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[This corrects the article DOI: 10.3389/fpls.2024.1471041.].

The use of hybrid vigor or heterosis in Coffea arabica L. cultivation has gradually been commercially explored worldwide since research has evolved in understanding this phenomenon and the vegetative propagation techniques or male-sterility making its use viable. Additionally, coffee producers adopting this technology have continuously increased. Therefore, we studied the existence and magnitude of heterosis and estimate the combining ability of parents in bi-parental crosses. The experiment was installed in 2019 using a randomized block design with three replications, with the experimental plot consisting of six plants. The experimental treatments consisted of 90 hybrids and 34 parental lines in which the grain yield in bags of processed coffee per hectare was evaluated according to the cumulative result of the first three harvests. Significant differences were observed between the 124 treatments, with the mean accumulated productivity values of the best hybrids surpassing those of the four most used commercial cultivars by more than 74 bags of coffee per hectare. The average yield mean heterosis was 64.2%, varying from − 26.1 to 184.4. Both the general combining ability (GCA) and the specific combining ability (SCA) were statistically significant, with the best-performing lines identified as potential parents being ‘Acauã Novo’, ‘IAC 125 RN’, ‘MGS Liberdade’, ‘Catiguá MG2’, and ‘Sarchimor MG 8840’. Promising hybrids for commercial exploitation were identified with a productive advantage of 30% relative to the best commercial standard cultivar, reinforcing the potential of this technology for C. arabica L. cultivation future.

This study aimed to assess the genetic basis and combining ability of 10 morphological traits in Indian mustard. The experiment involved eight parent lines and 28 crosses derived from a half-diallel mating design. Combining ability analysis is vital for identifying parents and hybrids with favorable genetic effects to enhance breeding efficiency. The study found significant variations across treatments, parents and parent vs. cross for all attributes related to seed yield. Some traits exhibited notable disparities between parents and crosses, underscoring the intricate genetic dynamics at play. The estimation of genetic components of variance underscored a predominant influence of non-additive gene action, especially in traits linked to yield. Specific combining ability (SCA) consistently surpassed general combining ability (GCA), underscoring the substantial role of non-additive genetic effects. Parental genotypes NPJ-194, DRMR-15-16, Kranti and NPJ-194 were identified as consistent and potent general combiners, indicating their potential to pass on favorable alleles to their offspring. Hybrid combinations such as SKJM-05 × Kranti, RW-85-59 × SKJM-05, and NPJ-194 × SKJM-05 exhibited notable GCA effects of parents, per se performance and SCA effects of hybrids for seed yield plant −1 . Heterosis breeding proved to be a viable strategy, with crosses such as RW-85-59 × SKJM-05, RW-85-59 × Giriraj, RW-85-59 × PHR-2, DRMR-15-16 × Giriraj, and SKJM-05 × PHR-2 exhibiting significant positive heterosis for OC over both mid-parent and better-parent values. Overall, this research provides valuable insights into the genetic basis of morphological traits in Indian mustard, offering strategic directions for focused breeding efforts and trait refinement.

Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.

