Evaluation of determinants on the financial performance of retirement benefit schemes in Kenya

Thesis submitted in partial fulfillment for the requirements for the Degree of Master of Commerce (MCOM) at Strathmore UniversityThe main objective of this study was to evaluate determinants on the financial performance of retirement benefit schemes in Kenya. The specific objectives were: to establish the effect of determinants on the financial performance of retirement benefit schemes in Kenya and to investigate the perception of stakeholders regarding determinants on the financial performance of retirement benefit schemes in Kenya. This study used quantitative design to determine the financial performance relationship with determinants of performance. The population for this study were the 1262 retirement benefit schemes registered with the Retirement Benefit Authority, RBA by close of 2013. Simple random sampling was used and Fishers formula was used to come up with sample size of 48 private pension funds. The study used primary and secondary data. The secondary data is quantitative in nature and was collected from the annual financial statements of the pension funds. Primary data was both qualitative and quantitative in nature and was collected using questionnaires. The study findings revealed that age of contributors, leverage of fund, contributions received, fixed income investment, equity investment, offshore investment, fund liquidity does not have an influence on the financial performance of retirement schemes. The study recommended that future studies focus on corporate governance practices as determinants of the financial performance of retirement benefit schemes in Kenya

Unravelling the physiology and genetics of salinity tolerance in chickpea (Cicer arietinum L.)

Chickpea (Cicer arietinum L.) is a nutritious legume predominantly grown in semi-arid environments under rain fed conditions, but is highly sensitive to soil salinity. Until recently, there has been slow progress in the application of molecular genetics in chickpea breeding. This is primarily because the available genetic variation in international chickpea germplasm collections has not been extensively characterised due to a lack of available genomics tools and high-throughput phenotyping resources. Molecular genetic approaches are needed to identify key loci with the potential to improve salinity tolerance in chickpea. In this project, genetic analysis was conducted on two populations: A recombinant inbred line (RIL) population of 200 individuals developed from a cross between Genesis836 and Rupali which are known to contrast in their tolerance to salinity and a diversity panel consisting of 245 chickpea accessions of diverse genetic background from ICRISAT. For phenotyping, an image-based high-throughput phenotyping platform was used. Data on growth rate, water use, plant senescence and necrosis, and agronomic traits were collected under both control and saline conditions (40 mM for diversity panel and 70 mM NaCl for RIL). In depth studies including differential metabolite accumulation and senescence detection were carried out to increase our understanding of the response of chickpea to salinity. Genesis836 and Rupali differentially accumulated metabolites associated with the TCA cycle, carbon and amino acid metabolism. Higher senescence scores were recorded in Rupali compared to Genesis836. On average, salinity reduced plant growth rate by 20%, plant height by 15% and shoot biomass by 28%. Additionally, salinity induced pod abortion and inhibited pod filling, which consequently reduced seed number and seed yield by 16% and 32%, respectively. Path analysis was utilised to understand the intricate relationship existing between the traits measured and aided in the identification of those most related to salinity tolerance. This analysis showed that seed number under salt was highly related to salinity tolerance in chickpea. To identify Quantitative Trait Loci (QTL) underlying salinity tolerance in chickpea, two complimentary genetic analysis approaches were used: genome-wide association studies (GWAS) and linkage mapping. Phenotypic data was combined with genotypic data from both the diversity panel (generated through whole-genome resequencing) and RIL population (from DArTseq). Linkage mapping and GWAS identified a total of 57 QTL and 54 marker-trait associations (MTAs), respectively. The loci identified were linked to growth rate, yield, yield components and ion accumulation. A novel major QTL for relative growth rate on chromosome 4 that explained 42.6% of genetic variation, was identified by both genetic analyses. This QTL co-located with several other QTL identified, including those associated with projected shoot area, water use, 100-seed weight, the number of filled pods, harvest index, seed number and seed yield under salt. Near-isogenic lines will be developed to allow for targeted fine mapping that will help identify candidate genes for molecular analysis. Molecular markers tightly linked to this QTL will be validated as a selection tool in breeding to improve salinity tolerance in chickpea.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Agriculture, Food and Wine, 2017

Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.

Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions

Evaluation of the performance of our senescence analysis algorithm as applied to chickpea images.

<p>Evaluation of the performance of our senescence analysis algorithm as applied to chickpea images.</p

Comparison of senescence estimations using the method proposed here (curves denoted Overall, Mid and Bottom) and a direct application of the color analysis software provided by The Plant Accelerator’s LemnaTec imaging system.

<p>For the Lemnatec results, only a whole-of-plant measure is available with which we compare a corresponding measure, which in turn is broken down into the senescence levels determined in the bottom and middle zones.</p

Whole-of-plant assessment of growth (plant area) and senescence as a function of time for two individual chickpea plants.

<p>Top figure shows the time developments of total projected plant area (all leaves and stems) for the two plants, depicting qualitatively similar but quantitatively different growth behavior. Bottom figure shows the percentage of senescence present in the leaves of these plants relative to their total plant area. The two individual plants exhibit different rates of senescence development.</p

Summary of senescence dependence on nitrogen treatment (<i>N</i>1 − <i>N</i>5, horizontal axis) for the Gladius wheat plant variety.

<p>Shown are mean values of onset determination (days after sowing, DAS, dashed curve and solid triangles, left vertical axis) and final degree of senescence (percentage of total project leaf area, solid curve and open diamonds, right vertical axis). Error bars show the variation across three repeats.</p

Whole-of-plant assessment of growth (plant area) and senescence as a function of time for two individual plants under N2, chosen arbitrarily.

<p>Top figure features the time developments of total projected plant area (all leaves and stems) for the two plants, depicting similar growth behavior. Bottom figure shows the percentage of senescence present in the leaves of these plants relative to their total plant area. The two individual plants exhibit different rates of senescence development as well as different onset dates.</p

Demonstration of the image segmentation process and zonal partitioning of plant foreground.

<p>(a) is the original image, while (b) shows just the segmented plant image with overlayed horizontal lines partitioning the image foreground into three zones.</p

Two examples of senescence patterns.

<p>(a) and (b) depict similar sized leaf areas of the same plant on different days; in (b) the plant has grown a little larger. Figs (c) and (d) show the same plant (different from (a) and (b)) on different days but at a much later stage of development when more leaves have become senescent and after some senescent leaves have fallen off.</p