Appraising the national road transport system in the light of the South African economic development plan

Since the mid 70’s politicians have realized how important transport has become in the economy of South Africa and the advantages it has on growth, job creation and infrastructure. The result was involvement in the rules and regulations that govern South African transport in our country today. Economic growth has become a critical factor for the survival of economies as well as the future prospects of generations to come. The global recession which had a direct and indirect effect on South Africa has highlighted the importance. The question on how the National Development Plan could have an influence on local and national economic growth has developed as well as what the impact will be of the contribution road transport can make on the growth of the South African society and the economy. When the current road transport sector is taken into consideration, the need for a constructive plan that can assist transporters, drivers and businesses to grow and expand has been identified. The purpose of this study is thus to determine what the current economic situation in the world and in South Africa is as well as how the current political spheres are contributing to the national economy. An in-depth analysis of the National Development Plan has been done with emphasis on the effect on transport in general and the effect on road transport in South Africa. In order to accomplish this objective a detailed literature study was done which highlighted the academics information that pertains to the above mentioned topics. An empirical study that would measure the thought process and feelings about the National Development Plan and road transport has been conducted by means of a questionnaire. The results of the study have indicated that the National Development Plan could be an important tool and could have an enormous positive effect on the overall economic situation of South Africa and its citizens. In addition, the study has revealed that in the long term the effect of the plan could be to the advantage of the road transport sector. Factors that could contribute to the success would be features such as the planned improvements on the main transport corridors, upgrade of infrastructure and the controlling of environmental matters would have give a positive ring to it. On the other hand, recommendations to rectify and improve other issues such as corruption, leadership, maintenance backlogs and stricter laws and policies have been identified that are hindering businesses to grow and expand. Literature and data gained through the empirical study has indicated that the National Development Plan will improve South Africa’s economic situation. Respondents were optimistic about the plan and the overall feeling were that the plan will succeed in improving the road transport sector thus contribute to the National Development Plan objective to eliminate poverty and inequality by 2030

Exploring rock climbing discourses

Climbing has been part of human nature since time immemorial, our ancestors used it to escape predators, to flee from flooding valleys, to gather food and to move to new territories. However it was not until the middle 1700’s that man started to use climbing not as a means to ensure survival, but as a source of pleasure and desire to climb and explore. For almost two centuries climbing has evolved through, what has often been referred to as a trial and error method, into a state of the art, modern day sport with various sub disciplines like sport climbing, trad - climbing, ice climbing, free climbing and bouldering. In its purest form it is one of the most awe inspiring sports to watch and take part in, and for those select few that dedicate their lives to it, it is a means to make a living, and a way to live on into eternity. Over the past 15 years climbing has become a widely practised and one of the fastest growing sports around the world, and is practised by people from all walks of life, from pre-primary school children right through to retired pensioners, from unemployed students to the most successful business men and women. With this growing interest among the population there also came a growing interest in the use of climbing for various other purposes like psycho-therapy, rehabilitation, team building. But more importantly, for this study, it has urged the researcher to ask what are the discursive resources and strategies that are employed by modern day climbers, seeing as the climbing community consists of such a large variety of people. This study was done from a Discursive Psychology perspective, and was strongly influenced by the work of Jonathan Potter and Derek Edwards, as well as the work of the Rhetoric Group from Loughborough University. The Discursive Psychology approach focuses on management and accomplishment of action and interaction through talk. Discourse is viewed as a resource that functions to accomplish action and Discursive Analysis focuses on the manner that discursive resources are being employed to achieve certain actions in interaction. For Discursive Psychology it is important to view both the material context and embodiment as important in the construction of action. So too in Rock Climbing are these two aspects very important and very relevant because of the prominence of physical activity in the sport. The research focused on how climbers talk during climbing and what discursive resources and strategies they employ during rock climbing discourses. The most prominent of these resources and strategies that were found in the analysis were laughter, pauses and delays, intensifiers ( words that are used to emphasize and pinpoint other words), loud uttering of words, change-of-state tokens, disclaimers, discourse markers, extreme case formulations, agreement-implicative acknowledgement tokens, hedge words / devices, speech-overlapping, previous experiences, and footing. This research hopes to offer alternative explanations in sport and psychology, by studying naturally occurring conversations between climbers, instead of the more traditional pre – and – post experience testing that has dominated studies in psychology for so long.Dissertation (MA)--University of Pretoria, 2006.Psychologyunrestricte

