Department of Biotechnology

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.

INFLUENCE OF ENVIRONMENT AND ITS INTERACTION EFFECTSON NEW POTATO [(Solanum tuberosum (L.)] MUTANT LINES

Sun, 01 Jun 2025 00:00:00 GMT

INFLUENCE OF ENVIRONMENT AND ITS INTERACTION EFFECTSON NEW POTATO [(Solanum tuberosum (L.)] MUTANT LINES Muasya, Mutati; Kinyua, Miriam; Chepkoech, Emmy

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.

Hybrid Performance, Heritability and Trait Associations in Yellow Maize (Zea mays L) Inbred Lines

Wed, 01 Jan 2025 00:00:00 GMT

Hybrid Performance, Heritability and Trait Associations in Yellow Maize (Zea mays L) Inbred Lines Shiundu, Daniel W.; Pkania, Kennedy; Chepkoech, Emmy The challenge of decreasing maize yields in Kenya and Sub-Saharan Africa due to biotic and abiotic stresses is further compounded by limited supply of improved hybrid varieties. This study aimed to evaluate the hybrid performance, heritability and examine phenotypic correlations between grain yield and yield-related traits in yellow maize inbred lines in Western Kenya. One hundred and thirty F1 testcrosses produced using a line-by-tester mating design of two-line testers on 65 yellow lines were evaluated across three locations using a 7×19 alpha lattice with two replications. Phenotypic data on grain yield and yield related traits were used to compute best linear unbiased estimates of means and variance components in META-R while R package ‘corrplot’ computed phenotypic correlations. Significant (p=0.001) genotypic and genotype-by-environment variances were observed for grain yield and related traits, except for plant height which did not show significant genotypic variance. With a trial mean of 9T/Ha and an LSD0.05 1.7, the testcross L45×TA produced the highest grain yield across sites at 12.4T/Ha. Studied traits showed high heritability across sites, with the exception of the Northern leaf blight, which had moderate heritability (46%). Significant phenotypic correlations were found between traits, with ear height showing the highest positive correlation with grain yield (r = 0.67, p=0.001). With higher genotypic variance than genotype-by-environment interaction variance, high heritability for grain yield and related traits and significant correlations between them, this germplasm offers opportunities for both direct and indirect selection in maize breeding programs aimed at yield improvement since most of the measured traits are largely dependent on the genetic value of the germplasm.

Agro-Morphological Characterization and Diversity Analysis of Selected Sorghum Genotypes in Kenyan Agro-Ecologies

Wed, 01 Jan 2025 00:00:00 GMT

Agro-Morphological Characterization and Diversity Analysis of Selected Sorghum Genotypes in Kenyan Agro-Ecologies Rutto, Emmah Cheluget; et. al... Sorghum (Sorghum bicolor) is a very important cereal crop cultivated globally, primarily in arid and semi-arid regions. It ranks fifth among the most important cereal crops globally, after wheat, rice, maize, and barley. Despite its crucial role in food security and climate resilience in arid and semiarid regions, sorghum production remains suboptimal, with yields consistently falling below the crop’s genetic potential. Understanding its morphological diversity is essential for effective breeding programs and genetic resource conservation. This study aimed to determine valuable morphological variation among selected sorghum genotypes against released varieties using a diverse set of traits and their correlations. The experiment was laid out using a Randomised Complete Block Design with three replications using 13 genotypes sourced from the University of Eldoret, and 03 checks from the Kenya Seed Company in Kenya. The genotypes were grown in Endebess and Sigor for one season and evaluated based on morphological traits. Using GenStat statistical software 14th Edition, data on qualitative and quantitative traits were analysed at 5% level of significance. The significant differences among the sorghum genotypes were tested using analysis of variance (ANOVA). Correlation Matrices, first, second and third principal component (PCA) were performed. Principal component analysis revealed the three most important PCs that contributed 81.78%, 15.33% and 1.5% of the total variation, respectively. At the Endebess site, grain yield exhibited the highest genotypic variation among the evaluated sorghum genotypes. E1291 recorded the longest leaves (67.87 cm), whereas Kalatur exhibited the shortest (36.13 cm). Moreover, mean comparisons between the two environments showed that Sigor recorded a higher mean grain yield (2.01 t ha⁻¹) compared to Elgon Downs (1.73 t ha⁻¹). Plant height (0.889) was the trait that contributed most to the variation in the first PC. Number of days to harst (0.814) contributed most to the variation in the second PC, whereas leaf length (0.842) was the largest contributor to the variation observed in the third PC. Correlation analysis showed significant positive relationships between 50% days to emergence to 50% days to flowering and days to maturity (r=0.7 and r=0.9), respectively, suggesting that these traits can be used as selection criteria in breeding programs. The frequency distribution analysis indicated a high occurrence of pigmented leaves (93.75%) and brown grain colour (68.75%), reflecting the natural variability within the studied population. The phenotypic evaluation of sixteen sorghum genotypes revealed significant agro-morphological diversity, confirming the genetic variability. These findings support informed selection and genetic improvement to boost yield and stress resilience in sorghum breeding for Kenya and similar regions.

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.

IDENTIFICATION OF DROUGHT TOLERANT KENYAN BRACHIARIA ECOTYPES USING DROUGHT TOLERANCE INDICES

Thu, 01 Jun 2023 00:00:00 GMT

IDENTIFICATION OF DROUGHT TOLERANT KENYAN BRACHIARIA ECOTYPES USING DROUGHT TOLERANCE INDICES Awalla, B. J.; Were, B. A; Binnot, J. Drought is one of the major abiotic stress factors limiting agricultural productivity globally. Brachiaria also known as signal grass is a native of sub-tropical and tropical Africa and important in livestock production. The grass has many advantages including; high biomass, high nutritional value, adaptation to drought and low fertility soils, sequestration of carbon, enhanced nitrogen uses efficiency and low greenhouse gas emissions. Emergence of climate change with increased global temperatures has led to prolonged drought which has adversely affected the improved Brachiaria hybrids. Locally available ecotypes are a rich source of unique genes and characteristics that could be key in developing drought resilient hybrids. The objectives of this study were to i) assess the effectiveness of various indices in selection of drought tolerant Kenyan Brachiaria ecotypes, ii) evaluate the relationship between the indices and iii) to identify high yielding and stable ecotypes under stressed condition. The design of the experiment was completely randomized design (CRD) with three replications in a factorial arrangement (3 x 25). A total of 11drought tolerance indices; tolerance (TOL), stress Tolerance Index (STI), mean productivity (MP), yield stability index (YSI), Geometric Mean Productivity (GMP), stress susceptibility index (SSI), Yield Index (YI), harmonic Mean (HM), drought intensity index (DII), modified stress tolerance k1 and modified stress tolerance k2 were calculated based on shoot biomass production under non-stressed (YP) and stressed (YS) conditions. Rank means, rank sum and standard deviation were also used to identify the tolerant materials. In the previous experiment, various physiological parameters were scored which included; leaf relative water content, relative chlorophyll content using SPAD -502 Chlorophyll meter (Minota Co), leaf fresh weight, leaf dry weight and leaf relative water content. Relative water content was also estimated and comparative scores were done between control, medium and extreme or water deficit experiments. Based on all the indices and ranking, BrK 1, BrK 6, BrK 7, BrK13 and BrK 18 were the most tolerant in stressed condition. These ecotypes can be recommended for planting in areas prone to drought. More studies on the identified tolerant ecotypes are essential to ascertain whether the materials hold unique genes that could later be introgressed into various breeding schemes to confer tolerance.