Biology subjects

Alahmad, S.

Publications and source records attributed to Alahmad, S..

6 recordsLinked to original sources

bioRxiv2025

The potential to breed for genetic legacy effects in sustainable farming systems

Legume crops provide protein-rich food, critical disease breaks in cereal rotations, and contribute to soil fertility through symbiotic nitrogen fixation. However, crop improvement programs typically focus on within-crop performance rather than system-level benefits. We hypothesise that legacy effects (the influence of one crops genotype on subsequent crop performance) are under genetic control and could be leveraged in breeding programs. To test this, we evaluated how 309 genetically diverse mungbean genotypes influence subsequent wheat performance. The mungbean panel was grown, followed by a single wheat cultivar sown in the same plot locations. Remarkably, wheat yield varied by nearly 1 t ha{square}{superscript 1} (2.52-3.49 t ha{square}{superscript 1}) depending solely on the preceding mungbean genotype, with legacy traits displaying moderate heritability (H{superscript 2}: 0.43-0.65), demonstrating untapped genetic potential for breeding. Analyses of mungbean traits, soil properties, and volatile organic compounds implicated root architecture, symbiotic nitrogen fixation and soil microbiome as potential biological drivers of legacy effects. Haplotype mapping identified genomic regions in mungbean associated with wheat yield and protein, revealing trade-offs between within-crop and legacy performance. Genetic simulations using empirically derived marker effects compared genomic selection strategies targeting mungbean yield, wheat yield, or both simultaneously. Balanced selection (50:50 weighting) achieved simultaneous gains in both crops (19.5% and 7.6%), highlighting the opportunity to breed for system-level productivity with reduced input requirements.

plant biology↗

bioRxiv2024

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars (Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. HighlightIntegrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

physiology↗

bioRxiv2024

Stacking haplotypes from the Vavilov wheat collection to accelerate breeding for multiple disease resistance

Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS and SNB, by up to 48.1%, 35.2%, 29.1%, 12.8%, 18.8% and 32.8%, respectively. Overall, our results highlight the genetic potential to improve MDR in wheat. The haploblock-based catalogue with novel forms of resistance provides a useful resource to guide desirable haplotype stacking for breeding future wheat cultivars with MDR.

plant biology↗

bioRxiv2024

Seminal root angle is associated with root system architecture in durum wheat

Optimal root system architecture (RSA) is critical for efficient resource capture in soils, hence being an interest in crop breeding. Seminal root angle (SRA) at the seedling stage in durum wheat has been suggested to be a good indicator of RSA. However, research on correlating such lab-based seedling root phenotyping to RSA at later phases of plant growth is limited, resulting in the importance of root trait variation seen in seedlings often being overstated. To explore the role of SRA in modifying RSA at later phases of plant growth, we assessed 11 genotypes contrasting in SRA (wide and narrow), grown in a rhizobox designed for phenotyping root systems of plants during late-tillering. Above-ground traits and root dry mass in different soil depths and across the entire soil volume were measured manually, while root architectural traits were extracted using image analysis and summarised by multiple factor analysis to describe RSA. When comparing the wide and narrow genotypes, no differences were detected for above-ground traits and total root dry mass. However, differences were observed in the allocation of root dry mass at different depths. The wide and narrow genotypes showed distinct RSAs, particularly in the upper soil (0 - 30 cm). The wide genotypes exhibited a spread-out root system with dense and thin roots, whereas the narrow genotypes had a compact root system with fewer but thicker roots. Our study demonstrated a clear difference in RSA between the wide and narrow genotypes, highlighting the association between SRA on the direction and distribution of root growth in plants at later growth stages.

plant biology↗

bioRxiv2021

A new protocol for speed vernalisation of winter cereals

There are many challenges facing the development of high-yielding, nutritious crops for future environments. One limiting factor is generation time, which prolongs research and plant breeding timelines. Recent advances in speed breeding protocols have dramatically reduced generation time for many short-day and long-day species by optimising light and temperature conditions during plant growth. However, winter crops with a vernalisation requirement still require up to 6-10 weeks in low-temperature conditions before transition to reproductive development. Here, we tested a suite of environmental conditions and protocols to investigate if vernalisation can be satisfied more efficiently. We identified a vernalisation method consisting of exposing seeds at the soil surface to an extended photoperiod of 22 h day:2 h night at 10{degrees}C with transfer to speed breeding conditions that dramatically reduces generation time in both winter wheat (Triticum aestivum) and winter barley (Hordeum vulgare). Implementation of this protocol achieved up to five generations per year for winter wheat or barley, instead of the two typically obtained under standard vernalisation and plant growth conditions. The protocol has great potential to enhance training and to accelerate research, pre-breeding, and breeding outcomes focussed on winter crop improvement.

plant biology↗

bioRxiv2021

Physiological and genetic drivers underpinning canopy development are associated with durum wheat yield in rainfed environments

New durum wheat (Triticum turgidum L. ssp. Durum) cultivars with improved adaptation to variable rainfall environments are required to sustain productivity in the face of climate change. Physiological traits related to canopy development underpin the production of biomass and yield, as they interact with solar radiation and affect the timing of water use throughout the growing season. This study explored the temporal canopy dynamics of durum wheat using a nested-association mapping population evaluated for longitudinal normalized difference vegetation index (NDVI) measurements. Association mapping was performed to identify quantitative trait loci (QTL) for time-point NDVI and spline-smoothed NDVI trajectory traits. Yield effects associated with QTL for canopy development were investigated using data from four rainfed field trials. Four QTL associated with slower canopy closure, improved yield in specific environments, and notably, were not associated with a yield penalty in any environment. This was likely due to optimised timing of water-use and pleiotropic effects on yield component traits, including spike number and spike length. Overall, this study suggests that slower canopy closure is beneficial for durum wheat production in rainfed environments. Selection for traits or loci associated with canopy development may improve yield stability of durum wheat in water limited environments.

genetics↗