Das Projekt gibt einen Literaturüberblick zu Rassenkreuzungen beim Milchrind, wertet eine Befragung sächsischer Betriebe, die Kreuzungsverfahren anwenden, aus und beschreibt die Methodik eines im Rahmen des Projektes angepassten ökonomischen Kalkulationsschemas. Anhand beispielhafter Berechnungen nach dem Prinzip der Leistungs- und Kostenrechnung lassen sich für die Rassenkreuzungen Holstein-Friesian mit Fleckvieh, Braunvieh und skandinavischem Rotvieh vergleichende ökonomische Kalkulationen, auch im Vergleich zu den Ausgangsrassen, durchführen. Das Kalkulationsmodell ermöglicht eine Vielzahl von Variantenrechnungen. Es steht als Excel-Anwendung auf Anfrage allen interessierten Praktikern zu Verfügung.

No description available.

Four alternative methods were examined with computer simulated data to improve accuracy of animal model genetic evaluations by including records lacking sire identification. Methods 1 and 2 assumed genetic values of cows missing sire identity were population and management group average, respectively. Methods 3 and 4 accounted for genetic values through producing abilities estimated as random and fixed effects, respectively. Correlations between true and estimated management group effects and breeding values of cows and sires were used as measures of estimation accuracy. Alternative methods were examined to determine 1) optimum, minimum management group size, 2) increases in estimation accuracy of alternative methods relative to the conventional method of discarding records lacking sire identity, 3) the effects on accuracy of missing sire identity for lower true breeding value sires, and 4) the potential to use different alternative methods in herds of varying size, proportion of cows sire identified, and level of variation. Management group effects were estimated more accurately as minimum management group size increased (3 to 6 to 9), but breeding values were less accurate. Accuracies of alternative methods slightly exceeded those of the conventional method for all estimated effects and all minimum group sizes. Accuracies of alternative and conventional methods were compared in 60 population with 250 sires and averages of 11,139 cows with 23,849 records. Alternative methods were always more accurate than the conventional method for estimating group effects. Methods 1 and 3 were uniformly more accurate in estimating breeding values of cows, and estimated breeding values of sires more accurately in 55 and 54, respectively, of 60 populations. Increases in accuracy were largest for method 3, but small for all methods. Intentionally omitting identity for daughters of sires with low breeding value reduced accuracy of estimation for breeding values but not for group effects. However, alternative methods were more accurate than the conventional method. Alternative methods were relatively most accurate for estimating breeding values in small herds having high variance and low proportions of sire identified cows. Method 3 had uniformly highest accuracy but method 1 often was similar with less computing cost. / Ph. D.

Variation among sires in daughters' reproductive performance was analyzed using data on 4,360 cows from nine herds obtained from the American Simmental Association. Cows were required to have at least 50% Simmental breeding, to have calved first at 2 yr of age and to have been born and subsequently calved in the same herd and season. Traits analyzed included first and second calving dates, first calving interval and the percentage of cows that returned to calve in the same season as 3-yr-olds. Data were adjusted for effects of percentage Simmental and first-calf calving ease score. At second calving, purebred Simmentals