Waterlogging Impacts on Crop Growth: Comparison
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Please note this is a comparison between Version 3 by Craig Beverly and Version 2 by Catherine Yang.

Waterlogging has the greatest impact on photosynthesis, followed by phenology and leaf expansion, suggesting a need for improved equations linking waterlogging to carbon assimilation. In agricultural fields, soil waterlogging can occur for many reasons. These may include excessive rainfall or irrigation, poor soil drainage, rising or perched water tables, as well as lateral surface or subsurface flows. This may lead to reduced oxygen within soil pores, causing reduced growth and, sometimes, crop death. 

  • aeration stress
  • saturation
  • soil depth

1. Aeration Stress

In many models, waterlogging is described using air-filled pore space or excess water factors defined as a function of actual soil water content (θ), soil water content at saturation (θsat) and drained upper limit (θdul) or proximity to a water table and waterlogging duration. Soil saturation occurs in the presence of a high water table (perched or regional groundwater table) which could result from excessive rainfall or irrigation and poor soil drainage. The water table in these models is represented in various ways that range from a prescribed water table (AquaCrop) to a water table simulated based on the bottom boundary condition and soil hydraulic properties (APSIM, DRAINMOD, DSSAT, SWAP) or based on other variables that are not directly linked to soil water processes (EPIC). The excess water stress factor is described using various terminologies such as ‘aeration stress factor’, ‘oxygen deficit stress factor’, ‘growth limiting factor’, ‘waterlogging coefficient’, ‘water stress factor’, ‘oxygen shortage reduction factor’ and ‘stress day index’. Some of these terminologies are intuitive e.g., ‘aeration’ stress while others such as ‘growth limiting factor’ and ‘stress day index’ are not. 
All the models use soil water content to define aeration stress, except DRAINMOD which uses a ‘stress day index’ (SDI) based on a crop susceptibility factor for wet conditions, proximity to a water table and SWAP which uses pressure heads. APSIM, AquaCrop and SWAP use comparable forms of aeration stress equations where APSIM’s anaerobiosis point is defined as the drained upper limit. Equations describing aeration stress can be bilinear, making use of critical thresholds in soil water content (APSIM, AquaCrop,) or take more complex forms that consider other variables such as crop types, species and phenology (APSIM, EPIC). In AquaCrop, the aeration stress factor is applied directly on transpiration while a product of multiple stresses which include aeration stress, is used as a multiplier on yield (DRAINMOD), root water flux (SWAP) and transpiration (WOFOST). More complex applications of aeration stress factors on specific growth process such as root growth dynamics and transpiration are present in APSIM, SWAP and WOFOST.
Aeration stress factors are derived from soil water content measurements such as field capacity, wilting and saturation points and therefore the accuracy of these measurements is critical. Such stress factors are a simple extension of the existing pathways with which water stress is used to impact on crops and pastures [1][2][3]. The application of these stress factors in plant growth models requires knowledge of critical thresholds in soil air content for different crops and soils. In APSIM, vertical root growth in soybean is inhibited when volumetric soil moisture approaches 3% below saturation [4][5]. AquaCrop considers these thresholds as a percentage below soil saturation based on a crop’s tolerance to waterlogging. The values range from about 3% for tolerant crops to 15% for sensitive crops. The SWAP model provides critical pressure head values of root water extraction for various crops while DSSAT uses a minimum pore space value necessary for supplying oxygen to roots which also varies with crop type. The EPIC model sets the critical aeration factor, a fraction of soil porosity, at 0.85 (15% air-filled porosity) for many crops. A critical air-filled porosity of 0.1 to 0.15 m3m−3 is often assumed to be crucial for plant growth [6][7][8]. However, when held constant, this threshold can result in erroneous estimates of transpiration and plant growth [8][9][10]. Mohammadi et al. [11] derived critical air content based on root depth and the rate of oxygen consumption and showed that it varied linearly with soil depth. Bartholomeus et al. [9] found that a value of 0.1 can be too high for certain crop, soil and environmental conditions. Meskini-Vishkaee et al. [8] reported differences in critical aeration porosity, matric heads at field capacity (FC) and plant available water (PAW) between different soils and crops. Different methods such as those based on drainage flux and matric heads may also create differences in FC values, thus influencing aeration stress factors derived using FC. Certain soil and crop characteristics influence the critical values of aeration stress and thus should be considered simultaneously to accurately determine the impact of oxygen stress [12].
In addition to aeration stress, there are other stresses related to nutrients, temperature, salinity, pests and soil compaction that impact crop growth. These stresses are treated independently in most models but in reality, they occur simultaneously and plants can develop adaptation mechanisms, producing a unique response which can be considered as a new emergent stress [13][14][15]. This unique response to concurrent stresses is rarely captured in models which use simplistic approaches to account for this phenomenon. For example, the most limiting stress (Law of the Minimum), which may exclude aeration stress, is often used as a multiplier on variables such as biomass (APSIM and EPIC) and N-fixation (DSSAT) while other models such as AquaCrop, SWAP and WOFOST use a multiplicative approach. The models that use the most limiting stress approach assume that the contribution of all other stresses is insignificant, even though plant responses to combined stresses can differ markedly from those due to a single stress [14][16][17][18]. On the other hand, a multiplicative model of the stresses can be inadequate and could lead to unreliable yield predictions [19]. Impacts of a single stress on various crops have been investigated more than those of combined stresses. Further, combined stresses involving aeration stress have not had as much attention as other stresses. Studies on combined stresses that include aeration stress are aeration and salt stress in tomatoes [20], waterlogging and pathogen (Fusarium poae) stress on wheat and barley [21], and temperature and waterlogging stress on rice [22]. Given that concurrent stresses on a crop can produce a synergistic effect, the multiplicative and the most limiting stress approaches in models may be unrealistic and further research is needed to understand the various stress response pathways for combined stresses. Decline in oxygen is accompanied by a decline in redox potential which in turn causes the crop to deteriorate. The redox status of the soil is a good indicator of waterlogging intensity but is not captured in these crop models.

