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Key messageSpecific combinations of LFY and WAPO1 natural alleles maximize spikelet number per spike in wheat. Spikelet number per spike (SNS) is an important yield component in wheat that determines the maximum number of grains that can be formed in a wheat spike. In wheat, loss-of-function mutations in LEAFY (LFY) or its interacting protein WHEAT ORTHOLOG OF APO1 (WAPO1) significantly reduce SNS by reducing the rate of formation of spikelet meristems. In previous studies, we identified a natural amino acid change in WAPO1 (C47F) that significantly increases SNS in hexaploid wheat. In this study, we searched for natural variants in LFY that were associated with differences in SNS and detected significant effects in the LFY-B region in a nested association mapping population. We generated a large mapping population and confirmed that the LFY-B polymorphism R80S is linked with the differences in SNS, suggesting that LFY-B is the likely causal gene. A haplotype analysis revealed two amino acid changes P34L and R80S, which were both enriched during wheat domestication and breeding suggesting positive selection. We also explored the interactions between the LFY and WAPO1 natural variants for SNS using biparental populations and identified significant interaction, in which the positive effect of the 80S and 34L alleles from LFY-B was only detected in the WAPO-A1 47F background but not in the 47C background. Based on these results, we propose that the allele combination WAPO-A1-47F/LFY-B 34L 80S can be used in wheat breeding programs to maximize SNS and increase grain yield potential in wheat.

Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (UGA230, UA48 and CSX8308) and updating the TM-1 cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from Pima cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

Sugarcane, the world's most harvested crop by tonnage, has shaped global history, trade and geopolitics, and is currently responsible for 80% of sugar production worldwide1. While traditional sugarcane breeding methods have effectively generated cultivars adapted to new environments and pathogens, sugar yield improvements have recently plateaued2. The cessation of yield gains may be due to limited genetic diversity within breeding populations, long breeding cycles and the complexity of its genome, the latter preventing breeders from taking advantage of the recent explosion of whole-genome sequencing that has benefited many other crops. Thus, modern sugarcane hybrids are the last remaining major crop without a reference-quality genome. Here we take a major step towards advancing sugarcane biotechnology by generating a polyploid reference genome for R570, a typical modern cultivar derived from interspecific hybridization between the domesticated species (Saccharum officinarum) and the wild species (Saccharum spontaneum). In contrast to the existing single haplotype ('monoploid') representation of R570, our 8.7 billion base assembly contains a complete representation of unique DNA sequences across the approximately 12 chromosome copies in this polyploid genome. Using this highly contiguous genome assembly, we filled a previously unsized gap within an R570 physical genetic map to describe the likely causal genes underlying the single-copy Bru1 brown rust resistance locus. This polyploid genome assembly with fine-grain descriptions of genome architecture and molecular targets for biotechnology will help accelerate molecular and transgenic breeding and adaptation of sugarcane to future environmental conditions.

High-throughput sequencing-based methods for bulked segregant analysis (BSA) allow for the rapid identification of genetic markers associated with traits of interest. BSA studies have successfully identified qualitative (binary) and quantitative trait loci (QTLs) using QTL mapping. However, most require population structures that fit the models available and a reference genome. Instead, high-throughput short-read sequencing can be combined with BSA of k-mers (BSA-k-mer) to map traits that appear refractory to standard approaches. This method can be applied to any organism and is particularly useful for species with genomes diverged from the closest sequenced genome. It is also instrumental when dealing with highly heterozygous and potentially polyploid genomes without phased haplotype assemblies and for which a single haplotype can control a trait. Finally, it is flexible in terms of population structure. Here, we apply the BSA-k-mer method for the rapid identification of candidate regions related to seed spot and seed size in diploid potato. Using a mixture of F1 and F2 individuals from a cross between 2 highly heterozygous parents, candidate sequences were identified for each trait using the BSA-k-mer approach. Using parental reads, we were able to determine the parental origin of the loci. Finally, we mapped the identified k-mers to a closely related potato genome to validate the method and determine the genomic loci underlying these sequences. The location identified for the seed spot matches with previously identified loci associated with pigmentation in potato. The loci associated with seed size are novel. Both loci are relevant in future breeding toward true seeds in potato.

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum, a drought and heat-tolerant C4 species, requires a vast, large-scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large-scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)-induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis-regulatory elements. A comparison of the distribution of mutations in the large-scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS-induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5' UTR, gene body, and 3'-UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the Ka /Ks value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB (https://www.depts.ttu.edu/igcast/sorbmutdb.php) and SorghumBase (https://sorghumbase.org/). This current large-scale sequence-indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.

Development of high yielding cowpea varieties coupled with good taste and rich in essential minerals can promote consumption and thus nutrition and profitability. The sweet taste of cowpea grain is determined by its sugar content, which comprises mainly sucrose and galacto-oligosaccharides (GOS) including raffinose and stachyose. However, GOS are indigestible and their fermentation in the colon can produce excess intestinal gas, causing undesirable bloating and flatulence. In this study, we aimed to examine variation in grain sugar and mineral concentrations, then map quantitative trait loci (QTLs) and estimate genomic-prediction (GP) accuracies for possible application in breeding. Grain samples were collected from a multi-parent advanced generation intercross (MAGIC) population grown in California during 2016-2017. Grain sugars were assayed using high-performance liquid chromatography. Grain minerals were determined by inductively coupled plasma-optical emission spectrometry and combustion. Considerable variation was observed for sucrose (0.6-6.9%) and stachyose (2.3-8.4%). Major QTLs for sucrose (QSuc.vu-1.1), stachyose (QSta.vu-7.1), copper (QCu.vu-1.1) and manganese (QMn.vu-5.1) were identified. Allelic effects of major sugar QTLs were validated using the MAGIC grain samples grown in West Africa in 2017. GP accuracies for minerals were moderate (0.4-0.58). These findings help guide future breeding efforts to develop mineral-rich cowpea varieties with desirable sugar content.