calved 1.7 ± 1.2 d later than 75% Simmental cattle and 5.1 ± 1.4 d later than 50% Simmental cattle. When compared to cows that calved without assistance at first calving, cows experiencing easy pulls were 1.7 ± 1.4% less likely to calve as 3-yr-olds and had 4.9 ± 1.0 d longer calving intervals. Cows with hard pulls were 9.0 ± 2.1% less likely to return and had 6.5 ± 1.6 d longer calving intervals. Cows requiring Cesarean section were 23.1± 2.5% less likely to return and had 19.6 ± 2.4 d longer calving intervals. Heritability estimates were .17 ± .04 for first calving date, .07 ± .06 for second calving date, .04:105 for calving interval and .11 ± .04 for percent return. Calving interval does not appear to be a useful selection criterion to improve reproduction. Phenotypic and genetic correlations of first calving date with calving interval were -.58 and -.83 ± .37, respectively. The genetic correlation between first and second calving dates was .66 ± .41. Given current data recording procedures, calving date appears to be the most useful potential selection criterion to improve reproductive fitness. / Master of Science

Opportunity cost of postponed replacement (OC) is the income forfeited by keeping a cow for an extra day and is estimated by the income produced by an average replacement. The effect of adjusting a measure of net income, relative net income (RNI), for OC (RNIOC) by lactation was studied. After edits, the data set consisted of 2,982,001 Holstein cows. Prediction factors were developed for RNI and days of productive life (DPL) so that OC could be estimated from cows with shorter herd life opportunities. Within-herd correlations of RNI estimated from 84 month herdlife opportunity with that predicted from cows still alive at 36, 48, 60 and 72 months were .46, .59, .72 and .76, respectively and predictions reflected phenotypic trends of increased net income over time. Corresponding correlations for predicted 84 month DPL at the same ages were .28, .36, .41 and .47 and predictions conflicted with phenotypic trends of decreased herdlife over time. Total OC for cows with 84 month opportunity were raised by an average of $34 when OC was estimated specific to each lactation. The 433,116 cows with classification records and 84 month herdlife opportunity were used to estimate genetic and phenotypic (co)variances among type traits, production, and months in milk (MIM), RNI and RNIOC with a multiple trait Sire model. Production information from all cows in classified herds indicated that classified cows are not a random sample of cows in those herds. Heritability of RNIOC (.17) was higher than RNI (.12), but the genetic correlation between the traits was high (.97). Heritability of MIM was .06. Genetic correlations of MIM to the yield and linear type traits were less than -31 in absolute value. Evaluation of net merit using economic weights developed with RNIOC was more accurate than indirect prediction of MIM. Approximate reliability of a first crop AI sire evaluation for net merit is .65 compared to .42 for MIM. / Ph. D.

No description available.

Text in English, Tswana / Low nutrient intake affects metabolism and growth in pregnant heifers and limits milk production in lactating cows on communal area smallholder dairy farms of the subtropics. Two studies were conducted during the current research. The first study evaluated effects of nutrient supply in standardized dairy diets on the growth and body reserves of pregnant Jersey heifers raised on communal area smallholder farms in a semi-arid zone of South Africa. Twenty-two farms with a total of 42 heifers, aged 22 to 28 months which were seven months pregnant at the beginning of the study were