Profitability and adoption of improved shrimp farming technologies in the aquatic agricultural systems of southwestern Bangladesh

Aquaculture 428–429 (2014) 61–70

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Profitability and adoption of improved shrimp farming technologies in the aquatic agricultural systems of southwestern Bangladesh Manjurul Karim a,⁎, R.H. Sarwer a,1, Michael Phillips b, Ben Belton a,1 a b

WorldFish, House 22B, Road 7, Block F, Banani, Dhaka 1213, Bangladesh WorldFish, Jalan Batu Maung, Batu Maung, 11960 Bayan Lepas, Penang, Malaysia

a r t i c l e

i n f o

Article history: Received 1 August 2012 Received in revised form 28 December 2013 Accepted 25 February 2014 Available online 5 March 2014 Keywords: Shrimp Southwest Bangladesh White spot syndrome virus Adoption Profitability Aquatic agricultural systems

a b s t r a c t This paper assesses factors influencing adoption of new shrimp aquaculture technologies within aquaticagricultural farming systems in southwestern Bangladesh. The impacts of three new technologies were assessed: two Modified Traditional Technologies (MTT 1 and MTT 2) and a Closed System Technology (CST). A total of 789 farmers from 10 sub-districts in Khulna Division were surveyed randomly, including a control group of 350 farmers using traditional technologies. Farmers gained significantly higher (P b 0.05) net returns when practicing improved shrimp farming systems as compared to traditional farms. The profitability of CST farms was more than double that of MTT farms, and the profitability of MTT farms was more than that of traditional farms. Similar (P N 0.05) financial benefit was derived from adoption of MTT1 and MTT2 technologies. Feed use, stocking density, gher size and white spot syndrome virus incidence were key factors associated with the economic returns of CST farms, while various supplementary feed inputs made a significant positive contribution towards increased return for the MTT farms. Lime was an important input for increased return both for MTTs and traditional farms. Farmer age and access to training influenced adoption of both technologies, and gher size and access to financing were significant for the more intensive Closed System Technology (CST). © 2014 Elsevier B.V. All rights reserved.

1. Introduction Black tiger shrimp (Penaeus monodon) contributes significantly to the national economy of Bangladesh, with shrimp exports being the second highest export income earner in the country (Bangladesh Bureau of Statistics, 2008), worth over US$ 457 million (DOF, 2012). In 2008–2009, Bangladesh farmed just under 98,000 tonnes of shrimp and prawn, of which the majority was black tiger shrimp, and exported more than 54,000 tonnes (processed including both head-on and headoff) (DOF, 2012). More than 244,000 ha of land in southern Bangladesh is now reported by the Department of Fisheries as registered for shrimp or prawn culture (Belton et al., 2011) and the sub-sector supports the livelihoods of more than 600,000 people, including farmers and service providers such as traders and processors (USAID, 2006). (See Fig. 1.) Farming in Southwest Bangladesh has undergone rapid growth and change since the 1980s as a result of a strong global market for shrimp and prawn and high profits to producers of these crops, the development of hatcheries and expansion of the area under production (Alam and Phillips, 2004; BFFEA, 2007; Islam et al., 2003). Land and waterscapes in Southwest Bangladesh are profoundly interconnected. These coastal

⁎ Corresponding author. Tel.: +880 2 8813250; fax: +880 2 8811151. E-mail addresses: [email protected], [email protected] (M. Karim). 1 Tel.: +880 2 8813250; fax: +880 2 8811151.

http://dx.doi.org/10.1016/j.aquaculture.2014.02.029 0044-8486/© 2014 Elsevier B.V. All rights reserved.

and freshwater agro-ecosystems, in which aquatic productivity contributes significantly to household food security, nutrition and income, are collectively referred to as ‘Aquatic Agricultural Systems’ (AAS) (WorldFish Center, 2011). Shrimp and prawn production in Bangladesh takes place in ghers — modified low-lying rice fields with raised dykes, used for seasonal production of shrimp, fish and other aquatic products. Shrimp (mainly Penaeus monodon) farming takes place primarily in saline areas, whereas freshwater prawn (Macrobrachium rosenbergii) farming takes place primarily in freshwater areas. Because salinities vary seasonally and the salinity range of the two species is partially overlapping, farmers often stock shrimp, prawn in the same system, either sequentially or concurrently (Ahmed et al., 2002; Barmon et al., 2004). However, farms surveyed for this paper were located mainly in brackish water or mixed salinity areas, and their production was dominated by shrimp. Thus, in the remainder of the paper we refer to them as shrimp farming systems. Shrimp farming in Bangladesh can be broadly categorized into three types, according to the level of inputs used: • Extensive culture, where shrimp depend entirely on naturally occurring organisms in the ponds for their growth • Improved extensive culture, which utilizes both natural productivity, application of fertilizer and occasional supplementary feeding to enhance growth

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M. Karim et al. / Aquaculture 428–429 (2014) 61–70

Fig. 1. Map showing major shrimp farming and sampled farmers/project working locations.

• Semi-intensive culture, in which shrimp obtain nutrients primarily from artificial feeds and are stocked at higher densities, necessitating management practices such as aeration and pond drainage to maintain water quality employed. Numerous factors have shaped the development of shrimp farming in Bangladesh. These include shrimp disease, changes in salinity, trade-related shocks, social conflicts and rising input prices, but farming has remained based largely on traditional practices with a low per unit area productivity of 160–230 kg/ha/year (Belton et al., 2011). Productivity is low compared to most neighboring shrimp-producing countries in Asia (Alday-Sanz, 2010; Gammage et al., 2006; Karim et al., 2012; Nguyen and Ford, 2010). Attempts to intensification of shrimp farming were not quite successful due to high risk of crop losses due to White Spot Disease (WSD) caused by white spot syndrome virus (54% crop loss reported by Karim et al., 2012), requirement of high investment, and no insurance provision on shrimp farming in Bangladesh. In response, government and non-government organizations have implemented

various programs to improve the productivity and sustainability of shrimp culture in southern Bangladesh (Shrimp Foundation, 2012). WorldFish's Shrimp Quality Support Program (SQSP), funded by USAID and launched in December 2005, was one such program. SQSP aimed to ‘improve the quality and quantity of Bangladesh's shrimp export in socially and environmentally acceptable ways’ (SQSP, 2006). This was attempted by providing assistance to shrimp farmers in Bagerhat, Khulna and Shatkhira districts to facilitate the adoption of improved production technologies. Most farmers selected by the project were small-scale, typically owning or operating less than 2 ha of ghers. In order to achieve its goals, the project promoted two alternative systems: a ‘Modified Traditional Technology’ (MTT), which, as the name suggests, was based on improvements to the widely used traditional culture system, and a semi-intensive ‘Closed System Technology’ (CST) (See Table 1.). A key aim of both systems, alongside increasing productivity, was to reduce the incidence of White Spot Disease (WSD), caused by the White Spot Syndrome Virus (WSSV). Better management practices, including the use of Polymerase Chain Reaction

