Assessing Impacts of Climatic Variations on Crop Production In

Bangladesh By

Taslima Imam

Bangladesh is highly vulnerable to severe weather events; floods, draughts,(cyclones) and heavy rainfalls have a grave impact on the agriculture of the country. Due to the Global Climate Change, the frequency of these natural disasters is increasing. The most affected crop by these natural disasters is rice, the major cereal of the country

Rice is the dominant crop, grown in three distinct rice growingseasons namely Aus (March to August), Aman (June to November), and Boro (December to May).

. The occupation of about 63% of the people in Bangladesh is agriculture related.

The agriculture sector contributes to a major share of about 30% to the gross domestic product.

In this presentation the impact of climate change on rice production in Bangladesh has been discussed.

Introduction

Table 1: Crop statistics of major cerealsfor the fiscal year: 2000 – 2001

Crop Area (thousand ha) Average yield

(tones ha-1 )

Current production (thousand tones)

HYV Aus 415 2.42 702

HYV Aman 2,146 2.96 4,484

HYV Boro 2,409 3.56 6,200

Other Rice 4,951 1.63 5,447

Rice Total 9,921 2.29 16,833

Wheat 592 1.85 890

Major Cereal Total 10,513 17,723

Source: BBS

. The most affected crop by floods and droughts is rice, which contributes to 93.96 % of the total cereal production in the country, followed by wheat 5.81% and other crops at 0.23%

Bangladesh has three most dominating seasons summer, winter and rainy season (monsoon) and three main varieties of rice Aus,Boro and Aman produce in respective seasons.

, Aus (March to August), Aman (June to November), and Boro(December-May).

Data used and Methodology

The long term flood data (1953-2004), temperature and rainfall data (1975-2001) of 35 observatories of the country have been analyzed to investigate the seasonal and inter-annual variability and trends.

The annual crop production data (1971-2003) of Aus,Aman and Boro have been analyzed to investigate the variation of crop production in response with climatic parameters.

88 89 90 91 92 93

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Dhaka

Mymensingh

Faridpur

Madaripur

Tangail

Chittagong

Rangamati

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Teknaf

HatiyaSandwip

Kutubdia

FeniM.Court

ChandpurComilla

Sylhet

SrimangalRajshahi

Bogra

RangpurDinajpur

Ishwardi

Khulna

Jessore

Chuadanga

Satkhira BarisalBhola

Patuakhali

Khepupara

Figure 1: The locations of thirty-five meteorological observatories.

Location of the Stations

India

India

India

Bay of Bengal Myanmar

Ranges of Mean temperature and precipitation in different seasons

MAM = March, April and May; JJAS = June, July, August and September; ON = October and November;

DJF = December, January and February.

Analysis :1

Figure 2: Annual variation of rice production in response with lowest minimum temperature.

Annual variation of rice (Aus, Am an & Boro) production in response w ith low est m inim um tem perature of Bangladesh

during 1976-2000

y = 0.0638x - 119.69

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Aus Aman Boro Lowest min

Analysis :1

• Temperature variations have an observable effect on crop yield. It has been found that the lowest minimum temperature has an increasing trend with the rate of 0.638o C per 10 years.

• Boro rice is a winter crop. It has been found that the Boro production is increasing very significantly with the increase of lowest minimum temperature.

• This increasing trend in the Boro production might also be credited in part to the scientific cultivation techniques which are increasingly being used, the proper use of fertilizers, the use of proper harvesting methods, use of hybrid seed, following of meteorological forecasts, proper use of pesticides, proper land management and increasing awareness among the cultivators.

Analysis :2

Annual variation of rice (Aus, Aman and Boro) production with response of Highest maximum temperature during 1976-2000

y = 0.0083x - 12.541

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Aus Aman Boro Highest max.Temp Linear (Highest max.Temp)

Analysis :2

• The trend of highest maximum temperature over the period 1976-2001 has a slightly increasing rate of .083 o C per ten years.

• The summer crop Aus production is decreasing significantly. The Aus production may decrease due to the increasing trend of highest maximum temperature.

• The decreasing trend of Aus production might also be related as because of flash flood due to heavy rainfall, Nor’weters, Tornados and early onset of monsoon.

Analysis :3

Annual variation of Boro rice production with periodic rainfall during 1976-2000

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Boro Rainfall (December-May)

Analysis :3

• The inter-annual variation in the amount of winter season (December to May) rainfall is little.

• Boro production, therefore, is insignificantly affected by this variable.

• However, Boro production is increasing prominently. This might be due to supplementary irrigation and other related factors.

Analysis :4

Annual variation of Rice (Boro) production with periodic rainfall during 1976-2000

y = -0.039x + 87.014

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Boro Rainfall Linear (Rainfall)

Analysis :5

Annual variation of Rice(Aus) production with rainfall during 1976-2000

y = -0.0028x + 7.5297

y = -0.0748x + 151.22

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Aus Rainfall (March-August)Linear (Rainfall (March-August)) Linear (Aus)

Analysis :5

• It reveals that rainfall during the months of March to August is in a decreasing trend.

• The Aus rice production is also decreasing significantly.

• So it may be inferred, that the decreasing trend of rainfall adversely affects the production of Aus.

Analysis :6 Monsoon rainfall in Bangladesh (June)

Trend of country averaged rainfall in June in Bangladesh during 1971-2004)

y = 2.3652x - 4235.8

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Analysis :6 Monsoon rainfall in Bangladesh (June)

• Variation of the country averaged rainfall for this month was more or less high with the exception of the period between 1988 and 1997 when the variation was less.

• The overall periodic analysis shows that the average monsoon rainfall in June has an increasing trend with the value of 2.3652 mm per year

Analysis :7 Monsoon rainfall in Bangladesh (July)

Trend of country averaged rainfall in July in Bangladesh during 1971-2004)

Figure 2.3: Trend of country-averaged rainfall in July in Bangladesh durng 1971-2004

y = -0.1375x + 801.67

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Analysis :7 Monsoon rainfall in Bangladesh (July)

• In July, it has been found that the country averaged monsoon rainfall varies from the lowest value of 310 mm to the highest value of 737 mm. The periodic analysis of average rainfall in July has shown that the variation was very high near 1974, 1978 and 1998,

• The overall periodic trend of average monsoon rainfall has a very little decreasing trend with the value of 0.1375 mm per year

Analysis :8 Monsoon rainfall in Bangladesh(August)

Figure 2.4: Trend of country-averaged rainfall of August in August in Bangladesh during 1971-2004

y = -1.6616x + 3713.1

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Analysis :8 Monsoon rainfall in Bangladesh (August)

During the overall period the average rainfall has a decreasing trend of 1.6616 mm per year.

From the analysis of country-averaged rainfall in August, it is found that the country-averaged rainfall varies from 175 to 623 mm.

The periodic variation shows that average rainfall has less variation from year to year with the higher values in 1983 and 1998 and the lowest value in 1989.

Analysis :9 Monsoon rainfall in Bangladesh(September)

Figure 2.5: Trend of country-averaged rainfall in September in Bangladesh during 1971-2004

y = 2.6662x - 4982.6

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Analysis :9 Monsoon rainfall in Bangladesh (September)

.The country averaged rainfall during September month varies from 129 mm to 583 mm.The average rainfall in September has a sharp

increasing tend of 2.6662 mm per year during the period 1971-2004.

Seasonal country-averaged rainfall over Bangladesh varies from 316 mm to 527 mm.

Overall it has also increasing trend of 1.3265 mm per year during 1971-2004.

Analysis :9 Trend of country-averaged monsoon rainfall in Bangladesh during

1971-2004

Trend of country-averaged monsoon rainfall in Bangladesh during 1971-2004

y = 1 . 3 2 6 5 x - 2 2 0 8 . 4

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Aman (J-N) production in Bangladesh (1976 –2004)

Trend of Aman Production

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Figure 2.6: Trend of country-averaged monsoon rainfall in Bangladesh during 1971-2004

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Historical flood events – extent and crop damage.

