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Introduction: Rice is a vital staple food for many countries, and its yield is known to be significantly affected by various abiotic stresses, which are expected to intensify with climate change, posing a threat to global food security. Abscisic acid (ABA), a crucial plant growth regulator, plays a crucial role in plant responses to these abiotic stresses. It influences several processes, such as seed dormancy, leaf gas exchange, reactive oxygen species (ROS) scavenging, ion toxicity reduction, and root elongation, all of which contribute to enhancing plant survival under stress. Methods: This article reviews recent research on ABA-mediated gene responses and expressions involved in rice plant architecture and its response to abiotic stress. Results and discussion: Abscisic acid responses were primarily driven by changes in gene expression. Expression analyses of the gene related to ABA biosynthesis or catabolism indicated several changes in plant architecture, such as changes in leaf angle, delayed flowering, and modifications in growth regulators. Additionally, tolerance-related mechanisms, such as increased ROS scavenging, reduced membrane leakage, and vacuolar compartmentation of toxic radicals, were activated under single or multiple stress conditions. While these adaptations may improve plant survival and yield sustainability under stress, they may not necessarily enhance yield potential in environments affected by drought, salinity, or heat stress. ABA expression was also associated with improved pollen viability, grain-filling potential, and seed setting under abiotic stresses such as heat, which could enhance seed yield in such challenging environments. [ABSTRACT FROM AUTHOR]

Summary: Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy‐based phenotypic trade‐offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non‐pleiotropy‐induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large‐scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability. [ABSTRACT FROM AUTHOR]

Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts. [ABSTRACT FROM AUTHOR]

Sesame seeds and their edible oil are highly nutritious and rich in mono- and polyunsaturated fatty acids. Bioactive compounds such as sterols, tocopherols, and sesamol provide significant medicinal benefits. The high oil content (50%) and favorable mono- and polyunsaturated fatty acid balance, as well as resilience to water stress, make sesame a promising candidate crop for global agricultural expansion. However, sesame production faces challenges such as low yields, poor response to agricultural inputs, and losses due to capsule dehiscence. To enhance yield, traits like determinate growth, dwarfism, a high harvest index, non-shattering capsules, disease resistance, and photoperiod sensitivity are needed. These traits can be achieved through variation or induced mutation breeding. Crossbreeding methods often result in unwanted genetic changes. The gene editing CRISPR/Cas9 technology has the potential to suppress detrimental alleles and improve the fatty acid profile by inhibiting polyunsaturated fatty acid biosynthesis. Even though sesame is an orphan crop, it has entered the genomic era, with available sequences assisting molecular breeding efforts. This progress aids in associating single-nucleotide polymorphisms (SNPs) and simple sequence repeats (SSR) with key economic traits, as well as identifying genes related to adaptability, oil production, fatty acid synthesis, and photosynthesis. Additionally, transcriptomic research can reveal genes involved in abiotic stress responses and adaptation to diverse climates. The mapping of quantitative trait loci (QTL) can identify loci linked to key traits such as capsule size, seed count per capsule, and capsule number per plant. This article reviews recent advances in sesame breeding, discusses ongoing challenges, and explores potential strategies for future improvement. Hence, integrating advanced genomic tools and breeding strategies provides promising ways to enhance sesame production to meet global demands. [ABSTRACT FROM AUTHOR]

Biostimulants play a crucial role in enhancing crop yields while promoting sustainability and environmental responsibility. Evaluating the efficacy of biostimulants on-farm requires rigorous, multiyear trials conducted across various locations and with different cultivars. This study was conducted in Uruguay from 2015 to 2023 to assess the impact of a single application of a humic biostimulant (HB) during the R3 phenological stage on irrigated rice (Oryza sativa L.). The study encompassed 103 farms situated in diverse cropping zones, each characterized by distinct cultivars, soil qualities, radiation, and temperature conditions across the East and North regions. Results revealed that the HB treatment elicited an average yield increase of 7.4% across all sites. Notably, 93% (97) of the trials exhibited a positive yield response, with an average increase of 8.5%, while only six trials (all in the eastern zone) showed a negative response to the HB treatment. A combined analysis of variance indicated that the biostimulant’s effect did not significantly differ between production zones, years, or rice cultivars when negative responses were excluded. Furthermore, relationships with environmental variables were nonsignificant, underscoring the positive effect of the biostimulant regardless of location. These findings hold significant implications for Uruguay’s rice sector, that is, integrating HBs into standard management practices could substantially boost irrigated rice yields in rice-producing areas. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction. [ABSTRACT FROM AUTHOR]

Agriculture is now facing the 'perfect storm' of climate change, increasing costs of fertilizer and rising food demands from a larger and wealthier human population. These factors point to a global food deficit unless the efficiency and resilience of crop production is increased. The intensification of agriculture has focused on improving production under optimized conditions, with significant agronomic inputs. Furthermore, the intensive cultivation of a limited number of crops has drastically narrowed the number of plant species humans rely on. A new agricultural paradigm is required, reducing dependence on high inputs and increasing crop diversity, yield stability and environmental resilience. Genomics offers unprecedented opportunities to increase crop yield, quality and stability of production through advanced breeding strategies, enhancing the resilience of major crops to climate variability, and increasing the productivity and range of minor crops to diversify the food supply. Here we review the state of the art of genomic-assisted breeding for the most important staples that feed the world, and how to use and adapt such genomic tools to accelerate development of both major and minor crops with desired traits that enhance adaptation to, or mitigate the effects of climate change.

Climate change affects agricultural productivity worldwide. Increased prices of food commodities are the initial indication of drastic edible yield loss, which is expected to increase further due to global warming. This situation has compelled plant scientists to develop climate change-resilient crops, which can withstand broad-spectrum stresses such as drought, heat, cold, salinity, flood, submergence and pests, thus helping to deliver increased productivity. Genomics appears to be a promising tool for deciphering the stress responsiveness of crop species with adaptation traits or in wild relatives toward identifying underlying genes, alleles or quantitative trait loci. Molecular breeding approaches have proven helpful in enhancing the stress adaptation of crop plants, and recent advances in high-throughput sequencing and phenotyping platforms have transformed molecular breeding to genomics-assisted breeding (GAB). In view of this, the present review elaborates the progress and prospects of GAB for improving climate change resilience in crops, which is likely to play an ever increasing role in the effort to ensure global food security.

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding. [ABSTRACT FROM AUTHOR]

Potato genetic improvement begins with crossing cultivars or breeding clones which often have complementary characteristics for producing heritable variation in segregating offspring, in which phenotypic selection is used thereafter across various vegetative generations (Ti). The aim of this research was to determine whether tetrasomic genomic best linear unbiased predictors (GBLUPs) may facilitate selecting for tuber yield across early Ti within and across breeding sites in inbred (S1) and hybrid (F1) tetraploid potato offspring. This research used 858 breeding clones for a T1 trial at Umeå (Norrland, 63°49′30″ N 20°15′50″ E) in 2021, as well as 829 and 671 clones from the breeding population for T2 trials during 2022 at Umeå and Helgegården (Skåne, 56°01′46″ N 14°09′24″ E), respectively, along with their parents (S0) and check cultivars. The S1 and F1 were derived from selfing and crossing four S0. The experimental layout was an augmented design of four-plant plots across testing sites, where breeding clones were non-replicated, and the parents and cultivars were placed in all blocks between the former. The genomic prediction abilities (r) for tuber weight per plant were 0.5944 and 0.6776 in T2 at Helgegården and Umeå, respectively, when T1 at Umeå was used as the training population. On average, r was larger in inbred than in hybrid offspring at both breeding sites. The r was also estimated using multi-environment data (involving at least one S1 and one F1) for T2 performance at both breeding sites. The r was strongly influenced by the genotype in both S1 and F1 offspring irrespective of the breeding site. [ABSTRACT FROM AUTHOR]