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Background: Genomic mating (GM) can effectively control the growth rate of inbreeding in population and achieve long-term sustainable genetic progress. However, the design of GM method and assessment of its effects during long-term selection have not been fully explored in beef cattle breeding. Results: In this study, we constructed a simulated population based on the real genotypes of Huaxi cattle, where five generations of simulated breeding were carried out using the genomic optimal contribution selection (GOCS), genetic algorithms strategy and three traditional mating strategies. During the breeding process, genetic parameters including average genomic estimated breeding value (GEBV), genetic gain values ( Δ G ), the rate of inbreeding values ( Δ F ) were calculated and compared across generations. Our results showed that the GM method could significantly improve the genetic gain while effectively controlling the inbreeding accumulation within the population. When using the GM method, there was an increase in genetic gain for Huaxi cattle ranging from 1.1% to 25.6% compared to traditional mating strategy, with inbreeding decreasing in the range of 5.8% to 36.2%. Validation using the real dataset from Huaxi cattle further confirmed our findings from the simulated study, offspring populations using the GM strategy exhibited a 7.3% increase in genetic gain compared to positive assortative mating. Conclusions: These findings suggest that the GM method shows potential for achieving sustainable genetic gain and could be utilized during long-term selection in beef cattle breeding. [ABSTRACT FROM AUTHOR]

ABSTRACT Check varieties are used in plant breeding and variety testing for a number of reasons. One important use of checks is to provide connectivity between years, which facilitates comparison among genotypes of interest that are tested in different years. When long‐term data are available, such comparisons allow an assessment of realized genetic gain (RGG). Here, we consider the question of how many check varieties are needed per cycle for a reliable assessment of RGG. We propose an approach that makes use of variance component estimates for relevant random effects in a linear mixed model and plugs them into an analysis of dummy datasets set up to represent the design options being considered. Our results show that it is useful to employ a larger number of checks and to keep the replacement rate low. Furthermore, there is intercycle information to be recovered, especially when there are few checks and replacement rates are high, so modelling the cycle main effect as random pays off. [ABSTRACT FROM AUTHOR]

Introduction: Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits. Methods: This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling). Results: The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme. Discussion: These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally. [ABSTRACT FROM AUTHOR]

The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre‐ and post‐weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS. [ABSTRACT FROM AUTHOR]

Genetic diversity and genetic gain are valuable parameters in plant breeding for assembling new cultivars. The presented study sought to identify the genetic diversity of the black soybean (Glycine soja L. Merrill) populations from crossbreeding and identify the broad sense heritability and genetic gain in yield-related traits. The set experiments proceeded in a randomized complete block design with three replications at two different environments/locations--Indramayu and Majalengka, Indonesia. The genetic material comprises 19 black soybean genotypes. The results showed that the genetic diversity in black soybean genotypes for grain yield and its attributes were broad, based on the principal component analysis, with a cumulative value of 80.55%. The environment greatly influenced heritability and genetic gains. High heritability and genetic gain were notable for seed length in the Majalengka location. In the Indramayu location, the demonstration of high heritability and genetic gains appeared for the number of fertile nodes and seed length. This information can serve as a basis for plant breeders to develop black soybean high-yielding cultivars in the future. [ABSTRACT FROM AUTHOR]

The development of sorghum breeding must continue with an assessment of genetic parameters in developing sorghum lines. Hence, the presented study sought to estimate the genetic variation, heritability, and gain in the selected F3 populations obtained by crossing the sorghum parental genotypes 'Bioguma-1' and 'Gando keta.' The study transpired from June to October 2022 in the experimental field of Citayam Depok, Indonesia. Plant materials consisted of 160 lines from 18 selected genotypes in F2 using an augmented experimental design, with 'Bioguma-1' and 'Gando keta' as checks and parents. Based on performance, the selection of sorghum lines G01 (4-1), G02 (1-5), G03 (3-3), G04 (3-4), G05 (3-6), and G06 (3-8) proceeded for evaluation in the next generation for seed weight traits and similarity traits to cultivar Bioguma-1. The predicted genetic gain in the F4 populations will further enhance seed weight compared with the F3 populations and parental lines by 33.3% and, consequently, the superior sorghum genotypes for releasing as new cultivars with high yield components, broader stem and panicle diameter, and shorter plant. [ABSTRACT FROM AUTHOR]

Rapid advances in genomics, phenomics, and molecular biology are accelerating crop breeding into the era of artificial intelligence (AI). Addressing the formidable challenge of managing the 'data deluge' is crucial for transitioning from disparate datasets to an integrated data infrastructure in AI-enabled plant breeding. Although AI has revolutionized various aspects of crop breeding, such as phenomics, variant calling models, gene discovery, genomic selection, and gene editing, there is a pressing need to synergize these components into an integrated breeding technology for future crop development. Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype–phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field. [ABSTRACT FROM AUTHOR]

The present article describes the development of an improved Pinus taeda clonal seed orchard adapted to the edaphoclimatic conditions of Uruguay. Initially, 2068 hectares distributed in nine companies were prospected, and 124 plus trees were identified based on growth, straightness, and health traits. These trees were clonally propagated via grafting to establish a clonal seed orchard. For the genetic evaluation of the orchard, two progeny tests were carried out in the Rivera and Paysandú municipalities. Quantitative genetic analyses allowed us to identify a simple genotype–environment interaction and an expected genetic gain for volumes of 17%, 13%, and 8% for selection intensities of 12%, 25%, and 50%, respectively. Moreover, the genetic diversity of the 124 clones of the orchard was assessed using 10 microsatellite markers. The fingerprinting profiles allowed us to identify a total of 224 alleles. The polymorphism information content of the different markers was in the range of 0.594 to 0.895. The combined probability of identity and probability of identity among siblings had a discrimination power of 8.26 × 10–14 and 5.91 × 10–5, respectively. Analysis of the genetic structure demonstrated that the seed orchard population was not structured by the supplier. [ABSTRACT FROM AUTHOR]

