Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates

Abstract

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3–11% historically to 10–20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Full text

Article https://doi.org/10.1038/s41467-023-36129-4
Silver lining to a climate crisis in multiple
prospects for alleviating crop waterlogging
under future climates
Ke Liu
1,2,29
, Matthew Tom Harrison
1,29
, Haoliang Yan
3,29
,DeLiLiu
4,5
,
Holger Meinke
1
, Gerrit Hoogenboom
6
,BinWang
4
,BinPeng
7,8,9
,
Kaiyu Guan
7,8,9
, Jonas Jaegermeyr
10,11,12
,EnliWang
13
, Feng Zhang
14
,
Xiaogang Yin
15
, Sotirios Archontoulis
16
, Lixiao Nie
17
,AnaBadea
18
,
Jianguo Man
19
, Daniel Wallach
20
, Jin Zhao
21
, Ana Borrego Benjumea
18
,
Shah Fahad
22
, Xiaohai Tian
2
, Weilu Wang
23
,FuluTao
24,25
, Zhao Zhang
26
,
Reimund Rötter
27
,YouluYuan
3
, Min Zhu
18
, Panhong Dai
28
,JiangwenNie
15
,
Yadong Yang
15
, Yunbo Zhang
2
& Meixue Zhou
1
Extreme weather events threaten food security, yet global assessments of
impacts caused by crop waterlogging are rare. Here we first develop a para-
digm that distils common stress patterns across environments, genotypes and
climate horizons. Second, we embed improved process-based understanding
into a farming systems model to discernchangesinglobalcrop waterlogging
under future climates. Third, we develop avenues for adapting cropping sys-
tems to waterlogging contextualised by environment. We find that yield
penalties caused by waterlogging increase from 3–11% historically to 10–20%
by 2080, with penalties reflecting a trade-off between the duration of water-
logging and the timing of waterlogging relative to crop stage. We document
greater potential for waterlogging-tolerant genotypes in environments with
longer temperate growing seasons (e.g., UK, France, Russia, China), compared
with environments with higher annualised ratios of evapotranspiration to
precipitation (e.g., Australia). Under future climates, altering sowing time and
adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%,
while earlier sowing of winter genotypes alleviates waterlogging by 8%. We
highlight the serendipitous outcome wherein waterlogging stress patterns
under present conditions are likely to be similar to those in the future, sug-
gesting that adaptations for future climates could be designed using stress
patterns realised today.
Increasingly frequent and compound extreme weather events driven
by the intensification of the global water cycle threaten the sustain-
ability and consistency of agri-food production1–3. Coupled with global
population growth and a burgeoning demand for food, exposure
to weather extremes demand the development of new knowledge,
technologies and practices that enable scalable, sustainable
intensification4,5.
Robust projections of climate impacts on crop growth under-
pinned by process-based models6,7are fundamental in the quest to
design effective and credible systems-based adaptations that minimise
Received: 15 July 2022
Accepted: 16 January 2023
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A full list of affiliations appears at the end of the paper. e-mail: matthew.harrison@utas.edu.au
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downside risk associated with future climate8–10. Application of such
models enables consideration of nonlinear,integrated crop responses
to environmental, genetic and management conditions7,11,supporting
the development of socially-acceptable and profitable climate change
adaptation and/or greenhouse gas emissions mitigation strategies12–14.
However, while the overwhelming majority of previous climate change
assessments have used a lens focused on either drought, heat or gra-
dual climate change1,3,15–17,ourknowledgeoftheimpactsofsoil
waterlogging on crop growth is very much in its infancy18–21.
Globally, around 27% of cultivated lands areimpacted by flooding
each year, with annual costs of flood damage over the last half-century
reaching a headline value of US$19 billion22–25.Intensification of the
global water cycle called forth bythe climate crisis would appear to be
driving a higher prevalence of waterlogging, placing pressure on the
use-efficiency of economic, natural and social capital20.Whilegeno-
type (G) × environment (E) × management (M) studies pertaining to
climate change adaptation abound26–31,suchworkisoftennotcon-
ducted in a way that facilitates scaling to other regions or transfer-
ability from other studies. Here, we develop a new approach for
assimilating manifold results from crop models into common, discrete
sets of groups. These groups—characterised by daily stress trajectories
plotted over the crop lifecycle as a function of phenology—invoke
plant stress, because perceived stress represents an integrated mea-
sure of biomass, canopy leaf area, cumulative water supply, vapour
pressure deficit and several other factors interacting across an
atmosphere-plant-soil continuum. As such, plant stress has longbeen a
ubiquitous target for quantification and manipulation in molecular,
breeding and agronomic studies32–35.
While G × E × M factorial studies are useful, attempts to interpret
results using the association between management interventions and
maturity biomass or yield36 can make it difficult to derive functional,
rationally bounded37 insights across all of the interventions deployed.
In contrast, we suggest that crop stress patterns characterised as a
function of phenology are limited in type; when grouped across an
entire factorial analysis, such relationships can be aggregated into
common groups and recurrence intervals, even though individual
stress trajectories may appearunique. To standardise contrasts across
treatments, we grouped waterlogging stress as a function of phenol-
ogy. We focus on waterlogging stress and barley as case studies, but
the principles could be generically applied to any crop or biological
variable. A fundamental contribution of our approach is the ability to
functionally categorise big datasets. Armed with knowledge of stress
prevalence and pattern using this method, scientific practitioners can
(1) more intuitively identify the most appropriate adaptation within
stress patterns that are more probable for their environment and (2)
transfer adaptations across regions within any given stress type35,38.
Building on foundational insights from our previous waterlogging
experiments conducted using a range of genotypes and treatments in
controlled environments39, we enumerate the effects of waterlogging
on photosynthesis and phenology and then use these insights to
improve the capacity of the internationally renowned model APSIM to
simulate the impacts of waterlogging on crop growth40. Although past
work has shown that our new waterlogging algorithms reproduce the
effects of waterlogging stress on contemporary barley genotypes40
with reasonable precision, the validity of our new algorithms across a
broad array of global cropping environments remains unknown. To fill
these knowledge gaps, we first calibrated and evaluated the
waterlogging-enabled version of APSIM using measured field data
from five countries. We then applied the waterlogging-enabled model
and novel clustering paradigm in each of the major barley production
zones across the world with the specific objectives of (1) quantifying
the effects of climate change on waterlogging, (2) characterising
common waterlogging stress patterns and frequencies across envir-
onments, (3) determining the extent with which common stress pat-
terns change under future climate, and (4), quantifying the extent with
which waterlogging tolerance genotypes, genotypic phenology and
sowing time mitigate effects of waterlogging under future climates.
Results and discussion
Conceptualising impacts of waterlogging on phenology and
photosynthesis
Past work has shown that crop sensitivity to waterlogging stress is
critically dependent on the developmental stage in which water-
logging occurs31. As such, we modelled waterlogging stress as a func-
tion of phenology, which is in itself a significant advance on the
majority of previous studies, the latter assuming that waterlogging
stress is primarily a function of water-filled pore space and has negli-
gible effect on crop ontogeny (e.g., ref. 40). We developed new func-
tions to account for experimentally observed effects of waterlogging
on photosynthesis and phenology (oxdef photo and oxdef pheno,
respectively; Fig. 1a)40. Each dimensionless function assumes multi-
pliers ranging from unity to nil in the form of y=f(x), where yis the
stress factor and xis soil moisture. When xis at or below field capacity,
y=1;ylinearly decreases with increasing xuntil the point at which the
soil is saturated (y= 0). These functions were incorporated into the
APSIM software platform to enable improved simulation of crop
responses to waterlogging as part of an integrated system. We cali-
brated the waterlogging-enabled framework using published data
from field observations across five countries (Australia, Argentina,
China, Canada and Ireland; Supplementary Table 1). Including the new
waterlogging functions significantly improved the performance of
APSIM in simulating the biophysical impacts of waterlogging relative
to the default version of the model, with the root mean square error
(RMSE) for waterlogged yield loss predictions decreasing from 0.3 to
0.1 (Fig. 1b). The modified model adequately captured the variation in
grain yield of multiple genotypes in response to a range of water-
logging treatments across environments (Fig. 1b), with simulations
accounting for 70% of the variation in observed yield.
Impacts of a changing climate on global soil waterlogging and
barley yield
Using downscaled projections from Assessment Report 6 (AR636) from
27 global circulation models (GCMs; Supplementary Table 2), we
quantified how current waterlogging frequencies may change under
future climates. Following recent reports41, we simulated crop growth
and development using the most plausible greenhouse gas emissions
scenario (ie. SSP585) for climate horizons of 2030–2059 and
2070–2099 (hereafter respectively referred to as 2040 and 2080). To
account for variable growing season durations under future climates,
we examine crops sown relatively early and late at each site in factorial
combination with shorter-growing season genotypes (‘spring’)and
longer growing season genotypes (‘winter’; Supplementary Table 3).
Our simulations suggest that even though the risk of severe
waterlogging will increase under future climate (2-10% increase across
GCMs, sites and sowing dates; Supplementary Fig. 1), yields will also
slightly increase due to fertilisation from atmosphericCO
2
enrichment
and mitigation of cold stress at high latitudes (Fig. 2a–dandSupple-
mentary Fig. 1). Our work suggests that past estimates of yield that do
not account for soil waterloggingmay be overestimated: here we show
that simulated future yields decreased by 8–18% in 2040 and 17–26% in
2080 when physiological effects were embedded in the modelling
framework (Fig. 2a, d). This modulating effect of waterlogging on yield
was especially pronounced in winter genotypes regions (Fig. 2c), likely
because such crops have longer growing seasons and greater annual
rainfall. Globally, the average yield penalty caused by waterlogging was
11% for the historical baseline, 14% in 2040 and 20% in 2080 for winter
barley (median yield penalty 130–591 kg ha−1; Fig. 4d), while for spring
barley yield penalties were 3% for the historical baseline, 6% in 2040
and 10% in 2080 (median yield penalty 50–91 kg ha−1; Fig. 4a) across
GCMs, sites and sowing dates.
Article https://doi.org/10.1038/s41467-023-36129-4
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Despite increased impacts of waterlogging, spring barley yields
increased by 5% in 2040 and 13% in 2080 for early sowing (ES) and by
7% in 2040 and 18% in 2080 for late sowing (Supplementary Fig. 1).
Future climates had variable effects on spring barley yield, ranging
from positive (e.g., Australia, Germany, Spain, France, United King-
dom, Ukraine and Russia) to antagonistic (Argentina, Canada, Central
Ethiopia, Ukraineand United states; Supplementary Table4). Averaged
across sites and climate horizons, yields increased by 22% and 9% for
early and late sown winter barley (Supplementary Fig. 1). For both
future climate horizons, winter barley yields increased for most
regions under early sowing, with greater gains expected in Europe
(18%; Supplementary Table 4). These changes suggest that forward
shifts in sowing time of long-season genotypes may benefit yields,
congruent with other work42.
Distilling common stress patterns across diverse environments,
genotypes and management approaches
Improved understanding of common waterlogging-stress seasonal
patterns allows insight into the timing of waterlogging stress relative
to crop phenology, which then governs cumulative effects on growth,
tillering, floral development and yield35,40,43. When applied in the pre-
sent study, these results help explain differences between yield
penalties caused by waterlogging stress between winter and spring
barley (Fig. 3a–f). Using waterlogging stress outputs from the model
computed as a function of historical climate, soil physics, atmospheric
demand, plant biology and agronomy, we calculate stress indices for
each day of crop growth.
We applied unsupervised k-means clustering to many thousand
individual trajectories of discretised waterlogging stress as a function
of the phenological stage into four common clusters (Figs. 3and 4);
within each stage, the algorithm minimises within-cluster variances.
The four clusters accounted for 71% of the variance for spring barley
and 80% for winter barley (increasing to five clusters accounted for
74% and 85% of total variance for spring and winter barley and was
deemed superfluous accuracy; Supplementary Fig. 2). While we
showcase barley and waterlogging stress as exemplars, the principles
shown here could be applied to any crop, region, stress type or bio-
physical model output.
Winter genotypes experienced substantially different patterns of
seasonal waterlogging stress relative to spring genotypes at the global
scale(cf.Fig.3a–f); waterlogging primarily occurred in the juvenile
phase of winter barley (WW3) cf. during reproductive development of
spring types (SW2-3). While cereals are more likely to experience yield
losses when exposedto waterlogging during their reproductive phases
(yield formation of cereals being tightly coupled with kernel number
and mass) we showed that winter genotypes exposed to waterlogging
during their juvenile phases had lower yields than spring genotypes
exposed to waterlogging during their reproductive phases, because
the magnitude of waterlogging experienced by winter types was
greater. Put another way, yield penalties caused by waterlogging
Fig. 1 | Framework invoked for modelling waterlogging (WL) stress, including
conceptual design of crop physiological responses to waterlogging and model
evaluation before (default) and after (improved) modification. a Schematic of
genotypic traits influenced by waterlogging and linkage with existing soil and water
sub-models in APSIM. bComparison of observed (Obs) and simulated (Sim)
waterlogged yield loss compared with controls across environments simulated by
improved and defaultversions of APSIM.Data in (b) represent contemporary barley
genotypes with varying waterlogging tolerance (n= 36). Parameterdescriptionsare
provided in Supplementary Table 4.
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reflected an important trade-off between the duration of waterlogging
experienced within a given phase and the timing of waterlogging
relative to the crop stage; across simulations, yield penalties asso-
ciated with winter barley were more severe thanthose of spring barley
(Fig. 4a, d).
While recurrence frequencies for each of the four main water-
logging stress patterns for spring genotypes remained similar under
future climate, frequencies of early severe (WW3) and mild (WW1)
waterlogging during the juvenile phases of winter genotypes increased
under future climate at the expense of seasons with minimal water-
logging. Stress pattern WW1 increased from 7% to 17% (under early
sowing; Fig. 4e) while WW3 from 3% to 8% (under late sowing; Fig. 4f)
compared with the baseline and 2080 periods (Fig. 3). Increased fre-
quencies of severe waterlogging underpin the greater reductions in
yields observed for winter genotypes compared with spring genotypes
under future climate (Figs. 2a, c and 4d), primarily due to increased
waterlogging in France, the UK, Russia and China (Supplemen-
tary Fig. 3).
Pathways for adapting agricultural systems to waterlogging
Adaptation of agricultural systems to climate change has and will
require cross-disciplinary action: new knowledge, practices and tech-
nologies that integrate agronomic, environmental, molecular, social
and institutional dimensions will be required5,44,45.By2080,early
sowing of spring barley reduced the occurrence of low waterlogging
(SW0; Fig. 4b), while later sowing of spring barley increased the like-
lihood of low waterlogging occurrence but did not affect the fre-
quency of the most severe type of waterlogging SW3 (Fig. 4c). In
contrast, earlier sowing of winter barley diminished frequencies of
both severe and low waterlogging stress (WW1 and WW3; Fig. 4e),
while later sowing of winter types increased risk of early-onset severe
and moderate waterlogging (WW1-WW3; Fig. 4f). Overall, we suggest
that sowing time of spring barley in 2080 had relatively little effect on
the magnitude of the type ofwaterlogging stress, while later sowing of
winter barley was likely to increase the likelihood of exposure to
waterlogging stress.
Altering sowing time coupled with the adoption of superior
genetics resulted in further gains in yield. Based on experimental
observations, we developed in silicogenotypes tolerant to soil hypoxia
and anoxia typically experienced when soils become waterlogged46.
After verifying the ability of the improved model to capture behaviour
of tolerant genotypes during and after waterlogging (Fig. 1b), we
examined the long-term performance and yield benefitexpectedwhen
waterlogging tolerant spring and winter genotypes were coupled with
other prospective adaptations (altered sowing time and/or phenolo-
gical duration). New genotypes with waterlogging tolerance demon-
strably increased barley yield under wetter years (Fig. 5) and in general
(Supplementary Fig. 4) under future climates. Across sites, the average
1000
3000
6000
0
20
40
60
80
20
40
180 150 120 90 60 30 0 30 60 90 120 150
0
20
40
60
80
20
40
180 150 120 90 60 30 0 30 60 90 120 150
Yield penalty
Site
Spring barley
Winter barley
1000
3000
6000
60
30
0
-30
90
60
30
0
Yield (kg ha-1)
Yield (kg ha-1)
180
180
Spring barley yield change (%)
Winter barley yield change (%)
E L
2040 2080
2040 2080
Yield penalty
Site
EL
E L E L
Fig. 2 | Impacts of waterlogging on yield under future climates (2040, 2080)
relative to the historical baseline(1985–2016)for early and late sowing(ES, LS).
a,cSimulated yield differences under future climates with and without water-
logging (WL) for genotypes with early (spring sowing barley) or late maturity
(autumn/winter sowing barley). b,dSimulated yields (pie charts; dark segments
denote yield penalty) under late sowing for spring barley and early sowing for
winter barley in 2040 (results for early or late sowing in 2040 and 2080 can be
found in supplementary Fig. 12). Yields were simulated with APSIM using down-
scaledprojections from27 GCMs (n= 27). Boxplotsindicate simulated yield change
across sites and GCMs; boxboundaries indicate 25th and 75th percentiles, whiskers
below and above each box denote the 10th and 90th percentiles, respectively.
Green regions in the maps define predominant barley cropping areas. The mapwas
modified using Rpackage ggplot2’maps (version 3.4.0)’with the Natural Earth
dataset in a publica domain (https://www.naturalearthdata.com).
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yield benefit of waterlogging tolerant lines was 14% and 18% (s.d., 23%
and 34%) for early- and later sowing in the 2040 s compared with the
baseline genotypes. Similar yield benefits were observed in 2080
(Supplementary Fig. 5). Mean yield benefits were greater for winter
genotypes (480–620 kg ha−1) than spring genotypes (194–213 kg ha−1;
Fig. 5). Importantly, yield benefits associated with waterlogging toler-
ance of new genotypes did not come at the expense of yield in drier
years, and reduced downside risk associated with low yielding years
(Supplementary Fig. 4).
Our results suggest that there would be more scope for and
potential impact of waterlogging tolerant genotypes in environments
with longer, cooler and more temperate growing seasons (e.g., the UK,
France, Russia and China; Fig. 5and Supplementary Fig. 6), compared
with shorter-growing season environments requiring fast-maturity
genotypes. This result may reflect the fact that longer growing season
environments have higher rainfall, more frequent soil saturation, and/
or greater propensity for extreme rainfall events. In countries with
higher annualised ratios of evapotranspiration to precipitation and
lower risk of waterlogging (e.g., Australia), genotypes with water-
logging tolerance conferred relatively little benefitoverthelong-term.
Future crop waterlogging stress patterns remain similar to
those occurring historically
We developed a new approach for clustering common stress patterns
to facilitate functional insight into big data that would otherwise be
outside the bounds of reasonable cognitive capacity. This character-
isation of the timing of waterlogging stress as afunction of phenology
across diverse management, environments and climate types revealed
two fundamental insights when assessed at the global scale. First,
winter genotypes experience earlier seasonal patterns of waterlogging
stress relative to spring genotypes (cf. Fig. 3a–f).Eventhoughcereal
crops are more sensitive to waterlogging during their reproductive
phases, winter genotypes experienced greater yield penalty under
early waterlogging (than spring genotypes under later waterlogging),
because waterlogged durations experienced by winter genotypes were
generally longer (Figs. 3and 4a, d). Second, even though future crop
waterlogging events are likely to increase by 2–10% (Supplementary
Fig. 7), we revealed the serendipitous outcome in which waterlogging
stress patterns for each of winter and spring genotypes under present
conditions are likely to be similar to those expected in future climate
(Supplementary Fig. 8). Equipped with such knowledge, agronomists
and cropbreeders wouldlikely achieve more widespread impactif new
spring genotypes were adapted to late-season waterlogging, while
proposed development of new winter barley genotypes would likely to
achieve wider impact if designed with early waterlogging in mind. It
should be noted that while situations with minimal waterlogging stress
(SW0 and WW0) would predominate (Fig. 3c,f);thisresultdoesnot
guarantee that such environments will not experience waterlogging
stress under future climate, rather, that low waterlogging stress is
more likely to emanate over the long-term40,47.
