1 Supplementary Data 1 2 Section S1: Current knowledge of lethal temperature thresholds 3 High temperatures affect crop growth and development through several mechanisms with the 4 most sensitive process depending on the stage of crop development. Serious effects on the 5 emergence of wheat seedlings were found for soil temperatures between 40 and 45°C [1-3]. 6 During vegetative growth, photosynthesis seems to be the most temperature sensitive process 7 [4] with an irreversible inhibition of Rubisco activation after incubation of wheat leaf tissue 8 at 45°C for 5 minutes [5]. The exposure of whole wheat plants to temperatures of 42.5 and 9 45°C for 1h (rapid and gradual increases in temperature) resulted in the complete inhibition 10 of the carbon exchange rate [4]. While crop yield is indirectly affected during the previous 11 growth stages, it is directly affected during the reproductive and seed filling stages. The most 12 temperature susceptible reproductive stages are the period prior to flowering and during 13 flowering and fertilization [6]. Three days of 30°C showed a reduction of grain set by almost 14 70% [7] and temperature regimes of 36/31°C (day/night) for 2 days resulted in 55 to 85% 15 grain sterility [8]. ‘Wheat failure’ has been reported with temperatures of 34°C [9]. Grain size 16 depends on the duration and rate of grain filling and high temperatures can reduce grain size 17 [8]. However, sensitivities to heat stress can vary widely between wheat cultivars [10]. 18 Many controlled environment experiments investigate the effect of high temperatures on 19 wheat development and yield but there is a lack of studies looking at temperatures high 20 enough to be lethal to the plant. There is also some ambiguity to the definition of lethal 21 temperature limits. Levitt [11] defined ‘heat-killing temperatures’ as the temperature at which 22 50% of the plant is killed whereas Porter & Gawith [12] define them as when ‘function is lost 23 beyond recovery’. We adopt the latter definition of lethal temperatures. Temperature 24
Transcript
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Supplementary Data 1
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Section S1: Current knowledge of lethal temperature thresholds 3
High temperatures affect crop growth and development through several mechanisms with the 4
most sensitive process depending on the stage of crop development. Serious effects on the 5
emergence of wheat seedlings were found for soil temperatures between 40 and 45°C [1-3]. 6
During vegetative growth, photosynthesis seems to be the most temperature sensitive process 7
[4] with an irreversible inhibition of Rubisco activation after incubation of wheat leaf tissue 8
at 45°C for 5 minutes [5]. The exposure of whole wheat plants to temperatures of 42.5 and 9
45°C for 1h (rapid and gradual increases in temperature) resulted in the complete inhibition 10
of the carbon exchange rate [4]. While crop yield is indirectly affected during the previous 11
growth stages, it is directly affected during the reproductive and seed filling stages. The most 12
temperature susceptible reproductive stages are the period prior to flowering and during 13
flowering and fertilization [6]. Three days of 30°C showed a reduction of grain set by almost 14
70% [7] and temperature regimes of 36/31°C (day/night) for 2 days resulted in 55 to 85% 15
grain sterility [8]. ‘Wheat failure’ has been reported with temperatures of 34°C [9]. Grain size 16
depends on the duration and rate of grain filling and high temperatures can reduce grain size 17
[8]. However, sensitivities to heat stress can vary widely between wheat cultivars [10]. 18
Many controlled environment experiments investigate the effect of high temperatures on 19
wheat development and yield but there is a lack of studies looking at temperatures high 20
enough to be lethal to the plant. There is also some ambiguity to the definition of lethal 21
temperature limits. Levitt [11] defined ‘heat-killing temperatures’ as the temperature at which 22
50% of the plant is killed whereas Porter & Gawith [12] define them as when ‘function is lost 23
beyond recovery’. We adopt the latter definition of lethal temperatures. Temperature 24
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thresholds might refer to daily mean temperatures [6], mean temperatures during certain 25
hours of the day (e.g. day/night or morning hours only) [13] or daily Tmin/Tmax [14]. Not 26
every study is clear about the length of exposure to high temperatures and if thermotolerance, 27
the ability of a plant to survive normally lethal temperatures after a short exposure to a sub-28
lethal heat stress or a gradual increase in temperatures prior to reaching the normally lethal 29
temperature, was accounted for. A shortcoming of most crop simulation models is to apply 30
thresholds which are defined at the leaf or canopy level but drive the model with air instead 31
of foliage temperature (exception e.g. JULES [15]). Depending on the available soil water 32
and the vapour pressure deficit (VPD), canopy and air temperature can differ by about 10°C 33
[16-18]. 34
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Section S2: Crop Model 36
The acceleration of senescence was parameterised according to the approach taken for the 37
Agricultural Production Systems sIMulator (APSIM) [19], i.e. Tmax hasten leaf senescence 38
threefold at just above 34°C and sixfold at 40°C. Relative leaf area reductions resulting from 39
including increased senescence in GLAM was tested against results from APSIM and 40
controlled environment studies [19]. Leaf senescence can accelerate during the whole crop 41
life cycle, resulting in reduced vegetative growth during the early crop developmental stages 42
and a shortening of the grain-filling period towards the end of the crop life cycle. 43
The effect of lethal temperatures was included by terminating crop development if daily Tmax 44
exceeds a threshold for a given number of days. During early crop development this would 45
lead to yield equal or close to zero. Maximum lethal temperature limits are not well defined 46
and are influenced by the ability of plants to acclimatise to high temperatures (this does not 47
apply to pollen) and to cool themselves down if enough water is available (transpiration 48
cooling). There can be a difference of several degrees Celsius between air temperature, 49
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commonly used in crop models, and leaf or canopy temperature, commonly reported for 50
controlled environment studies. The temperature differences and the lack of knowledge of 51
lethal temperature thresholds were considered by using a wide range of lethal temperature 52
thresholds. Three thresholds were applied, 40, 45, and 50°C. They had to be exceeded for 1 to 53
5 consecutive days in order to be lethal. For simplicity it was assumed that the same threshold 54
holds during the whole crop life cycle even though certain stages are more susceptible to heat 55
stress than others [12]. Quantitative information on these differences is not enough for 56
parameterisation. 57
In order to optimize GLAM, some crop specific global (i.e. site-independent) model 58
parameters are required, i.e. a single varietal type is modelled. Additionally two parameters 59
which vary spatially, the yield gap parameter (YGP) and the planting date, are needed. The 60
YGP is used to calibrate GLAM by accounting for impacts other than weather, e.g. pests, 61
diseases and non-optimal management of the crop, i.e. nutrient limitations and water stress in 62
places that are rainfed or have non-optimal irrigation. The YGP acts on the leaf area index 63
and the value is chosen which minimizes a measure of difference between the predicted and 64
observed yield. The root mean squared error (RMSE) was used to compare simulated yield 65
driven with observed and ERA40-reanalysis data. For crop model simulations driven by 66
global climate model outputs it cannot be assumed that the timing of specific years matches, 67
i.e. observed yields can only be used to compare the mean and standard deviation of 68
simulated yields [20]. For this reason, for the climate model output, the squared difference 69
between the simulated and observed yield mean and standard deviation was used [20]. The 70
calibration is a form of mean bias-correction and can compensate to some degree for input 71
climate bias [21]. Ranges for the global model parameters were taken from the literature. 72
Optimal parameter-sets were determined by randomly varying one parameter at a time and 73
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choosing the parameter-set which had the lowest measure of difference between the 74
simulated and observed yield after 5000 repetitions. 75
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Section S3: Mean and variability of simulated yield 77
Simulations with the raw climate model output show a decrease in mean yield for India from 78
about 1500 kg/ha for the baseline to just over 1000 kg/ha in 2070 to 2089 (Figure S3). Yield 79
simulations with the bias-corrected climate model data agree on a slight decrease in mean 80
yield towards the end of the century. Baseline simulations with the observed and ERA40-81
reanalysis data show similar mean yields compared to simulations with the bias-corrected 82
climate model data for 2030 to 2049 (Figure S3). Unrealistically high yield outliers were 83
simulated with both bias-corrected climate model data and the values increase with time 84
(Figure S3). They do not occur in such extremes in simulations using the raw climate model 85
output. The yield outliers are a combination of longer crop durations for the thermal time 86
development setting CT1 and CT2 compared to CT3 (which has ‘optimal’ development up to 87
50°C, Table S1) and a higher leaf area compared to simulations with the raw climate model 88
output. The bias-corrected climate data have lower temperatures than the raw climate model 89
output so that increased leaf senescence has less impact. The yield outliers simulated with the 90
bias-corrected climate model data are confined to an area in West India (Figure 1), where 91
instances of Tmax > 34°C do not occur frequently in the baseline or the future climate (Figure 92
S1). The coefficient of variation (CV), relating changes in the variability of simulated yield to 93
changes in mean yield, show an increase with time especially in the southern half of India 94
and substantially more for simulations using the raw climate model output (Figure S4). 95
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Section S4: CO2 fertilization and water stress 99
We did not include the effect of CO2 fertilization, i.e. an increase in the rate of photosynthetic 100
carbon fixation and net primary production for C3 crops like wheat with elevated CO2. Many 101
crop models overestimate the effect of CO2 fertilization as non-FACE (Free-Air CO2 102
Enrichment), i.e. chamber experiments, were used for parameterization [22]. FACE 103
experiments show an increase in yield under elevated CO2 of about 14% compared to 30% 104
for chamber experiments [22, 23]. Using the data from [22], it was estimated that there was a 105
global boost in wheat production of about 3% due to rising CO2 levels from 1980 to 2008 106
[24]. Experimental studies show that even though elevated CO2 increases the size of grain 107
yield, it can lead to a reduction in grain quality [25, 26]. It further can enhance the effect of 108
lethal temperatures as stomatal conductance decreases by an average of 22% (for CO2 109
increases from on average 366 to 567 µmol mol-1
) [27]. A reduction in stomatal opening can 110
lead to a reduction in transpiration cooling and therefore an increase in canopy temperature. 111
Furthermore there is evidence supporting a CO2 induced negative impact on the ability of 112
plants to absorb nitrogen [25]. However, the importance to model C-N effects together [28] is 113
beyond the capacity of the crop simulation model used in our study. 114
Our study looks at irrigated yields only, but water availability into the future is not 115
guaranteed. High temperatures often coincide with water stress. Currently over 90% of wheat 116
in India is irrigated [29] which can reduce heat stress through transpiration cooling but is 117
highly dependent on water availability and vapour pressure deficit. Canopy and air 118
temperature can differ up to 10°C, with canopy temperature being cooler if water is available 119
[16], and warmer under water stress [17, 18]. Widespread declines in water tables in India 120
[30] may suggest that current irrigation practices are not sustainable [31]. This might lead to 121
an increased risk of drought, higher temperatures experienced by the plant, and with it an 122
increased risk of exceeding crop temperature stress thresholds. 123
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Supplementary Table S1: Cardinal temperatures (CT, in °C) for wheat, i.e. base, optimum 124
and maximum temperature for the four crop developmental stages and the function used for 125
thermal time development. CT3 using a trapezoid function is characterised by ‘optimal’ 126