M. Karim et al. / Aquaculture 428–429 (2014) 61–70

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Table 1 Key management practices for different shrimp farming technologies. Source: adapted from Karim et al. (2012). Key management features

Traditional

MTT (1 and 2)

CST

Gher dike

Low and insecure dike

Moderately raised

Fencing with net Nursery Nursery water treatment Reservoir Reservoir water treatment Grow out water Grow out gher water exchange

No barrier No Not applicable No Not applicable Not treated Water is exchanged when needed

Barrier on nursery only PL are nursed for 15–20 days Treated with bleaching powder No Not applicable Not treated Water is exchanged when needed

WSSV-negative screened PL stocking Stocking Density Supplementary feed use

No 3–8 batches of PL are released @ 0.8–1 PL/m2 per time No

Harvesting

Multiple harvesting

MTT1: Yes and mixed with non-screened PL MTT2: Yes 3–5 batches of PL are released @ 0.8–1 PL/m2 per time Regularly and irregularly in nursery and grow out respectively Multiple harvesting

Dikes are cleaned and compacted to prevent entry of virus-contaminated outside water Barriers are established to prevent carriers entering the gher PL are nursed for 15–20 days Treated with bleaching powder Yes Water disinfected using bleaching powder Water disinfected using bleaching powder No water exchange during production cycle (water topped up from reservoir) Yes

(PCR)-tested post larvae (PL) and the nursing of PL on-farm prior to stocking, were advocated in order to support this goal. Social, economic and environmental aspects of Bangladesh's shrimp industry have been well documented in the literature covering topics including conflicts, impacts on livelihoods, environmental sustainability, productivity, diseases and governance (e.g., Alam et al., 2007; Deb, 1998; FAO, 2007; Ito, 2002; Karim et al., 2012; Rahman et al., 2006; Rasul and Thapa, 2004; Zhen and Routry, 2003). Ling et al. (1999) attempted to determine comparative advantages and resource use efficiencies among different Asian shrimp-producing countries. Alauddin and Tisdell (1998) and Haque (2011) explored the economic profitability in shrimp culture in Bangladesh. Analyses assessing the technical efficiency of traditional and improved shrimp aquaculture have also been conducted by a number of researchers for countries including Bangladesh (Kumar et al., 2004; Rahman et al., 2010; Uma Devi and Prasad, 2004). This paper adds to the literature on the economics of shrimp culture by examining factors that affect the profitability of the three ‘improved’ farming technologies promoted by WorldFish under SQSP. In addition, the paper analyzes factors contributing to the adoption or rejection by project participants of the production systems promoted by the project. Attaining a clearer understanding of these issues is important if Bangladesh's shrimp production and associated economic benefits are to be increased sustainably in future. 2. Methods 2.1. Description of technologies The main features of traditional, CST and MTT shrimp farming technologies are listed in Table 1. Use of WSSV-negative PL was promoted in CST and MTT systems on the basis that stocking disease-free PL would minimize the likelihood of infection later in the culture cycle (Karim et al., 2012; Padiyar et al., 2011). Polymerase Chain Reaction (PCR) laboratory testing was used to supply WSSV-free shrimp PL to project farmers. The PCR lab facilities were established in 2007 with technical and instrumental support from the Shrimp Quality Support Project of WorldFish funded by USAID. The lab was later modified with the assistance from the Food and Agricultural Organization (FAO) under its Shrimp Seed Certification Project in 2009. The lab used an IQ2000 (WSSV Detection and Prevention System) kit, certified by World Organization for Animal Health (OIE) for detecting and isolating viruses from shrimp brood, nauplii and PL. The costs of testing shrimp PL were supported by the project; still due to standard packing and transportation system, the price of tested PLs was 10–20% higher than that of non-tested PL, but similar to that of PL from wild sources.

2 batches of PL are released @ 6–8 PL/m2/cycle Commercial pelleted feeds are used both in nursery and grow out gher Complete harvesting at the end of each cycle

Another key management practice promoted to all farmers in the project was on-farm nursing of PL prior to stocking. The Modified Traditional Technology was divided into two sub-categories based on with the observation that many project farmers stocked both WSSVscreened and non-screened PL (MTT 1). Farmers who stocked only WSSV-screened PL as per project recommendations were considered to be practicing MTT2. 2.2. Technology promotion activities The SQSP project began operating in early 2006 and promoted CST and MTT systems by training 1355 shrimp farmers through Farmer Field School (FFS), in groups of 20–25. The project chose these shrimp farmers from five upazilas (sub-districts) in Bagerhat; two upazilas in Khulna and five upazilas in Satkhira (Fig. 1). Many farmers in these areas produce both shrimp and prawn. Only a relatively limited number of shrimp farmers were supported by the project due to budgetary constraints and the pioneering nature of the extension initiative, which was the first to promote improved shrimp farming practices in the region. The project supported employees of private sector shrimp collection points, known as depots, to provide technical support and advice. Farmers who adopted CST or MTT technologies after attending FFS were ensured access to a supply of PCR-tested shrimp PL to stock their ghers. One hundred and twenty farmers who had previously practiced traditional techniques began practicing CST during the project period, while 647 began practicing MTT. Another 588 farmers who had discontinued their participation in training and still followed traditional method, were considered as a control group for comparison with those adopting CST and MTT systems. 2.3. Farm survey A farm survey to assess the productivity and profitability of the technologies promoted by the project and identify factors influencing the likelihood of their adoption by farmers was conducted at the end of shrimp production season in December of 2006. A random sample totaling 88 out of 120 CST farmers, 315 out of 611 MTT1 farmers and 350 out of 588 traditional farmers was surveyed. All of the 36 farmers who followed the MTT2 system were surveyed, for a total of 789 shrimp farmers (Table 2). The number of farmers surveyed was sufficient to ensure 95% confidence level. In most cases, male shrimp farmers were the survey respondents, but their wives were also present during the interviews in some instances. Farmers were randomly selected from all the project working areas which, as Fig. 1 shows, were representative of the major shrimp farming areas.

