per conception were required. They also reported lower average blood serum inorganic phosphorus in this group. P supplement- ation of range cows grazing in areas deficient in phosphorus improved fertility in lactating cows over the 12 controls (Theiler and Green, 1931, Hart and Mitchell, 1965). Availability of minerals in soils depends upon their concentration in soil solution and it has been indicated that a general association exists between available soil P and P concentrations in forage, cereal and vegetable crops, (Reid and Horvath, 1980). Edye 33 31. (1971) used superphosphate as pasture fert- ilizer. Cows grazing in these pastures had a better conception rate, calving rate and weight increment than controls (no fertilizer). It is important to point out the fact that stocking rate also has a significant effect on.conception rate. In a trial conducted by Hecht 33 31. (1977), no differences were found between 76 heifers fed low levels of P (0.13 - 0.22% of Ration D.M.), and those heifers fed supplemental P (0.40% P of Ration D.M.). The variables measured were estrus exhibition, services per conception and pregnancies. Noller 33 31. (1977), in a one-year study, failed to show a significant effect on conception rate when 56 Holstein heifers were given complete mixed rations containing all phosphorus from.natural feedstuffs (.22%) or a .10% increase in P content of the ration (.32%). Call 33_31. (1978) reported no differences in reproductive performance and age to puberty in 96 Hersford heifers fed either 66% or 174% of NRC-recommended levels of P during two years. 13 Carstairs 33 31. (1980) suggested that phosphorus status did not influence reproduction in dairy cows fed rations with 98% or 138% of the P levels recommended by NRC. Phosphorus should be fed according to recommend- ations and excess phosphorus may be detrimental in post partum.dairy cows (Carstairs 33 31., 1981). Effect of Roughage Level in the Ration and Reproductive Performance Literature reports a definite relationship between dietary fiber, expressed as a roughage-to-concentrate ratio, crude fiber level and type, and percentage of fat in the milk produced (Van Soest, 1963). It has been suggested that a diet low in fiber adversely affects fertility because of the low production of acetic acid (Francos, 1968). Acetic acid is involved in the formation of steroid hormones, such as estradiol and progesterone (Francos, 1969). Restricted roughage with high grain rations have been shown to promote: changes in Lipoprotein lipase activity, stearic acid and cholesterol linoleate concentration, which are associated with an increased flux of fatty acids toward adipose tissues (Benson 33 31., 1972). A negative relationship exists between serum insulin and milk fat production and rumen acetate: propionate ratios (walker and Elliot, 1973). 14 A highly significant correlation between the milk butter-fat percentage of a herd and its conception rate has been reported (Bar—Anan, 1968, Ayalon 33 31., 1971). Refsdal in 1977 (quoted by Engvall, 1980) demon- strated a delayed start in the ovarian function of cows after parturition when the animals had been experi- mentally fed so that the milk-fat percentage was re- duced. Several researchers have found significant differences in the amount of roughage (32.2 to 34.1%) in the dry matter intake in the high fertility herds compared to the intake (20.3 to 23.3%) in the low fertility herds. (Francos 33 31., 1977, Mayer 33 31., 1978, Davidson 33 31., 1978, Tong 33 31., 1979). Trimberger 33 31. (1972) concluded that feeding liberal amounts of grain to compensate restricted forage is not a satisfactory procedure under normal economic conditions. They found that cows fed a liberal concen- trate ration has significantly longer calving intervals and required more services per conception than the controls. Buchanan-Smith 33 31. (1964) fed beef heifers either an all-concentrate ration ad libitum or a roughage ration composed by corn silage. The data suggested that 15 the all-concentrate ration has a triggering effect on the onset of estous compared to the roughage ration. Engvall (1980) found no significant difference in fertility in low milk-fat cows (3 3.0% B.F., 2.14 services per conception and 105 days open) when compared to controls cows (> 3.2% B.F., 2.25 services per con- ception and 112 days open). A 60:40 forage-to-grain ratio fed to Holstein cows showedaidelay to postpartum estrus, due to body weight loss and energy stress, when compared to 50:50, 65:35 and 85:15 ratios during early lactation. However, the average number of services per conception was not different among groups (Everson 33 31., 1976), Markusfeld (1970). Kali and Amir (1970) found that at the time of first insemination after parturition milk yield, butter fat percentage and butter fat production were higher in cows considered repeat breeders. Zamet 33 31. (1979), fed hay, hay crop silage and corn silage to postpartum Holstein cows in a 60%-40% forage concentrate mixture. Significant results showed that calving interval was shorter, more cows conceived and lower services per conception were achieved in cows fed hay compared to cows fed either hay crop silage or corn silage. 16 Energy and Reproductive Performance The relationships between energy intake and energy metabolism.must be taken into account when considering the influence of nutrition, body condition and milk production on reproductive performance in the lactating cow. In heifers, onset of puberty and subsequent re- productive efficiency can be affected by energy intake. Reid 33 31. (1957) fed 65, 100 and 145% of the recommended TDN levels to Holstein heifers and feeding levels did not affect the average number of services per conception, however, increasing levels of nutrient intake tended to reduce the percentage of heifers conceiving at the first service. Onset of puberty occurred at 20, 11 and 9 months of age respectively for the low, medium.and high TDN intake groups. The authors pointed out that although the age at the time of the first heat is affected markedly by feeding level, all heifers ex- perience the first heat at about the same size and height. These data suggest that body weight, rather than age, is more important for onset of the first estrus to occur. Similar results were reported by Gardner 33 31. (1977). Low energy diets for heifers delay onset of puberty and onset of estrus, decrease pregnancy rates, first service conception and alter reproductive performance 17 (Wiltbank 33 31., 1965, Dunn 33 31., 1969, Short and Bellows, 1971, Lemenager 33 31., 1980). Arnett 33 31. (1977) used 12 sets of twin beef females to compare normal and obese females postweaning through three lactations. Normal females were produced by feeding one twin a ration adequate in minerals, vitamins and protein according to NRC recommendations but containing only sufficient energy to gain approximately 1/3 kg. per day and to maintain a healthy, thrifty condition. Obese females were produced by feeding the other twin additional energy to induce and maintain a high degree of body fatness by varying the proportion of corn and cottonseed hulls. They found that normal heifers required fewer services per conception, less assistance at calving, weaned more calves and produced more‘milk. Leaver (1977) reported that heifers fed levels of nutrition above maintenance, the effects on fertility was small, using pregnancy rate to first service and total calving rate as reproductive performance measures. On the contrary, Pendlum 33 31. (1977) fed beef heifers different levels of supplemental energy as shelled corn (0.3 or 6 lbs. per head daily) plus corn silage and protein supplement. They reported that energy intake of all treatment groups was adequate for conception. 18 It is known that the high milk production dairy cows, during early lactation, are in negative energy balance because of the inability to consume sufficient feed to meet the requirements of the increased level of production. Butler 33 31. (1981) suggested that energy balance during the first 20 days of lactation is important in determining the onset of ovarian activity following parturition. They concluded that this activity was inversely related to average energy balance during the first 20 days of lactation: the greater the average deficit incurred, the longer the delay to ovulation. The same conclusions were reported previously by Ayalon 33 31. (1971) and Sonderegger and Schruch (1977). Carstairs et a1. (1981) reported that excess energy should be avoided for the first month of lactation and then gradually increased in primiparous Holstein cows, in this study high energy fed groups had almost twice as much incidence of disease and cows did not begin to yield more milk than low energy groups until week five after parturition. The authors suggested that primiparous cows should be fed rations moderate in energy immediately after calving and gradually building up their energy intake to a high by four to five weeks of lactation. 19 In another study, Carstairs 33 31. (1980) fed either high (135% NRC) or low (85% NRC) energy to Holstein cows and the data suggested that energy, within the ranges studied, did not influence reproduction of dairy cows. Animals fed on a high plane of nutrition prior to calving had a shorter interval to first estrus than cows fed on a low level of nutrition prior to calving, re- gardless of the post calving level of nutrition. The postpartum level of nutrition had a marked effect on cows fed below requirements before parturition, delaying estrus to 90 days postpartum but it had almost no effect on reproductive performance of cows in good body con- dition at calving (Wiltbank 33 31., 1962, Wiltbank 33 31. 