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Affected Area Crop Damage

FloodBangladesh is one of the most flood prone countries in the world. The floods are mainly caused by one or a combination of hydro-meteorological factors such as rainfall in the upper catchments of India and Nepal, in-country local rainfall, snow melt in the Himalayas and impacts of the tide levels in the Bay of Bengal. The total catchment area of the river systems is approximately 1,554,00 sq km of which only 7.5% lies in Bangladesh (Bangladesh occupies an area of 147570 sq.km). The most catastrophic floods mainly occur when the peaks of the three major rivers (Ganges,Brahmaputra & Meghna) coincide.

Bay of Bengal

Fig.1 River system of Bangladesh

Brahmaputra

Ganges

Meghna

Padma

LowerMeghna

Table 3 : Flood affected Area during Extreme Hydrological Year :(Source FFWC)

Year Affected Area

Sq.Km. Percentage

1954 36,800 25%

1955 50,500 34%

1974 52,600 36%

1987 57,300 39%

1988 89,970 61%

1998 100,250 68%

2004 55,000 38%

Table 4: Cropped area damaged due to flood

Year Flood affected area (m.ha)

Percent of total area

Cropped area damaged (m.ha)

1987 5.70 39 1.21

1988 8.90 61 2.12

1998 10.02 68 1.74

1999 3.28 22 0.13

2000 3.57 24 0.19

2004 5.56 38 0.85

Source: Economic Review Reports

Figure : Crop and Flooding Calendar of Bangladesh

Types of flood Nature of Damage

Early flash flood Boro, B.Aman

Early river over flow Aus, T.Aman, B.Aman,

Normal river over flow Aus, T.Aman ,B.Aman

Late river over flow T. Aman. B.Aman

Table 5: Crop loss (rice) due to different types of floods:

Model studyThis study considers the possible impacts of climate

change mainly on high yielding varieties (HYV) of rice and to an extant on wheat production.

For rice, six locations and for wheat three locations were chosen for the study that represent major rice and wheat growing areas of the country.

Six locations representing comparatively drier (Jessore andRajshahi), wetter (Sylhet and Mymensingh) and coastal regions (Chittagong and Barisal) of the country were chosen for investigation.Simulation was conducted for three HYV rice crops, grown during three different growing seasons a) Aus (March –August), b) Aman (July – November) and c) Boro (December – May).The simulation runs for rice and wheat were made by using CERES-Rice and CERES-Wheat models of DSSAT version 3.0 (Tsuji et al., 1994) respectively.

Data and methodology

Data and methodologyEighteen experiments (3 crops × 6 locations) for rice crop and three

experiments for wheat (1 crop × 3 locations) were created. Each experiment is composed of 11 treatments.

Three scenarios including two GCMs (Global circulation Models) were evaluated. The GCMs were CCCM (Canadian Climate Center) model and GFDL(3) (Geophysical Fluid Dynamics Laboratory) model.

For the sensitivity analyses; 0°C, 2°C and 4°C temperature rise at three levels of CO2 (330, 580 and 660 ppmv) were used.

This was done to see how the model outcome varies in response to the changes in climatic variables.

Data and methodology

The selected varieties ,Aus rice was planted on 1 April, Aman on 15 July and Boro on 1 February. Wheat was planted on 5 November.

The genetic cofficients for the varieties were not available, they were estimated by using GENCALC of DSSAT v3.0.

Crop data were collected from BRRI.

Ten years of weather data (daily rainfall, max,mini temp,and bright sunshine hours) for the sites was obtained from BMD.

RiceUnder the baseline scenario, Aus rice yields varied from a

minimum of 5.10 tonnes ha-1 in Chittagong to a maximum of 5.66 t ha-1 in Mymensing. Similarly a minimum of 5.31 t ha-1 and a maximum of 6.35 t ha-1 of Aman rice yields were obtained for Sylhet and Chittagong respectively. For Boro rice a Maximum yield of 8.61 tonnes ha-1 and a minimum yield of 8.02 tonnes ha-1 was recorded in Sylhet and Chittagongrespectively.

Yield reduction for all rice varieties in all locations were noted for both CCCM and GFDL scenarios. In case of Aus rice, yield reductions varied between 19 and 35% for CCCM scenario and between 22 and 31% for GFDL scenario at different locations

RiceFor Aman rice, yield reductions varied from 3 to

18%, and from 8 to 19% for the CCCM and GFDL scenarios, respectively.

In case of Boro rice, yields under the CCCM and GFDL scenarios were also reduced but the yield reductions ranged from 0 to 11%

Rice yields under different climate change scenarios.Chittagong Barisal Mymensing

Simulation Aus Aman Boro Aus Aman Boro Aus Aman Boro

Baseline(t/ha) 5.101 5.311 8.026 5.571 6.178 8.328 5.661 6.031 8.235

Percent Change over Baseline

CCCM -25 -18 -6 -28 -10 -3 -24 -7 0

GFDL -24 -19 -6 -25 -11 -4 -27 -14 -10

330ppm CO2+2°C -19 -24 -8 -18 -12 -4 -17 -12 -1

330ppm CO2+4°C -37 -42 -10 -40 -23 -6 -30 -20 -5

580ppm CO2+0°C 32 31 23 30 23 24 29 24 23

580ppm CO2+2°C 13 7 15 13 10 20 13 11 22

580ppm CO2+4°C -5 -10 13 -8 1 18 0 5 17

660ppm CO2+0°C 41 40 29 39 31 31 37 31 30

660ppm CO2+2°C 22 16 22 22 17 28 21 18 29

660ppm CO2+4°C 5 -1 20 2 8 25 9 12 24

Sylhet Rajshahi Jessore

Simulation Aus Aman Boro Aus Aman Boro Aus Aman Boro

Baseline(t/ha) 5.528 6.355 8.613 5.487 6.118 8.291 5.067 5.717 8.260

Percent Change over Baseline

CCCM -19 -12 -6 -31 -3 -3 -35 -4 -4

GFDL -22 -12 -11 -31 -12 -6 -30 -8 -4

330ppm CO2+2°C -16 -13 -9 -18 -11 -3 -22 -10 -2

330ppm CO2+4°C -34 -22 -13 -41 -22 -6 -47 -22 -9

580ppm CO2+0°C 29 24 22 30 24 24 34 24 24

580ppm CO2+2°C 14 9 14 13 12 21 13 14 22

580ppm CO2+4°C -3 2 8 -9 2 18 -14 5 14

660ppm CO2+0°C 37 31 29 39 31 32 44 32 32

660ppm CO2+2°C 22 15 20 23 19 28 24 21 30

660ppm CO2+4°C 6 9 14 1 10 25 -4 13 22

Rice yields under different climate change scenarios

Wheat:Under the baseline scenario, a maximum 3.32 t

ha-1 wheat yield was recoded at Mymensing and a minimum 2.90 t ha-1 was recoded at Jessore.

The yields were reduced under the CCCM and GFDL scenarios.

Under the CCCM scenario yield reductions varied from 17 to 22% and under the GFDL scenario yield reductions were as high as 58-63%.

Simulation Mymensingh Rajshahi Jessore

Baseline(t/ha) 3,317 3,265 2,901

Percent Change over BaselineCCCM -17 -21 -22

GFDL -58 -62 -63

330 ppm CO2+2°C -29 -39 -44

330 ppm CO2+4°C -59 -71 -73

580 ppm CO2+0°C 32 39 44

580 ppm CO2+2°C 4 -2 -4

580 ppm CO2+4°C -29 -43 -44

660 ppm CO2+0°C 41 49 56

660 ppm CO2+2°C 12 9 7

660 ppm CO2+4°C -21 -34 -35

Wheat yields under different climate change scenarios

Wheat:Increased wheat yields were noted for increased levels of CO2 with out any change in temperature. Yields were, however, found to have drastically reduced due to a temperature rise even at higher levels of CO2.Yield reductions for a 2°C temperature rise at 330ppm CO2 level ranged from 29% in Mymensing to 44% in Jessore and for 4°C rise of temperature it varied 59% and 73% (the same locations). Significant yield reductions were also observed for the 4°C temperature increase, even at 580 and 660ppm CO2levels. At 580 ppm yields were reduced between 29% and 44%, and at 660ppm CO2, level, the reductions ranged from 21% to 35%.

• Aggregated rice and wheat production under different climate change scenarios

SimulationAus(10t)

% Aman(10t)

% Boro(10t)

% Rice total(10t)

% Wheat(10t)

%

Baseline 0.77 - 4.02 - 6.26 - 11.05 - 1.07 -

CCCM 0.56 -27 3.75 -7 6.09 -3 9.93 -10 0.85 -20

GFDL 0.56 -27 3.51 -13

5.81 -7 9.18 -17 0.42 -61

330 ppm CO2+2°C 0.63 -19 3.48 -13

6.04 -4 9.46 -14 0.67 -37

330 ppm CO2+4°C 0.48 -38 3.00 -25

5.81 -7 7.99 -28 0.34 -68

580 ppm CO2+0°C 1.01 31 5.02 25 7.73 23

15.01 36 1.47 38

580 ppm CO2+2°C 0.87 13 4.47 11 7.49 20

13.39 21 1.06 -1

580 ppm CO2+4°C 0.72 -6 4.06 1 7.24 16

12.03 9 0.64 -40

660 ppm CO2+0°C 1.08 40 5.33 33 8.17 30

16.19 47 1.58 48

660 ppm CO2+2°C 0.94 22 4.76 19 7.95 27

14.56 32 1.16 9

660 ppm CO2+4°C 0.80 4 4.37 9 7.69 23

13.24 20 0.74 -31

• Sensitivity analyses:Rice:

• Usually, increased levels of CO2 increased all rice yields at all locations. The maximum yield increase of 44% was noted for a CO2 level of 660ppm.

• With increased temperatures, yields of Aus,Aman and Boro rice decreased for CO2 levels of 330 ppm. However, Boro yields generally increased, given a 2°C warming with CO2 levels of 580 and 660ppm.

• Even at 4°C tempera rise, yield increases were generally found for Aman and Boro rice at 580 and 660 ppm CO2 levels.

• It was observed that higher temperature reduced the yields in almost all study locations in all seasons and was particularly pronounced with a 4°C increase in temperature.

• The harmful effect of temperature rise was observed, even with increased CO2 levels.

• The adverse effect of increased temperature was more pronounced for wheat than for rice at all levels of increased CO2.

• The greatest reductions in aggregated production for both crops were noticed at 330 ppm CO2 with 4°C temperature rise. The greatest increases in aggregated production for both crops were noticed at 660 ppm CO2 with no temperature increase.

Conclusions:

Conclusions:•The results show that the effect of CCCM and GFDL scenarios on Aus and Aman rice crops were detrimental. Increased temperatures during the growing season caused grain sterility.

• At flowering, the rice plant is most susceptible to high temperature, which causes high spikelet sterility (Satakeand Yoshida, 1978). In Bangladesh, this problem is encountered in the dry season crop, grown in the drier region.

•The effect of temperature rise, on Boro rice was mild because the increase in temperature was within the tolerance level of the crop.

• Wheat was severely affected by temperature rise, even at higher concentrations of CO2.

Conclusions:The statistical analysis and the model analysis indicate that the impact of climate change on crop production in Bangladesh is indeed adverse. Given that the climatic variation maintains its current rate, in the future this impact will be more pronounced. In Bangladesh the population pressure is also affecting the sustainability of agricultural development. So the actions that must be taken are: limiting greenhouse gas emission, developing rice cultivars having high photosynthetic efficiency, developingflood tolerant modern rice cultivars with short duration for growth, substituting low yielding local varieties with High Yield Varieties, reducing dependency on rice, and reducing the population growth rate.

Thank You