Influences of increasing temperature on Indian wheat: quantifying limits to predictability

Three sources of climate data were used for the baseline period 1969–1988: observed weather data (monthly interpolated to daily T_min and T_max from the Climate Research Unit from the University of East Anglia, UK, 0.5° × 0.5°, averaged on to 1° × 1°), ECWMF ERA40-reanalysis data (daily T_min and T_max calculated from six hourly data and solar radiation, 1.125° × 1.125°, gridded to 1° × 1°), and the historical data from the coupled atmosphere–ocean simulations of the Hadley Centre quantifying uncertainty in model prediction (QUMP) project (2.5° latitude × 3.75° longitude). QUMP uses the perturbed physics approach to sample uncertainties [22]. We use daily solar radiation, T_min and T_max from the unperturbed baseline climate, and the 17-member projection ensemble forced by the SRES A1B scenario for the future. The projections were divided into three 20-year time periods from 2030 to 2089.

Yearly wheat district level production data were available for India (P K Aggarwal; Socio-economic Policy Division of the International Crops Research Institute for the Semi-arid Tropics (ICRISAT), Patancheru, India). The yield data were linearly detrended to account for improved crop varieties and management methods and scaled to the climate data grid. To upscale the data, the area under cultivation was assumed to be evenly spread throughout each district. The historical yield data were used to calibrate the crop model (see section S2 available at stacks.iop.org/ERL/8/034016/mmedia).

We use the spring wheat version of the General Large Area Model for annual crops (GLAM), a regional-scale process-based crop model running on a daily time-step [21, 23]. GLAM was chosen as it was designed to operate on spatial scales commensurate with those of global climate models. Its complexity is low compared to field scale crop models, giving the opportunity to compute a large number of simulations. Over 90% of wheat in India is irrigated [2] as it is traditionally sown in the dry season. For this reason, and to single out the impact of temperature on wheat yield, fully irrigated conditions are assumed. The two main processes that are influenced by temperature in GLAM are (i) the crop development which is determined by cardinal temperatures, a function for thermal time accumulation and a thermal-time-requirement for each developmental stage [21], and (ii) anthesis which is sensitive to high temperatures decreasing the number of set grains [20, 23]. Two additional processes were implemented in order to represent all major heat stress impacts on wheat: (iii) the acceleration of leaf senescence with high T_max, and (iv) a lethal temperature effect on the wheat plant. The reduction of transpiration efficiency to represent reduced photosynthesis with high temperatures was not included in the uncertainty analysis. Details on how process (iii) and (iv) are represented in GLAM and how GLAM is optimized can be found in the supplementary data section S2.

2.3.1. Uncertainty in climate.

2.3.2. Crop model uncertainty.

2.3.3. Uncertainty decomposition.

The uncertainty in climate predictions can be partitioned into internal variability of the climate system, model uncertainty, and scenario uncertainty [26]. We perturbed crop and climate model parameters and used a similar approach to decompose climate and crop model uncertainty. We distinguish six sources of uncertainty as introduced above: climate, lethal, thermal, optimization, planting, and HTS. The uncertainty decomposition was separately computed for (i) yield and crop duration; (ii) raw, BC and CF bias-corrected climate model output; (iii) the three time periods 2030–49, 2050–69, 2070–89; and (iv) for each grid cell. For each source i (i = 1,...,6) of crop and climate model uncertainty, the fractional uncertainty in yield y (and crop duration) is calculated as the fraction of one source of uncertainty divided by the sum of all sources of uncertainty. The absolute uncertainty of each source i is the range, i.e. the maximum minus the minimum yield (crop duration) across all simulations y_ij, with j being the subcategories of each uncertainty source i, e.g. j = 1,...,17 for the uncertainty attributed to the different QUMP ensemble members.

$$\begin{eqnarray} &&\text{Fractional uncertainty }i=\frac{\max ({y}_{i j})-\min ({y}_{i j})}{\mathop{\sum }_{i}\max ({y}_{i j})-\min ({y}_{i j})}. \end{eqnarray} \tag{ 1 }$$

The geographical distribution of fractional yield uncertainty does not change much over the three future time periods (figures 2, and S5, S6 available at stacks.iop.org/ERL/8/034016/mmedia), thus we focus on 2050–2069. Figure 2 shows the uncertainty decomposition for this period, which quantifies the extent to which yield variations are due to the climate model ensemble ('climate'), different choices of lethal temperature thresholds and days of exceedence ('lethal'), the choice of thermal time development ('thermal'), the data used for optimizing the crop model ('optimization'), and the planting date ('planting') in relation to the total uncertainty for each grid cell in India where wheat is grown. Yield simulations using the two bias-corrected climate data agree on the contribution of the different sources of uncertainty and their geographical distribution. Thermal is the largest source of uncertainty for yield simulations with highest values in the south-west of the country and lowest in the north-east. In south-west India, compared to the north-east, crop durations increase and become more variable as mean temperatures increase above the optimum temperature for crop development, but maximum temperatures do not frequently exceed the threshold for increased senescence shortening crop durations (figure S2). Following thermal, three sources of uncertainty have almost equal contributions to the total: climate, optimization, and planting. The fractional uncertainty from lethal contributes least to the total uncertainty (figure 2). The contribution of heat stress during anthesis (high temperature stress—'HTS') to total yield uncertainty was assessed using a separate set of simulations. HTS uncertainty is higher than lethal uncertainty but low compared to thermal uncertainty (figures 3 and S7 available at stacks.iop.org/ERL/8/034016/mmedia). HTS uncertainty increases with later planting.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

The temporal trend of countrywide average yield uncertainty shows an increasing trend with time (figure S8 available at stacks.iop.org/ERL/8/034016/mmedia; additionally shows result for crop duration uncertainty). The main increase in total uncertainty can be attributed to an increase in the uncertainty due to thermal. This is likely due to increasing mean temperatures above the optimal temperature for crop development towards the end of the century. The functions for thermal time development differ substantially above the optimal temperature with either reduced development or continued optimal development up to the maximum temperature.

We show the overall important contribution cardinal temperatures and the function for thermal time accumulation ('thermal') have on crop yield uncertainty. This is an often overlooked source of uncertainty in crop simulation studies as crop models are commonly run with a single setup. Temperature impacts the timing of events like anthesis and physical maturity and accordingly the temperature range experienced during those stages, thus the importance of better understanding crop thermal time development. Different functions for modelling crop development have been proposed [11–13] and are currently used in crop models, which can lead to substantial variations in simulated crop duration [14]. The importance of the representation of phenology in crop models was confirmed by the wheat Agricultural Model Intercomparison and Improvement Project (AgMIP) pilot study which compared 26 wheat models in four contrasting wheat growing environments. They identified the simulation of temperature effects as one of the largest limitations in modelling climate change impacts and could partly relate them to simulated phenology [27]. A recent study aiming at identifying cardinal temperatures for wheat leaf appearance using experimental data was able to suggest values for the base and lower optimum temperature but failed determining an upper optimum or maximum temperature [24]. The authors ascribed this to a low frequency of experimental data with mean temperatures above 25 ° C and stress responses to extreme low and high temperatures even though they included data from a T-FACE (Temperature-Free Air CO₂ Enrichment) experiment. The uncertainty in crop development can be expected to be high in all wheat growing areas where mean temperature frequently exceeds the optimal temperature. With increasing temperatures in the future it is essential to understand the behaviour of crop development beyond the optimum temperature.

Moreover, thermal time accumulation of crops might not be constant under varying climate conditions [28], an assumption made by crop modellers. An increase in thermal time accumulation between 7 and 10 ° C yr⁻¹ was found for a rice variety over a 20-year time period [28]. Extending the study to nine rice cultivars confirmed a correlation between temperature and phenological prediction error but the cultivars did not agree on the direction of correlation [29]. The study concluded that a rapid decrease in development rate above the optimal temperature, characteristic for the triangular function, can lead to systematic errors. This might be the reason for unrealistically high yield values simulated in our study for the simulations using the triangular function for thermal time accumulation (supplementary data section S3).

Figure 4 contrasts uncertainty from the climate model (climate; blue means more climate model uncertainty) versus uncertainty due to temperature-driven processes in the crop model (thermal + lethal + HTS; red means more crop model uncertainty) for early, middle, and late planting. There are regional differences where climate or crop model uncertainty dominates. On average over India fractional crop model uncertainty contributes about 0.6, 0.55, and 0.5 to total fractional uncertainty for early, middle and late planting, respectively. Figures 3 and S7 show that the main changes in fractional uncertainty with later planting come from a shift in thermal to climate model uncertainty. The decrease in thermal uncertainty with later planting is likely due to an increase in the importance of senescence as high temperatures are a common problem towards the end of the wheat growing season. Increased senescence can shorten total crop duration and makes it somewhat independent of thermal.