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Table 2 Sampling distribution by shrimp and prawn farming systems and districts. Shrimp and prawn farming system

Traditional MTT(1) MTT(2) CST Total

Table 3 Test for equality of means between MTT1 and MTT2 systems.

Study districts Bagerhat

Khulna

Satkhira

Total

179 161 17 36 393

48 34 14 27 123

123 120 5 25 273

350 315 36 88 789

2.4. Data analysis Data were entered in Microsoft Excel, and screened, cleaned, summarized and analyzed using SPSS software. Local currency was converted into US$ with the conversion rate of BDT 70.00 per US dollar (www. oanda.com —average for Jan–Dec 2006). Some descriptive analyses were conducted using standard error of mean to compare socioeconomic characteristics of farmers adopting the different shrimp production systems. Economic analyses were performed to assess the profit margins of the improved farming systems. Both fixed and variable costs were included in the profitability analysis. Variable costs included: organic fertilizer (cow dung); inorganic fertilizers (Urea, Triple Super Phosphate (TSP), Muriate of Potash (MP)); chemicals (oxygen tablets etc.); shrimp and prawn PL; fin fish seed; feed; fuel used for water exchange; labor (including family and hired labor); other miscellaneous costs (e.g. vitamins, feed transportation costs); and miscellaneous nondurable items (bamboo, wood, rope, tubes, bulbs, batteries, torches). Labor was converted to standard person-days (8 h/day) and final figures obtained through multiplying the person-days with respective daily wage rate. Returns refers to earnings gained from production of shrimp, prawn and fin fishes (e.g. rohu (Labeo rohita), catla (Katla katla), mrigel (Chrhinnus chrinnosis)). Finally, benefit cost ratio (BCR) was also calculated. Profitability analysis was conducted as follows: Π ¼ ΣQiPi–ΣXjPj

Data types

t

Sig. (2-tailed)

Data types

t

Sig. (2-tailed)

Gher size Molasses Rice bran Lime

−0.29 0.09 0.43 0.63

0.78 0.93 0.67 0.53

−0.06 −1.79 0.04 0.44

0.95 0.07 0.97 0.66

Wheat bran Soybean oil cake

1.40 2.02

0.17 0.05

1.97 −0.53

0.05 0.60

Commercial feed Homemade feed

0.68 −1.09

0.50 0.28

−0.71 0.11

0.48 0.92

Shrimp PL Returns

−2.84 0.50

0.03 0.62

Age of farmers Years of schooling Family size No. of adult male members Farming experience Number of cultured ghers Total gher areas Project selected gher size No. of gher owners Household income

−1.32 −1.41

0.19 0.17

Accordingly, one dummy variable was used and a single multiple regression model (written as MTT) was fitted for MTT instead of fitting two separate models for MTT1 and MTT2. The dummy variable MTT was fitted by numbering MTT1 as value 1 and MTT2 as value 0. A multiple linear regression function was employed to identify associations between shrimp production and profitability and the type and quantity of inputs used. Farmers used a variety of inputs depending on the culture system adopted. It was hypothesized that the following factors might affect economic returns: gher size, gher age, use of urea, use of TSP, labor (hired + family), shrimp stocking density, shrimp stocking frequency, lime, use commercial pelleted feed or homemade feed, application of molasses, WSSV incidence, sludge removal, use of reservoir for water exchange, algae control, use of feed storage and drying ghers before stocking. The definition and method of calculation of these factors are presented in Table 4. The following regression function was employed to fit these factors for all three shrimp farming technologies (traditional, MTT and CST). Y ¼ a þ βi X i þ ei where

where, Π Q P X i j

profit quantity of output (shrimp and fish) price of output (shrimp and fish) and inputs (seed, feed, fertilizer, medicine, equipment, and labor) quantity of the inputs number of outputs number of inputs

(Dillon and Hardaker, 1993) Moreover, to see the significant differences among production and cost items of the adopted technologies post hoc test and Analysis of Variance (ANOVA) were done. There was no difference in management practices between MTT1 and MTT2 farming, except for the exclusive stocking of screened shrimp PL in MTT2. A t-test for equality of means was conducted for inputs assumed to be the major contributing factors linked with returns. This indicated that the use of inputs and returns in MTT1 and MTT2 farms did not differ significantly, other than the use of soya bean oil cake as feed and PL stocking frequency (Table 3). The t-test also confirmed that there were no significant differences between these two groups for factors assumed to be linked with system adoption (Table 3). According to econometric theory, it is not necessary to run separate models for similar types of production system. Rather, it is appropriate to use a dummy variable to explain the situation. Thus it was decided to run single regression function for the MTT systems, rather than two separately regression functions, following Doran and Guise (1984).

Y a X β e i

returns from shrimp farming constant factors coefficient of factors random error number of factors (1, 2, 3,........)

(Gujarati, 2003) As noted above, some farmers who had received training in improved production technologies (CST and MTT) chose to adopt them, while others did not. The following factors were all considered likely to influence adoption: age; length of education; number of adult family members; years of experience of aquaculture; number of ghers owned; size of the selected gher; number of owners of the selected gher; whether the farmer had received training; and household income. It was assumed that younger and relatively more educated farmers would be less risk averse than older less educated farmers, and thus more likely to adopt new technology. It was also assumed that families with more adult members would be more likely to adopt new technology. Length of experience with aquaculture was also assumed likely to influence adoption of new technology, but could be either a positive or negative factor. Gher ownership was assumed to be a positive influence on adoption, on the basis that the greater the number of ghers available to a farmer, the greater the freedom they would have ghers to apply new technology in one of their ghers. Gher size was hypothesized to be negatively associated with likelihood of adoption, as the costs applying new technology in a large gher would be greater than those in a small gher.

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Table 4 Definition and methods of calculation of hypothesized factors. Factors

Definition and methods of calculation of factors

Factors included in the regression analysis by technology

Dummy for WSSV affected (No = 1, otherwise = 0) Gher area (ha) Molasses (US$/ha)

Whether ghers were affected by virus in the study year (in 2006) was considered. If a gher was not affected, it is counted as 1 and if affected, it is counted as 0. Gher area includes both water and dike, and local unit is converted into hectare. Molasses was used in gher to increase natural feed and value of used molasses calculated in US$ per hectare of gher area. Value of used urea in ghers was standardized as US$ per hectare of gher area. Value of used TSP (Triple super phosphate) in ghers was standardized as US$ per hectare of gher area. Value of used lime in ghers was standardized as US$ per hectare of gher area. Both family and hired labor were considered. Total spent hours by human labor in the ghers were first converted into man-days divided by 8 h. Then the man-days are multiplied with wage rate to get the value of the human labor used. Age of a gher is calculated by deducting newly construction year from the year of data collection for this study. Farmers stocked shrimp seeds several times during production season. Numbers of times seed stocked are calculated considering that the seeds were stocked how many times in a season. Whether farmers removed slug from the bottom of their ghers are considered and counted. If a farmer removed slug, it is counted as 1 and if the farmer did not remove, it is counted as 0. Whether farmers used water reservoir to exchange water of grow out gher. If a farmer uses reservoir, it is counted as 1, if not, it is counted as 0. Whether farmers controlled algae or not was considered. If a farmer controls algae, it is counted as 1, if not it is counted as 0. Whether farmers used feed storage to stock shrimp feed was considered. If a farmer has feed storage it is counted as 1, if not, it is counted as 0. During gher preparation, whether farmers dried their ghers or not was considered. If a farmer dries his/her gher it is counted as 1, if not, it is counted as 0. This is commercial or processed feed is available in the market and is also called industrial feed. Value of amount of industrial feed used in gher is calculated. Different types of ingredients like wheat bran, rice bran, flour, etc. were used to prepare homemade feed. Total cost for preparing the homemade feed is calculated. Average depth of gher during rainy season is considered and unit is expressed as feet. Value of used Bagda/shrimp PLs was calculated. Value of used Golda/prawn PLs was calculated. Value of used fish fingerlings was calculated.

Yes

Yes

Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes Yes Yes No

Yes Yes Yes Yes

Yes Yes No No

Traditional MTT CST

Urea (US$/ha) TSP (US$/ha) Lime (US$/ha) Human labor (US$/ha)

Gher age (year) Number of times seed stocked (no./year) Dummy for slug remove from bottom of gher (yes = 1, otherwise = 0) Dummy for use reservoir (yes = 1, otherwise = 0) Dummy for algae control (yes = 1, otherwise = 0) Dummy for feed storage use (yes = 1, otherwise = 0) Dummy for gher drying (yes = 1, otherwise = 0) Industrial feed (US$/ha) Homemade feed (US$/ha) Average water depth of gher (feet) Bagda seed (USD/ha) Golda seed (USD/ha) Fish seed (USD/ha)

Ownership of a gher by multiple owners was thought likely to hinder adoption difficulties in reaching consensus among the owners. A large proportion (58%) of project farmers did not participate the training sessions on regular basis, and it was assumed that the adoption rate would be higher among farmers who had attended the sessions. Household income was also considered an important factor as households with higher income are more likely to be able to risk adopting a new technology, as well as possessing greater capacity to investment in new enterprises. The following multinomial logistic regression was applied in order to determine factors might be significantly associated with the adoption of improved shrimp production technologies: P ðY Þ ¼

constant predictor variable coefficient of predictor variable number of predictors (1, 2, 3......) error term

(Gujarati, 2003) 3. Results 3.1. Farmers' socioeconomic characteristics

1 Farmers' average age was 37.8 years, and there was no difference (P N 0.05) in the ages of farmers adopting different shrimp farming technologies. Farmers were, on average, educated up to secondary school (9.2 years of schooling), with those adopting CST technologies relatively more educated than traditional farm owners. Mean household size was 5.1. There was no significant difference in household size (P N 0.05)

1 þ e−ðb0 þbi Xi þ∈i Þ

where, P(Y) e

b0 X b i ∈

is the probability of Y occurring base of natural logarithms

Table 5 Socioeconomic characteristics of shrimp farmers. Socioeconomic characteristics

Traditional

MTT(1)

MTT(2)

CST

All categories

Age of farmers (years) Farmers' schooling (number of years) Family size (number) Shrimp farming experience (years) No. of cultured gher Total owned gher area (ha)/household Gher area (ha)/household under improved technology Number of project gher with N1 operator

37.06 (±10.38) 8.51 (±3.64) 5.31 (±2.28) 8.08 (±6.33) 1.69 (±1.06) 1.99 (±5.63) N/A 3%

37.97 (±10.70) 9.11 (±3.61) 4.96 (±1.98) 9.67 (±6.07) 2.04 (±1.31) 3.04 (±5.55) 1.53 (±2.69) 5%

38.08 (±8.99) 10.28 (±4.31) 4.94 (±2.48) 7.60 (±4.96) 2.19 (±1.67) 4.69 (±13.90) 1.48 (±1.86) 14%

40.15 (±10.49) 11.78 (±2.99) 4.86 (±1.85) 9.88 (±7.47) 2.80 (±1.78) 23.75 (±103.03) 0.37 (±0.17) 2%

37.82 (±10.49) 9.20 (±3.73) 5.11 (±2.13) 8.89 (±6.36) 1.98 (±1.33) 4.96 (±35.38) 1.17 (±1.93) 4%

Note: Figures in parentheses indicate standard error of mean.

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Table 6 Level of production from different shrimp farming technologies. Technologies

Shrimp (Kg/ha/year)

Prawn (Kg/ha/year)

White fisha (Kg/ha/year)

Traditional (n = 350) MTT1 (n = 315) MTT2 (n = 36) CST (n = 88) Total (n = 789) F value (with 3df between group and 785df within group)

168.73a (±118.41) 310.38b (±164.63) 218.09ab (±133.89) 1410.59c (±926.15) 366.04 (±503.87) 382.82

76.51a (±121.63) 111.30b (±131.90) 205.33a (±239.57) 0c 87.74 (±133.43) 28.87

155.18a (±196.68) 193.57b (±182.26) 223.14a (±371.14) 0c 156.30 (±199.94) 127.45

Note: Figures in parentheses indicate standard deviation of mean. Mean values followed by different subscript letters indicate significant differences (P b 0.05) based on ANOVA. a Fish species raised in ghers are commonly collectively referred to as ‘white fish’. These include Rohu (Labeo rohita), Catla (Catla catla), Mrigel (Cirrhinus mrigala), Silver carp (Hypophthalmichthys molitrix), Mullet (Rhinomugil corsula) and Bata (Labeo bata).

among those practicing the different technologies (Table 5). Farmers had around 8.9 years of experience in shrimp culture, with no significant (P N 0.05) differences among households adopting the three farming technologies. Households practicing the traditional system owned an average of 1.69 ghers, totaling 1.99 ha. Farmers practicing CST owned significantly larger (P b 0.05) areas of gher (23.75 ha) than those practicing other technologies (Table 5). However, CST farmers rented out higher numbers of their ghers than farmers practicing other technologies and, on average, cultured 2.8 ghers in total, with a mean area of 0.37 ha each.

farmers practicing CST (US$18,190; P b 0.05) were significantly higher than those of farmers practicing MTT (MTT1 US$ 5198 and MTT2 $6934) and traditional systems (US$3131) (Table 8). There was no significant difference (P N 0.05) in total household income between farmers adopting MTT1 and MTT2 and no significant difference even between traditional and MTT farmers. 3.3. Factors affecting economic returns from shrimp farm technologies

3.2. Production, profitability and household income by technology

The following three sub-sections present the results of regression analysis for determining factors affecting production and financial returns for each of the three farming systems.

