Climate Change and Wheat Production in Pakistan: Calibration, Validation and Application of CERES- Wheat Model

Transcription

1 Research Report GCISC RR 14 Climate Change and Wheat Production in Pakistan: Calibration, Validation and Application of CERES- Wheat Model M. Mohsin Iqbal, Syed Sajidin Hussain, Muhammad Arif Goheer, Humaira Sultana, Kashif Majeed Salik, Muhammad Mudasser,Arshad M. Khan June 2009 Global Change Impact Studies Centre Islamabad, Pakistan

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3 Research Report GCISC-RR-14 Climate Change and Wheat Production in Pakistan: Calibration, Validation and Application of CERES- Wheat Model Muhammad Mohsin Iqbal, Syed Sajidin Hussain, Muhammad Arif Goheer, Humaira Sultana, Kashif Majeed Salik, Muhammad Mudasser, Arshad Muhammad Khan June 2009 Global Change Impact Studies Centre (GCISC) National Centre for Physics (NCP) Complex Quaid-i-Azam University Campus, P.O. Box 3022, Islamabad, Pakistan

4 Published by: Global Change Impact Studies Centre (GCISC) National Centre for Physics (NCP) Complex Quaid-i-Azam University Campus P.O. Box 3022 Islamabad Pakistan ISBN: GCISC Copyright. This Report, or any part of it, may not be used for resale or any other commercial or gainful purpose without prior permission of Global Change Impact Studies Centre, Islamabad, Pakistan. For educational or non-profit use, however, any part of the Report may be reproduced with appropriate acknowledgement. Published in: June 2009 This Report may be cited as follows: Iqbal, M, M., S.S. Hussain, M.A. Goheer, H. Sultana, K.M. Salik, M. Mudasser and A.M. Khan, (2009), Climate Change and Wheat Production in Pakistan: Calibration, Validation and Application of CERES-Wheat Model, GCISC-RR-14, Global Change Impact Studies Centre (GCISC), Islamabad, Pakistan. ii

5 CONTENTS Foreward Preface List of Tables List of Figures i ii iii iv 1. Introduction 1 2. Model used at GCISC Model Data Requirements 2 3. Model Calibration 5 4. Model Evaluation 8 5. Impact Studies on Wheat Productivity Past trends of wheat yield Hypothetical scenarios Climatic Zones Effect of temperature increase on grain yield of wheat Interactive effects of increase in temperature and CO 2 alongwith change in water supply on grain yield of wheat Length of crop life cycle under climate change and water management scenarios Effect of changes in rain and irrigation water availability on grain yield of wheat A2 and B2 Scenarios Impact of climate change on national food security Limitations of CERES-Wheat Model Conclusions 32 References 34

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7 F O R E W O R D Global Change Impact Studies Centre (GCISC) was established in 2002 as a dedicated research centre for climate change and other global change related studies, at the initiative of Dr. Ishfaq Ahmad, NI, HI, SI, the then Special Advisor to Chief Executive of Pakistan. The Centre has since been engaged in research on past and projected climate change in different sub regions of Pakistan, corresponding impacts on the country s key sectors, in particular Water and Agriculture, and adaptation measures to counter the negative impacts. The work described in this report was carried out at GCISC and was supported in part by APN (Asia Pacific Network for Global Change Research), Kobe, Japan, through its CAPaBLE Programme under a 3-year capacity enhancement cum research Project titled Enhancement of National capacities in the Application of Simulation Models for the Assessment of Climate Change and its Impacts on Water Resources, and Food and Agricultural Production, awarded to GCISC in 2003 in collaboration with Pakistan Meteorological Department (PMD). It is hoped that the report will provide useful information to national planners and policymakers as well as to academic and research organizations in the country on issues related to impacts of climate change on Pakistan. The keen interest and support by Dr. Ishfaq Ahmad, Advisor (S & T) to the Planning Commission, and useful technical advice by Dr. Amir Muhammed, Rector, National University of Computer and Emerging Sciences and Member, Scientific Planning Group, APN, throughout the course of this work are gratefully acknowledged. Dr. Arshad M. Khan Executive Director, GCISC i

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9 P R E F A C E The work described in this report was partially carried out under the framework of APN CAPaBLE Project (No CRP01CMY-Khan) entitled Enhancement of National Capacities in the Application of Simulation Models for the Assessment of Climate Change and its Impacts on Water Resources and Food and Agricultural Production awarded to Global Change Impact Studies Centre (GCISC) in collaboration with Pakistan Meteorological Department (PMD) by Asia Pacific Network for Global Change Research, Japan, in 2003 for a 3-year period. Firstly, the performance of crop simulation model CERES-Wheat was evaluated under local conditions. The evaluated model was then used for the climate change impact studies. Initially, arbitrary scenarios of temperature and CO 2 increase were used for climate change impact assessment; later IPCC-SRES based A2 and B2 scenarios of changes in CO 2 concentration, temperature and precipitation were used. The hydrological regimes in which crops grow will surely change with global warming. So, different scenarios of irrigation water supplies were applied to see their impact on wheat crop along with the scenarios of climate change. The results of model evaluation demonstrate satisfactory performance of CERES-Wheat model under the agro-climatic conditions of Pakistan, despite limited availability of detailed observed data sets and a number of discrepancies in the accuracy of observed data. It is found that increasing the ambient temperature results in shortening of growing season length (crop life cycle) in all the agro-climatic regions of Pakistan (Northern mountainous, Northern submountainous, Southern semi-arid plains and Southern arid plains), with reduction being the largest in the Northern mountainous region and smallest in the Southern arid region. These reductions, however, do not have similar impacts on crop yields. In the Northern mountainous region the crop yield will increase due to more favourable temperature conditions, while in the other regions the crop yield will decrease because of increased thermal stress. The impact of climate change using arbitrary scenarios of temperature and CO 2 increase, suggest that for no changes in ambient temperature, the increased concentration of CO 2 will have a positive effect in all regions while for no changes in CO 2 concentration, higher temperatures will drastically reduce yields in arid, semi-arid and sub-humid zones but will have beneficial effects in mountainous humid zone for temperature increases up to 4 o C. In all zones, changes in water supply from rainfed to full irrigation are expected to respond positively to yield. Crop response to elevated CO 2 will be relatively greater when water is taken as a limiting factor, compared to well-watered conditions. This suggests that increased CO 2 can increase water use efficiency when water is in short supply. The impacts of IPCC-SRES based A2 and B2 scenarios on wheat crop are found to result in markedly increased yield in northern mountainous region and reduced yields in southern plains of Pakistan. However, because of the meager share (2%) of northern mountainous region in national wheat production, the increased production from these areas will not be able to make any significant impact on national production, resulting in 5-6% drop in net production by 2080s. ii

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11 LIST OF TABLES Table 1 Agro-climatic characteristics of the study sites used for model evaluation 3 Table 2 Genetic coefficients developed for the Wheat cultivar Inqlab-91 6 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Observed and simulated data of growth and development in the model calibration phase at Faisalabad location at two sowing dates and nitrogen level of 150 Kg ha -1 Percentage difference of growth data with their RMSE and d-stat values in the model evaluation phase A2 and B2 scenarios of temperature and precipitation for Northern and Southern Pakistan Scenarios of temperature and precipitation change in Northern and Southern parts of Climate change impact on Growing Season Length (GSL) wheat in Northern mountainous region and Southern plains under A2 & B2 scenarios Impact of climate change on Wheat Production in Pakistan by 2080 under A2 and B2 Scenarios iii

