Sustainable intensification of small-scale aquaculture production in Myanmar through diversification and better management practices

Myanmar is one of the major aquaculture producers in Southeast Asia (FAO 2021). Aquaculture production has increased almost elevenfold over the past two decades, from 98.9 thousand tones in 2000 to 1082.1 thousand tones in 2019 (FAO 2021). Currently, more than 95% of Myanmar's aquaculture production comes from freshwater fish (Karim et al 2020), with large farms being a major source of freshwater fish production (Belton et al 2015). One of the most striking features of aquaculture in Myanmar is that small-scale aquaculture was almost absent since land-use regulations were thought to hinder the conversion of paddy land to ponds (Belton et al 2018). However, the small-scale aquaculture sector has been expanding rapidly throughout the country often via overseas development assistance for projects that seek to enhance food security and rural development over the past decade (Htoo et al 2021).

Although many areas in Myanmar have a good potential for small-scale aquaculture production (Karim et al 2020), we focused our analysis on five states/regions in the Central Dry Zone (i.e. Mandalay, Magway, Sagaing), and Northern and Eastern Regions (i.e. South Shan and East Shan, Kachin) (figure 1). These states/regions share similar socioeconomic and demographic characteristics such as religion (dominated by Buddhism), age structure (the highest proportion of the population is 10–19 yr old), education level (males tend to be more educated than females), livelihoods (almost half of the population is employed), and poverty (approximately 30% poverty rate) (Box S3, supplementary material).

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We developed a set of criteria and an extensive scoping approach for township inclusion from the five states/regions in the study (Box S4, supplementary material). The selection criteria include: (a) the availability of resource endowment to enable small-scale aquaculture (i.e. percentage of agriculture land, area of fish ponds), (b) demographic characteristics (i.e. population density, income per capita), and (c) social risk and security (i.e. access to safe sanitation, percentage of underweight children, internally displaced persons). In addition, these areas are characterized by low-income generation capacity, food insecurity and livelihoods challenges, poor access to sanitation, poor access to farming technologies, high weather variability and risk of natural disasters, and physical security threats from conflict (Torbick et al 2017, WorldFish 2021). In this sense small-scale aquaculture has great potential to improve rural livelihoods and food security in these areas. Following these criteria, we assigned each township in the identified states/regions a normalized score and then selected the five townships with the highest scores, with a high score indicating the high potential for small-scale aquaculture, and 30 townships were finally selected (see more details in table S1, supplementary material).

2.2.1. Data collection

2.2.2. Analytical approach and variables

2.2.3. Empirical analysis

The original data (n = 1112 households) from the survey were processed prior to the analysis. The extra answers beyond the proposed classification in variables were recategorized. To finalize the final selected sample of the surveyed households for analysis, first, we removed 14 households located outside the proposed townships. Second, we excluded 315 households that reported zero value of aquaculture production since farmers failed to harvest anything or miss-reported harvested numbers. Third, we also omitted 90 households that miss-reported the aquaculture cost and 35 households that miss-reported feed or fertilizer input. Furthermore, we identified 34 extreme values regarding outcome variables based on the top 95th percentiles (Shukla et al 2019) and omitted them to minimize bias (Nyambo et al 2019). In the end, after data processing we selected and used 624 valid household-level samples in the empirical analysis.

Before fitting the empirical models, we measured variance inflation factors (VIF) to check the multicollinearity between explanatory variables through the car package of R version 4.0.4 (Nkomoki et al 2018). The VIF test shows that there is no significant multicollinearity among the explanatory variables, suggesting the relative independence of the variables (table S8, supplementary material).

We used linear mixed-effects models (LMM) to estimate the effects of the different explanatory variables outlined in section 2.2.2 on the different sustainability outcomes. The LMM is a generalization of the regression approach, which considers both the fixed effects and random effects on the outcome variables (Barca et al 2019). The model is usually applied when there is non-independence in the dataset and to help explain processes (Wenng et al 2020). Considering that spatial-autocorrelation may exist within a study village as found in other studies in agricultural and aquacultural settings (Boillat et al 2019), we used the village as the random effect variable in all models.