Among the diseases that affect passion fruit (Passiflora edulis Sims), fruit woodiness is considered the most economically important viral disease of the crop, causing great production losses worldwide. This study was developed to estimate the genetic parameters for resistance and agronomic performance, using the REML/BLUP (Restricted Maximum Likelihood/Best Unbiased Linear Prediction) procedure; and compare predicted genetic gains for resistance and agronomic traits, using five selection-index approaches, to determine the most appropriate method for reliably selecting superior genotypes for a new cycle of recurrent selection aimed at effectively increasing the concentration of favorable alleles in the population. The experiment was carried out in the municipality of Campos dos Goytacazes, Rio de Janeiro, Brazil, in a randomized block design consisting of 90 full-sib families (constituting the C0 population of recurrent selection), involving three replicates with three plants per plot. The following variables were evaluated: mean area under the disease progress curve, number of fruits per plant, total production per plant, fruit length, fruit diameter, average fruit weight, pulp weight, mesocarp thickness, soluble solids content, and pulp yield. Simultaneous selection of superior genotypes was carried out by the selection index approach using the Mulamba and Mock rank-sum index; the additive index; the multiplicative index; the mean rank index adapted from Mulamba and Mock; and the combined selection index. The additive index was the most consistent for the combined selection of the 36 genotypes for the evaluated traits, especially resistance, showing that these genotypes have high genetic potential to form the next population. [ABSTRACT FROM AUTHOR]

Mango stands as a critical tree crop with significant economic value on a global scale. This is attributed to the advantageous effects of its mineral content, abundance of vitamins, and biologically active compounds, all of which contribute to the overall health and well-being of consumers. Therefore, it needs special attention for its emergence through the phenotypic characterization of the existing varieties. To do this, we characterized agro-morphologically different mango varieties in some parts of Nigeria. Thus, the objective of this study was to analyze the genetic variation and morpho-agronomic traits of different mango varieties in Nigeria. Qualitative and quantitative data from mango leaves were recorded from 10 mature leaves and mango fruits in 4 States of different agroecological zones of Nigeria. The genetic variability, phenotypic and genotypic variances, coefficients of genotypic and phenotypic variation, broad sense heritability, and genetic advance, based on morpho-agronomic traits, were evaluated. Analysis of variance and descriptive analysis through the coefficient of variability revealed an extensive diversity and the prospect of genetic selection for profitable characters. Broad sense heritability for all the characters varied from 44.61% for petiole length to 98.48% for pulp weight and fruit weight, indicating moderate to high inheritability. Genetic advance as a percentage of the mean (GAM) was high in all the traits studied and ranged from 28.27% to 172.58%. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV), demonstrating the extent of environmental effects. Edible rate is significantly and positively corrected with fruit length, fruit width, fruit weight, peel weight, pulp weight, seed length, seed width, pulp%, and pulp thickness.

Abstract Background Genomic mating (GM) can effectively control the growth rate of inbreeding in population and achieve long-term sustainable genetic progress. However, the design of GM method and assessment of its effects during long-term selection have not been fully explored in beef cattle breeding. Results In this study, we constructed a simulated population based on the real genotypes of Huaxi cattle, where five generations of simulated breeding were carried out using the genomic optimal contribution selection (GOCS), genetic algorithms strategy and three traditional mating strategies. During the breeding process, genetic parameters including average genomic estimated breeding value (GEBV), genetic gain values ( $$\Delta G$$ Δ G ), the rate of inbreeding values ( $$\Delta F$$ Δ F ) were calculated and compared across generations. Our results showed that the GM method could significantly improve the genetic gain while effectively controlling the inbreeding accumulation within the population. When using the GM method, there was an increase in genetic gain for Huaxi cattle ranging from 1.1% to 25.6% compared to traditional mating strategy, with inbreeding decreasing in the range of 5.8% to 36.2%. Validation using the real dataset from Huaxi cattle further confirmed our findings from the simulated study, offspring populations using the GM strategy exhibited a 7.3% increase in genetic gain compared to positive assortative mating. Conclusions These findings suggest that the GM method shows potential for achieving sustainable genetic gain and could be utilized during long-term selection in beef cattle breeding.

The study of adaptability, stability, and productivity is essential for selecting and recommending superior genotypes. This fact is particularly the case for the introduction of common bean cultivars in Rio de Janeiro, Brazil, whose production is negligible and does not meet the internal demand. Thus, this study estimated genetic parameters and selection gains and undertake a simultaneous selection for adaptability, stability, and productivity in black bean genotypes via mixed models. The investigation was carried out in three municipalities in the state of Rio de Janeiro during three crop years. The trials were set up in a randomized block design with 11 genotypes and three replications. High mean heritability (81%) and selection accuracy (90%), as well as good selection prospects, were observed. Gains between 1.03 and 9.49% were achieved for grain yield. Simultaneous selection was efficient, indicating two black bean lines (CNFP 15290 and CNFP 15361) as the most productive, adaptable, and stable. As such, these lines have the potential to be released as new black bean cultivars for the state of Rio de Janeiro. [ABSTRACT FROM AUTHOR]

Abstract The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

Abstract Background Mating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones’ descent, the breeder’s control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs. Methods By computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony. Results SDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed. Conclusion The opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen’s spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose.

Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep. Until now, there has been no use of genomic selection in the German Merino sheep breeding programme. In this simulation study, different genomic selection strategies were compared with a reference scenario with a breeding value estimation based on pedigree BLUP. A simplified version of the German Merino sheep breeding programme, including a health and a production trait in the breeding goal, was simulated via the R package Modular Breeding Program Simulator (MoBPS). Real genotype data were used to create a population specific simulation. The reference scenario was compared with several alternative scenarios in which selection was based on single‐step GBLUP (ssGBLUP) breeding value estimation with varying genotyping strategies. In addition to scenarios in which all male and all male plus all female lambs were genotyped, scenarios with a preselection of lambs, that is only a certain proportion (top 25%, top 50%) genotyped, were simulated. The results revealed that genetic gain increased with increasing numbers of available genotypes. However, marginal gains decreased with increasing numbers of genotypes. Compared with the reference scenario, genotyping the top 25% of male lambs increased the genetic gain for the breeding ram population by 13% for both traits whereas genotyping the top 50% of male lambs or all male lambs led to increases of 18% (17%) or 26% (21%) for the health (production) trait, respectively. The potential of genotyping females in addition to male lambs was less evident on the male side with no significant differences between the scenarios with different proportions of genotyped females. The results have shown that genomic selection can be a valuable tool to increase genetic gain in the German Merino sheep population and that the genotyping of a certain proportion of animals might lead to substantial improvement over pedigree‐based breeding value estimation. Nevertheless, further studies, especially economic evaluations, are needed before practical implementation. [ABSTRACT FROM AUTHOR]

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models. [ABSTRACT FROM AUTHOR]

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program. [ABSTRACT FROM AUTHOR]

Forage plant breeding aims to deliver cultivars that increase on-farm productivity through improved seasonal and annual yield, persistence of yield in perennials, and nutritive and feeding value. Breeding is generally undertaken in highly controlled field trials with individual plants or rows. However, the correlation of trait values between spaced plant trials and sward plots of forages is often low and non-significant. Heritability of traits, a measure of likely gains that can be achieved from plant breeding, can be moderate to high for many traits. The upper level of genetic gains in forage yield measured in small plot trials ranges between 6% and 15% per decade depending on species, indicating significant and lasting improvements have been achieved. Results from some farmlet scale grazing trials measuring animal performance support this but some others, particularly with dairy cows, fail to show advantage to improved cultivars. Factors contributing to this include aspects of systems management, systems trial design, and/or breeding objectives and evaluation systems; occurrence of environmental stresses; insufficient statistical power; and scaling interactions that restrict or dilute trait expression. Several factors are identified that should be considered as evaluation systems evolve in response to the changing economic, climatic, and regulatory forces affecting grassland farming. [ABSTRACT FROM AUTHOR]

Understanding phenology, its genetics and agronomic consequences, is critical for crop adaptation. Here we aim to (i) characterize lentil response to photoperiod with a focus on five loci: the lentil ELF3 orthologue Sn , two loci linked to clusters of lentil FT orthologues, and two loci without candidates in chromosomes 2 and 5 (Experiment 1: 36 lines, short and long days in a phytotron), and (ii) establish the phenology–yield relationship (Experiment 2: 25 lines, 11 field environments). A vintage perspective, where we quantify time trends in phenotype over three decades of breeding, links both experiments. Yield increased linearly from older to newer varieties at 29 kg ha–1 year–1 or 1.5% year–1, correlated negatively with flowering time in both winter- and summer-rainfall regimes, and decoupled from biomass in favourable environments. Time to flowering shortened from older to newer varieties at –0.56% year–1 in the field, and –0.42% year–1 (short days) and –0.99% year–1 (long days) in the phytotron. Early-flowering lines of diverse origin carried multiple early alleles for the five loci, indicating that at least some of these loci affect phenology additively. Current germplasm primarily features the early-flowering haplotype for an FTb cluster region, hence the potential to increase phenological diversity with yield implications. [ABSTRACT FROM AUTHOR]

Background: Mating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones' descent, the breeder's control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs. Methods: By computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony. Results: SDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed. Conclusion: The opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen's spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose. [ABSTRACT FROM AUTHOR]

The latest study aimed to evaluate the genotypes’ genetic potential and estimate the genetic parameters for yield-related traits in two faba beans (Vicia faba L.) cultivars with the influence of nano-silicon different concentrations. The experiment comprised a randomized complete block design (RCBD) with a factorial arrangement of two factors and three replications. Faba bean cultivars (Turkish and Italian) were content in the main plots, while the subplots comprised nano silicon different levels (0, 1, and 2 ml L -1 ). The analysis of variance exhibited significant differences between the cultivars for all studied traits. The superiority of the Turkish cultivar was remarkable for the pods per plant, seeds per pod, seed yield, and biological yield. The results showed that the phenotypic and genotypic variations were high for all traits at the nano silicon level of 2 ml L -1 ). However, they decreased at the control treatment. Heritability (broad sense) was extremely high for all traits and nano-silicon concentrations, indicating that the variation among the cultivars was mainly genotypic. The genetic gain displayed the highest for the 100-seed weight while low for the rest of the yield-related traits in faba beans. [ABSTRACT FROM AUTHOR]

Cotton is a valuable industrial fiber crop grown in many regions worldwide. Four cotton (Gossypium hirsutum L.) cultivars, i.e., Ishonch, Navbakhor-2, C-6524, and Tashkent-6, and their F1-2 diallel hybrids’ cultivation comprised a randomized complete block design with a factorial arrangement and four replications during 2019–2021 in the Tashkent Region, Uzbekistan. Significant (P ≤ 0.01) differences were notable among the parental genotypes and their F1 hybrids for boll weight and seed cotton yield. The parental cultivars Ishonch and Navbakhor-2 and their F1 diallel hybrids showed more stability and performed better than other genotypes. Broad-sense heritability estimates were the highest for boll weight and seed cotton yield while lowest for bolls per plant. Based on this trait’s yield, heritability, and variability, the inbreeding depression was positive in the F2 populations Ishonch × Navbakhor-2 and Navbakhor-2 × Tashkent-6. According to yield, the cultivars Ishonch, Navbakhor-2, and Tashkent-6 were outstanding as positive donors. [ABSTRACT FROM AUTHOR]

Grain yield plateau is a serious concern in many countries, including Italy. A specific‐adaptative strategy might apply to contrast this unfavourable condition. This study aimed to investigate the gain in grain yield and yield‐related traits of bread wheat in Italy over a 16‐year period. A historical dataset concerning 105 sites across Italy and observations on grain yield, heading date, plant height and thousand kernel weight was used. The target population of environments (TPEs) were defined using several environmental parameters. The results showed that while there was a general increase in thousand kernel weight over the 16 years, there was no significant improvement in grain yield and heading date. By contrast, plant height showed a slight decrease over the years. The study also identified five TPEs presenting different climate scenarios in Italy. The TPEs showed varying levels of performance for the traits of interest, with the Po valley (TPE4) showing the highest values for grain yield and thousand kernel weight. The study also identified the best‐performing genotypes for each TPE, providing valuable insights for future wheat breeding programmes in Italy. [ABSTRACT FROM AUTHOR]

Almost all pineapple crops in Brazil are grown using the cultivar Perola. This scenario can be changed through the development of new cultivars with better fruit quality and resistance. The novel approach of the present study was to select pineapple clones that combine both resistance to fusariosis and characteristics related to plant and fruit quality as alternatives to the traditional cultivar Perola. Therefore, the objectives of this work were to estimate genetic parameters and select clones through the REML/BLUP methodology, based on morpho-agronomic characteristics and resistance to fusariosis. Thus, a selection index was used for quantitative characteristics and, later, for qualitative characteristics, such as fruit quality, presence or absence of leaf spines, and resistance to fusariosis. Significant differences were found for most variables, denoting genetic variability among the evaluated clones. Fruit weight with and without crown and D-leaf length presented the highest heritability estimates (above 50%); however, fruit length, mean fruit diameter, soluble solids, titratable acidity, plant height, and number of active leaves presented the lowest heritability estimates due to greater residual variances. Characteristics related to plant and fruit development are strongly affected by the environment and may result in phenotypic changes. Dominance variance was higher than additive variance, which enables the obtaining of heterosis through vegetative propagation. Eleven out of the 20 superior selected clones presented absence of leaf spines and multiple crowns, higher fruit weight and soluble solids content, and desirable fruit shape and pulp color; seven of them showed resistance to fusariosis, making them suitable for final testing for release as new cultivars. [ABSTRACT FROM AUTHOR]

Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999-2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha-1 year-1 (1.94% year-1) and 26 kg ha-1 year-1 (0.42% year-1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of -2.65% year-1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with -1.19% and -0.27% year-1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of -0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya's highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including markerassisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development. [ABSTRACT FROM AUTHOR]

Monitoring genetic gains within breeding programs is a critical component for continuous improvement. While several national breeding programs in Africa have assessed genetic gain using era studies, this study is the first to use two decades of historical data to estimate genetic trends within a national breeding program. The objective of this study was to assess genetic trends within the final two stages of Zimbabwe's Department of Research & Specialist Services maize breeding pipeline between 2002 and 2021. Data from 107 intermediate and 162 advanced variety trials, comprising of 716 and 398 entries, respectively, was analyzed. Trials were conducted under optimal, managed drought stress, low nitrogen stress, low pH, random stress, and disease pressure (maize streak virus (MSV), grey leaf spot (GLS), and turcicum leaf blight under artificial inoculation. There were positive and significant genetic gains for grain yield across management conditions (28-35 kg ha-1 yr-1), under high-yield potential environments (17-61 kg ha-1 yr-1), and under low-yield potential environments (0-16 kg ha-1 yr-1). No significant changes were observed in plant and ear height over the study period. Stalk and root lodging, as well as susceptibility to MSV and GLS, significantly decreased over the study period. New breeding technologies need to be incorporated into the program to further increase the rate of genetic gain in the maize breeding programs and to effectively meet future needs. [ABSTRACT FROM AUTHOR]

Speed breeding (SB) is an effective solution to enhance genetic gain. We advanced 14 intraspecific and interspecific lentil populations from F2 to F6 generation under a SB growth chamber applying an extended photoperiod of 18h light (23-25 °C)/6h darkness (14-16 °C). Six generations per year were achieved with an average generation cycle ranging from 62 to 76 days, demonstrating the effectiveness of SB in reducing generation time. More than 1500 F6-7 single plant-row advanced lines obtained from the populations were grown as observation nurseries under field conditions for seed multiplication and preliminary selection prior to their introduction in yield trials in the perspective of identifying new improved varieties. In addition to higher genetic gain, the SB method is resource efficient and easy to implement in small-scale breeding programs. To our knowledge, this is the first report showing effective implementation of SB protocol in lentil breeding pipeline in Africa and the Middle East. [ABSTRACT FROM AUTHOR]