Improved shrimp farming technologies all generated higher annual yields and profit than traditional farming systems, although they also incurred higher variable costs (Tables 6 and 7). The CST was significantly different (P b 0.05) from MTT and traditional farming systems in terms of variable, fixed and total costs. Large differences in fixed costs were associated with pond infrastructure, with CST farmers investing more in repairing dykes and gher excavation. The differences in operating costs reflect the greater investment in all inputs for CST technologies. Gross return and gross margin were significantly higher in the CST farms, followed by MTTs and traditional ghers. Farmers gained more (P b 0.05) than double net economic returns from adopting CST compared with MTT, and four times more than traditional systems (Table 7). There was no difference (P N 0.05) between MTT1 and MTT2 in terms of the various cost items and returns. ANOVA results are shown in Table 7. Shrimp farming contributed a high proportion of family income, accounting for between 61% and 85% of the total when production of prawn, fish and crabs was included (Table 8). The average annual household income was US$ 5810. Annual household incomes for

3.3.1. Factors affecting returns from CST ghers An R-squared value of 0.81 indicated a good fit for the regression function for returns from CST shrimp farms, indicating that the factors included in the analysis contributed 81% in variation of economic returns from CST. The factors included in the function are as follows: ghers area; water depth; gher age; sludge removal; gher drying; use of reservoir; algal control; use of lime; use of molasses; use of urea; use of TSP; use of industrial feed; use of homemade feed; use of feed storage facilities; numbers of times PL stocked; ghers affected by WSSV; use of human labor; and value of PL. The F value (16.96) was significant at a level of less than 1%, indicating that variables included in the regression function were appropriate and significant. Farms adopting CST only stocked shrimp PL screened for WSSV. PL stocking rate (as indicated by the proxy cost of stocking in US$/ha) was significantly positively correlated with returns from CST shrimp farms. All CST farmers also used commercial feed in their ghers to feed stocked shrimp (Table 13). The value of the coefficient and standard error for commercial pelleted feed also indicated that it made a

Table 7 Cost, return and profit from different shrimp farming technologies. Particulars

Traditional

MTT1

MTT2

CST

Miscellaneous (US$/ha) Organic fertilizer (US$/ha) Inorganic fertilizer (US$/ha) Chemical (US$/ha) Shrimp seed (US$/ha) Prawn seed (US$/ha) Fin fish seed (US$/ha) Feed (US$/ha) Fuel (US$/ha) Human labor (US$/ha) Other (US$/ha) Variable cost (US$/ha) Fixed cost (US$/ha) Total cost (US$/ha) Gross return (US$/ha) Gross margin (US$/ha) Net return (US$/ha) Benefit cost ratio

49.47a (±62.96) 14.56a (±35.00) 17.71a (±20.94) 16.90a (±24.46) 253.83a (±147.63) 183.44a (±236.36) 51.47a (±78.29) 181.57a (±322.07) 7.50a (±25.21) 112.90a (169.41) 17.59a (±24.76) 907a (±681) 205a (±190) 1,112a (±742) 1,763a (±1475) 856a (±1056) 651a (±1065) 2.14a (±1.11)

48.04a (±52.99) 19.86a (±25.30) 17.87b (±235.00) 55.41a (±236.70) 315.77b (±200.20) 189.74a (±250.36) 51.56a (±64.49) 150.23a (±274.06) 21.69a (±574.57) 162.33b (175.80) 20.64a (±27.97) 1,053a (±735) 202a (±194) 1,255a (±781) 2,724b (±1541) 1,671b (±1190) 1,469a (±1206) 2.94b (±1.22)

52.79a (±68.56) 18.39a (±32.34) 11.27a (±12.79) 38.04a (±35.07) 145.49c (±78.04) 339.50b (±326.37) 51.63a (±110.97) 209.61a (±194.11) 21.46a (±27.19) 202.49b (192.47) 14.17a (±16.06) 1,105a (±683) 215a (±234) 1320a (±789) 2,890b (±2166) 1,785b (±1659) 1,570a (±1543) 2.73b (±1.15)

270.71b (±136.00) 98.16b (±63.40) 7.09b (±10.20) 596.27b (±363.19) 606.44d (±379.03) 0.00c (±0.00) 0.00b (±0.00) 2497.36b (±1816.10) 207.11b (±288.24) 1173.44c (619.63) 129.63b (±341.54) 5,586b (±2850) 580b (±389) 6,166b (±3027) 9,311c (±6683) 3,725c (±4488) 3,145c (±4399) 1.63c (±0.83)

Note: Figures in parentheses indicate standard deviation of mean. The subscript symbols a, b and c indicate significant differences among technologies based on ANOVA test.

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Table 8 Annual household incomes in 2006 by shrimp farming technology. Contribution ($ value and %) of income sources to household income by technology adopted Income sources

Shrimp Prawn Fishes Crab Agricultural crops Business Services Other (remittance, labor selling, rickshaw/van pulling etc.) Total income (US$/year)

Traditional

MTT1

MTT2

CST

All averages

$

%

$

%

$

%

$

%

$

%

1503 501 282 31 250 250 125 188 3131 (±3167)

48 16 9 1 8 8 4 6 100

3171 780 312 0 208 364 156 208 5198 (±5214)

61 15 6 0 4 7 3 4 100

2635 1248 277 69 277 832 763 901 6934 (±7350)

38 18 4 1 4 12 11 13 100

15,462 0 0 0 364 1455 182 728 18,190 (±18,929)

85 0 0 0 2 8 1 4 100

3777 581 291 0 232 465 174 291 5810 (±8818)

65 10 5 0 4 8 3 5 100

Note: Figures in parentheses indicate standard error of mean.

significant contribution to increasing shrimp production and generating higher returns from CST ghers. The regression indicates that, all other things being constant, farmers could increase returns by US$1.95 for every additional US$1.00 spent on commercial feed per hectare of gher area (Table 9). There was a significant negative correlation between white spot disease (WSD) and financial return; i.e., if a WSD outbreak occurred, financial losses resulted. Other factors included in the analysis did not show significant effects on profitability.

3.3.2. Factors affecting returns from MTT ghers Table 10 shows that the R-squared value (0.57) indicated a reasonable fit of the regression function for MTT ghers (i.e. variables included in the function were able to explain 57% of the variations in returns from shrimp). The F value (21.84) was significant at a level of less than 1%, indicating that the variables included were reasonably appropriate and made significant contributions to returns from MTT ghers. Application of molasses, lime, all types of seed (shrimp, prawn and fish), and both commercial and homemade feed were all positively and significantly correlated with financial returns from this system. Higher stocking frequency of shrimp PL resulted in significantly increased return, and each additional stocking could result in an increased return of US$71.65 per hectare. As in CST farming, WSD incidence significantly reduced returns. The use of TSP as a fertilizer was negatively correlated with return. This final result is unexpected, and the reasons for it are not clear.

3.3.3. Factors affecting returns from traditionally managed ghers An R-squared value of 0.66 indicates that the inputs included in the model for traditional shrimp cultivation systems contributed 66% of the variation in returns. The F value (32.06) also confirms that factors included in the model made a significant contribution to financial returns (Table 11). Coefficient values for stocking density of shrimp, prawn and fish confirmed that higher stocking densities are positively correlated with higher returns. This result is similar result to that found for MTT farms. In addition, use of homemade feed was also found to be positively associated with financial returns. Interestingly, unlike in the other systems, there was not a significant negative association (P b 0.05) between the occurrence of WSD and financial returns. 3.4. Factors affecting the adoption of MTT and CST This section investigates factors influencing the likelihood of adoption of CST and MTT by farmers who had previously practiced traditional shrimp culture techniques. A total of 9% and 48% farmers who previously practiced traditional techniques began practicing CST and MTT shrimp technologies, respectively, during the project period following exposure to training for both systems. Of the other farmers who received training, 43% continued to use traditional practices, or began to employ a small number of the recommendations given during training rather than adopting an entire technological package. The logistical regression analysis presented in this section is aimed at identifying

Table 9 Coefficient, standard error and significance levels of factors affecting returns from CST. Factors

(Constant) Dummy for WSSV affected (No = 1, otherwise = 0) Gher area (ha) Molasses (US$/ha) Urea (US$/ha) TSP (US$/ha) Lime (US$/ha) Human labor (US$/ha) Gher age (years) Number of times seed stocked (No./year) Dummy for sludge removal from bottom of gher (yes = 1, otherwise = 0) Dummy for use reservoir (yes = 1, otherwise = 0) Dummy for algae control (yes = 1, otherwise = 0) Dummy for feed storage use (yes = 1, otherwise = 0) Dummy for gher drying (yes = 1, otherwise = 0) Industrial feed (US$/ha) Homemade feed (US$/ha) Average water depth of gher (feet) Shrimp seed (US$/ha) Model fitting criteria: R-squared value 0.81 and F value 16.96 (P b 0.01).

Unstandardized coefficients B

Std. Error

t

Sig.

−14971.3 5239.65 2943.03 8.01 46.14 −84.94 −3.01 8.19 32.06 23.21 −70.29 245.61 924.55 998.44 1566.86 1.95 0.31 250.05 6.98

4832.60 1074.64 2585.81 5.61 100.25 105.10 4.69 11.79 69.38 628.93 1108.69 1381.19 1307.71 1441.82 3691.17 0.26 3.07 927.75 1.56

−3.10 4.88 1.14 1.43 0.46 −0.81 −0.64 0.70 0.46 0.04 −0.06 0.18 0.71 0.69 0.42 7.55 0.10 0.27 4.48

0.00 0.00 0.26 0.16 0.65 0.42 0.52 0.49 0.65 0.97 0.95 0.86 0.48 0.49 0.67 0.00 0.92 0.79 0.00

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Table 10 Coefficient, standard error and significance levels of factors affecting returns from MTT technologies. Factors

(Constant) Dummy for WSSV affected (no = 1, otherwise = 0) Gher area (ha) Molasses (US$/ha) Urea (US$/ha) TSP (US$/ha) Lime (US$/ha) Human labor (US$/ha) Gher age (year) Number of times seed stocked (no./year) Dummy for sludge remove from bottom of gher (yes = 1, otherwise = 0) Dummy for use reservoir (yes = 1, otherwise = 0) Dummy for algae control (yes = 1, otherwise = 0) Dummy for feed storage use (yes = 1, otherwise = 0) Dummy for gher drying (yes = 1, otherwise = 0) Industrial feed (US$/ha) Homemade feed (US$/ha) Average water depth of gher (feet) Shrimp seed (US$/ha) Prawn seed (US$/ha) Fish seed (US$/ha)

Unstandardized coefficients B

Std. Error

t

Sig.

−181.08 282.01 −25.04 7.66 −6.58 −9.44 7.55 −1.24 −2.40 71.65 48.56 121.74 235.99 207.37 210.14 1.15 1.39 −11.01 1.53 1.79 4.77

488.55 121.82 24.40 3.75 6.21 4.33 1.90 1.13 10.58 32.42 161.49 450.33 201.60 144.80 295.45 0.33 0.57 109.16 0.37 0.28 0.90

2.32 −1.03 2.04 −1.06 −2.18 3.98 −1.09 −0.23 2.21 0.30 0.27 1.17 1.43 0.71 3.46 2.44 −0.10 4.11 6.37 5.33

0.02 0.31 0.04 0.29 0.03 0.00 0.28 0.82 0.03 0.76 0.79 0.24 0.15 0.48 0.00 0.02 0.92 0.00 0.00 0.00

Model fitting criteria: R-squared value 0.57 and F value 21.84 (P b 0.01).

which characteristics were important in determining farmers' decisions to adopt or reject the technological packages promoted. The pseudo R-squared values given in Table 12 confirm that between 73% and 89% of variation in adoption behavior occurred due to factors included in the model. Overall, the model offers a good fit with factors predicting the adoption of improved shrimp farming systems (Table 12). The chi-squared test for each factor included in the model shows that number of adult family members, number of ponds operated by each household, number of years spent in education and number of gher owners did not contribute significantly to the adoption of improved shrimp farming technologies. Farmer age was positively correlated with the adoption of CST and MTT shrimp farming technologies (Table 12). As reference of Table 4, though there was no significant difference among farmers practicing different systems in terms of their age but positive correlation between average farmers' age and adopting more intensive technologies

