In conditions where supplementation of poor quality diets is a major challenge, forage legumes such as Sericea lespedeza can be a good alternative supplement for protein at lower cost than most commercial concentrates. From studies done on Sericea lespedeza it is found plausible and valuable to supplement urea with Sericea lespedeza to strategically combat the deleterious effect of condensed tannins in the Sericea lespedeza and provide nitrogen in the rumen. This study was aimed to evaluate the effect of replacing sunflower oilcake with urea (a rumen degradable protein RDP source) or Sericea lespedeza (rumen undegradable protein RUP source) mixed with urea as nitrogen/protein sources on nutrient utilization, milk yield and milk composition of Saanen dairy goats. A digestibility and lactation study were conducted at the University of Pretoria Research Farm and chemical analysis performed at the University Nutrilab. A 30 days digestibility study was conducted on male Saanen goats after the lactation study, with 23 days adaptation and 7 days data collection period. Nine male goats were randomised and allocated to the three treatments in metabolism cages. In the lactation study 36 dairy goats were blocked according to milk collected on first month of lactation into high, medium and low milk yielders, then allocated to the three treatments of total mixed rations containing sunflower oilcake (T1) at 7% main protein source, T2 (urea at 1%) and T3- Sericea lespedeza at 12.5% mixed with urea according to a complete randomised block design (CRBD). Milk samples were collected from individual goats monthly at two consecutive milking’s. The samples were analysed for milk fat, protein, lactose, somatic cell count and milk urea nitrogen using a Milko-Scan analyser (at Irene Lacto lab). In the digestibility study, dry matter intake was significantly higher for goats fed on Sericea lespedeza with urea (T3) diet than goats fed on T1 and T2 diet. Goats on T3 diet had also significantly higher organic matter and crude protein intake than those goats fed on the other two TMR diets. The results also shows that the mean daily milk yields for the goats in the T1, T2 and T3 were 2.56, 2.46 and 2.52 kg per day respectively. T2 group had higher milk fat % (3.61) and higher milk urea nitrogen (MUN - 25.70 mg N/dl) than the other two treatments. T1 had significantly higher milk protein %. There was a great difference in milk composition of the afternoon milk as compared to the morning milk. The three TMRs had no significant difference in the nitrogen utilization and nitrogen excretion. It is then concluded that Sericea lespedeza mixed with urea can be used as subsititutes for sunflower oilcake in the diets of dairy goats since no negetive effect was found. However further investigations are needed. / Dissertation (MSc Agric)--University of Pretoria, 2017. / DAAD-NRF / International Foundation for Science (IFS) / Animal and Wildlife Sciences / MSc (Agric) / Unrestricted

The traditional design of printed circuit board assembly focuses on finding a set of parameter values (that characterizes the process), such that the desired circuit performance specifications are met. It is usually assumed that this set of values can be accurately realized when the circuit or the assembly is built. Unfortunately, this assumption is not realistic for assemblies produced in mass scale. Fluctuations in manufacturing processes cause defects in actual values of the parameters. This variability in design parameters, in turn, causes defects in the functionality of the assemblies. The ratio of the acceptable assemblies to total assemblies produced constitutes the yield of the assembly process. Assembly yields of area array packages are heavily dependent on design of the board as much as package and process parameters. The economics of IC technology is such that the maximization of yield rather than the optimization of performance has become the topic of prime importance. The projected value of yield has always been a factor for consideration in the advancement of Integrated Chip technology. Due to considerable reduction in the package size, minimum allowable tolerance and tight parameter variations, electronic assemblies have to be simulated, characterized and tested before translating them to a production facility. Also, since the defect levels are measured in parts per million, it is impractical to build millions of assemblies for the purpose of identifying the best parameter. A mathematical model that relates design parameters and their variability to assembly yield can help in the effective estimation of the yield. This research work led to the development of a mathematical model that can incorporate variability in the package, board and assembly related parameters and construction of an effective methodology to predict the assembly yield of area array packages. The assembly yield predictions of the model are based on the characteristics of input variables (whether they follow a normal, empirical or experimental distribution). By incorporating the tail portion of the parameter distribution (up to ±6 standard deviation on normal distribution), a higher level of accuracy in assembly yield prediction is achieved. An estimation of the interaction of parameters is obtained in terms of the expected number of defective joints and/or components and a degree of variability around this expected value. As an implementation of the mathematical model, a computer program is developed. The software is user friendly and prompts the user for information on the input variables, it predicts the yield as expected number of defective joints per million and expected number of defective components (assemblies) per million. The software can also be used to predict the number of defects for a user-specified number of components (less or more than one million assemblies). The area array assembly yield model can be used to determine the impact of process parameter variations on assembly yields. The model can also be used to assess the manufacturability of a new design, represent the capability of an assembly line for bench marking purposes, help modify designs for better yield, and to define the minimum acceptable manufacturability standards and tolerances for components, boards and designs. / Master of Science

