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Journal of Arid Land  2023, Vol. 15 Issue (5): 545-561    DOI: 10.1007/s40333-023-0056-x
Research article     
Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments
1Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran 19839-69411, Iran
2Department of Production Engineering and Plant Genetics, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad 68151-44316, Iran
3Department of Agrotechnology, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran 18151-63111, Iran
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Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders. Nevertheless, there exists inherent uncertainty in such cases. General Circulation Models (GCMs) and future climate change scenarios (different Representative Concentration Pathways (RCPs) in different future time periods) are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield. This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments (Shiraz, Hamedan, Sanandaj, Kermanshah and Khorramabad) in eastern and southern Iran. These five representative locations can be categorized into three climate classes: arid cold (Shiraz), semi-arid cold (Hamedan and Sanandaj) and semi-arid cool (Kermanshah and Khorramabad). Accordingly, the downscaled daily outputs of 29 GCMs under two RCPs (RCP4.5 and RCP8.5) in the near future (2030s), middle future (2050s) and far future (2080s) were used as inputs for the Agricultural Production Systems sIMulator (APSIM)-wheat model. Analysis of variance (ANOVA) was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield. Years from 1980 to 2009 were regarded as the baseline period. The projection results indicated that wheat grain yield was expected to increase by 12.30%, 17.10%, and 17.70% in the near future (2030s), middle future (2050s) and far future (2080s), respectively. The increases differed under different RCPs in different future time periods, ranging from 11.70% (under RCP4.5 in the 2030s) to 20.20% (under RCP8.5 in the 2080s) by averaging all GCMs and locations, implying that future wheat grain yield depended largely upon the rising CO2 concentrations. ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations, followed by scenarios, GCMs, and their interactions. Specifically, at the semi-arid climate locations (Hamedan, Sanandaj, Kermanshah and Khorramabad), most of the variations arose from the scenarios (77.25%), while at the arid climate location (Shiraz), GCMs (54.00%) accounted for the greatest variation. Overall, the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions, particularly in dryland environments characterized by large fluctuations in rainfall and temperature. Moreover, the current research suggested some GCMs (e.g., the IPSL-CM5B-LR, CCSM4, and BNU-ESM) that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.

Key wordswheat grain yield      climate change      Agricultural Production Systems sIMulator (APSIM)-wheat model      General Circulation Models (GCMs)      arid climate      semi-arid climate      Iran     
Received: 19 August 2022      Published: 31 May 2023
Corresponding Authors: *Sajjad RAHIMI-MOGHADDAM (E-mail:
Cite this article:

Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments. Journal of Arid Land, 2023, 15(5): 545-561.