selected for the study. These represented the total number of farms with dairy cows in the area that were supported through a structured Dairy Development Program (DDP) of South Africa. Each farm had at least two pregnant Jersey heifers during the summer season of 2016. Each heifer was supplied 2.5 kg of a far-off (60-30 d prepartum) dry cow concentrate and increased to 3.3 kg of the same concentrate at close-up period (29-0 d prepartum). Feeding of concentrate was based on a standardized feeding program as recommended by DDP. During this study, no feeding treatment was imposed on the heifers. Eragrostis curvula hay was supplied by DDP. Daily intake of 7.2 and 5.4 kg; respectively for heifers at 60-30 d prepartum and 29-0 d prepartum was determined based on residual hay. Heifer diet (HD1) and heifer diet HD2 were therefore simulated respectively for cows at 60-30 d preparpartum and 29-0 d prepartum, respectively. Diets were assessed for nutrient composition using chemical analyses and in vitro ruminal degradation. Post ruminal nutrient absorption and animal responses were predicted using the Large Ruminant Nutrition System (LRNS) version 1.0.33 (level 1). Actual measurements of body weight (BW), body condition score (BCS) were done and blood was collected and analysed for proteins monthly. Heifers’ responses were validated against the model predicted values and comparative analysis of animal performance during pregnancy was done against the National Research Council (NRC, 2001) reference values. Relative to the minimum requirement for ruminants, both HD1 and HD2 diets had relative feed value (RFV) below 144. About 35% of HD1 dietary crude protein (CP) was within the slowly degrade neutral detergent fibre (NDF) fraction which is the neutral detergent fibre insoluble crude protein (NDFICP) while 32% was not available as the acid detergent insoluble crude protein (ADICP). Equally, HD2 diet had effectively 5.2% of CP as available protein and the fraction of the slowly degraded NDF constituted only 52.3% of the effective available protein. Energy density of HD1 and HD2 were 25% and 16% higher than expected at far-off and close-up period, respectively. The intake of metabolzable protein (MP) were 32 and 25% higher than predicted for the far-off and close-up period, respectively. Supply of MP was 37 % and was higher than NRC predictions of daily requirement in Jersey cow. This allowed BW gain of 29 kg and BCS of 0.33 which was within 25th percentile for pregnant heifers. Mean concentration of blood urea at both far-off and close-up periods deviated by 25% from NRC values. Creatinine (CR) concentration was 145 μmol /L at far-off and 155 μmol /L at close-up period. The second study assessed the adequacy of two lactation diets fed to 42 primiparous Jersey cows, aged 24 to 30 months during early (1-30 d postpartum) and peak (31-60 d postpartum) periods on the lactation performance of the cows. Cows received 4.5 and 5 kg of dairy concentrate at 1-30 d postpartum and peak milk (31-60 d postpartum) respectively. Eragrostis curvula hay was supplied ad libitum and dry matter intake (DMI) was estimated at 7.2 kg of hay/cow/day from residual hay. No feeding treatment was imposed except for the standardised diets typical to the production environment. Two simulated lactation diets (LD1 and LD2) were prepared based on dry matter intake (DMI) of grass hay and lactation concentrate. Diets were assessed for nutrient composition using wet chemistry and in vitro ruminal degradation. Nutrient supply of diets and absorption from the small intestines as well as cows’ responses were predicted using the Large Ruminant Nutrition System (LRNS) version 1.0.33 (level 1). Body weight