The Texas High Plains produces the most substantial amount of cotton in Texas. The region is a semi-arid area with limited precipitation, and it is, therefore, susceptible to climate change. Cotton production in the Texas High Plains is mostly dependent on irrigation to increase yield. The overall goal of this research was to study the interrelationships between climate change and cotton yield using correlation analysis and also to study how climate has changed in the region using trend analysis. A three-decade data (1987-2017) was analyzed to establish the relationship between climate change and cotton and also to determine how climate has changed in the area over the last 30 years. The research used precipitation and temperature data to assess climate change.The results of this research showed that annual mean temperature has lesser impacts on cotton yield, and the correlation between annual precipitation and cotton yield is insignificant. It also found out that high rates of temperature at the boll opening stage of cotton growth results in decreased cotton yield and that at the boll development and boll opening stages, precipitation is needed. Again, the research indicated that, on average, there had been a significant increase in temperature, but precipitation trends are insignificant. About 60% of cotton acreage in the area is irrigated. Therefore the research also found out that increasing trends of cotton yield may contribute to the decline of groundwater in the area.

The STAX and SCO shallow loss crop insurance products were introduced in the 2014 farm bill. This research investigates the farm-level performance of STAX for cotton growers. Using 10 years of actual farm yield data for the period 1999 to 2008, certainty equivalent gains were evaluated under four treatments in Texas, Mississippi and Louisiana for non-irrigated and irrigated cotton production. Following the current practice for STAX, county yield is estimated using yield data from YP, RP, and RP-HPE policies rather than NASS county level yield data. Findings show that, assuming actuariallyair premiums, certainty equivalent gains for RP tend to be higher than for STAX. But with subsidized premiums, the opposite outcome sometimes occurs. Furthermore, with subsidized premiums the findings indicate that almost all farms would benefit from purchasing STAX as a complement to RP. The use of actual farm yield data highlights the heterogeneity of STAX farm-level impacts.

Understanding the impact of late season precipitation on the distribution of cotton yields provides insight into managing yield risks. This research combines Linear Moment Models with historical weather data to assess the impact of late season precipitation extremes on cotton production and revenue. The empirical analysis suggests that late season drought reduces both mean yield and variance. The shift in variance is coupled with an exchange of upside risk for downside risk implying that the variance reduction alone masks an important effect on producer’s risk. Revenue impacts suggest high revenue for irrigated acreage as compared to dryland acreage, and the late season drought impact on revenue shows that the use of irrigation causes increases in revenue as compared to dryland acreage.

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This research uncovers a novel pathway in precision agriculture, emphasizing the utilization of advanced supervised and self-supervised deep learning approaches for an innovative solution to weed detection and crop yield prediction. The study focuses on key weed species: Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, which are troublesome weeds in the United States. One of the most innovative components of this research is the debut of a self-supervised learning approach specifically tailored for soybean yield prediction using only unlabeled RGB images. This novel strategy presents a departure from traditional yield prediction methods that consider multiple variables, thus offering a more streamlined and efficient methodology that presents a significant contribution to the field. To address the monitoring of Italian ryegrass in wheat cultivation, a bespoke Convolutional Neural Network (CNN) model was developed. It demonstrated impressive precision and recall rates of 100% and 97.5% respectively, in accurately classifying Italian ryegrass in the wheat. Among three hyperparameter tuning methods, Bayesian optimization emerges as the most efficient, delivering optimal results in just 10 iterations, contrasting with 723 and 304 iterations required for grid search and random search respectively. Further, this study examines the performance of various classification and object detection algorithms on Unmanned Aerial Systems (UAS)-acquired images at different growth stages of soybean and Palmer amaranth. Both the Vision Transformer and EfficientNetB0 models display promising test accuracies of 97.69% and 93.26% respectively. However, considering a balance between speed and accuracy, YOLOv6s emerged as the most suitable object detection model for real-time deployment, achieving an 82.6% mean average precision (mAP) at an average inference speed of 8.28 milliseconds. Furthermore, a self-supervised contrastive learning approach was introduced for automating the labeling of Palmer amaranth and soybean. This method achieved a notable 98.5% test accuracy, indicating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. A separate study was conducted to detect common ragweed in soybean crops and the prediction of soybean yield impacted by varying weed densities. The Vision Transformer and MLP-Mixer models achieve test accuracies of 97.95% and 96.92% for weed detection, with YOLOv6 outperforming YOLOv5, attaining an mAP of 81.5% at an average inference speed of 7.05 milliseconds. Self-supervised learning-based yield prediction models reach a coefficient of determination of up to 0.80 and a correlation coefficient of 0.88 between predicted and actual yield. In conclusion, this research elucidates the transformative potential of self-supervised and supervised deep learning techniques in revolutionizing weed detection and crop yield prediction practices. Its findings significantly contribute to precision agriculture, paving the way for efficient and cost-effective site-specific weed management strategies. This, in turn, promotes reduced environmental impact and enhances the economic sustainability of farming operations. / Master of Science in Life Sciences / This novel research provides a fresh approach to overcoming some of the biggest challenges in modern agriculture by leveraging the power of advanced artificial intelligence (AI) techniques. The study targets key disruptive weed species, such as, Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, all of which have the potential to significantly reduce crop yields. The studies were first conducted to detect Italian ryegrass in wheat crops, utilizing RGB images. A model is built using a complex AI system called a Convolutional Neural Network (CNN) to detect this weed with remarkable accuracy. The study then delves into the use of drones to take pictures of different growth stages of soybean and Palmer amaranth plants. These images were then analyzed by various AI models to assess their ability to accurately identify the plants. The results show some promising findings, with one model being quick and accurate enough to be potentially used in real-time applications. The most important part of this research is the application of self-supervised learning, which learns to label Palmer amaranth and soybean plants on its own. This novel method achieved impressive test accuracy, suggesting a future where data collection and labeling could be done more cost-effectively. In another related study, we detected common ragweed in soybean crops and predicted soybean yield based on various weed densities. AI models once again performed well for weed detection and yield prediction tasks, with self-supervised models showcasing high agreement between predicted and actual yields. In conclusion, this research showcases the exciting potential of self-teaching and supervised AI in transforming the way we detect weeds and predict crop yields. These findings could potentially lead to more efficient and cost-effective ways of managing weeds at specific sites. This could have a positive impact on the environment and improve the economic sustainability of farming operations, paving the way for a greener future.

