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Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value < 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Rabies is a neglected disease endemic in Asia and Africa but is still a significant public and veterinary health threat. Whilst a key delicacy for the local diet, bats are a natural reservoir host for many viral zoonotic agents including lyssaviruses, the causative agent of rabies. Studies on knowledge and practices linked to the disease will help to identify gaps and define preventive strategies that may subsequently result in a reduction and the potential elimination of human rabies. In order to assess the public health awareness of bat rabies among specific population groups in Makurdi (Nigeria), structured questionnaires (n = 154) were administered by face-to-face interviews to bat handlers and persons residing near bat roost sites. A total of 59.7% of the respondents were persons residing near bat roost sites, 13% were bat hunters, 25.3% were bat meat consumers and 1.9% were university researchers. Only 6.5% of respondents reported using some form of personal protective equipment (PPE) ranging from hand gloves, face/nose masks and protective boots to lab coats/coveralls while handling bats, whilst the majority (93.5%) did not use any form of PPE. With a mean knowledge score of 8.34 out of a possible 12 points, 50.6% of respondents had good knowledge of bats and their disease-carrying potential, 39.6% had fair knowledge, while 9.7% had poor knowledge. Log linear models showed significant associations between knowledge score and level of education, as well as knowledge score and occupation. The latter highlights the requirement to enhance public education among bat handlers and persons residing near bat roosts on the need to protect themselves better, while handling bats particularly during processing of bats for food and on steps to take when exposed to bites from bats.

The geographic distribution of a species is governed by climatic conditions, topography, resources and habitat structure determining the fundamental niche, while the local distribution expressed via home range occupation may be compressed by biotic interactions with competitors and predators, restricting the realised niche. Biotic influences could be especially important for relatively rare species. We investigated how rainfall, geology, land type and abundance of other ungulate species serving as competitors or prey for predators contributed to the patchy distribution of sable antelope herds within Kruger National Park. Data were provided by annual aerial surveys of ungulate populations conducted between 1978 and 1988. Sable herds were more commonly present on granitic and sandstone substrates than on more fertile basalt. They occurred both in the moist south-west and dry north of the park. They were most abundant in sour bushveld and mopane savanna woodland, and mostly absent from knob thorn-marula parkland. The presence of sable was negatively associated with high concentrations of impala and wildebeest, less consistently related to the abundance of zebra, and positively associated with the occurrence of buffalo herds. Best supported models included the separate effects of the most abundant grazers along with land type. Interspecific relationships seemed more consistent with vulnerability to predation as the underlying mechanism restricting the distribution of sable herds than with competitive displacement. Sable favoured land types distinct from those where wildebeest, the most preferred prey of lions, and impala, numerically the most important resident prey species, were most abundant. Hence the risk of predation, associated with habitat conditions where abundant prey species are most concentrated, can exert an overriding influence on the distribution of rarer species in terms of their home range occupation.

Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

The most prominent drivers of international migration across borders and internal migration-rural to urban areas are explained by the ‘push and pull’ migration model. However, this model falls short in addressing migrations driven by the impacts of climate change, such as the movement from rural to urban area as a coping strategy for environmental degradation. Factors like deforestation, desertification, droughts, and floods are key drivers of such migration. Additionally, bush encroachment and the spread of invasive alien plant species disrupt landscapes and negatively affect ecosystem goods and services in many arid and semi-arid regions around the world. This phenomenon directly affects the livelihoods of rural communities by depriving them of their croplands, rangelands, and ranches. Despite this, there is a lack of sufficient information on how these factors are linked to migratory movements, whether from rural to urban areas or between rural regions. To explore this connection, this study reviewed scientific publications, including journal articles and books using key phrases such as, ‘push and pull migration factors’, ‘impact of bush encroachment on migration’ and ‘impact of invasive alien plants on migration factors’, among others. A total of 155 documents were downloaded via Google Scholar, of which 99 were thoroughly reviewed and included in the study. The remaining 53 documents were skimmed and excluded due to their irrelevance, or limited contribution to the research. The study found that the bush encroachment and invasive plant species in rangelands are significant push factors for driving migration, both between rural areas and rural to urban areas. It is, therefore, recommended that these two factors be given a greater attention when addressing outmigration from rural regions.

We investigated the potential effects of genetic modification of Bt maize on the community composition and functions of bacterial endophytes associated with transgenic maize (Bt MON 810) in comparison with its isogenic parental line at two developmental stages. Bacterial isolates were obtained from transgenic (Bt) and non-transgenic (non-Bt) maize at 50- and 90-day-old developmental stages. Isolated bacterial endophytes were screened for their capabilities in phosphate solubilisation, nitrogen fixation, production of antifungal metabolites and production of indole acetic acid. After molecular identification, 60 isolates were obtained and clustered into 19 and 18 operational taxonomic units from 50- and 90-day-old maize, respectively. The isolates belonged to the genera Bacillus, Pantoea, Serratia, Yersinia, Enterobacter, Pseudomonas, Acinetobacter and Stenotrophomonas. Functional attributes and diversity of the isolated endophytes at both developmental stages were not significantly different for both maize varieties. However, functional attributes were significantly affected by plant growth stage. Isolates from younger plants were more efficient producers of indole acetic acid, but exhibited little or no capabilities for nitrogen fixation, phosphate solubilisation and antifungal activity in both maize genotypes. Based on these outcomes, Bt modification in maize does not seem to affect the community composition or functional attributes of bacterial endophytes. Significance: Bt modification in maize does not affect the ecological guild or functional attributes of cultivable bacterial endophytes.

