Theses & Desertations

EVALUATION OF YELLOW MAIZE (Zea mays L) INBRED LINES’ PERFORMANCE AND COMBINING ABILITY USING LINE BY TESTER ANALYSIS IN WESTERN KENYA

Wed, 01 Jan 2025 00:00:00 GMT

EVALUATION OF YELLOW MAIZE (Zea mays L) INBRED LINES’ PERFORMANCE AND COMBINING ABILITY USING LINE BY TESTER ANALYSIS IN WESTERN KENYA SHIUNDU, DANIEL WAMACHE There is need to continuously develop and deploy highly adaptable and productive maize hybrid varieties for use by farmers against the greatly dynamic biotic and abiotic stresses that face production of this crop in the country. The objective of this study was to estimate the hybrid performance and the combining abilities of yellow maize inbred lines and their testcrosses for grain yield and yield-related traits across three locations. Sixty-five yellow maize inbred lines were crossed to two-line testers; Cimmyt maize lines (CML) 486 (Tester A) and 451 (Tester B) using a line by tester design. Resultant a hundred and thirty F1 testcrosses with three check varieties were evaluated on three locations in western Kenya using a 7×19 alpha lattice with two replications. Data on grain yield and yield-related traits was collected. Means and variance components on hybrid performance were computed in META-R version VI and combining ability analysis done using Restricted maximum likelihood (REML). Grain yield means ranged between 12.4T/Ha and 2.8T/Ha with testcross L45×TA producing the highest grain yield mean across sites. High heritability (>60%) was recorded for grain yield and other yield-related traits except for northern leaf blight which was moderate. All yield-related traits in the study except northern leaf blight had significant phenotypic correlations with grain yield. Ear height had the highest positive correlation at 0.7(P<0.001). Across sites Analysis of Variance (ANOVA) revealed highly significant (p<0.001) mean squares for sites, hybrids, line general combining ability (GCA) line GCA by site, hybrid by site, specific combining ability (SCA) as well as SCA by site. L45 had the highest positive GCA for grain yield at 2.7 (p<0.05). L23, L65, L29 and L25 crossed with tester A showed positive significant SCA estimates for grain yield whereas L36×TA had a negative but significant SCA for grain yield at -1.9 (p<0.05). Based on SCA estimates with the testers, the inbred lines grouped into two heterotic groups A and B with 60% and 38.5% of the inbred lines respectively. L45 and other 33 lines that had positive GCA for grain yield could be exploited in the development of high yielding yellow maize hybrids. Testcrosses L45xTA, L47xTA and L35xTB showing equivalent or better performance to the mean of the checks have potential for further evaluation and consideration for release as adaptable and stable superior yielding yellow maize single cross hybrids.

DIVERSITY OF SELECTED SORGHUM GENOTYPES USING MORPHOLOGICAL, MOLECULAR AND BIOCHEMICAL MARKERS

Wed, 01 Jan 2025 00:00:00 GMT

DIVERSITY OF SELECTED SORGHUM GENOTYPES USING MORPHOLOGICAL, MOLECULAR AND BIOCHEMICAL MARKERS RUTTO, CHELUGET EMMAH Sorghum (Sorghum bicolor L. Moench) is a cereal ranked the fifth most vital cereal crop globally following maize, rice, wheat, and barley. It is versatile and is used in numerous culinary and feed products around the world. Sorghum is an economic staple crop and the genetic diversity in its germplasm is an invaluable aid for its improvement. Characterization of the available Kenyan germplasm of sorghum is important in comprehending the dynamics of the genetic material/pool and in improving and sustaining its productivity. The purpose of this study was to assess the genetic diversity among selected sorghum genotypes in Kenya. Thirteen Sorghum genotypes sourced from the University of Eldoret/Rongo university and three checks from Kenya seed Company were analysed using morphological traits, biochemical profiles and ISSR DNA Markers. The field experiments were conducted at Endebess (35°28'10" E longitude and 1°29'17" N latitude) and Sigor (34°51'24" E longitude and 1°4'26" N latitude), replicated three times and arranged in Randomized Complete Block Design. Biochemical and molecular analysis were carried out at the Chemistry and Biotechnoloy Laboratories respectively. Clustering was carried out using UPGMA, AMOVA and PCoA to assess their genetic relationships. PCA revealed that the 3 important PCs contributed 81.78%, 15.33% and 1.5% of the total variation. AMOVA revealed 97% and 3% genetic variation within and among populations respectively. Shannon Weiner Diversity Index (H=2.74) and Shannon-Weiner Evenness Index (J=0.988) revealed a moderate to high level of biochemical diversity and relatively uniform distribution. Genotypes E95A, E1 and T53B were high yielding, early, dual and nutrient dense and could be promoted for commercialization. These findings offer informed precision in selection and improvement for high yield performance drought resistance and nutritious sorghums in the breeding programs in Kenya and Similar Agro-ecologies.