Bambara groundnut ( Vigna subterranea (L.) Verdc.) is a highly nutritious legume crop supporting sustainable food systems in arid and semi-arid agroecologies. However, modern varieties with high yield and desirable product profiles are yet to be bred in sub-Saharan Africa (SSA). Therefore, this study aimed to determine the combining ability effects and gene action in Bambara groundnut for yield and related traits to identify the best parents and derived families for breeding and genetic advancement. Ten complementary and contrasting parents were selected and crossed using a 10 × 10 half-diallel mating design, and 45 progenies developed. The progenies and parents were field evaluated using a 5 × 11 alpha lattice design with two replications in two contrasting locations in South Africa. Data was collected on agronomic traits and subjected to statistical analyses to compute genetic parameters. The results revealed that the genotype × location interaction effect was significant ( P  < 0.001) for the studied agronomic traits. The general combining ability (GCA) effects were significant ( P  < 0.05) for the number of nodes per stem, number of secondary branches, number of pods per plant and seed length, while the specific combining ability (SCA) effects were significant ( P  < 0.05) for yield per plant and assessed traits. A relatively lower Baker’s ratio were recorded for most assessed traits at the F 1 , including grain yield per plant, indicating the preponderance of non-additive gene effects conditioning the traits. The parental lines such as ARC Bamb25, ARC Bamb8 and ARC Bamb55 recorded positive and desirable GCA effects for grain yield per plant and serve as suitable genetic resources for future breeding. The families ARC25 × ARC8, ARC44 × ARC9 and ARC6 × ARC9 had desirable SCA effects for grain yield per plant. Therefore, the newly developed crosses with high yield potential are recommended for selection and genetic advancement using pedigree or pure line selection methods for variety registration and commercialisation.

Drought is a major constraint on maize ( Zea mays L.) production and productivity in Sub-Saharan Africa (SSA). The increase in frequency and severity of drought, driven by climate change, is expected to worsen in the future. These occurrences are likely to adversely affect maize production and productivity, threatening the economic and social stability of millions of smallholder farmers. Understanding the genetics of hybrid performance under drought stress is crucial for designing breeding strategies to develop high-yielding hybrids. This study aimed to (i) evaluate the performance of three-way cross hybrids developed from elite inbred lines, including several drought-tolerant lines, using a line-by-tester mating design, and (ii) estimate the general combining ability (GCA) and specific combining ability (SCA) effects of the tropical maize inbred lines under managed drought and optimum conditions. A total of 265 maize inbred lines from the CIMMYT global maize breeding program were used as parents and crossed to six single cross testers to generate 795 testcross hybrids. These hybrids, along with six commercial hybrids as a check, were evaluated under managed drought and optimum conditions. Significant ( p < 0.001) variations were observed among genotypes and genotypes-by-environment interactions (GEIs) for grain yield and other traits. There was a preponderance of GCA variance (lines and tester) over SCA variance, indicating that additive effects were more important in determining grain yield and other key traits under both managed drought and optimum conditions. Ten inbred lines (S2_8, S10_1, S6_4, S10_14, S2_14, S10_15, S8_7, S2_3, S8_15, and S13_5) with desirable GCA effects for grain yield and other traits were identified. Fourteen testcross hybrids were identified with high grain yield and desirable agronomic traits under both drought and optimum conditions. The identified lines and hybrids are useful sources to be used in breeding and deploying as stress-tolerant hybrids. High correlations observed between observed and GCA-predicted hybrid performance suggest the possibility to evaluate more hybrids with fixed resources. The study demonstrates that it is feasible to obtain high-yielding and drought-tolerant lines and hybrids. These testcross hybrids should undergo rigorous on-farm trials to ensure consistent performance before commercialization and release. Deploying these hybrids could help in mitigating the effects of drought stress in SSA and contribute to improved maize productivity in the region.