An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

Late-maturity alpha-amylase (LMA) is a key concern for Australia’s wheat industry because affected grain may not meet receival standards or market specifications, resulting in significant economic losses for producers and industry. The risk of LMA incidence across Australia’s wheatbelt is not well understood; therefore, a predictive model was developed to help to characterise likely LMA incidence. Preliminary development work is presented here based on diagnostic simulations for estimating the likelihood of experiencing environmental conditions similar to a potential triggering criterion currently used to phenotype wheat lines in a semi-controlled environment. Simulation inputs included crop phenology and long-term weather data (1901–2016) for >1750 stations across Australia’s wheatbelt. Frequency estimates for the likelihood of target conditions on a yearly basis were derived from scenarios using either: (i) weather-driven sowing dates each year and three reference maturity types, mimicking traditional cropping practices; or (ii) monthly fixed sowing dates for each year. Putative-risk ‘footprint’ maps were then generated at regional shire scale to highlight regions with a low (66%) likelihood of experiencing temperatures similar to a cool-shock regime occurring in the field. Results suggested low risks for wheat regions across Queensland and relatively low risks for most regions across New South Wales, except for earlier planting with quick-maturing varieties. However, for fixed sowing dates of 1 May and 1 June and varying maturity types, the combined footprints for moderate-risk and high-risk categories ranged from 34% to 99% of the broad wheat region for South Australia, from 12% to 97% for Victoria, and from 9% to 59% for Western Australia. A further research component aims to conduct a field validation to improve quantification of the range of LMA triggering conditions; this would improve the predictive LMA framework and could assist industry with future decision-making based on a quantifiable LMA field risk

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space

Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

Sorghum, a genetically diverse C(4) cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V (cmax)), phosphoenolpyruvate (PEP) carboxylation (V (pmax)), and electron transport (J (max)), quantified using a C(4) photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V (cmax) (R (2) = 0.83), V (pmax) (R (2) = 0.93), J (max) (R (2) = 0.76), SLN (R (2) = 0.82), and LMA (R (2) = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V (cmax), V (pmax), J (max), SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V (cmax) and two QTL for J (max). Candidate genes within 200 kb of the V (cmax) QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J (max) QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity

Building a better Mungbean: Breeding for reproductive resilience in a changing climate

Mungbean (Vigna radiata (L.) R. Wilczek var. radiata) is a significant food and cash crop grown in tropical and subtropical regions. Mungbean production and consumer demand have increased substantially over the last two decades, owing to its agronomic, nutritional and economic benefits. Despite increased breeding efforts and the expansion of mungbean production in various agro-climatic regions, further production is hindered by low yield and variability, which is partly attributed to the impacts of abiotic stress. Abiotic stress impacts on the physiology, morphology and reproductive ability of mungbean which influences yield. Exposure to abiotic stresses at the reproductive stage is considered the most critical for yield production. In this review, we evaluate how abiotic stress impacts mungbean growth and productivity when occurring during the reproductive stage and traits that may confer adaptation. We present the limitations of current research including limited number of genotypes, lack of field experiments and detailed experimental information. We highlight the opportunities to exploit new tools and technologies, such as high-throughput phenotyping platforms, gene editing, and genomic selection, to accelerate breeding efforts to develop more resilient mungbean cultivars for today and tomorrow

Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

Sorghum, a genetically diverse C(4) cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V (cmax)), phosphoenolpyruvate (PEP) carboxylation (V (pmax)), and electron transport (J (max)), quantified using a C(4) photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V (cmax) (R (2) = 0.83), V (pmax) (R (2) = 0.93), J (max) (R (2) = 0.76), SLN (R (2) = 0.82), and LMA (R (2) = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V (cmax), V (pmax), J (max), SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V (cmax) and two QTL for J (max). Candidate genes within 200 kb of the V (cmax) QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J (max) QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity