Understanding Striga occurrence and risk under changing climatic conditions across different agroecological farming systems at local and regional scales122

Philosophiae Doctor - PhDThe invasion by Striga in most cereal crop fields in Africa has posed an acute threat to food security and socioeconomic integrity. Consequently, numerous technological and research developments have been made to minimize and even control the Striga impacts on crop production. So far, efforts to control Striga have primarily focused on the manipulation of the genetics of the host crops, as well as understanding the phenological and physiological traits, along with the chemical composition of the weed

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041–2060)

Understanding climate change effects on the potential distribution of an important pollinator species, Ceratina moerenhouti (Apidae: Ceratinini), in the Eastern Afromontane biodiversity hotspot, Kenya

Monitoring key pollinator taxa such as the genus Ceratina requires precise near real-time predictions to facilitate better surveillance. The potential habitat suitability of Ceratina moerenhouti was predicted in the Eastern Afromontane biodiversity hotspot (EABH) in Kenya using presence-only data, to identify their potential distribution and vulnerability due to climate change. Bioclimatic, edaphic, terrain, land surface temperature, and land use and land cover (LULC) variables were used as predictors. Three machine learning techniques, together with their ensemble model, were evaluated on their suitability to predict current and future (the shared socioeconomic pathways (SSPs), i.e., SSP245 and SSP585) habitat suitability. Predictors were subjected to variable selection using the variance inflation factor resulting in a few (n = 9) optimum variables. The area under the curve (AUC) and true skill statistic (TSS) were used for the accuracy assessment of the modeling outputs. The results indicated that 30% and 10% of the EABH in Murang’a and Taita Taveta counties are currently suitable for C. moerenhouti occurrence, respectively. However, future projections show a ±5% decrease in C. moerenhouti habitats in the two counties. Further, the ensemble model harnessed the algorithm differences while the random forest had the highest individual predictive power (AUC = 0.97; TSS = 0.96). Clay content, LULC, and the slope were the most relevant variables together with temperature and precipitation. Integrating multi-source data in predicting suitable habitats improves model prediction capacity. This study can be used to support the maintenance of flowering plant communities around agricultural areas to improve pollination services

Climate change and specialty coffee potential in Ethiopia

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience

Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)

After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS).Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)publishedVersio

Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterisation in semi-arid regions?

We explore the potential contribution of Sentinel-2 (S2) wavebands and biophysical parameters, i.e. Leaf Area Index (LAI), Chlorophyll content (Cab), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction of Vegetation Cover (FVC) and Canopy Water Content (CWC) in mapping land use and land cover (LULC) in Zimbabwe. Random forest (RF) and naïve Bayes (NB) were used to classify S2 imagery. S2 biophysical variables resulted in LULC overall accuracy (OA) of 96% and 86% for RF and NB respectively, whereas S2 wavebands produced slightly higher accuracies of 97% and 88% for RF and NB respectively. Combining wavebands and biophysical variables enhanced classification results (OA = 98%: RF and 91%: NB). Variable importance analysis showed that FAPAR, red-edge 2, green, red-edge 3, FVC and band 8a, are the most relevant in the classification. Our work shows the strength and capability of biophysical variables in discerning different LULC attributes in semi-arid environments

A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity

Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions

Examining the suitability of landscape patches for endangered species enhances critical insights and indicators into the processes of population structure, community dynamics, and functioning in ecosystems particularly in protected areas (PAs). While PAs are the cornerstone in biodiversity conservation, there is debate on their efficacy to retain their conservation superiority over unprotected areas under climate change. In the present study, we examined the spatial and temporal effectiveness of PAs at maintaining suitable habitat for the “vulnerable” Southern Ground-hornbill (SGH), Bucorvus leadbeateri compared with the unprotected areas in Zimbabwe. We used a landscape-scale analysis of 182 PAs, their surrounding buffer zones, and unprotected areas coupled with three machine learning models (maximum entropy: MaxEnt, random forest, and support vector machines) to simulate SGH habitat suitability. Bioclimatic, vegetation seasonality and terrain variables were used as predictors against SGH “presence-only” observations and the models were projected for 2050 as future climatic scenarios (i.e. representative concentration pathways: RCP2.6 and RCP8.5). The true skill statistic (TSS) and area under the curve (AUC) were used to evaluate the performance of the modeling framework. Our results show that the PAs network in Zimbabwe is extremely relevant for the conservation of SGH, with 8% of the suitable habitat within PAs projected to become unsuitable by 2050. Higher levels of protection status resulted in higher levels of suitable habitat for the SGH while the suitability of eastern-based PAs showed a decrease and the western-based PAs will potentially increase in suitability. Thus, conservation strategies should take the eastern PAs range contraction and associated westward shift into account. The established potential increase in suitability outside the PAs network (23%–31%) might increase conflicts between agriculture and conservation. We, therefore, suggest an expanded cross-boundary institutional alliance and policy development with all stakeholders to implement a holistic conservation plan. Our work demonstrates the importance of combining multi-source remotely sensed data in predicting habitat suitability for endangered species such as the SGH as key indicators of biological conservation and PAs’ effectiveness