and BCS were monitored, blood was collected and analysed for proteins monthly. A record of milk yield was taken daily, and milk was analysed for fat, protein, lactose and urea nitrogen weekly. Cows had DMI of 11.2 kg which was 12% higher than the expected at 1-30 d postpartum period and 11.6 kg which was 21% higher than the expected in 31-60 d postpartum cows. Diets had low available protein as % of dietary protein (LD1=46%; LD2=45%) and the slowly degraded NDF fraction (NDFICP) constituted 64% of the available protein. Intake of energy was 20% and 17% lower than the predicted value for the cows, respectively, at 1-30 d postpartum and 31-60 d postpartum period. Cows had negative energy balance of -6.5 and -5.6 Mcal respectively at 1-30 d postpartum and 31-60 d postpartum cows. Protein intake of lactating cows was low, which resulted in negative protein balance of 59% and 42% of cow’s daily requirement, respectively, at 1-30 d postpartum period and 31-60 d postpartum period. There was loss of BW and BCS, low milk yield, energy corrected milk (ECM: 9.50 kg/d) and feed efficiency (FE) of less than 1 (LD1= 0.85; LD2 =0.89) in cows at both periods. Composition of fat, protein and lactose in milk were negatively affected by the low level of dietary protein. Somatic cell count (SCC) in milk was 121 ± 13 x 103/ml and cows did not show signs of illness. Mean milk urea nitrogen (MUN) concentration was 12 ± 2.7 mg/dl reflecting the low protein status of the lactating cows. Cows had high creatinine concentration of 116 and 102 μmol /L at 1-30 d postpartum and 31-61 d postpartum period, respectively, which may indicate muscle breakdown due to heat stress relative to the hot production environment. Results showed that diets fed to dairy cows on communal area smallholder farms in Sekhukhune and Vhembe districts in Limpopo province had low feeding value and their low nutrient supply affected rumen fermentation, heifers’ ‘growth, body reserves and early lactation in Jersey dairy cows. In conclusion, diets supplied to dairy cows raised on smallholder farms are low in nutrients and do not support efficient growth in heifers and optimal milk production in early lactation. Development of a nutrition plan for improved dairy diets is required to maximise production and longevity in cows and enhance sustainability of dairy production on the smallholder farms in South Africa. / Go ja dijo tse di nang le dikotla tse di kwa tlase go ama metaboliseme le kgolo ya meroba e e dusang mme e ngotla tlhagiso ya mašwi ya dikgomo tse di tlhagisang mašwi mo dipolaseng tse dinnye tse di tlhakanetsweng mo mafelong a a mogote. Go dirilwe dithutopatlisiso di le pedi jaaka karolo ya patlisiso ya ga jaana. Thutopatlisiso ya ntlha e sekasekile ditlamorago tsa tlamelo ya dikotla mo dijong tsa teri tse di rulagantsweng mo kgolong le dirasefe tsa mmele tsa meroba ya Dijeresi e e dusang mo dipolaseng tse dinnye tse di tlhakanetsweng mo karolong e e batlileng e nna sekaka mo Aforika Borwa. Go tlhophilwe dipolase di le 22 tse di nang le meroba e le 42, e e bogolo jo bo magareng ga dikgwedi tse 22 le 28 mme e na le dikgwedi tse supa e ntse e dusa kwa tshimologong ya thutopatlisiso. Tsone di emetse palogotlhe ya dipolase tse di mo karolong eo tse di tshegediwang ke Lenaneo le le rulaganeng la Tlhabololo ya Teri (DDP). Polase nngwe le nngwe e ne e na le bonnye meroba ya Jeresi e le mebedi e e dusang ka paka ya selemo sa 2016. Moroba mongwe le mongwe o ne o fepiwa ka 2.5 kg ya dijo tse di omileng tsa dikgomo tsa fa go sa ntse go le kgakala (malatsi a le 60-30 pele ga go tsala) mme tsa okediwa go nna 3.3 kg fa malatsi a atamela (malatsi a le 29-0 pele ga go tsala). Dijo tseno di ne di di rulagantswe go