1964, Davis 33 31., 1977, Tong 33 31., 1979. Morrow 33 31. (1969) concluded that cows fed a liberal concentrate ration had significantly longer calving intervals and required more services per con- ception than the controls. The authors found that the interval from parturition to first estrus, the subsequent estrus interval and the occurrence of standing estrus and ovulation wererun:affected by liberal concentrate feeding. Large deviations from the desirable levels of energy in the ration may result in declining fertility 20 performance. Francos (1970) suggested that excessive feeding in the second half of pregnancy contributes to a state of postpartum stress, which in turn predisposes the uterus to faulty involution and to metritis. He noted this situation in two herds in which cows and heifers received 200 to 400 Scandinavian feeding units above normal requirements, including two to three times the standard amounts of protein. When the rations were reduced to conventional norms, metritis percent was reduced from 20% to 7% in one herd and from 35% to 14% in the other herd. Francos (1974) suggested that the "repeat breader” syndrome is associated with feeding a ration deficient in energy during any stage of lactation and the final stages of pregnancy. The same results have been reported by Francos 33 31. (1977). During the 4-year study conducted by Armstrong 33 31. (1966), 170 cows were fed different levels of concentrates ranging from a low of 464 kg. per lactation to 4790 kg. per lactation and the data suggested that high levels of concentrate feeding are not related to conception rate. Hodgson 33 31. (1980), in a study using crossbreed cows, obtained limited evidence that conception rate 21 using artificial insemination following estrus synchronization was not affected by plane of nutrition in early lactation. Changes in body weight prior to and during the mating period have been suggested as causes of impaired reproductive function in cows. Leaver (1977) concluded that there appeared to be an interaction between body condition and level of nutrition in relation to pregnancy rate in British Friesian heifers. In the dairy cows, there appears to be an association between the rate of body weight change and fertility over a long term (McClure, 1970a and 1970b, Youdan and King, 1977). The results indicate that improvements in fertility are possible if cows are managed so that they are gaining in body weight at the time of service (Schilling and England, 1968, King, 1968, Moller and Shannon, 1972, Sommerville 33 31., 1979). Carstairs 33 31. (1980) fed two levels of energy and phosphorus (100 and 75% of NRC requirements) to primiparous Holstein heifers and although the high energy group gained weight and the low energy group lost weight, there was little difference between groups in time to first ovulation. Holness 33 31. (1978) working with 160 Africander and Mashona cows, found a significant negative correlation 22 (P < .05) between liveweight postpartum and time between calving and first estrus. This indicates that postpartum anestrus was significantly shorter in cows that lost weight than in those that gained weight postpartum” Broster (1973) reviewed liveweight change and fertility in the lactating dairy cow and pointed out the lack of agreement in investigations between liveweight change and fertility in dairy cattle is due to the interaction between long and short term effects of nutrition on fertility. Other researchers have failed to find a relationship between body weight changes and fertility (Oxenreider and Wagner, 1971, Folman 33 31., 1973, Gardner, 1969, Boyd, 1972, Downie and Gelman, 1976). Another possible cause of infertility mentioned in the literature is the concentration of glucose in blood. McClure (1968) suggested that acute energy deficiency rapidly causes hypoglycaemia and this effect could lead to a hypothalamic failure. Increasing blood glucose concentrations appears to be associated with improved fertility (Hunger, 1977, McClure and Payne, 1978). Asignificant rise in plasma glucose levels before service in fertile cows has been demonstrated (Downie and Gelman, 1976). 23 In a recent work conducted by Carstairs 33 31. (1980), the only indication that glucose might be involved in a reproductive dysfunction, was a negative correlation between blood glucose and days to reach 3 ng./ml. serum progesterone which in this study may be involved in conception. Oxenreider and Wagner (1971) studied the effect of three levels of energy (66,100 and 133% of NRC require- ments) upon postpartum reproductive function. They found that energy intake had a significant effect on plasma glucose levels during the first eight weeks postpartum and there was a significant negative corre- lation between plasma glucose level and postpartum interval to occurrence of a 10 mm. follicle and ovulation. In extensive studies conducted in England and Sweden, Blowey 33 31. (1973) and Hewett (1974) failed to find a relationship between fertility and glu- cose concentrations in blood. Herdt 33 31. (1981) found that high concentrate diets (60% of dry matter) compared with low concentrate diets (40% of dry matter) increased mean plasma glucose values and reduced mean blood B-hydroxybutyrate concentration. However, they concluded that plasma glucose and blood B-hydroxybutyrate con- centrations cannot be used as valid indicators of energy balance. Blood B-hydroxybutyrate might be used as an 24 indicator of the relative glucogenic potential of dairy rations and blood concentrations of this metabolite could potentially be used to adjust factors in the ration which influence glucose availability to the cow. Russel et a1. (1979) also reported that plasma 3-hydroxybutyrate concentrations in late pregnancy were closely and inversely related to energy intake. Several experiments have dealt with the effect of different energy levels upon hormones. Changes in hor- mone concentration of animals in a low plane of nutrition suggest that energy may alter endocrine function. Hill 33 31. (1970) reported that undernutrition in heifers (85% of NRC requirements of energy and protein) reduced plasma levels of progesterone within five days. It also altered the length of the estrus cycle and re- duced the proportion of animals with normally fertilized ova. They reported no change in plasma LH. Folman 33 31. (1973) pointed Out that cows that conceived after one insemination has significantly higher progesterone levels during the estrus cycle preceding insemination than did cows that failed to conceive. At the same time cows maintained on a high level of nutrition required fewer inseminations per conception, conceived earlier and had a high plasma progesterone level 23 days earlier than cows maintained on a standard level of nutrition. In 25 cows that conceived after one insemination, level of nutrition had no effect on progesterone concentration, but it had a profound effect in cows that needed more inseminations for conception. It has been suggested that restricted energy intake reduces the response to LH by the corpus luteum, synthe- sizing and releasing less progesterone (Gombe and Hansel, 1973 and Apgar 33 31., 1975). Supporting these data, Beal 33_31. (1978) concluded that dietary energy restriction may influence the LH release directly at the pituitary level as well as indirectly through effects on ovarian steroid production. Lishman 33 31. (1979) found that the pattern of release of LH was altered by plane of nutrition (maximum rise occurred 30 minutes earlier in high plane of feeding than in underfed animals) and estradiol did not vary with plane of nutrition. Spitzer 33 31. (1978) reported the same results for progesterone and LH in heifers fed either 100% or 30% of NRC recommendations for energy. Corah 33 31. (1974) concluded that there was no signficant effect of energy on preipheral levels of progesterone or estradiol either prior to or following parturition. Carstaires 33 31. (1980) conducted an experiment in which progesterone secretion was not changed by either energy of phosphorus. 26 status of the ration. However; they reported that no cow with peak serum progesterone below 2.7 ng./m1. before insemination conceived to that insemination. MATERIALS AND METHODS The state of Chihuahua is located in the Northern Plateau of Mexico between 25°37', 31°46' north latitude and 103°39', 109°07' west longitude with an area of 247,087 sz (24,708 700 million hectares) that accounts for 12% of the national territory. The State is divided in three geographical areas: - Mountain Region (Sierra Tarahumara) - located in the western part, accounts for 30% of the total area, climate is classified as wab according to Koppen's climatic classification, with an annual rainfall of 700-1200 mm. and snow during the winter, and elevations up to 4000 ml - Semi-Desertic and Desertic Area - located to the northeast and east, it accounts for 52% of the total area, climate is classified as BWHw according to Koppen's climatic classification with an annual rainfall of 200-300 mm. - Central Plains - located between the two later regions: it accounts for 18% of the total area, climate is classified as BSkw according to Koppen's climatic classification, with an annual rainfall of 300-400 mm. 27' 28 The study areas are the counties of Chihuahua and Delicias, which are located in the Central Plains region. Average temperatures during the year for Chihuahua and Delicias are 16.9°C and l9.6°C respectively. Most dairy herds in the area are composed of Holstein-Friesian cattle (approximately 13,500 head), in which all cows are kept in large, outdoor drylots or corrals with high shades provided as a protection agains the sun. Feed bunks are located along one side of the lot to facilitate forage distribution. Double - 4 to 12 herringbone milking parlors are the most popular depending upon the size of herds. Alfalfa hay and commercial concentrate mixes are the main feeds offered to the cows throughout the year. Feedstuffs are located in centralized feed storage and processing units and a high percentage of roughages and concentrates, if not all, are purchased. Use of artificial insemination is common in sampled herds, and good herd husbandry and disease-prevention practices are observed. SamplinggProcedure In this study, Mexican farms were chosen on the basis of: availability of records such as DHI (Provo, Utah) or the Computerized Dairy Record System of the 29 Department of Agriculture of Mexico, willingness to provide the data and since most of the dairy cattle in Northern Mexico are of the Holstein breed, only eight Holstein herds were selected. No other factors were considered for selecting the farms. The Mexican herds were matched with herds in the Michigan DHI with similar size, breed, production level and reproductive parameters such as days open and services per conception. Design of the Questionnaire In the implementation of the questionnaire the guidelines for design and structure suggested by Kucker (1970) and Erickson (1972) were followed. The questionnaire was divided in three sections: Breeding, Reproduction, Herd Health and Nutrition. Preliminary drafts were analyzed by faculty members and graduate students of the Department and the final document consists of 74 questions. A copy of the questionnaire is included in Appendix. ‘ Mexican farmers were interviewed during the Summer of 1980. During the interview, they used either all the records available for more accurate answers or provided a copy of the records for later analysis. 