Displacement and degradation
Sinhalese (Buddhist) farming households have more viable agricultural livelihoods than Tamil (Hindu) or Moor (Muslim) households. While all of the Tamil and Moor household in our sample reside in communities that were directly affected by the war, only a small subset of Sinhalese communities are within the former conflict zone. Notably, Sinhalese households in these two communities reported higher yields than the average for both Tamil and Moor household also in the conflict zone and Sinhalese households throughout the Dry Zone. Although the virtual ethnic homogeneity of communities makes it challenging to rule out a spurious community-level effect through statistical models alone, the integration of our quantitative and qualitative results helps us to clarify and uncover some of the specific drivers of this rural ethno-religious inequality, connecting these patterns directly to socio-political phenomena related to the war. Most critical, we identified that Tamil and Moor farmers in the former conflict zone have significantly higher proportions of land that is purely rainfed than do farmers in the Sinhalese communities directly affected by the war. Access to either state- or community-managed irrigation systems are vital to both yala (dry) season rice production in Sri Lanka, as well as buffering rice production systems against wet-season droughts and floods (Lakmali et al. 2015; Williams and Carrico 2017). Further, as described by FGD participants in Community #14, the instability faced by households that lack access to functional irrigation systems pushes them away from rice farming towards alternative income generating activities. Critically, data collected through community focus groups highlight that this lack of irrigation water is not simply a relic of a pre-war history. Households in our sample’s most marginalized communities described the degradation of irrigation infrastructure during their period of war-driven displacement and inaction by the Sinhalese-dominated government in the years since their post-war resettlement as the primary challenges facing the viability of their agricultural livelihoods. Therefore, the war has perceivable, lasting impacts on the livelihoods of Tamil and Moor farming households and communities, while the agricultural livelihoods of Sinhalese farmers appear relatively unaffected.
Finally, our results also cast light on the particular plight of Moor farmers, which reported some of the lowest yields and the lowest household assets in our sample.
by Nicholas E Williams 1,✉, Malaka Dhamruwan 2, Amanda R Carrico ,3 Ambio ( A Journal of Environment & Society), Sweden, January 17, 2023
Abstract
Our understandings of the effects of war on land and resource access following armed conflicts are often shaped (and limited) by a reliance upon remotely sensed data. Here, we analyze household-level survey and community-level focus group data collected in Sri Lanka following the end of the nation’s ethno-religiously rooted civil war (1983–2009) to determine if and how the war differently affected the nation’s rice farmers. Our synthetic analyses revealed geographic variation in agricultural livelihood viability in post-war Sri Lanka, demonstrating how the protracted effects of war are exacerbating the vulnerability of rural Sri Lanka’s ethno-religious minority (Tamil and Moor) populations by (re-)shaping access to critical natural resources, including both land and irrigation water.
Armed conflicts have lasting effects on the state of natural resources and the livelihoods of those who depend on them (Lamb 1985; Baumann and Kuemmerle 2016). While war-related activities may encourage or deliberately cause habitat modification and biodiversity loss (Nackoney et al. 2014; Butsic et al. 2015; Hanson 2018), the displacement that often characterizes wartime has been shown to (at least temporarily) reduce pressure on some resources (Stevens et al. 2011; Alix-Garcia et al. 2013; Sánchez-Cuervo and Aide 2013). People not only seek refuge from the immediate threats of conflict (McNeely 2003), but also to seek viable livelihoods outside of conflict zones (Raleigh 2011). In addition to the direct and indirect implications displacement has on the status of local natural resources, the degradation of infrastructure that occurs during wartime and the socio-political reorganization that armed conflict engenders may dramatically reshape future resource access and use (Benson et al. 2008; Miguel and Roland 2011; Jaafar et al. 2015).
These issues are especially pertinent to agricultural livelihoods and land uses. Displacement associated with armed conflict has been shown to cause regional-scale agricultural land abandonment (Baumann and Kuemmerle 2016) and degradation of critical infrastructure, such as irrigation systems (Dinar and Keck 1997; Jaafar et al. 2015). Yet, empirical studies elucidate heterogenous long-term impacts of war on agricultural land use, particularly after armed conflicts cease. In certain instances, such as in Iraqi Kurdistan, agricultural activity in the post-war reconstruction era parallels that of the pre-war period (Eklund et al. 2016, 2017). In Caribbean Nicaragua, extensification of farmland followed the end of the Contra War and was primarily attributed to in-migration and land colonization (Stevens et al. 2011). In contrast, only a small fraction of agricultural lands abandoned during civil conflicts in Aceh, Indonesia (Czaika and Kis-Katos 2009) or the Caucasus (Baumann et al. 2015) were re-cultivated. Similar patterns have been identified in Bosnia, where the agricultural system failed to recover in the post-conflict period (Witmer 2008)—likely, in part, because of the dangers posed by residual land mines (Andersson et al. 1995). Finally, researchers also have identified instances of ‘telecoupling’ rooted in conflict-driven land abandonment. For example, agricultural lands abandoned in South Sudan paralleled land extensification occurring near refugee camps in Northern Uganda (Gorsevski et al. 2012) and elsewhere in the region (Hagenlocher et al. 2012; Alix-Garcia et al. 2013). Similarly, spatial variation in land-use patterns driven by the activities of armed groups has been observed in Columbia (Sánchez-Cuervo and Aide 2013).
