Yoseph Beyene

Maize lethal necrosis (MLN) is a severe disease caused by the combined infection of maize chlorotic mottle virus (MCMV) and a potyvirus, most often sugarcane mosaic virus (SCMV). This disease seriously threatens food security across sub-Saharan Africa (SSA). We investigated a major-effect quantitative trait locus for resistance on chromosome 6, named the maize lethal necrosis susceptibility locus 1 ( qMLNS1 ), derived from the Thai line KS23-6. Fine mapping and CRISPR-Cas9 editing of the candidate genes within the narrowed 105 kb interval revealed a peroxisomal peptidase as the underlying cause of susceptibility. Confocal microscopy confirmed the localization of the MLNS1 protein within peroxisomes. Targeted knockout of the Mlns1 gene in the susceptible elite line CML536 from SSA conferred resistance comparable to KS23-6 in field trials conducted in Naivasha, Kenya. This knockout specifically blocked MCMV accumulation without affecting SCMV. The edited lines showed no yield penalty or agronomic defects under disease-free conditions. Our findings uncover a mechanistic link between a peroxisomal enzyme and viral susceptibility. They also establish a rapid, scalable gene editing strategy for incorporating MLN resistance into elite germplasm, offering a model for combating similar viral diseases in staple crops globally.

Maize is a staple food crop in sub‐Saharan Africa, where increasing productivity is essential for food security. This study assessed testcross performance and combining ability of early‐ to medium‐maturity doubled haploid (DH) maize lines adapted to eastern Africa. A total of 732 testcross hybrids derived from 244 DH lines crossed with six testers, along with six commercial checks, were evaluated across eight locations. Agronomic and disease‐related traits were recorded and analysed using mixed models, line × tester analysis and stability tools (AMMI and GGE biplots). Significant genetic variation ( p ≤ 0.05) was detected among genotypes, environments and their interactions. Additive genetic effects were predominant, as indicated by high Baker's ratios (> 0.70) and narrow‐sense heritability estimates (0.71–0.95), highlighting the importance of general combining ability (GCA) in hybrid prediction. Several lines, including CKDHL141370, CKDHL142389 and CKDHL143580, exhibited favourable GCA effects for grain yield and earliness, while also contributing to resistance against turcicum leaf blight and grey leaf spot. The top‐performing hybrids, such as (CKDHL120312/CML312)//CKDHL142124, CKDHL143094 and CKDHL142908, yielded 7.4–8.2 t ha ⁻¹ , representing a 29%–79% advantage over commercial checks. GGE biplot analysis explained 71.27% of the variation and identified several hybrids combined high yield with stability across environments. Overall, these results demonstrate that integrating line × tester and GGE biplot approaches is highly effective for identifying superior parental lines and predicting their hybrid performance. The identified superior hybrids warrant further validation in on‐farm trials for potential commercialization to enhance maize productivity and resilience in sub‐Saharan Africa.

Low grain yield and recurrent drought remain major constraints to maize production in Sub-Saharan Africa (SSA), underscoring the urgent need for hybrids with improved resilience. The use of ex-PVP (expired Plant Variety Protection) temperate maize lines is important to improve yield potential and genetic diversity in tropical adapted lines. In this study, 15 temperate introgressed lines and 6 tropical lines were crossed with eight single cross testers to generate 168 testcross hybrids. All genotypes were evaluated in 8 sites, with 6 well-watered (WW) and 2 drought-stressed (DS) conditions in Kenya. Analysis showed significant genotypic and site effects for grain yield (GY) and other agronomic traits, with strong genotype × environment interactions under drought. General combining ability (GCA) and specific combining ability (SCA) were important, with additive variance dominating under both WW and DS. Temperate introgression (TI) improved yield performance in most tropical backgrounds, with hybrids from TI outperforming tropical hybrids by 1.20–2.04 t/ha under optimal conditions and yielding similarly under drought stress (TI, 1.88 t/ha; tropical, 1.89 t/ha). Hybrids showed up to 74% reduction in GY, 22% lower PH, 18% lower EH, and a 72% increase in ASI under DS compared to WW. Lines L2, L10, L16 and testers T1, T2, T3, T4, T7 showed consistently positive GCA for GY and shorter anthesis–silking interval, suggesting they can be used in breeding programs to develop high yielding drought tolerant hybrids suitable for sub-Saharan Africa. These findings emphasize the value of ex-PVP lines as source to enhance yield in tropical adapted germplasm. Both additive and non-additive gene action are important, combining pedigree methods with genomic selection for additive effects along with strategic heterosis use can accelerate the development of high-yielding, climate-resilient maize hybrids for stress-prone regions of SSA.

A systematic evaluation of maize hybrid performance and combining ability was conducted to enhance resistance to maize lethal necrosis (MLN), drought tolerance, and grain yield (GY) in eastern and southern Africa. Thirty-eight early- to intermediate-maturing maize inbred lines, including MLN-tolerant and high-yielding genotypes with drought tolerance and resistance to multiple foliar and insect pests, were crossed with 29 single-cross testers to generate 437 testcross hybrids. These hybrids were evaluated under managed MLN inoculation, drought stress, and optimum conditions across multiple locations. Continuous variation in GY, disease severity, and agronomic traits confirmed quantitative inheritance, with strong positive correlations between GY and ears per plant and negative correlations between MLN severity and yield. Variance analyses revealed highly significant genotypic and genotype × environment interactions, with additive effects predominating across environments (Baker’s ratios 0.85–0.99; heritability 0.69–0.88), supporting effective selection based on general combining ability (GCA). Superior MLN-tolerant hybrids, such as (CKLMARSI0037/CKLTI0139)//CKDHL120312, achieved up to 5.75 t ha ⁻ ¹ under MLN, exceeding commercial checks by over fivefold. Under optimum and drought conditions, top hybrids maintained high yield, foliar disease resistance, short anthesis–silking intervals, and delayed senescence. Specific combining ability (SCA) effects highlighted stress-specific non-additive interactions, particularly under drought, underscoring the need for targeted parental selection. GCA analyses identified across environment and environment-specific favorable parents, including CKDHL120312, CKDHL140910, CKLMARSI0037/CKLTI0139, and CML322/CML543, while GGE biplots confirmed tester discrimination and representativeness. These findings demonstrate that integrating MLN resistance, drought tolerance, and high yield is achievable without compromising other agronomic performance. The study provides a robust framework for selecting elite parents and testers, exploiting additive and non-additive genetic effects, and developing resilient, high-performing maize hybrids for sub-Saharan Africa.

Key message: Sparse testcrossing with 3-5 testers enhances genetic gain in hybrid breeding programs , offering a practical balance of simple testcross designs , resource efficiency , and increased prediction accuracy for general combining ability. Sparse testcrossing is an effective strategy for increasing both short- and long-term genetic gain in hybrid breeding programs. Maize hybrid breeding programs aim to develop new hybrid varieties by crossing genetically distinct parents from different heterotic pools, exploiting heterosis for improved performance. The programs typically consist of two main components: population improvement and product development. The population improvement component aims to enhance the heterotic pools through reciprocal recurrent selection based on general combining ability (GCA). However, especially in the early stages of testing, evaluating large numbers of hybrid combinations to estimate GCA is impractical due to considerable logistical challenges and costs. Therefore, breeders often evaluate the initial population of selection candidates using only a single tester to narrow down the candidate pool before further evaluation. Using a single tester, however, may not adequately represent the heterotic pool, leading to inaccurate GCA estimates and suboptimal selection decisions. To address this, we propose sparse testcrossing for early-stage testing, where subsets of candidate genotypes are testcrossed with different testers, connected through a genomic relationship matrix. We conducted stochastic simulations to compare various sparse testcrossing designs with a conventional testcross strategy using a single tester over 15 cycles of reciprocal recurrent genomic selection. Our results show that using 3–5 testers, sparsely distributed among full-sibs, sparse testcrossing offers breeders a practical balance between simple testcross designs, resource efficiency, and increased prediction accuracy for GCA, ultimately resulting in increased rates of genetic gain.

