Biswanath Das

Monitoring genetic gain is critical for evaluating breeding program performance. This study assessed genetic trends in the Zambia national maize breeding program using historical data (2001–2017) from 2225 hybrids tested across years and locations. Best linear unbiased estimates (BLUEs) were calculated, and genetic trends were determined by regressing entry means on first-year testing data. Mean heritability was moderate for grain yield, plant height, and ear height, and high for anthesis and silking dates, indicating strong reliability for flowering traits. Significant positive genetic gains were observed for most traits except days to silking. Grain yield (GY) increased at 0.021 t ha⁻¹ per year (0.85% annually), reflecting progress but remaining below levels required to meet regional future production demands. Plant and ear height increased by more than 1.3 cm annually, suggesting directional selection for taller plant architecture. Grain texture declined by 1.28% per year, indicating a shift toward flint-type kernels. Anthesis date and ears per plant showed minimal genetic variation. Regression models explained more than 15% of the total variation in plant height, ear height, ear number, and grain texture, confirming consistent genetic progress. Although measurable gains were achieved, the study’s baseline indicates that accelerating yield improvement will require rapid-cycle breeding, enhanced trait heritability, modern breeding tools, and a strategic reallocation of resources to sustain long-term impact.

Monitoring genetic gain is critical for evaluating breeding program performance. This study assessed genetic trends in the Zambia national maize breeding program using his-torical data (2001–2017) from 2,225 hybrids tested across years and locations. Best linear unbiased estimates (BLUEs) were calculated, and genetic trends were determined by re-gressing entry means on first-year testing data. Mean heritability was moderate for grain yield, plant height, and ear height, and high for anthesis and silking dates, indicating strong reliability for flowering traits. Significant positive genetic gains were observed for most traits except days to silking. Grain yield increased at 0.021 t ha⁻¹ per year (0.85% annually), reflecting progress but remaining below levels required to meet future production demands. Plant and ear height increased by more than 1.3 cm annually, suggesting directional selection for taller plant architecture. Grain texture declined by 1.28% per year, indicating a shift toward flint-type kernels. Anthesis date and ears per plant showed minimal genetic variation. Regression models explained over 15% of the total variation for plant height, ear height, ear number, and grain texture, confirming consistent genetic progress. Although measurable gains were achieved, accelerating yield improvement will require rapid-cycle breeding, enhanced trait heritability, modern breeding tools, and strategic reallocation of resources to sustain long-term impact.

Assessing genetic gains from historical data provides insights to improve breeding programs. This study evaluated the Mozambique National Maize Program’s (MNMP’s) genetic gains using data from advanced germplasm trials conducted at 21 locations between 2014 and 2020. Genetic gains were calculated by regressing the genotypic best linear unbiased estimates of grain yield and complementary agronomic traits against the initial year of genotype evaluation (n = 592). The annual genetic gain was expressed as a percentage of the trait mean. While grain yield, the primary breeding focus, showed no significant improvement, significant gains were observed for the plant height (0.67%), ear height (1.74%), ears per plant (1.31%), ear position coefficient (1.22%), and husk cover (4.7%). Negative genetic gains were detected for the days to anthesis (−0.5%), the anthesis–silking interval or ASI (−9.31%), and stalk lodging (−5.01%). These results indicate that while MNMP did not achieve the desired positive genetic gain for grain yield, progress was made for traits related to plant resilience, particularly the ASI and stalk lodging. MNMP should seek to incorporate new breeding technologies and human resources to enhance genetic gains for grain yield and other key traits in the maize breeding program, while developing and deploying high-yielding, climate-resilient maize varieties to address emerging food security challenges in Mozambique.

Key message: Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.

