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ABSTRACTFlower-visiting insects that are pollinators play a critical role in promoting biodiversity in agroecosystems and agricultural food production through their pollination ecosystem service. However, several factors affect the survival of these pollinators and flower visitors, including the heavy and indiscriminate application of agrochemicals to control crop insect pests, which is impacted by various cropping patterns in a landscape and by shifting environmental conditions. Thus, this study focused on investigating the influence of cropping patterns on the spatial distribution of pollinators (Apis mellifera, Hymenoptera other than A. mellifera, and Syrphidae), flower visitors (Calliphoridae), and pests, i.e. fruit fly (Bactrocera dorsalis) and false codling moth (Thaumatotibia leucotreta) of the avocado, a pollinator-dependent crop. Cropping patterns, earth observation data and relevant environmental variables were used as the predictor variables for modeling the potential distribution and abundance of avocado pollinators, flower visitors and pests in one of the leading regions in avocado production in Kandara, Maragua, and Gatanga sub-Counties in Murang’a County, Kenya. In specific, species distribution modeling (SDM) and species abundance modeling (SAM) techniques, i.e. the maximum entropy (MaxEnt) model (presence-only data) and negative binomial (NB) distribution in a generalized linear model (GLM) (abundance data) were used, respectively. Additionally, the spatial distribution probability of the co-occurrence of the pollinators, flower visitors and pests was also analyzed. This study revealed that cropping patterns was the most consistent influential predictor variables for the distribution of avocado pollinators, flower visitors and pests. A large area of Kandara and some parts of Maragua and Gatanga sub-Counties showed a high spatial distribution probability of the studied avocado pollinators, flower visitors and pests. However, only the majority of Kandara sub-County had a high spatial distribution probability score of the potential co-occurrence of the avocado pollinators, flower visitors and pests. Further, A. mellifera was the most abundant flower-visiting pollinator compared with the other studied pollinators, while B. dorsalis was the most abundant avocado pest compared with T. leucotreta. In addition, GLM analysis indicated that no environmental variable was significant in explaining the abundance of the studied avocado pollinators, whereas precipitation and elevation derivatives of aspect and hillshade were statistically significant (p ≤ 0.05) in explaining the abundance of B. dorsalis. Solar radiation was significant in explaining only the abundance of T. leucotreta. Our study revealed that SDM and SAM modeling outputs can be used to inform decision-making for the implementation of sustainable management efforts regarding pollinators, flower visitors, and insect pests.

Abstract Desert locust (Schistocerca gregaria) infestations cause significant damage to crops and pastureland, impeding food security and livelihoods globally. In recent years, some East African countries have suffered severe desert locust outbreaks, causing significant harm to agriculture and local communities. To comprehensively understand and mitigate the socio‐economic impacts of this pest, an integrated assessment approach is crucial. This study proposes an integrated assessment model that combines ecological, economic, and social dimensions to analyze the multifaceted impacts of the desert locust on the rate of urbanization changes, farming expansion, and food production and how they lead to food unavailability (demand, supply, and price) and food and nutrition insecurity in Eastern Africa. A system dynamics‐based assessment model was first developed for Sudan using the dynamic relations among the factors and their variations over time and how they affect the socio‐economic variables. The developed model was used to spatially simulate the impacts over the entire Eastern Africa. Various scenarios were further simulated and analyzed, incorporating different policy options to effectively mitigate the impacts. Results demonstrated that in all the countries, desert locusts extensively contribute to the slowing down of farming expansion and food production and supply. Consequently, these factors lead to increased urbanization rates through people rural‐urban migration and food demand at different magnitudes across different countries. By considering both short‐term and long‐term effects, this approach aims to provide policymakers, researchers, and practitioners with a holistic understanding of the complex dynamics involved and inform effective management strategies. For example, communities equipped with accurate breeding detection tools and employing an integrated pest management strategy combining chemical pesticides and biopesticides had the highest potential for effectively mitigating the future impacts of desert locusts for enhancing community livelihood.

Bee farming and beehealth are threatened by climate change, agricultural and agrochemicals intensification, and bee pests and diseases. Among these threats, bee pests have particularly been identified as a major obstacle to beehealth. Although previous studies have endeavoured to establish bee pests’ spatial distribution, their seasonal abundance in the landscape remains poorly understood. Hence, this study sought to determine factors that influence the abundance and spatial proliferation of bee pests in Kenya. Abundance data on Varroa destructor, Oplostomus haroldi, Galleria mellonella and Aethina tumida were collected from apiaries in Kenya during the wet and dry seasons. The abundance data were fitted to non-conflating human footprint datasets, satellite derived vegetation phenological, topographical and bioclimatic variables. The results indicated a significant (p ≤ 0.05) seasonal influence on bee pests’ abundance, while precipitation was the most relevant on most bee pests’ abundance prediction models. Topographic and vegetation phenological influence varied across the landscapes while anthropogenic influence was comparatively low. High seasonality in bioclimatic variables influenced the projected (year 2055) spatial and abundance risk levels of bee pests across the study area. The V. destructor and A. tumida prediction models for current and future epochs ranked excellent in their performance, while O. haroldi and G. mellonella were ranked good and fair, respectively. Due to their precision, this study concluded that these models could reliably be used to establish bee pests’ high-risk areas for management and mitigation purposes.

Abstract Reliable metrics to monitor human impacts on biodiversity are essential for informing conservation policy. As insects are indicators of global change, whose declines profoundly affect ecosystems, insect diversity may predict biodiversity status. Here we present an unbiased and straightforward biodiversity status metric based on insect diversity (richness) and landscape naturalness. Insect diversity was estimated using spatially explicit earth observation data and insect species assemblages across microhabitats in two agro-ecological zones in Africa. Landscape naturalness was estimated using various human impact factors. Biodiversity status values differed considerably (p

Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species documented on the African continent. Out of these, only 12 species have been well documented in Kenya. Moreover, interest on meliponine bees has increased amid climate change, agricultural intensification, and other anthropogenic effects. Generally, stingless bees are under-researched, with no previous documented evidence of their ecological niche (EN) distribution in most African countries. Hence, this study sought to establish the influence of bioclimatic, topographic, and vegetation phenology on their spatial distribution and change patterns. Stingless response variables from 490 sample points were collected and used in conjunction with 11 non-conflating features to build stingless ecological niche models. Six machine learning-based EN models were used to predict the distribution of seven stingless bees’ species combined. The results from the EN models showed that annual precipitation was the most influential variable to stingless bee distribution (contributing 43.09% logit), while potential evapotranspiration and temperature seasonality contributed 21.18% of the information needed to predict the spatial distribution of stingless bees. Vegetation phenology (21.36%) and topography (14.36%) had moderate effect on stingless bees’ distribution. On the other hand, high seasonality in precipitation and temperature indicated high stingless niche variability in the future (i.e. 2055). The performance of six EN algorithms used to predict distribution of stingless bees was found to be “excellent” for random forest (true skills statistics (TSS) = 0.91) and ranger (TSS = 0.90) and “good” for generalized additive models (TSS = 0.87), multivariate adaptive regression spline (TSS = 0.80), and boosted regression trees (TSS = 0.80), while they were “fair” for recursive portioning and regression trees (TSS = 0.79). These EN models could be utilized to inform stingless bee farming and insects pollinated crops by highlighting regions that provide highly suitable conditions for stingless bees. Additionally, the findings could be harnessed to increase both bee and agricultural productivity and forest conservation efforts through supplementary pollination services.

Spatiotemporal information on crops and cropping systems can provide useful insights into disease outbreak mechanisms in croplands. In September 2011, a severe outbreak of Maize Lethal Necrosis (MLN) disease was reported in Bomet County, Kenya. We aimed to detect severely MLN-infected fields and discriminate mono, inter, and continuous maize cropping systems. We collected in-situ MLN severity observations and acquired multi-date and multi-sensor data viz., RapidEye (RE), Sentinel-1 (S1), digital elevation model (DEM), and Landsat-8 (L8) imagery. A hierarchical classification approach was used to map the cropping systems and severely MLN-infected fields during the short rainy season (September 2014–February 2015) using the random forest (RF), one-class support vector machine (OCSVM) and biased SVM (BSVM) classifiers. RF showed better performance when a balanced multi-class dataset was available. Both OCSVM and BSVM did not lead to an accurate high severity MLN class separation. Moreover, the BSVM classifier was able to separate the mono and intercropping systems. During the long rainy season (March–August 2015), only maize crop data were available, hence the BSVM as one class classifiers (OCC) was used and maize fields were successfully mapped even with high confusion rate. Furthermore, the distribution of maize intercropping system increased in low rainfall sites, and the continuous cropping system limited to only 31% of total maize cropland.

Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12th and 28th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment

Increasing conflicts between farmers and pastoralists continue to be a major challenge in the Sahel. Political and social factors are in tandem important underlying determinants for conflicts in the region, which are amplified by the variability and scarcity of natural resources, often as a result of climate variability and climate change. This study aimed at holistically assessing the main environmental parameters that influence the patterns of seasonal migratory movements (transhumance) in a transboundary area in the southern Republic of Chad and northern Central African Republic through a broad set of Earth observation (EO) data and data from the Transhumance Tracking Tool. A spatial model was applied to the datasets to determine the spatiotemporal dynamics of environmental suitability that reflects suitable areas and corridors for pastoralists. A clear difference in environmental suitability between the origin and destination areas of herders was found in the dry season, proving the main reason for pastoralists’ movements, i.e., the search for grazing areas and water. Potential conflict risk areas could be identified, especially along an agricultural belt, which was proven by conflict location data. The results demonstrate the potential and innovation of EO-derived environmental information to support the planning of transhumance corridors and conflict prevention in the Sahel. In the future, a combination of real-time tracking of herders and EO-derived information can eventually lead to the development of an early warning system for conflicts along transhumance corridors in the Sahel.

The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.

Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

Rift Valley fever (RVF) is a zoonotic disease affecting humans and animals. It is caused by RVF virus transmitted primarily by Aedes mosquitoes. The data presented in this article propose environmental layers suitable for mapping RVF vector habitat zones and livestock migratory routes. Using species distribution modelling, we used RVF vector occurrence data sampled along livestock migratory routes to identify suitable vector habitats within the study region which is located in the central and the north-eastern part of Kenya. Eleven herds monitored with GPS collars were used to estimate cattle utilization distribution patterns. We used kernel density estimator to produce utilization contours where the 0.5 percentile represents core grazing areas and the 0.99 percentile represents the entire home range. The home ranges were overlaid on the vector suitability map to identify risks zones for possible RVF exposure. Assimilating high spatial and temporal livestock movement and vector distribution datasets generates new knowledge in understanding RVF epidemiology and generates spatially explicit risk maps. The results can be used to guide vector control and vaccination strategies for better disease control. Keywords: Home range estimation, Vector distribution, Rift Valley fever

The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. This study hypothesized that utilizing the unique advantages of multi-date and medium spatial resolution freely available Sentinel-2 (S2) reflectance bands (S2 bands), their vegetation indices (VIs) and vegetation phenology (VP) derivatives, and Sentinel-1 (S1) backscatter data would improve cropping pattern mapping in heterogeneous landscapes using robust machine learning algorithms, i.e., the guided regularized random forest (GRRF) for variable selection and the random forest (RF) for classification. This study’s objective was to map cropping patterns within three sub-counties in Murang’a County, a typical African smallholder heterogeneous farming area, in Kenya. Specifically, the performance of eight classification scenarios for mapping cropping patterns was compared, namely: (i) only S2 bands; (ii) S2 bands and VIs; (iii) S2 bands and VP; (iv) S2 bands and S1; (v) S2 bands, VIs, and S1; (vi) S2 bands, VP, and S1; (vii) S2 bands, VIs, and VP; and (viii) S2 bands, VIs, VP, and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The GRRF algorithm was used to select the optimum variables in each scenario, and the RF was used to perform the classification for each scenario. The highest overall accuracy was 94.33% with Kappa of 0.93, attained using the GRRF-selected variables of scenario (v) S2, VIs, and S1. Furthermore, McNemar’s test of significance did not show significant differences (p ≤ 0.05) among the tested scenarios. This study demonstrated the strength of GRRF in selecting the most important variables and the synergetic advantage of S2 and S1 derivatives to accurately map cropping patterns in small-scale farming-dominated landscapes. Consequently, the cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions. Additionally, these results can be used to understand the sustainability of food systems and to model the abundance and spread of crop insect pests, diseases, and pollinators.

Honeybees (Apis mellifera) are principal insect pollinators, whose worldwide distribution and abundance is known to largely depend on climatic conditions. However, the presence records dataset on potential distribution of honeybees in Indian Ocean Islands remain less documented. Presence records in shape format and probability of occurrence of honeybees with different temperature change scenarios is provided in this article across Zanzibar Island. Maximum entropy (Maxent) package was used to analyse the potential distribution of honeybees. The dataset provides information on the current and future distribution of the honey bees in Zanzibar Island. The dataset is of great importance for improving stakeholders understanding of the role of temperature change on the spatial distribution of honeybees.

Maize is the main staple crop in the East African Mountains. Understanding how the edaphic characteristics change along altitudinal gradients is important for maximizing maize production in East African Highlands, which are the key maize production areas in the region. This study evaluated and compared the levels of some macro and micro-elements (Al, Ca, Fe, K, Mg, Mn, Na and P) and other soil parameters (pH, organic carbon content, soil texture [i.e. % Sand, % Clay and % Silt], cation exchange capacity [CEC], electric conductivity [EC], and water holding capacity [HC]). Soil samples were taken from maize plots along three altitudinal gradients in East African highlands (namely Machakos Hills, Taita Hills and Mount Kilimanjaro) characterized by graded changes in climatic conditions. For all transects, pH, Ca, K and Mg decreased with the increase in altitude. In contrast, % Silt, organic carbon content, Al and water holding capacity (HC) increased with increasing altitude. The research provides information on the status of the physical–chemical characteristics of soils along three altitudinal ranges of East African Highlands and includes data available for further research.

Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.

Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%–88.15% overall accuracy for the peak flowering season imagery versus 80.82%–83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees’ foraging behavior.

Abstract Background Knowledge of vector ecology is important in understanding the transmission dynamics of vector borne disease. In this study, we determined the distribution and diversity of mosquitoes along the major nomadic livestock movement routes (LMR) in the traditional pastoral ecozone of northeastern Kenya. We focused on the vectors of Rift Valley fever virus (RVFv) with the aim of understanding their ecology and how they can potentially influence the circulation of RVFv. Methods Mosquito surveys were conducted during the short and long rainy seasons from November 2012 to August 2014 using CO2-baited CDC light traps at seven sites selected for their proximity to stopover points that provide pasture, water and night bomas (where animals spend nights). We compared mosquito abundance and diversity across the sites, which were located in three ecological zones (IV, V and VI), based on the classification system of agro-ecological zones in Kenya. Results Over 31,000 mosquitoes were trapped comprising 21 species belonging to 6 genera. Overall mosquito abundance varied significantly by ecological zones and sites. Mansonia species (Ma. uniformis and Ma. africana) were predominant (n = 12,181, 38.3 %). This was followed by the primary RVF vectors, Ae. ochraceus and Ae. mcintoshi comprising 17.9 and 14.98 %, respectively, of the total captures and represented across all sites and ecological zones. The Shannon diversity index ranged from 0.8 to 2.4 with significant zone, site and seasonal variations. There was also significant species richness of RVF vector across ecological zones. Conclusion Our findings highlight differential occurrence of RVFv vectors across ecological zones and sampling sites, which may be important in determining areas at risk of emergence and circulation of RVFv. Moreover, the vector distribution map along LMR generated in this study will guide potential interventions for control of the disease, including strategic vaccination for livestock.

BACKGROUND:Rift Valley fever (RVF) is a mosquito-borne viral zoonosis of ruminants and humans that causes outbreaks in Africa and the Arabian Peninsula with significant public health and economic consequences. Humans become infected through mosquito bites and contact with infected livestock. The virus is maintained between outbreaks through vertically infected eggs of the primary vectors of Aedes species which emerge following rains with extensive flooding. Infected female mosquitoes initiate transmission among nearby animals, which amplifies virus, thereby infecting more mosquitoes and moving the virus beyond the initial point of emergence. With each successive outbreak, RVF has been found to expand its geographic distribution to new areas, possibly driven by available vectors. The aim of the present study was to determine if RVF virus (RVFV) transmission risk in two different ecological zones in Kenya could be assessed by looking at the species composition, abundance and distribution of key primary and secondary vector species and the level of virus activity. METHODOLOGY:Mosquitoes were trapped during short and long rainy seasons in 2014 and 2015 using CO2 baited CDC light traps in two counties which differ in RVF epidemic risk levels(high risk Tana-River and low risk Isiolo),cryo-preserved in liquid nitrogen, transported to the laboratory, and identified to species. Mosquito pools were analyzed for virus infection using cell culture screening and molecular analysis. FINDINGS:Over 69,000 mosquitoes were sampled and identified as 40 different species belonging to 6 genera (Aedes, Anopheles, Mansonia, Culex, Aedeomyia, Coquillettidia). The presence and abundance of Aedes mcintoshi and Aedes ochraceus, the primary mosquito vectors associated with RVFV transmission in outbreaks, varied significantly between Tana-River and Isiolo. Ae. mcintoshi was abundant in Tana-River and Isiolo but notably, Aedes ochraceus found in relatively high numbers in Tana-River (n = 1,290), was totally absent in all Isiolo sites. Fourteen virus isolates including Sindbis, Bunyamwera, and West Nile fever viruses were isolated mostly from Ae. mcintoshi sampled in Tana-River. RVFV was not detected in any of the mosquitoes. CONCLUSION:This study presents the geographic distribution and abundance of arbovirus vectors in two Kenyan counties, which may assist with risk assessment for mosquito borne diseases.

Citrus is considered one of the most important fruit crops globally due to its contribution to food and nutritional security. However, the production of citrus has recently been in decline due to many biological, environmental, and socio-economic constraints. Amongst the biological ones, pests and diseases play a major role in threatening citrus quantity and quality. The most damaging disease in Kenya, is the African citrus greening disease (ACGD) or Huanglongbing (HLB) which is transmitted by the African citrus triozid (ACT), Trioza erytreae. HLB in Kenya is reported to have had the greatest impact on citrus production in the highlands, causing yield losses of 25% to 100%. This study aimed at predicting the occurrence of ACT using an ecological habitat suitability modeling approach. Specifically, we tested the contribution of vegetation phenological variables derived from remotely-sensed (RS) data combined with bio-climatic and topographical variables (BCL) to accurately predict the distribution of ACT in citrus-growing areas in Kenya. A MaxEnt (maximum entropy) suitability modeling approach was used on ACT presence-only data. Forty-seven (47) ACT observations were collected while 23 BCL and 12 RS covariates were used as predictor variables in the MaxEnt modeling. The BCL variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data (spanning the years 2014–2016) and annually-summed land surface temperature (LST) metrics (2014–2016). We developed two MaxEnt models; one including both the BCL and the RS variables (BCL-RS) and another with only the BCL variables. Further, we tested the relationship between ACT habitat suitability and the surrounding land use/land cover (LULC) proportions using a random forest regression model. The results showed that the combined BCL-RS model predicted the distribution and habitat suitability for ACT better than the BCL-only model. The overall accuracy for the BCL-RS model result was 92% (true skills statistic: TSS = 0.83), whereas the BCL-only model had an accuracy of 85% (TSS = 0.57). Also, the results revealed that the proportion of shrub cover surrounding citrus orchards positively influenced the suitability probability of the ACT. These results provide a resourceful tool for precise, timely, and site-specific implementation of ACGD control strategies.

Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km2 large study area in western Burkina Faso and southern Mali (West Africa). A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class “Wetland”) is 9,350 km2, corresponding to 4.3% of the total study area size. The classes “No wetland”/“Wetland” could be separated with very high certainty; the overall agreement (KHAT) was 84.2% (0.67) and 97.9% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN) models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI) variables were used to model their ecological niches using Maximum Entropy (MaxEnt). Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055) indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.

Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural rangelands to intensively irrigated croplands. In this study, we analyzed the environmental drivers of vegetation dynamics in five Central Asian countries by coupling key vegetation parameter “overall greenness” derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI time series data, with its possible factors across various management and climatic gradients. We developed nine generalized least-squares random effect (GLS-RE) models to analyze the relative impact of environmental factors on vegetation dynamics. The obtained results quantitatively indicated the extensive control of climatic factors on managed and unmanaged vegetation cover across Central Asia. The most diverse vegetation dynamics response to climatic variables was observed for “intensively managed irrigated croplands”. Almost no differences in response to these variables were detected for managed non-irrigated vegetation and unmanaged (natural) vegetation across all countries. Natural vegetation and rainfed non-irrigated crop dynamics were principally associated with temperature and precipitation parameters. Variables related to temperature had the greatest relative effect on irrigated croplands and on vegetation cover within the mountainous zone. Further research should focus on incorporating the socio-economic factors discussed here in a similar analysis.