Enhancement of fruit quality in <i><scp>C</scp>apsicum annum</i> through pollination by <i><scp>H</scp>ypotrigona gribodoi</i> in <scp>K</scp>akamega, <scp>W</scp>estern <scp>K</scp>enya

Kiatoko, Nkoba; Raina, Satish; Muli, Elliud; Mueke, J. M.

doi:10.1111/ens.12030

Cited by 34 publications

(29 citation statements)

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“…Since stimulation with odors affects food choices of stingless bees , this could be used in an applied research context. Stingless bees are important pollinators in East Africa (Kajobe and Echazarreta 2005;Kiatoko et al 2014;Macharia et al 2007;Raina et al 2011). A pre-stimulation of foraging bees with a specific floral odor inside and outside the hive might enhance targeted pollination, especially in greenhouses.…”

Section: Final Conclusion and Outlookmentioning

confidence: 99%

Olfactory associative learning in two African stingless bee species (Meliponula ferruginea and M. bocandei, Meliponini)

et al. 2015

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Worldwide only a few of the more than 500 stingless bee species has been studied in any detail. Most studies on stingless bees have been conducted in the Neotropics, whereas a very few have been undertaken in Africa. Foraging success is dependent to a greater or lesser extent on olfactory cues or signals. A prerequisite to effective foraging via odors is that the bees are able to associate odors with a nectar reward and memorize this information. In the context of olfactory learning, only five species of stingless bees have been studied using classical conditioning of the proboscis extension reflex (PER). Foragers are capable of using previously experienced olfactory information when choosing food sources, but they rarely respond in differential PER assays. This study examines for the first time the olfactory learning abilities of African stingless bees. A differential conditioning assay was used to study learning and memory of Meliponula ferruginea and Meliponula bocandei. Our results clearly show that both stingless bee species associate odors with a reward. Learning performance of both stingless bee species was poor compared to Apis m. scutellata. This might reflect that the experimental procedure has been optimized for Apis mellifera. However, the PER paradigm seems to be suitable to study learning, memory, and olfactory perception in Meliponula. As in honeybees, this paradigm will open the way to answer ecological, psychological, and neurobiological questions in these species.

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Section: Final Conclusion and Outlookmentioning

confidence: 99%

Olfactory associative learning in two African stingless bee species (Meliponula ferruginea and M. bocandei, Meliponini)

et al. 2015

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“…Its pollinator services are particularly indispensable to global food production and nutritional security [1,2]. Additionally, bee keeping aids natural resource conservation, especially in communities living around forests.…”

Section: Introductionmentioning

confidence: 99%

Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

Makori

Fombong

Abdel‐Rahman

et al. 2017

IJGI

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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.

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“…These savannahs are characterized by unreliable and erratic rainfall that necessitates for alternative sources of income to supplement the unpredictable crop yields (1,2). Apiculture and supplemental income as well as ecosystem services such as pollinator services boosts local economies, food and 30 nutritional security and improve biodiversity, hence valuable and sustainable socio-ecological practices in the African savannah (3,4). Therefore, there is need to enhance bee health and improve hive integrity and productivity contributing to food security and environmental sustainability in the African Savannahs.…”

Section: Introductionmentioning

confidence: 99%

Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

Makori

Abdel‐Rahman

Landmann

et al. 2020

Preprint

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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 5 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 10 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 15 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 20 approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.Keywords: Melliferous plants, beehealth, AISA Eagle, resampled multispectral data sets, random forest 45 multicollinearity, especially when using conventional parametric classification and regression procedures often make the use of hyperspectral data unfeasible. Specifically, the Hughes effect and the high redundancy rates of some bands in models developed using hyperspectral data impede landscape modelling (13)(14)(15)(16). In this regard, it is paramount to explore the utility of multispectral images with fewer broad bands for optimal discrimination of flowering plant 50 species (9,(17)(18)(19). However, whereas broadband multispectral data of medium spatial resolution such as the Landsat series have become popular in landscape mapping, they could mask out specific spectral features of flowering plant species, resulting in very low mapping accuracy. The relatively newly launched higher spectral and/or spatial resolution sensors such as WorldView-2, RapidEye, Spot-6 and Sentinel-2 offer great potential in detecting flowering 55 plants (20)(21)(22). Such sensors are specifically designed to capture spectral properties at additional wavebands such as red-edge and yellow spectrum that mimic over 90% of plant biophysiological information (23-25). Ho...

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Enhancement of fruit quality in Capsicum annum through pollination by Hypotrigona gribodoi in Kakamega, Western Kenya

Cited by 34 publications

References 10 publications

Olfactory associative learning in two African stingless bee species (Meliponula ferruginea and M. bocandei, Meliponini)

Olfactory associative learning in two African stingless bee species (Meliponula ferruginea and M. bocandei, Meliponini)

Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

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