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Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains
Field Crops Research 80 (2003) 223±234
Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains H. Pathaka, J.K. Ladhab,*, P.K. Aggarwala, S. Pengb, S. Dasc, Yadvinder Singhd, Bijay Singhd, S.K. Kamrae, B. Mishraf, A.S.R.A.S. Sastrig, H.P. Aggarwalh, D.K. Dasi, R.K. Guptaj a
Center for Applications of Systems Simulation, Indian Agricultural Research Institute, New Delhi 110012, India b International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines c Fertilizer Association of India, New Delhi 110016, India d Punjab Agricultural University, Ludhiana 141004, India e Central Soil Salinity Research Institute, Karnal 132001, India f G.B. Pant University of Agriculture and Technology, Pantnagar, India g Indira Gandhi Agricultural University, Raipur, Chhattishgarh, India h Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India i Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia 741252, West Bengal, India j Rice±Wheat Consortium for Indo-Gangetic Plains, IARI, New Delhi 110012, India Received 2 March 2002; received in revised form 26 August 2002; accepted 30 September 2002
Abstract Rice and wheat are the two most important cereals in the Indo-Gangetic Plains (IGP) and are responsible for the food security of the region. To understand the productivity trends in the transects of the IGP: (1) the climatic potential yields of rice and wheat were simulated using a crop simulation modeling approach and (2) the long-term trends of potential and on-farm yields were compared. The potential yields of rice and wheat in the IGP ranged from 7.7 to 10.7 and 5.2 to 7.9 Mg ha 1, respectively. The upper transects of the IGP are more productive and yield decreases by 27% for rice and by 32% for wheat from transect 2 to transect 5. The rate of change in the potential yield trend of rice from 1985 to 2000 ranged from 0.12 to 0.05 Mg ha 1 per year. Negative yield trends were observed at six of the nine sites, four of which were statistically signi®cant P < 0:05. The decrease in radiation and increase in minimum temperature were the reasons for the yield decline. The potential yield trend of wheat, however, appeared to be stable. On-farm yields of rice also showed a negative trend but for wheat the trend was mostly positive. The adverse changes in the weather parameters and declining trends of potential and on-farm yields of rice should be taken as an indication of a future problem. Regular on-farm monitoring of crops and climatic factors is urgently needed for predicting problems and allowing measures to be taken to improve productivity. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Climatic potential yield; Indo-Gangetic Plains; Simulation modeling; Rice; Wheat; Yield gap; Yield trends
Abbreviations: DAT, days after transplanting; FYM, farmyard manure; IGP, Indo-Gangetic Plains; LTE, long-term experiment Corresponding author. Tel.: 63-2-845-0563; fax: 63-2-891-1292. E-mail address: [email protected] (J.K. Ladha). *
0378-4290/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 0 2 ) 0 0 1 9 4 - 6
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H. Pathak et al. / Field Crops Research 80 (2003) 223±234
1. Introduction Rice and wheat are the two most important cereals in the world. These crops, grown in rotation on 13.5 million hectares of land in the Indo-Gangetic Plains (IGP) spread over Bangladesh, India, Nepal and Pakistan, provide food for more than 400 million people (Ladha et al., 2000). Over the past 20 years, rice and wheat production have shown a tremendous increase and remained the major source of the marketed surplus of food grains for feeding the growing urban population. Yields of both crops are showing signs of stagnation/decline as evidenced from a recent analysis of several long-term experiments (LTE) carried out throughout Asia (Bhandari et al., 2002; Dawe et al., 2000; Duxbury et al., 2000; Ladha et al., 2002; Regmi et al., 2002; Yadav et al., 2000). Ladha et al. (2002) analyzed 33 rice±wheat LTE in the IGP and China and observed that in treatments where recommended rates of N, P and K were applied, yields of rice and wheat stagnated in 72 and 82% of the LTE, respectively, while 22 and 6% of the LTE showed a signi®cant P < 0:05 declining trend for rice and wheat yields, respectively. This decline/stagnation has raised concerns about the long-term sustainability of intensive rice±wheat production systems and food security of the region. The possible causes of yield decline are depletion of soil nutrient supplying capacity, delay in planting, increase in pest incidence, and change in climatic variables like decrease in solar radiation and increase in temperature. Climate in¯uences plant life in many ways and can inhibit, stimulate, alter or modify crop performance. Its components, temperature, solar radiation, rainfall, relative humidity and wind velocity, independently or in combination in¯uence crop growth and productivity. All over the world concern exists about the possible climate change caused by the increase in the concentration of greenhouse gases such as CO2, CH4 and N2O in the atmosphere (Watson et al., 1996). Using general circulation models, it has been predicted that a doubling of the current CO2 level in the atmosphere will cause an increase of 1.5±4.0 8C in average global surface air temperature, with accompanying changes in rainfall pattern by the end of the 21st century (Cohen, 1990; Adams et al., 1995). For the Indian subcontinent, it is predicted that the mean atmospheric temperature will increase by 1±4 8C
(Sinha and Swaminathan, 1991). Although the solar radiation received at the surface will be variable geographically, on average it is expected to decrease by about 1% (Hume and Cattle, 1990). Crop growth simulation models can be used to understand the in¯uence of climatic variables on crop productivity when conventional ®eld experiments have limitations because of various confounding factors. Several studies have been done to develop an integrated assessment of the effect of climate change on regional and global food supplies and demand (Rosenzweig and Parry, 1994; Adams et al., 1995). Major rice models indicate a reduction in yield of about 5% per 8C rise in the mean temperature (Matthews et al., 1995). This would largely offset any increase in yield as a consequence of increased CO2. A few studies focused on South Asia, although food security concerns are very serious in the region. Moreover, no attempt has been made to simulate crop yield trends with actual weather data of the region. The objectives of this paper are to (1) simulate the climatic potential yields of rice and wheat in the IGP and (2) estimate trends of potential and on-farm yields of rice and wheat in the IGP. 2. Methodology 2.1. Site characterization The IGP is located within subtropical to warm temperate climates characterized by cool and dry winters and warm and wet summers. In the IGP, rice is usually grown in the wet summer season (May±June to October±November) and wheat in the dry winter season (November±December to March±April). The IGP, based on physiography and bioclimate, has been subdivided into ®ve broad transects (Narang and Virmani, 2001) (Fig. 1). TransGangetic Plains (transects 1 and 2) occupy large areas of Pakistan, and Punjab and Haryana in India. Transects 3 and 4 comprise areas in Uttar Pradesh, Bihar and Nepal. Lower parts of the Gangetic Plains in West Bengal, India, and parts of Bangladesh constitute transect 5. Seven sites covering the various transects of the IGP were selected for the study (Fig. 1). In addition, two sites (Pantnagar and Raipur), located in the foothills of
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225
Fig. 1. Study sites in the IGP.
the Himalayas at the upper side of the IGP and in central India at the lower side of the IGP, respectively, were also included for a comparison of productivity between the inside and outside of the IGP (Fig. 1). The sites were distributed from 21.20 to 30.938N latitude and 75.86 to 88.438E longitude (Table 1). Annual rainfall in the IGP follows a distinct pattern of increase from the transects 2±5. Transect 2 receives only 650 mm of rainfall per annum, but transect 5 receives more than 1600 mm. The annual average solar radiation decreases by 18% from transects 2±5. Minimum temperature increases from IGP transects 2±5 by 5.3 8C. This is also true for maximum temperature but the magnitude of increase is 1.9 8C. 2.2. Potential yield simulation Potential yield is de®ned as the maximum yield of a variety restricted only by the season-speci®c climatic
conditions. This assumes that other inputs (nutrient, water, etc.) are not limiting and cultural management is optimal. Thus, the potential yield of a crop depends on the temporal variation in CO2 level in the atmosphere, solar radiation, maximum and minimum temperatures during the crop season and physiological characteristics of the variety. Mechanistic crop growth models are routinely used to estimate potential yield and assess the effects of climate change (Muchow et al., 1990; Jansen, 1990; Penning de Vries, 1993; Boote and Tollenar, 1994; Kropff et al., 1994, 1997; Hundal and Kaur, 1996; Aggarwal et al., 1997, 2000). To simulate potential yield, CERES-RICE 3.5 (98.0) (Singh et al., 1998) and GENERIC-CERES 3.5 (98.0) (Ritchie et al., 1998) models for rice and wheat, respectively, were used. These processes oriented, daily time-step, mechanistic models simulate the main processes of crop growth and development, such as timing of phonological events, the
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Table 1 Characterization of the study sites in the IGPa Site
IGP transect
Latitude (8N)
Longitude Rainfallb (mm) (8E)
Radiationb Min. (MJ m 2 temperatureb per day) (8C)
Max. Organic Olsen Pd b temperature Cc (%) (mg kg 1) (8C)
NH4OAc Ke (mg kg 1)
Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24-Pargana Raipur Pantnagar
2 2 2 3 4 4 5 Non-IGP Foothills
30.93 29.72 28.67 25.43 25.30 26.67 22.75 21.20 29.00
75.86 75.95 77.20 80.57 83.50 82.13 88.43 81.70 79.30
20.1 19.2 18.6 18.3 19.7 18.7 17.0 18.1 16.7
29.6 30.0 31.1 31.4 31.8 30.9 31.5 32.5 30.0
46 160 130 82 109 161 64 167 125
650 700 750 818 1100 1100 1666 1388 1350
16.9 16.9 17.8 16.4 19.3 18.6 22.2 19.3 16.9
0.31 0.30 0.45 0.29 0.42 0.37 0.71 0.65 1.48
5 15 7 6 20 6 19 6 18
a Sources from: Abrol et al. (2000); Singh and Swarup (2000); Saha et al. (2000); Bhandari et al. (2002); Ladha et al. (2002); Pathak et al. (2002). b Radiation, minimum and maximum temperature and rainfall are average annual values across the period listed in Table 2. c Organic carbon content of soil was estimated by Walkley and Black (1934) method. d Olsen P content of soil was estimated by Olsen et al. (1954) method. e NH4OAc K of soil was estimated by ammonium acetate extraction method (CSTPA, 1974).
