Plant Breeding Research Articles

Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction

Abstract The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding.

Participatory research at scale: A comparative analysis of four approaches to large-scale agricultural technology testing with farmers

Tailoring agricultural technology options to the diverse conditions of smallholder farmers requires innovative approaches for testing these technologies with farmers across varied contexts, while incorporating their feedback into learning and decision-making processes. This study compares four such approaches: the Farmer Field School on Participatory Plant Breeding (FFS-PPB), Farmer Research Network (FRN), Crowdsourcing–Triadic comparisons of technologies (Tricot), and adapted Mother–Baby Trial (MBT) as implemented by four concrete projects. The objectives are to provide detailed descriptions of these approaches and their project-specific implementations, identify and analyze common aspects and differences, and derive insights to guide future farmer-inclusive projects aiming at contextual scaling of agricultural technologies. A literature review, key informant interviews, and a systematic content analysis were conducted for the analysis. Common features include cascade training models, simple farmer-managed experiments, and the use of digital tools for data collection. Major differences lie in the extent of farmer–researcher collaboration and decision-making, as well as how technology option-by-context interactions are addressed. The FRN, FFS-PPB, and adapted MBT approaches involve farmers in decision-making throughout most stages of research, including co-learningcycles that adapt the research design and technology options to farmers’ needs. Although these approaches require more training and expertise, they increase the likelihood of achieving relevant results that farmers can implement in practice. In contrast, more standardized approaches like the Crowdsourcing–Tricot streamline the implementation, data management and analysis of large-scale trials, but have limitations in capturing the underlying reasons for farmers’ preferences. Among the studied approaches, the FRN as implemented by the Women's Fields project in Niger is particularly effective in identifying which options best suit specific farming contexts.

Omics based approaches to decipher the leaf ionome and transcriptome changes in Solanum lycopersicum L. upon Tomato Brown Rugose Fruit Virus (ToBRFV) infection

The Tomato Brown Rugose Fruit Virus (ToBRFV) is a pathogen that mostly affects plants from the Solanaceae family. This virus severely affects the yield of tomato (Solanum lycopersicum L.) plants, thus creating an urgent need to research the basis of resistance to manage the disease. To understand the molecular basis of resistance, we employed omics-based approaches involving leaf ionomics and transcriptomics to help us decipher the interaction between elemental and nutritional composition and investigate its effect on the gene expression profile upon the ToBRFV infection in tomatoes. Ionomics was used to investigate the accumulation of trace elements in leaves, showcasing that the plants resistant to the virus had significantly higher concentrations of iron (p-value = 0.039) and nickel (p-value = 0.042) than the susceptible ones. By correlating these findings with transcriptomics, we identified some key genes involved in iron homeostasis and abscisic acid pathways, potentially responsible for conferring resistance against the pathogen. From the obtained list of differentially expressed genes, a panel of mutation profile was discovered with three important genes—Solyc02g068590.3.1 (K+ transporter), Solyc01g111890.3.1 (LRR), and Solyc02g061770.4.1 (Chitinase) showing persistent missense mutations. We ascertain the role of these genes and establish their association with plant resistance using genotyping assays in various tomato lines. The targeted selection of these genetic determinants can further enhance plant breeding and crop yield management strategies.

Canadian Organic Wheat Breeding with On-farm Selection: A Case Study using Landrace and Modern Parents

Participatory plant breeding (PPB) has the potential to be an alternative to a centralized breeding model for niche markets that are historically underserved. Our objectives were to evaluate the performance of two parental cultivars from a Canadian PPB program: a modern spring wheat (Triticum aestivum L.) cultivar (5602HR) and a landrace (Red Fife) - with the progeny of the cross when selected by two farmers located 1800 km apart (denoted Farm1 and Farm2) and evaluate progeny differences from each other under organic conditions. Red Fife was 18 cm taller, matured 5 days later, had greater lodging susceptibility, greater seed mass, and 3.5% lower protein concentration than 5602HR. Farmer genotypes were similar to 5602HR in protein concentration and lodging severity, and similar to Red Fife in plant height (14 cm taller than 5602HR) and seed mass. Farmer genotypes did not yield differently from either parent. Farmer genotypes did not differ from each other in most parameters measured, however, Farm1 had greater lodging resistance under high fertility conditions than Farm2 despite similar plant height. This research provides a proof of concept for the role that farmer selectors can play in selecting for positive traits for organic production and provide insight into organic farmers’ preferences. The research also demonstrated that using an older landrace (ie., Red Fife) allowed positive features such as tall stature to be incorporated into the resulting progeny.

Genetic Variability Studies in Chickpea (Cicer arietinum L.) for Yield and Contributing Traits Through Half-diallel Mating Strategy under Late-sown Conditions

Background: One of the oldest cultivated legumes, chickpeas (Cicer arietinum L.), are essential for world nutrition, especially with climate change. Chickpea output is poor despite rising demand for its nutritional value. Improved genetics can enhance yields to fulfil local and global demands. By selecting better parents and potential hybrids utilising half-diallel mating, breeders can hasten the development of high-yielding, durable cultivars. Methods: This study examined 55 half-diallel mating genotypes, comprising 10 parental lines and 45 F1 hybrids. At the Acharya Narendra Deva University of Agriculture and Technology Genetics and Plant Breeding Farm in Ayodhya, U.P., these were evaluated using a randomised block design with three replications under late-sown circumstances in 2022-23 rabi. Normal agricultural procedures were used. A four-meter row of each genotype was planted with 30 cm between rows and 10 cm between plants. Genetic indices like GCV, PCV, heritability, genetic progress, correlation coefficient and path analysis were evaluated. Result: ANOVA revealed significant genetic variability across all traits. Biological yield plant-1 had the highest PCV and GCV, followed by seed yield plant-1. Heritability and genetic progress were highest for days to 50% flowering and biological yield plant-1, indicating strong genetic control. Days to 50% flowering and maturity inversely linked with most yield-related characteristics. Seed yield was closely associated with primary and secondary branches plant-1, plant height, pods per plant, seeds pod-1, 100-seed weight and biological yield. Path coefficient study showed that all characteristics except days to 50% flowering, days to maturity and plant height influenced grain yield.

Masculinities in a feminist pedagogy: lessons for transformative gender and agriculture training

Masculinities and femininities are closely interconnected with men and women farmers’ everyday lives; hence critical reflection on these interconnections should be central in gender training in agriculture. While a focus on men and masculinities is crucial for sustainable transformation of deep-rooted gender norms and practices that limit the attainment of gender equality, there are insufficient empirically tested pedagogical models for this purpose. We share a case study, the Gender Responsive Researchers Equipped for Agricultural Transformation (GREAT) model, which incorporates masculinities in a feminist pedagogy. We use external monitoring, evaluation, and learning data for two case study courses that integrate gender in plant breeding, seed systems, and agronomy to demonstrate the efficacy of integrating the concept of masculinity and reflections on male farmers’ expectations, behaviors, and practices within a feminist approach to gender training. We conclude that feminist pedagogical practices offer insights into how gender training can integrate a masculinities perspective to move beyond divisive and narrow gender polarities towards addressing masculine norms that often hinder the attainment of gender transformation.

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

Maize Hybrids Performance Evaluation with Data Fusion by Matrix Factorization Algorithm

ABSTRACT Crop breeders often face challenges due to limited data availability when making crucial decisions, such as selecting top-performing varieties/hybrids for further experiments, registration, and commercialization. Evaluating all varieties/hybrids across all fields is impractical due to high experimental and time costs, as well as the limited number of locations for planting. This article aims to evaluate the performance of various maize hybrids in untested locations using historical data. The problem is approached through a matrix framework, where hybrids and fields correspond to rows and columns, respectively, with entries representing the yield of a specific hybrid at a given location. As this matrix is typically sparse, the task is to fill in missing data. Agronomists are primarily interested in the performance of top hybrids at specific locations for smart seed selection. To address this, we introduce a novel application of the Data Fusion by Matrix Factorization (DFMF) algorithm for predicting crop yields using maize data from the 2019 Syngenta Crop Challenge. The DFMF results are compared with the Random Forest (RF) algorithm as a benchmark, focusing on model performance for smart seed selection. Our analysis highlights the advantages of the DFMF approach over the traditional RF method in this context.

Rediscovering and Unrediscovering Gregor Mendel: His Life, Times, and Intellectual Context.

Two things about Mendel were "rediscovered" in 1900: His famous paper of 1865 and the story of his life and long neglect. Unlike the paper, which anyone could read in its entirety, the story came out only gradually, and many of its elements were misconstrued by Western European scientists. They pictured him as a pure scientist like themselves and were puzzled by or disinterested in his career as a clergyman, his intellectual community in far-off Moravia, and the importance to him of practical plant breeding. This paper recapitulates the process of mythmaking that followed the rediscovery, then shows how more recent historical research has been able to undo it and, in a sense, "unrediscover" Mendel.

