Ph.D. (Geography) / Skillful management and planning of the earth's natural resources and of agricultural production necessitates a great deal of Information regarding the resources and relevant soclo-economlc factors, as well as better Information on crop yield expectations throughout the year. These Intricate processes can often be simplified Into models. Most of Nature's systems (for example climatic systems) are, however, too complex to allow realistic models to be evaluated numerically and are therefore examined by means of simulation models through which the Interaction with time between physical processes Is established. The purpose Is to understand these processes In order to forecast the results of these changes and Interactions. The aim of this study Is to construct a composite climate model that (1) computes missing climate values, and (2) extrapolates climate values until the expected date of harvesting, by simulation using the random sampling of values from reference ("look-up") tables, In order that (3) the climate files, with simulated climate values, could be used with the parameter files as Input files for the CERES-Maize model. The CERES-Maize model uses dally values to simulate the growth, development and yield of the maize plant. The respective crop forecasting results obtained for actual and simulated climate values are then to be evaluated. Climate files, with four variables, were obtained on magnetic computer tape from the South African Weather Bureau for the study area In the Eastern Transvaal. The preliminary processing was done by the use of SA5-programmes and these files were then exported from the mainframe computer to a personal computer and stored on floppy disks. Climate reference flies were compiled from the original climate flies by sorting the climate data according to the Julian date. The missing values In the climate reference flies and the original climate files were restored from the files of neighbouring weather stations, as calculated orestimated values by.means of a suitable method of computation. Some of the methods used, were derived after comparing the graphs of the time-series of a number of climate files. Aclimate simulation model was compiled In which climatic elements were simulated by sampling values a set number of times randomly from the climate reference files. The mean of these sampled values were adjusted by multiplying It with a factor representing the climatic change over time. A climate file, also containing simulated values, and a theoretical parameter Input file were then used as the Input flies for a revised edition of the CERES-Maize model. A comparison of the results obtained for the 1986/87 growing season when the climate files, with actual and simulated values respectively, were used as Inputs for the CERES-Maize model, Indicated very promising results. The values predicted for two climate flies (1962-1987) differed by about 18%, whereas a difference of only about 8% between those predicted for two smaller climate files (actual and simulated values respectively), representing only the 1986/87 season, was recorded. The difference between values predicted for the climate file, mentioned last, and consisting only of simulated climate values, and those forecasted for the original and complete climate data file, was only 5%. As Indicated by the arithmetic mean, there is again a tendency towards the mean values.

D.Litt. et Phil. (Geography) / The aim of this study is to integrate LANDSAT multispectral digital data with agricultural statistics, to analyse, explain and forecast the spatial variation of crop production in the Vermaas development area (south of Lichtenburg, Western Transvaal). This aim answers the urgent need for a reliable agricultural data base that can be quickly and cheaply obtained and used for the timely planning of an environment's limited agricultural resources. With such a data base available, early decisions about imports and exports can be taken in connection with the expected agricultural commodities of an area: the year-to-year fluctuation in crop yields is still the main problem in relation to the overall planning of agricultural food production. The study has been conducted according to two main analytical phases, i.e. (i) the interpretation of the data, which in turn was subdivided into: - the cartographic-analytical evaluation of the agricultural information, and - the recognition of rural land-use patterns from LANDSAT digital data. (i i) the integration process. The LANDSAT land-use information was integrated with the observed agricultural statistics with the aid of two integration models: an empirical and an operational model. The data for the research consisted of the multispectral digital data of LANDSAT-l and available agricultural statistics. The LANDSAT data was acquired from the Satellite Remote Sensing Centre at Hartbeeshoek, while the agricultural data was obtained from the Department of Agriculture (Highveld Region) and other official soures. These analytical phases were conducted at the computer centres of the CSIR and RAU. Existing computer programme packages were used - the VICAR system for pattern recognition, and the BMD and SYMAP systems for the analytical evaluation of the agricultural information and for the implementation of the integration models. The following results were obtained: 3.1 The integration of the LANDSAT information with the agricultural statistics was reasonably successful. The success of any study of this nature can be ascertained from the accuracy with which the necessary information is derived from the LANDSAT multispectral digital data. 3.2 This analysis highl ighted the cultivated area as a major factor for consideration. The type of crop and the area covered by it are the two most important sets of information that can be obtained from the LANDSAT data and used in an integration model. 3.3 The results (predicted crop yields) that were obtained from the integration process could probably be improved, if the detrimental influence of collinearity, which existed between some of the agricultural variables, was el iminated. 3.4 The identification of different crops from the LANDSAT digital data was not possible - a fact which can be attributed to the lack of a crop calendar for this farming area. Besides the above-mentioned results, the following can also be listed: 4.1 The spatial variation In maize production was well analysed in terms of the integration results, In spite of the fact that the accuracy of the agricultural statistics was, in certain cases, questionable. 4.2 The important influence of time upon the spatial variation in crop production could not be implicated, because of the one point in time consideration of this study. 4.3 Only the agricultural variables that were directly related to farm area could be used as input data for this study. 4.4 The potential usefulness of the LANDSAT digital data as geographical information is mainly determined by its quality (cloudcover, resolution, etc.). 4.5 The application of multispectral digital data depends on certain specific techniques, with which the researcher must acquaint himself for a successful and useful interpretation of the digital data.