Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge

Future of durum wheat research and breeding: Insights from early career researchers

Durum wheat (Triticum turgidum ssp. durum) is globally cultivated for pasta, couscous, and bulgur production. With the changing climate and growing world population, the need to significantly increase durum production to meet the anticipated demand is paramount. This review summarizes recent advancements in durum research, encompassing the exploitation of existing and novel genetic diversity, exploration of potential new diversity sources, breeding for climate-resilient varieties, enhancements in production and management practices, and the utilization of modern technologies in breeding and cultivar development. In comparison to bread wheat (T. aestivum), the durum wheat community and production area are considerably smaller, often comprising many small-family farmers, notably in African and Asian countries. Public breeding programs such as the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) play a pivotal role in providing new and adapted cultivars for these small-scale growers. We spotlight the contributions of these and others in this review. Additionally, we offer our recommendations on key areas for the durum research community to explore in addressing the challenges posed by climate change while striving to enhance durum production and sustainability. As part of the Wheat Initiative, the Expert Working Group on Durum Wheat Genomics and Breeding recognizes the significance of collaborative efforts in advancing toward a shared objective. We hope the insights presented in this review stimulate future research and deliberations on the trajectory for durum wheat genomics and breeding.

Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces

Lentils are a vital staple crop in a world seeking sustainable and secure food, but their cultivation face a threat due to yield instability, mainly arising from a lack of genetic diversity in breeding programmes. In this study, we assembled and characterized the genetic and phenotypic diversities of a collection of 106 lentil genotypes, to evaluate their breeding and cropping potential. Lentil landraces from Italy and beyond, either abandoned or still cultivated, were collected from genebanks, seed savers, universities and farmers. We characterized their phenotypic diversity with an augmented block design, using a control plot enabling a spatial analysis. We phenotyped the collection during two cropping seasons for its agronomic performance, involving local practitioners in a participatory variety evaluation. Meanwhile, we genotyped the landrace collection with a DNA sequencing approach, obtaining 91,136 high quality single nucleotide polymorphisms (SNPs). We used SNPs to describe the phylogenetic relation among landraces, unveiling their uniqueness, and combined SNP data with measured traits to conduct a genome-wide association study (GWAS) that led to the identification of 32 unique marker-trait associations highlighting lentil genomic loci related with adaptation and performance. The results of this study offer new tools to unlock agrobiodiversity for lentil breeding in the Mediterranean, towards the identification of genetic factors responsible for traits of agronomic interest and providing possible sources of parental material.

Harnessing genomic technologies for one health solutions in the tropics

BACKGROUND

The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. MAIN: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement.

CONCLUSION

Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population.

Independent genetic factors control floret number and spikelet number in Triticum turgidum ssp

Wheat grain yield is a complex trait resulting from a trade-off among many distinct components. During wheat evolution, domestication events and then modern breeding have strongly increased the yield potential of wheat plants, by enhancing spike fertility. To address the genetic bases of spike fertility in terms of spikelet number per spike and floret number per spikelet, a population of 110 recombinant inbred lines (RILS) obtained crossing a Triticum turgidum ssp. durum cultivar (Latino) and a T. dicoccum accession (MG5323) was exploited. Being a modern durum and a semi-domesticated genotype, respectively, the two parents differ for spike architecture and fertility, and thus the corresponding RIL population is the ideal genetic material to dissect genetic bases of yield components. The RIL population was phenotyped in four environments. Using a high-density SNP genetic map and taking advantage of several genome sequencing available for Triticeae, a total of 94 QTLs were identified for the eight traits considered; these QTLs were further reduced to 17 groups, based on their genetic and physical co-location. QTLs controlling floret number per spikelet and spikelet number per spike mapped in non-overlapping chromosomal regions, suggesting that independent genetic factors determine these fertility-related traits. The physical intervals of QTL groups were considered for possible co-location with known genes functionally involved in spike fertility traits and with yield-related QTLs previously mapped in tetraploid wheat. The most interesting result concerns a QTL group on chromosome 5B, associated with spikelet number per spike, since it could host genes still uncharacterized for their association to spike fertility. Finally, we identified two different regions where the trade-off between fertility related traits and kernel weight is overcome. Further analyses of these regions could pave the way for a future identification of new genetic loci contributing to fertility traits essential for yield improvement in durum wheat.