Similar frequencies of waterlogging under historical and future
conditions is a fortuitous outcome, because it suggests that practi-
tioners could effectively develop today’s adaptations for the tem-
poral waterlogging patterns of tomorrow. If future waterlogging-
stress patterns were dissimilar to those occurring historically, then
the design of effective adaptations to future conditions would be
more hindered due to the need to establish controlled-stress
environments48 or create synthetic waterlogging stress patterns
similar to those expected in future. However, similar historical and
future waterlogging stress patterns suggest that beneficial adapta-
tions within each waterlogging stress pattern—e.g., the early-onset
severe pattern of winter barley—could be readily transferred
between regions, production systems and time periods, provided
that other factors remained unchanged (e.g., local-adaptation of
genotypes for disease resistance). Clustering stress patterns into
common groups allows us to move away from locally specific factors
causing the waterlogging stress (e.g., poor drainage, rising
groundwater, superfluous rainfall, sowing time, genotype, soil type
Fig. 3 | Waterlogging (WL) stress patterns and frequencies and grain yields for
the baseline (1985–2016), 2040 (2030–2059) and 2080 (2070–2099). Data
shown for spring (a–c) and winter barley (d–f) across sites, sowing times and
genotypes. Four key waterlogging stress patterns across sites and genotypes are
depicted: stress patterns for spring barley include SW0 (minimal waterlogging);
SW1 (low moderate-late waterlogging); SW2 (late-onset moderate waterlogging);
SW3 (late-onset severe waterlogging) and winter barley WW0 (minimal water-
logging); WW1 (low early-onset waterlogging relieved later); WW2 (moderate early-
onset waterlogging); WW3 (severe early-onset waterlogging). Boxplots indicate
grain yields for spring and winter barley across sites and GCMs; box boundaries
indicate the 25th and 75th percentilesacross 27 GCMs, whiskers below and above the
box indicate the 10th and 90thpercentiles.Growth stagesinclude the earlyjuvenile
phase (JV1, 10 <=APSIM growth stage <21; late juvenile phase (JV2, 21 <= APSIM
growth stage <32); floral initiation to heading (FIN, 32 <= APSIM growth stage <65);
flowering to grain filling (FIN,65 <= APSIM growth stage <71; early grainfilling (GF1,
71 <= APSIM growth stage <80) and late grain filling (GF2, 80 <= APSIM growth
stage <87).
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etc.) to the stress pattern that would most likely be realised in a
given environment as a function of crop phenology.
On the implications of regional climate change for waterlogging
and grain yield
Our work has shown that mean grain yield penalty caused by water-
logging increased from 6 to 14% in 2040 to 10–20% by 2080 across
GCMs, genotypes, management and sites. This result encompasses
locally specificfindings for Europe (e.g., France, Germany, UK and
Spain)49 and China43 under superfluous precipitation scenarios. In
these regions, yields were higher under future climate due to elevated
atmospheric CO
2
concentrations and moderate alleviation of cold
stress when water was not limiting50–52, analogous to yield gains seen in
US dairy systems3,16.Ourfindings also align with previous work which
suggests that winter crop yields in Europe will rise by 205053 due to
greater biomass production, grain number and grain weight asso-
ciated with a fertilisation effect of atmospheric CO
2
and moderate
warming11. In waterlogging-prone regions within Australia, we showed
that yields are likely to increase under future climate due to a lower
incidence of waterlogging (Fig. 2). Less rainfall in regions with high
precipitation (>600 mm/year) may reduce disease susceptibility (e.g.,
stripe rust), improve crop health and further raise yield under future
climate, although it should be noted that biotic pressures were not
accounted for in the modelling framework used here (e.g. Snow
et al.54). To avoidconfounding impacts of superfluous water with those
of nitrogen stress, we ran simulations without N stress invoked,
albeit effects of waterlogging on mineral N and the corollary of such
interplay would be a fruitful endeavor for future research (Rawnsley
et al.55).
Climatic transition towards drier and hotter conditions by the end
of 21st century is projected for many regions, often with an increased
likelihood of extreme weather events40,41,50,52. Even though future cli-
mate were conducive to a 2–10% higher risk of severe waterlogging
across the entire solution space (Supplementary Fig. 8), high variation
between regions and genotypic lifecycles (Supplementary Figs. 3 and
8) was offset by the beneficial effects of climate change that collec-
tively improved yield by 8–17% under future climate. As part of this, we
found higher frequencies of early-onset severe waterlogging stress in
Argentina, Ethiopia, China, the UK, France and Germany, in line with
reports of increased flash flooding in some regions towards the end of
the 21st century, particularly Asia and Africa56. We suggest that parti-
cular attention should be placed on the development of waterlogging
Yield lose (kg ha−1)Yield lose (kg ha−1)
Ye a r s
Baseline
20 0
20 0
pring inter
0
500
1000
1500
2000
0
500
1000
1500
2000
Yield lose (kg ha−1)Yield lose (kg ha−1)
Fig. 4 | Grain yield penalty and waterlogging stress patterns for the baseline
(1985–2016), 2040 (2030–2059) and 2080 (2070–2099). Grainyield penaltiesare
shown for spring (a) and winter (d) barley across sites and genotypes for relatively
early or late sowing (ES, LS) at each site. Boxplots indicate yield penalty for spring
and winter barley across sites and GCMs (n= 27); box boundaries indicate 25th and
75th percentiles, whiskers below and above the box indicate the 10th and 90th
percentiles, respectively. Waterlogging stress patterns for spring barley include
SW0 (minimal waterlogging); SW1 (low moderate-late waterlogging); SW2 (late-
onset moderate waterlogging); SW3 (late-onset severe waterlogging) and winter
barley, WW0 (minimal waterlogging); WW1 (low early-onset waterlogging relieved
later); WW2 (moderate early-onset waterlogging); WW3 (severe early-onset water-
logging). Data in (b), (c), (e)and(f) are presented as mean ± standard errors of the
mean of GCMs (n= 27). Data were analysed using one-way analysis of variance
followed by least significant difference(LSD) post-hoc tests.Different letters in (b),
(c), (e)and(f) above the bars indicate significant differences in the frequency of
stress patterns between climate periods (P<0.05).ExactPvalues include:4.96e-08
(SW0 for ES in spring barley), 0.46 (SW1 for ESin spring barley), 1.47e-04 (SW2 for
ES in spring barley), 2.61e-08 (SW3 for ES in spring barley), 7.44e-07 (SW0 for LS in
spring barley), 2.88e-05(SW1 for LS in spring barley),5.39e-04 (SW2 for LSin spring
barley), 0.49 (SW3 for LS in spring barley), 1.42e−11 (WW0 for ES in winter barley),
2.30e-08(WW1 for ES in winter barley),5.66e-06 (WW2for ES in winter barley), 0.84
(WW3 for ES in winter barley), 3.97e-07 (WW0 for LS in winter barley), 0.97 (WW1
for LS in winter barley),4.93e-04 (WW2 for LS in winter barley) and 6.01e-09(WW3
for LS in winter barley).
Article https://doi.org/10.1038/s41467-023-36129-4
Nature Communications | (2023) 14:765 6
Content courtesy of Springer Nature, terms of use apply. Rights reserved

mitigation approaches for smallholders and the rural poor in lower-
latitude countries where increased flood frequency is projected and
prevailing rainfall is already high; women, youth and marginalised
groups need to be empowered for proposed adaptation approaches to
be successful. To engender adoption, appropriate research, develop-
ment, policy and extension packages will be required to ensure that
proposed adaptations are cost-effective, demand-driven, socially
responsible and equitable57,58.
A requirement for contextualised adaptation to future climate
change
The effectiveness of genotypic adaptation (i.e., introduction of crops
with waterlogging tolerance genes) was higher in Ethiopia, China,
Germany, France and UK. Across countries analysed, we showed that
the adoption of waterlogging tolerant genotypes could mitigate up to
18% yield penalty caused by waterlogging under future climate, sug-
gesting further research and development of such genotypes would be
a worthwhile investment. In other regions, converting from longer-
season winter genotypes to short-season spring genotypes could 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 sug-
gest that contextualised adaptation will be key: there is no panacea,
and certainly no singulargeneric solution for all environments. Fruitful
future research may include ‘stacking’or combining of several bene-
ficial adaptations to determine whether the benefit from individual
adaptationsissynergisticorantagonistic
5,51.
As far as we are aware, the present study is the first experimental
quantification of waterlogging expected under future climate in each
of the major barley cropping zones of the world. To quantify water-
logging stress patterns, we develop and exemplify a simple, transfer-
able approach for clustering crop stress patterns across regions,
climate, management and genotypes. We show that even though the
frequencies of global waterlogging will become higher, these changes
will be outweighed overall by reduced waterlogging in other regions
together with elevated CO
2
and warmer growing season temperatures.
Our clustering approach comprises a pathway in which diverse bio-
climatic applications may be able to categorise big data outputs into
functional and biologically-meaningful patterns. While we apply this
method to waterlogging and barley, although the frameworkcould be
readily applied to any crop, production system, or temporal biological
variable. With regards to adaptation, we show that waterlogging tol-
erance genetics will have benefit in Ethiopia, France and China, but
particularly in regions were long-season ‘winter’genotypes are com-
monplace. Shifting from relatively late to early sowing or from late to
early maturity genotypes may alleviate waterlogging-induced yield
penalties in some environments (Australia, Canada, Spain, Turkey
and USA).
In this study, we used APSIM based on evidence that suggests that
this farming systems framework is one of the most reliable models for
simulating waterlogging dynamics57,59. Increasingly, however, multi-
model ensemble studies for predicting agroecological variables are
becoming commonplace, associated with the rise of high-performance
computing, big data and cloud analytics30,31,60. Some ensemble studies
suggest that taking either the ensemble mean or median of simulated
values provide more accurate estimates than any individual model
when variables related to growth are considered61,62. Indeed, the
authors of the present study are working as part of an international
research team in an Agricultural Modelling Intercomparison Project
(AgMIP)59 to test the applicability of our new approaches in a global
study of crop waterlogging. This will allow us to scale our develop-
ments from barley to other genotypes, management options and
environments using a range of models and, together with co-design as
part of a community of AgMIP practitioners, improve the rigour of the
approaches developed here.
While we only used one crop model, we invoked projections from
an ensemble of outputs from 27 global climate models (GCMs). This
aspect could be construed as both a strength and a weakness; the
former because the ensemble mean of climatic projections should be
more reliable than a projection from any one GCM (as discussed
above), the latter bec ause the va riability in modelled outputs incr eases
associated with greater variability in climatic realisations. Larger
variability in outputs increases the uncertainty associated with the
projection and can make results from such studies more difficult to
comprehend in a rationally bounded way50,63. In fact, such diversity in
potential simulated results across sites, seasons, genotypes and man-
agement was a key reason we developed the new approach to cluster
waterlogging stress patterns.
Of all GCM outputs, rainfall is perhaps the most uncertain. A key
reason for using the data from 27 GCMs was to better quantify the
spatiotemporal distribution of and variability in precipitation during
the growing season. We downscaled the GCM datasets usi ng the NASA/
POWER gridded historical weather database64. However, previous
work has shown that interpolated gridded data tends to be conducive
to producing rainfall events that are smaller in quantum but more
frequent, which can lead to lower surface runoff and higher soil
evaporation64. In a crop model, this could reduce plant water and
nitrogen uptake, resulting in the propagation of errors that impact on
variables such as biomass and yield. Using agricultural systems models
with observed data before spatially interpolating point-based results
may thus represent a more preferable approach for reducing
SEM
0−5
5−10
10−15
Fig. 5 | Mean and standard error of the mean (SEM) for grain yield benefit
associated with waterlogging tolerant genotypes relative to waterlogging
susceptible genotypes for 2040 (2030–2059). Values were computed across
years and27 GCMs in whichbarley growingseason rainfall was higher thanthe 90th
percentile; numerical values shown in each panel represent mean grain yield ben-
efit across sites, years and GCMs. The map was modified using R package
ggplot2’maps (version 3.4.0)’with the Natural Earth dataset in a public domain
(https://www.naturalearthdata.com).
Article https://doi.org/10.1038/s41467-023-36129-4
Nature Communications | (2023) 14:765 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved

uncertainty in model outputs. While the present study avoids the
aforementioned issue associated with nitrogen uptake because nitro-
gen stress was not invoked, in practice, mineral nitrogen deficiencies
associated with waterlogging may be present because waterlogging
impacts on the ability of plant roots to uptake nutrients65,66.
Although we revealed multiple prospects for alleviating crop
waterlogging under future climates, the variability in simulated yield
and phenology responses under future climates highlights the
importance of genotypic sensitivity to waterlogging stress. Across
scenarios, mean yield penalty from waterlogging increased from 3–11%
(baseline) to 6–14% (2040) and 10–20% (2080). Potential yield losses
largely depend on genotypic sensitivity to waterlogging stress, in
general with greater yield gains for tolerant genotypes of early sown
(winter maturity) waterlogging tolerant genotypes, and the lowest
gains for later sowing of (spring maturity) waterlogging tolerant gen-
otypes (Fig. 5). We obtained genotypic parameters for waterlogging
tolerance and phenology from previous empirical studies40,67 but
additional parameters from local genotypes would help improve the
rigour of projected changes under future climates. However, we
emphasise that the relative difference between scenarios is more
important than the absolute values in this study.
Methods
Experimental data used for parameterisation and evaluation
Measured data from five two-year experiments (Exp1, Exp2, Exp3,
Exp4, Exp5) conducted in five countries (Australia, Argentina, Canada,
China and Ireland) were used for model development and evaluation.
