The base layer is then modified to account for inaccuracies. A separate raster using the NDWI equation (McFeeters 1996) is created from the ENVI output and river pixels are extracted. As this would include flooded land, this dataset is vectorised and a manual pass of identifying rivers done. The aim of this is to distinguish between what are clearly rivers and temporarily flooded fields. The identified rivers are then rasterised and mosaicked or "stamped" back onto the majority-filtered grid. The perimeter of the urban areas Khulna and Barisal are also digitised and stamped back onto the grid. Areas of irrigated agriculture were distinguished from mangrove areas using a manually digitised mask to reclassify pixels that are erroneously classified by the isocluster tool into the mangrove class. A separate dataset that distinguished between irrigated and non-irrigated agriculture is also utilised. The newly identified irrigated agriculture polygons are also rasterised and stamped back onto the grid. These cells corresponded well to what was being classified as simply agriculture by the isocluster tool.

A further modification of the base layer uses a land use dataset for 2014 created by CEGIS in Bangladesh which differentiates between saline and freshwater aquaculture. This is supported by stakeholder evidence (see Chap. 10) and reference to available literature. The resultant map, Fig. 20.5, provides the basis for other research as well as the integrated model (e.g. Chaps. 21, 22 and 28).

**Fig. 20.5** Classification of 2010 Landsat Imagery of the study area into nine land use classes using remote sensing supported by ground truth data relating to the saline shrimp farming in the west of the study area (Reprinted under Creative Commons Attribution 4.0 International License from Amoako Johnson et al. 2016)

## **20.6 Scenarios of Future Land Use**

Rather than simply extrapolating historical trends, future land use scenarios were developed for the study area based on stakeholder's scenario narratives for 2050 under the business as usual (BAU) scenario (e.g. brackish shrimp area slightly increased due to conversion of natural vegetation). The narratives are quantified through a process of qualitative to quantitative translation by experts and, after a final stakeholder workshop, the quantifications were projected to 2050 using agreed per cent changes from the 2011 land use baseline. The analysis does not extend beyond 2050, not least due to the large uncertainties in social and political trends more than 40 years into the future.

The land use scenarios were therefore linked to the expert demographic and socio-economic scenarios. Land use scenarios utilised in this context are a novel way to unify socio-economic and biophysical scenarios with changes in land use and land cover acting as a main driver of the scenario modelling process. Whilst such an approach allowed for land use to act as a central driver for modelling the relationship between people and ecosystem services, it also critically allows stakeholders to envisage a future in terms with which they are familiar, land use being an aspect of the dayto-day lives of the local population and decision makers.

#### **20.7 Discussion**

The land use base map and scenarios outlined here are fundamental to integrated analysis of ecosystems, environmental change and social sustainability in the delta. The land use information, for example, can be used to establish the role of tenure, or exposure to environmental stress, as a constraint on livelihoods and the spatial distribution of poverty (see Chap. 21). The maps developed show a picture of rapid change over the past two or three decades in the delta, notably the consolidation of polders and agriculture within them, and a significant increase in the prevalence of brackish shrimp farming with aquaculture mainly replacing rice fields. This is a process that might reasonably be set to continue into the future and almost certainly within the context of the development of the Bangladesh Delta Plan 2100 (BDP2100 2015), which will see substantial coastal infrastructural development raising the value of land and formalising agricultural structures. This is likely to include the further development of current polders and possibly see the introduction of further water and sediment management processes.

The trend of conversion for aquaculture has occurred concurrently with an increase in salinity which itself has been associated with a rise in poverty across the study region (Amoako Johnson et al. 2016). Indeed this region has shown a substantial growth in shrimp farming in the last 20 years as shown in the land use maps. This process is often associated with the presence of salinity and is thus highly likely to continue with future levels of salinity (see Chaps. 17 and 18). The growth of the shrimp-based aquaculture appears to be associated with both the intrusion of salt but also the proximity of a substantial settlement, in this case of Khulna, where rapid processing of the perishable product can be undertaken. This association is supported by access to main road systems which can be seen to be associated with the main hotspots of shrimp industry development.

At one level this growth might be viewed as a useful adaptation to increasing salinity intrusion in the region (Belton et al. 2011; Primavera 1997; Paul and Vogl 2011; Kamruzzaman 2014). Indeed, the high demand and perceived monetary benefits of shrimp has inspired many farmers to convert farmlands intruded by saline water into shrimp farms, whilst others have actively encouraged saline water from marine sources into their farmlands to produce shrimp (Rahman et al. 2013). However, the environmental impacts of this intense aquaculture practice (e.g. increasing soil toxicity) raise concerns over its sustainability. Intensive aquaculture, as identified in the mapping, has consequences for land tenure, livelihood displacements and income loss, food insecurity and health, rural unemployment, social unrest, conflicts and forced migration (Hossain et al. 2013; Swapan and Gavin 2011; Paul and Vogl 2011). As such the monitoring of shrimp farming by remote sensing offers a valuable insight into changes in socio-environmental context linked via land use change and offers future scenarios the trend in growth of brackish shrimp farming.

## **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **21**

## **A Geospatial Analysis of the Social, Economic and Environmental Dimensions and Drivers of Poverty in South-West Coastal Bangladesh**

**Fiifi Amoako Johnson and Craig W. Hutton**

## **21.1 Introduction**

What are the causes of persistent poverty in the Ganges-Brahmaputra-Meghna (GBM) delta and how important are environmental dimensions and ecosystem services in explaining the uneven distribution of the observed rates of poverty? Investigation of the fine-grained linkages between the distribution of poverty and the state and health of the environment are particularly important for policy decisions and planning for the allocation of resources for development intervention. In this research, spatially aggregated population level data are used to (i) examine the extent

C. W. Hutton

F. Amoako Johnson (\*)

Social Statistics and Demography, Faculty of Social, Human and Mathematical Sciences, University of Southampton, Southampton, UK

Geodata Institute, Geography and Environment, University of Southampton, Southampton, UK

of geographical variations in poverty in the delta, (ii) identify the key socio-economic and environmental drivers of poverty and (iii) investigate how the drivers of poverty are spatially distributed and associated with spatially explicit socio-economic and environmental factors. Underlying the approach adopted is the hypothesis that if changes in the socio-economic and environmental functions in deltas have a substantial impact on the well-being of the local population, it should be reflected in the associated poverty levels measured.

Research has revealed wide social and geographical variations in poverty in Bangladesh (Amoako Johnson et al. 2016). In addition to the social, economic and cultural determinants, recent studies have shown that environmental and ecosystem services are also important associative factors of poverty (Suich et al. 2015; Amoako Johnson et al. 2016; Islam et al. 2016). Suich et al. (2015) also documented that ecosystem services act as a safety net for the poor and marginalised populations; however there is little evidence to suggest that availability of ecosystem services acts as a route out of poverty (see Chap. 2). Other studies have shown that the impact of ecosystem services on poverty are mediated by other factors including access to land, land tenure arrangements and availability of human capital (McKay and Lawson 2003; Daw et al. 2016). Yet, the ambiguity about the causes of the uneven distribution of poverty across space and across society remains unexplained. In this research, the hypothesis that the spatial dynamics of the factors associated with poverty including environmental services and human capital affect the incidence of poverty across space is explored.

Conventional approaches for measuring poverty (poverty headcount, income share and the poverty gap) rely on indicators such as income, expenditure and/or consumption which are either not covered by censuses or, where they are reported, are often not reliable (Meyer and Sullivan 2003; Nicoletti et al. 2011). To overcome these limitations, studies have used approaches based on households' ownership of assets and amenities (e.g. Filmer and Pritchett 2001). Validation of these approaches have shown that asset poverty robustly captures the multidimensionality of poverty (Filmer and Pritchett 2001) and represents chronic poverty and lack of human capital (McKay and Lawson 2003; Cooper and Bird 2012; Stein and Horn 2012; Wietzke 2015). Most censuses in low- and middle-income countries collect detailed information on ownership of assets and amenities, which could be used to evaluate the poverty status of local communities and the associative effects of climate-related hazards and environmental stressors.

In this chapter, socio-economic data from the Bangladesh Population and Housing Census (BPHC) is linked with environmental data derived from Landsat Imagery to examine the geospatial differentials in poverty with social, economic and environmental vulnerability, including salinisation, development of shrimp and prawn farms, loss of agriculture, water logging and infrastructure development in the GBM delta. The geospatial unit of analysis is the Union, which is the lowest tier of local level administrative structure in Bangladesh and typically represent perhaps 5,000 people.

#### **21.2 Background**

Although deltas are major source of diverse ecosystem services, vital for sustaining human well-being, they remain exposed to the impacts of climate change, environmental hazards, sea-level rise and land cover changes on local ecosystems. In turn, livelihoods and survival of residents, particularly poor and vulnerable communities, are affected (Nicholls et al. 2016). Communities of the GBM delta are therefore not only marginalised by environmental dynamics but also in their social and economic development. This is reflected in the region's adult illiteracy rate, education, access to health care, nutrition, employment, transportation and gender empowerment indicators which remain very low, with high geographical inequalities (Biswas 2008). For example, a study by Szabo et al. (2016) reported large intra-urban inequalities in education and access to health care services, whilst a study by Sohel et al. (2010) identified clusters of high foetal loss and infant death in the localities of the Meghna River.

A major challenge to the ecosystem services within the delta has been the increasing salinisation of the region which has had a substantial impact on land use and land cover changes. This is illustrated by the decline of traditional (rice) agriculture and the increase in brackish shrimp farming (although freshwater prawn farming also has a 20-year history within the area). Shrimp farming, due to the high demand and perceived monetary benefits, is an economic adaptation to the impacts of the rapidly salinising delta with many farmers converting their permanently flooded farmlands into shrimp farms and others actively encouraging saline water from marine sources into their farmlands to enable shrimp production (Rahman et al. 2013). Large-scale commercial shrimp farming in the delta has also developed, leading to deforestation, loss of agricultural land and increasing soil toxicity. For the local population, this has generated issues around land tenure, livelihood displacements, income loss, food insecurity and negative health impacts, loss of rural unemployment, social unrest, conflicts and forced migration (Paul and Vogl 2011; Swapan and Gavin 2011; Hossain et al. 2013). These issues raise concerns on the benefits and sustainability of the everexpanding shrimp farms to the vulnerable and marginalised populations of the delta.

Analysis of historical data also shows a decline in mangrove areas of about 17 per cent in the Sundarbans of the GBM delta, through both sea-level rise and deforestation (see Chap. 26 and Mukhopadhyay et al. (2015)), with projections anticipating a further decline of between 3 and 24 per cent by 2100 (Nicholls et al. 2016). Alongside salinisation and land loss, waterlogging of agricultural land is a growing phenomenon in the study area and is the result of the slow dissipation of annual flooding due to poorly maintained and overwhelmed drainage systems. These changes have important implications for provisioning ecosystem services (e.g. agriculture, fisheries) and regulating services such as the protective role of mangroves during storms.

Evidently, there is the need to examine the complex interactions between the socio-economic and environmental dynamics of the GBM delta to support policy and programmes aimed at alleviating poverty and develop sustainable approaches to preserve the regions' ecosystems and environment. In this research, multiple data sources including Census, Landsat Satellite Imagery 5 TM and Soil Salinity Survey data are used to examine the extent of geospatial clustering in poverty in the delta and their associative relationships with selected socio-economic and environmental factors in the GBM delta.

## **21.3 Study Area**

The analysis is conducted at the Union level, which is the lowest local government administrative unit in Bangladesh (Panday 2011; MoHFW 2012). The spatial distribution of Unions is shown in Fig. 21.2. The study area focused on the south-central (Barisal, Bhola and Patuakhali districts) and south-western (Bagerhat, Barguna, Jhalokati, Khulna, Pirojpur and Satkhira districts) coastal zones of the Bangladeshi GBM delta (Fig. 21.1).

The study area covers the 653 Unions which make up the central and western coastal zones of the GBM delta, classified into 497 rural and 156 urban (cities, municipalities and *Upazila* headquarters) Unions. It is important to note that four of the nine districts in the study area (Bagerhat, Satkhira, Pirojpur and Khulna) are classified amongst the major shrimp-producing districts in Bangladesh (FAO 2015).

**Fig. 21.1** South-central and south-western coastal zones of the Bangladeshi GBM delta. Map shows districts and land use (Reproduced from Amoako Johnson et al. 2016 under Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/))

## **21.4 Data and Methodology**

#### **21.4.1 Socio-economic Data**

Unlike environmental and climate-related data which can be generated across multiple spatial resolutions, social and economic data is more problematic as the infrastructure and resources to conduct surveys to collect data representative for small geographic areas are limited. A major source of representative local level social and economic data for most low- and middle-income countries is therefore the national Population and Housing Census (PHC).1 In Bangladesh, although these censuses are less regular and expensive compared to population level surveys, they do provide important demographic and human capital information representative for small geographic units such as Unions.

The outcome variable of interest 'asset poverty' is a multidimensional score based on ownership of assets and amenities derived from the 2011 Bangladesh PHC (BBS 2012). The assets and amenities data include detailed information on the type of housing structure (*pucka*, *semi-pucka*, *kutcha* and *jhupri*), sources of drinking water (tap, tube well and others), type of toilet facility (water sealed, non-water sealed, non-sanitary and no toilet) and electricity connectivity. *Pucka* refers to houses built with permanent materials such as burnt bricks or concrete, *kutcha* are those built with nondurable materials such mud floors and metal sheet roofs and/or walls, whilst *semi-pucka* is a hybrid of *pucka* and *kutcha* (e.g. floors and/ or walls are bricks or concrete but the rest are sheets) (Bern et al. 1994; Nenova 2010; GFDRR et al. 2014). A maximum likelihood factor analysis technique (Filmer and Pritchett 2001; Rutstein and Johnson 2004) is used to derive an asset poverty score at Union level. Maximum likelihood factor analysis is used as it circumvents the problem of multicollinearity and assigns indicator weights based on the variations in ownership of assets and amenities (Jones and Andrey 2007). The first factor score is categorised into quintiles and mapped to show the extent of spatial clustering in asset poverty.

The 2011 Bangladesh PHC also collected detailed information on demographic, economic, human capital and tenure. The socio-economic covariates derived from the 2011 Bangladesh PHC include employment status, adult literacy, school attendance, population density, dependency ratio and average household size. Information on major road density in each Union was derived from the 2011 Bangladesh Department of Roads and Highways data.

#### **21.4.2 Environmental Data**

The environmental covariates for the analysis are derived from 2009 Bangladesh Soil Salinity Survey (BSSS) (Ahsan 2010) and the 2010 Bangladesh Landsat 5 TM supplemented with the Bangladesh 2010 MODIS Terra Satellite Imagery (MODIS TSI). The 2009 BSSS is used to derive the percentage of Union area affected by soil salinity, whilst the Landsat 5 TM remote sensing images is used to extract Union area used for brackish shrimp and freshwater prawn farming. Union area affected by soil salinity is classified into four intensities: (i) low salinity (2–4 deciSiemens per metre (dS/m)), (ii) moderate salinity (4.1–8 dS/m), (iii) high salinity (8.1–12 dS/m) and (iv) very high salinity (12 dS/m or higher). The percentage of Union area used for brackish shrimp farming is classified into four categories: (i) no brackish shrimp farming, (ii) low brackish shrimp farming (less than one per cent of Union area), (iii) moderate brackish shrimp farming (one to ten per cent of Union area) and (iv) high brackish shrimp farming (greater than ten per cent of Union area). The percentage of Union area used for freshwater prawn farming was categorised into three categories: Unions with (i) no freshwater prawn farming, (ii) low freshwater prawn farming (less than one per cent of Union area) and (iii) high freshwater prawn farming (greater than one per cent of Union area). It is important to note that freshwater prawn farming is very limited in the study area, with only eight Unions where more than ten per cent of the Union area used for freshwater prawn farming. Additional environmental predictors extracted from the Landsat 5 TM include (i) the waterlogged agricultural land in a Union, (ii) Union area that is mangrove forest, (iii) permanent open water bodies and (iv) wetland and mudflats (see Amoako Johnson et al. 2016 for a more detailed discussion on the variables and their extraction). Table 21.1 shows the environmental, socioeconomic and important controls variables selected for the analysis.

**390**

#### **21.4.3 Methods**

To examine the extent of spatial clustering in asset poverty, the join-count spatial autocorrelation technique is used to examine whether the observed spatial patterns of asset poverty amongst the Unions in the study area are significantly random or clustered (Cliff and Ord 1981). A Bayesian Geoadditive Semi-parametric (BGS) regression is used to examine the geospatial differentials in asset and the extent to which the socio-economic and environmental predictors are associative with the observed spatial differentials in asset poverty (Brezger et al. 2005). The BGS techniques were adopted for this analysis because it allows for the unobserved spatial heterogeneity (both spatially structured and unstructured) to be accounted for as well as the simultaneous estimation of non-linear effects of continuous covariates as well as fixed effects of categorical and continuous covariates in addition to the spatial effects (Brezger et al. 2005).

The outcome variable of interest 'asset poverty' y*i* is coded 1 if a Union is in the bottom quintile of the score and 0 otherwise. The outcome variable y*i* follows a binomial distribution with parameters n*i* and π*i*, that is y*i*~B(n*i*, π*i*), where π*i* is the probability of a Union being in the bottom quintile and n*i* is the number of Unions in the study area. The model linking the probabilities of a Union being in the bottom quintile with the predictors follows a logistic model of the form:

$$\pi\_i = \mathbb{P}(\mathbf{y}\_i \mathbf{1} \mid \boldsymbol{\eta}\_i) = \frac{\exp(\boldsymbol{\eta}\_i)}{1 + \exp(\boldsymbol{\eta}\_i)} \tag{21.1}$$

where η*<sup>i</sup>* is the predictor of interest. With a vector *x x x i i ik* ′ ′ = … ( ) <sup>1</sup>,, ,, of *k* continuous covariates and λ λ *i i* λ*id* ′ ′ = … ( ) <sup>1</sup>, a vector of *d* categorical covariates, then predictor η*<sup>i</sup>* can be specified as:

$$
\eta\_i = \alpha \lambda\_i^{'} + \beta x\_i^{'} \tag{21.2}
$$

where α is a *d*-dimensional vector of unknown regression coefficients for the categorical covariates λ*<sup>i</sup>* ′ ,β is a *k*-dimensional vector of unknown regression coefficients for the continuous covariates x*<sup>i</sup>* ′ . To account for non-linear effects of the continuous covariates and spatial dependence in asset wealth, the BGS framework which replaces the strictly linear predictors with flexible semi-parametric predictors was adopted. The BGS model is then specified as shown in Eq. 21.3:

$$
\eta\_i = \alpha \lambda\_i^{'} + f\_k \lambda\_{ik}^{'} + f^{\text{sptat}(S\_i)} \tag{21.3}
$$

where *fk*(x) are non-linear smoothing function of the continuous variables *xik* and *f Si* spat( ) accounts for unobserved spatial heterogeneity at location *i* (*i*=1,…,S), some of which may be spatially structured and others unstructured. The spatially structured effects show the effect of location by assuming that geographically close areas are more similar than distant areas, whilst the unstructured spatial effect accounts for spatial randomness in the model. Then, Eq. 21.4 can be specified as:

$$
\boldsymbol{\eta}\_{i} = \boldsymbol{\alpha} \boldsymbol{\lambda}\_{i}^{\prime} + \boldsymbol{f}\_{k} \boldsymbol{x}\_{ik}^{\prime} + \boldsymbol{f}^{\mathrm{str}(\boldsymbol{S}\_{i})} + \boldsymbol{f}^{\mathrm{unstr}(\boldsymbol{S}\_{i})} \tag{21.4}
$$

where *f* str is the structured spatial effects and *f* unstr is the unstructured spatial effects and *f f f Si* spat( ) str unstr = + . In the case of this study, the spatially structured effects depict the extent of clustering of asset poverty and the influence of unaccounted predictor variables that themselves may be spatially clustered or random. The smooth effects of continuous factors are modelled with P-spline priors, whilst the spatial effects are modelled using Markov random field priors.

#### **21.5 Results**

The geospatial patterns in poverty derived using ownership of assets and amenities are shown in Fig. 21.2. The figure shows a strong clustering of asset poverty in the Bhola district and the Unions close to the Sundarbans. More than half (58.2 per cent) of all the Unions in the Bhola district are classified in the bottom quintile. The Pirojpur district recorded the second

**Fig. 21.2** Geospatial variations in asset poverty in the GBM delta of Bangladesh (Reproduced from Amoako Johnson et al. 2016 under Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))

highest percentage (34.9 per cent) of Unions in the bottom quintile. About a quarter of all Unions in the Barguna (25.9 per cent), Patuakhali (25.6 per cent) and Bagerhat (25.0 per cent) districts are also in the bottom quintile. Asset poverty is lowest in the Unions in Jhalokati (2.9 per cent), Barisal (5.7 per cent), Satkhira (9.5 per cent) and Khulna (9.7 per cent) districts where less than one-tenth of Unions in those districts are in the bottom quintile. A joint count spatial autocorrelation analysis showed that Unions in the bottom quintile are nearly three times more likely to be neighbours than would be expected under a random spatial pattern (Z[BW] = −18.87, *p* < 0.05). This demonstrates that the poorest Unions are more concentrated in some parts of the study area (see Fig. 21.2).

Table 21.2 shows the posterior odds ratios and their corresponding 95 per cent credible intervals of the effect of the socio-economic and environmental predictors on poverty, after controlling for the location (the divisional administrative effect and rural/urban status of the Union) effects. Only predictors significant at the five per cent significance (*p* < 0.05) level

**Table 21.2** Estimated posterior odds ratios and their corresponding 95 per cent credible intervals of the associative effects of the environmental and socio-economic predictors on poverty

are retained in the model, except for the shrimp farming because of its perceived economic importance and an alternative coping mechanism for salinity intrusion in the delta. The posterior odds ratios show that the divisional administrative effect and rural/urban status of the Unions are not significantly associated with poverty. For environmental predictors, the results show that whilst high levels (greater than 4 ds/m) of soil salinity in a Union are significantly associated with the probability of a Union being poor, areas used for both brackish shrimp and freshwater prawn farming are not. The estimated posterior odds ratios show that increasing intensity of soil salinity increases the odds of Union being poor. This suggests that whilst poverty is pronounced in Unions affected by high levels of salinity, large shrimp farms do not reduce the incidence of asset poverty.

The results further show that Unions with mangroves are six times more likely to be in the poorest quintile when compared with Unions with no mangrove. For permanent open water bodies, the results show that the higher percentage of permanent open water bodies within a Union, the higher the odds of the Union being in the poorest quintile. On average, if permanent water body area within a Union increases by one per cent, the odds of the Union being in the poorest quintile increases by 1.03. Similarly, the higher the wetland and mudflats area in a Union, the higher the likelihood of the Union being poor, however, the relationship is not linear.

Considering socio-economic factors, the results show that employment, literacy, school attendance and access to major road with a Union are significantly associated with poverty. The posterior odds ratios show that increase in employment rate, adult literacy and school attendance are all associated with a decline in the odds of a Union being poor (Table 21.2). Also, the higher the density of major roads within a Union, the lower the odds of the Union being poor.

The posterior mode of the structured spatial effects and their corresponding posterior probabilities at the 95 per cent nominal level are used to examine the spatial drivers of poverty. The posterior mode of the structured spatial effects could be used to identify Unions where asset poverty is high, where they are low and where they are trivial. To identify the spatial drivers of poverty, a sequential model building approach was adopted by first including the environmental predictors in the fitted model, accounting for the location effects, followed by the socio-economic predictors. The posterior probabilities are used to identify the spatial correlations of the predictors with poverty by examining Unions where the estimated posterior mode of the structured spatial effects become statistically non-significant after covariates are added to the model.

Figure 21.3 shows the spatial correlates of asset poverty for the poorest Unions in the GBM delta based on the asset score. The results show that the environmental controls exhibit significant geospatial associations with asset poverty predominantly with Unions close to the Sundarbans in the Satkhira and Khulna and Bagerhat districts, as well as those in the Barguna, Patuakhali and Pirojpur districts.