Ultimately, these dramatically varied impacts of armed conflict on agricultural land use depend on a multitude of factors, many of which are particular to the local context. However, in part because of the complexities and dangers associated with on-the-ground research, much of our understanding of the links between armed conflicts, land abandonment, and post-war land-use activities are derived from analyses of remotely sensed data (Witmer 2008; Stevens et al. 2011; Gorsevski et al. 2012; Hagenlocher et al. 2012; Qamer et al. 2012; Alix-Garcia et al. 2013; Sánchez-Cuervo and Aide 2013; Wilson and Wilson 2013; Baumann et al. 2015; Eklund et al. 2016; Yin et al. 2019). Few researchers have collected ground-level data, whether quantitative or qualitative, to complement the patterns identified through landscape-level data (Gorsevski et al. 2013; Eklund et al. 2017). Consequently, while the role of armed conflict as a driver of rapid land-use change is clear, there is a paucity of empirical research directed at the long-term effects of armed conflicts on agricultural and other natural resource-based livelihoods and regional food systems (Baumann and Kuemmerle 2016), and particularly research that seeks to document the socio-political and other processes that shape varied patterns observed globally.
Here, we present a case study from Sri Lanka, a country entrenched in civil war between the Sinhalese-controlled government and the Liberation Tigers of Tamil Eelam (LTTE) from 1983 to 2009. Small-scale agriculture, particularly rice farming, has been fundamental to the Sri Lankan economy, politics, and culture for millennia (Zubair 2005). Agriculture remains central to the livelihoods of over one-quarter of the nation’s 22 million people (Department of Census and Statistics 2019). However, the country’s civil war drove rural populations in regions of the country where the armed conflict raged (e.g., the north and east of the island) to abandon their agricultural lands and livelihoods as they fled to urban centers (Suthakar and Bui 2008). Following the war’s formal end in 2009, resettlement, infrastructure development, and other rural development projects have reinvigorated agricultural land use in war-affected regions (Kumar 2011). In some areas, analyses of remotely sensed data show that agricultural activity has exceeded pre-war levels (Ranagalage et al. 2020; Rathnayake et al. 2020). These findings are corroborated by the district-level rice production statistics as reported by the Sri Lankan government (Agriculture and Environmental Statistics Division 2022). Yet, paralleling post-war land-use research conducted elsewhere, the factors shaping and how livelihoods have been affected by this post-conflict re-agrarianization have yet to be explored.
Using household survey and focus group data collected in 2015 and 2016 in 25 communities throughout Sri Lanka’s Dry Zone, the main agricultural region, we assess whether households in formerly active combat zones have less viable agricultural livelihoods, as determined by lower yields and household assets, than households in areas less-directly affected by the quarter-century conflict. Additionally, we draw on a political ecology perspective to consider how socio-political dynamics before, during, and after a war, as well as the effects of the war on natural resources and infrastructure, have differently affected who has maintained or regained access to natural resources (Paulson et al. 2003). While the civil war in Sri Lanka was a conflict between the Sinhalese (Buddhist)-led government and Tamil (Hindu) ethnic minority population, it also inevitably affected the country’s ‘Moor’ (Muslim) and ‘Indian’, or ‘Up-Country’, Tamil (Hindu) populations. Therefore, we specifically aim to determine if and characterize how the conflict differently affected the agricultural livelihoods of households who identify with each of the country’s main ethno-religious groups.
Ethnic history (and conflict) in Sri Lanka
The contemporary Sri Lankan state has relatively recent origins, with independence in 1948 following Portuguese, Dutch, and British colonial periods. Yet, historical narratives that shaped and were shaped by the county’s decades-long civil war describe roots of the ethnic conflict going back millennia (Rajanayagam 1994). Some Tamil histories claim that their populations preceded the Sinhalese, with small numbers of merchants, artisans, and fisherfolk settling throughout the island (Nissan and Stirrat 1990). Regardless, neither the Tamil nor Sinhalese generally claim to be the island’s first inhabitants. Rather, Sinhalese ancestors came (c. 543 B.C.) with a fabled Indian prince, Vijaya, who was exiled from modern-day Bengal and settled alongside the island’s native Vedda people (an identity still maintained by a small group) (Gunawardana 1979). Under repeated pressure from waves of South Indian Tamil ‘invaders’ who crossed from the mainland, the Buddhist (converted in the third century) Sinhalese were ultimately pushed out of the islands northern and eastern regions as recently as 1000 years ago (De Silva 1987; Nissan and Stirrat 1990). Permanent Tamil settlements evolved into a kingdom centered in the city of Jaffna, which was sustained by small-scale rice cultivation and the region’s productive fisheries. The Sinhalese and Tamil populations inhabited distinct regions of the island over the centuries that followed, separated by jungle and an abandoned region of the central Dry Zone, which inform contemporary notions of discrete ethno-religious territories (Arasaratnam 1994).
Beginning at the end of the sixteenth century, European colonists sought to conquer, settle, and ultimately govern the island in its entirety, and in doing so worked to further accentuate and solidify historical ethnic boundaries and shape modern Sri Lankan history, politics, and life for all of the country’s populations. While the Portuguese and Dutch worked to supplant local religions with Christianity in their coastal colonial strongholds (in some cases quite successfully), the British acknowledged the sociocultural and political distinctions that characterized the island they called Ceylon (Manogaran 1994). Following their typical colonization protocol, the British formally pledged in 1815 to preserve Sinhalese (Buddhist) and Tamil (Hindu) religious and socio-political structures. Further, although ruling through a local Sinhalese monarchy in the Kingdom of Kandy, the British instated a racialized system of political representation in 1833 through the establishment of the Legislative Council of Ceylon that provided (marginal) political voice to the Tamil and Burgher (people of mixed European and Sri Lankan descent) communities (Wickramasinghe 2006).
British governors also encouraged the development of a plantation economy, focused on coffee and tea in the cool climate found in the Kandyan (now Central) highlands, and introduced a new ethno-political dynamic with the importation of indentured Tamil workers from mainland India. By the turn of the twentieth century, the continued immigration of these workers and their families brought the numbers of ‘Indian Tamils’ to nearly 500 000 people, accounting for around 12% of the total population (Wickramasinghe 2006). Despite a shared country of origin and Hindu religion, the immigration process and relationship with the British imperial project influenced the distinction of this group’s identity (Kanapathipillai 2009), often referred to as ‘Up-Country’ Tamils, from ‘Sri Lankan’ Tamils in the post-colonial era (Bass 2012).
