1. Climate Change, Submergence and Rice Yield: Evidence from Coastal Barisal, Bangladesh (Afsana Haque and Sarwar Jahan) Presented by Md. Ashikur Rahman Student No: MSS 141511 Economics Discipline Khulna University Presented to Mohammed Ziaul Haider, Ph.D Professor Economics Discipline Khulna University

2. Introduction Bangladesh, in the basin of Padma, Meghna, and Jamuna rivers, is the largest deltaic country in the world. On the one hand, the river- hydrology based soil ecology and climatic conditions offer fertile land for agricultural production. On the other, this makes the land vulnerable to both traditional water related phenomenon such as submergence and new ones such as – sea level rise (SLR) and more intense storm surge events. Bangladesh is gearing up for increased climatic changes. By the year 2030, estimates based on global climate models, suggest that annual mean temperature in Bangladesh will increase by 1°C, accompanied by a 5% increase in annual precipitation and a 14 cm rise in sea level.

3. Objectives of the Study Given the vulnerability of coastal farming to climatic changes, this study seeks explore more deeply how water submergence influences crop productivity. So, the main objectives of this paper is: 1. To examine the impact of submergence on rice production by comparing yields of rice cultivars in ‘high’ and ‘low submergence prone’ areas 2. To examine temporal and seasonal differences due to submergence. Also to ask how different varieties of rice are affected by submergence 3. Finally, to forecast probable rice yield loss due to SLR coupled with storm surge in the monsoon season for the year 2050. Scope of the Study In this paper, the researcher focus on the concerns climate change raises for coastal agricultural production in Bangladesh.

4. Researchers selected Barisal district as study area, which is located in the south-central part of Bangladesh. The district is crisscrossed by many rivers and faces submergence due to heavy monsoon rains, high tides and cyclonic storm surges. For the study, ten Upazila of Barisal district were first sub-divided into two categories -‘high submergence prone’ region and ‘low submergence prone’ region. Next, ten Unions from five Upazila (2 per each upazila) were selected for further examination. Six unions from ‘high submergence prone’ regions and four unions from ‘low submergence prone’ regions were purposefully selected to collect plot level data Sampling Technique

5. A comprehensive farm survey was carried out during January, 2011 in the selected Unions. 120 farmers who were owners or tenants of the plot (but not agricultural laborers) were surveyed. The survey questionnaire included questions on land holdings, cropping practices, crop production, losses and profits over the years along with their agricultural knowledge. The author tried to obtain recall data from the farmers for the period 2006-2010. In the final analysis, the author was able to use data only from113 plots. Also, in the final data set, the ratio of samples from two groups of Unions was 71:42 due to exclusion of some outliers in yield data. Survey Design and the Intervention

6. AttributesVariablesAll cases Socio-economic characteristics Distribution (Percentage ) according to firm size Small farm (operated area 0.05 to 2.49 acres) Medium farm (operated area 2.50 to 7.49 acres) Large farm (operated area is more than 7.50 acres) 47.8 35.8 1.5 Major cropHYV Aman Local aman/ T. Aman 43% 57% Plot level statistics, 2010 Average rice yield (kg/decimal) Kharif I season Kharif II season Robi season 7.20 9.46 16.70 Plot under different duration of submergence Less than 3 days 3-7 days 8-15 days More than 15 days 13.6 49.5 13.6 23.3 plots under different level of submergence Less than 0.5 meter 0.5-1.0 meter 1.0-3.0 meter More than 3.0 meter 14.8 26.2 56.6 2.5 Farmers and crop submergence in coastal Barisal Table 1: Summary statistics on sample

7. The authors tries to answer several questions related to the effect of water submergence on rice yield. I.Whether there is a difference in rice productivity in high and low submergence areas. II.Then they examine the temporal (2006- 2010) impact of submergence by comparing average yield over time and across rice varieties. They use panel data comprising both cross- sectional and recall data at plot levels from 2006-2010 for the above analyses. They also use regression analyses to estimate the effect of different submergence factors on Aman rice yield ( Kharif II rice crop, i.e. during the monsoon season between July and January month), the widely grown crop in coastal Barisal. A final aspect of their study is a projection of yield losses due to submergence in 2050. Methods

8. Table 2 shows the summary statistics of all the variables considered in the regression analysis. Table 2: Summary statistics of the regression variables VariablesDescriptionMinMaxMean Yield_2010* Yield (kg/decimal) of rice crop (including HYV and local variety) 0.5520.009.34 LNyield_2010* Natural logarithm of Yield_2010* -0.603.002.08 LN_ Input Cost Natural logarithm of total cost of input (Tk./decimal) 3.014.673.88 Elevation Dummy for land elevation; 1= high submergence prone region, 0= otherwise 010.69 Crop type Dummy for crop type; 1= local variety, 0= HYV Aman 010.55 Depth Dummy for average water depth (meter) during inundation in Kharif II season, 2010; 1=More than 0.75 m, 0=Otherwise 010.66 Duration Dummy for maximum duration (days) of inundation in Kharif II season, 2010; 1= More than 7 days, 0=Otherwise 010.36 Crop typeX Depth Interaction variable of Crop type and Depth 010.39 Crop typeX Duration Interaction variable of Crop type and Duration 010.28 *Dependent variable and season is Kharif II, 2010

9. #Difference in rice yield in high and low submergence areas Results Table 3: Average rice crop yield in ‘high’ and ‘low’ submergence prone regions, Barisal district, 2006- 2010 Average yield rate (Kg/decimal) of rice varieties, 2006-2010 All varietiesHYV AusHYV AmanHYV BoroLocal Paddy High submergence prone region 10.77 (5.41)8.54 (4.73)10.13 (5.41)15.17 (8.42)10.49 (4.78) Low submergence prone region 12.12 (8.59)10.27 (3.21)11.76 (6.82)20.64 (11.45)6.31 (3.79) Table 3 shows during 2006-2010, average annual rice yield (all varieties) is higher by almost 10% in ‘low submergence prone’ areas relative to ‘high submergence prone’ areas. Yield for HYV is always higher in low submergence areas relative to high submergence areas. In contrast, local paddy (T. Aus, T. Aman and Boro) yield is higher in ‘high submergence areas’ (relative to low submergence areas) by approximately 40% relative to ‘low submergence areas’. These deviations between mean yields are statistically significant.

10. Average yield rate (Kg/decimal) of rice varieties according to depth of submergence water and duration of inundation.5m- 1m1m- 3m HYV AmanLocal AmanHYV AmanLocal Aman < 3 days15.64 (0.94)--- 3-7 days13.33 (4.71)10.98 (3.99)7.55 (5.24)9.71 (4.80) 8-15 days-6.69 (4.19)7.80 (2.54)9.10 (1.94) 15 days >7.35 (2.47)8.40 (5.09)-7.26 (2.49) Table 4: Average yield of Aman varieties by depth of submergence water and duration of inundation, Barisal district, 2010 Table 4 shows the decline in yield in both HYV and local Aman when they experience rising levels of submergence for longer time horizons. HYVs do reasonably well if the water level in the field is below one meter. If water height exceeds this range, however, local Aman has higher yields relative to HYV.

11. Model 1Model 2Model 3Model 4 R20.1330.1770.1060.108 Constant5.53 (4.79)6.17 (4.71)9.91 (5.21)*9.69 (5.28)* Crop type-0.39 (1.04)-2.97 (1.69)*-0.26 (1.07)-0.00 (1.28) Depth-2.80 (1.09)**-4.77 (1.48)*** Duration-2.64 (1.2)**-1.98 (2.15) Crop typeX Depth3.93 (2.07)* Crop typeX Duration-0.90(2.40) Elevation2.05 (1.14)*1.64 (1.13)0.33 (1.21)0.43 (1.24) Ln_Input Cost1.15 (1.23)1.36 (1.20)0.07 (1.27)0.08 (1.28) Table 5: Results of regression analysis (Dependent variable:Yield_2010;n=74) Standard errors in parentheses; ***, ** and * indicate significance at 1%, 5% and 10% level respectively. Table 5 presents the estimated ordinary least square (OLS) regression results with Aman yield as the dependent variable. Alternative models are estimated to assess the robustness of the estimates to changes in submergence variables. Models 1 and 2 regress yield on depth and other variables, while in Models 3 and 4 we regress yield on duration and other independent variables.

