Genomic analysis is central to our effort to decode and enhance crop yield performance, an endeavor that, while promising, comes with its challenges. Yield traits are notoriously intricate; they weave a complex tapestry of genetic and environmental interactions that require extensive datasets for meaningful analysis. This research paper explores the complicated nature of these yield traits, emphasizing the urgency of developing large-scale databases and understanding the nuanced interplay between genotype and environment. By identifying genes linked to markers associated with yields, we can streamline breeding programs, making them faster and more precise. However, we must be candid about the limitations inherent in genomic analysis. It is undeniably powerful for boosting crop productivity, but recognizing its boundaries is equally essential. It improves our data analysis techniques and fosters a comprehensive understanding of yield genetics. SNP markers on high-density arrays may indicate genetic associations with phenotypic variation, but GBS-based genotyping methods may be better suited for identifying causal genetic variants in complex crop species, influenced by rare alleles not adequately represented on SNP arrays. Only then can we promote advancements in plant breeding, ultimately ushering in an era of increased crop productivity. Techniques like genome-wide association studies (GWAS), QTL mapping, and marker-assisted selection help accelerate breeding programs by directly targeting specific genes or loci responsible for these traits. High-throughput genotyping allows for a detailed assessment of genetic variation within and between crop populations. We study genome size, heterozygosity, and identify regions of the genome associated with traits of interest such as yield, stress tolerance, or disease resistance. We detect genomic regions where natural or artificial selection has favored specific alleles, leading to reduced genetic diversity and altered patterns within and between populations. We introduce and utilize genetic variation within specific regions of the genome. Understanding the genetic mechanisms that result in increased vigor and performance in hybrids compared to their inbred parents is critical. Generally, genomic-assisted breeding (GAB) revolutionizes crop improvement by using modern molecular tools to enhance accuracy and efficiency in plant breeding. GAB leverages techniques like marker-assisted selection, association mapping, and genomic selection to identify desirable traits, genes, and genomic regions associated with specific traits, ultimately accelerating the development of new crop varieties.
| Published in | Science Development (Volume 6, Issue 1) |
| DOI | 10.11648/j.scidev.20250601.12 |
| Page(s) | 17-24 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Agronomic Traits, Crop Diversity, DNA Sequence, and Genetic Architecture
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APA Style
Anteneh, M. (2025). Exploring the Intricacies of Crop Yield Performance Through Genomics. Science Development, 6(1), 17-24. https://doi.org/10.11648/j.scidev.20250601.12
ACS Style
Anteneh, M. Exploring the Intricacies of Crop Yield Performance Through Genomics. Sci. Dev. 2025, 6(1), 17-24. doi: 10.11648/j.scidev.20250601.12
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Download@article{10.11648/j.scidev.20250601.12,
author = {Melkam Anteneh},
title = {Exploring the Intricacies of Crop Yield Performance Through Genomics
},
journal = {Science Development},
volume = {6},
number = {1},
pages = {17-24},
doi = {10.11648/j.scidev.20250601.12},
url = {https://doi.org/10.11648/j.scidev.20250601.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.scidev.20250601.12},
abstract = {Genomic analysis is central to our effort to decode and enhance crop yield performance, an endeavor that, while promising, comes with its challenges. Yield traits are notoriously intricate; they weave a complex tapestry of genetic and environmental interactions that require extensive datasets for meaningful analysis. This research paper explores the complicated nature of these yield traits, emphasizing the urgency of developing large-scale databases and understanding the nuanced interplay between genotype and environment. By identifying genes linked to markers associated with yields, we can streamline breeding programs, making them faster and more precise. However, we must be candid about the limitations inherent in genomic analysis. It is undeniably powerful for boosting crop productivity, but recognizing its boundaries is equally essential. It improves our data analysis techniques and fosters a comprehensive understanding of yield genetics. SNP markers on high-density arrays may indicate genetic associations with phenotypic variation, but GBS-based genotyping methods may