2. Waterlogging Duration

The duration of waterlogging affects crop growth and the models that consider this are APSIM, AquaCrop, DRAINMOD and WOFOST. In APSIM, three days with aeration stress below a given threshold are required for impact on root depth while WOFOST requires four days of waterlogging for a maximum impact on transpiration. For WOFOST, if there is oxygen deficit on the fifth day, the reduction in transpiration remains similar to that on the fourth day. In AquaCrop, the number of days when waterlogging stress fully affects transpiration is specified, in addition to setting a threshold for the aeration coefficient. DRAINMOD considers waterlogging duration using the SEW30 method as an indicator of waterlogging severity and is a scalar applied to the final grain yield rather than incrementally affecting crop growth processes. SEW30 is “Sum of Excess Water” in the top 30 cm of soil, calculated from depth of the water table beneath the ground surface. Waterlogging duration impacts also depend on growth stage. Generally, the longer the waterlogging duration the more adverse are the effects on crop growth [23][24]. Marti et al. [24] found that wheat yields were affected according to the length of waterlogging during stem elongation. Ren et al. [25] reported variable results from various studies on sensitivity of waterlogging duration for maize at the three and six-leaf stages. This variability in results is more likely due to the piecemeal nature of published information regarding the design of field experiments, varying field conditions etc. which hinders a systematic comparison across the studies. Due to the interaction of different waterlogging durations, crop types and varieties, the duration of waterlogging before the plant is affected should not be fixed in the model to allow further testing using field observations. More research is needed to investigate how the number of consecutive days of waterlogging affects different crops, and how this interacts with waterlogging frequency.

3. Root Growth and Transpiration

Root growth in waterlogging conditions is simulated in APSIM, DSSAT and SWAP and depends on the extent of soil saturation, although the fraction of roots affected is determined differently in these models. The differences are due to consideration of other variables such as the aeration stress threshold and saturation duration (APSIM), soil water deficit factor (DSSAT) and a minimum gas filled porosity at which aeration stress occurs based on plant physiological and soil physical processes (SWAP). Impacts of waterlogging on transpiration/water-uptake are simulated in AquaCrop and WOFOST. In AquaCrop the waterlogging coefficient (the air-filled pore space in the root zone) is used to adjust transpiration while specifying the number of days of saturation to account for crop resistance to transient periods of waterlogging. WOFOST reduces transpiration when the soil water content exceeds a critical value for aeration while also taking into consideration the number of consecutive days with oxygen stress. The SWAP model calculates a reduction factor using the multiplicative approach of multiple stresses, including an aeration stress factor, to calculate root water uptake. Root growth and uptake of water and nutrients are affected by waterlogging. In certain conditions, root tissues can adapt to low oxygen levels. For example, wheat roots can survive anoxic conditions longer when exposed first to relatively reduced oxygen levels compared to a sudden change from aerated to anoxic conditions [26]. This phenomenon is not captured in the models reported here.