Positioned as temporary foreign labor, the British colonial governs neglected to give political recognition to Up-Country Tamils. However, reformations to the Legislative Council in 1889 acknowledged the (mostly) Tamil-speaking ‘Moor’ (Muslim) ethno-religious population. Sri Lankan Moors are descendants of Arab and Persian maritime traders that began arriving on the island at least as early as the ninth century C.E. (Ali 1984). While concentrated in the eastern region of the island, Moor livelihoods, languages, and socioeconomic positions diversified as they settled in cities and towns throughout Sri Lanka (Pfaffenberger 1994). As a potential threat to the European trade monopoly, the Portuguese and Dutch placed restrictions on coastal Moors that encouraged inland migration, further distributing their population into Sinhalese-dominated regions (McGilvray 2016). As with Tamil and Sinhalese populations, the formal recognition granted by the British colonial government through their inclusion in the Legislative Council brought political unity among the increasingly diverse group, particularly in the post-colonial period.
The British use of ‘race-based’ representation was most fundamentally a recognition of the pluralistic nature of Sri Lankan society, and like the Legislative Councils established in other British colonies stemmed from liberal philosophy of the Victorian Era that advocated for ‘native’ inclusion in governance (Stultz 1972). However, despite the unequivocal diversity within each of these groups, the Legislative Council’s focus on ethno-religious categories resulted in the development of political blocks based on these specific divisions competing for power as the colonial period came to an end (Wickramasinghe 2006), which revived and strengthened the historical territorial separation between the Tamil (Hindu) and Sinhalese (Buddhist) communities (Orjuela 2005). The Tamil (and other) ethno-religious minority population(s) became increasingly marginalized by the Sinhalese majority in the post-colonial, democratic Sri Lankan state through a series of formal policies, such as legislation that declared Sinhala as the only official language (Wickramasinghe 2006), inciting political organization among and anti-government actions led by Tamil youth.
Sri Lanka’s political instability began to come to a head in 1971 with a failed insurrection attempt by a Marxist group and waivered through a series of violent riots until the onset of the civil war in 1983 (Wickramasinghe 2006). The Liberation Tigers of Tamil Eelam (LTTE) attempted to secure sovereign territory in their historical strongholds in the north and east of the island. The LTTE’s use of extraordinary violence to establish a separatist state and the Sri Lankan military’s brutal counterinsurgency efforts over the course of the 26-year war affected the life and safety of all Sri Lankas (DeVotta 2009). Thus, wartime activities resulted in the long-term internal displacement of an estimated 800 000 Sri Lankans (Kumar 2011), and left ethno-religious minority populations, such as Moors in the Tamil regions, in a particularly vulnerable position as they were caught between the warring groups (Mcgilvray 2011; Yusoff et al. 2018). This volatility and reformulation of settlement patterns undeniably affected resource use and access during the war (Korf 2004; Bohle and Fünfgeld 2007), and there are documented cases of land encroachment during the war by both Tamil and Sinhalese farmers (Korf 2003). The war ended in 2009 with the Sinhalese government declaration of victory, and domestic refugee camps were officially disbanded in 2015, releasing refugees to return home and (in theory) working to restore public utilities and rehabilitate infrastructure in war-affected communities (Kumar 2011). Yet, how post-war resettlement has affected longer-term land use and access, and whether this process has differently affected farmers who identify with each Sri Lanka’s ethno-religious groups, has remained unclear.
The data used to investigate the differential, lasting impacts of Sri Lanka’s armed conflict on the agricultural livelihood of members of the nation’s various ethno-religious groups was collected as part of a broader research effort to better understand and improve agricultural decision making and adaptation to precipitation trends in Sri Lanka (ADAPT-SL). Climate change is increasingly affecting rainfall patterns in Sri Lanka, resulting in some monsoon seasons being marked by severe droughts while others experience devastating flooding events (Gunda et al. 2016). This seasonal variability poses serious challenges to small-scale farmers, who serve as the foundation of Sri Lanka’s food supply (Williams and Carrico 2017). ADAPT-SL was a collaborative effort between academic researchers and various offices and officials within the Sri Lankan national government that worked to identify and advance effective practices that both farmers and the government were implementing to combat climate variability.
Sampling and data collection
ADAPT-SL conducted a series of surveys with 1 340 farming households and community focus groups between 2013 and 2017. Households and communities were selected through a stratified random sampling strategy, which we employed to select 30 Grama Niladhari (GN) divisions, which usually comprise 1–3 villages with less than 500 households per village, distributed across each of the three regions of Sri Lanka’s Dry, or main agricultural, Zone (i.e., North, North-Central, East, and Southeast) where rice production dominates. Unique in Sri Lanka, the Dry Zone experiences a single monsoon season. For millennia, farmers have relied on systems to capture and store monsoonal rainfall and surface runoff for distribution during dry periods (Lakmali et al. 2015; Bebermeier et al. 2017; Burchfield et al. 2018). Beginning in the 1960s, the Sri Lankan government began developing a network of state-managed irrigation systems through the diversion of the nation’s largest river, the Mahaweli, and relocated households from coastal regions to newly viable farming regions within the Dry Zone (Zubair 2005). Therefore, to account for the institutional and social variability inherent to irrigation systems in contemporary Sri Lanka, half the GNs selected through our sampling design receive state-managed irrigation water, while the other 15 GNs self-manage (or in some cases lack) community irrigations systems. A single village from each GN was selected for surveying, and thirty to eighty rice farming households in each village (proportional to village size) were selected at random from voter lists validated by local farmer organizations leaders (for additional details regarding sampling protocol, see (Truelove et al. 2015)).