The cultivation of the criollo pocket melon (Cucumis melo L.) has received little attention in agronomic research, despite its productive potential and its remarkable phenotypic diversity. Its fruits show phenological, morphological and organoleptic differences that offer an opportunity for genetic improvement of the Criollo variety, which is necessary because it has lower yields than other imported varieties and hybrids. In order to evaluate the potential of a selected population in the improvement of agronomically important traits, recombinant individuals from a selected population (Ps) were evaluated against individuals from the original population (Po) of the Dudaim variety, under a randomized complete block design with two treatments and three replications in two different locations in the canton of Palenque, Province of Los Ríos, Ecuador. According to statistical comparisons, the selected population showed significantly higher mean values than the original population (p=0,05) in 7 of the 9 fruit traits evaluated, showing high genetic variability and high heritability in a broad sense for traits related to fruit quality. The studies allow us to conclude that there is a high potential to achieve genetic gains by selection, especially for fruit weight, total soluble solids and fruit yield traits. The studies allow us to conclude that there is a high potential to achieve genetic gains by selection, especially for fruit weight, total soluble solids and yield per hectare. [ABSTRACT FROM AUTHOR]

ABSTRACT: The study of adaptability, stability, and productivity is essential for selecting and recommending superior genotypes. This fact is particularly the case for the introduction of common bean cultivars in Rio de Janeiro, Brazil, whose production is negligible and does not meet the internal demand. Thus, this study estimated genetic parameters and selection gains and undertake a simultaneous selection for adaptability, stability, and productivity in black bean genotypes via mixed models. The investigation was carried out in three municipalities in the state of Rio de Janeiro during three crop years. The trials were set up in a randomized block design with 11 genotypes and three replications. High mean heritability (81%) and selection accuracy (90%), as well as good selection prospects, were observed. Gains between 1.03 and 9.49% were achieved for grain yield. Simultaneous selection was efficient, indicating two black bean lines (CNFP 15290 and CNFP 15361) as the most productive, adaptable, and stable. As such, these lines have the potential to be released as new black bean cultivars for the state of Rio de Janeiro.

IntroductionGenetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits. MethodsThis study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling). ResultsThe 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme. DiscussionThese results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally.

ABSTRACT: The cost benefits of herd genotyping and the benefits of using sexed semen have been affected by recent improvements in sexing technologies, incorporation of direct health traits in the German total merit index for Holstein cattle, deteriorating prices for purebred heifer calves and bull calves, and introduction of herd genotyping programs. Inseminating genetically superior dams with female-sexed Holstein semen increases the mean breeding value of heifer calves and can produce more Holstein heifer calves than are needed for replacement. This provides an opportunity to increase the selection response in health and production traits at the farm level. A deterministic model is introduced that predicts the increase or decrease in net profit when a farmer takes part in a herd genotyping program and follows a certain insemination strategy. The types of semen allocated to cows and heifers may be sexed or unsexed and Holstein or beef breed. Genetically superior heifers and cows are inseminated with female-sexed Holstein semen, intermediate dams with unsexed Holstein semen, and genetically inferior dams with unsexed or male-sexed beef breed semen. In general, participating in a herd genotyping program is beneficial for German Holstein breeders. The optimum proportions of cows and heifers that should be inseminated with a certain type of semen are sensitive to farm-specific peculiarities. A small price difference between crossbred bull calves and crossbred heifer calves often makes the use of male-sexed beef breed semen uneconomic. Under the conditions considered, it was found to be advantageous to inseminate approximately 50% of heifers and 10% of cows with the highest genetic merit with female-sexed Holstein semen. The optimum proportion of cows that should be inseminated with unsexed beef breed semen was found to be approximately 40%. In a herd with a low replacement rate, the selected heifers can exhibit their genetic superiority over a longer period of time, and a larger proportion of cows can be inseminated with beef breed semen. Participation in a herd genotyping program is, therefore, particularly beneficial for herds with low replacement rates.

Finger millet is a climate‐resilient and highly nutritious small grain crop widely grown in the semi‐arid tropics. It has multiple uses, including for food, feed and beverage preparations. However, finger millet is an under‐utilized and under‐researched crop with a mean yield of <1.0 t/ha despite a potential productivity of up to 8 t/ha. The yield gap is attributed to several production constraints, such as biotic and abiotic stresses, a lack of access to improved seeds and production inputs and poor agronomic management practices. There are valuable genetic resources and genetic variability of finger millet in its centres of diversity and global gene banks for variety design, product development and commercialization. The genetic variability can be harnessed further to integrate essential traits into candidate varieties through conventional and modern breeding methods. Breeding and genetic innovations such as genomics‐assisted breeding, mutation breeding and genome editing would accelerate finger millet breeding and new variety design and deployment. The objective of this review was to document the opportunities, challenges and prospects of finger millet improvement as a guide for variety development and deployment with enhanced grain yield and nutritional contents. The first section describes global production status and yield gains, major production and productivity challenges in finger millet. This is followed by an in‐depth presentation on breeding and genetic progress on variety development with improved agronomic and nutritional quality traits, drought and salinity tolerance, and fungal diseases, weeds and insect pest resistance. Further, the review summarized finger millet's genetic and genomic resources, reference genomes, whole genome re‐sequencing and transcriptomics of finger millet technologies, genetic engineering and genome editing and their integration with conventional breeding methods for variety design with desired end‐use traits. The review provides foundational information to expedite the development of new‐generation finger millet cultivars with desirable product profiles, including high grain yield potential, early maturity, desirable seed colour, compact head type, food and feed nutrients quality and high marketability through modern breeding approaches. [ABSTRACT FROM AUTHOR]