Applications of Artificial Intelligence in Wheat Breeding for Sustainable Food Security

In agriculture, especially in crop breeding, innovative approaches are required to address the urgent issues posed by climate change and global food security. Artificial intelligence (AI) is a revolutionary technology in wheat breeding that provides new approaches to improve the ability of crops to withstand and produce higher yields in response to changing climate circumstances. This review paper examines the incorporation of artificial intelligence (AI) into conventional wheat breeding methods, with a focus on the contribution of AI in tackling the intricacies of contemporary agriculture. This review aims to assess the influence of AI technologies on enhancing the efficiency, precision, and sustainability of wheat breeding projects. We conduct a thorough analysis of recent research to evaluate several applications of artificial intelligence, such as machine learning (ML), deep learning (DL), and genomic selection (GS). These technologies expedite the swift analysis and interpretation of extensive datasets, augmenting the process of selecting and breeding wheat varieties that are well-suited to a wide range of environmental circumstances. The findings from the examined research demonstrate notable progress in wheat breeding as a result of artificial intelligence. ML algorithms have enhanced the precision of predicting phenotypic traits, whereas genomic selection has reduced the duration of breeding cycles. Utilizing artificial intelligence, high-throughput phenotyping allows for meticulous examination of plant characteristics under different stress environments, facilitating the identification of robust varieties. Furthermore, AI-driven models have exhibited superior predicted accuracies for crop productivity and disease resistance in comparison to conventional methods. AI technologies play a crucial role in the modernization of wheat breeding, providing significant enhancements in crop performance and adaptability. This integration not only facilitates the growth of wheat cultivars that provide large yields and can withstand stressful conditions but also strengthens global food security in the context of climate change. Ongoing study and collaboration across several fields are crucial to improving and optimizing these AI applications, ultimately enhancing their influence on sustainable agriculture.

Ethnobotanical studies on rice landraces under on-farm conservation in Xishuangbanna of Yunnan Province, China

BACKGROUND

A complex interaction and mutual influence exists among landscapes, cultures, and landraces, with rice culture being a typical embodiment of this relationship. The conservation of landraces operates alongside preserving traditional practices. The Xishuangbanna region stands out as a hub for the genetic diversity of landraces, boasting rich genetic resources. Despite the diverse rice resources in this region, a comprehensive and systematic study has not been undertaken.

METHODS

From October to November 2023, we collected rice landraces under the on-farm conservation in 18 townships including Menghai, Mengla and Jinghong in Xishuangbanna. Employing semi-structured interviews and various methods, we investigated factors influencing the preservation and loss of rice landraces in the region. Statistical analysis was applied to the agronomic traits of collected local rice, encompassing indica or japonica, glutinous or non-glutinous, grain shape, and hull color as second category traits. The second category included quantitative traits like thousand grain weight and grain length. Rice diversity among different regions, traits, and ethnic groups was assessed using the Shannon-Wiener index. Additionally, clustering analysis via the UPGMA method depicted the distribution characteristics of the resources.

RESULTS

A total of 70 rice landraces were collected in the Xishuangbanna region, each exhibiting distinct characteristics. Differences were observed across regions, trait, naming, and ethnic groups. Diversity analysis revealed that Mengla had the highest diversity, followed by Menghai, while Jinghong exhibited the lowest diversity. The second category of traits displayed broader diversity than the first, with the Dai people's glutinous rice showcasing greater diversity than other ethnic groups. Cluster analysis categorized the 70 samples into seven groups at a genetic distance of 1.15. Ethnobotanical interviews emphasized the rapid loss of rice landraces resources in Xishuangbanna, with indigenous ethnic cultures playing a vital role in the conservation of rice landraces. Dai traditions, in particular, played a crucial role in protecting glutinous rice resources, showcasing a mutual dependence between Dai culture and glutinous rice.

CONCLUSIONS

The rich natural environment and diverse ethnic cultures in Xishuangbanna have given rise to various rice landraces. The Dai, primary cultivators of glutinous rice with higher diversity, intertwine their traditional ethnic culture with the conservation of glutinous rice resources. At the same time, the preserving glutinous rice resources promotes the inheritance of Dai ethnic culture. However, rice landraces are facing the risk of loss. Hence, collecting and documenting rice landraces is crucial. Encourage local communities to sustain and expand their cultivation, promoting on-farm conservation. These measures contribute valuable germplasm and genes for rice breeding and serve as a means of cultural preservation.

Is CRISPR/Cas9-based multi-trait enhancement of wheat forthcoming?

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technologies have been implemented in recent years in the genome editing of eukaryotes, including plants. The original system of knocking out a single gene by causing a double-strand break (DSB), followed by non-homologous end joining (NHEJ) or Homology-directed repair (HDR) has undergone many adaptations. These adaptations include employing CRISPR/Cas9 to upregulate gene expression or to cause specific small changes to the DNA sequence of the gene-of-interest. In plants, multiplexing, i.e., inducing multiple changes by CRISPR/Cas9, is extremely relevant due to the redundancy of many plant genes, and the time- and labor-consuming generation of stable transgenic plant lines via crossing. Here we discuss relevant examples of various traits, such as yield, biofortification, gluten content, abiotic stress tolerance, and biotic stress resistance, which have been successfully manipulated using CRISPR/Cas9 in plants. While existing studies have primarily focused on proving the impact of CRISPR/Cas9 on a single trait, there is a growing interest among researchers in creating a multi-stress tolerant wheat cultivar 'super wheat', to commercially and sustainably enhance wheat yields under climate change. Due to the complexity of the technical difficulties in generating multi-target CRISPR/Cas9 lines and of the interactions between stress responses, we propose enhancing already commercial local landraces with higher yield traits along with stress tolerances specific to the respective localities, instead of generating a general 'super wheat'. We hope this will serve as the sustainable solution to commercially enhancing crop yields under both stable and challenging environmental conditions.

Integrative and inclusive genomics to promote the use of underutilised crops

Underutilised crops or orphan crops are important for diversifying our food systems towards food and nutrition security. Here, the authors discuss how the development of underutilised crop genomic resource should align with their breeding and capacity building strategies, and leverage advances made in major crops.

Genetic Diversity of Durum Wheat (Triticum turgidum L. ssp. durum, Desf) Germplasm as Revealed by Morphological and SSR Markers

Ethiopia is considered a center of origin and diversity for durum wheat and is endowed with many diverse landraces. This research aimed to estimate the extent and pattern of genetic diversity in Ethiopian durum wheat germplasm. Thus, 104 durum wheat genotypes representing thirteen populations, three regions, and four altitudinal classes were investigated for their genetic diversity, using 10 grain quality- and grain yield-related phenotypic traits and 14 simple sequence repeat (SSR) makers. The analysis of the phenotypic traits revealed a high mean Shannon diversity index (H' = 0.78) among the genotypes and indicated a high level of phenotypic variation. The principal component analysis (PCA) classified the genotypes into three groups. The SSR markers showed a high mean value of polymorphic information content (PIC = 0.50) and gene diversity (h = 0.56), and a moderate number of alleles per locus (Na = 4). Analysis of molecular variance (AMOVA) revealed a high level of variation within populations, regions, and altitudinal classes, accounting for 88%, 97%, and 97% of the total variation, respectively. Pairwise genetic differentiation and Nei's genetic distance analyses identified that the cultivars are distinct from the landrace populations. The distance-based (Discriminant Analysis of Principal Component (DAPC) and Minimum Spanning Network (MSN)) and model-based population stratification (STRUCTURE) methods of clustering grouped the genotypes into two clusters. Both the phenotypic data-based PCA and the molecular data-based DAPC and MSN analyses defined distinct groupings of cultivars and landraces. The phenotypic and molecular diversity analyses highlighted the high genetic variation in the Ethiopian durum wheat gene pool. The investigated SSRs showed significant associations with one or more target phenotypic traits. The markers identify landraces with high grain yield and quality traits. This study highlights the usefulness of Ethiopian landraces for cultivar development, contributing to food security in the region and beyond.

gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation

To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data synthesis exercises, among others. To address this need, we developed the R package gosset, which provides functionality for rank-based data and models. The gosset package facilitates data preparation, modeling and results presentation stages. It introduces novel functions not available in existing R packages for analyzing ranking data. This paper demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua.