In der vorliegenden Arbeit wurde die Bedeutung des Einflusses von Laktationsnummer, Laktationsstadium, Körperkondition und Absetzverfahren auf Merkmale der Milchleistung und Eutergesundheit einer Herde Ostfriesischer Milchschafe untersucht. Die Tiere wurden nach dem Ablammen zufällig einer Früh- bzw. Spätabsetzergruppe zugeordnet (Absetzen der Lämmer 3 bzw. 42 Tage post partum). An insgesamt 40 Terminen wurden über eine vollständige Laktation Vorgemelks-, Hälftenanfangsgemelks- und Einzeltiergemelksproben gewonnen. Das Spektrum der untersuchten Merkmale umfasste die Milchmenge, die Milchinhaltsstoffe (Fett, Eiweiß, Laktose), die klinische Untersuchung des Euters, die somatische Zellzahl, die elektrische Leitfähigkeit und bakteriologische Untersuchungen. Zusätzlich wurden das Körpergewicht und die Körperkondition der Mutterschafe erfasst. Das durchschnittliche Leistungsniveau der Herde betrug 301±101,3 kg Milch, bei mittleren Milchfett-, Milcheiweiß-, und Laktosegehalten von 5,00, 5,14 bzw. 5,00 % (150-Tageleistung). Die Laktationsnummer hatte keinen signifikanten Effekt auf die Milchleistung. Schafe der Spätabsetzer-Gruppe produzierten im Anschluss an die Säugephase eine um 300 g signifikant höhere Testtagsmilchmenge als Tiere der Frühabsetzer-Gruppe. Das Absetzverfahren hatte keinen nachweisbaren Effekt auf die Milchinhaltsstoffe. Die Herde wies eine gute Eutergesundheit auf. Der Anteil bakteriologisch positiver Schafmilchproben betrug 28,5 %. Als dominante Erreger wurden in 96,6 % der bakteriologisch positiven Schafmilchproben Koagulasenegative Staphylokokken nachgewiesen. Der Anteil bakteriologisch positiver Euterhälftenbefunde stieg signifikant mit dem Fortschreiten der Laktation und zunehmender Laktationsnummer. Eine Euterinfektion mit Koagulasenegativen Staphylokokken sowie das Verfahren des Spätabsetzens beeinflussten alle drei untersuchten Eutergesundheitsparameter (logarithmierte Zellzahl, elektrische Leitfähigkeit und Laktosegehalt) negativ (p < 0,001).

Die Belastung des Energiestoffwechsels der Hochleistungskuh ist in der peripartalen Phase am größten. Die Regulation der Futteraufnahme und des Energiestoffwechsels durch den Nucleus arcuatus (ARC) des Hypothalamus spielt eine entscheidende Rolle während dieser Phase. Zahlreiche Metabolite und Hormone, wie z.B. das Peptidhormon Ghrelin, beeinflussen die Expression des orexigenen (futteraufnahmesteigernden) Neuropeptids Agouti-related Protein (AgRP) im ARC des Hypothalamus. Das Ziel dieser Arbeit war es, den Zusammenhang zwischen Körperfettmobilisierung und orexigenen Signalen im peripartalen Zeitraum der Hochleistungskuh näher zu untersuchen. Hierfür wurden 20 multipare Hochleistungskühe der Rasse Deutsch-Holstein (2.-4. Laktation) 7 Wochen ante partum (ap) bis 6 Wochen post partum (pp) untersucht. Die Tiere wurden in Anbindehaltung aufgestallt und entsprechend der jeweiligen Produktionsperiode bedarfsgerecht energetisch versorgt. Die ad libitum Futteraufnahme und Milchleistung wurden täglich gemessen und die Milchzusammensetzung wöchentlich analysiert. Das Körpergewicht und die Rückenfettdicke (RFD) wurden ebenfalls wöchentlich bestimmt. Einmal wöchentlich wurden Blutproben genommen, um die Konzentration von nicht-veresterten Fettsäuren (NEFA), Triglyceriden (TG) und Aminosäuren zu bestimmen. Eine Leberbiopsie wurde am 34. Tag ap und am 3., 18. und 30. Tag pp entnommen. In der 5. Woche ap und in der 2. Woche pp wurden die Tiere in eine Respirationskammer eingestallt und darin jeweils am 1. Tag ad libitum versorgt, während ihnen am 2. Tag das Futter für 10 h entzogen wurde. Die Futteraufnahme bzw. die kompensatorische Futteraufnahme nach Futterentzug wurden ebenfalls gemessen. Mit Hilfe der indirekten Kalorimetrie wurden der Sauerstoffverbrauch, die Bildung von Kohlenstoffdioxid und Methan gemessen, und der Respiratorische Quotient (RQ), die Fettoxidation (FOX) und die Kohlenhydratoxidation (KOX) berechnet. An beiden Tagen des Aufenthaltes in der Respirationskammer wurden stündlich Blutproben entnommen und die Konzentration von Acyl- und Gesamtghrelin, NEFA und TG bestimmt. Am 40. Tag pp wurden die Tiere geschlachtet und der ARC entnommen. In Studie 1 wurden 16 Tiere, basierend auf ihren Leberfettgehalt (LFC) am 18. Tag pp in eine Gruppe mit hohem LFC (H, n=8) und eine mit niedrigem LFC (L, n=8) eingeteilt. Für die Studie 2 wurden 18 Kühe entsprechend ihrer NEFA-Blutplasmakonzentration am Schlachttag in eine Gruppe mit hoher NEFA- (H, n=9) und eine mit niedriger NEFA-Plasmakonzentration (L, n=9) eingeteilt. In Studie 1 konnte gezeigt werden, dass die Acyl- und Gesamtghrelin- Plasmakonzentrationen nicht mit der pp Futteraufnahmesteigerung von Hochleistungskühen korrelierten. H-Kühe, die im Vergleich zu L-Kühen einen höheren Leber- und Milchfettgehalt, eine größere RFD und einen geringeren RQ aufwiesen, zeigten während des 10-stündigen Futterentzuges den größeren Anstieg der Acylghrelinkonzentration sowie ein größeres Acyl- und Gesamtghrelin-Verhältnis. Signifikante Korrelationen zwischen dem präprandialen Acyl:Gesamtghrelin- Verhältnis und zahlreichen Parametern des Fettstoffwechsels, wie