The LMM allows for the integration of continuous and categorical data (Kuznetsova et al 2017, Boillat et al 2019) and can be expressed as follows:

$$\begin{equation*}Log{\boldsymbol{Y}} = {\boldsymbol{X}}\beta + {\boldsymbol{Z}}\mu + \varepsilon \end{equation*}$$

where $LogY$ is the vector of the log-transformed outcome variable (i.e. six sustainability outcomes); ${\boldsymbol{X}}$ denotes the fixed variables (i.e. diversification practices and/or BMPs, individual BMPs, household and farm characteristics); ${\boldsymbol{Z}}$ indicates the random predictor (i.e. the village); $\beta$ and $\mu$ represent the vector of a parameter related to fixed-effects and random-effects respectively; $\varepsilon$ is the observation error vector.

We used standard LMM to conduct three rounds of analysis to compare the effects of different production models on sustainability outcomes. We compared: (a) diversification models (with and without BMPs) vs. unimproved monoculture, (b) diversification models with BMPs vs. diversification models without BMPs, and (c) production models with individual BMPs vs. production models without individual BMPs. Table S6 in the supplementary material summarizes these different comparisons.

To further explore the observed heterogeneity (Dias and Belcher 2015), we developed an extended LMM with the inclusion of variables related to household and farm characteristics to test whether these factors would influence sustainability outcomes. In the extended LMM we included five household characteristics and five farm factors (section 2.2.2). The coefficient estimates (CEs) and the 95% confidence intervals (CIs) of explanatory variables denote the effect size (i.e. the direction and magnitude of the effects) of (a) diversification practices and/or BMPs, (b) individual BMPs, and (c) household and farm characteristics (Nakagawa and Cuthill 2007).

Models' goodness-of-fit was assessed through the Bayesian information criterion (BIC) (Aguilar et al 2018, Starkweather 2010). The lower the BIC the better the model fit. The LMM was conducted using the lme4 package of R version 4.0.4 (Bates et al 2014).

2.2.4. Sensitivity analysis

Despite its multi-dimensional approach and robust analysis of the sustainability outcomes of adopting diversification practices and BMPs, our study has a series of limitations. The four most important ones relate to (a) farmer selection, (b) comprehensiveness of sustainability outcomes, (c) uncertainty of nitrogen and phosphorus use efficiency estimations, and (d) actual implementation of BMPs.

Regarding (a), there are no comprehensive lists of small-scale aquaculture producers in Myanmar or accurate estimates about their total numbers and distribution across regions/townships. To identify the farmers participating in this study we relied on a list of small-scale producers developed with the assistance of local partners. Although we aimed to be as comprehensive as possible in the identification of farmers in each area, it is likely that the lists were not complete. This possibly inserts biases in the sampling distribution between the study regions and townships.

Regarding (b), we focused on two environmental outcomes, namely nitrogen and phosphorus use efficiency. However several other environmental outcomes can be equally important when exploring sustainable intensification such as ecotoxicity, greenhouse gas emissions, and acidification (Henriksson et al 2017, 2018). We focused on these specific environmental outcomes because eutrophication is one of the main environmental issues in freshwater aquaculture, which is mainly caused by the nutrient inputs from feed and fertilizer (Zhang et al 2015).

Regarding (c), we calculated nitrogen and phosphorus use efficiency based on the nutrient compositions of feed, fertilizer, and fish species from general and Myanmar-specific reports and literature (table S4, supplementary material). To the extent possible we prioritized the use of coefficients (e.g. rice bran, fish meal, small indigenous species) from Myanmar-specific resources. However, for coefficients that Myanmar-specific resources were unavailable items we used nutrient compositions from general reports and literature (Tacon et al 2009, Bogard et al 2015). It should also be noted that the nutrient compositions of feed, fertilizer, and fish species vary depending on the species grown and the quality of the feed (Kong et al 2020). Furthermore, in this study we did not consider the input of nitrogen and phosphorus from PDC systems or other natural sources through the runoff into the pond, which may cause uncertainties in the assessment of nitrogen and phosphorus use efficiency (Boyd et al 2007).

Regarding (d), in this study we assumed the proper implementation of the different BMPs by all relevant households. However, several factors such as the capacity and the availability of resources (in the broadest sense) of small-scale producers can affect their ability to implement BMPs effectively, dictating at the same time to a large degree the actual outcomes of their adoption. This assumption might insert uncertainties in the assessment of the actual outcomes of BMPs adoption.

Figure 2 shows the adoption rates for diversification practices and BMPs across the entire sample, while table S9 (supplementary material) presents the adoption rates for each study region. The aggregate results in table S2 (supplementary material) suggest that most households in the study area have adopted some form of diversification, either related to polyculture (77.88% of households) or PDC (51.12% of households). Although many households have adopted 'polyculture only' (26.44% of households), much fewer households have adopted 'PDC only' (4.81% of households). At the same time 42.47% of the households have adopted at least some BMPs (table S2, supplementary material), usually in conjunction with some diversification strategies (figure 2(a)). It is interesting to note that comparatively few households have not adopted any diversification strategies (11.06% of households), of which 4.17% have adopted 'BMPs only' and 6.89% 'unimproved monoculture'.

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Coming to the adoption of individual BMPs, we observe relatively low adoption rates for individual BMPs (figure 2(b)). The highest adoption rate is for the use of liming products (29.49% of households), followed by proper post-harvest fish handling (24.20% of households) and proper species selection (22.60% of households). These findings indicate that smallholder households tend to prefer BMPs associated with the increase in fish production. Conversely, low adoption rates are observed for BMPs associated with the limitations of drugs and chemicals (6.09% of households) and the use of organic feed (9.29% of households). These results imply that BMPs targeting the improvement of environmental performance are relatively overlooked by small-scale producers in the study region. The remaining five BMPs (i.e. proper farm site selection, alternatives to antibiotics, natural food adequacy in water, proper stock density, and quality seed selection) have similar adoption rates ranging from 15% to 20% of the households.

The adoption networks of diversification practices and BMPs (figure 3), suggest that the households that have adopted polyculture-oriented models tend to rely on combinations of fish species such as silver barb (Barbonymus gonionotus), rohu (Labeo rohita), and common carp (Cyprinus carpio) (see large node size and edge weight in figure 3(a)). Small indigenous species are much less prevalent in the sampled households (figure 3(a)). For pond-dike species, the sampled households mainly conduct the combined cultivation of roselle, long bean, mustard, and bottle gourd on their pond dikes (figure 3(b)). BMPs such as the use of liming products, proper species selection, quality seed selection, proper stock density, and proper post-harvest fish handling are often jointly adopted (figure 3(c)). This implies that for smallholder households the main aim of adopting BMPs is to increase fish production rather than reduce environmental impacts.

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Overall, for most sustainability outcomes the households adopting diversification strategies (with or without BMPs) tend to perform better than households relying on 'unimproved monoculture', though the differences are not always statistically significant (figure 4).

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Specifically, aquaculture yield is higher for all groups adopting diversification strategies, BMPs, and their combinations compared to the 'unimproved monoculture', but the differences are not statistically significant (figure 4(a)). Similar trends are also found for aquaculture BCR, though households adopting 'polyculture only' have significantly higher BCR compared to 'unimproved monoculture' (figure 4(b)).

Regarding environmental performance, all production models have high nitrogen use efficiency compared to phosphorus use efficiency. Several production models, i.e. 'polyculture + PDC', 'BMPs only', and 'polyculture + PDC + BMPs' have lower or similar nitrogen use efficiency compared to 'unimproved monoculture', but the differences are not statistically significant (figure 4(c)). Although for most production models phosphorus use efficiency (except for 'polyculture + PDC', and 'polyculture + PDC + BMPs') is better compared to 'unimproved monoculture', again all differences are not statistically significant (figure 4(d)).

In terms of food security performance, fish self-consumption for the production models 'polyculture + PDC', 'polyculture + BMPs', and 'polyculture + PDC + BMPs' is significantly higher than 'unimproved monoculture' (figure 4(e)). The other groups have lower fish self-consumption than 'unimproved monoculture', but the differences are not statistically significant. Finally, the differences in HDDS between all production models and 'unimproved monoculture' are significantly higher (except for 'PDC only') (figure 4(f)).

Figure 5 reports the effects of different production models on the different sustainability outcomes using the standard LMM. These results are quite similar with those obtained through the extended LMM (table S10, supplementary material). However, as the standard LMM has a better goodness-of-fit compared to the extended LMM indicated by the BIC values (table S11, supplementary material), in the subsequent sections we focus on the standard LMM results. We present the regional variation (Box S1) and effects of household and farm characteristics (Box S2) in the supplementary material.

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For the sensitivity analysis (i.e. robustness check), we should note that in the LMM we detect several observations in which Cook's Distance is higher than three times the mean, considering the influential outliers (Figure S3, supplementary material). We exclude the outliers and recalculate the adoption effects of (a) diversification practices and/or BMPs, and (b) individual BMPs. Our sensitivity analysis reveals that the results are robust as the coefficient estimates are similar to the original coefficient estimates (table S12–14, supplementary material).

3.3.1. Effects of production models on sustainability outcomes

3.3.2. Effects of individual BMPs on sustainability outcomes

In terms of economic outcomes (figures 6(a) and (b)), in most cases the adoption of an individual BMP does not seem to have significant effects. However, we have to point out that these are highly aggregated samples that contain very diverse production models. Nevertheless we observe a positive effect on aquaculture yield through the adoption of the 'limitations in drugs and chemicals use' BMP (CE = 0.53, 95%CI = 0.10–0.96). In terms of aquaculture BCR, a significant positive effect is also found for the 'proper farm site selection' BMP (CE = 0.37, 95%CI = −0.05–0.79), while the 'use of liming products' BMP could have a negative effect (CE = −0.51, 95%CI = −0.87 to −0.15).

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In terms of environmental outcomes (figures 6(c) and (d)), we find that adopting the BMP 'natural food adequacy test in water' could significantly increase nitrogen use efficiency (CE = 0.45, 95%CI = 0.13–0.76). Similar positive signs are found for the BMP 'proper post-harvest fish handling' (nitrogen use efficiency: CE = 0.41, 95% CI = 0.02–0.80; phosphorus use efficiency CE = 0.58, 95%CI = 0.10–1.07). Conversely, adopting the BMP 'use of liming products' seems to negatively affect nitrogen and phosphorus use efficiency (CE = −0.30, 95%CI = −0.59–0, CE = −0.51, 95%CI = −0.88 to −0.15, respectively).

In terms of food security outcomes (figures 6(e) and (f)), the adoption of BMPs such as 'limitations in drugs and chemicals use' (CE = 0.11, 95%CI = 0.01–0.21) and 'natural food adequacy test in water' (CE = 0.06, 95%CI = 0–0.13) can have significant positive effects on HDDS. However, there is no significant difference in fish self-consumption.

Notably, our results demonstrate that the small-scale aquaculture producers in the surveyed area have a sound yield, achieving fish production of 4179.49–5015.47 kg ha⁻¹ yr⁻¹ across all production models (figure 4(a)). Similarly, the aquaculture BCR is quite positive ranging from 7.61 to 27.13 in all cases (figure 4(b)). All production models have high nitrogen use efficiency of 50.83–89.59% (figure 4(c)), while phosphorus use efficiency is relatively low, ranging from 9.59–20.83% (except for 'BMPs only': 53.67%) (figure 4(d)). Fish self-consumption can be considered quite sufficient standing at 16.76–49.01 kg yr⁻¹ (figure 4(e)), with households having acceptable household dietary diversity of 6.23–7.37 for all production models (figure 4(f)). The above suggests that the small−scale aquaculture systems have multiple positive sustainability outcomes, which nevertheless vary between the different production models.

In terms of effects on economic outcomes, on the one hand that the adoption of aquaculture diversification practices can indeed offer certain benefits related to yield and BCR (blue bars in figures 5(a) and (b)). This finding is consistent with previous studies that integrating polyculture and PDC in small-scale aquaculture contexts could enhance economic performance (Karim et al 2011, Limbu et al 2017). This is mainly because fish growth could be promoted through complementarity among species, and such polyculture systems allow the production of multiple products with commercial value (Thomas et al 2021). In particular, the combined adoption of polyculture and PDC results in the highest aquaculture yield (figure 4(a)), possibly due to the runoff of nutrients from PDC systems (fertilization) into the pond (Karim et al 2011). On the other hand the economic benefits can also increase when integrating BMPs into diversification practices, i.e. 'polyculture + BMPs' (green bars in figures 5(a) and (b)). This reflects recent studies that the adoption of BMPs in polyculture systems had greater potential to achieve economic growth (Dickson et al 2016, Kassam and Dorward 2017), possibly due to the compatibility among the combined taxa of fish species (i.e. proper species and seed selection BMPs) or stock density optimization.