APPLICATION OF GAMMA INDUCED MUTATION IN BREEDING FOR BACTERIAL WILT (Ralstonia solanacearum) DISEASE RESISTANCE IN POTATO (Solanum tuberosum L.

Mon, 01 Jan 2018 00:00:00 GMT

APPLICATION OF GAMMA INDUCED MUTATION IN BREEDING FOR BACTERIAL WILT (Ralstonia solanacearum) DISEASE RESISTANCE IN POTATO (Solanum tuberosum L. CHEPKOECH, EMMY Potato (Solanum tuberosum L.) is the second most important staple food crop in Kenya after maize and fourth in the world, therefore, plays a vital role in food and nutrition security, and sustainable development. Despite its importance, potato production in Kenya is still low due to biotic and abiotic constraints. Of the biotic factors, bacterial wilt in potato is regarded an important disease causing significant yield decline of about 50 to 100 %. It has been reported to affect 77 % of the potato farms in Kenya. Breeding for resistant varieties can play an important role in managing the disease. However, improvement of potato through conventional breeding has been difficult due to the narrow genetic diversity of the crop. Desired genetic variations could be generated through the application of induced mutations from which putative mutants can be selected. The objective of this study was to induce mutation on potato varieties to create variation and identify desirable allelic variants of genes underlying important quantitative traits. The study involved irradiation of three commercially grown high yielding Kenyan potato varieties: Asante, Kenya Mpya and Kenya Sherekea. A total of 570 mutant microtubers were developed using gamma rays from Co60 source under different dose rates (0 – 30 Gray) for the three varieties. The microtubers were then established at M1V1 and developed to M1V2, M1V3, and M1V4 generations at the University of Eldoret. The mutant populations were assessed for morphological, ploidy and genetic diversity. Bacterial wilt resistance screening was carried out at M1V4 generation at KALRO-Kabete station using alpha lattice design. The results showed that the total number of irradiated potato mutants that survived to produce tubers at the M1V1 stage was less than half for each genotype that was initially irradiated in all dosage rates across the three genotypes used. The highest tuber weight was at dosage rates 9 Gy in Asante (22.0 and 57.0 tons/ha), 15 Gy in Kenya Mpya (31.0 and 46.8) and 10 Gy in Kenya Sherekea (48.4 and 49.0) at M1V2 and M1V3 generations respectively. The number of ploidy level distribution was decreasing in diploids and triploids and were increasing in tetraploids from M1V1, M1V2 to M1V3 in all the three potato mutant populations. The reactions of potato mutants to bacterial wilt were varied and there was significant difference in selected agronomic traits and bacterial wilt resistance among varieties and between families of individual varieties. The days to onset of wilting, area under the disease progress curve and percentage of symptomatic tubers of total tuber number per ha was significantly different in all the three potato mutant populations. The genetic variability of the potato mutants showed that 20 SSR primers were polymorphic with 211 alleles (average eleven), Asante, Kenya Mpya and, Kenya Sherekea generating 69, 75 and 67 alleles respectively. The dendrogram and PCoA analyses showed that the 160 potato mutants and three parents were clustered into three groups, though the STRUCTURE analysis supported by the dendrogram confirm that each sub-population affiliate gave six clusters. Success in the use of gamma-induced mutation in the development of new varieties was observed and will lead to improved potato production, which will respond to enhanced food and nutrition security. The information from this study will inform potato variety release for commercial production and sustainable development and for future potato breeding programme

