Trade-off assessments between environmental and economic indicators in cropping systems of Pampa region (Argentina)

Ecological Indicators 83 (2017) 328–337

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Original Articles

Trade-off assessments between environmental and economic indicators in cropping systems of Pampa region (Argentina)

MARK

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Diego O. Ferraroa,b, , Mercedes Gagliostroa,b a

IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Av. San Martín 4453 (C1417DSE), Buenos Aires, Argentina Universidad de Buenos Aires, Facultad de Agronomía, Departamento de Producción Vegetal, Cátedra de Cerealicultura, Av. San Martín 4453 (C1417DSE), Buenos Aires, Argentina

b

A R T I C L E I N F O

A B S T R A C T

Keywords: Sustainability indicators Agriculture Tradeoff Emergy Ecotoxicity

An overall sustainability assessment should include changes in the economic return, the social benefits and the human intervention on the biophysical resources in order to highlight potential trade-off or synergies among them. In this work, we studied the performance of 36 cropping systems (CS) of the Pampa region, Argentina, which include three different crops, three increasing levels of technology adoption in four contrasting site conditions. For each CS, we simultaneously assessed 1) the ecosystem energy flow using the emergy synthesis; 2) the pesticide ecotoxicity using a simple dose-response model; and 3) the economic profit, in order to evaluate the influence of crop identity, technological level, and site location on the indicators values as well as to detect potential trade-offs between indicators. Results revealed that maize crop entailed the most sustainable indicator profile by exhibiting relative high emergy return, low non-renewable emergy use, low pesticide ecotoxicity, and high gross income. In addition, results showed a significant trade-off between economic profit and ecotoxicological risk in the CS studied. Further studies should be conducted for including more contrasting indicators in order to explore the potential trade-off among other ecosystem components as a promising way to identify sustainable crop management regimes for different production zones.

1. Introduction Agricultural systems are ecosystems human-modified in order to obtain a product that generates a profit. However, recently it arose the idea of a potential trade-off between productivity-enhancing technical change (i.e. agricultural intensification) and the maintenance of ecosystem integrity (Müller and Burkhard, 2010). Consequently, it led to a demand of analytic tools that can measure progress toward a broad range of social, environmental and economic goals (Reed et al., 2006). However, these goals should be clearly identified and the indicators should be goal-oriented for allowing course corrections. Despite the complexity of economic, ecological, and social aspects of agroecosystems, the economic performance is readily assessed using the economic return. However, when the ecological counterpart is assessed the multiplicity of both components and process entails a significant compromise between relevance and feasibility (Bockstaller et al., 2009). This compromise leads to find both robust and ecologically sound indicators to complement with the economic return indicators. The use of energy can be used as an indicator of both structural and functional integrity in agroecosystems due to,

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like any biological system, they are subject to the basic laws of physics, such as energy exchange and the resulting thermodynamic balances (Bakshi, 2002). Although thermodynamics are required to obtain a proper understanding of the physics underlying biological systems, economics, and the environmental sciences are useful complements for understating the path towards more sustainable agricultural systems. Thus, the use of energy-related analysis should be considered as one tool amongst several quantitative approaches that should be employed to study agricultural systems. The components of this “sustainability toolkit” (Hammond, 2007) would also include environmental assessments and cost-benefit analysis. In this work, we assessed the thermodynamic, environmental and the economic outcome of the most frequent cropping systems in the Pampa region (Argentina) in order to evaluate their performance as well as to highlight any potential tradeoff among components. The energetic performance of the cropping systems analyzed was assessed using the emergy synthesis (Odum, 1996; Zhang et al., 2007; Zhang et al., 2012; Wu et al., 2014). This is an energy evaluation method and specifically, emergy is defined as “the total amount of available energy of one kind (most often of the solar kind) that is used up directly or indirectly in a process to deliver an

Corresponding author at: Cátedra de Cerealicultura, Facultad de Agronomía, Universidad de Buenos Aires, Av. San Martín 4453 (C1417DSE), Buenos Aires, Argentina. E-mail address: [email protected] (D.O. Ferraro).

http://dx.doi.org/10.1016/j.ecolind.2017.08.020 Received 11 January 2017; Received in revised form 31 July 2017; Accepted 4 August 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.

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D.O. Ferraro, M. Gagliostro

output product, flow, or service” (Odum, 1996). Thus, emergy accounting is a measure of the past and present environmental support to a process, and it allows to explore the interplay of the natural ecosystem and human activities (Franzese et al., 2009). For analyzing one of the direct environmental effects we assessed the ecotoxicological risk associated with the pesticides used in each cropping system (Ferraro et al., 2003) in order to understand some potential effect on both insects and mammal diversity. Finally, the cropping systems were evaluated in terms of economical profit using historical economic data. The study was conducted in four locations located in the Pampa region of Argentina, each one representing different environmental cropping conditions. In this region, some issues regarding sustainability have recently arisen; among these are concerns that sustainability may be hampered by the replacement of mixed grazing–cropping systems with permanent agriculture mainly based on soybean crops, and that the impacts of increasing productivity by increasing inputs could derive in critical trade-offs between various economic and ecological services (Viglizzo and Frank, 2006; Rositano and Ferraro, 2014). As a measure of this intensification process, the pesticide consumption in the studied area increased from 6 million kilograms in 1992–32 million kilograms in 2012 (Solis et al., 2016). More recently, the cases of herbicide resistance in the study area (Valverde, 2007) contributed to increase the environmental load due to chemical compounds (Matin Qaim, 2005). Based on these antecedents, the main goal of this work is to conduct a comprehensive multiple assessments of the most conspicuous cropping systems of the Pampa region (Argentina), including the economic, and the environmental performance.

average annual rainfall is 900 mm (Viglizzo et al., 2004). 2.2. Cropping systems: management description and crop yield simulation Our analysis was restricted to 36 cropping systems (CS) that derived from a full combination of three crops, three incremental level of technological adoption and four site locations. In the four site locations described above, we selected the three most frequent crop systems in the Pampa region: (1) the wheat/soybean double cropping (W/S); (2) maize cropping (M), and spring soybean cropping (S). Within each crop, we defined three incremental technological level (TL): low (L), average (A) and high (H). The incremental technological level entails increasing input usage (e.g pesticides, fertilizers, yield potential due to genotype constraints). TL characterization was built by using several sources (BOLCER, 2015; Margenes_Agropecuarios, 2015). The scarcity of reliable data sources of the average crop yield for the whole set of CS led us to explore the outcome of these alternative management strategies by simulating crop yields into the Decision Support System for Agrotechnology Transfer (DSSAT) package (Jones et al., 2003) that has been calibrated for the studied locations (Mercau et al., 2007). The advantage of using crop simulations models is that they are able to capture climate-management interactions in a process-based structure as well as the simulated a representative average yield value using longterm weather records. Crop simulation models focus on how weather (especially temperature and the amount of radiation intercepted by the crop) and genetic characteristics affect potential yield, given a specified management scheme. DSSAT need many auxiliary inputs such as daily weather variables and soil characteristics in addition to crop genetics and management conditions. There are four types of input data to the DSSAT model: weather, plant, soil, and management. The weather input data are the daily sum of global radiation (MJ m−2), daily minimum and maximum air temperatures (°C), and the daily sum of precipitation (mm). Plant parameters and physiological characteristics are given in the form of genetic coefficients, which describe physiological processes such as development, photosynthesis, and growth for individual crop varieties in response to soil, weather, and management during a season (He et al., 2010). Soil inputs describe the physical, chemical, and morphological properties of the soil surface and each soil layer within the root zone. The management information includes planting density, row spacing, planting depth, irrigation, application of fertilizer and they were representative of the most frequent situation of each the cropping systems of the selected site locations. The average crop yield value from 1971 to 2008 historical weather record period was used as the representative crop yield for each CS. Factors associated with management and weather, however, are limited to plant-water supply and plant-nitrogen supply (Ghaffari et al., 2001) excluding important factors such as weeds, diseases, and pests. Therefore, we empirically adjusted the attainable crop yield (van Ittersum and Rabbinge, 1997) resulting from DSSAT simulations in order to model actual crop yield. Local data of simulated versus observed crop yield were used for obtaining the adjusting coefficients (attainable to actual yield) at each TL for each crop species (Mercau et al., 2001; Satorre et al., 2005; Mercau et al., 2007)

2. Materials and methods 2.1. Study site The cropping systems analyzed in this work are located in the Pampa region (Argentina). The Pampa is a fertile plain originally covered by grasslands, which during the 1900s and 2000s was transformed into an agricultural land mosaic by grazing and farming activities (Soriano et al., 1991). However, since 1990 the traditionally mixed grazing–cropping systems were being replaced by permanent agriculture. The most frequently cropped soils in the region are Mollisols, developed from eolian sediments of the Pleistocene era, with dominantly udic and thermic water and temperature regimes, respectively (Moscatelli et al., 1980). We assessed the cropping system performance in four typical agricultural locations: 1) Pergamino, 2) Balcarce, 3) Villegas, and 4) Gualeguay. Pergamino (33°53′00″S; 60°34′00″O) is located in the Rolling Pampas, the most productive subregion of the Pampa where annual cropping is concentrated (Hall et al., 1992). The predominant soil is a typical Argiudolls (Soil-Survey-Staff, 1999) and the annual rainfall averaged 950 mm. Balcarce (37°49′00″S 58°15′00″O) is considered representative of the predominant land uses in the southeast part of the Pampa Region. It includes part of the Flooding Pampas, mostly a cattle-breeding area dominated by lowlands with small differences in topography, soil quality, problems of salinity, water drainage and flood risk (Barral and Oscar, 2012). Predominant soils can be used for cultivated crops and pasture implantation, with an average annual rainfall of 700 mm (Viglizzo et al., 2004). Villegas is located to the west of the province of Buenos Aires (35° 02′00 “S; 63° 01′00” W), in the sub-region of the Sandy Pampa. Predominant soils with an aptitude for agricultural and livestock use, classified as typical Hapludolls (Soil-Survey-Staff, 1999). Soils with good depth and good drainage alternate with soils with hardened horizons, which limit the root development of plants. It is a sub-humid zone, with an average annual rainfall of 700 mm (Viglizzo et al., 2004). Finally, Gualeguay (33° 09′ 00“S; 59° 20′00” W) belongs to the southeast sub-region of Entre Ríos. The representative soils are the vertic Argiudolls, developed on colluvium-alluvial materials that are suitable for tillage, with a moderate risk of water erosion (Mantel and van Engelen, 1997). The

2.3. Indicator description 2.3.1. Emergy based-indicators (ELR and EYR) A typical diagram of the crop production system is presented in Fig. A1 in supplementary materials. The diagram illustrates the boundary, main components, and interactions. Inputs of the crop production system can be categorized into four types as shown in that diagram: (1) local renewable resources (R), such as sunlight, rain, and the wind, (2) local non-renewable resources (NR), such as net loss of topsoil, (3) purchased materials (M), such as mechanical equipment, purchased diesel, chemical fertilizers, seeds and pesticides, and (4) purchased services (S), such as labor, and technical management (Tao et al., 329

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2.3.3. Gross margin (GM) Using DSSAT and historical economic data we built 1) a crop yield matrix and b) a cost matrix for a full combination of crop type (3 levels), TL (3 levels) and site location (4 levels). The gross margin (GM) was calculated for each crop type as gross income (yield times product price) minus production costs. Production costs include fixed and variable components. Fixed direct costs do not depend on crop yield (e.g., seed and agrochemicals). Oppositely, variable direct costs depend on yields (e.g., harvest, marketing fees, and grain transportation). Output prices and production costs involve prices time series of maize, soybeans, and wheat as well as production costs (i.e. fertilizers; seeds; pesticides; and harvest and sale costs). They were extracted from the Argentine trade magazine “Márgenes Agropecuarios” (http://www. margenes.com). In all scenarios, we used median prices for the 2008–2015 period. In order to facilitate the data analyses, we standardized the GM values using the highest CS value, in order to obtain a relative gross margin (GMr) for each CS.

2013). This categorization is the based for calculating the relationships (i.e. emergy-based indicators) among emergy flows. Thus, we assessed the emergy flow in each CS using the emergy synthesis procedure (Odum, 1996). Briefly, the emergy accounting methodology uses the emergy theory to account for both the natural and human-made capital storages. The emergy accounting method value these storages using a common unit, the solar equivalent joule (seJ). The method accounts for the environmental support provided directly and indirectly by nature to resource generation and processing; it focuses on the valuation of the intrinsic properties of ecosystems (Mellino et al., 2015). In the emergysynthesis procedure, it is mandatory to assess the whole input as yearly emergy inflows. Therefore, the whole amount of annual solar energy in the studied location is considered (both the received and the not received by crops). The emergy-synthesis procedure assesses if the crop is able to capture this radiation due to multiple factors (i.e management, nutrient, and water availability). For further details on emergy synthesis methodology refer to (Brown et al., 2001). As in (Ferraro and Benzi, 2015), we followed the emergy accounting procedure for the studied location, by using the input (from the production cost data) and environmental data (soil organic matter, rain, the wind) (Table A1 in supplementary materials). On the bases of the fluxes described in Fig. A1 in supplementary materials, we used a basic set of emergy-based indicators for assessing the system performance. These indicators are related to efficiency; yield; environmental load; and investment return. The first emergy indicator is calculated as follows: EYR = (NR + R + M + S)/(M + S)

2.4. Data analysis We applied a combined set of descriptors and both univariate and multivariate to assess the indicator variability due to the crop identity, the technological level, and site location. The whole data set was evaluated using a linear mixed model for assessing the effects of each factor. Also, data were analyzed using k-means clusters, for finding homogeneous groups within the CS set and classification and regression trees (CART) for finding multiple explanatory models for the determines k-means clusters. A k-means cluster analysis (Jain and Dubes, 1988) is based on a least sum-of squares estimation and attempts to group the CS increasing cluster internal homogeneity and external or between group heterogeneity. For selecting the optimal number of CS clusters, we inspected the set of solutions for detecting: (1) a cutoff value of 5% in the percentage decrease in the misclassification error when adding one more cluster and (2) the lowest number of clusters that meet the above condition. Both conditions determine a proper balance between accuracy and complexity for the final number of clusters (Koziol, 1990). CART was used for partitioning the clustered CS groups into subsets (or nodes) with the highest attainable homogeneity defined by the explanatory factors (i.e. site, TL and crop). Basically, a classification tree partitions the space of all possible attributes (both categorical and continuous), starting with all attributes (at the root of the tree) and successively splitting that space in nodes in which each node is more likely to be assigned to one of the k-means clusters than the node from which it is split (Breiman et al., 1984)

(1)

where EYR is the Emergy Yield Ratio; NR is the nonrenewable emergy from nature; R is the renewable emergy from nature; M is the emergy embodied in the materials purchased from the economy, and S is the emergy embodied in the services purchased from the economy. This indicator is calculated as the ratio of the emergy output divided by the non-renewable emergy input as feedback from the outside economy. The higher the value, the greater the return obtained per unit of emergy invested, indicating the potential contribution that is made to the main economic system from the exploitation of local resources. The second emergy indicator is calculated as follows: ELR = (NR + M + S)/R

(2)

where ELR is the Emergy Loading Rate; NR is the nonrenewable emergy from nature; R is the renewable emergy from nature; M is the emergy embodied in the materials purchased from the economy, and S is the emergy embodied in the services purchased from the economy. This indicator shows the ratio of the non-renewable emergy flows to renewable emergy flows, indicating the load of the environment generated by human-dominated non-renewable flows. The lower the ratio, the lower the stress to the environment. Details about the items associated with each kind of flux (R, NR, M, and S) along with the data sources and the calculation procedure can be found in the Appendix. We have included the emergy evaluation table for one CS in order illustrate the table structure, the emergy calculations, and data sources. The other tables calculated for the combination of crop species, year and cropping systems has the same structure and data sources

3. Results Results of the linear mixed model showed most of the variance associated to crop specie when analyzing both the emergy (EYR and ELR) and the ecotoxicity (P) indicators (Table 1). However, when the performance was assessed using the economic indicator (GMr) only the technological level, as the main factor, resulted in significant variance component explanation (Table 1). Although there were significant main effects due to crop species, site, and technological level, there was also significant interactions that affected the magnitude of the differences among them (Table 1, Figs. 1–4). When analyzing the individual crop performance using the emergy indicators, the spring soybean (S) exhibited the highest emergy return, in all sites and under all technological levels as well as the lowest value of nonrenewable emergy usage (i.e. low ELR). Both patterns were associated with a sustainable functioning as reveals high efficiency on the exploitation of local resources and low environmental impact. Oppositely, the wheat/soybean (W/S) double cropping showed the lowest emergy return as well as the highest ELR values (Figs. 1 and 2). The significant crop effect on the ecotoxicity risk was mainly associated

2.3.2. Pesticide ecotoxicity (P) The ecotoxicological risk (P) was assessed using a fuzzy logic-based and field scale indicator (RIPEST) that consider the toxicity effects on mammals and insects, using the number, type, and rate of applied pesticides. Toxicity and the dose applied are considered as the major factors in determining the final impact of a pesticide application, rather than the type of formulation, mode of action or chemical classification (Ferraro et al., 2003). Further details about RIPEST structure and functioning are available on its web site (http://malezas.agro.uba.ar/ ripest/) 330

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Table 1 Relative Variance component (RVC) of a linear mixed model using the factors studied. Factor

Variable EYR

Site (1) Crop (2) TL (3) 1*2 1*3 2*3 1*2*3

ELR

P

GMr

RVC

F

P

RVC

F

P

RVC

F

P

RVC

F

P

0.173 0.738 0.026 0.056 0.001 0.006 0.000

10.12 47.04 11.56 497.1 12.06 75.78

0.008 0.000 0.012 0.000 0.000 0.000

0.100 0.860 0.024 0.011 0.001 0.004 0.001

26.48 203.6 15.21 61.39 4.422 33.11

0.000 0.000 0.008 0.000 0.014 0.000

0.000 0.933 0.045 0.002 0.001 0.012 0.007

0.597 177.4 10.21 1.97 1.270 7.600

0.644 0.000 0.025 0.149 0.340 0.003

0.302 0.211 0.072 0.366 0.007 0.010 0.030

3.362 3.163 10.20 37.33 1.727 2.385

0.093 0.111 0.018 0.000 0.198 0.109

Values in bold represent statistical significance at P < 0.05.

threshold in EYR = 2, which represents a condition of equal emergy contribution from economy and nature. The double cropping W/S was below this threshold in all the site x TL combinations, as well as spring soybean (S) resulted in EYR > 2 in all the CS analyzed (Fig. 1). Maize (M) was the only crop that oscillated both above and below this functional threshold, which indicates that the crop ability to capture natural emergy through the use of external and acquired emergy (i.e. EYR) depends on the site and also on the technological level (Fig. 1). Also, a sustainable threshold could be set at ELR = 1 that represents an equal contribution of renewable and nonrenewable emergy during the cropping period of each CS (Fig. 2). Only spring soybean and maize crops at Villegas site showed ELR < 1 values, and the double cropping W/S exhibited ELR values in the range of 1.8–2.8.

with low P values of maize comparing to S and W/S, which probed similar values both in the medium and high TL, across sites (Fig. 3). Site location also resulted as a main significant explanatory factor for both emergy indicators, but it showed a no significant effect for explaining variability on the ecotoxicological P index (Table 1). One specific location (Villegas) showed a significant and consistent emergy performance pattern of relatively more sustainable condition with high EYR and low ELR values (Figs. 1 and 2). The technological level was the only explanatory factor significantly associated with all the calculated indicators (Table 1). However, the magnitude of the relative variance component (RVC) was remarkably lower than both site and site factor (Table 1). When inspecting the variability in the emergy indicators due to the technological level through sites and crops, the high technological level showed the lowest EYR values and the highest ELR values in all sites and locations (Figs. 1 and 2). The level of technological adoption also resulted in a significant main effect on P value (Table 1). However, only the low TL exhibited a consistent pattern of lower ecotoxicity risk across sites and crops with the exception of W/S on Villegas site (Fig. 3). The technological level also resulted in a main significant effect on GMr, showing a pattern of highest economic return under the high level of technological adoption (Fig. 4). In relation to, the economic dimension of the CS assessment, GMr results showed also only a marginal main site location effect, as well as the higher data variance component was associate to the site x crop interaction (Table 1). When assessing the emergy return it is possible to set a functional

3.1. Trade-off assessment The trade-off assessment among CS indicators showed some significant effects (Table 2). The overall correlation matrix exhibited a significant and positive correlation between the gross margin (the economic performance) and both the fraction of nonrenewable emergy used (ELR) and the pesticide index of ecotoxicity (P) (the environmental impact). The strongest (negative) correlation was between the CS ability to capture natural emergy using purchased inputs (EYR) and the environmental impact associated to high usage of nonrenewable emergy fraction used (ELR). Fig. 1. Means values of Emergy Yield Ratio (EYR) for the crops, technological level, and the four locations analyzed. Locations are a) Pergamino; b) Balcarce; c) Villegas; and d) Gualeguay. Crops are M) Maize; S) spring soybean; and c) wheat/soybean double cropping. Technological levels are H) high; M) medium; and L) low. The dotted line shows EYR = 2, the state of equal emergy contribution from economy and nature (see text for calculation details). The arrow in the first panel shows the direction of change of the indicator towards a more sustainable condition.

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Fig. 2. Means values of Emergy Load Ratio (ELR) for the crops, technological level, and the four locations analyzed. Locations are a) Pergamino; b) Balcarce; c) Villegas; and d) Gualeguay. Crops are M) Maize; S) spring soybean; and c) wheat/soybean double cropping. Technological levels are H) high; M) medium; and L) low. The dotted line shows ELR = 1, the state of equal emergy contribution from renewable and non-renewable sources (see text for calculation details). The arrow in the first panel shows the direction of change of the indicator towards a more sustainable condition.

was possible to detect some variability regarding both GMr and the ELR indicator (Fig. 6). Finally, the indicator pattern regarding the technological level (TL) showed the highest similarity among the explanatory factors studied (Fig. 7) between the three TL (Fig. 7).

The spider diagrams (Figs. 5–7) are useful for highlighting not only some potential trade-off between indicators but also, the dissimilarity between the CS performance and a theoretical sustainable condition. When inspecting the crop-related results (Fig. 5) the maize crop showed the most similar diagram respect to the theoretical sustainable conditional, exhibiting a low P value, EYR and ELR values closer to the sustainable threshold, but with a relatively low GMr. Otherwise, the others two crops (S and W/S) showed higher GMr values, but their results reinforced the idea of the economical-environmental trade-off, by showing a noticeable undesirable P (S and W/S) and ELR (W/S) values (Fig. 5). In the set of spider diagrams that show the site effect, it was possible to observe the similarity between localities regarding the indicator pattern (i.e the spider shape) (Fig. 6). All sites showed high P values (in the average of all TL and crops) and similar EYR values, closer to the sustainable threshold. However, it

3.2. Multivariated assessment The multivariate assessment of the indicator set data was useful for detecting a number of contrasting clusters (Fig. 8) and the explanatory factors associated (Fig. 9). Five k-means clusters were selected when analyzing the matrix data. Two of them (Clusters 1 and 4) showed high GMr values associated with high values of both P and ELR (Fig. 8). The remaining clusters (Clusters 2, 3 and 5) exhibited relatively lower values of GMr (ca. 0.5). This group could be split in two contrasting performance. Both clusters 2 Fig. 3. Means values of Pesticide Index (P) for the crops, technological level, and the four locations analyzed. Locations are a) Pergamino; b) Balcarce; c) Villegas; and d) Gualeguay. Crops are M) Maize; S) spring soybean; and W/S) wheat/soybean double cropping. Technological levels are H) high; M) medium; and L) low. The dotted line shows P = 1, the state of the maximum acute toxicity registered for the cropping systems analyzed (see text for calculation details). The arrow in the first panel shows the direction of change of the indicator towards a more sustainable condition.

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Fig. 4. Means values of Emergy Load Ratio (ELR) for the crops, technological level, and the four locations analyzed. Locations are a) Pergamino; b) Balcarce; c) Villegas; and d) Gualeguay. Crops are M) Maize; S) spring soybean; and c) wheat/soybean double cropping. Technological levels are H) high; M) medium; and L) low. The dotted line shows MBr = 1, the state of maximum gross income (U$S/ha) of the 36 cropping systems analyzed (see text for calculation details). The arrow in the first panel shows the direction of change of the indicator towards a more sustainable condition.

4. Discussion

Table 2 Pearson correlation matrix between all indicators estimated. Bold values are significant at P < 0.01.

ELR P GMr

EYR

ELR

P

−0.91 −0.03 −0.15

0.30 0.42

0.51

Results from the cropping system assessment of this work reinforced the functioning pattern evidenced in previous results where the long term trend in the emergy-related indicators went toward lower emergy return (EYR) and high environmental impact (ELR) (Ferraro and Benzi, 2015). Moreover, most of the CS analyzed showed EYR values below the desirable threshold of EYR = 2 and below ELR = 1. Despite the scarcity of both EYR and ELR assessments, similar cropping systems exhibited values of EYR values in the range of 1.18–2.32 and ELR in the range of 2.49 and 22.25 (Brown and Ulgiati, 1997; Panzieri et al., 2000; Ortega et al., 2002; Lefroy and Rydberg, 2003; Chen et al., 2006; de Barros et al., 2009; Ghisellini et al., 2014; Ma et al., 2015). The observed ELR ranges (0.73–2.80) of the CS analyzed indicates an exploitation condition of relatively low dependency from nonrenewable emergy (ELR). However, the observed EYR range (1.45–3.03) showed values toward a condition where the ability to crop systems to capture an equivalent amount of energy of nature regarding inverted from the economy (i.e. EYR = 2) is jeopardized. The Pampa region exhibits excellent agroecological conditions for crop production, that are clearly related to the higher return on investments in the emergy purchased. However, the agronomic decisions associated with the use of inputs, the

Values in bold represent statistical significance at P < 0.05.

and 5, exhibited also a desirable condition of higher values of emergy return (EYR) with relatively low use of nonrenewable emergy (ELR), but the pesticide profile showed high values of ecotoxicity risk (P). A final cluster (Cluster 3) showed the relatively closer performance regarding the environmental indicators of the theoretical sustainable cluster (Fig. 8). Specifically, this cluster was mainly associated with CS under maize crop (Fig. 9). By inspecting the CART results, the rest of the clusters were sequentially explained by also using crop and site factors, with no significant predictor effects due to the technological level (Fig. 9)

Fig. 5. Mean values of performance indicators of the crops analyzed: a) maize, b) spring soybean and c) wheat/soybean double cropping. Both EYRr and ELRr values are relativized to the maximum observed values (ELR and EYR = 3). The dotted lines show a theoretical sustainable condition: P = 0; EYRr = 0.67 (EYR = 2); ELRr = 0.33 (ELR = 1) and GMr = 1. The arrows in the first panel show the direction of change of the indicators towards a more sustainable condition.

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Fig. 6. Mean values of performance indicators of the locations analyzed: a) Balcarce; b) Villegas; c) Gualeguay; and d) Pergamino. Both EYRr and ELRr values are relativized to the maximum observed values (ELR and EYR = 3). The dotted lines show a theoretical sustainable condition: P = 0; EYRr = 0.67 (EYR = 2); ELRr = 0.33 (ELR = 1) and GMr = 1. The arrows in the left panel show the direction of change of the indicators towards a more sustainable condition.

Fig. 7. Mean values of performance indicators of the technological levels analyzed: a) high; b) medium; c) low. Both EYRr and ELRr values are relativized to the maximum observed values (ELR and EYR = 3). The dotted lines show a theoretical sustainable condition: P = 0; EYRr = 0.67 (EYR = 2); ELRr = 0.33 (ELR = 1) and MBr = 1. The arrows in the left panel show the direction of change of the indicators towards a more sustainable condition.

detect trade-offs with the movement towards economic targets accompanied by reverses in ecological targets. Specifically, the ecosystem integrity (Müller and Burkhard, 2010) assessed using both the emergy usage and the ecotoxicological risk resulted in a significant conflict with the economic profit. The ecological integrity reflects the ability of ecosystems to sustain services to humans (De Leo and Levin, 1997), and the results here showed that some of this capability could be risked as the management move towards farm design decision mainly motivated for raising the economical return. Although some conflict arise when characterizing the relationship between ecosystem features and system

management decision and mainly some potential environmental deterioration (i.e fertility loss, biological resistance to pesticides, soil erosion) should be accounted for assessing a potential effect on emergy return. Trade-off analyses have been recognized as a main driving force towards sustainable development (Kanter et al., 2016). The multiple goals involved in the agroecosystem design (i.e profit, productivity, cultural preferences, policy objectives) require the ability to track multiple outcomes well as to highlight critical aspects of working on better farm designs (Groot et al., 2007). In this work, we were able to 334

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One of the most representative goals that arise from both monitoring and ecosystem assessment is to link the emerging results to actual decision-making in agricultural management (De Groot et al., 2010). Although we worked using modal CSs in order to represent the most frequent management regimes, some operational result could be identified from our assessment. Spring soybean was the only crop far above the sustainable threshold of emergy return (ELR = 2) and closer to ELR = 1. Additionally, these favorable values were associated with relatively high values of economic return. However, the environmental cost was evidenced in high ecotoxicological risk. In Argentina, the soybean area has experienced a high rate of expansion in the last 20 years up to cover almost 60% of cropping land (Grau et al., 2005; Castanheira and Freire, 2013). This change has not been free of controversies when analyzing the ecosystem consequences either in positive terms of profit or welfare effects (Matin Qaim, 2005) or negatives when considering the soybean crop as the major driver of deforestation in dry forest/savanna ecosystems (Grau et al., 2008) or the key factor for increasing the agriculturization process that leads to both the increase of external inputs dependence (Viglizzo et al., 2001). Moreover, the rapid evolution of herbicide resistance in the weed populations in the study area (Rubione and Ward, 2016) could exacerbate this dependence due to the increase in pesticide usage. In a previous study presenting an economic-environmental model for land use in Argentina's Pampas, results suggest that it would be appropriate to encourage farmers to reduce the area of land assigned to spring soybeans for both increasing soil carbon and nutrient balance (Cabrini and Calcaterra, 2016). Our results show that this process of agriculture intensification due to soybean adoption leads to an acceptable ecological integrity, in thermodynamics terms, and economical return; but there is still an environmental risk on biodiversity. However, the most desirable overall assessment associated to maize crops should encourage for policy options that alter the relative prices of crops, to avoid the predominance

Fig. 8. Mean and error standard values of each indicator for the five k-means values identified. The thick line (S) shows a theoretical cluster under a sustainable condition.

functioning (Levin, 1998; Loreau et al., 2001), the emergy-related indicators depicted in this paper are closely linked with a strong sustainability approach by offering solid thermodynamic insights (Romero and Linares, 2014) by providing important insights into the state of the ecosystem metabolism that support the ecosystem services that support humans by providing materials, goods, and services. Moreover, these trade-offs were reinforced by the ecotoxicological risk results that highlighted a biodiversity risk associated with some unsustainable pesticide usage.

Fig. 9. Classification and regression tree (CART) of the k-means clusters depicted in Fig. 5. Right and left branches indicate that the group satisfies, or do not satisfy, respectively, the split condition at a decision node. The number associated to each branch shows the number of crop fields splitted to each child node. Columns inside each box indicate the frequency of distribution each k-means clusters in that node, and the top-left number is the node number (ID). The centered number inside the boxes indicates the most frequent cluster (the column order is 1–2-3-4-5). Dotted boxes indicate terminal nodes. The CART model explained the whole dataset variance.

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of monocultures or systems highly dependent on the soybean crop. These new policy options, which may have direct effects on variables such as prices, should be tested for identifying potential tradeoffs between different agroecosystem domains (i.e. economic, environmental) in order to assess agricultural sustainability (Tittonell, 2014). 5. Conclusions The multiple assessments of the most representative cropping systems (CS) of the Pampa regions showed that the crop identity modified the most the ecosystem performance, and maize crop resulted in the indicator pattern most closely related to a theoretical sustainable condition. In addition, the cropping systems set showed a significant tradeoff between the economic return and the ecosystem integrity measured through the ecotoxicity risk and the use of nonrenewable emergy. The effect of increasing levels of the technological level (TL) was clearly observed in the final values of economic return. No single CS was clearly associated with a theoretical sustainable indicator pattern as significant trade-offs were observed between economic and environmental performance. Further studies should be conducted for including more contrasting indicators in order to explore the potential trade-off among other ecosystem components. Acknowledgements This work was supported by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET − Grant PIP 2012/ 555), the Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT − Grant PICT 2013/1559), and the Universidad de Buenos Aires (Grant UBACYT PDE 2016-17). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2017.08.020. References BOLCER, 2015. Relevamiento De Tecnología Agrícola Aplicada De La Bolsa De Cereales Argentina. In: BOLCER, B.C.d (Eds.), . Available at: http://www.bolsadecereales. com/retaa (in Spanish). Bakshi, B.R., 2002. A thermodynamic framework for ecologically conscious process systems engineering. Comp. Chem. Eng. 26, 269–282. Barral, M.P., Oscar, M.N., 2012. Land-use planning based on ecosystem service assessment: A case study in the Southeast Pampas of Argentina. Agriculture. Ecosyst. Environ. 154, 34–43. Bockstaller, C., Guichard, L., Keichinger, O., Girardin, P., Galan, M.-B., Gaillard, G., 2009. Comparison of methods to assess the sustainability of agricultural systems: a review. Sustainable Agriculture. Springerpp. 769–784. Breiman, L., Friedman, R., Olshen, R., Stone, C., 1984. Classification and Regression Trees. Pacific Grove, California. Brown, M.T., Ulgiati, S., 1997. Emergy-based indices and ratios to evaluate sustainability: monitoring economies and technology toward environmentally sound innovation. Ecol. Eng. 9, 51–69. Brown, M., Campbell, D., Comar, V., Huang, S., Rydberg, T., Tilley, D., Ulgiati, S., 2001. Emergy Synth Esis: Theory and Applications of the Emergy Methodology. Center for Environmental Policy, University of Florida, Gainesville. Cabrini, S.M., Calcaterra, C.P., 2016. Modeling economic-environmental decision making for agricultural land use in Argentinean Pampas. Agric. Syst. 143, 183–194. Castanheira, É.G., Freire, F., 2013. Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems. J. Clean. Prod. 54, 49–60. Chen, G.Q., Jiang, M.M., Chen, B., Yang, Z.F., Lin, C., 2006. Emergy analysis of chinese agriculture. agriculture. Ecosyst. Environ. 115, 161–173. De Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complexity 7, 260–272. De Leo, G.A., Levin, S., 1997. The multifaceted aspects of ecosystem integrity. Conserv. Ecol. 1, 3. Ferraro, D., Benzi, P., 2015. A long-term sustainability assessment of an Argentinian agricultural system based on emergy synthesis. Ecol. Modell. 306, 121–129. Ferraro, D.O., Ghersa, C.M., Sznaider, G.A., 2003. Evaluation of environmental impact indicators using fuzzy logic to assess the mixed cropping systems of the Inland Pampa Argentina. Agric. Ecosyst. Environ. 96, 1–18.

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