Increasing the sucrose yield and the disease resistance of plants are two major objectives of the transgenic sugarcane plant programme in South Africa. The sugarcane culm has thus been identified as one of the main target areas for transgene expression. A shortage of reliable promoter elements as well as patent limitations have necessitated the isolation of promoters that are preferentially expressed in the sugarcane culm. In the present study two different approaches were followed to isolate such promoters, and the bacterial promoter, rolC, was evaluated for tissue-specific expression in sugarcane. Differential display is a non-directed technique that was used to identify genes that are differentially expressed in the mature sugarcane culm. The original method was modified, and four putative culm-preferential fragments were isolated. Sequence and hybridisation analyses revealed that these fragments were false positives, and could therefore not be used to obtain a culm-specific promoter. Activity of the Agrobacterium rolC promoter was evaluated by analysing expression patterns of two reporter genes in the mature culm of transgenic sugarcane plants. Nucleic acid analyses indicated that the foreign DNA was incorporated into the sugarcane genome, and that mRNA transcripts were produced. Histochemical analysis was done to visualise rolC-driven GUS and GFP expression in the mature sugarcane culm. In both cases the reporter gene expression was restricted to the vascular bundles and specifically to the phloem. A directed approach was followed to isolate the gene and subsequently the promoter of the β-subunit of pyrophosphate-dependent phosphofructokinase (PFP-β). An incomplete cDNA clone was obtained from a mature culm cDNA library, and was used for the screening of a sugarcane genomic library. Two clones containing different parts of the PFP-β gene were isolated. A Deletion Factory™ system was used to analyse the clone containing the 5' end of the gene. The first five exons and 1747 bp of the 5' flanking region of the gene were sequenced. Preliminary activity analysis of the promoter region was done by constructing two expression vectors, and analysing transient GUS expression in sugarcane callus. Results indicated that the promoter is capable of driving foreign gene expression in callus. Transient expression levels were lower than that of the maize Ubi-1 promoter. Further analysis of the 5' flanking region will be done to establish whether cis-acting elements outside the analysed area have an influence on the activity of the promoter. / Thesis (Ph.D.)-University of Natal, Pietermaritzburg, 1997.

South Africa is the leading producer of sugarcane in Africa and one of the largest sugarcane producers in the world. Sugarcane is grown under a wide range of climatic, agronomic, and socio-economic conditions in the country. Stress factors such as water and nutrient deficiencies, and insect pests and diseases are among the most important factors affecting sugarcane production in the country. Monitoring of stress in sugarcane is therefore essential for assessing the consequences on yield and for taking action of their mitigation. The prediction of sugarcane yield, on the other hand is also a significant practice for making informed decisions for effective and sound crop planning and management efforts regarding e.g., milling schedules, marketing, pricing, and cash flows. In South Africa, the detection of stress factors such as nitrogen (N) deficiency and sugarcane thrips (Fulmekiola serrata Kobus) damage and infestation are made using traditional direct methods whereby leaf samples are collected from sugarcane fields and the appropriate laboratory analysis is then performed. These methods are regarded as being time-consuming, labour-intensive, costly, and can be biased as often they are not uniformly applied across sugarcane growing areas in the country. In this regard, the development of systematically organised geo-and time-referenced accurate methods that can detect sugarcane stress factors and predict yields are required. Remote sensing offers near-real-time, potentially inexpensive, quick and repetitive data that could be used for sugarcane monitoring. Processing techniques of such data have recently witnessed more development leading to more effective extraction of information. In this study the aim was to explore the potential use of remote sensing to quantify stress in and predict yield of sugarcane in South Africa. In the first part of this study, the potential use of hyperspectral remote sensing (i.e. with information on many, very fine, contiguous spectral bands) in estimating sugarcane leaf N concentration was examined. The results showed that sugarcane leaf N can be predicted at high accuracy using spectral data collected using a handheld spectroradiometer (ASD) under controlled laboratory and natural field conditions. These positive results prompted the need to test the use of canopy level hyperspectral data in predicting sugarcane leaf N concentration. Using narrow NDVI-based vegetation indices calculated from Hyperion data, sugarcane leaf N concentration could reliably be estimated. In the second part of this study, the focus was on whether leaf level hyperspectral data could detect sugarcane thrips damage and predict the incidence of the insect. The results indicated that specific wavelengths located in the visible region of the electromagnetic spectrum have the highest possibility of detecting sugarcane thrips damage. Thrips counts could also adequately be predicted for younger sugarcane crops (4–5 months). In the final part of this study, the ability of vegetation indices derived from multispectral data (Landsat TM and ETM+) in predicting sugarcane yield was investigated. The results demonstrated that sugarcane yield can be modelled with relatively small error, using a non-linear random forest regression algorithm. Overall, the study has demonstrated the potential of remote sensing techniques to quantify stress in and predict yield of sugarcane. However, it was found that models for detecting a stress factor or predicting yield in sugarcane vary depending on age group, variety, season of sampling, conditions at which spectral data are collected (controlled laboratory or natural field conditions), level at which remotely-sensed data are captured (leaf or canopy levels), and irrigation conditions. The study was conducted in only one study area (the Umfolozi mill supply area) and very few varieties (N12, N19, and NCo 376) were tested. For practical and operational use of remote sensing in sugarcane monitoring, the development of an optimum universal model for detecting factors of stress and predicting yield of sugarcane, therefore, still remains a challenging task. It is recommended that models developed in this study should be tested – or further elaborated – in other South African sugarcane producing areas with growing conditions similar to those under which the predictive models have been developed. Monitoring of sugarcane thrips should also be evaluated using remotely-sensed data at canopy level; and the ability of multispectral sensors other than Landsat TM and ETM+ should be tested for sugarcane yield prediction. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

There is a need in the South African sugar industry to investigate improved techniques for forecasting seasonal sugarcane yields. An accurate and timely forecast of seasonal cane yield is of great value to the industry, and could potentially allow for substantial economic savings to be made. Advances by climatologists have resulted in increasingly accurate and timely seasonal climate forecasts. These advances, coupled with the ongoing advances made in the field of crop yield simulation modelling, present the sugar industry with the possibility of obtaining improved cane yield forecasts. In particular, the lead time of these forecasts would be improved relative to traditional techniques. Other factors, such as the flexibility offered by simulation modelling in the representation of a variety of seasonal scenarios, would also contribute to the possibility of obtaining improved cane yield forecasts. The potential of applying crop yield simulation models and seasonal rainfall forecasts in cane yield forecasting was assessed in this research project. The project was conducted in the form of a case study in the Eston Mill Supply Area. Two daily time step cane yield simulation models, namely the ACRU-Thompson and CANEGRO-DSSAT models, were initially evaluated to test their ability to accurately simulate historical yields given an observed rainfall record. The model found to be the more appropriate for yield forecasting at Eston, the ACRU-Thompson model, was then used to generate yield forecasts for a number of seasons, through the application of seasonal rainfall forecasts in the model. These rainfall forecasts had previously been translated into daily rainfall values for input into the model. The sugarcane yield forecasts were then evaluated against observed yields, as well as against forecasts generated by more traditional methods, these methods being represented by a simple rainfall model and Mill Group Board estimates. Although the seasonal rainfall forecasts used in yield forecasting were found not to be particularly accurate, the proposed method provided more reliable cane yield forecasts, on average, than those using the traditional forecasting methods. A simple cost-benefit analysis indicated that the proposed method could potentially give rise to the greatest net economic benefits compared to the other methods. Recommendations are made for the practical implementation of such a method. Future areas of research are also identified. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2000.

South Africa is the largest producer of sugar in Africa and one of the ten largest sugarcane producers in the world. Sugarcane in South Africa is grown under a wide range of agro-climatic conditions. Climate has been identified as the single most important factor influencing sugarcane production in South Africa. Traditionally, sugarcane mill committees have issued forecasts of anticipated production for a region. However, owing to several limitations of such committee forecasts, more advanced technologies have had to be considered. The aim of this study has been to develop, evaluate and implement a pertinent and technologically advanced operational sugarcane yield forecasting system for South Africa. Specific objectives have included literature and technology reviews, surveys of stakeholder requirements, the development and evaluation of a forecasting system and the assessment of information transfer and user adoption. A crop yield model-based system has been developed to simulate representative crops for derived Homogeneous Climate Zones (HCZ). The system has integrated climate data and crop management, soil, irrigation and seasonal rainfall outlook information. Simulations of yields were aggregated from HCZs to mill supply area and industry scales and were compared with actual production. The value of climate information (including climate station networks) and seasonal rainfall outlook information were quantified independently. It was concluded that the system was capable of forecasting yields with acceptable accuracy over a wide range of agro-climatic conditions in South Africa. At an industry scale, the system captured up to 58% of the climatically driven variability in mean annual sugarcane yields. Forecast accuracies differed widely between different mill supply areas, and several factors were identified that may explain some inconsistencies. Seasonal rainfall outlook information generally enhanced forecasts of sugarcane production. Rainfall outlooks issued during the summer months seemed more valuable than those issued in early spring. Operationally, model-based forecasts can be expected to be valuable prior to the commencement of the milling season in April. Current limitations of forecasts include system calibration, the expression of production relative to that of the previous season and the omission of incorporating near real-time production and climate information. Several refinements to the forecast system are proposed and a strong collaborative approach between modellers, climatologists, mill committees and other decision makers is encouraged. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.

As is the case in many other regions in the world, sugarcane production in southern Africa is affected by a wide range of climatic conditions, which can vary considerably from location to location and from year to year. As a result, the season length and growth cycles of sugarcane in southern Africa differ greatly. Such conditions include the hot and dry regions of northern KwaZulu-Natal, Swaziland and Mpumalanga, where sugarcane is mostly irrigated, to the humid sub-tropical coastal belt extending from the far north coast of KwaZulu-Natal to areas in the Eastern Cape, as well as the cool frost prone midlands regions of KwaZulu-Natal. Owing to the wide range of climatic conditions in which sugarcane is grown in southern Africa, there are many different external factors that affect sugarcane production, including a range of pests and diseases, frost occurrences and variations in soil water. The objective of this research was to (1) identify a number of important variables that affect cane production in southern Africa, (2) employ suitable models to reflect these variables, and (3) simulate and map the extent and severity of these variables at a high spatial resolution over southern Africa. Such variables include the Eldana saccharina and Chilo sacchariphagus stalk borers, sugarcane rust fungus, heat units with selected base temperatures, frost, soil water content, soil compaction, irrigation water demand, conducive and non-conducive growing conditions, flowering proficiencies for sugarcane, sugarcane yields and yield increments per unit of irrigation. The distribution patterns of the above-mentioned variables relied greatly upon the various models employed to represent them, as well as the accuracy of the temperature and rainfall databases to which the various models were applied. Although not definitive, the models used to reflect the variables which had been identified were considered to be generally satisfactory. The resolution at which the variables which had been identified in this study were mapped, was also found to be adequate. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2008.