School of Biological and Physical Sciences

Analysis of climate change impacts on plant biodiversity and livelihoods among Maasai women in Narok County, Kenya

Wed, 01 Jan 2025 00:00:00 GMT

Analysis of climate change impacts on plant biodiversity and livelihoods among Maasai women in Narok County, Kenya Sinteria, John Kishoyian Climate change poses critical threats to plant biodiversity and pastoral livelihoods in sub-Saharan Africa, yet knowledge gaps persist regarding gender-differentiated impacts of climate-biodiversity interactions on women's livelihood vulnerability. This study assessed climate change impacts on plant species diversity and implications for Maasai women's livelihoods in Narok County, Kenya. A mixed-methods convergent design was employed, integrating quantitative climate data analysis (1990-2020), systematic botanical surveys, structured questionnaires (n=100 Maasai women), and qualitative assessments through focus group discussions (n=24 groups) and key informant interviews (n=15 traditional experts). Climate data from Kenya Meteorological Department and NASA POWER database were analyzed using Mann- Kendall trend tests and Sen's slope estimators. Ethnobotanical surveys utilized systematic transect-quadrat sampling across eight locations. Vulnerability assessment employed Hahn's Livelihood Vulnerability Index framework. Results revealed statistically significant warming of 0.35°C per decade (Mann-Kendall τ=0.312, p<0.01) with extreme temperature events reaching 2.35°C above baseline. Precipitation showed high inter-annual variability (coefficient of variation=31.2%) with significant seasonal shifts including September increases (τ=0.338, p=0.009) and February decreases approaching significance (τ=-0.251, p=0.054). Botanical surveys documented 89 plant species across 33 families, with medicinal uses dominating (36% of species), followed by construction materials (13%) and fodder (11%). Diversity indices indicated moderate levels (Shannon-Weiner H'=1.335; Simpson's D=0.421). Critical conservation concerns emerged with 31 species (35%) occurring in single locations and 25 species at critically low densities, indicating high extinction risk. The Climate Vulnerability Index (4.4) demonstrated moderate vulnerability, with strong adaptive capacity (10.4) buffering high plant-based sensitivity (3.8) and moderate climate exposure (2.2). Climate awareness was exceptionally high (91% of respondents), with strong correspondence between women's perceptions and meteorological data validating traditional ecological knowledge systems. The study conclusively demonstrates that climate change significantly impacts plant biodiversity with direct implications for Maasai women's livelihoods. Despite strong traditional knowledge and social capital through cooperatives, communities face climate risks and biodiversity loss that threaten healthcare access, food security, and cultural practices. Key recommendations include establishing community conservancies with women as primary managers, implementing climate-smart plant management integrating traditional and scientific knowledge, strengthening women's cooperatives for economic resilience, developing integrated climate information systems, and creating intergenerational knowledge transfer programs. These findings advance understanding of the climate-biodiversity-gender nexus and inform evidence-based policy interventions for pastoral communities navigating climate uncertainty.

Mathematical modeling of energy mix and optimization of renewable resources

Wed, 01 Jan 2025 00:00:00 GMT

Mathematical modeling of energy mix and optimization of renewable resources Sigei, Kipkirui Robert Energy, as both a direct and indirect fundamental life-supporting resource, has experienced a steady rise in domestic and industrial demand, driven by technological advancement, population growth, and economic expansion. Various sources of energy including fossil fuels, hydroelectric power, geothermal energy, wind, solar, and nuclear are available in different proportions, each with distinct cost structures and environmental impacts. The challenge of meeting these diverse needs while minimizing production and distribution costs, conserving the environment, and reducing wastage has evolved into a complex multi-objective problem. This research focuses on the mathematical modelling of the optimal energy mix and the optimization of renewable resources, with particular emphasis on individualized demand profiles. The objectives are threefold: first, to formulate a mathematical model for analysing the dynamics of energy demand, production, and distribution; second, to determine the parameter thresholds that guarantee stability and robustness of the optimal energy mix; and third, to develop a smart grid feedback model using adaptive neural networks capable of automatically maintaining the desired energy balance. The methodology entails formulating a system of differential equations to represent the energy system, expressing it in state-space form, and applying Laplace transforms to derive transfer functions. These will be analysed for sensitivity, stability, and robustness using Nyquist and Bode plot criteria. MATLAB–Simulink, equipped with neural network modules, will then be employed to simulate and implement an intelligent, adaptive feedback control system. Through these simulations, the study will integrate real-time learning and self-adjustment capabilities to align production with demand in the most efficient manner. The anticipated outcome is an automated, smart distribution system capable of dynamically meeting individualized energy requirements at the lowest possible cost, while enhancing the utilization of renewable sources and reducing reliance on non-renewable options. Ultimately, this approach aims to promote environmental sustainability through increased adoption of green energy technologies.