The development of cereal crops with high nitrogen use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improving crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse Sorghum bicolor lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly impacted the root-associated bacterial communities and root metabolite composition of sorghum. We found a positive correlation between sorghum NUE and bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxon, Pseudomonas. Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to low N stress. This indicates that the shift in the sorghum microbiome due to low N is associated with the root metabolites of the host plant. Taken together, our findings suggest that host genetic regulation of root metabolites plays a role in defining the root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low N stress.IMPORTANCEThe development of crops that are more nitrogen use-efficient (NUE) is critical for the future of the enhanced sustainability of agriculture worldwide. This objective has been pursued mainly through plant breeding and plant molecular engineering, but these approaches have had only limited success. Therefore, a different strategy that leverages soil microbes needs to be fully explored because it is known that soil microbes improve plant growth through multiple mechanisms. To design approaches that use the soil microbiome to increase NUE, it will first be essential to understand the relationship among soil microbes, root metabolites, and crop productivity. Using this approach, we demonstrated that certain key metabolites and specific microbes are associated with high and low sorghum NUE in a field study. This important information provides a new path forward for developing crop genotypes that have increased NUE through the positive contribution of soil microbes.

Rapid advancements in technologies provide various tools to analyze fruit crop genomes to better understand genetic diversity and relationships and aid in breeding. Genome-wide single nucleotide polymorphism (SNP) genotyping arrays offer highly multiplexed assays at a relatively low cost per data point. We report the development and validation of 1.4M SNP Axiom® Citrus HD Genotyping Array (Citrus 15AX 1 and Citrus 15AX 2) and 58K SNP Axiom® Citrus Genotyping Arrays for Citrus and close relatives. SNPs represented were chosen from a citrus variant discovery panel consisting of 41 diverse whole-genome re-sequenced accessions of Citrus and close relatives, including eight progenitor citrus species. SNPs chosen mainly target putative genic regions of the genome and are accurately called in both Citrus and its closely related genera while providing good coverage of the nuclear and chloroplast genomes. Reproducibility of the arrays was nearly 100%, with a large majority of the SNPs classified as the most stringent class of markers, "PolyHighResolution" (PHR) polymorphisms. Concordance between SNP calls in sequence data and array data average 98%. Phylogenies generated with array data were similar to those with comparable sequence data and little affected by 3 to 5% genotyping error. Both arrays are publicly available.

Summary Chromosomal inversions can preserve combinations of favorable alleles by suppressing recombination. Simultaneously, they reduce the effectiveness of purifying selection enabling deleterious alleles to accumulate. This study explores how areas of low recombination, including centromeric regions and chromosomal inversions, contribute to the accumulation of deleterious and favorable loci in 225 Mangifera indica genomes from the Australian Mango Breeding Program. Here, we identify 17 chromosomal inversions that cover 7.7% (29.7 Mb) of the M. indica genome: eight pericentric (inversion includes the centromere) and nine paracentric (inversion is on one arm of the chromosome). Our results show that these large pericentric inversions are accumulating deleterious loci, while the paracentric inversions show deleterious levels above and below the genome wide average. We find that despite their deleterious load, chromosomal inversions contain small effect loci linked to variation in crucial breeding traits. These results indicate that chromosomal inversions have likely facilitated the evolution of key mango breeding traits. Our study has important implications for selective breeding of favorable combinations of alleles in regions of low recombination. [ABSTRACT FROM AUTHOR]

BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines. [ABSTRACT FROM AUTHOR]

Summary Flavor is a crucial aspect of the eating experience, reflecting evolving consumer preferences for fruits with enhanced quality. Modern fruit breeding programs prioritize improving quality traits aligned with consumer tastes. However, defining fruit‐quality attributes that significantly impact consumer preference is a current challenge faced by the industry and breeders. This study proposes a data‐driven approach to statistically model the relationship between fruit‐quality parameters and consumers' overall liking. Our primary hypothesis suggests that the interplay between fruit‐quality attributes and consumer preferences may reach a critical value, serving as new empirical benchmarks for fruit quality. Using extensive historical datasets accounting for sensory, biochemical, and genomic information described in blueberry, we first demonstrated that multivariate adaptive regression splines (MARS) could be used to identify specific values of fruit‐quality traits that significantly affect consumer perception by using nonlinear spline regressions on estimating threshold points. We harnessed genomic information and carried out genomic selection (GS) for five fruit‐quality traits evaluated on the original scale and after classified via the MARS approach. This study provides a pioneering consumer‐centric and data‐driven approach to defining fruit‐quality standards and supporting molecular breeding that has broad applications to breeding programs from any species. [ABSTRACT FROM AUTHOR]

ABSTRACT Check varieties are used in plant breeding and variety testing for a number of reasons. One important use of checks is to provide connectivity between years, which facilitates comparison among genotypes of interest that are tested in different years. When long‐term data are available, such comparisons allow an assessment of realized genetic gain (RGG). Here, we consider the question of how many check varieties are needed per cycle for a reliable assessment of RGG. We propose an approach that makes use of variance component estimates for relevant random effects in a linear mixed model and plugs them into an analysis of dummy datasets set up to represent the design options being considered. Our results show that it is useful to employ a larger number of checks and to keep the replacement rate low. Furthermore, there is intercycle information to be recovered, especially when there are few checks and replacement rates are high, so modelling the cycle main effect as random pays off. [ABSTRACT FROM AUTHOR]

The goal of transgenesis in plant breeding is to make step‐change improvements in traits of interest. However, improving quantitative traits, such as yield in maize (Zea mays L.), with transgenes has been difficult. Traditionally, transgene testing is done on a few isogenic lines, and results are extrapolated to entire breeding populations. Testing on limited germplasm does not provide a robust estimate of a transgene's value. Incorporating transgenes directly into breeding populations could increase genetic variance and the rate of genetic gain. Here, we used a transgene that reduces ethylene as a case study and investigated event, transgene, family, and environment effects and their interactions. We also determined whether introduction of the transgene into a breeding population would result in transgenic lines being preferentially selected over nontransgenic lines for yield. We found significant variation in transgene effects across clustered environments and families for multiple traits including yield. In environmental Cluster 2, the transgenic lines yielded 0.4 Mg ha−1 more than nontransgenic lines in family KC22; yet, in family QY43, transgenic lines yielded 0.3 Mg ha−1 less. Similarly, within Cluster 4, the QY43 family had preferential selection of transgenic over nontransgenic lines, whereas in families YE41 and AY91, nontransgenic lines were selected more frequently. These results show the critical importance of evaluating transgenes across broad germplasm diversity to assess their general value to a program. Integrating transgenes, or using gene editing, directly in a breeding program can expand genetic variation for quantitative traits and potentially accelerate genetic gain. [ABSTRACT FROM AUTHOR]

Premise Methods Results Discussion Leveraging DNA markers, particularly single‐nucleotide polymorphisms (SNPs), in parentage analysis, sib‐ship reconstruction, and genomic relatedness analysis can enhance plant breeding efficiency. However, the limited availability of genomic information, confined to the most commonly used species, hinders the broader application of SNPs in species of lower economic interest (e.g., most tree species). We explored the possibility of using universal target capture probes, namely Angiosperms353, to identify SNPs and assess their effectiveness in genomic relatedness analysis.We tested the approach in 11 tree species, six of which had a half‐sib family structure. Variants were called within species, and genomic relatedness analysis was conducted in species with two or more families. Scalability via amplicon sequencing was tested by designing primers and testing them in silico.Adequate SNPs for relatedness analysis were identified in all species. Relatedness values from Angiosperms353‐based SNPs highly correlated with those from thousands of genome‐wide DArTseq SNPs in Cordia africana, one of the species with a family structure. The in silico performance of designed primers demonstrated the potential for scaling up via amplicon sequencing.Utilizing universal target capture probes for SNP identification can help overcome the limitations of genomic information availability, thereby enhancing the application of genomic markers in breeding plant species with lower economic interest. [ABSTRACT FROM AUTHOR]

Background: Beyond carrying the plant embryo, seeds harbour intricate microbial communities whose transmission across successive plant generations can significantly influence the ecological and evolutionary dynamics of plant–microbe symbioses. The process of plant domestication has potential repercussions in genes involved in plant-microbiome interactions. However, the extent to which breeding can impact the seed microbiome is sparsely explored. Cannabis is a high-value crop but sparsely subjected to agricultural innovations established in other crop species during the last century. Here, we conduct a large-scale analysis of the bacterial seed microbiome of Cannabis across different domestication grades and investigate the potential of seed-associated endophytes as plant growth-promoting agents under both controlled and field conditions. Results: Analysis of Cannabis seed endophyte composition and diversity across 46 plant genotypes revealed 813 different bacterial genera with a predominance of Gammaproteobacteria, Bacilli, Actinobacteria and Alphaproteobacteria but a genotype-specific microbiome. The assessment of domestication and breeding on microbial assembly revealed a higher bacterial diversity in low domestication genotypes (Shannon index, H′: 1.21 vs. 1.05) and a higher homogeneity in bacterial composition caused by line development. Further, a seed bacterial isolate (Bacillus frigoritolerans C1141) associated with low domestication genotypes, and with genes associated with bio-fertilization, bioremediation and phytohormone production, increased plant growth by 42.3% at the time of harvest, under field conditions. Conclusion: This study addresses critical knowledge gaps related to the assembly of the Cannabis seed-endophytic microbiome. It reveals that Cannabis breeding is linked to alterations of seed microbial communities, which potentially led to the loss of bacteria with functional significance. These results highlight the importance of preserving seed microbiomes in plant breeding to support sustainable plant health and growth enhancement in Cannabis. CAHvc7HPtw2foEoMit4yw- Video Abstract [ABSTRACT FROM AUTHOR]

Drought had affected the crops production in Morocco, during the last decade. Plants breeding is still a solution to increase crops tolerance for water scarcity. Using natural biofertilizer based on microorganisms still a good practice to enhance the resilience of agriculture to drought. The objective of this study is to investigate the effect of water shortage and use of a biofertilizer based on the strain of Pseudomonas putida on five genotypes of eggplants selected for drought tolerance under the semi-arid of the northeast of Morocco. Two irrigations regimes: 100% (amount of water irrigation made by growers) and 50% of this amount with and without the biofertilizer (1 × 108 UFC/g). The biofertilizer was applied three times during the plant growth stages. The experiment was conducted at commercial farm production and using a randomized complete block design. Plants were organized in blocks containing 3 plants for each genotype and repeated in 5 repetitions. Crops were planted on August 3rd, 2022, and experiments ended on January 2nd, 2023. The results showed different responses among the genotypes in terms of growth. The effect of Pseudomonas on plant height showed that there was a significant increase, at 100% irrigation for C14, B3, C8, B5 and C11 with 20%, 19%, 17%, 14.29% and 12,5%, respectively compared with the control. For C8 and B3, when subjected to 100% water with biofertilizer, there was an increase in the average number of fruits compared to 100% water without the biofertilizer. The highest yield was recorded with B5 under 100% irrigation + fertilizer (1.35 kg/plant). Water shortage impacted the productivity of all genotypes and the fruit number and yield increased with the use of the biofertilizer. Our study is still valuable under the conditions of this trial and more experiments will be needed at several seasons and at different growing conditions. [ABSTRACT FROM AUTHOR]

Evaluation of genetic biodiversity for micronutrients is crucial for breeding high-quality crops and addressing the negative impacts of mineral deficiencies. The objectives of this research were to assess genetic variation and the relationship between grain Fe and Zn levels and agronomic traits in a diverse collection of wheat varieties. Additionally, the study aimed to determine the correlation between microsatellite markers (SSR) and micronutrient quantities. A total of 42 genotypes (Iranian commercial cultivars, landraces, and Afghan and Swiss varieties) were evaluated over a two-year period. Fe and Zn levels were measured using two semi quantitative staining assays and atomic absorption spectrophotometry (AAS) facility. Semi-quantitative staining methods and AAS showed high correlations for micronutrient contents. Landraces exhibited higher Fe (63.79 mg/kg) and Zn (44.76 mg/kg) but lower grain yield compared with commercial cultivars. Heritability estimates ranged 53%-79.43%, suggesting that genetic variance played a higher contribution in the phenotypic variation of traits than environmental factors. Notably, Fe content displayed significant correlations with days to maturity. Canonical correlation analysis (CCA) revealed that Zn content was correlated with four agronomic traits. Evaluation of genetic diversity using SSR markers demonstrated high genetic variation among the genotypes tested. The analysis of polymorphism information content (PIC) indicated that SSR primers had an average PIC of 0.75, with the Xgwm192 primer exhibiting higher PIC than others. Several SSR markers revealed association with micronutrient content that can be used in marker-assisted selection (MAS) programs aimed at selection of high micronutrient genotypes. In conclusion, the findings underscored the substantial genetic diversity present in micronutrient levels among global wheat genotypes, the potential of landraces for micronutrients biofortification of wheat cultivars through cross hybridization, the utility of staining methods for screening high/low micronutrient genotypes, and use of microsatellite markers for marker-assisted breeding aiming to micronutrient improvement in breeding programs. [ABSTRACT FROM AUTHOR]