The post‐emergent herbicide options to control grasses in sorghum have historically been limited. Therefore, a mass selection program was implemented to increase the tolerance of sorghum to the broad‐spectrum herbicide tembotrione. The objectives of this study are to determine (i) which observation timing and vegetative indices are most effective in assessing sorghum injury to tembotrione using unoccupied aerial system imagery and (ii) if mass selection can be used to increase the tolerance of sorghum to tembotrione. Using an unoccupied aerial system, several vegetative indices, collected at either 14 or 21 days after tembotrione application, accurately measured sorghum injury. Over four cycles of selection, tembotrione tolerance increased in a linear and consistent manner. This demonstrates that mass selection can be used as a strategy for developing herbicide tolerance in crops. The modest, but steady genetic gain indicates both the quantitative nature of the trait and sufficient heritability to improve it. Given further development, tembotrione‐resistant sorghum hybrids could provide an effective means of post‐emergent weed control for a range of common weeds. [ABSTRACT FROM AUTHOR]

Safflower (Carthamus tinctorius L.) is a multipurpose minor crop consumed by developed and developing nations around the world with limited research funding and genetic resources. Genomic selection (GS) is an effective modern breeding tool that can help to fast-track the genetic diversity preserved in genebank collections to facilitate rapid and efficient germplasm improvement and variety development. In the present study, we simulated four GS strategies to compare genetic gains and inbreeding during breeding cycles in a safflower recurrent selection breeding program targeting grain yield (GY) and seed oil content (OL). We observed positive genetic gains over cycles in all four GS strategies, where the first cycle delivered the largest genetic gain. Single-trait GS strategies had the greatest gain for the target trait but had very limited genetic improvement for the other trait. Simultaneous selection for GY and OL via indices indicated higher gains for both traits than crossing between the two single-trait independent culling strategies. The multi-trait GS strategy with mating relationship control (GS_GY + OL + Rel) resulted in a lower inbreeding coefficeint but a similar gain compared to that of the GS_GY + OL (without inbreeding control) strategy after a few cycles. Our findings lay the foundation for future safflower GS breeding. [ABSTRACT FROM AUTHOR]

Implementing an appropriate breeding program is crucial to control fluctuation in performance, enhance adaptation, and further improve the crossbred population of dairy cattle. Five alternative breeding programs (BPs) were modeled considering available breeding units in the study area, the existing crossbreeding practices, and the future prospects of dairy research and development in Ethiopia. The study targeted 143,576 crossbred cows of 54,822 smallholder households in the Arsi, West Shewa, and North Shewa zones of the Oromia Region, as well as the North Shewa zone of the Amhara Region. The alternative BPs include conventional on-station progeny testing (SPT), conventional on-farm progeny testing (FPT), conventional on-station and on-farm progeny testing (SFPT), genomic selection (GS), and genomic progeny testing (GPT). Input parameters for modeling the BPs were taken from the analysis of long-term data obtained from the Holetta Agricultural Research Center and a survey conducted in the study area. ZPLAN+ software was used to predict estimates of genetic gain (GG) and discounted profit for goal traits. The predicted genetic gains (GGs) for milk yield (MY) per year were 34.52 kg, 49.63 kg, 29.35 kg, 76.16 kg, and 77.51 kg for SPT, FPT, SFPT, GS, and GPT, respectively. The GGs of the other goal traits range from 0.69 to 1.19 days per year for age at first calving, from 1.20 to 2.35 days per year for calving interval, and from 0.06 to 0.12 days per year for herd life. Compared to conventional BPs, genomic systems (GPT and GS) enhanced the GG of MY by 53%-164%, reduced generation interval by up to 21%, and improved the accuracy of test bull selection from 0.33 to 0.43. The discounted profit of the BPs varied from 249.58 Ethiopian Birr (ETB, 1 USD = 39.55696 ETB) per year in SPT to 689.79 ETB per year in GS. Genomic selection outperforms SPT, SFPT, and FPT by 266, 227%, and 138% of discounted profit, respectively. Community-based crossbreeding accompanied by GS and gradual support with progeny testing (GPT) is recommended as the main way forward to attain better genetic progress in dairy farms in Ethiopia and similar scenarios in other tropical countries. [ABSTRACT FROM AUTHOR]

Intercropping is considered advantageous for many reasons, including increased yield stability, nutritional value and the provision of various regulating ecosystem services. However, intercropping also introduces diverse competition effects between the mixing partners, which can negatively impact their agronomic performance. Therefore, selecting complementary intercropping partners is the key to realizing a well-mixed crop production. Several specialized intercrop breeding concepts have been proposed to support the development of complementary varieties, but their practical implementation still needs to be improved. To lower this adoption threshold, we explore the potential of introducing minor adaptations to commonly used monocrop breeding strategies as an initial stepping stone towards implementing dedicated intercrop breeding schemes. While we acknowledge that recurrent selection for reciprocal mixing abilities is likely a more effective breeding paradigm to obtain genetic progress for intercrops, a well-considered adaptation of monoculture breeding strategies is far less intrusive concerning the design of the breeding programme and allows for balancing genetic gain for both monocrop and intercrop performance. The main idea is to develop compatible variety combinations by improving the monocrop performance in the two breeding pools in parallel and testing for intercrop performance in the later stages of selection. We show that the optimal stage for switching frommonocrop to intercrop testing should be adapted to the specificity of the crop and the heritability of the traits involved. However, the genetic correlation between the monocrop and intercrop trait performance is the primary driver of the intercrop breeding scheme optimization process. [ABSTRACT FROM AUTHOR]

