Amalia Zucaro, Salvatore Mellino, Silvio Viglia, Sergio Ulgiati

1Department of Biology, University “Federico II”, Naples, Italy

2Department of Science and Technology, Parthenope University of Naples, Italy

Assessing the Environmental Performance and Sustainability of National Agricultural Systems

Amalia Zucaro1?, Salvatore Mellino2, Silvio Viglia2, Sergio Ulgiati2

1Department of Biology, University “Federico II”, Naples, Italy

2Department of Science and Technology, Parthenope University of Naples, Italy

Submission Info

Communicated by Pier Paolo Franzese

Agricultural systems are a crucial interface between human societies and nature, in that they “amplify” the human-controlled investments by concentrating natural energies of sun, rain, and nutrients and make them converge to food production. Even if gross economic value, labor and energy expenditures associated to agriculture are unlikely to ever represent a large fraction of the total performance of a national developed economy, yet the role of such a sector goes much beyond the actual food production and calls for higher attention by concerned policy makers. The evaluation of the dynamics and performance of agricultural systems must be carefully investigated from different sustainability points of view (energy, material, economic, environmental, social) to point out how the system and its main driving forces are evolving over time and how can they support a national economy. In this study we compare the agricultural systems of Scotland and Italy over time, by means of an integrated analysis model, SUMMA (SUstainability Multimethod Multiscale Assessment) capable to take into account different dimensions of the investigated systems. The final goal is to understand what are the steps of the investigated processes that are characterized by the lowest performances as well as how the system can be made more robust and resilient in spite of the existing problems (among which increasing energy and resource prices, decreasing land quality, and decreasing marginal productivity).

? 2013 L&H Scientific Publishing, LLC. All rights reserved.

1 Introduction

Agriculture is one of the key drivers of the global economy. It supports the livelihood and subsistence of avery large number of people worldwide and it is vital to rural development and poverty alleviation, in addition to food and non-food items production. Croplands and pastures have become among the largest land use categories on the planet, also exceeding the extent of forest cover, occupying about 50% of global land surface [1]. Additionally, agriculture is the main activity by which mankind continually organizes and defines interactions with the surrounding ecosystem. The multiple functions of agriculture [2] and their links to other global concerns, including loss of biodiversity and ecosystem services, climate change and water scarcity, must be integrated and accounted for when dealing with evaluations of the performance of agro-systems [3]. The performance of the agricultural sectors is essential to the achievement of sustainable development and wellbeing worldwide [4], with special focus on sustainable and equitable use of natural resources for all generations [5]. Biophysical evaluations of productive systems help understand the main driving forces of their performance as well as their dynamical interaction with the surrounding environment and the main economy in which they are embedded [6,7]. For this to happen, a system must be carefully investigated from different points of view (energy, economic, material, environmental, social) as well as on a significantly extended time scale, to point out also how the system and its driving forces evolve over time.

The first step of such assessment is the development of an integrated model, able to take into account all different aspects: energy and material flows, land use, rate of resources use, interrelations of socioeconomic and natural systems, among other parameters. In general, the economic performance is the aspect that policy makers and managers consider with more interest, due to its links to employment and social parameters (economic and social sustainability). However, a comprehensive evaluation cannot disregard resource use and environmental aspects, that also contribute to affect the sustainability of the investigated sector or process, by focusing on crucial factors such as energy demand, material resource availability and use as well as environmental integrity.

In the present study, the SUstainability Multicriteria Multiscale Assessment (SUMMA) [8,9] framework is applied to the performances of the Scottish and Italian agricultural sectors at national scales over the last two decades, in order first of all to calculate and compare their performance indicators, and secondly to ascertain which of these indicators can be usefully extended to other national and local systems for more informed policy making.

2 Materials and Methods

The performances of national Italian and Scottish agricultural systems were investigated over time (1985, 1993, 2002, 2006 and 2010 for the Italian system; 1991, 2001, 2007 and 2010 for the Scottish one). Land cover and land use of these countries are shown in Figure 1, with large fractions of intensive agriculture in Italy and large fractions of semi-natural and natural landscape in Scotland. Of course, the two systems are also characterized by very different climate, temperature and cropping systems.

2.1 The investigated systems

High soil fertility, good climate conditions and water abundance characterize the Italian agriculture, thus enabling the production of a large variety of high-quality fruit and vegetable products [6]. The agricultural sector in Italy is still a very important economic, environmental and social activity in support of a large fraction of population directly involved in agricultural production and agro-industrial food manufacture. Nevertheless, the Italian agricultural sector is far from reaching a high share of the Italian GDP (in the year 2010 agricultural GDP is only 6% of the national GDP) [10]. The northern part of Italy produces primarily grains, sugar beets, soybeans, meat, and dairy products, while the southern part is specialized in fruits, vegetables, olive oil, wine, and durum wheat.

Fig. 1 Investigated systems in dominant landscape types of Europe. Source: Corine land cover 2010 (http://www.eea.europa.eu/dataand-maps/data/corine-land-cover-2006-raster).

Scotland, located in the north of Britain, is very well known for its mountainous landscape rich with forests, rivers, and lakes. Scottish landscape makes difficult to carry out productive activities: the European Union acknowledges the existence of natural and geographic disadvantages and, as a consequence of this problem, 85% of Scotland’s land is considered “Less Favoured Area”. Agriculture is one of the most important economic activities carried in this country; the 94% of total land of Scotland is defined rural by the Scottish government (http://www.scotland.gov.uk/Publications/2010/08/2010UR), about 65,000 people are directly employed in agriculture and it is estimated that more or less 250,000 jobs (1 out of 10 of all Scottish jobs) derive from this sector.

2.2 Methods

The dynamics and performance of agriculture can be evaluated in many ways by means of economic, social and environmental parameters. The main idea of SUMMA, the framework adopted in this study, is that only investigating a single dimension (e.g., energy consumption) and seeking maximization of one parameter (efficiency, production cost, jobs, etc.) is unlikely to provide sufficient insight for sustainable policy making. Instead, if suitable approaches are selected, applicable at different scales and designed so that they complement each other, an integrated assessment would be feasible.

SUMMA is based on the joint application of different evaluation methods, which can be divided in two broad categories (Figure 2): methods focused on resources supplied (“upstream” methods), and those that deal with the consequences of the system’s operations (“downstream” methods). This “upstream” and“downstream” categorization is different than the usual “foreground” and “background” categorization used in LCA. The latter categorization refers to the typology of data used, respectively indicating if they are under the direct control of the process operator or not [11], while the former focuses on the aggregate methods used to describe the impacts. The two points of view are complementary and come into play in two different steps of the analysis (Impact Assessment and Inventory).

The upstream methods selected in SUMMA are Material Flow Accounting (MFA), Embodied Energy Analysis, Exergy Analysis (not used in this study) and Emergy Synthesis, while the downstream methods mainly rely on assessing the impacts of airborne, waterborne and solid waste releases. The global downstream method used for these impacts is the CML 2000 approach. Each method supplies a piece of information about a system’s performance at an appropriate scale, to which other methods may not be applicable. Integration provides an overall picture, characterized by an ‘a(chǎn)dded value’ (understanding the whole as a whole) that could not be achieved through each approach individually. SUMMA, based on a unique set of input data to be used in the calculation of all indicators for increased consistency of results, is capable to expand the focus of the evaluation beyond the more traditional accounting of energy or economic costs.

The MFA method [13-16] aims at evaluating the environmental disturbance associated to the withdrawal or diversion of material flows from their natural ecosystemic pathways. Similarly to the MFA method, the Embodied Energy Analysis (or Gross Energy Requirement, G.E.R.) [17,18] deals with the gross (direct and indirect) energy requirement of the analyzed system and offers useful insights into the first-law energy efficiency on the global scale, considering all the cumulative use of commercial energy supplies. Exergy [19,20] is not applied here because of its main focus on process efficiency at process scale, while we are here concerned on the global scale assessment of national agricultural systems. The Emergy Synthesis method [21-24] takes into account the free environmental inputs (sunlight, wind, rain etc.), the time for resource generation as well as the indirect environmental support embodied in human labor and services, which are not usually included in traditional embodied energy analyses.

Fig. 2 Flow diagram of the SUMMA approach. The system is treated as a black box. Input and output flows are multiplied by specific exergy, energy, matter, emergy and emission factors to yield estimates of upstream and downstream impacts on resource and environmental dynamics ([12] modified).

Finally, the CML 2000 method [25] allows the evaluation of an extensive list of impact categories. The method consists in assigning impact characterization factor to the emission flows from each process (determined by an accurate mass balance, both locally and globally). These factors may be more than one for each flow if the latter contributes to several categories of impact simultaneously. Detailed description of these method can be found in Ulgiati et al. [8,9].

2.2.1 Systems diagram

The generic system diagram of an agricultural system is shown in Figure 3(a), with symbols explained in Figure 3b. The renewable sources (sun, rain, wind and deep heat) are shown in the left side of the diagram. These sources go directly and indirectly in support of the whole investigated system. In addition to renewable flows, further imported flows from the main economy (fertilizers, pesticides, fossil fuels, electricity, goods, machinery and labor) support agricultural production. These flows are shown as entering from the top of the system diagram. The “assets” symbol represents in aggregate form the most typical infrastructures of agricultural systems (barns, storage buildings, irrigation system, etc).

Agricultural products are exported and market pays for them. Such money adds up to the total budget of the agricultural sector, indicated in the diagram as money storage. The agricultural budget is mainly composed by the money that farm workers receive as an income of productive activities (products sold), as well as contribution from external investments. Money is then used to pay for the resources imported in support to the system.

Fig. 3 (a) System diagram for a generic agricultural system ([7], modified). (b) Legend of systems symbols used in Figure 3-a, from [22].

2.2.2 Calculation procedure

Based on the systems diagram of Figure 3(a), tables of input and output flows were constructed for the two investigated systems (Italian and Scottish agricultures). All data were collected, on a yearly basis, in order to account for the supporting matter, energy and money flows. Firstly, an inventory of all the input and output flows is generated, on the local scale of the system. This inventory forms the common basis for all subsequent impact assessments, which are carried out in parallel, thus ensuring the maximum consistency of the input data and inherent assumptions. The raw amounts of input and output flows from the inventory phase are multiplied by suitable conversion coefficients specific of each method previously described, which express the “intensity” of the flow, i.e. quantify to what extent cumulative material, energy, or environmental costs are associated to each flow over its whole life cycle. Such coefficients are available in life cycle assessment, energy and environmental accounting literature. Material, energy, and environmental “costs” associated to each flow are calculated, according to the following generic equation:

whereC= material, energy or environmental cost associated to the investigated process, i.e. cumulative matter, energy, emergy and emissions associated to that process on the biosphere scale;Ci= material, energy or environmental cost associated to theith inflow or outflow of matter or energy;fi= raw amount of theith flow of matter, energy, labor;ci= material, energy or environmental unit cost coefficient of theith flow (from literature or calculated in this work). The material, energy or environmental costCis finally divided by the process productp(in our case the dry mass, money value and energy content of Italian and Scottish agricultural yields), in order to generate production cost indicators according to the method applied. A large set of performance indicators can also be calculated, e.g. EROI in energy analysis, and EYR, ELR, ESI among others in emergy analysis. The calculated indicators are then interpreted within a comparative procedure, in which the results of each method are set up against each other and contribute to a comprehensive picture, on which scientific and policy conclusions can be drawn.

The livestock sector is not included in the present study, but agricultural production of forage and other livestock feedstock do.

3 Results

3.1 Performance of the Italian agricultural sector

Based on the energy system diagram (Figure 3a), an inventory of input and output flows was constructed for each investigated year [10,26-30] to evaluate the agricultural trend over a 25 year time-frame (Table 1). In the investigated period, land cropped decreased, and so did fertilizers and pesticides, while electricity, liquid fuels and machinery increased. Direct labor decreased in terms of hours applied, but its money cost increased, together with the cost of services. The mass and energy content of agricultural yield decreased, while instead the current price economic value increased although with some fluctuations.

Inventory data were then converted into cumulative material demand, cumulative energy demand, environmental support, and emissions, to generate performance indicators over time. The main calculated indicators are listed in Table 2 as: abiotic material intensity (MIabiot) and water intensity (MIwater) (i.e., abiotic matter and water degraded in all the steps of the process); energy intensities (cumulative commercial energy demand expresses as joule or oil equivalent, goileq.); emergy intensities (demand for global environmental support to the process); airborne and waterborne emission intensities (according to selected LCA impact categories). Indicators are calculated in relation to selected functional units (dry mass produced, energy made available in the product; economic value of the yield; hectares of cropped land).

For example, according to Table 2, one euro of GDP generated by the agricultural production required in the year 2010 about 3 kg of abiotic matter, 100 m3of water, 11 MJ of energy (translating into 263 grams of oil equivalent), 5.00E+12 seJ of environmental support, and finally generated a global warming contribution of 827 g of CO2-equivalent.

A selection of indicators from Table 2 is graphically shown in the radar diagram of Figure 4. To compare data with different orders of magnitude in the same radar diagram, a normalization procedure was applied (all values divided by the value of the first year of investigation) so that a larger area suggests a higher relative impact.

Table 1 Direct supply, land use and product generated: Agricultural sector in Italy.

Increasing areas in the diagram clearly point out that the Italian agriculture is becoming less sustainable and is day-by-day turning into a fossil fuel based economy. This is affecting its ability to serve as a source of renewable materials, food, energy and ecosystem services (e.g. water holding ability and stabilization of organic matter in soil). The decreasing performance is affected by a mix of factors: large rainfall oscillations and related variation of irrigation practices, decreased amount of arable land, variation of the mix of crops, change in technology (increased agricultural machinery use), decrease of labor, increased use of fertilizers and other chemicals. Such variations of input values translate into important changes of the performance indicators that in turn globally translate into a different shape and area of the radar diagrams.

3.2 Performance of the Scottish agricultural sector

The inventory data of the Scottish agricultural sector (Table 3, [31-33]) show increasing cropped land, decreasing use of fertilizers and pesticides (with some fluctuation), decreasing electricity, increasing fuels and machinery. Labor increases both in terms of hours invested and money cost. Instead, services show a relatively constant trend. The mass of agricultural production increases and so do its energy content and economic value. The declining ￡/€ exchange rate between euro and sterling partially hides the constant increase of economic value when expressed in Euro (in 2007, 1 ￡ was equivalent to 1.467 € while instead in 2010 it was only valued 1.167 €).

Table 4 lists the main indicators obtained through the application of the SUMMA framework. The indicators are the same as in Table 2 for Italy, in order to ease comparison.

Table 2 Performance of the Italian agricultural sector in selected years.

Fig. 4 The radar diagram shows the performance indicators of Italian agriculture over time. Values normalized from Table 2.

A pictorial overview of selected results is provided in Figure 5 showing oscillating performances around the reference year 1991. It should be noted that the radar diagram was generated by only using intensive indicators, not extensive ones, so that the diagram’s behavior is not dependent on the different physical area of the system in the investigated years.

4 Discussion

The possibility to compare selected impact categories of investigated systems (e.g. energy depletion, demand for environmental support, contribution to global warming, acidification, eutrophication) is an important aspect of the SUMMA approach specially if the assessment aims at process improvement, resource use policy making or finally large scale development planning. Much more important is that the SUMMA calculation procedure allows to identify what are the categories that are responsible of the largest impacts and, within each category, what is the process step that generates the highest loading; finally, it can be seen, within each step, what is the item to be charged for the heaviest contribution and therefore needing improvement effort. Comparison can be made between the present process performance and performances in previous years over a time series, between two processes yielding the same product or service, and finally between scenarios based on improvement assumptions.

In the present study we focus on two very different agricultural systems. The case studies show the way the assessment approach is applied and highlight its potentialities for further application to agricultural systems at all scales.

Table 3 Direct supply, land use and product generated: agricultural sector in Scotland

4.1 Time trends of national agro-systems

Trends of resource use, cropped land, labor invested, and yields harvested allow the calculation of performance and sustainability indicators for the two systems (Tables 2 and 4). Focusing on material and energy costs, it can be seen that 1 g d.m. of agricultural product required in Italy 2010 about 1 g of abiotic material (66% more than in 1985) and 3.3 kJ of energy (50% more than in 1985). Unit water demand dropped from 29.5 g water per g of product, more than 50% less compared to 1985. Instead, the Scottish agricultural system, in spite of its less favorable climate conditions, only required 0.51 g abiotic matter per g of product (20% less than in 1991) and 1.29 kJ of energy (9% less than in the reference year). Water demand dropped by 25% (from 2.49 g water per gram product in 1991 to 1.88 g water/g product in 2010). The much lower water use in Scotland is certainly due to the different climate conditions and mix of agricultural crops (e.g. wheat in Italy demanding more water than barley in Scotland); instead, the increase of abiotic material and energy demand in Italy is linked to the still intensive agricultural system (especially in Northern Italy), based on increasing mechanization and fuel use and doubling of electricity use, coupled to decreased agricultural yields (in mass and energy content terms) (Table 1). The opposite is true in Scotland, where electricity use decreases by about one third, coupled to increased yield. The energy investment compared to the energy content of the yield provides an additional information about the ability of the systems to capture and store the solar energy through photosynthesis: the EROI of Italian agricul-ture is constantly around the value of 7:1 (7 joule yielded versus 1 J fossil energy invested), while EROI is in the range 7-10:1 for Scotland (with an increasing trend), showing a much higher ability to capture the solar energy and making it available.

Of course, data trends oscillate in both countries and the final energy and material costs are the cumulative result of changes occurring in all input and product flows.

It is important to point out that the total current price economic value of agricultural production in Italy was 2.37E+10 € (28% higher than in 1985), compared to an economic expenditure of 2.36E+10 € in 2010, for labor and services: the activity was hardly capable to pay its own expenses, in the average. Instead, the Scottish agriculture still shows an economic value of agricultural production equal to 2.22E+09€ (in 2010, 50% higher than in 1991), compared to 1.65E+09 € in the same year, still providing a sufficient net income. The reasons for such trends must be investigated in individual input items of Tables 1 and 3, with special focus on the oscillations of each flow and their consequences on the final results.

Contributions to global warming, rain acidification and water body eutrophication are also shown in Tables 2 (Italy) and 4 (Scotland), globally showing a better performance of Scottish agriculture (smaller unit values and decreasing trends in all categories) compared to Italian agriculture (increasing trends and higher absolute unit values).

4.2 The added value of emergy

The emergy synthesis methods provide another interesting set of indicators. While mass and energy indicators shed light on the efficiency of the system in using available resources, emergy indices and ratios allow to investigate the quality of these resources. In other words, a system may be efficient in using fossil energies, but still be unsustainable due to their nonrenewability. Focusing on resource generation time and patterns, emergy assigns a quality label to each resource flow and calculates indicators of efficiency (transformities: solar equivalent energy/unit of product), local self-reliance (EYR=total emergy use/imported emergy), carrying capacity (ELR= nonrenewable and imported emergy/local renewable emergy), economic and environmental sustainability (ESI= EYR/ELR), renewability (%REN= renewable emergy use/total emergy use). Looking at the emergy indicators in Tables 2 and 4, it is possible to extract a clear picture of the performance of the two systems at the scale of biosphere, i.e. at the scale of the larger system that generates and provides resources over time. Very important is to point out that emergy accounting also includes the generation of minerals in the crust and other life-supporting processes, that are not included in material and energy accounting methods.

The demand for such environmental support doubled in Italian agriculture during the investigated time period: very interesting is to observe that while the emergy per unit mass (seJ/g d.m.) doubles, the emergy demand per unit economic value (seJ/€) only increases by 20% (inflation affects results to some extent) and the emergy demand per unit of energy delivered increased by 2.5 times, likely affected by a lowered energy content of the delivered product. The Sustainability Index (ESI) decreased from 0.14 in 1985 to 0.08 in 2010.

The Scottish agriculture shows a much better performance also in emergy terms: its emergy demand per unit of product (either g d.m., J and €) decreased, while its overall sustainability is more than twice the one for Italy and shows a slow decreasing trend.

4.3 Large scale versus local scale burdens

Tables 2 and 4 also show another set of interesting indicators, the so-called global to local ratios. They are defined as the ratio of cumulative material, energy, emission burden (indirect + direct investments) to the locally invested amounts, thus measuring how the local specificity of a process is capable to amplify the resource demand at larger scales. Considering, for example, the energy global-to-local ratio, its value close to 3 for Italy and close to 3.5 for Scotland means that each joule of energy used locally (directly inthe farm) is supported by more or less 3 joules invested directly or indirectly in the process (also due to the energy invested for material goods like tractors and pesticides). A change in the amount or in the mix of direct energy and resources used locally affects the global scale, since it depends on the resource metabolism of the production chain (and process efficiency) that is followed to deliver the input resources and since there is energy embodied in goods, materials and infrastructures. For example using steel locally involves the whole chain that provides such a steel, from mining of iron ore to the refining of the product in a life cycle perspective. Therefore, if more or less steel is used, or if steel is replaced by aluminum, or if recycling patterns are implemented, this may translate into a bigger or smaller burden placed on the larger scale. Global-to-local use ratios can be calculated for almost all impact categories (matter demand, water demand, impact of emissions): Tables 2 and 4 show an abiotic material global-to-local ratio of about 1.5 for Italian agriculture in 2010 and about 2.0 for Scotland (both decreasing trends). Similar ratios can also be calculated for water use and emissions, in order to highlight the burden generated outside of the local system and increase awareness for responsible and planned use of resources. Providing a clear assessment of such aspects is important for policy. The global-to-local ratios may change due to a multiplicity of factors (efficiency of the productive chain, mix of supply, etc.) and it is possible to affect these factors through improvement strategies.

The global-to-local ratio cannot be calculated for emergy that is by definition focused on the global scale only.

4.4 Use of biophysical indicators for planning and policy making

Once time series of inventories are made available (Tables 1 and 3)and a suitable set of performance and sustainability indicators has been calculated (Tables 2 and 4), it is possible to generate an overall comparison of the systems behavior over time (Figure 6) similar to the radar diagrams used for assessment of time trends (Figures 4 and 5). The Figure helps understand in a global way what are the parameters that affect to a larger extent the performance of each investigated system, so that it is easier to a manage, a stakeholder or a policy maker to go back to the analytical Tables of calculated indicators, identify the most crucial and then refer to the inventory and the supply chain for suitable improvement actions and regulations.

Figure 6 suggests that the agriculture of Italy 2010 is globally more environmental impacting than the Scottish agriculture in the same year. The figure also includes material and energy flows per ha, higher for Italy than for Scotland. When indicators are calculated on “per gram” or per € basis, costs and impacts are hidden, because of the Italian higher productivity per hectare and because of the conversion ratio U.K￡/€.

Figures similar to Figures 4, 5 and 6 may be a useful starting point for a debate about a system’s performance (not only an agricultural one), for comparison among systems, for scenario-making and for detailed discussion about the reasons and the drivers of calculated performances, in order to involve all potential actors in concerned policies and responsible use of resources.

5 Conclusion

The study shows that analytical inventory procedures, joint use of different evaluation methods characterized by different spatial and time-scales, as well as comparison of intensive and extensive indicators over time provide a powerful tool for comprehensive understanding and policy-making.

Table 4 Efficiency and performance indicators of the Scottish agricultural sector in selected years.

Fig. 5 The radar diagram shows the performance indicators of Scottish agriculture over time. Values normalized divided by the value of the first year of investigation from Table 4.

Fig. 6 Radar diagram showing the comparison of the performance indicators of agriculture in Italy and Scotland. Values normalized with reference to the total impact generated (the total impact is calculated by adding the values of the two systems, then, in order to calculate its fraction or percentage, the value of the indicator is divided by the sum of the two) from Tables 2 and 4.

The two investigated case studies (Italian and Scottish agricultures) underlined a very different dynamics. The Italian agricultural system seems increasingly becoming less sustainable, because of its heavy dependence on fossil fuels. The Scottish system shows instead a better global performance. A problem suggested by the assessment is that the best performing system is not also the one characterized by the highest productivity. Such an aspect must be taken into account by policy makers, in that it affects the ability of the system to supply food, energy and materials to meet the growing needs of local populations.

It clearly appears that both investigated systems generate not only local but also and mainly global impacts in the surrounding regions where primary input are processed as well as all over the supply chain. This means that an improvement of the local performance (efficiency, change of resource mix, change of crop mix, etc.) may lead to a positive feedback effect on the regional and global scales. The SUMMA approach presented and applied in this study is suitably designed in such a way to allow comparative assessment and “scenario making” experiments, based on selectively assuming technical changes or better use of the most crucial production factors in order to ascertain how these changes affect the final performance indicators.

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[33] Scottish Agricultural Statistics (2013), http://www.scotland.gov.uk/Topics/Statistics/Browse/AgricultureFisheries/PubAbstract /Abstract2013.

11 September 2013

?Corresponding author.

E-mail address: amalia.zucaro@unina.it (A. Zucaro).

ISSN 2325-6192, eISSN 2325-6206/$- see front materials ? 2013 L&H Scientific Publishing, LLC. All rights reserved.

10.5890/JEAM.2013.11.006

Accepted 13 November 2013

Available online 1 January 2014

Integrated Evaluation

Agriculture

Sustainability