The Tomato Brown Rugose Fruit Virus (ToBRFV) is a pathogen that mostly affects plants from the Solanaceae family. This virus severely affects the yield of tomato (Solanum lycopersicum L.) plants, thus creating an urgent need to research the basis of resistance to manage the disease. To understand the molecular basis of resistance, we employed omics-based approaches involving leaf ionomics and transcriptomics to help us decipher the interaction between elemental and nutritional composition and investigate its effect on the gene expression profile upon the ToBRFV infection in tomatoes. Ionomics was used to investigate the accumulation of trace elements in leaves, showcasing that the plants resistant to the virus had significantly higher concentrations of iron (p-value = 0.039) and nickel (p-value = 0.042) than the susceptible ones. By correlating these findings with transcriptomics, we identified some key genes involved in iron homeostasis and abscisic acid pathways, potentially responsible for conferring resistance against the pathogen. From the obtained list of differentially expressed genes, a panel of mutation profile was discovered with three important genes—Solyc02g068590.3.1 (K+ transporter), Solyc01g111890.3.1 (LRR), and Solyc02g061770.4.1 (Chitinase) showing persistent missense mutations. We ascertain the role of these genes and establish their association with plant resistance using genotyping assays in various tomato lines. The targeted selection of these genetic determinants can further enhance plant breeding and crop yield management strategies. [ABSTRACT FROM AUTHOR]

Background: Yellow lupine (Lupinus luteus L.) is a high-protein crop of considerable economic and ecological significance. It has the ability to fix atmospheric nitrogen in symbiosis with Rhizobium, enriching marginal soils with this essential nutrient and reducing the need for artificial fertilizers. Additionally, lupine produces seeds with a high protein content, making it valuable for animal feed production. However, drought negatively affects lupine development, its mutualistic relationship with bacteria, and overall yield. To understand how lupine responds to this stress, global transcriptome sequencing was conducted, along with in-depth biochemical, chromatography, and microscopy analyses of roots subjected to drought. The results presented here contribute to strategies aimed at mitigating the effects of water deficit on lupine growth and development. Results: Based on RNA-seq, drought-specific genes were identified and annotated to biological pathways involved in phytohormone biosynthesis/signaling, lipid metabolism, and redox homeostasis. Our findings indicate that drought-induced disruption of redox balance characterized by the upregulation of reactive oxygen species (ROS) scavenging enzymes, coincided with the accumulation of lipid-metabolizing enzymes, such as phospholipase D (PLD) and lipoxygenase (LOX). This disruption also led to modifications in lipid homeostasis, including increased levels of triacylglycerols (TAG) and free fatty acids (FFA), along with a decrease in polar lipid content. Additionally, the stress response involved alterations in the transcriptional regulation of the linolenic acid metabolism network, resulting in changes in the composition of fatty acids containing 18 carbons. Conclusion: The first comprehensive global transcriptomic profiles of lupine roots, combined with the identification of key stress-responsive molecules, represent a significant advancement in understanding lupine's responses to abiotic stress. The increased expression of the Δ12DESATURASE gene and enhanced PLD activity lead to higher level of linoleic acid (18:2), which is subsequently oxidized by LOX, resulting in membrane damage and malondialdehyde (MDA) accumulation. Oxidative stress elevates the activities of superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT), while the conversion of FFAs into TAGs provides protection against ROS. This research offers valuable molecular and biochemical candidates with significant potential to enhance drought tolerance. It enables innovative strategies in lupine breeding and crop improvement to address critical agricultural challenges. [ABSTRACT FROM AUTHOR]

The control of plant diseases represents a significant challenge in agriculture, particularly with the current emphasis on reducing pesticide use, necessitating alternative approaches. Among them, resistant plant breeding programs offer a promising alternative. Considering the stressful conditions in space, space conditions emerge as an innovative method for advancing crop improvement. This study explored the unique potential of the International Space Station (ISS) environment to induce beneficial modifications in grapevine, resulting in plants with reduced susceptibility to diseases. Canes of two Vitis vinifera L. cultivars, Cabernet Sauvignon (CS) and Merlot (Me), have undergone a 10-month space journey on-board the ISS, while others stayed on Earth (Ground Control, GC). We conducted a 2-year analysis of the response to downy mildew (Plasmopara viticola) of plants developed from ISS and GC canes, called Mother plants. The disease development of progenies produced from Merlot Mother plants was also evaluated. A higher proportion of individuals with low-susceptibility was noted in the ISS Mother group compared to the controls for both cultivars. This trend was also observed for the Merlot progenies. Finally, 11% of Merlot individuals (Mother and progenies, and for the 2 years of study) were distributed in the low susceptibility class. This study suggests that grapevine canes exposure to ISS conditions resulted in an enhanced percentage of plants showing reduction of susceptibility to downy mildew. [ABSTRACT FROM AUTHOR]

Summary Rapeseed (Brassica napus) is a globally significant oilseed crop with strong heterosis performance. Recessive genic male sterility (RGMS) is one of the key approaches for utilizing heterosis in B. napus. However, this method faces the inherent challenge of being time‐consuming and labour‐intensive for removing fertile plants during seed production. Here, we report a hypocotyl length‐regulated gene, BnHL, which is closely linked to a known fertility gene, BnMs2, serving as a seedling morphology marker. This marker could be used to identify fertile plants in the breeding of RGMS lines based on hypocotyl traits. By targeting the BnHL gene, both homozygous and heterozygous edited mutants exhibited significantly longer hypocotyls than the wild type (WT). Furthermore, germination experiments revealed that 7 days after seed germination, the difference in hypocotyl length between the mutant and the WT seedlings reached its maximum, effectively distinguishing fertile plants under both white (W) and red/far‐red (R/FR) light. Mutations in BnHL did not result in significant changes in main agronomic traits. Thus, this study provides a comprehensive strategy for screening and identifying a new morphological marker gene for early screening in RGMS hybrid breeding with completely non‐transgene during the whole production. [ABSTRACT FROM AUTHOR]