The socio-economic predictors were significantly associated with asset poverty for Unions in the Bhola and Patuakhali districts, as well as a few Unions in the Barisal district. The socio-economic factors are associated with asset poverty in 34 of the 39 Unions in the bottom quintile in the

**Fig. 21.3** Spatial correlates of poverty amongst Unions in the bottom quintile (Reproduced from Amoako Johnson et al. 2016 under Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))

Bhola district, making up about half of all the Unions in the district. In the Patuakhali district, the socio-economic factors are associated with asset poverty in 12 of the 18 Unions in the bottom quintile. The socio-economic factors are also significantly associated with asset poverty in six Unions in the Barisal district—Dhul Khola, Hizla Gaurabdi, Alimabad, Char Gopalpur, Jangalia and Bhasan Char Unions and also the Atharagashia Union in the Barguna district.

### **21.6 Discussion**

The analysis conducted in this research illustrates that the benefits of integrating census data at lower geographic units are beneficial for designing and promoting policy-relevant research and the development of sustainable poverty-relevant programmes and targeted interventions. Using census data, this research has been able to explore the geospatial differentials in asset poverty, an important indicator and correlate of chronic poverty and lack of human capital (McKay and Lawson 2003; Cooper and Bird 2012; Stein and Horn 2012; Wietzke 2015), and provide input into the integrated modelling described in Chap. 28. Linking census and environmental data derived from Landsat 5 TM, MODIS TSI and Soil Salinity Survey, the research has explored the spatial correlates of poverty in the GBM delta.

The results show a strong clustering of poverty in the GBM delta, predominantly clustered amongst Unions in the Bhola, Bagerhat, Barguna, Patuakhali and Pirojpur districts. The multivariate analysis revealed that the environmental predictors—intensity and extent of salinity intrusion in a Union, presence of mangrove forest, waterlogged agricultural land, permanent open water bodies and wetland and mudflats—are significantly associated with Union level poverty. Whilst increasing intensity of salinity intrusion is significantly associated with poverty, the results also show that both large brackish shrimp and freshwater prawn farms do not impact on poverty in the GBM delta. This reveals that the impact of shrimp farming on alleviating poverty amongst the local population is trivial. The strong association identified between salinity intrusion and poverty could be attributed to loss of arable land, reduced agricultural productivity and income, food insecurity, rural unemployment, social unrest, conflicts and forced migration (Paul and Vogl 2011; Swapan and Gavin 2011; Hossain et al. 2013; Sá et al. 2013).

Socio-economic predictors, employment rate, adult literacy and school attendance are also significant predictors of poverty as well as access to major roads. The results show that increased employment rate, adult literacy, school attendance and access to major roads reduce the odds of a Union being poor.

The overall finding of the research is that the socio-economic and environmental drivers of poverty in the GBM delta are discernible spatially. As captured in the Bangladesh Government coastal zone policy, the coastal zone is slow in socio-economic development and lacks the resources to cope with environment deterioration and hazards (MoWR 2005). As such policy formulation aimed at improving the well-being of residents of the GBM delta should be geospatially focused and targeted. These findings provide relevant input to addressing the geospatial inequalities in poverty in the GBM delta in the government's coastal zone policy plan.

#### **Note**

1. Available at https://unstats.un.org/unsd/demographic/sources/census/ censusdates.htm

## **References**

Ganges–Brahmaputra–Meghna Delta of Bangladesh. *Sustainability Science* 11 (3): 423–439. https://doi.org/10.1007/s11625-016-0356-6.

Wietzke, F.B. 2015. Who is poorest? An asset-based analysis of multidimensional wellbeing. *Development Policy Review* 33 (1): 33–59. https://doi. org/10.1111/dpr.12091.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **22**

## **Defining Social-Ecological Systems in South-West Bangladesh**

**Helen Adams, W. Neil Adger, Munir Ahmed, Hamidul Huq, Rezaur Rahman, and Mashfiqus Salehin**

## **22.1 Introduction**

Deltas are diverse environments with spatially and temporally variable ecosystem services (Barbier et al. 2011). This chapter argues that different parts of the delta can be identified as distinct social-ecological systems. In doing so it proposes that recognising these distinct systems is crucial to understanding the persistence of poverty in natural resource dependent communities. In effect, the social mechanisms that have evolved to manage and govern access to diverse bundles of ecosystem services are distinctive and associated with different land uses (Rodriguez et al. 2006).

H. Adams (\*)

Department of Geography, King's College London, London, UK

W. Neil Adger Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

M. Ahmed TARA, Dhaka, Bangladesh

Ecosystem services are co-produced in bundles in different socialecological systems. A social-ecological system is the amalgamation of physical, ecological, and social phenomena into a set of recognisable and distinct systems of interaction. Social-ecological systems theories have a long and varied intellectual history, drawing on ideas of hybrid environments, human ecology, and cultural geography. Social-ecological systems as an analytical framework emphasise the interdependent nature of the human and ecological systems (Cote and Nightingale 2012).

The chapter describes the social-ecological systems (SESs) within the study area and how they have been identified and characterised. The first section shows how social-ecological systems were integrated as a guiding principle through the project. The second section presents the results of analysis of the ways in which social systems differ according to the ecological system to form distinct sub-systems of the delta. The chapter concludes by summarising some of the key findings on the relationship between poverty reduction and ecosystem services revealed through a systems perspective.

## **22.2 Social-Ecological Systems as an Analytical Framework**

Managing natural resources, promoting sustainability, and enhancing human well-being require knowledge of populations, resources, and institutions within distinct social-ecological systems. Many studies have highlighted the benefits of understanding social-ecological interactions to promote sustainable management. Analysis of landscape change in Mediterranean Spain, for example, required modelling of both the intensity of

H. Huq

Institute of Livelihood Studies, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

R. Rahman • M. Salehin

Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

agriculture and the socio-economic context in which it was implemented (De Aranzabal et al. 2008). In the marine sector, fisheries management progressed from species-based to ecosystem-based approaches that embedded humans and their associated cultural values and different management practices within the ecosystem (Levin et al. 2009).

Social-ecological perspectives are particularly suited to incorporating diverse forms of lay and scientific knowledge in natural resource management (Hill et al. 2012), co-management practices (Cinner et al. 2012), common pool resources (Nagendra and Ostrom 2014), and governance issues (Karpouzoglou et al. 2016). As such, studies have applied system approaches to better manage and conserve ecosystem services, rather than explicitly seeking to design interventions with social goals such as alleviation of poverty. However, one major and common insight from this field which is relevant for this research is that social institutions are directly affected by the underlying characteristics of the natural resource base, including the resource fluctuations, variability, and divisibility (Ostrom 1990).

Many social mechanisms employed by poor populations to access natural resources are specific to that particular resource. In capture fisheries, for example, loans to buy equipment are paid back as a proportion of the catch because of the variable and unpredictable nature of catches and thus income (Allison and Ellis 2001). Systems of sharecropping, where landowners take significant proportions of agricultural outputs as rents, emerge as a mechanism in agricultural areas dominated by private land ownership, surplus labour, and insecure livelihoods (Wood 2003). Thus, mechanisms of rent capture in agriculture and fisheries are adapted to the specific characteristics of the bundle of ecosystem services but have a similar outcome of systematically extracting surplus value away from the poorest.

## **22.3 Methodology**

Social-ecological systems are identified as articulated and experienced by those engaged in resource use across the study area. Categorisation was based on data generated using open-ended questions focussed on social factors that influence the ability of ecosystem services to produce well-being for the poor through livelihoods, environment, and perceptions of causal mechanisms (described in Chap. 1). Other studies have used this method for exploratory analyses of this type: respondents converge on issues of multiple causation, threshold effects, and the social dynamics and processes by which social and ecological dimensions of their lives and livelihoods are constructed (e.g. Davis 2009; Crane 2010; Fabinyi et al. 2014).

Data from 70 households purposefully sampled across the dominant land cover types of the delta region was collected between September 2012 and May 2013. Effort was made to include geographically remote areas, diverse administrative districts, different land uses, and livelihood systems in households with a range of wealth statuses. The data are available in open-access form as notes and transcripts of the interviews (see Adams and Adger 2016 to access Reshare depository). Interviews were carried out in Bengali and lasted from between 30 minutes and one hour.

Analysis of the data suggests seven distinct SESs across the study area: irrigated and rain-fed agriculture, brackish and freshwater aquaculture, char-dominated (eroding islands) areas, areas that are dependent on the Sundarbans mangrove forest, and coastal areas with easy access to offshore fisheries. The distribution of these SESs is shown in Fig. 22.1. The majority of the study area is dedicated to crop cultivation (63 percent), predominantly rice, and the Sundarbans mangrove forest, which constitutes almost a third of the land area (29 percent). Eight per cent of the land area is dedicated to freshwater prawn, brackish shrimp, and white fish aquaculture while waterways and wetlands compose around one percent. Thus, the SESs defined can correspond directly to the dominant land use (agriculture and aquaculture) or defined by proximity to a key geographical feature (Sundarbans dependence, char areas and offshore fisheries). For an examination of the ecological characteristics of these systems, please see Chaps. 21, 24, 25, and 26 and Adams et al. (2013).

Once defined, the SESs are identified and mapped using satellite imagery and GIS analysis (see Adams et al. 2016 for a full description of this process). The SESs form the basis of analysis of the ecosystem services throughout this research highlighting the diversity of delta

**Fig. 22.1** Distribution of social-ecological systems based on survey information in the study area

environments and social trajectories across the delta region. The systems are used, for example, to analyse the influence of environmental conditions on the incidence of poverty (see Chap. 1) and form the first-level stratification of the household survey (see Chap. 23 and Adams et al. 2016). The integrated modelling approach also takes into account these different system dynamics through incorporation of the household survey data (see Chap. 28).

The remainder of this chapter presents some initial results highlighting some of the causal mechanisms linking ecosystem services and poverty in the study area and how these vary between SESs. These themes are summarised in Table 22.1, which also provides some summary statistics of the respondents. Numbers cited in brackets throughout this chapter correspond to the metadata file that accompanies the transcripts in the online database.1

**410**

## **22.4 Poverty Ecosystem Service Dynamics by Social-Ecological System**

## **22.4.1 Agriculture**

In agricultural regions, two distinct SESs emerged from the interviews based on the presence or absence of irrigation infrastructure. These two systems were kept analytically separate because (i) the cost of, and access to, irrigation serves to deepen inequalities between the landed and landless, (ii) multiple cropping seasons impact on the value of produce, labour opportunities, and associated mobility, (iii) each system has a differentiated vulnerability to external pressures such as fluctuating market prices and climate change (e.g. through an ability to withstand drought or alternate between crops), and (iv) the geographic location; irrigated areas tended to be those in direct land conflict with brackish shrimp cultivation. However, some common themes emerged across interviews in both types of agricultural zone. These include access to land for food security and/or route out of poverty, breakdown of intra-community moral economy in rural villages, and a shortage of off-farm livelihoods for the large landless population.

Large landowners interviewed had leveraged their assets to start profitable business activities such as supplying organic fertiliser, painting and decorating, intensive chicken rearing, educate their families so that they could take up professional jobs, or fund international labour migration with resulting remittances. This supports similar findings from Bangladesh (Tourfique 2002). However, there is another kind of landowner, constrained by the status in society conferred by land ownership but lacking sufficient assets to access profitable off-farm activities: "*Being part of the middle class society has closed the path of asking help from someone. My father was elected member [local politician] twice, this has given us an illusion of aristocracy and also prevents me from taking any small jobs*." (56)*.*

Nearly half the rural population in Bangladesh is landless (Saha 2002). Landless interviewees mentioned that the landed could use loans to further consolidate their wealth. Agricultural banks and NGO loans require land as collateral: *"if we give them our land documents temporarily"* (65). Thus, loans are not so easily accessible for the landless. Furthermore, they felt landowners could use their loans on economic activities rather than subsistence agriculture because they can grow their own food. This is supported by statements from land owners who avoid selling their land to maintain the subsistence security it confers. One farmer said, he "*would no more sell his land than kill a crazy son*" (48); meaning although agricultural activities are not always profitable, he would be reluctant to sell land.

Some respondents mention that a strong intra-community moral economy is breaking down: "*Everyone is guided by his or her own judgement. All of us think why shall we be guided by others?*" (41), but the role of large landowners as patrons to the poor continues. The poorest landless households, when in trouble, "*seek help from other rich people. If we tell them that we're in big trouble, they help as much as they can*" (42). Sometimes this assistance to the poor also takes the form of a place to stay, in return for work and political support: "*Since we live on their land, we must do as they say*" (45). Respected village members such as teachers and war veterans still play a role in mediator of disputes, even if the role of the informal village head has diminished (56, 5).

Seasonality remains a driving factor. The landless respondents described the wet season as the most difficult because reduced opportunities for agricultural labour combined with fewer off-farm activities and opportunities to buy food. One respondent said: "*(We) have needs all year round- but the rainy season is the worst time for us…at that time we don't have work like loading-unloading [at the border with India]*" (42). Another said: "*Rainwater wets everything. They can't sow paddy and can't sell that*" (57). Although there is a year-round shortage of opportunities for the landless: "*Do your sons go outside for work?" "Of course. Otherwise what will we eat?*" (57).

#### **22.4.2 Aquaculture**

Aquaculture ponds (*ghers*) can be used to cultivate shrimp, prawn, white fish, or rice (or a combination of them all) depending on the elevation of the plot, the salinity of surface water, access to irrigation, precipitation, and season. An economy exists supporting the *ghers* by collecting shrimp post-larvae from natural waters (31), providing fertiliser and other inputs such as snail meat (35), and delivering supplies to the pond owners (7, 8). This goes some way to remedying the loss of farm labouring opportunities, caused by low labour requirements on *ghers*, particularly shrimp farming (Swapan and Gavin 2011).

Aquaculture is associated with a loss of open-access resources. In brackish water shrimp areas, salinity precludes other ecosystem services. However, freshwater prawn can be cultivated in rotation with other crops, vegetables can be grown on pond walls, and there is sufficient plant life for animal fodder. Freshwater prawn cultivation is also associated with a loss of open-access wetland areas which has provided security of income for those with land but reduced open-access resources for the landless (5): *" Earlier we had the freedom for fishing in the beel [a wetland], now we earn more [from aquaculture]*"(32). Furthermore, nets used to collect the shrimp larvae to supply the ponds are perceived almost universally to have led to a decrease in fish stocks due to the by-catch. These nets are illegal but bans against them are not followed or well enforced.

Distinct differences can be observed between areas that are dominated by brackish shrimp aquaculture and areas that are dominated by freshwater prawn that, in turn, differ from those of farmers who concentrate solely on rice cultivation. Therefore, two different systems were defined, one for dominantly brackish shrimp cultivation, another for freshwater prawn dominated areas. The systems are separated based on: (i) forms of investment and issues of land rights, (ii) impacts on the poorest, and (iii) geographic location of the areas and associated stresses.

Forms of investment in, and access to, aquaculture practices differ. Expansion of brackish water shrimp aquaculture has been driven by external investors: "*The owners of those large ghers were people coming from outside the area*" (39). Thus, benefits tend to accrue outside the area to absentee landlords. Land conflicts arise, as rice farmers adjacent to shrimp areas have no choice but to convert to shrimp due to the negative effects of saline water intrusion on the productivity of their crops (see Faruque et al. 2017). In freshwater prawn areas, external investors and absentee landlords were not mentioned. What did arise in conversation were the large debts to set up the ponds for those who own land and different prices of land inside the polder (where cultivation can occur) and outside.

The issues raised by the most marginalised households differ between the two systems. In brackish shrimp areas the poorer and landless respondents stated that their well-being would be improved by the return of a local landlord cultivating crops to whom they could go in emergences for food, loans, and help with medical costs (62). The return of agriculture would also provide bare subsistence for the ultra-poor: one destitute respondent mentioned that if there were still agriculture in the surrounding areas she could collect rice that had fallen to the ground during harvesting (9). More of the *gher* owners in the freshwater shrimp areas are owner-cultivators, and a wider range of crops can be cultivated (including through sharecropping opportunities), perhaps why such issues were not raised by respondents from this area.

Brackish shrimp areas are more remote from health services, markets, and alternative livelihood sources and more exposed to risks of storm surges and cyclones. However, they are also very close to the Sundarbans mangrove forest and the coast with easy access to offshore fisheries. This means that the poor are able to diversify into these alternative but precarious livelihoods of fishing, shrimp larvae collection, and forest collection. The case is different in the freshwater prawn areas; there are few open-access resources to support those without access to private property. Canals belong to the government or are on private land so the landless are unable to fish in them (35). Thus job opportunities are found in the nearby city of Khulna.

#### **22.4.3 Riverine Areas and Charlands**

Riverbank erosion occurs across the study area, often exacerbated by man-made river diversion or by malfunctioning systems of dykes and sluice gates due to siltation. By contrast in the eastern part of the delta, adjacent to the Meghna River, high levels of erosion and accretion lead to highly dynamic char islands. These stretches of land are, in effect, sandbanks in the river, attached to or detached from river banks, but with their own highly mobile and sometimes marginal populations.

The physical dynamics of chars has led to them being an important SES in their own right, recognised throughout the south Asian region and described as having "livelihoods defined by water" and populations living constantly with risk of displacement (Lahiri-Dutt and Samanta 2013). The principal characteristics of Charland SESs, as articulated by interview respondents, are (i) highly seasonal income and seasonal shifts in income between fishing and rain-fed agriculture, (ii) loss of land resulting in sudden changes in material well-being, (iii) high mobility of landless households, (iv) high reliance on richer patrons to help landless households, and (v) high insecurity of land tenure for those whose land has been submerged with the constant threat of land-grabbing.

The data from interviews with char dwellers reveal a high seasonality of income between crops in the wet season and fishing in the dry season. Cultivation is principally for subsistence. Charlands share characteristics common to agriculture and fisheries SESs. For example, sharecropping for the landless and mortgaging land to others when in financial difficulty and loans for fishing equipment that are paid back through a percentage of the profit on the catch.

Insecure land tenure was perceived by char dwellers as a critical issue in accessing ecosystem services. When land is eroded (and thus submerged) property rights remain with the owner if sediment accretes above the water level within 30 years. If not, the land returns to government ownership and can be redistributed to the landless. However, the interviewees mentioned that land can be appropriated by more influential people before it is reclaimed by the family (75), or names are changed on title deeds within government offices.

Associated with the constant erosion of riverbanks and islands is constant mobility. Households living on unclaimed strips of land on dykes and riverbanks find themselves continually moving as the riverbank erodes. In turn, constant mobility and the constant search for new land on which to build a household can create a dependence on wealthier neighbours and relations for support and patron-client relationships. One landless respondent who had been forced to move multiple times was able to generate income by raising cattle for a wealthier resident (73). Another respondent was allowed to live on a relative's land in return for work (75).

Loss of land is associated with change in livelihood. A person who has lost all their agricultural land to erosion must find alternative sources of income; respondents often mentioned that people become fishermen (e.g. 63). Loss of land through river erosion is also associated with a fall in income. When asked who were the poorest in the village, one respondent replied it was those who had lost their land to river erosion (27).

#### **22.4.4 Sundarban Mangrove Dependence**

This SES takes into account the people living directly adjacent to the Sundarbans mangrove forest. People fish on its margins and interior, and enter on a daily or weekly basis to collect firewood, honey, fish, crabs, and thatching among other resources. The Sundarbans is a nature reserve so people are prohibited from living within its boundaries. Households therefore live on its border among the brackish water shrimp ponds. Three themes emerged from the interviews with mangrove collectors: (i) highly seasonal incomes, (ii) high levels of livelihood and personal insecurity, and (iii) systems of permits that are difficult to navigate and/or ineffective.

Interviews with these natural resource users revealed livelihoods highly affected by seasonality. Wet season rains make collection of firewood difficult (9); in the dry season there is a scarcity of freshwater and people have to drink salty water (9); fishermen fish in different locations in different seasons (8, 14); resource collection is banned during certain periods (68); and the quality of resources (e.g. the size of crabs) changes with the season (11).

Some respondents move between different resource and day labour opportunities, while others exhibit extremely low livelihood mobility due to a strong livelihood-based identity (e.g. traditional fishermen), or a lack of human capital. For example, despite collection of firewood being prohibited, a woman of around 50 still went into the forest every day. In doing so, she faced the threat of both being detected and physical punished by forest guards as well as exposure to extreme weather and natural hazards (9).

This theme of insecurity commonly arose in interviews. Not just in terms of a stable income source but also in terms of the potential of physical harm from encounters with pirates, forest guards or wild animals, physically demanding working conditions, and the periodic threat of cyclones. This physical insecurity is partly a result of a lack of alternative livelihood sources outside the forest. One resource collector said: "*You know in his heart nobody wants to go to the Sundarbans*" because of fear (68). The loss of agriculture in the area to make way for shrimp *ghers* (described in Sect. 22.4.2) has removed a key alternative livelihood (10) increasing reliance on the forest resources to fill income gaps.

Another theme frequently occurring in this set of interviews is the system of permits and moratoriums on resource collection in the forest and the efficacy of the government forest regulators in enforcing them. Some interviewees collected wood despite it being illegal while others paid for the permits and respected periods when resource collection is prohibited. Respondents perceive a decrease in the quantity and diversity of fish catch and blame people using fine nets (14) as well as organised gangs collecting fish illegally in ways that are destructive to other species (e.g. poison—15). That is to say, because they perceive that, forest regulators are not enforcing rules effectively.

#### **22.4.5 Offshore Fisheries and the Coastal Periphery**

It is difficult to geographically define an SES based on fisheries. Reliance on local inland fisheries (in *beels*, canals, and rivers) is ubiquitous across the study area. People will also travel from inland areas to access offshore fishing activities. There are fishing villages (often majority Hindu), where fishing is a traditional livelihood and closely linked to identity, where men will fish from nearby rivers during the wet season, and offshore during the dry season; these villages can be found across the study area. Also, Sundarbans fishermen often live between the brackish shrimp *ghers*, and coastal fishermen live among agricultural land to take advantage of the subsistence agricultural opportunities it offers.

However, those living adjacent to the coast have easier access to the resources the ocean offers. Thus, this research defined a SES based on those areas with direct access to the Bay of Bengal. Three aspects of this system are commonly highlighted by interviewees: (i) seasonal livelihoods, (ii) the long-term recovery from Cyclone Sidr in 2009, and (iii) the role of debt relations in accessing fisheries.

This SES is characterised by seasonal changes in livelihoods. Some fishermen alternative between species with different profitabilities: Hilsa (*Tenualosa ilisha*), in summer, and less profitable fish in other seasons or Hilsa in summer and shrimp post-larvae in the winter (31). Others alternate between livelihoods: one household collected forest resources from small patches of mangroves when food stores from subsistence agriculture were low (30).

Cyclone Sidr in 2009, and its continued impact on livelihoods, is a key feature for this system. People are still recovering from loss of assets and land damaged by salinity. One household had invested profits from fishing into cattle that were lost during Cyclone Sidr (54), a businessman lost his stock of dried fish that was on the beach when the cyclone hit (28).

Another key feature of this system is loans as a means to access fisheries. Loans are taken to access equipment, boats, and supplies, and are paid back as a proportion of the profit on the catch made. There is a more complex system for larger boats where groups of up to eight men travel offshore. These loans are accessed in advance per season and as a group through cooperation of the crew of the boat. Profits are subsequently apportioned to the crew and money lender (29, 68). These loans are associated with the accumulation of debt, and debt bondage as the catch is often insufficient to pay back the advance (54, 68).

However, while continuation of the loan system is not perceived as a positive aspect of household economies, some respondents felt there was no alternative (e.g. 29). Respondents also mentioned that loans have allowed people to access fisheries more easily as, whereas in the past men would wade off the beach up to their necks to catch fish, now they have boats, nets, and diesel and as such, access to more profitable species of fish that are found further offshore (29).

### **22.5 Conclusion**

The timing and nature of ecosystem services across the study area give rise to different livelihood opportunities, means of access to ecosystem services, and coping mechanisms. This research demonstrates how these patterns of ecosystem service and human-environment interactions form seven distinct SESs. Each of these systems has, in effect, different barriers and enablers of access to ecosystem services and hence potential pathways out of poverty. The seven social-ecological systems demonstrate a large spatial range of ecosystem services as well as high variation throughout the year. Given the prevalence of diverse livelihoods by individuals and households described within these systems, this analysis suggests that policy interventions need to take account of that diversification and also be tailored to the dominant mechanisms and institutions within diverse social-ecological systems.

The research has highlighted that ecosystem service access is critical for subsistence of poor parts of delta populations across all the seven systems and that these ecosystem services are critical in poverty prevention for marginal sections of society. Yet there are potential trade-offs between ecosystem services and the need for subsistence and for finding routes out of poverty. Income from shrimp post-larvae collection, for example, is a key safety net for the poorest when they lose land or livelihoods. However, the collection of this shrimp has negative impacts on fish catch. Fishing communities retain solidarity and identity from the traditions and institutions of fishing. Yet with successive generations entering fishing, the resource is stressed. The situation is further complicated by the management of floods via sluice gates for the benefit of agriculture. This limits the ability of fish fry to enter the polder early in the breeding season, and thus maintain fish stocks.

Across all seven social-ecological systems highlighted here, some social trends and mechanisms are common. A crucial example is that the part of each population most directly connected to the ecosystem services are least able to benefit from, their presence. For fishers, for example, surplus accrues to the money lenders and traders. For landless labourers, profits accrue to landowners through rent or sharecropping. Hence, a common issue across all seven systems is to design mechanisms by which the poorest populations retain value and benefits as the ecosystem service travels up the commodity chain. Such interventions would be a significant step in enabling allowing ecosystem services to alleviate poverty and building the sustainability of the diverse social and ecological circumstances within delta regions.

## **Note**

1. http://reshare.ukdataservice.ac.uk/852356/

## **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **23**

## **Characterising Associations between Poverty and Ecosystem Services**

**Helen Adams, W. Neil Adger, Sate Ahmad, Ali Ahmed, Dilruba Begum, Mark Chan, Attila N. Lázár, Zoe Matthews, Mohammed Mofizur Rahman, and Peter Kim Streatfield**

## **23.1 Introduction**

In order to develop insight and potential actions to co-manage ecosystem services for both healthier ecosystem functioning and to alleviate poverty in natural resource-dependent communities within deltas, it is necessary to understand how poverty is manifest and the level of dependence of populations on the ecosystems and social-ecological systems in which they live and work.

One strategy to develop this insight involves the direct observation of how people live, their own management of the resources around them,

H. Adams (\*) • M. Chan Department of Geography, King's College London, London, UK

W. Neil Adger Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

S. Ahmad Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany

the outcomes of that interaction with ecosystem services in terms of material well-being and their health and their perceptions of those relationships. Hence this research uses social science survey techniques to generate extensive data on the ways in which households use ecosystem services to generate well-being as part of diverse rural livelihoods. In doing so, the survey also provides a quantitative baseline understanding that is also essential to the integrated model (see Chap. 28). Simulations of winners and losers of future interventions on ecosystem services are thus based on real-life starting conditions.

There is significant value in generating primary observational data on ecosystem service use. Alternative sources of social data on life and livelihood include national census data and generalised livelihood surveys, such as the standard Household Income and Expenditure Surveys (HIES) carried out in low-income countries throughout the world (Deaton 1997). Census data are limited to demographic variables, typically with the main occupations of adults within households, and as such give an economic picture of populations. They are less useful to demonstrate where and how people interact with their local environments—the objective of this work (see Chap. 1). Household surveys typically provide detailed analysis of consumption patterns, expenditure patterns and economic activities, as well as demographic

A. N. Lázár

Faculty of Engineering and the Environment and Tyndall Centre for Climate Change Research, University of Southampton, Southampton, UK

Z. Matthews

Social Statistics and Demography, Faculty of Social, Human and Mathematical Sciences, University of Southampton, Southampton, UK

M. M. Rahman International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

P. K. Streatfield Formerly of the International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

A. Ahmed • D. Begum

Climate Change and Health, International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

variables. They do so using nationally representative samples of households from which inferences concerning national trends can be drawn, and the patterns of poverty and well-being are developed. Use of the Bangladesh HIES by Szabo et al. (2016) showed how food security at the household level is negatively associated with creeping salinity in the study area. Yet these data sources are limited in information about the direct benefits people derive from their ecosystems, about their mobility and other responses, and on the health and well-being of populations. Hence the survey here provides a unique set of insights on human-environment relations in this delta.

As highlighted in Chap. 21, ecosystem services are highly variable in space and time. This bespoke survey therefore builds in temporal variability by repeat interviews in three waves over a full calendar year: the analysis constructs detailed livelihood calendars. A further challenge for human-environment models is the multi-dimensional and contested nature of poverty, both as manifest in lack of material assets, an absence of health and also as a lived experience (Baulch 1996). Hence the survey is comprehensive in collecting specific variables that facilitate interdisciplinary analyses and consideration of material and subjective measurements of well-being alongside use of ecosystem services and livelihood diversity. It allows multilevel analysis and intra-household analyses: variables relate to individual men and women and to whole households.

This chapter first briefly outlines the survey methodology and implementation (Sect. 23.2). Section 23.3 summarises the data available from the household survey; it highlights unique variables and aspects of the survey and those that are comparable with other standard datasets. Section 23.4 describes each of the publicly available datasets associated with the household survey, illustrated with selected descriptive statistics. The publicly available datasets are land cover data by Union for the field area in Khulna and Barisal Divisions, household listing data of 9,300 households, a household roster dataset that presents separately the basic data of the 8,000 people living in the households surveyed and three rounds of household survey data for approximately 1,500 separate households taking into account attrition between the three rounds. The chapter closes with a reflection on the reuse potential of the dataset.

All data are available to download from the ReShare UK-based online data repository.1 The data are accompanied by English and Bengali versions of the questionnaire, as well as a glossary of terms used in the questionnaire. The survey design process itself is described in more detail in Adams et al. (2016).

## **23.2 Methodology**

This section explains how the survey was carried out. First, it describes the sampling strategy and how the concept of social-ecological systems was operationalised and used to stratify the sample; social-ecological systems were integrated from the beginning. Second, it describes the household listing process, required to ensure a random sampling from within villages. Third, it describes the process of implementing the survey.

## **23.2.1 Sampling Strategy**

Households were identified through systematic random sampling. The sample was stratified initially by social-ecological system (SES): rain-fed agriculture, irrigated agriculture, freshwater prawn aquaculture, brackish shrimp aquaculture, coastal aquaculture, riverine areas including eroding islands, and mangrove dependence. These were identified through expert elicitation and land cover maps and verified through semi-structured interviews (see Chap. 22). The sample was not driven by livelihood or ecosystem service use as these changed throughout the year and thus were not compatible with a seasonal approach.

In order to create a sampling frame, a land cover map is overlaid with an administrative map and Unions assigned to a SES. Agricultural and aquaculture systems could be directly assigned based on 80 per cent minimum land coverage per Union. Riverine, marine and Sundarbans systems were assigned based on contiguous boundaries with the associated feature. Some Unions do not have a clearly dominating land use. These Unions were excluded from the sampling process. Table 23.1 shows the number of Unions included in the sampling belonging to each of the strata.

Systematic random sampling was used to select three Unions from each SES and three *Mouzas* from each Union. A segment of approximately 125 households was listed in each *Mouza* to randomly select the 21 households that were interviewed. Households were eligible if both a man (aged 18–54) and women (aged 15–49) were present. The target

**Table 23.1** Number of Unions sampled included in each social-ecological system

respondent for the survey was the main earner, who completed the structured questionnaire. Information on global satisfaction of life, anthropometry (height and weight) and blood pressure was collected from both a male and a female member of the selected household.

The sample size was calculated based on a head count ratio of poverty prevalence, poverty defined by the inability of households to meet the costs of basic food needs (BBS 2011). In Barisal 27 per cent of people are below this poverty line, and in Khulna 15 per cent and so a population weighted average of 22 per cent was calculated. Ten per cent was added for potentially non-responses, and an additional ten per cent was added to take into account attrition between rounds (although actual attrition rates were below five per cent). Further information on sampling strategy can be found in Adams et al. (2016).

#### **23.2.2 Survey Implementation**

The survey was administered to selected households three times: first in June 2014, then over October to November 2014 and finally in March 2015, each time with a four-month recall period. Thus the data covers the period from February 2014 to February 2015. Attrition rates were low: 1,586 households were initially selected; this fell to 1,516 in the second round, and came back up to 1,531 in the third round. However, when all three rounds are considered, 1,478 households have consistent and complete records across the three surveys.

## **23.3 Survey Data**

The survey contains up to nearly 3,000 potential variables corresponding to hundreds of different survey questions contained within 15 different sections. Table 23.2 describes the type of variables that are contained in the survey.

Reuse of the data is facilitated by the interdisciplinary nature of the questions, the number of standard measures of multi-dimensional wellbeing that can be recreated from the data, and the multilevel nature of the data: it can be disaggregated by season, by social-ecological system, by Union, or by individual.

Questions were also included to allow the creation of standard measures to facilitate direct comparison with other surveys. This includes the variables required to recreate the Progress out of Poverty index2 , the Multi-Dimensional Poverty Index3 and the FANTA III food diversity score.4 The survey also contains multiple questions that are also contained within national censuses in order to facilitate comparison and validation.

The items on the asset list (Sect. 2 of the survey) and expenditure (Sects. 13 and 14) were taken from the Bangladesh Household Income and Expenditure 2010 (BBS 2011), with a few additions to take into account the ecosystem service-focus of this research. The global satisfaction with life scale is the same one that is applied in Gallup surveys and

**Table 23.2** Metadata summary of the survey data

the UK well-being survey (Evans 2015; Gallup 2015). The set of nine questions answered on Likert scales to measure place attachment has been previously developed, tested and applied within social psychology and human geography (Lewicka 2011; Devine-Wright 2013).

## **23.4 Datasets Available for Reuse**

Each of the datasets described here is openly available for reuse and analysis; some illustrative descriptive statistics are discussed here.

#### **23.4.1 Land Cover Database**

In addition to the main survey, an Excel spreadsheet is also available5 that provides land cover data for each of the Unions in the study area. While this information is used to create a sampling strategy based on SESs, it could also be used to create other land cover-based sampling strategies or to better understand the land use characteristics of Unions in Khulna and Barisal Divisions.

#### **23.4.2 Household Listing**

A listing was carried out at the start of the data collection process as a rapid census to determine eligibility of households for the full survey. Thus the data comprises a very limited set of variables. However, the value of this dataset lies in the sample size: 9,327 households were surveyed. Data is available for these households on sex and age of the main earner; primary, secondary and tertiary occupation of the earning member; estimated total monthly income in Bangladesh Taka (BDT); and floor, wall and roof materials.

Figure 23.1 shows the average income by social-ecological system disaggregated by sex of the main household earner. Men consistently earn more than women. The coastal periphery SES is worst for income parity, and the freshwater prawn aquaculture zone is the most favourable. However, the difference in average earnings across the systems is twice as great for women than for men. Average income for male household heads varies

**Fig. 23.1** Mean monthly income by social-ecological system and sex from listing data

from 8,508 BDT a month in the riverine system to 7,207 BDT a month in irrigated agricultural SES (a difference of 1,301 BDT). For women, average income varies from 6,447 in the brackish shrimp aquaculture SES to 3,532 in the coastal periphery zone (a difference of 2,915 BDT).

Figure 23.2 shows the most common income sources disaggregated by SES. This includes primary, secondary and tertiary incomes. The five most important income types are shown using two metrics: contribution to total income of the system and the proportion of households involved in this livelihood.

The most common income types vary in ways expected by SES; fishing as an income source is more common in the coastal and riverine zones, pond-based aquaculture is only present in the aquaculture zones (freshwater prawn and brackish shrimp), and agriculture (rain-fed or irrigated) exists across all zones. Interesting findings emerge in the differences between the most common and the most lucrative income types.

In the Mangrove dependent SES, for example, professional salaried jobs are most important economically, but most households are engaged as day labourers.

## **23.4.3 The Household Roster**

The household roster information is available as a separate dataset so the basic characteristics of all individuals included in the survey can be easily analysed. Although the survey was administered to 1,586 households, this comprises 7,993 different women, men and children. Information on whether the person was a visitor or permanent household member, relationship to household head, age, marital status, school attendance, highest level of education reached, number of times the person has attended school, employment status, whether the person is working away and birth place of person (by *Upazila* and urban/rural) is collected for all three rounds. Figure 23.3 shows the average household size in the survey,

**Fig. 23.3** Household size distribution

**Fig. 23.4** Educational attainment by age group

and Fig. 23.4 shows educational attainment of individuals by age group. Most households had four or five members and educational attainment increases with younger age groups, with people aged 55 and over least likely to have had any education.

Information on place of birth was included in the roster in addition to standard questions on relation to household head, age, education level and income. Analysis of the responses on place of birth show that almost everyone surveyed (90 per cent) was born in the same *Upazila* in which they were now living; therefore this is not an area of in-migration. The approximately ten per cent of the population born outside the *Upazila*, was made up of women (613 people born outside the *Upazila* compared to 132 men), suggesting that in-migration, where it does occur, is predominantly a result of marriage.

As the household roster was carried out for each round of the survey, a window into changing intra-household dynamics is provided. Table 23.3, for example, shows changes in the percentage of men and women working and the proportion of those working who are migrating outside the village to access these opportunities. A tenth of the number of women are working compared to men. However, in two of the rounds, a very similar proportion of working women as working men were seeking livelihood opportunities outside the village.

## **23.4.4 Seasonal Household Survey Variables**

There are three main categories of variable types in the household survey: those with an aim of measuring multi-dimensional poverty, those measuring ecosystem service use and those recording coping strategies used by the household to cope with variability in income.

#### **<IP_ADDRESS> Measuring Multi-dimensional Poverty**

Much of the survey instrument is dedicated to the measurement of multidimensional well-being. Various sections of the survey measure material poverty at the household level: Section 2 of the survey records household

**Table 23.3** Change in the percentage of men and women working within and outside the village by survey round

assets, Sect. 3 income type and livelihood diversification, Sect. 9D collects information on landholdings, Sect. 13 on household food diversity and food expenditure and Section 14 non-food expenditure.

Information is also collected to record health status at the individual level. Height, weight and blood pressure of a man, woman and child under five was measured in each household. Chapter 27 focuses on the health outcomes of the survey. Questions on subjective well-being, through a ten-point scale on satisfaction with life, were also asked to men and women separately in each household (Sect. 15 of the survey). Finally, in the third round of the survey, a section was included to measure levels of perceived empowerment of women in the household.

#### **<IP_ADDRESS> Ecosystem Use and Quality**

Section 4 of the survey contains four sections on agriculture, aquaculture, fisheries and mangrove activities, including species or variety, productivity, price and profit made. Section 9 contains questions on livestock and poultry and homestead forestry. In addition, questions on perceptions of different dimensions of the quality of the natural environment are asked (Sect. 12). Figures 23.5 and 23.6 provide two examples from this section: on perceptions of water quantity and quality by SES and season. Perceptions change with season and with different SES. While the aquifers from which most water is drawn do not follow the SES boundaries, perceptions of poor water quality are highest where problems of salinity intrusion are most acute. Due to an oil spill occurring in the Sundarbans between the second and third rounds, a module on the impacts of the oil spill was added to the final round.

#### **<IP_ADDRESS> Coping Strategies**

The survey instrument contains various sections dedicated to understanding coping strategies. Three sections are dedicated to understanding mobility: past migration and current migration strategies of the household (Sects. 5 and 6 of the survey) and place attachment of the main respondent (Sect. 11). Section 7 is devoted to understanding the use of loans and Sect. 10 examines costs and responses to specific shocks. Table 23.4 shows the source of loans by season. Formal loans from agricultural banks

**Fig. 23.5** Respondent perceptions of availability of drinking water by socialecological system throughout the seasons (i.e. rounds—R1–R3)

and non-governmental organisations are most commonly stated as sources of loans, reported approximately five times as often as loans from friends and family are reported. Loans from money lenders are reported slightly less, although people may not want to share the degree to which they owe money in this form.

#### **<IP_ADDRESS> Seasonal Changes in Productivity of Ecosystems**

The seasonal nature of the survey and four-month recall period allows for the reconstruction of seasonal calendars for the different ecosystem services. For example, Figs. 23.7 and 23.8 show the total production of

**Fig. 23.6** Respondent perceptions of salinity of drinking water by socialecological system in each season (i.e. rounds – R1–R3)

**Table 23.4** Number of households taking loans from different sources by survey round

**Fig. 23.7** Total *Aman* rice production in kilograms by social-ecological system and month

**Fig. 23.8** Total *Boro* rice production in kilograms by social-ecological system and month

*Aman* and *Boro* rice varieties by month for each of the SESs. *Aman* rice is typically grown during the wet, monsoon season and harvested during the winter; however, it can be grown in small quantities in other months as well. Surprisingly, the Charland SES produces the most *Aman* rice, followed by the mangrove forest and finally the rain-fed agriculture SES. *Boro* rice requires irrigation during the dry season and typically harvested during spring. Not surprisingly, the irrigated agriculture SES produces the most *Boro* rice. *Boro* rice often produced together with freshwater aquaculture crops, such as fish and prawn. *Boro* rice is dominantly produced in the irrigated agriculture and freshwater prawn SESs, but the riverine SES also harvests significant quantities of *Boro* rice.

Figure 23.9 shows seasonality in total fish catch by month for each of the SESs. Fish is generally available all year round. Easy access to the Bay of Bengal fisheries seem to invite fishers in larger quantities: caught fish is most important in the coastal periphery and Charland SESs although during the monsoon months and during the early dry season months (July to January), fishing has some importance in the Sundarban dependent and rain-fed irrigation SESs. Collected data generally refers to Bengali months

**Fig. 23.9** Total fish catch in kilograms by social-ecological system and month

that are slightly offset compared to Western months (e.g. Falgun runs from mid-February to mid-March), figures provides both Bengali and English month names for clarity.

## **23.5 Conclusion**

The survey described in this chapter provides key insights into the complexity of rural livelihoods in the coastal zone and helps us to understand the characteristics of households benefitting from ecosystem services and those of households using them as a last resort in the absence of other safety nets. Analysis of the data shows that having any proportion of household income originating in provisioning ecosystem services within farming, aquaculture, fisheries or forest-based livelihoods increases the likelihood of that household being above the poverty line. However, being above a poverty line does not necessarily mean the household is on a positive well-being trajectory, or that they will not fall under the poverty line again. High levels of ecosystem services are associated with high levels of well-being only in those with significant land assets and associated social capital to enter into agriculture-based business opportunities.

The data reported here for the GBM delta demonstrates a wide variety of levels of income and trajectory: while significant proportions of the population would not be classified as being under the poverty line, incomes and the multiple dimensions of well-being are limited throughout the populations surveyed. Further analysis of the survey results shows how these households combine ecosystem services with off-farm livelihoods and risk diversification strategies such as loans and migration, to maintain more secure rural livelihoods.

## **Notes**

## **References**

**444 H. Adams et al.**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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## **Part 5**

## **Present and Future Ecosystem Services**

## **24**

## **Prospects for Agriculture Under Climate Change and Soil Salinisation**

**Derek Clarke, Attila N. Lázár, Abul Fazal M. Saleh, and Mohammad Jahiruddin**

## **24.1 Agriculture as an Ecosystem Service in Bangladesh**

Agriculture is the largest and most important provisioning ecosystem in the world (Zhang et al. 2007), covering 38 per cent of the global land area (DeClerck et al. 2016). In addition to providing important ecosystem services, agriculture also relies on, is impacted by and alters many other ecosystem services (Fig. 24.1). As the population of the world and

D. Clarke (\*) • A. N. Lázár

A. F. M. Saleh Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

M. Jahiruddin Department of Soil Science, Bangladesh Agricultural University, Mymensingh, Bangladesh

Faculty of Engineering and the Environment and Tyndall Centre for Climate Change Research, University of Southampton, Southampton, UK

**Fig. 24.1** The role of the agriculture ecosystem within ecosystem services

urbanisation increases, agriculture is becoming more and more important and the trade-off between the natural ecosystem services and provisioning ecosystem services from agriculture has to be clearly identified and managed with care (DeClerck et al. 2016; Gordon et al. 2010; Power 2010; Zhang et al. 2007). For example, in 2010, approximately 20 per cent of the world's population was employed in agriculture, but in certain countries, such as Bangladesh, this is significantly higher at 47.5 per cent (World Bank 2017). Changes in agricultural productivity can therefore have implications for both health and well-being.

Agricultural production in the coastal areas of Bangladesh faces many challenges including climatic and environmental change, water shortages and natural hazards (pest, diseases, floods) (FAO and MA 2013). Constraints to crop production are heavy soil consistency, low soil fertility, flooding in the monsoon season, poor soil structure causing delayed draining, high osmotic pressure causing a reduction in the ability of plants to absorb water and nutrients and cyclonic storm surges (see Chap. 18). In addition, soil salinisation is a major concern to farmers in these coastal regions (Baten et al. 2015). Salt naturally occurs in both soil and water; however the amount of salt present depends on both the soil characteristics and the hydrological settings. A high level of salt in the soil limits the ability of the plant to take up water and nutrients. Under normal conditions, the root cells have higher concentration of solutes than the soil water, and the difference allows a free and efficient movement of water into the plant root (i.e. osmotic effect). Increased salinity in the soil water lowers the rate of water transfer to the root; therefore, the plant needs to adapt osmotically by either accumulating salts or synthesising organic compounds such as sugars and organic acids (Hanson et al. 2006). These extra processes use energy, and thus the plant will be sub-optimally developed. In addition salts, like chloride, boron and sodium ions, can also cause toxicities for the plants when these are accumulated in the stems and leaves causing leaf-burns and twig-die-backs (Brown and Shelp 1997). Chloride, boron and sodium ions can be also absorbed through the leaves, thus irrigation water quality is even more important. Crop tolerance to salinity varies widely: some crops are very salt tolerant, but most crops are sensitive to salinity. This sensitivity also depends on the plant growth stage (i.e. germination, vegetative growth or reproductive growth). Many crops are more sensitive to salinity during the early vegetative stage (Mondal et al. 2015).

The accumulation of salts in the soils of agricultural regions occurs progressively when water evaporates from irrigated or flooded fields, leaving residual minerals. In Bangladesh, this problem is reduced during the annual monsoon which brings fresh rainwater to the system and displaces accumulated salts vertically down through the soil profile. As a result, monsoon season rice crop yields are reasonably good and adequate to support a subsistence level of farming in the coastal regions (see Chap. 23). However, evaporation in the dry season causes the salinity to re-develop each year (see Fig. 24.3, lower panel) which results in reduced soil fertility.

Irrigation in the dry season can counteract some of these problems, but the quality of the irrigation water quality is important. Many famers use groundwater or river water which can be partially contaminated with dissolved minerals, and these minerals add to the soil salinity when the applied water evaporates. Extreme events such as cyclones cause inundation of polders with sea water. This can take several years to flush out and typically requires two to three monsoon seasons to return the soils to their pre-inundation salinity levels (Rabbani et al. 2013). The salinity problem is further exacerbated by the move from rice production to brackish water shrimp and fish farming in coastal areas of Bangladesh. Although shrimp production is economically valuable to the owners, the deliberate inundation of polders with brackish or salt water contaminates soils and groundwater in adjacent areas and causes detrimental effects on both biodiversity and crop production of the region (Kartiki 2011).

The aim of this chapter is to assess the effect of the present and future climate and irrigation practices on the crop growth potential in the southwest coastal zone of Bangladesh. Thus, Sect. 24.2 provides an overview of the crops and cropping systems in coastal Bangladesh, Sect. 24.3 introduces the methods, and Sect. 24.4 presents and discusses the results and implications.

## **24.2 Current Conditions and Crops in the Study Area**

The agricultural sector of Bangladesh constitutes an important component of the national economy accounting for around 21 per cent of the national gross domestic product (GDP) (Clarke et al. 2015). The study area is located in the south-west coastal zone of Bangladesh and covers an area of approximately 19,000 km2 dominated, in 2010, by agriculture (45 per cent), followed by natural vegetation (12 per cent), aquaculture (11 per cent), water (8 per cent) and wetland (8 per cent). This area represents some 30 per cent of the cultivated lands of Bangladesh (Karim et al. 1990; Lázár et al. 2015) and is mostly cultivated by subsistence farmers (Fig. 24.2). The warm humid climate of the region with relatively high annual rainfall (FAO CLIMWAT) provides farming communities excellent opportunities to grow crops on the silty clays of the delta (FAO and MA 2013).

