DSSAT modelling relevant to climate change impacts on agriculture

Crop modelling

1. Topic: ASSESSMENT OF CLIMATE SCENARIOS AND MANAGEMENT PRACTICES ON WHEAT PRODUCTION USING DSSAT MODEL IN SEMI-ARID REGION Dr Anshuman Gunawat Department of Environmental Science Bhaskaracharya College of Applied Sciences, University of Delhi 1 30 January 2024 “International Conference on Environmental and Society: Traditional and Contemporary Perspectives”

2. Climate change is evolving as one of the leading environmental problem facing the modern world. Rising temperatures directly linked with climate change which may harm crop production (Olesen et al., 2011). Wheat ( Triticum aestivum ) is one of the most dominant crop which covers about 32% of the total land under cereal cultivation at a global scale (Tari, 2016). Impacts of climate change are more devastating in South Asia and may result in 50 percent reduction in wheat productivity by 2050 (Ali et al., 2017). Introduction 2

3. E very 2°C increase in temperature, which has been predicted by 2030, the GDP will reduce by 5 percent ( Hirabayashi et al., 2008; Singh et al., 2014). India ranks fourth-largest producer of wheat (after Russia, USA, and China) and contributes about 8.7% of the total wheat production (Sharma and Wardhan , 2017). Wheat is the second most important food crop in India particularly in the north-western and northern parts of India. Total 86% of the India's wheat production comes from 5 states of UP, Punjab, Haryana, Rajasthan and MP (Singh et al., 2017). Adaptation strategies are useful for increase wheat production in coming climate change conditions (Hussain et al., 2018). Introduction 3

4. Experiments that are impractical, too lengthy or too expensive & allow hypothetical situations to be investigated. Testing scientific hypothesis , y ield prediction, forecasting & climate change adaptation strategies. S ome popular crop models such as DSSAT, APSIM, InfoCrop , etc. DSSAT-CERES considered as a primary model for crop modelling (Rinaldi et al., 2007). DSSAT-CERES model was used for the combination of different irrigation application, fertilizers rates (Babel et al., 2018) and different sowing dates (Parmar et al., 2013). 4 Crop Model Application

5. Application of DSSAT-CERES model for estimation of wheat yield under different management practices. To assess the impact of climate change under different scenarios on wheat production. Identification of adaptation measures for wheat yield using DSSAT-CERES model. Objectives 5

6. 6 Experiment site Experiment site located in Central University of Rajasthan campus, Ajmer The experiment site is located in semi-arid region of Rajasthan. Latitude and longitude of this site is (26.6287° N, 75.0324° E).

7. 7 Detailed methodology

8. S. No. Details 1 Crop Wheat 2 Cultivar Raj 3077 3 Date of sowing 28 November 4 Seed rate 125 kg ha -1 5 Seeding depth 5-7 cm 6 Spacing 10 cm 7 Method of sowing Manual ( kera method)/Line sowing Cultivation Details 8 Wheat is a Rabi crop that is grown in the winter season. Sowing : November – December Harvesting : March – April

9. S. No. Details of layout 1 Design Randomized Complete Block Design (RCBD) 2 Total No. of treatments 12 3 No. of Replication 4 4 Total number of plots 48 5 Individual plot size 2 m x 2 m 6 Area of each plot 4.0 m 2 7 Length of experimental site 30 m 8 Width of experimental site 10 m 9 Gross area 300 m 2 Experimental Details 9

10. 2 meter 2 meter T-1   T-9   T-11   T-6 T-8 T-3 T-7 T-2 T-10 T-4 T-12 T-5 T-7 T-1 T-9 T-3 T-11 T-4 T-2 T-10 T-6 T-8 T-5 T-12 T-11   T-9   T-1   T-7 T-6 T-3 T-8 T-2 T-5 T-4 T-12 T-10 T-2 T-7 T-4 T-5 T-11 T-9 T-1 T-10 T-3 T-8 T-6 T-12 Replicate 1 Replicate 2 Replicate 4 Replicate 3 Field Experiment Design (RCBD) 10