development of the canopy to intercept photosynthetically active radiation and its use to accumulate dry matter. The models calculate net photosynthesis based on a constant radiation use ef®ciency, leaf area index, extinction coef®cient and light absorption by the canopy (Mall and Aggarwal, 2002). The models have the capability to simulate the effect of CO2 on photosynthesis and water use based on the effects of stomatal conductivity (Jones et al., 1994). GENERICCERES can also deal with vernalization requirement and can allow for photoperiod sensitivity. Potential yields of rice variety PR 106 and wheat variety HD 2329, which are the most dominant varieties in the IGP, were simulated. Genotypic coef®cients used in the models for rice and wheat cultivars are given in Table 2. These coef®cients were estimated based on past ®eld experiments by repeated iterations until a close match between simulated and observed phenology and yield was obtained. The data of experiments not used for calibration was used for validation. There was generally a good agreement in the simulated and observed time course in phenological development and grain yield. Earlier the performance of the models has been well validated all over the world including rice±wheat growing environments of India (Hundal and Kaur, 1996; Lal, 1999; Aggarwal and Mall, 2002; Mall and Aggarwal, 2002) and Bangladesh (Timsina et al., 1998) using data from ®eld
experiments and the accuracy of model predictions was estimated using the coef®cient of determination between simulated and observed values. The daily weather data (solar radiation, maximum temperature and minimum temperature) required for simulation of potential yields were collected from the Table 2 Genotypic coef®cients of rice and wheat varieties used in the DSSAT model Genetic coefficients
Values
Rice (Variety PR 106) Juvenile phase coefficient (P1), GDDa Photoperiodism coefficient (P2R), GDD h 1 Grain-filling duration coefficient (P5), GDD Critical photoperiod (P20), h Spikelet number coefficient (G1) Single grain weight (G2), g Tillering coefficient (G3) Temperature tolerance coefficient (G4)
500 150 300 11.5 60 0.024 1.0 1.0
Wheat (Variety HD 2329) Vernalization coefficient (PIV) Photoperiodism coefficient (PID), GDD h 1 Grain-filling duration coefficient (P5), GDD Phyllochron interval (PHINT) Kernel number coefficient (G1) Kernel weight coefficient (G2) Spike number coefficient (G3)
0.5 3.24 2.6 95 3.37 3.5 4.23
a
GDD, growing degree days (8C).
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meteorological observatories located in the region. Rice was transplanted on 1 July and wheat sown on 15 November every year. These are the optimum dates of planting rice and wheat in the IGP (Aggarwal and Kalra, 1994). Crop yields were simulated for the last 15 years and yield trends in the transects of the IGP were estimated using linear regression analysis. The CO2 concentration in the atmosphere was considered as 330 ppm in 1985 and increasing with an annual increment of 1.5 ppm (Jones et al., 1994). Sensitivity of the model to the changes in solar radiation, and minimum and maximum temperatures on potential yields of rice and wheat was done for Ludhiana site using the weather data of 1985±1986, 1986±1987 and 1987±1988. 2.3. District yield trend The Government of India reports grain yields of various crops obtained by farmers on a district basis every year. Yields of rice and wheat for the last 15 years for the various districts in which the study sites are located were collected from the Fertilizer Association of India Regional Statistics (FAI, 1986±2000), New Delhi, India, and a yield trend analysis was done. 2.4. Data analysis Linear regression analyses were done to determine the trends (slopes) in weather parameters, simulated potential yield and farmers' yield over the years. The P values on the slopes were used to test whether observed yield changes were signi®cantly different from 0 P < 0:05. 3. Results and Discussion 3.1. Analysis of weather data Analyses of weather data during the period showed that solar radiation decreased over the years in both the rice and wheat seasons at all the sites except Kanpur and Faizabad in the rice season (Table 3). The annual change in the solar radiation trend in rice ranged from 0.25 MJ m 2 per day per year at Delhi to 0.04 MJ m 2 per day per year at Kanpur and Faizabad. Negative trends were observed in seven of the nine
227
data sets, four of which were statistically signi®cant P < 0:05. These declining trends were observed throughout the transects of the IGP, indicating that the decline in radiation is not localized. On the other hand, positive trends were observed at two sites, but they were statistically not signi®cant. In wheat, solar radiation also showed a negative trend, ranging from 0.00 to 0.23 MJ m 2 per day per year at all the sites, with three sites (Delhi, Varanasi and 24-Pargana) statistically signi®cant. Sinha et al. (1998) observed that there was a 10% decline in solar radiation in northwestern India during the last two decades. It is widely perceived that in all the major cities of India aerosol concentration has been increasing, resulting in decreased solar radiation and increased minimum temperature (Hundal and Kaur, 1996; Aggarwal et al., 2000). Besides the effect of global warming, an increase in minimum temperature could be the result of decreased solar radiation (John Sheehy and T. Horie, personal communication). There has also been an increase in the percent particulate matter in the air around cities. This would attenuate the light reaching the plant to higher, red wavelengths that are less photosynthetically active and so lower yields. Since, most weather stations are located in urban areas, it cannot be concluded whether these changes are also occurring in rural agricultural areas. The minimum temperature in rice showed a negative trend at three sites, with one (Kanpur) statistically signi®cant, whereas six sites showed a positive trend, with one site (Ludhiana) signi®cantly different from 0 (Table 3). A similar signi®cant increase in the minimum temperature trend in rice was also recorded in Adampur, located near Jalandhar, Punjab (Bijay Singh, personal communication). In wheat, ®ve sites showed a negative trend, with Delhi and Faizabad being signi®cantly different. Four sites showed a positive trend in minimum temperature but were not signi®cant statistically. The analysis of weather data for more than two decades by Sinha et al. (1998) showed that there has been a 1.5 8C increase in the minimum temperature at many places in northwestern India. Kukla and Karl (1993) analyzed data from several countries, including the United States, Canada, China, Commonwealth of Independent States, Australia, Sudan, Japan, Denmark, Finland, several Paci®c islands, Pakistan, South Africa, and some European countries, covering 50% of the land in the Northern
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Table 3 Weather parameters and their changes over time during rice and wheat crop seasons in the IGP Site
Year
Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24 Pargana Raipur Pantnagar *
1985±1999 1985±1999 1985±1999 1990±1999 1985±1999 1985±1997 1985±1998 1985±1999 1985±1999
Solar radiation
Minimum temperature
Average (MJ m 2 per day)
Change Average (MJ m 2 (8C) per day per year)
Rice
Wheat
Rice
21.3 20.0 19.3 17.3 18.9 18.3 10.8 14.9 16.8
15.6 15.0 15.4 16.4 17.5 15.7 17.0 19.1 14.0
0.12* 0.13* 0.25* 0.04 0.04 0.04 0.21* 0.03 0.04
Wheat
Rice
Wheat
0.05 0.03 0.23* 0.01 0.11* 0.10 0.28* 0.09 0.00
23.4 23.5 23.9 21.7 24.6 24.6 26.0 22.9 23.0
8.9 9.3 9.8 9.7 12.1 11.0 18.0 13.7 9.4
Maximum temperature
Change (8C per year)
Average (8C)
Rice
Rice
Wheat
33.0 32.7 33.9 32.5 32.9 32.1 33.0 30.8 31.8
22.5 23.6 24.6 26.5 27.2 26.3 31.0 30.4 24.5
0.05* 0.02 0.04 0.32* 0.04 0.02 0.06 0.08 0.02
Wheat 0.001 0.06 0.07* 0.11 0.02 0.09* 0.002 0.08 0.05
Change (8C per year) Rice 0.04 0.05 0.06 0.10 0.00 0.01 0.004 0.05 0.03
Wheat 0.05 0.08 0.05* 0.09 0.07 0.10 0.15* 0.06 0.09*
Signi®cant at P 0:05.
Hemisphere and 10% of the land in the Southern Hemisphere. The minimum temperature revealed a general rise worldwide, with the exception of the eastern coast of North America, where it decreased. They observed that (a) an increase in natural and anthropogenic clouds, (b) haze from cities, factories, and burning ®elds and forests, (c) vapor trails of highaltitude aircraft, (d) irrigation that keeps the soil surface warmer at night, (e) anthropogenic greenhouse gases and (f) warming of the urban zones, which keeps the night temperature high, are the probable causes of such an increase in minimum temperature. The maximum temperature in rice remained stable over the years, with ®ve sites showing a negative trend and four positive but non-signi®cant trends (Table 3). In wheat, however, seven sites showed negative trend, three of which were statistically signi®cant. A decrease in maximum temperature increases the vegetative and grain-®lling period and increases crop yield in wheat provided the minimum temperature remains constant (Horie et al., 1995; Matthews et al., 1995). 3.2. Sensitivity analysis Decreased solar radiation by 1.7 MJ m 2 per day reduced rice and wheat yields from 10.9 to 10.3 and 8.3 to 7.5 Mg ha 1, respectively (Fig. 2). Increased minimum temperature by 1.7 8C also decreased yields of rice and wheat from 10.9 to 10.0 and 8.3 to
8.1 Mg ha 1, respectively. Increase in maximum temperature increased rice yield marginally but decreased wheat yield signi®cantly. The analysis showed that the model is sensitive to the changes in radiation and temperature, and therefore, could be used for simulating the effect of changes in weather parameters on rice and wheat yields. 3.3. Simulated potential yields The potential yields of rice and wheat in the IGP ranged from 7.7 to 10.7 and 5.2 to 7.9 Mg ha 1, respectively (Table 4). The yields of both rice and wheat were the highest in Ludhiana (IGP 2) and decreased toward the lower part of the IGP. The yield declined by 28% for rice and by 34% for wheat from transect 2 (Ludhiana) to transect 5 (24-Pargana). This is because of the lower solar radiation and higher daily minimum temperature in the lower part of the IGP, resulting in decreased photosynthesis, and a shortened vegetative and grain-®lling period. Outside the IGP, rice yields were higher than those of IGP transect 5. Pantnagar, at the upper side of the IGP, had a higher yield potential than Raipur, at the lower side of the IGP. Aggarwal et al. (2000) estimated similar potential yields of rice and wheat in the IGP using daily weather data generated from monthly means by WGEN-weather and observed that Punjab and Haryana had 10 Mg ha 1 or more potential rice yield,
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229
Fig. 2. Sensitivity of simulated potential yields of rice and wheat to the changes in solar, radiation, and minimum and maximum temperatures.
which decreased in an eastward direction in the IGP. The potential yield of wheat was about two-thirds of that of the rice crop and followed the same declining trend from northwest to east along the transect. In another study (Mohandas et al., 1995), potential rice yield in Kapurthala district of Punjab in the upper Gangetic Plains was estimated to be 10.5 Mg ha 1, whereas in Cuttack, Orissa, located in the southwest of the lower IGP, it was 7.1 Mg ha 1. The potential yield trend in rice ranged from 0.12 Mg ha 1 per year at Delhi to 0.05 Mg ha 1 per year at Kanpur (Fig. 3 and Table 4). Negative
yield trends were observed in six of the nine data sets, four of which were signi®cantly different from 0 P < 0:05. These declining trends were observed throughout the transects of the IGP, indicating that yield declines are not localized. On the other hand, positive trends were observed at three sites, but none was statistically signi®cant P < 0:05. The changes in radiation and minimum temperature are the reasons for the potential yield decline of rice. In wheat, the rate of annual yield change ranged from 0.07 Mg ha 1 per year at Delhi to 0.04 Mg ha 1 per year at Faizabad and Pantnagar (Fig. 3 and Table 4).
Table 4 Climatic potential and district average yields and yield changes over the years in the IGP Site
Potential yielda Year
District yieldb
Rice
Wheat c
Yield (Mg ha 1) Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24-Pargana Raipur Pantnagar
1985±1999 10.7 1985±1999 10.4 1985±1999 9.8 1990±1999 9.5 1985±1999 9.2 1985±1997 9.1 1985±1998 7.7 1985±1999 8.2 1985±1999 9.0
Change (Mg ha 1 per year) 0.07* 0.08** 0.12* 0.05 0.01 0.00 0.10** 0.03 0.00
c
Yield (Mg ha 1) 7.9 7.3 7.1 7.0 7.0 6.7 5.2 6.2 6.5
Year
Wheat c
Change (Mg ha 1 per year) 0.02 0.02 0.07 0.00 0.01 0.04 0.07 0.03 0.04
Rice Yield (Mg ha 1)
1985±1998 1985±1998 1985±1999 1985±1998 1985±1998 1985±1999 1985±1998 1987±1998 1989±1998
5.6 3.8 2.4 2.8 3.2 2.8 2.8 2.1 4.2
Change (Mg ha 1 per year) 0.05 0.01 0.15* 0.07* 0.13** 0.03 0.08* 0.03 0.03
Yieldc (Mg ha 1) 4.3 3.6 2.5 2.8 2.2 2.3 2.1 1.0 2.7
Change (Mg ha 1 per year) 0.06* 0.05** 0.11* 0.04** 0.06* 0.03* 0.02 0.03 0.07**
a Potential yields were simulated using CERES-RICE 3.5 (Singh et al., 1998) and GENERIC-CERES 3.5 (Ritchie et al., 1998) models for rice and wheat, respectively. b Source: Fertilizer Association of India Regional Statistics (1986±2000). c Average yield. * Signi®cant at P 0:05. ** Signi®cant at P 0:01.
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Fig. 3. Climatic potential yield trends of rice and wheat at the various sites of the IGP. The coef®cients of regression analysis are given in Table 4.
Of the nine sites, six showed a negative trend and three showed positive trends, but none is signi®cantly different from 0. Thus, wheat yield appeared to be more stable than rice yield. When simulation was done keeping the CO2 concentration constant (330 ppm) over the years, similar negative yield trends of both crops were observed, but the magnitude of decline was higher indicating that increased CO2 concentration compensated for the decline in crop yield due to decreased solar radiation and increased temperature up to some extent. 3.3.1. Relationship between simulated potential yield and weather parameters The potential yield of a crop depends on the temporal variation in solar radiation, maximum and minimum temperatures during the crop season and
physiological characteristics of the varieties and their interactions. A decrease in radiation reduces photosynthesis and contributes a decline in yield, while an increase in radiation improves photosynthesis (Yoshida and Parao, 1976; Horie et al., 1995). An increase in the CO2 level in the atmosphere would increase crop yield mainly by stimulating photosynthetic processes and improving water use ef®ciency by increasing the number of productive tillers per plant (Baker et al., 1990). The effect of increased temperature would largely be negative because of increased respiration (Penning de Vries, 1993) and a shortened vegetative and grain-®lling period (Horie et al., 1995). It has been shown in a number of ®eld studies that increased temperature reduces ¯oral reproduction, causes sterility due to stomatal closure and reduces fertilization (Satake and Yoshida, 1978; Nishiyama
H. Pathak et al. / Field Crops Research 80 (2003) 223±234
and Satake, 1981; Matsui et al., 1997). Yoshida and Parao (1976) and Horie et al. (1995) observed that as the average temperature increased above the optimum (22±23 8C), rice yield declined linearly with an increase in temperature up to 30 8C, followed by a sharp decline thereafter. The initial linear decrease was due to the shorter crop duration caused by increased temperature and the sharp decline after 30 8C was because of spikelet sterility from high-temperature damage. The net effect of an increase in CO2 and temperature is complicated and depends on the relative effects of both variables in a given region. The signi®cant decline in climatic potential rice yields at Ludhiana, Karnal, Delhi and 24-Pargana (Table 4) was the result of a decrease in solar radiation, while at Ludhiana it was also partly due to an increase in minimum temperature. Hundal and Kaur (1996) also observed that increasing temperature and decreasing radiation levels were the reasons for the
231
decline in simulated potential yields of rice and wheat at Ludhiana. A decrease in solar radiation at Delhi in wheat also could have lowered the potential yield of wheat signi®cantly P < 0:05, but a decrease in minimum temperature, which increased yield, may have compensated for this to some extent. At Varanasi and 24-Pargana, though solar radiation decreased signi®cantly (Table 3), wheat yield remained stable (Table 4) mainly because of a decrease in maximum temperature, which compensated for the adverse effect of the reduction in solar radiation. 3.4. District yield trends The district average yields of rice varied from 2.1 Mg ha 1 at Raipur, located outside the IGP, to 5.6 Mg ha 1 at Ludhiana in transect 2 of the IGP (Table 4). Rice yields decreased from the upper to
Fig. 4. Farmers' yield trends of rice and wheat at the various sites of the IGP. The coef®cients of regression analysis are given in Table 4.
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lower IGP. As with to rice, farmers in the upper transects harvested more wheat than those in the lower regions. Among other factors, favorable climate is responsible for the greater yields in the upper transects (Narang and Virmani, 2001). The time trend of rice yield ranged from 0.15 Mg ha 1 per year at Delhi to 0.13 Mg ha 1 per year at Varanasi (Fig. 4 and Table 4). Negative yield trends were observed in ®ve of the nine data sets and one (Delhi) was signi®cantly different from 0 P < 0:05. Positive trends were observed at four sites and two (Kanpur and Varanasi) were statistically signi®cant. The district yield trend resembled the trend of potential yield of rice to a great extent, indicating that climate also affected farmers' yields considerably. For example, all the ®ve sites (Ludhiana, Karnal, Delhi, Raipur and Pantnagar) which showed negative yield trends also recorded decline in solar radiation (Table 3). Similarly, all these sites except Karnal showed an increase in minimum temperature. Thus the study showed that a reduction in solar radiation and an increase in minimum temperature may be responsible for the yield decline of rice. In wheat, the rate of annual yield change differed markedly from that of rice (Fig. 4). The estimated yield trend ranged from 0.02 Mg ha 1 per year in 24-Pargana to 0.11 Mg ha 1 per year at Delhi (Table 4). All the sites except 24-Pargana had a positive yield trend and at seven sites the positive trends were signi®cantly different from 0 P < 0:05. Wheat yields appeared to be increasing throughout the transects of the IGP. 4. Conclusion Recent trends of a decline or stagnation in the yield of rice and wheat in the IGP have raised serious concerns about the region's food security. The effect of possible climate change on crop production adds to the already complex problem. An analysis of weather parameters of the last several years showed negative trends in solar radiation and an increase in minimum temperature in several places, resulting in declining trends of potential simulated yields of rice and wheat. This is a matter of serious concern, as climate, once it deteriorates is dif®cult to restore. The adverse changes in weather parameters and decrease in potential yields
should be taken as an indication of future problems. Farmers' yields should be continuously monitored for their stability. While breeders should try to break the yield barrier with improved germplasm with more adjustability in the changed climate, soil scientists and agronomists should ®nd out ways to improve input use ef®ciency and planners ways to disseminate the technology to farmers. Regular monitoring of crops and climatic factors in farmers' ®elds would help in predicting problems and allowing measures to be taken to improve productivity. References Abrol, I.P., Bronson, K.F., Duxbury, J.M., Gupta, R.K., 2000. Long-term soil fertility experiments in rice±wheat cropping systems. Rice±Wheat Consortium Research Series #6. New Delhi, India. Adams, R.M., Flemming, R.A., Chang, C.C., McCarl, B.A., Rosenzweig, C., 1995. A reassessment of the economic effects of global climate change on US agriculture. Climat. Change 30, 147±167. Aggarwal, P.K., Kalra, N., 1994. Analyzing the limitations set by climatic factors, genotype, water and nitrogen availability on productivity of wheat. II. Climatically potential yields and optimal management strategies. Field Crops Res. 38, 93± 103. Aggarwal, P.K., Mall, R.K., 2002. Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climat. Change 52, 331±343. Aggarwal, P.K., Kropff, M.J., Cassman, K.G., ten Berge, H.F.M., 1997. Simulating genetic strategies for increased yield potential in irrigated, tropical environments. Field Crops Res. 51, 5±18. Aggarwal, P.K., Talukdar, K.K., Mall, R.K., 2000. Potential yields of the rice±wheat system in the Indo-Gangetic Plains of India. Rice±Wheat Consortium Paper Series 10. Rice±Wheat Consortium for the Indo-Gangetic Plains, New Delhi, India, and Indian Agricultural Research Institute, New Delhi, India, 11 pp. Baker, J.T., Allen Jr., L.H., Boote, K.J., 1990. Growth and yield responses of rice to carbon dioxide concentration. J. Agric. Sci. (Camb.) 115, 313±320. Bhandari, A.L., Ladha, J.K., Pathak, H., Padre, A.T., Dawe, D., Gupta, R.K., 2002. Trends of yield and soil nutrient status in a long-term rice±wheat experiment in the Indo-Gangetic Plains of India. Soil Sci. Soc. Am. J. 66, 162±170. Boote, K.J., Tollenar, M., 1994. Modelling genetic yield potential. Physiology and Determination of Crop Yield. ASA, Madison, WI, pp. 533±565. Cohen, S.J., 1990. Bringing the global warming issue closer to home: the challenge of regional impact studies. Bull. Am. Meteorol. Soc. 71, 520±527. CSTPA, 1974. Handbook on Reference Methods for Soil Testing. Council on Soil Testing and Plant Analysis, Athens, GA.
H. Pathak et al. / Field Crops Research 80 (2003) 223±234 Dawe, D., Dobermann, A., Moya, P., Abdulrachman, S., Singh, B., Lal, P., Li, S.Y., Lin, B., Panaullah, G., Sariam, O., Singh, Y., Swarup, A., Tan, P.S., Zhen, Q.X., 2000. How widespread are yield declines in long-term rice experiments in Asia? Field Crops Res. 66, 175±193. Duxbury, J.M., Abrol, I.P., Gupta, R.K., Bronson, K., 2000. Analysis of long-term soil fertility experiments with rice±wheat rotation in South Asia. In: Abrol, I.P., Bronson, K., Duxbury, J.M., Gupta, R.K. (Eds.), Long-term Soil Fertility Experiments in Rice±Wheat Cropping Systems. RWC Research Series #6. New Delhi, pp. vii±xxii. FAI, 1986±2000. Fertilizer Association of India Regional Statistics (1986±2000). Fertilizer Association of India, New Delhi. Horie, T., Nakagawa, H., Ceneno, H.G.S., Kropff, M.J., 1995. The rice crop simulation model SIMRIW and its testing. In: Matthews, R.B., Kropff, M.J., Bachelet, D., van Laar, H.H. (Eds.), Modelling the Impact of Climate Change on Rice Production in Asia. CAB International and International Rice Research Institute, The Philippines, pp. 51±66. Hume, C.J., Cattle, H., 1990. The greenhouse effect: meteorological mechanisms and methods. Outlook Agric. 19, 17±23. Hundal, S.S., Kaur, P., 1996. Climatic change and its impact on crop productivity in Punjab, India. In: Abrol, Y.P., Gadgil, S., Pant, G.B. (Eds.), Climatic Variability and Agriculture, pp. 377±393. Jansen, D.M., 1990. Potential rice yields in future weather conditions in different parts of Asia. Neth. J. Agric. Sci. 38, 661±680. Jones, J.W., Hunt, L.A., Hoogenboom, G., Godwin, D.C., Singh, U., Tsuji, G.Y., Pickering, N.B., Thornton, P.K., Bowen, W.T., Boote, K.J., Ritchi, J.T., 1994. Crop models. In: Tsuji, G.Y., Uehera, G., Balas, S. (Eds.), DSSAT Version 3, vol. 2. University of Hawaii, Honolulu, Hawaii, p. 284. Kropff, M.J., van Laar, H.H., Matthews, R.B., 1994. ORYZA1: an eco-physiological model for irrigated rice production. In: SARP Research Proceedings, IRRI, Los BanÄos, Philippines, and ABDLO, Wageningen, Netherlands, 110 pp. Kropff, M.J., Teng, P.S., Aggarwal, P.K., Bouman, B.A.M., van Laar, H.H., 1997. Applications of Systems Approaches at the Field Level, vol. 2. Kluwer Academic Publishers, Netherlands, 465 pp. Kukla, G., Karl, T.R., 1993. Nighttime warming and the greenhouse effect. Environ. Sci. Technol. 27, 1469±1474. Ladha, J.K., Fischer, K.S., Hossain, M., Hobbs, P.R., Hardy, B., 2000. Improving the productivity and sustainability of rice± wheat systems of the Indo-Gangetic Plains: a synthesis of NARS-IRRI partnership research. Discussion Paper No. 40. International Rice Research Institute, Philippines, pp. 1±31. Ladha, J.K., Dawe, D., Pathak, H., Padre, A.T., Yadav, R.L., Singh, B., Singh, Y., Singh, P., Kundu, A.L., Sakal, R., Ram, N., Regmi, A.P., Gami, S.K., Bhandari, A.L., Amin, R., Yadav, C.R., Bhattarai, E.M., Gupta, R.K., Hobbs, P.R., 2002. How extensive are yield declines in long-term rice±wheat experiments in Asia? Field Crops Res., in press. Lal, M., 1999. Climate variability and rice productivity in Bihar, Uttar Pradesh, West Bengal and Madhya Pradesh. Project Report, Rice±Wheat Consortium for the Indo-Gangetic Plains.
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International Crops Research Institute for the Semi-arid Tropics, New Delhi, 25 pp. Mall, R.K., Aggarwal, P.K., 2002. Climate change and rice yields in diverse agro-environments of India. I. Evaluation of impact assessment models. Climat. Change 52, 315±330. Matsui, T., Namuco, O.S., Ziska, L.H., Horie, T., 1997. Effects of high temperature and CO2 concentration on spikelet sterility in Indica rice. Field Crops Res. 51, 213±219. Matthews, R.B., Kropff, M.J., Bachelet, D., 1995. Introduction. In: Matthews, R.B., Kropff, M.J., Bachelet, D., van Laar, H.H. (Eds.), Modeling the Impact of Climate Change on Rice Production in Asia. CAB International and International Rice Research Institute, The Philippines, pp. 3±9. Mohandas, S., Kareem, A.K., Ranganathan, T.B., Jeyaraman, S., 1995. Rice production in India under current and future climates. In: Matthews, R.B., Kropff, M.J., Bachelet, D., van Laar, H.H. (Eds.), Modelling the Impact of Climate Change on Rice Production in Asia. CAB International and International Rice Research Institute, The Philippines, pp. 165±181. Muchow, R.C., Sinclair, T.R., Bennet, J.M., 1990. Temperature and solar radiation effects on potential maize yields across locations. Agron. J. 82, 338±343. Narang, R.S., Virmani, S.M., 2001. Rice±wheat cropping systems of the Indo-Gangetic Plains of India. Rice±Wheat Consortium Paper Series 11. Rice±Wheat Consortium for the Indo-Gangetic Plains, New Delhi, India, and International Crops Research Institute for the Semi-arid Tropics, Patancheru, Andhra Pradesh, India, 36 pp. Nishiyama, I., Satake, T., 1981. High temperature damages the rice plant. Jpn. J. Trop. Agric. 25, 14±19. Olsen, S.R., Cole, C.V., Watanabe, F.S., Dean, L.A., 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Circular No. 939, Washington, DC. Pathak, H., Bhatia, A., Jain, M.C., Kumar, S., Singh, S., Kumar, U., 2002. Emission of nitrous oxide from soil in rice±wheat systems of Indo-Gangetic Plains of India. J. Environ. Monit. Assess. 77 (2), 163±178. Penning de Vries, F.W.T., 1993. Rice production and climate change. In: Penning de Vries, F.W.T., et al. (Eds.), Systems Approaches for Agricultural Development. Kluwer Academic Publishers, Dordrecht, Netherlands, pp. 175±189. Regmi, A.P., Ladha, J.K., Pathak, H., Pasuquin, E., Bueno, C., Dawe, D., Hobbs, P.R., Joshy, D., Maskey, S.L., Pandey, S.P., 2002. Analyses of yield and soil fertility trends in a 20-year rice± wheat experiment in Nepal. Soil Sci. Soc. Am. J. 66, 857±867. Ritchie, J.T., Singh, U., Godwin, D.C., Bowen, W.T., Wilkens P.W., Baer, B., Hoogenboom, G., Hunt, L.A., 1998. GENERICCERES V3.5. IFDC, Michigan State University, University of Georgia, USA. Rosenzweig, C., Parry, M.L., 1994. Potential impact of climate change on world food supply. Nature 367, 133±138. Saha, M.N., Saha, A.R., Mandal, B.C., Ray, P.K., 2000. Effects of long-term jute±rice±wheat cropping system on crop yields and soil fertility. In: Abrol, I.P., Bronson, K.F., Duxbury, J.M., Gupta, R.K. (Eds.), Long-term Soil Fertility Experiments in Rice±Wheat Cropping Systems. RWC Research Series #6, New Delhi, pp. 94±104.
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Satake, T., Yoshida, S., 1978. High-temperature induced sterility in indica rice at ¯owering. Jpn. J. Crop Sci. 47, 6±11. Singh, K.N., Swarup, A., 2000. Effect of long-term rice±wheat cropping sequence on yield and soil properties in reclaimed sodic soils. In: Abrol, I.P., Bronson, K., Duxbury, J.M., Gupta, R.K. (Eds.), Long-term Soil Fertility Experiments in Rice± Wheat Cropping Systems. Rice±Wheat Consortium Research Series #6, New Delhi, pp. 40±49. Singh, U., Godwin, D.C., Ritchie, J.T., Bowen, W.T., Wilkens, P.W., Baer, B., Hoogenboom, G., Hunt, L.A., 1998. CERESRICE V3.5. IFDC, Michigan State University, University of Georgia, USA. Sinha, S.K., Swaminathan, M.S., 1991. Deforestation, climate change and sustainable nutritional security: a case study of India. Climat. Change 19, 201±209. Sinha, S.K., Singh, G.B., Rai, M., 1998. Decline in Crop Productivity in Haryana and Punjab: Myth or Reality? Indian Council of Agricultural Research, New Delhi, 89 pp.
Timsina, J., Singh, U., Badaruddin, M., Meisner, C., 1998. Cultivar, nitrogen, and moisture effects on a rice±wheat sequence: experimentation and simulation. Agron. J. 90, 119±130. Walkley, A., Black, I.A., 1934. Estimation of soil organic carbon by the chromic acid titration method. Soil Sci. 37, 29±38. Watson, R.T., Zinyowera, M.C., Moss, R.H., Dokken, D.J., 1996. Climate change 1995, impacts, adaptations and mitigation of climate change: scienti®c-technical analyses. Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p. 879. Yadav, R.L., Dwivedi, B.S., Orasad, K, Tomar, O.K., Shurpali, N.J., Panday, P.S., 2000. Yield trends, and changes in soil organic-C and available NPK in a long-term rice±wheat system under integrated use of manures and fertilizers. Field Crops Res. 68, 219±246. Yoshida, S., Parao, F.T., 1976. Climatic in¯uence on yield and yield components of lowland rice in the tropics. Climate and Rice. International Rice Research Institute, The Philippines, pp. 471±494.
Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains H. Pathaka, J.K. Ladhab,*, P.K. Aggarwala, S. Pengb, S. Dasc, Yadvinder Singhd, Bijay Singhd, S.K. Kamrae, B. Mishraf, A.S.R.A.S. Sastrig, H.P. Aggarwalh, D.K. Dasi, R.K. Guptaj a
Center for Applications of Systems Simulation, Indian Agricultural Research Institute, New Delhi 110012, India b International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines c Fertilizer Association of India, New Delhi 110016, India d Punjab Agricultural University, Ludhiana 141004, India e Central Soil Salinity Research Institute, Karnal 132001, India f G.B. Pant University of Agriculture and Technology, Pantnagar, India g Indira Gandhi Agricultural University, Raipur, Chhattishgarh, India h Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India i Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia 741252, West Bengal, India j Rice±Wheat Consortium for Indo-Gangetic Plains, IARI, New Delhi 110012, India Received 2 March 2002; received in revised form 26 August 2002; accepted 30 September 2002
Abstract Rice and wheat are the two most important cereals in the Indo-Gangetic Plains (IGP) and are responsible for the food security of the region. To understand the productivity trends in the transects of the IGP: (1) the climatic potential yields of rice and wheat were simulated using a crop simulation modeling approach and (2) the long-term trends of potential and on-farm yields were compared. The potential yields of rice and wheat in the IGP ranged from 7.7 to 10.7 and 5.2 to 7.9 Mg ha 1, respectively. The upper transects of the IGP are more productive and yield decreases by 27% for rice and by 32% for wheat from transect 2 to transect 5. The rate of change in the potential yield trend of rice from 1985 to 2000 ranged from 0.12 to 0.05 Mg ha 1 per year. Negative yield trends were observed at six of the nine sites, four of which were statistically signi®cant P < 0:05. The decrease in radiation and increase in minimum temperature were the reasons for the yield decline. The potential yield trend of wheat, however, appeared to be stable. On-farm yields of rice also showed a negative trend but for wheat the trend was mostly positive. The adverse changes in the weather parameters and declining trends of potential and on-farm yields of rice should be taken as an indication of a future problem. Regular on-farm monitoring of crops and climatic factors is urgently needed for predicting problems and allowing measures to be taken to improve productivity. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Climatic potential yield; Indo-Gangetic Plains; Simulation modeling; Rice; Wheat; Yield gap; Yield trends
Abbreviations: DAT, days after transplanting; FYM, farmyard manure; IGP, Indo-Gangetic Plains; LTE, long-term experiment Corresponding author. Tel.: 63-2-845-0563; fax: 63-2-891-1292. E-mail address: [email protected] (J.K. Ladha). *
0378-4290/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 0 2 ) 0 0 1 9 4 - 6
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1. Introduction Rice and wheat are the two most important cereals in the world. These crops, grown in rotation on 13.5 million hectares of land in the Indo-Gangetic Plains (IGP) spread over Bangladesh, India, Nepal and Pakistan, provide food for more than 400 million people (Ladha et al., 2000). Over the past 20 years, rice and wheat production have shown a tremendous increase and remained the major source of the marketed surplus of food grains for feeding the growing urban population. Yields of both crops are showing signs of stagnation/decline as evidenced from a recent analysis of several long-term experiments (LTE) carried out throughout Asia (Bhandari et al., 2002; Dawe et al., 2000; Duxbury et al., 2000; Ladha et al., 2002; Regmi et al., 2002; Yadav et al., 2000). Ladha et al. (2002) analyzed 33 rice±wheat LTE in the IGP and China and observed that in treatments where recommended rates of N, P and K were applied, yields of rice and wheat stagnated in 72 and 82% of the LTE, respectively, while 22 and 6% of the LTE showed a signi®cant P < 0:05 declining trend for rice and wheat yields, respectively. This decline/stagnation has raised concerns about the long-term sustainability of intensive rice±wheat production systems and food security of the region. The possible causes of yield decline are depletion of soil nutrient supplying capacity, delay in planting, increase in pest incidence, and change in climatic variables like decrease in solar radiation and increase in temperature. Climate in¯uences plant life in many ways and can inhibit, stimulate, alter or modify crop performance. Its components, temperature, solar radiation, rainfall, relative humidity and wind velocity, independently or in combination in¯uence crop growth and productivity. All over the world concern exists about the possible climate change caused by the increase in the concentration of greenhouse gases such as CO2, CH4 and N2O in the atmosphere (Watson et al., 1996). Using general circulation models, it has been predicted that a doubling of the current CO2 level in the atmosphere will cause an increase of 1.5±4.0 8C in average global surface air temperature, with accompanying changes in rainfall pattern by the end of the 21st century (Cohen, 1990; Adams et al., 1995). For the Indian subcontinent, it is predicted that the mean atmospheric temperature will increase by 1±4 8C
(Sinha and Swaminathan, 1991). Although the solar radiation received at the surface will be variable geographically, on average it is expected to decrease by about 1% (Hume and Cattle, 1990). Crop growth simulation models can be used to understand the in¯uence of climatic variables on crop productivity when conventional ®eld experiments have limitations because of various confounding factors. Several studies have been done to develop an integrated assessment of the effect of climate change on regional and global food supplies and demand (Rosenzweig and Parry, 1994; Adams et al., 1995). Major rice models indicate a reduction in yield of about 5% per 8C rise in the mean temperature (Matthews et al., 1995). This would largely offset any increase in yield as a consequence of increased CO2. A few studies focused on South Asia, although food security concerns are very serious in the region. Moreover, no attempt has been made to simulate crop yield trends with actual weather data of the region. The objectives of this paper are to (1) simulate the climatic potential yields of rice and wheat in the IGP and (2) estimate trends of potential and on-farm yields of rice and wheat in the IGP. 2. Methodology 2.1. Site characterization The IGP is located within subtropical to warm temperate climates characterized by cool and dry winters and warm and wet summers. In the IGP, rice is usually grown in the wet summer season (May±June to October±November) and wheat in the dry winter season (November±December to March±April). The IGP, based on physiography and bioclimate, has been subdivided into ®ve broad transects (Narang and Virmani, 2001) (Fig. 1). TransGangetic Plains (transects 1 and 2) occupy large areas of Pakistan, and Punjab and Haryana in India. Transects 3 and 4 comprise areas in Uttar Pradesh, Bihar and Nepal. Lower parts of the Gangetic Plains in West Bengal, India, and parts of Bangladesh constitute transect 5. Seven sites covering the various transects of the IGP were selected for the study (Fig. 1). In addition, two sites (Pantnagar and Raipur), located in the foothills of
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225
Fig. 1. Study sites in the IGP.
