Plant Breeding Programs Research Articles

Rate of double reduction and genetic variability in yield, quality, and senescence related traits in tetraploid potato (Solanum tuberosum L.).

The amount of genetic variability is the foundation for genetic change in any plant breeding program, and the amount of double reduction can influence genetic gain and the amount of future genetic diversity in polyploid species. Our study investigates these factors using variance components analysis on a dataset comprising 13,131 potato breeding lines and phenotypic data from Scandinavian environments spanning 17 years (2003 to 2021). Pedigree information was used in quantitative genetic models to estimate additive genetic variance and the relative importance of additive and non-additive genetic variance. We used two models, a baseline model (M1) without effects due to specific combining ability (SCA) and M2 (including SCA due to interaction between parental genomes). Two cross-validation (CV) schemes [5-Fold and leave-one-breeding-cycle-out (LBCO)] were used to evaluate the prediction ability (PA) of each model. We estimated the rate of double reduction phenomenon (DRP) by determining the rate best fitting the data using a marginal likelihood approach. Our findings showed a wide range of variation in different traits, with very large proportion of additive genetic variance in dry matter content (DMC), but intermediate additive genetic variance for relative yield (RY), germination (GR), and withering (WNG). All traits showed modest non-additive genetic variance. Furthermore, genotype x environment interaction played a significant role in trait variability but is still much smaller than the additive genetic variance. After using different DRP rates, we found that a model with a 0.05 DRP rate provided the best fit to the data. Heritability estimates indicated a strong genetic basis for DMC, while other traits showed more moderate heritability, which shows contributions from both additive and interaction factors. Model comparison by 5-Fold CV and LBCO and the log likelihood ratio test (LRT) highlighted the importance of considering SCA when capturing trait variability. In 5-Fold CV, PA ranged from 0.296 to 0.812 in M1 and 0.300 to 0.813 in M2. Under LBCO CV, PA ranged from 0.180 to 0.726 in M1 and 0.180 to 0.728 in M2. However, an increase in PA in Model 2, which incorporates SCA, compared to Model 1, can be attributed to the inclusion of SCA effects. Furthermore, the LRT results indicated a highly significant difference between the models. CV and LRT suggest the need for genetic models that account for both additive and SCA effects. Our analysis also showed that genotype x environment interactions should be accounted for in order to maximize the accuracy of predicted breeding values of tetraploid potato clones. The rate of double reductions was small and insignificant.

Influence of parental background on growth, fruit size and shelf life in F1 NIL Sletr1-2 tomatoes

Sletr1-2 is a tomato mutant known for its extended fruit shelf life, making it a valuable genetic resource for plant breeding programs. However, its small fruit size limits its commercial viability. To address this limitation, NIL Sletr1-2 has been developed, integrating the long shelf-life trait of Sletr1-2 with improved fruit size, offering new opportunities for commercial tomato production and genetic improvement. The objective of this study was to identify and characterize the growth, and fruit shelf life of three generations of F1 NIL-Sletr1-2 tomatoes generation. NIL Sletr1-2 tomatoes were crossed with three commercial tomato varieties, namely Costoluto, Oxheart, and Sart Roloise to obtain the F1 generation. The results showed that there were differences in growth and yield characteristics of the three generations of F1 NIL Sletr1-2. NIL Sletr1-2 crossed with Oxheart produced the tallest plants and the most flowers, but lower leaf number, while the largest fruit was obtained from NIL Sletr1-2 crossed with Sart Roloise. NIL Sletr1-2 crossed with Sart Roloise and Oxheart had a longer fruit shelf life, which was 6.47 and 6.60 days longer than Costoluto. The addition of exogenous ethylene did not significantly affect the decrease in fruit shelf life of the three F1 NIL Sletr1-2 generations.