Documenting current species distribution patterns and their association with habitat types is important as a basis for assessing future range shifts in response to climate change or other influences. We used the adaptive local convex hull (a-LoCoH) method to map distribution ranges of 12 ungulate species within the Kruger National Park (KNP) based on locations recorded during aerial surveys (1980â??1993). We used log-linear models to identify changes in regional distribution patterns and chi-square tests to determine shifts in habitat occupation over this period. We compared observed patterns with earlier, more subjectively derived distribution maps for these species. Zebra, wildebeest and giraffe distributions shifted towards the far northern section of the KNP, whilst buffalo and kudu showed proportional declines in the north. Sable antelope distribution contracted most in the north, whilst tsessebe, eland and roan antelope distributions showed no shifts. Warthog and waterbuck contracted in the central and northern regions, respectively. The distribution of impala did not change. Compared with earlier distributions, impala, zebra, buffalo, warthog and waterbuck had become less strongly concentrated along rivers. Wildebeest, zebra, sable antelope and tsessebe had become less prevalent in localities west of the central region. Concerning habitatoccupation, the majority of grazers showed a concentration on basaltic substrates, whilst sable antelope favoured mopane-dominated woodland and sour bushveld on granite. Buffalo showed no strong preference for any habitats and waterbuck were concentrated along rivers. Although widespread, impala were absent from sections of mopane shrubveld and sandveld. Kudu and giraffe were widespread through most habitats, but with a lesser prevalence in northern mopane-dominated habitats. Documented distribution shifts appeared to be related to the completion of the western boundary fence and widened provision of surface water within the park. Conservation implications: The objectively recorded distribution patterns provide a foundation for assessing future changes in distribution that may take place in response to climatic shifts or other influences.

Documenting current species distribution patterns and their association with habitat types is important as a basis for assessing future range shifts in response to climate change or other influences. We used the adaptive local convex hull (a-LoCoH) method to map distribution ranges of 12 ungulate species within the Kruger National Park (KNP) based on locations recorded during aerial surveys (1980–1993). We used log-linear models to identify changes in regional distribution patterns and chi-square tests to determine shifts in habitat occupation over this period. We compared observed patterns with earlier, more subjectively derived distribution maps for these species. Zebra, wildebeest and giraffe distributions shifted towards the far northern section of the KNP, whilst buffalo and kudu showed proportional declines in the north. Sable antelope distribution contracted most in the north, whilst tsessebe, eland and roan antelope distributions showed no shifts. Warthog and waterbuck contracted in the central and northern regions, respectively. The distribution of impala did not change. Compared with earlier distributions, impala, zebra, buffalo, warthog and waterbuck had become less strongly concentrated along rivers. Wildebeest, zebra, sable antelope and tsessebe had become less prevalent in localities west of the central region. Concerning habitat occupation, the majority of grazers showed a concentration on basaltic substrates, whilst sable antelope favoured mopane-dominated woodland and sour bushveld on granite. Buffalo showed no strong preference for any habitats and waterbuck were concentrated along rivers. Although widespread, impala were absent from sections of mopane shrubveld and sandveld. Kudu and giraffe were widespread through most habitats, but with a lesser prevalence in northern mopane-dominated habitats. Documented distribution shifts appeared to be related to the completion of the western boundary fence and widened provision of surface water within the park. Conservation implications: The objectively recorded distribution patterns provide a foundation for assessing future changes in distribution that may take place in response to climatic shifts or other influences.

Documenting current species distribution patterns and their association with habitat types is important as a basis for assessing future range shifts in response to climate change or other influences. We used the adaptive local convex hull (á-LoCoH) method to map distribution ranges of 12 ungulate species within the Kruger National Park (KNP) based on locations recorded during aerial surveys (1980-1993). We used log-linear models to identify changes in regional distribution patterns and chi-square tests to determine shifts in habitat occupation over this period. We compared observed patterns with earlier, more subjectively derived distribution maps for these species. Zebra, wildebeest and giraffe distributions shifted towards the far northern section of the KNP, whilst buffalo and kudu showed proportional declines in the north. Sable antelope distribution contracted most in the north, whilst tsessebe, eland and roan antelope distributions showed no shifts. Warthog and waterbuck contracted in the central and northern regions, respectively. The distribution of impala did not change. Compared with earlier distributions, impala, zebra, buffalo, warthog and waterbuck had become less strongly concentrated along rivers. Wildebeest, zebra, sable antelope and tsessebe had become less prevalent in localities west of the central region. Concerning habitat occupation, the majority of grazers showed a concentration on basaltic substrates, whilst sable antelope favoured mopane-dominated woodland and sour bushveld on granite. Buffalo showed no strong preference for any habitats and waterbuck were concentrated along rivers. Although widespread, impala were absent from sections of mopane shrubveld and sandveld. Kudu and giraffe were widespread through most habitats, but with a lesser prevalence in northern mopane-dominated habitats. Documented distribution shifts appeared to be related to the completion of the western boundary fence and widened provision of surface water within the park. CONSERVATION IMPLICATIONS: The objectively recorded distribution patterns provide a foundation for assessing future changes in distribution that may take place in response to climatic shifts or other influences.