Integrated assessment of agro-ecological systems: The case study of the
“Alta Murgia” National park in Italy
Michele Moretti
a,b,⁎
, Annalisa De Boni
a, Rocco Roma
a, Mariano Fracchiolla
a, Steven Van Passel
b,c aUniversity of Bari“Aldo Moro”, Department of Agro-environmental and Territorial Sciences, Via Amendola 165/A, 70126 Bari, Italy
bHasselt University, Faculty of Business Economics, Centre for Environmental Sciences, Martelarenlaan 42, 3500 Hasselt, Belgium c
University of Antwerp, Faculty of Applied Economics, Department of Engineering Management, Prinsstraat 13, 2000 Antwerp, Belgium
a b s t r a c t
a r t i c l e i n f o
Article history: Received 1 July 2015
Received in revised form 17 February 2016 Accepted 19 February 2016
Available online 27 February 2016
Several indicators and methods are already applied for sustainability assessment in agriculture. The links between sustainability indicators, agricultural management and policies are not well explained. The aim of this study is to combine biophysical and monetary sustainability assessment tools to support agriculture policy decision-making. Three methodological steps are considered: i) the environmental impacts of farms are assessed using terrestrial acidification, freshwater eutrophication, soil and freshwater ecotoxicity as well as natural land transformation; ii) the most relevant indicators of agriculture damages on ecosystems quality are aggregated into an index; iii) the farms' index scores are integrated with farm assets, land and labor, into the Sustainable Value approach (SVA), as indicator of natural resources used by farms. As a case study, the methodology was ap-plied to arable farms with and without animal husbandry of the“Alta Murgia” National Park. The crop farms, in our sample, have a higher sustainable value using their economic and environmental resources. Mixed farms need to improve their resources use efficiency. Although crop farms have lower land-use efficiency compared to mixed farms, our results suggest supporting, by means of policy options, the specialized crop farms that, on average, perform better in terms of ecosystems quality preservation. Finally, wefind that Life Cycle Assessment (LCA) to soundly measure the environmental impacts clearly enriches the SVA.
© 2016 Elsevier Ltd. All rights reserved.
Keywords: Farm sustainability Ecosystem quality damage Sustainable value Integrated assessment
1. Introduction
Sustainable development aims at improving the quality of life of human populations that guarantees a sustainableflow of goods and ser-vices. The Brundtland Commission defined it in 1987 as: “development that meets the needs of the present without compromise the ability of future generations to meet their own needs” (WCED, 1987). Since that time, several efforts have been done to integrate the principles of sus-tainable development into country policies. Therefore, sustainability as-sessment is considered an important step to shift toward sustainability of human activities (Pope et al., 2004). Scientists have developed several different sustainability evaluation tools in the last thirty years such as biophysical, monetary tools and sustainability indicators to deal with the triple bottom dimensions of sustainability (environmental, econom-ic and social) (Gasparatos and Scolobig, 2012; Van Passel and Meul, 2012; Kloepffer, 2008). Interesting reviews of different approaches
for sustainability assessment can be found in Neumayer (2003),
Gasparatos et al. (2008),Jeswani et al. (2010)andVan Passel and Meul (2012). However, the scientific debate between supporters of
monetary or biophysical tools still remains unsolved (Gasparatos and Scolobig, 2012). Monetary tools were strongly criticized both on the
ethical ground for their strong“anthropocentric” view and for the
high level of subjectivity that arise from the imperfect knowledge in many relevantfields of sustainability issues, especially those that cannot be traded into existing goods and services markets. On the other hand, an important advantage of monetary tools is that they provide decision makers information in a format they are familiar withFarrell and Hart (1998)andAtkinson (2000)). Biophysical models address sustainability from an“ecocentric” perspective, and are considered more “objective” when compared to monetary approaches, but at the same time, they are not able to capture some economic and social issues such as capital, education and culture (Gasparatos et al., 2008, 2009andMaes and Van Passel, 2014). Moreover, biophysical and monetary assessment methods differ also in their basic concept of value, relying on cost of pro-duction and utility theories of value respectively (Gasparatos et al., 2009). According toGasparatos et al. (2009), approaching sustainability using only monetary or biophysical tools means to leave out the interre-lationships between the two different approaches to sustainability resulting into a great deterioration of the decision making process out-come. Whereas, the combination of biophysical and monetary tools may evolve in a wider sustainability perspective in performing the assess-ment. These types of“hybrid approaches” (Gasparatos et al., 2008) Agricultural Systems 144 (2016) 144–155
⁎ Corresponding author at: University of Bari “Aldo Moro”, Department of Agro-environmental and Territorial Sciences, Via Amendola 165/A, 70126 Bari, Italy.
E-mail address:michele.moretti@uhasselt.be(M. Moretti).
http://dx.doi.org/10.1016/j.agsy.2016.02.007
0308-521X/© 2016 Elsevier Ltd. All rights reserved.
Contents lists available atScienceDirect
Agricultural Systems
were strongly fostered in order to balance the simplicity, the wider ac-ceptance and the easy communication characteristics of monetary tools with the more strict and objective relation with ecosystems func-tion andflows of the biophysical ones, with a logical effect on the im-provement of systems' sustainability. If the problematic issues of consistency and of weighting between environmental, economic and societal priorities were properly assessed (Hoogmartens et al., 2014), then the integration of monetary and biophysical sustainability assess-ment approaches could supply decision maker with simplified and stan-dardized sustainability assessment (Jeswani et al., 2010).
Shields et al. (2002), defining the basic characteristics of sustainabil-ity indicators, argue that they have to provide users with information they need in a form they could understand. For these purposes, aggre-gated sustainability indicators are particular useful to compare policy options (Farrell and Hart, 1998) because they are able to summarize complex and multidimensional issues avoiding information overload and reducing the risk of being misinterpreted (Costanza, 2000). Several indicators such as water withdrawal, threatened species, soil organic carbon content, soil nutrient retention capacity, and fertilizers and pes-ticides use (Reytar et al., 2014) were developed to understand the com-plex relationships between agriculture and environment, but links between sustainability indicators and agricultural management are not well explained (Wei et al., 2009).
The aim of this study is to cover this lack by exploring the possibility to combine biophysical with monetary sustainability assessment tools to support agriculture policies decision-making at local, regional or national level. To achieve this goal, the Life Cycle Assessment (LCA) methodology (biophysical tool) was integrated into a monetary sustain-ability assessment tool: the Sustainable Value Approach (SVA). LCA has been used to define the environmental impacts of the agricultural activ-ities at the farm level, while SVA allows local policy makers to compare the sustainability performances of different farm's management strate-gies. As a case study, the proposed methodology was applied to the ag-ricultural system of the“Alta Murgia” National Park (hereinafter simply referred as Park). According to the Reg.(CE) 1242/2008 (2010)the ty-pologies of agricultural holdings inside the Park are: mixed crops –live-stock; specialistfield crops and specialist grazing livestock (Ente Parco Nazionale dell'Alta Murgia, 2010).
This paper is further structured in the following way.Sections 2 and 3focus on the logical framework and the methodologies used in the as-sessment of environmental and socio-economic impacts of the farms' activities inside the Park. InSection 4the main results are presented. The paper ends with a discussion and conclusions (Section 5 and 6). 2. A pathway to a more integrated sustainability assessment 2.1. Life cycle analysis as sustainable assessment tool
The increased importance of Life Cycle Assessment (LCA) in policy evaluation, (Meul et al., 2014; Moretti et al., 2014; Thabrew et al., 2009), led to a strong impulse in its applications to assess sustainable development projects, plans and programs. LCA can be considered a decision-support tool for environmental burdens and impacts that can be used at different level (firms, sector, region, etc.) and by managers, suppliers, customers and policy-makers. (Jeswani et al., 2010). The ca-pability of LCA to capture high relevant environmental issues, its
analyt-ical soundness and its efficiency in simplifying complex systems
characterizes, according toMeul et al. (2014), the success of this method as decision making tool. On the other hand, the different allocation rules, the very restrictive assumptions used for defining e.g. equivalence factors (Gasparatos et al., 2009) as well as the demand for large amount of data and the complexity of the logical framework behind LCA may hider this potential and reduce its practical application (Jeswani et al., 2010). A large number of scientific studies exist on the application of LCA to agriculture and agricultural systems (e. g.,Huysveld et al., 2015; Tendall and Gaillard, 2015; Ripoll-Bosch et al., 2013; Nemecek
et al., 2011; van der Werf et al., 2009; Thomassen et al., 2008; Pluimers et al., 2000). Moreover, LCA can provide estimates of environmental im-pacts of entire farming systems (Van der Werf and Petit, 2002). Most of these studies are usually based on“typical” production systems that use average farm data or“representative farms” (Meul et al., 2014). Howev-er, the applications of LCA are still a daunting task because most of the impact categories used are defined by means of characterization factors usually estimated at a global (in some cases even lower— e.g. US and Europe) scale and thus only partially linked to policy makers concern and political goals (Sleeswijk et al., 2008). Even if LCA addresses the most pertinent environmental problems (Jeswani et al., 2010) this does not mean that all the environmental issues are included (e.g. eco-system services1) and that the proposed indicators adequately address all the potential impacts (e.g. the use of pesticides and fertilizers, and land use). Methodological frameworks and models to include missing environmental topics and improve some already accounted indicators have been already proposed e.g. ecosystem services (Y.I. Zhang et al., 2010), biodiversity (Jeanneret et al., 2014; Curran et al., 2010; Schmidt, 2008), land use (de Baan et al., 2013; Koellner and Scholz, 2008), toxicity (Rosenbaum et al., 2008), as well as the consequential LCA for capturing the benefits of the effects of climate-change mitiga-tion policies (Plevin et al., 2014). Further developments are needed in order to make them applicable to all the cases and at different spatial levels. The use of LCA coupled with different types of methods and ap-proaches to sustainability assessment testifies its relevance in capturing the environmental impacts of human activities, but also, the needs to placing its outcomes in a broader sustainability context. For example,
Mohamad et al. (2014)were able to estimate the environmental and economic impacts of organic and conventional olive farms using a com-bined LCA and Life Cycle Costing (LCC) approach.Meul et al. (2014)
using Flemish dairy farms as case study, used LCA combined with a
broader farm sustainability assessment tool (MOTIFS;Meul et al.,
2008) to exploring the potential of LCA as a decision supporting tool
to support decision making at farm level. Whereas,Moretti et al.
(2014)coupled LCA with market demand analysis to assess the most ef-ficient subsides scheme for improving profitability of forest sector in Apulia Region.
2.2. Sustainable value approach as a sustainability assessment tool The Sustainable Value Approach (SVA) is a value oriented method for assessingfirms' contribution to sustainability at different macroeco-nomic and microecomacroeco-nomic scales: national economy (Ang et al., 2011); economic sector or single economic entities (Van Passel et al., 2007; Van Passel et al., 2009). This property of multi-spatial level analysis is partic-ularly relevant for the agricultural sector, especially for environmental sustainability; which can be strongly affected by farms' management strategies. Moreover, it allowed integrating different indicators in a composite index, the sustainable value, that was found to be useful to compare sustainability performance of different agricultural sectors (Van Passel and Meul, 2012) and policy options.
The SVA was developed byFigge and Hahn (2004a,b, 2005), extend-ing the capital efficiency theory to resource use. The SVA applies the logic of opportunity costs to natural resources evaluation. It is a numer-ical path for the integration of sustainability assessment indicators (e.g.
Van Passel et al., 2007, 2009), used for several interesting applications: oil company (Figge and Hahn, 2005); the European manufacturing com-panies (Hahn et al., 2007); agricultural systems (Van Passel et al., 2007, 2009; Hou et al., 2014) and European countries (Ang et al., 2011). These applications confirm its utility to advice policy-makers in decision mak-ing at different levels (Van Passel and Meul, 2012). This approach cate-gorizes all the resources used by an economic entity into:financial
1
According toY. Zhang et al. (2010)the numerous life cycle oriented environmental as-sessments need to be converged to methods that combine different disciplines to consider the role of ecosystem services in supporting human activities.
capital, natural capital and labor. The SVA accounts for how much value has been created as a result of the economic entity using the resources instead of a benchmark (Ang et al., 2011). If the return that an economic unit achieves through the use of a defined amount of resources exceeds the opportunity costs of these resources, then the economic unit con-tributes to the sustainable use of resources at the benchmark level. The opportunity cost indicates how much return the benchmark alter-native would create with the same amount of resources. It indicates how efficiently resources have been allocated between different eco-nomic entities (Figge and Hahn, 2004a). Hence sustainable value is clearly a monetary measure of sustainability. A complete review of the advantages and disadvantages, assumptions and inherent sustainability perspective of SVA can be found inAng et al. (2011).
2.3. An integrated sustainability assessment of agro-ecosystems
Agro-ecosystems are arguably the most managed ecosystem in the world (Stoorvogel et al., 2004; Wei et al., 2009). In the past, agro-ecosystems were managed and evaluated overemphasizing their social and economic components (Wei et al., 2009). According to different au-thors, this has caused many alterations of ago-ecosystems like land deg-radation, loss of biodiversity, groundwater depletion, greenhouse gasses emission and erosion (Conway, 1985; van der Werf and Petit, 2002; Dale and Polasky, 2007). The increasing concern about the nega-tive impacts of agricultural activities on natural resources underlies the development of many methods for their evaluation (for a thorough
re-view seesVan der Werf and Petit, 2002; Payraudeau and van der
Werf, 2005). In this context, sustainable agriculture can be defined as the management of agro-ecosystem in such a way that it can maintain its biological diversity, productivity and regeneration capacity today and for the future (Van Cauwenbergh et al., 2007). More in detail,
Pretty (2008), defined agriculture sustainability as the capability of ag-ricultural systems to: (i) integrate biological and ecological processes, (ii) minimize the human-made inputs, and (iii) make productive use of farmers'knowledge and their collective capabilities. Several models integrate biophysical and economic assessment of agro-ecosystems sus-tainability (for a thorough review seeJanssen and van Ittersum, 2007).
Stoorvogel et al. (2004)propose an integrated biophysical and econom-ic approach (the Tradeoff Analysis Model) to assess sustainability of agro-ecosystems, highlighting the role of temporal and spatial scales to supply policy-makers with useful indicators.Wei et al. (2009)used the force-pressure-state-impact-response approach to identify the in-teractions between biophysical and economic models in order to
pro-vide a comprehensive evaluation of farm's performance.Paracchini
et al. (2015)presented another approach to sustainability assessment at different spatial level (farm, farming region, etc.) in combination with a wide range of indicators. According toDantsis et al. (2010), the application of multiple criteria in agricultural system sustainability as-sessment requires several assumptions and simplifications although it brings several benefits (e.g. representing the current agricultural man-agement practices, simplifying composite concepts and being applied to different spatial scales). An interesting examination of the pros and cons of using aggregate indicators for agricultural sustainability
assess-ment is presented byGómez-Limón and Sanchez-Fernandez (2010).
Usually, these“indicator lists” (Gasparatos et al., 2009) are created in order to capture sustainability issues relevant for a specific context and therefore, they are not widely applicable. One example of this ap-proach is the project“Agroecosistemi”2supported by the Park. This was based on the AESIS (Agro-Environmental Sustainability Informa-tion Systems) framework, developed byPacini et al. (2011). The project aimed to identify a list of indicators according to the different sustain-ability dimensions (environmental, economic and social), in order to
assess farms' sustainability performances and establish their contribu-tion to the needs of the“Park System”. The maintenance of the econom-ic, biological and physical components that make up an agro-ecosystem is determinant for its sustainability (Belcher et al., 2004). Moreover, the complex trade-offs between these components claim for a holistic ap-proach to agro-ecosystems sustainability assessment in order to identify sustainable management practices (Pacini et al., 2015). However, the dependency of farm activities on natural resources and human-made resources asks for a better understanding of the links between environ-mental indicators, farm management activities and policies. Integrated sustainability assessment tools could be appropriate to identify policy priorities toward sustainable agro-ecosystems.
2.4. Methodological framework
To account for the requirements of sustainability assessment of agro-ecosystems described above, we structured our analysis in three stages: (i) the life cycle environmental impacts assessment of the stud-ied farms, (ii) the aggregation of some impact categories into the eco-system quality damage index, and (iii) the incorporation of this index into the SVA algorithm.Fig. 1illustrates the approach to assess sustain-ability of agricultural production systems combining LCA and SVA.
The sustainable value of different farms and agricultural sectors (specialized crop and mixed farms) is calculated to compare their role in safeguarding the sustainability of agro-ecosystems. The farms' contri-bution to environmental sustainability can be monitored using the LCA. Within the LCA methodological framework, the ReCiPe impact assess-ment method has been chosen in order to combine both a problem and a damage oriented approaches. Although traditional LCA is a steady-state tool which does not account for the uniqueness of the environmen-tal systems affected and their sensitivities to emission sources (Reap et al., 2008) this bias has been reduced by means of:
1. Choosing the most affected environmental impact categories by this site-specificity bias, such as: acidification, eutrophication, toxicity (Reap et al., 2008).
2. Further reducing the impact categories used according to the main geo-morphological and ecological characteristics of the studied area (seeSections 3.1 and 3.2)
Moreover, while the ReCiPe method uses the data on registered species at the European or Global level, in this study, the selected impact categories were normalized according the data at the Mediterranean spa-tial level.3The ReCiPe methodology assumes that the quality of ecosys-tems is adequately represented by the diversity of species (Goedkoop et al., 2009). Hence, the chosen impact categories related to terrestrial acidification, terrestrial ecotoxicity and freshwater ecotoxicity, freshwater eutrophication and natural land transformation (measured in terms of species lost∗ yr) have been considered as good proxy of the damages caused to ecosystems quality. Assuming a linear addictive relationship, an aggregated index has been designed (the ecosystem quality damage index), accounting for the overall effects of farms' management activities on ecosystems quality. The ecosystem quality damage index has been in-corporated into the SVA algorithm representing the natural resources used by farms to create value added for the society. However, the SVA outcomes compensates for the negative impacts generated by farms with the positive ones. Therefore, the value contribution (the Return to Cost ratio) for each form of capital was calculated in order to identify on which resource form (capital, land, labor, natural) the efforts should be focused in order to achieve a more sustainable agro-ecosystem within the Park.
2
http://www.parcoaltamurgia.gov.it/officinadelpiano/index.php?option=com_ content&view=article&id=856&catid=41.
3
Data form 2000 have been used according toBrooks et al. (2002)in order to be con-sistent with the normalization procedure used into ReCiPe impact assessment method. 146 M. Moretti et al. / Agricultural Systems 144 (2016) 144–155
3. Materials and methods
To broaden the general insights on the integration and combination of sustainability assessment tools and to answer the call for methodological pluralism in holistic sustainability assessment (Gasparatos et al., 2009), this study performs a sustainability evaluation of farming systems both
at the farm level and at the regional level. Therefore LCA and SVA are in-tegrated. Combining these two methods is feasible because they satisfy the request of complementarity, consistency and ability to address all the perspective of sustainability (Van Passel and Meul, 2012).
This paper addresses the following research questions: a) is it possi-ble to combine biophysical and monetary sustainability assessment
Fig. 1. A framework for an integrated sustainability assessment of Agro-ecosystems.
Fig. 2. Geographical location and zoning of the“Alta Murgia” National Park. Source: adapted fromCotecchia (2010)andPerrino et al. (2006).
tools in a meaningful and consistent way to agro-ecosystems; b) could this methodology be used to structure policy measures to improve the sustainability of agriculture in natural areas. Application of this method is illustrated in a case study involving 14 mixed and specialized crop farms located in the Park.
3.1. The study area
The Park is located in the Apulia (S–E of Italy) on the top of the cal-careous Murgia highland (Fig. 2). Its total surface is about 68.077 ha for the most occupied by pasture, agricultural land and a limited amount of forests and natural areas (Cotecchia, 2010). The whole Park's area was split into three zones: zone A1 of significant interest in nature, landscapes and cultural heritage; zone A2 characterized by agricultural landscapes and zone B of ecological connection and promotion of economic activities compatible with the purposes of the Park. The zone A2 is the most consis-tent farming area, both in terms of quantity and quality of production, contributing significantly to the socio-economic development of the en-tire area (Fig. 2).
The relevance of this natural protected area, accounting only for the natural resources, is due to the richness of its environment in terms of vascularflora (over 500 taxa), and the peculiarity of the steppic grass-land ecosystem that prevails in this area (Perrino et al., 2006). As argued byCotecchia (2010), the presence of this specific habitat is the results of the human activities, especially agriculture and pasture, on the natural environment. Therefore, agriculture has a fundamental role in defining the ecological, economic and social structure of the Park area. At the
same time,Perfecto and Vandermeer (2002)argued that biodiversity
conservation in protected area in general, and specifically, in this case, is greatly affected by agricultural activities. This is true especially for the Murgia area that holds a huge karst formation (Canora et al., 2008) that is highly susceptible to anthropogenic disturbance (North et al., 2009). A trade-off between human activities (agriculture in this case), ecosystem diversity and ecosystems preservation should be found in order to guar-antee the sustainable management of the Park.