11. Note: RD is 150 kg/ha (Singh et. al., 2008) Treatments Details of treatment T-1 Control & water irrigation at 21 days after sowing (DAS) T-2 Water irrigation at 25 DAS T-3 Water irrigation at 15 DAS T-4 Recommended dose (RD) of Urea (U)+ water at 21 DAS T-5 1.25 RD of U+ water at 15 DAS T-6 RD of U + Water irrigation at 25 DAS T-7 RD of U+ Water irrigation at 15 DAS T-8 RD of U/2 + water irrigation at 21 DAS T-9 1.25 RD of U+ water irrigation at 25 DAS T-10 RD of U/2+ Water irrigation at 25 DAS T-11 RD of U/2+ Water irrigation 15 DAS T-12 1.25 RD of U+ water irrigation at 21 DAS Treatment Details 11

12. Sample No. Depth (cm) pH Bulk Density (g cm -3 ) Particle Density (g cm -3 ) Porosity (%) Water Holding Capacity (%) Moisture Content (%) S1 0-15 7.2 1.43 2.61 34.59 29.6 15.27 S2 15-30 7.6 1.44 2.63 32.81 26.4 14.97 S3 30-60 7.9 1.46 2.65 32.54 28.2 15.04 S4 60-90 8.1 1.48 2.66 32.38 27.8 16.37 S5 90-120 8.3 1.50 2.69 31.86 28.6 15.84 S6 120-150 8.1 1.52 2.71 31.02 26.4 14.37 S7 150-180 8.5 1.53 2.73 30.21 27.4 15.68 12 Soil Profile Physico -chemical properties of experimental site

13. Months Temperature (℃) Rainfall (mm) RH (%) Wind Speed (m s −1 ) Solar Radiation (W/m 2 ) Minimum Maximum Nov 13.5 30.0 0.0 36.1 1.2 15.4 Dec 11.7 27.9 0.0 30.9 1.1 13.7 Jan 8.2 24.1 14.5 38.4 1.7 14.1 Feb 10.8 29.3 0.0 25.8 1.8 18.4 Mar 16.2 33.7 11.6 22.7 1.9 21.5 Apr 22.9 40.4 2.3 15.9 3.0 24.6 INSTRUMENTS: Wind speed meter, Hygrometer, Maximum-Minimum thermometer, Barometer Solar radiation ( https://power.larc.nasa.gov/data-access-viewer/ ) Weather Data 13

14. (A) Seeding (B) Fertilizer (C) Irrigation and (D) Treatment Sub-plot (A) (B) Field Activities (C) (D) 14

15. (A) (A) Sample Wash (B) Sample Dry (C) Wheat plant and (D) Grain Weight Data Collection (B) (C) 15

16. (A) Root Length (B) Shoot Length (C) Spikelet's Length and (D) Plant Count Lab/Field Work (A) (C) (B) (D) 16

17. 17 Maturated crop at Experimental site

18. Morphological parameters Treatments T-1 T-2 T-3 T-4 T-5 T-6 T-7 T-8 T-9 T-10 T-11 T-12 Plant height (cm) 110.7 112.3 114.7 113.0 119.0 118.0 119.3 119.0 111.3 112.0 112.7 115.3 Spike height (cm) 14.3 13.3 18.7 15.0 24.3 19.7 22.7 21.3 15.7 14.0 15.3 19.7 Spike weight (g) 3.2 3.4 3.4 3.4 4.6 4.3 4.5 4.4 3.1 3.3 3.2 3.5 Spike wheat count 67.3 64.7 65.7 66.3 79.0 74.0 77.7 73.3 64.0 65.7 64.3 69.0 Root length (cm) 11.0 11.0 12.7 12.3 15.3 14.0 16.0 13.7 11.0 12.3 11.3 13.0 Root width (cm) 3.0 3.1 3.5 3.3 4.4 3.7 4.5 3.7 3.1 3.2 3.1 3.5 Stem Diameter (mm) 4.0 4.0 4.2 4.4 4.8 4.2 4.8 4.5 4.0 4.2 4.2 4.7 Length b/w second node (cm) 21.7 21.0 22.0 22.7 25.3 23.0 25.0 24.0 20.3 21.7 22.0 22.7 Average Morphological Parameters at Maturity 18 Three plant samples were taken from every treatment replicates. Maximum in treatment T-5 and T-7 Minimum in treatment T-2 and T-9

19. Yield (kg/plot) T. No. 1 Year 2 Year 3 Year R1 R2 R3 R4 R1 R2 R3 R4 R1 R2 R3 R4 T-1 1.99 1.65 1.64 1.48 2.24 0.95 1.16 1.2 1.5 1.12 1.33 1.26 T-2 1.7 1.57 1.98 1.99 1.99 0.74 1.16 0.83 1.57 1.36 1.75 1.87 T-3 1.83 1.81 1.79 1.8 1.89 1.52 1.2 0.71 1.66 1.34 1.52 1.6 T-4 1.63 1.52 1.97 1.53 1.84 1.1 1.67 1.59 1.13 1.21 1.82 1.33 T-5 2.25 2 2.13 1.65 2.87 2.56 1.92 1.47 2.11 2.61 1.77 1.36 T-6 1.85 1.9 1.78 1.12 1.78 1.93 2.01 1.23 1.46 1.9 1.56 0.9 T-7 1.53 1.85 2.47 1.7 1.83 2.44 1.19 1.37 1.26 1.63 2.23 1.44 T-8 1.74 1.65 1.62 1.96 1.52 1.62 2.01 1.61 1.45 1.36 1.53 1.81 T-9 1.53 1.58 1.43 1.37 1.3 1.28 1.52 0.88 1.23 1.36 1.17 1.17 T-10 1.57 1.79 1.89 1.58 1.28 1.59 2 0.66 1.27 1.53 1.78 1.37 T-11 1.63 1.92 1.45 1.47 2.13 1.68 1.31 0.76 1.24 1.79 1.22 1.28 T-12 1.43 1.89 1.99 1.64 1.62 2.1 1.66 0.74 1.25 1.62 1.8 1.43 Experimental Wheat Yield 19

20. The crop models are effective tools to predict crop productivity under different management options and climatic conditions (Chaudhary et al., 2018). In the context of climate change, the importance of crop model in simulating crop production under different climatic scenarios was increasing day by day (White et al., 2011; Araya et al., 2020 ). The DSSAT is a software application program that comprises crop simulation models for over 42 crops (Jones et al., 2003; Sachin et al., 2019). The use of DSSAT model is to simulate and estimate wheat yield prediction under different climate change scenarios ( Hoogenboom et al., 2017 ; Liu et al., 2017). DSSAT provides an efficient method for evaluating the impact of changing climate on wheat production. DSSAT has been used for more than 30 years in the research world. DSSAT 20

21. The DSSAT model needs to be calibrated and validated for each variety and region. (Warnock et. al., 2005) Calibration provides the genetic coefficients with the best adjustment between simulated and observed data. After calibration, the model is validated by comparing it to an observed dataset used in the calibration. (Maldonado-Ibarra et. al., 2015) For DSSAT-CERES model, seven genetic coefficients were adjusted by trial and error approach for fine-tuning of the model in order to match the model outputs of grain yield with the observed values. (Babel et. al., 2018) 21 DSSAT

22. Data on treatments, field conditions, crop management and simulation controls Time course data (average values) for a wheat experiment Crop species coefficients for a model Weather daily data, Soil profile data Experiment input files Weather and Soil input files Experimental data input file Crop input file DSSAT Model Inputs 22

23. 23 Main Layout of DSSAT Model

24. Experiment Information 24

25. Planting Details 25

26. Treatment Details 26

27. Simulation Details 27

28. Yield (kg/ha) T. No. Fertilizer dose Irrigation Yield 1 Year 2 Year 3 Year Observed Simulated Observed Simulated Observed Simulated T1 0% Fertilizer 21 Days 3882 3474 2998 2796 2998 3165 T2 25 Days 4185 3425 2560 2497 3775 2704 T3 15 Days 3825 3439 2744 3161 3213 3335 T8 50% fertilizer 21 Days 3461 3866 3336 3083 3041 3537 T10 25 Days 4179 3857 3281 2623 3618 3060 T11 15 Days 3635 3904 3240 3579 3106 3766 T4 100% fertilizer 21 Days 3454 3921 3176 3119 2826 3611 T6 25 Days 3777 3936 3942 2630 3300 3103 T7 15 Days 4077 4060 3578 3738 3547 3940 T5 125% fertilizer 15 Days 4202 4087 4525 3784 4014 3964 T9 25 Days 3731 3939 3095 2626 3112 3069 T12 21 Days 3676 3920 3150 3105 3222 3608 28 High irrigation with high amount of fertilizer can produce high yield. Low irrigation with high fertilizer is not useful for yield. Water deficiency might be responsible for low yield. Model Performance for All Treatments

29. Assessment of the impact of climate change under different scenarios on wheat production 29

30. Gridded datasets (26.5° N, 75.5° E) of precipitation and temperature were obtained from the India Meteorological Department (IMD) and the CORDEX-SA domain to be used in this study. The resolution of the gridded precipitation and temperature data was 0.5°×0.5° and 1°×1°. The bias-corrected ensemble mean of future climate projected by three CORDEX-SA driving GCMs experiments (CNRM-CM5, CCSM4 and MPIESM-LR) were used in the DSSAT Model. The resolution of the CORDEX-SA data was 0.44°×0.44°. Climate Data 30

31. The long-term (1980–2010) baseline observation data that included minimum and maximum temperatures, daily precipitation and solar radiation for nearby experimental site were obtained from IMD. For this present study time scale is divided in three periods as baseline (1981-2010), near future (2021-2050) and far future (2051-2080). 31 S. No. Time scale Period From To 1. Baseline 1981 2010 2. Near Future 2021 2050 3. Far Future 2051 2080 Simulation Period

32. Months Baseline (1) RCP 4.5 (2) Difference RCP 4.5 (3) Difference (1981-2010) (2021-2050) (2-1) (2051-2080) (3-1) Tmax Rain Tmax Rain Tmax Rain Tmax Rain Tmax Rain (°C) (mm) (°C) (mm) (°C) (mm) (°C) (mm) (°C) (mm) Jan 15.65 1 17.2 7.2 1.55 6.2 17.4 7.9 1.75 6.9 Feb 18.6 3.3 19.4 13.1 0.8 9.8 20.15 16.7 1.55 13.4 Mar 24.35 2.2 24.6 1.2 0.25 -1 25.85 2 1.5 -0.2 Apr 30 3.9 30.75 8.6 0.75 4.7 32.5 11.2 2.5 7.3 May 33.9 9.8 34.25 9.9 0.35 0.1 35.7 12.5 1.8 2.7 Jun 33.95 57.2 34.9 42.2 0.95 -15 34.85 54.7 0.9 -2.5 Jul 30.5 170.5 31.75 183.4 1.25 12.9 31.45 213.5 0.95 43 Aug 28.9 142 30.15 154 1.25 12 29.7 178.2 0.8 36.2 Sep 29.1 50.2 30 52.3 0.9 2.1 29.8 63.4 0.7 13.2 Oct 26.75 10.5 27.8 13.5 1.05 3 27.65 17.6 0.9 7.1 Nov 21.55 5.7 23.45 1.5 1.9 -4.2 23.35 1.6 1.8 -4.1 Dec 17.05 1.4 18.85 4.2 1.8 2.8 18.8 4.2 1.75 2.8 32 Change in Climatic Parameters (RCP 4.5) In near future monthly average temperature is increasing from (0.25 – 1.9 °C) with comparison to baseline (1981-2010) data. Whereas, in far future the overall monthly average temperature is increasing from (0.7 – 2.5 °C ).

33. Months Baseline (1) RCP 8.5 (2) Difference RCP 8.5 (3) Difference (1981-2010) (2021-2050) (2-1) (2051-2080) (3-1) Tmax Rain Tmax Rain Tmax Rain Tmax Rain Tmax Rain (°C) (mm) (°C) (mm) (°C) (mm) (°C) (mm) (°C) (mm) Jan 15.65 1 17.15 4.7 1.5 3.7 19.05 6.5 3.4 5.5 Feb 18.6 3.3 19.85 9.2 1.25 5.9 21.8 8.9 3.2 5.6 Mar 24.35 2.2 25.7 1.4 1.35 -0.8 27.3 2.4 2.95 0.2 Apr 30 3.9 32.15 8.8 2.15 4.9 33.85 11.1 3.85 7.2 May 33.9 9.8 35.45 12.5 1.55 2.7 36.8 15.2 2.9 5.4 Jun 33.95 57.2 34.8 53.6 0.85 -3.6 35.65 55.8 1.7 -1.4 Jul 30.5 170.5 31.15 218.6 0.65 48.1 31.8 243 1.3 72.5 Aug 28.9 142 29.75 176 0.85 34 30.35 191.9 1.45 49.9 Sep 29.1 50.2 29.6 66.8 0.5 16.6 30.4 68.2 1.3 18 Oct 26.75 10.5 27.75 16 1 5.5 28.75 17.4 2 6.9 Nov 21.55 5.7 23.15 1.4 1.6 -4.3 24.9 1.6 3.35 -4.1 Dec 17.05 1.4 18.55 3.2 1.5 1.8 20.35 2.6 3.3 1.2 33 Change in Climatic Parameters (RCP 8.5) In near future monthly average temperature is increasing from (0.5 – 2.15 °C) with comparison to baseline (1981-2010) data. Whereas, in far future the overall monthly average temperature is increasing from (1.3 – 3.85 °C ).

34. T. No. Fertilizer dose Irrigation Yield 1 Year 2 Year 3 Year Baseline RCP 4.5 RCP 8.5 (kg/ha) (kg/ha) ( kg/ha) 1981-2010 (kg/ha) Near future (kg/ha) Far future (kg/ha) Near future (kg/ha) Far future (kg/ha) T1 0% Fertilizer 21 Days 3474 2796 3165 3425 3251 2832 3055 2748 T2 25 Days 3425 2497 2704 3121 2982 2468 2951 2443 T3 15 Days 3439 3161 3335 3516 3332 2883 3347 2874 T8 50% fertilizer 21 Days 3866 3083 3537 3810 3663 3150 3434 3101 T10 25 Days 3857 2623 3060 3428 3278 2685 3174 2626 T11 15 Days 3904 3579 3766 4066 3791 3250 3739 3209 T4 100% fertilizer 21 Days 3921 3119 3611 3880 3829 3272 3562 3193 T6 25 Days 3936 2630 3103 3460 3348 2734 3198 2665 T7 15 Days 4060 3738 3940 4231 3992 3419 3909 3339 T5 125% fertilizer 15 Days 4087 3784 3964 4283 4051 3464 3959 3384 T9 25 Days 3939 2626 3069 3437 3368 2767 3191 2676 T12 21 Days 3920 3105 3608 3865 3849 3298 3576 3198 34 DSSAT Simulation for Future Period In all the treatments, the projected simulated yield of RCP 4.5 and RCP 8.5 for near and far future observed less than from baseline period. For both RCP 4.5 and RCP 8.5, the near future simulated yield observed greater than the far future simulated yield. Yield is decreasing for far future period. So, there is a clearly shown impact of increasing temperature over year on crop yield.

35. Sowing Window The model were simulated to analyse the impact of different sowing date on crop yield for future scenarios One-week early sowing (21 November) One-week late sowing (05 December) Two-week early sowing (14 November) Two-week late sowing (12 December) Plant Density The model were simulated to analyse the impact of plant density on crop yield for different plant population Low Scenario (100 plants m -2 ) Normal Scenario (150 plants m -2 ) High Scenario (250 plants m -2 ) Adaptation Measures 35

36. T. No. Fertilizer dose   Irrigation Baseline ( 1981-2010 ) (kg/ha) RCP 4.5 2021-2050 2051-2080 2021-2050 2051-2080 1 W Early (kg/ha) 1 W Late (kg/ha) 1 W Early (kg/ha) 1 W Late (kg/ha) 1 WE (% diff) 1 WL (%diff) 1 WE (% diff) 1 WL (%diff) T1 0% fertilizer 21 Days 3425 4156 4008 3443 3307 21.34 17.02 0.53 -3.45 T2 25 Days 3121 4050 3913 3449 3255 29.77 25.38 10.51 4.29 T3 15 Days 3516 4253 4165 3564 3488 20.96 18.46 1.37 -0.8 T8 50% fertilizer 21 Days 3810 4231 4001 3545 3326 11.05 5.01 -6.96 -12.7 T10 25 Days 3428 4084 3921 3514 3284 19.14 14.38 2.51 -4.2 T11 15 Days 4066 4263 4165 3609 3510 4.85 2.43 -11.2 -13.7 T4 100% fertilizer 21 Days 3880 4247 3988 3583 3301 9.46 2.78 -7.65 -14.9 T6 25 Days 3460 4054 3887 3542 3272 17.17 12.34 2.37 -5.43 T7 15 Days 4231 4263 4165 3610 3510 0.76 -1.56 -14.7 -17 T5 125% fertilizer 15 Days 4283 4263 4165 3610 3510 -0.47 -2.76 -15.7 -18.1 T9 25 Days 3437 4046 3872 3549 3264 17.72 12.66 3.26 -5.03 T12 21 Days 3865 4251 3979 3588 3290 9.99 2.95 -7.17 -14.9 36 One-Week Early/Late Sowing in RCP 4.5 In near future, one-week early and one-week late sowing crop observed more than baseline yield. Whereas in far future, the one-week early and one-week late sowing crop shows less productive than baseline yield. Therefore, in both the near and far future the one-week early sowing crop shows high production comparatively one-week late sowing. Low fertilizer treatments sowing good yield production in far future.