bspw. LFC, Milchfettgehalt, RQ und RFD, lassen einen Zusammenhang zwischen Ghrelin, dem Fettstoffwechsel und der Fettverteilung erkennen. In Studie 2 konnte nachgewiesen werden, dass die in der Frühlaktation auftretende unterschiedliche Aktivierung hypothalamischer AgRP-Neurone von H- und L-Kühen nicht mit deren Futteraufnahme assoziiert ist. Die höhere NEFA-Plasmakonzentration, die höhere RFD, die höhere FOX und der höhere Sauerstoffverbrauch der H-Kühe waren jeweils signifikant mit der geringeren Aktivierung hypothalamischer AgRP-Neurone korreliert. Diese Korrelationen belegen einen Zusammenhang zwischen dem prozentualen Anteil aktivierter AgRP-Neurone und dem Sauerstoffverbrauch sowie der Substratverstoffwechselung während der Frühlaktation. Zusammenfassend lässt sich schlussfolgern, dass die untersuchten Signale des orexigenen Systems im peripartalen Zeitraum der Hochleistungskuh nicht mit der Futteraufnahme, jedoch mit dem Fett- und Energiestoffwechsel assoziiert waren. Ferner lassen die Resultate den Schluss zu, dass die Futteraufnahme bereits vor der Kalbung durch den Körperfettgehalt determiniert ist, und dass die Fettmobilisierung per se kaum einen Einfluss auf die Futteraufnahmesteigerung in der Frühlaktation besitzt.

Text in English / Cow size influences biological efficiency of individual animals, which influences herd composition and stock flow. This in turn influences the economic efficiency of the herd. This research followed the thread from animal size, to biological efficiency, to economic efficiency for beef cattle production under a typical production system in semi-arid South Africa. Cattle were grouped into three groups namely small, medium and large cattle, with mature weights of 300kg, 450kg and 600kg respectively. The net energy requirements of individual cattle were calculated for maintenance, growth, lactation and foetal production, for each of the three sizes. Growth rates, milk yield, reproduction rates, and management practices were assumed from existing research. Next the stock flow for a herd of small, medium and large cattle were calculated from the above. Income and expenses as commonly used in the research area were calculated from the stock flow. Gross profit above allocated costs were subsequently calculated for the three herds under the above-mentioned conditions. When assuming similar reproduction and growth rates for small, medium and large mature cattle, the following results were obtained: more heads of small cattle could be held on a set resource base, but the total live weight of a herd of large cattle that could be held on the same resource base was greater. This was mostly due to proportionately lower maintenance energy requirements in the herd of large cattle. In the simulation in this study, maintenance energy requirements for the herd of large cattle was 71.2%, compared to 72.0% for the herd of medium cattle and 73.1% for the herd of small cattle. Income from the herd of small cattle was the lowest, as less kilograms of beef were available to sell. Allocated costs for the herd of small cattle were the highest, due to a large number of expenses being charged per head of cattle. As a result, the herd of large cattle were more economically efficient than their smaller counterparts. Income above allocated costs for the herds of large, medium and small cattle were R1,182,865, R1,085,116 and R946,012 respectively. Larger cattle generally have a lower reproduction rate under similar conditions. No equation exists that directly links size to reproduction rates, especially considering the vast number of variables that influences reproduction rates. However, in the form of scenarios, it could be calculated that, given a reproduction rate of 80% for mature small cattle, when reproduction rates of large cattle were 24.7% lower than that of small cattle and the reproduction rates of medium cattle were 15.4% lower than that of small cattle, the large and medium herds became less profitable than the small herd. Smaller cattle mature faster than larger cattle which provides the opportunity for early breeding. When small cattle were bred early, at 15 months, at a calving rate of only 44.5% it was more profitable than when the same cows were bred at 24 months. When medium cattle were bred at 15 months, a calving rate of 37.0% was needed to be more profitable than when they were bred at 24 months. Even when the herd of small cattle were bred at 15 months with a reproduction rate of 100%, it could still not match the profitability of the herd of large cattle bred at 24 months given the reproduction rates of all other classes of animals were similar. When the herd of medium cattle were bred at 15 months, at a calving rate of 53.7%, it matched the profit of the herd of large cattle that were bred at 24 months, when the reproduction rates of other classes were equal. Scenarios were considered were feed intake was limited. When feed was limited to a specific amount, smaller cattle were more biologically efficient and cattle with potential for small mature sizes would grow to a larger size than cattle with potential for medium and large mature sizes. When