Evaluation of salinity tolerance of Yemeni chilli pepper genotypes during gemination by using different statistically models

Evaluating the genotypes of vegetables is a critical component in establishing effective plant breeding programs. In this study, nine genotypes of Yemeni Capsicum spp. were collected from various regions in Yemen to assess their germination capabilities under different salinity levels (0, 50, 100, 150, 200, and 250 mM). The experiment was conducted using a factorial completely randomized design (CRD) with three replicates. Results indicated that increasing salinity levels led to a gradual decline in germination percentage (GRP), mean germination rate (MGR), germination time (MGT), and seedling dry matter (DM%). Additionally, variations in the genotypes’ responses to salt stress were evaluated using four models: the slope of the regression line (b), the integrated evaluation approach (DV), Principal components, and the genotypes’ salinity susceptibility index (GSSI). All the classified of genotypes was different by analysis models. Based on the integrated value (DV), the genotypes were classified into four sensitivity categories: resistant (A, D, and G), moderately resistant (F and V2), sensitive (S and Z), and highly sensitive (H and V3) to salinity stress. The findings demonstrate that the slope of the regression line is a reliable indicator for assessing genotype sensitivity to salinity, aligning consistently with the integrated value model (DV). The insights gained from this research are expected to significantly inform breeding strategies aimed at developing salt-tolerant chilli pepper cultivars, which are essential for successful cultivation in challenging environmental conditions.

Accuracy of Multi‐Environmental Trials in Predicting New Environments Using Different Approaches Based on Environmental Covariates: A Case in Barley (Hordeum vulgare L.) Breeding

ABSTRACTOne of the current innovations in predicting genotype performances in a target population of environments is integrating environmental covariates (ECs) into multi‐environment trial (MET) data analysis. In this study, a MET data set of a barley (Hordeum vulgare L.) breeding program in the years 2016 and 2017 was used. We evaluated and compared different approaches of using ECs in predicting genotype performances into new environments. The comparison was done using the mean squared error of predicted differences (MSEPD) under different linear mixed models. The MSEPD was computed for the new environments using a cross‐validation mechanism that drops out one environment at a time. Our results show that models with ECs resulted in smaller MSEPD compared with the model without ECs. Among the different approaches, the reduced rank regression approach with one component resulted in the smallest MSEPD followed by fitting both the first and the second synthetic covariates of the extended Finlay–Wilkinson regression. Overall, there is a potential gain in predictive accuracy in MET with integrating ECs in plant breeding programs.

Development of Low Erucic Acid Content Rapeseed Mutant Variety BINA Sarisha12

Any plant breeding program must have genetic variability, and mutagenesis is a proven method of creating new variability. Gamma irradiation was used to create novel genetic variability in rapeseed at different doses. Recently released new rapeseed (Brassica napus) mutant variety, BINA sarisha12 developed from popular variety Binasarisha-9 by gamma irradiation. Though Binasarisha-9 is a high yielding variety but duration is around 90 days and erucic acid content is 39.14%. For developing early maturing and low erucic acid content new variety, initially the seeds of Binasarisha-9 were exposed to different doses of gamma rays (550, 650, 750, 800 and 900 Gy) and M1 was grown in 2013-14. Irradiated populations were evaluated in M2, M3 and M4 generations during 2014-15, 2015-16 and 2016-17, respectively. Selected mutants carried out of various types of yield trial in 2017-18, 2018-19 and 2019-20 in experimental farms and in the farmers’ fields also. Performance of selected three mutants, derived from 650 and 850 Gy of gamma rays (one from 650 and two from 850 Gy), were found to be superior to other mutant lines and mother variety Binasarisha-9. The mutant RM-005 grown in various places around the country following researcher and farmer’s management practices during 2020-21 and 2021-22. Comparative assessment done in respect of seed yield, maturity period, plant height, number of primary branches plant-1, number of siliqua plant-1, number of seeds siliqua-1 etc. Reactions to major disease (Alternaria blight), insect-pest (aphid) infestation and analyzing fatty acid profile also studied. Based on the superior performance of RM-005, like as, tolerant to Alternaria blight, showed the lower incidence of aphid infestation and low erucic acid content than the check variety, National Seed Board of Bangladesh registered this mutant as an improved rapeseed variety named BINA sarisha12.