Corrigendum on: Manigben KA, Beyene Y, Chaikam V, Tongoona PB, Danquah EY, Ifie BE, Aleri I, Chavangi A, Prasanna BM and Gowda M (2024). Testcross performance and combining ability of intermediate maturing drought tolerant maize inbred lines in Sub-Saharan Africa. Front. Plant Sci. 15:1471041. doi: 10.3389/fpls.2024.1471041 In the published article, there was an error in Figure 1 and 3 as published. Figures 1 and 3 were interchanged. The corrected Figure 1 and its caption are shown below: Figure 1. Frequency distribution of grain yield, anthesis date, silking date, anthesis–silking interval, plant height, and ear height recorded under managed drought and optimum conditions. Blue color denotes traits measured under drought stress and orange color denotes trait performance under optimum condition.The corrected Figure 3 and its caption are shown below:Figure 3. Proportional contribution of GCA-Line, GCA-Tester, and SCA-Line by Tester interaction to total genotype variance of testcrosses for GY and other secondary traits under drought (DS), optimum (Opt), and across all environments (ACR) conditions. The lower bar denoted by red color indicates the GCA-Line; the middle bar denoted by sea blue represents the GCA-Tester, and the upper bar denoted by yellow indicates the SCA-Line by Tester interaction.

Maize ( Zea mays L.) is a vital staple food crop for millions of people in Sub-Saharan Africa, but its productivity is affected by fall armyworm ( Spodoptera frugiperda , FAW), a transboundary pest native to the Americas. This study aimed to (i) assess the severity of foliar and ear damage due to FAW under natural and artificial infestation (ii) evaluate the grain yield (GY) performance and stability of three-way cross maize hybrids and agronomic traits across maize growing locations in East Africa. A total of 84 hybrids were evaluated over two years across 15 locations under natural and/or artificial infestation of FAW. The phenotypic coefficient of variation was higher than genotypic coefficient of variation for all traits in artificial and natural infestation experiments. Experimental hybrids were tolerant to moderately susceptible under artificial infestation as well as under natural infestation. GY was positively correlated with ears per plant (EPP) ( r = 0.37, p ≤ 0.001) and grain moisture at harvest MOI ( r = 0.27, p ≤ 0.05) and negatively correlated with anthesis silking interval (ASI) ( r = -0.31, p ≤ 0.01), ear Aspect (EA) ( r = -0.57, p ≤ 0.001) and foliar damage at 21 days after infestation (FD3) ( r = -0.27, p ≤ 0.05) under artificial infestation. Under natural infestation GY was positively correlated with anthesis date (AD) ( r = 0.33, p ≤ 0.001), plant height (PH) ( r = 0.43, p ≤ 0.001), and MOI ( r = 0.43, p ≤ 0.001), while negative correlations were observed with (ASI) ( r = -0.42, p ≤ 0.001), EA ( r = -0.58, p ≤ 0.001), ear rot (ER) ( r = -0.25, p ≤ 0.05), and gray leaf spot (GLS) ( r = -0.43, p ≤ 0.001). Three experimental hybrids CKHFAW180299, CKHFAW190474 and CKHFAW190529 were among the highest yielding genotypes under both artificial and natural infestation while CKHFAW190386 and CKHFAW190477 exhibited the highest yield stability. These hybrids are promising candidates for national performance trials and future commercialization as FAW tolerant hybrids in East Africa.

Key message: The relatedness between the genotypes of the training and the testing set using sparse phenotyping experiments helps optimize the line allocation by utilizing the relationship measurements to reduce cost without compromising the genetic gain. The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing sets (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, and balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs.