Predicting inbred parent synchrony at flowering for maize hybrid seed production by integrating crop growth model with whole genome prediction

Crop Science, Journal Year: 2025, Volume and Issue: 65(1)

Published: Jan. 1, 2025

Abstract One of the challenges maize ( Zea mays ) hybrid seed production is to ensure synchrony at flowering two inbred parents a hybrid, which depends on specific parental combination and environmental conditions field. Maize can be simulated using mechanistic crop growth model that converts thermal time accumulation leaf numbers based inbred‐specific physiological parameter values. Heretofore, these parameters need measured or assigned prior knowledge. Here, we leverage genetic, environmental, management data predict simulate phenotypes by whole genome prediction methodology combined with (CGM–WGP) as part in‐field in‐season development. We use estimation sets differ in terms weather information test robustness our approach. As findings, demonstrate importance defining informative priors generate biologically meaningful predictions unobserved parameters. Our CGM–WGP infrastructure efficient simulating phenotypes. An important practical application method ability recommend differential planting intervals for male female inbreds used commercial fields synchronize flowering.

Language: Английский

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Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 757 - 757

Published: March 20, 2025

This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep (DL), in advancing genetic improvement Solanaceous crops. AI has emerged as a powerful solution to overcome limitations traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms models, ML DL facilitate identification optimization key traits, including higher yield, improved quality, pest resistance, tolerance extreme climatic conditions. By integrating big data analytics omics, these methods enhance genomic selection (GS), support gene-editing technologies like CRISPR-Cas9, accelerate crop breeding, thus enabling development resilient adaptable highlights role improving Solanaceae crops, such tomato, potato, eggplant, pepper, aim developing novel varieties superior agronomic quality traits. Additionally, this study examines advantages AI-driven compared Solanaceae, emphasizing contribution agricultural resilience, food security, environmental sustainability.

Language: Английский

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Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Abstract Predicting phenotypes from a combination of genetic and environmental factors is grand challenge modern biology. Slight improvements in this area have the potential to save lives, improve food fuel security, permit better care planet, create other positive outcomes. In 2022 2023 first open-to-the-public Genomes Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using large dataset including genomic variation, phenotype weather measurements field management notes, gathered project over nine years. The attracted registrants around world with representation academic, government, industry, non-profit institutions as well unaffiliated. These participants came diverse disciplines include plant science, animal breeding, statistics, computational biology others. Some had no formal genetics or plant-related training, some were just beginning their graduate education. teams applied varied methods strategies, providing wealth modeling knowledge based on common dataset. winner’s strategy involved two models combining machine learning traditional breeding tools: one model emphasized environment features extracted Random Forest, Ridge Regression Least-squares, focused genetics. Other high-performing teams’ included quantitative genetics, classical learning/deep learning, mechanistic models, ensembles. used, such genetics; weather; data, also diverse, demonstrating that single far superior all others within context competition.

Language: Английский

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Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 22, 2024

Abstract Predicting phenotypes from a combination of genetic and environmental factors is grand challenge modern biology. Slight improvements in this area have the potential to save lives, improve food fuel security, permit better care planet, create other positive outcomes. In 2022 2023, first open-to-the-public Genomes Fields initiative Genotype by Environment prediction competition was held using large dataset including genomic variation, phenotype weather measurements, field management notes gathered project over 9 years. The attracted registrants around world with representation academic, government, industry, nonprofit institutions as well unaffiliated. These participants came diverse disciplines, plant science, animal breeding, statistics, computational biology, others. Some had no formal genetics or plant-related training, some were just beginning their graduate education. teams applied varied methods strategies, providing wealth modeling knowledge based on common dataset. winner's strategy involved 2 models combining machine learning traditional breeding tools: 1 model emphasized environment features extracted random forest, ridge regression, least squares, focused genetics. Other high-performing teams’ included quantitative genetics, learning/deep learning, mechanistic models, ensembles. used, such weather, data, also diverse, demonstrating that single far superior all others within context competition.

Language: Английский

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Unlocking Wheat Drought Tolerance: The Synergy of Omics Data and Computational Intelligence

Food and Energy Security, Journal Year: 2024, Volume and Issue: 13(6)

Published: Nov. 1, 2024

ABSTRACT Currently, approximately 4.5 billion people in developing countries consider bread wheat ( Triticum aestivum L.) as a staple food crop, it is key source of daily calories. Wheat is, therefore, ranked the second most important grain crop world. Climate change associated with severe drought conditions and rising global mean temperatures has resulted sporadic soil water shortage causing yield loss wheat. While responses crosscut all omics levels, our understanding water‐deficit response mechanisms, particularly context wheat, remains incomplete. This can be significantly advanced aid computational intelligence, more often referred to artificial intelligence (AI) models, especially those leveraging machine learning deep tools. However, there an imminent continuous need for AI integration. Yet, foundational step this integration clear contextualization drought—a task that long posed challenges scientific community, including plant breeders. Nonetheless, literature indicates significant progress fields, large amounts potentially informative data being produced daily. Despite this, questionable whether reported big datasets have met security expectations, translating into pre‐breeding initiatives challenge, which likely due accessibility or reproducibility issues, interpreting poses review, focuses on these perspectives explores how might act interface make insightful. We examine stress, focus

Language: Английский

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