Factors Affecting Farmers’ Knowledge on Using Antibiotics and Gas Reducers in Pangas (Pangasius pangasius) Fish Health Management

doi:10.21203/rs.3.rs-2220963/v1

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Factors Affecting Farmers’ Knowledge on Using Antibiotics and Gas Reducers in Pangas (Pangasius pangasius) Fish Health Management

https://doi.org/10.21203/rs.3.rs-2220963/v1

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Most fish farmers of the global south had limited knowledge on how or why to use aqua drugs. The study was undertaken mainly to explore the extent of knowledge of pangas (Pangasius pangasius) fish farmers on using antibiotics and gas reducers in Pangasius fish health management. Moreover, the study will explore the factors, which affect farmers knowledge on using antibiotics and gas reducers in Pangasius fish health management. For the first time, we divide the knowledge in to four dimensions, including factual, conceptual, procedural, and metacognitive dimensions to understand farmers’ knowledge on different aspects of using aqua drugs and merged with Innovation-decision model. The study was conducted at Bailar union under Trishal Upazila (sub-district) of Mymensingh district of Bangladesh. All pangas fish farmers’ (87) in the study area were considered as a population while 72 farmers were selected as sample by random sampling technique. A structured interview schedule was used for data collection using face to face survey method in winter 2022 following a quantitative correlational approach. Findings showed that survey participants had better knowledge of conceptual dimensions that is followed by metacognitive, procedural, and factual knowledge and age, education, annual income, experience, extension media contact, mixed antibiotics in feed before feeding, and pond management were identified as influential determinants of the farmers’ knowledge. The findings of this study will be implemented by different government and non-government extension organizations to enhance the knowledge of the farmers on using antibiotics and gas reducers to manage pangas fish health. Thus, in the long run, the findings of this study will help the practitioners and policy planners to create situations, which will help to produce better quality pangas fish that is safe for humans and the environment.

Earth and environmental sciences/Environmental social sciences

Biological sciences/Ecology/Freshwater ecology

Aquaculture

gas reducers

antibiotics

fish health

Pangasius pangasius

Bangladesh

Background Of The Study

Aquaculture is one of the key methods of fish production in Bangladesh. The mainstream of aquaculture production goes toward freshwater ponds (Singha et al., 2020). Department of Fisheries (2019) noted that in the years 2018 and 2019, inland capture fisheries contribute only 28.19%, whereas inland culture fisheries contribute 56.76%, and marine fisheries contribute 15.05% to the total fish production of Bangladesh. Aquaculture production, including ponds, ditches, haors, baors, etc. showed an increasing trend from 2007–2008 with a value of 39.23% to 2018-19 with a value of 56.76% (Department of Fisheries, 2019). Bangladesh is now ranked third for inland water capture fish producing country in the world and the fifth largest producer of aquaculture fish in the world by major producers (Food and Agriculture Organization of the United Nations, 2020).

Over the last decade, aquaculture has expanded intensely and mechanically in Bangladesh (Rasul et al., 2017). Several types of diseases were seen in farmed aquatic animals in Bangladesh, including red spot, tail rot, anal protrusion, bacterial, fungal disease, etc. (Rasul et al., 2017). In fish health management and disease treatment, farmers use a variety of aqua drugs such as Gas-reducers like (Geo-rich, Yucca, Geo-prime, Bio-pond, No Gas, Gasonex plus, Acme’s zeolite, Aqua Magic), and antibiotics like (Renamox, Renamycin, Virex, Pathonil, Aquakleen, Povin vet, Bactiab, Aquakleen, Novamix-104) (Supplementary File 1) (Akter et al., 2020; Hasan et al., 2020; Singha et al., 2020). Aqua drugs and chemicals like antibiotics and gas reducers play a significant role not only in fish health management as well as in pond construction, soil, and water management, increasing natural aquatic productivity, feed formulation, reproduction manipulation, growth promotion and processing, and value addition of the final product (Singha et al., 2020).

Pharmaceutical companies and chemical sellers play a significant role in introducing aqua chemicals to fish farmers (Akter et al., 2020). In general, the most widespread problem in developing countries is the use of aqua drugs and medicines in the aquaculture and fisheries industries without knowing the adverse effect of aqua drugs (Singha et al., 2020). On the other hand, excessive use of aqua drugs leads to environmental degradation in some areas, which makes water quality unsuitable for aquaculture (Haque et al., 2019). Moreover, these unlicensed, non-biodegradable drugs or chemicals have several effects on human health (Singha et al., 2020). Particularly, adding drugs to the water with or without feeds results in the environmental disturbance of the microbiota (Plumb & Hanson, 2011). Rodgers and Furones (2009) stated using antibiotics in fisheries routinely in aquaculture that are in use for human medication is risky. Aquaculturists are advised to use other prevention approaches rather than antibiotics administering but if the use of antibiotics is necessary for some circumstances, they should administer only approved drugs for fish (Rodgers & Basurco, 2009). Antibiotics and gas reducers residues may accumulate in sediments, drive modifications in microbial communities, enforce toxic effects in non-target species, and modify phyto and zooplankton diversity, which caused alteration of the food chain and changes every level within the ecosystem (Lulijwa et al., 2020). Thus, the consequences of the inappropriate use of antibiotics and gas reducers are wide and dangerous. Therefore, antibiotics and gas reducers used in aquaculture must be approved by regulatory agencies, which may set rules for their use, including transportation, dose rates, withdrawal times, and tolerances (Romero et al., 2012).

