Adoption Determinants of Adapted Climate Smart Agriculture Technologies Among Smallholder Farmers in Machakos, Makueni, and Kitui Counties of Kenya

The study examined the adoption determinants of adapted climate smart agriculture (CSA) technologies among smallholder farmers. A multi-stage sampling procedure was used to select a total sample of 384 households. Percentages and regression were employed in data analysis. The results revealed that 47.4% adapted to climate change using integrated farming system, intercropping, crop rotation and agroforestry. Sex (0.9%), education level (9.2%) significantly influenced adoption of the adaptation strategies. Moreover, information sources such as mobile phones (0.9%), and neighbors/friends (0.2%) negatively affected the adaptation strategies. Future policy should aim at creating more awareness through different information sources and provide local extension services.Keywords: Adaptation, Smallholder farmers, Multinomial Logistic regression model

Adoption Determinants of Adapted Climate Smart Agriculture Technologies Among Smallholder Farmers in Machakos, Makueni, and Kitui Counties of Kenya

The study examined the adoption determinants of adapted climate smart agriculture (CSA) technologies among smallholder farmers. A multi-stage sampling procedure was used to select a total sample of 384 households. Percentages and regression were employed in data analysis. The results revealed that 47.4% adapted to climate change using integrated farming system, intercropping, crop rotation and agroforestry. Sex (0.9%), education level (9.2%) significantly influenced adoption of the adaptation strategies. Moreover, information sources such as mobile phones (0.9%), and neighbors/friends (0.2%) negatively affected the adaptation strategies. Future policy should aim at creating more awareness through different information sources and provide local extension services.Keywords: Adaptation, Smallholder farmers, Multinomial Logistic regression model

Adoption Determinants of Adapted Climate Smart Agriculture Technologies Among Smallholder Farmers in Machakos, Makueni, and Kitui Counties of Kenya

The study examined the adoption determinants of adapted climate smart agriculture (CSA) technologies among smallholder farmers. A multi-stage sampling procedure was used to select a total sample of 384 households. Percentages and regression were employed in data analysis. The results revealed that 47.4% adapted to climate change using integrated farming system, intercropping, crop rotation and agroforestry. Sex (0.9%), education level (9.2%) significantly influenced adoption of the adaptation strategies. Moreover, information sources such as mobile phones (0.9%), and neighbors/friends (0.2%) negatively affected the adaptation strategies. Future policy should aim at creating more awareness through different information sources and provide local extension services.Keywords: Adaptation, Smallholder farmers, Multinomial Logistic regression model

Biopsychosocial risk factors and knowledge of cervical cancer among young women: A case study from Kenya to inform HPV prevention in Sub-Saharan Africa

Background: Cervical cancer is the second most common female reproductive cancer after breast cancer with 84% of the cases in developing countries. A high uptake of human papilloma virus (HPV) vaccination and screening, and early diagnosis leads to a reduction of incidence and mortality rates. Yet uptake of screening is low in Sub-Saharan Africa and there is an increasing number of women presenting for treatment with advanced disease. Nine women in their twenties die from cervical cancer in Kenya every day. This paper presents the biopsychosocial risk factors that impact on cervical cancer knowledge among Kenyan women aged 15 to 24 years. The findings will highlight opportunities for early interventions to prevent the worrying prediction of an exponential increase by 50% of cervical cancer incidences in the younger age group by 2034. Methods: Data from the 2014 Kenya Demographic and Health Survey (KDHS) was analysed using complex sample logistic regression to assess biopsychosocial risk factors of knowledge of cervical cancer among young women aged 15 to 24 years (n = 5398). Findings: Close to one third of the participants were unaware of cervical cancer with no difference between participants aged 15–19 years (n = 2716) and those aged 20–24 years (n = 2691) (OR = 1; CI = 0.69–1.45). Social predisposing factors, such as lack of education; poverty; living further from a health facility; or never having taken a human immunodeficiency virus (HIV) test, were significantly associated with lack of awareness of cervical cancer (p<0.001). Young women who did not know where to obtain condoms had an OR of 2.12 (CI 1.72–2.61) for being unaware of cervical cancer. Psychological risk factors, such as low self-efficacy about seeking medical help, and an inability to refuse unsafe sex with husband or partner, perpetuated the low level of awareness about cervical cancer (p<0.001). Conclusions: A considerable proportion of young women in Kenya are unaware of cervical cancer which is associated with a variety of social and psychological factors. We argue that the high prevalence of cervical cancer and poor screening rates will continue to prevail among older women if issues that affect young women’s awareness of cervical cancer are not addressed. Given that the Kenyan youth are exposed to HPV due to early sexual encounters and a high prevalence of HIV, targeted interventions are urgently needed to increase the uptake of HPV vaccination and screening

The moderating effect of gender on the contribution of social enterprises to social transformation in Kiambu County, Kenya

This study sought to establish the moderating effect of social entrepreneurs’ gender on the contribution of social enterprises to social transformation in Kiambu County, Kenya. The study used a descriptive survey design guided by mixed methods research on 322 sampled social enterprises drawn from a target population of 1944 social enterprises distributed across the 12 sub-counties of Kiambu County. Data were collected using survey questionnaires and interview guide instruments.  A simple random sampling technique was used to get the proportionate sample for each stratum.  In data analysis, both descriptive statistics (mean, percentages, standard deviation and frequencies) and inferential statistics (correlation and regression analysis) were applied. The findings showed the influence of the independent variable on the dependent variable being explained by R of 0.568. The findings also revealed that social entrepreneurs’ gender was likely to have an effect on the relationship between social entrepreneurship and social transformation of R2 = 0.322. By the estimates of the F-test, social entrepreneurship was found to predict social transformation by F (3, 281) = 77.330, p&lt;.05

The moderating effect of gender on the contribution of social enterprises to social transformation in Kiambu County, Kenya

This study sought to establish the moderating effect of social entrepreneurs’ gender on the contribution of social enterprises to social transformation in Kiambu County, Kenya. The study used a descriptive survey design guided by mixed methods research on 322 sampled social enterprises drawn from a target population of 1944 social enterprises distributed across the 12 sub-counties of Kiambu County. Data were collected using survey questionnaires and interview guide instruments.  A simple random sampling technique was used to get the proportionate sample for each stratum.  In data analysis, both descriptive statistics (mean, percentages, standard deviation and frequencies) and inferential statistics (correlation and regression analysis) were applied. The findings showed the influence of the independent variable on the dependent variable being explained by R of 0.568. The findings also revealed that social entrepreneurs’ gender was likely to have an effect on the relationship between social entrepreneurship and social transformation of R2 = 0.322. By the estimates of the F-test, social entrepreneurship was found to predict social transformation by F (3, 281) = 77.330, p&lt;.05