MACHINE LEARNING IN AGRICULTURE WITH APPLICATION IN MAIZE (Zea mays) YIELD PREDICTION MODELING IN UASIN GISHU COUNTY, KENYA

Mon, 01 Jan 2024 00:00:00 GMT

MACHINE LEARNING IN AGRICULTURE WITH APPLICATION IN MAIZE (Zea mays) YIELD PREDICTION MODELING IN UASIN GISHU COUNTY, KENYA SITIENEI, MIRIAM CHEPLETING Artificial intelligence is a subfield of computer science that aims to bring forth machines capable of emulating human behavior and replicating the cognitive and behavioral processes exhibited by humans. It is the discipline of making computers behave without explicit programming. The system functions by consolidating large amounts of data through efficient and repetitive processing, alongside the utilization of intelligent algorithms. This allows the software to independently acquire knowledge from patterns or attributes included in the data. Machine learning is a subfield of artificial intelligence that facilitates the autonomous acquisition of knowledge by computers through the analysis of past data, without the need for explicit programming. The primary objective of implementing machine learning techniques in the agricultural domain is to enhance both crop productivity and quality. This is motivated by the rise of big data technology and highperformance computation. It has propelled advancements in unraveling, quantifying, and comprehending data-intensive agricultural operational processes. The nature of machine learning models might vary between descriptive and predictive, depending on the specific research challenge and queries at hand. This study undertook a systematic literature review to assess the adoption of machine learning techniques in agricultural research in the Science Direct database to evaluate trends in its adoption, particularly in agricultural research. To evaluate machine learning applications in crop production, animal production, soil management, and agricultural mechanization, as well as the specific areas of study. Crop modeling and yield prediction is a decision tool used by farmers and other decision-makers in the agricultural sector to increase production efficiency and assist them in making swift decisions that affect the standard of agricultural output. Crop yield forecasting models can reasonably estimate the actual yield, but it would be preferable if they performed better. It is one of the most important precision agriculture topics. The need to adopt modern regression techniques of machine learning to attain sufficient amount of maize for sustainable agriculture, for food security, economic stability and nutritional benefits to the farmer. The study applied Random Forests, K Nearest Neighbor, and Extreme gradient boosting-XGBOOST machine learning regression algorithms to predict maize yield in Uasin Gishu county-Kenya using field-collected questionnaire data from 900 farmers spread across 30 wards in the five sub-counties. It utilized the R software and a train-totest ratio of 80:20. All the models could predict maize yield. Finally, model evaluation was done using Root Mean squared error-RMSE, Mean Squared Error-MSE, Mean Absolute Error-MAE, Mean Absolute Percentage Error-MAPE, Nash-Sutcliffe Efficiency Coefficient- NSE and Willmott's Index-WI to select the best model for yield prediction. Overall, XGBOOST emerged as the best regression algorithm in four evaluation metrics with RMSE of 0.4563, MSE =0.2082, MAE =0.3532, and Willmott’s index of 0.3264. XGBOOST was followed by Random Forest regression and K Nearest Neighbor regression algorithm. The findings recommend an XGBOOST machine learning regression model to predict maize yield in Uasin Gishu-Kenya to optimize maize yield for economic stability and food security. XGBOOST is an ensemble learning algorithm, there is a need to evaluate other ensemble regression algorithms from bagging and stacking in yield prediction and appreciate the need to fast implement machine learning techniques to make Agriculture more sustainable for future generations in Kenya

THE BEHAVIOURAL ECOLOGY OF BALE MONKEYS (Chlorocebus djamdjamensis) IN THE DISTURBED ENVIRONMENT OF HARENNA BAMBOO FOREST, ETHIOPIA

Mon, 01 Jan 2024 00:00:00 GMT

THE BEHAVIOURAL ECOLOGY OF BALE MONKEYS (Chlorocebus djamdjamensis) IN THE DISTURBED ENVIRONMENT OF HARENNA BAMBOO FOREST, ETHIOPIA KUMARA, WAKJIRA GEMEDA This study aimed to understand the behavior and ecology of Bale monkey in disturbed environment of bamboo forest. The study site was located in the southern escarpment of Bale Mountains National Park, specifically known as Rira locality. A focal animal sampling method was used based on a 10-minute observation period across the seasons. Data were collected on social system with associated behavioral and ecological components from 1973 focal animals watched, which included the deployment of a total group count to determine group size. The minimum convex polygon method was used to elucidate the effects of attractive food availability on the home range size of the group. Data were analyzed using a Generalised Linear Model (GLM). This study revealed that Bale monkey has a large group size in fragmented forest, which might be linked to the availability of attractive crops that further facilitated boundary overlap among the neighboring groups. Bale monkey group lives in a multi-male-multi-female social system. The group size was variable in every count, possibly due to the surrounding environmental conditions. Relatively larger group size recorded in wet season than in dry season (84 v 74, P<0.001). With the social partner, adult females were more associated with other adult females, juveniles and infants, whereas adult males did mainly with adult females. The group showed larger home range size in wet season (753.2 ha) than in dry season (27.8ha). Likewise, its daily travel length in wet season (6205m.) was significantly further than in dry season (1301m), p=0.032. This leads to evidence that Bale monkey group exhibits larger home range area and longer day range in disturbed forest than its published reports in undisturbed forest. Bale monkey exhibited a semiterrestrial behaviour (50.4% on trees and 49.6% on ground) unlike most forest - living guenons. This suggests that the temporal and spatial availability of attractive food sources and forest fragments significantly influence the substrate use of the Bale monkey. Arundinaria (bamboo plant) was the most frequently used substrate by Bale monkey across seasons. Plant species selected as food sources indicated that the bulk of the diet largely comprised of arundinaria, barley and grass species. Seasonal differences significantly influenced the proportion of the food items selected by Bale monkeys (p = 0.01561). In wet season, young Arundinaria leaf and shoot was the most preferred food items (p < 0.001), while barley is the most attractive food items in dry season. Bale monkey spent more time feeding but less so in dry season (51%) than in wet season (64%), P<0.001. The group spent more time resting in dry season (P<0.001), which might be influenced by the availability of cultivated food items. The proportion of activity patterns varied with time of the day. Resting and grooming were more pronounced around noon (P<0.001), while feeding largely took place around the mooring and evening. This study generally concluded that habitat fragments, availability of cultivated crop and other attractive food items and seasonal variability significantly influence the behaviors and ecological adoptions of Bale monkeys

DEVELOPMENT OF MICROSATELLITE MARKERS AND ANALYSIS OF GENETIC DIVERSITY AND POPULATION STRUCTURE OF SANDALWOOD (Osyris lanceolata Hochst. & Steud.) IN KENYA

Mon, 01 Apr 2024 00:00:00 GMT

DEVELOPMENT OF MICROSATELLITE MARKERS AND ANALYSIS OF GENETIC DIVERSITY AND POPULATION STRUCTURE OF SANDALWOOD (Osyris lanceolata Hochst. & Steud.) IN KENYA OTIENO, JOHN OCHIENG African sandalwood (Osyris lanceolata Hoscht and Steud) is a multipurpose and drought tolerant, African tropical hemi-parasitic tree species belonging to Santalaceae family. It is an evergreen dioecious shrub or small tree growing to a height of 1-7 meters depending on soil-type, climate and genetics of the tree. The species is valued globally for its essential oils used in perfumery and pharmaceutical industries. The increased demand for its essential oils and other products is bringing pressure to bear on the dwindling O. lanceolata populations and habitats in Kenya and East African region. due to overexploitation through anthropogenic activities that include illegal trade, overgrazing bush burning and destruction of host plant species for fuel wood, timber, charcoal burning and building materials. Consequently, the Convention on International Trades in Endangered Species (CITES) recently issued notification to review and gather information on the conservation status of O. lanceolata among other concerns. Although protected under CITES, the species continued to be heavily smuggled and overexploited. However, knowledge regarding the genetic diversity and population structure of the extant Kenyan populations, which is vital in informing conservation and sustainable management strategies of the species is still limited. Therefore, the aim of this work was to develop microsatellite (SSR) markers and use them to evaluate the genetic diversity and population structure of the species across the geographical distribution range in Kenya. A set of 12 polymorphic and five monomorphic microsatellite markers were developed and characterised using standard genome assembly, SSR identification and primer design protocols. Ten highly polymorphic microsatellite loci were used to characterise 288 individuals over ten natural populations, namely Baringo, Embu, Gachuthi, Gwasi, Kibwezi, Kitui, Makueni, Meru, Mau and Mt Elgon. The loci produced 178 alleles with a high Shannon’s Information index (I) values ranged from 0.805 to 1.6. The average observed heterozygosity across all loci varied from 0.112 to 0.815. A high level of genetic diversity was inferred from the genetic diversity parameters (He = 0.587, I = 1.302 and PPL = 97 %). The unweighted pair group method of arithmetic averages (UPGMA) and population structure analysis grouped these 288 individuals into two major groups. The AMOVA results indicated that 62% of the total genetic variation was found within populations, while only 38% was observed among populations. Evaluating genetic diversity is vital for identifying populations for conservation priority and establishing baseline data for informed conservation strategies at the local level. This study represents the first examination of the genetic diversity and population structure of O. lanceolata using SSR markers. The newly developed microsatellite markers will be valuable for future breeding programs and genetic studies aimed at formulating effective conservation plans.