Surprisingly, Anwar et al (2018) and Akter et al (2020) noted that most fish farmers had limited knowledge on how or why to use aqua drugs. This is due to their poor knowledge about the status and consequences of aqua drugs used in fish health management (Akter et al., 2020). Considering the above information, the present study was conducted to explore the level of knowledge of farmers' on using antibiotics and gas-removals in Pangasius fish health management in specific areas of Mymensingh district in Bangladesh.

In this study, aquaculture related to pangas was considered because Pangasius fish production is higher, at 12.31% than other fish species in inland water bodies (Department of Fisheries, 2019). Rui (Labeo rohita) and Tilapia (Oreochromis niloticus) production were 10.55% and 10.49%, respectively in 2018 (Department of Fisheries, 2019). Moreover, of the fish production regions of Bangladesh, Mymensingh district ranked third that is preceded by Chattogram and Khulna divisions (Department of Fisheries, 2019). Therefore, for this study we considered Mymensingh division and pangas fish species.

Therefore, the overarching research question of this study was “what was the level of knowledge of pangas fish farmers to ensure adequate use of antibiotics and gas-reducers in fish health management?” To explore the overarching research questions, the following specific research questions were formulated:

What are the demographic characteristics of the pangas fish farmers, including personality variables, socioeconomic characteristics, and communication behavior of the study area?

What is the level of farmers’ knowledge regarding the use of antibiotics and gas reducers in pangas fish health management?

What are the influential factors, which have significantly impacted the knowledge of the respondents on using antibiotics and gas reducers in pangas fish health management?

The main goal of this study was to explore the extent of knowledge of Pangasius fish farmers on using antibiotics and gas reducers in fish health management. Through this study, the knowledge gap of the farmers in using antibiotics and gas reducers can be identified. Therefore, in the future, the concerning authority, including the Department of Fisheries and other government and non-government organizations would formulate specific knowledge enhancement programs for the pangas fish farmers in using antibiotics and gas reducers in fish heath management, which will help farmers to use safe doses of aquatic drugs in fish health management. Thus, in the long run, the findings of this study will help for safe fish production and secure human and environmental health. Finally, the study addressed first, second, third, 12th, and 14th sustainable development goals of the United Nations, which are eliminating poverty, zero-hunger, good health and wellbeing, ensure sustainable consumption and production patterns (12.4), and climate action, respectively (United Nations, n.d.).

The Theoretical Framework Of The Study

This study was concerned with farmers’ knowledge of using antibiotics and gas-reducers in Pangasius fish health management. Thus, knowledge on using antibiotics and gas reducers in Pangasius fish health management was the focus of this study. Selected demographic characteristics, including personality, socioeconomic, and communication variables of the farmers were considered as independent variables in this study. The aforesaid factors may also influence the knowledge stage of Innovation-decision model (Rogers et al., 2014). Moreover, knowledge was categorized as factual, conceptual, procedural, and metacognitive knowledge (Krathwohl, 2002).

Factual knowledge can be easily defined by terminology, precise descriptions, and basic elements within any domain (Krathwohl, 2002). Tapia (2018) stated that factual knowledge is the information that can and must be learned through exposure, repetition, and commitment to memory. Related to factual knowledge, conceptual knowledge can be understood as knowing the interrelationships and/or functions among the details and elements that make up a larger structure (Krathwohl, 2002). Tapia (2018) also mentioned that conceptual knowledge is knowing the facts that can be organized in meaningful ways. Patel (2021) described that procedural knowledge can simply be said to know how to do something. It is defined as the knowledge acquired by practicing or exercising a task or a skill. It is also called practical knowledge, imperative knowledge, or task knowledge (Patel, 2021). Krathwohl (2002) stated that metacognitive knowledge involves knowledge about cognition in general as well as awareness of and knowledge about one’s own cognition.

Knowledge, the first stage of the innovation-decision process was considered as the focus of this study. The selected personality variable, socio-economic characteristics, and communication behavior of the respondents, including age, education, annual income, experience, training on aquaculture, farm size, operating season, fish feeding, pond management, disease occurrence, and extension media contact may affect the knowledge of farmers for using antibiotics and gas-reducers use in Pangasius fish health management. Appropriate knowledge on using antibiotics and gas reducers would impact better persuasion followed by decision, implementation, and confirmation to use antibiotics and gas reducers. Therefore, appropriate knowledge on using antibiotics and gas-reducers may help the farmers to adopt a better decision about using these chemicals in their ponds accurately. Therefore, in the long-term, safe fish will be produced, which will secure human and environmental health. The conceptual framework of the study has been presented in Fig. 1.

Objective 1: Demographic characteristics of the pangas fish farmers, including personality variables, socioeconomic characteristics, and communication behavior of the study area

Age of the respondents was found to range from 18 to 68 years, with an average of 38 years and standard deviation 11.54. Data presented in the Table 1 indicated that the 47.2% respondents of this study were categorized as young-aged compared to 44.2% and 11% of the respondents were categorized as middle-aged and old-aged, respectively. Abdullah (2013); Haque et al. (2019); and Sakib (2014) found significant positive relationship between the ages of the fish farmers and their extent of knowledge on utilization of aqua drugs.

Data presented in the Table 1 also indicated also that 84.7% of the farmers belong to secondary to higher education levels compared to 8.3% were illiterate, 6.9% primary level, 30.6%, respectively. Muddassir et al. (2019); and Haque et al. (2019) noticed that educated farmers tend to be more likely to have positive perceptions and knowledge regarding new aquaculture production procedures, compared to less educated farmers. Moreover, Uddin (2021) indicated that educated farmers faced less constraints in fish farming. The average annual household income of the respondents and annual income from pangas fish culture were Bangladeshi Taka 8.79 and 6.47 hundred thousand, respectively. Data presented in Table 1 revealed that the overwhelming majority, 91.7% respondents of this study area had medium to high annual income. In case of annual income from pangas fish culture, majority 63.9% respondents of this study area had medium to high annual income from pangas fish culture too. Haque et al. (2019) mentioned that the annual income of the fish farmers had a positive significant correlation with the extent of utilization of aqua drugs.

The findings of the study indicated that the 59.8% respondents of this study area possessed medium to high farming experience. Buncharoen and Anal (2021) found that older adopters had significant positive attitude towards aquacultural activities. Data contained in Table 1 indicated that only 13% respondents of this study area had short term training on antibiotics and gas-reducers use in fish health management comparatively a large portion of the respondents 81.9% of this study area had no training on antibiotics and gas reducers used in fish health management. Abdullah (2013); and Haque et al. (2019) found that training on aquaculture of the fish farmers had a significant positive relationship with the extent of utilization of aqua drugs.

It was also found that a large proportion of the respondents 72.2% of this study area had no access to credit comparatively 15.3% of the respondents in this study area received high amount of credit. A relatively small percentage (12.5%) of the respondents of this study area obtained low to medium amount of credit. Of these respondents, 15.3% received credit from government banks, 8.3% received credit from private banks, and 4.2% received credit from NGOs. Extension media contact play a crucial role towards knowledge enhancement as Haque et al. (2019) stated that there was a positive significant relationship between the extension media contact of the fish farmers and their extent of utilization of aqua drugs.

From the Table 1 it was also found that surprisingly almost all of the survey participants (98.6%) had low to medium contact with local extension office and other extension practitioners and media, including model fish farmers, input dealers, NGO workers, radio, and TV. Moreover, data presented in Table 1 also showed that 77.8% respondents possessed small to medium farms in this study area and 80.5% of respondents possessed small to medium pangas ponds in this study area. Hassan et al. (2020); and Haque et al. (2019) found that there was a positive significant relationship between the size of the farm of the fish farmers and the amount of their aqua medicine use.

The average operating season was 11.58 months with a standard deviation of 1.54. It is also found that 93.1% of the respondents in this study area cultured pangas fish for 12 months in their pond compared to 6.9% of respondents who cultured pangas fish for 6 months in their pond. The average of antibiotics mixed in feed before feeding was 2.36 times with a standard deviation of 0.93. Similarly average of gas reducers mixed in feed before feeding was 1.46 times with a standard deviation of 0.82. Data also indicated that 58.3% of the respondents in this study area possessed medium pond management activities compared to 34.7% of respondents in this study area who possessed higher pond management activities. Data presented in the Table 1 also indicated that 79.17% of respondents’ pangas pond were affected by diseases in past comparatively only 20.83% of respondents reported symptoms of no disease in their pangas pond throughout the farming cycle. Major diseases, including red spot, anal protrusion, tail and fin rot, pop eye, etc. were found in the study area. Hossain et al. (2018) observed that 60% of fish farms were affected by the disease in past.

Table 1

Selected characteristics of the pangas fish farmers (n = 72)
Characteristics (Measuring Units)	Category	Famers (percent)	Mean	SD
Age (Years)	Young (up to 35)	47.2	38.0	11.54
	Middle age (36–55)	44.4
	Old age (> 55)	8.3
Education (Years of schooling)	Illiterate (0)	8.3	10.82	4.79
	Primary level (1–5)	6.9
	Secondary level (6–10)	30.6
	Higher Secondary level (11–12)	20.8
	Higher education (> 12)	33.3
Annual Income (‘00000’ Tk.)
Annual family income	Low income (up to 1.50)	8.3	8.79	17.99
	Medium income (1.51-4.00)	37.5
	High income (> 4.00)	54.2
Annual income from fish culture	Low income (up to 1.50)	36.1	6.47	13.34
	Medium income (1.51-4.00)	22.2
	High income (> 4.00)	41.7
Pangas Fish Farming Experience (Actual years)	Low experience (up to 7 years)	40.2	10.54	6.86
	Medium experience (8–15 years)	41.7
	High experience (> 15 years)	18.1
Training on (No. of days)
Antibiotics	No (0)	81.9	0.52	1.38
Antibiotics	Short term (1–15)	18.1	0.52	1.38
Gas reducers	No (0)	81.9	0.58	1.35
Gas reducers	Short term (1–15)	18.1	0.58	1.35
Access to Credit (‘00000’ Tk.)	No (0)	72.2	4.18	16.10
	Low (0.10–0.99)	2.8
	Medium (1.0–3.0)	9.7
	High (> 3.0)	15.3
Extension Media Contact (Scale score)	Not at all (0)	0	7.59	2.72
	Low contact (1–7)	45.8
	Moderate contact (8–14)	52.8
	High contact (> 14)	1.4
Farm size (Hectares)
Total farm area	Marginal farm area (0.02–0.2)	5.6	2.16	4.16
	Small farm area (0.21–0.99)	48.6
	Medium farm area (1.0–3.0)	29.2
	Large farm area (> 3.0)	16.7
Pangas fishpond area	Marginal farm area (0.02–0.2)	5.6	1.94	4.13
	Small farm area (0.21–0.99)	56.9
	Medium farm area (1.0–3.0)	23.6
	Large farm area (> 3.0)	13.9
Operating season (Month)	4 months	0	11.58	1.54
	6 months	6.9
	12 months	93.1
Fish Feeding (score)
Mixed antibiotics in feed before feeding	Do not mix (0)	30.6	2.36	0.93
	Occasionally mix (1)	2.8
	Mix when needed (2)	66.7
Mixed gas reducers in feed before feeding	Do not mix (0)	75.0	1.46	0.82
	Occasionally mix (1)	4.2
	Mixed when needed (2)	20.8
Pond Management	low management (1–5)	6.9	9.45	3.05
	Medium management (6–10)	58.3
	Higher management (11–15)	34.8
Disease occurrence (scale score)	No Disease (0)	20.83	-	-
Disease occurrence (scale score)	Disease affected (1)	79.17	-	-
Note. Poss. = Possible Range; Obs. = Observed range; No. = Number; % = Percent; SD = Standard Deviation.

Objective 2: Determine the level of farmer’ knowledge on using antibiotics and gas reducers in farmed fish health management

The knowledge regarding the use of antibiotics and gas reducers in pangas fish health management of the respondents ranged from 7 to 44 with an average score 26.28 and standard deviation 7.86. Based on the knowledge regarding the use of antibiotics and gas reducers in pangas fish health management the farmers were classified into three categories as shown in Table 2.

Table 2

Distribution of the farmers according to their knowledge on using antibiotics and gas reducers in *pangasius* fish health management, n = 72
Characteristics (Measuring Units)	Range		Category	Farmers		Mean	SD
	Poss.	Obs.		No.	%
Knowledge (score)	0–48	7–44	Poor knowledge (1–16)	7	9.8	26.28	7.86
			Moderate knowledge (17–32)	50	69.4
			Fair knowledge (> 32)	15	20.8
Note. Poss. =Possible range, Obs. =Observed range, No. =Number, % =Percent, SD = Standard Deviation

Data presented in Table 2 indicated that the highest proportion 69.4% of the respondents of this study area had moderate knowledge on using antibiotics and gas reducers in pangas fish health management, while 20.8% had fair knowledge and 9.8% of the respondents of this study area had poor knowledge. The findings of this study indicated that most of the respondents had medium to fair knowledge on using antibiotics and gas reducers in pangas fish health management. Hosen (2020) found that most of the respondents had moderate fish farming knowledge in the study entitled as awareness of fish farmers regarding public and private sector extension services.

Average scores of factual, conceptual, procedural, and metacognitive knowledge of the respondents were 5.95, 7.46, 6.31, and 6.56, respectively. Categorization and percentages of survey respondents for each dimension of knowledge were presented in Table 3.

Table 3

Distribution of farmers regarding the knowledge on using antibiotics and gas reducers in *Pangasius* fish health management under the dimensions of knowledge, n = 72
Characteristics (Measuring Units)	Category	Farmers		Mean	SD
		No.	%
Factual knowledge (score)	Poor knowledge (up to 4)	28	38.9	5.95	2.61
	Moderate knowledge (5–8)	30	41.7
	Fair knowledge (> 8)	14	19.4
Conceptual knowledge (score)	Poor knowledge (up to 4)	9	12.5	7.46	2.40
	Moderate knowledge (5–8)	41	56.9
	Fair knowledge (> 8)	22	30.6
Procedural knowledge (score)	Poor knowledge (up to 4)	24	33.3	6.31	2.42
	Moderate knowledge (5–8)	37	51.4
	Fair knowledge (> 8)	11	15.3
Metacognitive knowledge (score)	Poor knowledge (up to 4)	20	27.8	6.56	2.52
	Moderate knowledge (5–8)	36	50.0
	Fair knowledge (> 8)	16	22.2
Note. Poss. =Possible range, Obs. =Observed range, Num. =Number, Per. =Percent, SD = Standard Deviation

Data presented in Table 3 indicated that most participants of this study had moderate knowledge in all dimensions on knowledge, including 56.9% in conceptual knowledge, 51.4% in procedural knowledge, 50.0% in metacognitive knowledge, and 41.7% in factual knowledge. Surprisingly, the highest percentage of respondents (38.9%) had low factual knowledge that is followed by procedural knowledge (33.33%), metacognitive knowledge (27.8%), and conceptual knowledge (2.5%). Besides, 30.6% of participants have fair knowledge in the conceptual dimension that is followed by metacognitive (22.2%), factual (19.4%), and procedural knowledge (15.3%). Therefore, for these findings, it could be concluded that 87.5% of participants in my study had moderate to fair knowledge in the conceptual dimension of knowledge, which is followed by metacognitive knowledge (72.2%), procedural knowledge (66.7%), and factual knowledge (61.1%).

Objective 3: Explore The Influential Factors, Which Have Significantly Impacted The Knowledge Of The Respondents

4.3.1 Pearson’s Product Moment Correlation Analysis

The information of Table 4indicated that annual income (X₃), training on aquaculture (X₅), extension media contact (X₇), mixed antibiotics in feed before feeding, and pond management (X₁₁) had significant positive relationship with knowledge of the pangas fish farmers regarding the use of antibiotics and gas reducers in fish health management.

Table 4

Factors associated with the knowledge on using antibiotics and gas reducers in *Pangasius* fish health management, n = 72
Influential Characteristics of the farmers	Correlation (p value, p < 0.01, p < 0.05, and p < 0.10)
Age (X₁)	0.457
Education (X₂)	0.135
Annual income (X₃)
Annual family income	0.065*
Income from pangas culture	0.048**
Experience (X₄)	0.213
Training on aquaculture (X₅)
Received on antibiotics	0.055*
Received on gas reducers	0.082*
Access to credit (X₆)	0.873
Extension media contact (X₇)	0.015**
Farm size (X₈)
Total farm size	0.186
Area under pangas pond	0.245
Operating season (X₉)	0.232
Fish feeding(X₁₀)
Mixed antibiotics in feed before feeding	0.000***
Mixed gas reducers in feed before feeding	0.769
Pond management (X₁₁)	0.019**
*** Correlation is significant at the 0.01 level (2-tailed).
** Correlation is significant at the 0.05 level (2-tailed).
*Correlation is significant at the 0.10 level (2-tailed).