Peanuts play a crucial role as an oil crop, serving not only as a primary source of edible oil but also offering ample protein and vitamins for human consumption. The lateral branch angle of peanuts is the angle between the main stem and the first pair of lateral branches, which is an important agronomic trait of peanuts, significantly impacts the peg penetration into the soil, plant growth, and pod yield. It is closely intertwined with planting density, cultivation techniques, and mechanized harvesting methods. Therefore, the lateral branch angle holds substantial importance in enhancing peanut yield and facilitating mechanization. In order to conduct in-depth research on the lateral branch angle of peanuts, this research is grounded in the QTL mapping findings, specifically focusing on the QTL qGH associated with the lateral branch angle of peanuts located on chromosome B05 (142610834-146688220). By using Jihua 5 and PZ42 for backcrossing, a BC1F2 population comprising 8000 individual plants was established. Molecular markers were then developed to screen the offspring plants, recombine individual plants, conduct fine mapping. he results showed that using the phenotype and genotype of 464 recombinant individual plants selected from 8000 offspring, narrow down the localization interval to 48kb, and designate it as qLBA. The gene Arahy.C4FM6Y , responsible for the F-Box protein, was identified within qLBA through screening. Real-time quantitative detection of Arahy.C4FM6Y was carried out using M130 and Jihua 5, revealing that the expression level of Arahy.C4FM6Y at the junction of the main stem and the first lateral branch of peanuts was lower in M130 compared to Jihua 5 during the growth period of the first lateral branch from 1 to 10 centimeters. Consequently, Arahy.C4FM6Y emerges as a gene that restrains the increase in the angle of the first lateral branch in peanuts. This investigation offers novel genetic reservoirs for peanut plant type breeding and furnishes a theoretical foundation for molecular marker-assisted peanut breeding. [ABSTRACT FROM AUTHOR]

Lentil (Lens culinaris Medikus) is a nutrient-rich, cool-season food legume that is high in protein, prebiotic carbohydrates, vitamins, and minerals. It is a staple food in many parts of the world, but crop performance is threatened by climate change, where increased temperatures and less predictable precipitation can reduce yield and nutritional quality. One mechanism that many plant species use to mitigate heat and drought stress is the production of disaccharides, oligosaccharides and sugar alcohols, collectively referred to as low molecular weight carbohydrates (LMWCs). Recent evidence indicates that lentil may also employ this mechanism – especially raffinose family oligosaccharides and sugar alcohols – and that these may be suitable targets for genomic-assisted breeding to improve crop tolerance to heat and drought stress. While the genes responsible for LMWC biosynthesis in lentil have not been fully elucidated, single nucleotide polymorphisms and putative genes underlying biosynthesis of LMWCs have been identified. Yet, more work is needed to confirm gene identity, function, and response to abiotic stress. This review i) summarizes the diverse evidence for how LMWCs are utilized to improve abiotic stress tolerance, ii) highlights current knowledge of genes that control LMWC biosynthesis in lentil, and iii) explores how LMWCs can be targeted using diverse genomic resources and markers to accelerate lentil breeding efforts for improved stress tolerance. [ABSTRACT FROM AUTHOR]

Conventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype–phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties. [ABSTRACT FROM AUTHOR]

Pseudomonas syringae pv. tomato (Pst) is the causal agent of bacterial speck disease in tomatoes. The Pto/Prf gene cluster from Solanum pimpinellifolium was introgressed into several modern tomato cultivars and provided protection against Pst race 0 strains for many decades. However, virulent Pst race 1 strains that evade Pto -mediated immunity now predominate in tomato-growing regions worldwide. Here we report the identification of resistance to a Pst race 1 strain (Pst 19) in the wild tomato accession S. pimpinellifolium LA1589 (hereafter LA1589), using our rapid high-throughput seedling screen. LA1589 supports less bacterial growth than cultivars, and does not exhibit a hypersensitive response to Pst 19. We tested an existing set of 87 Inbred Backcross Lines (IBLs) derived from a cross between susceptible Solanum lycopersicum E-6203 and Solanum pimpinellifolium LA1589 for resistance to Pst 19. Using single-marker analysis, we identified three genomic regions associated with resistance. Bacterial growth assays on IBLs confirmed that these regions contribute to resistance in planta. We also mapped candidate genes associated with resistance in a cross between the Solanum lycopersicum var. lycopersicum cultivar Heinz BG-1706 and S. pimpinellifolium LA1589. By comparing candidates from the two mapping approaches, we were able to identify 3 QTL and 5 candidate genes in LA1589 for a role in resistance to Pst 19. This work will assist in molecular marker-assisted breeding to protect tomato from bacterial speck disease. [ABSTRACT FROM AUTHOR]

The editorial discusses the importance of microbial OMICS in advancing sustainability in agricultural and environmental microbiology. Microorganisms provide essential ecosystem services and play crucial roles in global ecosystems. The use of OMICS technologies has led to significant progress in sustainable agriculture and environmental management by elucidating microbial functions and interactions in diverse ecosystems. Studies highlighted in the editorial demonstrate how microbial OMICS can impact soil health, crop productivity, and the diversity of soil bacterial communities, offering potential solutions to challenges such as food scarcity and environmental protection. [Extracted from the article]

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types. [ABSTRACT FROM AUTHOR]

• Crop wild relatives are valuable sources of genetic material that needs to be conserved, using both in situ and ex situ methods. • South Africa contains 258 prioritized crop wild relatives that have been used in 121 documented cases of plant improvement to date. • As part of the southern African development Committee, South Africa has access to various resources, gene banks, toolkits and projects that can assist with the in situ and ex situ conservation of crop wild relatives. • Priority areas for in situ conservation should be included in the South African national protected area expansion strategy. • Crop wild relatives within gene banks should be characterised to allow for optimal use by plant breeders for crop improvement. Crop Wild Relatives (CWR) are good sources of unexplored genetic diversity that can assist plant breeders to increase the yield and resilience of their crops. These species are valuable plant genetic resources (PGR) that have been used in more than 4,157 documented cases of plant improvement to date. South Africa has 258 prioritized CWR, selected based on their distribution, threat status and potential as gene donors. In light of ongoing habitat destruction, global warming and mismanagement of resources, the conservation of these PGR is vital. Complementary conservation approaches allow for the continuous development of CWR, while harnessing and applying the available diversity in plant breeding programs. The South African National Biodiversity Strategy and Action Plan (NBSAP) strives to utilize conservation resources to build and maintain an effective complementary, in situ to ex situ conservation pipeline. As part of the Southern African Development Community (SADC), South Africa has access to numerous resources that can assist to protect its rich floral diversity, including the SADC Plant Genetic Resource Centre (SPGRC), the SADC CWR Project and CAPFITOGEN3. [ABSTRACT FROM AUTHOR]

An epigenetic phenomenon has three features: not a DNA mutation, heritable, and reversible. Epigenetic memory is related to DNA methylation, chromatin modification, and/or RNA-mediated mechanisms. Both external (environmental) and internal (genomic) stresses and signals can induce heritable epigenetic variation. Most internally induced epigenetic variations are relatively stable and can be explored to improve crop yield and resilience. Beyond plants, epigenetic principles are shared across sexually reproducing organisms with relevance to medicine and human health. Plants can program and reprogram their genomes to create genetic variation and epigenetic modifications, leading to phenotypic plasticity. Although consequences of genetic changes are comprehensible, the basis for transgenerational inheritance of epigenetic variation is elusive. This review addresses contributions of external (environmental) and internal (genomic) factors to the establishment and maintenance of epigenetic memory during plant evolution, crop domestication, and modern breeding. Dynamic and pervasive changes in DNA methylation and chromatin modifications provide a diverse repertoire of epigenetic variation potentially for transgenerational inheritance. Elucidating and harnessing epigenetic inheritance will help us develop innovative breeding strategies and biotechnological tools to improve crop yield and resilience in the face of environmental challenges. Beyond plants, epigenetic principles are shared across sexually reproducing organisms including humans with relevance to medicine and public health. [ABSTRACT FROM AUTHOR]

Plant breeding started when humans transitioned to settled agriculture, focusing on domestication, selection, and crop introduction. Initially, the processes were slow until the scientific revolution accelerated the development of new varieties. The establishment of the first plant breeding institute in the 18th century highlighted its economic importance. Mendel's experiment established a scientific foundation for plant breeding, which was further advanced by cytological studies focusing on chromosomes, pure-line theory, inbreeding depression, backcross, single seed descent (SSD), and recurrent selection. Later, mutation breeding added new variations. The 20th century's Green Revolution resulted in high-yielding crop varieties. In the 21st century, biotechnology and molecular biology such as genetic engineering, genomics, marker-assisted selection, CRISPR, and bioinformatics techniques revolutionized plant breeding. These technologies have led to the development of novel, resistant, and high-yielding varieties, contributing to sustainable agriculture and food security. This review explores plant breeding evolution, discussing traditional breeding methods, the impact of Mendelian genetics, the Green Revolution, and recent biotechnological advancements. By integrating these diverse materials, this review provides a comprehensive resource for breeders, highlighting how the innovations improved crop varieties to address food challenges. This information is particularly valuable for developing countries in their breeding strategies for sustainable food security. [ABSTRACT FROM AUTHOR]

SUMMARY: Chloroplasts are organelles that are derived from a photosynthetic bacterium and have their own genome. Genome editing is a recently developing technology that allows for specific modifications of target sequences. The first successful application of genome editing in chloroplasts was reported in 2021, and since then, this research field has been expanding. Although the chloroplast genome of several dicot species can be stably modified by a conventional method, which involves inserting foreign DNAs into the chloroplast genome via homologous recombination, genome editing offers several advantages over this method. In this review, we introduce genome editing methods targeting the chloroplast genome and describe their advantages and limitations. So far, CRISPR/Cas systems are inapplicable for editing the chloroplast genome because guide RNAs, unlike proteins, cannot be efficiently delivered into chloroplasts. Therefore, protein‐based enzymes are used to edit the chloroplast genome. These enzymes contain a chloroplast‐transit peptide, the DNA‐binding domain of transcription activator‐like effector nuclease (TALEN), or a catalytic domain that induces DNA modifications. To date, genome editing methods can cause DNA double‐strand break or introduce C:G‐to‐T:A and A:T‐to‐G:C base edits at or near the target sequence. These methods are expected to contribute to basic research on the chloroplast genome in many species and to be fundamental methods of plant breeding utilizing the chloroplast genome. Significance Statement: The current status of recently developed technologies for plastid genome editing is summarized and discussed. This technology offers advantages in precision, inheritability, social and national acceptance, and feasibility across a wider range of species, benefiting both basic biology and future agricultural applications. [ABSTRACT FROM AUTHOR]

As a major staple food crop for the global human population, climate-resilient wheat is the key to global food security. In this context, there is an urgent need to combine advances in genomic technologies and cutting-edge tools in the crop breeding pipelines. The genetic improvement of wheat starts with a clear understanding of genes and genomic components. The past 4–5 years have seen unprecedented growth in wheat genomes, pangenomes, and functional genomics studies. More than 50 reference-level genome assemblies are available for wheat and its wild and related species. Wheat's wild and related species provide a rich source of new genes and alleles to improve wheat. Reference genome assemblies of wheat's wild and related progenitor species have provided a new, helpful resource for targeted gene discovery for wheat improvement. This review explores the journey of wheat genomics starting in 2003. There is an urgent need to improve wheat for upcoming challenges, including biotic and abiotic stresses. Sustainable wheat improvement requires the introduction of new genes and alleles in high-yielding wheat cultivars. Using new approaches, tools, and technologies to identify and introduce new genes in wheat cultivars is critical. High-quality genomes, transcriptomes, and pangenomes provide essential resources and tools to examine wheat closely to identify and manipulate new and targeted genes and alleles. Wheat genomics has improved excellently in the past 5 years, generating multiple genomes, pangenomes, and transcriptomes. Leveraging these resources allows us to accelerate our crop improvement pipelines. This review summarizes the progress made in wheat genomics and trait discovery in the past 5 years. [ABSTRACT FROM AUTHOR]

Social Impact Statement: Cassava is a vital food source for millions worldwide, crucial for food security and economic stability. This study analyzed cell wall polysaccharides in cassava roots to understand their impact on cooking properties. We found these polysaccharides influence the textural attributes of cassava roots, essential for both cooking and consumption. The research highlights the need to further identify and analyze cassava cell wall components. By improving our understanding of these components, we can improve food security, affordability, and resilience in diverse regions, ultimately contributing to global food security and better aligning with consumer preferences. Summary: The textural attributes of cassava roots significantly influence preferences in cooking and consumption as a food source, yet the specific components dictating these properties remain unclear. We aimed to identify the factors shaping the cooking properties of cassava roots.We conducted a compositional analysis of 22 traits in a diverse F1 biparental population derived from soft‐ and hard‐boiling progenitors. The traits encompassed cooking qualities, starch properties, and cell wall composition.Specific cell wall components including cellulose, xylan, un‐esterified, and methyl‐esterified homogalacturonan (HG) demonstrated a correlation with cooking quality attributes: sensory assessments related to texture (SAT) and water absorption during boiling (WAB). Correlation and regression analyses revealed that these wall components collectively contribute to 20% of SAT variability and 14% of WAB variability. SAT appeared to be influenced by methyl‐esterified and un‐esterified HGs, xylan, and cellulose, impacting the tensile strength of the cell wall. Conversely, WAB appeared to be associated with methyl‐esterified HG, potentially altering water absorption properties. Although genome‐wide association studies (GWAS) were unable to identify significant SNPs for SAT and WAB, notable associations emerged for cellulose, xylan, and un‐esterified‐ and methyl‐esterified HG. Candidate genes associated with these SNPs point towards diverse cell wall‐related proteins, transcription factors, sugar metabolism‐related genes, and glycosyl hydrolases.This study provides insights into the relationship between cassava root compositions and cooking characteristics and the role of wall components in determining the cooking quality of edible cassava. This information represents a substantial contribution towards the development of protocols for selecting varieties with texture preferences. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Can modern breeding technologies, such as genome editing, contribute to reduced pesticide usage? This question has been accentuated by a recent legal proposal to exempt genome‐edited plants from the strict regulations applied to classical genetically modified (GM) crops within the European Union (EU). Using official statistics on crop cultivation and pesticide usage for two example crops commonly grown in Sweden, we calculate that cereal farmers collectively could save up to 70 million € in pesticide usage for wheat alone and that a late blight‐resistant potato could reduce pesticide usage by over 80% provided that the EU legislation is amended. Summary: The European Commission has set goals to reduce the use of chemical pesticides, and one way to meet these goals in the agricultural sector is to breed disease‐resistant crops. Here, we ask whether modern breeding technologies, for example, genome editing using site‐directed nucleases, can contribute to these goals. This question has been accentuated by recent legal proposals in the European Union (EU) and several other jurisdictions worldwide to exempt genome‐edited plants from the strict regulations often applied to classical genetically modified (GM) crops. Using official statistics on crop cultivation and pesticide usage for two example crops commonly grown in Sweden (wheat and potato), we show that cereal farmers collectively could potentially save up to 70 million € in pesticide usage for wheat alone and that a late blight‐resistant potato could reduce pesticide usage by over 80% provided that the EU legislation is amended. Given the immense potential of genome‐edited crops, we further discuss details in the legal proposal currently being negotiated in the EU on the so‐called new genomic techniques that includes both genome editing and targeted insertions of cisgenes. Although promising, we argue that several technical limitations in the legal proposal will, if implemented, hamper the development of disease‐resistant crops and make the suggested legislation less future‐proof. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Agricultural sustainability depends on the adaptation of crops to their local environment. Smallholder farmers who save seed provide an essential "evosystem" service by growing locally adapted seed varieties that can recruit biodiversity to enhance their growth and defense. While professional plant breeding has diverted evolutionary processes away from local adaptation, smallholder farmers, particularly those in centers of origin for crops, benefit society by selecting and propagating diverse crop varieties that allow local adaptation processes to perpetuate. Given that smallholders support society through the generation of evosystem services, changes in policy and practice are needed to support the livelihoods of smallholder farmers in ways that mitigate risk and recognize their important contributions to agricultural sustainability. To enhance the reach of this work, a Spanish language version of the paper is available in the Supporting Information (see Translation_ES). [Correction added on 18 June 2024, after first online publication: The preceding sentence has been added in this version.] Summary: Long‐term food security and agricultural sustainability depend on protecting the eco‐evolutionary processes that select for local adaptation in crops. Since seed systems structure how people acquire seed, institutional and social changes influence evolutionary processes within agroecosystems. Since World War II, the rise of professional breeding has bifurcated seed systems into traditional and formal systems, which has negatively affected agrobiodiversity, crop evolution, and agricultural sustainability. In traditional seed systems, farmers often save seed from plants that best provide desired qualities, selecting landrace crop varieties to adapt to local environmental conditions. In formal or centralized seed systems, farmers buy seeds bred primarily for maximizing yield under ideal conditions. When farmers source seeds externally, evolutionary processes underlying local adaptation are disrupted. Here, we argue that traditional seed systems provide important evosystem services, or the evolutionary processes resulting from the maintenance and use of genetic diversity that benefit society. We present a framework on how seed systems influence the evolutionary processes that enable local adaptation, which is necessary for sustainable agriculture. We discuss how changes in human values underlying traditional and formal seed systems can alter evolutionary processes that underlie local adaptation. We conclude that developing policies that support people in managing ecological and evolutionary processes within seed systems is needed to address current and future challenges of global food security and agricultural sustainability. [ABSTRACT FROM AUTHOR]

Summary: Structural variations (SVs) are major genetic variants that can be involved in the origin, adaptation and domestication of species. However, the identification and characterization of SVs in Spinacia species are rare due to the lack of a pan‐genome. Here, we report eight chromosome‐scale assemblies of cultivated spinach and its two wild species. After integration with five existing assemblies, we constructed a comprehensive Spinacia pan‐genome and identified 193 661 pan‐SVs, which were genotyped in 452 Spinacia accessions. Our pan‐SVs enabled genome‐wide association study identified signals associated with sex and clarified the evolutionary direction of spinach. Most sex‐linked SVs (86%) were biased to occur on the Y chromosome during the evolution of the sex‐linked region, resulting in reduced Y‐linked gene expression. The frequency of pan‐SVs among Spinacia accessions further illustrated the contribution of these SVs to domestication, such as bolting time and seed dormancy. Furthermore, compared with SNPs, pan‐SVs act as efficient variants in genomic selection (GS) because of their ability to capture missing heritability information and higher prediction accuracy. Overall, this study provides a valuable resource for spinach genomics and highlights the potential utility of pan‐SV in crop improvement and breeding programmes. [ABSTRACT FROM AUTHOR]

The use of plant growth-promoting rhizobacteria presents a promising addition to conventional mineral fertilizer use and an alternative strategy for sustainable agricultural crop production. However, genotypic variations in the plant host may result in variability of the beneficial effects from these plant–microbe interactions. This study examined growth promotion effects of commercial vegetable crop cultivars of tomato, cucumber and broccoli following application with five rhizosphere bacteria. Biochemical assays revealed that the bacterial strains used possess several nutrient acquisition traits that benefit plants, including nitrogen fixation, phosphate solubilization, biofilm formation, and indole-3-acetic acid (IAA) production. However, different host cultivars displayed genotype-specific responses from the inoculations, resulting in significant (p < 0.05) plant growth promotion in some cultivars but insignificant (p > 0.05) or no growth promotion in others. Gene expression profiling in tomato cultivars revealed that these cultivar-specific phenotypes are reflected in differential expressions of defense and nutrient acquisition genes, suggesting that plants can be categorized into "microbe-friendly" cultivars (with little or no defense responses against beneficial microbes) and "microbe-hostile" cultivars (with strong defense responses). These results validate the notion that "microbe-friendly" (positive interaction with rhizosphere microbes) should be considered an important trait in breeding programs when developing new cultivars which could result in improved crop yields. [ABSTRACT FROM AUTHOR]