[pt] Este trabalho investiga experimentalmente a ação dos efeitos de parede na diferença de pressão adicional, delta P mais, ocasionada pela sedimentação de uma partícula esférica ao longo do eixo de colunas circular e triangular cheias de fluido viscoso, com movimento em números de Reynolds da partícula inferiores a 2. Diferenças de pressões foram determinadas para relações de dimensões características da partícula e do duto (a/R0) entre 0,20 e 0,82 numa faixa de números de Reynolds compreendida dentro do Regime de Oseen. Os resultados plotados como diferença de pressão adimensional, delta P mais A/D, VS. a/R0, onde A é a secção transversal da coluna e D a força de arraste sobre a partícula, foram aproximados por séries de polinômios de Chebyshev para a/R0 variando de 0 a 0.82. A equação de HAPPEL e BYRNE2 que inclui correção para efeitos de parede foi comparada com os resultados encontrados para coluna circular, verificando-se a validade dessa equação até aproximadamente a/R0 igual 0,34 e movimento em números de Reynolds da partícula inferiores a 2. / [en] In this work we investigate the wall effects on the addicional pressure difference induced by the settling of spherical particles along the axis of cylinders of circular and triangular cross sections, which is full of a viscous fluid, for particle Reynolds numbers less than two. Pressure differences were determinated for relationships of the particle characteristic dimension to the tube radius (a/R0) between 0,20 and 0,82, in a range for the Reynolds numbers within the Oseen regime. The results for the pressure difference, delta P more A/D VS. a/R0, where A is the cross section área of the tube and D the drag on the particle, are plotted for a a/R0 varying from 0 to 0,82, and were approximated by on interpolating Chebyshev polinomial. The equations of HAPPEL and BYRNE2 which includ correction terms for the wall effects, was compared with the results obtained for the circular tube. The experimental results indicate that the HAPEL and BYRNE equation is valid for values of a/R0 up to 0,34, approximately, and particle Reynolds numbers less than two.

[pt] O objetivo deste trabalho é estudar o comportamento de um fluido real com densidade estratificada quando passa por um obstáculo. A partir das equações gerais da Mecânica dos Fluidos desenvolvida para vorticidade e função de corrente, obtém-se duas equações diferenciais parciais ambas elípticas, constituindo o modelo matemático do escoamento. O método numérico TUBE AND TANK, desenvolvido por Gosman et al. (1), análogo ao das diferenças finitas, é utilizado para transformar as equações diferenciais em equações algébricas. As condições de contorno necessárias ao problema foram tais que o escoamento estudado se tornou idêntico àquele analisado por Forchtgott(3). Com o auxílio de um computador IBM/370 foi possível obter os resultados que estão de acordo com as observações de Forchtgott(3). / [en] The main purpose of this work is to study the behavior of the real density stratified fluid flowing over an obstacle. From the general equations of fluid mechanics developed in terms of vorticity and stream functions two elliptical partial differential equations which constitute the mathematical flow model were obtained. The numerical method TUBE AND TANK, developed by Gosman et al. (1), analogous to the finite differences method, was used to transform the partial differential equations into algebraic equations. The boundary conditions used in the problem were such that the flow was indentical to that analyzed by Forchtgott(3). Using an IBM/370 computer it was possible to obtain the resultswhich are in agreement with those observed by Forchtgott(3).

Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire a deeper insight into the environmental factors affecting their productivity. In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms. We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors. Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.