The Influence of Land Quality on Allocation of Land for Farm Forest in Kenya: The Case of Vihiga County

Kenya has long history of promoting tree growing on farms for various purposes ranging from  laying claim to property and boundary marking in 1940s to response to socioeconomic drivers such commercial interests through vibrant market for tree products. The Rural Afforestation and Extension Services Division (RAES) started in 1971 was aimed at accelerating tree growing on farms through training of farmers, establishment of tree nurseries countrywide and deployment of extension staff to offer technical services to rural farmers. Farms within agricultural landscapes are not uniform but differ in various forms such as slope, drainage, soil texture, fertility, water holding capacity, stone/rock outcrops and other attributes that impose land quality variation hence influencing their potential uses. The study was therefore undertaken to evaluate the influence of land quality on farm forest land use allocation through use of land quality concept developed by von Thunnen in 1826.  The study was done in one of the highly populated counties in western Kenya, the Vihiga County where farm forests occupies 30% of household land. Samples of 112 households were surveyed in 4 sub-counties. The study mapped quality aspects within households land profile into four categories  gentle,  steep, steep and rocky and flood plain and swampy and intensity of trees in respective category. OLS regression analysis was used to determine the influence of land quality on farm forest land allocations. The results indicate that farm forest allocations was not significantly influenced by poor land quality aspects across the study household lands. This is because the land sizes were very small and farm forests were adopted across the household land profile irrespective of quality aspects. However, households indicated that poor quality lands were preferable for farm forest largely for they were not favourable for crop production. The study observes that farm forests were highly influenced  by high population density and small land sizes that has masked the importance land quality in land use allocation decisions. Keywords: farm forest, land quality, land use allocatio

Farm Forestry Development in Kenya: A Comparative Analysis of Household Economic Land Use Decisions in UasinGishu and Vihiga Counties

Tree growing on farms in Kenya is an important land use that has evolved over the last 100 years into multi-billion subsistence and commercial oriented enterprises.  The smallholder farms in medium and high potential areas are facing serious shortage of quality farming land that has created severe competition among various competing land uses mostly agriculture and farm forestry. Therefore the economic competitiveness of farm forestry as a land use is assumed to be proportional to the size of household land allocated to its use. Understanding household decisions making in allocation of land to competing land uses has increasingly become an important subject to resource economists and policy makers. Therefore a study was undertaken in 2011/2012 to evaluate the socioeconomic decisions making in relations to farm forestry in two counties in high potential agricultural areas of western Kenya. The two counties were selected for the study differed settlement in history, agricultural land use, farm forestry development and demographic characteristics. Uasin Gishu represents the recently settled former European settler farms and Vihiga to represents the former African Reserves. The study was based on range of models such as spatial land use concepts, integrated land use decision making and land use efficiency criterion to underpin the household production function.  260 households were surveyed using systematic sampling methods with questionnaires being administered randomly to households in locations within selected divisions.  The main data extracted from the standard questionnaire were household structure, ratio of land used for cropping, grazing and farm forestry, product output, prices, market information, marketing procedures and distribution of trees by species.  Data was analysed by use of OLS regression models to generate key farm forestry decision making parameters.  The results show that household land size had strong influence on farm forestry decisions irrespective of household’s production strategy.  Farm forestry incomes proved to be an importance driving force in decisions to plant trees thus supporting the importance of economic objectives on household land use decisions. A farm forestry income was stronger in areas where markets and marketing infrastructure were better developed.  The density of planted trees increased with decreasing land size attested the strength of subsistence and commercial dimension of trees within an agricultural landscape. The study points out some policy lessons for development of farm forestry in developing countries like Kenya that include putting in place policies and regulations that attract, expand and sustain farm forestry product demand and infrastructure that improve marketing efficiency and thus better income to farmers from sale of trees. Keywords: Farm forestry, Land use, Household decision makin

A Review Farm Forestry Evolution for the Last 100 Years in Kenya: A Look at Some Key Phases and Driving Factors

The study reviews the evolution of tree growing in Kenya from pre-colonial through colonial to the present day in order to understand some factors that have influenced such developments. The study is based on desktop literatures reviews of various studies done in the country over the years and the authors’ experiences. The study indicates that forest resources management during pre-colonial period were based on individual communities’traditional structures that ensured that its members had abundant supplies of land and resources to support their socioeconomic activities. Forestlands were viewed as reserves for future agricultural expansion depending on community population growth and settlement patterns. In 1895 the country was declared British Protectorate that heralded the entry of colonial settlers that drastically changed land ownership through displacement and concentration of indigenous populations. Improved health services led to drastic population growths that further shrunk available productive land and forest resources to levels that could not adequately accommodate traditional land uses. The resultant was seriousland degradation in Africa reserves that prompted the Colonial Government to initiate agricultural and land use transformations that included afforestation in highly populated for environmental conservation, boundary marking and supply of tree products. Another parallel development was forest reservation and expansion of public plantation by Forest Department that involved planting of fast growing exotic species such as Eucalyptus, Pines and Cypress among others that diffused to neighbouring farms, missionary centres, schools and emerging elite Africans for amenity and social status. The emergence of Acacia mearnsii as a cash crop for African farmers in Central and western Kenya in 1930s was another development that enhanced adoption of tree growing on farms in the country. After independence in 1963 more policies and strategies to promote tree growing on former settler farms and African reserves for environmental conservation and subsistence needs implemented.  The last chapter of the farm forestry evolution was the commercialization of farm forestry operations due increased demand for various forest products beyond the capacity of public forests. The key markets niches mostly for firewood in tea processing, transmission poles manufacturing, charcoal and sawnwood for rural and urban markets were lucrative enough to motivate millions of smallholder farmers to expand their farm forestry investments. The markets based incentives to meet the growing demand for various products has transformed farm forestry in Kenya into multibillion sector enterprises that competes with public and private plantations products in local markets. The lessons learnt in Kenya case is the multiple factors that have shaped farm forestry development over the last 100 years and the critical role played by market related factors that enabled smallholder tree growers to enter into lucrative short rotation wood product markets. Keywords: Farm forestry evolution, phases, driving factor