the Himalayas at the upper side of the IGP and in central India at the lower side of the IGP, respectively, were also included for a comparison of productivity between the inside and outside of the IGP (Fig. 1). The sites were distributed from 21.20 to 30.938N latitude and 75.86 to 88.438E longitude (Table 1). Annual rainfall in the IGP follows a distinct pattern of increase from the transects 2±5. Transect 2 receives only 650 mm of rainfall per annum, but transect 5 receives more than 1600 mm. The annual average solar radiation decreases by 18% from transects 2±5. Minimum temperature increases from IGP transects 2±5 by 5.3 8C. This is also true for maximum temperature but the magnitude of increase is 1.9 8C. 2.2. Potential yield simulation Potential yield is de®ned as the maximum yield of a variety restricted only by the season-speci®c climatic
conditions. This assumes that other inputs (nutrient, water, etc.) are not limiting and cultural management is optimal. Thus, the potential yield of a crop depends on the temporal variation in CO2 level in the atmosphere, solar radiation, maximum and minimum temperatures during the crop season and physiological characteristics of the variety. Mechanistic crop growth models are routinely used to estimate potential yield and assess the effects of climate change (Muchow et al., 1990; Jansen, 1990; Penning de Vries, 1993; Boote and Tollenar, 1994; Kropff et al., 1994, 1997; Hundal and Kaur, 1996; Aggarwal et al., 1997, 2000). To simulate potential yield, CERES-RICE 3.5 (98.0) (Singh et al., 1998) and GENERIC-CERES 3.5 (98.0) (Ritchie et al., 1998) models for rice and wheat, respectively, were used. These processes oriented, daily time-step, mechanistic models simulate the main processes of crop growth and development, such as timing of phonological events, the
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Table 1 Characterization of the study sites in the IGPa Site
IGP transect
Latitude (8N)
Longitude Rainfallb (mm) (8E)
Radiationb Min. (MJ m 2 temperatureb per day) (8C)
Max. Organic Olsen Pd b temperature Cc (%) (mg kg 1) (8C)
NH4OAc Ke (mg kg 1)
Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24-Pargana Raipur Pantnagar
2 2 2 3 4 4 5 Non-IGP Foothills
30.93 29.72 28.67 25.43 25.30 26.67 22.75 21.20 29.00
75.86 75.95 77.20 80.57 83.50 82.13 88.43 81.70 79.30
20.1 19.2 18.6 18.3 19.7 18.7 17.0 18.1 16.7
29.6 30.0 31.1 31.4 31.8 30.9 31.5 32.5 30.0
46 160 130 82 109 161 64 167 125
650 700 750 818 1100 1100 1666 1388 1350
16.9 16.9 17.8 16.4 19.3 18.6 22.2 19.3 16.9
0.31 0.30 0.45 0.29 0.42 0.37 0.71 0.65 1.48
5 15 7 6 20 6 19 6 18
a Sources from: Abrol et al. (2000); Singh and Swarup (2000); Saha et al. (2000); Bhandari et al. (2002); Ladha et al. (2002); Pathak et al. (2002). b Radiation, minimum and maximum temperature and rainfall are average annual values across the period listed in Table 2. c Organic carbon content of soil was estimated by Walkley and Black (1934) method. d Olsen P content of soil was estimated by Olsen et al. (1954) method. e NH4OAc K of soil was estimated by ammonium acetate extraction method (CSTPA, 1974).
development of the canopy to intercept photosynthetically active radiation and its use to accumulate dry matter. The models calculate net photosynthesis based on a constant radiation use ef®ciency, leaf area index, extinction coef®cient and light absorption by the canopy (Mall and Aggarwal, 2002). The models have the capability to simulate the effect of CO2 on photosynthesis and water use based on the effects of stomatal conductivity (Jones et al., 1994). GENERICCERES can also deal with vernalization requirement and can allow for photoperiod sensitivity. Potential yields of rice variety PR 106 and wheat variety HD 2329, which are the most dominant varieties in the IGP, were simulated. Genotypic coef®cients used in the models for rice and wheat cultivars are given in Table 2. These coef®cients were estimated based on past ®eld experiments by repeated iterations until a close match between simulated and observed phenology and yield was obtained. The data of experiments not used for calibration was used for validation. There was generally a good agreement in the simulated and observed time course in phenological development and grain yield. Earlier the performance of the models has been well validated all over the world including rice±wheat growing environments of India (Hundal and Kaur, 1996; Lal, 1999; Aggarwal and Mall, 2002; Mall and Aggarwal, 2002) and Bangladesh (Timsina et al., 1998) using data from ®eld
experiments and the accuracy of model predictions was estimated using the coef®cient of determination between simulated and observed values. The daily weather data (solar radiation, maximum temperature and minimum temperature) required for simulation of potential yields were collected from the Table 2 Genotypic coef®cients of rice and wheat varieties used in the DSSAT model Genetic coefficients
Values
Rice (Variety PR 106) Juvenile phase coefficient (P1), GDDa Photoperiodism coefficient (P2R), GDD h 1 Grain-filling duration coefficient (P5), GDD Critical photoperiod (P20), h Spikelet number coefficient (G1) Single grain weight (G2), g Tillering coefficient (G3) Temperature tolerance coefficient (G4)
500 150 300 11.5 60 0.024 1.0 1.0
Wheat (Variety HD 2329) Vernalization coefficient (PIV) Photoperiodism coefficient (PID), GDD h 1 Grain-filling duration coefficient (P5), GDD Phyllochron interval (PHINT) Kernel number coefficient (G1) Kernel weight coefficient (G2) Spike number coefficient (G3)
0.5 3.24 2.6 95 3.37 3.5 4.23
a
GDD, growing degree days (8C).
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meteorological observatories located in the region. Rice was transplanted on 1 July and wheat sown on 15 November every year. These are the optimum dates of planting rice and wheat in the IGP (Aggarwal and Kalra, 1994). Crop yields were simulated for the last 15 years and yield trends in the transects of the IGP were estimated using linear regression analysis. The CO2 concentration in the atmosphere was considered as 330 ppm in 1985 and increasing with an annual increment of 1.5 ppm (Jones et al., 1994). Sensitivity of the model to the changes in solar radiation, and minimum and maximum temperatures on potential yields of rice and wheat was done for Ludhiana site using the weather data of 1985±1986, 1986±1987 and 1987±1988. 2.3. District yield trend The Government of India reports grain yields of various crops obtained by farmers on a district basis every year. Yields of rice and wheat for the last 15 years for the various districts in which the study sites are located were collected from the Fertilizer Association of India Regional Statistics (FAI, 1986±2000), New Delhi, India, and a yield trend analysis was done. 2.4. Data analysis Linear regression analyses were done to determine the trends (slopes) in weather parameters, simulated potential yield and farmers' yield over the years. The P values on the slopes were used to test whether observed yield changes were signi®cantly different from 0 P < 0:05. 3. Results and Discussion 3.1. Analysis of weather data Analyses of weather data during the period showed that solar radiation decreased over the years in both the rice and wheat seasons at all the sites except Kanpur and Faizabad in the rice season (Table 3). The annual change in the solar radiation trend in rice ranged from 0.25 MJ m 2 per day per year at Delhi to 0.04 MJ m 2 per day per year at Kanpur and Faizabad. Negative trends were observed in seven of the nine
227
data sets, four of which were statistically signi®cant P < 0:05. These declining trends were observed throughout the transects of the IGP, indicating that the decline in radiation is not localized. On the other hand, positive trends were observed at two sites, but they were statistically not signi®cant. In wheat, solar radiation also showed a negative trend, ranging from 0.00 to 0.23 MJ m 2 per day per year at all the sites, with three sites (Delhi, Varanasi and 24-Pargana) statistically signi®cant. Sinha et al. (1998) observed that there was a 10% decline in solar radiation in northwestern India during the last two decades. It is widely perceived that in all the major cities of India aerosol concentration has been increasing, resulting in decreased solar radiation and increased minimum temperature (Hundal and Kaur, 1996; Aggarwal et al., 2000). Besides the effect of global warming, an increase in minimum temperature could be the result of decreased solar radiation (John Sheehy and T. Horie, personal communication). There has also been an increase in the percent particulate matter in the air around cities. This would attenuate the light reaching the plant to higher, red wavelengths that are less photosynthetically active and so lower yields. Since, most weather stations are located in urban areas, it cannot be concluded whether these changes are also occurring in rural agricultural areas. The minimum temperature in rice showed a negative trend at three sites, with one (Kanpur) statistically signi®cant, whereas six sites showed a positive trend, with one site (Ludhiana) signi®cantly different from 0 (Table 3). A similar signi®cant increase in the minimum temperature trend in rice was also recorded in Adampur, located near Jalandhar, Punjab (Bijay Singh, personal communication). In wheat, ®ve sites showed a negative trend, with Delhi and Faizabad being signi®cantly different. Four sites showed a positive trend in minimum temperature but were not signi®cant statistically. The analysis of weather data for more than two decades by Sinha et al. (1998) showed that there has been a 1.5 8C increase in the minimum temperature at many places in northwestern India. Kukla and Karl (1993) analyzed data from several countries, including the United States, Canada, China, Commonwealth of Independent States, Australia, Sudan, Japan, Denmark, Finland, several Paci®c islands, Pakistan, South Africa, and some European countries, covering 50% of the land in the Northern
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Table 3 Weather parameters and their changes over time during rice and wheat crop seasons in the IGP Site
Year
Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24 Pargana Raipur Pantnagar *
1985±1999 1985±1999 1985±1999 1990±1999 1985±1999 1985±1997 1985±1998 1985±1999 1985±1999
Solar radiation
Minimum temperature
Average (MJ m 2 per day)
Change Average (MJ m 2 (8C) per day per year)
Rice
Wheat
Rice
21.3 20.0 19.3 17.3 18.9 18.3 10.8 14.9 16.8
15.6 15.0 15.4 16.4 17.5 15.7 17.0 19.1 14.0
0.12* 0.13* 0.25* 0.04 0.04 0.04 0.21* 0.03 0.04
Wheat
Rice
Wheat
0.05 0.03 0.23* 0.01 0.11* 0.10 0.28* 0.09 0.00
23.4 23.5 23.9 21.7 24.6 24.6 26.0 22.9 23.0
8.9 9.3 9.8 9.7 12.1 11.0 18.0 13.7 9.4
Maximum temperature
Change (8C per year)
Average (8C)
Rice
Rice
Wheat
33.0 32.7 33.9 32.5 32.9 32.1 33.0 30.8 31.8
22.5 23.6 24.6 26.5 27.2 26.3 31.0 30.4 24.5
0.05* 0.02 0.04 0.32* 0.04 0.02 0.06 0.08 0.02
Wheat 0.001 0.06 0.07* 0.11 0.02 0.09* 0.002 0.08 0.05
Change (8C per year) Rice 0.04 0.05 0.06 0.10 0.00 0.01 0.004 0.05 0.03
Wheat 0.05 0.08 0.05* 0.09 0.07 0.10 0.15* 0.06 0.09*
Signi®cant at P 0:05.
Hemisphere and 10% of the land in the Southern Hemisphere. The minimum temperature revealed a general rise worldwide, with the exception of the eastern coast of North America, where it decreased. They observed that (a) an increase in natural and anthropogenic clouds, (b) haze from cities, factories, and burning ®elds and forests, (c) vapor trails of highaltitude aircraft, (d) irrigation that keeps the soil surface warmer at night, (e) anthropogenic greenhouse gases and (f) warming of the urban zones, which keeps the night temperature high, are the probable causes of such an increase in minimum temperature. The maximum temperature in rice remained stable over the years, with ®ve sites showing a negative trend and four positive but non-signi®cant trends (Table 3). In wheat, however, seven sites showed negative trend, three of which were statistically signi®cant. A decrease in maximum temperature increases the vegetative and grain-®lling period and increases crop yield in wheat provided the minimum temperature remains constant (Horie et al., 1995; Matthews et al., 1995). 3.2. Sensitivity analysis Decreased solar radiation by 1.7 MJ m 2 per day reduced rice and wheat yields from 10.9 to 10.3 and 8.3 to 7.5 Mg ha 1, respectively (Fig. 2). Increased minimum temperature by 1.7 8C also decreased yields of rice and wheat from 10.9 to 10.0 and 8.3 to
8.1 Mg ha 1, respectively. Increase in maximum temperature increased rice yield marginally but decreased wheat yield signi®cantly. The analysis showed that the model is sensitive to the changes in radiation and temperature, and therefore, could be used for simulating the effect of changes in weather parameters on rice and wheat yields. 3.3. Simulated potential yields The potential yields of rice and wheat in the IGP ranged from 7.7 to 10.7 and 5.2 to 7.9 Mg ha 1, respectively (Table 4). The yields of both rice and wheat were the highest in Ludhiana (IGP 2) and decreased toward the lower part of the IGP. The yield declined by 28% for rice and by 34% for wheat from transect 2 (Ludhiana) to transect 5 (24-Pargana). This is because of the lower solar radiation and higher daily minimum temperature in the lower part of the IGP, resulting in decreased photosynthesis, and a shortened vegetative and grain-®lling period. Outside the IGP, rice yields were higher than those of IGP transect 5. Pantnagar, at the upper side of the IGP, had a higher yield potential than Raipur, at the lower side of the IGP. Aggarwal et al. (2000) estimated similar potential yields of rice and wheat in the IGP using daily weather data generated from monthly means by WGEN-weather and observed that Punjab and Haryana had 10 Mg ha 1 or more potential rice yield,
H. Pathak et al. / Field Crops Research 80 (2003) 223±234
229
Fig. 2. Sensitivity of simulated potential yields of rice and wheat to the changes in solar, radiation, and minimum and maximum temperatures.
which decreased in an eastward direction in the IGP. The potential yield of wheat was about two-thirds of that of the rice crop and followed the same declining trend from northwest to east along the transect. In another study (Mohandas et al., 1995), potential rice yield in Kapurthala district of Punjab in the upper Gangetic Plains was estimated to be 10.5 Mg ha 1, whereas in Cuttack, Orissa, located in the southwest of the lower IGP, it was 7.1 Mg ha 1. The potential yield trend in rice ranged from 0.12 Mg ha 1 per year at Delhi to 0.05 Mg ha 1 per year at Kanpur (Fig. 3 and Table 4). Negative
yield trends were observed in six of the nine data sets, four of which were signi®cantly different from 0 P < 0:05. These declining trends were observed throughout the transects of the IGP, indicating that yield declines are not localized. On the other hand, positive trends were observed at three sites, but none was statistically signi®cant P < 0:05. The changes in radiation and minimum temperature are the reasons for the potential yield decline of rice. In wheat, the rate of annual yield change ranged from 0.07 Mg ha 1 per year at Delhi to 0.04 Mg ha 1 per year at Faizabad and Pantnagar (Fig. 3 and Table 4).
