Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants

Review of applications of artificial intelligence (AI) methods in crop research

Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.

Unraveling the genetics of heat tolerance in chickpea landraces (Cicer arietinum L.) using genome-wide association studies

Chickpea, being an important grain legume crop, is often confronted with the adverse effects of high temperatures at the reproductive stage of crop growth, drastically affecting yield and overall productivity. The current study deals with an extensive evaluation of chickpea genotypes, focusing on the traits associated with yield and their response to heat stress. Notably, we observed significant variations for these traits under both normal and high-temperature conditions, forming a robust basis for genetic research and breeding initiatives. Furthermore, the study revealed that yield-related traits exhibited high heritability, suggesting their potential suitability for marker-assisted selection. We carried out single-nucleotide polymorphism (SNP) genotyping using the genotyping-by-sequencing (GBS) method for a genome-wide association study (GWAS). Overall, 27 marker-trait associations (MTAs) linked to yield-related traits, among which we identified five common MTAs displaying pleiotropic effects after applying a stringent Bonferroni-corrected p-value threshold of <0.05 [-log10(p) > 4.95] using the BLINK (Bayesian-information and linkage-disequilibrium iteratively nested keyway) model. Through an in-depth in silico analysis of these markers against the CDC Frontier v1 reference genome, we discovered that the majority of the SNPs were located at or in proximity to gene-coding regions. We further explored candidate genes situated near these MTAs, shedding light on the molecular mechanisms governing heat stress tolerance and yield enhancement in chickpeas such as indole-3-acetic acid-amido synthetase GH3.1 with GH3 auxin-responsive promoter and pentatricopeptide repeat-containing protein, etc. The harvest index (HI) trait was associated with marker Ca3:37444451 encoding aspartic proteinase ortholog sequence of Oryza sativa subsp. japonica and Medicago truncatula, which is known for contributing to heat stress tolerance. These identified MTAs and associated candidate genes may serve as valuable assets for breeding programs dedicated to tailoring chickpea varieties resilient to heat stress and climate change.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Comprehensive identification of glutathione peroxidase (GPX) gene family in response to abiotic stress in pepper (Capsicum annuum L.)

Plant glutathione peroxidase (GPX) plays an important role in the maintenance of cell homeostasis and in the antioxidant response in plants. In this study, the peroxidase (GPX) gene family was identified in the whole genome of pepper using bioinformatic method. As a result, a total of 5 CaGPX genes were identified, which were unevenly distributed on 3 of the 12 chromosomes of pepper genome. Based on phylogenetic analysis, 89 GPX genes in 17 species from lower plants to higher plants can be divided into 4 groups GroupⅠ, Group Ⅱ, Group Ⅲ, Group Ⅳ). The MEME Suite analysis of GPX proteins shows that all these proteins contain four highly conserved motifs, as well as other conserved sequences and amino acid residues. Gene structure analysis revealed the conservative exon-intron organization pattern of these genes. In the promoter region of CaGPX genes, many cis elements of plant hormone and abiotic stress response were identified in each of CaGPX proteins. In addition, expression patterns of CaGPX genes in different tissues, developmental stages and responses to abiotic stress were also performed. The results of qRT-PCR showed that the transcripts of CaGPX genes varied greatly under abiotic stress at different time points. There results suggest that the GPX gene family of pepper may play a role in plant development andstress response. In conclusion, our research provides new insights into the evolution of pepper GPX gene family, and understanding for functional of these genes in response to abiotic stresses.

Identification of passion fruit HSF gene family and the functional analysis of PeHSF-C1a in response to heat and osmotic stress

Heat stress transcription factors (HSFs) are the major regulators of plant response to environmental stress, especially heat and drought stress. To gain a deeper understanding of the mechanisms underlying HSFs in the abiotic stress response of passion fruit, we conducted an in silico analysis of the HSF gene family. Through bioinformatics and phylogenetic analyses, we identified 18 PeHSF members and classified them into A, B, and C groups. Collinearity analysis results revealed that the expansion of the PeHSF gene family was due to the presence of segmental duplication. Furthermore, gene structure and protein domain analysis illustrated that PeHSFs in the same subgroup are relatively conserved. Conserved motif and function domain analysis suggested that PeHSF proteins possess typical conserved functional domains of the HSF family. A protein interaction network and 3D structure prediction were used to study the potential regulatory relationship of PeHSFs. Additionally, the subcellular localization results of PeHSF-A6a, PeHSF-B4b, and PeHSF-C1a were consistent with the predictions. RNA-seq and RT-qPCR analysis revealed the expression patterns of PeHSFs in different tissues of passion fruit floral organs. Promoter analysis and the expression patterns of the PeHSFs under different treatments demonstrated their involvement in various abiotic stress processes. Notably, overexpression of PeHSF-C1a consistently enhanced tolerance to drought and heat stress in Arabidopsis. Overall, our findings provide a scientific basis for further functional studies of PeHSFs that could contribute to improvement of passion fruit breeding.

Genetic resources and breeding of maize for Striga resistance: a review

The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.

The battle of crops against drought: Genetic dissection and improvement

With ongoing global climate change, water scarcity-induced drought stress remains a major threat to agricultural productivity. Plants undergo a series of physiological and morphological changes to cope with drought stress, including stomatal closure to reduce transpiration and changes in root architecture to optimize water uptake. Combined phenotypic and multi-omics studies have recently identified a number of drought-related genetic resources in different crop species. The functional dissection of these genes using molecular techniques has enriched our understanding of drought responses in crops and has provided genetic targets for enhancing resistance to drought. Here, we review recent advances in the cloning and functional analysis of drought resistance genes and the development of technologies to mitigate the threat of drought to crop production.

Engineering drought and salinity tolerance traits in crops through CRISPR-mediated genome editing: Targets, tools, challenges, and perspectives

Prolonged periods of drought triggered by climate change hamper plant growth and cause substantial agricultural yield losses every year. In addition to drought, salinity is one of the major abiotic stresses that severely affect crop health and agricultural production. Plant responses to drought and salinity involve multiple processes that operate in a spatiotemporal manner, such as stress sensing, perception, epigenetic modifications, transcription, post-transcriptional processing, translation, and post-translational changes. Consequently, drought and salinity stress tolerance are polygenic traits influenced by genome-environment interactions. One of the ideal solutions to these challenges is the development of high-yielding crop varieties with enhanced stress tolerance, together with improved agricultural practices. Recently, genome-editing technologies, especially clustered regularly interspaced short palindromic repeats (CRISPR) tools, have been effectively applied to elucidate how plants deal with drought and saline environments. In this work, we aim to portray that the combined use of CRISPR-based genome engineering tools and modern genomic-assisted breeding approaches are gaining momentum in identifying genetic determinants of complex traits for crop improvement. This review provides a synopsis of plant responses to drought and salinity stresses at the morphological, physiological, and molecular levels. We also highlight recent advances in CRISPR-based tools and their use in understanding the multi-level nature of plant adaptations to drought and salinity stress. Integrating CRISPR tools with modern breeding approaches is ideal for identifying genetic factors that regulate plant stress-response pathways and for the introgression of beneficial traits to develop stress-resilient crops.

Engineering Abiotic Stress Tolerance in Crop Plants through CRISPR Genome Editing

Environmental abiotic stresses challenge food security by depressing crop yields often exceeding 50% of their annual production. Different methods, including conventional as well as genomic-assisted breeding, mutagenesis, and genetic engineering have been utilized to enhance stress resilience in several crop species. Plant breeding has been partly successful in developing crop varieties against abiotic stresses owning to the complex genetics of the traits as well as the narrow genetic base in the germplasm. Irrespective of the fact that genetic engineering can transfer gene(s) from any organism(s), transgenic crops have become controversial mainly due to the potential risk of transgene-outcrossing. Consequently, the cultivation of transgenic crops is banned in certain countries, particularly in European countries. In this scenario, the discovery of the CRISPR tool provides a platform for producing transgene-free genetically edited plants-similar to the mutagenized crops that are not extensively regulated such as genetically modified organisms (GMOs). Thus, the genome-edited plants without a transgene would likely go into the field without any restriction. Here, we focused on the deployment of CRISPR for the successful development of abiotic stress-tolerant crop plants for sustaining crop productivity under changing environments.

Genome-Wide Identification and Expression Analyses of the Chitinase Gene Family in Response to White Mold and Drought Stress in Soybean (Glycine max)

Chitinases are enzymes catalyzing the hydrolysis of chitin that are present on the cell wall of fungal pathogens. Here, we identified and characterized the chitinase gene family in cultivated soybean (Glycine max L.) across the whole genome. A total of 38 chitinase genes were identified in the whole genome of soybean. Phylogenetic analysis of these chitinases classified them into five separate clusters, I–V. From a broader view, the I–V classes of chitinases are basically divided into two mega-groups (X and Y), and these two big groups have evolved independently. In addition, the chitinases were unevenly and randomly distributed in 17 of the total 20 chromosomes of soybean, and the majority of these chitinase genes contained few introns (≤2). Synteny and duplication analysis showed the major role of tandem duplication in the expansion of the chitinase gene family in soybean. Promoter analysis identified multiple cis-regulatory elements involved in the biotic and abiotic stress response in the upstream regions (1.5 kb) of chitinase genes. Furthermore, qRT-PCR analysis showed that pathogenic and drought stress treatment significantly induces the up-regulation of chitinase genes belonging to specific classes at different time intervals, which further verifies their function in the plant stress response. Hence, both in silico and qRT-PCR analysis revealed the important role of the chitinases in multiple plant defense responses. However, there is a need for extensive research efforts to elucidate the detailed function of chitinase in various plant stresses. In conclusion, our investigation is a detailed and systematic report of whole genome characterization of the chitinase family in soybean.

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

The influence of endophytes on rice fitness under environmental stresses

KEY MESSAGE

Endophytes are crucial for the promotion of rice growth and stress tolerance and can be used to increase rice crop yield. Endophytes can thus be exploited in biotechnology and genetic engineering as eco-friendly and cost-effective means for the development of high-yielding and stress-tolerant rice plants. Rice (Oryza sativa) crop is continuously subjected to biotic and abiotic stresses, compromising growth and consequently yield. The situation is exacerbated by climate change impacting on ecosystems and biodiversity. Genetic engineering has been used to develop stress-tolerant rice, alongside physical and chemical methods to mitigate the effect of these stresses. However, the success of these strategies has been hindered by short-lived field success and public concern on adverse effects associated. The limited success in the field of stress-tolerant cultivars developed through breeding or transgenic approaches is due to the complex nature of stress tolerance as well as to the resistance breakdown caused by accelerated evolution of pathogens. It is therefore necessary to develop novel and acceptable strategies to enhance rice stress tolerance and durable resistance and consequently improve yield. In the last decade, plant growth promoting (PGP) microbes, especially endophytes, have drawn the attention of agricultural scientists worldwide, due to their ability to mitigate environmental stresses in crops, without causing adverse effects. Increasing evidence indicates that endophytes effectively confer fitness benefits also to rice under biotic and abiotic stress conditions. Endophyte-produced metabolites can control the expression of stress-responsive genes and improve the physiological performance and growth of rice plants. This review highlights the current evidence available for PGP microbe-promoted tolerance of rice to abiotic stresses such as salinity and drought and to biotic ones, with special emphasis on endophytes. Associated molecular mechanisms are illustrated, and prospects for sustainable rice production also in the light of the impending climate change, discussed.

Identification of superior haplotypes in a diverse natural population for breeding desirable plant height in soybean

KEY MESSAGE

Plant height of soybean is associated with a haplotype block on chromosome 19, which classified 211 soybean accessions into five distinct groups showing significant differences for the target trait. Genetic variation is pivotal for crop improvement. Natural populations are precious genetic resources. However, efficient strategies for the targeted utilization of these resources for quantitative traits, such as plant height (PH), are scarce. Being an important agronomic trait associated with soybean yield and quality, it is imperative to unravel the genetic mechanisms underlying PH in soybean. Here, a genome-wide association study (GWAS) was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with PH in a natural population of 211 cultivated soybeans, which was genotyped with NJAU 355 K Soy SNP Array and evaluated across six environments. A total of 128 SNPs distributed across 17 chromosomes were found to be significantly associated with PH across six environments and a combined environment. Three significant SNPs were consistently identified in at least three environments on Chr.02 (AX-93958260), Chr.17 (AX-94154834), and Chr.19 (AX-93897200). Genomic regions of ~ 130 kb flanking these three consistent SNPs were considered as stable QTLs, which included 169 genes. Of these, 22 genes (including Dt1) were prioritized and defined as putative candidates controlling PH. The genomic region flanking 12 most significant SNPs was in strong linkage disequilibrium (LD). These SNPs formed a single haplotype block containing five haplotypes for PH, namely Hap-A, Hap-B, Hap-C, Hap-D, and Hap-E. Deployment of such superior haplotypes in breeding programs will enable development of improved soybean varieties with desirable plant height.

Genome-Wide Identification of the AP2/ERF Gene Family and Functional Analysis of GmAP2/ERF144 for Drought Tolerance in Soybean