Evaluation of Genetic Variability for Yield and Associated Traits in Breeding Lines Derived from the Cross RNR-15048 × Tunga

Aims: To evaluate the genetic variability of 45 rice breeding lines for 12 quantitative and 2 qualitative traits, aiming to identify and enhance traits that contribute to improved crop yield and nutritional quality in rice. Study Design: The experiment was carried out in Randomised Complete Block Design with two replications. Place and Duration of Study: During kharif 2023 at Agricultural and Horticultural Research Station, Kathalagere, Davangere. Methodology: The research utilized 45 breeding lines resulting from the cross RNR15048 × Tunga, sourced from the Department of Genetics and Plant Breeding, College of Agriculture Shimoga, along with 5 checks. Data was recorded and analyzed statistically using R software. Results: The significant differences among the breeding lines identified through the analysis of variance highlight the potential for selecting superior genotypes within the studied rice breeding lines. High to moderate values of the genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) suggest that there is substantial genetic diversity for the traits evaluated, indicating a promising opportunity for genetic improvement through selective breeding. Conclusion: The findings of this study highlight the importance of focusing yield improvement programs on specific traits, including plant height, number of tillers per plant, productive tillers per plant, grains per panicle, filled grains per panicle, test weight, protein content, and overall grain yield. Emphasizing these traits is essential for enhancing the productivity and nutritional value of rice, ultimately supporting food security efforts.

Enhancing Across-Population Genomic Prediction for Maize Hybrids

In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency.

Transgenic rice with microbial high-temperature-resistant β-glucosidase gene significantly improved 2-acetyl-1-pyrroline content and edible quality.

The content of 2-acetyl-1-pyrroline (2-AP) directly affects the aroma and taste of rice. Δ1-Pyrroline and methylglyoxal are the precursors of 2-AP synthesis, and β-glucosidase plays an important role in the synthesis of methylglyoxal. In this study, β-glucosidase gene cloned from Pyrococcus furiosus was molecularly modified to obtain the high-temperature-resistant β-glucosidase gene 371-β-glucosidase (T371A), which was transformed into kitaake varieties (Oryza sativa L. subsp. japonica) by Agrobacterium-mediated transformation method, and transgenic rice with heterologous expression of T371A was obtained. Experiments were conducted in transgenic rice to investigate whether this gene had an effect on the synthesis of 2-AP. Under the optimum reaction temperature of 50°C and cooking temperature of 100°C, the enzyme activity of β-glucosidase in transgenic rice seeds was prominently increased by 260-280% and 419-426% over that of the control, respectively. The content of 2-AP in transgenic rice seeds significantly increased by 75-105% under normal temperature and high-temperature cooking conditions compared with the control. It was also found that transgenic rice increased the content of methylglyoxal and decreased the expression of betaine aldehyde dehydrogenase (BADH2). The high-temperature-tolerant β-glucosidase gene obtained in this study provides an innovative technical strategy for molecular breeding of high-edible aroma crops and has wide application potential. © 2024 Society of Chemical Industry.

Cleaved Amplified Polymorphic Sequence Markers in Horticultural Crops: Current Status and Future Perspectives

DNA markers have broad applications, including marker-assisted selection (MAS) for breeding new cultivars. Currently, single nucleotide polymorphisms (SNPs) have become a preferred choice of markers for molecular geneticists and breeders. They offer many advantages, such as high abundance and coverage in the genome, codominant inheritance, locus specificity, and flexibility for high-throughput genotyping/detection formats, and they are relatively inexpensive. The availability of reference genome sequences enables precise identification of candidate genes and SNPs associated with a trait of interest through quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS). Such SNPs can be converted into markers for their application in MAS in crop breeding programs. Cleaved amplified polymorphic sequence (CAPS) markers amplify short genomic sequences around the polymorphic endonuclease restriction site. This review provides insight into the recent advancements made in the development and application of CAPS markers in several horticultural plants. We discussed many new tools that aid faster and more accurate design of CAPS markers from the whole genome resequencing (WGRS) data. The developed CAPS markers offer immense application in germplasm screening and field trials, genomic loci mapping, identifying candidate genes, and MAS of important horticultural traits such as disease resistance, fruit quality and morphology, and genetic purity.

Phenotyping in Plant Breeding using Modern Tools: A Review

Phenotyping in Plant Breeding using Modern Tools: A Review

Application of a Multi-Spectral UAV Imagery in Germplasm Characterization: Prediction of Forage Biomass and Growth Patterns of Cicer Milkvetch (Astragalus cicer L.) Populations

Unmanned aerial vehicles (UAV)-based multi-spectral imaging could reduce the intensive labour required in phenotyping germplasm in crop breeding. The objectives of this study were to examine if UAV-based imaging could differentiate cicer milkvetch (Astragalus cicer L.) germplasm and identify UAV-based vegetation indices with correlations to its dry matter yield (DMY). A spaced nursery from 27 cicer milkvetch populations was established near Saskatoon, SK, Canada, in 2019. From 1 June to 15 October in 2020 and 2021, phenotypic traits including maximum stem length, leaf number per stem, rhizome spread rate, and stem density, along with two UAV-measured traits, green area and canopy volume, were measured bi-weekly. Forage DMY was determined in late June and mid-October of each year. In this study, normalized difference vegetation index (NDVI) green area and NDVI canopy volume data differentiated the three selected populations. NDVI green area had the highest correlation with forage DMY among the traits (June harvest: r = 0.91, October harvest: r = 0.77). Among measured phenotypic traits, maximum stem length had the highest correlation with forage DMY (June harvest: r = 0.74, October harvest: r = 0.83), which was significantly correlated to NDVI green area. The results indicated potential use of UAV-phenotyping in single plant evaluation in plant breeding.

