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Article

Water, Food and Ecosystem Nexus in the Coastal Zone of Northeast Pará, Eastern Amazon, Brazil

by
Vitor Abner Borges Dutra
1,
Aline Maria Meiguins de Lima
2,*,
Peter Man de Toledo
3 and
Yuri Antonio da Silva Rocha
2
1
State Secretariat for Environment and Sustainability (SEMAS) of Pará, Lomas Valentinas Street 2717, Marco, Belém 66093-677, PA, Brazil
2
Graduate Program in Environmental Sciences, Institute of Geosciences, Federal University of Pará, Augusto Corrêa Street 01, Guamá, Belém 66075-110, PA, Brazil
3
National Institute for Space Research, Astronauts Avenue 1758, Jardim da Granja, São José dos Campos 12227-010, SP, Brazil
*
Author to whom correspondence should be addressed.
Geographies 2025, 5(4), 73; https://doi.org/10.3390/geographies5040073 (registering DOI)
Submission received: 24 October 2025 / Revised: 18 November 2025 / Accepted: 26 November 2025 / Published: 1 December 2025
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Abstract

The aim of this research was to identify the challenges and opportunities associated with the Water-Food-Ecosystem (WFE) Nexus approach in coastal river basins in Northeastern Pará and Eastern Amazonia. The methodology considered Sustainable Development Goals (SDGs) axes 2, 6, 8, 10 and 13 through a Nexus indicator matrix. It involved a statistical analysis of distribution and correlation coefficients. The coefficient indicates the strength of the relationship and the presence of positive or negative correlations. The final distribution of the variables discussed was to zone the region into areas of higher and lower potential for water sustainability. The results showed significant variability in consumptive use along the water axis. Castanhal had the highest level of consumptive use due to its public water supply, which increased in line with population growth between 2000 and 2022 (r = 0.76), in accordance with SDG 6. In the food axis, fishing and aquaculture activities were prevalent in the coastal municipalities of Maracanã and São Caetano de Odivelas (SDGs 2 and 8). In the ecosystem axis, significant deforestation was observed (39.45% to 86.88%), accompanied by low environmental compliance. Regarding the relationship between water and food, only the proportion of rural properties with irrigation and temporary crops showed a significant negative correlation (r = −0.62). The results indicate the consolidation of measures pertaining to water security in the region, exerting a direct influence on food security and strategies employed for the administration of ecosystems imperative for the sustenance of multiple extractive communities in the region. The Nexus approach highlighted various challenges in the region, including poor environmental compliance, overuse of water and forest resources, degraded pastures, and underdeveloped socioeconomic indicators.

1. Introduction

The Nexus approach can be considered an instrument for formulating and implementing coherent policies aimed at the 2030 Agenda for the Sustainable Development Goals (SDGs), seeking to promote fundamental aspects for achieving sustainability at the global level [1]. The term “Nexus” refers to the understanding of these interdependencies, tensions, and trade-offs [2]. The Water-Food-Ecosystem (WFE) context has been developed for various applications, including studies focused on water security [3], the evolution of deforestation and its impact on carbon sequestration potential [4], public policies and agricultural planning [5], and social adaptation in response to the consequences of climate change [6].
The multiple responses are grounded in the relationship between water, economy, and ecosystems, with their direct interaction with the processes that indicate climate variability conditions. This demonstrates that water security is essential for socioeconomic security [7,8], where increasing water scarcity implies direct competition for water resources, with indirect consequences such as economic crises and social instability, reinforcing the interdependencies among water, food, ecosystems, and disaster risk reduction. Water scarcity and pollution also threaten ecosystems, reducing biodiversity and weakening ecological resilience [9,10].
The Nexus approach has generated derivatives promoting integration in public policies, such as the Water-Soil-Waste Nexus, which brings together Integrated Natural Resource Management, Water Resources, and Solid Waste. Other examples include Water-Energy-Environment (WEE), Water-Energy-Food-Ecosystem (WEFE), and Water-Energy-Land-Climate (WELC) [11]. This diversity is linked to the potential for synergy, the detection of trade-offs, the improvement of planning and decision-making processes, and the support of governance and integrated management mechanisms [12].
The applicability of the WFE Nexus in the process of socioeconomic adaptation to climate change is evident in Brazil. Conditions associated with various climate-related events, especially droughts, have exacerbated the effects of the water crisis in several regions of the country already affected by poor water quality for consumption. Episodes in the states of the Southeast (2014 and 2015), the Northeast (2010 and 2016), and the North (2005, 2010, 2016, and 2022 to 2024) posed challenges to health, infrastructure, agriculture, biodiversity, and water resources, and thus had significant environmental and socioeconomic implications [13,14].
Another important factor for Brazil is ensuring food production. Most Brazilian states maintain a strong economic relationship with agriculture, which interacts with other areas of the productive sector, such as industry, leading to high levels of water and energy consumption, the latter being heavily dependent on hydropower potential [15]. The implementation of the SDGs and Nexus approaches plays a role in supporting the formulation of public policies by assisting in the control of relevant factors such as large-scale deforestation rates, land use changes, as well as water and energy scarcity [16].
In the states that comprise the Brazilian Legal Amazon (Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, and Tocantins), which mainly cover the country’s northern region and influence the Amazon biome and river basin [17], the WFE Nexus can contribute to the organization of territorial management actions and the establishment of regulatory mechanisms for natural resources, highlighting the relevance of the region’s biodiversity and water potential [18].
The coastal zone of the Eastern Amazon represents a unique context characterized by fluvial and estuarine river basins, the presence of numerous insular areas, and the Amazon River delta-estuary. This complexity makes it necessary to consider the potential of the WFE Nexus as an instrument aimed at optimizing the use of natural resources to ensure the social and economic demands of a region highly vulnerable to climate-related risks and marked by low resilience [19].
Within this context lies the coastal region known as Pará-Maranhão, which is part of the Brazilian Legal Amazon [20]. The WFE Nexus analysis becomes relevant for decision-makers when considering the landscape scale, as it seeks to reconcile agriculture, environmental conservation, and other land uses across social, ecological, and economic spheres [21]. In a highly sensitive region, it should prioritize forest protection to ensure the continued provision of ecosystem services (ES) and to guarantee that other important land uses, such as agriculture and livestock, are properly managed and do not encroach upon Conservation Units (CUs) and Permanent Preservation Areas (PPAs) [22,23]. Therefore, the objective of this study was to identify challenges and opportunities in applying the WFE Nexus approach within the landscape defined by a coastal region belonging to the Amazon River delta-estuary (ADE) in the Eastern Amazon.
Studies using the Nexus approach tend to adopt either a national (Brazil) or a macro-regional (the Legal Amazon region) perspective [13,14,18]. In the context of the Legal Amazon, the following points justify the Nexus approach in relation to the themes of water, food, and ecosystems: the demand for policies that address uncertainties and information gaps regarding the measurement of the impacts of climate variability on the water balance; the promotion of integrated and strengthened water resource management; and the increase in the resilience and adaptive capacity of society, ecosystems, and the economy in the scenario of reduced future water availability. Water accessibility would ensure the implementation of social protection and food security policies, particularly for populations most vulnerable to climate risks. This includes consolidating adaptation approaches based on conserving and restoring ecosystems that are essential for climate regulation, as well as ensuring food and water security [18].
This research therefore hypothesizes that the eastern Amazon is a transitional area between the typical Amazonian environment and northeastern Brazil, where the effects of seasonal climate variation are more pronounced. Consequently, the water-food-ecosystem nexus approach is a manageable strategy for this region. This environment is characterized by an estuarine region with river basins exhibiting fluvial behavior and marine influence due to tidal effects. This creates a zone of high erosion in the coastal strip, which is home to a dense mangrove forest and high levels of land use activity related to forest cover alteration. This describes the eastern part of the Legal Amazon landscape.
The landscape unit defined by the fluvial-estuarine coastal zone of the ADE [24] is justified by the central role of water, which, in this region, is the key factor in food production and ecosystem maintenance. The estuarine zone encompasses a vast floodplain with mangrove forests that serve as a means of subsistence for numerous communities. Thus, there is a strong correlation between the use of natural resources and the need for their conservation to ensure the social and economic sustainability of the local population.