Maize lethal necrosis (MLN) disease is a recent outbreak in eastern Africa and has emerged as a significant threat to maize production in the region. The disease is caused by the co-infection of Maize chlorotic mottle virus and any member of potyviridae family. A total of 28 maize inbred lines with varying levels of tolerance to MLN were crossed in a half-diallel mating design, and the resulting 340 F 1 crosses and four commercial checks were evaluated under MLN artificial inoculation at Naivasha, Kenya in 2015 and 2016 using an alpha lattice design with two replications. The objectives of the study were to (i) investigate the magnitude of general combining ability variance (σ GCA 2 ) and specific combining ability variance (σ SCA 2 ) and their interaction with years; (ii) evaluate the efficiencies of GCA based prediction and hybrid performance by means of a cross-validation procedure; (iii) estimate trait correlations in the hybrids; and (iv) identify the MLN tolerant single cross hybrids to be used as female parents for three-way cross hybrids. Results of the combined analysis of variance revealed that both GCA and SCA effects were significant (P < 0.05) for all traits except for ear rot. For MLN scores at early and late stages, GCA effects were 2.5–3.5 times higher than SCA effects indicating that additive gene action is more important than non-additive gene action. The GCA based prediction efficiency for MLN resistance and grain yield accounted for 67–90% of the variations in the hybrid performance suggesting that GCA-based prediction can be proposed to predict MLN resistance and grain yield prior to field evaluation. Three parents, CKDHL120918, CML550, and CKLTI0227 with significant GCA effects for GY (0.61–1.21; P  < 0.05) were the most resistant to MLN. Hybrids “CKLTI0227 × CML550”, “CKDHL120918 × CKLTI0138”, and “CKDHL120918 × CKLTI0136” ranked among the best performing hybrids with grain yield of 6.0–6.6 t/ha compared with mean yield of commercial check hybrids (0.6 t/ha). The MLN tolerant inbred lines and single cross hybrids identified in this study could be used to improve MLN tolerance in both public and private sector maize breeding programs in eastern Africa.

Geraniums (Pelargonium spp.) are ornamental plants that are widely popular because of their abundant flowering, color variability, different flower patterns, and ease of cultivation. Genetic breeding of this species aims to reduce the plant size and flower color. The objective of this study was to carry out morphoagronomic characterization of parents and F1 hybrids and to estimate the combined capacity and hybrid performance in the circulating diallel in F2 geranium (Pelargonium sp.). We obtained 18 and 275 plants from the F1 and F2 generations, respectively. Characterization of the parental genotypes and F1 and F2 hybrids was performed based on the descriptors for Pelargonium. Parents and F1 hybrids were grouped using the Tocher and UPGMA methods and diallel analyses in the F2 generation. The F1 hybrids G8, G11, and G17 exhibited color combinations suitable for commercialization and are promising for inclusion in breeding programs. The effects of general combining ability (GCA) and specific combining ability (SCA) were significant for these traits were also significant. The results showed the presence of both additive and non-additive genes. However, non-additive and dominant genes were predominant in most characteristics studied. Diallel analysis of the F2 hybrids revealed that the best hybrid combinations for reducing plant height were 14 × 11, 14 × 13, and 15 × 12. Therefore, the implementation and use of diallel analysis were efficient in selecting superior parental genotypes and producing hybrids with high yields.

Core Ideas Genomic prediction of hybrid wheat Introducing pedigree and environmental covariables Modeling genomic × environment interaction In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1–M5) under four cross‐validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat (Triticum aestivum L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments.

Thirty-four full-sib Pinus koraiensis families were used to evaluate and identify elite P. koraiensis material. Tree height (H) and diameter at breast height (dbh) were assessed. The results from variance analyses showed that familial variance sources for different traits in different growth years were extremely significantly different. The average phenotypic variation coefficients of H, dhb and volume (V) among families in different growth years ranged from 7.57 to 15.70, from 10.37 to 12.89 and from 24.44 to 28.13%, respectively. The family heritabilities of all traits ranged from 0.910 to 0.990, which are high values. A significant and positive correlation was observed among all traits, with values ranging from 0.43 to 0.99. According to the analyses of general and special combining ability, female parents F4 and F2 and male parents M7 and M13 had high levels of general combining ability for all three traits evaluated. Families PK05 (F9 × M14) and PK06 (F2 ×  M14) showed the highest and the lowest specific combining ability values in all traits evaluated. Using the comprehensive multiple-traits method to evaluate the families by traits in the 18th growth year at the rate of < 10%, we selected families PK40, PK05 and PK22 as elite families; the genetic gains for these families in H, dbh and V were 14.43, 11.29 and 24.72%, respectively. This study provides materials and basic theoretical knowledge that can be used to improve seed orchards and develop special hybrid seed orchards.