30 Originally, sixty Michigan farmers were selected and after December 1, 1981, only nineteen answered the questionnaire sufficiently accurate for inclusion in the data set. Data Alfalfa hay and commercial concentrate mix samples were carried from Mexico and analyzed in the Research- Extension Analytical Laboratory (WOoster, OH) for Dry Matter, Crude Protein, Crude Fiber, P, Ca, Mg, S, estimated Net Energy, and estimated TDN. The composition of the Mexican commercial concen- trate mix basically is: rolled sorghum grain, cottonseed meal and/or soybean meal, dehydrated alfalfa meal, molasses, rock phophate, salt, limestone, cobalt sulphate, iron sulphate, copper sulphate, manganese sulphate, zinc oxide and potassium iodine, and Vitamins A, D and E. The data from the questionnaire interviews and ration analysis were transferred to 80-column computer cards. Variables and card format are listed in Appendix. Answers to the questionnaire were encoded either as numeric answers with a specific key for each one, yes or no type answers (0 = No, 1 = Yes) or a rank-type answer. Reproduction, Herd Health and Breeding data 31 were collected in an attempt to identify management practices which could be affecting herd reproductive performance. Nutrition data were utilized to evaluate different types of rations fed to the cows according to level of milk production. Nutrient data were provided either by laboratory analysis as in the case of the Mexican herds or by information provided by farmers through the questionnaires or by the NRC 1978 Feed Analysis Tables in the case that farmers data were missing or incomplete. Ration evaluation was performed using the Tel Cal 56:3 Dairy Ration Evaluation program developed by Hlubik and Thomas (1979). This Tel-Cal program accomp- lishes several tasks. First, it calculates the amount of D.M. and seven nutrients needed by milking cows (N.E. for lactation, Protein, C.F., Ca, P, Mg and S). Then, it compares these totals to amounts furnished in a ration composed of up to eight feeds. The program also converts feeds entered from an as-fed basis to a D.M. basis, calculates total lbs. of D.M. Percent D.M. of the ration, estimates pounds of feed that the COWS‘Would consume and enters nutrient densities per 1b. .of D.M, The program has the flexibility to allow for changes of any nutrient density for any feed, to estimate lbs. of nutrient required as well as the minimum 32 concentration of crude protein and net energy that should be contained per pound of ration dry matter. The program calculates the amount of each of the seven nutrients provided by the ration, excess or deficiency of every nutrient and nutrient densities per lb. of dry matter in the ration. Method of Analysis The dependent variables selected for analysis were: Days Open, Average Days from Calving to First Service, Services per Conception and First Service Conception. The 20 independent variables selected are defined in the regression model description. One of the objectives of this study was to deter- mine if interrelationships exist among the nutrient content of feeds and reproductive performance of dairy cattle. The statistical methods chosen were the multiple linear regression by lease squares analysis and analysis of variance. The procedure of least squares ascertains which combination of variables is the most accurate predictor of the dependent variable under study. :4 N N N 94 >4 N UINJ-‘NwNNNl-‘NUI b w >4 N H x N X H N W XIXA 33 The regression equation is described below: - 2 2 2 2 2 L + b7x2 + b8x3 + ng4 + b10x5 n bllxlx2 + b12x1x3 + b13x1x4 + b14xlx5 + b15x22x3 + b16x2x4 + b17"2"s + blsxsxa + b19"3"5 + bzoxaxs + ei = Dependent Variable a Intercept = Crude Protein (AVPROT) = Net Energy (AVNE) 8 Calcium (AVCA) a Phosphorus (AVFO) = Crude Fiber (AVFI) = Quadratic Effect of Crude Protein (AVPROTZ) 8 Quadratic Effect of Net Energy (AVNEZ) a Quadratic Effect of Calcium (AVCAZ) a Quadratic Effect of Phosphorus (AVFOZ) = Quadratic Effect of Crude Fiber (AVFIZ) = Cross Product Crude Protein and Net Energy (PRONE) = Cross Product Crude Protein and Calcium.(PROCA) = Cross Product Crude Protein and Phosphorus (PROFO) = Cross Product Crude Protein and Crude Fiber (PROFI) 34 x2x3 = Cross Product Net Energy and Calcium (NECA) xzx4 = Cross Product Net Energy and Phosphorus (NEFO) xe5 a Cross Product Net Energy and Crude Fiber (NEFI) x3x4 = Cross Product Calcium and Phosphorus (CAFO) x3x5 = Cross Product Calcium and Crude Fiber (CAFI) xsx5 = Cross Product Phosphorus and Crude Fiber (FIFO) ei = Residual Random Error Associated with Yi b1, b2, ... b20 - Regression Coefficients of Y1.- on all the effects considered. With a stepwise backward elimination procedure, variables can be removed one at a time, starting with the variable that contributes the least to the total variation (Nie 33 31., 1975). In this fashion, each in dependent variable eliminated generates a different model and these will be discussed further. Because the dependent variables (days open, first breeding after calving, first service conception, first heat after parturition and total number of services per conception) were herd averages, and the nutrition data were provided for cows according to production levels (namely high, average and low production), new variables were created. 35 In order to compute these variables, a weighted average was obtained for the analysis. Where data for the three production groups were available, the total was divided by three to obtain an average; otherwise, in most cases only two groups (high and low) were taken into consideration. List of new variables is included in Appendix Table 36 (variables 203-207). In order to avoid the high correlation between the linear and quadratic terms and to build a more accurate model, the linear effects of the variables were centered. The approach used for centering was to substract the variable from the overall mean value. The quadratic and cross-products were calculated using the centered variables. A dummy variable (Country) was fitted into the model for interpreting its significance as a difference between Mexican and Michigan herds. The second objective of this study was to detect possible interaction between management and feeding prac- tices that could be affecting reproduction of dairy cows and since the factors included in the survey were essen- tially multivariate, many of them are highly interrelated and several may covary greatly, Factor Analysis was the statistical method chosen. Factor Analysis is a procedure that allows to identify the best linear combinations of variables that 36 would account for more of the variance in the data as a whole than any other combinations (Nie 33 31., 1975). Factor Analysis attempts to account for the corre- lations among many observed variables in terms of a small number of more general variables called factors and the correlations are regrouped into patterns (Gill, 1978). After the interpretation of the analysis, data were regrouped, according to the factors, in new general variables (Appendix, variables 208-210) Table 36, the hypotheses concerning association between feeding and management procedures and reproductive parameters was tested using the method of least squares and the follow- ing model: + b x + b x + b x + e. Yi‘bo 11 22 33 1 Y. a Dependent Variable x1 8 Reproductive Management (RMGT) N I - Nutrition Factors (NUTl) 2 x3 = Nutrition Management (NMGT) b0 = Intercept ei = Residual Random Error Associated with Yi bl’ b2, b3 = Regression Coefficient of Yi on all the effects considered. (n 37 These new general variables were composed of several of the original variables, grouped together. For fitting the new general variables in the regress- ion model, Composite Indices or Factor Scores were built from the original variable. The method used for this approach was to standardize values as follows: General Variable (Correlation Coefficient of Original Variable l) x (Original Variable 1 Value - Mean of Var. l)/(Standard Deviation of 0. V. l) + ... + (Correlation Coefficient of O. V. n) x (O. V. n Value - Mean of O. V. n)/(Standard Deviation of O. V. n). In addition to this regression model a dummy variable for COUNTRY was also included to test the variation be- tween regions and in farms within region. The Statistical Package for the Social Sciences or SPSS (Nie 33 31., 1975) was utilized for the computer processing of the data. p: De of P}. RESULTS AND DISCUSSION Table 1 shows means and standard errors for the dependent variables under study. Differences between regions will be discussed further in the regression models when the variable COUNTRY is fitted into the model. Tables 2 and 3 shows the nutrient concentrations in the ration of dairy c0ws in Mexico and Michigan, as well as an analysis of variance performed to detect differences between regions in nutrient concentration. Table 4 shows the stepwise backward elimination procedure used with the dependent variable Days Open. Deleted variables were: Phosphorus, Quadratic Effect of Phosphorus, Quadratic Effect of Calcium, Fiber and Phosphorus, Protein and Phosphorus, and Net Energy and Phosphorus interactions. Overall significance for the model was .071, the percentage of variation explained by this model as indicated by R2 = .73457 and adjusted R2 = .4249, which for small samples it is a more valid estimate of the proportion of the variablity of the dependent variable, which can be explained by the set of independent variables. 38 39 NH.o mm.H HHH.o «H.N Nmo.o 33.3 :oeuamoaoo Mom moofl>umm mw.m mo.eea em.me me.msfi we.m mm.HoH - ammo mama mw.~ «3.30 oe.m om.mm mm.~ mm.oe masseuse uma on waH>Hmu Scum whom ommuo>< N~.m Hm.oe ea.e oo.om Hmm m mo.¢m couuamuaoo wow>umm umuwm .m.m 24m: m.m zen: .m.m zemz zo mum .GOHuamunou Mme moofi>umm can some mzmn .mcwpooum umuah ou wca>amo Beam when mwmuo>< .cowunoocou oofi>uom umuam pom muchum pumpcmum one name: .H mgm<9 40 «Ho.o mmN.o NHH.o oou.H omc.m Om¢.m~ oom.~ omm.om Nmm.o oom.o~ mao.o ¢NN.O HNo.c me.o oHo.a omo.om omm.H ou~.co oa¢.c oma.< N EDHonU N Hmnwm mcaho owmuo>< owmho>< ABH\Hmu so u umz mwmum>< N :Hououm owmuo>¢ o~0.o wm~.o coo.o coo.o mHo.o owo.wa qow.o oom.om mom.o ooH.¢H .m.m zO mam fiOflUNHUfiQOCOU “Cmfihufiz COHUNM .N MAm< omoo.o mm mmeH.o aonmm\3 sham anyone moo. o-o.o H ouuo.o muucsoo Ho aonmm -moem mmmum>< Heeo.o mm “~3.H aonmm\s spam Hoo.v mmo.a H mmc.H huucnoo Ho fionmm Ebfloamo owmuo>< Hme.- mm cmm.mmo :onmm 3 spam mwwocm Hoo.v oem.~oHH H o~m.~oHH muucsoo no sonmm “oz oweum>¢ mmm.m mm mee.mm conom\s spam soc. omm.em H omm.em muucaoo Ho :onmm chuoum mmmum>< mommm mmm<=om zoammmm mmm<=om one H mmma z .Honwm cam msuonemonm .Epwoamu .mwuocm uoz .cmououm How ooamwum> mo mamzaos< .m m4m<fi 42 Significant linear effects (P < .05) that contri- buted to explain the variation were Fiber, Net Energy and Calcium. Significant (P < .05) quadratic effects were Net Energy and Fiber. In this model, interactions which showed to be highly significant (P < .01) were: Net Energy and Fiber, Calcium and Phosphorus, Protein and Net Energy, Net Energy and Calcium, Protein and Fiber and Fiber and Calciumm Since days open was one of the variables used for matching the Mexican herds with Michigan herds, no significant differences (P > .05) were found when the variable COUNTRY was fitted into the model (Table 5). In this study, ration protein content (AVPROT) seems to have no significant effect (P > .05) on days open; although, a highly significant difference in protein content among regions was observed (P < .01) in Table 3. These findings are in agreement with those of Edwards 33 31. (1980), in which no relationship exists between days open and protein content of the ration. Fiber content of the ration had a significant effect (P < .05) on days open (Table 4), and Table 3 shows a highly significant difference (P < .01) between Mexican farms (25.4%) and Michigan farms (18.6%). 43 Apparently a higher roughage content in the ration is fed to cows in Mexico and it seems to have a positive effect on days open. This effect has been reported by Francos 33 31. (1977), Mayer 33 31. (1978) and Tong 33 31. (1979). It is important to point out that the main roughage fed to dairy cows in Mexico is alfalfa hay compared to corn or alfalfa silage in Michigan. Zamet 33 31. (1979) found a shorter days open interval in cows fed alfalfa hay compared to the interval in cows fed either corn or alfalfa silage. Net Energy content of the ration also had a signi- ficant effect on days open (P < .05). And in Table 3 a highly significant difference between Mexican (56.8 Meal/lb) and Michigan farms (70.86) was found (P < .01). ' According to NRC Nutrient Requirements of Dairy Cattle (1978), the recommended levels of Net Energy for lactation range from 64 to 78 Mcal/lb for cows producing 18 to 78 lbs of milk daily, Net Energy con- centration for Mexican cows is lower than the recommended levels. Evidence of the effect of Net Energy on days open is contradictory. Morrow 33 31. (1969) reported that longer days open intervals were found in high energy diets, and Carstairs 33 31. (1980) concluded that 44 energy seemed not to influence reproductive performance in dairy cows. In this study, lower levels of net energy seems to be favorable for shorter days open intervals. Calcium content is significantly affecting days open intervsl (P < .05), and in Table 3, differences between Mexico (1.2%) and Michigan (0.66%) are highly significant (P < .01). These results do not agree with Hignet (1950) and King (1971). However, in this case Ca appears to be influencing the days open interval and maybe due to earlier ovulation as suggested by Ward 33 31. (1971). In Table 6, the regression model for average days from calving to first service is shown. Also, a deletion procedure was used and the variables excluded were: Protein, Quadratic Effects of Protein and Calcium, Net Energy and Fiber, and Protein and Net Energy interactions. Overall significance of this model was .001 and the model helps to explain 91% of the variation of average days from calving to first service (R2 = .91481 and adjusted R2 = .79863). In this case, only Calcium as a linear effect was significant (P < .05). Highly signficant (P < .01) quadratic effects were Net Energy, Fiber and Phosphorus. 45 TABLE 4. Model for Days Open with the Variables Left After the Stepwise Backward Eli- mination Procedure. Model : R2 = .73457 Adj. R2 - .42490 Degrees of Freedom Regression = 14 Mean Squares Regression = 2843.73273 Degrees of Freedom Residual = 12 Mean Squares Residual = 1198.81181 F Significance = .071 Variablesa: Slope Standard Error Type I of Slope Error AVPROT 28.8111 15.8386 .094 AVNE2 -2.3357 .6259 .003 NEFI -3.8752 .9419 .001 CAFO 3309.8008 921.0457 .004 AVPROTZ -3.7335 3.1746 .262 PRONE 8.7811 2.5942 .005 AVFIZ 5.8224 1.7583 .006 AVFI 17.0987 5.8777 .013 NECA -86.7407 21.6637 .002 AVCA -520.5686 192.5526 .019 PROFI 24.4123 7.7604 .008 AVNE -l7.9710 6.1962 .013 PROCA 44.1193 36.7559 .253 FICA 0356 68.2102 .007 -244. aFor description of variables, see Table 35 46 TABLE 5. Days Open with the Variables Left After the Stepwise Backward Elimin- ation Procedure Including COUNTRY as a Variable. Model: R2 = .73463 Adj. R2 = .37276 Degrees of Freedom Regression = 15 Mean Squares Regression = 2654.36631 Degrees of Freedom Residual = 11 Mean Squares Residual a 1307.50048 F Significance = .12 Variablesa: Slope Standard Error Type I of Slope Error COUNTRY 1.7355 34.8845 .961 PRONE 8.8465 3.0110 .013 CAFO 3327.1951 1023.4597 .008 AVPROTZ -3.6959 3.4005 .300 AVFI2 5.8514 1.9263 .011 NEFI -3.8958 1.0670 .004 AVPROT 28.9791 16.8825 .114 NECA -87.2215 24.6022 .005 AVFI 17.2574 6.9175 .030 AVNEZ -2.3524 .7355 .008 PROFI 24.6182 9.1003 .020 AVCA -524.0784 213.1066 .032 PROCA 43.7229 39.2041 .289 AVNE- -17.9117 6.5798 .020 FICA -225.7766 79.3667 .016 aFor description of variables, see Table 35 47 Highly significant (P < .01) interactions were Calcium and Phosphorus, Fiber and Calcium, Net Energy and Phosphorus. Signficant (P < .05) interactions were Net Energy and Calcium, Protein and Calcium and Protein and Fiber. Table 7 shows that differences of average days from calving the first service were not significant (P > .05) between regions. In this model, overall significance is .002 and R2 a .91481 an adjusted R2 = .78846. No linear effects showed to be significant (P > .05). Highly significant (P < .01) quadratic effects were Phosphorus, Fiber and Net Energy. Interaction effects that showed to be highly significant (P < .01) were Calcium and Phosphorus, Fiber and Calcium.and Net Energy and Phosphorus. Net Energy and Calcium, and Protein and Calcium Interactions were significant (P < .05) . In Table 3, the difference in ration Calcium content between regions is highly significant (P < .01). It was hypothesized that Calcium content of the ration had a significant effect on Days Open, but in this case Calcium content of the ration did not have a significant (P > .05) effect on average days from calving to first service. 48 TABLE 6. Model for Average Days from Calving to First Service with the Variables Left After the Stepwise Backward Elimination Procedure Model: R2 - .91481 2 Adj. R = .79863 Degress of Freedom Regression = 15 Mean Squares Regression = _ 343.15373 Degrees of Freedom Residual = 11 Mean Squares Residual = 43.57824 F Significance = .001 Variablesa: Slope Standard Error Type I of Slope. Error AVNEZ .4566 .1009 .001 AVFI -.0737 1.0600 .946 AVF02 6156.9123 1206.3444 .0001 PROFO -86.7381 47.2611 .094 NECA 9.7708 3.2083 .011 PROCA 17.5725 6.2259 .017 AVFIZ -l.l625 .1949 .0001 FIFO -27.1768 26.2885 .323 AVCA 32.7045 12.8844 .028 PROFI -1.6801 .7530 .047 AVFO -l35.7896 66.8410 .067 AVNE .1855 .9205 .844 NEFO -13l.2926 28.7648 .001 FICA 26.8222 6.4387 .002 CAFO -2439.4617 488.9348 .0001 aFor description of variables, see Table 36 49 TABLE 7. Model for Average Days from Calving to First Service with the Variables Left After the Stepwise Backward Elimination Procedure Including COUNTRY as a Variable. Model: R2 . .91864 Adj.R2 = .78846 Degress of Freedom Regression = 16 Mean Squares Regression - 323.0551 Degrees of Freedom Residual = 10 Mean Squares Residual = 45.7784 F Significance - .002 Variablesa: Slope Standard Error Type I of Slope Error COUNTRY 7.1397 10.3999 .508 AVFOZ 5623.4962 1460.2919 .003 FIFO -38.7700 31.7986 .251 AVFIZ -1.1503 .2005 .0001 AVNE2 .4610 .1036 .001 NECA 9.2183 3.3854 .021 PROFO -91.0729 48.8493 .092 PROCA 16.2375 6.6709 .035 AVFI .1240 1.1245 .914 AVCA 28.8987 14.3220 .071 PROFI -1.5433 .7971 .082 AVFO -68.4050 119.6977 .580 AVNE .2881 .9552 .769 NEFO -l38.1904 31.1471 .001 FICA 25.0152 7.1048 .006 CAFO -2203.5664 607.6148 .005 aFor description of variables, see Table 35 50 Since average days from calving to first service, there is no consistency in the hypothesis. The regression model for the variable services per conception is shown in Table 8. Variables deleted were: Phosphorus, Quadratic Effects of Phosphorus, Calcium and Protein, Interactions between Protein and Energy, Calcium and Protein. Overall significance for the model was .406, 32 a .53344 and adjusted R2 a .06689. When COUNTRY was used as a dummy variable in the model, no significant differences (P > .05) existed between region (Table 9) as expected because this variable was used to match the herds. Only Calcium, Quadratic Effects of Net Energy and Fiber and the interaction between Net Energy and Phosphorus showed to be significant (P < .05) in this model. Regression model for First Service Conception is shown in Table 10. Overall significance for the model is .117, and the variation explained by R2 = .58621 and adjusted R2 8 .28277. Variables deleted from the model were: Net Energy, Fiber, Quadratic Effects of Net Energy and Fiber and the interactions between Net Energy and Fiber, Calcium and Fiber, Protein and Fiber, Protein and Phosphorus