2. Materials and Methods

The region under study encompasses coastal, rural and urban areas and includes various stakeholders such as riverine populations, community members, extractive workers, small-scale rural producers, and urban residents. It is located in the eastern Amazon in northeastern Pará and encompasses 12 municipalities: Castanhal, Curuçá, Igarapé-Açu, Magalhães Barata, Maracanã, Marapanim, Santo Antonio do Tauá, São Caetano de Odivelas, São Francisco do Pará, São João da Ponta, Terra Alta and Vigia (Figure 1). According to the 2022 Demographic Census of the Brazilian Institute of Geography and Statistics (IBGE), these municipalities have an estimated population of 467,354 inhabitants and share common characteristics of settlement and socioeconomic dynamics.
The coastal municipalities constitute Sectors 3 (Continental Estuarine) and 4 (Fluvial-Maritime) of the Coastal Zone of Pará (ZCPA) [25]. The ZCPA was established under the State Coastal Management Policy (PEGC/PA, State Law n. 9.064-2020). The region comprises nine conservation units, which cover approximately 100,000 ha and primarily protect the mangrove ecosystem: (i) Padre Sérgio Tonetto Wildlife Refuge (REVIS); (ii) Mãe Grande de Curuçá Extractive Reserve (RESEX); (iii) Maracanã RESEX; (iv) Cuinarana Marine RESEX; (v) Mestre Lucindo Marine RESEX; (vi) Mocapajuba Marine RESEX; (vii) São João da Ponta RESEX; (viii) Campo das Mangabas Sustainable Development Reserve (RDS); and (ix) Algodoal-Maiandeua Environmental Protection Area (APA).
The Nexus approach was adapted as a conceptual tool for the Water, Food, and Ecosystem axes, based on matrices that represent the variables with the greatest intervention in the region [1,26] (Figure 2). These matrices are based on two main points: (1) they highlight the Nexus relationships for achieving the 2030 Agenda, interpreting the matrix in the context of a literature review focused on sustainable and resilient cities [26]; and (2) they explain that Nexus analysis provides useful knowledge for planning, risk identification and mitigation, and resilience building [1].
The purpose of the Nexus matrix is thus understood as increasing the availability and accessibility of environmental resources, improving productivity, and achieving the SDGs through the understanding of local socioeconomic and environmental interactions. In this regard, data on Water, Food, and Ecosystems were collected and systematized for the 12 municipalities of the study area, also encompassing aspects of the SDGs of the 2030 Agenda (Table 1).
The temporal definition and chosen variables are justified by an analysis based on the SDGs and agendas for promoting public policies in the region: Ecological-Economic Zoning of the state of Pará (ZEE, State Law n. 6745/2005); Sustainable Territories Program (STP, State Decree n. 334/2019); State Coastal Management Policy (PEGC/PA, State Law n. 9.064/2020); State Climate Change Policy (PEMC/PA, State Law n. 9.048/2020) and Amazon Now State Plan (PEAA, State Decree n. 941/2020). The PEAA aims to achieve the Sustainable Development Goals (SDGs) of the 2030 Agenda at state level. To this end, it promotes the implementation of measures to ensure the requirements of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism are observed. These measures encourage activities that prevent and mitigate Greenhouse Gas (GHG) emissions, prevent and control deforestation, and promote environmental, economic, financial and fiscal strategies for environmental protection in the State of Pará, in accordance with the State Policy on Climate Change. These policies aim to stimulate economic investment in the region while also considering the need for environmental conservation and maintaining water availability in the context of future climate variability scenarios.
The data on water, food, and ecosystems were organized in spreadsheets, forming a Nexus indicator matrix. The axes were discussed individually based on this matrix, with emphasis on descriptive statistics related to the municipality or the specific variable itself (Figure 3).
The variables were individualized by axis (water, food, and ecosystem) for each of the 12 municipalities, and the values obtained were normalized to a 0–1 scale. The aim of normalization was to organize the data in a way that ensured features from different scales or units were treated equitably. It was only necessary to scale feature values to a specific range (from 0 to 1) for values that were not collected based on their percentage distribution. Values that were not expressed as percentages were evaluated according to their representativeness in relation to the total consumed or used. The analysis of socioeconomic conditions was added to support the understanding of the other Nexus indicators.
The average values obtained for each axis were defined based on the influence of each variable. Where Ni is defined as the descriptive indicator of the Water (W), Food (F), and Ecosystem (E) axes; and Ij represents the individual indicators and the effect of their summation on the total area set: N i   =   I j ∑ I j .
The variables obtained as percentage rates, or those that could be transformed into percentages proportional to the territorial area of each municipality, were selected and statistically processed in the R (software (version 4.5)) environment. The percentage transformations served to avoid quantitative discrepancies among the municipalities.
The selected data were used for Pearson correlation analysis, which measures the similarity or correlation between two variables, resulting in a range from −1 to +1, with the positive extreme indicating high similarity, proximity to zero indicating absence of correlation, and the negative extreme indicating the lowest similarity. Pearson’s correlation coefficient is commonly used to describe patterns of relationships between variables, or to draw valid conclusions about a population based on sample data. However, it should be noted that it is strongly influenced by the mean of the distribution [27]. Therefore, it should be emphasized that a relationship between 19 variables was assumed for each of the 12 municipalities.
Based on these correlations, a correlogram of water–food, water–ecosystem, and ecosystem–food interrelationships was developed. In addition to analyzing the axes by comparing the various relationships obtained among them, the effect of population was also compared using x/y/z diagrams, with the aim of assessing how one variable relates to two predictor variables.
The statistical analysis was therefore made up of two steps. Firstly, variables were correlated using a heat map. Secondly, the data was integrated using 3D diagrams. Cartographic processing (in QGis software (version 3.44)) involved aggregating variables with the same value, adjusting for the individual scale indicator and its potential contribution to water sustainability: Population increase between 2000 and 2022; Human consumption proportional to total consumption; Rural consumption proportional to total consumption; Industrial consumption proportional to total consumption; Agricultural establishments; Average area of agricultural establishments; Conservation Unit Area proportional to the area of the municipality; Water Permanent Preservation Areas (PPA) proportional to the area of the municipality; Legal Reserve (LR) area proportional to the area of the municipality; and Rural properties eligible for registration with Rural Environmental Registry (RER) records.
Furthermore, the Nexus WFE was defined according to the distribution of total water consumption in the region. This includes human, rural and industrial supply, which is grouped under the Water axis. It also accounts for the total area of establishments intended for agricultural and livestock use, which is grouped under the Food axis. Finally, it considers the total area impacted by deforestation, which is grouped under the Ecosystem axis.

3. Results

The correlation results (Figure 4) show that population growth is positively related to urban water supply (r = 0.76) and rural irrigation (r = 0.65), indicating greater water demand. In terms of land use, temporary crops exhibited a negative correlation with both permanent crops (r = −0.85) and livestock (r = −0.64), reflecting patterns of productive substitution. Degraded and non-degraded pasture areas were strongly correlated with each other (r = 0.78) and with accumulated deforestation (r = 0.95), reinforcing the association between livestock and forest loss. The RER also showed a positive association with deforestation (r = 0.94) and livestock, while the Conservation Units (CUs) presented consistent negative correlations (r = −0.7 to r = −0.84), confirming their protective effect against agricultural expansion.
The presence of municipalities with low levels of education, health, and income is associated with inadequate sanitation conditions and urban infrastructure. This scenario contrasts with the presence of conservation areas that form the coastal zone and support the economic livelihoods of various communities through fishing and extractive activities (both animal and plant). In this context, local governance mechanisms are structured by institutional arrangements aimed at ensuring local livelihoods and the traditional use of natural resources [28].
The processes occurring in the region directly affect the subsistence dynamics of the coastal population (Figure 5A). The intensification of land use practices has led to a decline in soil quality, particularly in older agricultural colonization areas associated with several productive cycles [29].
The correlogram in Figure 4 was developed to indicate significant correlations between percentage rate variables and proportions of variables relative to the territorial area of each municipality. The total population of the evaluated region, comprising the 12 municipalities, is 454,657 inhabitants according to the 2022 census of the Brazilian Institute of Geography and Statistics. The observed variation ranged from approximately 50% growth (in Castanhal) to a 6% reduction (in Maracanã) during the period from 2000 to 2022, demonstrating that the region is heterogeneous and does not reflect the indicators in the same way (Figure 5A).
The responses among the variables do not imply a uniform trend in WFE behavior, but rather demonstrate the influence of the region’s economic development history, the slow progress of social indicators, and the increasing impact on land cover types, especially those designated for conservation [30,31,32,33,34]. The region exhibits an unfavorable pattern for maintaining water sustainability conditions due to the pressure from food production and forest cover modification, even when considering medium to low levels of water consumption pressure (Figure 5B–J).
Table 2 covers the significant correlations identified in the Ecosystem–Food relationship. The specificities of these correlations involve both the characteristics of mutually exclusive land use and land cover classes, as well as statistical proxies, in line with studies that seek to understand the relationship between ecosystems and food in order to identify opportunities and challenges for important Amazonian supply chains related to these two axes [31,33,34].

3.1. Water Axis

There was great variability in consumptive water uses among the municipalities. Castanhal, which concentrates about 42% of the total population, was the municipality with the highest water consumption (0.21 m3/s) and the highest percentage of public supply (43.9%) (Figure 6). This is reinforced by the positive and significant correlation between population growth from 2000 to 2022 and urban supply during the analyzed period (r = 0.76). Irrigation also showed a positive and significant correlation with population growth (r = 0.65). The municipalities that account for less than 10% of the total sampled population concentrate the WFE indicators.
Meeting water supply demands for multiple purposes and at multiple scales is always challenging, especially when the priority is human consumption. This challenge is even greater in regions where the most intensive forms of land use are agricultural practices and livestock farming [5]. Nexus WFE guidelines promote collaboration among public administrators, distribute economic benefits to farmers more equitably, and mitigate the environmental impacts of these activities.
The demand pressure (Figure 6) is connected to the inaccurate water supply for the area. The presence of tidal-influenced river basins, associated with a lack of representative river flow monitoring stations, renders the water supply data unreliable. The most accurate scenario is based on water entering the system through rainfall. The wettest months (monthly average from 1981 to 2020) are February (605.4 mm), March (764 mm) and April (707 mm), while the driest months are September (224.5 mm), October (256.1 mm) and November (215.6 mm). The spatial distribution of the annual average rainfall shows values ranging from a maximum of 3584 mm to a minimum of 2098 mm (average annual accumulated rainfall from 1981 to 2020) [35]. Consequently, a more pronounced monthly fluctuation is evident than the annual distribution, which is primarily perceived by the local population during the process of supplying water for consumption.

3.2. Food Axis

Family farming activities occurred at a high proportion in most of the studied municipalities (Table 3). In addition, the presence of oil palm plantations is noteworthy in the study area, a crop not native to the Amazon region that has been cultivated mainly in a monoculture system in Northeastern Pará [36]. It is important to note that the numbers relating to extractive activities and aquaculture are an underestimation of the local situation. Extractive and aquaculture activities are fundamental for the subsistence of the inhabitants of the extractive reserves (RESEX), ensuring food security and financial sustainability for local communities (SDGs 2 and 8).
Analysis of mapped land cover and land use changes [32] revealed a significant (32%) reduction in forest cover between 1985 and 2019. This area is almost the size of the municipality of Castanhal (1029 km2), the largest area covered by the study. One of the key findings was the conversion of forest to pasture; 40% of the mapped area represented this change, accounting for a 92% increase over 34 years. The area under temporary and perennial crops increased from 1.42 km2 to 5.76 km2 and from 3.24 km2 to 10.18 km2, respectively, between 1985 and 2019. This increase may be associated with the presence of agroforestry systems (SAFs) in the region.
Artisanal fishing and crab harvesting are the main economic activities in the municipality of São Caetano de Odivelas, due to the presence of the Mocapajuba Marine RESEX [37]. Additionally, it is estimated that 60% of the crabs harvested in Pará originate from federal Conservation Units (CUs) [38], highlighting the importance of this species for the food security and economic sustainability of local populations. The indicators of population concentration suggest that the higher the concentration, the less favorable the ecosystem component becomes, exerting greater pressure on food production (Figure 7).

3.3. Ecosystem Axis

With regard to the Conservation Units (CUs), it is noteworthy that the municipalities of Castanhal, Igarapé-Açu, Santo Antônio do Tauá, São Francisco do Pará, Terra Alta, and Vigia are not covered by state or federal CUs. On the other hand, the remaining municipalities that contain such protected areas hold large territorially protected zones in proportion to their total area, such as Curuçá (36.32%), São Caetano de Odivelas (34.14%), and Magalhães Barata (33.52%).
The CU areas present in the study region form a belt of ecological corridors composed mainly of mangroves, but which could be more consistently integrated with other local water-related Permanent Preservation Areas (PPAs) and Legal Reserve (RL) areas. The presence of nature conservation units has supported the maintenance of permanent preservation areas, while their absence has favored pressure toward the consolidation of legal reserve areas (Figure 8). These environmentally protected areas are notably subject to pressure from the expansion of temporary crops, perennial crops, and pasture in the region, as observed in the large consolidated areas surrounding the vegetation classes.
An individual analysis within the Nexus-Ecosystem context takes into account factors such as ecological connectivity, management effectiveness, the impact of deforestation associated with land use patterns and the decline of ecosystem services. According to the Geoenvironmental Zoning perspective developed for the region [25], conservation units represent an ecological and socio-environmental response aimed at promoting the sustainable use of forests and the conservation of natural resources. More than 30% of the territory of the municipalities of Curuçá, Magalhães Barata and São Caetano de Odivelas belongs to this category in the form of extractive reserves. However, the Nexus-Food context reveals conflicting overlaps with agricultural production and animal farming, including the expansion of deforested areas [28,29]. Therefore, as long as the extractive reserves succeed in their objective of maintaining mangrove areas, ecological connectivity is guaranteed, enabling the provision of ecosystem services. However, if deforestation continues to increase [32] and public policies for the protection and conservation of the coastal zone are weakened, the result will be the loss of the region’s natural heritage.

4. Discussion

4.1. WFE Nexus Assessment

The overall behavior of the region shows a negative trend, with food production exerting the greatest pressure on the water and ecosystem axes (Figure 9). In the Water–Food relationship, only the variables “proportion of rural properties with irrigation” and “proportion of rural properties with temporary crops” showed a significant correlation (p < 0.05), with r = −0.62, indicating a moderate negative correlation. This implies that the more rural properties in a municipality had irrigation, the lower the likelihood of them having some type of temporary crop. This reinforces the characteristic water demand of permanent crops in the region, which require irrigation management, such as oil palm, citrus, and black pepper.
No significant correlations were identified in the Water–Ecosystem relationship for the selected variables. However, it is well known that mangroves and riparian forests are PPAs that have interdependence with water resources. Mangroves are fundamental for ensuring carbon sequestration in coastal zones, and their structural behavior is determined by climatic factors strongly related to water, such as precipitation, temperature, and tidal movement [39].
The variables analyzed did not allow for the assessment of changes in the water balance, as hydrological data (from surface and groundwater) were not included, with only water consumption rates being considered. To quantify such changes, continuous monitoring in the region would be necessary, which is not the reality observed. Thus, the best quantitative indicators were the land use and land cover change themselves, which may have direct consequences for the hydrological regime of the watersheds in the study area, given that alterations in the percentage distribution of vegetation imply the loss of associated riparian ecosystems, threatening water recharge systems, modifying soil removal rates (erosion), and reducing the resilience of both systems [40,41].
The positive responses (r > 0.5) for the relationships favorable to maintaining the legal reserve reinforce the argument that the process of regularizing rural properties contributes to the preservation of protected areas, while also highlighting the influence of productive activities in controlling deforestation rates. An example of positive intervention in the PPA × LR relationship and in deforestation control are Agroforestry Systems (AFS). These function as alternatives to degraded pastures, and the promotion of socioeconomic and environmental improvements can be associated with groups such as non-timber forest product extraction, fishing and tropical aquaculture, and tropical horticulture [42,43].
In this sense, it is essential to pay attention to the categories of temporary and permanent crops. On the one hand, forest-compatible products that can be adopted in agroforestry systems (AFS) under temporary cropping systems—such as pineapple, cassava, and watermelon—are already part of the production portfolio of family farmers in the region. On the other hand, the category of permanent crops deserves special attention, given that productions of black pepper, coconut, banana, passion fruit, orange, and oil palm were identified in the region.
While respecting the ongoing discussion about how compatible oil palm is with forests and its potential socio-environmental threats when cultivated as a monoculture [35,44], it is important to highlight that this commodity is also found within agroforestry systems (AFS), such as the oil palm–açaí–cocoa AFS [45,46]. When well managed and supported by technical assistance and rural extension, these production chains can yield satisfactory economic benefits and ensure the achievement of SDG 2 (Zero Hunger and Sustainable Agriculture), 8 (Decent Work and Economic Growth), 10 (Reduced Inequalities), 13 (Climate Action), and 15 (Life on Land).
The region encompasses different agricultural products, such as the following palm groves: açaí (Euterpe oleracea), murumuru (Astrocaryum murumuru), buriti (Mauritia flexuosa), tucumã (Astrocaryum vulgare), and marajá (Bactris acanthocarpa). These palm groves are responsible for positive local impacts—social (ensuring food security), economic (increasing income), and ecological (ecological and hydrogeomorphological stability) [47]. To a lesser extent and with a more localized role, the crab is a species characteristic of mangroves and plays an ecological function in maintaining this ecosystem [38]. These aspects reinforce the Ecosystem–Food relationship, emphasizing SDG 2.
Land use and land cover changes indicated that shifting family farming practices did not significantly affect the region’s mangrove areas, while the mosaic of occupations (such as subsistence agriculture) shifted in relation to the secondary vegetation class during the same period [48]. Thus, the results corroborate the interface of consolidated areas in the region, while not disregarding the ecosystem importance of Legal Reserve (LR) areas. It is relevant to note that the region is classified as “consolidated” by the Ecological-Economic Zoning (ZEE) of Pará, which reflects the high proportion of family farming and the low adherence to the RER.
The distribution of the total water consumption response in the region—including human, rural, and industrial supply, comprising the Water axis; the total area of establishments intended for agricultural and livestock use, comprising the Food axis; and the total area impacted by deforestation, comprising the Ecosystem axis—allowed for the spatialization of the Nexus response (Figure 10).
Water consumption and the need for food production compromise the southeastern edge of the region. Meanwhile, the ecosystem component showed that the region as a whole maintained a consistent pattern regarding deforestation, except in areas already heavily altered, which sustained low rates during the evaluated period. The resulting mosaic reinforces the need for public land use planning policies, as the existing fragmentation is evident, and planning actions that prioritize water sustainability would ensure better development of food production as well as greater water availability for multiple forms of consumption.