PERFORMANCE EVALUATION OF A PROTOTYPE VARIABLE PITCH IRISH POTATO GRADER

Sun, 01 Oct 2023 00:00:00 GMT

PERFORMANCE EVALUATION OF A PROTOTYPE VARIABLE PITCH IRISH POTATO GRADER KIBOR, DAVID TIROP The manual grading of potatoes in Kenya has resulted in inconsistencies, quality variations and financial losses for small-scale farmers due to low market prices for ungraded products. To mitigate these challenges, this study aimed to develop and evaluate the performance of a cost-effective potato grading machine to enhance uniformity and overall quality of potato tubers. The research involved determining the physical and mechanical properties of Shangi potato variety. The prototype potato grader consisted of a feeding hopper, conveyor belt, grading unit, and collection trays. Grading capacity, grading efficiency, and mechanical damage index were assessed by varying grading unit speeds, angles of inclination and feed rates. The results showed that the grading capacity increased with higher grading unit speeds, inclination angles, and feed rates. The optimal operating conditions were observed at a grading unit speed of 4 rpm, an inclination angle of 0 degrees, and a feed rate of 3400 kg/hr. The prototype potato grader achieved a commendable grading capacity of 3968 kg/hr, with an efficiency of 89.34% and a low mechanical damage index of 2.94%. The results demonstrate that the potato grading machine effectively enhances grading while minimizing mechanical damage. This grading machine offers efficiency a practical and sustainable solution for small-scale farmers to produce high-quality graded potatoes in line with market demands. It is recommended that future research may include further optimization by exploring various grading unit speeds, feed rates, and inclination angles. Implementing padding on the collection trays could further reduce mechanical damage. Additionally, investigating alternative power sources may enhance the grader's versatility and extending testing to other fruits and vegetables would broaden its applicability in the agricultural industry.

MORPHO-GENETIC DIVERSITY OF GAMMA IRRADIATED DOLICHOS BEAN (Lablab purpureus (L.) Sweet) GENOTYPES FOR CLIMATE CHANGE ADAPTATION.