and Net Energy and Phosphorus. Variables which shown to be significant (P < .05) were: quadratic effect of protein, and the interactions 51 TABLE 8. Model for Services per Conception with the Variables Left After the Stepwise Backward Elimination Procedure. Model: R 3 .53344 Adj. R2 a .06689 Degrees of Freedom Regression = 13 Mean Squares Regression = .24156 Degrees of Freedom Residual = 13 Mean Squares Residual - .21127 F Significance = .406 Variablesa: Slope AVNE .0826 PROFI -.0681 FIFO -3.5331 NEFI .0081 AVPROT -.2119 NECA .1436 NEFO -2.9220 AVNEZ .0138 PROFO -5.7525 AVCA 3.7706 AVFI .0792 AVFIZ -.0424 FICA .8746 Standard Error Type I of Slope Error .0419 .071 .0426 .135 .7642 .067 .0054 .158 .1320 .133 .1284 .284 .0813 .018 .0048 .013 .7874 .153 .5087 .027 .0625 .227 .0159 .020 .4400 .068 aFor description of variables, see Table 35 52 TABLE 9. Model for Services per Conception with the Variables Left After the Stepwise Backward Elimination Pro- cedure Including COUNTRY as a Variable. Model : R2 = .53726 Adj. R2 - 0 Degrees of Freedom Regression = 14 Mean Squares Regression = .22591 Degrees of Freedom Residual = 12 Mean Squares Residual = .22700 F Significance = .509 Variablesa: Slope Standard Error Type I of Slope. . Error COUNTRY -.1212 .3853 .758 NEFI .0077 .0058 .208 NECA .1314 .1386 .362 FIFO -3.4435 .8507 .087 NEFO -2.8801 .1288 .025 AVPROT -.2078 .1375 .157 PROFI -.0680 .0442 .150 AVNEZ .0136 .0050 .019 PROFO -5.8507 .9383 .163 AVCA 3.7878 .5648 .032 AVFI .0760 .0656 .270 AVNE .0718 .0554 .219 AVFIZ -.0414 .0168 .030 FICA .8781, .4563 .078 aFor description of variables, see Table 35 53 between Calcium and Phosphorus, Fiber and Phosphorus and Net Energy and Calcium, When COUNTRY was used in the model, the overall significance is lowered to .091 and this variable has a significance of .152 (Table 11). Significant (P < .05) variables for this model were: Protein, Quadratic Effect of Protein, and Net Energy and Calcium interaction. The highly significant (P < .01) difference in protein content of the ration between Mexican herds (16.6%) and Michigan herds (14.1%) seems to indicate the difference in first service conception (56% and 60.3%, respectively). The variability of the factors studied ranged from size of herd to amount of time spent detecting estrus as well as amount of number of times concentrate and hay were fed to the cows. Factor analysis was applied to determine if some pattern of relationship among the different measurements existed and if the data could be reduced or rearranged into subsets of factors or variables that account for sources of variation. The patterns defined in Factor Analysis can be visualized through a geometric interpretation. Each of the variables included in the analysis could be considered as an axis of a geometric space, in this. way, the space would have a total of 49 dimensions, one 54 TABLE 10. Medal for First Service Conception with the Variables Left After the Stepwise Backward Elimination Pro- cedure. Model: R2 = .58621 2 Adj. R = .28277 Degrees of Freedom Regression = 11 Mean Squares Regression = 554.92929 Degrees of Freedom Residual = 15 Mean Squares Residual = 287.24938 F Significance = .117 Variablesa: Slope Standard Error Type I of Slope Error AVPROT —9.2104 4.9733 _ .084 CAFO -2097.3982 851.9888 .026 FIFO 125.8073 58.3743 .048 PRONE -.9740 .6524 .156 AVPROTZ 3.2112 1.3685 .033 NECA 12.0149 5.3350 .040 AVCA -l4.0044 41.9565 .743 AVCAZ 213.3930 115.8053 .085 AVFO -43.8821 146.6152 .769 PROCA -41.3610 28.9462 .174 AVFOZ 3526.7925 2002.6318 .099 aFor description of variables, see Table 35 TABLE 11. 2 Adj. R =- .64443 .33966 55 Model for First Service Conception with the Variables Left After the Stepwise Backward Elimination Procedure Including COUNTRY as a Variable. Degrees of Freedom Regression = 12 Mean Squares Regression = -559.204O Degrees of Freedom Residual = 14 Mean Squares Residual = 264.4653 F Significance - .091 Variablesa: COUNTRY PRONE AVFOZ FIFO NECA AVPROTZ AVPROT AVCAZ AVCA AVFO PROCA CAFO Slope Standard Error Type I , of Slope Error 19.9503 13.1770 .152 —1.4328 .6955 .058 1279.1290 2428.2398 .607 119.9637 56.1443 .051 12.5362 5.1306 .028 3.1495 1.3137 .031 -12.7875 5.3248 .031 253.4099 114.2179 .044 -11.6116 40.2892 .777 198.7656 213.2520 .367 ~58.5050 29.9941 .071 -1375.66l6 946.3363 .168 aFor description of variables, see Table 35 56 for each of the variables under consideration. In the space defined by the dimensions, every variable is represented by a point depending on its value. A line can be drawn from the point to the origin for a vector representation and the angle between any two of these vectors is a measure of the relationship between the two characteristics. If the angle approximates 90 degrees of relationship is less, if the angle approximates 0 degrees the relationship is stronger, if the angle between the two vectors is 180 degrees an inverse relation- ship exists, therefore variables that are highly inter- related will be grouped together in a pattern. The initial analysis was performed using SPSS (Nie 33 31., 1975). The first analysis is for exploring the data-reduction possibilities by constructing a set of new variables on the basis of the interrelation of variables in the data. This approach, which uses defined factors, is called principal-component analysis . Factors are shown in Table 12. Variation in Factor 1 is characterized by the grouping of variables related to nutrient concentration in each feedstuff, nutrient concentration (%) in total ration and total amount of nutrient in the ration. 57 Variability of Factor 2 is related to daily amount of feed materials fed to the animals. In Factor 3, a combination of variables related to general management and nutrient concentration in each feedstuff was included. The fourth factor included variables related to reproductive management such as: daily time for observing estrus of % of retained placenta. Included in Factor 5 are a small grouping of variables without relationships, for that reason, this factor was not considered. Eigenvalue is a measure of the relative importance of the function and the sum of eigenvalues is a measure of the total variance existing in the variables. In Table 13, the factors are ordered in terms of decreasing variation. New general variables were created and the criteria used was to select the two highest correlations (independ- ently of the sign) and regroup them under a single factor. In this way, the new variables created were: Nutrition (NUTl) characterized by Factor 1, Nutrition Management (NMGT) characterized by Factor 2, Reproductive Management (RMGT) characterized by Factor 4. Factor 3 and Factor 5 were discarded due to the low number of variables grouped in them. 58 It is important to point out that the analysis grouped the variables in a very defined way. Related variables were clustered under the same factor and unrelated variables were excluded as was the case for Factor 5. Subsequently, the data were fitted into three well defined newly generated variables to facilitate testing a general hypothesis. Hypothesis to be tested is that some feeding and reproductive management procedures could be affecting specific reproductive parameters. Before the new variables were tested for prediction in multiple regression analysis, they were standardized using the procedure described in Methods of Analysis. The goal was to develop a more accurate prediction model. The models are included in Tables 14-17. Using Days Open as dependent variable and COUNTRY as dummy variable (Table 14), the regression showed that the variables RMGT, NMGT and NUTl did not account for a significant difference between farms in Mexico and Michigan (P > .2). In Table 15, there was a highly significant difference between countries (P < .01) using first service after parturition as a dependent variable. 59 However, the variables under consideration did not account for the difference between the regions (P > .25). No significance between countries or variables was found for first service conception or services per conception (Tables 16 and 17). According to this analysis, no significant dif- ferences between Mexico and Michigan existed in relation to reproductive management, as shown in Appendix Tables 1, 37, 48, 51, 52. The differences among farms could be due to managerial decisions such as: policy for first service after parturition, use of heat detection aids, uterine infusion of antibiotics after calving, technical assistance, record keeping system or operator's level of competency as stated by Erickson (1972). During the analysis, management differences related to Nutrition were found. Most Michigan farms use complete rations for feeding dairy cows and operator utilized one or two production level groups for supplementing concentrates individually in the parlor, or only 1 group regardless or production. Due to larger herd size, all Mexican farms used three production level groups and allowed milking cows to consume their entire ration, with concentrate placed on the roughage, apart from the milking operation. 60 These differences between feeding practices were not significant and the results agreed with those of Wilk 33 31. (1978) and Clark 33 31. (1980). Cows fed a constant amount of concentrate (7.3 Kg/cow) during the entire lactation regardless of yield were compared to cows alloted to high and low subgroups on production of fat-corrected milk (1 Kg of concentrate per 2.25 Kg fat-corrected milk) (Wilk 33 31., 1978). The groups did not differ significantly in reproductive performance. Clark 33 31. (1980) assigned cows randomly to either a herd with high, medium and low production groups or a one-group control herd. Also, the groups did not differs significantly in reproductive performance. we expected to find differences that accounted for the variation in reproductive performance of dairy cows in two different region; thus, the complete analysis showed that the effect of management practices on reproductive performance in a dairy herd is not easily measured because of the complexity of the process. Also, it showed that although statistical data were obtained, cause and effect relationships between herd management and reproductive performance could not be established. 