Households in 5 of the 30 communities participated in a pilot survey in 2013 that was developed in collaboration with local agricultural specialists that focused on such topics as land tenure, farming methods and yields, household and farm assets, household livelihood strategies, and farmer perceptions of agricultural and environmental change. Following revisions to the survey instrument, a first cohort of households (N = 607 across 13 communities) was surveyed over three one-hour sessions by trained local enumerators in May and June 2015. These sessions included interviews with both men and women heads of household. An additional cohort (N = 541 across 12 communities) was surveyed using the same instrument in May and June 2016. Because of the incongruity between the pilot and final surveys, only data collected in 2015 and 2016 were included in these analyses (N = 1 148). Additional details regarding the survey, titled the Sri Lankan and Agricultural Decision-Making Survey (SEADS), and the resulting dataset can be found through the Inter-university Consortium for Political and Social Research (ICPSR) archive, project #37051.
In parallel and as a complement to survey data collection, semi-structured focus group discussions (FGD), attended by 15–45 farmers and local agricultural stakeholders, were conducted in each of our 25 survey communities in 2015 and 2016 to obtain qualitative data that could contextualize trends identified in quantitative survey data. Participants were selected by a mixture of purposive and snowball sampling and not limited to survey participants. In particular, purposive sampling was used to ensure that views of key stakeholders and community leaders (e.g., farmer organization leader and rural development society chair) were represented. Women farmers, a minority in Sri Lanka (~ 20%) (Carrico et al. 2019), were represented in FDGs, but due to their relatively small population and gender norms relating to community leadership, FGD participants were predominantly men. FGDs were moderated by the ADAPT-SL in country coordinator with the assistance of various local project scientists and research assistants. FGDs were conducted in both Sinhalese and Tamil, depending on community, and audio recorded. Because of inconsistencies in recording quality and the need for multiple translations, full transcriptions were not completed. Rather, written field notes were taken by at least one member of the ADAPT-SL team during FGDs, which were drawn from in this analysis to help to explain the community-level dynamics that shape the patterns observed in household survey data.
Data preparation and analysis
Along with demographic data, surveys collected seasonal farming and household asset information which we used to generate the dependent variables for statistical models. Because rice is by far the most commonly grown crop among Dry Zone farmers, wet (i.e., primary rice farming) season rice yield (Yield) is a comparable indicator of agricultural livelihood viability and, therefore, served as one of these variables. Yields were calculated using the area (in acres) farmers reported having planted in the season and total output (in bushels) then converted to the international standard of metric ton per hectare (1 mt = 47.92 bushels (Central Bank of Sri Lanka 2020)). Outliers were identified using interquartile range (IQR) criterion during preliminary data exploration, which resulted in the exclusion of 7 households from analyses that included Yield. Household assets, the other metric we use to distinguish livelihood viability, was constructed using a principal component analysis (PCA) series, with the final index normalized with mean 0 and standard deviation 1 (Lalloué et al. 2013). Initial inputs were the presence or absence of a range of material assets documented through surveys, including vehicles, home construction, appliances, consumer electronics, and farm implements (Williams et al. 2018).
We ran a series of models that considered two primary outcome variables: seasonal rice Yields and Household Assets. Preliminary analyses revealed that inter-seasonal variability significantly affected Yields between those communities surveyed in 2015 and those surveyed in 2016 (Cohort), t(1 108.1) = − 5.29, p < 0.001. However, the relationship between Cohort and Household Assets was not significant. Further, while community of residence has a significant relationship with both Yields and Household Assets, community is autocorrelated with a key independent variable included in our models (i.e., Ethnicity as described below). Therefore, hierarchical models, including only Cohort as a random effect (or nesting factor), were used to predict Yields, while models predicting Household Assets do not include random effects.
The first set of models were regression analyses exploring whether there are quantifiable, lasting effects of the Sri Lankan civil war on the agricultural livelihoods of households within the former Conflict Zone. While the war affected all regions of Sri Lanka to varying degrees, to construct this variable (Conflict Zone) we drew on results of FGDs to identify communities directly affected (coding fieldnotes and identifying communities that experienced active combat, war-related displacement, or commandeering of property during wartime) by the conflict. Nearly all of these communities are located in provinces in the North and East of Sri Lanka (see Fig. 1) in which control was contested during the civil war (Siriwardhana and Wickramage 2014).
Fig. 1.
The second and third sets of models determined whether members of Sri Lanka’s main ethnic groups have fared differently in terms of agricultural livelihood recovery in the post-war era. Specifically, these ANOVA models tested the relationships between a household’s self-identification with an ethnic community (Ethnicity) and the household’s Yields and Household Assets and were followed by Tukey’s HSD (honestly significant difference) post-hoc tests to determine pair-wise differences between ethnic groups. Residential patterns in rural Sri Lanka result in near-homogenous ethnic identification among households in small communities. By selecting villages throughout the Dry Zone, our sampling design failed to include an Up-Country Tamil community, who namely reside in the Central Highlands or urban centers. As such, the small sample of Up-Country Tamils (N = 4) in the dataset were omitted from the analyses to maintain statistical power. Further, our sampling protocol did not result in the selection of one of Sri Lanka’s few Tamil or Moor communities outside of the former Conflict Zone. Therefore, while the second set of models tested the relationships between Ethnicity and our two agricultural livelihood metrics among farmers throughout the country to assess nation-level ethnic disparities, the third set of models examined these relationships using only the subsample of households within the Conflict Zone to explore whether post-war re-agrarianization has differently affected ethnic communities.
In addition to these models, we conducted a final series of ANOVAs and chi-squared tests to identify correlations between the Ethnicities of farming households in the former Conflict Zone and a suite of agricultural resources and strategies shown in previous work to affect agricultural livelihood outcomes (Williams and Carrico 2017; Williams et al. 2018). These analyses helped us to better understand the various factors that may be influencing the patterns relating to agricultural livelihood variability identified through our previous models. All statistical analyses were performed in R (R Core Team 2020) and included the package lme4 (Bates et al. 2015), and preliminary tests were performed to confirm that data structure did not violate assumptions of linear regressions or ANOVAs.
Finally, while the statistical analyses described above helped us to begin to explore the effects of Sri Lanka’s civil conflict on the ethnic dynamics of agricultural livelihoods, we were able to further elucidate these relationships by drawing on the results of FGDs, particularly from those conducted in the country’s and Conflict Zone‘s most marginalized farming communities. Qualitative results presented are summaries of field notes documenting FGDs in the three communities that represent the lowest average Yields and Household Assets within our dataset. Fieldnotes were edited for grammar and thematically coded to identify information regarding prominent factors and processes described in FGDs that affect agricultural livelihoods within these communities, highlighting but not limited to those that pertain to the effects of the civil war on the community and farmers livelihoods.
Results of quantitative analyses
Descriptive information regarding the sample population and the key dependent variables used in our models to assess agricultural livelihood viability are displayed in Table 1. Model Sets 1 and 2 (detailed below) include the total sample population to investigate the effects of a household’s location in the former Conflict Zone and Ethnicity on both Yield and Household Assets. The subsample used in Model Sets 3 and 4 explores ethnic variation in and possible drivers of agricultural livelihood success among households within the former Conflict Zone. Therefore, the subsample used for these final model sets is constrained to the 12 (of 25 total) Conflict Zone communities. While the total sample population of Tamil and Moor households are maintained within this subset, only 98 Sinhalese households (exclusively residing in two communities) within our sample are living and farming within the former Conflict Zone.
Table 1.
Summary statistics of variables (1137 household across 25 communities)
Variable | n (%) | Mean | SD | Min | Max |
---|---|---|---|---|---|
Yield (mt/ha) | 4.29 | 1.89 | 0 | 9.06 | |
Household assets (standardized) | 0 | 1 | − 3.16 | 0.94 | |
Cohort | |||||
C1 | 604 (53) | ||||
C2 | 533 (47) | ||||
Ethnicity | |||||
Sinhalese | 825 (73) | ||||
Tamil | 254 (22) | ||||
Moor | 58 (5) | ||||
Conflict zone | 417 (36) | ||||
Ethnicity × conflict zone | |||||
Sinhalese | 98 (9) | ||||
Tamil | 254 (22) | ||||
Moor | 58 (5) |
Model Set 1 identified significant relationships between agricultural livelihood metrics and a household’s location within a community that experienced the direct effects of violence during Sri Lanka’s civil war (Conflict Zone). Farming households in the former Conflict Zone have lower seasonal rice Yields (M = 4.12 mt/ha, SD = 1.93) than farming households in areas not directly impacted by the war (M = 4.37 mt/ha, SD = 1.85). The effect of a household being located within the former Conflict Zone, therefore, was significant, F(1, 1 139) = 10.7, p < 0.001. Similarly, asset indices among farming households in former Conflict Zones were significantly lower than among households in areas less or indirectly affected by the war, R2 = 0.10, F(1, 1 146) = 140.1, p < 0.001.
ANOVAs used in Model Set 2, which also include households in all 25 survey communities throughout the Dry Zone, show significant effects of Ethnicity on both rice Yields, F(2, 1 133) = 25.87, p < 0.001, and Household Assets, F(2, 1 134) = 117.6, p < 0.001. Post-hoc analyses using Tukey’s HSD tests identified significant differences in the inter-group means between all groups in both analyses (Table 2). Members of the Sinhalese community have significantly higher Yields (M = 4.47 mt/ha, SD = 1.84) and Household Assets (M = 0.25, SD = 0.85) than the Tamil (3.85 ± 2.00 and − 0.57 ± 1.07, respectively) or Moor households (3.57 ± 1.57 and − 1.04 ± 1.04, respectively). Because households identifying as Tamil or Moor live exclusively in the former Conflict Zone, the patterns exposed by post-hoc tests parallel the results of analyses that included Conflict Zone as a predictor. Augmenting the previous models, however, these post-hoc analyses also revealed that Tamil households have significantly higher Yields and Assets than Moor households (p = 0.05 and p < 0.001, respectively).
Table 2.
Post-hoc test results for ANOVAs including ethnicity and agricultural livelihood metrics of households throughout Sri Lanka’s Dry Zone (n = 1137)
Comparison | Difference of means | SE of difference | Significance |
---|---|---|---|
(a) Effects of Ethnicity on seasonal rice Yields | |||
Sinhalese—Tamil | 0.75 | 0.13 | *** |
Sinhalese—Moor | 1.36 | 0.23 | *** |
Tamil—Moor | 0.61 | 0.27 | * |
(b) Effects of Ethnicity on Household Assets | |||
Sinhalese—Tamil | 0.82 | 0.07 | *** |
Sinhalese—Moor | 1.29 | 0.12 | *** |
Tamil—Moor | 0.47 | 0.13 | *** |
*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001
When restricted to households only within the former Conflict Zone for Model Set 3, ANOVAs continue to show significant effects of Ethnicity on rice Yields, F(2, 263.54) = 21.12, p < 0.001, and Household Assets, F(2, 407) = 34.06, p < 0.001. Tukey’s HSD tests of the Conflict Zone-bound models reveal regional patterns regarding agricultural livelihoods of Sri Lanka’s different ethnic communities similar to those observed in analyses including households throughout the Dry Zone. Within the former Conflict Zone (Table 3), the Yields and Household Assets of Sinhalese households (M = 5.15 mt/ha, SD = 1.56 and M = 0.23, SD = 0.79) are significantly higher than that of Tamil households (3.85 ± 2.01 and − 0.57 ± 1.07, respectively) and Moor households (3.58 ± 1.58 and − 1.04 ± 1.04, respectively). Also paralleling patterns observed throughout the Dry Zone, Tamil households have significantly higher Household Assets than Moor households (p = 0.004). Notably, although Tamil households within the former Conflict Zone have slightly higher Yields than Moor households, the difference is not statistically significant (Table 3b).
Table 3.
Post-hoc test results for ANOVAs including ethnicity and agricultural livelihood metrics of households located within former Conflict Zone (n = 417)
Comparison | Difference of means | SE of difference | Significance |
---|---|---|---|
(a) Effects of Ethnicity on seasonal rice Yields | |||
Sinhalese—Tamil | 1.33 | 0.22 | *** |
Sinhalese—Moor | 1.68 | 0.32 | *** |
Tamil—Moor | 0.36 | 0.28 | |
(b), Effects of Ethnicity on Household Assets | |||
Sinhalese—Tamil | 0.81 | 0.12 | *** |
Sinhalese—Moor | 1.27 | 0.17 | *** |
Tamil—Moor | 0.47 | 0.15 | ** |
*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001
A final set of analyses explored potential correlations between the Ethnicities of households in the former Conflict Zone and a suite of factors identified in previous research to influence agricultural outcomes among farming households in the Dry Zone (Table 4). Most notable among these results, Tamil and Moor farmers in the former Conflict Zone on average have significantly higher proportions of paddyland that are purely rainfed than do Sinhalese farmers, F(2, 407) = 16.91, p < 0.001, signifying that many ethno-religious minority farming households lack access to either state- or community-managed irrigation systems.
Table 4.
Results for tests exploring correlations between farmers within the former Conflict Zone’s Ethnicities and a suite of agricultural resources and strategies that influence farming outcomes
Agricultural resource or strategy | Sinhalese | Tamil | Moor |
---|---|---|---|
Age (years) | 49.37 ± 11.38 | 47.56 ± 12.38 | 50.52 ± 13.73 |
Education level (years)** | 3.18 ± 1.13 | 2.83 ± 1.00 | 2.64 ± 0.89 |
FO participation* | 93.86% | 91.73% | 77.59% |
Proportion of paddy rainfed*** | 4.22% | 30.07% | 37.93% |
Total paddy acreage* | 3.31 ± 2.64 | 4.73 ± 4.50 | 4.18 ± 4.22 |
Receiving drought information*** | 71% | 38% | 39% |
Presence of an agrowell*** | 27% | 16% | 5% |
Use of drought-tolerant seeds** | 50% | 69% | 55% |
Crop diversity** | 0.62 ± 1.13 | 0.78 ± 1.24 | 0.17 ± 0.70 |
*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 and standard deviations (SD) accompany all reported means
Qualitative results from FGDs
Each of these sets of analyses, which test the relationships between location and/or ethnic community membership with our two agricultural livelihood metrics, expose the two Moor communities in our study maintained significantly lower Yields and Household Assets than the Sinhalese or Tamil communities. The only deviation from this pattern occurs in the analysis of ethnic variation in rice yields restricted to communities within the former Conflict Zone. Notably, this aberration can be explained by the acutely low yields suffered by a particular Tamil community. Thus, the results of FGDs conducted by our research team in these three communities (all situated in areas of the Sri Lanka’s Eastern Province that was heavily affected by the civil war) help to contextualize and clarify how events related to the war have affected the agricultural livelihoods of households in these communities.
Community#12(Yield=2.95mt/ha,SD=1.52andHouseholdAssets=−0.45,SD=0.86). |
Farmers and community leaders in this Moor farming community described the links between many of their current community issues, which beyond climate-related challenges include issues with both transportation to farmland and lack of grain storage, and events relating to the war. Initially settled in the 1960s, during the civil war almost all of the households in the area were displaced to regional centers, abandoning their homes and fields to temporarily settle with and around other displaced Moor farmers and long-term Moor residents. During that time, an existing grain storage facility was taken over by the military to serve as an outpost and the canal systems critical to irrigated rice farming fell into disrepair.
After 2008, as the war concluded, these families started to resettle in their original lands. As of 2017, around 300 families have resettled the area. Although the community was once supplied irrigation water through a canal that that diverted water from the Mahaweli Delta (Sri Lanka’s largest river system), the canal was severely damaged during the war. Thus, most farmers depend on rainfed rice farming in the wet season as their primary livelihood. The FGD members vented that the community has still yet to receive government assistance for canal rehabilitation following the war, and farmers are therefore unable to grow a second yearly rice crop during the dry season. Further, the grain storage facility continues to be used by the national military and has not been replaced. Finally, while prior to the war government-provided boats helped to transport farmers across a major river to their rice fields, farmers now cross the waterway using home-made boats or swimming. Farmers and community leaders cite these factors as the major impediments to their abilities to achieve higher yields and ultimately to secure viable agricultural livelihoods. As a results, most households in the community are increasingly shifting their livelihoods to livestock farming and daily wage labor, farming rice only to meet their household needs.
Community#22(Yield=2.98mt/ha,SD=0.93andHouseholdAssets=−0.5,SD=0.81). |
The farmers captured by our survey in this peri-urban area represent a somewhat permanently displaced population whose lives and livelihoods have been greatly affected by the civil war. These Moor farmers were originally settled 25–30 km inland in late 1969 alongside resettled Tamil farmers and given access to state-managed irrigation water as part of a government resettlement program to develop agriculture and promote food self-sufficiency. In 1987, as wartime activities began to directly affect their region, some Moor farming households began relocating to a formerly small, regional center originally settled by Moor fishermen and traders, but controlled by the Sinhalese government throughout the war. The initially resistant Moor households in the inland farming community migrated as combat began occurring in the area. While most joined their community members in the same regional center, the Sri Lankan army sent some families to other nearby Moor towns, resulting in population booms in Moor urban centers throughout the region. Displaced from their land, these families ceased farming during the war and shifted their livelihoods to wage labor and trade.
