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The success in identifying heterosis in hybrid maize (Zea mays L.) breeding depends on the availability of reliable genetic diversity among maize inbred lines. Conventional methods of breeding have been boosted by the availability and efficiency of molecular markers. Coupling simple sequence repeat (SSR) markers with morphological markers provides thorough starting information for new inbred lines, especially from different genetic backgrounds. Furthermore, recent evidences that the environment can influence the epigenetic structure of the genome have necessitated morphological screening of crops during breeding programmes. This study used 28 agronomic traits and 14 SSR markers which are distributed uniformly in ten (1-10) inbred lines, namely EM11-133, EM12-210, OSU23i, CML395, CML202, CML442, CML444, CML208, CML312 and CML204 from Kenya, International Centre for the Improvement of Maize and Wheat (CIMMYT), and another (OSU 23i) from USA. The aim was to investigate their morphological and genetic diversity, categorise the inbred lines into useful groups based on the molecular profiles and morphological traits, and lastly determine the level of phenotype-genotype correlation. The dissimilarity calculated using SSR markers had a mean morphological dissimilarity of 0.895403, an r value of -0.1421 and a p -0.9840. The dissimilarity between the molecular and morphological traits was 0.860465. Comparison between the molecular and morphological data had a dissimilarity matrix with an r -0.2323 and a p value of 0.0120. This was probably due to intrinsic synteny in maize genome. The dendrograms generated with hierarchical Unweighted Pair Group Method with Arithmetic mean (UPGMA) cluster analysis of the Jaccard's similarity coefficient matrices revealed four major clusters. The Co-ancestry distance showed six tied groups with the Kenya cluster showing some differentiation with Exact Tests for population differentiation with a p = 0.0513. The American inbred line (OSU 23i) segregated alone, while the Kenya lines (EM11-133 and EM12-210) had close homology with the CIMMYT inbred lines (CMLs). A total of 2.0 alleles were detected among the inbred lines using bulk DNA samples and 14 SSR loci. Clustering analysis based on the genetic similarity coefficients separated the inbred lines into 4 groups with the American inbred line seeming to be genotypically more diverse from the others.Key Words: Genetic diversity, molecular markers, SSR, Zea mays RÉSUMÉLe succès dans l'identification des hétérosis de maïs hybrides (Zea mays L.) dépend de la disponibilité d'une diversité génétique fiable dans les lignées endogames du maïs. Les méthodes conventionnelles de l'hybridation avaient été améliorées par la disponibilité et l'efficacité des marqueurs moléculaires. Le couplage des marqueurs simples de sequence répétée (SSR) avec les marqueurs morphologiques fournit des informations fondamentales précises pour les nouvelles lignées endogames, principalement de différente constitution génétique. En outre, les recentes évidences selon...

Background:Maize lethal necrosis (MLN) disease continues to reduce the productivity of maize drastically threatening food security in the affected regions. It continues to cause yield loss of 30-100 percent in farmers' fields, depending on the time of infestation which is valued at $198 million in Kenya. This has not only threatened regional trade, but also seed industry. It has been reported in the major maize belts of Uasin Gishu, Trans-Nzoia, Bomet, Narok and Nandi Counties. MLN is caused by the synergistic interaction between Sugarcane Mosaic Virus (SCMV) and Maize Chlorotic Mottle Virus (MCMV). The disease has then spread to other Eastern and Central African countries with devastating food security and economic consequences. Objectives:This study highlights result after screening selected maize inbred lines for resistance to MLN, SCMV and MCMV in identifying promising lines for integration into the breeding program for MLN resistance. Methods:Sixty-five (65) maize genotypes were artificially inoculated using virus strains collected from Bomet County in Kenya at 3-4 leaf stage. Data on disease severity and incidence, AUDPC and flowering were recorded. Results:From the result, the inbred lines had significant differences for SCMV, MCMV and MLN reactions. Based on Area Under Disease Progress Curve (AUDPC) score and ELISA analysis, genotypes MLN001 and MLN006 have the lowest score of 270, whereas OH28 had a maximum at 1259 under MCMV. Genotypes MLN042 and MLN041 were identified as the most promising sources of resistant against SCMV. However, no genotype was identified to have acceptable levels of tolerance to MLN, but MLN001 and MLN013 were identified as the best performers under MLN. This study also validated the presence of MLN tolerance in MLN013 (CKDHL120312) and MLN001 (CKDHL120918) as earlier reported by CIMMYT. These tolerant genotypes are now serving as donors in the introgression of the tolerance into the Kenyan adapted maize backgrounds and development of improved MLN tolerant varieties. This will go a long way in restoring and ensuring sustainable maize productivity in improving the livelihoods of the smallholder farmers who form 75% of the major maize producers in Kenya.The identified inbred lines would be recommended for use in varietal development, MLN management and to enhance maize productivity, in the MLN endemic regions and further research in understanding the mode of gene action for MLN tolerance.

Maize (Zea mays L.) productivity in the sub‐Saharan Africa is constrained by biotic and abiotic stresses that reduce yield. In the region, one of the most serious abiotic factor is frequent intermittent droughts, which has been attributed to climate change. The purpose of this paper was to use on‐farm demonstration studies and farmer field days to demonstrate new drought mitigation technology and provide information on how small farmers can reduce yield losses. A total of 4814 demonstration plots of 39 DroughtTEGO maize hybrids and 19 commercial check hybrids were established in 17 counties across the low‐to‐mid‐altitude maize‐growing agroecologies of Kenya between 2015 and 2017. A total of 246 field‐day workshops were conducted. Combined analyses across years and locations showed that top five DroughtTEGO hybrids increased maize yields 33 to 54% (5.5–6.3 Mg ha−1) relative to conventional hybrids. The highest yield advantage of DroughtTEGO hybrids over commercial checks was observed in the drier lower eastern region in Kenya. Farmers particularly women, preferred the DroughtTEGO hybrids because of the stay‐green character, whiteness of flour (milling quality), root lodging resistance, drought‐tolerance and shelling percentage. Results from this study suggested that smallholder farmers can reduce the impact of drought by seeding drought‐tolerant maize hybrids. Core Ideas High yields and farmer‐preferred traits determine adoption of new varieties. Conducting on‐farm demonstrations can overcome adoption barriers. Planting of drought‐tolerant hybrids mitigates drought stress for smallholder‐farmers.

A study was conducted using maize samples collected from different agroecological zones of Kenya (n = 471) and Tanzania (n = 100) during the 2013 maize harvest season to estimate a relationship between aflatoxin B1 concentration and occurrence with weather conditions during the growing season. The toxins were analysed by the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Aflatoxin B1 incidence ranged between 0–100% of samples in different regions with an average value of 29.4% and aflatoxin concentrations of up to 6075 µg/kg recorded in one sample. Several regression techniques were explored. Random forests achieved the highest overall accuracy of 80%, while the accuracy of a logistic regression model was 65%. Low rainfall occurring during the early stage of the maize plant maturing combined with high temperatures leading up to full maturity provide warning signs of aflatoxin contamination. Risk maps for the two countries for the 2013 season were generated using both random forests and logistic regression models.