be better suited for identifying causal genetic variants in complex crop species, influenced by rare alleles not adequately represented on SNP arrays. Only then can we promote advancements in plant breeding, ultimately ushering in an era of increased crop productivity. Techniques like genome-wide association studies (GWAS), QTL mapping, and marker-assisted selection help accelerate breeding programs by directly targeting specific genes or loci responsible for these traits. High-throughput genotyping allows for a detailed assessment of genetic variation within and between crop populations. We study genome size, heterozygosity, and identify regions of the genome associated with traits of interest such as yield, stress tolerance, or disease resistance. We detect genomic regions where natural or artificial selection has favored specific alleles, leading to reduced genetic diversity and altered patterns within and between populations. We introduce and utilize genetic variation within specific regions of the genome. Understanding the genetic mechanisms that result in increased vigor and performance in hybrids compared to their inbred parents is critical. Generally, genomic-assisted breeding (GAB) revolutionizes crop improvement by using modern molecular tools to enhance accuracy and efficiency in plant breeding. GAB leverages techniques like marker-assisted selection, association mapping, and genomic selection to identify desirable traits, genes, and genomic regions associated with specific traits, ultimately accelerating the development of new crop varieties.
},
year = {2025}
}
TY - JOUR T1 - Exploring the Intricacies of Crop Yield Performance Through Genomics AU - Melkam Anteneh Y1 - 2025/06/30 PY - 2025 N1 - https://doi.org/10.11648/j.scidev.20250601.12 DO - 10.11648/j.scidev.20250601.12 T2 - Science Development JF - Science Development JO - Science Development SP - 17 EP - 24 PB - Science Publishing Group SN - 2994-7154 UR - https://doi.org/10.11648/j.scidev.20250601.12 AB - Genomic analysis is central to our effort to decode and enhance crop yield performance, an endeavor that, while promising, comes with its challenges. Yield traits are notoriously intricate; they weave a complex tapestry of genetic and environmental interactions that require extensive datasets for meaningful analysis. This research paper explores the complicated nature of these yield traits, emphasizing the urgency of developing large-scale databases and understanding the nuanced interplay between genotype and environment. By identifying genes linked to markers associated with yields, we can streamline breeding programs, making them faster and more precise. However, we must be candid about the limitations inherent in genomic analysis. It is undeniably powerful for boosting crop productivity, but recognizing its boundaries is equally essential. It improves our data analysis techniques and fosters a comprehensive understanding of yield genetics. SNP markers on high-density arrays may indicate genetic associations with phenotypic variation, but GBS-based genotyping methods may be better suited for identifying causal genetic variants in complex crop species, influenced by rare alleles not adequately represented on SNP arrays. Only then can we promote advancements in plant breeding, ultimately ushering in an era of increased crop productivity. Techniques like genome-wide association studies (GWAS), QTL mapping, and marker-assisted selection help accelerate breeding programs by directly targeting specific genes or loci responsible for these traits. High-throughput genotyping allows for a detailed assessment of genetic variation within and between crop populations. We study genome size, heterozygosity, and identify regions of the genome associated with traits of interest such as yield, stress tolerance, or disease resistance. We detect genomic regions where natural or artificial selection has favored specific alleles, leading to reduced genetic diversity and altered patterns within and between populations. We introduce and utilize genetic variation within specific regions of the genome. Understanding the genetic mechanisms that result in increased vigor and performance in hybrids compared to their inbred parents is critical. Generally, genomic-assisted breeding (GAB) revolutionizes crop improvement by using modern molecular tools to enhance accuracy and efficiency in plant breeding. GAB leverages techniques like marker-assisted selection, association mapping, and genomic selection to identify desirable traits, genes, and genomic regions associated with specific traits, ultimately accelerating the development of new crop varieties. VL - 6 IS - 1 ER -
Debre Zeit Agricultural Research Center, Ethiopian Institute of Agricultural Research, Debre Zeit, Ethiopia
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