Alliance Motives among Manufacturing SMES: Evidence from an Emerging Economy

This study was designed to investigate the effect of environmental-based motives on firm performance. The study targeted manufacturing Small and Medium Enterprises based in Kenya whose performance has been negatively affected by high industry competition, low technology uptake, and industry regulation. The study was geared towards establishing the environmental-based motives pushing and pulling manufacturing Small and Medium Enterprises toward strategic alliance formation. To this end, the study was set to investigate whether compliance with government regulation, the need to grow market share, and the need to increase customer base motivates manufacturing Small and Medium Enterprises in Kenya to form strategic alliances. The target population for the study consisted of 74 SMEs and the study adopted descriptive and explanatory research designs and collected data from company CEOs or senior managers. Descriptive and inferential statistics were used to analyze the survey data. The study findings indicated that environmental-based motives have a positive and significant effect on the performance of manufacturing Small and Medium Enterprises in Kenya (Adj R2 = 0.484). Based on these findings, the study concluded that environmental-based motives of compliance with the government regulation, market share, and customer base motivate manufacturing Small and Medium Enterprises to form strategic alliances and that these motives have a positive and significant effect on firm performance. The study contributed to the general body of knowledge by bridging the contextual and empirical gaps identified after the literature review. The study recommends that top management teams in the manufacturing industry should map environmental-based motives and align such motives to specific aspects of their value chain activities

An Assessment of the Challenges facing the Implementation of SMASSE Project Activities in Bomet District, Kenya

The Poor performance of students in science subjects (physics, chemistry and biology) in Kenyan secondary schools has been a persistent problem. In an attempt to stop this, the government implemented the SMASSE program in conjunction with JAICA from the government of Japan. The implementation of this program has encountered copious challenges over the years which are threatening its success. Thus, this study aimed to investigate the challenges facing the implementation of SMASSE Project Activities. The study was conducted in Bomet County in Kenya. The target population comprised of all the 121 secondary school head teachers and science teachers in the study area. Out of these, a sample size of 50 respondents was randomly selected from principals and teachers of public secondary schools in the district. The schools were stratified into boys’, girls’ and mixed secondary schools to ensure uniformity. Data was collected from the respondents using structured questionnaires. Data was analyzed descriptively using SPSS. Results were presented in form of frequencies and percentages. The results indicate that 65% of the teachers sampled were not adequately prepared for the program. In addition, 75% of the teachers stated that the boarding facilities during SMASSE were inadequate and of low standards. 70% were of the opinion that catering services offered to them during the training were of low quality. The findings further revealed that 75% of the teachers lacked sufficient time to apply ASEI-PDSI concept in lessons, while 90% stated that heavy teaching load was a challenge. 100% considered low morale among teachers a challenge. 75% of the head teachers agreed that conflict of interest was a major challenge, that is, science and mathematics teachers attend INSET during the holidays while their art-based counterparts were free to attend their personal interests. 90% agreed that another challenge during the implementation of SMASSE was non- collection/non- remittance of SMASSE funds to District Planning Committee (DPC). Finally 75% of the head teachers agreed that high staff turnover and the transfer of trainers to non-curriculum implementing posts challenged the implementation process. The study concluded from the findings that SMASSE project implementation is facing major challenges. The ministry of Education should employ more mathematics and science teachers to address the widespread teachers‟ shortage and in the process deal with some of these challenges. In addition, the negative attitude towards the project can be changed through payment of allowances for those attending the INSET, provide decent boarding and catering facilities during the training. Finally, the principles of SMASSE INSET should be incorporated into the training curriculum of secondary teacher training institutions. This would save on costs and time

Validating commonly used drought indicators in Kenya

Drought is a complex natural hazard that can occur in any climate and affect every aspect of society. To better prepare and mitigate the impacts of drought, various indicators can be applied to monitor and forecast its onset, intensity, and severity. Though widely used, little is known about the efficacy of these indicators which restricts their role in important decisions. Here, we provide the first validation of 11 commonly-used drought indicators by comparing them to pasture and browse condition data collected on the ground in Kenya. These ground-based data provide an absolute and relative assessment of the conditions, similar to some of the drought indicators. Focusing on grass and shrublands of the arid and semi-arid lands, we demonstrate there are strong relationships between ground-based pasture and browse conditions, and satellite-based drought indicators. The Soil Adjusted Vegetation Index (SAVI) has the best relationship, achieving a mean r2 score of 0.70 when fitted against absolute pasture condition. Similarly, the 3-month Vegetation Health Index (VHI3M) reached a mean r2 score of 0.62 when fitted against a relative pasture condition. In addition, we investigated the Kenya-wide drought onset threshold for the 3-month average Vegetation Condition Index (VCI3M; VCI3M<35), which is used by the country’s drought early warning system. Our results show large disparities in thresholds across different counties. Understanding these relationships and thresholds are integral to developing effective and efficient drought early warning systems (EWS). Our work offers evidence for the effectiveness of some of these indicators as well as practical thresholds for their use

Assessing drivers of intra-seasonal grassland dynamics in a Kenyan savannah using digital repeat photography