The cost benefits of herd genotyping and the benefits of using sexed semen have been affected by recent improvements in sexing technologies, incorporation of direct health traits in the German total merit index for Holstein cattle, deteriorating prices for purebred heifer calves and bull calves, and introduction of herd genotyping programs. Inseminating genetically superior dams with female-sexed Holstein semen increases the mean breeding value of heifer calves and can produce more Holstein heifer calves than are needed for replacement. This provides an opportunity to increase the selection response in health and production traits at the farm level. A deterministic model is introduced that predicts the increase or decrease in net profit when a farmer takes part in a herd genotyping program and follows a certain insemination strategy. The types of semen allocated to cows and heifers may be sexed or unsexed and Holstein or beef breed. Genetically superior heifers and cows are inseminated with female-sexed Holstein semen, intermediate dams with unsexed Holstein semen, and genetically inferior dams with unsexed or male-sexed beef breed semen. In general, participating in a herd genotyping program is beneficial for German Holstein breeders. The optimum proportions of cows and heifers that should be inseminated with a certain type of semen are sensitive to farm-specific peculiarities. A small price difference between crossbred bull calves and crossbred heifer calves often makes the use of male-sexed beef breed semen uneconomic. Under the conditions considered, it was found to be advantageous to inseminate approximately 50% of heifers and 10% of cows with the highest genetic merit with female-sexed Holstein semen. The optimum proportion of cows that should be inseminated with unsexed beef breed semen was found to be approximately 40%. In a herd with a low replacement rate, the selected heifers can exhibit their genetic superiority over a longer period of time, and a larger proportion of cows can be inseminated with beef breed semen. Participation in a herd genotyping program is, therefore, particularly beneficial for herds with low replacement rates. [ABSTRACT FROM AUTHOR]

The main challenge facing agricultural crop production in the current global climate change scenario is sustainability. Drought, as a yield‐limiting factor, has become a major threat to international food security. Tolerance to drought is a complex trait and its response is carried out by various genes, transcription factors, microRNAs, hormones, proteins, cofactors, ions and metabolites. The complexity of the trait has limited the development of drought‐tolerant sweet potato cultivars by traditional breeding. Advances in sweet potato breeding to exploit the full potential of the crop to contribute to improved and higher performing sweet potato cultivars, adapted to increasingly risky rainfed conditions, mainly drought, are key to making food production systems more efficient and more tolerant to pressure from drought and other stressors. Genetic gain for yield potential in sweet potato has improved mostly in African countries, mainly as a result of an accelerated breeding scheme. The focus on maximising the utilisation of molecular tools for sweet potato improvement and yield has been recently explored in breeding programmes in sub‐Saharan Africa (SSA). This article provides an update on the trends and gaps in sweet potato breeding in SSA, reviewing the relevant strategies used to improve sweet potato in the region. Finally, the perspectives of using new advanced tools including genetic engineering and genome editing, and the challenges associated with climate change for further improvement of the crop are highlighted. Collaborative efforts in African countries are driving advances in breeding methods through the incorporation of molecular tools to develop drought‐tolerant sweet potato varieties that are important to global food security despite challenges posed by the drastic change in climate. [ABSTRACT FROM AUTHOR]

The Pacific oyster Crassostrea gigas is one of the most commercially important aquaculture species worldwide. The occurrence of inbreeding is common in the breeding practice of oysters due to limited population size, successive high selection pressure, and genetic drift. To determine the potential effects of inbreeding on the potential of genetic improvement for fast growth of oysters, genetic parameters of growth traits in the 7th to 9th generation of the orange-shell strain (a typical inbred population) were estimated. At the grow-out stage, the average genetic gain (GG), selection response (SR), and realized heritability ( h R 2 ) for shell height were 6.06–7.06%, 0.43–0.50, and 0.24–0.27, respectively. Meanwhile, the average GG, SR, and h R 2 for body weight were 6.03–7.36%, 0.38–0.41, and 0.21–0.22. There were no significant differences in the corresponding genetic parameters among the three generations (P > 0.05). Genetic parameters were estimated for an unselected population of C. gigas that served as a control group and were significantly higher than those of the orange-shell strain (P < 0.05). The results of this study suggest that inbreeding reduced the potential for genetic improvement of the rapid growth of oysters. However, some easy-to-operate and low-cost measures including the large size of broodstock, balanced sex ratio, and artificial spawning have been applied to the mass selection of the orange-shell strain, which prevents the situation from getting worse. These results will contribute to a better understanding of the negative effects of inbreeding on C. gigas and the management of inbreeding in shellfish breeding programs. [ABSTRACT FROM AUTHOR]

