Early yield prediction of a maize crop is important for planning and policy decisions. Many countries, including South Africa use the conventional techniques of data collection for maize crop monitoring and yield estimation which are based on ground-based visits and reports. These methods are subjective, very costly and time consuming. Empirical models have been developed using weather data. These are also associated with a number of problems due to the limited spatial distribution of weather stations. Efforts are being made to improve the accuracy and timeliness of yield prediction methods. With the launching of satellites, satellite data are being used for maize crop monitoring and yield prediction. Many studies have revealed that there is a correlation between remotely sensed data (vegetation indices) and crop yields. The satellite based approaches are less expensive, save time, data acquisition covers large areas and can be used to estimate maize grain yields before harvest. This study applied Landsat 8 satellite based vegetation indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Moisture Stress Index (MSI) to predict maize crop yield. These vegetation indices were derived at different growth stages. The investigation was carried out in the Kopanong Local Municipality of the Free State Province, South Africa. Ground-based data (actual harvested maize yields) was collected from Department of Agriculture, Forestry and Fisheries (DAFF). Satellite images were acquired from Geoterra Image (Pty) Ltd and weather data was from the South African Weather Service (SAWS). Multilinear regression approaches were used to relate yields to the remotely sensed indices and meteorological data was used during the development of yield estimation models. The results showed that there are significant correlations between remotely sensed vegetation indices and maize grain yield; up to 63 percent maize yield was predicted from vegetation indices. The study also revealed that NDVI and SAVI are better yield predictors at reproductive growth stages of maize and MSI is a better index to estimate maize yield at both vegetative and reproductive growth stages. The results obtained in this study indicated that maize grain yields can be estimated using satellite indices at different maize growth stages.

Several models exist for investigating the effects of plant competition within and among species, i.e. intra- and inter-specific competition. The models for interspecific competition are based upon additive and replacement (de Wit) series experiments. Each approach has previously been used almost exclusively to study the effects of binary mixtures, and each has been used to derive various indices of competitiveness among species. Studies were undertaken in 1980 and 1981 to compare and evaluate the different models, and to investigate the relative performance of species in ternary combinations. Monoculture (density), additive and replacement series experiments involving binary mixtures of barnyard grass (Echinochloa crusgalli), redroot pigweed (Amaranthus retroflexus ) and green foxtail (Setaria viridis) were undertaken in both years; monoculture (density) and replacement series involving binary and ternary mixtures of rapeseed (Brassica napus) with pigweed and foxtail were also investigated in both years; in 1981, the rapeseed-pigweed-foxtail experiments also included additive series mixtures, the experimental design for which permitted the investigation of binary replacement series at different total densities. Monoculture experiments showed that yield of all four species was related to the density according to de Wit's spacing formula. Additive series experiments involving barnyard grass or rapeseed with the other two species showed that the yield of the indicator species followed the Dew's relationship between yield and the square root of the density of the associated species. The present studies have shown that the yield of the latter (in the presence of the indicator species) can also be described by the spacing formula. In binary replacement series experiments, de Wit's relative crowding coefficients (k) were calculated. Estimates of yield obtained from the k-values were usually found to agree well with observed yields. Dew's Index of Competition (CI) was calculated from additive series data for each combination of species tested. Relative crowding coefficients (k), Willey and Rao's Competition Ratio (CR) and McGilchrist's Aggressivity (A) were calculated from binary replacement series data. Both k- and CR- values contain components which relate to intra- and inter-specific competition. The actual relative contributions of intraspecific and interspecific competition were calculated by comparing the effect on a given species of adding equal densities of its own kind or of a second species, to the same total density; the ratio of the former to the latter is a new parameter, termed the Interference Ratio (IR), and is related to the relative crowding coefficient. Intercomparisons of the various measures of competitiveness showed that in both years k-values were highly correlated with A and CR, and in 1980 were also correlated with CI. However, there was only a weak correlation between k and IR. In general, CR-, k- and A-values suggested that barnyard grass and rapeseed were the most competitive species. However, IR-values indicated that the greatest competitiveness was exhibited by pigweed against foxtail. Pigweed was much more sensitive to its own kind than to foxtail. Estimates of k-values for untested combinations based upon either the use of de Wit's spacing formula or upon k-values determined for binary mixtures involving each of the untested pair with a common third species were found to be unreliable. In several mixtures, k was found to be density dependent. In ternary mixtures, the effects on the yield of a given species could not be predicted from its behaviour in the presence of either of its competitors in binary combinations. For example, pigweed and foxtail behaved synergistically against high density rapeseed, but tended to act antagonistically at low rapeseed densities. Although foxtail was consistently the weakest competitor in any binary mixture, it had the greatest effect of any species in determining the competitive interaction between the other species. In order to estimate yield losses, e.g. in crop-weed systems, only additive series data are shown to be of general applicability. / Land and Food Systems, Faculty of / Graduate

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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.

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Call number: LD2668 .T4 1961 J65

Call number: LD2668 .T4 1964 L23 / Master of Science

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