(i.e. MTT farmers were comparatively older than traditional farmers, and CST farmers were oldest than those of both traditional and MTT farmers) may contribute to show a positive correlation in adopting technology. However, as noted above, farmers' level of education was not a significant factor associated with the adoption of either technology. In addition, and perhaps surprisingly, the length of experience in aquaculture farming was significantly negatively correlated with technology adoption, with more experienced farmers being less inclined to adopt both of the improved technologies. Participation of farmers in training was found to be the single most important factor influencing adoption of improved MTT and CST systems. Likelihood of adoption was also positively correlated with household income (more strongly for CST than for MTT), which is unsurprising given that both technologies are more investment-intensive than traditional farming practices—CST considerably more so than MTT. Pond size was also strongly negatively correlated with adoption of CST; farmers with larger ghers being less

Table 11 Coefficient, standard error and significance levels of factors affecting returns from traditional technologies. Factors

(Constant) Dummy for WSSV affected (no = 1, otherwise = 0) Gher area (ha) Molasses (US$/ha) Urea (US$/ha) TSP (US$/ha) Lime (US$/ha) Human labor (US$/ha) Gher age (year) Number of times seed stocked (no./year) Dummy for sludge removed from bottom of gher (yes = 1, otherwise = 0) Dummy for reservoir use (yes = 1, otherwise = 0) Dummy for algae control (yes = 1, otherwise = 0) Dummy for feed storage use (yes = 1, otherwise = 0) Dummy for gher drying (yes = 1, otherwise = 0) Industrial feed (US$/ha) Homemade feed (US$/ha) Average water depth of gher (feet) Shrimp seed (US$/ha) Prawn seed (US$/ha) Fish seed (US$/ha) Model fitting criteria: R-squared value 0.66 and F value 32.06 (P b 0.01).

Unstandardized coefficients B

Std. Error

t

Sig.

−524.70 150.74 −19.03 27.43 4.24 1.01 1.77 2.62 −1.31 74.78 147.24 284.46 143.29 78.31 −35.21 0.29 0.61 14.04 1.42 3.84 1.45

319.94 109.26 46.33 14.14 3.49 3.17 1.81 1.72 7.04 21.06 131.35 217.55 121.13 141.20 120.04 0.27 0.20 72.94 0.38 0.30 0.62

−1.64 2.38 −0.41 1.94 1.22 0.32 0.98 1.52 −0.19 3.55 1.12 1.31 1.18 0.56 −0.29 1.05 3.03 0.19 3.73 13.03 2.34

0.10 0.17 0.68 0.05 0.23 0.75 0.33 0.13 0.85 0.00 0.26 0.19 0.24 0.58 0.77 0.30 0.00 0.85 0.00 0.00 0.02

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Table 12 Probability of factors influencing adoption of improved technologies. Factors

MTT

Intercept Age of farmers Farmers' schooling (years) No. of adult family members Aquaculture farming experience (years) No. of ghers Size of pond (ha) No. of owners of project ghers Training received on shrimp farming (1 = Yes, 0 = No) Household income (thousand US$/year)

CST

B (Std. Error)

Sig.

B (Std. Error)

Sig.

−7.352 (±1.559) 0.074 (±0.025) −0.093 (±0.060) −0.282 (±0.181) −0.130 (±0.043) −0.046 (±0.186) 0.282 (±0.187) 0.980 (±0.681) 8.162 (±0.753) 0.186 (±0.069)

0.000 0.002 0.117 0.118 0.002 0.804 0.131 0.150 0.000 0.007

−8.096 (±3.054) 0.093 (±0.030) 0.073 (±0.078) −0.189 (±0.223) −0.138 (±0.052) −0.235 (±0.222) −4.023 (±0.873) −0.266 (±2.306) 7.100 (±1.237) 0.398 (±0.074)

0.008 0.002 0.350 0.398 0.008 0.291 0.000 0.908 0.000 0.000

Model fitting criteria: −2 Log Likelihood 409.81, chi-square 1113.82 with 18 df significance at less than 1% level. Pseudo R-Square: Cox and Snell 0.756, Nagelkerke 0.885 and McFadden 0.731.

likely to adopt the new system. Gher size was not a significant factor in the adoption of MTT, however. 4. Discussion This study shows that the adoption of improved shrimp farming technologies in southern Bangladesh improved farm productivity and profitability. The factors identified as being positively associated with productivity and profitability in this study are similar to those of studies carried out in other countries, and include stocking density of shrimp PL, feeding rates and use of lime for water quality control (Alday-Sanz, 2010) (Kureshy and Davis, 2000; Padiyar et al., 2011; Tacon, 1987). The results indicate that significant opportunities exist to improve shrimp production in Bangladesh through intensification and the adoption of better management practices. Among the three technologies, CST provided the highest returns, but farmers practicing this technology were not interested in increasing the area under CST due to the higher investment costs incurred, and the risk associated with WSD outbreaks, are potentially catastrophic for farmers when they occur in semi-intensive systems (Karim et al., 2012). Gher size also was negatively correlated with CST adoption, indicating management difficulties associated with large CST ghers. Analysis indicated that shrimp farming contributed 85% of total income for households practicing CST. Although households with the capacity to invest in closed system intensification are relatively well resourced, even these are highly dependent on incomes from shrimp farming and, as such, are vulnerable in the event of crop failure WSD. Provision of crop insurance for CST farmers and bank loans for system intensification could be one of the means of mitigating these barriers to CST adoption. More widespread intensification might be beneficial because of the land sparing effect of the technology, which could reduce the need to convert additional farmland in order to increase production. MTT appears to be a more easily replicable and lower-risk technology for farmers, with the use of addition of feeds (commercial pelleted diets and homemade feed) generating higher returns. MTT farmers are aware of commercial pelleted feed, but 35% were not interested in using it due to the higher level of investment required (Table 13). This suggests the need to consider ways of improving farmer access to feed such as, for instance, establishing market information systems for the

Table 13 WSD incidence and input application by technology. Particulars

Traditional

MTT1

MTT2

CST

% of ghers affected by WSD % of farmers using molasses % of farmers using lime % of farmers using industrial feed % of farmers using homemade feed

56.3 2.6 86.6 46.9 55.4

48.3 52.8 91.7 88.9 47.2

19.4 60.7 91.4 64.9 60.7

17.0 100.0 98.9 62.2 14.4

distribution of feed price information, or encouraging the establishment of small-scale local feed mills in shrimp farming areas. The study also indicates that increasing the application of homemade feed and increasing stocking density of shrimp, prawn and fish seed slightly increase returns. Lime was a highly significant input for increasing returns with MTT. Prior to the project interventions, a majority of MTT farmers (87 to 99%) used lime, but on an irregular basis and in insufficient quantities. The project was successful in raising awareness about lime use, though there is room for further improvement in developing more effective mechanisms to convey messages about effective lime application. It is assumed that all shrimp farmers could easily adopt this practice as the costs involved in lime application are relatively low (2.6% of total cost) compared to other inputs used in shrimp ghers, as also noted by Chowdhury et al. (2010a, 2010b). More confusingly, the use of TSP was found to be negatively correlated with returns. The reasons for this are unclear, and require further investigation. Despite the high prevalence of WSSV in traditional and MTT farms, production levels appear to be maintained by farmers' indigenous technical strategy of multiple stocking and harvesting. This is one of the important findings of this study, and to some extent undermines the justification for stocking screened PL in the MTT systems if biosecurity measures are not followed. This coping strategy has also been identified in a number of other studies (Alam et al., 2010; Islam et al., 2005; Karim et al., 2012). The prawns and fin fish produced in MTT and traditional systems also helped in mitigating economic vulnerability, as well as (in the case of fin fish) contributed directly to home consumption. Analysis of the factors affecting the adoption of improved shrimp production systems appears to suggest that it is easier to encourage older people to adopt the improved systems. Although farmers' education level was not a significant factor in determining adoption, this indicates that it may be easier to convince the farmers who are not highly educated to adopt MTT and CST systems. Perhaps because they were more set in their ways than younger farmers, more experienced farmers also appeared to be less inclined to switch from existing traditional production systems to improved farming systems. Finally, training in aquaculture farming was a highly significant factor determining the adoption of MTT and CST systems, suggesting that further investments in training would support the scaling up and out of improved technologies. The Farmer Field School approach represents a potentially important tool for further training investments, as also noted in a number of other studies (Davis et al., 2010). Training via small groups can help create opportunities for farmer-to-farmer exchange of experiences, and appears to be an efficient way of sharing learning and problem solving (Padiyar et al., 2011). Such approaches are efficient in reaching larger numbers of farmers, in terms of investment per trainer. They are useful for trainers in gathering feedback and knowledge from farmers, thereby helping to improve the skills and effectiveness of trainers.

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The study demonstrates the potential to increase productivity and profitability of shrimp farms in southern Bangladesh through the adoption of improved technologies, but this will require raising awareness about, and improving access to, key inputs such as quality feed, seed and lime, as well as making investments in both farmer training and associated value chain development activities. As the capacity of most farmers to intensify up to CST level is limited promotional efforts should focus mainly on dissemination of the MTT system, which combines the resilience and relatively low risk of the indigenously developed traditional system with more scientific management practices, and higher resultant productivity and margins. Acknowledgments The authors are grateful to the USAID (Grant No: 388-A-00-0700008-00) for the financial support provided to the Shrimp Quality Support Project (SQSP). The authors thank all field staff of the SQSP for conducting the survey. The preparation of this paper was supported by the CGIAR Research Program on Aquatic Agricultural Systems. We also thank three anonymous reviewers for their insightful and constructive comments. References Ahmed, S.A., Mallick, D.L., Ali, M.L., Rahman, A.A., 2002. Literature Review on Shrimp. Policy Research for Sustainable Shrimp Farming in Asia (PORESSFA): A Comparative Analysis of Bangladesh, India, Thailand and Vietnam with Particular Reference to Institutional and Socio-economic Aspects. Bangladesh Center for Advanced Studies, Dhaka, Bangladesh. Alam, S.M.N., Phillips, M.J., 2004. Coastal shrimp aquaculture systems in Southwestern Bangladesh. Asian Fish. Sci. 17, 175–189. Alam, S.M.N., Pokrant, E.B., Yakupitiyage, A., Phillips, E.M.J., 2007. Economic returns of disease-affected extensive shrimp farming in southwest Bangladesh. Aquac. Int. 15, 363–370. Alam, M.J., Islam, M.L., Shah, S.B., Tuong, T.P., Joffre, O., 2010. Improving the productivity of the rice-shrimp system in the south-west coastal region of Bangladesh. In: Hoanh, C.T., Szuster, B.W., Suan-Pheng, K., Ismail, A.M., Noble, A.D. (Eds.), Tropical Deltas and Coastal Zones: Food Production, Communities and Environment at the Land-Water Interface, Comprehensive Assessment of Water Management in Agriculture Series. Vol. 9. CAB International, Wallingford, UK, pp. 93–105. Alauddin, M., Tisdell, C., 1998. Bangladesh's shrimp industry and sustainable development: resource-use conflict and the environment. Asian Fish. Sci. 11, 97–110. Alday-Sanz, V., 2010. The Shrimp Book. Nottingham University Press, Nottingham, UK. Bangladesh Bureau of Statistics (BBS), 2008. Bangladesh Agricultural Census. Government of Peoples' Republic of Bangladesh, Dhaka, Bangladesh. Bangladesh Frozen Foods Exporters Association, 2007. Shrimp & fish. BFFEA Newsl. 2, 1–28. Barmon, B.K., Kondo, T., Osanami, F., 2004. Labor demand for rice-prawn gher farming in Bangladesh. A case study of Khulna district. Rev. Agric. Econ. 60, 273–287. Belton, B., Karim, M., Thilsted, S., Jahan, K.M., Collis, W., Phillips, M., 2011. Review of aquaculture and fish consumption in Bangladesh. Studies and Reviews 2011–53The WorldFish Center, Penang 76. Chowdhury, M.A., Yahya Khairun, Y., Rahman, M.M., Ganesh, P., Shivakoti, G.P., 2010a. Production economics as an indicator for sustainable development of shrimp farming. Asia Pac. J. Rural Dev. XX (1). Chowdhury, M.A., Khairun, Y., Rahman, M.M., Ganesh, P., Shivakoti, G.P., 2010b. Production Economics as an indicator for sustainable development of shrimp farming. Asia Pac. J. Rural Dev. XX (No. 1) (July). Davis, K., Nkonya, E., Kato, E., Mekonnen, D.A., Odendo, M., Miiro, R., Nkuba, J., 2010. Impact of farmer field schools on agricultural productivity and poverty in East Africa. IFPRI Discussion Paper 00992. International Food Policy Research Institute (IFPRI). Deb, A.K., 1998. Fake blue revolution: environmental and socio-economic impacts of shrimp culture in the coastal areas of Bangladesh. Ocean Coast. Manage. 41 (1), 63–88.

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