Zoom In Zoom Out Reset image size

We found that crop model uncertainty was on average larger than climate model uncertainty. The majority of studies looking at uncertainty in climate impact projections focus on either input uncertainty [30, 31] or impact model uncertainty [32]. Few studies quantify both, with the extent varying from uncertainty quantification of single processes to various model parameters [33–38]. Contrary to our results they suggest that climate model uncertainty dominates total projected uncertainty. For example, assessment of climate and crop model uncertainty for groundnut in India found relatively low crop model uncertainty, attributable to good observational constraints [33, 34]. Here, as in most studies, we do not include uncertainty due to different crop models. Small scale crop model intercomparisons including two to three models showed that this can be an important source of uncertainty [14, 39, 40]. A large scale AgMIP is currently being carried out to quantify uncertainty across multiple models, scenarios, locations, and crops [32].

We did not include the effect of CO₂ fertilization and water stress in the yield simulations. Increasing CO₂ levels increase the rate of photosynthetic carbon fixation and net primary production for C₃ crops like wheat. From 1980 to 2008 increasing CO₂ levels were estimated to have had a global boost on wheat production of about 3% [3]. The response of yield to changes in CO₂ is complex as it additionally affects for example stomata opening changing canopy temperature, grain quality, and the absorption of nitrogen. The availability of water further complicates the matter. If water availability is guaranteed, it can help cooling down plants through transpiration cooling but water stress would increase canopy temperatures. A more detailed discussion on our reasoning not to include CO₂ fertilization and water stress and their likely influence on crop yield can be found in the supplementary data section S4 (available at stacks.iop.org/ERL/8/034016/mmedia).

Figure 5 shows the influence of lethal temperatures on simulated yield for thresholds of 40, 45, and 50 ° C and their exceedence for 1, 3, and 5 consecutive days in order to lead to plant death. The BC and CF bias-corrected climate model data give similar results. The choice of lethal temperature limit has a strong impact on simulated yield and crop duration. If the lethal threshold is 40 ° C and has to be exceeded for one day, simulated yield reductions for both bias-corrected climate model outputs show possible reductions of about 10–15% by the end of the century. Crop duration can be reduced between 10 and 30% for the same time period (figure S9 available at stacks.iop.org/ERL/8/034016/mmedia). For lethal temperature thresholds of 45 ° C fewer grid cells are affected and 50 ° C does not seem to harm wheat yields in India (figure 5).

Zoom In Zoom Out Reset image size

Due to the lack of experimental data, we chose a broad range of lethal temperature thresholds. On average over India, lethal temperatures had a small impact on simulated yield and yield uncertainty compared to other sources of uncertainty, but locally they can be important. The small impact is likely due to the effect of increased senescence. Increased senescence was shown to substantially shorten crop duration and crop yield in India and Australia [14, 15]. In many cases increased senescence may reduce yield so that lethal limits result in little additional yield loss. If only increased senescence but not lethal limits are included in crop simulations, the total crop duration stays in a feasible range for wheat growth. A possible way to adapt to these conditions is (1) to plant earlier, or (2) to grow shorter crop duration varieties. Planting dates of wheat are often dependent on the harvest time of rice, as a rice–wheat cropping system is common in India. Lobell et al [41] found for some of the main wheat growing areas in India, that wheat was sown on average one week earlier by 2010 than at the beginning of the decade. They also found that sowing dates seem to be near or already at the optimum window for yields, implying that further significant increases in yield from changes in planting date are not very likely [41]. Shorter crop duration varieties do exist but shorter durations mean less light interception and biomass accumulation leading to lower yields in irrigated systems. The risk presented by lethal temperatures is that even if Indian farmers adapt to heat stress towards the end of the growing season by planting shorter crop duration varieties, locally lethal temperatures might have the ability to reduce crop duration up to a point where wheat growth is not feasible anymore. In this case a combination of shorter crop duration varieties with increased heat tolerance is essential for adaptation. As discussed above, a follow up study should investigate the influence of plant water status on canopy temperatures and how this would change the frequency of crossing crop temperature stress thresholds.