>Magister Scientiae - MSc / Maize streak disease (MSD), caused by variants of the Maize streak virus (MSV) A strain, is the world's third and Africa’s most important maize foliar disease. Outbreaks of the disease occur frequently and in an erratic fashion across Africa and Islands in the Indian Ocean causing devastating yield losses such that the emergence, resurgence and rapid diffusion of MSV-A variants in this region presents a serious threat to maize production, farmer livelihoods and food security. To compliment current MSD management systems, a total of 689 MSV-A full genomes sampled over a 32 year period (1979-2011) from 20 countries across Africa and the adjacent Indian Ocean Islands, 286 of which were novel, were used to estimate: (i) the levels of genetic diversity using MEGA and the Sequence Demarcation Tool v1.2 (SDT); (ii) the times of occurrence and distribution of recombination using the recombination detection program (RDP v.4) and the genetic algorithm for recombination detection (GARD); (iii) selection pressure on codon positions using PARRIS and FUBAR methods implemented on the DATAMONKEY web server; (iv) reconstruct the history of spatio-temporal diffusion for MSV-A using the discrete phylogeographic models implemented in BEAST v1.8.1; (v) characterize source-sink dynamics and identify predictor variables driving MSV-A dispersal using the generalized linear models, again implemented in BEAST v1.8.1. Isolates used displayed low levels of genetic diversity (0.017 mean pairwise distance and ≥ 98% nucleotide sequence identities), and a well-structured geographical distribution where all of the 233 novel isolates clustered together with the -A1 strains. A total of 34 MSV inter-strain recombination events and 33 MSV-A intra-strain recombination events, 15 of which have not been reported in previous analyses (Owor et al., 2007, Varsani et al., 2008 and Monjane et al., 2011), were detected. The majority of intra-strain MSV-A recombination events detected were inferred to have occurred within the last six decades, the oldest and most conserved of these being events 19, 26 and 28 whereas the most recent events were 8, 16, 17, 21, 23, and 29. Intra-strain recombination events 20, 25 and 33, were widely distributed amongst East African MSV-A samples, whereas events 16, 21 and 23, occurred more frequently within West African MSV-A samples. Events 1, 4, 8, 10, 14, 17, 19, 22, 24, 25, 26, 28, and 29 were more widely distributed across East, West and Southern Africa and the adjacent Indian Ocean Islands. Whereas codon positions 12 and 19 within motif I in the coat protein transcript, and four out of the seven codon positions (147, 166, 195, 203, 242, 262, 267) in the Rep transcript (codons 195 and 203 in the Rb motif and codons 262 and 267 in site B of motif IV), evolved under strong positive selection pressure, those in the movement protein (MP) and RepA protein encoding genes evolved neutrally and under negative selection pressure respectively. Phylogeographic analyses revealed that MSV-A first emerged in Zimbabwe around 1938 (95% HPD 1904 - 1956), and its dispersal across Africa and the adjacent Indian Ocean Islands was achieved through approximately 34 migration events, 19 of which were statistically supported using Bayes factor (BF) tests. The higher than previously reported mean nucleotide substitution rate [9.922 × 10-4 (95% HPD 8.54 × 10-4 to 1.1317 × 10-3) substitutions per site per year)] for the full genome recombination-free MSV-A dataset H estimated was possibly a result of high nucleotide substitution rates being conserved among geminiviruses such as MSV as previously suggested. Persistence of MSV-A was highest in source locations that include Zimbabwe, followed by South Africa, Uganda, and Kenya. These locations were characterized by high average annual precipitation; moderately high average annual temperatures; high seasonal changes; high maize yield; high prevalence of undernourishment; low trade imports and exports; high GDP per capita; low vector control pesticide usage; high percentage forest land area; low percentage arable land; high population densities, and were in close proximity to sink locations. Dispersal of MSV-A was frequent between locations that received high average annual rainfall, had high percentage forest land area, occupied high latitudes and experienced similar climatic seasons, had high GDP per capita and had balanced maize import to export ratios, and were in close geographical proximity. / National Research Foundation (NRF), the Poliomyelitis Research Foundation (PRF), and the Thuthuka Board

Thesis (PhD. (Geography)) --University of Limpopo, 2019 / Of late climate change and consequently, the spread of crop diseases has been identified as one of the major threat to crop production and food security in subSaharan Africa. This research, therefore, aims to evaluate the role of in situ hyperspectral and new generation multispectral data in detecting maize crop viral and fungal diseases, that is maize streak virus and grey leaf spot respectively. To accomplish this objective; a comparison of two variable selection techniques (Random Forest’s Forward Variable, (FVS) and Guided Regularized Random Forest: (GRRF) was done in selecting the optimal variables that can be used in detecting maize streak virus disease using in-situ resampled hyperspectral data. The findings indicated that the GRRF model produced high classification accuracy (91.67%) whereas the FVS had a slightly lower accuracy (87.60%) based on Hymap when compared to the AISA. The results have shown that the GRRF algorithm has the potential to select compact feature sub sets, and the accuracy performance is better than that of RF’s variable selection method. Secondly, the utility of remote sensing techniques in detecting the geminivirus infected maize was evaluated in this study based on experiments in Ofcolaco, Tzaneen in South Africa. Specifically, the potential of hyperspectral data in detecting different levels of maize infected by maize streak virus (MSV) was tested based on Guided Regularized Random Forest (GRRF). The findings illustrate the strength of hyperspectral data in detecting different levels of MSV infections. Specifically, the GRRF model was able to identify the optimal bands for detecting different levels of maize streak disease in maize. These bands were allocated at 552 nm, 603 nm, 683 nm, 881 nm, and 2338 nm. This study underscores the potential of using remotely sensed data in the accurate detection of maize crop diseases such as MSV and its severity which is critical in crop monitoring to foster food security, especially in the resource-limited subSaharan Africa. The study then investigated the possibility to upscale the previous findings to space borne sensor. RapidEye data and derived vegetation indices were tested in detecting and mapping the maize streak virus. The results revealed that the use of RapidEye spectral bands in detection and mapping of maize streak virus disease yielded good classification results with an overall accuracy of 82.75%. The inclusion of RapidEye derived vegetation indices improved the classification accuracies by 3.4%. Due to the cost involved in acquiring commercial images, like xviii RapidEye, a freely available Landsat-8 data can offer a new data source that is useful for maize diseases estimation, in environments which have limited resources. This study investigated the use of Landsat 8 and vegetation indices in estimating and predicting maize infected with maize streak virus. Landsat 8 data produced an overall accuracy of 50.32%. The inclusion of vegetation indices computed from Landsat 8 sensor improved the classification accuracies by 1.29%. Overally, the findings of this study provide the necessary insight and motivation to the remote sensing community, particularly in resource-constrained regions, to shift towards embracing various indices obtained from the readily-available and affordable multispectral Landsat-8 OLI sensor. The results of the study show that the mediumresolution multispectral Landsat 8-OLI data set can be used to detect and map maize streak virus disease. This study demonstrates the invaluable potential and strength of applying the readily-available medium-resolution, Landsat-8 OLI data set, with a large swath width (185 km) in precisely detecting and mapping maize streak virus disease. The study then examined the influence of climatic, environmental and remotely sensed variables on the spread of MSV disease on the Ofcolaco maize farms in Tzaneen, South Africa. Environmental and climatic variables were integrated together with Landsat 8 derived vegetation indices to predict the probability of MSV occurrence within the Ofcolaco maize farms in Limpopo, South Africa. Correlation analysis was used to relate vegetation indices, environmental and climatic variables to incidences of maize streak virus disease. The variables used to predict the distribution of MSV were elevation, rainfall, slope, temperature, and vegetation indices. It was found that MSV disease infestation is more likely to occur on low-lying altitudes and areas with high Normalised Difference Vegetation Index (NDVI) located at an altitude ranging of 350 and 450 m.a.s.l. The suitable areas are characterized by temperatures ranging from 24°C to 25°C. The results indicate the potential of integrating Landsat 8 derived vegetation indices, environmental and climatic variables to improve the prediction of areas that are likely to be affected by MSV disease outbreaks in maize fields in semi-arid environments. After realizing the potential of remote sensing in detecting and predicting the occurrence of maize streak virus disease, the study further examined its potential in mapping the most complex disease; Grey Leaf Spot (GLS) in maize fields using WorldView-2, Quickbird, RapidEye, and Sentinel-2 resampled from hyperspectral data. To accomplish this objective, field spectra were acquired from healthy, moderate and xix severely infected maize leaves during the 2013 and 2014 growing seasons. The spectra were then resampled to four sensor spectral resolutions – namely WorldView-2, Quickbird, RapidEye, and Sentinel-2. In each case, the Random Forest algorithm was used to classify the 2013 resampled spectra to represent the three identified disease severity categories. Classification accuracy was evaluated using an independent test dataset obtained during the 2014 growing season. Results showed that Sentinel-2 achieved the highest overall accuracy (84%) and kappa value (0.76), while the WorldView-2, produced slightly lower accuracies. The 608 nm and 705nm were selected as the most valuable bands in detecting the GLS for Worldview 2, and Sentinel-2. Overall, the results imply that opportunities exist for developing operational remote sensing systems for detection of maize disease. Adoption of such remote sensing techniques is particularly valuable for minimizing crop damage, improving yield and ensuring food security.