feed was limited by a factor of the calculated energy requirements of small, medium and large cattle, large cattle were more effective. This is because large cattle use proportionately less energy for maintenance, which allows more energy to be allocated to growth, lactation and foetal production. When energy was limited to an amount per unit of metabolic weight, small cattle were more efficient than medium and larger cattle in the growth and production phases. Small, medium and large cattle were equally efficient (or inefficient) in the maintenance and lactation phases. Energy requirements of cattle in South Africa are commonly calculated using the Large Stock Unit (LSU). The LSU typically overestimates energy requirements for cattle, except in the lactation phase. When using the LSU to match small, medium or large cattle to a resource base, the LSU overestimates energy requirements of large cattle proportionately more than that of small and medium cattle. This is excluding the lactation phase, where energy requirements for all three sizes are underestimated and that of large cattle underestimated proportionately more. There are more considerations when matching cow size to managerial practices. A smaller body size is a natural adaptation to a semi-arid environment and this adaptation can be expressed in different ways. The number of animals on a resource base has implications on management practices. Having more heads of cattle on a resource base increases genetic variation of the herd, allowing for genetic progress to be made faster than in herd of fewer cattle. / Agriculture and  Animal Health / M.Sc. (Agriculture)

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)

<p>  </p> <p>Higher education has embedded systemic disadvantages for women within Science, Technology, Engineering, and Mathematics (STEM) disciplines. As a result, parenting women who pursue doctoral degrees in STEM fields face an uphill battle; yet the literature has given short shrift to the experiences of women who have children while training to become scientific professionals. This absence exists despite the fact that parenting is frequently an underlying theme in the literature on women’s decreased participation in STEM disciplines. Further, studies that do address parenting women’s experiences in higher education at large focus on individual characteristics and are limited by an emphasis on gender at the expense of other social inequalities. These inequalities have remained persistent and poorly understood. To re-imagine STEM higher education as an institution, it is necessary to understand the everyday social relations embedded within organizations that are a part of the institution. This institutional ethnography addresses these gaps. This study aimed to explore the social relations of the STEM higher education that shaped women’s experiences in STEM doctoral programs. Using Intersectionality and Inequality Regimes frameworks, this study examined women’s interactions with the institution, thereby providing a highly contextualized perspective on the STEM higher education institution. Data collection followed an emergent design with interviews with parenting women in STEM doctoral programs. Through these interviews, narrative events were identified that helped to isolate institutional processes that shaped their experiences. From there, data collection involved interviews with institutional informants and analysis of institutional texts (e.g., graduate handbooks, university policies). Data analysis followed narrative analytic methods using the Listening Guide, Labovian narrative analysis, and institutional ethnographic ruling relations mapping. Therein, three key studies from the data are shared. First, a narrative analysis with interpretation by Inequality Regimes showed how regimes of inequality shaped the experiences of two women who were pregnant and parenting while pursuing STEM doctorates. Second, an institutional ethnographic inquiry into the institutional relations that made up the lactation rooms and women’s interactions with them and revealed a decentralized organization that made accessing the spaces challenging for doctoral student women. And third, an institutional ethnographic analysis of women’s experiences with parental leave illustrated the lack of responsibility to ensure that students know about parental leave and could use the policy. Findings examine the institution’s organization around an ideal worker that many participants struggled to perform; this resulted in a diffuse and disorganized approach to policy and procedures for parenting women. Findings indicate that the neoliberal discourses in the institution shaped these experiences. The institution's masculine, white, classed nature results in it being insular to parenting women. While women persist within this environment, they face adversity emergent from the relations that make up the institution. I offer recommendations to improve gaps in consideration for parenting students, and a call to transform the overall institution to support parenting women at this critical juncture in their training. </p>