Impact of Climate Change on the Rice Wheat Cropping System of Pakistan

Transcription

1 Chapter 7 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan Ashfaq Ahmad 1, Muhammad Ashfaq 2, Ghulam Rasul 3, Syed Aftab Wajid 1, Tasneem Khaliq 1, Fahd Rasul 1, Umer Saeed 1, Muhammad Habib ur Rahman 1, Jamshad Hussain 1, Irfan Ahmad Baig 4, Syed Asif Ali Naqvi 2, Syed Ahsan Ali Bokhari 3, Shakeel Ahmad 5, Wajid Naseem 6, Gerrit Hoogenboom 7, and Roberto O. Valdivia 8 1 University of Agriculture, Faisalabad, Pakistan 2 University of Agriculture, Faisalabad, Pakistan 3 Pakistan Meteorological Department, Islamabad, Pakistan 4 PMAS-Arid Agriculture University Rawalpindi, Pakistan 5 Bahauddin Zakariya University, Multan, Pakistan 6 COMSATS Institute of Information Technology, Vehari, Pakistan 7 Washington State University, Pullman, WA, USA 8 Oregon State University, Corvallis, OR, USA Introduction In terms of area, Pakistan is the second largest country in SouthAsia and 36th largest in the world. The total geographical area of Pakistan is 79.6 million ha, of which 22 million ha are used for crop production. Most of this cultivated land is irrigated, which encompasses about 19 million ha. The majority of farmers have small land holdings; about 86% of the farms have less than 5 ha and only 5% of the farms are greater than 10 ha (Government of Pakistan, 2010). Punjab is the largest province of Pakistan both with respect to population and agricultural production. Historically the name Punjab is derived from two words of a local language, namely Punj meaning five and Ab meaning water. Punjab encompasses five rivers, and, as a result, it has a well-developed canal system for irrigation. There are two cropping seasons in Pakistan, Rabi and Kharif. Rabi crops are grown normally from November to April and Kharif crops are grown from May to October. These two seasons determine the agricultural economy of Pakistan. During the Rabi season wheat is dominant; during the growing season , it was 219

2 220 A. Ahmad et al. Table 1. Statistics of rice and wheat in Pakistan ( ). Rice Wheat Area Production Area Production 000 % 000 % % 000 % Year ha Change tonne Change 000 ha Change tonne Change (Government of Pakistan, 2013) cultivated on 9.04 million ha in with total production of 25.3 million tonnes. During the Kharif season rice is also an important crop; it was cultivated on 2.79 million ha in with a production of 6.8 million tonnes. Sustainable production of wheat and rice is crucial for food security in Pakistan as wheat is the staple food of the people and it accounts for almost 10.3% of the total value added in agriculture. Rice is the second major source of food after wheat and accounts for 3.1% of the total value added in agriculture. Historic data (Table 1) show that the area under cultivation and production of rice and wheat was inconsistent. This inconsistency was because of water availability, shift in the monsoon patterns, and government policies for support prices of crops and subsidies for inputs (Government of Pakistan, 2014). Climate of Pakistan Pakistan is located on a large landmass north of the tropic of cancer between the latitude 23 to 38 N and longitude of 60 to 80 E. The climate is defined as continental characterized by extreme variation in temperature, both seasonally and daily. Pakistan is also noted for its cold winters and hot summers. Two thirds of its arable land ranges from semi-arid to arid climatic conditions, while a small zone of northeast Punjab adjoining Kashmir experiences subhumid to humid conditions. The study region lies in the semi-arid to subhumid agroclimatic zone, based on moisture index (MI) (Chaudhry and Rasul, 2004), where the maximum summer monsoon occurs during July and August. The monsoon season from July to September is generally associated with the monsoon depressions that are formed over the Bay of Bengal. They reach Pakistan via India due to their westward motion, depending upon their strength. Another cause of summer precipitation is the southwesterly

3 January 19, :41 Handbook of Climate Change and Agroecosystems 9.75in x 6.5in b2010-v2-ch07 page 221 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan (a) Annual Rainfall (mm) Normal ( ) Fig (b) Annual Mean Temperature ( C) Normal ( ) Climate of Pakistan (a) annual rainfall (mm) (b) annual mean temperature ( C). flow of moisture from the Arabian Sea which is activated with the persistence of a mesoscale low pressure over land. Both phenomena of monsoon and moisture from the Arabian Sea reinforce the precipitation process and produce high-intensity rainfall. The rice crop mainly depends upon rainfall during monsoon season (July to September). Winter precipitation is produced by western disturbances which are troughs of westerly waves passing across the mid-latitudes. Due to such weather systems, the northern half of Pakistan receives a reasonable amount of precipitation in the form of rainfall as well as snowfall over the north and west mountainous regions (Chaudhary and Rasul, 2004). Chaudhary and Rasul (2004) characterized Pakistan s arable area into different agroclimatic zones on both an annual and seasonal basis using a MI. Water from snow and glacier melt is important during May and June when rice is sown and coincides with the extremely hot and dry pre-monsoon season. Winter rains are very important for wheat in the rainfed areas, especially at crown root initiation and reproductive stages. The area used in this study was mostly irrigated during Rabi season (Ghazala et al., 2009). In the rice wheat cropping system, wheat is generally sown in December after the rice harvest and moisture deficit has never been a problem. However, the heavy rain just after sowing hampers the emergence of seedlings out of the crusted soil. As the wheat crop is mainly irrigated, air temperature, relative humidity, and solar radiation play a more important role than rainfall for crop growth and development as well as the incidence of pests and diseases during the growing season (Rasul et al., 2012).

4 222 A. Ahmad et al. Fig. 2. Cropping Systems in Pakistan Location and elevation of integrated assessment study region. Wheat, rice, sugarcane, maize, and cotton are the most important agricultural crops grown in Pakistan. As a result, Pakistan has three main cropping systems, namely rice wheat, mixed wheat, and cotton wheat on the basis of Kharif crops. These cropping systems are mainly found in the subhumid area in the northern part of the country, the semi-arid area in the central part of the country, and the arid area in southern part of the country, with different systems for the different regions of Punjab (Fig. 1). Small to medium land holdings and a very low percentage of tenancy is the character of the rice wheat zone. The number of very small farmers (1.5 ha) and very large farmers (>20 ha) are relatively small, although several very large farmers operate over 100 ha, especially in Gujranwala District. The rice wheat cropping system covers 1.1 million ha, centered in the districts of Sialkot, Gujranwala, Sheikhupura, Nankansahib, and Hafizabad (Fig. 2). Issues and Challenges A change in local weather conditions due to climate change threatens the productivity of the agricultural sector, which makes it vulnerable both biophysically and

5 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 223 economically. Some regions are likely to benefit from climate change while other regions could be adversely affected. An increase in temperature, changes in rainfall trends and extreme weather events are raising concerns about the prospective impact on water, food, and energy. There are five main risks related to climate change in Pakistan: A rise in sea level, glacial retreats, more frequent and heavier floods, higher temperatures, and an increase in the frequency of droughts. Such risks raise major challenges for current and future decision-making and have a resounding impact on agriculture, water resources, urban rural management, and the overall economy. Since 2001, Pakistan has repeatedly borne witness to history s worst disasters, and indeed Pakistan ranked 12th in the 2014 Global Climate Risk Index and was among the three worst affected countries worldwide over three consecutive years in terms of climate-related calamities (Kreft and Eckstein, 2013). Pakistan suffered from severe flooding in both 2010 and 2011 and was struck again by a rough monsoon season in 2012, which killed over 650 people. Floods in 2010 were the worst in its history, causing nearly 3000 deaths and affecting 20 million people (WMO-No.1119). The 2012 monsoon seasonal rainfall averaged over Pakistan was 257%, Sindh 1100%, Punjab 405%, and Baluchistan 338%, compared to the long-term average total due to unusually intense rainfall during September. The 2012 floods affected around three million people in Pakistan, damaged thousands of hectares of agricultural crops, and claimed approximately 450 lives (Blunden and Arndt, 2012). Agriculture is always vulnerable to climate change, and this is especially so in Pakistan because of its arid and semi-arid ecosystems (Janjua et al., 2010) (an increase in temperature would affect arid and semi-arid areas more than humid regions). Although changes in precipitation are variable, there are indications of an overall increase in precipitation over South Asia during the coming decades. Heavy rainfall events are expected to increase during the rainy season, increasing the chances of floods, while the dry seasons are expected to get drier. According to the IPCC (2002), precipitation increased an average 40% in the southeast, 20% in the north and 10% in central part of Pakistan during the 20th century (Gitay et al., 2002). Developing economies are often more climate-sensitive because they rely on labor-intensive technologies, whereas developed economies can cope with climate extremes as technology is more readily available and adoption rates are higher (Kurukulasuriya et al., 2006; Mendelsohn et al., 2001; Seo and Mendelsohn, 2008). Productivity in Pakistan is affected by a number of climatic variables including rainfall patterns, rising temperature, and elevated CO 2. In , the agricultural sector contributed 53% to Pakistan s GDP, which dropped to 31% during , and to 21.4% during The floods of 2010 affected 20% of the land area and the overall production loss of sugar cane, paddy, and cotton was

6 224 A. Ahmad et al. estimated at 13.3 million tones. Two million ha of standing crops were either lost or damaged. Between 60% and 88% of the farming households reported losses of more than 50% of their major crops, including rice, vegetables, cotton, sugar, and fodder (GOP, 2011). Agricultural growth suffered a serious setback during as a result of drought. The major crops registered decreased growth of almost 10%, while overall decrease in growth was 2.6%. The extent of hazards due to climate change in Pakistan is large and could be more challenging in the future. It is, therefore, important to quantify the impact of climate change on all vulnerable systems and particularly on agriculture; keeping in view the magnitude of the problem. The team engaged with the Agricultural Model Intercomparison and Improvement Project (AgMIP) has started to quantify climate change effects on the rice wheat cropping system at the farm level through the use of climate, crop, and economic models. The overall goal is to develop adaptation strategies to counter the potential negative effects of climate change, to improve the livelihood of small-holder farmers in the project area, to disseminate this information to farmers, policymakers, and extension workers, and to strengthen partnerships among the relevant stakeholders. Rice Wheat Cropping System The rice wheat cropping system is centered in the districts of Sialkot, Gujranwala, Sheikhupura, Nankansahib, and Hafizabad and occupies 1.1 million ha of farming land. Most of the fields are sown in an annual rice wheat cropping pattern and about 72% of wheat is sown after rice. The rice wheat system has special problems and conflicts in crop management for the two crops. Rice requires puddled compacted soils to hold standing water during the growing season. Hence for rice cultivation, the puddling operation to form a hard pan is important for water retention. It decreases soil porosity and markedly alters the porosity distribution by mixing soil separates to enhance water retention of soil. Wheat grows best in well-drained soils that allow deep penetration of the root system. However, unless this hard pan is broken, wheat could suffer from problems of water-logging. Another issue in rice wheat management is caused by the dominance of late-maturing rice varieties that allows little time for land preparation for wheat that ultimately delays the wheat cultivation. Issues of water scarcity and management figure highly in this system. Many farmers list water stress as an issue. Labor shortages during the major cultural operations, especially at planting, harvesting, and threshing, are also increasing. Nutrient imbalance and mining by rice and wheat have led to problems in some areas. Nitrogen and phosphorus are two of the most important macro-nutrients that have become limiting in the rice wheat system. Zinc, boron, and manganese are micro-nutrient problems in some areas. Declines in soil organic matter could be due to problems with the rice wheat system.

7 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 225 The sowing time for wheat in this region is from 20 November to the end of December. The common seeding rate is kg ha 1 and the seed bed is prepared by two to three cultivations followed by two plankings. Some farmers prepare the seed bed by using residual moisture after harvesting rice crop to save time and avoid delay in wheat sowing. The sowing method that is normally practiced by farmers is a broadcast; the fertilizer level that is commonly used in the area is 135:61:0 kg ha 1 N:P:K. Farmers usually apply four to five irrigations at critical growth stages (crown root initiation, stem elongation, booting and heading, and grain development) to wheat depending on weather conditions. It is generally harvested by mid-april (Table 2). However, progressive farmers use a drill for sowing and a combine harvester for harvesting wheat. Average yield levels range from kg ha 1. Common wheat varieties in the region include Sahar-2006, Faisalabad- 2008, and Lasani The rice nursery is generally sown from 20 May to 20 June and 30- to 40- day-old seedlings are used for transplanting. Approximately 5 7 kg of seed is used in the nursery for transplanting on an area of one ha. Land is prepared with two cultivations after the harvesting of wheat two months prior to transplanting. At the time of transplanting, four to five cultivations are done followed by planking in standing water to puddle the soil and creation of the hardpan to keep water standing in the crop and create anaerobic conditions. Generally no fertilizer is applied at the time of land preparation, but is applied after one week of transplanting. The average fertilizer levels that are used are 120:100:0 kg ha 1 N:P:K. The number of irrigations ranges from Rice is harvested in October to November (Table 2), with an average yield of 4000 kg ha 1. The common varieties that are sown in the region include Super Basmati, Basmati-385, and Basmati-2,000. Methodology Socio-economic data Survey data for rice, wheat, and livestock for the rice wheat cropping zone of Punjab were collected for this study. An extensive farm survey for 155 farms from the selected five districts was conducted. Because the population was heterogeneous in nature, a stratified random sampling technique was used. The districts of this study included Sheikhupura, Nankana Sahib, Hafizabad, Gujranwala, and Sialkot. Two villages were taken randomly from each district. Each district was taken as a separate stratum, because of its own climatology and topography. From each stratum at least 30 respondents and at least 15 farms from each village were chosen randomly so that the selected sample could be a true representation of the farming population. Missing values, like the management practices (sowing and harvesting

8 Table 2. Crop management practices data of rice and wheat used for crop modeling analysis for the rice wheat cropping system of Punjab. Rice T Irri. Irri. T Irri. Irri. Serial No. Stratum PD HD N P (no) (No) (mm) PD HD N P (no) (No) (mm) 1 Sheikhupura 20th October ,586 20th March Nankana Sahib May to ,721 November to Hafizabad to End November ,636 to April Gujranwala of June ,617 end Sialkot ,584 December PD: Planting date, HD: Harvesting date, N: Nitrogen (kg ha 1 ), P: Phosphorus (kg ha 1 ), T: Number of tillage applications, Irri.: Irrigations. Wheat 226 A. Ahmad et al.

9 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 227 Fodder Farm Yard Manure Rice Wheat Crops N IDE PESTIC Inputs Fig. 3. Grains Grains Household Milk & Meat Income Rice wheat farming system of Pakistan. Livestock Milk & Meat Market dates, non-farm income, and other crops, etc.) were supplemented from the same farmers later on through personal contact. The economic analysis was conducted on a per farm basis. The farming system diagram for the region surveyed is shown in Fig. 3. Climate The past climate of the study region was analyzed by using the available weather station data. The climate change projections for the region were generated by using output of five general circulation models (GCMs) of the latest CMIP5 (Coupled Model Intercomparison Project) family. Based on meteorological data of five surrounding locations in the area of study, the gradients of precipitation and temperature were developed as the terrain and ecological features were not much different (Fig. 2). The delta method was applied for projecting the future climate under the relatively high-emission climate change scenario, i.e., RCP (representative concentration pathway) 8.5. Observed trends in temperature and precipitation The annual rainfall ( ) in the study region varied from 400 to 1000 mm with most of the rain in the Sialkot district falling during summer monsoon (Fig. 1). The observed meteorological data for Sialkot and Sheikhupura districts were analyzed

10 228 A. Ahmad et al. for annual daily mean temperature ( C) and annual total precipitation (mm) as well as decadal averages for the period from For Sialkot, an increase in temperature of 1.91 C was recorded from and after that a decrease was observed until 2000 with a total decline of 2.13 C. The most recent decade was the warmest among the past seven decades. For the Sheikhupura district during the first four decades starting from 1931, the mean temperature varied between 24 C and 24.5 C. After 1970, there was a rising trend until 2010 with an increase of 1 C as compared to mean temperature during 1960s and 1970s. For rainfall there was a large interannual variability in both districts, with Sialkot having the highest decadal average of 1108 mm during Sheikhupura received the highest decadal average rainfall of 719 mm during (Fig. 4a). The Fig. 4. Observed trends in annual mean temperature ( C) and annual precipitation (mm) and decadal average ( ) for rice-wheat districts Sialkot (a & c) and Sheikhupura (b & d).

11 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 229 maximum annual rainfall in Sialkot was observed in 1973 with a total of 1886 mm, while Sheikhupura received its maximum rainfall in 1955 that amounted to 1317 mm. The minimum annual total for Sialkot was 368 mm in 2011, while Sheikhupura received a minimum rainfall of 277 mm in Baseline climate data Sialkot and Sheikhupura are the only two districts where meteorological observations were available for the complete baseline period ( ). The quality of the available weather station data was checked and the datasets were converted to.agmip files as described in the AgMIP protocols (Rosenzweig et al., 2013a). For preparation of baseline data for the rest of the three districts, background daily weather time series ( ) were obtained from the global AgMERRA (Modern Era-Retrospective Analysis for Research and Applications) dataset (see Part 1, Chapter 3 in this volume). The AgMERRA time-period covers the modern era of remotely sensed data, from 1979 until present, and the special focus of the atmospheric assimilation is the hydrological cycle (Ruane et al., 2014). Baseline data for the corresponding three districts (Hafizabad, Nankana Sahib, and Gujranwala) were estimated in a manner similar to the gap-filling bias adjustment of the AgMIP protocols (Rosenzweig et al., 2013a). The differences in monthly climatology were calculated between the AgMERRA dataset for Hafizabad and Nankana Sahib, where distance was greater than 50 km from the weather station (Ruane et al., 2014) and the WorldClim dataset (Hijmans et al., 2005) for the Gujranwala, where the distance was less than 50 km from the weather station. For the temperature, the monthly bias was subtracted from the corresponding month s daily AgMERRA data, while for rainfall the monthly bias was multiplied by the corresponding month of the AgMERRA data. This resulted in a continuous, complete, physically consistent daily climate series for all five districts from Climate projections Delta scenarios (Wilby et al., 2004) are based on historical baseline daily weather data; with each day s weather variables perturbed by using the changes in climate model outputs for future mid-century RCP8.5 (Moss et al., 2010) versus those same model outputs for the historical time-period. Five CMIP5 GCMs, CCSM4, GFDL- ESM2M, HadGEM2-ES, MIROC5, and MPI-ESM-MR (Taylor et al., 2012), were used for the generation of climate projections (Part 1, Chapter 3 in this volume). The selection criterion of these GCMs was based on several facts such as the track record of publications, better performance in monsoon regions and reputation of the model developing institute (Rosenzweig et al., 2014).

12 230 A. Ahmad et al. Fig. 5. Climatology (Tmax, Tmin, and precipitation) for the baseline period ( ) and projections for mid-century RCP8.5 scenario for (a) Hafizabad, (b) Sialkot, (c) Sheikhupura, (d) Nankana Sahib, and (e) Gujranawala. The baseline ( ) was taken as reference for comparison to assess the change in the projected climatic parameters. The future mean, maximum, and minimum temperatures and precipitation the RCP8.5 mid-century projections for all stations are presented in Fig. 5 along with the baseline. All five selected models performed consistently to project the thermal regime for the future in terms of daily maximum and minimum mean temperatures. Figure 5 shows an increase of about 2 C in temperature during the mid-century ( ) period under the RCP8.5 scenario. However, heterogeneous model output was seen in the case of precipitation projections for the selected study area. There is a remarkable difference among the output of the GCMs in the projected values of precipitation for all five districts of Punjab. The HadGEM2-ES was the wettest of all five, projecting a 200-mm increase

13 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 231 in precipitation on average in the area of study, while GFDL-ESM2M was the driest showing an expected decrease of about 100 mm for The ensemble mean of the five GCMs shows a slight increase in annual rainfall (mm) as compared to the baseline. In order to further examine projected climate change for the mid-century RCP8.5 scenario for the selected stations, the annual cycle of maximum temperature, minimum temperature, and precipitation was calculated for each district. There is a clear signal of warming for all five selected districts as the five CMIP5 GCMs show an increase of 2 Cto3 C as compared to the baseline. However, this increase is not uniform for all five CMIP5 GCMs. For example, CCSM4 and GFDL-ESM-2M are showing more warming for the months of April and September, respectively. There are some exceptional cases like projection of MPI-ESM-MR, in which the rainfall shows abrupt increases and decreases as compared to the normal pattern. There is mixed behavior of GCMs towards projected precipitation as compared to the baseline. Some CMIP5 GCMs (e.g., HadGEM2-ES and MIROC5) are wet while other GCMs (e.g., CCSM4 and GFDL-ESM-2M) are dry as compared to the precipitation of the baseline period. Crop modeling Crop models are being used to evaluate both the spatial and temporal production uncertainties due to crop management and climate scenarios (Hoogenboom, 2000). Crop models have their applications in the fields of research, decision support, education, and training (Jones et al., 2003). Crop simulation models have the potential to help understand the impact of climate change, climate variability, and agronomic practices on plant growth and development as the models integrate the soil plant atmosphere complex. Two crop growth models were used in this study, including DSSAT and APSIM. Decision Support System for Agrotechnology Transfer (DSSAT) DSSAT is a comprehensive decision-support system (DSS) for assessing crop management options. Crop simulation models use one set of code for simulating soil water, nitrogen, carbon dynamics, crop growth and development, and yield of crops (Hoogenboom et al., 2012; Jones et al., 2003). The simulations are based on physiological processes that describe the response of the crop to soil and aerial environmental conditions. DSSAT has been used widely to simulate the collective effects of plant genetics, management practices, weather and soil conditions on the growth, development and yield of wheat and rice in Pakistan and other countries in

14 232 A. Ahmad et al. South Asia (Ahmad et al., 2009; Ahmad et al., 2011; Ahmad et al., 2012; Ahmad and Hasanuzzaman 2012; Mall and Aggarwal, 2002; Sarkar and Kar, 2006). The inputs that are required for model operation include management practices, environmental factors, and daily weather data. A set of cultivar-specific coefficients that characterize the cultivar being grown in terms of plant development and grain biomass; and crop management information, such as plant population, row spacing, seeding depth, and application of fertilizer and irrigation. The cropping system model (CSM) CSM-CERES-Wheat and CSM-CERES-Rice require a set of seven and eight cultivar coefficients for simulation of phenology, growth, and yield of cultivars, respectively. The cultivar coefficients were estimated through iteration approach using the sensitivity analysis option of DSSAT (Ahmad et al., 2011; Ahmad et al., 2012). The cultivar coefficients were determined sequentially, starting with phenological parameters, followed by the grain-filling parameters, and finally total biomass and grain yield (Hunt and Boote, 1998). The models simulate phenological development, biomass accumulation and partitioning, leaf area index, stem and leaf growth, and the water and N balance from planting until harvest at daily time steps. Agricultural Production Systems Simulator (APSIM) APSIM is a cropping system modeling framework that was developed by the Agricultural Production Systems Research Unit in Australia (Keating et al., 2003). It is a highly advanced simulator of agricultural systems to evaluate management options, agronomic practices for supporting on-farm decision-making, designing farming systems for production and experimental design or resource management objectives, and assessing the value of seasonal climate forecasting as well as plant, animal, soil, climate, and management interactions (Buckthought et al., 2011). APSIM can estimate profitability, economic risk, yield, animal production, and effects on the environment. It can be used to analyze risk and explore alternative management options such as crop choice, planting date, and fertilizer rate, using local climate data and paddock-specific soil data. When used interactively with farmers, it can also take into account the social and/or economic values or goals that influence an individual farmer s management decisions (Gaydon et al., 2012; Keating et al., 2003; Stone and Heinemann, 2012). Experimental data used for model calibration Rice The data on rice phenology, yield, yield components, and management practices were obtained from field trials conducted on fine aromatic rice cultivars Basmati- 385, Super Basmati, and Basmati-2000 at the Agronomic Research Farm, University

15 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 233 of Agriculture, Faisalabad, Pakistan during the 2000, 2001, 2004, 2005, 2009, and 2010 growing seasons. In these experiments rice was transplanted and grown under flooded conditions until maturity. Different crop management practices such as transplanting date, plant population density, irrigation levels, nitrogen rates, and split application of nitrogen and varietal behavior were tested. A brief description of methodology adopted in the field experimentation is given below. i. Experiments conducted during 2000 and 2001 Two years of field trials for assessing the effect of nitrogen management and planting density on growth, development and yield of rice were conducted at the Agronomic Research Area (Ahmad et al., 2009). The performance of an aromatic fine rice cultivar Basmati-385 under an irrigated semi-arid environment was evaluated. Three planting densities, i.e., one, two, and three seedlings per hill, respectively, were randomized in the main plots and five nitrogen application rates, 0, 50, 100, 150, and 200 kg N ha 1 were randomized in subplots. The models were calibrated with the best treatment which was 200 kg N ha 1. A more detailed description can be found in Ahmad et al. (2012). ii. Experiments conducted during Data from a two-year field study data were used to calibrate the cultivar coefficients, including Super Basmati, Basmati-385, and Basmati The study was conducted at the same experimental farm as previously described during the rice-growing season of 2009 and The experiments were laid out in randomized complete block design (RCBD) with split arrangements that had three replications. The treatments consisted of three planting dates, i.e., 1, 15, and 30July, and three cultivars, i.e., Super Basmati, Basmati-385, and Basmati The model was calibrated with a planting date of 1 July. Wheat Two years of field trials for the assessment of nitrogen management on growth, development, and yield of wheat were conducted at the agronomic research area, University of Agriculture, Faisalabad, Pakistan, to evaluate the performance of various genotypes under irrigated conditions in a semi-arid environment. An RCBD with split arrangement was employed with three replications. In both years the experiment was planted in the same field that had a silt clay loam soil named as Lyallpur series. Four nitrogen rates, including 0, 55, 110 (standard), and 220 kg ha 1 were randomized in the main plot and ten wheat varieties were located in the subplot. Wheat was planted during the second week of November and

16 234 A. Ahmad et al , with the help of a single-row hand-drill, keeping a row-to-row distance of 30 cm, and using a seeding rate of 100 kg ha 1. Phosphorus and potassium were applied in all plots at the rate of 85 and 60 kg ha 1, respectively, in the form of triple super phosphate and sulphate of potash, and nitrogen in the form of urea at the time of seed-bed preparation. All cultural practices such as weeding, interculturing practices, irrigation, etc., were kept uniform for all the experimental treatments. Two split doses of nitrogen fertilizer were applied: The first dose of half the nitrogen fertilizer was applied at the time of first irrigation after 35 days of sowing and the second split of half of nitrogen fertilizer at the time of second irrigation 25 days after the first irrigation in both years. Data on phenology and development were recorded during the vegetative and reproductive phases in both years. Anthesis was recorded when ears were visible outside the leaf sheath on 50% of the plants in each plot. Physiological maturity was determined by regular sampling of grains from primary tillers. For the growth analysis, seven harvests were obtained at 14-day intervals during each growing season. Plants from a one-meter row were harvested from each experimental unit randomly. The plants were separated into individual components like leaves, stem, and grains. A subsample of g of green leaves and branches were oven-dried to a constant weight at 60 C to determine the dry weight. A subsample of g of green leaves was taken and the leaf area was measured with an electronic leaf area meter (Licor, model 3100). The leaf area index (LAI) was calculated as the ratio of total leaf area to occupied surface area. At maturity, half the plot was harvested manually to record the final biomass, grain yield, and yield components. Statistical analysis All observed and simulated results of calibration and evaluation were compared to assess the accuracy of models using statistical analyses, including root-mean-square error (RMSE), d-stat, coefficient of determination (R 2 ), percentage difference (PD), and mean percentage difference (MPD). Calibration and evaluation results DSSAT Rice Data from two field experiments were used to calibrate CSM-CERES-Rice. Basmati-385 cultivar was used in the first field experiment to calibrate the model with a treatment of two seedlings per hill and nitrogen at 200 kg ha 1, because this treatment had the highest yield in the field. In the second field trial, the model was calibrated for cultivars Super Basmati and Basmati-2000 with a transplanting date of 1 July. During calibration, the model predicted the number of days to anthesis with 0%, 0%, and 1.58% PD for Basmati-385, Super Basmati, and Basmati-2000,

17 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 235 respectively. For grain yield, the PDs were 1.4%, 3%, and 1.4% for Basmati-385, Super Basmati, and Basmati-2000, respectively. Corresponding results of biomass production were 2.1%, 1.6%, and 1.3%. For model evaluation, the RMSE in Basmati-385 for grain yield ranged between 41 and 156 kg ha 1 during 2000 and between 154 and 461 kg ha 1 in 2001 with MPD of 3 6% in 2000 and % in The R 2 between observed and simulated grain yield was 0.99 (2000) and 0.98 (2001). The RMSE for grain yield of Super Basmati and Basmati-2000 remained 203 kg ha 1 in 2009 and 237 kg ha 1 in 2010, while for Basmati-2000, it was 194 kg ha 1 during 2009 and 206 kg ha 1 during MPD for grain yield was 4% and 6% during 2009 and 2010 for Super Basmati and 4% and 5% during 2009 and 2010 for Basmati-2000, respectively. The R 2 for the evaluation dataset was 0.98 in grain yield for both cultivars. APSIM Rice APSIM Rice was calibrated using data from the second field experiment regarding varietal response to sowing dates. During calibration, the model predicted days to anthesis with PDs of 1.6%, 0.0%, and 3.3% for cultivars Basmati-385, Super Basmati, and Basmati-2000 respectively. So far as the PDs of yield were concerned, the model predicted a yield with a PD of 2.7%, 0.45%, and 0.56% for cultivar Basmati-385, Super Basmati, and Basmati-2000, respectively. Model simulations for above-ground biomass showed PDs of 4.6%, 6%, and 4.5% for Basmati-385, Super Basmati, and Basmati-2000, respectively. The model was evaluated with the remaining experimental treatments of 2009 and evaluated with data collected in The RMSE for grain yield of Basmati-385 was 377 kg ha 1 (2009) and 463 kg ha 1 (2010). The d-stat for Basmati-385 was 0.65 for 2009 and 0.69 for In the case of Super Basmati, the MPD for grain yield was 8.9% for 2009 and 5.4% for The RMSE for grain yield of Super Basmati was 422 and 685 kg ha 1 for 2009 and 2010, respectively. The values of d-stat were (2009) and 0.31 (2010). Model evaluation was good in the case of Basmati-2000 as MPDs for grain yield were 3.7% and 7.2% for 2009 and 2010, respectively. The values for RMSE were 202 kg ha 1 and 770 kg ha 1 for 2009 and 2010, respectively. The value for d-stat (0.59) for 2010 was good as compared to the evaluation d-stat (0.35) value. DSSAT Wheat For the calibration of the CSM-CERES-Wheat model with experimental data, the 110 kg N ha 1 treatment performed well in the field during both the years, so it was used for model calibration ( ), while the other treatments (0, 55, 220 Nkgha 1 ) were used for model evaluation to judge the accuracy of model and

18 236 A. Ahmad et al. genetic coefficient performance. A close agreement was obtained between observed and simulated values for phenology, LAI, biomass, and grain yield for all cultivars (Fasisalabad-2008, Lasani-2008, and Sahar-2006). The values of PDs were 0.8%, 1%, and 5% for Faisalabad-2008, Lasani-2008, and Sahar-2006, respectively for the grain yield. As regards biomass, the PDs were 1.6% for Faisalabad-2008, 1.5% for Lasani-2008, and 3.3% for Sahar For evaluation of the CSM-CERES-Wheat model, it was run with the remainder of the treatments of the experiment that were conducted in and then evaluated with the data of the experiment. The values of MPDs for grain yield were 3%, 4.5%, and 13% for cultivars Faisalabad-2008, Lasani-2008, and Sahar-2006, respectively, for Corresponding values for MPDs for grain yield were 6%, 6.5%, and 3%. The values for RMSE of grain yield were 176 kg ha 1 for Faisalabad-2008, 204 kg ha 1 for Lasani-2008, and 415 kg ha 1 for Sahar for In , corresponding values of RMSE for grain yield were 282, 221, and 324 kg ha 1. The index of agreement (d-statistics) was 0.99 for Faisalabad-2008, 0.98 for Lasani-2008, and 0.92 for Sahar-2006 for the year For evaluation with the data of the experiment, the d-statistics were 0.97 for Faisalabad-2008, 0.98 for Lasani-2008, and 0.96 for Sahar APSIM Wheat The APSIM Wheat model was calibrated for the same three wheat cultivars (Faisalabad-2008, Lasani-2008, and Sahar-2006) at the optimum nitrogen rate of 110 kg ha 1 during Calibration performance of model for all recorded parameters was good. Statistical indices showed that there was a close agreement between observed and simulated phenology, grain yield, and biomass for all cultivars. The PDs for days to anthesis were 0.9%, 1.9%, and 1.9% for Faisalabad-2008, Lasani-2008, and Sahar-2008, respectively. PDs of maximum leaf area index (LAIX) were the highest (13%) for Sahar-2006 and the lowest (5.6%) for Lasani-2008, while for Faisalabad-2008 had a value of 10.6%. Genetic coefficients were adjusted with little difference in observed and simulated grain yield. The PDs between simulated and observed were 0.80%, 0.5%, and 3.2% for the cultivars Faisalabad- 2008, Lasani-2008, and Sahar-2006, respectively. The PDs for simulated biomass of Faisalabad-2008 and Sahar-2006 were 0.04% and 6.1%, respectively, but 0.3% for Lasani For evaluation of the APSIM model, it was run with rest of the treatments of experiment conducted in and then evaluated with the data of the experiment. The values for MPDs for grain yield were 14%, 13%, and 10% for cultivars Faisalabad-2008, Lasani-2008, and Sahar-2006, respectively, for During , the values for MPDs were 1.5%, 8.4%, and 3.7% for

19 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan 237 Faisalabad-2008, Lasani-2008, and Sahar-2006 respectively. The values for RMSE for grain yield were 609 kg ha 1 for Faisalabad-2008, 629 kg ha 1 for Lasani-2008, and 498 kg ha 1 for Sahar-2006 for the year In , the values for RMSE for grain yield were 340 kg ha 1 for Faisalabad-2008, 650 kg ha 1 for Lasani-2008, and 581 kg ha 1 for Sahar So far as d-stat is concerned, it was 0.92 for Faisalabad-2008, 0.89 for Lasani-2008, and 0.94 for Sahar-2006 for For evaluation with the data of , the values for d-stat were 0.98 for Faisalabad-2008, 0.89 for Lasani-2008, and 0.93 for Sahar On-farm model evaluation The household surveyed data described above were used to create crop management files for both models. The initial conditions, input levels, and soil characteristics of the field that were obtained from the surveys were used to define the input data for both models. Then the models were run with observed weather data of the cropping year, i.e., DSSAT Rice The CSM-CERES-Rice model was evaluated with farmers field data for 155 fields of rice from five districts of Punjab in the rice wheat cropping zone. There was a good agreement between predicted and observed farmer rice yield with an RMSE of 409 kg ha 1, a d-stat of 0.80, and a bias of 0.94 (Fig. 6). The CSM-CERES-Rice model simulated rice yield with percent error ranging from 17.6% to 24% and R 2 value of 0.53 (Fig. 6). The performance of the model differed for good and poor management practices of the farmers. The difference between simulated and observed yield was less for those farmers whose management practices were according to recommendations. Planting time, plant population, number of irrigation, irrigation at critical stages, fertilizer application dates, application at crop critical stages, weed management, and disease control were better in the case of progressive farmers fields and the model also simulated almost the same yield as that observed. APSIM Rice The APSIM-Oryza-2000 was evaluated with farmers field data. Model performed well with an R 2 of 0.44, a RMSE of 440, and a d-stat of 0.78 for the final yield for 155 farmers fields (Fig. 7). The performance of model was different for different farmers. The performance of the model was different for different farmers. The reason for this difference was the management practices of the farmers and different amounts of inputs. The difference between simulated and observed yield was less for those farmers who used recommended cultural practices to grow their crops.

20 238 A. Ahmad et al Y = X R 2 = 0.53 RMSE = 409 d-stat = 0.80 Observed Yield (kg ha -1 ) Simulated Yield (kg ha -1 ) Fig. 6. Relationship between observed and simulated yield in 155 farmers rice fields (using DSSAT) in five strata of the rice wheat cropping zone of Punjab. DSSAT Wheat The CSM-CERES-Wheat model was evaluated with data from 155 farmers fields to predict wheat yield. A close agreement was observed between the simulated and observed for farmers yield. When models were evaluated at the farmers field data, goodness of model (R 2 ) was 0.64 for CSM-CERES-Wheat between observed and simulated yield of 155 farmers as shown in Fig. 8. Comparison of individual farmers yields showed that DSSAT simulated wheat yield with percent error ranging from 25% to 17% having RMSE 436 kg ha 1 and d-statistic of Overall, the DSSAT model predicted the yield with an error of 1.6% and bias was calculated as The difference between simulated and observed yield was less for those farmers who were growing wheat according to agriculture department recommendations. Good cultural practices by progressive farmers were the key factors in better simulation by the model.

21 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan Y = X R 2 = 0.44 RMSE = 440 d-stat = 0.78 Observed Yield (kg ha -1 ) Simulated Yield (kg ha -1 ) Fig. 7. Relationship between observed and simulated yield in 155 farmers rice fields (using APSIM) in five strata of the rice wheat cropping zone of Punjab. APSIM Wheat The APSIM Wheat was evaluated with field data of 155 farmers of five regions. The relationship was drawn between farmers observed yields and the simulated yield of wheat by APSIM. The model performed well with an R 2 value of 0.37, an RMSE of 592, and a d-stat of 0.88, as shown in Fig. 9. APSIM wheat simulated yield with more error as compared to DSSAT as the value for RMSE was slightly higher. So far as the value of R 2 was concerned, it was less than CSM-CERES-Wheat which showed that the performance of CSM-CERES-Wheat was slightly better as compared to APSIM. However, the wheat data from the farmers fields had the same uncertainty as the rice data. Economic analysis The Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA- MD) Version Beta was used to determine the impact of climate change on

22 240 A. Ahmad et al Y = X R 2 = 0.64 RMSE = 436 d-stat = 0.87 Observed Yield (kg ha -1 ) Simulated Yield (kg ha -1 ) Fig. 8. Relationship between observed and simulated yield in 155 farmers wheat fields (using DSSAT) in five strata of the rice wheat cropping zone of Punjab. socio-economic indicators. It simulates technology adoption and impact in a population of heterogeneous farm households. Farmers are assumed to be economically rational and thus choose between management systems based on expected economic returns. This model uses data on the spatial variability in economic returns to represent heterogeneity in the farm population. One of the important implications of this model is that incomplete adoption of a new technology can simply be due to heterogeneity in farming conditions such as soils, climate, water, costs, and the farm household s characteristics in technology adoption analysis. Most of the literature attributes incomplete adoption to attitudes such as risk aversion or constraints like access to the technology or finance that are typically difficult to observe and quantify (Suri, 2011). While these factors may indeed contribute to low adoption rates in some cases, in several cases observable heterogeneity in biophysical and economic characteristics of farms can be sufficient to explain low adoption rates (Antle et al., 2005). In climate change assessment, the TOA-MD model implies that all farms are not affected in the same way; in most cases, some farms lose and some farms gain from climate change. Similarly, some farms may be willing to adopt technologies that facilitate adaptation

23 Impact of Climate Change on the Rice Wheat Cropping System of Pakistan Y = X R 2 = 0.37 RMSE = 592 d-stat = 0.88 Observed Yield (kg ha -1 ) Simulated Yield (kg ha -1 ) Fig. 9. Relationship between observed and simulated yield in 155 farmers wheat fields (using APSIM) in five strata of the rice wheat cropping zone of Punjab. to climate change, while others will not. The TOA-MD model allows researchers to simulate the impacts of the full range of adoption rates from 0% to 100%. In this study, farmers are presented with a simple binary choice: They can operate with a current or base production System 1, or they can switch to an alternative System 2. Under the climate change analysis, it is necessary to distinguish between three factors that affect the expected value of a production system: The production methods used, referred to here as the technology, the physical environment in which the system is operated, i.e., the climate, and economic and social environment in which the system is operated, namely, the socio-economic setting that has been referred to as representative agricultural pathways (RAPs) (Rosenzweig et al., 2013b). These RAPs are basically qualitative storylines that can be translated into model parameters such as farm and household size, prices, and costs of production and policy. A major challenge in scenario design for climate impact assessment is the dimensionality of the analysis. Farmers are initially operating a base technology with a base climate known as System 1, while System 2 is defined as the case in which farmers continue using the base technology under a perturbed climate. There is also

24 242 A. Ahmad et al. a System 3, which is defined as an adapted technology used to withstand the adverse impacts of perturbed climate. The analysis was conducted for each of 5 GCMs discussed previously. Their overall impacts were observed in each scenario for both the DSSAT and APSIM crop model outcomes and for livestock. Three core questions were answered through the analyses. In Core Question 1, sensitivity of current agricultural production system to climate change was assessed. Impact of climate change on future agricultural production was addressed in Core Question 2. Core Question 3 aimed to find out benefits of potential adaptations to climate changes (Rosenzweig et al., 2013b). First, climate change impact assessments (CC-IA) were made without using the RAPs to find out the sensitivity of current agricultural production system to climate change (Core Question 1). After applying the RAPs the analyses were again carried out for evaluating the impacts of climate change on future production system (Core Question 2). Then analysis was made for future adapted production system by using RAPs and adaptations to address Core Question 3. The outputs of the rice and wheat simulations of DSSAT and APSIM were used as inputs of TOA-MD. Data used for economic analysis The data used for TOA-MD analysis for the base system is given in the form of the averages of all selected farms. The average farm size of the sample in the study area was 4.5 ha with coefficient of variation (CV) of 0.5, while the average yields of rice and wheat crops were 18,349 and 18,915 kg, with standard deviation of 9435 and 9840 kg, respectively, on a per farm basis. The output price per kg was Rs. 43 for rice and Rs. 29 for wheat with standard deviation of 0.3 and 0.4, respectively. The cost of production for rice was Rs with standard deviation of Rs. 293,186 per farm and for wheat it was Rs. 338,396 with standard deviation of Rs. 187,340 per farm. Mean net farm returns for rice and wheat crops were Rs. 256,558 and Rs. 204,804 per farm, with standard deviation of Rs. 187,329 and 165,076, respectively. These values can be converted into USD at the rate of USD1 = Rs Outreach Activities and Interaction with Stakeholders Stakeholder interactions and key decisions Regional RAPs were developed by conducting two meetings with interdisciplinary stakeholders including economists, plant breeders, irrigation specialists, soil scientists, agronomists, plant pathologists, entomologists, policymakers, progressive farmers, extensionists, and other experts. A nested approach was followed to develop RAP scenarios and the framework to follow for the development of RAPs was based on intermediate challenges (SSP2) (O Neill et al., 2012) with an optimistic scenario