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Location Climate class Latitude Longitude Altitude
(m a.s.l.)
Cultivated area
(×103 hm2)
Shiraz Arid cold 29°36′37′′N 52°31′52′′E 1585 1.30×10-6 0.23 0.39 0.51 13.50
Hamedan Semi-arid cold 34°47′57′′N 48°30′52′′E 1850 1.35×10-6 0.19 0.35 0.49 45.60
Sanandaj Semi-arid cold 35°18′53′′N 46°59′55′′E 1500 1.33×10-6 0.26 0.39 0.50 41.10
Kermanshah Semi-arid cool 34°18′51′′N 47°03′54′′E 1400 1.37×10-6 0.13 0.31 0.48 8.30
Khorramabad Semi-arid cool 33°27′59′′N 48°21′20′′E 1147 1.33×10-6 0.22 0.37 0.50 38.00
Table 1 Climate classes and soil properties of the five representative locations
Location Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
Maximum temperature (°C)
Shiraz 12.50 15.10 19.00 24.80 31.30 36.50 38.40 37.70 34.10 28.20 20.50 15.00 26.09
Hamedan 3.00 5.60 12.00 18.30 23.60 30.40 34.30 34.10 29.30 21.70 13.20 6.30 19.32
Sanandaj 6.20 8.40 14.10 20.30 25.90 33.20 37.30 37.00 32.20 24.50 15.40 9.30 21.98
Kermanshah 9.30 10.80 14.90 20.70 27.20 33.10 36.40 36.00 31.70 25.00 16.60 11.70 22.78
Khorramabad 10.50 12.60 16.90 22.50 28.70 35.70 39.30 39.00 34.60 27.40 18.80 12.70 24.89
Location Minimum temperature (°C)
Shiraz 0.40 2.20 5.50 9.90 14.70 18.60 21.30 20.40 15.80 10.60 5.40 1.70 10.54
Hamedan -8.10 -5.70 -0.70 4.30 7.40 10.70 14.20 13.00 7.90 4.10 0.00 -4.60 3.59
Sanandaj -5.50 -4.00 0.20 4.90 8.60 12.70 17.40 16.50 10.20 6.20 1.40 -2.90 5.48
Kermanshah 0.30 1.20 4.50 9.40 14.50 19.10 22.40 22.00 17.80 13.00 6.20 2.20 11.05
Khorramabad -0.90 0.30 3.20 7.20 10.60 14.40 18.60 17.80 12.90 9.10 4.20 0.70 8.17
Location Mean temperature (°C)
Shiraz 6.45 8.65 12.25 17.35 23.00 27.55 29.85 29.05 24.95 19.40 12.95 8.35 18.32
Hamedan -2.55 -0.05 5.97 11.30 15.50 20.55 24.25 23.55 18.60 12.90 6.60 0.85 11.46
Sanandaj 0.35 2.20 7.15 12.60 17.25 22.95 27.35 26.75 21.20 15.35 8.40 3.20 13.73
Kermanshah 4.80 6.00 9.70 15.05 20.85 26.10 29.40 29.00 24.75 19.00 11.40 6.95 16.92
Khorramabad 4.80 6.45 10.05 14.85 19.65 25.05 28.95 28.40 23.75 18.25 11.50 6.70 16.53
Location Cumulative rainfall (mm)
Shiraz 80.20 53.60 53.80 21.40 5.80 0.20 0.40 1.10 0.00 3.60 30.50 70.30 320.90
Hamedan 29.50 33.70 44.50 38.00 20.70 2.60 2.70 1.90 1.00 22.80 32.70 34.90 265.00
Sanandaj 52.10 59.00 68.50 66.10 32.70 2.10 1.00 0.10 0.80 34.40 65.90 53.50 436.20
Kermanshah 109.10 84.90 98.30 58.70 16.50 0.10 0.50 0.90 1.20 36.20 81.90 80.80 569.10
Khorramabad 57.60 66.40 71.70 54.50 18.90 1.00 0.20 0.10 0.60 20.70 48.40 70.40 410.50
Table 2 Long-term (1980-2009) monthly and annual climate characteristics of the five representative locations
Parameter Unit Value
Thermal time at the end of juvenile stage °Cd 380.00
Number of grains per gram of stem Kernel/(g•stem) 25.00
Thermal time at floral initiation stage °Cd 500.00
Maximum grain size g 0.04
Thermal time from the start grain filling stage to maturity °Cd 400.00
Photoperiod sensitivity - 2.50
Vernalization sensitivity - 2.00
Table 3 Description of the parameters in the APSIM-wheat model used to predict the grain yield of 'Azar-2' cultivar
Trait R2 d-index nRMSE (%) MBE N
Days to flowering (d) 0.99 0.96 3.180 4.000 9
Days to maturity (d) 0.99 0.97 2.150 1.880 9
Grain yield (t/hm2) 0.88 0.99 12.300 -0.105 55
Soil moisture (cm3/cm3) 0.99 0.93 8.700 0.002 56
Table S1 Summarized results of APSIM model evaluation (adapted from Rahimi-Moghaddam et al., 2021)
Fig. 1 Projected changes in future seasonal mean temperature (a) and seasonal cumulative rainfall (b) from 29 CMIP5 GCMs under RCP4.5 and RCP8.5 in the near future (2030s; RCP4.5_Near and RCP8.5_Near), middle future (2050s; RCP4.5_Mid and RCP8.5_Mid) and far future (2080s; RCP4.5_Far and RCP8.5_Far) compared to the baseline period (1980-2009) at the five representative locations. CMIP5, Coupled Model Intercomparison Project Phase 5; GCMs, General Circulation Models; RCP, Representative Concentration Pathway. Box boundaries indicate the 25th and 75th percentiles across GCMs, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The black horizontal line within each box indicates the median of multi-GCM ensembles.
Fig. 2 Changes of projected future wheat grain yield from 29 CMIP5 GCMs under RCP4.5 and RCP8.5 in Hamedan (a), Kermanshah (b), Khorramabad (c), Sanandaj (d) and Shiraz (e) in the near future (2030s), middle future (2050s) and far future (2080s) compared to the baseline period (1980-2009), and the averaged wheat grain yields of the five representative locations in the baseline period (f). Box boundaries indicate the 25th and 75th percentiles across GCMs, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The black horizontal line within each box indicates the median of multi-GCM ensembles.
Fig. 3 Projected future wheat grain yield from 29 CMIP5 GCMs under RCP4.5 and RCP8.5 in Hamedan (a and b), Kermanshah (c and d), Khorramabad (e and f), Sanandaj (g and h) and Shiraz (i and j) in the middle future (2050s). Length of boxplot indicates the variability across years. Box boundaries indicate the 25th and 75th percentiles across years, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The black horizontal line within each box indicates the median of multi-GCM ensembles.
Fig. 4 Analysis of variance (ANOVA) for projected future wheat grain yield as affected by locations, scenarios (RCPs_future time periods) and GCMs, as well as their interactions. The share of the uncertainty related to scenarios, GCMs and their interactions (scenario×GCM) for the projected future wheat grain yield are presented in donut charts. (a), Hamedan; (b), Kermanshah; (c), Khorramabad; (d), Sanandaj; (e), Shiraz. S.O.V., sources of variation; df, degrees of freedom; MS, mean of squares; ***, significant at the 0.001 probability level.
Fig. 5 Projected wheat grain yield (a1-a5) and the length of the wheat growth period (b1-b5) at the five representation locations in the baseline period and in the future under two climate change scenarios: elevating both CO2 concentration and temperature (blue boxes); and only rising temperature without elevating CO2 concentration (red boxes). (a1 and b1), Hamedan; (a2 and b2), Kermanshah; (a3 and b3), Khorramabad; (a4 and b4), Sanandaj; (a5 and b5), Shiraz. The length of boxplots indicates the variability among the 29 CMIP5 GCMs. Box boundaries indicate the 25th and 75th percentiles across GCMs, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The black horizontal line within each box indicates the median of multi-GCM ensembles.
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