Crop Science最新文献

{"title":"Shaping the rhizosphere: How barley domestication altered root traits and microbiome interactions","authors":"Babak Malekian,&nbsp;Hamid Rouina,&nbsp;Somayeh Gholizadeh,&nbsp;Mohammad Javad Ahmadi Lahijani,&nbsp;Heinrich Grausgruber,&nbsp;Katarzyna Retzer","doi":"10.1002/csc2.70368","DOIUrl":"10.1002/csc2.70368","url":null,"abstract":"<p>Climate change and soil degradation are rapidly transforming agricultural systems, underscoring the need for a deeper understanding of how crops interact with their surrounding microbiomes. Barley, as one of the earliest domesticated cereal crops, serves as a valuable model for investigating the effects of domestication on root traits, exudation patterns, and microbial associations. Domestication has influenced not only crop yield but also root morphology and function. This review synthesizes current knowledge on the structural and biochemical modifications in barley roots induced by domestication and examines their implications for microbial recruitment and function. Particular attention is given to how variations in root morphology, the composition of root exudates, and the assembly of microbial communities shape plant–microbe interactions and influence overall crop performance. The impact of modern breeding practices is also explored, with a focus on whether elite cultivars may have lost beneficial microbial partnerships that could be harnessed for future crop improvement. By evaluating contrasting findings and identifying research gaps, this review highlights the need for integrative research approaches, especially those involving multi-omics and genotype–environment–microbiome frameworks, to better understand the causal links between domestication traits and microbiome dynamics. Ultimately, it advocates for microbiome-informed breeding strategies that reintroduce beneficial microbial associations from wild barley genotypes. Such approaches hold strong potential to enhance nutrient-use efficiency, stress resilience, and sustainability in modern agriculture.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The US NPGS is a critical resource for plant breeding","authors":"Jeffrey F. D. Dean,&nbsp;Don R. La Bonte,&nbsp;Amol N. Nankar,&nbsp;Sachin Rustgi,&nbsp;Nicholas Santantonio,&nbsp;Gerald R. Smith,&nbsp;Shuhao Yu,&nbsp;Charles Chen","doi":"10.1002/csc2.70372","DOIUrl":"10.1002/csc2.70372","url":null,"abstract":"<p>The recent paper from Hale et al. (<i>Crop Science</i>, 2025;65:e70094) does great service in highlighting issues and making recommendations to address the many challenges facing public-sector plant breeding programs at Land Grant Universities. Here we wish to draw further attention to the National Plant Germplasm System (NPGS) mentioned in the report as a critical partner for crop improvement efforts. Organized and operated by the USDA-ARS, the NPGS comprises 22 genebanks containing over 200 crop species and more than 600,000 unique accessions of plant germplasm. Researchers and support staff working in these centers distribute more than 200,000 samples each year to plant breeders and researchers nationwide. These samples have been the source of many important traits that now ensure improved crop yield and resilience to extreme weather as well as disease and pest resistance.</p><p>However, the NPGS has suffered significant declines in operational capacity through declining budgets, aging infrastructure, and the attrition of personnel. This shortfall in capacity creates backlogs in moving germplasm to long-term storage in a timely fashion, and it delays the testing of germplasm stores quality and viability, it slows the regeneration of germplasm stocks maintained in storage, and it reduces the ability of NPGS researchers to identify and characterize the promising new traits critical for future crops. Additionally, the lack of technical knowledge limits the capacity for the NPGS to bring onboard new species and germplasm for protection.</p><p>To address these issues and ensure the NPGS continues as a reliable partner for plant breeding activities in the United States, we advocate for the full adoption of The National Strategic Germplasm and Cultivar Collection and Assessment Plan released in 2023 (https://www.ars-grin.gov/Pages/NPGS). This 10-year plan outlines a clear vision for revitalizing the NPGS and safeguarding the precious genetic resources housed therein, ensuring that they will continue to benefit public plant breeding programs into the future.</p><p><b>Jeffrey F. D. Dean</b>: Conceptualization; writing—original draft; writing—review and editing. <b>Don R. La Bonte</b>: Writing—review and editing. <b>Amol N. Nankar</b>: Writing—review and editing. <b>Sachin Rustgi</b>: Writing—review and editing. <b>Nicholas Santantonio</b>: Writing—review and editing. <b>Gerald R. Smith</b>: Writing—review and editing. <b>Shuhao Yu</b>: Writing—review and editing. <b>Charles Chen</b>: Writing—review and editing.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Granular nitrogen source and rate affect bermudagrass phytotoxicity","authors":"Travis W. Shaddox,&nbsp;P. Agustin Boeri,&nbsp;J. Bryan Unruh","doi":"10.1002/csc2.70373","DOIUrl":"10.1002/csc2.70373","url":null,"abstract":"<p>Fertilizer burn is a common concern among turfgrass managers. Risk of burn is largely assumed by using the fertilizer salt index. However, the validity of the salt index as a predictor of turfgrass phytotoxicity under field conditions has not been confirmed. The objective of this study was to determine the phytotoxicity of 12 granular N sources applied at five rates to Latitude 36 hybrid bermudagrass [<i>Cynodon dactylon</i> (L.) Pers. × <i>Cynodon transvaalensis</i> (Burtt-Davy)] under field conditions at two locations. Treatments were applied as a single application at N rates of 0, 49, 98, 196, and 392 kg ha⁻¹ to plots arranged in a split plot design with four replications in Jay, FL, and Lexington, KY, in August 2020, and were irrigated immediately following application. Phytotoxicity, turfgrass quality, normalized difference vegetation index, soil electrical conductivity (EC), and volumetric water content were measured for 22 days after initiation. Nitrogen source and rate influenced most response variables at both locations. Monoammonium phosphate (MAP) and diammonium phosphate (DAP) resulted in the greatest phytotoxicity, reaching 51.6% and 48.4% of the plot area affected at an N rate of 392 kg ha⁻¹ in Jay, despite resulting in less soil EC than ammonium sulfate and calcium nitrate, which resulted in no phytotoxicity. Only MAP and DAP resulted in phytotoxicity in Lexington. No N source resulted in phytotoxicity using 49 kg ha⁻¹ of N, and turfgrass managers should be able to apply any N source evaluated to bermudagrass maintained at 2.5 cm with little risk of phytotoxicity provided N applications are ≤49 kg ha⁻¹ and the turfgrass is immediately irrigated with 0.6 cm of water. These results were not consistent with salt index rankings, suggesting the salt index may not be a valid predictor of nitrogen fertilizer burn risk in turfgrass.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomewide prediction of stability across environments in maize populations","authors":"Isaías Ariza-Hernández,&nbsp;Rex Bernardo","doi":"10.1002/csc2.70371","DOIUrl":"10.1002/csc2.70371","url":null,"abstract":"<p>Stability models characterize the performance of genotypes across environments. While genomewide prediction is effective for the mean performance of maize (<i>Zea mays</i> L.) hybrids across environments, its potential for predicting stability has not been reported. Our objective was to assess if genomewide prediction of stability is accurate for yield, moisture, and test weight in maize populations. Stability of performance in seven testcross populations, evaluated in 10–18 environments, was estimated as the regression coefficient from classical Eberhart–Russell analysis. Slope values for each trait in each population had the expected mean of 1.0 and, across populations, ranged from 0.41 to 1.72 for yield (t ha⁻¹ at 130 g H₂O kg⁻¹), 0.65 to 1.27 for moisture (g kg⁻¹), and 0.39 to 1.87 for test weight (g L⁻¹). Across populations, predictive ability for stability ranged from −0.07 to 0.41 for yield, −0.08 to 0.52 for moisture, and −0.18 to 0.62 for test weight. Five different prediction models, including ridge regression and Bayesian models, did not lead to any significant differences (<i>p</i> = 0.05) in predictive ability for stability. Predictive ability was always lower for stability than for the across-environment mean of a genotype. Slopes were also estimated from the predicted performance of each genotype in each environment, but the correlations between observed and predicted slopes remained low. Split-correlation analysis indicated that the low predictive ability for the slope was due primarily to a low signal-to-noise ratio. Our findings indicated that predicting stability in maize is too inconsistent to be routinely useful.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic selection using random regression to predict hybrid performance in untested environments by leveraging environmental data","authors":"Thiago C. Brommonschenkel,&nbsp;Maria M. Pastina,&nbsp;Marcio F. R. Resende,&nbsp;Lauro J. M. Guimarães,&nbsp;Marco A. Peixoto,&nbsp;Aluízio Borém,&nbsp;Cláudia T. Guimarães,&nbsp;Roberto S. Trindade,&nbsp;Paulo E. O. Guimarães,&nbsp;José Crossa,&nbsp;Rafael T. Resende,&nbsp;Kaio Olimpio G. Dias","doi":"10.1002/csc2.70369","DOIUrl":"10.1002/csc2.70369","url":null,"abstract":"<p>Genotype-by-environment interaction (G×E) remains one of the major challenges to achieving genetic gains in tropical maize (<i>Zea mays</i> L.) breeding. To address this, we implemented the geographic information system–factor analytic (GIS-FA) framework, which integrates factor-analytic (FA) models with environmental covariates and genomic kernels to predict hybrid performance in untested environments. Unlike traditional G×E models, GIS-FA links environmental descriptors directly to the latent factor loadings estimated by FA models. These loadings are then extrapolated to new environments using partial least squares regression, enabling accurate predictions of genotypic performance across untested locations. By incorporating a nonlinear Gaussian genomic kernel into the FA framework, potentially capturing additive and nonadditive genetic effects, predictive accuracy improved by up to 13.0% relative to the environment-only model across four cross-validation schemes. This integrative approach enabled the development of high-resolution predictive maps that translate complex G×E patterns into visual tools, supporting decision-making in hybrid positioning. The results highlight the practical advantages of GIS-FA for tropical maize breeding programs: (1) robust prediction of hybrid performance beyond experimental networks, (2) strategic genotype recommendation in the target population of environments, and (3) improved accuracy of site-specific predictions through the integration of genomic information. In summary, GIS-FA provides a scalable, data-driven framework to guide hybrid recommendation, deployment, and resource allocation in tropical maize breeding.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining methods for efficient line development and seed increase in winter wheat: Speed breeding with minibulks and a single-plant nursery","authors":"Jeremy B. Logrono,&nbsp;Luis Gehrke,&nbsp;Eric Olson,&nbsp;Richard E. Boyles,&nbsp;Jessica E. Rutkoski","doi":"10.1002/csc2.70363","DOIUrl":"10.1002/csc2.70363","url":null,"abstract":"<p>Speed breeding (SB) can accelerate winter wheat (<i>Triticum aestivum</i> L.) generations, but optimized protocols that integrate rapid early-generation advancement with efficient field seed increase for multi-year and multi-location testing remain limited. Nitrogen (N) and seeding rate management of bulks during SB (“minibulks”) were evaluated to determine whether intergenotypic competition in F₂ bulk populations could be minimized to reduce natural selection. Additionally, a pipeline combining minibulk advancement with rapid line derivation was designed and studied for feasibility and efficiency. A 4 × 4 × 3 factorial greenhouse experiment evaluated genotype mixtures, N rates (50–300 mg N L⁻¹), and seeding rates (300–7000 seeds m⁻²) for their effects on intergenotypic competition, heading date, maturity date, and seed yield. Results indicated that shifts in genotype frequency were minor and could be minimized using a density of 3500 seeds m⁻² (80 seeds per 232.25 cm² pot), combined with 150–300 mg N L⁻¹, while maintaining seed yield and rapid generation times. Field experiments conducted over 4 years demonstrated that F₃ seeds from minibulks could be space planted in the field to establish a single-plant nursery, with single plants producing sufficient seed for 4.76 m² increase plots, achieving rapid seed amplification. Quadratic relationships between seeding rates and yield were observed in increase plots, illustrating that seed from a single plant is sufficient for seeding an increase plot that produces enough seeds for multi-environment testing. Overall, this study advances SB methods in winter wheat with a focus on efficiency during line development and seed increase.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can artificial intelligence effectively mimic a maize breeder's selection decisions?","authors":"Isaías Ariza-Hernández,&nbsp;Rex Bernardo","doi":"10.1002/csc2.70367","DOIUrl":"10.1002/csc2.70367","url":null,"abstract":"<p>Genetic improvement depends on multi-trait selection decisions whereby breeders advance a subset of candidates from a larger population. Our objective was to assess whether artificial intelligence via machine learning can effectively mimic selection decisions in maize (<i>Zea mays</i> L.). Seven experienced maize breeders independently made selection decisions in seven biparental populations from within- and across-environment phenotypic data. Selection decisions were mimicked via two linear models and four nonlinear models. In across-population prediction, the probability that a random breeder-selected candidate was ranked ahead of a random non-selected candidate was about 0.90 for the best prediction models. This high probability, which did not depend on the proportion selected by the breeder, was supported by about 0.70–0.80 of the breeder's decisions being exactly replicated by machine learning. The models performed better in identifying discarded candidates than selected candidates. Logistic regression and gradient boosting performed the best, whereas <i>k</i>-nearest neighbors performed the poorest. Hyperparameter tuning did not improve model performance. Including check hybrids in the prediction models was not helpful. As expected, yield was the most important trait, with selection being weaker but generally consistent for lower moisture, higher test weight, shorter plants, and lower ears. Overall, our findings indicated that breeder selection decisions in maize have a learnable structure that is consistent enough to be modeled effectively in a decision-support system. We speculate that the effectiveness of machine learning to mimic selection decisions in maize will only increase as more data become available to train the models.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-based optimization of mega-environmental designs for sparse multi-environment testing","authors":"Raegan Hoefler,&nbsp;Lucia Gutierrez","doi":"10.1002/csc2.70366","DOIUrl":"10.1002/csc2.70366","url":null,"abstract":"<p>Plant breeding programs aim to identify superior varieties by evaluating potential varieties in a network of locations, and these evaluations are often called multi-environment trials. It is important to optimize multi-environment trials in terms of resource allocation to ensure accurate and efficient experiments. It has been shown that mega-environmental designs (MEDs), which are sparse designs for multi-environment trials that explicitly account for the mega-environment structure within a target population of environments, can substantially increase the response to selection compared to other common designs. The objective of this simulation study was to further optimize MEDs in three areas to provide general guidelines for their use: the amounts of replication and sparsity to use in the designs, the choice of model for analysis, and the ideal structure for the target population of environments. Optimized MEDs were compared to other commonly used designs in simulated multi-environment trials using real yield data and the real genotype-by-environment interaction structure of 223 barley (<i>Hordeum vulgare</i> L.) genotypes evaluated in 30 location-year environments in the United States and Canada. MEDs always had the highest response to selection when heterogeneous genetic variances across mega-environments were modeled, and sparser MEDs were generally best. However, interactions among trait heritability, strategies in modeling the relationships between genotypes, and the alignment of the environments with historical mega-environment structure make choosing the most appropriate experimental design case-specific.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of combining ability in expired plant variety protection maize lines for Southern US adaptation and aflatoxin resistance","authors":"Joseph E. Knoll,&nbsp;Daniel Jeffers,&nbsp;Ebrahiem M. Babiker","doi":"10.1002/csc2.70364","DOIUrl":"10.1002/csc2.70364","url":null,"abstract":"<p>Contamination of maize (<i>Zea mays</i> L.) grain with aflatoxin, produced by the fungus <i>Aspergillus flavus</i>, is a major concern in Southern US environments. Development of adapted hybrids with genetic resistance to aflatoxin accumulation is one strategy to mitigate this problem. Crossing inbred lines with recently expired plant variety protection (ex-PVP) provides a new, relatively untested, source of germplasm with improved agronomic traits to utilize in breeding aflatoxin-resistant hybrids. This study assessed the combining abilities of 32 ex-PVP lines and six aflatoxin-resistant USDA-Agricultural Research Service (ARS) lines in four environments for yield, agronomic traits, and aflatoxin. USDA-ARS lines Mp313E, Mp715, and Mp717 had positive general combining ability (GCA) for anthesis, silking, and plant height, resulting in later maturing, taller hybrids, though many had low aflatoxin. Mp717 consistently demonstrated negative GCA for aflatoxin level. The USDA-ARS line GT1309 had negative GCA for flowering time and plant height. Ex-PVP lines from both major US heterotic groups (PHGV6, PHJ65, PHM57, LH195, F118, FBLL, LH200, LH236, and PHMK0) demonstrated a favorable combination of GCA for yield, ear rot resistance, and/or reduced aflatoxin when crossed with the USDA-ARS lines. Among tested hybrids, Mp717 × PHMK0 had positive specific combining ability (SCA) for yield and negative SCA for aflatoxin level, suggesting that PHMK0 is a promising ex-PVP inbred to use in second-cycle breeding.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149020006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to enviromically predict breeding genotypes as if they were commercial cultivars?","authors":"Rafael Tassinari Resende","doi":"10.1002/csc2.70362","DOIUrl":"10.1002/csc2.70362","url":null,"abstract":"<p>Early trial rankings may not translate into cultivar-stage performance, especially when genotype-by-environment interaction changes the target response across breeding stages. This study implemented and evaluated a bi-trait genomic–enviromic reaction-norm framework to predict breeding genotypes “as cultivars” by combining genomic relationships, environmental covariates, and covariance between trait-stage expressions. Breeding trial yield (BTY) and cultivar target yield (CTY) were treated as distinct but genetically related responses, with variance–covariance components estimated through derivative-free restricted maximum likelihood optimization. An illustrative simulation was used to develop the model, inspect its estimates, and visualize behavior. In this simulation, the single nucleotide polymorphism–based relationship matrix produced a more coherent BTY–CTY covariance structure than the identity matrix and enabled spatial recommendation maps. An empirical CassavaBase case study evaluated BTY-to-CTY prediction using records from multiple genotypes and locations, including bridge genotypes observed at both trait-stages. In this setting, three models were compared: a genomic BTY-only baseline, a bi-trait genomic model, and a bi-trait genomic–enviromic model using environmental principal components. The genomic–enviromic model showed its strongest advantage at the genotype level under environmentally supported leave-one-CTY-location-out (LOLO) validation, increasing prediction accuracy from 0.491 to 0.797 and reducing mean absolute error from 26.7% to 18.2% in LOLO-<i>strict</i> relative to the BTY-only baseline. These results show that breeding-stage genotypes can be projected onto the CTY scale across target environments by combining genomic, enviromic, and bi-trait information.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"66 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.70362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}