4. Leaf Growth

The models APSIM, EPIC and WOFOST directly incorporate aeration stress on leaf growth. APSIM uses the proportion of the affected root system due to aeration stress to adjust leaf area. EPIC calculates leaf area index using the most limiting of a number of stresses, including aeration stress. The leaf area index is a function of the crop development stage, the minimum of all crop stress factors and a heat unit factor. The WOFOST model uses the product of water and oxygen shortage reduction factors, including a crop-specific maximum death rate of leaves, to determine the death rate of leaves due to water stress.

5. Phenology and Crop-Specific Factors

The models APSIM, WOFOST and SWAP/WOFOST incorporating improvements by [9] have some capability in simulating waterlogging impacts taking into consideration important variables such as phenology, waterlogging duration and crop-specific factors for waterlogging. Growth stage is considered in APSIM and WOFOST while crop-specific waterlogging stress factors are incorporated in APSIM, DRAINMOD, EPIC and SWAP. In an approach implemented in GLAM (General Large Area Model) based on the WOFOST/CGMS (Crop Growth Monitoring System) models, the critical air content is modified using a crop specific parameter to define the lower limit of soil water content where a plant suffers waterlogging stress [27]. A limitation of most crop specific factors is that they are fitting parameters with no physical meaning, rendering them untransferable across models due to the diverse formulations of aeration stress factors in the models.
Waterlogging can cause delays in phenological events such as shoot development, ear emergence and maturity [28][29]. Additionally, some crop varieties have some tolerance to waterlogging conditions and show reduced sensitivity in yield hence the need for variety-specific waterlogging stress factors. Sensitivities of different growth stages and crop species are important in evaluating impacts of waterlogging in crop models. For instance, a crop might show greater sensitivity to waterlogging when it occurs early in the growth cycle compared to that at a later period as shown in the case of barley and rapeseed in [30], while a crop such as field pea is adversely affected in both early and late waterlogging. Wang et al. [31] observed larger reductions in yield when cotton was waterlogged during flowering than at the boll-opening stage.

6. Nitrogen Cycling Processes Impacted by Waterlogging

A reduction in nitrate concentrations in soil can occur due to waterlogging [32][33] due to effects of superfluous soil water on denitrification, leaching, N uptake, runoff, gaseous losses etc. [34][35][36]. None of the models reported here have explicitly incorporated aeration stress impacts on nitrogen losses and uptake except for DSSAT, which calculates N-fixation rate based on excess water stress.

7. Crop Recovery after Waterlogging (Phenotypic Plasticity)

Crop recovery, adaptation and plasticity after waterlogging have not been accounted for in any of the crop models presented here. The assumption in some models is that the crop recovers when waterlogging ceases e.g., recovery from waterlogging in APSIM AgPasture happens every day when soil water is below saturation and is proportional to the water free porosity. However, for some crops, short waterlogging durations can have longer-term adverse effects. For instance, three days of waterlogging severely retarded development in wheat and barley even after drainage [37]. Colmer et al. [26] showed that seminal roots of wheat stopped growing soon after waterlogging and the capacity for re-growth after drainage was diminished. On the other hand, adventitious roots can grow in waterlogged soils and can even grow longer when waterlogging ceases [26]. Sometimes waterlogging can lead to complete crop failure depending on crop stage and duration of waterlogging [38]. Therefore, the basic assumption in some models of crop recovery when waterlogging ceases needs further investigation. Crop growth during the recovery period is key in estimating yield losses due to waterlogging [39]. Zhang et al. [40] found that the spraying of cotton stem leaves with sodium nitroprusside (SNP) during waterlogging increased nitric oxide even after waterlogging stress which led to enhanced photosynthetic recovery and increased yield compared to the non-SNP waterlogged treatment. The re-aeration impact on crops that occurs after waterlogging can cause stress but is fundamentally disregarded in crop models. This re-aeration stress has been discussed in few studies [41][42][43]. Rapid re-aeration after hypoxia causes damage mainly attributed to formation of excessive reactive oxygen species (ROS), by-products of aerobic metabolism [41]. An imbalance between ROS and antioxidant systems activated by re-aeration can occur due to environmental stresses, creating oxidative stress. Re-aeration can also induce photosynthetic stress in crops due to an abrupt increase in light intensity when floods recede. Therefore, plant adaptation mechanisms under waterlogging conditions and transition between hypoxic/anoxic conditions and re-aeration deserve further research.