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Genotype–environment interaction (GEI) information is essential in plant breeding programs. The objectives of this study were to select high‐yielding and stable yellow‐fleshed sweet potato (YFSP) genotypes in different environments and identify the favorable environment for testing. The trials were conducted in four different locations in West Java, Indonesia, using randomized complete block design (RCBD) with three replications. The results showed that yield was the primary source of variation, accounting for 46.947% of the total variation, followed by GE effect (29.938%) and genotype (23.115%). Additive Main Effect and Multiplicative Interaction (AMMI) Stability Value (ASV) analysis showed Prai (838) and high‐yielding KMDR were the genotypes with the most stability performance in all environments. Parametric and nonparametric stability analysis showed that Prai (838) produced high yields with stability performance in all environments. AMMI showed that Prai (838) and 264 were stable in all environments. Genotype + genotype by environment (GGE) biplot analysis identified Prai (838), 8(11), and Rancing, of which 8(11) and Rancing were the best performing vertex genotypes in Jatinangor. Prai (838) was the stable genotype in all stability analyses used. AC Putih was categorized as having a high level of stability (73.663%) according to coefficient of variability (CVi) with the sustainability index (SI) ranked first. Stable genotypes can be recommended as new genotypes, while Jatinangor can be used in favorable environment for testing. [ABSTRACT FROM AUTHOR]

Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, providing higher accuracy when predicting agronomic variables using the VIs select by path analysis as input. Future studies should evaluate other plant traits, such as physiological or nutritional ones, as well as different spectral variables from those evaluated here, with a view to contributing to an in-depth understanding about cause-and-effect relationships between plant traits and spectral variables. Such studies could contribute to more specific HTP at the level of traits of interest in each crop, helping to develop genetic materials that meet the future demands of population growth and climate change. [ABSTRACT FROM AUTHOR]

Unlike early land plants, flowering plants have evolved a pollen tube that transports a pair of non-motile sperm cells to the female gametophyte. This process, known as siphonogamy, was first observed in gymnosperms and later became prevalent in angiosperms. However, the precise molecular mechanisms underlying the male–female interactions remain enigmatic. From the landing of the pollen grain on the stigma to gamete fusion, the male part needs to pass various tests: how does the stigma distinguish between compatible and incompatible pollen? what mechanisms guide the pollen tube towards the ovule? what factors trigger pollen tube rupture? how is polyspermy prevented? and how does the sperm cell ultimately reach the egg? Successful male–female communication is essential for surmounting these challenges, with cysteine-rich peptides (CRPs) playing a pivotal role in this dialogue. In this review, we summarize the characteristics of four distinct classes of CRPs, systematically review recent progress in the role of CRPs in four crucial stages of pollination and fertilization, consider potential applications of this knowledge in crop breeding, and conclude by suggesting avenues for future research. [ABSTRACT FROM AUTHOR]

ABSTRACT The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)‐aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding. [ABSTRACT FROM AUTHOR]

Summary Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single‐parameter model that outperforms the state‐of‐the‐art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user‐friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems. [ABSTRACT FROM AUTHOR]

Flooding stress is a growing threat to global soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations are increasing due to climate change. Soybean is highly sensitive to flooding and substantial yield losses are observed due to a cascade of negative physiological responses induced by hypoxia. Hence, there is a pressing need for the development of flood‐tolerant genotypes. This study evaluated the grain yield and seed protein and oil content of 31 soybean genotypes over 2 years under both non‐flooding and flooding conditions, where flooding entailed a 4‐day partial water submergence during the early reproductive growth stages R1/R2. Mixed‐effects linear models were utilized to assess the impact of flood damage scores (FDSs, 1–4 scale) on observed phenotypes, as well as differences in observed phenotypes between tolerant, moderate, and susceptible genotypes across flooding and non‐flooding treatments. No significant impact of FDS was observed for seed protein and oil content. In addition, no significant differences in these phenotypes were observed between flooding and non‐flooding treatments across the various genotype categories. On average, for each unit increase in FDS, grain yield decreased by 432.7 kg ha−1 (17.4%). Tolerant genotypes experienced roughly 33% yield losses between flooding and non‐flooding treatments, while moderate and susceptible genotypes experienced 44% and 51% yield losses, respectively. The advancements in genomics and phenomics are promising for the identification and incorporation of novel flood‐tolerant alleles through plant breeding, potentially mitigating flooding‐induced yield losses across diverse environmental conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT The global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable food production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding methods bolstered with efficient and nondestructive plant trait documentation approaches is vital. High‐throughput crop phenotyping (HTCP) platforms have prominently emerged as a mainstream approach for reducing the phenotyping bottleneck in breeding programmes. HTCP has the potential to provide detailed quantitative information of large plant populations under different growth stages across diverse environmental regimes, facilitating accelerated plant breeding strategies. New imaging platforms also enable nondestructive characterization of a wide range of above and below‐ground crop parameters. The specificity in use of sensors, automation of data collection, large‐scale data handling systems and accurate analytical tools have a substantial role in dynamic crop monitoring and big data interpretation. HTCP platforms are capable of making precise measurements of a wide range of physiological, morphological, biochemical and stress responses in plants. Developments of sensors with improved precision, intervention of unmanned aerial vehicles, robotics, computed tomography and machine learning have given a dramatic developmental leap to precise and large‐scale crop phenotyping. This review provides an avenue for understanding various high‐throughput phenotyping platforms, working principles, current developments and contributions to high‐throughput phenotyping of various crops under laboratory and field conditions. A detailed comparative idea on the advantages and pitfalls of these available platforms can help researchers in choosing the right technology suiting specific practical requirements. Furthermore, the review aims to provide novel future prospects and developmental requirements that can potentially widen the application and utilization of these HTCP technologies in agriculture. [ABSTRACT FROM AUTHOR]