In terms of effects on environmental outcomes, phosphorus use efficiency ranges from 9.59 to 20.83% (except for 'BMPs only' with 53.67%) in all production models, which is similar to the results (8.70–21.20%) from a review study in China (Zhang et al 2015). We observe that adopting diversification practices and their combinations without BMPs can increase phosphorus use efficiency (blue bars in figure 5(d)). Previous studies also found that integrating a semi-intensive polyculture system with PDC can enhance nutrient use efficiency through the connections between terrestrial and aquatic production units (Karim et al 2011, Thomas et al 2021). Such positive effects for phosphorus use efficiency are also observed when integrating BMPs into diversification practices, i.e. 'polyculture + BMPs', and 'polyculture + PDC + BMPs' (green and red bars in figure 5(d)). A recent study also suggested that the adoption of BMPs in tilapia polyculture systems could offer a significant improvement in environmental performance regarding eutrophication, as this might be linked to the adoption of BMPs such as natural food adequacy test in water, and improved feed management (Henriksson et al 2017). However, such production models do not seem to have any major effects on nitrogen use efficiency (blue and green bars in figure 5(c)). This observed lack of significant effects might be likely because the surveyed farms already had high nitrogen use efficiency (50.83–89.59%) in different production models (figure 4(c)), resulting in that the differences might not be significant between production models. Such underlying mechanisms need to be understood better in order to achieve sustainable intensification through diversification and BMPs in Myanmar and possibly other developing contexts. Furthermore, for individual BMPs we can see that the use of liming products has negative effects on nitrogen and phosphorus use efficiency (figures 6(c) and (d)), possibly because farmers do not check liming requirements. This points to the possible need for extension support to indicate further good practices to improve the performance of BMPs, such as using liming products only as needed (Tucker and Hargreaves 2008).

In terms of effects on food security outcomes, our findings point to the vital role that some diversification practices can play in the food security of small-scale aquaculture households (figures 5(e) and (f)). To begin with, the adoption of some diversification practices can provide essential benefits for fish self-consumption and HDDS, as indeed evidenced in other small−scale aquaculture contexts (Ahmed et al 2014, Castine et al 2017). For example, species diversification in polyculture models could increase fish consumption in producing households, while diverse fish species rich in micronutrients can be an integral part of the diets and food security of small-scale producers (Dam Lam et al 2022). Similar to our results, diversified aquaculture systems integrating polyculture and PDC have also been found to improve household dietary diversity through the intake of vegetables and fish rich in micronutrients (Ahern et al 2021). However, our results suggest that the adoption of BMPs in diversified systems does not seem to have significant effects for food security outcomes. Indeed, although previous studies have explored the effects of BMPs on profitability (Dickson et al 2016), poverty alleviation (Kassam and Dorward 2017), and environmental performance (Henriksson et al 2017), there is scarce literature examining the food security outcomes of BMPs adoption. In this sense our findings contribute to current BMPs literature suggesting that integration of BMPs into diversification practices might not add substantial value in terms of food security benefits to small-scale aquaculture households.

As mentioned in the Introduction the adoption of diversification practices and BMPs have been core elements for the sustainable intensification of small-scale aquaculture production (Henriksson et al 2021). Our study makes two important observations for their actual potential to achieve sustainable intensification. First, while many of the studied diversified production models (with or without BMPs) can have significant positive effects on most economic, phosphorus use efficiency, and food security outcomes compared to 'unimproved monoculture' (blue and green bars in figures 5(a), (b), (d), (e), and (f)), we see practically no effect for nitrogen use efficiency (blue and green bars in figure 5(c)). Second, the adoption of BMPs for each diversified production model has practically no effect for practically all sustainability outcome (red bars in figure 5). Collectively these suggest that (a) although diversification and BMPs can have certain sustainability benefits, their full potential for sustainable intensification might be constrained in the absence of nitrogen use efficiency, and (b) the adoption of BMPs in diversified production models has virtually no added effect for the studied sustainability outcomes. This suggests the possible need for context-specific improvements in the studied BMPs or even the development of new BMPs. Below we discuss potential implications and ways forward.

Nevertheless, our findings point out the strong potential of diversification for enhancing economic, phosphorus use efficiency and food security outcomes. First, we highlight the importance of polyculture since polyculture-based models such as 'polyculture + PDC', 'polyculture + BMPs' and 'polyculture + PDC + BMPs' have significant positive effects on aquaculture yield, phosphorus use efficiency, fish self-consumption, and HDDS (figures 5(a), (d), (e), and (f)). In such models the combination of silver barb (Barbonymus gonionotus) in the upper layer, rohu (Labeo rohita) in the middle layer, and common carp (Cyprinus carpio) or mrigel (Cirrhinus cirrhosus) in the bottom layer (the main polyculture species in the sampled households) (figures 3(a) and 7) might add significant value. In this polyculture system, the foraging behaviors of carp can benefit rohu and silver barb through resuspending nutrients accumulated in the sediment into the water body (Thomas et al 2021) (figure 7). In such polyculture system, we also observe that the differences in yield between production models are small (figure 4(a)), which likely because most small-scale aquaculture ponds are unaerated, and the production volumes are limited by the oxygen budget. To further improve sustainability performance, we suggest applying mechanical aerators (if financially feasible) to provide oxygen and keep the aerobic organisms suspended and mixed with water to increase nutrient use efficiency and aquaculture yield and BCR. Second, the integration of PDC into the polyculture model can have added value for dietary diversity in terms of HDDS as the producing households not only benefit from high fish self-consumption which characterizes polyculture-based models, but also diverse vegetables from the land around the pond (figures 5(e) and (f)). In addition to the crop species currently cultivated (figure 3(b)) the possible addition of vegetables rich in micronutrients (e.g. orange sweet potato, dark green leafy vegetables) can have added nutritional benefits (Ahern et al 2021) (figure 7).

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Furthermore, we should note that in diversified small-scale aquaculture systems, it is hard to simultaneously achieve maximum potential yield and minimize environmental footprint, as there are several tradeoffs, especially when considering the full life-cycle of inputs, such as feed, water, and energy (Henriksson et al 2021). Such challenges could be addressed through targeted interventions that are readily available, including BMPs (Henriksson et al 2021). However, the low adoption rates for individual BMPs (ranging from 8% to 28%) among the sampled households are observed (figure 2(b)). For example, environment-related BMPs such as limitations in drugs and chemicals use, use of organic feed, and alternatives to antibiotics are much less popular compared to other BMPs targeting productivity gains (figure 3(c)). On the one hand, compared to the environment-related BMPs, BMPs aiming at productivity gains (e.g. proper species selection, quality seed selection) are often more easily accessible for households, likely due to observation from neighbors, friends, and family. Likewise, some adoption challenges are the resource constraints and the often insufficient knowledge and skills of small-scale aquaculture households (Belton et al 2018). On the other hand, farmers will likely invest in and adopt BMPs aiming at the environmental performance if they receive incentives and signals from government policies that set clear goals, if they expect that the investment will be profitable, and if they have the right education, information, and motivation (Viatte 2001, Piñeiro et al 2020). These strongly imply the provision of economic or in-kind incentives from governments or NGOs, such as providing fish seed, feed, and alternatives to antibiotics for the targeted farmers. In addition, to achieve the full potential of BMPs for sustainable intensification in small-scale production contexts, there should be greater efforts to ensure both that farmers are not excluded from adopting such BMPs and that BMPs are implemented properly (Aung et al 2021, Henriksson et al 2021).

Toward that end, it is crucial to improve the extension capability through future interventions from appropriate government agencies and NGOs. We suggest several pathways to improve these processes to help small-scale producers establish sustainable aquaculture production systems. First, there is a need to improve the necessary training packages, such as identifying context-specific needs and solutions and injecting economic, environmental, and food security concerns into the process of developing and introducing diversification practices and BMPs (Viatte 2001, Dam Lam et al 2022). It would be necessary to improve the BMPs to fit the local contexts, for example by fine tuning certain operation variables, and even develop new BMPs to improve sustainability outcomes in the local context (figure 7). For example, feeding and fertilization management can be a new BMP that focuses on lowering the nutrient inputs (e.g. 10% less fertilizer, 15% less feed or only feeding 5 out of 7 d), while maintaining production. Second, a participatory method would require the promotion of farmer awareness about the merits of such farming practices (e.g. productive, input-efficient, and environmentally beneficial) and facilitate the dissemination of these practices. In particular, a key point for such participation will be to involve and empower all relevant local actors (and especially women and women-led households) across multiple entry points (e.g. household level, community level) (Dam Lam et al 2022). Furthermore, a communication approach should be implemented to engage such actors in self-reflection exercises that best accommodate the intervention context (e.g. lessons learned, workshops, practical training), where socioeconomic barriers and resource limits can be identified, and their possible effects on the expected outcomes become clear (Htoo et al 2021). Finally there would be a need to ensure the proper implementation of BMPs over time, for example by asking farmers to record properly all inputs and outputs from their aquaculture operation in the first few cycles to both receive feedback on whether they implement properly the BMPs and how to further improve them. Such an integrated approach combining different techniques and actively considering the barriers to their adoption and proper implementation could possibly reconcile different sustainability dimensions and achieve sustainable intensification in small-scale aquaculture systems.