8. Crop Yields

Numerous experimental studies have shown that waterlogging generally reduces crop yield, but this varies depending on other factors discussed previously. In most dynamic models, yield is an outcome of climate-soil-crop process interactions. However, in some large-scale models where processes such as crop growth are less detailed compared to those in field scale models, a yield gap parameter is used to adjust crop yield. The yield gap parameter lumps processes or conditions such as sub-optimal management and biotic stresses not captured in the model. For example, GLAM [44] does not explicitly incorporate factors that impact yield such as waterlogging. Rather, GLAM uses a yield gap parameter that can be calibrated to match simulated yields with observed yields. The limitation with this approach is obvious in that it is difficult to attribute a simulated yield reduction to a particular factor unless it is the only factor impacting growth and the rest have been well controlled. DRAINMOD uses a different approach to the aeration stress factor method: the model calculates SDI which adjusts yield for a given stress type. SDI is a function of water table depth (up to 30 cm from the surface), a crop susceptibility factor for wet conditions [45] which depends on crop species and growth stage, and potential yield. Although this index uses the water table depth, it does not directly influence growth variables and processes such as transpiration, root and leaf growth among others.

9. Soil Water Dynamics

The discussion presented above linking crop growth to soil water content and aeration stress implies that the success in simulating crop growth under waterlogged conditions is contingent on adequate mathematical formulations of soil water dynamics, among other processes [46][47]. Soil saturation can be caused by water ponding on the surface due excessive rainfall or irrigation, perching water tables above a layer of low permeability in the subsoil, rising of the groundwater table into the root zone and lateral surface or subsurface flows. Waterlogging is simulated in crop models via soil water balance modules that vary in their structure. Some crop models also contain a water table that may cause sub-surface waterlogging. The two main approaches for simulating soil water balance are based on Richard’s equation and the cascading bucket model. The Richard’s equation is used in APSIM (SWIM3 module) and SWAP to simulate soil water flow while the cascading bucket model is used in APSIM (SoilWAT module), AquaCrop, WOFOST and DSSAT, where drainage is calculated as a fraction of the soil water content above the drained upper limit (θdul). Hysteresis in soil is not accounted for in AquaCrop, APSIM, DSSAT and WOFOST, which could lead to uncertainties in simulated soil water related processes such as infiltration and lateral flow. Capillary rise is important especially where the water table is close to the surface. APSIM-SWIM and SWAP use the Richard’s equation and simulate capillary rise and the water table depth is updated. Capillary rise in AquaCrop and DSSAT is simulated based on soil hydraulic properties, water table depth and soil water. The depth and variation in time of the water table in AquaCrop is specified as an input to the model and is thus not updated by the model while DSSAT can simulate perched water tables. It would be a worthwhile contribution to waterlogging impact studies to investigate the influence of capillary rise and hysteresis on waterlogging and crop growth. It is necessary that in environments where shallow water tables exist, and capillary flow or bypass flows are presumed dominant that the adopted crop model adequately predict soil water dynamics, including surface water ponding.
Due to the central role that soil water content plays in defining waterlogging, it is imperative that soil hydraulic properties be properly quantified and soil water dynamics adequately represented in the model. Parameters such as soil field capacity and porosity are often assumed constant but can be variable [1][2][48][49] and this may bias the values of excess water stress factors calculated using these variables. In waterlogged conditions, the simulation of soil water content depends on the movement of the water table in and out of the soil profile or rootzone, which is represented in a rudimentary fashion in many crop models. The simplistic coupling of the soil profile and the water table in these models has led researchers to link groundwater models to crop models [50][51] to calculate the water table more accurately and improve soil water simulation. A question for future research is whether accuracy would be more improved by better simulating crop growth processes or better simulating fluctuating water tables, or both.

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