ya ka lenaneo le le rulagantsweng la kotlo le le atlenegisitsweng ke DDP. Mo nakong ya thutopatlisiso eno, ga go na kalafi epe ya kotlo e e neng e patelediwa meroba. DDP e ne e tlamela ka furu ya eragrostis curvula. Go ja ga letsatsi le letsatsi ga meroba ga 7.2 le 5.4 kg ka nako ya malatsi a le 60-30 pele ga go tsala le malatsai a le 29-0 pele ga go tsala go ne go ikaegile ka furu e e setseng. Ka jalo go ne ga tlhagisiwa gape kotlo ya meroba ya 1 (HD1) le kotlo ya meroba ya 2 (HD2) mo dikgomong tse di mo malatsing a le 60-30 pele ga go tsala le malatsi a le 29-0 pele ga go tsala. Dikotlo tseno di ne tsa sekwasekwa go bona go nna gona ga dikotla mo go tsona go dirisiwa tshekatsheko ya dikhemikale mo mogodung. Go ne ga bonelwa pele monyelo ya dikotla morago ga go feta mo mpeng ya ntlha le tsibogo ya diphologolo go ya ka Thulaganyo ya Kotlo ya Diotli tse Dikgolo (LRNS) mofuta wa 1.0.33 (legato 1). Go dirilwe tekanyo ya boima jwa mmele (BW) le maduo a seemo sa mmele (BCS) mme go ne ga tsewa madi le go a sekaseka go bona diporoteini kgwedi le kgwedi. Tsibogo ya meroba e ne ya tlhomamisiwa ka dipalo tse di bonetsweng pele tsa sekao mme ga dirwa tshekatsheko e e tshwantshanyang ya tiragatso ya diphologolo ka nako ya go dusa go dirisiwa dipalo tsa Lekgotla la Bosetšhaba la Dipatlisiso (NRC, 2001). Malebana le ditlhokegopotlana tsa diotli, HD1 le HD2 di ne di na le boleng jo bo tshwantshanyegang jwa kotlo (RFV) jo bo kwa tlase ga 144. Poroteini e e tala (CP) ya dijo e e ka nnang 35% ya HD1 e ne e le mo karolwaneng ya tekanyetso ya faeba e e bolang ka iketlo (NDF) e leng poroteini e e tala ya faeba e e lekanyediwang (NDFICP), fa 32% di ne di seyo jaaka poroteini e tala e e sa monyelegeng ya esete (ADICP). Fela jalo, HD2 e na le 5.2% tsa CP e e dirang jaaka poroteini e e teng mme karolo ya NDF e e bolang ka iketlo e ntse fela 52.3% tsa poroteini e e dirang e e gona. Bogolo jwa maikatlapelo a HD1 le HD2 bo ne bo le kwa godimo ka 25% le 16% go na le jaaka go ne go solofetswe mo dipakeng tse di kgakala le tse di atamelang. Go jewa ga poroteini e e silegang (MP) go ne go le kwa godimo ka 32% le 25% go na le jaaka go ne go solofetswe mo dipakeng tse di kgakala le tse di atamelang. Tlamelo ya MP e ne e le 37%, e leng e e kgolwane go na le diponelopele tsa NRC tsa ditlhokego tsa letsatsi le letsatsi tsa dikgomo tsa Jeresi. Seno se letlile gore go nne le koketsego ya BW ya 29 kg le BCS ya 0.33 e leng se se neng se le mo diperesenteng tsa bo25 tsa meroba e e dusang. Go nna teng ga urea ya madi mo dipakeng tse dikgakala le tse di atamelang go ne go farologane ka 25% go tswa mo dipalong tsa NRC. Go nna teng ga kereitini (CR) e ne e le 145 μmol/L mo pakeng e e kgakala le 155 μmol/L mo pakeng e e atamelang. Thutopatlisiso ya bobedi e sekasekile ditlamorago tsa dijo tse pedi tsa tlhagiso ya mašwi mo tiragatsong ya tlhagiso ya mašwi ya dikgomo tsa Jeresi di le 42 tse e leng la ntlha di tsala tsa bogolo jwa dikgwedi tse di magareng ga 24 le 30 mo pakeng ya ntlha (malatsi a le 1-30 morago ga go tsala) le ya setlhoa (malatsi a le 31-60 morago ga go tsala). Dikgomo di amogetse 4,5 le 5 kg ya motswako wa teri mo dipakeng tsa mašwi tsa ntlha (malatsi a le 1-30 morago ga go tsala) le tsa setlhowa (malatsi a le 31-60 morago ga go tsala). Go ne go tlamelwa ka furu ya eragrostis curvula go ya ka tlhokego mme go ja dijo tse di omileng (DMI) go ne go lekanyediwa go 7.2 kg ya furu/ka kgomo/ka letsatsi go tswa mo furung e e neng e setse. Go ne go sa patelediwe kalafi epe ya phepo, kwa ntle fela ga dijo tse di rulagantsweng tse di tshwanetseng tikologo ya tlhagiso. Go ne ga baakanngwa dijo tsa tlhagiso ya mašwi tse di tlhagisitsweng gape (LD 1 le LD 2) di ikaegile ka go jewa ga tse di