Some families attempted to move back to their farming community to revive their agricultural livelihoods during a ceasefire between 2002 and 2004, and many of the displaced households did so as the war began to end in 2008. In both cases, however, most of these families found that their farm and residential lands had been taken over by ‘stayed-back’ Tamil families and Tamil families that migrated into the area during the war. The encroachment into Moor agricultural lands was less severe than the residential take-over (FGD participants described this perhaps relating in part due to the poor state of the reservoir and canals due to lack of maintenance during the war). However, many of the farmers described being unable to obtain deeds for their land before war broke out or losing existing deeds during the forced migration and war, complicating the process of repossessing their former residential land in the inland community. Thus, while some Moor farmers were able to resettle their pre-war residential lands, the Moor farming families in our survey community make a daily 25–30 km commute during the rainy season to farm rice in their agricultural holdings.
Further complicating their agricultural livelihoods, unlike most Sri Lankan farmers with access to government-managed irrigation water, these farmers described being unable to farm during the dry season with reserved irrigation water. FGD participants described the poor state of their community tank (reservoir) and canal system, which fell into disrepair during the war and have not been restored. Additionally, farmers described needing to commute daily across a river (the bridge destroyed during the war had not been rebuilt) to maintain their crops (including protecting against elephants), making cultivation during the dry season too risky for the meager yields. FGD participants did state that because of their proximity to their fields, Tamil farmers who reside in their former village are able to cultivate other field crops, like groundnut, mung bean, and okra as a source of income during the dry season.
In addition to issues relating to their commute, because the Moor farmers are no longer residents of the area in which they farm, they fail to receive certain forms of government support, such as a (now volatile) fertilizer subsidy or disaster compensation. The local ‘farmer organization’, which serve as a connection between farming communities throughout Sri Lanka and formal extension services and government programs, is also headed by Tamil community members, and Moor farmers feel that this results in their requests and needs being ignored. Despite the potential benefits of developing a new residential area and resettling nearer to their agricultural lands, FGD participants expressed that the lingering tensions between Tamil and Moor communities and the access to off-farm income in town have kept them from doing so.
Community#14(Yield=0.52mt/ha,SD=1.57andHouseholdAssets=−1.48,SD=1.02) |
This Tamil farming community was largely abandoned during the war because of active conflict in the region and was resettled in 2009. The farmers in this community pointed to the effects of war and post-war government neglect as key factors shaping their remarkably low rice yields. The village historically had two functional tanks to provide irrigation water. However, both reservoirs fell into disrepair during the war, one of which is now completely nonfunctional. Significant financial investment from the divisional government would be required to rehabilitate these tanks, and there is no plan for repairs to be undertaken. As a result, farmer do not plant rice in the yala (or dry) season, nor do they have adequate irrigation water to help buffer against drought-affected maha (or wet) seasons.
Additionally, the Irrigation Department utilizes a nearby canal to manage excess water from one of the large-scale irrigation system the state-level institution manages. This canal is in poor condition and leaking water often floods community members’ paddylands. This process is what caused the crop failures documented by our survey. Many farmers work as laborers during the maha season in the agricultural communities nearby that have better water management and/or plant alternative crops in highland plots to buffer their households against regular rice crop failures.
Finally, deeds of inherited land were lost by many farmers (or descendants of pre-war inhabitants) during the war or tsunami that affected much of Sri Lanka in 2004. While the government worked to reissue paper deeds, they were only granted for one acre of land. Many farmers claim to own larger fields, but the discrepancy results in these farmers receiving lower fertilizer subsidies (from the now capricious program) and having less collateral for bank loans than they feel they are entitled.
While war-related displacement often temporarily relieves pressure on natural resources, research has identified dramatic variation in post-war resettlement and natural resource use patterns. This is particularly apparent among agricultural communities and livelihoods; in some instances agricultural activities fail to resume following a conflict (Witmer 2008; Czaika and Kis-Katos 2009; Baumann et al. 2015), while elsewhere agricultural land use has been observed to parallel or extend beyond pre-war levels (Stevens et al. 2011; Eklund et al. 2016, 2017). However, because most insights about how war affects natural resource and land-use patterns are derived from remotely sensed data (Witmer 2008; Stevens et al. 2011; Hagenlocher et al. 2012; Alix-Garcia et al. 2013; Baumann et al. 2015; Jaafar et al. 2015; Eklund et al. 2016; Yin et al. 2019), we lack a thorough understanding of how the socio-political dynamics of war and post-war (re-)organization may shape land and resource access. The study presented here enables us to not only detect geographic variation in agricultural livelihood viability in post-war Sri Lanka, but also recognize how the lasting effects of war are exacerbating the vulnerability of rural Sri Lanka’s ethno-religious minority populations.
Previous research utilizing remotely sensed data shows that despite land abandonment during periods of the Sri Lankan civil war (Suthakar and Bui 2008), re-agrarianization has occurred in war-affected regions with extensification above pre-war levels in some areas (Ranagalage et al. 2020; Rathnayake et al. 2020). Yet, it is clear from our analysis of household- and community-level data from throughout post-war Sri Lanka that the lingering effects of war result in variation in the viability of agricultural livelihoods. Resettlement has indeed occurred in communities directly affected by the armed conflict, but as hypothesized, farming households in these communities have lower rice yields and fewer assets than households in communities indirectly affected by the war. Further, both in general and specifically within the former conflict zone, we found that some communities have fared much better than others in the re-agrarianization process and that the observed patterns fall along ethno-religious lines.