Tester selection for combining ability estimation of storage root yield and sweetpotato virus disease in sweetpotato breeding

General combining ability (GCA) is the major selection criterion for new sweetpotato (Ipomoea batatas) parents in a reciprocal recurrent selection (RRS) scheme. Here we aimed to estimate GCA and specific combining ability (SCA) by using 16 potential testers involved in an 8 × 8 partial diallel and propose a procedure to identify testers in sweetpotato breeding. Data on storage root yield in tons per hectare (rytha), and sweetpotato virus disease (vir2) from 64 families (1,913 clones) were collected in five trials at two locations in Uganda. The estimates of the female GCA accounted for the largest additive genetic variation for storage root yield compared to the male GCA for both traits. Mid-parent heterosis ranged from − 6.2 to 7% for rytha, and − 1.1 to 1.3% for vir2 in the progeny families. A stepwise procedure to identify testers top-ranked ‘NASPOT 7’ as a dual tester for both traits. Besides this parent, ‘Ejumula’ and ‘NASPOT 10 O’ for rytha, and ‘NASPOT 1’, ‘NK259L’, ‘SPK004’, and ‘NASPOT 11’ for vir2 are particularly suitable as respective single-trait testers. Testers are important in many plant breeding programs to enhance efficiency of RRS, and thus other crop species might benefit from the strategy and methods applied herein.

Applications of Artificial Polyploid Induction for Genetic Improvement of Plants

Polyploidy is the presence of more than two homologous sets of chromosomes. It has been one of the strong driving forces for higher plant evolution and also significant transforming tool for plant breeding programs. The review deals with the involvement of natural and artificial polyploids in enhancing traits and diversifying the species of plants. Artificial polyploidy induction (APPI) is mainly done using antimitotic agents like colchicine, trifluralin, and oryzalin. This has been very highly utilized for improving yield and tolerance to biotic and abiotic stresses. Besides, it has been utilized in aesthetic and economic traits in crops, medicinal, and ornamental plants. In many cases, polyploid organisms show better vigor, bigger organs, more biomass, and higher primary and secondary metabolite production. Ornamental plants tend to produce bigger flowers with better fragrance, color, and vase life. The chromosome doubling alters the phytochemical profile and increases the content of desired compounds in medicinal plants. Tissue culture methods for polyploid induction are efficient, and direct (chromosome counting, flow cytometry) and indirect (morphological and anatomical markers) techniques are available for ploidy determination. However, optimizing the type, dosage, and duration of antimitotic agents is critical for successful polyploid breeding. Reduced fertility, a frequent consequence of polyploidy, facilitates the production of seedless fruits, while genome doubling in hybrids restores fertility for further breeding applications. This paper reports recent advances in polyploidy research and its application in creating custom-designed plants with higher marketability, thus emphasizing the economic potential of APPI. Genetic diversity and advanced protocols in polyploidy manipulation provide great opportunities for improving plant yield, quality, and stress tolerance, which will ensure its place as a cornerstone of modern plant breeding.

Towards Streamlining the Choice of Crossing Combinations in Plant Breeding by Integrating Model-Based Recommendations and Plant Breeder’s Preferences

Selecting crossing combinations crucial for successfully developing new improved crop varieties and genomic data from DNA markers have become invaluable for guiding plant breeders in evaluating and choosing promising crosses between potential parents. However, navigating the vast array of thousands of possible parental combinations, even with extensive genomic information, can be challenging, even for experienced breeders with deep knowledge of their crop’s gene pool. This case study aimed to evaluate the effectiveness of a recommender system to support plant breeders in this complex decision-making process. It took a retrospective approach, analyzing selection decisions made by an experienced breeder across several thousand potential crossing combinations over six years. The results indicated that a recommender system could significantly reduce the time and effort needed to identify promising crosses aligned with the breeder’s preferences. However, active feedback from the breeder to the recommender system appeared to be essential for achieving a satisfactory prediction. Integrating model-based recommendations and plant breeder’s preferences in a recommender system featuring such a reciprocal fine-tuning scheme, where the breeder actively provides feedback to the machine in the style of hybrid human–artificial intelligence, represents one step towards streamlining the choice of crossing combinations in plant breeding programs.

Integrated characterization of Greek fennel genotypes through morpho‐agronomical characteristics, yield components and phytochemical compounds

AbstractBackgroundFennel (Foeniculum vulgare Mill.) is a widely cultivated vegetable and aromatic‐medicinal plant. In this research, field studies assessed 12 fennel genotypes of diverse origin, comprising 10 Greek accessions and two European commercial varieties, focusing on their morpho‐agronomical traits, phenolic compounds and antioxidant activity in the aboveground biomass. Emphasis gave on the post‐harvest determination of seed yield and essential oil's (EO's) profile for each genotype.ResultsMultivariate data analysis on both morpho‐agronomical descriptors and EO's components demonstrated clear distinction of Greek fennel accessions (groups I and III) from commercial varieties (group II). Accessions of groups I and III were ranked with intermediate values of height, foliage density, leaf length, green color intensity and primary canopy diameter. In addition, fennel accessions differentiated in their EO's yield (ranged from 2.2% to 7.8%), although they exhibited similar qualitative profile. The main EO's compound of group I was trans‐anethole, estragole and fenchone for group III, while commercial varieties characterized by high concentration in trans‐anethole and fenchone. Variations among genotypes were also demonstrated in the content of total phenolics (7.0–11.6 mg g−1 on dry weight basis, dw) and flavonoids (5.9–12.4 mg g−1 dw), while the antioxidant activity determined by ABTS test, varied from 9.0 to 16.2 mg Trolox equivalents g−1 dw.ConclusionThe observed variability in morpho‐agronomical descriptors and phytochemical profile among fennel genotypes recorded for the first time the potential of Greek native genetic material. The data of this research provides useful information for direct future uses in pharmaceutical industry and for applications in plant breeding programs, aiming to produce new type of fennel varieties.

Cost-Effective Detection of SNPs and Structural Variations in Full-Length Genes of Wheat and Sunflower Using Multiplex PCR and Rapid Nanopore Kit.

The rapid identification of allele variants in target genes is crucial for accelerating marker-assisted selection (MAS) in plant breeding. Although current high-throughput genotyping methods are efficient in detecting known polymorphisms, they are limited when multiple variant sites are scattered along the gene. This study presents a target amplicon sequencing approach using Oxford Nanopore Technologies (ONT-TAS) to rapidly sequence full-length genes and identify allele variants in sunflower and wheat collections. This procedure combines multiplex PCR and a rapid sequencing kit, significantly reducing the time and cost compared to previous methods. The efficiency of the approach was demonstrated by sequencing four genes (Ahasl1, Ahasl2, Ahasl3, and FAD2) in 40 sunflower genotypes and three genes (Ppo, Wx, and Lox) in 30 wheat genotypes. The ONT-TAS revealed a complete picture of SNPs and InDels distributed over the individual alleles, enabling rapid (4.5 h for PCR and sequencing) characterization of the genetic diversity of the target genes in the germplasm collections. The results showed a significant diversity of the Ahasl1/Ahasl3 and Wx-A/Lox-B genes in the sunflower and wheat collections, respectively. This method offers a high-throughput, cost-effective (USD 3.4 per gene) solution for genotyping and identifying novel allele variants in plant breeding programs.

Genetic diversity and population structure analyses of tropical maize inbred lines using Single Nucleotide Polymorphism markers.

Analyses of the genetic distance and composition of inbred lines are a prerequisite for parental selection and to exploit heterosis in plant breeding programs. The study aimed to assess genetic diversity and population structure of a maize germplasm panel comprising 182 founder lines and 866 derived inbred lines using Single Nucleotide Polymorphism (SNP) markers to identify genetically unique lines for hybrid breeding. The founder lines were genotyped with 1201 SNPs, and the derived lines with 1484 SNPs. Moderate genetic variation, with genetic diversity ranging from 0.004 to 0.44 with a mean of 0.25, was recorded for the founder lines, while corresponding values of 0.004 to 0.34 with a mean of 0.13 were recorded for the derived lines. Heterozygosity values ranging from 0.00 to 0.24 and a mean of 0.08 were recorded for both lines. Of the SNP markers used, 82% of the 1201 markers and 84% of the 1484 markers exhibited polymorphism information content ranging from 0.25 to 0.50. Analysis of molecular variance revealed significant genetic differences (P ≤ 0.001) among and within populations in the founder and derived lines. Most detected variations, i.e., 97% and 88.38%, were attributed to within populations in the founder and derived lines, respectively. Population structure analysis identified three distinct subpopulations among founder lines and two among derived lines. Cluster analysis supported the population structure The following genetically distant founder and derived inbred lines were selected: G15NL337 and G15NL312 (Cluster 1), 15ARG152 and RGS-PL44 (Cluster 2), RGS-PL44 and 15ARG149 (Cluster 2), and RGS-PL33 and RGS-PL44 (Cluster 2), respectively. The selected lines are genetically distinct and recommended for marker-assisted hybrid maize breeding to exploit the frequency of beneficial alleles. This study provides valuable insights for maize breeding programs, enabling the exploitation of beneficial alleles and contributing to improved crop yields and food security through hybrid breeding.

Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost.

Conducting genomic selection in plant breeding programs can substantially speed up the development of new varieties. Genomic selection provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative. Despite the availability of new technologies, the cost of large-scale genotyping remains a major limitation to the implementation of genomic selection. We suggest to combine pooled genotyping with population-based imputation as a cost-effective computational strategy for genotyping SNPs. Pooling saves genotyping tests and has proven to accurately capture the rare variants that are usually missed by imputation. In this study, we investigate adding iterative coupling to a joint model of pooling and imputation that we have previously proposed. In each iteration, the imputed genotype probabilities serve as feedback input for adjusting the per-sample prior genotype probabilities, before running a new imputation based on these adjusted data. This flexible setup indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take advantage of the strengths in both pooling and imputation when an appropriate set of reference haplotypes is available for imputation. The iterations improve greatly upon the initial genotype predictions, achieving very high genotype accuracy for both low and high frequency variants. We enhance the average concordance from 94.5% to 98.4% at limited computational cost and without requiring any additional genotype testing.

Balancing Sensitivity and Specificity Enhances Top and Bottom Ranking in Genomic Prediction of Cultivars.

Genomic selection (GS) is a predictive methodology that is revolutionizing plant and animal breeding. However, the practical application of the GS methodology is challenging since a successful implementation requires a good identification of the best lines. For this reason, some approaches have been proposed to be able to select the top (or bottom) lines with more Precision. Despite the varying popularity of methods, with some being notably more efficient than others, this paper delves into the fundamentals of these techniques. We used five models/methods: (1) RC, known as the Bayesian Best Linear Unbiased Predictor (GBLUP); (2) R, which is like RC but uses a threshold; (3) RO, Regression Optimum, that leverages the RC model in its training process to fine-tune the threshold; (4) B, Threshold Bayesian Probit Binary model (TGBLUP) with a threshold of 0.5 to classify the cultivars as top or non-top; (5) BO is the TGBLUP but the threshold used is an optimal probability threshold that guarantees similar Sensitivity and Specificity. We also present a benchmark comparison of existing approaches for selecting the top (or bottom) performers, utilizing five real datasets for comprehensive analysis. For methods that necessitate a rigorous tuning process, we suggest a streamlined tuning approach that significantly decreases implementation time without notably compromising performance. Our analysis revealed that the regression optimal (RO) method outperformed other models across the five real datasets, achieving superior results in terms of the F1 score. Specifically, RO was more effective than models R, B, RC, and BO by 60.87, 42.37, 17.63, and 9.62%, respectively. When looking at the Kappa coefficient, the RO model was better than models B, BO, R, and RC by 37.46, 36.21, 52.18, and 3.95%, respectively. In terms of Sensitivity, the RO model outperformed models B, R, and RC by 145.74, 250.41, and 86.20, respectively. The second-best model was the model BO. It is important to point out that in the first stage, the BO and RO approaches train a classification and regression model, respectively, to classify the lines as the top (bottom) or not the top (not the bottom). However, both the BO and RO approaches optimize a threshold in the second stage to perform the classification of the lines that minimize the difference between the Sensitivity and Specificity. The BO and RO methods are superior for the selection of the top (or bottom) lines. For this reason, we encourage breeders to adopt these approaches to increase genetic gain in plant breeding programs.

Pre-breeding in alfalfa germplasm develops highly differentiated populations, as revealed by genome-wide microhaplotype markers

Plant genebanks contain large numbers of germplasm accessions that likely harbor useful alleles or genes absent in commercial plant breeding programs. Broadening the genetic base of commercial alfalfa germplasm with these valuable genetic variations can be achieved by screening the extensive genetic diversity in germplasm collections and enabling maximal recombination among selected genotypes. In this study, we assessed the genetic diversity and differentiation of germplasm pools selected in northern U.S. latitudes (USDA Plant Hardiness Zone 7 or below) originating from Eurasian germplasm. The germplasm evaluated included four BASE populations (C0) from different geographical origins (Central Asia, Northeastern Europe, Balkans-Turkey-Black Sea, and Siberia/Mongolia), 20 cycle-one populations (C1) derived from each of the four BASE populations selected across five locations in the U.S. and Canada, and four commercial cultivars. Using a panel of 3,000 Diversity Array Technologies (DArTag) marker loci, we retrieved 2,994 target SNPs and approximately 12,000 microhaplotypes. Microhaplotypes exhibited higher genetic diversity values than target SNPs. Principal component analysis and discriminant analysis of principal components revealed significant population structure among the alfalfa populations based on geographical origin, while the check cultivars formed a central cluster. Inbreeding coefficients (FIS) ranged from − 0.1 to 0.006, with 27 out of 28 populations showing negative FIS values, indicating an excess of heterozygotes. Interpopulation genetic distances were calculated using Rho pairwise distances (FST adapted for autotetraploid species) and analysis of molecular variance (AMOVA) parameters. All BASE populations showed lower Rho values compared to C1 populations and check cultivars. AMOVA revealed that most of the genetic diversity was among individuals within populations, especially in BASE populations (92.7%). This study demonstrates that individual plants in BASE populations possess high genetic diversity, low interpopulation distances, and minimal inbreeding, characteristics that are essential for base-broadening selection. The populations developed in this project serve as valuable sources of novel alleles for North American alfalfa breeding programs, offering breeders access to diverse, regionally adapted pools for improving various alfalfa traits.

Agronomic and morphological evaluation of six genotypes and two hybrids of Poblano peppers in field conditions

Objective: to evaluate agronomic and morphological traits of six Poblano-type pepper genotypes, compared to a commercial hybrid and an experimental hybrid, in order to select genotypes with potential to continue with a plant breeding program. Design/ Methodology/ Approach: treatments and statistical model were arranged in randomized complete blocks, with eight treatments (six F2 genotypes, and two hybrids Carranza (commercial F1) and F402 (experimental F1). Treatments were analyzed with analysis of variance (p ≤ 0.05); then tested with Multiple Mean Comparison (Tukey, p ≤ 0.05) and Pearson Correlation Analysis. Results: no statistical differences were found in yield per plant (RPP), number of fruits per plant (NFP), average weight per fruit (PPF), plant height (AP), stem thickness (GT), leaf length (AL) and leaf width (AH). Regarding width at the base of the fruit (ABF) the hybrids were superior; in average width of the fruit the genotypes G4, G6 and the hybrids were better; in fruit length G2, G5 and G6 stood out; in calyx depth G1, G3 and G4; in length of the peduncle G4 was different from the others; and in thickness of the mesocarp (GM) the genotypes G2, G4, G6 and hybrids were superior. According to Pearson's correlation, the yield depended on NFP (0.66), MG (0.46), ABF (0.38), as it is shown by their coefficients. Limitations/ Implications of the study: F1 hybrids do include market features preferred by consumers and producers, since those hybrids were created to favor those traits; for this reason, those hybrids were compared to second generation (F2) genotypes obtained by directed manual pollination. Findings/ Conclusions: The evaluated G2, G4 and G6 second generation (F2) genotypes are highlighted for their agronomic potential compared even to the tested hybrids. So, their genetic potential is inferred, and could be useful to continue selecting them within a plant breeding program.

Application of ultra-weak photon emission imaging in plant stress assessment

The oxidative damage induced by abiotic stress factors such as salinity, drought, extreme temperatures, heavy metals, pollution, and high irradiance has been studied in Arabidopsis thaliana. Ultra-weak photon emission (UPE) is presented as a signature reflecting the extent of the oxidation process and/or damage. It can be used to predict the physiological state and general health of plants. This study presents an overview of a potential research platform where the technique can be applied. The results presented can aid in providing invaluable information for developing strategies to mitigate abiotic stress in crops by improving plant breeding programs with a focus on enhancing tolerance. This study evaluates the applicability of charged couple device (CCD) imaging in evaluating plant stress and degree of damage and to discuss the advantages and limitations of the claimed non-invasive label-free tool.

DETERMINATION OF MOLECULAR MARKERS BY RAPD TECHNIQUE ASSOCIATED WITH FOUR MELON GENOTYPES UNDER 50% IRRIGATION CONDITIONS

Genetic diversity is a practical and attractive method for finding drought tolerant genotypes. Therefore, the purpose of this investigation is to find single RAPD markers linked to studied traits as indicator of drought stress tolerant. Seven random primers were selected as molecular marker for drought stress tolerance among four melon genotypes. According to results, 69 polymorphic bands were obtained out of 72 bands. Jaccard coefficient was calculated to study genetic diversity and relationship among genotypes using unweighted pairwise group method with arithmetic mean (UPGMA). The range of genetic similarity ranged from 0.167 to 0.324. In addition, two separated groups with two genotypes in each were identified by phylogenetic tree in form of topology. In the context of the analysis of single marker, 14 RAPD markers were associated with the studied morphological traits, which make them valuable source for genetic expression in plant breeding programs.

Image-based yield prediction for tall fescue using random forests and convolutional neural networks.

In the early stages of selection, many plant breeding programmes still rely on visual evaluations of traits by experienced breeders. While this approach has proven to be effective, it requires considerable time, labour and expertise. Moreover, its subjective nature makes it difficult to reproduce and compare evaluations. The field of automated high-throughput phenotyping aims to resolve these issues. A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. This approach was used in the present study to assess the dry matter yield of tall fescue and its accuracy was compared to that of the breeder's evaluations, using field measurements as ground truth. RGB images of tall fescue individuals were processed by two types of predictive models: a random forest and convolutional neural network. In addition to computing dry matter yield, the two methods were applied to identify the top 10% highest-yielding plants and predict the breeder's score. The convolutional neural network outperformed the random forest method and exceeded the predictive power of the breeder's eye. It predicted dry matter yield with an R² of 0.62, which surpassed the accuracy of the breeder's score by 8 percentage points. Additionally, the algorithm demonstrated strong performance in identifying top-performing plants and estimating the breeder's score, achieving balanced accuracies of 0.81 and 0.74, respectively. These findings indicate that the tested automated phenotyping approach could not only offer improvements in cost, time efficiency and objectivity, but also enhance selection accuracy. As a result, this technique has the potential to increase overall breeding efficiency, accelerate genetic progress, and shorten the time to market. To conclude, phenotyping by means of RGB-based machine learning models provides a reliable alternative or addition to the visual evaluation of selection candidates in a tall fescue breeding programme.

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for viral disease resistance in maize (Zea mays L.).

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for viral disease resistance in maize (Zea mays L.).

Radicle emergence and seed imbibition tests as estimators of seed vigour in mexican Pepitilla maize landrace

Seed vigour of Mexican native maize was evaluated in 10 Pepitilla populations (PEP) and three improved varieties by radicle emergence vigour (REVT) and seed imbibition tests, in order to design a plant breeding programme to obtain native varieties with high seed vigour. As part of the REVT, protrusion percentage (PR), radicle emergence (RE), non-germinated seeds (NGS) and radicle length (RL) were measured. Additionally, the imbibition curve and the percentage of testa rupture were recorded. Radicle protrusion in Pepitilla seed lots was 73-94%; for improved varieties, it ranged from 69 to 94%. For 'PEP-2401NS', 'PEP-2402AG', 'PEP-2406GU' and 'PEP-2410RP', RE was equal to PR, so, neither of these genotypes produced a radicle < 2 mm; while, RE in 'V 236P' was 50% lower than PR. In addition, the R 2 of 0.9630 between RE and standard germination suggests that RE can be considered a reliable test to predict maize seed germination. For NGS, 'PEP-2402AG' and 'H-565' showed 6%, and RL in Pepitilla was three times greater than for the improved varieties. After 36 hours imbibition, an improved variety registered the highest water absorption (49.8%), but all Pepitilla landraces showed higher testa rupture. In general, the Pepitilla populations had a high seed-vigour due to the rapid RE and a greater RL.