Jane Mugo

The right to development, an inherent human right for all, emphasizes that all individuals and communities have the right to participate in, contribute to, and benefit from development that ensures the full realization of human rights. In Kenya, where a significant portion of the population faces poverty and vulnerability to climate change, access to rights-based needs such as clean water, healthcare, and education still remains a critical challenge. This study explored the implementation of a Human Rights-Based approach to community development through a Public–Private Development Partnership model (PPDP), with a focus on alleviating poverty and improving access to rights-based services at the community level in Narok and Nakuru counties. The research aimed to identify critical success factors for scaling the PPDP model and explore its effects on socio-economic empowerment. The study employed a mixed-methods approach for data collection, using questionnaires to obtain quantitative data, focus group discussions, and key informant interviews with community members, local leaders, and stakeholders to gather qualitative data. We cleaned and analyzed all our data in R (version 4.4.3) and used the chi-square to establish the significance of differences between areas where the PPDP model was implemented and control areas where it was not. Results reveal that communities with the PPDP model experienced statistically significant improvements in employment, income levels, and access to rights-based services compared to control areas. The outcomes underscore the potential of the PPDP model to address inclusive and sustainable development. This study therefore proposes a scalable pathway beginning with access to rights-based needs, followed by improved service delivery, and culminating in economic empowerment. These findings offer valuable insights for governments, development practitioners, investment agencies, and researchers seeking community-driven developments in similar socio-economic contexts across Africa. For the first time, it can be adopted in the design and implementation of development projects in rural and local communities across Africa bringing into focus the need to integrate rights-based needs at the core of the project.

Rüppell's vultures are critically endangered, primarily due to anthropogenic activities such as habitat degradation, climate change, and intentional and unintentional poisoning, which have led to the loss of nesting and breeding sites. To aid in the conservation and protection of these species, habitat evaluation and niche mapping are crucial. Species distribution modeling (SDM) is a valuable tool in conservation planning, providing insights into the ecological requirements of species under conservation concerns. This study employed an ensembling modeling approach to assess the habitat suitability and distribution of Rüppell's vultures across Kenya. We utilized four algorithms; Gradient Boosting Machine, Generalized Linear Model, Generalized Additive Model, and Random Forest. Data on Rüppell's vultures were sourced from the Global Biodiversity Information Facility, while key environmental variables influencing the species' distribution were obtained from WorldClim. The resultant species distribution map was overlaid with a conservation area map to evaluate the overlap between suitable habitats and existing protected areas. Our analysis identified suitable habitats in regions such as the Masai Mara Game Reserve, Mount Kenya National Park, Nairobi National Park, Tsavo East National Park, and Hell's Gate National Park, with the majority of these habitats located outside protected areas, except those within Hell's Gate National Park. Precipitation and elevation emerged as the primary environmental predictors of the distribution of Rüppell's vultures. Based on these findings, we recommend establishing vulture sanctuaries in suitable habitats and hotspots to enhance the conservation of Rüppell's vultures outside the protected areas.

Traditional simulation models are often point based; thus, more research is needed to emphasize spatial simulation, providing decision‐makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine‐resolution data from coarse‐resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the artificial neural network, creating a hybrid modeling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modeling workflows with the spatial APSIM next‐generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg N ha⁻¹ to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89) between the spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha⁻¹) relative to the potential yield.

Raptors face global threats like electrocution, collisions, and habitat fragmentation. Many species remain understudied, and their distribution patterns are unknown. Understanding their current and future distribution is crucial for conservation. Protecting these top predators requires knowledge of their spatial distribution and environmental influences. This study addresses knowledge gaps in raptor habitats and distributions in Kenya, considering current and future climate changes. Using species distribution models and occurrence data from the Global Biodiversity Information Facility, we evaluated suitable habitats for four endangered Kenyan raptor species: Martial eagle, Secretarybird, Bateleur, and Steppe Eagle. We assessed the impact of climatic predictors on their distribution, considering two climate change scenarios for 2020–2040. Our findings reveal that raptor distribution in Kenya is predominantly concentrated in the southwestern region, extending into the central region of the country. The most significant predictors of raptor species distribution varied for each species, with Steppe eagle and Secretarybird being highly influenced by precipitation during the warmest quarter, Martial eagle being influenced by mean temperature during the driest quarter, and Bateleur being primarily influenced by precipitation during the coldest quarter. When projecting our model into the climate change scenarios for 2020–2040, all species except the Bateleur exhibited a negative range shift. The results of our study suggest that climate change may have adverse impacts on the raptor species examined. In light of these findings, we recommend implementing targeted monitoring and conducting surveys in accordance with our current model predictions. Specifically, our focus should be on monitoring areas that exhibit the highest climate suitability, as these areas are likely to undergo significant shifts in the near future. By conducting regular monitoring and engaging in further research, we can enhance our understanding of these raptor species and gather valuable data to improve the accuracy and reliability of our model predictions.