61 mmmeo.o cummo.o mommm.o: ~N©N¢.o Hmmmm.ou mm: o~¢c¢.Ou «memo.o HHHmH.o: mm¢c~.¢: cemmn.o mum< mmqu.on onHm.o momoo.o: mmawm.o- mmmmm.ou mmm< mnomm.ou mHHoo.o- «momm.cu NHNo«.o mqmmn.o .HssssHsHuusss cowumaohuouv Houomm HmeHocHHm momma xmuumz Houomm .NH mqm< «gems.o «mono.o mmmHH.o- moNHs.o mmmm~.o om>< Hmsem.o mmamo.o mseso.o- ommmm.o oAmHA.o < mmmmH.o- mmsHo.o muesH.o- m-o~.o mHoem.o- mz>< emomm.o smaH~.o- memos.o- eHaos.o HomMH.o Homm>< NAHm~.o- «memo.o mm~n~.o ammoe.o smmHo.o- mmm< «HmsH.o osmsm.o mmmmu.o esmmm.o- osHss.o mm< mmsHH.o- mmaoo.o mmsHm.o mmHms.o- omsHo.o mmu<, msoom.o NeHmm.o mHso~.o smNHs.o ssmHN.o- mum mammm.o mdmmm.o- NAOMH.¢ mHNms.o. odHeH.o- mum mmmm~.o mmNmH.c HmamH.o- mmoms.o mmmss.¢- em: memo~.o- sasso.o mmdmm.o msm~s.o nmmeH.o- mmn momo~.o eaoso.o- mmmmm.o- memes.o- mmmsH.o mHHz Hmnec.o- soss~.o memoo.o essen.o maHss.o- mNHmm_ amomm.o aeomH.o smsoH.o messm.o mmomm.o omH As.soov NH mHm o>Huoaneso mo unmouom mnHm>aowwm Houomm smsHmscdem .mH mHmme 64 TABLE 14. Regression Model for Days Open with the Variables Derived from Factor Analysis. Model: R2 - .03810 Adj. R2 - 0 Degrees of Freedom Regression = 3 Mean Squares Regression = 688.2697 Degrees of Freedom Residual = 23 Mean Squares Residual = 2266.6604 F Significance - .822 Variables: Slope Standard Error Type I of Slope Error RMGT -l.5611 5.6934 .786 NMGT -l.376l 1.7820 .448 NUTl -.l448 .1658 .392 INCLUDING COUNTRY AS A VARIABLE: Model: R2 - .0856 .Adj. R2 - 0 Degrees of Freedom Regression = 4 Mean Squares Regression = 1160.5141 Degrees of Freedom Residual = 22 Mean Squares Residual = 2252.5428 F Significance = .725 Variables: Slope Standard Error Type I of Slope Error COUNTRY -21.5089 20.1083 .296 RMGT .3862 5.9605 .949 NMGT -l.8384 1.8283 .326 NUTl .0864 .2721 .751 65 TABLE 15. Regression Model for Average Days from Calving to First Service with the Variables Derived from Factor Analysis. Model: R2 - .1941 Adj. R2 = .08901 Degrees of Freedom Regression = 3 Mean Squares Regression = 364.0885 Degrees of Freedom Residual = 23 Mean Squares Residual = 197.1478 F Significance = .167 Variables: Slope Standard Error Type I of SlOpe ' Error RMGT - .9926 1.6790 .56 NMGT -.2959 .5255 .579 NUTl .0782 .0489 .123 INCLUDING COUNTRY AS A VARIABLE: Model: R2 - .4127 Adj. R2 - .3059 Degrees of Freedom Regression = 4 Mean Squares Regression 8 580.5845 Degrees of Freedom Residual = 22 Mean Squares Residual s 150.1967 F Significance = .016 Variables: Slope Standard Error Type I " of Slope Error COUNTRY 14.8595 5.1924 .009 RMGT -.3526 1.5391 .821 NMGT .0234 .4721 .961 NUTl -.0815 .0702 .258 66 TABLE 16. Regression Model for Services per Conception with the Variables Derived From Factor Analysis. Model: R2 = .2165 Adj. R2 = .1144 Degrees of Freedom Regression = 3 Mean Squares Regression = .4250 Degrees of Freedom Residual = 23 Mean Squares Residual = .2005 F Significance = .125 Variables Slope Standard Error Type I of Slope Error RMGT .0569 .0535 .298 NMGT -.0275 .0167 .114 NUTl .0009 .0015 .49 INCLUDING COUNTRY AS A VARIABLE: Medel: 32 = .2731 Adj. R2 - .1409 Degrees of Freedom Regression = 4 Mean Squares Regression = .4019 Degrees of Freedom Residual = 22 Mean Squares Residual 8 .1945 F Significance = .12 Variables: Slope Standard Error Type I of Slopg Error COUNTRY -.2443 .1868 .204 RMGT .0790 .0553 .167 NMGT -.0328 .0169 067 NUTl .0035 .0025 3173 TABLE 17. R - .0300 Adj. R2 = 0 67 Regression Model for First Service Conception with the Variables Derived from Factor Analysis. Degrees of Freedom Regression = 3 Mean Squares Regression = 104.1292 Degrees of Freedom Residual = 23 Mean Squares Res F Significance = Variables RMGT NMGT NUTl idual - 439.1551 .87 Slope Standard Error Type I of Slope ‘ ‘ Error .9779 2.5060 .70 -.3114 .7844 .695 -.0481 .0730 .516 INCLUDING COUNTRY AS A VARIABLE: Model: R2 - .0306 Adj. R2 = 0 Degrees of Freedom Regression = 4 Mean Squares Regression = 79.7541 Degrees of Freedom Residual = 22 ‘Mean Squares Res F Significance - Variables: COUNTRY RMGT NMGT NUTl idual = 458.8157 .949 Slope Standard Error Type I of Slope Error -1.090 9.075 .905 1.076 2.690 .693 -.3349 .8251 .689 —.0364 .1228 .77 68 In an overview of all the models, we observe that the variable which accounted for most variation in the analyses was calcium in Days Open (P < .05), average days from calving to first service (P < .05) and Services per Conception (P < .05). Also interactions of Ca with P, Net Energy, Protein and Fiber showed to be significative in all four models. Calcium levels for Mexican herds (1.2% of ration D.M.) are well above the NRC recommendations (0.7% of ration D.M.), whereas Michigan herds have a good Ca bal- ance in the ration (.66%). However, no significant differences among countries were found for the variables days open and services per conception, the results of this study agree with those of Littlejohn and Lewis (1960), and Steevens 33 31. (1971). These authors concluded that fertility was not affected by the Ge content of the diet. Ward 33 31. (1971) reported that high levels of Ca did not affect signifi- cantly services per conception. For the variable average days from calving to first service a highly significant differences was found (P < .01) among regions. The results seem to agree with ward and Call (1979). These authors suggested that recommended levels of Calcium promote rapid uterine involution and early ovulation 69 in dairy cows. And, this could be the case for Michigan dairy cows which are fed an adequate level of Calcium. However, the variable average days from.ca1ving to first service should be considered related to im- portant management decisions such as: interval for first breeding after parturition, post partum.reproductive check and others which are going to influence the average number of days from calving to first service. Since average days from calving to first service influences greatly days open and to lesser extend services per conception, along with the management de- cisions described before, the influence of Ca alone seems to be doubtful for explaining the variance for the most part. It is important to point out that the results observed for phosphorus and protein are in agreement with the reports found in the literature. Phosphorus approaches significance (P < .06) only in the variable average days from calving in first service. Differences relative to this variable among regions are not signi- ficant (P > .05). Also, the results are similar to those reported before by Noller 33 31. (1977). Hecht 33 31. (1977), Call 33 31. (1978) and Carstairs 33 31. (1980) in which phosphorus levels in the ration did not influence reproduction dairy cows. 70 Protein approaches significance (P < .08) only in First Service Conception, the difference relative to this variable between regions is highly significant (P > .01). Since no significant differences among countries were found for the dependent variables, except for average days from calving to first service. The results agree with those of Bond and Wiltbank (1970), Wohlt and Clark (1978) and Edward33 31. (1980), in which ration protein levels did not affect reproductive performance. High protein levels in the ration of Mexican cows could be detrimental for first service conception and services per conception. As Jordan and Swanson (1979) suggested, this condition could create a suboptimal uterine or ovarian environment and reduce reproductive efficiency. Also, the practice of feeding excess protein is wasteful and could impair reproductive efficiency without increasing milk production. In the regression model for Days Open, Net Energy was significant (P < .05) and showed to have a negative effect. The results did not agree with literature reports of Sonderegger and Schurch (1977) and Ayalon 33 31. (1971). Theseauthors suggested that deficient levels of energy increased the interval from parturition to conception. In this study, although no significant differences among countries were observed for Net Energy, average days open 71 were lower for Mexican farms compared to Michigan farms. The results agree with Tong 33 31. (1979) and Carstairs 33 31. (1980) for showing no influence of energy on first service conception, services per conception and average days from calving to first service. Crude fiber was another variable attempting to explain the variation observed in Days Open (P < .05). The results agree with literature reports of Zamet 33 31. (1979), Francos 33 31, (1977), Mayer 33 31. (1978) and Tong 33 31. (1979). Table 18 shows a correlation analysis between Protein, Net Energy, Calcium, Phosphorus and Fiber. This approach was used in an attempt to explain the interactions among the nutrients. Highly significant positive (P <:.Ol) correlations were: Calcium and Protein, Fiber and Protein and Fiber and Calcium. Significant positive correlation (P :=.05) was Phosphorus and Net Energy. Significant negative correlation (P<= .05) was Calcium and Net Energy. The correlations between variables and their interactions are not consistent through the analyses and they do not help to explain how the interactions are affecting reproductive per- formance. Another attempt to understand the interaction effects was made by dividing our herds in high and low 72 33¢. u m Hoo. m coo. u m moo. n m Hemo.- NNHo. 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Criteria Used For Selection of Siresa VARIABLE OVERALL MEXICO MICHIGAN 1 2 3 4 1 2 3 4 1 2 3 4 Dam's Milk and Fat Prod. 2 2 2 1 O 0 0 0 2 2 2 1 Pedigree 1 0 1 1 0 0 0 0 1 0 l 1 USDA Predicted Difference 12 9 3 0 2 5 1 0 10 4 2 0 Color Markings 0 0 0 0 0 0 0 0 0 O 0 0 Conception Rate 2 6 3 5 2 1 0 3 0 5 3 2 Price 3 2 4 4 2 0 O 0 1 2 4 4 Repeatability Factor 3 6 7 7 0 2 4 2 3 4 3 5 Type Traits 3 1 5 5 2 0 2 2 1 1 3 3 A.I. Summary Lists 0 1 0 0 0 0 0 0 0 1 0 0 Recognition Programs 0 0 0 0 0 0 0 0 0 0 0 0 Other (Mastitis Resistance) 1 0 l 0 0 0 1 0 1 0 0 0 aNumber of Respondents Ranking the Criteria for Sire Selection. bHighest Ranking=1 118 .NN u 00044600 000 m4 n cmw4£ofiz .w u 004x02 ”:04on 0 Mom oncoamou mo Hanson Hmuou onu m0 unmouma 0 mm commounxm ohm madam N4 N N 4 am m hm>me x44: m4 4 m o em 4 aaaoaoe o4 a m m m4 «4 “was: a aaaauamz a ca 4 N 04 N4 coauoaaouamm a4 m m m AH m swam who maaoo oz may 02 mm» oz may mam<4m<> zaonon oonmz aqamm>o mm3oo mafionpoum 5&4: :4 00u4u muopuomwn 008500 umoz .o¢ m4mo N4N<4N<> Nasoo Na444so No4 Nana .44 @4449 120 TABLE 42. Comparison of A.I. and Bull In Terms of Conception Rate VARIABLE OVERALL MEXICO MICHIGAN Much More a b Convenient 7 (25.9) 4 (50.0) 3 (15.8) More Convenient 8 (29.6) 4 (50.0) 4 (21.1) Same 6 (22.2) 0 6 (31.6) Less Convenient 5 (18.5) 0 5 (26.3) Much Less Convenient 1 (3.7) 0 1 (5.3) aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents 121 TABLE 43. Percentage of Respondents That Use Grain Feeding Guide VARIABLE OVERALL MEXICO MICHIGAN Milk Production 188(66.6)b 6 (75.0) 12 (63.3) Fat Production 1 (3.7) 0 1 (5.3) Cows Condition 1 (3.7) 1 (12.5) 0 Same For All Cows 7 (25.9) 1 (12.5) 6 (31.3) aNumber of ReSpondents bNumber of Parenthesis are Percentage of Respondents 122 TABLE 44. Uterine Medication After Parturition VARIABLE OVERALL 'MEXICO MICHIGAN Yes 63(22.2)b 2 (25.0) 4 (21 1) No 21 (77.8) 6 (75.0) 15 (78.9) Z of Cows 0 22 (81.5) 6 (75.0) 16 (84.2) 25 - 79 1 (3.7) o 1 (5.3) 80 - 89 1 (3.7) 0 1 (5.3) 90 - 99 3 (11 1) 2 (25.0) 1 (5.3) aNumber of Respondents bNumber in Parenthesis are Percentage of Respondents 123 TABLE 45. Criteria For Cutting Alfalfa Hay VARIABLE OVERALL MEXICO MICHIGAN Pre-Bloom 6"5‘(22.2)b 1 (12.5) 5 (26.3) 1/10 Bloom 14 (51.9) 7 (87.5) 7 (36.8) Half Bloom 7 (25.9) 0 7 (36.8) Mature 0 0 o aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents 124 TABLE 46. Availability of Trace Mineral Salt VARIABLE OVERALL MEXICO MICHIGAN Free Choice 10307.0)b 4 (50.0) 6 (31.6) Mixed in Ration 6 (22.3) 0 6 (31.6) Both Methods 11 (40.7) 4 (50.0) 7 (36.8) aNumber of Respondents bNumbers in Parentheses are Percentage of Respondents 125 TABLE 47. Management of Cows in Estrus VARIABLE OVERALL ‘MEXICO MICHIGAN Separated 63(22.2)b 3 (37.5) 3 (15.8) Leave the Cow ‘with the Herd 21 (77.8) 5 (62.5) 16 (84.2) aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents 126 TABLE 48. Schedule for Observing Cows for Estrus VARIABLE OVERALL 4 . MEXICO MICHIGAN Before Feeding 7a(25.9)b 3 (37.5) 4 (21.1) During Feeding 6 (22.2) 1 (12.5) 5 (26.3) After Feeding 6 (22.2) 1 (12.5) 5 (26.3) Other (Milking, Barn Cleaning) 8 (29.6) 3 (37.5) 5 (26.3) I_ 8Number of Respondents bNumbers in Parenthesis are Percentage of Respondents 127 TABLE 49. Season to Which Better Conception Rate is Obtained ‘ (Z of Respondents) VARIABLE OVERALL MEXICO MICHIGAN Summer I 83(29.6)b 1 (12.5) 7 (36.8) Winter 14 (51.9) 5 (62.5) 9 (47.4) Same All Year 5 (18.5) 2 (25.0) 3 (15.8) aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents 128 TABLE 50. Use of Clean-Up Bull on the Farm VARIABLE OVERALL . IMEXICO MICHIGAN Number of Services with A.I. Before Using Bull: Never 63(22.2)b 1 (12.5) 5 (26 1 2 (7.4) 0 2 (10. 2 3 (11.1) 0 3 (15 3 7 (25.9) 2 (25.0) 5 (26. 4 6 (22.2) 4 (50.0) 2 (10 >4 3 (11.1) 1 (12.5) 2 (10. Z of Calvings From Bull 15.15 3.57 21 .3) 5) .8) 3) .5) 5) .91 aNumber of Respondents b Numbers of Parenthesis are Percentage of Respondents. 129 TABLE 51. Artificial Insemination Variables VARIABLE OVERALL MEXICO MICHIGAN Z of Herd Artificially Inseminated: 5 to 70 33(11.1)b O (0) 3 (15.7) 71 to 80 3 (11.1) 1 (12.5) 2 (10.6) 81 to 100 21 (77.8) 7 (87.5) 14 (73.6) Years Using A.I. 3 to 10 9 (33.3) 2 (25.0) 7 (37.0) 11 to 20 8 (29.6) 5 (62.5) 3 (21.0) 21 to 35 10 (37.0) 1 (12.5) 9 (42.0) Heat Detected A;M. A.I. Carried Out: Immediately 2 (7.4) 0 2 (10.5) Same Day, P.Mi 23 (85.2) 8 (100.0) 15 (75.9) Next Day 2 (7.4) 0 2 (10.5) Heat Detected P.M. A.I. Carried Out: Immediately 3 (11.1) 0 3 (15.8) Next Day, A.M. 23 (85.2) 8 (100.0) 15 (78.9) Next Day, PgM. 1 (3.7) 0 1 (5.3) aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents TABLE 51 (con't.) 130 VARIABLE OVERALL MEXICO _ .MICHIGAN Cows Detected in Estrus: Early A.M. 16 (59.3) 5 (62.5) 11 (57.9) Late A.M. 2 (7.4) 0 2 (10.5) Noon 3 (11.1) 1 (12.5) 2 (10.5) Evening 63(22.2)b 2 (25 O) 4 (21.1) Breeding Time During Summer Early A.M. 10 (37.0) 7 (87.5) 3 (15.8) Late A.M. 6 (22.0) 0 6 (37.6) Noon 0 0 0 Evening 11 (40.9) 1 (12.5) 10 (52.6) aNumber of Respondents b Numbers in Parenthesis are Percentage of Respondents 131 TABLE 52. Heat Detection Variables VARIABLE OVERALLI . MEXICO MICHIGAN Very Difficult 2a(7.4)b O (0) 2 (10 5) Difficult 10 (37.0) 4 (50.0) 6 (31.6) Easy 13 (48.0) 3 (37.5) 10 (52.6) Very Easy 2 (7.4) 1 (12.5) 1 (5.3) Winter Heat Obs. Frequency per Day 1 1 (3.7) 0 (0) 1 (5.3) 2 10 (37.0) 3 (37.5) 7 (36.8) 3 9 (33.3) 3 (37.5) 6 (31.6) :3 7 (25.9) 2 (25.0) 5 (26.3) Observer: Operator or Owner 21 (77.8) 2 (25.0) 19 (100.0) Wife 4 (14.8) 0 (0) 4 (21.1) Children 4 (14.8) 0 (0) 4 (21.1) Hired Labor 17 (63.0) 8 (100.0) 9 (47.4) aNumber of Respondents b Numbers in Parenthesis are Percentage of Respondents. 132 TABLE 52 (con't.) VARIABLE OVERALL V MEXICO MICHIGAN Summer Heat Obs. Frequency per Day: 1 0 (O) O (O) 0 (O) 2 12 (44.4) 3 (37.5)- 9 (47.4) 3 6 (22.2) 3 (37.5) 3 (15.8) 3 9 (33.3) 2 (25.0) 7 (36.9) 85§§§‘°” or 21 (77.8) 2 (25.0) 19 (100.0) Wife 6 (22.2) 0 (O) ' 6 (31.6) Children 5 (18.5) 0 (O) 5 (26.3) Hired Labor 183(66.7)b 8 (100.0) 10 (52.6) Time for Heat Detection (Mins.): 5 to 20 10 (37.0) 2 (25.0) 8 (42.1) 21 to 40 11 (40.7) 4 (50 0) 7 (36.9) >41 6 (22.3) 2 (25.0) 8 (24.0) aNumber of Respondents bNumbers in Parenthesis are Percentage of Respondents 133 TABLE 53. Reproductive Management of Cows AfterICalving VARIABLE OVERALL IIMEXICO MICHIGAN Post Partum Check Before Breeding: No 83(29.6)b 1 (12.5) 7 (36.8) Yes 19 (70.4) 7 (87.5) 12 (63.2) Pregnancy Check After Breeding: No 2 (7.4) 0 2 (10.5) Yes 25 (92.6) 8 (100) 17 (89.5) Post Partum Check Carried Out By: Veterinarian 23 (92.0) 6 (75.0) 17 (100) Other (Tech- nician, Operator) 2 (8.0) 2 (25.0) 0 Pregnancy Check (days): 30 to 50 13 (52.0) 5 (62.5) 8 (47.0) 51 to 70 10 (40.0) 1 (12.5) 9 (52.9) > 71 2 (8.0) 2 (25.0) 0 8Number of Respondents bNumbers in Parenthesis are Percentage of Respondents TABLE 53 (con't.) 134 VARIABLE _ OVERALL MEXICO MICHIGAN Interval from Calving to Breeding (days): 40 - SO 9 (33.3) 2 (25.0) 7 (36.8) 51 - 60 17 (62.9) 6 (75.0) 11 (57.9) > 61 1 (3.7) 0 1 (5.3) 135 TABLE 54. Use of Heat Detection Aids VARIABLE OVERALL II MEXICO _MICHIGAN Yes 103(37.O)b 0 10 (52.6) No 17 (63.0) 8 (100.0) 9 (17.4) aNumber of Respondents bNumbers of Parenthesis are Percentage of Respondents ‘\ e 10. 136 BREED INC What percent of your herd is bred artificially? How long have you been using A.I.? veers Do you inseminate any of your own cows? (If yes, all, skip to question 5). __ yes, ell YO. , some 00 How well do you feel your inseminator is trained for this job: adequately partially inadequately Heat detection is difficult for some people. In your case. it is: very difficult difficult _.“y very easy How many times a day are your cows observed for heat in the winter? By whom? Operator Wife Children Hired Labor How many times a day are your cows observed for heat in the summer? By whom? Operator Wife _ Children Hired Labor _____ How long a time each day are the cows observed for heat? If standing to be mounted is used as a symptom of heat. and your cow shows symptoms in the morning. A.I. is carried out: Immediately The same day, in the evening The next day If your cow stands to be mounted in the evening, she will be bred: Immediately Next day in the morning Next day in the evening 5% What percent of your cows settle with one breeding? 12. l3. 14. 15. 16. 17. 18. 19. 21. 137 In terms of conception rate - that is getting a cow to settle - would you say AI, compared to a bull is? Much more convenient More convenient Same Less convenient Much less convenient How do you rank (in order or importance) the following in making your decision about what bull to use? Dam's milk and fat production Repeatability factor Ancestry (Pedigree) Type traits of site's daughters USDA Predicted Difference MSU-Ext. AI Summary List Breed Pleasing color markings Association Sire Conception Rate Recognition Programs Price of Site Other (specify) What is your average calving interval? Do you obtain better conception rate during: Summer or Winter During summertime, most of your cows are bred during: Early Morning Late Morning Noon Evening Do you have more cows in heat during: Early morning Late morning Noon Evening What type of heat detection aids do you use? Chalk Comer Bull Paint Others (Specify) K-Mar Heat Detector How many services do you wait before to use the clean-up bull in a cow: 1 , 2 , 3 , 4 , More then 4 ' ‘0 Z of calvings due to clean-up bulls: Are your cows infused with antibiocics after breeding: Always Seldom Often Never Sometimes According to your Vet instructions 23. 138 When you observe a cow in heat, do you: Separate her Leave her with the herd Other Procedure (Specify) Do you catch up cows in heat: Before Feeding Time During Feeding Time After Feeding Time Other Time (Specify) Where do cows calve? what is the sanitation of calving area? What is the average number of days from calving to first breeding? What is the average number of days from calving to first heat? REPRODUCTION AND HERD HEALTH Are cows examined after calving to determine if they are ready to rebreed? Y0! no Are cows examined for pregnancy after breeding? yes no If so. when are they examined after breeding? days By Whom? Veterinarian: Other (specify) How long after calving are your cows bred for the first time? How many cows aborted in the past year? How many cows had milk fever last year? How many cows had ketosis last year? How effective do you feel the use of A.I. is in combating the spread of disease? Very effective Effective Not effective 10. ll. 12. 13. 14. 15. 16. 17. 139 Are your calves vaccinated for: Brucella TBR (red nose) Leptospirosis BVD (virus diarrhea) PI3 (shipping fever) Pasteurella (shipping fever) How many cows were culled last year? How many cows died last year? How would you describe your dairy operation-type of facilities? Stanchion barn or tie stalls Open lot-free stalls and parlor were enclosed - free stalls and parlor Cold covered-free stalls and parlor._____ Loose housing and parlor Stanchion barn and parlor Stanchion barn and free stalls Stanchion barn and loose housing Carrels and Milking Parlor Other What problems do you have more frequently with your high producing cows that you do nor have with your average cows: Going off feed Reproduction problems Mastitis and Udder Problems Ketosis Milk fever Other Rank in order of importance the following for culling cows: Milk Production Reproduction Feet and Legs Type Mastitis and Udder Problems Disposition Other (specify) How many cows had retained placenta last year? How many cows had Metritis last year? When do you have more calving problems? Summer Winter Do you routinely medicare the uterus of cows after normal calving? yes no 5 so, what I are medicated? 