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Agriculture is an important source of human food. However, current agricultural practices need modernizing and strengthening to fulfill the increasing food requirements of the growing worldwide population. Genome editing (GE) technology has been used to produce plants with improved yields and nutritional value as well as with higher resilience to herbicides, insects, and diseases. Several GE tools have been developed recently, including clustered regularly interspaced short palindromic repeats (CRISPR) with nucleases, a customizable and successful method. The main steps of the GE process involve introducing transgenes or CRISPR into plants via specific gene delivery systems. However, GE tools have certain limitations, including time-consuming and complicated protocols, potential tissue damage, DNA incorporation in the host genome, and low transformation efficiency. To overcome these issues, nanotechnology has emerged as a groundbreaking and modern technique. Nanoparticle-mediated gene delivery is superior to conventional biomolecular approaches because it enhances the transformation efficiency for both temporal (transient) and permanent (stable) genetic modifications in various plant species. However, with the discoveries of various advanced technologies, certain challenges in developing a short-term breeding strategy in plants remain. Thus, in this review, nanobased delivery systems and plant genetic engineering challenges are discussed in detail. Moreover, we have suggested an effective method to hasten crop improvement programs by combining current technologies, such as speed breeding and CRISPR/Cas, with nanotechnology. The overall aim of this review is to provide a detailed overview of nanotechnology-based CRISPR techniques for plant transformation and suggest applications for possible crop enhancement.

The changing climate scenarios harshen the biotic stresses including boosting up the population of insect/pest and disease, uplifting weed growth, declining soil beneficial microbes, threaten pollinator, and boosting up abiotic stresses including harsh drought/waterlogging, extremisms in temperature, salinity/alkalinity, abrupt rainfall pattern)) and ulitamtely  affect the plant in multiple ways. This nexus review paper will cover four significant points viz (1) the possible impacts of climate change; as the world already facing the problem of food security, in such crucial period, climatic change severely affects all four dimensions of food security (from production to consumption) and will lead to malnutrition/malnourishment faced by low-income peoples. (2) How some major crops (wheat, cotton, rice, maize, and sugarcane) are affected by stress and their consequent loss. (3) How to develop a strategic work to limit crucial factors, like their significant role in climate-smart breeding, developing resilience to stresses, and idiotypic breeding. Additionally, there is an essence of improving food security, as much of our food is wasted before consumption for instance post-harvest losses. (4) Role of biotechnology and genetic engineering in adaptive introgression of the gene or developing plant transgenic against pests. As millions of dollars are invested in innovation and research to cope with future climate change stresses on a plant, hence community base adaptation of innovation is also considered an important factor in crop improvements. Because of such crucial predictions about the future impacts of climate change on agriculture, we must adopt measures to evolve crop.

In response to the climate change, it is essential to provide smallholder farmers with improved field crop genotypes that may increase the resilience of their farming system. This requires a fast turnover of varieties in a system capable of injecting significant amounts of genetic diversity into productive landscapes. Crop improvement is a pivotal strategy to cope with and adapt to climate change. Modern breeding may rely on the genomics revolution to speed up the development of new varieties with adaptive potential. However, centralized breeding may not adequately address smallholder farmers’ needs for more locally acclimatized varieties or groups of varieties. This, in turn, constrains adoption of new varieties that reduces the effectiveness of a resource-intensive breeding process, an issue that may be overcome with participatory, decentralized approaches. Whether high-tech centralized breeding or decentralized participatory approaches are better suited for smallholder farmers in the global South is hotly debated. Sidestepping any false dichotomies and ideological issues in these debates, this review provides a perspective on relevant advances in a breeding approach that combines the two approaches and uses genomics for trait mining from ex situ collections of genetic materials, participatory multilocation trials and crowdsourced citizen science. It argues that this new combination of high-tech centralized and participatory decentralized methods can provide a coherent and effective approach to breeding for climate adaptation and the present review advocates on a different way forward for the future research.

Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.

Climate change can decrease the global maize productivity and grain quality. Maize crop requires an optimal temperature for better harvest productivity. A suboptimal temperature at any critical stage for a prolonged duration can negatively affect the growth and yield formation processes. This review discusses the negative impact of temperature extremes (high and low temperatures) on the morpho-physiological, biochemical, and nutritional traits of the maize crop. High temperature stress limits pollen viability and silks receptivity, leading to a significant reduction in seed setting and grain yield. Likewise, severe alterations in growth rate, photosynthesis, dry matter accumulation, cellular membranes, and antioxidant enzyme activities under low temperature collectively limit maize productivity. We also discussed various strategies with practical examples to cope with temperature stresses, including cultural practices, exogenous protectants, breeding climate-smart crops, and molecular genomics approaches. We reviewed that identified quantitative trait loci (QTLs) and genes controlling high- and low temperature stress tolerance in maize could be introgressed into otherwise elite cultivars to develop stress-tolerant cultivars. Genome editing has become a key tool for developing climate-resilient crops. Moreover, challenges to maize crop improvement such as lack of adequate resources for breeding in poor countries, poor communication among the scientists of developing and developed countries, problems in germplasm exchange, and high cost of advanced high-throughput phenotyping systems are discussed. In the end, future perspectives for maize improvement are discussed, which briefly include new breeding technologies such as transgene-free clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-mediated genome editing for thermo-stress tolerance in maize.

Main conclusionGenomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time.The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO ], salinity, and ozone. The \"omics\" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

The Green Revolution must evolve to meet global food demand in times of climate change and global crisis.The architectype represents an important breeding target to secure yields under varying climatic and environmental conditions.Optimized physiological traits, defined as the physiotype, need to be integrated with optimized morphological traits to enhance yield potential, reduce resource input, and maximize environmental resilience.The synergy between ideal architectype and optimal physiotype can enable a new Green Revolution driven by advancements in genomics, transgene, genomic selection, genome editing, molecular design, and precision management practices.Emerging technologies, including high-throughput phenotyping, multi-omics approaches, machine learning, and artificial intelligence, will facilitate the discovery of gene sets for critical traits and accelerate the breeding of next-generation crop varieties. In the middle of the last century, the Green Revolution dramatically increased crop yields and transformed global agriculture. As current food production is increasingly challenged by the demands of the growing population, climate change, and environmental degradation, a new Green Revolution is urgently needed. This Review highlights recent progress in defining the morphological ideotypes of four major crops, and proposes essential physiological traits critical for crop improvement and environmental adaptation. We introduce two concepts: the ‘architectype’ representing optimized morphological features, and the ‘physiotype’ encompassing improved physiological traits. By integrating these concepts through advanced genomic technologies and precision management practices, the next Green Revolution could potentially enhance crop yields and resource use efficiency by over 20–30%, thereby ensuring sustainable food production. In the middle of the last century, the Green Revolution dramatically increased crop yields and transformed global agriculture. As current food production is increasingly challenged by the demands of the growing population, climate change, and environmental degradation, a new Green Revolution is urgently needed. This Review highlights recent progress in defining the morphological ideotypes of four major crops, and proposes essential physiological traits critical for crop improvement and environmental adaptation. We introduce two concepts: the ‘architectype’ representing optimized morphological features, and the ‘physiotype’ encompassing improved physiological traits. By integrating these concepts through advanced genomic technologies and precision management practices, the next Green Revolution could potentially enhance crop yields and resource use efficiency by over 20–30%, thereby ensuring sustainable food production.

Background A comprehensive study of the genome and genetics of superior germplasms is fundamental for crop improvement. As a widely adapted protein crop with high yield potential, the improvement in breeding and development of the seeds industry of faba bean have been greatly hindered by its giant genome size and high outcrossing rate. Results To fully explore the genomic diversity and genetic basis of important agronomic traits, we first generate a de novo genome assembly and perform annotation of a special short-wing petal faba bean germplasm (VF8137) exhibiting a low outcrossing rate. Comparative genome and pan-genome analyses reveal the genome evolution characteristics and unique pan-genes among the three different faba bean genomes. In addition, the genome diversity of 558 accessions of faba bean germplasm reveals three distinct genetic groups and remarkable genetic differences between the southern and northern germplasms. Genome-wide association analysis identifies several candidate genes associated with adaptation- and yield-related traits. We also identify one candidate gene related to short-wing petals by combining quantitative trait locus mapping and bulked segregant analysis. We further elucidate its function through multiple lines of evidence from functional annotation, sequence variation, expression differences, and protein structure variation. Conclusions Our study provides new insights into the genome evolution of Leguminosae and the genomic diversity of faba bean. It offers valuable genomic and genetic resources for breeding and improvement of faba bean.

Background Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants. Results In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis , which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding. Conclusions Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.