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This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

Thesis (M.S.)--Northwest Missouri State University, 2008. / The full text of the thesis is included in the pdf file. Title from title screen of full text.pdf file (viewed on July 25, 2008) Includes bibliographical references.

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Thesis (M.Sc. (Crop Science)) --University of Limpopo, 2008 / Stenocarpella ear rot caused by Stenocarpella maydis (Berck) Sutton is the most important disease of maize in South Africa. It is a sporadic disease which makes it difficult for farmers to prepare for its occurrence and consequently of the control measure. The objectives of this study were to genetically characterise the resistance of Stenocarpella maydis ear rot and to identify agronomically suitable Stenocarpella ear rot resistant inbred lines with good combining ability for grain yield. The experimental design was a randomised complete block design with three replications. Studies were conducted at Bethlehem, Cedara and Potchefstroom. To facilitate the comparison, separate trials were established for inbred lines and top cross hybrids. Fifty-four inbred lines were compared against four inbred lines vs. E739, DO620Y, H111 and Mo17 that are well adapted and stable yielders possessing variable resistance to Stenocarpella maydis ear rot. Fifty-four top crosses were compared against one open pollinated variety (SAM 1066), which was used as a tester line as well as three commercial hybrids vs. PAN 6124BT, PAN 6026 and CRN 3505. At Potchefstroom there was an inoculation trial using both inbreds and top crosses. The inbred and top cross materials were obtained from 2004/05 breeding nursery under natural infestation of Stenocarpella maydis at Agricultural Research Council-Grain Crops Institute, Potchefstroom. Data collected were number of days to 50% silking, plant and ear height in centimetres, husk cover, ear position, stand count, total number of ears, number of diseased ears and lodging resistance,. Entries 43 and 4 were the most stable inbred lines with a beta close to 1, while entries 9 and 25 had the smallest deviation from regression. Among the tested inbred lines entry 47 was superior over other inbred lines for grain yield followed by entry 4. Entry 47 showed grain yield of 2.84 tons ha-1 at Bethlehem and 4.42 tons ha-1 at Potchefstroom. While entry 4 had a grain yield of 2.19 tons ha-1 at Bethlehem and 4.58 tons ha-1at Potchefstroom. The two lines, however, are poor combiners for both grain yield and Stenocarpella maydis ear rot resistance. Using SAM 1066 as a tester the grain yield observed for top crosses at Bethlehem, Cedara and Potchefstroom were 5.94, 7,15 and 9.95 tons ha-1, respectively. Entries 57 and 14 were the most stable top cross hybrids with a beta close to one, while entries 46 and 47 had the smallest deviation from regression. Entries 56 and 28 were the most superior top cross hybrids. Entry 56 showed grain yield of 5.58 tons ha-1 at Bethlehem, at Cedara it showed the yield of 5.90 tons ha-1 and at Potchefstroom it was 9.95 tons ha-1 and for the average of three sites it was 7.14 tons ha-1. Entry 28 showed grain yield of 5.80 tons ha-1 at Bethlehem, at Cedara it was 5.80 and at Pothefstroom it was 9.35 tons ha-1 and the combined average was 6.98 tons ha-1. These values compared favourably with the commercial standards. The checks entries 58 and 57 had proved to be resistant over locations. The best combiners for Stenocarpella maydis resistance were entries 29 and 52. Stenocarpella maydis ear rot was found to be of polygenic resistance with additive genetic effects. / The government of Lesotho, Agricultural Research Council-Grain Crops Institute (ARC-GCI), and the Maize Trust

Thesis (D.Agr.Sc.) - University of Queensland, 2004. / Includes bibliographical references.

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Geographical Information Systems and Remote Sensing, 2019 / Prosopis species (also known as Mesquite), in particular P. glandulosa (Honey Mesquite) have a negative impact on indigenous biodiversity and the livelihood of communities in the semi-arid and arid parts of South Africa. The spread of these species is a threat to the environments in which they have been introduced as they spread at high rates, increase the mortality of indigenous trees and disrupt important ecosystem processes such as hydrological and nutrient cycles. Due to the negative impacts of Prosopis on important ecosystem services and South Africa’s native biodiversity, it is essential for the distribution of these species to be identified, controlled and monitored in order to mitigate their spread and restore damaged ecosystems. The objectives of this study were to use Remote Sensing and Geographic Information Systems (GIS) tools to: (i) delineate the distribution of Prosopis using high resolution satellite imagery, (ii) determine the changes in spatial distribution of these species in the period 2003-2017, and (iii) use moderate spatial resolution satellite imagery and ancillary environmental data to predict areas susceptible to future invasion.. The study area used in this investigation is the Square Kilometre Array (SKA SA) site, situated in Northern Cape Province, South Africa. Satellite images were classified using Multi-layer Perceptron (MLP) Neural Network classification algorithm to improve the land use land cover classification accuracy. A WordView-3 image with 1.24 m spatial resolution was used to delineate the distribution of Prosopis in the study area for the year 2016. Landsat images from the years 2003, 2008, 2013 and 2017 were used to conduct a change detection analysis. The prediction model developed in the study was able to predict Prosopis cover for the years 2017 and 2022 cover using ancillary environmental data and land use land cover maps. The study was also able to quantify the area covered by Prosopis species for the years 2017 and 2022. / XN2020