Exp1 was conducted under controlled conditions (Mt Pleasant
Laboratories, Launceston, Tasmania, Australia) with four waterlogging
treatments using six contemporary Australian barley genotypes dif-
fering in their waterlogging tolerance from 2019 to 2020 (see
refs. 39,40). In Exp2, barley yields were measured under five water-
logging treatments in the greenhouse and field conditions at the
School of Agronomy, University of Buenos Aires, Argentina in 2010
(see ref. 68). In Exp3, barley genotypes were evaluated for waterlogging
tolerance in controlled field conditions at Brandon Research and
Development Centre, Brandon, Manitoba, Canada from 2016 to 2017.
Waterlogging treatments were initiated at tillering by adding the water
to heights of 0.5–1 cm above the soil surface (see ref. 69). In Exp4, barley
yields were measured in field conditions carried out at Oak Park,
Carlow, Ireland from 2017 to 2018. Waterlogging treatments were
initiated at the tillering stage using a boom irrigator (see ref. 70). In
Exp5, field experiments were conducted in 2003–2004 and
2005–2006 at Zhejiang University, Hangzhou, China. Waterlogging
treatments were imposed at tillering (see refs. 71,72). All experiments
were carefully managed to provide adequate nutrition and control of
biotic pressures. Original datasets used for model evaluation (e.g.,
yield under ambient conditions and those subject to waterlogging)
were compiled from a range of environments, including field experi-
ments and environmentally controlled experiments. Given the diver-
sity in data origins and variation in units used for reporting (e.g.,
gplant
−1,gm
−2,kgha
−1), we standardised grain yield dimensions to kg
ha−1before computing yield loss using Eq. (1):
Y ield loss %ðÞ=ðY iel dck Y ieldWLÞ
Yieldck
×100% ð1Þ
Where Yield
CK
is the yield (kg ha−1) obtained from control treatment
and yield
WL
represents yield measured for the waterlogging treatments
(kg ha−1).
Advancing the process basis of APSIM-Barley for the simulation
of waterlogging
We embedded the aforementioned experimental data into APSIM-
Barley using physiological constructs detailed below to improve the
ability of the model to simulate waterlogging40. The waterlogging
tolerance genes examined here enable plants to tolerate saturated
soils by accelerating aerenchyma formation and increasing root por-
osity following a waterlogging event. To account for this, we con-
ceptualised three stages of plant response and adaptation
(Supplementary Fig. 9). Stage one is the immediate plant response to
waterlogging at which time water supply to the plant is unlimited and
with soilstrength lowered, root growth has little physical impediment.
In this phase, biological functioning is not limited by oxygen or water
availability and growth processes are not affected. During the second
stage, soil water pores become fully saturated and oxygen-dependent
bioprocesses are negatively influenced. The default version of APSIM
contains photosynthesis functions for waterlogging (oxdef_photo),
but these do not account for the effects of waterlogging on phenolo-
gical rate. To compare the improved version of APSI M( detailed below)
with the default version, we ran the improved model twice: once with
oxdef_photo set to 1, and again with these parameters using values
detailed as below. To account for genetic differences in waterlogging
tolerance in APSIM, we developed and added the function y_oxde-
f_lim_photo, where values of unity or nil equate to no stress or full
stress, respectively. The third stage encompasses adaptation respon-
ses, the net result of which is a variable level of adaptation depending
on waterlogging tolerance genetics. After the adaptation stage, geno-
types tolerant to waterlogging tend to exhibit similar photosynthetic
rates compared with before waterlogging, analogous to plants that
grow aerenchyma after waterlogging39, whereas genotypes sensitive to
waterlogging can exhibit decreased growth after waterlogging events
if y_oxdef_lim_photo remains less than unity. Wedid not mathematically
transcribe a process for crop failure under waterlogging, because in
the majority of cases, waterlogging is realised as a transient event (viz.
Fig. 3) and our experimental work suggests that intolerant genotypes
persist for up to two months of waterlogging without failing40.These
concepts were programmed into the source code of APSIM; the
executable containing the modified source code and XML files are
available online: https://github.com/KeLiu7/Waterlogging-Barley.
Sites for factorial simulations
Soil waterlogging occurs when soils become saturated and plant roots
cannot respire, while flooding refers to excessive surface water
accumulation65. Waterlogging may be present without surface flood-
ing. Waterlogging can be caused by extreme rainfall events, prolonged
seasonal rainfall, poor soil hydraulic conductivity, lateral surface and/
or groundwater flows, rising/perched water tables,improper irrigation
or combinations of these factors57. Despite the diversity of ways in
which waterlogging can occur, the result is oxygen levels in pore
spaces that are insufficient for plant roots to adequately respire73.To
account for soil and climatic variability across regions, simulations
were conducted using sites across thirteen countries based on national
barley production and planting area. In each country, simulated sites
were prioritised based on dominant soil types in cropping zones from
the Digital Soil Map of the World74. These representative sites (Sup-
plementary Table 3 and Supplementary Fig. 10) where barley isgrown75
have documented reports of waterlogging76. Soil parameters at each
site (soil texture, bulk density, pH and organic carbon content etc.)
were obtained from the International Soil Reference and Information
Centre77.
Model calibration and evaluation
In APSIM-Barley (version 7.9)78, phenology is described in terms of
thermal time accumulation using 11 crop stages and 10 phases (time
between stages). Further model details, including phenology and
growth are detailed in refs. 78,79. Site-specific genotype selection, crop
management (e.g., sowing date) were based on local expert advice and
experimental records (Supplementary Table 3). Genotypes were cre-
ated such that lifecycles were in line with local sowing, flowering and
Article https://doi.org/10.1038/s41467-023-36129-4
Nature Communications | (2023) 14:765 8
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maturity times80. This was conducted by setting APSIM phenological
parameters for vernalisation (vern_sens), photoperiod (photop_sens)
and thermal time between emergence and the end of the juvenile
phase (tt_end_of_juvenile). These parameters were chosen due to their
high influence on crop flowering times47. Winter barley requires
greater exposure to cold temperature to evoke reproductive devel-
opment, whereas spring barley flowers without a cold exposure pre-
condition. In APSIM-Barley, vern_sens refers to vernalisation
representing cumulative cold temperature requirement to initiate
reproductive development (range 0–5), while photop_sens refers to
day length sensitivity (range 0–5); higher values denote greater sen-
sitivity. We assigned vern_sens values based on maturity group (‘spring’
maturity = 1 and ‘winter’maturity values of either 2.5 or 4, depending
on vernalisation requirement)81. Similarly, photop_sens values were set
to 1 for ‘spring’maturity and ‘winter’maturity values ranged from 2.5 to
4. tt_end_of juvenile values were set to 400 for ‘spring’maturity and
‘winter’maturity values ranged from 400 to 750. We first para-
meterised vern_sens according to the maturity group, we then adjus-
ted photop_sens and tt_end_of juvenile until the simulated flowering
days match with local flowering and maturity days.