Sinhalese (Buddhist) farming households have more viable agricultural livelihoods than Tamil (Hindu) or Moor (Muslim) households. While all of the Tamil and Moor household in our sample reside in communities that were directly affected by the war, only a small subset of Sinhalese communities are within the former conflict zone. Notably, Sinhalese households in these two communities reported higher yields than the average for both Tamil and Moor household also in the conflict zone and Sinhalese households throughout the Dry Zone. Although the virtual ethnic homogeneity of communities makes it challenging to rule out a spurious community-level effect through statistical models alone, the integration of our quantitative and qualitative results helps us to clarify and uncover some of the specific drivers of this rural ethno-religious inequality, connecting these patterns directly to socio-political phenomena related to the war. Most critical, we identified that Tamil and Moor farmers in the former conflict zone have significantly higher proportions of land that is purely rainfed than do farmers in the Sinhalese communities directly affected by the war. Access to either state- or community-managed irrigation systems are vital to both yala (dry) season rice production in Sri Lanka, as well as buffering rice production systems against wet-season droughts and floods (Lakmali et al. 2015; Williams and Carrico 2017). Further, as described by FGD participants in Community #14, the instability faced by households that lack access to functional irrigation systems pushes them away from rice farming towards alternative income generating activities. Critically, data collected through community focus groups highlight that this lack of irrigation water is not simply a relic of a pre-war history. Households in our sample’s most marginalized communities described the degradation of irrigation infrastructure during their period of war-driven displacement and inaction by the Sinhalese-dominated government in the years since their post-war resettlement as the primary challenges facing the viability of their agricultural livelihoods. Therefore, the war has perceivable, lasting impacts on the livelihoods of Tamil and Moor farming households and communities, while the agricultural livelihoods of Sinhalese farmers appear relatively unaffected.
Finally, our results also cast light on the particular plight of Moor farmers, which reported some of the lowest yields and the lowest household assets in our sample. Focus group data expose that farmers in one Moor community are not only stymied by government neglect, struggling like other ethno-religious minority farmers to maintain rice farming systems without (consistent) irrigation water, but that their minority status within a Tamil-dominated region has also directly affected their livelihoods. While these farmers were dislocated from their rural community during the war, the neighboring Tamil farmers were not and, along with Tamils that immigrated during the war, seized the opportunity to commandeer many of their residential lands. These Moor farmers now must make an arduous daily commute from the peri-urban area where they reside to cultivate their fields in their former village, compromising their ability to protect their modest rice crops against elephants or diversify their production with alternative or more drought-tolerant crops like their Tamil neighbors. The persecution of Moor communities and anti-Muslim campaigns in the post-war period, particularly in urban centers, has been noted (Ali 2011; Mcgilvray 2011, 2016; Yusoff et al. 2018). Although the number of Moors households in our study was small (reflective of the small national population), our results provide additional empirical evidence of the marginalization of these populations throughout Sri Lanka. Over the course of their long history in Sri Lanka, Sri Lankan Moors have been caught in the middle of broader political and economic conflicts and, today, continue to suffer from government inaction.
The findings presented here are particularly relevant to Sri Lanka, but the results have implications beyond this small island nation. Rice farming is critical to the livelihoods of people throughout rural Sri Lanka, regardless of ethno-religious identity. As witnessed in the county’s recent food crisis, driven in part by the abrupt reversal of decades-old policies that encouraged chemical fertilizer overuse, sound agricultural policies are critical to the stability of the nation’s food supply (OCHA 2022). Policymakers currently debating how to develop a more sustainable domestic food system, and economy more generally, must consider the lasting inequalities associated with the country’s multidecadal civil war on farming and farmers. This is especially important for those government entities that manage irrigation infrastructure. Water is fundamental to rice cultivation in the Dry Zone and critical to the viability of agricultural livelihoods on an island where rainfall patterns are being increasingly affected by climate change. Priority should be placed on rehabilitation projects that would increase the resilience of the region’s most marginalized communities.
Additionally, issues of post-war inequality, especially as they relate to rural recovery and redevelopment, are not unique to Sri Lanka. Yet, few studies have collected ground-level social data to understand drivers and consequences of war-related land-use change. While remotely sensed data can identify patterns, it does not provide an ability to explain important social dynamics tied to or embedded within the land-use patterns. Here, we show that the sustainability of livelihoods and food systems does not depend solely on whether land is in use, but also on who has access to that land and the resources necessary to successfully cultivate it. This work is part of a growing body of research that aims to integrate diverse methodologies and data types to understand complex natural resource problems and provide actionable insights for policymakers. As our work, which draws on a political ecology perspective, highlights, future research in this domain must explicitly consider whether and how socio-political inequalities shape and are shaped by resource access. Throughout the world, local and regional food supplies are dependent on small-scale producers (Lowder et al. 2014). Therefore, even beyond in areas recovering from periods of conflict, recognizing and working to resolve inequalities tied to rural livelihoods and food production are critical to supporting food system resilience in our era of unprecedented global environmental change.
Acknowledgements
We thank the other members of the ADAPT-SL research team, our collaborators at National Building and Research Organization (NBRO), Sri Lanka, and our local research assistants. Support for this project was provided by a grant from the National Science Foundation (EAR-1204685).
Biographies
Nicholas E. Williams
is an Assistant Professor in the Natural Resources Management and Environmental Sciences Department at California Polytechnic State University, San Luis Obispo. His research interests include the socio-ecological dynamics of food systems.
Malaka Dhamruwan
is a graduate student in the Department of Economics at the University of Colombo, Sri Lanka. His research interests include macroeconomic policy, institutions and sectoral disparities.
Amanda R. Carrico
is an Associate Professor in Department of Environmental Studies at University of Colorado Boulder. Her research interests include the behavioral dimensions of environmental conservation and adaptation to environmental change.
Author contributions
NEW: Conceptualization, Formal analysis, Data curation, Writing – Original Draft MD: Validation, Investigation, Writing – Review & Editing ARC: Methodology, Resources, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Nicholas E. Williams, Email:
Malaka Dhamruwan, Email:
Amanda R. Carrico, Email:
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