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Sustainability in the Brazilian pampa biome: A composite index to integrate beef production, social equity, and ecosystem conservation
Ecological Indicators 98 (2019) 317–326
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Sustainability in the Brazilian pampa biome: A composite index to integrate beef production, social equity, and ecosystem conservation David Santos de Freitasa, a b
⁎,1
T
, Tamara Esteves de Oliveirab,2, Juliano Morales de Oliveiraa
Laboratory of Plant Ecology, Universidade do Vale do Rio dos Sinos (UNISINOS), Brazil Center for Studies and Research in Agribusiness (CEPAN/UFRGS), Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Grazing ecosystems Livestock production Natural grasslands Sustainable analysis Sustainable tension index
The natural grasslands of the Pampa biome (PB) are maintained by moderate grazing of large ruminants that traditionally occupy the area, offering a place where food production and natural habitat conservation may coexist. The conversion of natural grasslands (NG) into other land covers, and the consequent modifications of natural landscapes, highlights the need for better management strategies to maintain grasslands in good natural conditions. However, there are many factors that influence sustainability, and to understand the reality of each location and its needs to achieve a sustainable status are a challenge for public managers and for entrepreneurs. Therefore, it is important to evaluate where sustainable beef production is more indicated and which criteria demands more attention for a better sustainable scenario. Hence, this study proposed a composite index to evaluate areas of tension based on the concepts of sustainability and efficient beef cattle farming for NG in the Brazilian PB. The Sustainable Tension Index (STI) is a composite index of descriptor variables that represent the economic, social, and environmental dimensions. The selection of this variables was based on a literature review and governmental documents. For each variable a weight was calculated by a principal components analysis. Each standardized variable was multiplied by the value of its weight, and the results were summed for each municipality to obtain the STI. The STI was classified into intervals by dividing the amplitude of the values by five from very low to very high tensions. Municipalities with low tensions across the three dimensions were considered closer to sustainable development. To evaluate the differences among mesoregions, a PERMANOVA was applied in R software. The STI was able to describe the localities regarding sustainable beef production variables and identified the critical factors in each municipality. Most of Pampa's municipalities presented intermediaries STI, but only three presented low tension in all the dimension, emphasizing the complexity of this issue. The southwest and southeast mesoregions were more apt to sustainable beef production, and the metropolitan mesoregion was the least indicated for this activity. Variables associated to education, income and land use had higher weight for the index. The proposed STI is a tool for both farm managers and political representants to support important decisions and its application presented an optimistic reality regarding the Brazilian PB, which if well managed and supported, has a great potential for sustainable beef cattle farming.
1. Introduction Concerns regarding sustainable food production have arisen relatively recently and the associated problems have been underestimated in Brazilian government policies (Gianezini et al., 2014; Maiello et al., 2015). In this study, as in many others, sustainability is understood as an ideal scenario that improves productive processes, ensuring a balance between economic development, social well-being, and ecological conservation (Moldan et al., 2012; Holden et al., 2014; 2016).
However, integrating these dimensions is a complex task when planning sustainable policies and strategic actions, which highlights the need for a tool to measure and correlate the factors in these dimensions. The inability to balance between these three dimensions impairs sustainable agricultural practices in many developing countries, including Brazil, a major food producer with high biodiversity (Bettencourt and Kaur, 2011). For instance, cattle farming, an important socioeconomic activity in Brazil, has been greatly criticized for its environmental impact, particularly in terms of deforestation and the
⁎
Corresponding author at: Campus São Leopoldo/RS. Av. Unisinos, 950, Bairro Cristo Rei, CEP: 93.022-750, Brazil. E-mail address: [email protected] (D.S. de Freitas). 1 Scholarship holder by the institutions CAPES/PROSUP. 2 Financial support CAPES. https://doi.org/10.1016/j.ecolind.2018.10.012 Received 15 December 2017; Received in revised form 30 September 2018; Accepted 4 October 2018 1470-160X/ © 2018 Elsevier Ltd. All rights reserved.
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Fig. 1. Characterization of the research area: (a) Brazilian biomes with Rio Grande do Sul highlighted; (b) The state of Rio Grande do Sul and its mesoregions and municipalities. The mesoregions of Rio Grande do Sul are (1) Southwest (SW), (2) Southeast (SE), (3) Metropolitan Region of Porto Alegre (MP), (4) Eastern Central (EC), (5) Western Central (WC), (6) Northwest (NW), and (7) Northeast (NE). Since the municipality of Pantâno Grande is the only one in region 4, it is incorporated in region 3.
in each location, to ensure the overall well-being of the local society. Furthermore, there are many factors that influence sustainability, and understanding the reality of each location and its needs to achieve a sustainable status are a challenge for public managers, researchers and for entrepreneurs whom which to invest in these projects. Hence, fundamental research is imperative to understand the measurement and classification of the sustainability of productive processes in specific locations (Pires et al., 2014; Salvati and Carlucci, 2014; Maiello et al., 2015). Tension indexes can be used to evaluate regions’ difficulties in this regard but most previous studies have considered only one factor or have not applied these indexes to a specific production process (Gleeson et al., 2012; Mueller et al., 2012). The indexes are metrics that express the depletion of resources in a region such that low tension indicates high sustainability (Gleeson et al., 2012). For this purpose, combining variables into complex indicators has proved to be an efficient method (Salvati and Carlucci, 2014) that allows a detailed characterization since it enables the simultaneous consideration of economic, social, and environmental variables (Moldan et al., 2012). Such analyses can guide regional management and enable the development of appropriate public policies and strategic actions (Yang et al., 2014; Maiello et al., 2015). Moreover, since the PB has experienced a drastic drop in NG areas, which are being replaced by more profitable crops (De Oliveira et al., 2017), it is extremely relevant to understand ways to make cattle more lucrative while conserving local biodiversity and identify the constraints and opportunities specific to each locality (Godfray et al., 2010). Thus, this study proposes a composite index to identify areas of tension based on the concept of sustainability related to beef cattle production. This study also characterizes municipalities based on factors that require specific actions for sustainable development according to the sustainability dimensions.
emission of greenhouse gases (GHGs) (Steinfeld et al., 2006; Ruviaro et al., 2015). This issue has generated conflicts between environmentalists and stakeholders in the beef supply chain, contributing to the complexity of developing sustainable strategies. Nonetheless, in theory, the Brazilian Pampa biome (PB), a region where cattle farming contribute to the conservation of natural grasslands (NG), is a setting where sustainable beef cattle production can facilitate the biodiversity conservation process (BRASIL, 2005; Carvalho and Batello, 2009; Overbeck et al., 2015; De Oliveira et al., 2017). The original vegetation over large areas of this biome is natural grassland, which harbors a unique biodiversity and a close historical association with livestock production since the period of European colonization (Overbeck et al., 2013). In this region, the moderate grazing of ruminants delays ecological succession and maintains the physiognomy and diversity of the grassland (Fedrigo et al., 2017). Ruminant grazing can also mitigate GHG emissions if combined with appropriate management practices (Neely et al., 2009; Vasconcelos et al., 2018), as overgrazing compromises areas of this biome (Carvalho and Batello, 2009). In the PB, this perspective is of great importance not only because it allows for ecological beef production but also because most grassland is on private property and grazed by cattle for productive purposes (Marchand, 2014). The state of Rio Grande do Sul has a cattle herd of about 13 million, mainly located in the PB region, which contributed more than US $8.6 billion to the state’s GDP in 2015 (Informativo NESPro and Embrapa Pecuária Sul, 2018; SCPRS, 2015). Moreover, beef has been produced in the PB for hundreds of years, and this tradition shaped the cultural aspects associated with the local lifestyle (Gaúcho) (De oliveira and Freitas, 2017). Given this background, public and private actions must work together toward conservation and maintain local know-how regarding traditional production. Nevertheless, sustainability is rarely seen as a priority because individuals tend to prioritize issues of immediate personal risk (e.g., hunger) (Weber, 2006). Therefore, despite the importance of private property in the PB, conservation policies should be based on strategic tools, such as biodiversity surveys, geographical landscape analyses, and indicators that can support the identification of the specific needs and challenges
2. Materials and methods 2.1. Research area The Rio de la Plata grasslands are located in Argentina, Brazil, 318
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Paraguay, and Uruguay (Soriano, 1991). The northern part of this region is the Brazilian PB, which occupies 176,496 km2 (i.e., 2.07% of Brazil’s land area) and covers the southern half of the state of Rio Grande do Sul (RS) (i.e., 63% of the state’s land area). RS is composed of 168 municipalities, 94 of which are completely inside the PB (Fig. 1). This PB is characterized by the dominance of subtropical grassland. These formations are natural remnants of the cold and dry glacial periods of the Pleistocene (Behling et al., 2009. In the Holocene, the vegetation was maintained by grazing large prehistoric mammals; this function is currently performed by domestic herds of cattle or horses, which were introduced in the south of the continent in the 18th century (Boldrini et al., 2015).
Table 1 Variables that compose the Sustainable Tension Index according to the dimensions and forms of contribution of each variable to the index.
Data were obtained from government databases and Brazilian research institutions. The main source of data was the Brazilian Institute of Geography and Statistics (IBGE), particularly the most recent Agricultural (IBGE, 2006) and Demographic Census (IBGE, 2010). The Statistical Foundation of the State of Rio Grande do Sul (FEE) provided some additional data. The sampling was at the municipality level, because it is the lowest level of territorial coverage with databases available for many variables of interest (Godfray et al., 2010) and allows the portrayal of local characteristics and the extrapolation of standards for the mesoregions. This analysis considered the 94 municipalities whose territories are completely inside the political delimitations of the PB (IBGE, 2004). The variables of interest were grouped into three dimensions based on the criteria for sustainable development, as shown in Table 1 (Brundtland and Khalid, 1987; Holden et al., 2014). These variables were selected based on a literature review and were considered in accordance with previous research and governmental legal documents considering the conservation of NG in the PB. Variables that describe the efficiency of cattle farming, trade relations, and economic growth (17 variables) were included in the economic dimension. Those related to the quality of life, social justice, education, basic sanitation, and social inequality of the residents (22 variables) were included in the social dimension. Finally, variables associated with the conditions of the NGs and natural forests within the PB landscape as well as degraded areas (24 variables) were included in the environmental dimension. Within each dimension, variables directly related to beef cattle production were also considered (see Table 1).
2.3.1. Standardization and normalization of variables The variables were standardized so that variables of different magnitudes could be compared (OECD, 2008). Negative or positive contributions to the index were determined according to previous publications (Salvati and Carlucci, 2014) and reference studies on the PB (Carvalho and Batello, 2009; Pillar et al., 2009; Gautreau, 2014; De Oliveira et al., 2017) (Table 1). Eq. (1) was used for variables that positively contributed to the index, and Eq. (2) was used for those that negatively contributed to the index. ' ' (x max j − x i, j )
z (x i, j ) = 1 −
(1)
' ' (x max j − x i, j ) ' ' (x max j − x minj )
Source
Economic dimension (17 variables) General indicators GDP per capita Income per capita Theil index Total exports Economically inactive population Total expenses Taxes collected
+ + − + − − +
IBGE IBGE IBGE FEE IBGE FEE IBGE
+ + + + + + − +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
+
IBGE
Social dimension (22 variables) General Indicators % population without basic sanitation Illiteracy rate Child mortality rate Life expectancy % of unemployed persons Rate of relationship connections with employers Gini index Total crime Demographic density Rate of rural and urban population % illiterate voters % population with water supply % garbage collected
− − − + − + − − − + − + +
IBGE IBGE IBGE IBGE IBGE FEE IBGE FEE IBGE IBGE FEE IBGE IBGE
Specific beef cattle indicators % of managers who cannot read and write % of farms in which the farmer is associated % of farms in which the farmer owns the land % of farms in which the farmer lives on the farm Proportion of women who manage farms % of managers with higher education % of area allocated to family farming Number of farms
− + + + + + + +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
− − − + − − + + − +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
− − − − + +
IBGE IBGE FEE FEE IBGE IBGE
+ + + − + −
IBGE IBGE IBGE IBGE IBGE IBGE
Environmental dimension (23 variables) General indicators Altitude Temporary crops Crops - area planted with forage for cutting Natural grasslands Planted grasslands–degraded Planted grasslands–in good condition Natural forests – for preservation or legal reserve Other natural forests Planted forests Agroforestry systems - area planted with forest species also used for crops and grazing by animals Degraded lands (eroded, desertified, salinized, etc.) Unsuitable land for agriculture or livestock (sand, quarries, etc.) Municipal pollution potential index Vehicle rate of residents % of farms engaged in organic agriculture Area of the municipality
2.3. Construction of the tension indexes
' ' (x max j − x minj )
Sign
Specific beef cattle indicators % of livestock and breeding stock farms using technical guidance Livestock GDP % aggregated value of the municipality % contribution of cattle farming to state GDP % of farms that control diseases and/or parasites in cattle % of total investments in 2006 invested in farms % of farms with debt Number of agricultural farms with production in the year of 2006 (%) Offtake
2.2. Data collection
z (x i, j ) =
Dimensions and variables
Specific beef cattle indicators % of agricultural farms that rotate pastures (units) % of farms that use soil preparation % of farms that perform crop rotation % of farms that carry out burnings % of farms that perform protection and/or conservation of slopes % of farms that use fertilization and do not need to
(2)
In the equations: z(xi,j) = observed value of variable i standardized in sample unit j x’i,j = value of variable i for sample unit (municipality) j x’max,j = maximum value of the variable’s distribution across
(continued on next page) 319
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3. Results
Table 1 (continued) Dimensions and variables
Sign
Source
% of farms that use pesticides and do not need to % of farms with more than 50 cattle with confined animals
− −
IBGE IBGE
The STI was calculated to demonstrate which municipalities present bigger challenges regarding sustainable livestock production and to indicate the specific issues to be addressed in each locality with respect to economic development, social welfare, and environmental conservation. In this analysis, higher tension represents lower sustainability in a given municipality. All of the variables contributed to the composite index with weights based on the variables’ correlations with the PCA axes. For a given variable, a higher correlation implied a higher weight.
municipalities x’min,j = minimum value of the variable’s distribution across municipalities. 2.3.1.1. Calculating variable weights. The weight of each variable was calculated according to Eq. (3) based on principal components analysis (PCA) according to the Pearson correlation matrix between the standard variables:
ωi =
m ∑k = 1 |(αi, m × βm)| n m ∑ j = 1 ∑k = 1 |(αi, m × βm)|
3.1. Description of component analysis and weights of the variables PCA was performed with 61 variables for the 94 municipalities that belong to the PB and resulted in six variation axes with eigenvalues above two and an overall explanatory power of about 50%, as shown in Table 2. Component 1 explained 14.4% of data variation and was mainly positively related to economic factors such as population income, expenses, and taxes collected, but it was negatively related to the contribution of cattle farms to the state’s GDP. Social factors were negatively associated with an illiterate population and basic sanitation. The municipalities showed a gradient such that the highest association to the axis was in the metropolitan region, followed by regions with significant cattle farming, and, finally, by those with precarious sanitation and financial conditions. Of the three dimensions evaluated, cattle farming and farm financials were the main variables captured by component 2, which explained 12.4% of data variation. Along this axis, there was a positive association with cattle farming, NG, and municipalities’ areas and an inverse association with variables regarding family farming and soil preparation. Municipalities with the largest grassland areas and more productive farms had greater associations with this axis, were mainly located in the southwest region, and were less associated with the metropolitan region. Component 3 explained 6.8% of data variation, and the highest correlations were observed within the economic dimension (e.g., municipal expenses and crime rates). The highest correlations in component 4, which explained 6.1% of data variation, were observed in land use, particularly in areas with temporary crops, which were inversely associated with natural and planted forests. Component 5, which explained 4.8% of data variation, mainly captured the social relationships of farmers, whereas component 6, which explained 4.7% of data variation, showed the associations between land use and the environmental features of farms. Calculating the weights of the variables allowed the contributions of each variable and each dimension to be analyzed, excluding collinearity factors. In general, the environmental variables constituted the most weight in the index (39%), followed by the economic (31%) and social variables (29%). Among the 20 variables with the largest weights, nine represented the economic dimension (cattle farming GDP, contribution of cattle farming to state GDP, municipalities with debt, Theil Index, investments made, disease control, economically inactive population, total municipal expenses, and taxes collected), three were social (crime rate, Gini index, and number of illiterate managers), and eight were environmental (% of farms that conduct rotation of pastures, % of farms that perform crop rotation, area of municipality, % of area with natural forests, % of area with NG, % of area with temporary crops, and number of farms).
(3)
In this equation: ωi = weight of variable i α = correlation of variable i with axis m β = variation explained by axis m. This formula allows the contribution of each variable to the index to be observed while eliminating problems associated with collinearity and, thus, avoiding an overestimation of the index. The selected axes had an eigenvalue above two, and the variables had a total explanatory power above 40% (OECD, 2008). 2.3.2. Index construction Each standardized variable was multiplied by the value of its weight, and the results were summed for each municipality to obtain the sustainable tension index (STI), as in Eq. (4): m
STIj =
∑ (xi',j × ωi') k=1
(4)
In this equation: STIj = sustainable tension index for municipality j x'i = value of variable i in municipality j ω’i = weight of variable i. Similarly, the variables for each dimension were summed to assess the following three pillars of sustainability: Economic tension (EcTI), Social tension (SocTI), and Environmental tension (EnTI) indices. 2.3.3. Categorization of STI and the municipalities The STI was classified into the following five intervals by dividing the amplitude of the values by five: very low, low, intermediate, high, and very high tensions. After this categorization, the STI values were incorporated into a shapefile of municipalities and plotted on a thematic map using ArcGis 20.0 software (ESRI, 2015). Likewise, the values of EcTI, SocTI, and EnTI were classified by the same criteria and plotted on the map. Municipalities with low or very low tension rates were considered throughout the proposed dimensions. Those with low tensions across the three dimensions were considered closer to sustainable development. To evaluate the differences among mesoregions, a PERMANOVA test was applied using the vegan package in R software (R Core Team, 2017). In all of the analyses, a < 0.05 level of significance was considered.
3.2. Sustainable tension index (STI) The descriptive analysis of the STI found an average of 0.474, a range of 0.145, and a standard deviation of 0.031, as shown in Fig. 2. 320
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Table 2 Factor loadings (axis correlations) and variable weights.
Green cells represent a positive correlation with the axis. Red cells represent a negative correlation with the axis. 321
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Fig. 2. Description of indices and municipalities with higher and lower STI values. The abbreviations in the figure are defined as Sustainable Tension Index (STI), Economic Tension Tndex (EcTI), Social Tension Index (SocTI), and Environmental Tension Index (EnTI).
with the axes, had larger weights in the index, and were relevant in their respective dimensions. Despite its relevance, the PB has been a neglected biome in Brazilian conservation policies (Overbeck et al., 2007; Pillar and Vélez, 2010), as these policies excluded the PB from conservation campaigns and public awareness strategies. Nevertheless, some organizations are currently investing time and resources in maintaining beef farming in NGs as a conservation strategy. For example, the Alianza del Pastizal organization supports rural farms and certifies those that produce cattle according to the conservation principles for the biodiversity of the PB (Marchand, 2014). Despite the efforts of the Alianza del Pastizal, however, most of its actions are local, making it difficult for the organization to impact the Brazilian PB as a whole and compromising the continuity of its beneficial strategies. However, these actions may contribute to reducing tension locally, especially with respect to environmental variables, since the practices recommended by the organization focus on maintaining the physiognomy and their heterogeneity of NGs, recovering degraded areas, and promoting sanitary animal control and hydric management for associated farms (Perera and Carriquiry, 2014). Although the STI offers an overview of the municipalities, the specific variables associated with livestock illustrate the sustainability of beef production, and its relationship with the broader municipality variables indicates the relationship between this economic practice and the location. This perspective offers a broader view for stakeholders in the beef supply chain and government managers, offering subsidies for the creation of applied strategies to promote sustainability in the PB. The understanding of these associations is also key to understanding the impact of beef production on the socioeconomic characteristics of a municipality. However, the index offers a broad overview of the situation in a given location since the variables do not represent the realities of individual production systems within farms, as presented by other studies, such as that of Van Passel et al. (2007). Hence, despite the importance of understanding the overall situation of a municipality, it is important to further analyze the conditions of farms. For instance, many NGs experience degradation and overgrazing, and, therefore, are not conservation sites even though they are deemed “natural”. Nevertheless, each farm has distinct characteristics, hindering a realistic analysis based on individual data. Moreover, the effort needed to collect this level of information may limit the feasibility of such studies. The component analysis in this study demonstrates a disturbing
The analysis identified 32 municipalities with high tension. Overall, 38 municipalities presented intermediate STI values, and 23 indicated low tension. Of the ten municipalities with the highest tension, eight were located in the metropolitan mesoregion of Porto Alegre, whereas the municipalities with the lowest tension levels were found in the southeast and southwest mesoregions of the state, as shown in Fig. 3. The average STIs differed across the state’s mesoregions. The metropolitan mesoregion had the highest STI, whereas the southwest and southeast mesoregions had the lowest STIs, revealing the longitudinal gradient of the STI, as shown in Fig. 4. Eighteen municipalities had low or very low EcTIs, 44 had SocTIs below the intermediate level, and 54 municipalities stood out for having low EnTIs, indicating that the economic dimension faced the most serious issues in the analyzed setting. Only three municipalities exhibited low or very low tension for all three indices, and these municipalities also had the lowest STIs, as shown in Fig. 5. Of the other 51 municipalities that had low or very low EnTIs, 10 had low EcTIs and 21 had below intermediate SocTIs, indicating that the three factors are not dependent on each other because the dimensions are not clearly associated. Although the sustainability scenario did not arise frequently in the analysis, the SocTI and EnTI dimensions were most frequently associated in the analyzed municipalities. Only nine municipalities had low EnTI and EcTI values, and only ten municipalities had low EcTI and SocTI values, as shown in Fig. 5.
4. Discussion Sustainable strategies in the PB have direct implications for the conservation of NG ecosystems. In recent decades, the PB has been strongly influenced by human activities, such as soy production and forestry, which have contributed to grassland fragmentation (Gautreau, 2014). Moreover, the loss of grassland areas is associated with lower richness of plant and invertebrate species (Staude et al., 2018). As expected, the percentage of forestry area was negatively related to the index, increasing the tension in a municipality. Moreover, given the increasing demand for beef, maintaining extensive beef farming through moderate grazing could prevent ecological succession and maintain NG areas (Carvalho and Batello, 2009). This notion implies that variables such as the percentage of NGs and livestock activity should reduce the STI. In fact, these variables were highly correlated 322
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Fig. 3. (a) Spatialization of the Sustainable Tension Index (STI) in the analyzed municipalities and mesoregions of Rio Grande do Sul; (b) Spatialization of the Economic Tension Index (EcTI); (c) Spacialization of the Social Tension Index (SocTI); and (d) Spatialization of the Environmental Tension Index (EnTI). Mesoregions of Rio Grande do Sul state: (1) Southwest (SW); (2) Southeast (SE); (3) Metropolitan region of Porto Alegre (MP); (4) Eastern Central (EC); (5) Western Central (WC); (6) Northwest (NW); and (7) Northeast (NE).
grassland) that is not being efficiently utilized (Barcellos et al., 2011). This, in turn, is due to low investments in cattle farming and the profiles of rural farmers in the region, who tend to be fearful of adopting new technologies (Borges et al., 2016) and have low levels of education. Nevertheless, it is difficult to identify the differences among municipalities and observe locations with higher tension. In general, a tension gradient that expands from west to east was observed, meaning that larger municipalities with more available pastures had lower tension. Regions with the lowest tension levels were traditionally cattle producers. Particularly in the southwest, high cattle production is associated with the cultural traditions of this state (De oliveira and Freitas, 2017), thus favoring the social aspect of sustainability.
result because of the inverse association between the economic and social variables. The first axis indicated an inverse relationship between economic growth and social equity. In the second axis, factors related to cattle and farm financials were directly associated with grassland areas and inversely related to variables describing the economic condition of the municipality. These results reinforce the importance of NGs in the analyzed scenario and highlight the disparity between the social quality of life and the economic development of local populations. The overall STI average was intermediate, indicating that the PB’s municipalities are in overall neutral scenarios, which does not mean that they are sustainable. This probably occurs because of the high potential for sustainable beef cattle production (e.g., areas with natural 323
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Fig. 4. Comparison of the Sustainable Tension Index (STI) across mesoregions (SQ = 0.15288, p = 0.001). The letters indicate significant differences between mesoregions according to contrast tests. EC × MP (p = 0.0301); EC × SE (p = 0.2364); EC × SW (p = 0.0002); MP × SE (p = 0.0002); MP × SW (p = 0.0001); SE × SW (p = 0.047).
allows farms to be profitable and respect Brazilian legislation (e.g., Native Vegetation Protection Law- Law n° 12.727, October 17, 2012) (BRASIL, 2012a,b). Therefore, areas with appropriate soil management tend to have low tensions by protecting other NG areas, and this variable has a greater weight in the composite index. This study also identified useful and accessible land use variables that are key factors for sustainability, particularly for natural forests and grasslands and temporary crops. Land use changes were also identified as a problem for RS (De Oliveira et al., 2017), mostly as a consequence of the increase in crop and forestry areas that reduced NG areas. Hence, altering the natural landscape and the perception that NG has no value may amplify the loss of biodiversity due to invasions of exotic species and the de-characterization of natural habitats (Gautreau, 2014). Only four municipalities presented low tension on all three dimensions, indicating that the associations between the dimensions are weak. The discussion focused on municipalities with low tensions because it was necessary to define a threshold for sustainability. One parameter that can be used as an indicator for this concept is the balance among the three dimensions (Holden et al., 2016). Therefore, the three municipalities mentioned in Fig. 5 are the only ones close to achieving sustainability. At the same time, the bearable scenario, with low tension in terms of social and environmental variables, was the most common, indicating that environmental and social characteristics are contemplated more often. The STI proposed in this study enabled the visualization of patterns in the analyzed regions and the observation of specific local realities. The application of this strategic tool identified dimensions that require interventions and revealed regional cattle farming patterns and their associations with sustainability in addition to the specific political demands of each municipality. Moreover, depending on data availability, other studies may apply this methodology to evaluate other regions and food supply chains. However, it is important that the effects of each variable are re-evaluated for each location (Godfray, 2015) because the contributions of variables may differ according to the ecosystem or biome evaluated. Nevertheless, the proposed index is limited by the provision of government data, since the set of variables presented depends on the Agricultural Census published by IBGE in Brazil, which has been overdue since 2010, indicating that many municipalities that exhibited low tension in this analysis may exhibit a different level of tension in practice today. Another possible direction for future work is to apply the same technique to another set of variables that are periodically available but are less specific. In addition, each time the index is updated, a new analysis must be performed to aggregate the previous
However, although these municipalities have lower STIs, they still have challenges to overcome, and this STI method can clarify the possibilities for public managers. For instance, Dom Pedrito, the region with the second smallest STI, exhibited low tension on the economic and social dimensions but required interventions in the environmental dimension, indicating that practices to maintain NGs and manage farms may be effective measures to decrease the region’s STI. This scenario highlighted the importance of variables associated with the financial resources obtained by a municipality, particularly from cattle farming, because the municipal GDP and municipal value added from cattle farming were associated with sustainable tension. Thus, economic well-being is fundamental for a municipality to be able to invest in actions that affect the environment and society (Weber, 2006). Likewise, specific credit lines for cattle farming with sustainable precepts could positively impact the sustainability of traditional beef cattle municipalities.3 Both economic and social inequality are also important because equality of resource distribution is a pro-sustainability factor. In addition, the criminality and illiteracy of rural managers increase sustainability tension. Moreover, the lack of perspective of the young rural population contributes to rural exodus, which, in turn, causes a lack of human resources for rural firms, which is one of the biggest problems for food production worldwide (Godfray, 2015). Regarding the environment, the impact of the rotation of crops and pastures, identified in the index, as well as that of occasional grazing exclusion, which has also been identified by other studies on livestock management, highlights the importance of the latter on grassland sustainability practices (Nabinger et al., 2009; Overbeck et al., 2015; Fedrigo et al., 2017). Nevertheless, once NGs are replaced by crops and planted grasslands, their recovery may be difficult, and this strategy should be approached with care. Hence, farmers should invest in strategic supplementation and better pasture management, such as adjusting cattle stocking rates, particularly in the winter, when they face limited biomass production and must increase grazing pressure, reducing plant covering and compacting soil. Appropriate soil management contributes to farms’ efficiency and sustainable intensification, preventing the expansion of agricultural areas (Godfray, 2015). This vertical increase in production ensures that new grassland areas are not degraded by the expansion of crop plantations and planted pastures, helps to maintain natural areas, and
3 See the ABC Plan that supports low carbon agriculture practices < http:// www.agricultura.gov.br/assuntos/sustentabilidade/plano-abc/plano-abcagricultura-de-baixa-emissao-de-carbono > .
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Fig. 5. Panel (a) shows the indices associated with the three dimensions of sustainability. The black lines indicate the reference low tension threshold. The blue lines indicate municipalities with lower STIs, and the red lines indicate the municipalities with the highest tension levels. Panel (b) shows the projection of economic and social indicators with municipalities in the fair scenario highlighted. Panel (c) shows the projection of environmental and economic indices with municipalities in the viable scenario highlighted. Panel (d) shows the projection of the environmental and social indices with municipalities in the bearable scenario highlighted. Dotted lines indicate the reference low tension threshold.
Another alternative in locations with high tension would be to disregard the investment in sustainable beef production. Efforts to improve processes, invest in the recovery of NGs, create good jobs, improve educational conditions for rural landowners, and provide subsidies for sustainable beef farming activities should be the focus of managers in localities with lower tensions since these variables had higher weights in the STI. For instance, investments to increase these municipalities’ per capita incomes, improve the basic and higher educations of rural farmers, and recover degraded grassland areas would be important actions for maintaining the STIs in these municipalities. Overall, the STI not only allows the visualization of a regional sustainable pattern but also serves as a management tool because it enables the evaluation of each municipality’s situation with all the variables analyzed. However, municipalities should invest in the collection and availability of their own data, because, at present, these studies are exclusively based on public domain sources. In addition, this analysis is static and outlines an overview based on the latest data
index so that the PCA weights are the same and the different periods can be compared.
5. Conclusion The index proposed in this study allowed the identification of tension areas based on the concept of sustainability and the precepts of efficient beef cattle farming. Moreover, the STI is an important tool for analyzing the key factors to be considered in sustainable beef farming. Thus, most of the analyzed municipalities have the capacity to improve their processes and achieve sustainable beef production. Although few municipalities had low tensions on the three dimensions contemplated, the STI revealed a general pattern of intermediate tensions. The results demonstrated that sustainable beef production is well-established in southwest and southeast mesoregions, especially Alegrete, Dom Pedrito, and Uruguaiana, whereas it requires public policy intervention and investment in the metropolitan mesoregion. 325
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available in Brazil. Hence, when new data become available, the paths traced by the municipalities toward sustainability should be analyzed.
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Available at: < http://ecoqua.ecologia.ufrgs. br/arquivos/Livros/CamposSulinos.pdf/ > . (Accessed May, 2018). Pillar, V.P., Vélez, E., 2010. Extinção dos Campos Sulinos em unidades de conservação: um fenômeno natural ou um problema ético? Natureza & Conservação 8 (1), 84–86. https://doi.org/10.4322/natcon.00801014. Pires, S.M., Fidélis, T., Ramos, T.B., 2014. Measuring and comparing local sustainable development through common indicators: constraints and achievements in practice. Cities 39, 1–9. https://doi.org/10.1016/j.cities.2014.02.003. R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: < https://www. R-project.org/ > . (Accessed May, 2018). Ruviaro, C.F., et al., 2015. Carbon footprint in different beef production systems on a southern Brazilian farm: a case study. J. Cleaner Prod. 96, 435–443. https://doi.org/ 10.1016/j.jclepro.2014.01.037. Salvati, L., Carlucci, M., 2014. A composite index of sustainable development at the local scale: Italy as a case study. Ecol. Indic. 43, 162–171. https://doi.org/10.1016/j. ecolind.2014.02.021. SCPRS – Secretaria da coordenação e planejamento do Rio Grande do Sul, 2015. Atlas Socioeconômico do Rio Grande do Sul. SCP, Porto Alegre. Soriano, A., 1991. Río de la Plata grassland. In: Coupland, R. (Ed.), Natural grasslands: Introduction and Western Hemisphere. Elsevier, Amsterdam, pp. 367–407. Staude, Ingmar R., et al., 2018. Local biodiversity erosion in south Brazilian grasslands under moderate levels of landscape habitat loss. J. Appl. Ecol. 55 (3), 1241–1251. https://doi.org/10.1111/1365-2664.13067. Steinfeld, H. et al., 2006. Livestock’s long shadow: environmental issues and options. 390 ISBN 92-5-105571-8. Available at: < http://www.fao.org/docrep/010/a0701e/ a0701e00.HTM/ > . (Accessed May, 2018). VAN Passel, Steven, et al., 2007. Measuring farm sustainability and explaining differences in sustainable efficiency. 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Acknowledgements We appreciate the fundamental financial support by Coordination for the Improvement of Higher Education Personnel (CAPES). Without it, this study would be impossible. The authors are also thankful to Luis Fernando da Costa Medina for support during the initial stages of this research. References Barcellos, J.O.J., et al., 2011. Technological innovation and entrepreneurship in animal production. Revista Brasileira de Zootecnia 40 (n. Special Supplement), 189–200 Available at: < https://goo.gl/tegf69 > . (Accessed May, 2018). Behling, H., et al., 2009. Dinâmica dos campos no sul do Brasil durante o Quaternário Tardio. In: Pillar, V.P. (Ed.), CAMPOS SULINOS - conservação e uso sustentável da biodiversidade, Instituto do Meio Ambiente e dos Recursos Naturais Renováveis; Brasília. Rede Campos Sulinos, Porto Alegre, pp. 13–25 (Accessed August, 2018). Bettencourt, L.M., Kaur, J., 2011. Evolution and structure of sustainability science. Proc. Natl. Acad. 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Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Sustainability in the Brazilian pampa biome: A composite index to integrate beef production, social equity, and ecosystem conservation David Santos de Freitasa, a b
⁎,1
T
, Tamara Esteves de Oliveirab,2, Juliano Morales de Oliveiraa
Laboratory of Plant Ecology, Universidade do Vale do Rio dos Sinos (UNISINOS), Brazil Center for Studies and Research in Agribusiness (CEPAN/UFRGS), Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Grazing ecosystems Livestock production Natural grasslands Sustainable analysis Sustainable tension index
The natural grasslands of the Pampa biome (PB) are maintained by moderate grazing of large ruminants that traditionally occupy the area, offering a place where food production and natural habitat conservation may coexist. The conversion of natural grasslands (NG) into other land covers, and the consequent modifications of natural landscapes, highlights the need for better management strategies to maintain grasslands in good natural conditions. However, there are many factors that influence sustainability, and to understand the reality of each location and its needs to achieve a sustainable status are a challenge for public managers and for entrepreneurs. Therefore, it is important to evaluate where sustainable beef production is more indicated and which criteria demands more attention for a better sustainable scenario. Hence, this study proposed a composite index to evaluate areas of tension based on the concepts of sustainability and efficient beef cattle farming for NG in the Brazilian PB. The Sustainable Tension Index (STI) is a composite index of descriptor variables that represent the economic, social, and environmental dimensions. The selection of this variables was based on a literature review and governmental documents. For each variable a weight was calculated by a principal components analysis. Each standardized variable was multiplied by the value of its weight, and the results were summed for each municipality to obtain the STI. The STI was classified into intervals by dividing the amplitude of the values by five from very low to very high tensions. Municipalities with low tensions across the three dimensions were considered closer to sustainable development. To evaluate the differences among mesoregions, a PERMANOVA was applied in R software. The STI was able to describe the localities regarding sustainable beef production variables and identified the critical factors in each municipality. Most of Pampa's municipalities presented intermediaries STI, but only three presented low tension in all the dimension, emphasizing the complexity of this issue. The southwest and southeast mesoregions were more apt to sustainable beef production, and the metropolitan mesoregion was the least indicated for this activity. Variables associated to education, income and land use had higher weight for the index. The proposed STI is a tool for both farm managers and political representants to support important decisions and its application presented an optimistic reality regarding the Brazilian PB, which if well managed and supported, has a great potential for sustainable beef cattle farming.
1. Introduction Concerns regarding sustainable food production have arisen relatively recently and the associated problems have been underestimated in Brazilian government policies (Gianezini et al., 2014; Maiello et al., 2015). In this study, as in many others, sustainability is understood as an ideal scenario that improves productive processes, ensuring a balance between economic development, social well-being, and ecological conservation (Moldan et al., 2012; Holden et al., 2014; 2016).
However, integrating these dimensions is a complex task when planning sustainable policies and strategic actions, which highlights the need for a tool to measure and correlate the factors in these dimensions. The inability to balance between these three dimensions impairs sustainable agricultural practices in many developing countries, including Brazil, a major food producer with high biodiversity (Bettencourt and Kaur, 2011). For instance, cattle farming, an important socioeconomic activity in Brazil, has been greatly criticized for its environmental impact, particularly in terms of deforestation and the
⁎
Corresponding author at: Campus São Leopoldo/RS. Av. Unisinos, 950, Bairro Cristo Rei, CEP: 93.022-750, Brazil. E-mail address: [email protected] (D.S. de Freitas). 1 Scholarship holder by the institutions CAPES/PROSUP. 2 Financial support CAPES. https://doi.org/10.1016/j.ecolind.2018.10.012 Received 15 December 2017; Received in revised form 30 September 2018; Accepted 4 October 2018 1470-160X/ © 2018 Elsevier Ltd. All rights reserved.
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Fig. 1. Characterization of the research area: (a) Brazilian biomes with Rio Grande do Sul highlighted; (b) The state of Rio Grande do Sul and its mesoregions and municipalities. The mesoregions of Rio Grande do Sul are (1) Southwest (SW), (2) Southeast (SE), (3) Metropolitan Region of Porto Alegre (MP), (4) Eastern Central (EC), (5) Western Central (WC), (6) Northwest (NW), and (7) Northeast (NE). Since the municipality of Pantâno Grande is the only one in region 4, it is incorporated in region 3.
in each location, to ensure the overall well-being of the local society. Furthermore, there are many factors that influence sustainability, and understanding the reality of each location and its needs to achieve a sustainable status are a challenge for public managers, researchers and for entrepreneurs whom which to invest in these projects. Hence, fundamental research is imperative to understand the measurement and classification of the sustainability of productive processes in specific locations (Pires et al., 2014; Salvati and Carlucci, 2014; Maiello et al., 2015). Tension indexes can be used to evaluate regions’ difficulties in this regard but most previous studies have considered only one factor or have not applied these indexes to a specific production process (Gleeson et al., 2012; Mueller et al., 2012). The indexes are metrics that express the depletion of resources in a region such that low tension indicates high sustainability (Gleeson et al., 2012). For this purpose, combining variables into complex indicators has proved to be an efficient method (Salvati and Carlucci, 2014) that allows a detailed characterization since it enables the simultaneous consideration of economic, social, and environmental variables (Moldan et al., 2012). Such analyses can guide regional management and enable the development of appropriate public policies and strategic actions (Yang et al., 2014; Maiello et al., 2015). Moreover, since the PB has experienced a drastic drop in NG areas, which are being replaced by more profitable crops (De Oliveira et al., 2017), it is extremely relevant to understand ways to make cattle more lucrative while conserving local biodiversity and identify the constraints and opportunities specific to each locality (Godfray et al., 2010). Thus, this study proposes a composite index to identify areas of tension based on the concept of sustainability related to beef cattle production. This study also characterizes municipalities based on factors that require specific actions for sustainable development according to the sustainability dimensions.
emission of greenhouse gases (GHGs) (Steinfeld et al., 2006; Ruviaro et al., 2015). This issue has generated conflicts between environmentalists and stakeholders in the beef supply chain, contributing to the complexity of developing sustainable strategies. Nonetheless, in theory, the Brazilian Pampa biome (PB), a region where cattle farming contribute to the conservation of natural grasslands (NG), is a setting where sustainable beef cattle production can facilitate the biodiversity conservation process (BRASIL, 2005; Carvalho and Batello, 2009; Overbeck et al., 2015; De Oliveira et al., 2017). The original vegetation over large areas of this biome is natural grassland, which harbors a unique biodiversity and a close historical association with livestock production since the period of European colonization (Overbeck et al., 2013). In this region, the moderate grazing of ruminants delays ecological succession and maintains the physiognomy and diversity of the grassland (Fedrigo et al., 2017). Ruminant grazing can also mitigate GHG emissions if combined with appropriate management practices (Neely et al., 2009; Vasconcelos et al., 2018), as overgrazing compromises areas of this biome (Carvalho and Batello, 2009). In the PB, this perspective is of great importance not only because it allows for ecological beef production but also because most grassland is on private property and grazed by cattle for productive purposes (Marchand, 2014). The state of Rio Grande do Sul has a cattle herd of about 13 million, mainly located in the PB region, which contributed more than US $8.6 billion to the state’s GDP in 2015 (Informativo NESPro and Embrapa Pecuária Sul, 2018; SCPRS, 2015). Moreover, beef has been produced in the PB for hundreds of years, and this tradition shaped the cultural aspects associated with the local lifestyle (Gaúcho) (De oliveira and Freitas, 2017). Given this background, public and private actions must work together toward conservation and maintain local know-how regarding traditional production. Nevertheless, sustainability is rarely seen as a priority because individuals tend to prioritize issues of immediate personal risk (e.g., hunger) (Weber, 2006). Therefore, despite the importance of private property in the PB, conservation policies should be based on strategic tools, such as biodiversity surveys, geographical landscape analyses, and indicators that can support the identification of the specific needs and challenges
2. Materials and methods 2.1. Research area The Rio de la Plata grasslands are located in Argentina, Brazil, 318
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Paraguay, and Uruguay (Soriano, 1991). The northern part of this region is the Brazilian PB, which occupies 176,496 km2 (i.e., 2.07% of Brazil’s land area) and covers the southern half of the state of Rio Grande do Sul (RS) (i.e., 63% of the state’s land area). RS is composed of 168 municipalities, 94 of which are completely inside the PB (Fig. 1). This PB is characterized by the dominance of subtropical grassland. These formations are natural remnants of the cold and dry glacial periods of the Pleistocene (Behling et al., 2009. In the Holocene, the vegetation was maintained by grazing large prehistoric mammals; this function is currently performed by domestic herds of cattle or horses, which were introduced in the south of the continent in the 18th century (Boldrini et al., 2015).
Table 1 Variables that compose the Sustainable Tension Index according to the dimensions and forms of contribution of each variable to the index.
Data were obtained from government databases and Brazilian research institutions. The main source of data was the Brazilian Institute of Geography and Statistics (IBGE), particularly the most recent Agricultural (IBGE, 2006) and Demographic Census (IBGE, 2010). The Statistical Foundation of the State of Rio Grande do Sul (FEE) provided some additional data. The sampling was at the municipality level, because it is the lowest level of territorial coverage with databases available for many variables of interest (Godfray et al., 2010) and allows the portrayal of local characteristics and the extrapolation of standards for the mesoregions. This analysis considered the 94 municipalities whose territories are completely inside the political delimitations of the PB (IBGE, 2004). The variables of interest were grouped into three dimensions based on the criteria for sustainable development, as shown in Table 1 (Brundtland and Khalid, 1987; Holden et al., 2014). These variables were selected based on a literature review and were considered in accordance with previous research and governmental legal documents considering the conservation of NG in the PB. Variables that describe the efficiency of cattle farming, trade relations, and economic growth (17 variables) were included in the economic dimension. Those related to the quality of life, social justice, education, basic sanitation, and social inequality of the residents (22 variables) were included in the social dimension. Finally, variables associated with the conditions of the NGs and natural forests within the PB landscape as well as degraded areas (24 variables) were included in the environmental dimension. Within each dimension, variables directly related to beef cattle production were also considered (see Table 1).
2.3.1. Standardization and normalization of variables The variables were standardized so that variables of different magnitudes could be compared (OECD, 2008). Negative or positive contributions to the index were determined according to previous publications (Salvati and Carlucci, 2014) and reference studies on the PB (Carvalho and Batello, 2009; Pillar et al., 2009; Gautreau, 2014; De Oliveira et al., 2017) (Table 1). Eq. (1) was used for variables that positively contributed to the index, and Eq. (2) was used for those that negatively contributed to the index. ' ' (x max j − x i, j )
z (x i, j ) = 1 −
(1)
' ' (x max j − x i, j ) ' ' (x max j − x minj )
Source
Economic dimension (17 variables) General indicators GDP per capita Income per capita Theil index Total exports Economically inactive population Total expenses Taxes collected
+ + − + − − +
IBGE IBGE IBGE FEE IBGE FEE IBGE
+ + + + + + − +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
+
IBGE
Social dimension (22 variables) General Indicators % population without basic sanitation Illiteracy rate Child mortality rate Life expectancy % of unemployed persons Rate of relationship connections with employers Gini index Total crime Demographic density Rate of rural and urban population % illiterate voters % population with water supply % garbage collected
− − − + − + − − − + − + +
IBGE IBGE IBGE IBGE IBGE FEE IBGE FEE IBGE IBGE FEE IBGE IBGE
Specific beef cattle indicators % of managers who cannot read and write % of farms in which the farmer is associated % of farms in which the farmer owns the land % of farms in which the farmer lives on the farm Proportion of women who manage farms % of managers with higher education % of area allocated to family farming Number of farms
− + + + + + + +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
− − − + − − + + − +
IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE IBGE
− − − − + +
IBGE IBGE FEE FEE IBGE IBGE
+ + + − + −
IBGE IBGE IBGE IBGE IBGE IBGE
Environmental dimension (23 variables) General indicators Altitude Temporary crops Crops - area planted with forage for cutting Natural grasslands Planted grasslands–degraded Planted grasslands–in good condition Natural forests – for preservation or legal reserve Other natural forests Planted forests Agroforestry systems - area planted with forest species also used for crops and grazing by animals Degraded lands (eroded, desertified, salinized, etc.) Unsuitable land for agriculture or livestock (sand, quarries, etc.) Municipal pollution potential index Vehicle rate of residents % of farms engaged in organic agriculture Area of the municipality
2.3. Construction of the tension indexes
' ' (x max j − x minj )
Sign
Specific beef cattle indicators % of livestock and breeding stock farms using technical guidance Livestock GDP % aggregated value of the municipality % contribution of cattle farming to state GDP % of farms that control diseases and/or parasites in cattle % of total investments in 2006 invested in farms % of farms with debt Number of agricultural farms with production in the year of 2006 (%) Offtake
2.2. Data collection
z (x i, j ) =
Dimensions and variables
Specific beef cattle indicators % of agricultural farms that rotate pastures (units) % of farms that use soil preparation % of farms that perform crop rotation % of farms that carry out burnings % of farms that perform protection and/or conservation of slopes % of farms that use fertilization and do not need to
(2)
In the equations: z(xi,j) = observed value of variable i standardized in sample unit j x’i,j = value of variable i for sample unit (municipality) j x’max,j = maximum value of the variable’s distribution across
(continued on next page) 319
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3. Results
Table 1 (continued) Dimensions and variables
Sign
Source
% of farms that use pesticides and do not need to % of farms with more than 50 cattle with confined animals
− −
IBGE IBGE
The STI was calculated to demonstrate which municipalities present bigger challenges regarding sustainable livestock production and to indicate the specific issues to be addressed in each locality with respect to economic development, social welfare, and environmental conservation. In this analysis, higher tension represents lower sustainability in a given municipality. All of the variables contributed to the composite index with weights based on the variables’ correlations with the PCA axes. For a given variable, a higher correlation implied a higher weight.
municipalities x’min,j = minimum value of the variable’s distribution across municipalities. 2.3.1.1. Calculating variable weights. The weight of each variable was calculated according to Eq. (3) based on principal components analysis (PCA) according to the Pearson correlation matrix between the standard variables:
ωi =
m ∑k = 1 |(αi, m × βm)| n m ∑ j = 1 ∑k = 1 |(αi, m × βm)|
3.1. Description of component analysis and weights of the variables PCA was performed with 61 variables for the 94 municipalities that belong to the PB and resulted in six variation axes with eigenvalues above two and an overall explanatory power of about 50%, as shown in Table 2. Component 1 explained 14.4% of data variation and was mainly positively related to economic factors such as population income, expenses, and taxes collected, but it was negatively related to the contribution of cattle farms to the state’s GDP. Social factors were negatively associated with an illiterate population and basic sanitation. The municipalities showed a gradient such that the highest association to the axis was in the metropolitan region, followed by regions with significant cattle farming, and, finally, by those with precarious sanitation and financial conditions. Of the three dimensions evaluated, cattle farming and farm financials were the main variables captured by component 2, which explained 12.4% of data variation. Along this axis, there was a positive association with cattle farming, NG, and municipalities’ areas and an inverse association with variables regarding family farming and soil preparation. Municipalities with the largest grassland areas and more productive farms had greater associations with this axis, were mainly located in the southwest region, and were less associated with the metropolitan region. Component 3 explained 6.8% of data variation, and the highest correlations were observed within the economic dimension (e.g., municipal expenses and crime rates). The highest correlations in component 4, which explained 6.1% of data variation, were observed in land use, particularly in areas with temporary crops, which were inversely associated with natural and planted forests. Component 5, which explained 4.8% of data variation, mainly captured the social relationships of farmers, whereas component 6, which explained 4.7% of data variation, showed the associations between land use and the environmental features of farms. Calculating the weights of the variables allowed the contributions of each variable and each dimension to be analyzed, excluding collinearity factors. In general, the environmental variables constituted the most weight in the index (39%), followed by the economic (31%) and social variables (29%). Among the 20 variables with the largest weights, nine represented the economic dimension (cattle farming GDP, contribution of cattle farming to state GDP, municipalities with debt, Theil Index, investments made, disease control, economically inactive population, total municipal expenses, and taxes collected), three were social (crime rate, Gini index, and number of illiterate managers), and eight were environmental (% of farms that conduct rotation of pastures, % of farms that perform crop rotation, area of municipality, % of area with natural forests, % of area with NG, % of area with temporary crops, and number of farms).
(3)
In this equation: ωi = weight of variable i α = correlation of variable i with axis m β = variation explained by axis m. This formula allows the contribution of each variable to the index to be observed while eliminating problems associated with collinearity and, thus, avoiding an overestimation of the index. The selected axes had an eigenvalue above two, and the variables had a total explanatory power above 40% (OECD, 2008). 2.3.2. Index construction Each standardized variable was multiplied by the value of its weight, and the results were summed for each municipality to obtain the sustainable tension index (STI), as in Eq. (4): m
STIj =
∑ (xi',j × ωi') k=1
(4)
In this equation: STIj = sustainable tension index for municipality j x'i = value of variable i in municipality j ω’i = weight of variable i. Similarly, the variables for each dimension were summed to assess the following three pillars of sustainability: Economic tension (EcTI), Social tension (SocTI), and Environmental tension (EnTI) indices. 2.3.3. Categorization of STI and the municipalities The STI was classified into the following five intervals by dividing the amplitude of the values by five: very low, low, intermediate, high, and very high tensions. After this categorization, the STI values were incorporated into a shapefile of municipalities and plotted on a thematic map using ArcGis 20.0 software (ESRI, 2015). Likewise, the values of EcTI, SocTI, and EnTI were classified by the same criteria and plotted on the map. Municipalities with low or very low tension rates were considered throughout the proposed dimensions. Those with low tensions across the three dimensions were considered closer to sustainable development. To evaluate the differences among mesoregions, a PERMANOVA test was applied using the vegan package in R software (R Core Team, 2017). In all of the analyses, a < 0.05 level of significance was considered.
3.2. Sustainable tension index (STI) The descriptive analysis of the STI found an average of 0.474, a range of 0.145, and a standard deviation of 0.031, as shown in Fig. 2. 320
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Table 2 Factor loadings (axis correlations) and variable weights.
Green cells represent a positive correlation with the axis. Red cells represent a negative correlation with the axis. 321
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Fig. 2. Description of indices and municipalities with higher and lower STI values. The abbreviations in the figure are defined as Sustainable Tension Index (STI), Economic Tension Tndex (EcTI), Social Tension Index (SocTI), and Environmental Tension Index (EnTI).
with the axes, had larger weights in the index, and were relevant in their respective dimensions. Despite its relevance, the PB has been a neglected biome in Brazilian conservation policies (Overbeck et al., 2007; Pillar and Vélez, 2010), as these policies excluded the PB from conservation campaigns and public awareness strategies. Nevertheless, some organizations are currently investing time and resources in maintaining beef farming in NGs as a conservation strategy. For example, the Alianza del Pastizal organization supports rural farms and certifies those that produce cattle according to the conservation principles for the biodiversity of the PB (Marchand, 2014). Despite the efforts of the Alianza del Pastizal, however, most of its actions are local, making it difficult for the organization to impact the Brazilian PB as a whole and compromising the continuity of its beneficial strategies. However, these actions may contribute to reducing tension locally, especially with respect to environmental variables, since the practices recommended by the organization focus on maintaining the physiognomy and their heterogeneity of NGs, recovering degraded areas, and promoting sanitary animal control and hydric management for associated farms (Perera and Carriquiry, 2014). Although the STI offers an overview of the municipalities, the specific variables associated with livestock illustrate the sustainability of beef production, and its relationship with the broader municipality variables indicates the relationship between this economic practice and the location. This perspective offers a broader view for stakeholders in the beef supply chain and government managers, offering subsidies for the creation of applied strategies to promote sustainability in the PB. The understanding of these associations is also key to understanding the impact of beef production on the socioeconomic characteristics of a municipality. However, the index offers a broad overview of the situation in a given location since the variables do not represent the realities of individual production systems within farms, as presented by other studies, such as that of Van Passel et al. (2007). Hence, despite the importance of understanding the overall situation of a municipality, it is important to further analyze the conditions of farms. For instance, many NGs experience degradation and overgrazing, and, therefore, are not conservation sites even though they are deemed “natural”. Nevertheless, each farm has distinct characteristics, hindering a realistic analysis based on individual data. Moreover, the effort needed to collect this level of information may limit the feasibility of such studies. The component analysis in this study demonstrates a disturbing
The analysis identified 32 municipalities with high tension. Overall, 38 municipalities presented intermediate STI values, and 23 indicated low tension. Of the ten municipalities with the highest tension, eight were located in the metropolitan mesoregion of Porto Alegre, whereas the municipalities with the lowest tension levels were found in the southeast and southwest mesoregions of the state, as shown in Fig. 3. The average STIs differed across the state’s mesoregions. The metropolitan mesoregion had the highest STI, whereas the southwest and southeast mesoregions had the lowest STIs, revealing the longitudinal gradient of the STI, as shown in Fig. 4. Eighteen municipalities had low or very low EcTIs, 44 had SocTIs below the intermediate level, and 54 municipalities stood out for having low EnTIs, indicating that the economic dimension faced the most serious issues in the analyzed setting. Only three municipalities exhibited low or very low tension for all three indices, and these municipalities also had the lowest STIs, as shown in Fig. 5. Of the other 51 municipalities that had low or very low EnTIs, 10 had low EcTIs and 21 had below intermediate SocTIs, indicating that the three factors are not dependent on each other because the dimensions are not clearly associated. Although the sustainability scenario did not arise frequently in the analysis, the SocTI and EnTI dimensions were most frequently associated in the analyzed municipalities. Only nine municipalities had low EnTI and EcTI values, and only ten municipalities had low EcTI and SocTI values, as shown in Fig. 5.
4. Discussion Sustainable strategies in the PB have direct implications for the conservation of NG ecosystems. In recent decades, the PB has been strongly influenced by human activities, such as soy production and forestry, which have contributed to grassland fragmentation (Gautreau, 2014). Moreover, the loss of grassland areas is associated with lower richness of plant and invertebrate species (Staude et al., 2018). As expected, the percentage of forestry area was negatively related to the index, increasing the tension in a municipality. Moreover, given the increasing demand for beef, maintaining extensive beef farming through moderate grazing could prevent ecological succession and maintain NG areas (Carvalho and Batello, 2009). This notion implies that variables such as the percentage of NGs and livestock activity should reduce the STI. In fact, these variables were highly correlated 322
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Fig. 3. (a) Spatialization of the Sustainable Tension Index (STI) in the analyzed municipalities and mesoregions of Rio Grande do Sul; (b) Spatialization of the Economic Tension Index (EcTI); (c) Spacialization of the Social Tension Index (SocTI); and (d) Spatialization of the Environmental Tension Index (EnTI). Mesoregions of Rio Grande do Sul state: (1) Southwest (SW); (2) Southeast (SE); (3) Metropolitan region of Porto Alegre (MP); (4) Eastern Central (EC); (5) Western Central (WC); (6) Northwest (NW); and (7) Northeast (NE).
grassland) that is not being efficiently utilized (Barcellos et al., 2011). This, in turn, is due to low investments in cattle farming and the profiles of rural farmers in the region, who tend to be fearful of adopting new technologies (Borges et al., 2016) and have low levels of education. Nevertheless, it is difficult to identify the differences among municipalities and observe locations with higher tension. In general, a tension gradient that expands from west to east was observed, meaning that larger municipalities with more available pastures had lower tension. Regions with the lowest tension levels were traditionally cattle producers. Particularly in the southwest, high cattle production is associated with the cultural traditions of this state (De oliveira and Freitas, 2017), thus favoring the social aspect of sustainability.
result because of the inverse association between the economic and social variables. The first axis indicated an inverse relationship between economic growth and social equity. In the second axis, factors related to cattle and farm financials were directly associated with grassland areas and inversely related to variables describing the economic condition of the municipality. These results reinforce the importance of NGs in the analyzed scenario and highlight the disparity between the social quality of life and the economic development of local populations. The overall STI average was intermediate, indicating that the PB’s municipalities are in overall neutral scenarios, which does not mean that they are sustainable. This probably occurs because of the high potential for sustainable beef cattle production (e.g., areas with natural 323
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Fig. 4. Comparison of the Sustainable Tension Index (STI) across mesoregions (SQ = 0.15288, p = 0.001). The letters indicate significant differences between mesoregions according to contrast tests. EC × MP (p = 0.0301); EC × SE (p = 0.2364); EC × SW (p = 0.0002); MP × SE (p = 0.0002); MP × SW (p = 0.0001); SE × SW (p = 0.047).
allows farms to be profitable and respect Brazilian legislation (e.g., Native Vegetation Protection Law- Law n° 12.727, October 17, 2012) (BRASIL, 2012a,b). Therefore, areas with appropriate soil management tend to have low tensions by protecting other NG areas, and this variable has a greater weight in the composite index. This study also identified useful and accessible land use variables that are key factors for sustainability, particularly for natural forests and grasslands and temporary crops. Land use changes were also identified as a problem for RS (De Oliveira et al., 2017), mostly as a consequence of the increase in crop and forestry areas that reduced NG areas. Hence, altering the natural landscape and the perception that NG has no value may amplify the loss of biodiversity due to invasions of exotic species and the de-characterization of natural habitats (Gautreau, 2014). Only four municipalities presented low tension on all three dimensions, indicating that the associations between the dimensions are weak. The discussion focused on municipalities with low tensions because it was necessary to define a threshold for sustainability. One parameter that can be used as an indicator for this concept is the balance among the three dimensions (Holden et al., 2016). Therefore, the three municipalities mentioned in Fig. 5 are the only ones close to achieving sustainability. At the same time, the bearable scenario, with low tension in terms of social and environmental variables, was the most common, indicating that environmental and social characteristics are contemplated more often. The STI proposed in this study enabled the visualization of patterns in the analyzed regions and the observation of specific local realities. The application of this strategic tool identified dimensions that require interventions and revealed regional cattle farming patterns and their associations with sustainability in addition to the specific political demands of each municipality. Moreover, depending on data availability, other studies may apply this methodology to evaluate other regions and food supply chains. However, it is important that the effects of each variable are re-evaluated for each location (Godfray, 2015) because the contributions of variables may differ according to the ecosystem or biome evaluated. Nevertheless, the proposed index is limited by the provision of government data, since the set of variables presented depends on the Agricultural Census published by IBGE in Brazil, which has been overdue since 2010, indicating that many municipalities that exhibited low tension in this analysis may exhibit a different level of tension in practice today. Another possible direction for future work is to apply the same technique to another set of variables that are periodically available but are less specific. In addition, each time the index is updated, a new analysis must be performed to aggregate the previous
However, although these municipalities have lower STIs, they still have challenges to overcome, and this STI method can clarify the possibilities for public managers. For instance, Dom Pedrito, the region with the second smallest STI, exhibited low tension on the economic and social dimensions but required interventions in the environmental dimension, indicating that practices to maintain NGs and manage farms may be effective measures to decrease the region’s STI. This scenario highlighted the importance of variables associated with the financial resources obtained by a municipality, particularly from cattle farming, because the municipal GDP and municipal value added from cattle farming were associated with sustainable tension. Thus, economic well-being is fundamental for a municipality to be able to invest in actions that affect the environment and society (Weber, 2006). Likewise, specific credit lines for cattle farming with sustainable precepts could positively impact the sustainability of traditional beef cattle municipalities.3 Both economic and social inequality are also important because equality of resource distribution is a pro-sustainability factor. In addition, the criminality and illiteracy of rural managers increase sustainability tension. Moreover, the lack of perspective of the young rural population contributes to rural exodus, which, in turn, causes a lack of human resources for rural firms, which is one of the biggest problems for food production worldwide (Godfray, 2015). Regarding the environment, the impact of the rotation of crops and pastures, identified in the index, as well as that of occasional grazing exclusion, which has also been identified by other studies on livestock management, highlights the importance of the latter on grassland sustainability practices (Nabinger et al., 2009; Overbeck et al., 2015; Fedrigo et al., 2017). Nevertheless, once NGs are replaced by crops and planted grasslands, their recovery may be difficult, and this strategy should be approached with care. Hence, farmers should invest in strategic supplementation and better pasture management, such as adjusting cattle stocking rates, particularly in the winter, when they face limited biomass production and must increase grazing pressure, reducing plant covering and compacting soil. Appropriate soil management contributes to farms’ efficiency and sustainable intensification, preventing the expansion of agricultural areas (Godfray, 2015). This vertical increase in production ensures that new grassland areas are not degraded by the expansion of crop plantations and planted pastures, helps to maintain natural areas, and
3 See the ABC Plan that supports low carbon agriculture practices < http:// www.agricultura.gov.br/assuntos/sustentabilidade/plano-abc/plano-abcagricultura-de-baixa-emissao-de-carbono > .
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Fig. 5. Panel (a) shows the indices associated with the three dimensions of sustainability. The black lines indicate the reference low tension threshold. The blue lines indicate municipalities with lower STIs, and the red lines indicate the municipalities with the highest tension levels. Panel (b) shows the projection of economic and social indicators with municipalities in the fair scenario highlighted. Panel (c) shows the projection of environmental and economic indices with municipalities in the viable scenario highlighted. Panel (d) shows the projection of the environmental and social indices with municipalities in the bearable scenario highlighted. Dotted lines indicate the reference low tension threshold.
Another alternative in locations with high tension would be to disregard the investment in sustainable beef production. Efforts to improve processes, invest in the recovery of NGs, create good jobs, improve educational conditions for rural landowners, and provide subsidies for sustainable beef farming activities should be the focus of managers in localities with lower tensions since these variables had higher weights in the STI. For instance, investments to increase these municipalities’ per capita incomes, improve the basic and higher educations of rural farmers, and recover degraded grassland areas would be important actions for maintaining the STIs in these municipalities. Overall, the STI not only allows the visualization of a regional sustainable pattern but also serves as a management tool because it enables the evaluation of each municipality’s situation with all the variables analyzed. However, municipalities should invest in the collection and availability of their own data, because, at present, these studies are exclusively based on public domain sources. In addition, this analysis is static and outlines an overview based on the latest data
index so that the PCA weights are the same and the different periods can be compared.
5. Conclusion The index proposed in this study allowed the identification of tension areas based on the concept of sustainability and the precepts of efficient beef cattle farming. Moreover, the STI is an important tool for analyzing the key factors to be considered in sustainable beef farming. Thus, most of the analyzed municipalities have the capacity to improve their processes and achieve sustainable beef production. Although few municipalities had low tensions on the three dimensions contemplated, the STI revealed a general pattern of intermediate tensions. The results demonstrated that sustainable beef production is well-established in southwest and southeast mesoregions, especially Alegrete, Dom Pedrito, and Uruguaiana, whereas it requires public policy intervention and investment in the metropolitan mesoregion. 325
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available in Brazil. Hence, when new data become available, the paths traced by the municipalities toward sustainability should be analyzed.
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