Primer pairs to identify physiological races of Plasmodiophora brassicae

Clubroot is a soil-borne disease caused by infection with Plasmodiophora brassicae, the causal agent of clubroot, and clubroot can directly cause enormous economic losses in cruciferous crops. The latent spores of P. brassicae are highly active, have a high rate of infection, and are transmitted over a wide range of channels. The manifestation of P. brassicae in fields is often caused by the mixed infection of multiple physiological races, and such mixed infections often pose a substantial challenge to the breeding of plants that are resistant to P. brassicae. In this study, a set of multiple molecular markers for the identification of the main physiological race of P. brassicae was established by utilizing a system to detect the clubroot microspecies-specific primers SCL14 (UP/LP), PBRA_000030-2 (F/R), PBRA_000303-1 (F/R), PBRA_009348-2 (F/R), and Novel342-1 (F/R). The molecular identification of clubroot physiological race resulted in identifying of five such races, including No. 1, 4, 7, 9, and 11. The primers used in this study were more specific, and some strains were not only identified as No. 4 but also as No. 7, such as strains P2, P3, and P4, or as No. 1, such as strain P60. The highest frequency and widest distribution of the small species No. 4 in the sampling site indicated that race No. 4 is the dominant race of P. brassicae. Based on the distribution of race in the sampling site, it was found that the isolated strains of P. brassicae were more differentiated and caused more types of disease. In this study, the primers obtained for the molecular identification of P. brassicae in previous studies were improved and combined to form a multi-molecular marker method to identify the physiological race isolates of P. brassicae, which will provide a theoretical basis to identify the isolates of P. brassicae using molecular markers.

Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction

In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes.

Assessing population structure and morpho-molecular characterization of sunflower (Helianthus annuus L.) for elite germplasm identification.

Sunflower (Helianthus annuus L.), known for its adaptability and high yield potential, is vital in global edible oil production. Estimating genetic diversity is a key pre-breeding activity in crop breeding. The current study comprised of 48 genotypes which were assessed for their biometrical traits at department of Oilseeds, Tamil Nadu Agricultural University, during the rainy season of 2022. The lines were subsequently characterised using 103 simple sequence repeat (SSR) markers for molecular diversity analysis. The results indicated that the net nucleotide distances indicated varying genetic divergence, with subpopulations II and V showing the highest (0.056) and I and IV the lowest (0.014). Subpopulation IV exhibited the highest heterozygosity (0.352), while subpopulation III had the lowest heterozygosity and a low Fst (0.173). Principal components analysis (PCA) and hierarchical cluster analysis were employed for assessing the morphological diversity, facilitating genotype grouping and parent selection for breeding programs. The first four components cumulatively accounted for 86.72% of the total variation. Cluster Analysis grouped 48 sunflower genotypes into three clusters based on genetic diversity. COSF 13B stands out for its high head diameter, oil content, seed yield, and oil yield based on mean performance of morphological data. Principal coordinate analysis (PCoA) mirrored the groupings from the Neighbor Joining method, with the first three components explaining 27.24% of the total variation. Molecular data analysis identified five distinct clusters among the germplasm. By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was revealed with the genotypes RHA 273 and GMU 325 consistently demonstrated high oil yield per plant. The genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 have been identified as suitable parents for enhancing oil content in sunflower breeding programs. These findings also aid in selecting SSR markers for genotype characterization and in choosing diverse parents for breeding programs.

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types.

The influence of plant genomics on plant evolution

With the increasing demand for high-quality tea, the genetic basis of ideal traits of tea plants has been widely concerned. The research on tea tree genomics, specifically focusing on “Longjing 43”, provides significant insights into plant evolution and breeding. “Longjing 43” was chosen due to its complex genome and economic importance. The study successfully assembled its genome to the chromosome level, revealing a large and heterogeneous genome with numerous repetitive sequences. Comparative genomics showed that hybridization has been a key driver of genetic diversity and adaptability in tea plants. These findings are crucial for developing marker-assisted selection (MAS) techniques, improving traits such as disease resistance, flavor, and stress response. Moreover, the research underscores the role of tea tree genomics in preserving cultural heritage and promoting economic benefits through enhanced tea quality. This study contributes to the broader understanding of plant genomics by illustrating the evolutionary processes and potential applications in agriculture, conservation, and industry, thus offering a comprehensive tool for future plant breeding and genetic improvement strategies.