Understanding grassland dynamics and their relationship to weather and grazing is critical for pastoralists whose livelihoods depend on grassland productivity. Studies investigating the impacts of climate and human factors on inter-seasonal grassland dynamics have focused mostly on changes to vegetation structure. Yet, quantifying the impact of these on the inter-seasonal dynamics of specific grassland communities is not known. This study uses digital repeat photography to examine how intra-seasonal grassland dynamics of different grassland communities are affected by precipitation, temperature, and grazing in a heterogeneous semi-arid savannah in Kenya. A low-cost digital repeat camera network allowed for fine-scale temporal and spatial variability analysis of grassland dynamics and grazing intensity. Over all grass communities, our results show precipitation driving mainly early-season and in some cases mid-season flushing, temperature driving end-of-season senescence, and grazing influencing mid-season declines. Yet, our study quantifies how these three drivers do not uniformly impact grassland species communities. Specifically, Cynodon and Cynodon/Bothriochloa communities are rapidly and positively associated with precipitation, where mid-season declines in Cynodon communities are associated with grazing and late-season declines in Cynodon/Bothriochloa communities are associated with temperature increases. Setaria communities, on the other hand, have weaker associations with the drivers, with limited positive associations with precipitation and grazing. Kunthii/Digitaria diverse communities had no association with the three drivers. Highly diverse mixed communities were associated with increased precipitation and temperature, as well as lower intensity grazing. Our research sheds light on the complex interactions between plants, animals, and weather. Furthermore, this study also demonstrates the potential of digital repeated photography to inform about fine-scale spatial and temporal patterns of semi-arid grassland vegetation and grazing, with the goal of assisting in the formulations of management practises that better capture the intra-annual variability of highly heterogeneous dryland systems

Mapping Opuntia stricta in the arid and semi-arid environment of Kenya using sentinel-2 imagery and ensemble machine learning classifiers

Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective is to improve the accuracy and precision of agricultural drought forecasting in spatially diverse regions with a hierarchical Bayesian model. Results showed that the hierarchical Bayesian model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian auto-regression distributed lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the hierarchical Bayesian model at 4- to 10-week lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The hierarchical Bayesian model also showed good transferable forecast skills over counties not included in the training data

A cross-sectional analysis of factors associated with detection of oncogenic human papillomavirus in human immunodeficiency virus-infected and uninfected Kenyan women

BACKGROUND: Cervical cancer is caused by oncogenic human papillomaviruses (HPV) and is one of the most common malignancies in women living in sub-Saharan Africa. Women infected with the human immunodeficiency virus (HIV) have a higher incidence of cervical cancer, but the full impact on HPV detection is not well understood, and associations of biological and behavioral factors with oncogenic HPV detection have not been fully examined. Therefore, a study was initiated to investigate factors that are associated with oncogenic HPV detection in Kenyan women. METHODS: Women without cervical dysplasia were enrolled in a longitudinal study. Data from enrollment are presented as a cross-sectional analysis. Demographic and behavioral data was collected, and HPV typing was performed on cervical swabs. HIV-uninfected women (n = 105) and HIV-infected women (n = 115) were compared for demographic and behavioral characteristics using t-tests, Chi-square tests, Wilcoxon sum rank tests or Fisher\u27s exact tests, and for HPV detection using logistic regression or negative binomial models adjusted for demographic and behavioral characteristics using SAS 9.4 software. RESULTS: Compared to HIV-uninfected women, HIV-infected women were older, had more lifetime sexual partners, were less likely to be married, were more likely to regularly use condoms, and were more likely to have detection of HPV 16, other oncogenic HPV types, and multiple oncogenic types. In addition to HIV, more lifetime sexual partners was associated with a higher number of oncogenic HPV types (aIRR 1.007, 95% CI 1.007-1.012). Greater travel distance to the clinic was associated with increased HPV detection (aOR for detection of \u3e /= 2 HPV types: 3.212, 95% CI 1.206-8.552). Older age (aOR for HPV 16 detection: 0.871, 95% CI 0.764-0.993) and more lifetime pregnancies (aOR for detection of oncogenic HPV types: 0.706, 95% CI, 0.565-0.883) were associated with reduced detection. CONCLUSION: HIV infection, more lifetime sexual partners, and greater distance to health-care were associated with a higher risk of oncogenic HPV detection, in spite of ART use in those who were HIV-infected. Counseling of women about sexual practices, improved access to health-care facilities, and vaccination against HPV are all potentially important in reducing oncogenic HPV infections

Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model

Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish early warning systems (EWSs) to save lives and livelihoods. Existing EWSs use a combination of satellite earth observation (EO)-based biophysical indicators like the vegetation condition index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWSs rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like auto-regression, Gaussian processes, and artificial neural networks can provide very skilled models for forecasting vegetation condition at short- to medium-range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. In a previous paper, a Gaussian process model and an auto-regression model were used to forecast VCI in pastoral communities in Kenya. The objective of this research was to build on this work by developing an improved model that forecasts vegetation conditions at longer lead times. The premise of this research was that vegetation condition is controlled by factors like precipitation and soil moisture; thus, we used a Bayesian auto-regressive distributed lag (BARDL) modelling approach, which enabled us to include the effects of lagged information from precipitation and soil moisture to improve VCI forecasting. The results showed a ∼2-week gain in the forecast range compared to the univariate auto-regression model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85, and 0.74, compared to the auto-regression model's R2 of 0.88, 0.77, and 0.65 for 6-, 8-, and 10-week lead time, respectively