Socioeconomic Factors Influencing Farm Forestry Investment Decisions in Kenya: The Case of Uasin Gishu and Vihiga Counties

In Kenya, traditional farm landscapes are an overlay agricultural crops livestock and various farm forest formations. Tree growing in agricultural landscapes in the country has a long history. However the intensity has developed over the last 100 years across the country at varying pace and configurations depending on various factors largely driven by demand and supply conditions. Therefore the study was premised on the fact that household land is allocated to tree growing based on the household subsistence needs and extra to satisfy market demands. The study to evaluate the socioeconomic factors that influenced adoption farm forestry by households in two counties in high potential agricultural areas of western Kenya was undertaken in 2015. The two counties were selected for the study differed in various attributes such as settlement history, agricultural land use, farm forestry development and demographic characteristics. Uasin Gishu represents the recently settled former European settler farms and Vihiga represents the former African Reserves settled hundreds of years ago. The study used integrated land use decision making concept to underpin the household production function.  The survey involved 260 households that were systematically sampled with questionnaires being administered randomly to households in locations within selected sub counties. The main data extracted from the questionnaire were household land sizes, age of household head, educational levels of household head, cultural factors, farm forest incomes, distance to forest product markets, farm employees, settlement years, household sizes and crop incomes.  Data was analysed by use of OLS regression models to generate key farm forestry decision making variables.  The results show that the most stable and significant explanatory variables were land size, farm forestry incomes and off-farm incomes. This shows that they were the most important variables in farm forestry land use decisions in western Kenya.  The study also revealed that the two counties were significantly different in their farm forestry activities with Vihiga being more intensive as compared to Uasin Gishu.  Farm forestry incomes proved to be an importance driving force in scaling up tree growing on individual farms hence indicating the importance of economic objectives on household land use decision making. Farm forest income was stronger in areas where markets and marketing infrastructure were better developed. The study provides some factors that policy makers need to consider in order to positively influence farm forest development in Kenya and other developing countries. Keywords: Farm forestry, Land use, farm incomes, household decision makin

Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Background The World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation. Methods Monthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019–2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period. Results Over 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1–4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1–4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4. Conclusion The implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred

Geographic accessibility and hospital competition for emergency blood transfusion services in Bungoma, Western Kenya

Background Estimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models of accessibility and competition to blood transfusion services in Bungoma County, Western Kenya. Methods Hospitals providing blood transfusion services in Bungoma were identified from an up-dated geo-coded facility database. AccessMod was used to define care-seeker’s travel times to the nearest blood transfusion service. A spatial accessibility index for each enumeration area (EA) was defined using modelled travel time, population demand, and supply available at the hospital, assuming a uniform risk of emergency occurrence in the county. To identify populations marginalized from transfusion services, the number of people outside 1-h travel time and those residing in EAs with low accessibility indexes were computed at the sub-county level. Competition between the transfusing hospitals was estimated using a spatial competition index which provided a measure of the level of attractiveness of each hospital. To understand whether highly competitive facilities had better capacity for blood transfusion services, a correlation test between the computed competition metric and the blood units received and transfused at the hospital was done. Results 15 hospitals in Bungoma county provide transfusion services, however these are unevenly distributed across the sub-counties. Average travel time to a blood transfusion centre in the county was 33 min and 5% of the population resided outside 1-h travel time. Based on the accessibility index, 38% of the EAs were classified to have low accessibility, representing 34% of the population, with one sub-county having the highest marginalized population. The computed competition index showed that hospitals in the urban areas had a spatial competitive advantage over those in rural areas. Conclusion The modelled spatial accessibility has provided an improved understanding of health care gaps essential for health planning. Hospital competition has been illustrated to have some degree of influence in provision of health services hence should be considered as a significant external factor impacting the delivery, and re-design of available services

Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Abstract Background The World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation. Methods Monthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019–2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period. Results Over 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1–4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1–4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4. Conclusion The implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred

Additional file 1 of Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Additional file 1: Table S1. Kenya national Extended Programme for Immunisation (EPI) vaccine schedule. Figure S1. Flowchart of the selection process to determine facilities routinely offering vaccination services in RTS, S/AS01 intervention areas (23 sub-counties) from DHIS2 listing. Figure S2. Gantt chart illustration of numerator and denominator counts used for coverage computation. Figure S3. Annual population density maps of children under 1 year within RTS,S/AS01 implementation sub-counties for the years 2019-2022. Table S2. Characteristics of 537 vaccinating facilities within the 23 implementation sub-counties. Figure S4. Animation of cumulative Penta 1 and RTS,S/AS01 vaccines administered at facility level from September 2019 to August 2022 (N = 537). Figure S5. Chart showing sub-county coverage rankings of RTS,S/AS01 vaccine doses for each denominator