Understanding climate change effects on the potential distribution of an important pollinator species, Ceratina moerenhouti (Apidae: Ceratinini), in the Eastern Afromontane biodiversity hotspot, Kenya

International audienceMonitoring key pollinator taxa such as the genus Ceratina requires precise near real-time predictions to facilitate better surveillance. The potential habitat suitability of Ceratina moerenhouti was predicted in the Eastern Afromontane biodiversity hotspot (EABH) in Kenya using presence-only data, to identify their potential distribution and vulnerability due to climate change. Bioclimatic, edaphic, terrain, land surface temperature, and land use and land cover (LULC) variables were used as predictors. Three machine learning techniques, together with their ensemble model, were evaluated on their suitability to predict current and future (the shared socioeconomic pathways (SSPs), i.e., SSP245 and SSP585) habitat suitability. Predictors were subjected to variable selection using the variance inflation factor resulting in a few (n = 9) optimum variables. The area under the curve (AUC) and true skill statistic (TSS) were used for the accuracy assessment of the modeling outputs. The results indicated that 30% and 10% of the EABH in Murang'a and Taita Taveta counties are currently suitable for C. moerenhouti occurrence, respectively. However, future projections show a ±5% decrease in C. moerenhouti habitats in the two counties. Further, the ensemble model harnessed the algorithm differences while the random forest had the highest individual predictive power (AUC = 0.97; TSS = 0.96). Clay content, LULC, and the slope were the most relevant variables together with temperature and precipitation. Integrating multi-source data in predicting suitable habitats improves model prediction capacity. This study can be used to support the maintenance of flowering plant communities around agricultural areas to improve pollination services

Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area

In the recent past, the Horn of Africa witnessed an upsurge in the desert locust (Schistocerca gregaria) invasion. This has raised major concerns over the massive food insecurity, socioeconomic impacts, and livelihood losses caused by these recurring invasions. This study determined the potential vegetation damage due to desert locusts (DLs) and predicted the suitable habitat at high risk of invasion by the DLs using current and future climate change scenarios in Kenya. The normalized difference vegetation index (NDVI) for the period 2018–2020 was computed using multi-date Sentinel-2 imagery in the Google Earth Engine platform. This was performed to assess the vegetation changes that occurred between May and July of the year 2020 when northern Kenya was the hotspot of the DL upsurge. The maximum entropy (MaxEnt) algorithm was used together with 646 DL occurrence records and six bioclimatic variables to predict DL habitat suitability. The current (2020) and two future climatic scenarios for the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the model for interdisciplinary research on climate (MIROC6) were utilized to predict the future potential distribution of DLs for the year 2030 (average for 2021–2040). Using Turkana County as a case, the NDVI analysis indicated the highest vegetation damage between May and July 2020. The MaxEnt model produced an area under the curve (AUC) value of 0.87 and a true skill statistic (TSS) of 0.61, while temperature seasonality (Bio4), mean diurnal range (Bio2), and precipitation of the warmest quarter (Bio18) were the most important bioclimatic variables in predicting the DL invasion suitability. Further analysis demonstrated that currently 27% of the total area in Turkana County is highly suitable for DL invasion, and the habitat coverage is predicted to potentially decrease to 20% in the future using the worst-case climate change scenario (SSP5-8.5). These results have demonstrated the potential of remotely sensed data to pinpoint the magnitude and location of vegetation damage caused by the DLs and the potential future risk of invasion in the region due to the available favorable vegetational and climatic conditions. This study provides a scalable approach as well as baseline information useful for surveillance, development of control programs, and monitoring of DL invasions at local and regional scales