Sun, 01 Oct 2023 00:00:00 GMT

MORPHO-GENETIC DIVERSITY OF GAMMA IRRADIATED DOLICHOS BEAN (Lablab purpureus (L.) Sweet) GENOTYPES FOR CLIMATE CHANGE ADAPTATION. KIMNO, STEPHEN KIPCHIRCHIR Dolichos bean (Lablab purpureus (L.) Sweet 2n=22 or 24 is a multipurpose legume mainly grown and used as a pulse, forage feed and in soil amendment for nitrogen fixation and green manure. Practically, it still yields below estimated potential of over 5000 kgha-1. Induced crop mutagenesis is a safer conventional breeding method and has played a major role in increasing global food security. The main objective of the study was to contribute to climate change adaptability through gamma ray irradiation of dolichos bean genotypes and selection of climate smart allelic accessions. Specific objectives were to: evaluate the effect of gamma irradiation doses on morpho-agronomic traits of mutant dolichos bean accessions, assess genetic variability estimates, determine the genetic diversity, and evaluate nutritional and mineral composition and to evaluate the adaptability potential of mutant accessions in north rift Kenya. Four dolichos bean varieties (maridadi, cream, black I and black II) were irradiated with 300 gy and 400 gy gamma rays in 2018 in Austria. The M1 to M4 generations of the accessions of four dolichos bean genotypes were advanced by forward genetics protocol at University of Eldoret in 2019 through 2021.M2 accessions were evaluated for effect of mutation and genetic estimates, 95 M3s for genetic diversity based on 20 SSR markers, 24 M4s were screened for nutritional and mineral composition and yield and adaptability potential. The results showed that dose 300gy and 400gy significantly (p=0.05) increased leaf length, raceme length, dry seed yield per plant and plant height across the accessions. Qualitative phenotypic variations were present in all mutant accessions except black I. There was a higher genetic estimate variability for the yield associated traits measured for eldo maridadi than for eldo black I indicating difference in genotype and impact of mutation. Genetic diversity of 95 accessions based on microsatellite markers produced 20 polymorphic primers mapping an average of 5.25 alleles per locus, polymorphic information content of 0.58 with analysis of molecular variance (AMOVA) among population of 45% and among and within individuals 54% and 1%.The nutritional test showed that accession BF032 (28.86±0.18%), MT076 (74.88±0.59%), BF137 (9.69±0.34%), MT049 (12.55±0.57%) and BT188 (449.69±0.02 kcal) had significantly higher percent crude protein, carbohydrate, crude fat, crude fibre and energy. WT018, BT114 and BT039 had significantly higher phosphorous, potassium, calcium and zinc (0.58±0.21mg/l, 2.81±0.00 mg/l, 175.65±2.27 mg/l and 3.64±2.29 mg/l respectively). Accessions BT188 (3919 kgha-1), MT049 (3315 kgha-1), GT032 (3512 kgha-1) and WT026 (4462 kgha-1) were identified as adaptable and best yielding while Baringo as the best location for dolichos production. The use of gamma irradiation in generating genetic variability in Kenyan dolichos bean genotypes for climate change adaptation was effective. The best accessions on nutrition and yield adaptability are an important genetic resource for building resilience to climate change in Kenya.

IDENTIFICATION OF NEW MOLECULAR MARKERS FOR DIVERSITY ANALYSIS AND BREEDING FOR EARLY MATURITY AND DETERMINATE LABLAB (LABLAB PURPUREUS) VARIETIES

Tue, 01 Feb 2022 00:00:00 GMT

IDENTIFICATION OF NEW MOLECULAR MARKERS FOR DIVERSITY ANALYSIS AND BREEDING FOR EARLY MATURITY AND DETERMINATE LABLAB (LABLAB PURPUREUS) VARIETIES KAMAU, ELIEZAH MAINA Lablab (Lablab purpureus (L.) Sweet) is a grain legume crop commonly grown in Africa and India and is used as human food, animal feed, in soil conservation, enhancing soil fertility and in weed management. In Kenya, most farmers grow landraces which are inherently low yielding and have other undesirable attributes like long maturity duration and indeterminate growth habit. The status of genetic diversity of the local lablab germplasm and how it relates to materials from other regions remain unclear. Unavailability of novel breeding selection tools such as molecular markers and lack of adequate information on the inheritance pattern of important traits have also hampered the crop improvement in Kenya. The objectives of the study were therefore: to develop new molecular markers for lablab; to assess the genotypic diversity of local, exotic and wild lablab accessions using simple sequence repeats, diversity array technology (DArT) and single nucleotide polymorphism (SNP) molecular markers; to identify the inheritance pattern of selected lablab qualitative traits and establish the linkage relationship of the genes controlling them; determine the heritability estimates, genetic gain and character association of important traits of determinate lablab. Transcriptome sequencing using 454 Titanium FLX system of mRNA isolated from leaves and shoots of lablab samples, was conducted to discover genic-SSRs and to develop SSR markers. Eight of these new developed SSR markers were used to characterize 189 lablab accessions. SilicoDArT and SNP markers were developed using DArTSeq technology and used to characterize 240 lablab accessions. The genetics of growth habit and other qualitative traits were studied in three generations (F1, F2 F3) of eight lablab populations. Selected F5 lines with determinate growth habit were grown using RCBD design at KALRO Thika and Katumani to determine heritability estimates, genetic gain and character association. Results indicated that there were 446 genic SSRs from 3140 assembled lablab contigs indicating an overall density of 202 SSR per Mbp. SSR primer pairs designed from the contigs sequences amplified on lablab genome. The gene diversity among the 189 accessions based on SSR loci ranged from 0.26 to 0.52 with an average of 0.38, with germplasm collected from Kenya showing a moderate genetic diversity of 0.36. Higher genetic diversity (He<0.5) was detected within the Ethiopian and South Africa populations. A total of 15,601 polymorphic DArT markers and 11,431 SNP markers were identified each with average reproducibility and genotype call rate of more than 90%. Based on both DArT and SNP markers the 240 lablab was of narrow genetic diversity with the expected mean heterozygosity of 0.030 (DArT) and 0.039 (SNP). However, genetic differentiation was most pronounced between the cultivated and the wild accessions. The growth habit in lablab is under control of three genes which could be temperature dependent. The genes controlling stem growth habit and time to flowering in lablab are linked. The study identified, moderate to high heritability, genetic advance estimates and significant positive correlations of pods per plant, raceme per plant, plant height, pod width, racemes per pod and number of flower nodes. The newly developed molecular markers are useful in grouping lablab genotypes into related clusters that breeders can use to enhance lablab productivity. Selection for high number of units of pods per plant, raceme per plant, plant height, pod width, racemes per pod and can be effective when targeting to develop high seed yielding determinate varieties.

SOIL EROSION PREDICTION USING MODIFIED UNIVERSAL SOIL LOSS EQUATION (MUSLE) IN TUGEN HILLS, BARINGO, KENYA

Sat, 01 Oct 2022 00:00:00 GMT

SOIL EROSION PREDICTION USING MODIFIED UNIVERSAL SOIL LOSS EQUATION (MUSLE) IN TUGEN HILLS, BARINGO, KENYA CHESIRE, ATHANUS KOMEN Soil erosion by water is one of the primary causes of land degradation and occurs throughout the world. Soil erosion is contributing negatively to the already declining agricultural productivity thereby negatively influencing people’s livelihoods and economic empowerment. Therefore, there is need to understand erosion processes, quantify sediment yield, identify and rank critical sources on spatial domain of sediment. This will help in formulation of prioritized catchment conservation strategies. This study focused on estimation of sediment yield from Tugen Hills particularly Saimo catchment in Baringo County using Modified Universal Soil Loss Equation (MUSLE) model with a view to develop an understanding of inter-relationships between soil erosion and sediment yield. The input model parameters of runoff volume (Q) and peak flow rate (qp) were determined from runoff plots of dimensions 4.8m by 2m set up in the catchment with average slope of 2%. Soil erodibility factor (K) was calculated mathematically based on soil samples collected. Cover management (C) was obtained by percentage cover and support practice (P) factor was determined through observation and use of conversion table. Apart from determination of model parameters, the study calibrated and validated MUSLE for use in future studies within Saimo catchment and other catchments with similar characteristics. The mean bulk densities for top soil and bottom soil are 1.05g/cm2 and 1.07 g/cm3. The total value for fine sand and silt gives 37.1%. The saturated hydraulic conductivity varied from 8.0 μm/s to 41.3 μm/s with a mean value of 24.1 μm/s. There were only two classes high and moderately high translating to code 2 and 3, respectively. The analysis of variance (ANOVA) of the observed data showed that rainfall intensity affected the sediment yield production in the runoff plots and that there was no evidence to suggest that the soil homogeneity in the runoff plots affected the sediment yields. The observed and simulated MUSLE model values for calibration were PBIAS (0.83), R2 (0.75), r (0.87) and KGE (-0.20) and those for validation were NSE (0.96), PBIAS (-0.44), R2 (0.60), r (0.78) and KGE (0.46). Hence it can be concluded that the MUSLE model can be used successfully as an effective tool in soil conservation management. Future work for several seasons is however needed in order to capture different slopes and the varying climatic conditions for the model to be robust and to be used widely.