Plant Genetic Resources (PGR) are material containing useful genetic variation in plant species, including their weedy and wild relatives. Collections of PGR are mined for useful traits as needed to respond to new biotic and abiotic stresses; incorporate new nutritional, yield, or value‐added traits; and create new markets or industries. PGR are also useful to identify genes encoding traits of interest; serve as training populations for new breeding methodologies such as genomic selection that integrate the latest genomic data with new statistical modeling; and underpin evolutionary and taxonomic studies. The genebanks of the USDA ARS National Plant Germplasm System have experienced progress and challenges in applying such tools and technologies to PGR maintenance and creation of data that will increase their utility. These tools include high‐throughput genotyping and phenotyping technologies that could be used by breeders to unlock their genetic potential, and to more efficiently and effectively manage the collections. Examples of these technologies used on PGR of the USDA ARS Plant Germplasm Introduction and Testing Research Unit are presented, as are genebank success stories of entries that were used to create new breeding lines and cultivars to solve a problem faced by US farmers. Finally, current challenges and opportunities in PGR management and utilization are offered.
By introducing desirable traits into an existing genome while maintaining the plant’s genetic integrity, plant genetic engineering is an effective method for enhancing plants. The introduction of valuable genes encoding traits like disease/insect resistance, herbicide tolerance, enhanced nutritional/medical/commercial properties, improved uptake and utilization of growth agents, and many more traits has been accomplished through the genetic modification of a wide variety of plant species. For reasons discussed later in this chapter, the public’s perception of genetically modified organisms (GMOs) is currently negative. However, it remains to be seen whether the advantages of reduced chemical use and improved nutritional aspects lead to a world with food security that is environmentally sustainable. Genetic engineering is especially important because grape is very heterozygous and standard breeding methods fail to produce true-to-type offspring. Grapes are also a commodity driven by consumers, and novel varieties, particularly wine cultivars, have little market acceptance. In order to produce indigenous genotypes with novel characteristics, technological breakthroughs like gene transfer must be utilized because vegetative propagation leaves little room for development.
A. Goswami, Bijendra Singh, R. P. Singh· Progressive Agriculture· 0 citations
The imprecise breeding methods including recombination breeding, physical/chemical mutagenesis, and marker-assisted breeding have been extensively utilized for trait improvement of rice crop. Despite tremendous progress made through these breeding methods, the critical issues, such as linkage drag, unintended phenotype, and longer duration of time required to breed a cultivar, have been the major limitations. Among the new breeding technologies, genome editing (GE) has become the most promising approach because of its specificity, precision, and speed. Despite its transformative potential, genome editing continues to face several limitations in crop improvement. These include well-recognized policy challenges, such as biosafety regulations and intellectual property constraints, alongside technical barriers like inefficient tissue culture and transformation systems. Additionally, researchers remain constrained by the limited availability of precise gene information necessary for accurate targeted editing and effective trait enhancement. This review presents an analysis of genes that regulate abiotic and biotic stresses, yield, grain quality and nutrition, plant architecture, nutrient absorption and use efficiency, and other agronomically important traits of rice. The trait-wise probable target genes for genome editing have been discussed in detail. This review will serve as a ready reckoner for rice researchers and funding agencies.
Manish Solanki, Faisal Yousuf, Akanksha Srivastava et al.· Physiologia Plantarum : An I...· 2 citations
The future of crop improvement using GEd technologies lies in the harmonisation or alignment of global policies and regulations to support the trade of agricultural produce and ensure that growers and consumers can benefit from GEd technology.
Michael G. K. Jones· Sugar Industry international· 0 citations
Legumes are vital for global food security, nutrition, and sustainable agriculture, yet their improvement is often hindered by species-specific recalcitrance and conventional breeding limitations. Tissue culture technologies have emerged as transformative tools, enabling rapid regeneration, somatic embryogenesis, haploid production, and in vitro mutagenesis to generate elite genotypes and novel genetic diversity. Their integration with molecular breeding, genome editing, and in vitro conservation has facilitated the development of stress-tolerant, disease-resistant, and nutritionally enhanced cultivars while safeguarding germplasm resources. Recent advances highlight the role of artificial intelligence, particularly machine learning models, in optimizing plant growth regulator combinations to overcome regeneration bottlenecks in recalcitrant legumes, thereby enhancing shoot regeneration frequency and efficiency. Coupled with high throughput phenotyping, mutational breeding, and genomics-based approaches, these computational innovations accelerate the exploitation of genetic diversity and introgression of novel traits beyond the natural legume gene pool. Collectively, tissue culture technologies, strengthened by AI-driven predictive modeling and precision phenotyping, stand as a cornerstone for sustainable legume improvement, addressing escalating global food and feed demands while meeting environmental challenges.
Suman Rawte, Z. Jha, Hemant Sahu et al.· Genetics and Molecular Resea...· 0 citations
Plant breeding has progressed from phenotype-based selection to increasingly precise genetic and agronomic interventions. Advances in molecular breeding, genome engineering, and crop management have improved productivity, but have also promoted the widespread use of genetically uniform cultivars optimized for controlled production systems. While uniformity facilitates predictability and mechanization, it may constrain adaptive capacity under increasingly variable environmental conditions. In parallel, recent developments in digital agriculture, including high-resolution phenotyping, remote-sensing, molecular diagnostics, and AI-assisted decision support, are transforming the ability to monitor and manage biological variation across spatial and temporal scales. In this review, we examine how these technological advances intersect with emerging concepts in crop diversity and reproductive biology. We discuss how digital agriculture enables improved characterization of genotype-environment interactions and consider reproductive mechanisms that expand the accessible breeding space beyond conventional biparental crossing schemes, including haploid induction and multi-parental breeding. These approaches provide opportunities to accelerate trait introgression, generate novel genetic combinations, and overcome reproductive barriers. We argue that digital and diagnostic agriculture provide an informational framework for the deployment and evaluation of genetically heterogeneous plant populations. Together, recent advances suggest that technological precision and biological diversity can be integrated into breeding strategies that improve productivity and resilience.
By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations