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13 LIST OF FIGURES Figure1 Data inputs to the CERES-Wheat Model for simulation studies 2 Figure 2 Weather data required for CERES-Wheat Model Operation 3 Figure 3 Figure 4 Figure 5 Figure 6 The mean annual cycle of monthly climatic characteristics over 40-year period (1960 to 2000) for Faisalabad, Sheikhupura and Bahawalpur locations, Tmax ( o C), Tmin ( o C), are maximum temperature and minimum temperature respectively. Observed and simulated growth stages (a), tops weight (b) and leaf area index (c) in S1 and S2 for the Faisalabad for model calibration. Observed and simulated tops weight in S1 and S2 at Faisalabad and Bahawalpur location with nitrogen fertilizer N1, N2 and N3 in model evaluation phase. Observed and simulated leaf area index in sowing one (S1) and two (S2) in Faisalabad, Sheikhpura and Bahawalpur location, in model evaluation phase with nitrogen fertilizer N1, N2 and N Figure 7 Past trends in wheat yields due to climate change in Faisalabad, Sheikhupura and Bahawalpur district ( ) 14 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Pakistan map showing the location of 10 sample meteorological stations with their altitude and precipitation effectiveness index value respectively. The 30-year mean monthly maximum (Tmax) and minimum (Tmin) temperature and rainfall (1971 to 2000). Trends in simulated wheat yields with temperature changes at 375 ppm CO 2 concentration in arid, semi-arid, sub-humid and humid zones. Percentage change in yield from base yield in response to temperature changes at 375 ppm CO 2 concentration in arid, semi-arid, sub-humid and humid zones. Trends in simulated wheat yields with changes in temperature at 550 ppm (a) and 700 ppm (b) CO 2 concentration in arid, semi-arid, sub-humid and humid zones iv

14 Figure 13 Change in total length of crop life cycle (days) from base temperature in respective climatic zones. 22 Figure 14 Impact of Changes in Rainfall on Wheat Yield 23 Figure 15 Figure 16 Impact of Changes in Irrigation Water Availability on Wheat Yield Impact of Changes in CO 2 Concentration, Temperature, and Irrigation Water Availability on Wheat Yield at (a) 360 ppm (b) 550 ppm CO Figure 17 Grids covering geographical areas of a) Northern (FHN) and b) Southern (FHS) parts 26 Figure 18 Agro-climatic zones of Pakistan based on aridity classes 27 Figure 19 Figure 20 Grids covering Agro-climatic zones of Pakistan: a) FHAB (Humid/Sub-humid+ Semi-arid) corresponding to Northern Pakistan and b) FHCD (Arid +Hyper-arid) corresponding to Southern Pakistan. Impact of climate change on wheat yield in Northern and Southern Pakistan under A2 and B2 scenarios v

15 1. Introduction Wheat is the single most important crop in the world in terms of total harvested weight and amount used for human and animal nutrition. Climate change is likely to be of great consequence for wheat production and, thus, human food supplies. Currently, about 600 Mt of wheat grain is produced annually which provides 20% of the energy and 25% of the protein requirements of world s 6.6 billion people. In addition, wheat makes a large contribution to the nutrition of animals that provide milk and meat for the human diet. In Pakistan, wheat is the staple food crop of masses. It is grown all over the country on an area of 8.4 million hectares producing on an average 2.52 t/ha grain yield (MinFAL, 2006). The country is almost self sufficient in wheat production. Punjab and Sindh provinces are the main granary of Pakistan as they contribute more than 90% of total wheat production in the country (PARC, 2002). Geographically, the whole of Pakistan, particularly the Punjab province, is located in arid and semiarid zone. Irrigation is a key input for crop production in the area. The country is already under water stress due to increasing population pressure, intensive agriculture and frequent droughts. Climate change in the form of global warming is likely to increase water demand for wheat crop further and bring severe water stresses to agriculture that could seriously endanger the food security of the country. Over the last two decades, various crop simulation models have emerged as valuable tools for studying impacts of climate change on agriculture. These models are able to address what if type questions and their use is becoming increasingly common all over the world. Crop growth simulation models reduce the need for expensive and timeconsuming field experimentation as they can be used to extrapolate the results of research conducted in one season or location to the other seasons, locations, or management options. They also provide a means to quantify the effects of climate, soil and management on crop growth, productivity and sustainability of agricultural production. 2. Model used at GCISC For conducting such studies at GCISC, the CSM-Cropsim-CERES-Wheat model version 4.0 ((Ritchie et al., 1998; Jones et al., 2003) that is part of the Decision Support System for Agro-technology Transfer (DSSAT) Version 4.0 (Tsuji et al., 1994; Hoogenboom et al., 2004) was used. CERES-Wheat is a dynamic and mechanistic crop growth model that simulates the duration of vegetative and reproductive stages, accumulation and partitioning of biomass, and grain yield for a specific cultivar (Hoogenboom et al., 1994; Ritchie et al., 1998). The model is capable of simulating the impact of main environmental factors, such as weather and soil type, and also can aid in providing farmers with information for their management decisions (Tsuji et al., 1998). The model was acquired from University of Georgia, Griffin, GA, USA by GCISC under the framework of an APN CAPaBLE project (GCISC, 2008). The model was firstly 1

16 calibrated and validated under local conditions and then used for climate change impact assessment studies. 2.1 Model Data Requirements Model requires the following type of data inputs (Figure 1): a. Field Experimental Crop Data (Crop Management Data, X File), Crop Yield and yield components data (A File) and Crop Growth Data (T File) b. Crop Genetic Coefficients Data (Cultivar file, Ecotype file and Species file) c. Soil Data d. Weather data (Maximum Temperature, Minimum Temperature, Rainfall, Solar Radiation) (Fig 2). Simulation Process Growth and Development Data Cultivar File Species File DSSAT.exe Irrigation Data Soil Data Weather Data X-File Ecotype File Simulation Output Figure 1: Data inputs to the CERES-Wheat Model for simulation studies The agronomic data on wheat growth, required for calibration and validation of the model, was arranged from University of Agriculture, Faisalabad after entering into a Memorandum of Understanding with the Agronomy Department of the University.The Agro-Climatology Lab of Agronomy Department facilitated conducting the relevant research as a part of thesis research of a Ph.D. student (Mr. M. Arif Goheer) who later joined GCISC. The thesis research was designed to determine the effect of sowing dates, nitrogen levels and spatial variability on wheat growth and development, in the year

17 Three sites were selected to represent varied weather conditions and soil types of Punjab province of Pakistan. These were: Faisalabad, Bahawalpur and Sheikhupura. Bahawalpur was the driest of the three sites, with an annual average rainfall of 164 mm and almost highest maximum and minimum temperatures, while Sheikhupura had the highest annual average rainfall of 592 mm. The annual average minimum temperatures were similar for Sheikhupura and Bahawalpur (Table 1). Monthly variations in temperatures and rainfall are shown in Figure 3 for each site. Daily Maximum Temperature Daily Solar Radiation Weather Data Daily Minimum Temperature Daily Rainfall Figure 2: Weather data required for CERES-Wheat Model Operation At each site, wheat was planted on two sowing dates; S1 ands2. Faisalabad site has silt loam soil; the crop was planted on November 21 and December 15. Sheikhupura site has silty clay loam soil; the crop was planted on November 18 and December 13. Bahawalpur site has clay loam soil, and the crop was planted on November 24 and December 19. The wheat crop at all the sites was supplied with three nitrogen levels, 100 kg ha -1 (N1), 125 kg ha -1 (N2) and 150 kg ha -1 (N3). The source of nitrogen was urea, applied in two equal splits. The first half dose was broadcast and incorporated at planting and the second half was broadcast, but not incorporated, one month after planting. Table 1: Agro-climatic characteristics of the study sites used for model evaluation. Site Latitude ( N) Longitude ( E) Altitude (m) Tmax ( C) Tmin ( C) Annual Rainfall (mm) Faisalabad Sheikhupura Bahawalpur

18 The experimental design was a randomized complete block with split plots and four replications. The size of each plot was 2.3 m x 8 m. The planting dates were randomized in the main plots and the nitrogen levels in the subplots. All experiments were conducted under irrigated conditions. Water was supplied in the form of flood irrigation with 75 mm of water per irrigation. Irrigation was applied to ensure the availability of water during the critical growth stages, but keeping into consideration the local rainfall. Weeds, pests and diseases were controlled with mechanical cultivation and pesticides. The cultivar used was Inqilab 91, which originated from WL 711/crow s and Pb a-oa-1a-oa 85060m-1. This cultivar covers about 70 percent of the total area under wheat production in Pakistan and has excellent characteristics for wider adaptation. It is a semi-erect variety with an average height of 106 cm and an average number of 42 grain per ear. It is a general purpose variety that is suitable for both early and late planting. The average growing season duration is 147 days and the average yield is 4500 Kg ha Faisalabad Tempeature ( o C) Rainfall (mm) Sheikhupura Tempeature ( o C) Rainfall (mm) Bahawalpur Tempeature ( o C) Rainfall (mm) Jan Feb Mar April May June July Aug Sep Oct Nov Dec 0 Month Tmax Tmin Total rainfall Figure 3: The mean annual cycle of monthly climatic characteristics (Tmax, Tmin, and total rainfall) over 40-year period (1960 to 2000) for Faisalabad, Sheikhupura and Bahawalpur locations. 4

19 Field observations were taken on growth stages of plant; total above-ground biomass (hereafter referred to as tops weight), grain number; unit grain weight; grain yield; and leaf area index. The growth stages recorded were: seedling emergence, tillering, stem elongation, spike emergence, anthesis and physiological maturity, corresponding to Zadoks growth stages 10, 20, 30, 50, 60 and 90, respectively (Zadoks et al., 1974). 3. Model Calibration The ultimate test of a simulation model is the accuracy with which it can describe or mimic the actual system, usually involving comparisons between simulated and observed data (Willmott 1982; Jones and Kiniry 1986; Oreskes et al. 1994). For this purpose, the d- index (index of agreement) and RMSE (root mean square error) were used as statistical criteria for model evaluation. Observed and simulated values of growth stages, tops weight and Leaf Area Index (LAI) were also plotted as a function of time. The d-index (Willmott, 1982) was computed in the following way: d n n 2 2 = 1 ( Pi Oi) / [ P' i + O' i ] i= 1 i= 1 where Oi and Pi are observed and predicted values for the i-th data pair, and P'i =Pi-O (average of the observed) and O'i=Oi-O. The index (d) is intended to be a descriptive measure. It is both a relative and a bounded measure, which can be widely applied in order to make cross comparisons between models. The value of d ranged from zero which indicates null result to one which indicates perfect accuracy. RMSE is one of the best overall measures of model performance, as it summarizes the mean difference in the units of observed and simulated values (Willmott 1982). It was computed as RMSE n 1 = N i= 1 ( Pi Oi) Where N is the number of observed values and Oi and Pi are observed and predicted values for the i-th data pair. To calibrate the model, a systematic approach was followed as suggested by Boote, For this purpose, the experimental data from the first and second planting dates under the nitrogen application rate of 150 kg ha-1 for Faisalabad location was used. The remaining treatments for the Faisalabad site and the experiments for the other two sites were subsequently used for model evaluation. The Faisalabad location was selected for calibration because the field experiment was conducted at the University Farm and was in the close proximity of the research investigators for detailed observations. The coefficients calculated for the cultivar Inqlab-91are presented in Table 2. 5

20 Table 2: Genetic coefficients developed for the Wheat cultivar Inqilab-91 Genetic coefficients description P1V. Days at optimum vernalizing temperature required to complete vernalization. P1D. Percentage reduction in development rate in a photoperiod 10 hour shorter than the threshold relative to that at the threshold. Estimated values P5. Grain filling (excluding lag) period duration ( o C.d) G1. Kernel number per unit canopy weight at anthesis (#g -1 ) 0 0 G2. Standard kernel size under optimum conditions (mg) G3. Standard, non-stressed dry weight (total, including grain) of a single tiller at maturity (g) PHINT. Phylochron interval ( o C.d) 95 Based on the visual observation of graphics, a close association was found between observed and simulated duration of emergence, tillering, stem elongation, spike emergence, anthesis and physiological maturity stages for both S1 and S2 (Figure 4). For the simulation of all these stages, RMSE of 7.24 days in S1 and 5 days in S2 was observed. Values of d were 0.99 in S1 and 1.0 in S2. This supports the degree of association between observed and simulated values with specific reference to anthesis stage which is underestimated by one day in S1 but overestimated by one day in S2. For maturity, there is an under-estimation of one day in S1 but exact simulation (118 days after sowing) in S2. The simulated tops weight showed a close fit with the observed tops weight, with d-stat values of 0.95 for S1 and 0.97 for S2. However, the slope of simulated dry matter accumulation was steep during the early growth period (Figure 4 b), while at maturity the tops weight accumulation was simulated with almost 98 % accuracy for both sowing dates (Table 3). Comparing the simulated and observed Leaf Area Index (LAI), the model has provided comparatively better fit for S1 with RMSE of 0.85 and d = 0.96 while in S2 value of RMSE is 0.95 and of d is The maximum LAI was simulated with exact accuracy in S1 but was underestimated with percent difference of 7.27 in S2, although the day of occurrence of maximum LAI accurately matched the observed day in S2 (Figure 4 c). 6

21 Growth stage (Zodok scale) (a) Tops weight (Kg ha -1 ) (b) Days after sowing Days after sowing 7 6 (c) Leaf area index Days after sowing Figure 4: Observed and simulated growth stages (a), tops weight (b) and leaf area index (c) in S1 ( )and S2( ) at Faisalabad for model calibration. Hollow and solid symbols represent observed and simulated values respectively. Grain yield simulated by the model was within +1.65% of the observed yield for S1 and within +0.17% for S2. Two important yield components i.e. unit grain weight and grain number m -2 were simulated with good accuracy for S2 but for S1 the difference between the observed and simulated unit grain weight was % and for the grain number m -2 was +5.47%. Harvest index was simulated well by the model with a difference of -0.73% for S1 and +2.15% for S2 (Table 3). 7

22 Table 3: Observed and simulated data of growth and development in the model calibration phase at Faisalabad location at two sowing dates and nitrogen level of 150 Kg ha -1. Planting dates S1 S2 Components Observed Simulated Difference* (%) Observed Simulated Difference* (%) Grain yield (kg ha -1 ) Unit grain weight (g) Grain number (m -2 ) Maximum leaf area index Tops weight at maturity (kg ha -1 ) Harvest index (%) *%Difference = ((Simulated - Observed)/Observed)) x Model Evaluation The accuracy of simulating the phenology of a crop is important for accurate simulation of crop growth and yield. In Sheikhupura, the duration of observed growth stages lagged behind the simulated growth stages, especially at stem elongation and spike emergence stages in both S1 and S2. The difference between the observed and simulated values for growth stages was worst in Bahawalpur. The anthesis is an important phenological event in the life cycle of a plant as it signals the possibility of pollen dispersal, fertilization and grain development. The anthesis stage was simulated with better accuracy (+1 day in S1 and +3 days in S2) in Sheikhupura compared to Bahawalpur (+7 days in S1 and +10 days in S2). Maturity date for Sheikhupura was under-predicted by 3 days in S1 and with exact accuracy in S2. In Bahawalpur, the maturity date was simulated with +5 days difference in S1 and with +8 days difference in S2. Disparity of simulated values from the observed values was worst in Bahawalpur. This can be partially attributed to imprecision of observed values. At Bahawalpur, the field was visited twice a week because of limited resources. The experimental sites were far apart and at each site different persons were recording data so human assessment error was also possible. It is a common experience that occurrence of a certain growth stages is not easy to distinguish and the decision is based on subjective assessment. The phenology component also helps simulate the effect of N deficit on rate of life cycle progress (Singh et al. 1999). In this study, there was no effect of different doses of fertilizer on the duration of key growth stages in both the observed and simulated outputs. It was the sowing time that mainly altered the duration of growth stages. For this reason the nitrogen levels in the context of phenology are not discussed in detail. 8

23 The slope of model-simulated dry matter accumulation was too steep during the early part of growth period in all the treatments at Faisalabad and Bahawalpur locations but some underestimated trends were noticed during later stages. Nevertheless, the value of d was satisfactory in all treatments including sowing dates and nitrogen levels (Table 4). This steep slope can partially be attributed to overestimated duration of early crop growth stages (tillering and stem elongation). This longer duration of early stages led to more biomass accumulation. Because of some other research interests, destructive harvest of tops weight was not possible at Sheikhupura location. For this reason, the tops weights of Sheikhupura data were available only at maturity which were over-estimated by the model with a maximum difference of -8.8% from the observed values in all the treatments. At maturity, tops weight at Bahawalpur was simulated with a difference ranging from 0.5 to -5.9% while at Faisalabad, the tops weight was underestimated by a maximum of 11% of observed values. Overall, the tops weight was simulated by the model with RMSE of 546 Kg ha -1 and d-stat value of Table 4: Percentage difference of growth data with their RMSE and d-stat values in the model evaluation phase. Location Sowing date Nitrogen level Yield (Kg ha -1 ) Unit grain weight (g) Grain Number Maximum LAI tops weight (Kg ha -1 ) Harvest Index Difference (%)* Faisalabad Bahawalpur Sheikhupura RMSE d-stat *%Difference = ((Simulated - Observed)/Observed)) x100 9

24 Plant growth is greatly affected by the supply of nitrogen. In this study, the model was sufficiently able to respond to nitrogen treatments according to the observed values (Figure 5). Heng et al. (2000) reported that model simulations were sensitive to N rate. In this study, there was a positive linear response of biomass accumulation to increasing nitrogen. Sowing dates have a significant effect on crop duration and thus on total biomass accumulation. The model was sensitive to the sowing date effect on biomass accumulation. In all the cases of second sowing date, total biomass was lesser than the first sowing date. This can be attributed to shorter life cycle of the crop in the second sowing date S1-Faisalabad S2-Faisalabad Tops weight (Kg ha -1 ) S1-Bahawalpur S2-Bahawalpur Days after sowing Figure 5: Observed and simulated tops weight in S1 and S2 at Faisalabad and Bahawalpur locations with nitrogen fertilizer N1 ( ), N2 ( ) and N3 ( ) in the model evaluation phase. Hollow and solid symbols represent observed and simulated values respectively. Leaf area index (LAI) is an important parameter for indication of grain and biomass yield. It is unlikely to achieve a good fit for biomass or grain yield if simulated incorrectly. In S1 of Faisalabad, the model captured comparatively better the peak (maximum) LAI than S2 which lagged behind the observed value, but S2 accurately captured the time of occurrence of maximum LAI compared to S1. Sheikhupura simulated well the LAI especially the value of maximum LAI as well as time of occurrence of maximum LAI both in S1 and S2. In Bahawalpur the LAI was not well simulated and had an under-estimated trend in the whole crop cycle (Figure 6). Underestimation of maximum LAI was worst in Bahawalpur with percent difference ranging 10

25 from -21.7% to -32.1% from the observed values. Heng et al. (2000) also observed an underestimated trend of LAI simulation over time. Leaf area index was responsive to nitrogen in both the observed and the simulated values. Degree of responsiveness was not further analyzed because of under-estimation in some treatments. 7 6 S1-Faisalabad S2-Faisalabad Leaf area index S1-Sheikhupura S2-Sheikhupura S1-Bahawalpur S2-Bahawalpur Days after sowing Figure 6: Observed and simulated leaf area index values in the two sowing dates (S1 and S2) at Faisalabad, Sheikhpura and Bahawalpur locations, in the model evaluation phase, with nitrogen fertilizer N1 ( ), N2 ( ) and N3 ( ). Hollow and solid symbols represent observed and simulated values respectively. The grain yield is the ultimate and most important component of a crop. The model simulated grain yield with a difference of -9.2% to +9.9% from the observed values (Table 4). The RMSE of 93 Kg ha -1 and d-stat value of 0.88 indicates that the model has simulated the grain yield reliably well. Hundal and Kaur (1997) also found generally close agreement between observed and simulated (CERES Wheat v.2.1) yield of wheat, cv. HD-2329, over eight years ( to ) in the subtropical environment of Ludhiana, India. In their study, the simulated grain yields were within 80 to 115% of the measured yields. In the present study, in most of the cases of first sowing date (S1), especially under lower nitrogen levels, the simulated grain yield was underestimated compared to S2 but there was no consistent pattern of underestimation and 11

26 overestimation. In all treatments, second sowing date had lower yield compared to first sowing date in the observed data which was also well captured by the model. Results are in accordance with those of Midmore et al. 1984; Saunders and Hettel 1994, who reported that delayed sowing caused the wheat crop to flower and fill grain during very high temperature conditions. These conditions led to shortened crop duration which resulted in reduced yields. The model was also sensitive to nitrogen treatments and followed well the trend of increasing yield with increasing amounts of nitrogen amount according to the observed values. Results are in accordance with Otter-Nacke et al. (1986), who tested the model for its sensitivity to N rates and splits and compared results of simulation against observations from a range of experiments. They concluded that model was sensitive to nitrogen rates ranging from 0 to 160 kg/ha. In Bangladesh, the CERES-Wheat simulated grain yield quite satisfactorily for eight treatment combinations of N, water, and sowing dates (Timsina et al., 1998). Heng et al. (2000) also reported good agreement between observed and simulated grain yields at many locations across the world including India, Bangladesh and China. The unit grain weight was simulated by the model with a difference of -11.6% to +5.6% from the observed values (Table 4). This variability was because of observed values. The model had set a single value of unit grain weight (0.038 g) while the observed values varied from to g. Temperature during the post-anthesis period influences productivity (Ritchie and NeSmith 1991). The post-anthesis phase of wheat in South Asia is different from that in temperate countries where it is commonly followed by declining temperature. In sub-temperate (tropical) countries such as South Asia, postanthesis period is usually the time for a sudden increase in temperature (Chauhan et al. 2005). Calderini et al. (1999) reported that the grain weight showed a clear relationship with the average temperature during the grain filling period. The final grain weight was significantly affected by sowing date. The grain weight is an important source of variation of grain yield in wheat. In order to further check the sensitivity of the model for simulation of unit grain weight with a wider range of changing temperature, a sensitivity analysis was run. In this analysis, temperature was raised upto 5 C by a unit rise in average temperature during the grain filling period but the model did not respond to variation in temperature for final unit grain weight, though it varied yield through variation in grain number. Grain yield in wheat has a stronger relationship with grain number than with grain weight (Sayre et al., 1997; Duggan et al., 2000; Brancourt-Hulmel et al., 2003). The unit grain weight, however, plays significant role in yield loss in sub-tropics under the conditions of rising temperature. Timsina and Humphreys (2003) concluded that response to high temperature during grain filling needed further consideration, as this could be a major limiting factor for yield in many rice-wheat locations. Accurate simulation of grain number for accurate prediction of grain yield is also important. In this study, the model simulated grain number with RMSE of 596 grains and d-stat value of Comparatively better simulation of yield compared to yield 12

27 components confirms the results of extensive evaluation by Otter-Nacke et al. (1986) who showed that CERES-Wheat had a slight tendency towards underestimation of grain number and overestimation of unit grain weight. They, however, were of the opinion that this tendency had no consequences for simulation results, provided appropriate compensation among yield components occurred. Dele colle et al. (1995) determined that CERES-Wheat model simulated apparently correct yields through a compensatory effect between poorly simulated yield components. Harvest Index explains the proportion of total biomass that goes into grain yield. The Harvest Index was simulated by the model with RMSE of 1.85 and d-stat value of 0.95.This indicates that the model was able to explain the partitioning of total biomass into grain yield fairly well. In conclusion, the model was calibrated and evaluated using experimental data from different locations, sowing dates and nitrogen levels. Apart from the limited availability of detailed observed datasets and a number of discrepancies in the accuracy of observed data set, the model simulated crop growth and development response reasonably well. The sensitivity of the model towards different nitrogen regimes and planting dates was similar to the observed data set. The model was also able to capture the environmental variability between locations satisfactorily for Faisalabad and Sheikhupura locations. However, the results for Bahawalpur were poor, especially the simulation of LAI over time. The Model was able to simulate fairly well the number of observed parameters upon which the model performance was tested (phenology, biomass accumulation, leaf area index, and grain yield). But the response of model to impact of high temperature on simulation of unit grain weight needs further consideration, as this can be a major limitation to yield in sub-tropical environments. To have a greater confidence, there is need to evaluate model performance with more detailed and accurate datasets especially on water regimes as this was an important missing component in the study. 5. Impact Studies on Wheat Productivity Crop growth simulation models provide means to quantify the effects of climate, soil and management factors on crop growth, productivity and sustainability of agricultural production. The development and application of system approaches and decision support methods can help identify adverse environmental impacts. The CERES models have been extensively used for the assessment of impacts of climate change on agricultural crop production in different regions of the world (Rao and Sinha, 1994; Rosenzweig and Iglesias, 1994; Otavio et al., 1994). The CERES-Wheat v 4.01 crop simulation model, calibrated and validated at GCISC, for its suitability to simulate wheat production was used for analyzing the impact of climate change on wheat productivity in Pakistan, initially under hypothetical scenarios (given in section 5.2) and then under A2 and B2 Scenarios developed by the Climatology Section of GCISC from an ensemble of six GCMs (Table 5 and 6). 13

28 5.1. Past trends of wheat yield Runs from the DSSAT-based CERES-Wheat simulation model on wheat yield for the past 40 years ( ) revealed a slightly declining trend of wheat yield in Faisalabad and Sheikhupura districts and almost an invariable trend in Bahawalpur district over time (Figure 7). At 5 percent level of significance the trend for Faisalabad and Bahawalpur is insignificant with P values of and 0.70 respectively. But the declining trend for Sheikhupura is significant at 5 percent level of significance with P value Values of 5500 Yield (kg/ha) Year Figure 7: Past trends in wheat yields due to climate change in Faisalabad (-- --), Sheikhupura (-- --) and Bahawalpur (-- --) district ( ) the regression coefficient are 0.31, and for Sheikhupura, Faisalabad and Bahawalpur respectively. These results suggest that other management practices remaining the same during all the study years, the aggregate impact of climatic parameters (changes in temperature, solar radiation and rainfall) exerted an overall negative impact in yield. However, considerable variation in wheat yield over the study years was observed, illustrating the sensitivity of prevailing cultivar to changes in these climatic parameters Hypothetical scenarios The rising carbon dioxide and temperature, and changes in rainfall are of direct physiological consequence to plant growth, development and yield. Since environmental control, especially of CO 2, is very difficult and expensive; there have been only a few studies all over the world in estimating the direct impact of climate change on crop plants. 14

29 A widely accepted approach to analyze the possible effects of different climate change parameters on crop yield is to determine the incremental changes (anomalies) in temperature, precipitation, CO 2 etc., and to apply these changes uniformly to a baseline climate e.g., the daily climatic records at a weather station (Rosenzweig and Iglesias, 1994). For CO 2, three scenarios were used: 375 parts per million on volume basis (ppmv) (recent), 550 ppmv (double the pre-industrial CO 2 concentration) and 700 ppmv (almost 2.5 times the pre-industrial CO 2 concentration). For temperature six scenarios (0 to 5ºC in the incremental steps of 1ºC) were used. Since future climate changes are expected to pose serious threats to our available water resources, the efficient use of water is of prime concern. Therefore, water as management factor was also taken into account with two scenarios of water supply i.e. rainfed (with no additional supplies of water) and full irrigation (water supply as per requirements of the crop) were considered. The long-term past ( ) daily weather data on solar radiation, maximum and minimum temperature and rainfall at the representative sites were used as the baseline climatic data. The weather series for simulations under the changed climate were obtained by direct modification of observed series using hypothetical climate change scenarios with assumption of a uniform change in temperature and CO 2 over the study area. The above synthetic scenarios may not be physically plausible but are inexpensive, quick, easy to construct, and easy to use. In addition, synthetic scenarios can capture a wide range of potential climate change and allow researchers to examine the impacts of small to large changes in temperature and other climatic parameters on crop productivity (Smith and Hulme, 1998) Climatic Zones Ten sites (Fig. 8) were selected from the entire Pakistan based on the availability of data and their ability to represent different climatic zones. These sites were categorized into four climatic zones mainly based on Thornthwaite s Precipitation Effectiveness index (Thornthwaite, 1931) by using climatic data of 30 year ( ) PE Index = 1.65 ( P ) K = 1971 k T k 0.9 Where, PE stands for precipitation effectiveness, PK for total amount of precipitation (mm) in the k th year and T k for average daily temperature of k th year. Fig. 8 shows the latitude, longitude, elevation and PE index value of each selected site. The climatic zones identified were arid, semi-arid, sub-humid and humid with their PE index values ranges less than 16, 16-31, 32-63, respectively. The mean monthly variations in the maximum and minimum temperature and rainfall are shown in Fig. 9. Arid zone is the driest zone, with 30-year annual average rainfall of 186 mm, while the 15

30 semi-arid, sub-humid and humid zones have an annual average rainfall of 516, 1062 and 1776 mm respectively. Humid zone has the lowest minimum and maximum temperatures and the highest rainfall compared to other zones. Figure 8: Pakistan map showing the location of 10 sample meteorological stations with their altitude and precipitation effectiveness index value respectively Arid Semi-arid Tempeature ( o C) Sub-humid Humid Rainfall (mm) Jan Feb Mar April May June July Aug Sep Oct Nov Dec Jan Feb Mar April May June July Aug Sep Oct Nov Dec 0 Month Tmax Tmin Total rainfall Figure 9. The 30-year ( ) mean monthly maximum (Tmax) and minimum (Tmin) temperature and rainfall values in the four climatic zones of Pakistan. 16

31 Effect of temperature increase on grain yield of wheat Compared to the respective baseline scenario of each zone, yield declined in all the zones with each unit rise in temperature except in the humid zone (Fig. 10). The magnitude of decline was the highest in sub-humid zone followed by semi-arid and arid zones (Fig. 11). The percentage decline in yield in arid and semi-arid zones is found to be statistically insignificant. But a significant difference exists in sub-humid zone from arid zone. The reason of the higher percentage decline in sub-humid zone compared to arid and semiarid zone could be attributed be their different water regimes, as the sub-humid zone is rainfed while arid and semi-arid zones are simulated under irrigated conditions. To cross test this reasoning, these three zones when run under homogeneous water regimes i.e. either rainfed or full irrigation, the percentage change in yield with increasing temperature became insignificant among these zones. The decrease in yield can largely be attributed to the shortened growth periods as discussed in the section This is because of accelerated phenology under increased air temperatures. Grain filling in wheat is a critical stage for high temperature injury (Johnson and Kanemasu, 1983). If the crop confronts high temperature at reproductive stages, it hampers normal grain development that leads to shriveled grain and subsequently drastic yield losses (Sultana, 2003). Results are also in accordance with the findings of Bender et al. (1999), who reported that a constant 1 o C increase in temperature over the whole growing season of spring wheat would decrease yields by 6-10% due to shorter duration of crop growth. Qureshi and Iglesias (1994) have also reported, based on their modeling work that higher temperatures will drastically reduce yields in Pakistan. This suggests that these zones are vulnerable to increase in temperature, especially given their existing water shortages and the high temperatures that already approach tolerance limits (CGIAR, ; Parry et al., 1988). In the scenario of temperature increase, humid zone is an optimistic zone. In this zone beneficial effects are likely to ensue with higher temperatures. Yield follows a positive trend of gain with rise in temperature up to 4 o C (Fig. 10). On the average, there was 20% gain in yield over base yield by increasing temperature up to 5 o C (Fig. 11). Though the length of crop life cycle also gets reduced in this zone but this shortening is beneficial. Hussain and Mudasser (2007) have recently reported based on econometric analysis that shortening of the length of crop life cycle could be beneficial for the mountain areas above 1500 m altitude because warmer temperatures will make it more likely that the crop will mature earlier and hence yield increases could be expected. In the humid zone, low temperature (cold stress) is a constraint for wheat crop. So, rise in temperature is in favor of this zone in terms of yield gain. A similar trend was simulated by Sultana et al., 2005, who studied wheat crop grown in sub-humid and humid zone and reported opposite yield trends in these zones; a decrease in yield in the sub-humid zone while increase in humid zone as a result of increase in atmospheric temperature. In spite of this positive yield trend, the humid zone has low average base yields compared to those of arid, semiarid and sub-humid zones. At temperature increase of 1 o C, it approximately approached 17

32 the yield level of sub-humid zone but lower than the yields of arid and semi-arid zone at this scenario of temperature increase. The comparatively lower yield in the humid zone may be attributed also to non availability of cold tolerant winter or facultative cultivars in this zone (Hashmi and Shafiullah, 2003). The expected future increases in temperature caused by global warming would, however, render the varieties of arid and semi-arid zones unsuitable there and these could be introduced in the humid zone. In USA, Rosenzweig (1985) found that the major effect of climate change would be regional shifts in the use of wheat cultivars. 5 Yield (ton ha -1 ) Temperature change ( o C) Arid Semi-arid Sub-humid Humid Figure 10: Trends in simulated wheat yields with temperature changes, at 375 ppm CO 2 concentration, in arid, semi-arid, sub-humid and humid zones. 18

33 Percent change Temperature change ( o C) Arid Semi-arid Sub-humid Humid Figure 11: Percentage change in yield from the base yield in response to temperature changes at 375 ppm CO 2 concentration in arid, semi-arid, sub-humid and humid zones Interactive effects of increase in temperature and CO 2 alongwith change in water supply on grain yield of wheat Under the baseline water regimes, the impact of increase in CO 2 concentration on wheat yield was simulated in all the climatic zones. The increase in CO 2 concentration, from the baseline level of 375 to 550 ppm, and to 700 ppm, exerted positive effect on wheat yield. This positive impact partially compensated the negative impact arising due to increased temperatures up to 2-3 o C by 550 ppm and up to 3-4 o C by 700ppm in arid, semi-arid and sub-humid zones (Figure 12 a and b). In these zones, increase in temperature beyond 2-4 o C could not sustain baseline yields and nullified the beneficial effects of enhanced carbon dioxide concentration. Kalra et al., 2003 also reported that increasing temperature nullifies the beneficial effects of enhanced carbon. In humid zone, the pattern of yield gain with rise in CO 2 concentration (550 and 700 ppm) was similar to the pattern of yield at 375 ppm CO 2. Changing CO 2 concentration level from 375 to 550 ppm and 700 ppm has not changed the trend but only affected the magnitude of yield positively at varying temperature regimes. It implies that at the CO 2 concentration level of 375, 550 or 700 ppm, a 4 o C increase in temperature in humid zone is the optimal one where maximum yield could be obtained. In the arid, semi-arid and 19

34 sub-humid zones, the temperature optima shift slightly upward with increasing CO 2 concentration from 550 to 700 ppm. Within a water regime, increase in CO 2 concentration from 375 to 550 and 700 ppm can exert positive effect on wheat yield but this positive effect is significantly variable in different climatic zones under rainfed conditions. The highest response in terms of percentage gain in yield by increasing CO 2 level from 375 to 550 ppm and 700 ppm respectively was shown by arid zone (77% and 147%) followed by semi-arid (66%, 125%), sub-humid (22%, 35%) and humid zones (11%, 18%). This suggests that increased CO 2 can increase water use efficiency. Chaudhuri et al., (1990) and Kimball et al., (1995) has also reported that crop response to elevated CO 2 is relatively greater when water is a limiting factor, compared to well-watered conditions. Under full irrigation conditions the relative response of different zones is almost similar. But if the current baseline water regimes (i.e. full irrigation in arid and semi-arid zones and rainfed in sub-humid and humid zones) persist in future, the percent gain in yield by increasing CO 2 level from 375 to 550 ppm and 700 ppm were found to be similar (insignificant differences) in arid, semi-arid and humid zones. But the sub-humid zone will be most benefited (significantly higher gain in yield compared to other zones). Reason of this can be that sub-humid zone is a rainfed zone while arid and semi-arid zones are run under full irrigation. This relatively limited water supply in sub-humid zone gets benefited from CO 2 enrichment. Because of this reason when all the four areas were run under full irrigation; the differences among zones in terms of percentage gain in yield by increase in CO 2 concentration (550 ppm and 700 ppm) became insignificant. The humid zone is also a rainfed zone but less limited in terms of water supply (high rainfall see Fig. 9) compared to sub-humid zone. This is why it didn t behave like sub-humid zone. Lawlor and Mitchell, (2000) also reported similar response in FACE (Free Air CO 2 Enrichment) studies, that is, a greater effect of CO 2 enrichment on grain yield under drought conditions relative to well watered conditions. In all zones, at any specified CO 2 concentration, changes in the water supply from rainfed to full irrigation resulted in yield gain, an indication that water supply is normally a limitation to crop production in these zones. Within CO 2 levels, the changes in water supply from rainfed to full irrigation shows an intense degree of responsiveness in terms of yield gain at 375 ppm CO 2 level compared to 550 ppm and 700 ppm. There was on average 324 % and 195 % gain in yield in arid and semi-arid zones respectively by changing water supply from rainfed to full irrigation in the scenario of 375 ppm CO 2 compared to 208% and 122% gain in yield in the same zones at 550 ppm CO 2 and 150% and 87% gain in yield at 700 ppm CO 2. In sub-humid zone and humid zone this response was 31% and 18% at 375 ppm CO 2 concentration; 16% and 15% at the scenario of 550 ppm CO 2 and 15% and 13% at 700 ppm CO 2 respectively. 20

35 (a) (b) Yield (ton ha -1 ) Temperature change ( o C) Arid Semi-arid Sub-humid Humid Figure 12: Trends in simulated wheat yields with changes in temperature at 550 ppm (a) and 700 ppm (b) CO 2 concentration in arid, semi-arid, sub-humid and humid zones. (Dotted line with same symbol represent constant base yield of respective zone) Length of crop life cycle under climate change and water management scenarios Temperature determines the rate at which a plant progresses through various phenological stages towards maturity. Rise in temperature, in the present study (Figure 13), reduced the length of crop life cycle in all the four areas, though at an accelerated rate in humid zone. On the average, there were 4, 5, 6 and 9 days reduction in the length of crop life cycle per degree centigrade rise in temperature in arid, semi arid, sub-humid and humid zones respectively. Reduction in crop life cycle from the base scenario was in the order of 12, 14, 18 and 27 days in arid, semi arid, sub-humid and humid zones, respectively. Hussain and Mudasser (2007) reported that increased temperatures correspond to increase in Growing Degree Days (GDDs) and decrease in crop life cycle. O'Brien (2000) reported the highest change in GDDs in humid regions and the lowest in arid regions. In humid zone, the accelerated shrinkage of crop life cycle was however associated with gain in yield. Apart from gain in yield, there is possibility of shift in cropping pattern due to reduction in crop life cycle because at prevalent temperatures wheat crop takes more than optimal time to complete its growth and development. As the temperature rises, the crop completes its life cycle in lesser time. This might offer the possibility of growing more than one successive crop in the region. The areas which are currently under mono-cropping (one crop a year) because of short growing season can become transitional cropping zone and those currently in transitional cropping zone can become double cropping zone. Rosenzweig and Liverman (1992) have also reported similar findings in response to temperature increase for temperate regions. 21

36 In arid, semi-arid and sub-humid zones, the loss of yield with rise in temperature is linked with the shrinkage of the length of crop life cycle. 5 Temperature increase ( o C) Reduction in lenght of wheat life cycle (days) from base scenario Arid Semi-arid Sub-humid Humid Figure 13: Reduction in total length of crop life cycle (days) from base temperature in the four climatic zones in Pakistan. Overall, higher temperatures drastically reduced yields in arid, semi-arid and sub-humid zones. But in humid zone, beneficial effects are likely to ensue with higher temperatures up to 4 o C. This positive yield trend in humid zone can be a significant research avenue to exploit this opportunity for improving livelihood of the dependent communities. Increase in concentration of CO 2 from the baseline level of 375 ppm to 550 ppm and 700 ppm will have substantial positive effect on wheat yield in all climatic zones. This positive impact will compensate the negative impact arising due to increased temperatures up to 3 o C in arid, semi-arid and sub-humid zones. But in humid zone at the given CO 2 concentration level of 375 or 550 ppm and 700 ppm, a 4 o C increase in temperature is the optimal one where maximum yield could be obtained. In all zones, changes in water supply from rainfed to full irrigation are expected to respond positively to yield. This positive yield response might be more intense in arid zone followed by semi-arid zone and sub-humid zones. Crop response to elevated CO 2 will be relatively greater when water is taken as a limiting factor, compared to wellwatered conditions. This suggests that increased CO 2 can increase water use efficiency. 22

37 To conclude, the existing zones for wheat production are likely to shift from arid to humid zone as warming and shortening of growth cycle are in favor of humid zone. Higher temperatures will increase the heat damage in arid, semi-arid and sub-humid zones which may render some of the arid areas unsuitable for wheat production in the long run; hence the varieties presently under cultivation in the arid zone will become unsuitable there. These varieties could then be used in the humid zones Effect of changes in rain and irrigation water availability on grain yield of wheat Keeping in view the future threats to water resources in the context of climate change, additional scenarios of water availability were included in the study. These scenarios were in the form of increase and decrease in the number of irrigations i.e. 2 irrigations and 4 irrigations and changes in the rainfall i.e. + 30% and -30% taking Faisalabad as representative irrigated area. iii) Impact of Rainfall on Wheat Yield Figure 14 reveals that increase or decrease in rainfall has positive and negative impact on wheat yield respectively but the magnitude of change is likely to be small. This is mainly because the experiment was simulated under irrigated conditions and the optimal amount of irrigation was applied. Wheat Yield (kg/ha) Baseline Change in Rainfall (%) Figure 14: Impact of assumed changes in rainfall amounts on wheat yield 23

38 iv) Impact of changes in irrigation water on Wheat Yield Increasing temperature would not only increase the water demand for agricultural crops due to increased evapotranspiration but also adversely affect the water availability for crops (Ullah et. al. 2001). The impact of changes in the availability of irrigation water was simulated on wheat yield. Results of analysis suggest that reduction in the number of irrigations from three (recommended) to two (under the scenario of 33% reduction in water supply) will reduce yield by about 34% over the baseline (Figure 15). On the other hand, only 20% increase in yield would occur if the number of irrigations is increased from 3 to 4 (under the scenario of 33% increase in water supply). Baseline Wheat Yield (kg/ha) Number of Irrigations Figure 15: Impact of changes in irrigation water availability on wheat yield at baseline temperature and CO 2 concentration, assuming that only water availability changes; the amount of water per irrigation does not change. Simulation was also done to assess the combined impact of increase in temperature and reduction in number of irrigations on wheat yield at increased CO 2 concentration level of 550 ppm. The results (Figure 16) revealed that a significant decline in yield would be expected if reduction in the number of irrigation is accompanied with an increase in temperature. The baseline yields would not be sustained (Figure 16-a) with two irrigations even at higher (550 ppm) CO 2 levels. On the other hand, the yield could be sustained up to 3 o C, if water availability was increased by 33%, i.e. four irrigations (Figure 16-b). 24

39 5000 If both Water Availability and Temperature Change (CO2 Level = 550 ppm) Wheat Yield (kg/ha) (a) Change in Temperature ( C) 4 Irrigations 2 Irrigations Baseline Wheat Yield (kg/ha) (b) Change in Temperature ( C) 4 Irrigations 2 Irrigations Baseline Figure 16: Combined impact of changes in CO 2 concentration (baseline and 550 ppm), temperature, and irrigation water availability on wheat yield. To conclude past climate change had an overall negative impact on wheat yield in Faisalabad. The sensitivity of wheat to climate change will be more pronounced under future climate change scenarios. Results indicate that even a 1 C rise in temperature will have a negative impact on wheat yield. On the other hand, increased CO 2 concentration in the atmosphere (from baseline to 550 ppm) will on wheat yieldvcaused by rising temperatures at least up to 2 o C. Thus the combined impact of CO 2 and temperature is expected to be positive only up to 2 C beyond which the overall impact will be negative. Water is the most important factor affecting wheat yield. While, decrease in rainfall shows some negative impact on wheat yield, the expected decrease in overall water availability due to climate change will have even more negative impact on wheat yield. On the other hand, if water availability increases, wheat crop would tolerate the adverse impacts of increasing temperature on yield up to 3 C A2 and B2 Scenarios The researchers in Climatology Section of GCISC have been working on the development of future climate scenarios for Pakistan in the light of past 50-year trends and based on the output of an ensemble of six Global Climate Change (GCM) Models. They are developing IPCC-SRES based A2 and B2 scenarios for likely changes in temperature and rainfall for different agro-climatic zones of Pakistan. The A2 scenario family describes a very heterogeneous world. The underlying theme is self reliance and preservation of local identities. Economic development is primarily regionally oriented and per capita economic growth and technological change is more fragmented and slower 25

40 than other storylines. The B2 scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. It is a world with continuously increasing global population, at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change. Two types of agro-climatic zone groupings have been developed: i) Initially, Pakistan was divided into Northern (FHN1) and Southern (FHS1) parts on the basis of two rectangular grid boxes encompassing some parts of neighboring countries, as depicted in Figure 17. (a) (b) Figure 17: Grids covering geographical areas of a) Northern (FHN) and b) Southern (FHS) parts of Pakistan. The scenarios developed for these areas are given in Table 5. For time horizons of 2020s, 2050s and 2080s, the projected increase in temperature is 1.00, 2.69 and 4.98 C in Northern and 1.03, 2.48, and 4.34 C in Southern parts of Pakistan under A2 scenario and 1.14, 2.50, and 3.75 C in Northern and 1.24, 2.37, and 3.47 C in Southern Pakistan under B2 scenarios, respectively. The increases in precipitation were from 1.03 to 1.95 % in Northern and to % in Southern Pakistan under A2 scenario. The corresponding changes in precipitation under B2 scenario were to 3.61 % in Northern and to % in Southern Pakistan form 2025 to In FHN and FHS, F stands for Pakistan, H for higher grid numbers, N for Northern Pakistan and S for Southern Pakistan 26

41 Table 5: A2 and B2 scenarios of temperature and precipitation for Northern and Southern Pakistan based on rectangular grids. Global CO Time 2 Northern Pakistan Southern Pakistan Concentration Horizon (ppm) T ( C ) ( P)% T ( C ) ( P)% Scenario A ± ± ± ± ± ± ± ± ± ± ± ± Scenario B ± ± ± ± ± ± ± ± ± ± ± ± 7.53 ii) Later on, scenarios of temperature and precipitation were developed for four agroclimatic zones of the country, viz. Humid areas (designated as Northern mountainous region), Sub-Humid areas (designated as Northern Sub-mountainous region), Semi-Arid areas (designated as Southern semi-arid plains) and Arid areas (designated as Southern arid plains) Fig 18. Figure 18: Agro-climatic zones of Pakistan based on aridity classes 27

42 Transposing these areas onto the map of Pakistan revealed that Humid and Sub-Humid zones can be merged together as they comprise a narrow vertical area band. Further, the Humid/ Sub-humid area (FHA) together with Semi-arid plains (FHB) can be grouped under Northern Pakistan (FHAB) because FHA and FHB were not significantly different due to coarse resolution of GCMs. Similarly the Arid (FHC) and Hyper-arid (FHD) can be grouped into Southern Pakistan (FHCD). In this way, Pakistan has been divided into two parts; FHAB (Humid/Sub-humid +Semi-arid) and FHCD ( Arid+Hyperarid) as shown in Figure 19. (a) (b) Figure 19: Grids covering Agro-climatic zones of Pakistan: a) FHAB (Humid/Subhumid+ Semi-arid) corresponding to Northern Pakistan and b) FHCD (Arid +Hyper-arid) corresponding to Southern Pakistan. The A2 and B2 scenarios for these zones are given in Table 6. Table 6: Scenarios of temperature and precipitation change in Northern and Southern parts of Pakistan as worked out by GCISC Climatology Section from an ensemble of six GCM outputs. Time Horizon Global CO 2 Concentration (ppm) Northern Pakistan Southern Pakistan T ( C ) P (%) T ( C ) P (%) Scenario A2 2020s ± ± ± ± s ± ± ± ± s ± ± ± ± Scenario B2 2020s ± ± ± ± s ± ± ± ± s ± ± ± ±