Construction of some third order optimum sequential rotatable designs in three and four dimensions

Wed, 01 Jan 2025 00:00:00 GMT

Construction of some third order optimum sequential rotatable designs in three and four dimensions Bittok, Chepleting Julieth Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving and optimizing processes. Response Surface Methodology has gained recognition as a useful tool in a number of fields, including industry, agriculture, and medicine. Given the limited resources currently accessible on the planet, researchers are looking for strategies to maximize resource utilization in order to meet the continually increasing demands at both the individual and society levels. This can only be achieved through an appropriate design of experiments such as in this study. The problem of this study was to construct some third order optimum sequential rotatable designs in three and four dimensions. The specific objectives of the study were; to construct third order sequential rotatable designs in three dimensions by combining pairs of second order rotatable point sets; to construct third order sequential rotatable designs in four dimensions by appending an extra factor in each of the second order rotatable point sets and; to obtain the A-, D-, T-, E- optimality criteria of the sequential third order rotatable designs in three and four dimensions. The third order rotatable arrangement in three and four dimensions were established after all variables were proved to be real and positive and their excess functions were found to be zero. These arrangements formed TORDs after they satisfied the non-singularity conditions required for rotatability, yielding 44, 58, and 46 points TORDs for the three-dimensional designs and 80a and 80b points TORDs for the four-dimensional designs. Most of researches in RSM are theoretical especially in third order rotatability. So, there is need to give hypothetical examples to these designs and existing designs for presentation in applicable formats for the three and four-dimensional rotatable designs to give maximum produce. The study also identified and presented A-, D-, T-, E- optimality criteria in order to obtain the effectiveness of the constructed third order rotatable designs. The design with the smallest (least) optimality criterion among these is considered to be optimal. Design B12 was determined to be D-optimal for TORDs in three dimensions, while design was identified as the D-optimal design for TORDs in four dimensions. Design B12 was found to be T-optimal for TORDs in three dimensions, while design was considered T-optimal for TORDs in four dimensions. Regarding the A-criterion, design B13 was deemed optimal in three dimensions, whereas design was identified as the A-optimal design for TORDs in four dimensions. Both designs B12 and were found to be E-optimal for the designs in three dimensions and four dimensions, respectively. In order to obtain optimality criteria and confirm the existence of optimal solutions in these and other design settings, the study recommends employing various methodologies. These methods include balanced incomplete block design and pairwise block design.

Forest degradation impact on soil physico-chemical properties and bacteria community structure in Mount Kenya forest ecosystem

Wed, 01 Jan 2025 00:00:00 GMT

Forest degradation impact on soil physico-chemical properties and bacteria community structure in Mount Kenya forest ecosystem Koech, Jebet Truphena The Mount Kenya forest ecosystem has experienced notable degradation in recent years largely due to uncontrolled anthropogenic land use activities such as deforestation, overgrazing and improper land management, and whose impact on soil microbial communities has not been fully elucidated. Hence, this study aimed to assess the effects of forest degradation on soil bio-physico-chemical properties within the Mt. Kenya Forest. The specific objectives were to: Assess the effects of forest degradation on selected soil physical properties; Analyze the effect of forest degradation on soil chemical properties; and evaluate the influence of forest degradation on soil bacteria community structure. Two contrasting and non overlapping forest patches –undisturbed and degraded– were selected, and a 100×100 m plot established in each patch. Six sampling locations, with a spacing of 18 m between points, were established diagonally across each plot. To account for microvariability at each sampling location, sterile soil auger was used to collect soil samples in three replicates –one at the center and two others 1 m to the left and right. A total of 36 soil samples were transported in cool boxes and refrigerated prior to laboratory analysis. The dry combustion and core ring methods were used to assess the soil physical properties of organic matter and bulk density, respectively. Calcium, potassium and magnesium chemical properties were extracted using ammonium acetate and quantified by atomic absorption spectroscopy, while soil pH was measured in situ using a pH meter. Soil microbial abundance was determined using the pour plate method, while bacterial isolates were characterized based on morphological, biochemical, and molecular traits. Molecular identification involved DNA extraction, sequencing and BLAST analysis using MEGA X and NCBI GenBank. Data were analyzed using analysis of variance in SAS version 9.4, with LSD for post hoc comparisons. Results showed that undisturbed patch had significantly higher (P<0.05) soil organic matter, bulk density, calcium and potassium levels, but supported significantly lower (62.5±27.83; F1, 94=139.10; P<0.0001) mean bacteria colony-forming units (CFUs) when compared to degraded patch with 152.74±48.74 CFUs (F1, 94=139.10; P<0.0001), whose soil pH was more alkaline. Molecular analysis based on16SrRNA gene sequences showed distinct clustering of bacterial isolates with bootrap support values ranging from 69% to 100% with known strains in GenBank. Dominant bacterial isolates in undisturbed forest included Bacillus aerius, Pseudochrobactrum saccharolyticum, Brucella pseudogrignonensis, Brevundimonas diminuta, Delftia tsuruhatensis, Stenotrophomonas maltophilia, and Pseudomonas fluorescens, while degraded patch yielded Vagococcus fluvialis, Achromobacter xylosoxidans, Cupriavidus sp., and Ochrobactrum sp., thus indicating that forest land use degradation can affect soil bacterial communities through modifying soil physical parameters, nutritional conditions, and biological interactions. In conclusion, this study shows that forest land use changes can drastically alter the number of soil bacterial communities through modifying soil physical parameters, nutritional conditions, and biological interactions. This study recommends continuous monitoring of soil bio-physico-chemical properties that serve as indicators of forest soil health, and incorporating findings into forest management and restoration plans.