18. \J 140 Prot. Prot. Prot. Prat. Pror. Prot. Prot. Prot. Prot. Prot. Prot. Prot. Prat. Prot. Tea Yes Yes Anhydrous Ammonia 2nd Test 7272 2 22222222 2 '4 0 8' i No Other Do you worm your cows? If so, with what product? NUTRITION What has been the composition of your grain mix since January 1? a. Shelled corn Amount b. Oats Amount c. Wheat Amount d. Beet pulp Amount e. Protein supple. Amount f. Salt Amount g. Minerals Amount h. Corn & cob Amount i. H. M. Corn Amount 3. Barley Amount k. Molasses Amount 1. Linseed meal Amount m. Soybean meal Amount o. Other Amount 0. Amount p. Amount Do you feed your cows in dry lOt during the summer? Do you change your grain mix composition during the year? If no supplement is included in the ration, is any additional supplement fed? Yes No How much? How fed? Mixed with feed by hand Top dressed Other (specify) Do you know the crude protein percent of your corn silage? If yes, what is it? Do you add any additive to your corn silage? Yes If yes, what? Urea Commercial Additive How much per ton? Do you have your hay tested? Yes No If yes, what percent protein? 1st Test 10. 14. 15. 16. 17. 18. 19. 20. What is the criteria for cutting alfalfa for hay: Prebloom 1/10 bloom Mature 141 Half bloom In your winter feeding program, how many pounds of grain per day was fed to your average producing cows? What is your feeding guide? Pounds of grain: According to Milk Production According to Fat Production According to Cows condition Give all the cows the same Other How many pounds of grain does the average dry cow get? Do you increase the pounds of grain to dry cows before calving? How many pounds of corn silage per day do you feed your milking cows in an average year? Yes How many pounds of hay is fed per day to your milking cows in an average year? No How many pounds of haylage is fed per day to your milking cows in an average year? If haylage is fed, what is the percent protein? Ts salt and mineral available? Free Choice Mixed in the Ration How many pounds of grain a day did your top cow receive last year? Do you think your cows would eat more grain? If yes, why not give them more? How many times per day are your cows Roughage Concentrate fed: Yes No What time of the year do you have more cows going off feed: Is Selenium included in the feed? Average body weight of your cows? Yes N0 lbs. 24. 25. 26. 28. 29. 30. 142 What is the ration given to dry cows: Kind of Feed Amount (lbs) oer dav # of cows in this group Ration given to High-Producing cows: Kind of Feed Amount (lbs) per day # of cows in this group average milk production lbs Ration given to Medium-Producing Cows: Kind Of Feed Amount (lbs) per dav # of cows in this group average milk production lbs Ration given to Low-Producing Cows: Kind of Feed Amount (lbs) oer dav # of Cows in this group Average Milk Production lbs How many cows are you milking today? How many dry cows do you have today? Number of services per conception (from you DHIA Report) LIST OF REFERENCES LIST OF REFERENCES Alderman, G. 1963. Mineral Nutrition and Reproduction in Cattle. Veterinary Record, 75:1015. Apgar, J., D. Aspros, J. E. Hixon, R. R. Saatman and W. Hansel. 1975. Effect of Restricted Feed Intake on the Sensitivity of the Bovine Corpus Luteum to LH in Vitro. J. Anim. Sci., 41:1120. Armstrong, D. V., L. D. Brown, J. W. Thomas and S. M. Getty. 1966. High Level Grain Feeding and Herd Health, J. of Dairy Sci., 49:730 (Abstr.). Arnett, D. W., G. L. Holland and R. Totusek. 1971. Some ngiigg of Obesity in Beef Females. J. Anim. Sci. Ayalon, N., H. H. Harrari, 1. Lewis, L. N. Posener and Y. Cohen. 1971. Relation of the Calving-to-Service Interval to Fertility in Dairy Cows With Different Reproductive Histories, Production Levels and Management Practices. Reufah Vet. 28:155. Bar-Anan, R. 1968. Correlations Between Fertility, Milk Yield and Butter Fat Percentage in the Israeli-Friesian Dairy Cow. Reufah Vet., 25:238. Beal, W. E., R. E. Short, R. B. Staigmiller, R. A. Bellows, C. C. Kaltenbach and T. G. Dunn.’ 1978. Influence of Dietary Energy Intake on Bovine Pituitary and Luteal Function. J. Anim. Sci., 46:181-188. Bedrak, E., A. C. warnick, J. F. Hentages, Jr. and T. J. Cunha. 1964. Effect of Protein Intake and Grains Reproduction and Blood Constituents of Beef Heifers. Tech. Bull. Fla. Agric. Expt. Stn. No. 678:30 pp. Benson, J. D., E. W. Askew, R. S. Emergy and J. W. Thomas. 1972. Metabolism of Fatty Acids by Adipose Tissue and Liver of Cows Fed Normal, Restricted Roughage 0r MgO Supplemented Rations. J. Dairy Sci., 55:83. 143 144 Blowey, R. W., D. W. WOOd and J. R. Davis. 1973. A Nutritional Monitoring System for Dairy Herds Based on Blood Glucose, Urea and Albumin Levels Vet. Rec., 93:417. Bond, J. and J. N. Wiltbank" 1970. Effect of Energy and Protein on Estrus, Conception Rate, Growth and Milk Production of Beef Females. J. Anim. Sci., 30:438. Boyd, H. 1972. weight Change and Fertility in One Herd of Dairy Cattle. Vet. Rec., 91:193. Broster, W. H. 1973. Liveweight Change and Fertility in the Lactating Dairy Cow: .A Review. Vet. Rec., 93:417-420. Buchanan-Smith, J. G., W: Bannister, R. M. Durham and S. E. Curl. 1964. Effect of All-Concentrate fed Ad Libitum versus Roughage Ration on Occurrence of Estrus in Beef Heifers. J. Anim. Sci., 23:902 (Abstr.). Butler, W. R., R. W. Everett and C. E. Coppock. 1981. The Relationship between Energy Balance, Milk Production and Ovulation in Postpartum Holstein Cows. J. Animr Sci., 53:742. Call, J. W., J. E. Butcher, J. T. Blake, R. A. Smart and J. 1“ Shupee 1978. Phosphorus Influence on. Growth on Beef Cattle. J. Anim. Sci., 47:216-225. Carson, R. L., A. B. Caudle and H. E. Riddle. 1978. The Relationship between Narrow Calcium-Phosphorus Ratio and Reproductive Problems in a Dairy Herd: A Case Report. TheriogenologY. 9:505. Carstairs, J. A., D. A. Morrow and R. S. Emery. 1980. Postpartum Reproductive Function of Dairy Cows as Influenced by Energy and Phosphorus Status. J. 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Sci., 39:380. Davidson, M., G. Francos and E. Meir. 1978. An Analysis of Several Nutritional Parameters in Kibutz Herds with High and Low Fertility. Reufah Vet., 35:167. Davis, D., R. R. Schalles, G. H. Kiracofe. 1977. In- fluence of Winter Nutrition on Beef Cow Reproduction. J. Anim. Sci., 46:430. Downie, J. G., and A. L. Gelman. 1976. The Relation- ship Between Changes in Body weight Plasma Glucose and Fertility in Beef Cows. Vet. Rec., 99:210-212. Dunn, T. G., J. E. Ingalls, D. R. Zimmerman and J. N. Wiltbank. 1969. Reproductive Performance of 2-Year-Old Hereford and An us Heifers as Influenced by Pre and Post-CaIving Energy Intake. J. Anim. Sci. 29:719. Darrell, W. B. 1951. A Survey of the Role of Nutrition in Sterility of Dairy Cattle. Can. J. Comp. Med. 15:1. 146 Eckles, C. H., L. S. Palmer, T. W. Gullickson, C. P. Fitch, W. L. Boyd, L. Bishop and J. W. Nelson. 1935. 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Patton, R. D. Randell, E. L. Monk, M. I. Udo-Aka and C. J. Callahan. 1976c. Dietary Urea for Dairy Cattle. IV. Effect on Reproductive Hormones. Theriogenology 5:213. Erickson, R. W. 1972. An Analysis of High and Average Milk Production Dairy Farms. Ph.D. Thesis. Michigan State University, E. Lansing. Everson, R. A., N. A. Jorgensen, J. W. Crowley, E. L. Jensen and G. P. Barrington. 1976. Input Output of Dairy Cows Fed a Complete Ration of a Constant or Variable ForageNKJGrain Ratio. J. Dairy Sci., 59:1776. 147 Fitch, C. P., W. L. Boyd, C. H. Eckles, T. W. Gullickson, L. S. Palmer and C. Kennedy. 1932. Report of an Experiment to Determine the Effect of a Low Calcium Ration on Reproduction in Cattle. Cornell Vet. 22:156. Folman, Y., M. Rosenberg. Z. Herz and M. Davidson. 1973. The Relationship Between Plasma Progesterone Concentration and Conception in Post-Partum Dairy Cows Maintained on Two Levels of Nutrition. J. Reprod. Fert. 34:267-278. Francos, G. 1970. Observations on the Relationship Between Overfeeding and the Incidence of Metritis in Cows After Normal Parturition. Reufah Vet., 27:148. Francos, G. 1968. The Relationship Between the Milk Fat Percentage and Fertility in Dairy Herds. Reufah Vet., 25:32. Francos, G. 1969. Observations on the Relationship Between Fertility and Butter Fat Percentage in Three Dairy Herds. Reufah. Vet., 26:121. Francos, G. 1974. Possible Effect of Different Nutri- tional Planes on the Incidence of Reproductive Disorders in Israeli Dairy Herds. Reufah Vet., 31:27. Francos, M. Davidson and E. Mayer. 1977. The Influence of Some Nutritional Factors on the Incidence of the Repeat Breeder Syndrome in High Producing Dairy Herds. Theriogenology. 7:107. Gardner, R. W. 1969. Interactions of Energy Levels Offered to Holstein Cows Prepartum and Postpartum. II. Reproductive Performance. J. of Dairy Sci., 52:1985. Gardner, R. W., J. D. Schuh and L. G. Vargus. 1977. Accelerated Growth and Early Breeding of Holstein Heifers. J. Dairy Sci., 60:1941-1948. Gibson, W. W. C. 1969. Unifying Hypothesis-Fertility Status, Palatability, Ketosis, Protein Intake. Vet. Rec., 85:728. 148 Gill, J. L. 1978. Design and Analysis of Experiments in the Animal and Medical Sciences, Vol. 1, Ames, Iowa: Iowa State University Press. Gombe, S. and W; Hansel. 1973. Plasma Luteinizing Hormone (LH) and Progesterone Levels in Heifers on Restricted Energy Intakes. J. Anim. Sci., 37:728. Gould, C. M. 1969. Unifying Hipothesis-Fertility Status, Palatability, Ketosis, Protein Intake. Vet. Rec., 85:662. Guilbert, H. R. 1942. Some Endocrine Relationships in Nutritional Reproductive Failure (A Review). J. Animn Sci., 1:3-13. Hart, B. and G. L. Mitchell. 1965. Effect of Phosphate Supplementation on the Fertility of an Open Range Beef Cattle Herd on the Barkly Tableland. Aust. Vet. J., 41:305-9. Hecht, D., M. E. wells, L. J. Bush and G. D. Adams. 1977. 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