4.2. WFE Nexus and Environmental Policies

Based on the readings of the Nexus variables and their interrelationships for the study area, several legal instruments stand out for mitigating negative anthropogenic impacts on environmental resources, including (i) political-administrative measures, such as the delimitation of new Conservation Units (CUs), as provided for in the Ecological-Economic Zoning (ZEE) of the state of Pará (State Law n. 6745/2005); (ii) economic measures, such as incentives for reforestation through payment for environmental services; and (iii) sociocultural, educational, and public communication measures, considered fundamental in all Brazilian environmental policies as instruments of environmental education.
The Ecological-Economic Zoning (ZEE) of Pará aimed to reconcile the use of natural resources with environmental preservation and conservation, as well as to conduct surveys and periodic monitoring of the state’s geographic area in accordance with scientific and technological trends and developments, ensuring the conservation of representative samples of the state’s ecosystems. The ZEE of Pará classified the municipalities of the study area into zones of productive activity consolidation and environmentally sensitive zones. Despite this, little progress has been observed with regard to improving the quality of life of local populations, as reflected by the low Human Development Index (HDI) and Per Capita GDP values [25].
In ecological terms, the region contains nine Conservation Units (CUs) with a reasonable degree of environmental protection. However, more protected areas could be created in the region, given its environmental vulnerability and the fact that it comprises a highly threatened center of endemism. Therefore, it is urgent to adopt effective measures for protecting the remaining vegetation and promoting the restoration of ecological corridors in the region in order to enable the movement of local fauna. In addition, the studied area includes narrow strips of water-related Permanent Preservation Areas (PPAs) and Legal Reserve (LR) areas. It is understood that information on land use and land cover changes could effectively support the reformulation and implementation of public policies according to the context of historically altered landscapes in the locality, also contributing to improving the communication of impacts to society and other stakeholders [46].
The studied region contains areas of degraded pastures that could either be improved in efficiency or converted into areas of temporary or permanent crops. It is possible to adopt systems for the recovery of degraded areas, ensuring economic returns, provided there is a joint effort among stakeholders for the proper implementation of the Degraded Area Recovery Plans (PRADs).
The development of conservation strategies at the edges of deforested areas is necessary, primarily due to the demand for environmental resources [49], since factors such as agricultural and pasture expansion, combined with population growth, land prices, and the demand for commodities, represent pressures on forest cover.
The temporal scale analysis was also important for the Nexus matrix, as it is directly related to more sustainable future scenarios and serves to support the interpretation of different aspects, such as the availability of natural resources, population projections, and climate change across different timeframes in the coming years [50].
The use of future scenarios encourages stakeholders to deepen their knowledge and consider political pathways, and in some cases, explicitly and transparently addressing uncertainty can build capacity for flexibility and policy adaptation [51]. In this regard, the study area shows limited environmental compliance (low percentage of RER), with the headwaters of watersheds being poorly protected, which may result in a loss of water quality in the region in the future. Some factors that may drive these changes include population growth, the expansion of agriculture and livestock, and urban sprawl, which exert pressure on other land use classes in the region [32].
Another relevant policy for the region is the State Coastal Management Policy (PEGC/PA, State Law n. 9.064/2020), which defines coastal management as the set of activities and procedures that, through specific instruments, enables the management of natural resources in the Coastal Zone in an integrated and participatory manner. Its objective is to improve the quality of life of local populations, preserve specific habitats essential for the conservation of fauna and flora, and align human activities with the carrying capacity of ecosystems [52,53].
The adoption of methodologies such as Nexus to inform public policy has an immediate impact on integrated territorial planning. However, as in this study, limitations are associated with the information base and possible external factors, which cannot be reflected without more detailed surveys and precise mapping.
A comparative analysis of previous studies on the water-food-ecosystem nexus involving the Amazon and other Brazilian biomes [2,13,14,16,18] supports the method’s efficiency but also identifies limitations in the choice of composite indicators or multivariate models. Despite these limitations, the same points of convergence were identified, demonstrating that Nexus analysis is promising for the region despite the heterogeneity of spatial and temporal information:
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In order to ensure a healthy relationship between ecosystems and food production, it is necessary to decouple deforestation from agricultural production. Otherwise, insufficient public policy action will result in continued deforestation and the marginalization of vulnerable groups [18].
-
The protective function of forests is significant in coastal zones. They control erosion, preserve water recharge areas and provide biomass for energy generation, food production and income generation. They also protect against the effects of droughts and floods and support fauna in highly sensitive environments. Therefore, consolidating the Ecosystem-Water Nexus directly impacts the conservation of coastal systems and tributary river basins [54].
-
The nexus approach is not without its critics, as there are limitations to how far it can encompass ecological or environmental processes that transcend the conceptualization of the axes. In the Amazon, the nexus is interconnected with factors involving climate change, land use patterns and the natural resources that sustain people’s livelihoods. Deforestation of the Amazon rainforest represents another threat to water and energy security [55].
-
In the coastal zone of the eastern Amazon, significant impacts on riparian habitats include biodiversity loss and the fragmentation of natural vegetation. Traditional communities have had their livelihoods transformed, resulting in economic losses and forcing them to alter their way of life. Therefore, it is necessary to consolidate public policies, regulatory processes, and oversight by responsible agencies, in addition to the continuous monitoring of hydroecological processes [56].
The Nexus evaluation approach supports sustainable decision-making by providing an additional means of combining data and developing environment-specific models based on population characteristics and regional factors [57]. It can integrate solutions and inform political decisions across various environmental and socio-economic scenarios. When the quality of the indicator data is high, multi-criteria decision-making methods can be used to engage a variety of stakeholders. The results showed that Nexus WFE feedback depends on the quality and quantity of the information provided, as well as the extent to which policymakers intend to define their priorities and respond to social demands.