37. T. No. Fertilizer dose Irrigation Baseline (1981-2010) (kg/ha) RCP 4.5 2021-2050 2051-2080 2021-2050 2051-2080 2 W Early (kg/ha) 2 W Late (kg/ha) 2 W Early (kg/ha) 2 W Late (kg/ha) 2 WE (% diff) 2 WL (%diff) 2 WE (% diff) 2 WL (%diff) T1 0% fertilizer 21 Days 3425 4193 4171 3477 3457 22.42 21.78 1.52 0.93 T2 25 Days 3121 4202 3744 3475 3128 34.64 19.96 11.34 0.22 T3 15 Days 3516 4273 4192 3658 3478 21.53 19.23 4.04 -1.08 T8 50% fertilizer 21 Days 3810 4262 4160 3631 3456 11.86 9.19 -4.7 -9.29 T10 25 Days 3428 4234 3747 3541 3166 23.51 9.31 3.3 -7.64 T11 15 Days 4066 4273 4192 3683 3478 5.09 3.1 -9.42 -14.46 T4 100% fertilizer 21 Days 3880 4258 4126 3671 3430 9.74 6.34 -5.39 -11.6 T6 25 Days 3460 4214 3690 3542 3123 21.79 6.65 2.37 -9.74 T7 15 Days 4231 4273 4192 3683 3478 0.99 -0.92 -12.95 -17.8 T5 125% fertilizer 15 Days 4283 4273 4192 3683 3478 - 0.23 -2.12 -14.01 -18.8 T9 25 Days 3437 4203 3664 3513 3104 22.29 6.6 2.21 -9.69 T12 21 Days 3865 4252 4108 3676 3420 10.01 6.29 -4.89 -11.51 37 Two-Week Early/Late Sowing in RCP 4.5 The near future sowing more productive when seeding at two-week earlier and two-week late. But in case of far future, crop yield decreases for two-week early and two-week late sowing.

38. T. No. Fertilizer dose Irrigation Baseline (1981-2010) RCP 8.5 2021-2050 2051-2080 2021-2050 2051-2080 1 W Early (kg/ha) 1 W Late (kg/ha) 1 W Early (kg/ha) 1 W Late (kg/ha) 1 WE (%diff) 1 WL (%diff) 1 WE (%diff) 1 WL (%diff) T1 0% fertilizer 21 Days 3425 4044 3743 3421 3101 18.07 9.28 -0.12 -9.46 T2 25 Days 3121 3696 3632 3353 3049 18.42 16.37 7.43 -2.31 T3 15 Days 3516 4231 4016 3554 3415 20.34 14.22 1.08 -2.87 T8 50% fertilizer 21 Days 3810 4151 3741 3524 3171 8.95 -1.81 -7.51 -16.77 T10 25 Days 3428 3770 3651 3399 3062 9.98 6.51 -0.85 -10.68 T11 15 Days 4066 4258 4016 3570 3434 4.72 -1.23 -12.2 -15.54 T4 100% fertilizer 21 Days 3880 4182 3725 3535 3162 7.78 -3.99 -8.89 -18.51 T6 25 Days 3460 3783 3597 3381 3028 9.34 3.96 -2.28 -12.49 T7 15 Days 4231 4258 4016 3570 3434 0.64 -5.08 -15.62 -18.84 T5 125% fertilizer 15 Days 4283 4258 4016 3570 3434 -0.58 -6.23 -16.65 -19.82 T9 25 Days 3437 3787 3570 3373 3010 10.18 3.87 -1.86 -12.42 T12 21 Days 3865 4184 3711 3535 3151 8.25 -3.98 -8.54 -18.47 38 One-Week Early/Late Sowing in RCP 8.5 The near future sowing more productive when seeding at one-week early and one week late comparing to baseline yield. In case of far future, the crop yield is less than baseline for one-week early and one-week late sowing.