Table 4 Climatic potential and district average yields and yield changes over the years in the IGP Site
Potential yielda Year
District yieldb
Rice
Wheat c
Yield (Mg ha 1) Ludhiana Karnal Delhi Kanpur Varanasi Faizabad 24-Pargana Raipur Pantnagar
1985±1999 10.7 1985±1999 10.4 1985±1999 9.8 1990±1999 9.5 1985±1999 9.2 1985±1997 9.1 1985±1998 7.7 1985±1999 8.2 1985±1999 9.0
Change (Mg ha 1 per year) 0.07* 0.08** 0.12* 0.05 0.01 0.00 0.10** 0.03 0.00
c
Yield (Mg ha 1) 7.9 7.3 7.1 7.0 7.0 6.7 5.2 6.2 6.5
Year
Wheat c
Change (Mg ha 1 per year) 0.02 0.02 0.07 0.00 0.01 0.04 0.07 0.03 0.04
Rice Yield (Mg ha 1)
1985±1998 1985±1998 1985±1999 1985±1998 1985±1998 1985±1999 1985±1998 1987±1998 1989±1998
5.6 3.8 2.4 2.8 3.2 2.8 2.8 2.1 4.2
Change (Mg ha 1 per year) 0.05 0.01 0.15* 0.07* 0.13** 0.03 0.08* 0.03 0.03
Yieldc (Mg ha 1) 4.3 3.6 2.5 2.8 2.2 2.3 2.1 1.0 2.7
Change (Mg ha 1 per year) 0.06* 0.05** 0.11* 0.04** 0.06* 0.03* 0.02 0.03 0.07**
a Potential yields were simulated using CERES-RICE 3.5 (Singh et al., 1998) and GENERIC-CERES 3.5 (Ritchie et al., 1998) models for rice and wheat, respectively. b Source: Fertilizer Association of India Regional Statistics (1986±2000). c Average yield. * Signi®cant at P 0:05. ** Signi®cant at P 0:01.
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Fig. 3. Climatic potential yield trends of rice and wheat at the various sites of the IGP. The coef®cients of regression analysis are given in Table 4.
Of the nine sites, six showed a negative trend and three showed positive trends, but none is signi®cantly different from 0. Thus, wheat yield appeared to be more stable than rice yield. When simulation was done keeping the CO2 concentration constant (330 ppm) over the years, similar negative yield trends of both crops were observed, but the magnitude of decline was higher indicating that increased CO2 concentration compensated for the decline in crop yield due to decreased solar radiation and increased temperature up to some extent. 3.3.1. Relationship between simulated potential yield and weather parameters The potential yield of a crop depends on the temporal variation in solar radiation, maximum and minimum temperatures during the crop season and
physiological characteristics of the varieties and their interactions. A decrease in radiation reduces photosynthesis and contributes a decline in yield, while an increase in radiation improves photosynthesis (Yoshida and Parao, 1976; Horie et al., 1995). An increase in the CO2 level in the atmosphere would increase crop yield mainly by stimulating photosynthetic processes and improving water use ef®ciency by increasing the number of productive tillers per plant (Baker et al., 1990). The effect of increased temperature would largely be negative because of increased respiration (Penning de Vries, 1993) and a shortened vegetative and grain-®lling period (Horie et al., 1995). It has been shown in a number of ®eld studies that increased temperature reduces ¯oral reproduction, causes sterility due to stomatal closure and reduces fertilization (Satake and Yoshida, 1978; Nishiyama
H. Pathak et al. / Field Crops Research 80 (2003) 223±234
and Satake, 1981; Matsui et al., 1997). Yoshida and Parao (1976) and Horie et al. (1995) observed that as the average temperature increased above the optimum (22±23 8C), rice yield declined linearly with an increase in temperature up to 30 8C, followed by a sharp decline thereafter. The initial linear decrease was due to the shorter crop duration caused by increased temperature and the sharp decline after 30 8C was because of spikelet sterility from high-temperature damage. The net effect of an increase in CO2 and temperature is complicated and depends on the relative effects of both variables in a given region. The signi®cant decline in climatic potential rice yields at Ludhiana, Karnal, Delhi and 24-Pargana (Table 4) was the result of a decrease in solar radiation, while at Ludhiana it was also partly due to an increase in minimum temperature. Hundal and Kaur (1996) also observed that increasing temperature and decreasing radiation levels were the reasons for the
231
decline in simulated potential yields of rice and wheat at Ludhiana. A decrease in solar radiation at Delhi in wheat also could have lowered the potential yield of wheat signi®cantly P < 0:05, but a decrease in minimum temperature, which increased yield, may have compensated for this to some extent. At Varanasi and 24-Pargana, though solar radiation decreased signi®cantly (Table 3), wheat yield remained stable (Table 4) mainly because of a decrease in maximum temperature, which compensated for the adverse effect of the reduction in solar radiation. 3.4. District yield trends The district average yields of rice varied from 2.1 Mg ha 1 at Raipur, located outside the IGP, to 5.6 Mg ha 1 at Ludhiana in transect 2 of the IGP (Table 4). Rice yields decreased from the upper to
Fig. 4. Farmers' yield trends of rice and wheat at the various sites of the IGP. The coef®cients of regression analysis are given in Table 4.
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lower IGP. As with to rice, farmers in the upper transects harvested more wheat than those in the lower regions. Among other factors, favorable climate is responsible for the greater yields in the upper transects (Narang and Virmani, 2001). The time trend of rice yield ranged from 0.15 Mg ha 1 per year at Delhi to 0.13 Mg ha 1 per year at Varanasi (Fig. 4 and Table 4). Negative yield trends were observed in ®ve of the nine data sets and one (Delhi) was signi®cantly different from 0 P < 0:05. Positive trends were observed at four sites and two (Kanpur and Varanasi) were statistically signi®cant. The district yield trend resembled the trend of potential yield of rice to a great extent, indicating that climate also affected farmers' yields considerably. For example, all the ®ve sites (Ludhiana, Karnal, Delhi, Raipur and Pantnagar) which showed negative yield trends also recorded decline in solar radiation (Table 3). Similarly, all these sites except Karnal showed an increase in minimum temperature. Thus the study showed that a reduction in solar radiation and an increase in minimum temperature may be responsible for the yield decline of rice. In wheat, the rate of annual yield change differed markedly from that of rice (Fig. 4). The estimated yield trend ranged from 0.02 Mg ha 1 per year in 24-Pargana to 0.11 Mg ha 1 per year at Delhi (Table 4). All the sites except 24-Pargana had a positive yield trend and at seven sites the positive trends were signi®cantly different from 0 P < 0:05. Wheat yields appeared to be increasing throughout the transects of the IGP. 4. Conclusion Recent trends of a decline or stagnation in the yield of rice and wheat in the IGP have raised serious concerns about the region's food security. The effect of possible climate change on crop production adds to the already complex problem. An analysis of weather parameters of the last several years showed negative trends in solar radiation and an increase in minimum temperature in several places, resulting in declining trends of potential simulated yields of rice and wheat. This is a matter of serious concern, as climate, once it deteriorates is dif®cult to restore. The adverse changes in weather parameters and decrease in potential yields
should be taken as an indication of future problems. Farmers' yields should be continuously monitored for their stability. While breeders should try to break the yield barrier with improved germplasm with more adjustability in the changed climate, soil scientists and agronomists should ®nd out ways to improve input use ef®ciency and planners ways to disseminate the technology to farmers. Regular monitoring of crops and climatic factors in farmers' ®elds would help in predicting problems and allowing measures to be taken to improve productivity. References Abrol, I.P., Bronson, K.F., Duxbury, J.M., Gupta, R.K., 2000. Long-term soil fertility experiments in rice±wheat cropping systems. Rice±Wheat Consortium Research Series #6. New Delhi, India. Adams, R.M., Flemming, R.A., Chang, C.C., McCarl, B.A., Rosenzweig, C., 1995. A reassessment of the economic effects of global climate change on US agriculture. Climat. Change 30, 147±167. Aggarwal, P.K., Kalra, N., 1994. Analyzing the limitations set by climatic factors, genotype, water and nitrogen availability on productivity of wheat. II. Climatically potential yields and optimal management strategies. Field Crops Res. 38, 93± 103. Aggarwal, P.K., Mall, R.K., 2002. Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climat. Change 52, 331±343. Aggarwal, P.K., Kropff, M.J., Cassman, K.G., ten Berge, H.F.M., 1997. Simulating genetic strategies for increased yield potential in irrigated, tropical environments. Field Crops Res. 51, 5±18. Aggarwal, P.K., Talukdar, K.K., Mall, R.K., 2000. Potential yields of the rice±wheat system in the Indo-Gangetic Plains of India. Rice±Wheat Consortium Paper Series 10. Rice±Wheat Consortium for the Indo-Gangetic Plains, New Delhi, India, and Indian Agricultural Research Institute, New Delhi, India, 11 pp. Baker, J.T., Allen Jr., L.H., Boote, K.J., 1990. Growth and yield responses of rice to carbon dioxide concentration. J. Agric. Sci. (Camb.) 115, 313±320. Bhandari, A.L., Ladha, J.K., Pathak, H., Padre, A.T., Dawe, D., Gupta, R.K., 2002. Trends of yield and soil nutrient status in a long-term rice±wheat experiment in the Indo-Gangetic Plains of India. Soil Sci. Soc. Am. J. 66, 162±170. Boote, K.J., Tollenar, M., 1994. Modelling genetic yield potential. Physiology and Determination of Crop Yield. ASA, Madison, WI, pp. 533±565. Cohen, S.J., 1990. Bringing the global warming issue closer to home: the challenge of regional impact studies. Bull. Am. Meteorol. Soc. 71, 520±527. CSTPA, 1974. Handbook on Reference Methods for Soil Testing. Council on Soil Testing and Plant Analysis, Athens, GA.
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