Chuka University

Solar-powered irrigation Systems (SPIS) are critical for agricultural production enhancement, food security and climate change adaptation, especially in Arid and Semi-Arid Lands (ASAL). There is increased attention towards shifting to more abundant and cleaner energy potential sources for revitalising irrigation strategies in ASAL areas. This study employed a geospatial approach to identify suitable locations for solar-powered irrigation systems (SPIS) in Baringo County, Kenya. Based on an integrated use of GIS spatial analysis and analytical hierarchy procedure (AHP), suitable locations for solar-powered irrigation were mapped. Precipitation, irrigated areas, proximity to rivers, slope, and solar radiation were analysed and processed to derive spatially explicit SPIS suitability classes ranging from very low to very high suitability. The thematic layers were assigned weights based on Saaty’s AHP method, where weights for each factor were determined from a pairwise comparison matrix, and a Weighted Linear Combination (WLC) approach was used to derive the final suitability classes for the county. The findings reveal that approximately 58

Abstract The paper examines the decorrelation gain achievable for M-ary Quadrature Amplitude Modulation (M-QAM) and Binary Phase-shift Keying (BPSK) schemes in spatial diversity configurations, where virtually synthesized signals are applied over Nakagami-m fading channels. The signals are embedded in additive white Gaussian noise (AWGN). The primary form of the suggested virtual signal synthesizer involves shifting the phase of the generated output signals. To obtain the decorrelation gain, an orthogonal transformation matrix is utilized. This matrix ensures energy preservation, is blind to channel correlation, and does not require explicit signal information. The aim is to generate virtual signal that would be equivalent to those received from a system with more antennas. An analytical framework is developed to assess how fading severity, diversity order, and branch correlation influence Symbol Error Rate (SER) characteristics. Monte-Carlo simulations validate the analytical results. Various antenna configurations and fading severities (m = 0.5, 1, 3, 5) are explored. Additionally, the impact of different antenna spacings (0.1, 0.2, 0.5 wavelengths) is analyzed under a uniform angle of arrival. The results indicate that decorrelation gains are obtained, with a moderate dependence on the fading severity parameter m. However, this dependence becomes negligible for higher values of m.

Land Use and Land Cover (LULC) changes due to human activities and natural factors significantly influence the variation in Ecosystem Service Values (ESVs). This study assessed the spatio-temporal dynamics of ESVs in Baringo, an Arid and Semi-Arid Land (ASAL) county located in Kenya’s Great Rift Valley, over 24 years (2000–2024). The Benefit Transfer Method (BTM) was employed to estimate ESV changes using LULC data for the years 2000, 2014, and 2024, alongside updated valuation coefficients from the Ecosystem Services Valuation Database (ESVD) published by de Groot and others in 2020. The analysis revealed a substantial decline in the total ESVs in Baringo County, from United States Dollars (US) 25.70 billion in 2000 to US14.92 billion in 2024, representing a loss of US-10.78 billion (-41.9

Farm-level uptake of improved agricultural technologies is a key lever for raising Irish potato incomes worldwide. As global population growth continues to strain the food system, there is a pressing need for strategies that lift productivity and, with it, farmer earnings. Yet despite the crop’s economic weight, many smallholders still record modest returns because they have not fully embraced modern production technologies. This study therefore investigated how technology adoption shapes the income of Irish potato growers in Ol Kalou Sub-County, Kenya. Using a descriptive cross-sectional design, 385 respondents were drawn from a population of 21,942 smallholder Irish potato farmers. Descriptive statistics and Propensity Score Matching (PSM) were used, via SPSS version 28 and STATA version 17, to compare farmers who had and had not adopted technologies such as certified seed, fertilizer use, mechanization, irrigation, and integrated pest management. Adopters earned a significantly higher average income of KES 45,546.25 per acre against KES 30,230.10 per acre for non-adopters, with an estimated Average Treatment Effect on the Treated (ATT) of KES 15,316.15 per acre, a 50.67% income gain attributable to adoption. The results show that adopting agricultural technologies meaningfully lifts farm profitability by raising productivity, cutting production risk, and strengthening market participation. The study therefore recommends wider adoption of these technologies as a route to more efficient, lower-cost production.

Despite improvements in net enrolment rates, equitable access to primary and secondary education in Kenya remains a major challenge, particularly in rural and low-income areas. Long walking distances to schools continue to limit enrolment, attendance, and completion rates, affecting vulnerable populations. This study presents the first nationwide spatial analysis of school accessibility in Kenya using a GIS-based composite indicator: the Kenya Education Accessibility Index (KEAI). The KEAI integrates seven standardised raster layers, including school density (primary and secondary), number of primary and secondary schools accessible within a 30-minute walking-time threshold, number of primary and secondary schools per 10,000 people, and travel time to the nearest primary and secondary school. Spatial autocorrelation analysis was conducted to identify statistically significant clusters of low accessibility, referred to as Education Deserts. These spatial clusters, mapped at the constituency level, indicate areas where school infrastructure is insufficient and where targeted interventions are most needed. Results highlight substantial disparities in access between urban and rural regions, validating the index against official education indicators at the county level. The study provides critical insights into the spatial distribution of education opportunities and introduces a replicable, data-driven methodology for identifying priority areas at the national scale for school development. Findings support evidence-based policies aimed at improving access to education and achieving Kenya's policy objective of a 100% transition rate from primary to secondary education, in line with Sustainable Development Goal 4 on inclusive and equitable quality education.