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A Socioeconomic Study on Farming Practices and Livelihood Status of Haor
Farmers in Kishoreganj District: Natural Calamities Perspective
M. T. Uddin1, A. R. Dhar1 and N. Hossain 2
Abstract
The study was conducted to assess the farming practices and livelihood status
considering natural calamities in haor areas of Kishoreganj district. A total of 120
farmers were selected from Mithamoin upazila on the basis of farm size category
following stratified random sampling technique. Data were analyzed with a
combination of descriptive statistics, mathematical and statistical techniques.
Descriptive statistics showed that average farm size of the farmers was 0.73 hectare,
where 73.9% was small farmers. Majority of the farmers were engaged in C-L-F
farming system (39.2 percent) which was followed by C-P-F, C-L-P and C-L-P-F
farming systems (30.0, 18.3 and 12.5 percent, respectively). Profitability analysis
and average productivity index revealed that crop production was profitable and
productivity was high in the study areas. Estimates of transcendental production
model indicated that human labour cost, chemical fertilizers cost, organic fertilizers
cost and irrigation cost had significant impact on profitability of rice crop
production. Cropping intensity was found as 132.4% from crop intensification index.
Livelihood component framework divulged that haor people’s asset possession,
activities and strategies, well being, and external policies and institutions was
improved by their production practices. Applying severity ranking model (SRM),
early flood, drought and hailstorm were found most severe natural calamities causing
damage to farmers’ cultivable land, crop, physical assets and basic necessities.
Lower price of output, and less availability and high price of inputs were the major
problems faced by the farmers. The study recommended that input subsidy and
output price support programmes should be properly implemented and sufficient
work opportunities should be created by government and non- government
organizations to support the haor dwellers in crisis period and for moving away from
a single cropping pattern to a double or triple cropping pattern.
Keywords: haor, production practices, natural calamities, livelihood,
socioeconomic study
Introduction
Bangladesh has witnessed respectable
improvements in its economic, social and
health conditions with annual GDP growth
of 6.6% (WB, 2016). The percent of
households living below the poverty line
has declined consistently. Health and
nutrition outcomes have improved
significantly. While the overall conditions
of the country are promising, the people
residing in the haor areas are relatively poor
and cannot even meet their basic needs in
compare to that of the people in the
mainland.
Haor is basically very low lying river basin
area below the level of flood plain, which is
also similar to swamp land covered by
Bangladesh Journal of Extension Education ISSN 1011-3916
Volume 30, No. 1, 2018: 27-42 Research Article
1Department of Agricultural Economics, Bangladesh Agricultural University, Mymensingh and
2Friedrich Naumann Foundation (FNF), Dhaka
Bangladesh Journal of Extension Education
28
water almost six months of a year starting
from the monsoon (Sharma, 2010). The
haor areas of north-eastern region in
Bangladesh cover about 2.0 million ha of
area and accommodate about 19.4 million
people. There are about 373 haors located in
the districts of Sunamganj, Sylhet,
Kishoreganj, Habiganj, Netrakona,
Maulvibazar and Brahmanbaria. These 373
haors cover an area of about 858 thousand
ha which is around 43% of the total area of
the haor region (MoWR, 2012). Haor areas,
i.e., the north-eastern part of Bangladesh
has a unique landscape, where natural
pattern of flooding has created very
productive fisheries resources in the wet
season, and allowed rice to grow in the dry
season. The productivity of this wetland has
contributed to the food security and there is
a potentiality for further increases of land
for agriculture. The haor is a single cropped
area where flashflood causes crop damage
which is considered as a big threat to the
people. Many people in haor areas are
involved in fish capturing (Rahman and
Salam, 2008).
Farming is the major economic activity of
the haor region. Almost 80% of this area is
covered by Boro rice (Huda, 2004). This
single crop remains under the threat of
damage from the early flash floods.
Although the economic development of
Bangladesh is moving steadily at a
moderate pace, the haor region is still
under-developed. It is difficult to foresee
the country’s overall progress without the
development of the haor region as it covers
a significant part of the country and
population which deserves special
development initiatives. Improvements in
these regions can lead to increase in
production, employment and poverty
reduction.
The livelihood patterns of haor farmers are
much more harsh and full of uncertainties,
which is totally different from that of main
land. There are very limited and seasonal
farm and non-farm work opportunities in
the haor areas. People living in these areas
endure very insecure livelihood because of
natural calamities which cause great
vulnerability. Since the cropped land area is
being continuously shrinking over time
leading to challenge towards increasing
productivity, it has indeed become
imperative to exploit the crop production
potentiality of the haor areas; it is because
those areas usually remain under-utilized
with quite low cropping intensity (Jabber
and Alam, 1996).
A few studies related to production
practices and livelihood status of haor
people have been conducted by different
researchers which are: Nowreen et al.
(2013) evaluated the change of future
climate extremes for the haor basin area of
Bangladesh and experienced the highest
variability in both rainfall and temperature
during the pre-monsoon season when flash
floods normally occurred; Parvin (2013)
performed an economic analysis of farm
and non-farm activities with their income
lingkages in Dingaputa haor of Netrokona
district, and found that project participants’
farm and non-farm income was higher as
compared to the non-project participants’
income; Khan et al. (2012) identified the
impacts of flood on crop production in haor
areas of Kishoreganj district and revealed
that Boro rice in Rabi season was damaged
by flash flood due to unavailability of
controlling measures; Alam et al. (2011)
conducted a study on crop production in the
haor areas of Bangladesh and reported that
Rabi-Fallow-T. Aman, Vegetable-Aus-T.
Aman and Rabi-B. Aman patterns were the
potential cropping patterns; and Sharma
(2010) explored the scenario of haor
vulnerabilities and other obstacles for
sustainable livelihood development in
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
29
Kishoreganj district and showed that 71%
haor households were effectively landless
where 78.9% households suffered from
food insecurity.
The above mentioned literatures clearly
indicate that a number of studies have been
conducted on economic and environmental
prospect of haor areas but there is lack of
specific study on existing farming practices,
crop intensification and livelihood status
considering natural calamities of the haor
farmers. Therefore, to minimize the
research gap and add valuable information
on the existing notions, the study will be
very helpful to the researchers as well as
policy makers to recommend policy
guidelines regarding the stated aspects in
haor areas. The specific objectives of the
study were: i) to identify haor farmers’
socioeconomic status and address their
farming practices; ii) to examine the status
of crop intensification in terms of
profitability, productivity and cropping
intensity; iii) to address the impact of
natural calamities on production practices
and livelihood of haor dwellers; and iv) to
investigate major constraints faced by the
farmers and recommend policy options.
Methodology
Study areas and sample size
The study was conducted at four villages
namely, Islampur, Sarker Haty, Paschim
Kholapara and Dhubajhora of Mithamoin
upazila in Kishoregonj district. The villages
were selected on the basis of high
vulnerability to natural calamities. A total of
120 farmers (i.e., 30 from each village)
were selected for primary data collection on
the basis of farm size category (i.e., small,
medium and large) following stratified
random sampling technique. Primary data
were collected from the respondents by
using a structured questionnaire during
November 2017 to February 2018. Key
informant interviews (KII) and focus group
discussions (FGD) were also conducted for
data collection.
Analytical Techniques
Descriptive statistics: Descriptive statistics
like sum, averages, percentages, etc. were
calculated to identify the farmers’
socioeconomic status and address their
farming practices.
Profitability analysis: Profitability of crop
production per hectare, from the view point
of individual farmers was measured in
terms of gross return, gross margin, net
return and benefit cost ratio (undiscounted).
The formula needs for the calculation of
profitability are discussed below:
GR = P × Q ; GM = GR – TVC ;
NR = GR – (TFC + TVC) ;
BCR = GR ÷ (TFC + TVC)
Where,
GR = Gross return; P = Sales price of
the product (Tk.); Q = Yield per hectare
(unit); GM = Gross margin; TVC =
Total variable cost; NR = Net return;
TFC = Total fixed cost (Tk.); and BCR
= Benefit cost ratio.
Transcendental production model
In order to investigate the extent of
influence of the determinants on
profitability of crop production,
transcendental production model was used
(Gujarati, 2003). In the present study, the
following transcendental production model
was used to identify the level of influence
of the factors influencing profitability of
crop production in the haor areas:
Bangladesh Journal of Extension Education
30
The model was made linear in the following
form:
lnYi = lnβ0 + β1lnX1 + β2lnX2 + β3lnX3 +
β4lnX4 + β5lnX5 + β6lnX6 + β7lnX7 +
β8X1 + β9X2 + β10X3 + β11X4 + β12X5
+ β13X6 + β14X7
Where,
Yi = Net return (Tk./ha); X1 = Human
labour cost (Tk./ha); X2 = Power tiller
cost (Tk./ha); X3 =
Seed/seedlings cost (Tk./ha); X4 =
Chemical fertilizers cost (Tk./ha); X5 =
Organic fertilizers cost (Tk./ha); X6 =
Insecticides cost (Tk./ha); X7 =
Irrigation cost (Tk./ha); β0 = Intercept;
β1 to β7 = Exogenous coefficients; β8 to
β14 = Stochastic coefficients; and ln =
Natural logarithm.
Average productivity index (API): Crop
productivity was measured using average
productivity index (API). The following
formula was used for calculation (Uddin
and Dhar, 2018):
Where,
Xi = Average crop yield in the cropped
area (metric ton/ha); = Average crop
yield in the entire region (metric
ton/ha); SD (Xi) = Standard deviation of
crop yield in the cropped area (metric
ton/ha); Yi = Average harvested extent
in the cropped area (metric ton/ha); =
Average harvested extent in the entire
region (metric ton/ha); and SD (Yi) =
Standard deviation of harvested extent
in the cropped area (metric ton/ha).
The productivity grade was determined
from the productivity range which is
represented in Table 1 as follows:
Table 1 Range and grade of productivity
Range of productivity
Grade of
productivity
87.5% and above
Very high
62.5% to 87.5%
High
37.5% to 62.5%
Medium
12.5% to 37.5%
Low
Below 12.5%
Very low
Source: Uddin and Dhar (2018).
Cropping intensity index (CII) : Cropping
intensity index was constructed to measure
the cropping intensity in a given cropland
per year (Uddin and Dhar, 2018). The
following formula was used for calculation:
Cropping intensity = (AreaGC ÷ AreaNC) × 100
Where,
AreaGC = Gross cropped area (ha); and
AreaNC = Net cropped area (ha).
Livelihood component framework (LCF):
Livelihood component framework was
constructed to measure the impact of
production practices on haor farmers’ asset
possession, activities and strategies, well
being, and external policies and institutions
(Uddin and Dhar, 2017).
Severity ranking model (SRM) : The
severity of damage in haor farmers’
agricultural and livelihood activities due to
the occurrences of different natural disasters
was quantified and represented using
severity ranking model (SRM) (Uddin and
Dhar, 2017). The major components of the
model were identified as agriculture, assets
and livelihood items. The sub-components
of agriculture, assets and livelihood items
were crop, livestock, poultry, and
homestead and agroforestry; cultivable land,
household area and physical assets; and
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
31
drinking water, sanitation, education and
employment; respectively. The damage of
the natural calamities (i.e., early flood,
drought and hailstorm) were characterized
as extreme (severity point = 4), high
(severity point = 3), medium (severity point
= 2) and low (severity point = 1). The
component severity score (CSS) of each
sub-component of the model was estimated
using the following formula:
CSSN = (NE × SPE) + (NH × SPH) + (NM ×
SPM) + (NL × SPL)
Where,
CSSN = Component severity score in
case of early flood, drought and
hailstorm; NE = Number of farmers in
extreme damage level; SPE = Severity
point of extreme damage level; NH =
Number of farmers in high damage
level; SPH = Severity point of high
damage level; NM = Number of farmers
in medium damage level; SPM =
Severity point of medium damage level;
NL = Number of farmers in low damage
level; and SPL = Severity point of low
damage level.
The CSS of each sub-component could
range from 120 to 480. The model severity
score (MSS) of each sub-component was
computed using the following formula:
MSS = CSSF + CSSD + CSSH
Where,
CSSF = Component severity score in
case of early flood; CSSD = Component
severity score in case of drought; and
CSSH = Component severity score in
case of hailstorm.
The MSS of each sub-component could
range from 360 to 1440. The severity of
destruction due to natural calamities was
ranked on the basis of MSS of each sub-
component.
Findings and Discussion
Socioeconomic Status of the Respondents
The socioeconomic status of the
respondents is represented in Table 1. It is
seen that average family size of the
respondents was 6.0, which was higher than
national average of 4.1 (HIES, 2016)
whereas 66.7% member of the household
were male and 33.3% were female. In terms
of respondents surveyed, 98.3% were male
where only 1.7% were female. Majority of
them (49.1%) were under the age group of
15.01 to 55.00 years (lower than national
average of 54.8%) that are considered as
active and working group (HIES, 2016).
Most of the respondents were illiterate
(43.3%) whereas 35.9% completed primary
level of education. In terms of occupation,
65.2% and 34.8% respondents were
involved with agriculture only, and
agriculture and other income generating
activities, respectively (Table 2).
Bangladesh Journal of Extension Education
32
Table 2 Socioeconomic characteristics of the respondents
Particulars about the respondents
Percentages (%) of respondents
Family size (no.)
6.0
(Male: 66.7%; Female: 33.3%)
Sex
Male
98.3
Female
1.7
Age
0.00 to 15.00 years
21.9
15.01 to 55.00 years
49.1
Above 55.00 years
29.0
Educational level
attained
Illiterate
43.3
Primary
35.9
Secondary
15.0
Higher secondary and above
5.8
Occupational status
Agriculture only
65.2
Agriculture and others
34.8
Source: Field survey, 2018.
Land Tenancy Arrangements of the
Farmers
Table 3 reveals the land tenancy status of
two categories of farmers (i.e., small and
medium). Most of the farmers were small
farmers (73.9% which is lower than the
national average of 76.7%) (HIES, 2016).
Average farm size of small and medium
farmers was 0.33 and 1.12 ha, respectively.
The major share of the farmers’ cultivable
land was own land (57.6% and 82.1% for
small and medium farmers, respectively).
Though a small portion of land (7.1%) was
rented/leased-out by the medium farmers,
no small farmer rented/leased-out any
cultivable land.
Table 3 Farmers’ land tenancy arrangements
Farmers’ categories
% of
farmers
Average
farm size
(ha)
Land leasing arrangements (ha)
Own land
Rented/
leased-in land
Rented/
leased-out land
Small farmers
(less than 1.00 ha)
73.9
0.33
0.19
(57.6)
0.14
(42.4)
-
Medium farmers
(1.01 to 3.00 ha)
26.1
1.12
0.92
(82.1)
0.12
(10.8)
0.08
(7.1)
Source: Field survey, 2018.
Note: Figures in the parentheses indicate percentages of average farm size.
Area under Crop Production
It is evident from Table 4 that total cropped
area of the farmers was 0.73 ha. About 76.0
percent of total cropped area of the
respondents was under rice cultivation
where 7.0% and 6.0% were under spices
and wheat cultivation, respectively. A very
negligible amount of area was under
homestead forestry (0.4 and 0.1 percent for
vegetables and fruits cultivation,
respectively).
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
33
Table 4 Area under crop production
Enterprises
Cultivated area (ha)
% of total cropped area
Crop
Rice
0.553
75.8
Wheat
0.044
6.0
Pulses
0.030
4.1
Oilseeds
0.031
4.3
Spices
0.051
7.0
Jute
0.017
2.3
Homestead forestry
Vegetables
0.003
0.4
Fruits
0.001
0.1
Total cropped area
0.730
100.0
Source: Field survey, 2018.
Major Farming Practices
The major farming practices identified in
the study areas were crop-livestock-poultry
(C-L-P), crop-livestock-fish capture (C-L-
F), crop-poultry-fish capture (C-P-F) and
crop-livestock-poultry-fish capture (C-L-P-
F), which were followed by 18.3%, 39.2%,
30.0% and 12.5% respondents, respectively
(Table 5).
Table 5 Major farming practices in the study areas
Farming systems
No. of farmers
(n = 120)
Percentages (%) of
farmers
Crop-Livestock-Poultry (C-L-P)
22
18.3
Crop-Livestock-Fish capture (C-L-F)
47
39.2
Crop-Poultry-Fish capture (C-P-F)
36
30.0
Crop-Livestock-Poultry-Fish capture (C-L-P-F)
15
12.5
Source: Field survey, 2018.
Profitability of Crop Production
Boro rice is the only crop in the haor
region, and almost all the farmers produce
this crop. Profitability of Boro rice
production in the study areas is represented
in Table 6. It is observed that total cost of
crop production was Tk. 104,182 per
hectare. Gross return and net return from
crop production was Tk. 132,485 and Tk.
28,303 per ha, respectively. The BCR was
found as 1.27 which implied that farmers
could earn Tk. 127 by investing Tk. 100 in
crop production. So, it can be said that crop
farming is profitable in the study areas. The
study is supported by Islam et al. (2011)
where the authors found that all of the
farming systems were profitable.
Bangladesh Journal of Extension Education
34
Table 6 Profitability of crop production
Particulars
Tk./ha
Variable costs
Human labour
21,254
Power tiller
10,295
Seed/seedlings
8,563
Fertilizers
Chemical
7,581
Organic
3,667
Total
11,248
Insecticides
590
Irrigation
43,258
i. Total variable cost
95,208
Fixed costs
Land lease value
7,416
Interest on operating capital
1,558
ii. Total fixed cost
8,974
iii. Total cost (i + ii)
104,182
iv. Gross return
132,485
v. Gross margin (iv - i)
37,277
vi. Net return (iv - iii)
28,303
vii. BCR (iv ÷ iii)
1.27
Source: Authors’ estimation, 2018.
Factors Affecting Profitability of Crop
Production
A transcendental production model was
used conveying the determinants
influencing profitability of crop production
in the study areas. Seven explanatory
variables were identified as major factors
for this study. The estimated equation was
as follows:
lnYi = 0.044 + 0.001lnX1 + 0.133lnX2 + 0.073lnX3 + 0.070lnX4 + 0.020lnX5 + 0.012lnX6 +
0.102lnX7 + 0.131X1 + 0.030X2 + 0.077X3 + 0.038X4 + 0.142X5 + 0.108X6 + 0.069X7
Table 7 Estimates of transcendental production model
Variables
Exogenous
coefficients
p-value
Stochastic
coefficients
p-value
Value
of R2
F-
value
Constant
0.044
0.306
-
-
0.613
42.719
Human labour cost (X1)
-0.001**
0.049
-0.131
0.239
Power tiller cost (X2)
0.133
0.122
0.030
0.130
Seed/seedlings cost (X3)
0.073
0.309
-0.077
0.117
Chemical fertilizers cost (X4)
-0.070***
0.001
0.038
0.853
Organic fertilizers cost (X5)
0.020*
0.075
0.142**
0.049
Insecticides cost (X6)
-0.012
0.148
0.108
0.700
Irrigation cost (X7)
0.102**
0.026
-0.069
0.306
Source: Authors’ estimation, 2018.
Note: ***, ** and * indicate significant at 1%, 5% and 10% percent probability level, respectively.
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
35
The exogenous estimates of transcendental
production model indicates that power tiller
cost, seed/seedlings cost, organic fertilizers
cost and irrigation cost had positive
impacts; while human labour cost, chemical
fertilizers cost and insecticides cost had
negative impacts on profitability of crop
production. Again, the stochastic estimates
indicates that power tiller cost, chemical
fertilizers cost, organic fertilizers cost and
insecticides cost had positive impacts; and
human labour cost, seed/seedlings cost and
irrigation cost had negative impacts on
profitability of crop farming. The
significant exogenous coefficients
demonstrated that 1 percent increase in
organic fertilizers cost and irrigation cost
will lead to increase in crop profitability by
0.020 and 0.102 percent, respectively,
whereas 1 percent increase in human labour
cost and chemical fertilizers cost will lead
to decrease in crop profitability by 0.001
and 0.070 percent, respectively. The
significant stochastic coefficient represented
that 1 unit increase in organic fertilizers cost
will result in increase in crop profitability
by 0.142 percent (Table 7).
The value of coefficient of determination
(R2) was found as 0.613 which implied that
61.3 percent variation of dependent variable
has been explained jointly by the
independent variables, i.e., the model is
well fitted. The F-value of 42.719 meant
that all of the explanatory variables
included in the model were important to
explain the variation in the dependent
variable (Table 7). The findings can be
compared with Ahmad et al. (2005) where
the authors found from Cobb-Douglas
production model that seed, fertilizer and
sowing of carrot in September and October
were yield enhancing variables while the
yield limiting factors were high prices of
inputs, limited financial resource and
inadequate availability of labour during
peak load period.
Measurement of crop productivity
Crop productivity is defined in agricultural
geography as well as in economics as
“output per unit of input” or “output per
unit of land area” (Dharmasiri, 2009). In
this study, crop productivity was estimated
using average productivity index which can
identify the spatial distribution pattern of
crop productivity of a specific region (Table
8). It is seen that average yield in the
cropped area was 2.12 metric ton per ha,
whereas average yield in the entire region
was 1.85 metric ton per ha (Upazila
Agriculture Office, 2018). On the other
hand, average harvested extent of the
farmers was 2.10 metric ton per ha, whereas
average harvested extent in the entire region
was 1.90 metric ton per ha (Upazila
Agriculture Office, 2018). Average crop
productivity of the farmers in haor areas
was estimated at 86.4%. The result is
relatively similar with Uddin and Dhar
(2018) where the authors observed that
productivity of Aus rice in plain areas was
138.0 and 100.0 percent in stare of
government input supported and non-
supported farmers, respectively.
Table 8 Average productivity index (API)
Particulars
Index values
Productivity grade
Average yield in the cropped area (metric ton/ha)
2.12
High
Average yield in the entire region (metric ton/ha)
1.85
Standard deviation of yield in the cropped area (metric ton/ha)
0.25
Average harvested extent in the cropped area (metric ton/ha)
2.10
Average harvested extent in the entire region (metric ton/ha)
1.90
Standard deviation of harvested extent in the cropped area
(metric ton/ha)
0.20
Average productivity (%)
86.4
Source: Authors’ estimation, 2018 and Upazila Agriculture Office, 2018.
Bangladesh Journal of Extension Education
36
Cropping Intensity Analysis
Cropping intensity is explained as the
number of crops grown in a given cropland
per year. It measures the productivity of per
unit gross cropped area in a year (Bhaskar,
2009). The whole process is named as crop
intensification. Intensification of crop
production in the study areas is represented
in Table 9. It is seen that gross and net
cropped area of the farmers in the study
areas were 0.73 and 0.55 ha, respectively.
Cropping intensity was found as 132.4%.
The study considered homestead gardening
beside Boro rice production, which was the
reason of cropping intensity being more
than 100.0%. The results implied that
farmers of the haor areas could grow crops
for nearly 1.3 times per year in a particular
cropland. The result is quite similar with
Islam and Uddin (2014) where the author
found variability in crop intensity from one
farming household to the next was higher
among high intensity households than those
of low intensity households.
Table 9 Cropping intensity index (CII)
Particulars
Index values
Gross cropped area (ha)
0.73
Net cropped area (ha)
0.55
Cropping intensity (%)
132.4
Source: Authors’ estimation, 2018.
Impact of production practices on haor
farmers’ livelihood
Livelihood component framework (LCF)
depicted farmers’ engagement with
different production practices (Table 10). In
terms of farmers’ asset possession, it is
observed that 43.5 percent haor farmers’
savings and cash at hand were increased,
income for purchasing assets and
equipments was increased for 39.0 percent
farmers, and land use efficiency and
optimized uses of open water resources
were increased for 27.2 percent farmers. On
the other hand, 60.0 percent farmers
experienced increasing ecological
imbalance and decreasing environmental
condition.
It is seen that 51.2 percent farmers stated
about increased crop productivity as well as
cropping intensity in the study areas which
allowed them to grow more crops in a year.
Additional income from farming activities
had been increased according to 40.0
percent farmers. Risks and uncertainties
involved in income generation from farming
activities were decreased accordingly. But
23.0 percent farmers opined that their
involvement in other income generating
activities was decreased to some extent.
Most of the farmers’ food security condition
was improved which helped to enhance
sustainable livelihood provision. Limited
and unpredictable cash earnings due to
natural calamities were experienced by 70.2
percent farmers. Also, market access and
control of the people was increased in the
study areas. Overall, it can be depicted from
Table 10 that farmers’ livelihood was
positively influenced through their
production practices. The findings are
supported by Uddin and Dhar (2017) where
the authors declared that livelihood status of
the char dwellers was improved by their
production practices.
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
37
Table 10 Livelihood component framework
Impacts on
Outcomes
Positive effects
% of
farmers
Negative effects
% of
farmers
i) Impact of production practices on farmers’ asset possession
Human capital
Income used for health and
educational purposes
38.3
-
-
Physical capital
Income used to buy household
assets, food, machineries and
equipments, housing construction,
etc.
39.0
-
-
Financial capital
Increased savings and cash in hand,
GO-NGO aid, and reduced capital
borrowing tendency
43.5
-
-
Natural capital
Increased land use efficiency
and optimized uses of
open water resources
27.2
Reduced environmental quality,
increased ecological imbalance,
increased use of chemical fertilizer
and pesticides, biodiversity
disturbance, etc.
60.0
Social capital
Reduced gender inequality,
increased training facilities,
involvement in social groups, etc.
31.3
Conflicts within community,
political unrest, etc.
45.5
ii) Impact of production practices on farmers’ activities and strategies
Farming,
schooling and
other activities
Increased crop productivity and
cropping intensity
51.2
Reduced involvement in other
income generating activities
23.0
Increased child enrollment
45.3
Work can be shared
within household
27.5
Further reducing tradeoff
with other works
21.0
Strategies for
selecting
activities:
- Diversify
- Minimize
risk
- Maintain
liquidity
Contributes to diversification
32.0
-
-
Reduce risk and uncertainty
27.3
Increased additional income
40.0
iii) Impact of production practices on farmers’ well being
Cash
Enhanced monetary income
46.2
Limited and unpredictable
70.2
Food security
Helps to ensure
households’ food security
62.5
-
-
Sustainability
of livelihood
Contributes to
livelihood sustainability
57.0
Some earn distrust
20.5
Empowerment
Increased empowerment,
especially haor women
40.5
Lack of self managerial
capability and capacity building
of groups
28.3
Reduced
vulnerability
Cannot rely on
unpredictable earnings
30.0
-
-
iv) Impact of production practices on farmers’ external policies and institutions
Market access
Gain access to market
69.2
-
-
Control access of members
52.5
Source: Field survey, 2018.
Bangladesh Journal of Extension Education
38
Natural calamities’ impact on haor
residents’ livelihood
The people of the study areas are victim of
frequent natural calamities like early flood,
drought and hailstorm. It is seen from Table
11 that 73.8 percent farmers were affected
by early flood which was followed by
drought and hailstorm (32.5 and 21.3
percent farmers, respectively). In monetary
term, the amount of loss for early flood,
drought and hailstorm were Tk. 43197, Tk.
17950 and Tk. 12430 per household,
respectively. Hossain et al. (2017) also
found that a considerable area of
agricultural land had been submerged in
Tanguar haor basin by flash flood leading
to loss of a significant amount of crop
which is partly supportive to the findings.
Table 11 Monetary loss of farmers due to
natural calamities
Types of
natural
calamities
Percentages
of farmers
faced
Average
monetary loss
(Tk./household)
Early flood
73.8
43197
Drought
32.5
17950
Hailstorm
21.3
12430
Source: Field survey, 2018.
Table 12 Severity ranking model for impact evaluation of natural calamities
Model components
Natural calamities
MSS
SR
Early flood
Drought
Hailstorm
Severity of damage
E
H
M
L
CSS
E
H
M
L
CSS
E
H
M
L
CSS
Agriculture
Crop
54
31
22
13
366
38
52
16
14
354
65
28
15
12
386
1106
2
Livestock
and poultry
40
21
49
10
331
47
33
21
19
348
35
55
10
20
345
1024
9
Fish
46
35
22
17
350
58
19
22
21
354
39
32
40
9
341
1045
7
Homestead
and
agroforestry
62
27
12
19
372
37
26
35
22
318
56
40
13
11
381
1071
4
Assets
Cultivable
land
64
35
12
9
394
60
32
17
11
381
58
30
20
12
374
1149
1
Homestead
area
47
27
31
15
346
30
37
35
18
319
36
25
39
20
317
982
10
Physical
assets
54
41
17
8
381
61
13
32
14
361
40
34
30
16
338
1080
3
Livelihood
items
Drinking
water
62
14
34
10
368
39
25
38
18
325
43
51
14
12
365
1058
6
Sanitation
40
15
46
19
316
50
29
21
20
349
55
43
8
14
379
1044
8
Education
52
25
30
4
347
43
15
38
24
317
31
45
22
10
313
977
11
Employment
43
39
24
14
351
56
42
12
10
384
29
49
22
20
327
1062
5
Source: Authors’ estimation, 2018.
Note: E = Extreme, H = High, M = Medium, L = Low, CSS = Component severity score, MSS = Model severity
score, and
SR = Severity ranking.
Severity points: Extreme = 4, High = 3, Medium = 2, and Low = 1.
Calculation of CSS (crop) for early flood = (54 × 4) + (31 × 3) + (22 × 2) + (13 × 1) = 366.
Calculation of CSS (crop) for other natural calamities was done accordingly.
Calculation of MSS (crop) = 366 + 386 + 354 = 1106.
Calculation of CSS and MSS of other model components for all stated natural calamities were
done following the same procedure, and ranked consequently.
Impact evaluation of natural calamities
The severity of damage in haor farmers’
agricultural and livelihood activities for the
occurrences of different natural calamities
was quantified through severity ranking
model (SRM). The model was composed of
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
39
three components which are: agriculture
(sub-components: crop, livestock and
poultry, fish and homestead and
agroforestry), assets (sub-components:
cultivable land, homestead area and
physical assets) and livelihood items (sub-
components: drinking water, sanitation,
education and employment). The
destruction severity in model sub-
components was ranked according to their
model severity score (MSS). Table 12
shows that the highest MSS in this model
was 1149 and the lowest one was 977. The
level of damage was the highest in case of
cultivable land which was ranked as 1st
(with MSS 1149). It was followed by crop
(with MSS 1106), physical assets (with
MSS 1080), homestead and agroforestry
(with MSS 1071) and employment (with
MSS 1062) ranking as 2nd, 3rd, 4th and 5th,
respectively (Table 12). The result is
partially supported by Khan and Nahar
(2014) where the authors showed that
natural calamities had destructive impacts
on human lives, health, education and
property damages.
Natural Calamities’ Consequences on
Production Practices
Major consequences of natural calamities on
production practices included reduced farm
production, increased cost of production,
damaged communication system, deformed land
topography, etc. Table 13 reveals that 27.3%,
24.1%, 24.0% and 24.6% respondents stated that
the severity of natural calamities were extreme,
high, moderate and low, respectively.
Table 13 Consequences of natural calamities in the study areas
Major consequences
Degree/severity of natural calamities (% of respondents)
Extreme
High
Moderate
Low
Reduced farm production
24.8
19.8
39.0
16.4
Damaged farm infrastructure
34.0
19.0
15.5
31.5
Disrupted communication system
21.9
32.8
23.4
21.9
Increased cost of production
33.6
27.7
17.7
21.0
Higher market prices of inputs
37.1
16.2
17.9
28.8
Enhanced soil erosion
25.0
22.5
30.0
22.5
Siltation and sedimentation
21.2
33.8
29.5
15.5
Deformed land topography
29.8
23.1
14.0
33.1
Average degree/severity
27.3
24.1
24.0
24.6
Source: Field survey, 2018.
Problems faced by the respondents and
probable solutions
Table 14 represents the problems faced by
the respondents and their probable
solutions. It is seen that 93.3% respondents
stated about lower price of output and
88.3% stated about less availability and
high price of inputs which pushed them to
rank the problems as 1st and 2nd,
respectively. In terms of the solution, 75.0%
gave opinion for government subsidy on
input price, and 68.3% stated to fix the
ceiling and floor price of the output in the
market. Uddin et al. (2017) supported the
findings where the authors found high price
of inputs, lack of institutional credit, lack of
knowledge about conservation agriculture,
etc. as major problems of crop production.
Bangladesh Journal of Extension Education
40
Table 14 Problems faced by the respondents and probable solutions
Major problems
Percentages (%)
of farmers stated
Rank
Probable solutions
to the problems
Percentages (%)
of farmers stated
Less availability and
high price of inputs
88.3
2
Government subsidy on
input price
75.0
Lower price of output
93.3
1
To fix the ceiling and floor
price of the output
68.3
Poor transportation
and storage facilities
57.5
4
Improvement of road
communication and storage
facilities by local
government
67.5
Frequent occurrence of
natural hazards like
flood, storm, etc.
71.7
3
Knowledge on pre-disaster
and post-disaster
management activities
73.3
Weak market
management system
50.8
5
Improve market
administration and
supervision structure
60.0
Source: Field survey, 2018. Conclusion
The study concludes that though the haor
people were intermittent victims of natural
calamities and had limited scope for crop
production, they coped up the
circumstances with diversified production
practices. Majority of the farmers were
found as small farmers having less than one
hectare of land. The most common farming
practices were C-L-P, C-L-F, C-P-F and C-
L-P-F. The study exposed that crop
production was profitable and productivity
was high in the study areas which resulted
in a moderate cropping intensity. Majority
of the farmers experienced positive impacts
of farming practices in terms of asset
possession, activities and strategies, well
being, and external policies and institutions.
Natural calamities like early flood, drought,
hailstorm, etc. caused a immense
destruction to the agriculture, non-
agriculture and day-to-day activities of the
haor people. Cultivable land, crop and
physical assets were obstinately affected by
those natural hazards. Considering the
findings of the study, some essential policy
recommendations have been arisen which
are: moving away from a single cropping
pattern of Boro rice to a double or triple
cropping pattern consisting of pulses,
oilseeds, vegetables and Boro rice with the
help of DAE and other research
organizations. Input subsidy and output
price support to the farmers as well as
essential pre-disaster and post-disaster
actions should be properly implemented by
government to support them in the crisis
period. Moreover, GOs-NGOs should create
work opportunities for the haor residents so
that they can be involved in income
generating activities (IGAs) throughout the
year.
A Socioeconomic Study on Farming Practices and Livelihood Status of HaorFarmers in Kishoreganj
41
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