39. T. No. Fertilizer dose  Irrigation Baseline (1981-2010) (kg/ha) RCP 8.5 2021-2050 2051-2080 2021-2050 2051-2080 2 W Early (kg/ha) 2 W Late (kg/ha) 2 W Early (kg/ha) 2 W Late (kg/ha) 2 WE (% diff) 2 WL (%diff) 2 WE (% diff) 2 WL (%diff) T1 0% fertilizer 21 Days 3425 3883 3843 3253 3146 13.37 12.2 -5.02 -8.15 T2 25 Days 3121 3855 3485 3326 2944 23.52 11.66 6.57 -5.67 T3 15 Days 3516 4131 3991 3499 3316 17.49 13.51 -0.48 -5.69 T8 50% fertilizer 21 Days 3810 4056 3838 3379 3162 6.46 0.73 -11.31 -17.01 T10 25 Days 3428 3946 3455 3385 2939 15.11 0.79 -1.25 -14.26 T11 15 Days 4066 4198 3991 3525 3328 3.25 -1.84 -13.31 -18.15 T4 100% fertilizer 21 Days 3880 4105 3815 3409 3143 5.8 -1.68 -12.14 -18.99 T6 25 Days 3460 3939 3397 3362 2894 13.84 -1.82 -2.83 -16.36 T7 15 Days 4231 4205 3991 3525 3328 -0.61 -5.67 -16.69 -21.34 T5 125% fertilizer 15 Days 4283 4205 3991 3525 3324 -1.82 -6.82 -17.7 -22.39 T9 25 Days 3437 3937 3365 3350 2875 14.55 -2.09 -2.53 -16.35 T12 21 Days 3865 4108 3801 3400 3130 6.29 -1.66 -12.03 -19.02 39 Two-Week Early/Late Sowing in RCP 8.5 Two-week early sowing crop is more efficient. The near future is more productive when seeding at two-week earlier and two-week late. But in case of far future, the crop yield is decreases for two-week early and two-week late sowing.

40. 40 (a) P lant De nsity (100 plants m -2 ) for Future Scenarios In RCP 4.5 scenario (near & far future) at 100 plants per meter square all the twelve treatments shows negative yield. But T-2 (for near future) gives approximately the same yield to baseline. In RCP 8.5 scenario (near & far future) at 100 plants per meter square all the treatments shows negative yield. (b) (c) (d)

41. 41 P lant D ensity (250 plants m -2 ) for Future Scenarios (a) In RCP 4.5 scenario (near future) at 250 plants per meter square, the nine treatments gives negative yield, but T-1, T-2 & T-10 shows positive yields. In RCP 8.5 scenario (near future) at 250 plants per meter square the eleven treatments gives negative yield, but T-2 shows positive yields. In case of far future, all treatments are sowing negative yield.

42. Overall Conclusion Treatments (T-5, T-7) is high productive among all treatments. Plant root length is directly linked with yield production and shoot length is related with biomass. In future scenarios, the RCP 4.5 shows high productivity rather than the RCP 8.5 scenario. Projected simulated yield of near and far future is less than from baseline period. Near future simulated yield is greater than far future simulated yield. Recent study shows that temperature increase in RCP 4.5 scenario (0.25-2.5 °C) and in RCP 8.5 scenario (0.5-3.85 °C) may reduce the crop yield. Temperature is increasing in far future period and yield is also decreasing for far future period it is sowing that increasing temperature is responsible factor for decreasing crop yield. 42

43. The DSSAT model can be used for finding the optimum sowing dates for future climate change scenarios. Crop model suggest that adjustments to planting dates would increase wheat yield. The result indicates that there may be a possible increase in yield with early sowing dates. These tendencies were similar for both one week and two-week early sowing dates, but t wo-week earlier transplanting is high productive in RCP 8.5 scenario. It is evaluated after model simulation with low amount of fertilizer farmers could produce a good amount of crop yield in future. It is suggested that sowing at normal seed rate with normal quantity of fertilizer is enough for producing wheat yield without harming of soil fertility. The present study suggests that the DSSAT model can be used to predict the effect of a change in yield in the different climate scenarios. 43 Overall Conclusion

44. Thank you... 44

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