Breeding for nitrogen use efficiency (NUE) is important to deal with food insecurity and its effect on grain quality, particularly protein. A total of 1679 hybrids were evaluated in 16 different trials for grain yield (GY), grain quality traits (protein, starch and oil content) and kernel weight (KW) under optimum and managed low soil nitrogen fields in Kiboko, Kenya, from 2011 to 2014. The objectives of our study were to understand (i) the effect of low soil N stress on GY and quality traits, (ii) the relationship between GY and quality traits under each soil management condition and (iii) the relationship of traits with low-N versus optimum conditions. From the results, we observed the negative effects of low N on GY, KW and the percentage of protein content, and a positive effect on the percentage of starch content. The correlation between GY and all quality traits was very weak under both soil N conditions. GY had a strong relationship with KW under both optimum and low soil N conditions. Protein and starch content was significantly negative under both optimum and low-N conditions. There was no clear relationship among quality traits under optimum and low N, except for oil content. Therefore, it seems feasible to simultaneously improve GY along with quality traits under both optimum and low-N conditions, except for oil content. However, the negative trend observed between GY (starch) and protein content suggests the need for the regular monitoring of protein and starch content to identify varieties that combine both high GY and acceptable quality. Finally, we recommend further research with a few tropical maize genotypes contrasting for NUE to understand the relationship between the change in grain quality and NUE under low-N conditions.

Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F₃ biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h²) estimates were moderate with 0.37–0.45 for the individual F₃ populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6–10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14–40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F₃ populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.

Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.

Sub-Saharan Africa is facing food security challenges due, in part, to decades of soil nitrogen (N) depletion. Applying N fertilizer could increase crop yields and replenish soil N pools. From 2010 to 2015, field experiments conducted in Embu and Kiboko, Kenya and Harare, Zimbabwe investigated yield and N uptake response of six maize (Zea maysL.) hybrids to four N fertilizer rates (0 to 160 kg N ha⁻¹) in continuous maize production systems. The N recovery efficiency (NRE), cumulative N balance, and soil N content in the upper 0.9 m of soil following the final harvest were determined at each N rate. Plant and soil responses to N fertilizer applications did not differ amongst hybrids. Across locations and N rates, NRE ranged from 0.4 to 1.8 kg kg⁻¹. Higher NRE values in Kiboko and Harare occurred at lower post-harvest soil inorganic N levels. The excessively high NRE value of 1.8 kg kg⁻¹at 40 kg N ha⁻¹in Harare suggested that maize hybrids deplete soil inorganic N most at low N rates. Still, negative cumulative N balances indicated that inorganic soil N depletion occurred at all N rates in Embu and Harare (up to − 193 and − 167 kg N ha⁻¹, respectively) and at the 40 kg N ha⁻¹rate in Kiboko (− 72 kg N ha⁻¹). Overall, maize N uptake exceeded fertilizer N applied and so, while yields increased, soil N pools were not replenished, especially at low total soil N levels (< 10,000 kg N ha⁻¹in top 0.9 m).

Understanding the genetic basis of maize grain yield and other traits under low-nitrogen (N) stressed environments could improve selection efficiency. In this study, five doubled haploid (DH) populations were evaluated under optimum and N-stressed conditions, during the main rainy season and off-season in Kenya and Rwanda, from 2014 to 2015. Identifying the genomic regions associated with grain yield (GY), anthesis date (AD), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear position (EPO), and leaf senescence (SEN) under optimum and N-stressed environments could facilitate the use of marker-assisted selection to develop N-use-efficient maize varieties. DH lines were genotyped with genotyping by sequencing. A total of 13, 43, 13, 25, 30, 21, and 10 QTL were identified for GY, AD ASI, PH, EH, EPO, and SEN, respectively. For GY, PH, EH, and SEN, the highest number of QTL was found under low-N environments. No common QTL between optimum and low-N stressed conditions were identified for GY and ASI. For secondary traits, there were some common QTL for optimum and low-N conditions. Most QTL conferring tolerance to N stress was on a different chromosome position under optimum conditions.

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10⁻⁶). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers’ specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.