Wheat is a globally dominant staple food and one of the highest-consumed products because of its taste, texture, and bread quality. Genetic variability, heritability, and genetic advancement are essential to learning about the yield potential of crops. Finding out wheat's heritability and genetic advance led to this study's design at the research area of the Department of Plant Breeding and Genetics, the University of Agriculture, Peshawar, in 2021-2022. The experiment began using 27 wheat genotypes comprising nine parents and 18 F3 populations evaluated in a random complete block design (RCBD) with three replications. Highly significant variations observed came from analysis of variance among parents and F3 populations for days to heading, plant height, tillers plant-1, flag leaf area, spikelet's spike-1, the number of grains spike-1, a thousand-grain weight, and biomass yield. The highest heritability estimates of 0.82, 0.87, 0.88, 0.89, 0.86, 0.76, 0.88, 0.86, 0.89, 0.87, 0.86, and 0.84 emerged from Watan x Janbaz, Fakhr-e-Sarhad x AUP-5008, Pirsabak-2005 x AUP-5008, Barsat x Tatara, Fakhr-e- Sarhad x Tatara, Pirsabak-2005 x Tatara, Watan x Tatara, Watan x AUP-5008, AUP-4008 x Janbaz, Barsat x Tatara, Watan x AUP-5008, and Barsat x Janbaz, respectively, for productive traits. The highest values of genetic advance were 32.71, 20.33, 35.08, and 34.24 for Fakhr-e-Sarhad x AUP-5008, Fakhr-e-Sarhad x Janbaz, Pirsabak-2005 x Tatara, and Watan x Tatara, respectively. The parental genotypes Janbaz and AUP-5008 were the most promising genotypes recommended for further evaluation in upcoming breeding schemes. [ABSTRACT FROM AUTHOR]

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP—theoretically reaching one when using the Pearson's correlation as a metric—is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding. This review explores the pivotal factors that have shaped genomic selection (GS) in plant breeding over the past two decades. A thorough literature review is conducted on three prominent crops (wheat, maize, and potato) to demonstrate the impacts of these key factors on the accuracy of genomic prediction. Two breeding programs are explored to showcase current achievements of GS, , and insights into future prospects are also provided. [ABSTRACT FROM AUTHOR]

In studies on plant genetic improvement, it is common to use multi-environmental tests that aim to evaluate and select different lines under different environmental conditions. Another objective is to measure the main quantitative characteristics, such as grain yield, in order to select the best lines under test (test lines). To achieve these and other goals, good experimental design and adequate statistical analysis are essential. In this perspective, the present work evaluated the use of simulated data from spatially optimized designs in multi-environmental tests to compare different partially replicated (p-rep) designs with grid-plot designs considering gain and quality of the genetic material selection. For p-rep, the percentages of repetitions of test lines and the number of standard varieties (checks) were varied. Joint and individual analyses were conducted using linear mixed models with spatial variation in plot errors. Results indicated that p-rep designs were superior to grid-plot designs on relative realized genetic gain, accuracy, and selection probability. When comparing different p-rep designs, it was observed that those without the presence of checks presented the best results in relation to genetic gain. Furthermore, it was observed that for each set of parameters, in which only genetic correlation varied, the greater the genetic correlation, the higher the gain and selection quality. In all locations considered, greater precision was observed in the estimates of variance components when obtained through joint analysis, as expected. [ABSTRACT FROM AUTHOR]

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020–21 and 2021–22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999–2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha−1 year−1 (1.94% year−1) and 26 kg ha−1 year−1 (0.42% year−1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of −2.65% year−1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with −1.19% and −0.27% year−1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of −0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya’s highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development.

The replacement of winter wheat varieties has contributed significantly to yield improvement worldwide, with remarkable progress in China. Drawing on two sets of data, production yield from the National Bureau of Statistics of China and experimental yield from literature, this study aims to (1) illustrate the increasing patterns of production yield among different provinces from 1978 to 2018 in China, (2) explore the genetic gain in yield and yield relevant traits through the variety replacement based on experimental yield from 1937 to 2016 in China, and (3) compare the yield gap between experimental yield and production yield. The results show that both the production and experimental yields significantly increased along with the variety replacement. The national annual yield increase ratio for the production yield was 1.67% from 1978 to 2018, varying from 0.96% in Sichuan Province to 2.78% in Hebei Province; such ratio for the experimental yield was 1.13% from 1937 to 2016. The yield gap between experimental and production yields decreased from the 1970s to the 2010s. This study reveals significant increases in some yield components consequent to variety replacement, including thousand-grain weight, kernel number per spike, and grain number per square meter; however, no change is shown in spike number per square meter. The biomass and harvest index consistently and significantly increased, whereas the plant height decreased significantly.

This study aims to understand the genetic basis of key industrial traits in Slash pine (Pinus elliottii Engelm. var. elliottii) to enhance improvement efficiency. Detailed analyses were conducted on inter-family differences, genetic parameters, correlations, and breeding values (BVs) for growth, wood properties, and resin traits of Slash pine planted in Changle Forest Farm of Hangzhou, leading to the identification of elite families. It indicates that growth traits are primarily influenced by environmental effects, while wood properties exhibit a significant impact of genetic effects. The variation in resin traits arises from both genetic and environmental effects. Notably, Beta-pinene exhibits the highest variability and genetic gains among the traits analyzed. The family heritability ranges for growth, wood properties, and resin traits are 0.543−0.794, 0.870−0.885, and 0.285−0.695, respectively. Significant positive correlations are evident between growth and resin traits, while a negative correlation is observed between growth and wood properties. Elite families identified through single-trait and multi-trait combined selection are 8−126 for growth traits, 2−325 and 0−373 for wood properties, and 8−131 for resin traits. The average genetic gains for these elite families are 7.44%, 7.17%, and 8.84%, respectively. These findings provide valuable insights for high-generation breeding of Slash pine and lay a genetic foundation for formulating effective breeding strategies for conifers.