Soil waterlogging occurs when water saturates soil pores for several hours or more. Since gas diffusion in water is slower than in air, the oxygen concentration of waterlogged soils decreases rapidly, triggering a cascade of processes that – for the vast majority of contemporary crop species - are individually and collectively detrimental to growth. Globally, waterlogging affects around 10% of land area and significantly reduces crop yield. With future climate change, more agricultural regions will be at greater risk of waterlogging due to higher frequencies of extreme events, including extreme rainfall and flash flooding.
Process-based crop models are key to designing effective and holistic adaptations to extrapolate short-term field experiments, helping minimise down-side risk associated with changes in future climates. Application of such models enables consideration of complex, nonlinear physiological crop feedbacks to environmental, genetic and management conditions, supporting the development of effective, profitable climate change adaptation opportunities. Currently however, process-based crop models cannot reliably quantify growth inhibition evoked by waterlogging, and thus our models tend to overestimate yield under waterlogged conditions.
In our recent study in Nature Communications, we first developed a novel paradigm for distilling manifold crop model simulations into simplified discrete groups. Application of this paradigm enables insight into the type and frequency of waterlogging stress relative to crop phenology, and thus the importance of a given stress group when thousands of model simulations are compared globally. Next, we advanced the process-based understanding of waterlogging in a farming systems model using results from several waterlogging field experiments across major global cropping zones (Fig. 1) then used the improved model to discern changes in global crop waterlogging under future climate. Using both the clustering paradigm and improved biophysical model, we revealed that past estimates of yield under future climate change may well be overestimated: simulated future yields decreased by 8–18% in the 2040s and 17-26% in the 2080s when physiological effects of soil anoxia and plant phenology were accounted for. More importantly, we showed that at the global scale, yield penalty caused by waterlogging will increase under future climate change.
Armed with knowledge of probable changes in yield penalty caused by waterlogging, we then conceive and compare several practical avenues for adapting cropping systems to waterlogging by conceptualising three stages of plant response and adaptation (Fig. 2). We showed that altering sowing time coupled with adoption of superior crop genotypes resulted in further gains in yield (these genotypes had waterlogging tolerance genes enabling tolerance of saturated soils by accelerating aerenchyma formation and increasing root porosity following waterlogging).
In some regions, converting from longer-season winter genotypes to short-season spring genotypes was found to help avoid waterlogging, but with regional specificity; viz. long-season waterlogging tolerant genotypes were shown to be more effective in Ethiopia, while short-season waterlogging tolerant genotypes were more effective in Europe and China.
Taken together, our results suggest that contextualised adaptation will be key: there is no panacea, no singular generic silver bullet that can be applied across all environments. Fruitful future research may include ‘stacking’ or combining of several beneficial adaptations to determine whether the benefit from individual adaptations is synergistic or antagonistic. Either way, adaptation of agricultural systems to climate change has and will continue to require cross-disciplinary action: new knowledge, practices and technologies that integrate agronomic, environmental, molecular, social and institutional dimensions are needed to ensure that proposed adaptations are fit-for-purpose.
This research was led by scientists (Ke Liu, Matthew Tom Harrison and Meixue Zhou) from the University of Tasmania with concerted collaboration with researchers in over 30 international institutions. Dr. Ke Liu and Associate Professor Matthew Tom Harrison from University of Tasmania are the first author and corresponding authors of this work, respectively.