Abstract
The article presents the summary of a dataset related to the risks factors of white spot disease (WSD) of farmed shrimp (Penaeus monodon) in Khulna, Bagerhat and Satkhira districts of Bangladesh. This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools were applied to get the conceptual framework for data collection regarding lists of farmers and the variables of the risk factors of WSD. In the second step, sampling of farmers, google featured questionnaire development, and mobile phone-assisted survey were carried out. The total surveyed farms were 233 consisting of 21 and 212 semi-intensive and extensive farms, respectively. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains some basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article.
Keywords: Disaggregated data, Shrimp farming, Risk factors, WSD, Bangladesh
Specifications Table
| Subject | Aquaculture, Aquatic Science, Epidemiology |
| Specific subject area | Aquatic Animal Health Management |
| Type of data | Table |
| How data were acquired | Applying participatory rural appraisal tools; mobile-phone assisted Google featured structured questionnaire survey with shrimp farmers; geographic location of each farm of the respondents |
| Data format | Raw in MS Excel Map for sampled farmers distribution |
| Parameters for data collection | This dataset was obtained from the shrimp farmers following two consecutive steps. Firstly, participatory rural appraisal tools were applied to get the conceptual framework for collecting lists of farmers and the variables associated with the risk factors of WSD. Later, sampling of farmers, google questionnaire development (provided as supplementary file and made available at https://goo.gl/forms/ckG1AIf9xMTxtPpf1), and data collection were undertaken by android mobile phone-assisted survey. Parameters of this dataset belong to the farmers and farm characteristics and management practices of shrimp farms by saline zones. |
| Description of data collection | Total number surveyed farms were 233 consisting of 21 semi-intensive and 212 extensive shrimp farms. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the farms. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article. |
| Data source location | Institution: Department of Aquaculture, Bangladesh Agricultural University City/Town/Region: Khulna, Bagerhat and Shatkhira districts Country: Bangladesh |
| Data accessibility | Repository name: Mendeley Data identification number: http://dx.doi.org/10.17632/nz96v5spbf.2 Direct URL to data: https://data.mendeley.com/datasets/nz96v5spbf/2 |
| Related research article | N.A. Hasan, M.M. Haque, S.J. Hinchliffe, J. Guilder, A sequential assessment of WSD risk factors of shrimp farming in Bangladesh: Looking for a sustainable farming system, Aquaculture. 526 (2020) 735,348. https://doi.org/10.1016/j.aquaculture.2020.735348. [1] |
Value of the Data
The dataset of WSD affected 233 shrimp farmers is disaggregated by their farm characteristics and management practices, and by saline zones in southwest Bangladesh which can be used to conduct comparative studies of the changes in shrimp farming on a temporal scale
The key strength of the dataset is that it has GPS coordinates of all the individual farms which researchers and policymakers can use for the establishment of farm traceability that Bangladesh shrimp farms lack severely
The data can be useful for spatial modelling of the impacts of climate change particularly the impact of saline water intrusion on shrimp farming and rural livelihoods
Overall, the data are important for various stakeholders including farmer, policymakers, researchers, scholars, academicians to mitigate the negative impacts of WSD on the entire shrimp farming area of Bangladesh towards sustainable farming
1. Data description
The dataset has been built in MS Excel format having two sheets. The first sheet (Dataset) is the main dataset of 46 variables and the second one (DataCoding) is about the coding of different nominal and binary data. The short descriptions of the whole dataset (N = 233) are given in the summary Tables 1–3. The data were collected mainly in the form of continuous variables along with some nominal and binary variables. In the summary Tables, continuous variables are presented in average, and nominal and binary variables are in frequency. The basic socio-economic data of shrimp farmers collected includes age, education, farming experiences and farm size are presented in the form of average and frequency (Table 1). The socio-economic data has the potential to disaggregate the whole dataset for comparative analysis within the dataset, and in the future by generating another round of survey data for temporal analysis. Table 2 contains the summary of the dataset for various variables under the domains of farm characteristics, environmental attributes and disease history of shrimp farms. The summary of the dataset related to the data of a range of farm management practices collected from the individual survey site is presented in Table 3. The variables were grouped into five categories in the survey questionnaire (provided as a supplementary file). The key strength of the dataset is that it contains GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. This dataset will facilitate the researchers to undertake a comparative research on a temporal scale within the same farms, or with neighbouring farms to illustrate the changes of culture practices, and to recommend the way forward towards sustainable shrimp farming in Bangladesh.
Table 1.
Basic socio-economic characteristics of shrimp farmers.
| Variables | Variables type | Variables narration | Average/Frequency |
|---|---|---|---|
| Farmer zone | NV* | Khulna | 150 |
| Bagerhat | 26 | ||
| Satkhira | 57 | ||
| Farmer age (average years) | CV** | Khulna | 42.5 |
| Bagerhat | 41.3 | ||
| Satkhira | 41.5 | ||
| Involved with shrimp farming (average years) | CV | Khulna | 14.2 |
| Bagerhat | 13.8 | ||
| Satkhira | 16.6 | ||
| Farmer education | NV | Primary (1–5) | 60 |
| Junior secondary (6–8) | 44 | ||
| Secondary (9–10) | 60 | ||
| Higher secondary (11–12) | 41 | ||
| Diploma (13–15) | 1 | ||
| Bachelor's (13–16) | 8 | ||
| Master's (17–18) | 2 | ||
| No education | 17 | ||
| Farm size (average in ha) | CV | Khulna | 1.28 |
| Bagerhat | 2.86 | ||
| Satkhira | 2.91 |
*Nominal Variable; **CV: Continuous Variable.
Table 3.
Summary of the dataset by different management variables of shrimp farming practices, and by zone.
| Variables category | Variables | Variables type | Variables narration | LSZ1 (Khulna) | ISZ2 (Bagerhat) | HSZ3 (Satkhira) |
|---|---|---|---|---|---|---|
| Average/Frequency | ||||||
| Management variables (Site/farm management) | Farm operated by owner | BV⁎⁎⁎ | No: 0 | 26 | 5 | 5 |
| Yes: 1 | 124 | 21 | 52 | |||
| Use of fertilizer | NV⁎ | No: 4 | 59 | 9 | 8 | |
| Inorganic: 3 | 62 | 9 | 41 | |||
| Organic: 2 | 10 | 8 | 1 | |||
| Mixed – inorganic and organic: 1 | 19 | 0 | 7 | |||
| Chemicals use (pond preparation) | NV | Chemical treatments: 3 | 24 | 7 | 7 | |
| Therapeutic treatments: 1 | 126 | 19 | 50 | |||
| Chemicals use (water treatment) | NV | Chemical treatments: 3 | 60 | 11 | 11 | |
| Therapeutic treatments: 1 | 90 | 15 | 46 | |||
| Use of aerator | BV | No: 0 | 132 | 22 | 56 | |
| Yes: 1 | 18 | 4 | 1 | |||
| Gher drying after harvest | BV | No: 0 | 6 | 1 | 0 | |
| Yes: 1 | 144 | 25 | 57 | |||
| Sludge removal method | NV | No: 5 | 18 | 3 | 13 | |
| Flushing, deposit sludge on farm: 3 | 62 | 9 | 24 | |||
| Flushing, deposit sludge on and off farm: 2 | 48 | 7 | 17 | |||
| Flushing, deposit sludge off farm: 1 | 22 | 7 | 3 | |||
| Sludge removal interval | NV | Never: 1 | 18 | 3 | 13 | |
| 1 year: 2 | 102 | 17 | 39 | |||
| ≥2 year: 3 | 30 | 6 | 5 | |||
| Management variables (Site/farm management) | Maintain and repair dikes | NV | No repaired dikes or repair with the pond bottom soil of other farms: 4 | 7 | 1 | 1 |
| Repaired dikes with the pond bottom soil of farm itself: 2 | 134 | 23 | 56 | |||
| Repaired dikes with the soil from fallow land: 1 | 9 | 2 | 0 | |||
| Period of fallow | CV⁎⁎ | Continuous variable | 55.56 | 57.3 | 45 | |
| Management variables (Water management) | Water source (direct natural) | NV | Rain water: 3 | 6 | 1 | 0 |
| Boring water: 2 | 21 | 0 | 3 | |||
| Direct from sea or river/tidal flow: 1 | 56 | 11 | 10 | |||
| If not direct natural: 0 | 67 | 14 | 44 | |||
| Water source (indirect natural) | NV | Water coming via other shrimp farms: 4 | 28 | 9 | 10 | |
| Canal from sea/river: 2 | 20 | 3 | 34 | |||
| Treated water: 1 | 19 | 2 | 0 | |||
| If not indirect natural: 0 | 83 | 12 | 13 | |||
| Water coming via other farms | BV | No: 0 | 122 | 17 | 47 | |
| Yes: 1 | 28 | 9 | 10 | |||
| Reservoir | BV | No: 0 | 135 | 25 | 57 | |
| Yes: 1 | 15 | 1 | 0 | |||
| Frequency of water exchange | NV | ≤ 7 – 28 days: 4 | 43 | 17 | 26 | |
| 29 – 42 days: 3 | 49 | 3 | 7 | |||
| > 42 days: 2 | 14 | 1 | 6 | |||
| No exchange: 1 | 44 | 5 | 18 | |||
| Same passes for inlet/outlet | BV | No: 0 | 65 | 8 | 7 | |
| Yes: 1 | 85 | 18 | 50 | |||
| Management variables (Culture management) | Culture method | NV | Monoculture: 4 | 20 | 4 | 1 |
| Polyculture (shrimp with prawn): 3 | 34 | 10 | 6 | |||
| Polyculture (shrimp with fish): 1 | 96 | 12 | 50 | |||
| Source of PL | NV | Mixed source or non-registered private hatchery: 3 | 19 | 1 | 7 | |
| Registered private hatchery: 2 | 99 | 17 | 48 | |||
| Wild: 1 | 32 | 8 | 2 | |||
| Stocking density | CV | Continuous variable | 229.7 | 208.7 | 257.1 | |
| Stocking age | CV | Continuous variable | 13.8 | 22.9 | 16 | |
| Quality of PL | NV | Low: 3 | 9 | 2 | 1 | |
| Medium: 2 | 115 | 23 | 56 | |||
| High: 1 | 26 | 1 | 0 | |||
| Crop rotation | BV | No: 0 | 82 | 11 | 26 | |
| Yes: 1 | 68 | 15 | 31 | |||
| Management variables (Feed management) | Types of feed use | NV | Live food: 5 | 12 | 1 | 20 |
| Homemade pellet feed: 4 | 25 | 9 | 7 | |||
| Mixed use of homemade and commercial pellet feed: 3 | 40 | 12 | 8 | |||
| Formulated commercial pellet feed: 2 | 50 | 4 | 3 | |||
| No: 1 | 23 | 0 | 19 | |||
| Use of feed additives | BV | No: 0 | 94 | 6 | 43 | |
| Yes: 1 | 56 | 20 | 14 | |||
| Management variables (Biosecurity management) | Bird scare net | BV | No: 0 | 57 | 25 | 57 |
| Yes: 1 | 0 | 1 | 0 | |||
| Crab fence | BV | No: 0 | 57 | 24 | 57 | |
| Yes: 1 | 0 | 2 | 0 | |||
| Footbath | BV | No: 0 | 57 | 24 | 57 | |
| Yes: 1 | 0 | 2 | 0 | |||
| Limited access | BV | No: 0 | 54 | 23 | 54 | |
| Yes: 1 | 3 | 3 | 3 | |||
| Same equipment for the whole farm | BV | No: 0 | 0 | 2 | 0 | |
| Yes: 1 | 57 | 24 | 57 | |||
LSZ: Low Saline Zone.
ISZ: Intermediate Saline Zone.
HSZ: High Saline Zone.
NV: Nominal Variable.
CV: Continuous Variable.
BV: Binary Variable.
Table 2.
Summary of dataset by the variables of site/farm characteristics, environmental aspects and disease history, and by zone.
| Variables category | Variables | Variables type | Variables narration | LSZ1 (Khulna) | ISZ2 (Bagerhat) | HSZ3 (Satkhira) |
|---|---|---|---|---|---|---|
| Average/Frequency | ||||||
| Site/farm characteristics | Prior land use | NV⁎ | Rich or other crops farming: 3 | 120 | 23 | 56 |
| Wetland or others: 1 | 30 | 3 | 1 | |||
| Dominant soil type | NV | Sandy soil: 3 | 38 | 3 | 18 | |
| Loamy soil: 2 | 93 | 16 | 33 | |||
| Clay soil: 1 | 19 | 7 | 6 | |||
| Average canal depth | CV⁎⁎ | Continuous variable | 4.52 | 4.73 | 3.32 | |
| Average farm depth | CV | Continuous variable | 2.7 | 3.03 | 1.96 | |
| Culture practice | NV | Extensive: 2 | 131 | 24 | 57 | |
| Semi-intensive: 1 | 19 | 2 | 0 | |||
| Environmental variable | Temperature | CV | Continuous variable | 30.2 | 27.1 | 29.3 |
| pH | CV | Continuous variable | 7.8 | 7.6 | 7.4 | |
| Salinity | CV | Continuous variable | 7.4 | 10.2 | 15.9 | |
| Disease history | Previous prevalence of WSD | CV | Continuous variable | 65.1 | 57.9 | 45.4 |
| Virus detected (current culture) | BV⁎⁎⁎ | No: 0 | 71 | 5 | 13 | |
| Yes: 1 | 79 | 21 | 44 | |||
LSZ: Low Saline Zone.
ISZ: Intermediate Saline Zone.
HSZ: High Saline Zone.
NV: Nominal Variable.
CV: Continuous Variable.
BV: Binary Variable.
2. Experimental design, materials, and methods
This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools such as key informant interview (KII), focus group discussion (FGD) and field observations were conducted to get the conceptual framework for generating lists of farmers and the variables associated with the risk factors of white spot disease (WSD). In the second step, sampling of farmers, google featured questionnaire development, and data collection were carried out by android mobile phone-assisted survey. In the beginning, through extensive literature review particularly reviewing the statistical report published by Fisheries Resource Survey System (FRSS) of the Department of Fisheries (DoF), the major shrimp producing sites were selected in Khulna, Satkhira and Bagerhat districts of Bangladesh (Table 4). Shrimp farming in Bangladesh is characterized by a large number of small farms (over 200,000 farms registered by DoF), weak traceability, extensive farming practices, mass mortality due to WSD almost every year, and vulnerable to climate change [2], [3], [4], [5], [6], [7], [8], [9].
Table 4.
Top shrimp producing districts in Bangladesh by volume of production (adapted from [2]).
| District | Shrimp production (MT) | % of total production |
|---|---|---|
| Khulna | 56,043.48 | 22.03 |
| Bagerhat | 64,607.96 | 25.4 |
| Satkhira | 64,875.91 | 25.5 |
| Jessore | 37,643.13 | 14.8 |
| Cox's Bazar | 22,944.93 | 9.02 |
These sites are collectively known as the ‘shrimp zone’ consisting of high saline, intermediate saline and low saline areas from where comprehensive lists of WSD experienced shrimp farmers were collected from the key informant, local Upazilas (sub-districts) Fisheries Officers of the DoF. The list of shrimp farmers in an individual farming site was cross-checked through FGD with farmers. From each of the farming sites populated with WSSV experienced shrimp farmers (Khulna – 500, Bagerhat – 90 and Satkhira – 190), about 30% of farmers each from Khulna (150), Bagerhat (26) and Satkhira (57) in a total of 233 farmers, who experienced WSD in the past years (from 2010 to 2017), were sampled using a simple random sampling technique that made a robust dataset for statistical analyses. The total number of semi-intensive and extensive farms were 21 and 212, respectively (Fig. 1). The questionnaire survey was conducted applying google survey form in the android mobile phone during December/2017 to July/2018. Before the survey, the paper-based questionnaire was tested at the farmer level, edited and finalized. Then the questionnaire was transformed into google featured questionnaire (made available at https://goo.gl/forms/ckG1AIf9xMTxtPpf1) and then applied by the trained enumerators to conduct the survey. After the survey, the dataset was downloaded in the computer from the Google in CSV (comma-separated values) format and then converted to a MS Excel file.
Fig. 1.
Map of Bangladesh showing the distribution of sampled shrimp farmers (SI=semi-intensive; E=extensive) in data collection area.
Declaration of Competing Interest
The authors declare that they have no known competing for financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.
Acknowledgments
The authors would like to thank the Ministry of Science and Technology, Bangladesh for providing fellowship for this work. Authors also express their sincere appreciation to Syed Arifuzzaman, Executive Director of ARBAN NGO for providing necessary supports of enumerators who collected the data using mobile phone in the shrimp farming areas.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.dib.2020.105936.
Appendix. Supplementary materials
References
- 1.Hasan N.A., Haque M.M., Hinchliffe S.J., Guilder J. A sequential assessment of WSD risk factors of shrimp farming in Bangladesh: looking for a sustainable farming system. Aquaculture. 2020;526:735348. doi: 10.1016/J.AQUACULTURE.2020.735348. [DOI] [Google Scholar]
- 2.DoF, Yearbook of Fisheries Statistics of Bangladesh 2017-18, Fisheries Resources Survey System (FRSS), Department of Fisheries, Ministry of Fisheries and Livestock, Bangladesh., 35 (2018) 129.
- 3.Deb A.K. Fake blue revolution: environmental and socio-economic impacts of shrimp culture in the coastal areas of Bangladesh. Ocean Coast. Manag. 1998;41:63–88. doi: 10.1016/S0964-5691(98)00074-X. [DOI] [Google Scholar]
- 4.Bhowmick B., Crumlish M. Aquaculture health management and biosecurity practices in South West of Bangladesh. Bangladesh J. Vet. Med. 2016;14:263–269. doi: 10.3329/bjvm.v14i2.31407. [DOI] [Google Scholar]
- 5.Islam H.M.R., Khana M.H., Roy D., Alam M.M., Ahmed K.K.U., Mahmud Y., Ahsan M.N., Shah M.S. Association of risk factors: WSSV proliferation in the shrimp (Penaeus Monodon) Farms of South-West Coastal Region of Bangladesh. Ann. Vet. Anim. Sci. 2014;1:39–51. [Google Scholar]
- 6.Naureen M., Kabir D.S., Ali M.S., Kamal M. Status of the shrimp sector in Bangladesh in the context of HACCP and trade issues: a review. Bangladesh J. Fish. Res. 2006;10:185–202. [Google Scholar]
- 7.Burgess J., Connell J. 1st ed. 2007. Globalisation and Work in Asia. Chandos Publishing. [DOI] [Google Scholar]
- 8.Ahmed N., Diana J.S. Threatening “white gold”: impacts of climate change on shrimp farming in coastal Bangladesh. Ocean Coast. Manag. 2015;114:42–52. doi: 10.1016/j.ocecoaman.2015.06.008. [DOI] [Google Scholar]
- 9.Balasubramanian G., Sarathi M., Venkatesan C., Thomas J., Sahul Hameed A.S. Oral administration of antiviral plant extract of Cynodon dactylon on a large scale production against White spot syndrome virus (WSSV) in Penaeus monodon. Aquaculture. 2008;279:2–5. doi: 10.1016/j.aquaculture.2008.03.052. [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.