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

Abstract

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.

Testing the EPIC Richards submodel for simulating soil water dynamics under different bottom boundary conditions

“…In our study, aeration stress factor was maximized to 1.0 for both maize and grain sorghum in order to null the effect of waterlogging on root growth. In addition to EPIC, other models that integrate ways for reducing total biomass production, potential transpiration, or photosynthesis in concomitance of shallow GW or waterlogging events have been developed (Shaw et al., 2013; Liu et al., 2020). On the other hand, enhanced root growth modules able to account for excessive moisture effects are quite rare (Ebrahimi‐Mollabashi et al., 2019).…”

Integrated Crop Management Practices Improve Grain Yield and Resource Use Efficiency of Super Hybrid Rice

“…These techniques have been shown to have favorable effects on rice growth and development as well as yield formation. Therefore, optimization of management by the interaction of environment and genotype increases the likelihood of reaching yield potential and maximum resource use efficiency ( Harrison et al, 2017 ; Ibrahim et al, 2019 ; Liu et al, 2020c ).…”

“…For example, N management techniques combining N fertilizer application limit soil fertility and precision and field nutrient management techniques have been examined ( Liu et al, 2009 ; Shah et al, 2021 ). Soil water management, such as alternating wet and dry, intermittent wetting, and controlled irrigation, can significantly improve water use efficiency while promoting rice growth, rice development, and yield formation ( Harrison et al, 2014b ; Liu et al, 2020c ). Variation in phenology is also important since crop development determines the life cycle duration, flowering time, and growing season duration ( Liu et al, 2020d ).…”

iTRAQ Proteomic Analysis of Wheat (Triticum aestivum L.) Genotypes Differing in Waterlogging Tolerance

“…Crop waterlogging (WL) may be caused by intense rainfall events, excessive irrigation, flash flooding, lateral surface or subsurface flow, and/or poor soil drainage ( Herzog et al, 2016 ; Chang-Fung-Martel et al, 2017 ; Kaur et al, 2020 ). Waterlogging can hinder or cease plant growth by reducing oxygen availability in soil pore spaces in the root zone ( Liu et al, 2020b ). Anoxic soils and severe hypoxia then inhibit several physiological processes, including root water absorption, plant hormone relations, ion uptake, and transport and superoxide dismutase activities ( Ghobadi et al, 2017 ; Manik et al, 2019 ).…”

“…WL has become a major constraint for wheat production in southeast of China due to excessive rainfall during the growing season, which is especially severe during the critical grain formation periods of anthesis and maturation ( Liu et al, 2020b ; Yan et al, 2022 ). Proteomics is a useful and important method for investigating crop responses to stress by detecting changes in expression and post-translational modification of proteins ( Komatsu et al, 2012 ).…”