Waterlogging results in the inhibition of processes in the meso-
phyll, photoassimilate transport in the phloem, gas conductance and
thus reduces photosynthetic rate57. In APSIM-Barley, these processes
are modelled per unit ground area. Effects of waterlogging on pho-
tosynthesis and phenology (‘waterlogging-stress days’) were modelled
using stress indices (oxdef_photo and oxdef_pheno)computedasa
function of the fraction of roots waterlogged (oxdef_photo_rtfr). For
oxdef_photo_rtfr levels of 0.8 and greater, oxdef_photo and oxdef_pheno
linearly decreased; for oxdef_photo_rtfr levels less than 0.8, no stress
was invoked, following experimental observations39,82. Photosynthetic
and phenological stress indices were defined as a function of crop
stage (x_oxdef_stage_photo, x_oxdef_stage_pheno), which is a significant
advance on the majority of previous studies which assume that
waterlogging stress depends only on the extent and duration of water-
filled porespace67. Partof the novelty ofthe current workis the delay in
phenology associated with the duration of waterlogging and the crop
stage/sin which it occurs. Waterlogging in early growth stages inhibits
leaf appearance rate and tiller development and delays flowering. If
waterlogging stress occurs during vegetative stages, plants may fully
recover by grain-filling stages; if waterlogging occurs during flowering,
plants cannot fully recover pre-waterlogging photosynthetic potential
before maturity40. Effects of waterlogging on phenology (oxdef_pheno)
were derived using information from environment-controlled
experiments40.Theparameteroxdef_pheno was computed as a func-
tion of the fraction of roots waterlogged (oxdef_pheno_rtfr). For
oxdef_pheno_rtfr levels of 0.8 or greater, oxdef_pheno linearly
decreased to 0.8 until the soil is fully saturated; for oxdef_pheno_rtfr
levels less than 0.8, no stress was invoked.
To account for varying effects on phenology, we invoke the
function y_oxdef_lim_pheno that is calculated according to crop stage
(x_oxdef_stage_pheno, i.e., APSIM stage code). The y_oxdef_lim_pheno
response function was adopted from our previous studies40.For
y_oxdef_lim_pheno levels less than 1, crop phenology is delayed, with
y_oxdef_lim_pheno increasing from 0.65 at stage 4.0 to 0.95 to APSIM
stage 5.5; for y_oxdef_lim_pheno levels greater than 1, grain-filling
durations are truncated, with y_oxdef_lim_pheno increasing from 1.0 to
1.5 between ASPIM stages 6 and 10. The delayed effect on phenology is
only triggered before flowering (i.e., x_oxdef_stage_pheno between 1
and 6) and the grain filling duration reduction is triggered after flow-
ering (i.e., x_oxdef_stage_pheno greaterthanorequalto6).Ingeneral,
the magnitude of delay is largely depended on the extent and duration
of waterlogging stress, as well as its timing relative to crop develop-
ment. The physiological basis for waterlogging-induced delays to
phenology is discussed in our previous studies40, with the rate of leaf
emergence determining the duration between emergence and
anthesis83. Waterlogging in later growth stages causes premature flag
leaf senescence and shortens the grain-filling period66. Reduced grain
growth in waterlogged plants is due to dec reased post-anthesis ca rbon
assimilation and culm reserves remobilised to grains84,85.
APSIM was initialised using the SWIM3 Module (soil water infil-
tration and movement; Supplementary Fig. 11).To examine the extent
with which the new processes described above improved the ability to
simulate crop growth and development under waterlogging, we also
run a default (unimproved) version of APSIM-Barley with waterlogging.
Simultaneous multi-objective optimisation63 of oxdef_photo and
oxdef_pheno for each genotype was performed for waterlogging
treatments by minimising the sum of squared residuals across data-
sets. Evaluation of phenology, biomass and yield components under
waterlogging have been described in our previous peer-reviewed
literature35,39,40. Details of the waterlogging algorithms, including their
implementation in the APSIM source code, are provided in peer-
reviewed literature40 and online: https://github.com/KeLiu7/
Waterlogging-Barley.
Historical and future climate data
Daily data for maximum and minimum temperature, rainfall and solar
radiation for 1985–2016 at each location were obtained from the
National Aeronautics and Space Administration/Prediction of World-
wide Energy Resources (NASA/POWER)86. NASA/POWER provides cli-
mate data at a horizontal resolution of 1° latitude–longitude. Yearly
atmospheric CO
2
concentration [CO
2
] for future periods were calcu-
lated based on the Shared Socio-economic Pathway 585 (SSP585), a
business-as-usual (high) emission scenario. This scenario most closely
represents the climate trajectory to date41. Yearly atmospheric con-
centrations [CO
2
] were calculated foreach year following the method87
that used empirical equations obtained by nonlinear least-squares
regression fitted to [CO
2
] from the 27 GCMs (see Supplementary
Methods).
½CO2SSP585 =757:44+ 84:938 1:537*y
2:2011 3:8289*y0:45242 +2:4712*104*y+15ðÞ
2
+1:9299*105*y1937ðÞ*105*y1937ðÞ
3
+5:1137*107*y1910ðÞ
4
ð2Þ
where ywas the calendar year from 1900 to 2100 (year = 1900,
1901, …, 2100).
To generate climate scenarios for 2040 and 2080, monthly
temperature, rainfall and radiation projected from 27 GCMs (Sup-
plementary Table 2) are available from the Coupled Model Inter-
comparison Project Phase 6 (CMIP6). Here we used the statistical
downscaling model (NWAI-WG)88 to downscale GCM monthly grid-
ded data to daily climate data for each of the study sites. Spatial
downscaling was achieved by using an inverse distance-weighted
(IDW) interpolation method in this study. The IDW interpolation
method was used to compute rainfall and temperature values for
each weather station based on its distance to the geographical
centres of the four nearest GCM grid cells89, then applied bias cor-
rection, resulting in bias-corrected monthly data using a relation-
ship derived from observations and GCM data for the historical
training period of 1985–2016. Bias-corrected and downscaled GCM
trends were then transcribed into time series of daily maximum and
minimum temperature, rainfall and radiation using a modified sto-
chastic weather generator. The major advantage of this statistical
downscaling method, particularly in comparison with more com-
putationally demanding dynamical downscaling, is that it can be
easily applied to any location for which a long-term daily historical
climate record is available. We did not use daily data from GCMs in
this study for three reasons: first, not all GCMs provided daily
Article https://doi.org/10.1038/s41467-023-36129-4
Nature Communications | (2023) 14:765 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved

climate data. Second, interpolation of daily GCM values can be error-
prone, especially for rainfall. Third, weather variables such as
radiation, precipitation, minimum and maximum temperature are
often interdependent (e.g., rainy days are often cooler and have
lower solar radiation); interpolation and bias-correction on a daily
time-step can confound such interdependence88. Instead, the
approach we used (NWAI-WG bias correction of monthly values)
accounts for interdependency between climatic variables89.
Factorial simulations
Simulations were run from 1985 to 2100; initial soil conditions were
reset annually at sowing to prevent potential ‘carry-over’effects from
previous seasons. Initial plant available water at sowing was set 15 mm
to ensure consistency of emergence across sites and sowing dates.
Barley was sown at 180 plants m2using a depth of 20 mm and row
spacing of 200 mm. Nitrogen was applied as NO
3
−and maintained
above 200 kg ha−1in the top 300mm throughout the season to ensure
that nitrogen supply did not limit growth. This assumption was made
so that waterlogging stress typologies described below were not
confounded by the presence or absence of N stress. Soil texture, bulk
density, pH, and organic carbon content were obtained from the
International Soil Reference and Information Centre77. Global
groundwater table depths used in model initialisation were obtained
from Aquaknow89.
Novel approach for clustering seasonal waterlogging-stress
typologies
To categorise waterlogging stress patterns, we output seasonal time
courses of waterlogging stress on photosynthesis as a function of
phenology (APSIM output variable oxdef_photo). Individual stresses
were clustered across simulation years, sites, genotypes and manage-
ment. For each environment, waterlogged days (i.e., days with
oxdef_photo lower than 1) were cumulated for each of six discrete
growth stages (i.e., early juvenile (JV1, 10 <= APSIM growth stage <21);
late juvenile (JV2, 21<= APSIM growth stage <32); floral initiation to
heading (FIN, 32 <=APSIM growth stage <65);flowering to grain filling
(FIN, 65<= APSIM growth stage <71); early grain filling (GF1,
71 <= APSIM growth stage <80); late grain filling (GF2, 80 <= APSIM
growth stage <87). oxdef_photo was averaged for each growth stage
across simulation years, sites, genotypes and management. Prevailing
seasonal waterlogging patterns were realised by applying unsu-
pervised k-means clustering to all seasonal trajectories of oxdef_photo
against phenology. Clustering was applied using the R statistical
package ‘stats’(R Development Core Team, 2013), with clusters being
defined such that total within-cluster variation was minimised (parti-
tioning nobservations into kclusters (the valueof K is assigned asfour)
where each observation belongs to the cluster with the nearest mean,
i.e., the cluster centroid).
Simulated impacts of waterlogging on yield
The impact of waterlogging stress on crop yield for a given location
was quantified by comparing the yield difference of each year simu-
lated by the default version of APSIM and improved APSIM with
waterlogging algorithms. The yield difference caused by waterlogging
(Yield percentage
WL
) was calculated using Eq. (3):
Y ield percentag eWL=Y iel dyY ield y,wl
Yieldy
×100% ð3Þ
Where Yield
y
represents simulated grain yield (kg ha−1) obtained using a
defaultAPSIM version with a climate period from 1985–2100 (Y = 1985,
1986, …,2100)andyield
WL,y
represents simulated yield (kg ha−1)
obtained from the modified version of APSIM using the aforemen-
tioned waterlogging algorithms for the said year.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The simulated yield data generated in this study are provided in the
Source data file. Genotypic parameters used in APSIM are available in
Supplementary Table 3. Soil data and downscaled climate datasets are
available online (https://doi.org/10.5281/zenodo.7444483)90.Source
data are provided with this paper.
Code availability
The R code containing the clustering algorithm and the APSIM
executable containing the improved waterlogging algorithms are
available in the GitHub repository (https://doi.org/10.5281/zenodo.
7444483)90.
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Acknowledgements
The research was financially supported by Grains Research and Devel-
opment Corporation grant (UOT1906-002RTX) issued to M.T.H. We are
grateful to Isaiah Huber from Iowa University for APSIM programming.
Author contributions
K.L. and M.T.H. conceived the study. K.L., H.L.Y. and D.L.L. conducted
the crop model simulations and downscaled global climate models.
K.L., M.T.H. and S.A. coded the waterlogging functions into APSIM
source code. K.L., M.T.H. and H.L.Y. created and analysed the results.
K.L. and M.T.H. wrote the paper. M.T.H. funded the study. All authors
edited and revised the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-023-36129-4.
Correspondence and requests for materials should be addressed to
Matthew Tom Harrison.
Peer review information Nature Communications thanks Jonathan
Ojeda and the other, anonymous, reviewer(s) for their contribution to the
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© The Author(s) 2023
1
Tasmanian Institute of Agriculture, University of Tasmania, Newnham Drive, Launceston, TAS, Australia.
2
MARA Key Laboratory of Sustainable Crop Pro-
duction in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou, China.
3
State Key Laboratory of Cotton Biology, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, China.
4
New South Wales
Department of Primary Industries,Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia.
5
Climate Change Research Centre, University of New
South Wales, Sydney, NSW, Australia.
6
Department of Agricultural and Biological Engineering, IFAS, University of Florida, Gainesville, FL, USA.
7
Agroeco-
system Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, IL, USA.
8
College of
Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA.
9
National Center for Supercomputing
Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA.
10
NASA Goddard Institute for Space Studies, New York, NY, USA.
11
Columbia
University, Center for Climate Systems Research, New York, NY, USA.
12
Potsdam Institute for Climate Impacts Research (PIK), Member of the Leibniz
Association, Potsdam, Germany.
13
Commonwealth Scientific and Industrial Research Organisation (CSIRO) Agriculture and Food, Canberra, ACT, Australia.
14
State Key Laboratory of Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China.
15
College of Agronomy and Biotechnology,
China Agricultural University, Beijing, China.
16
Department of Agronomy, Iowa State University, Ames, IA, USA.
17
Research Center for Physiology and Ecology
and GreenCultivation of Tropical Crops, College of Tropical Crops, Hainan University, Haikou, Hainan, China.
18
Brandon Research and Development Centre,
Agriculture and Agri-Food Canada, 2701 Grand Valley Road, Brandon, MB R7A 5Y3, Canada.
19
MARA Key Laboratory of Crop Ecophysiology and Farming
System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.
20
National Institute for Agricultural Research (INRAE), UMR AGIR, Castanet Tolosan, France.
21
College of Resources and Environmental Sciences, China
Agricultural University, Beijing, China.
22
Department of Agronomy, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan.
23
Joint International
Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Yangzhou University, Yangzhou, Jiangsu, China.
24
Key
Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing, China.
25
Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland.
26
School of National Safety and Emergency
Management, Beijing Normal University, Beijing, China.
27
University of Göttingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS),
Grisebachstr. 6, 37077 Göttingen, Germany.
28
School of Computer Science & Information Engineering, Anyang Institute of Technology, Anyang, China.
29
These authors contributed equally: Ke Liu, Matthew Tom Harrison, Haoliang Yan. e-mail: matthew.harrison@utas.edu.au
Article https://doi.org/10.1038/s41467-023-36129-4
Nature Communications | (2023) 14:765 13
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