12. An interesting result - though statistically insignificant in three models, but significant in Model 1- is that yield is higher in high submergence areas. Another interesting result in Model 2 is that the coefficient of the interaction between crop type and depth is positive and significant. Models 1 and 2 in Table 5 both depict significant negative effects of inundation depth on yield. Other factors being constant, Model 1 and 2 estimate around 2.8 kg/decimal and 4.8 kg/decimal yield loss due to more than 0.75 m water in the field respectively. As shown in Model 2, other things being constant, yield is lower with local varieties relative to HYV seeds. Model 3 and 4 show the results of duration of inundation on yield. Model 3 estimates around 2.6 kg/decimal decrease in Aman harvest because of seven days or longer submergence. Overall, Table 5 suggests that depth of submergence water has a stronger negative effect than duration of submergence on Aman yield.

13. Inundated Aman area (decimal) Newly inundated area (decimal) in 2050 Average Aman yield (kg/decimal) with 3-7 days inundation in 2010 (and by assumption for 2050) Decline in average productivity (kg/ decimal) with increased level of inundation Yield loss in 2050 (kg) Y 2005Y 2050 1234=(3-2)567=(6*4) Uninundated---12.60-- Below 1m34147855292061187727612.5512.60-12.55=0.0593864 1m-3m14560368149601633997958.5312.55-8.53=4.021607176 Above 3m91913641026467310733090.008.53-0=8.539155326 Total27166517305168973350380--1085636 6 Table 6: Land inundation and yield loss of Aman rice in 2050

14. Column 6 estimates total average yield losses in 2050 based on the total area expected to be inundated. Thus, they estimate that there will be a net loss of 10,856 tons (ranging from 3,661 ton to 18,052 ton) of Aman rice in 2050 because of submergence. This number amounts to about 5% of total Aman production in Barisal district in 2009-2010. The additional area of Aman that is likely to be submerged in 2050 is shown in Table 6 - additional area that is expected to be under less than one meter, 1-3 meters, and more than 3 meters of water in 2050 are 1,877,276 decimal (7,600 ha), 399,795 decimal (1,619 ha) and 1,073,309 decimal (4,345 ha) respectively. In total, they expect 5,964 ha of Aman producing land to face over one meter of inundation in 2050. However, the researcher do not bring into consideration in this study, the growth stage of rice plants during submergence.

15. They find that rice cultivation in Barisal district is at risk due to continuous submergence with high levels of water for considerable time period during the cropping seasons. On average, farmers faced a maximum of nine days of submergence of the main rice crop (Aman crop in the Kharif II season) in 2010. Eighteen percent of total Aman fields in our study area were under 0.5-1 meter of water for 3-7 days, while 31% were under 1-3 meters of water for the same number of days. Some plots suffer even more water for longer duration. Their study results emphasize that high yielding rice cultivars are more vulnerable in this region to water stress relative to local low yielding varieties. Conclusions and Policy Implications This study investigates temporal and seasonal variation in rice productions in coastal Barisal because of different levels of water submergence. Authors aim to understand the consequences of future climate change and SLR by examining how productivity is currently affected by water inundation into paddy fields.

16. Sustainable harvesting in Barisal may be possible with the use of modern submergence tolerant rice varieties accompanied by proper agricultural management practices. In 2010, the Bangladesh Rice Research Institute developed two submergence tolerant rice varieties, Dhan51 and Dhan52, for flash flood prone areas In 2050, they expect an additional 13,564 hectares of Aman fields in Barisal district, or 61% of agricultural land, to face three to seven days of submergence from water of varying heights because of SLR and increased storm surge events. Unless there are technological and productivity changes, this means that there will be a 5% reduction in total Aman (HYV and local Aman) production in 2050 relative to 2010. Development and introduction of low cost water control technologies such as rain water harvest and water conservation may also increase the possibility of modern varieties adoption.

17. Thank You