Drought is a major environmental constraint that causes substantial reductions in plant growth and yield. Expression of stress-related genes is largely regulated by transcription factors (TFs), including in soybean [Glycine max (L.) Merr.]. In this study, 301 GmAP2/ERF genes that encode TFs were identified in the soybean genome. The TFs were divided into five categories according to their homology. Results of previous studies were then used to select the target gene GmAP2/ERF144 from among those up-regulated by drought and salt stress in the transcriptome. According to respective tissue expression analysis and subcellular determination, the gene was highly expressed in leaves and encoded a nuclear-localized protein. To validate the function of GmAP2/ERF144, the gene was overexpressed in soybean using Agrobacterium-mediated transformation. Compared with wild-type soybean, drought resistance of overexpression lines increased significantly. Under drought treatment, leaf relative water content was significantly higher in overexpressed lines than in the wild-type genotype, whereas malondialdehyde content and electrical conductivity were significantly lower than those in the wild type. Thus, drought resistance of transgenic soybean increased with overexpression of GmAP2/ERF144. To understand overall function of the gene, network analysis was used to predict the genes that interacted with GmAP2/ERF144. Reverse-transcription quantitative PCR showed that expression of those interacting genes in two transgenic lines was 3 to 30 times higher than that in the wild type. Therefore, GmAP2/ERF144 likely interacted with those genes; however, that conclusion needs to be verified in further specific experiments.

Characterization of SOD and GPX Gene Families in the Soybeans in Response to Drought and Salinity Stresses

Plant stresses causing accumulation of reactive oxidative species (ROS) are scavenged by effective antioxidant defense systems. Therefore, the present study performed genome-wide identification of superoxide dismutase (SOD) and glutathione peroxidase (GPX) gene families in cultivated and wild soybeans, and 11 other legume species. We identified a total of 101 and 95 genes of SOD and GPX, respectively, across thirteen legume species. The highest numbers of SODs and GPXs were identified in cultivated (Glycine max) and wild (Glycine soja). A comparative phylogenetic study revealed highest homology among the SODs and GPXs of cultivated and wild soybeans relative to other legumes. The exon/intron structure, motif and synteny blocks were conserved in both soybean species. According to Ka/Ks, purifying the selection played the major evolutionary role in these gene families, and segmental duplication are major driving force for SODs and GPXs expansion. In addition, the qRT-PCR analysis of the G. max and G. soja SOD and GPX genes revealed significant differential expression of these genes in response to oxidative, drought and salinity stresses in root tissue. In conclusion, our study provides new insights for the evolution of SOD and GPX gene families in legumes, and provides resources for further functional characterization of these genes for multiple stresses.

Detecting QTL and Candidate Genes for Plant Height in Soybean via Linkage Analysis and GWAS

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

Insights on Genetic Diversity, Population Structure, and Linkage Disequilibrium in Globally Diverse Coconut Accessions Using Genotyping-by-Sequencing

Genotyping-by-sequencing (GBS) has emerged as a cost-effective approach for genome-wide discovery of single-nucleotide polymorphism (SNP) markers and high-throughput genotyping. In this study, 96 coconut palms, representing 16 accessions from globally diverse origins, were genotyped using the GBS strategy. A total of 10,835 high-quality SNPs, which were identified after stringent filtering, were utilized to assess genetic diversity, population structure, and linkage disequilibrium (LD) analyses. The polymorphism information content (PIC) values of SNPs ranged from 0.1 to 0.4, with a large proportion of SNPs (8633 nos.; 79.7%) having a higher PIC in the range of 0.3-0.4. The genetic diversity analysis revealed the existence of a high level of variation in coconut accessions, with an average expected heterozygosity (H_e) value of 0.43. Unweighted neighbor-joining phylogenetic tree and Bayesian-based model population structure grouped coconut genotypes into four main clusters. The accessions are generally clustered based on their height (tall or dwarf), with a few accession clusterings based on geographical origins. Investigation of LD pattern in coconut indicated a relatively rapid LD decay with a short range (9 kb). The results obtained in this study will contribute to enhancing the capacity of coconut researchers to utilize genetic diversity for further genetic improvement. In addition, it would open up possibilities for performing genomic studies such as genome-wide association studies and genomic selection to accelerate the efficiency and speed of coconut genetic improvement.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

The Effect of Silicon on Osmotic and Drought Stress Tolerance in Wheat Landraces

Drought stress reduces annual global wheat yields by 20%. Silicon (Si) fertilisation has been proposed to improve plant drought stress tolerance. However, it is currently unknown if and how Si affects different wheat landraces, especially with respect to their innate Si accumulation properties. In this study, significant and consistent differences in Si accumulation between landraces were identified, allowing for the classification of high Si accumulators and low Si accumulators. Landraces from the two accumulation groups were then used to investigate the effect of Si during osmotic and drought stress. Si was found to improve growth marginally in high Si accumulators during osmotic stress. However, no significant effect of Si on growth during drought stress was found. It was further found that osmotic stress decreased Si accumulation for all landraces whereas drought increased it. Overall, these results suggest that the beneficial effect of Si commonly reported in similar studies is not universal and that the application of Si fertiliser as a solution to agricultural drought stress requires detailed understanding of genotype-specific responses to Si.