Fóruns mundiais para a troca de informações de práticas e operações sustentáveis entre governos, pesquisadores e sociedade, têm sido organizados com foco no desenvolvimento de novas tecnologias e práticas gerenciais que visam o desenvolvimento sustentável (econômico, ambiental e social) da produção. Cenário de destaque e que também tem evoluído expressivamente no agronegócio nacional e mundial. Em razão da expressiva representatividade do setor leiteiro no Brasil, este trabalho tem como objetivo analisar a sustentabilidade de fazendas produtoras de leite na principal bacia leiteira do país. A partir da revisão de literatura, foi escolhido o modelo de análise de sustentabilidade utilizado por Dolman et at. (2014) aplicado na avaliação de fazendas de leite na Holanda. Este mesmo ferramental foi ajustado e utilizado na avaliação da produção de leite em Minas Gerais, com base no banco de dados do Projeto EDUCAMPO/Sebrae. Para o desempenho econômico, foi calculada: a renda da atividade e a relação das receitas sobre os custos da atividade. Para o desempenho ambiental, foram calculados indicadores ambientais derivados da ACV (Análise de Ciclo de Vida), cujo perímetro do estudo foi do berço à porteira, sendo estes: a ocupação da terra (OT); o uso de energia não renovável (UENR); o potencial de aquecimento global (PAG); o potencial de acidificação (PA) e o potencial de eutrofização (PE), todos na unidade funcional de um quilograma de leite corrigido pelo teor de gordura e proteína (FPCM). Para o desempenho social, foi considerada a relação da área de mata nativa em cada fazenda, a bonificação recebida pelo leite comercializado e o número de folgas mensais que os funcionários possuem. Ao comparar os indicadores das fazendas de leite de Minas Gerais com os resultados das fazendas holandesas de Dolman et al. (2014), verificou-se um melhor desempenho das fazendas europeias em praticamente todos os indicadores calculados, com exceção dos indicadores de desempenho econômico, esta diferença pode ser atrelada ao tipo de sistema de produção adotado e ao nível de intensificação da produção existente nas duas realidades. Após o comparativo, foi realizada uma análise de clusters entre as fazendas do EDUCAMPO. Com essa análise foi possível verificar diferenças nas características estruturais e de produção entre os clusters formado a partir dos indicadores de sustentabilidade. Os resultados mostram a grande relevância deste tema na produção de leite, destacando a necessidade do acompanhamento desses indicadores para o melhor direcionamento da gestão nas operações de produção de leite. A sustentabilidade é um direcionamento complexo que exige a junção e a sinergia de diversas áreas para o seu entendimento e desenvolvimento / World forums for information exchange of best practices and sustainable operations among governments, researchers and society have been organized focusing on the development of new technologies and management practices aiming the sustainable (economic, environmental and social) production development. This featured scenario has also evolved significantly in discussing sustainability in the domestic and global agribusiness operations. Therefore, due to the significant representation of the dairy sector in Brazil, this study aims to analyze the sustainability of dairy farms in the main dairy region of the country. In the literature review, the main frameworks for assessing the sustainability of milk production around the world were assessed. Within this range, it has been chosen the model proposed and utilized by Dolman et at. (2014) to evaluate the sustainability of dairy farms of EDUCAMPO Program database in Minas Gerais, Brazil. For economic performance it has been calculated: income of activity and the ratio of revenue over the costs of the activity. The environmental performance indicators have been derived from a cradle to farm-gate LCA (Life Cycle Assessment): land occupation (LO); non-renewable energy use (NREU); global warming potential (GWP); acidification potential (AP) the eutrophication potential (EP), and all those indicators were calculated in the functional unit of one kilogram of fat-protein-corrected milk (FPCM). For social performance, it has been considered the amount of native forest present in the farm, plus the bonus received by the milk sold and the number of monthly days off that the employees have. When compared with the results of the Dutch milk farms of Dolman et al. (2014), there has been a better performance of almost all indicators of the European reality than the calculated for the Brazilian farms. Nevertheless, the economic performance has been better in the Brazilian reality. This difference can be linked to the type of production system and the level of intensification of existing production in both realities. The analysis of clusters between farms in MG showed that structural and production characteristics affects each sustainable cluster performance. These results shows the great importance of this matter in milk production. Hence, the need for monitoring these indicators could lead to the better management of the operations in the sustainability view. Sustainability is a complex focus that requires the addition and synergy of several areas for its understanding and development. Projects like EDUCAMPO are critical to keep the continuously improvements in the farming operations of the national milk industry.

Fóruns mundiais para a troca de informações de práticas e operações sustentáveis entre governos, pesquisadores e sociedade, têm sido organizados com foco no desenvolvimento de novas tecnologias e práticas gerenciais que visam o desenvolvimento sustentável (econômico, ambiental e social) da produção. Cenário de destaque e que também tem evoluído expressivamente no agronegócio nacional e mundial. Em razão da expressiva representatividade do setor leiteiro no Brasil, este trabalho tem como objetivo analisar a sustentabilidade de fazendas produtoras de leite na principal bacia leiteira do país. A partir da revisão de literatura, foi escolhido o modelo de análise de sustentabilidade utilizado por Dolman et at. (2014) aplicado na avaliação de fazendas de leite na Holanda. Este mesmo ferramental foi ajustado e utilizado na avaliação da produção de leite em Minas Gerais, com base no banco de dados do Projeto EDUCAMPO/Sebrae. Para o desempenho econômico, foi calculada: a renda da atividade e a relação das receitas sobre os custos da atividade. Para o desempenho ambiental, foram calculados indicadores ambientais derivados da ACV (Análise de Ciclo de Vida), cujo perímetro do estudo foi do berço à porteira, sendo estes: a ocupação da terra (OT); o uso de energia não renovável (UENR); o potencial de aquecimento global (PAG); o potencial de acidificação (PA) e o potencial de eutrofização (PE), todos na unidade funcional de um quilograma de leite corrigido pelo teor de gordura e proteína (FPCM). Para o desempenho social, foi considerada a relação da área de mata nativa em cada fazenda, a bonificação recebida pelo leite comercializado e o número de folgas mensais que os funcionários possuem. Ao comparar os indicadores das fazendas de leite de Minas Gerais com os resultados das fazendas holandesas de Dolman et al. (2014), verificou-se um melhor desempenho das fazendas europeias em praticamente todos os indicadores calculados, com exceção dos indicadores de desempenho econômico, esta diferença pode ser atrelada ao tipo de sistema de produção adotado e ao nível de intensificação da produção existente nas duas realidades. Após o comparativo, foi realizada uma análise de clusters entre as fazendas do EDUCAMPO. Com essa análise foi possível verificar diferenças nas características estruturais e de produção entre os clusters formado a partir dos indicadores de sustentabilidade. Os resultados mostram a grande relevância deste tema na produção de leite, destacando a necessidade do acompanhamento desses indicadores para o melhor direcionamento da gestão nas operações de produção de leite. A sustentabilidade é um direcionamento complexo que exige a junção e a sinergia de diversas áreas para o seu entendimento e desenvolvimento / World forums for information exchange of best practices and sustainable operations among governments, researchers and society have been organized focusing on the development of new technologies and management practices aiming the sustainable (economic, environmental and social) production development. This featured scenario has also evolved significantly in discussing sustainability in the domestic and global agribusiness operations. Therefore, due to the significant representation of the dairy sector in Brazil, this study aims to analyze the sustainability of dairy farms in the main dairy region of the country. In the literature review, the main frameworks for assessing the sustainability of milk production around the world were assessed. Within this range, it has been chosen the model proposed and utilized by Dolman et at. (2014) to evaluate the sustainability of dairy farms of EDUCAMPO Program database in Minas Gerais, Brazil. For economic performance it has been calculated: income of activity and the ratio of revenue over the costs of the activity. The environmental performance indicators have been derived from a cradle to farm-gate LCA (Life Cycle Assessment): land occupation (LO); non-renewable energy use (NREU); global warming potential (GWP); acidification potential (AP) the eutrophication potential (EP), and all those indicators were calculated in the functional unit of one kilogram of fat-protein-corrected milk (FPCM). For social performance, it has been considered the amount of native forest present in the farm, plus the bonus received by the milk sold and the number of monthly days off that the employees have. When compared with the results of the Dutch milk farms of Dolman et al. (2014), there has been a better performance of almost all indicators of the European reality than the calculated for the Brazilian farms. Nevertheless, the economic performance has been better in the Brazilian reality. This difference can be linked to the type of production system and the level of intensification of existing production in both realities. The analysis of clusters between farms in MG showed that structural and production characteristics affects each sustainable cluster performance. These results shows the great importance of this matter in milk production. Hence, the need for monitoring these indicators could lead to the better management of the operations in the sustainability view. Sustainability is a complex focus that requires the addition and synergy of several areas for its understanding and development. Projects like EDUCAMPO are critical to keep the continuously improvements in the farming operations of the national milk industry.

The fate of the carbon and nitrogen in dairy farm effluent (DFE) applied onto soil was investigated through laboratory experiments and field lysimeter studies. They resulted in the development and testing of a complex carbon (C) and nitrogen (N) simulation model (CaNS-Eff) of the soil-plant-microbial system. To minimise the risk of contamination of surface waters, regulatory authorities in New Zealand promote irrigation onto land as the preferred treatment method for DFE. The allowable annual loading rates for DFE, as defined in statutory regional plans are based on annual N balance calculations, comparing N inputs to outputs from the farming system. Little information is available, however, to assess the effects that these loading rates have on the receiving environment. It is this need, to understand the fate of land-applied DFE and develop a tool to describe the process, that is addressed in this research. The microbially mediated net N mineralisation from DFE takes a central role in the turnover of DFE, as the total N in DFE is dominated by organic N. In a laboratory experiment, where DFE was applied at the standard farm loading rate of 68 kg N ha⁻¹, the net C mineralisation from the DFE was finished 13 days after application and represented 30% of the applied C, with no net N mineralisation being measured by Day 113. The soluble fraction of DFE appeared to have a microbial availability similar to that of glucose. The low and gradually changing respiration rate measured from DFE indicated a semi-continuous substrate supply to the microbial biomass, reflecting the complex nature and broad range of C compounds in DFE. The repeated application of DFE will gradually enhance the mineralisable fraction of the total soil organic N and in the long term increase net N mineralisation. To address the lack of data on the fate of faecal-N in DFE, a ¹⁵N-labelled faecal component of DFE was applied under two different water treatments onto intact soil cores with pasture growing on them. At the end of 255 days, approximately 2% of the applied faecal ¹⁵N had been leached, 11 % was in plant material, 11 % was still as effluent on the surface, and 40% remained in the soil (39% as organic N). Unmeasured gaseous losses and physical losses from the soil surface of the cores supposedly account for the remaining ¹⁵N (approximately 36%). Separate analysis of the total and ammonium nitrogen contents and ¹⁵N enrichments of the DFE and filtered sub-samples (0.5 mm, 0.2µm) showed that the faecal-N fraction was not labelled homogeneously. Due to this heterogeneity, which was exacerbated by the filtration of DFE on the soil surface, it was difficult to calculate the turnover of the total faecal-N fraction based on ¹⁵N results. By making a simplifying assumption about the enrichment of the ¹⁵N in the DFE that infiltrated the soil, the contribution from DFE-N to all plant available N fractions including soil inorganic N was estimated to have been approximately 11 % of the applied DFE-N. An initial two-year study investigating the feasibility of manipulating soil water conditions through controlled drainage to enhance denitrification from irrigated DFE was extended a further two years for this thesis project. The resulting four-year data set provided the opportunity to evaluate the sustainability of DFE application onto land, an extended data set against which to test the adequacy of CaNS-Eff, and to identify the key processes in the fate of DFE irrigated onto soil under field conditions. In the final year of DFE irrigation, 1554 kg N ha⁻¹ of DFE-N was applied onto the lysimeters, with the main removal mechanism being pasture uptake (700 kg N ha⁻¹ yr⁻¹ removed). An average of 193 kg N ha⁻¹ yr⁻¹ was leached, with 80% of this being organic N. The nitrate leaching decreased with increasing soil moisture conditions through controlled drainage. At the high DFE loading rate used, the total soil C and N, pH and the microbial biomass increased at different rates over the four years. The long-term sustainability of the application of DFE can only be maintained when the supply of inorganic N is matched by the demand of the pasture. The complex simulation model (CaNS-Eff) of the soil-plant-microbial system was developed to describe the transport and transformations of C and N components in effluents applied onto the soil. The model addresses the shortcomings in existing models and simulates the transport, adsorption and filtration of both dissolved and particulate components of an effluent. The soil matrix is divided into mobile and immobile flow domains with convective flow of solutes occurring in the mobile fraction only. Diffusion is considered to occur between the micropore and mesopore domains both between and within a soil layer, allowing dissolved material to move into the immobile zone. To select an appropriate sub-model to simulate the water fluxes within CaNS-Eff, the measured drainage volumes and water table heights from the lysimeters were compared to simulated values over four years. Two different modelling approaches were compared, a simpler water balance model, DRAINMOD, and a solution to Richards' equation, SWIM. Both models provided excellent estimation of the total amount of drainage and water table height. The greatest errors in drainage volume were associated with rain events over the summer and autumn, when antecedent soil conditions were driest. When soil water and interlayer fluxes are required at small time steps such as during infiltration under DFE-irrigation, SWIM's more mechanistic approach offered more flexibility and consequently was the sub-model selected to use within CaNS-Eff. Measured bromide leaching from the lysimeters showed that on average 18% of the bromide from an irrigation event bypassed the soil matrix and was leached in the initial drainage event. This bypass mechanism accounted for the high amount of organic N leached under DFE-irrigation onto these soils and a description of this bypass process needed to be included in CaNS-Eff. Between 80 and 90% of the N and C leached from the lysimeters was particulate (> 0.2 µm in size), demonstrating the need to describe transport of particulate material in CaNS-Eff. The filtration behaviour of four soil horizons was measured by characterising the size of C material in a DFE, applying this DFE onto intact soil cores, and collecting and analyzing the resulting leachate using the same size characterisation. After two water flushes, an average of 34% of the applied DFE-C was leached through the top 0-50 mm soil cores, with a corresponding amount of 27% being leached from the 50-150 mm soil cores. Most of the C leaching occurred during the initial DFE application onto the soil. To simulate the transport and leaching of particulate C, a sub-model was developed and parameterised that describes the movement of the effluent in terms of filtering and trapping the C within a soil horizon and then washing it out with subsequent flow events. The microbial availability of the various organic fractions within the soil system are described in CaNS-Eff by availability spectra of multiple first-order decay functions. The simulation of microbial dynamics is based on actual consumption of available C for three microbial biomass populations: heterotrophs, nitrifiers and denitrifiers. The respiration level of a population is controlled by the amount of C that is available to that population. This respiration rate can vary between low level maintenance requirements, when very little substrate is available, and higher levels when excess substrate is available to an actively growing population. The plant component is described as both above and below-ground fractions of a rye grass-clover pasture. The parameter set used in CaNS-Eff to simulate the fate of DFE irrigated onto the conventionally drained lysimeter treatments over three years with a subsequent 10 months non-irrigation period was derived from own laboratory studies, field measurements, experimental literature data and published model studies. As no systematic calibration exercise was undertaken to optimise these parameters, the parameter set should be considered as "initial best estimates" and not as a calibrated data set on which a full validation of CaNS-Eff could be based. Over the 42 months of simulation, the cumulative drainage from CaNS-Eff for the conventionally drained DFE lysimeter was always within the 95% CI of the measured value. On the basis of individual drainage bulking periods, CaNS-Eff was able to explain 92% of the variation in the measured drainage volumes. On an event basis the accuracy of the simulated water filled pore space (WFPS) was better than that of the drainage volume, with an average of 70% of the simulated WFPS values being within the 95% CI for the soil layers investigated, compared to 44% for the drainage volumes. Overall the hydrological component of CaNS-Eff, which is based on the SWIM model, could be considered as satisfactory for the purposes of predicting the soil water status and drainage volume from the conventionally drained lysimeter treatment for this study. The simulated cumulative nitrate leaching of 4.7 g NO₃-N m⁻² over the 42 months of lysimeter operation was in good agreement to the measured amount of 3.0 (± 2.7) g NO₃-N m⁻². Similarly, the total simulated ammonium leaching of 2.7g NH₄- N m⁻² was very close to the measured amount of 2.5 (± 1.35) g NH₄- N m⁻² , however the dynamics were not as close to the measured values as with the nitrate leaching. The simulated amount of organic N leached was approximately double that measured, and most of the difference originated from the simulated de-adsorption of the dissolved fraction of organic N during the l0-month period after the final DFE irrigation. The 305 g C m⁻² of simulated particulate C leached was close to the measured amount of 224 g C m⁻² over the 31 months of simulation. The dissolved C fraction was substantially over-predicted. There was good agreement in the non-adsorbed and particulate fractions of the leached C and N in DFE. However, the isothermic behaviour of the adsorbed pools indicated that a non-reversible component needed to be introduced or that the dynamics of the de-adsorption needed to be improved. Taking into account that the parameters were not calibrated but only "initial best estimates", the agreement in the dynamics and the absolute amounts between the measured and simulated values of leached C and N demonstrated that CaNS-Eff contains an adequate description of the leaching processes following DFE irrigation onto the soil. The simulated pasture N production was in reasonable agreement with the measured data. The simulated dynamics and amounts of microbial biomass in the topsoil layers were in good agreement with the measured data. This is an important result as the soil microbial biomass is the key transformation station for organic materials. Excepting the topsoil layer, the simulated total C and N dynamics were close to the measured values. The model predicted an accumulation of C and N in the topsoil layer as expected, but not measured. Although no measurements were available to compare the dynamics and amounts of the soil NO₃-N and NH₄-N, the simulated values appear realistic for an effluent treatment site and are consistent with measured pasture data. Considering the large amount of total N and C applied onto the lysimeters over the 42 months of operation (4 t ha⁻¹ of N and 42 t ha⁻¹0f C), the various forms of C and N in dissolved and particulate DFE as well as in returned pasture, and that the parameters used in the test have not been calibrated, the simulated values from CaNS-Eff compared satisfactorily to the measured data.

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)