Introduction: Smallholder farmers in Sub-Saharan Africa (SSA) are increasingly producing soybean for food, feed, cash, and soil fertility improvement. Yet, the difference between the smallholder farmers’ yield and either the attainable in research fields or the potential from crop models is wide. Reasons for the yield gap include low to nonapplication of appropriate fertilizers and inoculants, late planting, low plant populations, recycling seeds, etc.Methods: Here, we reviewed the literature on the yield gap and the technologies for narrowing it and modelled yields through the right sowing dates and suitable high-yielding varieties in APSIM.Results and Discussion: Results highlighted that between 2010 and 2020 in SSA, soybean production increased; however, it was through an expansion in the cropped area rather than a yield increase per hectare. Also, the actual smallholder farmers’ yield was 3.8, 2.2, and 2.3 times lower than the attainable yield in Malawi, Zambia, and Mozambique, respectively. Through inoculants, soybean yield increased by 23.8%. Coupling this with either 40 kg ha−1 of P or 60 kg ha−1 of K boosted the yields by 89.1% and 26.0%, respectively. Overall, application of 21–30 kg ha-1 of P to soybean in SSA could increase yields by about 48.2%. Furthermore, sowing at the right time increased soybean yield by 300%. Although these technologies enhance soybean yields, they are not fully embraced by smallholder farmers. Hence, refining and bundling them in a digital advisory tool will enhance the availability of the correct information to smallholder farmers at the right time and improve soybean yields per unit area.

This study purposed to evaluate the impact of climate change on green gram yield, biomass and days to maturity under the baseline and future climate scenarios in Kitui County, Kenya. A field experiment was conducted during the March–April–May (MAM) and October–November–December (OND) planting seasons of 2018 and 2019 in the South Eastern Kenya University (SEKU) farm. Data on soil physical and chemical properties, daily climate data on rainfall, maximum and minimum temperature, and solar radiation, and green gram phenology dates were collected from the site and used in the calibration and validation of the APSIM model for four varieties of green gram, namely Biashara, Tosha, N26, and KS20 varieties. The calibrated green gram model captured the observed yield, biomass and days to maturity of the four varieties of green gram well. The calibrated green gram model was used to simulate the effects of climate change using daily climate data from an equal-weight ensemble of the nine CORDEX RCA4 models under the baseline scenario (1971 to 2000), and the future RCP 4.5 and 8.5 scenarios (2021 to 2050). During the MAM and OND seasons, a statistically significant decline in yield, biomass, and days to maturity is expected under both the RCP 4.5 and RCP 8.5 scenarios. The high variability in rainfall amount under both the RCP 4.5 and RCP 8.5 scenarios will translate to a lower yield and biomass. The increase in temperature under both the RCP 4.5 and RCP 8.5 scenarios will reduce the days to maturity for green grams in Kitui County. A decline in green gram yield is expected under future climate scenarios in one of the highly suitable zones for Kitui County, Kenya. Given that the government aims to revive farming in the ASALs by promoting climate-smart agriculture through planting drought-resistance crops, there is a need to develop green gram varieties which are more tolerant to the expected change in climate.

Background: Kenya's rich wildlife is an important natural resource and is vital to the country's economy. However, rapid infrastructure development in key wildlife areas has threatened the survival of numerous species including raptors. Policy: The growing distribution of electricity grids in Kenya poses a significant risk to raptors and populations of other large birds. This research aimed to review the policy gaps in Kenya’s National Energy Policy (2018) examining the strategies for developing bird friendly transmission power lines in Kenya, with a goal to reduce the negative impact on raptor populations and improve their conservation. The findings indicate a high overlap between the ecological niche of raptors with medium and high voltage transmission power lines, with indication of high-risk hotspots for electrocution and collisions. Recommendations: This policy brief therefore proposes the adoption of bird friendly power lines in new transmission infrastructure projects and retrofitting existing power lines with bird friendly devices. This can be achieved through strengthening the legal and regulatory frameworks in the National Energy Policy. This will help prioritize avian conservation in power lines infrastructural development projects including the development of specific policies and guidelines for the placement, orientation, and marking of power lines to reduce the risk of electrocution and collisions of raptor species. Conclusions: Kenya committed to protecting migratory bird species and their habitats by signing the Convention on the Conservation of Migratory Species of Wild Animals in 1999. However, the National Energy Policy of 2018 lacks guidelines for bird-friendly power lines. Addressing this requires collaboration from the government, power companies, research institutions, conservation groups, and local communities. A concerted effort involving public education, targeted funding for research, and stakeholder collaboration can help Kenya meet its conservation obligations and safeguard vital habitats for migratory birds.

Raptors are apex predators threatened globally by electrocution, collisions, and habitat fragmentation. Most species of raptors are understudied and largely unexplored. Top predators like raptors depend on the sustainability of the ecosystems in which they live and migrate. Knowing how endangered raptors are geographically dispersed, as well as the factors that may influence how they use their habitat, is critical for their protection. This research focuses on Kenya, where there are gaps in knowledge on appropriate habitats and raptor dispersal patterns. With several species of raptors endangered, it is crucial to determine their distribution patterns for management and conservation. To evaluate the size of the realized niches for five Kenyan raptor species at the risk of extinction, we applied species distribution models (SDMs) through an ensembling approach using occurrence data from the Global Biodiversity Information Facility (GBIF) and environmental covariates. These species were: Martial eagle, Secretarybird, Bateleur, Steppe Eagle, and Southern ground hornbill. The five raptors’ distribution within and outside protected areas and the role of key environmental predictors in predicting their distribution was estimated. Our findings indicate raptor distribution in several areas in Kenya that is predominantly in the south-western region, extending into the country’s central region. Martial eagle had the largest niche range amounting to ca.49,169 km2 while the Southern ground hornbill had the smallest niche range amounting to ca.4,145 km2. Secretarybird had the highest distribution outside protected areas at 77.57% followed by the Martial eagle at 76.89%. Significant predictors of raptor species distribution in Kenya were; precipitation during the warmest quarter, precipitation during the driest month, and precipitation during the coldest quarter. Key areas for raptor conservation listed here could serve as foundation for a number of additional Important Bird Areas (IBAs) in Kenya, according to the A1 Global IBA Criterion for species that are globally threatened.

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.