omileng (DMI) e leng furu ya tlhaga le metswako ya tlhagiso ya mašwi. Go nna teng ga dikotla ga dijo tseno go ne ga lekanyediwa go dirisiwa khemisitiri e e bongola le go bola mo mpeng ga in vitro. Go ne ga bonelwa pele tlamelo ya dikotla ya dijo, monyelo go tswa mo maleng a mannye mme go ne ga bonelwa pele tsibogo ya dikgomo go dirisiwa Thulaganyo ya Kotlo ya Diotli tse Dikgolo (LRNS) mofuta wa 1.0.33 (legato 1). Go ne ga elwa tlhoko boima jwa mmele le BCS, go ne ga tsewa madi mme a sekasekwa go bona diporoteini kgwedi le kgwedi. Go ne ga rekotiwa tlhagiso ya mašwi letsatsi le letsatsi mme mašwi a sekasekwa go bona mafura, poroteini, laketose le urea naeterojini beke le beke. Dikgomo di ne di na le DMI ya 11.2 kg, e e neng e le kwa godingwaga ka 12% go na le jaaka go ne go solofetswe mo pakeng ya malatsi a le 1-30 morago ga go tsala, le DMI ya 11.6 kg, e e neng e le kwa godingwana ka 12% go na le jaaka go ne go solofetswe mo dikgomong tse di nang le malatsi a le 31-60 di tsetse. Dijo di ne di na le poroteini e e gona e e kwa tlase jaaka peresente ya poroteini ya dijo (LD1=46% le LD2=45%) mme karolwana ya NDF e e bodileng ka bonya (NDFICP) e nnile 64% tsa poroteini e e gona. Go jewa ga maikatlapelo go ne go le kwa tlasenyana ka 20% le 17% go na le dipalo tse dineng di bonetswe pele mo dikgomong mo dipakeng tsa malatsi a le 1-30 morago ga go tsala le malatsi a le 31-60 morago ga go tsala. Go rekotilwe balanse ya maikatlapelo a a tlhaelang a dikgomo ya -6.5 le -5.6 Mcal mo malatsing a le 1-30 morago ga go tsala le 31-60 morago ga go tsala. Go jewa ga poroteini ke dikgomo tse di tlhagisang mašwi go ne go le kwa tlase, mme seo sa baka balanse e e tlhaelang ya poroteini ya 59% le 42% tsa ditlhokego tsa letsatsi le letsatsi tsa dikgomo mo pakeng ya malatsi a le 1-30 morago ga go tsala le malatsi a le 31-60 morago ga go tsala. Go rekotilwe tatlhegelo ya BW le BCS, tlhagiso e e kwa tlase ya mašwi, mašwi a a baakantsweng maikatlapelo (ECM: 9.50 kg/ka letsatsi) le bokgoni jwa furu (FE) jo bo kwa tlase ga 1 (LD1=0.85; LD2=0.89) mo dikgomong mo dipakeng tseo tsotlhe. Go nna teng ga mafura, poroteini le laketouse mo mašwing di amegile ka tsela e e sa siamang ka ntlha ya seelo se se kwa tlase sa poroteini e e kwa tlase. Tekanyetso ya disele tsa somatiki (SCC) mo mašwing e ne e le 121±13x10³/ml mme dikgomo ga di a bontsha matshwao ape a bolwetsi. Motswako wa urea naeterojini ya mašwi (MUN) e ne e le 12±2.7mg/dl, e leng se se bontshang seemo se se kwa tlase sa poroteini sa dikgomo tse di tlhagisang mašwi. Dikgomo tseno di ne di na le motswako wa kereitine wa 116 le 102 μmol/L mo dipakeng tsa malatsi a le 1-30 morago ga go tsala le malatsi a le 31-61 morago ga go tsala, mme seo se ka supa go fokotsega ga mesifa ka ntlha ya kgatelelo ya mogote e e bakwang ke tikologo e e mogote e go tlhagisiwang mo go yona. Dipholo di bontshitse gore dijo tsa dikgomo tsa teri mo dipolaseng tse dinnye tse di tlhakanetsweng mo dikgaolong tsa Sekhukhune le Vhembe kwa Porofenseng ya Limpopo di na le boleng jo bo kwa tlase jwa kotlo le gore dijo tse di nang le dikotla tse dinnye di amile titielo ya dijo, kgolo ya meroba, dirasefe tsa mmele le tlhagiso ya mašwi ka bonako mo dikgomong tsa teri tsa Jeresi. Kwa bokhutlong, dijo tsa dikgomo tsa teri tse di godisediwang mo dipolaseng tse dinnye di na le dikotla tse di kwa tlase mme ga di tshegetse kgolo e e mosola ya meroba le tlhagiso e e siameng ya mašwi mo nakong ya ntlha ya tlhagiso ya mašwi. Go tlhokega leano la dikotla go tokafatsa dijo tsa teri go tokafatsa tlhagiso le go tshela sebaka ga dikgomo le go tokafatsa go nnela leruri ga tlhagiso ya teri mo dipolaseng tse dinnye mo Aforika Borwa. / Agriculture and  Animal Health / Ph.D. (Agriculture)