Genetic factors increasing barley grain yields under soil waterlogging

Harrison, Matthew T.; Ibrahim, Ahmed N.; Manik, S. M. Nuruzzaman; Johnson, Peter; Tian, Xiaohong; Meinke, Holger; Zhou, Meixue

doi:10.1002/fes3.238

Cited by 39 publications

(36 citation statements)

References 55 publications

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“…For example, in APSIM-Wheat and APSIM-Barley, aeration deficit impacts on crop growth range from maximum sensitivity at emergence to no sensitivity during grain filling, while the crop sensitivity factor in Equation 3 was 0.16 for crop establishment, 0.18 for early vegetative stages, 0.38 for late vegetative stages, 0.21 for flowering, and 0.06 for yield formation. These values contrast with experimental work by Liu, Harrison, Ibrahim, et al (2020), who showed that barley was least sensitive to early waterlogging and most sensitive to waterlogging near anthesis. Instead of arbitrary, crop-specific empirical factors related to waterlogging stress at various phenological stages, we suggest that models be based on crop physiological principles, which could then form the basis for simplified but well-informed phenomenological descriptions of such processes (Meinke et al, 1998).…”

Section: /2020ef001801contrasting

confidence: 98%

“…The growth stage i can be planting to terminal spikelet, terminal spikelet to flowering, flowering, grain filling, or maturity. This approach allows variation in the extent of waterlogging according to growth stage, but the model does not account for the delay in phenology caused by waterlogging in greenhouse experiments (Liu, Harrison, Ibrahim, et al, 2020). Li et al (2016) compared both methods in GLAM-WOFOST then revised the runoff, surface storage, and infiltration scheme in GLAM, resulting in an improvement in model validation (data set from wheat production data from 1985 to 2000 of Jiangsu, Anhui, and Zhejiang provinces in China), with RMSE and R 2 values of 307 kg ha −1 and 0.78 in using WSF HU (Hu et al, 2004) and RMSE and R 2 values of 307 kg ha −1 and 0.71 in using WSF HU .…”

Section: Glam-wofostmentioning

confidence: 99%

“…For example, for cereals waterlogging during vegetative stages could be linked with tiller number and kernels per spike, with greater aeration stress over longer periods reducing tiller number and kernels per spike (Masoni et al, 2016). During reproductive stages, waterlogging could be linked with spikelet fertility and/or grain weight (de San Celedonio et al, 2014; Liu, Harrison, Ibrahim, et al, 2020).…”

Section: Synthesis: Pathways Used To Model Waterloggingmentioning

confidence: 99%

Section: Modeling Crop Waterloggingmentioning

confidence: 99%

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The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know

et al. 2020

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Models are key tools in our quest to better understand the impacts of soil waterlogging on plant growth and crop production. Here, we reviewed the state of the art of modeling approaches and compared the conceptual design of these models with recent experimental findings. We show that many models adopt an aeration stress (AS) principle where surplus water reduces air-filled porosity, with implications for root growth. However, subsequent effects of AS within each model vary considerably. In some cases, AS inhibits biomass accumulation (e.g. AquaCrop), altering processes prior to biomass accumulation such as light interception (e.g. APSIM), or photosynthesis and carbohydrate accumulation (e.g. SWAGMAN Destiny). While many models account for stage-dependent waterlogging effects, few models account for experimentally observed delays in phenology caused by waterlogging. A model intercomparison specifically designed for long-term waterlogged conditions (APSIM-Oryza) with models developed for dryland conditions with transient waterlogging would advance our understanding of the current fitness for purpose of exsiting frameworks for simulating transient waterlogging in dryland cropping systems. Of the point-based dynamic models examined here, APSIM-Soybean and APSIM-Oryza simulations most closely matched with the observed data, while GLAM-WOFOST achieved the highest performance of the spatial-regional models examined. We conclude that future models should incorporate waterlogging effects on genetic tolerance parameters such as (1) phenology of stress onset, (2) aerenchyma, (3) root hydraulic conductance, (4) nutrient-use efficiency, and (5) plant ion (e.g. Fe/Mn) tolerance. Incorporating these traits/effects into models, together with a more systematic model intercomparison using consistent initialization data, will significantly improve our understanding of the relative importance of such factors in a systems context, including feedbacks between biological factors, emergent properties, and sensitive variables responsible for yield losses under waterlogging.

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Section: /2020ef001801contrasting

confidence: 98%

Section: Glam-wofostmentioning

confidence: 99%

Section: Synthesis: Pathways Used To Model Waterloggingmentioning

confidence: 99%

Section: Modeling Crop Waterloggingmentioning

confidence: 99%

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The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know

et al. 2020

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“…However, a lower population may allow for wider plant spacing, which impacts the leaf area, light interception, and canopy apparent photosynthesis ( Tao et al, 2018 ). This leads to the production of more photosynthetic products that may significantly increase the spike number and kernel number, compensating for detrimental effects of lower population on other yield components to some degree ( Liu et al, 2020 ). Ensuring early and vigorous crop establishment is a key factor in achieving maximum wheat yield under the RW cropping system.…”

Section: Discussionmentioning

confidence: 99%

Comparison of early season crop types for wheat production and nitrogen use efficiency in the Jianghan Plain in China

Yang

Geng

Zhang

et al. 2021

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The rice-wheat (RW) cropping system is one of the most prevalent double-cropping systems used to farm the Jianghan Plain in China. However, it can lead to low wheat yields and reduced nitrogen use efficiency compared with dryland wheat (DW). We evaluated wheat yield and nitrogen use efficiency for two rotations (summer rice-winter wheat and summer soybean-winter wheat) from 2017 to 2019 and applied the results to improve nitrogen management for planting wheat after rice in the Jianghan Plain. Field experiments were conducted over two years with two nitrogen treatments: traditional nitrogen management (M1: 90 kg N ha−1 was applied at sowing and jointing, respectively ) and optimized nitrogen management (M2: 60 kg N ha−1 was applied at sowing, wintering and jointing, respectively). The highest total wheat production was achieved under M2 for both cropping systems and the two-year average yield was 6,128 kg ha−1 in DW and 6,166 kg ha−1 in RW. The spike number in DW was 15% higher than RW in M1 and 13% higher in M2, but the kernel per spike and 1,000-grain weight was lower than RW. The nitrogen accumulation of DW was 24% higher than RW in M1 and 33% in M2. Compared with RW, DW had higher NO3− content in the soil surface layer (0–20 cm) and a higher root length density (RLD) in the deeper layer (40–60 cm), which may account for the higher N uptake in DW. Our results show that the grain yield of RW was comparable to that of DW by optimum nitrogen management. The rice-wheat cropping system combined with optimum nitrogen management may be of economic and agronomic benefit to the wheatbelt in the Jianghan Plain in China.

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Transcriptional analysis in multiple barley varieties identifies signatures of waterlogging response

et al. 2023

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Waterlogging leads to major crop losses globally, particularly for waterlogging‐sensitive crops such as barley. Waterlogging reduces oxygen availability and results in additional stresses, leading to the activation of hypoxia and stress response pathways that promote plant survival. Although certain barley varieties have been shown to be more tolerant to waterlogging than others and some tolerance‐related quantitative trait loci have been identified, the molecular mechanisms underlying this trait are mostly unknown. Transcriptomics approaches can provide very valuable information for our understanding of waterlogging tolerance. Here, we surveyed 21 barley varieties for the differential transcriptional activation of conserved hypoxia‐response genes under waterlogging and selected five varieties with different levels of induction of core hypoxia‐response genes. We further characterized their phenotypic response to waterlogging in terms of shoot and root traits. RNA sequencing to evaluate the genome‐wide transcriptional responses to waterlogging of these selected varieties led to the identification of a set of 98 waterlogging‐response genes common to the different datasets. Many of these genes are orthologs of the so‐called “core hypoxia response genes,” thus highlighting the conservation of plant responses to waterlogging. Hierarchical clustering analysis also identified groups of genes with intrinsic differential expression between varieties prior to waterlogging stress. These genes could constitute interesting candidates to study “predisposition” to waterlogging tolerance or sensitivity in barley.

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Genetic factors increasing barley grain yields under soil waterlogging

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 39 publications

References 55 publications

The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know

The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know

Comparison of early season crop types for wheat production and nitrogen use efficiency in the Jianghan Plain in China

Transcriptional analysis in multiple barley varieties identifies signatures of waterlogging response

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