Of these variables, a weak relationship was found to be existed between annual family income and farmers' knowledge on using antibiotics and gas reducers. Comparatively a stronger relationship was found between income from pangas culture and farmers' knowledge on using antibiotics and gas reducers. Shobuj (2020) found that the annual income of the fish farmers had significant negative relation with the adoption of Genetically Improved Farmed Tilapia (GIFT) at farm level in the study entitled as adoption of GIFT at farm level that contradicted with our findings. We argued that famers with better annual income can buy aqua drugs more easily than the farmers with less annual income. Therefore, the may process more knowledge on using these chemicals. Moreover, Tran et al. (2021) mentioned that GIFT is disease resistance and required less aqua drugs for their disease treatment. Therefore, the GIFT producers may use less aqua drugs that results their low knowledge on their uses.

Of these variables, a weak relationship was found to have existed between training received on antibiotics and farmers’ knowledge on using antibiotics and gas reducers comparatively a moderate weak relationship was found to have existed between training received on gas reducers and farmers' knowledge on using antibiotics and gas reducers. Hamid (2011) stated that training experience of the farmers had a significant positive relationship with their perception of the effect of shrimp farming in the study entitled as farmers’ perception of the effect of shrimp farming. There was moderate evidence of relationship found to have existed between extension media contact and farmers’ knowledge on using antibiotics and gas reducers. Haque et al. (2019) stated that the extension media contact of the farmers had positive significant relationship with their extent of use of aqua drugs.

A very strong relationship was found to have existed between mixed antibiotics in feed before feeding and farmers’ knowledge on using antibiotics and gas reducers that was not identified in any previous studies. Many of the respondents had low to medium experience of pangas fish farming. Thus, regardless the experience level of the respondents, all survey participants use some sorts of aqua drugs in their pangas fish health management practices. Moreover, most of the survey respondents have moderate to fair knowledge on aqua drugs use in their fish farms. Therefore, the experience variables may not be significantly related to knowledge of the participants for using antibiotics and gas reducers in their farms. Antibiotics and gas reducers are not very expensive; thus, farmers could buy it easily without any credit support therefore, there was no relationship may be found between farmers’ knowledge on using antibiotics and gas reducers and access to credit.

Moreover, farm size had no relationship with knowledge regarding the use of antibiotics and gas reducers. Thus, large, or small farms with proper management can go through the unwanted situation without the knowledge regarding the use of antibiotics and gas reducers. Therefore, farm size and use of aqua drugs may not be correlated.

Linear Multiple Regression Analysis

A Linear regression analysis was employed to identify the factors affecticing the knowledge of the farmers regarding the use of antibiotics and gas reducers in pangas fish health management. The results of the linear regression analysis were placed in Table 5.

Table 5

Regression coefficient of farmers’ knowledge on using antibiotics and gas reducers in pangas fish health management, n = 72
Demographic Characteristics of the farmers		Unstandardized Coefficients	Standardized Coefficients	t-value	Level of significant
Constant		-6.228		-0.619	0.539
Age (X₁)		-0.118	-0.174	-1.680	0.099*
Education (X₂)		0.277	0.169	1.743	0.087*
Annual income (X₃)	Annual family income	0.605	1.384	2.406	0.019**
Annual income (X₃)	Income from pangas culture	-0.762	-1.294	-2.171	0.034**
Experience (X₄)		0.235	0.205	2.242	0.029**
Training on aquaculture (X₅)	Received on antibiotics	1.788	0.315	1.124	0.266 ^NS
Training on aquaculture (X₅)	Received on gas reducers	0.346	0.059	0.232	0.817 ^NS
Access to credit (X₆)		0.063	0.128	1.360	0.179 ^NS
Extension media contact (X₇)		0.719	0.249	2.715	0.009***
Farm size (X₈)	Total farm size	2.866	1.511	1.554	0.126 ^NS
Farm size (X₈)	Area under pangas pond	-2.427	-1.275	-1.318	0.193 ^NS
Operating season (X₉)		2.413	0.079	0.766	0.447 ^NS
Feeding (X₁₀)	Mixed antibiotics in feed before feeding	3.379	0.397	4.162	0.000***
Feeding (X₁₀)	Mixed gas reducers in feed before feeding	-0.112	-0.012	-0.110	0.913 ^NS
Pond management (X₁₁)		0.841	0.326	3.088	0.003***
Note. R square = 0.601, Adjusted R square = 0.494, F change = 5.619, and p = < 0.01*, < 0.05, and < 0.10*
NS = non-significant

The results indicated that among eleven variables, six independent variables, including age (X₁), education (X₂), annual income (X₃), experience (X₄), extension media contact (X₇), mixed antibiotics in feed before feeding, and pond management (X₁₁) showing the significant contribution to the knowledge of farmers’ on using antibiotics and gas reducers in pangas fish health management. The adjusted R square value was 0.494 and the corresponding F change value was 5.619. The adjusted R square value indicates that 49.4 percent of the total variation in the respondents’ knowledge was explained by the eleven variables included in the regression analysis. Young to middle-aged group farmers were more interested to adopt new technology therefore, there might be an opportunity to increase their knowledge regarding the use of antibiotics and gas reducers. Hosen (2020) stated that awareness of fish farmers regarding public and private sector extension services increase with the increase of their age. Sakib et al. (2014) also mentioned that educated farmers know many things than less educated fish farmers as highly educated farmers collect information from various sources like mass media as well as through interaction with experts.

Experienced respondents were able to decide on when using of antibiotics and gas reducers needed which might help to increase the knowledge of farmers on using antibiotics and gas reducers. Booncharoen and Anal (2021) found similar result and mentioned that frequent exposure to extension media contact might be able to increase the farmers’ knowledge regarding the use of antibiotics and gas reducers as extension practitioners provide authentic knowledge on use of antibiotics and gas reducers.

The rest of the variables, including training on aquaculture, excess to credit, farm size, operating season, and mixed gas reducers in feed before feeding had no relationship with the knowledge of farmers’ on using antibiotics and gas reducers in pangas fish health management. Very few of the respondents in this study were found to have participated in the training, if most of the respondent had access to training then the respondent might be able to increase their knowledge on using antibiotics and gas reducers. Antibiotics and gas reducers are not very expensive; thus, farmers could buy it without any external financial supports, and therefore, there was no relationship between farmers’ knowledge on using antibiotics and gas reducers and taking credit and not taking it.

Farm size had not mattered with the knowledge regarding the use of antibiotics and gas reducers as all categories of the fish farmers use these chemicals for proper disease management. Most of the respondents operating fish for 12 months and mixed antibiotics and gas reducers with the foods when needed (as mean value in Table 1 close to 2.0). Therefore, the participants have adequate knowledge on this issue and therefore, no significant variations had been found for this variable. For example, if ammonia gas was found in water, then gas reducers were provided in the pond. Finally, we found that pond management positively affect the farmers knowledge on antibiotics and gas reducers use in pangas fish farming. Pond management is a complex skills and farmers should have adequate knowledge in different aspects of pond management, including sue of aua drugs. Therefore, the farmers who have better score in pond management may have better knowledge on using antibiotics and gas reducers.

Based on the findings of the research and logical interpretation of their meaning in the light of other relevant facts, the following conclusions were drawn:

Most of the participants of this study had moderate knowledge regarding the use of antibiotics and gas reducers in pangas fish health management. Moreover, the survey participants had better knowledge of conceptual dimensions that is followed by metacognitive, procedural, and factual knowledge. Therefore, it can be concluded that the level of the knowledge of the respondents was not found satisfactory on using antibiotics and gas reducers.
Correlation results indicated that annual income, training on aquaculture, extension media contact, mixed antibiotics in feed before feeding, and pond management had significantly positive correlation with the farmers’ knowledge on using antibiotics and gas reducers in pangas fish health management. Thus, it can be said that the above-mentioned factors of the respondents may play important role in increasing their knowledge on the issue.
Age, education, annual income, experience, extension media contact, mixed antibiotics in feed before feeding, and pond management were identified as influential determinants by linear multiple regressions analysis. Therefore, it leads to conclude that these determinants might significantly affect the farmers’ knowledge on using antibiotics and gas reducers in pangas fish health management.

Implications Of The Study

Based on the findings and conclusion of this study, several interventions can be considered to increase farmers’ knowledge on using aqua drugs in their farms. The extension practitioners should take different educational and training programs in the factual dimension of using antibiotics and gas reducers in fish health management as most of the participants had possessed least factual knowledge on using antibiotics and gas reducers in pangas fish health management. Moreover, extension practitioners should work with the farmers with high experience as the age and experience of the participants had positive significant relationship with using antibiotics and gas reducers in fish health management.

From the findings of this study, it would also be recommended that knowledge-building educational activities, including formal and non-formal education and training sessions, should be conducted by the local office of the Department of Fisheries and other NGOs in using antibiotics and gas reducers as they had a positive significant relationship with using of antibiotics and gas reducers in fish health management. In the case of socio-economic characteristics of the participants, annual income and pond management had positive significant relationship with farmers' knowledge on using antibiotics and gas reducers in fish health management. Therefore, the Department of Fisheries may introduce different projects and programs on pond management. However, the findings of this study also indicated that farmers with better annual income had good knowledge in antibiotics and gas reducers. Thus, the Department of Fisheries should work with farmers with low family incomes to enhance their knowledge on using antibiotics and gas reducers in fish health management. Finally, the Department of Fisheries should strengthen their communication with the local fish-farming communities to enhance the appropriate knowledge on using antibiotics and gas reducers in fish health management.

Limitations Of This Study

There is huge opportunity to pursue further research related to the knowledge of farmers on using antibiotics and gas-reducers in fish health management. The present study was conducted in one union Bailar of Trishal upazila (sub-district) under Mymensingh district. Therefore, the similar study can be replicated in other unions, Upazilas of Mymensingh districts even in other districts of Bangladesh to validate the findings of this study. The study was undertaken to explore the relationship of twelve selected characteristics with the knowledge of fish farmers regarding the use of antibiotics and gas reducers as the dependent variable. Thus, it would be recommended that more independent variables could be added for further study. Furthermore, from my theoretical framework, we only explored the knowledge factor of the innovation-decision process. Therefore, in future studies, other components of the innovation-decision process including persuasion, decision, implementation, and confirmation can be explored to understand the innovation-decision process of using antibiotics and gas reducers in fish health management. The present study was conducted on Pangasius fish species hence it could be recommended that further study should be conducted on other disease-susceptible species, including koi (Anabas testudineus), shing (Heteropneustes fossilis), etc. In this study, we only focused on using antibiotics and gas reducers. However, there are many others aqua drugs, which are important for aquaculture. Therefore, in future studies, researchers may explore the extent of knowledge on using other aqua drugs in fish health management.

Research Design, Population, Sapling Frame, and Sample Size

The research design is the detailed plan for an investigation (Creswell et al., 2003). The quantitative correlational approach (Apuke, 2017; and Rudolph, 2018) was used to determine the major socio-economic factors of the fish farmers, personality variables, and communication behaviors of the respondents on their knowledge on using antibiotics and gas reducers in fish health management. The study was conducted at Bailar union from Trishal upazila (sub-district) under Mymensingh District as Mymensingh, district as it has produced the highest percent of pangas fish (52.93%) in the 2018-19 fiscal year in Bangladesh (Department of Fisheries, 2019). Moreover, District Fisheries Officer of Mymensingh also confirmed that fish farmers of Trishal upazila have been producing highest amount of pangas fish (D. K. Saha, personal communication, March 24, 2022).

All Pangas fish farmers of the Bailor union of Trishal upazila were the population of this study. The researcher collected a list of farmers that comprises 87 pangas fish farmers from the local Extension Agent for Fisheries (LEAF) working at Department of Fisheries and those 87 farmers were the sampling frame of this study. From the sampling frame 72 pangas fish farmers were selected randomly at 95% confidence level and 5% marginal error.

Ethical Clearance And Informed Consent

As a survey study, the research team followed all ethical guidelines to conduct human subject research. All methods of this study were carried out in accordance with relevant guidelines and regulations of the Ethical Standard of Research Committee, Bangladesh Agricultural University Research System and approved prior to data collection (Ref# /BAURES/ESRC/AG/25). Moreover, prior to data collection, the researchers obtained informed oral consent from the survey participants and all their identify information were masked properly during data analysis and reporting. Furthermore, no identify marks of the survey participants were used in any parts of this report.

Development And Validation Of Research Protocol

Development and Validation of Research Protocol

The researchers developed an interview schedule based on literature review. During the literature review, the researcher identified 12 variables, which had significant relationship with knowledge. The drafted interview schedule was finalized by pre-test (Supplementary File 2). To conduct pre-test, the researcher selected six members from the sampling frame, who are not in the first 72 randomized farmers list on the sampling frame. Glen (n. d.) mentioned Cronbach's alpha (α) or coefficient alpha measures the reliability, or internal consistency of an Interview Schedule. Cronbach's Alpha (α) value for the pilot survey was 0.905 that indicated almost perfect internal consistency of the responses as Cronbach's alpha values greater than 0.7 indicate high internal consistency of responses (Bonett & Wright, 2015). None of the survey respondents suggested any further revisions to the questions.

Variables Of This Study

According to Krathwohl (2002), knowledge had four dimensions, including factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge, respectively. All the four dimensions of knowledge were taken in this study for the measurement of knowledge of the farmers. The researcher developed 24 open-ended questions from four dimensions of knowledge. The questions have been listed in the following Table 6.

Table 6

Open-ended structured questions based on the four dimensions of knowledge as defined by Krathwohl (2002)
Dimensions of Knowledge	Questions
Factual Knowledge	What are antibiotics?
	What are gas reducers?
	Mention the name of two antibiotics?
	Mention the name of two gas reducers?
	Can you tell me the two benefits of using antibiotics?
	Can you tell me the two benefits of using gas reducers?
Conceptual Knowledge	When do you apply the antibiotics during the day?
	When do you apply the gas reducers during the day?
	Why do you use antibiotics?
	Why do you use gas reducers?
	When do you catch fish after the using of antibiotics?
	When do you catch fish after the using of gas reducers?
Procedural Knowledge	How much dosage of antibiotics will you apply to one acre of a pond?
	How much dosage of gas reducers will you apply to one acre of a pond?
	How do you administer antibiotics in the culture pond?
	How do you administer gas reducers in the culture pond?
	Mention two symptoms for antibiotic use?
	Mention two symptoms for gas-reducers use?
Metacognitive Knowledge	How often do you use antibiotics according to the instructions given on the packet?
	How often do you use gas reducers according to the instructions given on the packet?
	How often do you consult an official (UFO/company representative/ farm input dealer) before using antibiotics?
	How often do you consult an official (UFO/company representative/ farm input dealer) before using gas reducers?
	Mention two adverse effects of using excessive antibiotics in culture pond?
	Mention two adverse effects of using excessive gas reducers in culture pond?

Each respondent was asked to answer all 24 questions. The researcher assigned two scores for each correct answer, one for the partially corrected answer, and zero for the completely wrong answer. After putting scores against each question as responded by the interviewee, the researcher calculated the sum score for each respondent. Therefore, the maximum score for an interviewee would be 48 for all corrected answers and zero for completely wrong answers for all questions. Afterward, the researcher categorized the total scores of the respondents as low knowledge, moderate knowledge, and fair knowledge with scores, of 0–16, 17–32, and 33–48, respectively as the similar categorization was used by Hamid (2011) to categorize the level of knowledge of the farmers. From Table 7, it was found that a list of personality variables, socio-economic characteristics, and communication behavior of the respondents along with their extent of relationship with the knowledge on fish farming in previous studies. For this study, these 12 variables were selected as they have significant relationship with the dependent variables and asked during data collection (Supplementary File 2).

Table 7

Independent variables enlisted from related articles
variables	Relationship with Knowledge	Extent of relationship	Authors
Age (years)	yes	Positive	(Abdullah, 2013; Booncharoen & Anal, 2021; Muddassir et al., 2019; and Sakib et al., 2014)
Age (years)	no	-	(Ali et al., 2016; Haque et al., 2019)
Education	yes	Positive	(Sakib et al., 2014)
Education	no	-	(Abdullah, 2013; Ali et al., 2016)
Annual income	yes	Positive	(Sakib et al., 2014; Haque et al., 2019)
Annual income	no	-	(Abdullah, 2013)
Experience (years)	yes	Positive	(Booncharoen & Anal, 2021)
Experience (years)	no	-	(Ali et al., 2016; Hasan et al., 2020)
Training on aquaculture	no no	- -	(Haque et al., 2019; Abdullah, 2013)
Credit received	no	-	(Shobuj, 2020)
Farm size	yes	Positive	(Haque et al., 2019; Sakib et al., 2014)
Farm size	no	-	(Hasan et al., 2020; Abdullah, 2013)
Operating Season	-	-	(Hossain et al., 2018)
Operating Season	-	-	(Ali et al., 2016)
Feeding	yes	Positive	(Booncharoen & Anal, 2021)
Feeding	-	-	(Rasul et al., 2017; Hossain et al., 2018)
Water Quality Management	-	-	(Booncharoen & Anal, 2021; Ali et al., 2016)
Disease Occurrence	-	-	(Hossain et al., 2018; Rasul et al., 2017)
Extension Media Contact	yes	Positive	(Haque et al., 2019; Abdullah, 2013)
Knowledge of fish farming	no	Negative	(Haque et al., 2019)
Water exchange (% farms practicing)	yes	Negative	(Ali et al., 2016)
Volume of water exchange per cycle (%)	yes	Negative	(Ali et al., 2016)
Repairing pond	yes	Positive	(Booncharoen and Anal, 2021)

Methods Of Data Collection, Processing, And Analysis

Data was collected by the researchers’ using survey method with the help of a prepared structured interview schedule. A total of 72 personal face-to face interviews using the survey as a script were carried out from 12th January to 20th January 2022. It took around 30 minutes to complete each survey. Firstly, the researcher established rapport with the respondents and clearly explained the objectives of the study using the local language as far as possible. As a result, the respondent furnished proper response to the question without any hesitations. The questions were clarified whenever any respondent had difficulties on understanding. After finishing the questions, the researchers checked the data for errors and then closed the interview by giving thanks.

After collecting data, the research team processed and analyzed all collected data. To facilitate tabulation, the collected data was identified using the appropriate quantitative variables and transferred from the interview schedule to a Microsoft Office Excel® sheet (The raw data set can be found at Supplementary File 3). Afterwards, data were transferred into the IBM SPSS® Version. 25 according to the data analysis plan (Supplementary File 4). Various descriptive statistical measures, including frequency, number, percentage, mean, and standard deviation (SD), were used for categorization and describing the variables. The Pearson's product-moment correlation coefficient and linear multiple regressions coefficient were calculated to determine the demographic factors, which influence the knowledge of the respondents on using antibiotics and gas reducers. It was found that the analyzed data set was normality distributed with linear relationship and independent to each other. We also found that there is homoscedasticity was presented in the data set.

The formula of Pearson’s product-moment correlation coefficient (r) is as follows (Corporate Finance Institute, n.d.).

$${r}_{xy}=\frac{\varSigma \left({x}_{i}-\stackrel{-}{x}\right)\left({y}_{\ddot{l}}-\stackrel{-}{y}\right)}{\sqrt{{\varSigma \left({x}_{i}-\stackrel{-}{x}\right)}^{2}{\varSigma \left({y}_{\ddot{l}}-\stackrel{-}{y}\right)}^{2}}}$$

Where,

${r}_{xy}=$ The correlation coefficient of the linear relationship between the

variable x and y

${x}_{i}=$ The values of the x-variable in a sample

$\stackrel{-}{x}$ =The mean of the values of the x-variable

${y}_{i}=$ The values of the x-variable in a sample

$\stackrel{-}{y}$ = The mean of the values of the x-variable

The equation of multiple regressions is as follows (Ramkumar, 2020):

$$Y= {\beta }_{0}+{\beta }_{1}{X}_{1}+\dots \dots \dots \dots \dots \dots .+{\beta }_{n}{X}_{n}+e$$

Source: (Obtained from, SuperDataScience, 2018)

Where, Dependent variable, Y = Farmers’ knowledge on using antibiotic and gas remover in Pangasius fish health management

β = Slopes associated with independent variables, X_i

${\beta }_{0}$ = Intercept

X_i = Independent variables

e = Error term

The formula to find the value of the regression coefficient is as follows (Glen, n.d.):

B₁ = b₁ = Σ [(x_i – x) (y_i – y)] / Σ [ (x_i – x)²]

Where,

B₁ is the regression coefficient

x_i and y_i are the observed data set

And x and y are the mean value.

Data Availability

The datasets generated and/or analyzed during the current study are available in the Supplementary File 3.

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No competing interests reported.

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Factors Affecting Farmers’ Knowledge on Using Antibiotics and Gas Reducers in Pangas (Pangasius pangasius) Fish Health Management

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Abstract

Figures

Introduction

Background Of The Study

The Theoretical Framework Of The Study

Findings And Discussions

Objective 3: Explore The Influential Factors, Which Have Significantly Impacted The Knowledge Of The Respondents

4.3.1 Pearson’s Product Moment Correlation Analysis

Linear Multiple Regression Analysis

Conclusions And Implications Of This Study

Implications Of The Study

Limitations Of This Study

Materials And Methods

Research Design, Population, Sapling Frame, and Sample Size

Ethical Clearance And Informed Consent

Development And Validation Of Research Protocol

Variables Of This Study

Methods Of Data Collection, Processing, And Analysis

Declarations

Data Availability

References

Additional Declarations

Supplementary Files

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