Improved Empirical Representation of Plant Responses to Waterlogging for Simulating Crop Yield

Abstract

Waterlogging causes apparent reductions in crop yields around the world. Crops undergo plant responses and adaptations due primarily to the reduction in soil oxygen concentrations in the plant root zone that occur during waterlogged conditions. Current methods of assessing and quantifying crop yield reductions due to waterlogging, such as the sum of excess water (SEW) and stress day index (SDI) accumulating methods, and the models DRAINMOD, Agricultural Production Systems Simulator (APSIM), and Salt Water And Groundwater MANagement (SWAGMAN) Destiny (Destiny) do not include plant physiological adaptation processes that may limit or avoid reductions in crop yield. This paper analyses results from field trials to create a unifying concept that recognizes the various responses and adaptations of crops to waterlogging. We propose an empirical three stage representation of crop responses and adaptations during waterlogging. Stage one represents increased plant function with unlimited water and adequate soil oxygen concentrations for root respiration for up to 3 d. Stage two follows and represents plant response to declining soil oxygen concentrations often resulting in decreased plant function. Finally, stage three represents species dependant plant adaptations. We test the sensitivity of SWAGMAN Destiny using our three stage empirical representation to estimate yield reductions due to waterlogging. Results are consistent with field trial observations, with decreased yield compared to simulations using 10% air‐filled pore space as the waterlogging criteria. We suggest the three stage empirical representation for specific crops can be used to improve simulation model estimations of crop yields due to waterlogging.

The Effects of Flood, Drought, and Flood Followed by Drought on Yield in Cotton

“…In the practice of field irrigation and drainage, it is crucial to establish and apply the relationship between water stress intensity and crop yield (or yield reduction). Thus, a commonly-used method called the stress day index method (SDI) [39,40] was used in the present work and calculated as follows:…”

Modeling Flood-Induced Stress in Soybeans

“…The models SWAGMAN Destiny (Meyer et al, 1996) and DRAINMOD (Skaggs et al, 2012) have been found to simulate waterlogging stress with the same level of accuracy as APSIM did prior to our additions, but fall short of incorporating the stage and nutrient dynamics of waterlogging-induced stress (Shaw et al, 2013;Shaw and Meyer, 2015). Currently, SWAGMAN Destiny (Meyer et al, 1996) uses the gas-filled soil pore volume to define the degree to which the soil is waterlogged (on a scale of 0-1 aeration), wherein dry matter accumulation is reduced after soil aeration remains at zero for 3 consecutive days.…”

Microtopography‐induced transient waterlogging affects switchgrass (Alamo) growth in the lower coastal plain of North Carolina, USA

“…In order to quantify the impacts of waterlogging on aboveground biomass yield, we adopted a metric called the sum of excess water (SEW) to quantify the intensity of waterlogging. This empirical metric is based on soil WTD and has been widely used to determine waterlogging intensity and provide a quantitative way of determining the amount of stress on crop growth during the growing season (Shaw & Meyer, 2015). Several studies found strong correlation between the SEW and crop yield (Kanwar et al, 1988;Malik et al, 2002;Marti et al, 2015).…”

“…Several studies found strong correlation between the SEW and crop yield (Kanwar et al, 1988;Malik et al, 2002;Marti et al, 2015). It has also been adapted by various models, such as DRAINMOD (Skaggs et al, 2012), Agricultural Production Systems Simulator (APSIM) (McCown et al, 1996), and Salt Water And Groundwater MANagement (SWAGMAN) Destiny (Destiny) model (Shaw & Meyer, 2015). The SEW method (Sieben, 1964) for a growing season lasting N days is calculated as:…”

“…The relative elevations were surveyed using the soil surface near the groundwater-level monitoring well as the reference. Meyer, 2015). Secondly, large natural fluctuations of WTD during our study period led to changes of treatment impacts on switchgrass leaf-level gas exchange or even opposite treatment impacts under waterlogging and drought conditions (Fig.…”