**Fig. 24.2** Subsistence farmers (left) and agricultural landscape (right) of the south-west coastal region of Bangladesh (Photographs: Derek Clarke)

#### **24.2.1 Crops, Seasonality and Irrigation**

The region experiences two contrasting seasons each year—wet (*Kharif* ) season, between June and October, when agriculture concentrates on wet foot rice production and the dry (*Rabi*) season, between November and May, where water availability is reduced (Fig. 24.3). The agriculture calendar allows up to three crops per year during the monsoon (*Kharif*-1), post-monsoon (*Kharif*-2) and dry season (*Rabi*). Rice is the dominant crop, and there are three broad categories of rice cultivated

**Fig. 24.3** (Upper) Simplified crop calendar adapted for the study area (based on FAO/WFP 2008); (lower) Rainfall distribution and soil salinity in southern Bangladesh (after Lázár et al. 2015)

in different 'agriculture seasons': *Aman* rice (May to December), *Boro* (November to May) and *Aus* (March to August) (Murshid 2012; Banglapedia 2012). *Aman* rice is the most important staple food, and due to the monsoon rains, it requires minimal irrigation. *Boro* rice on the other hand requires irrigation during the dry winter period. Increasing soil salinity in the dry season has encouraged farmers to diversify production to include salt-tolerant crops such a mustard seed and watermelon (Ibrahim et al. 2009). The lack of irrigation water, however, prevents all-year-round agriculture in many places of the south-east part of the study area.

#### **24.2.2 Soil Quality in Coastal Areas**

The soils of the coastal lands in southern Bangladesh are relatively poor compared to other areas of Bangladesh, requiring the use of fertiliser (FRG 2012). The organic matter content is low to medium (1.0–3.4 per cent, wet oxidation method) with low nitrogen (N) content (0.091–0.18 per cent, Kjeldahl method). The soil phosphorus (P) status is very low to low (1.0–15.0 mg/kg, 0.5 M NaHCO3 extractable), the potassium (K) status is medium to optimum (0.181–0.36 c mol/kg, 1 M NH4OAc extractable) and the sulphur (S) status is also medium to optimum (15.1–30.0 mg/kg, CaH2PO4 extractable). Micronutrients especially the zinc (Zn) status is low to medium (0.451–1.35 mg/kg, DTPA extractable), and the boron (B) status is medium to optimum (0.31–0.60 mg/kg, CaCl2 extractable).

Many of these soils are classified as alkaline soils. In the Ganges tidal floodplain and the Young Meghna Estuarine Floodplains, 25 per cent of the agricultural land is highly saline (soil electrical conductivity (EC) above 12.0 deciSiemens per metre (dS/m)), and the remaining 75 per cent suffers from slight to moderate soil salinity (EC value 2.0–6.0 dS/m) (Ahsan 2010). The relationship between irrigation water salinity and soil salinity is complex and depends on the volumes of water used, the effectiveness of the monsoon rainfalls and water management practices such as land drainage. Mondal et al. (2015) describe series of experiments on the impacts of irrigation water quality on rice production. Salinity stress on rice crops starts if the irrigation water quality exceeds 3 dS/m, and crop yields will fall by 80 per cent if the irrigation water quality is greater than 10 dS/m.

#### **24.2.3 Historical Changes in Crop Varieties**

Farmers are always willing to adopt new varieties of crops if they perceive the benefits. However, farmers in the coastal areas are generally more conservative and reluctant to use balanced doses of fertiliser application. With assistance from plant breeders and extension workers from the Bangladesh Department of Agricultural Extension (DAE),1 farmers have gradually moved from traditional low-yielding rice varieties and broadcast planting to higher-yielding varieties (HYV) and transplanted rice which, if kept well-watered and fertilised, can more than double production. In the 1990s, local crop varieties dominated the cropping patterns (e.g. broadcast *Aman*), but after 2000, the crop production shifted towards high-yielding and more resilient varieties (Fig. 24.4). At the same

**Fig. 24.4** Rice crop area (ha) grown in the Khulna region between 1980 and 2008 (Based on data from the Center for Environmental and Geographic Information)

**Fig. 24.5** Dry season salinity effects: (left) rice failing to germinate, (centre) maize yield reduction and (right) melon damaged by salinity and drought (Photographs: Derek Clarke)

time, diversity of the crops also increased (e.g. potato, wheat, chilli, etc.) thanks to the dissemination of modern technologies and knowledge by various agencies and Non-Governmental Organisations (NGOs).

However, despite these changes in the dry season, agriculture remains difficult and large areas of land are sometimes incapable of being used productively until the next monsoon rainfall dilutes the accumulated salts (Fig. 24.5). More salt-tolerant crop types and varieties, for example, mustard, chilli and rapeseed, have been adopted, but the more marginal areas continue to be constrained by soil conditions, high dry season soil salinity and lack of finance to invest in alternate crops.

## **24.3 Estimating Changes in Climate and Soil Salinity on Agriculture**

Research on the dry season salinity problem was undertaken to investigate the climate variability within coastal Bangladesh, to determine whether inter-annual variability is more important than the longer-term climatic trends associated with future climate change, whilst also identifying the key salt mechanism and its likely relationship with reductions in agricultural yield.

**Fig. 24.6** Information used to model and evaluate future salinity and impacts on crop production

#### **24.3.1 Methodology**

The modelling-based assessment used both soil moisture and salt balance models (e.g. Andales et al. 2011; Clarke et al. 2015), run using future estimates of rainfall and climate factors that affect evaporation (Fig. 24.6). The soil moisture model is based on the FAO CROPWAT methodology which is a vertical water balance model (Clarke et al. 1998). This was extended by the authors to include a calculation of salt mass transfer through a typical irrigated farm. Salt inputs are from irrigation water from groundwater pumps or river channels at known salinity concentrations (Clarke et al. 2015). The extended CROPWAT model calculates the volume of water needed to irrigate crops in the dry season together with the mass of salt accumulated from minerals in the slightly salty irrigation water. The model calculates the soil leaching requirement and the volume of fresh rainwater to remove the salts in the monsoon season. The models require information on the crop types grown, their tolerances to water and salt stress and soil water storage characteristics. These were obtained from the FAO guidelines for crop evapotranspiration (Allen et al. 1998). The models are run at a daily time step using daily values of rainfall of the 1980–2100 period (HADRM3P climate model, UK Met Office, pers. comm, described in Chap. 11 and Caesar et al. 2015). Evapotranspiration was calculated using the Penman-Monteith equations for potential evapotranspiration using climatic data for Barisal obtained from the FAO CLIMWAT climatic database (CLIMWAT 2017). Estimates of future potential evapotranspiration were made using the baseline FAO CLIMWAT climate data set perturbed according to temperature increases proposed by Agrawala et al. (2003).

The model runs include calculations of the soil moisture deficit (SMD),2 the masses of salts accumulated in the soils in the dry season and the masses removed by leaching by fresh rainwater in the wet season over a typical one hectare field near Barisal. The overall aim was to calculate changes in and draw conclusions on the future seasonal and inter-annual variability of evaporative demands, the amount and schedule of irrigation required in the dry season for growing vegetables, as well as determining the accumulation of annual unleached salts and its effect on potential crop yields.

## **24.3.2 Modelling Results**

#### **<IP_ADDRESS> Timing and Length of the Monsoon Season**

Using the rainfall and calculated SMD in each year of the simulations, it is possible to determine the start and length of the monsoon season, that is, when the soil is sufficiently wet to grow main season *Aman* rice. This is shown in Fig. 21.7. Inter-annual (i.e. year to year) variability remains the dominant forcing in terms of dry season climate throughout the twentyfirst century, despite the long-term climate trends also present within the future climate (see Chap. 11). This variability is most evident within the changes to the length of the seasons, whereas changes to both the onset and magnitude exhibit smaller trends. The source of this variability is not yet fully understood, but research suggests a significant part is due to the monsoon circulation and its interconnection with both the El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO—the largest element of the intra-seasonal variability in the tropical atmosphere).

The simulations indicate that the monsoon season is expected to shorten by two to three weeks, and the onset of the season will be four to five days later by the end of the twenty-first century. However, despite the reduced season length, the total annual rainfall is likely to remain the same or increase by five to ten per cent. This implies that the monsoon rainfall is expected to intensify by the end of the century. This shortening of the wet season, when combined with the year to year variability could result in some monsoon seasons being too short for a successful rice crop by the end of the twenty-first century (e.g. Fig. 24.7 upper—2055,

**Fig. 24.7** (Upper) Calculated changes in the start and duration of the Monsoon based on rainfall duration (Q8 realisation of the HadRM3P regional climate model); (lower) Calculated changes in the start and duration of the Monsoon based on soil moisture deficit.

2093; Fig. 24.7 lower—2061,2068,2083, as indicated by arrows). This phenomenon is also visible in other climate realisations (e.g. Q0 and Q16; see Chap. 11).

#### **<IP_ADDRESS> Dry Season Water Requirements and Salinity Accumulation in Soils**

The dry season exhibits long-term climate forcing with the length of the season expected to increase by 14 days towards the end of the twenty-first century. This is due to the fact that higher temperatures will result in more evapotranspiration and a longer period when the soils are dry, requiring more initial rainfall to re-wet the soil at the start of the monsoon season. Potential evapotranspiration was found to be less variable year to year and tended to rise slightly with increasing temperature. The dry season is thus expected to become drier with the SMD increasing by 25 mm of soil water and, with the onset of the maximum SMD occurring four to five days earlier, an earlier onset of the dry season is suggested. However, dry season irrigation requirements will not increase because the growing period of crops will remain unchanged. Although higher temperatures increase potential evapotranspiration (by five to ten per cent), actual crop evapotranspiration is controlled more by soil moisture storage and irrigation water applied by farmers then by changes in temperature. It is the inter-annual variability of rainfall which will cause changes to both the amount and timing in the application of irrigation water.

Human water management as well as natural environmental changes both have impact on future soil salinity accumulation. If farmers use available groundwater or river water to irrigate in the dry season, the salt load in the irrigation water will be deposited in the soils when the water is evaporated or transpired by crops. Hence the soil salinity conditions will depend on a combination of human factors such as which crop to grow, crop salinity tolerance, irrigation or no irrigation and maintenance of drainage systems to remove leached salts.

Additional environmental factors also exist, such as duration of the dry season, salinity of the irrigation water used, shallow water table problems, magnitude of the next monsoon season and its ability to naturally leach salts out of the soil profile. Typical 'good' irrigation water may contain one to two parts per thousand (ppt) of minerals. Medium-quality water contains 4–6 ppt, and low-quality water may contain 8–12 ppt or higher. Simulations explored the effect of irrigation water quality on salt accumulation in the soil.

The extended CROPWAT model results show that at present (2015) in the Barisal region, if the dry season irrigation water contains less than 4 ppt salts, the monsoon rains in the following season will be able to flush these salts away (the amount of rain is higher than leaching required). However in drier years or if farmers use lower-quality irrigation water, the residual salts will start to accumulate to be carried over to the next dry growing season, worsening soil salinity and decreasing crop yields. The models were run into the future (to 2100) with a range of assumed values of irrigation water quality (Fig. 24.8). Irrigation water qualities of less than 4 ppt result in a small impact of salinity causing less than ten per cent reduction in expected crop yields of dry season vegetable crops. If the irrigation water quality in the dry season remains at 4 ppt, then the monsoon in the following season is able to flush away the accumulated salts in almost every year (there is one exception in this simulation in year 2070. Note that this simulation uses stochastic methods, so the date is not absolute).

**Fig. 24.8** Changes in the leaching capacity of the monsoon season rains compared with salts introduced by dry season irrigation with water qualities of 4 ppt and 5 ppt dissolved mineral content

If the regional water quality was to worsen (e.g. caused by various factors which might include sea-level rise, cyclones, use of lower-quality groundwater for irrigation), then irrigating crops results in the salts not being removed during the following monsoon season (see 5 ppt irrigation quality on Fig. 24.8) in 48 of the 120 years of the simulation. The residual salts are carried over to the next dry crop season with consequences for crop yields; in this case the anticipated crop yield loss due to salinity is 25 per cent. Note that this is a cumulative process, and, if this occurs in consecutive years, soil salinity levels increase over time and even lower potential crop yields are produced.

With the saline intrusion along river estuaries and into the shallow groundwater along the coast associated with anticipated sea-level rise (Kay et al. 2015; Bricheno et al. 2016), the decrease in the quality of irrigation water sources will be a significant factor determining future dry season agricultural productivity within the coastal zone. These findings align with the work of the World Bank (Dasgupta et al. 2014) which indicated that the coastal regions with close proximity to the coast and/ or major rivers are expected to see crop yields reduced by more than 50 per cent by the end of the twenty-first century. In this situation farmer's incomes will be reduced, livelihoods threatened and they will have to be more and more reliant on the main season rice as a subsistence crop.

#### **<IP_ADDRESS> Temperature and Carbon Dioxide**

In addition to water and salinity stress, other effects on plants will develop due to anticipated climatic change. Crop response to changes in atmospheric carbon dioxide (CO2) and temperature differs between crop types and individual varieties of these crops. Changes in CO2 concentrations will assist plant development and biomass accumulation. Parry et al. (2004) suggest that grain crop production will increase by between two to five per cent if atmospheric CO2 rises from the 2016 value of 400 parts per million (ppm) to 500 ppm, which is anticipated to occur in the 2040s.

Fig. 24.9 shows a possible trajectory of CO2 (SRES A1B scenario— KNMI 2016) and the calculated CO2 fertilisation (i.e. atmospheric fertigation) response factor. This crop growth response factor is not a direct a multiplier of yield, rather just one factor of many that influences the crop

**Fig. 24.9** Projected changes in carbon dioxide in the study area and changes in the crop fertigation

**Fig. 24.10** Projected changes in air temperature in the study area compared with crop tolerance to heat stress

physiology. The overall crop yield is affected by numerous factors including crop type and variety, water availability, soil salinity, tolerance to heat, good farming practices and the appropriate use of adequate fertilisers on the poor soils.

Increased crop yields due to CO2 fertigation are likely to be counteracted by heat stress on plants as global temperatures increase (Sánchez et al. 2014; Peng et al. 2004). Figure 24.10 shows the projected changes in temperatures used in this study and the values which will cause the onset of reduced crop yields. Rice is not likely to be affected by heat limitations in the near future. However other key crops are already potentially constrained and this effect will increase as the twenty-first century progresses. This suggests that the staple rice crop will remain viable, but relatively recently adopted crops such as chilli may not be so successful and new non-rice crop types may have to be adopted in the future.

## **24.4 Discussion**

Agriculture is the single most important provider of ecosystem services to the people of south-west Bangladesh. Monsoon rains supply adequate water to grow a main season rice crop. However, income from agriculture is currently constrained by the limited availability of good quality irrigation water in the dry season, access to markets and the cost of fertilisers. In Bangladesh, the market prices for rice are low so farmers have little or no surplus income to develop coping strategies. Compounding these problems is the fact that the coastal farming communities are also most at risk of flooding due to cyclones.

Farmers attempt to improve their incomes by growing dry season crops such as oil seeds and vegetables, but in coastal regions, dry season soil salinity severely limits crops production (see Chap. 28). The responses to these stressors by low-income farmers are limited, for example, localised irrigation with water of low quality or flooding some areas with brackish river water for aquaculture. These desperate actions, however, serve to increase the salt loading introduced to the fields, and a successful rice crop in the following wet season will depend on removal of accumulated salts. Therefore, although the development of soil salinity is an environmental process, soil salinisation is closely linked to farmers' behaviour and land use practices. This, in turn, not only reduces the agriculture productivity but also is closely associated with the decline in other ecosystem services associated with water regulation (coastal wetlands, mangroves, etc.).

These issues are exacerbated by projections of regional changes in sea levels, higher temperatures and reduced dry season river flows. Higher sea levels will increase mean water levels and the potential overtopping of polders by sea water (Kay et al. 2015), reducing the effectiveness of drainage systems to remove saline water to the sea. Higher temperatures will increase evaporation rates which will increase the need for irrigation in the dry season. Reduced river flows (see Chap. 13 and Whitehead et al. (2015)) mean that brackish water will penetrate further up river channels (Chap. 17 and Bricheno et al. (2016)), and when this water is used for dry season irrigation, this will accelerate the salt loading in the fields. Impacts on groundwater quality have not been fully quantified, but there are strong linkages between river water quality and groundwater salinity (Chap. 18 and Salehin et al. (2014)), so increased use of groundwater for agriculture may accelerate the deterioration of soil productivity due to soil salinity.

These findings reveal that both climate change, inter seasonal climate variability and saline intrusion are important drivers determining the productivity of dry season agriculture in the coastal regions of Bangladesh. There is a tipping point of water quality around 4 ppt beyond which soil salinity development accelerates. In effect salinity is an impact resulting from the trade-offs between ecosystems system functioning, natural environmental change and human responses that lead in turn to trade-offs between provisioning and regulating ecosystems (see Chap. 2).

#### **Notes**

## **References**

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# **25**

## **Marine Ecosystems and Fisheries: Trends and Prospects**

**Manuel Barange, Jose A. Fernandes, Susan Kay, Mostafa A. R. Hossain, Munir Ahmed, and Valentina Lauria**

## **25.1 Introduction**

Bangladesh is a country deeply connected to water, dominated by extensive flood plains, a complex network of rivers, and a dynamic system of estuaries and islands, and a major coastal sea. A significant dependence on aquatic resources also results in growing pressures on coastal ecosystems and added risks from environmental change: the population of Bangladesh

M. Barange (\*)

Plymouth Marine Laboratory, Plymouth, UK

J. A. Fernandes • S. Kay • V. Lauria Plymouth Marine Laboratory, Plymouth, UK

M. A. R. Hossain Department of Fisheries Biology and Genetics, Bangladesh Agricultural University, Mymensingh, Bangladesh

M. Ahmed TARA, Dhaka, Bangladesh

Food and Agriculture Organization of the United Nations, Rome, Italy

in its coastal region has doubled since the 1980s. The dependence is further challenged by significant environmental variability, often experienced as a set of cyclones, floods and other hazards.

The dependence on aquatic ecosystems extends to its resources, such as fish and fisheries, which contribute significantly to Bangladeshi culture, economy and tradition. Fisheries supplies more than 60 per cent of the country's protein intake (DoF 2013) at an average per capita consumption of approximately 20 kg/year (DANIDA-DFID 2003). The sector contributes approximately four and a half per cent to the national gross domestic product (GDP), and provides direct employment to two million people on a full-time basis (DoF 2002) and indirect employment to a further 13 million people (Alam et al. 2012; World Bank 2015). Such is its fisheries dependence that Bangladesh is both the fourth largest inland capture producer in the world (2003–2012) and the sixth largest aquaculture producer (FAO 2016). Since independence in 1971 the fisheries industry has seen steady growth with production tripling in the last two decades (Alam et al. 2012). Between 1984 and 2009 annual average growth in fish production was more than five per cent largely driven by the expansion in inland aquaculture fisheries, which grew at a rate of almost ten per cent per year (Golub and Varma 2014).

This chapter explores how climate change will affect marine fisheries in Bangladesh, and how management interventions can minimise or exacerbate such impacts. It will demonstrate that the marine ecosystem of Bangladesh will remain productive but that the species benefiting from this production may change. This is illustrated by investigation of the specific impacts of climate change on Hilsa shad (*Tenualosa ilisha*) and Bombay duck (*Harpadon nehereus*), two of the fish that contribute the most to food security and nutrition in Bangladesh.

## **25.2 Structure of Fisheries Sector**

The fishery of Bangladesh includes marine, inland and aquaculture sectors (Fig. 25.1), each one with its own specific stressors and production trends. In 2012–2013 the full fisheries sector produced 3.4 million tonnes of fish. Marine and inland fisheries are connected because both target in particular

**Fig. 25.1** Fisheries production (expressed in tonnes) in Bangladesh 1950–2012: marine catches, inland catches and aquaculture production (Based on data from the FAO Global database)

Hilsa shad, a fish that spends part of its life cycle in the sea and part of it in rivers. Aquaculture activities are also connected with capture fisheries because they depend on the provision of larvae and juveniles from rivers and marine ecosystems (Kathun 2004). However, there are many processes involved in the development of aquaculture as an industrial activity, beyond the availability of natural fish and shellfish resources. Fisheries has been continuously growing in Bangladesh since 1950 (Fig. 25.1), with aquaculture providing the majority of production in recent years.

The marine-based fishery in particular, which relies entirely on natural production processes, is subdivided into subsistence, artisanal and industrial. The artisanal sector is the most productive with circa 500,000 tonnes of fish landings in 2012 (94 per cent of volume of landings). Subsistence fisheries are of greatest importance for national food provision, and focuses on catches of species of lower commercial value. The only industrial fishing developed in Bangladesh operates out of Chittagong on the east coast and comprises two distinct industrial fisheries: shrimp trawl and bottom trawl (FAO 2006).

## **25.3 Marine Fish Catches and Their Management**

Freshwater habitats in Bangladesh are inhabited by 260 fin fish species, 25 species of prawn and 25 species of turtles (Ali 1999), as well as 23 exotic fin fish species that were introduced for aquaculture purposes (Hossain 2010). Marine waters are habitat to 475 species of fin fish, of which 65 are of economic importance, and host 38 species of marine shrimps (Ali 1999).

Hilsa shad is the biggest fishery and the national fish of Bangladesh. Locally known as *ilish* or *ilisha*, it is a euryhaline anadromous fish found in marine, coastal and freshwater environments. A significant part of its catch is exported to India, where it is especially consumed in religious holidays and by Bangladeshis living in a number of outside countries. In 2012–2013, Hilsa contributed singlehandedly about ten per cent of the total fish production of Bangladesh, with a market value of US\$2250 million, about one per cent of Bangladesh's GDP. In the last two decades, Hilsa production from inland waters has declined by about 20 per cent, whereas marine water yield has increased threefold (Kathun 2004).

Bombay duck supplies the second largest fish catch in the Bangladesh coastal region and is the most popular subsistence fishery, representing over 12 per cent of the catch (1.7 million tonnes), and consumed fresh or dried. In addition to its food provision role, it is a lucrative fishery in the Bay of Bengal despite its price being six times lower than Hilsa (65 BDT. kg-1 versus 430 BDT.kg-1; 1US\$=79 Bangladesh Taka (BDT)).

Bangladesh has a centralised fisheries management system under the Department of Fisheries of the Ministry of Livestock and Fisheries. The present Fisheries Policy was adopted in 1998 to enhance resources and production, reduce poverty through self-employment in the sector, supply the need for animal protein, achieve economic growth and earn foreign exchange while maintaining ecological balance, biodiversity and public health. In 2006, the Ministry of Fisheries and Livestock adopted a Fisheries Strategy pushing further towards poverty reduction, co-management and conservation of resources. But even though the Hilsa fishery is regulated through some technical management measures, such as reduction in mesh size and fishing closures, the overall management of fishery resources in the country is currently insufficient to control effort. This is primarily due to the lack of funding and resources committed to sustainable fisheries management (FAO 2003).

## **25.4 Estimating the Effects of Climate Change on the Marine Ecosystems and Fisheries of Bangladesh**

In fish-dependent economies such as Bangladesh (Toufique and Belton 2014), the combination of climate change, population growth and protein consumption patterns is a significant amplifier of pressures on critical national resources (Delgado et al. 2003; Merino et al. 2012). Yet there are no major studies that have projected the structure and availability of aquatic resources and ecosystem services into the future accounting for the impacts of climate change. This research analyses the prospects for fisheries maintaining its critical role in providing employment and provisioning ecosystem services and nutrition across the delta region.

Previous analyses have predicted a decrease in the productive potential of fisheries in South and Southeast Asia as a result of climate change (Barange et al. 2014). Understanding how this impact translates into the future provision of fish products is crucial for the sustainability of fisheries-dependent communities in coming decades (see Chap. 23). In previous work based on simpler models, Merino et al. (2012) concluded that marine fisheries production in Bangladesh may be reduced by five per cent by 2050, leaving aquaculture to produce between 0.3 and 3.8 million tonnes of fish to achieve national fish consumption targets of between 13 and 31 kg fish/capita, accounting for expected human population growth. In order to better quantify how climate change will change the marine fish production potential in Bangladesh, a combination of atmospheric, hydrological, ocean circulation and ocean biogeochemical models is used (Fig. 25.2). A full description of these models is detailed in Fernandes et al. (2016): here, a summary of these models is provided.

Climate data were taken from the UK Met Office regional climate model (RCM) HadRM3P, which is dynamically downscaled from the