5. Conclusions

The municipalities of Northeastern Pará face different challenges involving land use and land cover changes. Through the systematization of indicators for the Water, Food, and Ecosystem Nexus approach, the analysis of their interrelationships, and their connection with current environmental policies, it was possible to identify that the region suffers from low environmental compliance, high pressure on its water and forest resources, degraded pasture areas, and weak socioeconomic indicators.
On the other hand, there are opportunities for socioeconomic and environmental improvements in the region in order to bring the local reality closer to the SDGs of the 2030 Agenda: implementation of existing and related environmental policies; incentives for the widespread adoption of Agroforestry Systems (AFS), given the predominant pattern of family farming in the region; adoption of techniques to increase the efficiency of local pastures; encouragement of the restoration of deforested water-related Permanent Preservation Areas (PPAs) at the headwaters of watersheds, with emphasis on springs and water sources; promotion of the adoption and proper management of diversified perennial crops with satisfactory economic returns, such as cocoa, citrus, and oil palm (in consortia); and establishment of permanent dialog with local stakeholders, ensuring that their needs and demands are heard by decision-makers.
The method has methodological limitations relating to its indicators and how they are distributed in space and time. This is problematic in the Amazon region, where difficulties in homogenizing information across the territory persist. Furthermore, quantifying potential impacts relies on secondary surveys due to the challenges involved in collecting primary data across the region.
Future research must focus on improving the quality of indicators and empirical validation mechanisms. Only then will it be possible to formulate more representative prospective scenarios for the region that provide adequate guidance for the implementation of public policies.
The Nexus analysis methodology has proven to be an arrangement that supports multiple approaches in the decision-making process through the aggregation of indicators that can shape different scenarios for assessing the present moment and establish prospective future relationships. However, it depends on the updating of the information employed and its uniformity across the entire study area. This context varies greatly depending on the effort of each administrative unit (municipality, state, or country) to invest in its maintenance.

Author Contributions

Conceptualization, V.A.B.D., A.M.M.d.L. and P.M.d.T.; methodology, V.A.B.D., A.M.M.d.L. and P.M.d.T.; formal analysis and investigation, V.A.B.D.; writing—original draft preparation, V.A.B.D.; writing—review and editing, A.M.M.d.L. and Y.A.d.S.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Coordination for the Improvement of Higher Education Personnel (CAPES), n. 88887.514635/2020-00.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CUConservation Unit
RESEXExtractive Reserves
GDPGross Domestic Product
LRLegal Reserve Area
MHDIMunicipal Human Development Index
RERPercentage of Rural Environmental Registry
SDGSSustainable Development Goals
PPAWater Permanent Preservation Areas
WFEWater-Food-Ecosystem

References

  1. Nhamo, L.; Ndlela, B.; Mpandeli, S.; Mabhaudhi, T. The water-energy-food nexus as an adaptation strategy for achieving sustainable livelihoods at a local level. Sustainability 2020, 12, 8582. [Google Scholar] [CrossRef]
  2. Bortoleto, A.P.; Franco Barbosa, P.S.; Maniero, M.G.; Guimarães, J.R.; Vieira Junior, L.C.M. A Water-energy nexus analysis to a sustainable transition path for Sao Paulo State, Brazil. J. Clean. Prod. 2021, 319, 128697. [Google Scholar] [CrossRef]
  3. Liu, Q. Interlinking climate change with water-energy-food nexus and related ecosystem processes in California case studies. Ecol. Process. 2016, 5, 14. [Google Scholar] [CrossRef]
  4. Yamagata, Y.; Hanasaki, N.; Ito, A.; Kinoshita, T.; Murakami, D.; Zhou, Q. Estimating water–food–ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2. 6). Sustain. Sci. 2018, 13, 301–313. [Google Scholar] [CrossRef]
  5. Zhang, Q.; Wu, Y.; Zhang, G.; Xu, Y.J.; Hu, B.; Sun, J.; Yu, Y.; Guo, Y. Optimizing the balance among water use, food production, and ecosystem integrity at the regional scale. Agric. Water Manag. 2025, 317, 109670. [Google Scholar] [CrossRef]
  6. Ji, J.S. Health is the landing zone for climate change adaptation. Nat. Hum. Behav. 2025, 9, 829–830. [Google Scholar] [CrossRef]
  7. Han, C.; Han, X.; Ma, B.; Li, D.; Wang, Z.; Hao, Z.; Zhang, X. Research on the Coupling Coordination Relationship and Spatial Equilibrium Measurement of the Water–Energy–Food Nexus System in China. Water 2025, 17, 527. [Google Scholar] [CrossRef]
  8. Zhu, J.; Liu, W.; Sun, Y. Comparative Study of Water–Energy–Food–Ecology Coupling Coordination in Urban Agglomerations with Different Development Gradients. Sustainability 2025, 17, 4332. [Google Scholar] [CrossRef]
  9. Mohtar, R.; Peng, J.; Daher, B.; He, X. Challenges and future actions on water-economy-ecology-society nexus under changing environment. River 2024, 3, 337–340. [Google Scholar] [CrossRef]
  10. Zhang, Y.; Zuo, Q.; Wu, Q.; Han, C.; Tao, J. An integrated diagnostic framework for water resource spatial equilibrium considering water-economy-ecology nexus. J. Clean. Prod. 2023, 414, 137592. [Google Scholar] [CrossRef]
  11. Endo, A.; Yamada, M.; Miyashita, Y.; Sugimoto, R.; Ishii, A.; Nishijima, J.; Fujii, M.; Kato, T.; Hamamoto, H.; Kimura, M.; et al. Dynamics of water–energy–food nexus methodology, methods, and tools. Curr. Opin. Environ. Sci. Health 2020, 13, 46–60. [Google Scholar] [CrossRef]
  12. Eriksson, N.; Avellán, T.; Teutschbein, C.; Blicharska, M. Towards a common understanding of water-energy-food nexus research: A view of the European nexus community and beyond. Sci. Total Environ. 2025, 967, 178775. [Google Scholar] [CrossRef]
  13. Alves, L.M.; Ometto, J.P.; Arcoverde, G.F.; Branco, E.A.; Toledo, P. Water-Energy-Food Nexus Under Climate Change: Analyzing Different Regional Socio-ecological Contexts in Brazil. In Water-Energy-Food Nexus and Climate Change in Cities; Lazaro, L.L.B., Giatti, L.L., Valente de Macedo, L.S., Puppim de Oliveira, J.A., Eds.; Sustainable Development Goals Series; Springer: Cham, Switzerland, 2022. [Google Scholar] [CrossRef]
  14. Moreira, F.A.; Dalla Fontana, M.; Malheiros, T.F.; Di Giulio, G.M. The Water-Energy-Food Nexus: What the Brazilian Research has to Say; USP School of Public Health: São Paulo, Brazil, 2022; 291p. [Google Scholar]
  15. Von Sperling, E. Hydropower in Brazil: Overview of positive and negative environmental aspects. Energy Procedia 2012, 18, 110–118. [Google Scholar] [CrossRef]
  16. Arcoverde, G.F.B.; Menezes, J.A.; Paz, M.G.A.; Barros, J.D.; Guidolini, J.F.; Branco, E.A.; Andrade, P.R.; Pulice, S.M.P.; Ometto, J.P.H.B. Sustainability assessment of Cerrado and Caatinga biomes in Brazil: A proposal for collaborative index construction in the context of the 2030 Agenda and the Water-Energy-Food Nexus. Front. Phys. 2023, 10, 1060182. [Google Scholar] [CrossRef]
  17. Haddad, E.A.; Araújo, I.F.; Feltran-Barbieri, R.; Perobelli, F.S.; Rocha, A.; Sass, K.S.; Nobre, C.A. Economic drivers of deforestation in the Brazilian Legal Amazon. Nat. Sustain. 2024, 7, 1141–1148. [Google Scholar] [CrossRef]
  18. Milhorance, C.; Bursztyn, M. Climate adaptation and policy conflicts in the Brazilian Amazon: Prospects for a Nexus+ approach. Clim. Chang. 2019, 155, 215–236. [Google Scholar] [CrossRef]
  19. Mansur, A.V.; Brondízio, E.S.; Roy, S.; Hetrick, S.; Vogt, N.D.; Newton, A. An assessment of urban vulnerability in the Amazon Delta and Estuary: A multi-criterion index of flood exposure, socio-economic conditions and infrastructure. Sustain. Sci. 2016, 11, 625–643. [Google Scholar] [CrossRef]
  20. Souza-Filho, P.W.M.; Lessa, G.C.; Cohen, M.C.L.; Costa, F.R.; Lara, R.J. The Subsiding Macrotidal Barrier Estuarine System of the Eastern Amazon Coast, Northern Brazil. In Geology and Geomorphology of Holocene Coastal Barriers of Brazil; Lecture Notes in Earth Sciences; Springer: Berlin/Heidelberg, Germany, 2009; Volume 107. [Google Scholar] [CrossRef]
  21. Timko, J.; Le Billon, P.; Zerriffi, H.; Honey-Rosés, J.; La Roche, I.; Gaston, C.; Sunderland, T.; Kozak, R.A. A policy nexus approach to forests and the SDGs: Tradeoffs and synergies. Curr. Opin. Environ. Sustain. 2018, 34, 7–12. [Google Scholar] [CrossRef]
  22. Cohn, A.S.; Mosnier, A.; Havlík, P.; Valin, H.; Herrero, M.; Schmid, E.; O’Hare, M.; Obersteiner, M. Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation. Proc. Natl. Acad. Sci. USA 2014, 111, 7236–7241. [Google Scholar] [CrossRef]
  23. Larsen, R.K.; Osbeck, M.; Dawkins, E.; Tuhkanen, H.; Nguyen, H.; Nugroho, A.; Gardner, T.A.; Wolvekamp, P. Hybrid governance in agricultural commodity chains: Insights from implementation of ‘No Deforestation, No Peat, No Exploitation’ (NDPE) policies in the oil palm industry. J. Clean. Prod. 2018, 183, 544–554. [Google Scholar] [CrossRef]
  24. Rocha, Y.A.D.S.; Lima, A.M.M.D.; Silva, C.M.S.E.; Franco, V.D.S.; Raiol, L.L.; Oliveira, I.S.D.; Dias, M.L.N.; Beltrão Júnior, P.R.E. Hydro-meteorological dynamics of rainfall erosivity risk in the Amazon River Delta-Estuary. J. Water Clim. Chang. 2025, 16, 1673–1694. [Google Scholar] [CrossRef]
  25. Abreu, W.L.D.; Rocha, G.D.M.; Flores, M.D.S.A.; Ribeiro, É.R.F.; Tamasauskas, C.E.P.; Gass, S.L.B. Zoneamento geoambiental a partir das unidades de conservação: Subsídios para a gestão integrada da zona costeira paraense-Brasil. Rev. Bras. De Geogr. Física 2020, 13, 3042–3059. [Google Scholar] [CrossRef]
  26. Cristiano, E.; Deidda, R.; Viola, F. The role of green roofs in urban water-energy-food-ecosystem nexus: A review. Sci. Total Environ. 2021, 756, 143876. [Google Scholar] [CrossRef]
  27. Asuero, A.G.; Sayago, A.; González, A.G. The Correlation Coefficient: An Overview. Crit. Rev. Anal. Chem. 2006, 36, 41–59. [Google Scholar] [CrossRef]
  28. Dahlet, L.; Barboza, R.S.L.; Van Putten, I.E.; Akpan, A.; Siriwardane-de Zoysa, R.; Glaser, M. Perceptions of governance and access in artisanal marine fisheries in northern Brazil. Ecol. Soc. 2025, 30, 1–30. [Google Scholar] [CrossRef]
  29. Krause, G.; Glaser, M. Co-evolving geomorphical and socio-economic dynamics in a coastal fishing village of the Bragança region (Pará, North Brazil). Ocean Coast. Manag. 2003, 46, 859–874. [Google Scholar] [CrossRef]
  30. Tanure, T.M.P.; Miyajima, D.N.; Magalhães, A.S.; Domingues, E.P.; Carvalho, T.S. The impacts of climate change on agricultural production, land use and economy of the legal amazon region between 2030 and 2049. Economia 2020, 21, 73–90. [Google Scholar] [CrossRef]
  31. Gama-Rodrigues, A.C.; Müller, M.W.; Gama-Rodrigues, E.F.; Mendes, F.A.T. Cacao-based agroforestry systems in the Atlantic Forest and Amazon Biomes: An ecoregional analysis of land use. Agric. Syst. 2021, 194, 103270. [Google Scholar] [CrossRef]
  32. Dutra, V.A.B.; Vancine, M.H.; Lima, A.M.M.; Toledo, P.M. Dinâmica da paisagem e fragmentação de ecossistemas em três bacias hidrográficas na Amazônia Oriental entre 1985 e 2019. Rev. Bras. De Geogr. Física 2023, 16, 936–949. [Google Scholar] [CrossRef]
  33. Costa, F.A.; Assad, E.D.; Bebbington, D.H.; Brondizio, E.S.; Fearnside, P.M.; Garrett, R.; Hecht, S.; Heilpern, S.; Mcgrath, D.; Oliveira, G.; et al. Complex, diverse and changing agribusiness and livelihood systems in the Amazon. Acta Amaz. 2024, 54, e54es22096. [Google Scholar] [CrossRef]
  34. Borges, M.D.M.R.; Sousa, L.M.; Ribas, G.G. Sustainable Production Systems in the Brazilian Amazon: A Systematic Review. Sustainability 2025, 17, 4745. [Google Scholar] [CrossRef]
  35. Sanches Ferreira, N.; Meiguins de Lima, A.M.; Cardoso Gomes, D.J. Vulnerabilidade das áreas úmidas e influência sazonal da precipitação pluviométrica na Amazônia Oriental. GEOFRONTER 2023, 9, 1–23. [Google Scholar] [CrossRef]
  36. Nahum, J.S.; Santos, L.S.; Santos, C.B. Formação da dendeicultura na Amazônia paraense. Mercator 2020, 19, e19007. [Google Scholar] [CrossRef]
  37. Vale, J.R.B.; Silva Leite, T.V.; Silva, E.L.S.; Santos, J.F. Análise espacial das unidades de paisagem da Reserva Extrativista Marinha Mocapajuba, zona costeira do Nordeste Paraense. Rev. Cerrados 2018, 16, 153–173. [Google Scholar] [CrossRef]
  38. Passos, P.H.S.; Coelho, M.C.M.; Ribeiro, S.C.A.; Coelho, J.L.; Almeida, M.C.; Vergara Filho, W.L. Manejo do Caranguejo-Uçá: O Método de Embalagem Para o Transporte Sustentável; IDSM: Belém, Brazil, 2020; 60p. [Google Scholar]
  39. Simard, M.; Fatoyinbo, L.; Smetanka, C.; Rivera-Monroy, V.H.; Castañeda-Moya, E.; Thomas, N.; Van der Stocken, T. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 2019, 12, 40–45. [Google Scholar] [CrossRef]
  40. Mello, K.; Randhir, T.O.; Valente, R.A.; Vettorazzi, C.A. Riparian restoration for protecting water quality in tropical agricultural watersheds. Ecol. Eng. 2017, 108, 514–524. [Google Scholar] [CrossRef]
  41. Hogeboom, R.J.; Borsje, B.W.; Deribe, M.M.; Van Der Meer, F.D.; Mehvar, S.; Meyer, M.A.; Özerol, G.; Hoekstra, A.Y.; Nelson, A.D. Resilience Meets the Water–Energy–Food Nexus: Mapping the research landscape. Front. Environ. Sci. 2021, 9, 630395. [Google Scholar] [CrossRef]
  42. Cardozo, E.G.; Celentano, D.; Rousseau, G.X.; Silva, H.R.E.; Muchavisoy, H.M.; Gehring, C. Agroforestry systems recover tree carbon stock faster than natural succession in Eastern Amazon, Brazil. Agrofor. Syst. 2022, 96, 941–956. [Google Scholar] [CrossRef]
  43. Souza, F.E.D.; Santos Oliveira, J.D.N.; Santos, C.R.C.; Oliveira Ferreira, E.V.; Silva, R.T.L.; Paula, M.T.; Alves, J.D.N.; Oliveira, J.S.R.; Rodrigues, J.I.M.; Martins, W.B.R. Physical and chemical soil quality and litter stock in agroforestry systems in the Eastern Amazonia. Agric. Ecosyst. Environ. 2025, 382, 109479. [Google Scholar] [CrossRef]
  44. Filgueiras, T.C.G.M.; Jacob, V.; Miranda, C.D.S.C.; Gonçalves, N.V.; Conceição Santos, A.; Nahum, J.S. As trabalhadoras do dendê: Uma análise sobre o trabalho da mulher na cadeia produtiva do dendê. Rev. Caribeña De Cienc. Soc. 2024, 13, e3685. [Google Scholar] [CrossRef]
  45. Homma, A. A terceira natureza da Amazônia. Rev. Parana. De Desenvolv. 2017, 38, 27–42. [Google Scholar]
  46. Homma, A.K.O.; Menezes, A.J.E.A.; Santana, C.A.M.; Navarro, Z. The more sustainable development of the Amazon region: Between (many) controversies and the possible path. COLÓQUIO—Rev. Do Desenvolv. Reg. 2020, 17, 1–27. [Google Scholar] [CrossRef]
  47. Melo, P.A.; Passos, M.M. Características e distribuição das formações de Mauritia flexuosa L. F. na microrregião do salgado paraense. In A Geografia do Pará em Múltiplas Perspectivas: Natureza, Urbano, Rural e Cultura, 1st ed.; Hespanhol, R.A.M., Melazzo, E.S., Eds.; Editora ANAP: Tupã, Brazil, 2017; pp. 75–88. [Google Scholar]
  48. Alves, R.J.M.; Gonçalves, W.G.; Gonçalves, J.P.; Nunes, G.D.L.; Silva, E.R.M.; Maia, J.S.; Adami, M.; Narvaes, I.S. Análise do uso e ocupação do solo em Marapanim-PA a partir de dados do projeto TerraClass. Holos 2018, 1, 81–90. [Google Scholar]
  49. Siqueira-Gay, J.; Yanai, A.M.; Lessmann, J.; Pessôa, A.C.M.; Borja, D.; Canova, M.; Borges, R.C. Pathways to positive scenarios for the Amazon forest in Pará state, Brazil. Biota Neotrop. 2020, 20, e20190905. [Google Scholar] [CrossRef]
  50. Bhave, A.G.; Conway, D.; Dessai, S.; Stainforth, D.A. Water resource planning under future climate and socioeconomic uncertainty in the Cauvery River Basin in Karnataka, India. Water Resour. Res. 2018, 54, 708–728. [Google Scholar] [CrossRef] [PubMed]
  51. Yung, L.; Louder, E.; Gallagher, L.A.; Jones, K.; Wyborn, C. How methods for navigating uncertainty connect science and policy at the water-energy-food nexus. Front. Environ. Sci. 2019, 7, 37. [Google Scholar] [CrossRef]
  52. Chen, J.; Chen, H.; Smith, T.F.; Rangel-Buitrago, N. Analyzing the impact and evolution of ocean & coastal management: 30 years in retrospect. Ocean Coast. Manag. 2023, 242, 106697. [Google Scholar] [CrossRef]
  53. Cruz-Ramírez, C.J.; Chávez, V.; Silva, R.; Muñoz-Perez, J.J.; Rivera-Arriaga, E. Coastal management: A review of key elements for vulnerability assessment. J. Mar. Sci. Eng. 2024, 12, 386. [Google Scholar] [CrossRef]
  54. Guimaraes, M.M.; Pedrozo, E.A. Nexo água-energia-alimentos e floresta: Integração necessária. Rev. De Adm. E Negócios Da Amaz. 2021, 13, 195–211. [Google Scholar] [CrossRef]
  55. Pezzuti, C.B.; Zuanon, J.; Ribas, C.; Wittmann, F.; d’Horta, F.; Sawakuchi, A.O.; Lopes, P.F.M.; Carneiro, C.C.; Akama, A.; Garzón, B.R.; et al. Belo Monte through the Food-Water-Energy Nexus: The disruption of a unique socioecological system on the Xingu River. In The Water-Energy-Food Nexus: What the Brazilian Research has to Say; Chapter 1; Moreira, F.A., Dalla Fontana, M., Malheiros, T.F., Di Giulio, G.M., Eds.; USP School of Public Health: São Paulo, Brazil, 2022; pp. 22–40. [Google Scholar]
  56. Moreira, R.M.; Paranhos Filho, A.C.; Sieber, S. A novel Water-Food-Energy nexus approach integrating Analytic Hierarchy Process and Google Earth Engine using global datasets for photovoltaic energy generation. Renew. Energy Focus 2022, 43, 210–227. [Google Scholar] [CrossRef]
  57. Bieber, N.; Ker, J.H.; Wang, X.; Triantafyllidis, C.; van Dam, K.H.; Koppelaar, R.H.; Shah, N. Sustainable planning of the energy-water-food nexus using decision making tools. Energy Policy 2018, 113, 584–607. [Google Scholar] [CrossRef]
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Figure 1. The Guamá Integration Region (IR): (A) Boundaries and municipal headquarters; (B) In the context of South America; (C) WFE Nexus and its connection with the Sustainable Development Goals (SDGs)/2030 Agenda. Source: Authors (2025).
Figure 1. The Guamá Integration Region (IR): (A) Boundaries and municipal headquarters; (B) In the context of South America; (C) WFE Nexus and its connection with the Sustainable Development Goals (SDGs)/2030 Agenda. Source: Authors (2025).
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Figure 2. Flowchart of methodological procedures. Source: Adapted from [1,26].
Figure 2. Flowchart of methodological procedures. Source: Adapted from [1,26].
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Figure 3. Variables from the analysis matrix according to the 12 study municipalities: (a) Water; (b) Food; (c) Ecosystem; (d) Socioeconomic aspects. Source: Authors (2025).
Figure 3. Variables from the analysis matrix according to the 12 study municipalities: (a) Water; (b) Food; (c) Ecosystem; (d) Socioeconomic aspects. Source: Authors (2025).
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Figure 4. Statistically significant associations between selected Nexus variables. Legend: Pop = population change (2000–2022) of municipalities (%); W1 = urban water supply (%); W2 = adequate sewage system (%); W3 = proportion of cisterns on rural properties (%); W4 = proportion of rural properties with irrigation (%); F1 = number of agricultural establishments (n); F2 = average area of agricultural establishments (ha); F3 = proportion of rural properties with temporary crops (%); F4 = proportion of rural properties with permanent crops (%); F5 = proportion of rural properties with livestock (%); F6 = proportion of rural properties with family farming (%); F7 = pasture area without degradation proportional to the area of the municipality (%); F8 = pasture area with some degree of degradation proportional to the area of the municipality (%); E1 = area of accumulated deforestation (1988–2007) proportional to the area of the municipality (%); E2 = area of increased deforestation (2008–2021) proportional to the area of the municipality (%); E3 = rural properties eligible for registration with Rural Environmental Registry (RER) records (%); E4 = water Permanent Preservation Area (PPA) proportional to the area of the municipality (%); E5 = Legal Reserve (LR) area proportional to the area of the municipality (%); E6 = Conservation Unit area proportional to the area of the municipality (%). Source: Authors (2025).
Figure 4. Statistically significant associations between selected Nexus variables. Legend: Pop = population change (2000–2022) of municipalities (%); W1 = urban water supply (%); W2 = adequate sewage system (%); W3 = proportion of cisterns on rural properties (%); W4 = proportion of rural properties with irrigation (%); F1 = number of agricultural establishments (n); F2 = average area of agricultural establishments (ha); F3 = proportion of rural properties with temporary crops (%); F4 = proportion of rural properties with permanent crops (%); F5 = proportion of rural properties with livestock (%); F6 = proportion of rural properties with family farming (%); F7 = pasture area without degradation proportional to the area of the municipality (%); F8 = pasture area with some degree of degradation proportional to the area of the municipality (%); E1 = area of accumulated deforestation (1988–2007) proportional to the area of the municipality (%); E2 = area of increased deforestation (2008–2021) proportional to the area of the municipality (%); E3 = rural properties eligible for registration with Rural Environmental Registry (RER) records (%); E4 = water Permanent Preservation Area (PPA) proportional to the area of the municipality (%); E5 = Legal Reserve (LR) area proportional to the area of the municipality (%); E6 = Conservation Unit area proportional to the area of the municipality (%). Source: Authors (2025).
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Figure 5. Highlights of the land use and natural resource process. (A) Human consumption proportional to total consumption; (B) Rural consumption proportional to total consumption; (C) Industrial consumption proportional to total consumption; (D) Population increase between 2000–2022; (E) Agricultural establishments; (F) Average area of agricultural establishments; (G) Conservation Unit Area proportional to the area of the municipality; (H) Water Permanent Preservation Areas (PPA) proportional to the area of the municipality; (I) Legal Reserve (LR) area proportional to the area of the municipality; (J) Rural properties eligible for registration with Rural Environmental Registry (RER) records. Source: Authors (2025).
Figure 5. Highlights of the land use and natural resource process. (A) Human consumption proportional to total consumption; (B) Rural consumption proportional to total consumption; (C) Industrial consumption proportional to total consumption; (D) Population increase between 2000–2022; (E) Agricultural establishments; (F) Average area of agricultural establishments; (G) Conservation Unit Area proportional to the area of the municipality; (H) Water Permanent Preservation Areas (PPA) proportional to the area of the municipality; (I) Legal Reserve (LR) area proportional to the area of the municipality; (J) Rural properties eligible for registration with Rural Environmental Registry (RER) records. Source: Authors (2025).
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Figure 6. The impact of WFE indicators on population distribution. Source: Authors (2025).
Figure 6. The impact of WFE indicators on population distribution. Source: Authors (2025).
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Figure 7. The impact of population distribution on the water (W), food (F) and ecosystem (E) axes. Source: Authors (2025).
Figure 7. The impact of population distribution on the water (W), food (F) and ecosystem (E) axes. Source: Authors (2025).
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Figure 8. The influence of the degree of permanent preservation area (PPA), legal reserve (LR) and nature conservation unit (NCU): Castanhal (1), Curuçá (2), Igarapé-Açu (3), Magalhães Barata (4), Maracanã (5), Marapanim (6), Santo Antônio do Tauá (7), São Caetano de Odivelas (8), São Francisco do Pará (9), São João da Ponta (10), Terra Alta (11) and Vigia (12). Source: Authors (2025).
Figure 8. The influence of the degree of permanent preservation area (PPA), legal reserve (LR) and nature conservation unit (NCU): Castanhal (1), Curuçá (2), Igarapé-Açu (3), Magalhães Barata (4), Maracanã (5), Marapanim (6), Santo Antônio do Tauá (7), São Caetano de Odivelas (8), São Francisco do Pará (9), São João da Ponta (10), Terra Alta (11) and Vigia (12). Source: Authors (2025).
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Figure 9. Trend associated with the relationships between the water, food and ecosystem axes. The color classification groups four similar behaviors. Source: Authors (2025).
Figure 9. Trend associated with the relationships between the water, food and ecosystem axes. The color classification groups four similar behaviors. Source: Authors (2025).
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Geographies 05 00073 g010
Figure 10. The Water (W), Food (F) and Ecosystems (E) Nexus. Source: Authors (2025).
Figure 10. The Water (W), Food (F) and Ecosystems (E) Nexus. Source: Authors (2025).
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Table 1. Municipal data for the Nexus WFE analysis, accounting for the SDGs of the 2030 Agenda.
Table 1. Municipal data for the Nexus WFE analysis, accounting for the SDGs of the 2030 Agenda.
Axes NexusSDGsDataSourcesYear
WaterGeographies 05 00073 i001Consumptive uses of waterANA 12022
Water supplySNIS 22020
Adequate sewage systemIBGE 32010
Cistern on rural propertiesIBGE2017
IrrigationIBGE2017
FoodGeographies 05 00073 i002Permanent crop productionIBGE, FAPESPA 42017 e 2019
Temporary crop productionIBGE, FAPESPA2017 e 2019
Livestock productionIBGE, FAPESPA2017 e 2019
Aquaculture productionSEDAP 52019
Degraded pasturesMapBiomas2020
EcosystemGeographies 05 00073 i003
Geographies 05 00073 i004		Accumulated DeforestationINPE 61988–2007
Annual Deforestation IncreaseINPE2008–2021
Percentage of Rural Environmental Registry (RER) (*)SICAR 7–SEMAS 82022
Water Permanent Preservation Areas (PPA)SICAR–SEMAS2022
Legal Reserve Area (LR) (**)SICAR–SEMAS2022
Conservation Unit Area (CUA)IDEFLOR-BIO 9 e ICMBIO 102022
Socioeconomic aspectsGeographies 05 00073 i005
Geographies 05 00073 i006		Estimated PopulationIBGE2000 e 2022
Gross Domestic Product (GDP) per capitaIBGE2019
Municipal Human Development Index (MHDI)PNUD 112010–2020
(1) National Water and Sanitation Agency; (2) National Sanitation Information System; (3) Brazilian Institute of Geography and Statistics; (4) Amazon Foundation for Support of Studies and Research; (5) State Secretariat for Agricultural and Fisheries Development; (6) National Institute for Space Research; (7) National Rural Environmental Registry System; (8) State Secretariat for Environment and Sustainability of Pará; (9) Institute for Forestry Development and Biodiversity of the State of Pará; (10) Chico Mendes Institute for Biodiversity Conservation; (11) United Nations Development Program. (*) The Rural Environmental Registry (RER) is a mandatory electronic public registry for all rural properties in Brazil. (**) The Forest Code defines a legal reserve as an area of native vegetation on a private rural property. The purpose of a legal reserve is twofold: firstly, to ensure the sustainable use of natural resources, and secondly, to protect native fauna and flora, as well as biodiversity. Source: Authors (2025).
Table 2. Significant correlations of the Ecosystem-Food Nexus and its peculiarities.
Table 2. Significant correlations of the Ecosystem-Food Nexus and its peculiarities.
rDescription
0.95As the region has undergone historical anthropization, native vegetation has been almost completely suppressed, giving way mainly to pastureland.
0.90The increase in rural property registrations in the RER reveals rural properties with land use focused on livestock activities.
0.87As the region has undergone historical anthropization, native vegetation has been almost completely suppressed, giving way mainly to pastureland.
0.83The LR area is a proxy variable for RER, explaining the positive correlation with livestock activity.
0.81The water PPA is a proxy variable for RER, explaining the positive correlation with livestock activity.
0.74The increase in rural property registrations in the RER reveals rural properties with land use focused on livestock activities.
0.70The water PPA is a proxy variable for RER, explaining the positive correlation with livestock activity.
0.66The LR area is a proxy variable for RER, explaining the positive correlation with livestock activity.
0.64
0.60The LR area is a proxy variable for RER, explaining the positive correlation with permanent crop activity.
0.59The increase in rural property registrations in the RER reveals rural properties with land use focused on livestock activities.
−0.83As they are mutually exclusive classes, the absence of NCU implies the proliferation of pasture in the anthropized region.
−0.70
Notes: Rural Environmental Registry (RER), Permanent Preservation Area (PPA), Legal Reserve (LR) and Nature Conservation Unit (NCU).
Table 3. Profile of agricultural establishments and their relevance to the WFE nexus evaluation.
Table 3. Profile of agricultural establishments and their relevance to the WFE nexus evaluation.
MunicipalityAgricultural EstablishmentsTemporary Crops (%)Permanent Crops (%)Livestock (%)Fishing and Aquaculture
(%)
(%)Average
Area (ha)
Castanhal10.8034.1051.1222.4714.060.81
Curuçá5.4812.8043.7431.754.761.06
Igarapé-Açu20.5522.9863.2221.217.150.8
Magalhães Barata4.2626.4983.456.352.272.95
Maracanã16.9810.9571.989.914.673.53
Marapanim8.8113.5378.279.663.620.22
Santo Antônio do Tauá8.5421.8039.9826.056.680.23
São Caetano de Odivelas6.0523.1350.9618.6911.821.6
São Francisco do Pará6.6351.7256.8521.7211.080
São João da Ponta1.7538.4477.3513.264.970
Terra Alta1.0120.3053.8537.506.730
Vigia9.148.7335.5633.2312.590.32
Source: Brazilian Institute of Geography and Statistics; Amazon Foundation for Support of Studies and Research; State Secretariat for Agricultural and Fisheries Development.
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Dutra, V.A.B.; Lima, A.M.M.d.; Toledo, P.M.d.; Rocha, Y.A.d.S. Water, Food and Ecosystem Nexus in the Coastal Zone of Northeast Pará, Eastern Amazon, Brazil. Geographies 2025, 5, 73. https://doi.org/10.3390/geographies5040073

AMA Style

Dutra VAB, Lima AMMd, Toledo PMd, Rocha YAdS. Water, Food and Ecosystem Nexus in the Coastal Zone of Northeast Pará, Eastern Amazon, Brazil. Geographies. 2025; 5(4):73. https://doi.org/10.3390/geographies5040073

Chicago/Turabian Style

Dutra, Vitor Abner Borges, Aline Maria Meiguins de Lima, Peter Man de Toledo, and Yuri Antonio da Silva Rocha. 2025. "Water, Food and Ecosystem Nexus in the Coastal Zone of Northeast Pará, Eastern Amazon, Brazil" Geographies 5, no. 4: 73. https://doi.org/10.3390/geographies5040073

APA Style

Dutra, V. A. B., Lima, A. M. M. d., Toledo, P. M. d., & Rocha, Y. A. d. S. (2025). Water, Food and Ecosystem Nexus in the Coastal Zone of Northeast Pará, Eastern Amazon, Brazil. Geographies, 5(4), 73. https://doi.org/10.3390/geographies5040073

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