According toCotecchia (2010), the farms located in the Park area have been classified, according to the Reg.(EC) n.1242/2008, into
three“types of farm” (TF): mixed farms with crops and livestock;
specialized crop farms and specialized livestock farms. A sample of four-teen farms, chosen among those who were engaged in the project “AgroEcosistemi” and representative of the mixed and specialized crop farms respectively, was used as case study to analyze the sustain-ability of agricultural activities in the Park area.
The most relevant TF in the park area were equally represented in our case study sample (7 mixed farms and 7 specialized crop farms). In order to deal with the difference in farm size, the last national agricul-tural census (ISTAT, 2010) was used as a reference for farm classi fica-tion. The specialized crop farms (hereafter simply“specialized farms”) and the mixed farms, were included into the highest three classes in terms of Utilized Agricultural Area (UAA). Those classes represented almost the 2.5% of Apulian farms, and thus all the farms in our sample could be considered as“large farms”. All input–output raw materials and budget data were collected directly on farm and were referred to year 2013 as baseline. Both farm types cultivated cereals and legumes crops; while livestock activities are characterized by the presence of mainly sheep and to a lesser extent both sheep and cows. The main live-stock product produced by the mixed farms is sheep milk, only one farm breed sheep for meat production. Both types of farms use mainly organ-ic production method for crop or forage production; while the herds are raised according the conventional method. The only exceptions are tree specialized farms, located in the higher fertile south Apennines geolog-ical foredeep, called“Bradanic Trough” (Tropeano et al., 2002; Canora et al., 2008) that uses conventional agriculture technologies for corps production. Others relevant farm characteristics are summarized in
Table 1.
3.2. The LCA approach
A LCA was performed to analyze the interactions between agricul-tural activities and the environment, allowing the evaluation of the main environmental impacts of farms' activities in the Park area. The goal of this LCA study was to assess the relationships between farms ac-tivities and ecosystems quality loss within the Park. Data of the
com-mercial farms that participated into the project“Agroecosistemi”
(n = 14) were used and refer to the year 2013. Data related to farm management strategies, yield, fertilizers and pesticides uses, water consumptions, as well as techniques of animal husbandry (semi-wild or tethering), types and amount of animals feeding materials, etc., were collected by means of questionnaires directly submitted to farmers. An area based functional unit (FU) was selected for this study, considering that the sampled farms belonged to the same class of“land use intensity” (seeSection 3.1). In order to account for land size effect, each farm has been regarded as a single produc-tion unit, and it has been employed as reference for the estimaproduc-tion of environmental impacts.
Therefore, the FU used in this study was a farm with 40 ha of UAA, which was the surface of the less extensive farm in our sample. For each farm, a detailed cradle-to-farm-gate life cycle assessment, in-cluding on and off farm pollution and avoided impacts, was performed (Fig. 3).
For additional data, the Ecoinvent database (version 2.2) was consulted, especially for raw materials production and transports. Simapro 7.3.3 was used as a calculation platform. This study was a cradle-to-farm gate study, in which all the raw materials and processes take place form raw material extraction or production, till crops or live-stock production. Transports inside the farm were excluded from the system boundaries. The use of manure and recycling of seeds were accounted in the system as prevented impacts due to the avoided pro-duction of, respectively, nitrogen and phosphorus fertilizers and com-mercial seeds. The amount of fertilizer produced was determined based on the mean N and P content of respectively bovine and sheep manure (Brentrup et al., 2000; Azeez and Van Averbeke, 2010). The emissions of N fertilizer and manure were calculated according to
Brentrup et al. (2000), using different references to estimate the
N-balance for the different crops (Ryden et al., 1984a,b; Köpke and
Nemecek, 2010; Garabet et al., 1998). The leaching fraction of applied P fertilizers was estimated according toNest et al. (2014). Pesticide
emissions were assessed using the PestLCI model (Dijkman et al.,
2012). Methane emissions to air and N2O emissions to water and soil from livestock breeding and grazing were assessed using the IPCC tier 2 approach (IPCC, 2006) (Table 2).
Table 1
Average descriptive statistics of the data sample of crop and mixed farms. Unit Crop farms Mixed farms
Mean value
Range Mean value
Range
Farm size and land use
Cultivated area (UAA) ha 178 40–410 313 94–1040 Crops area ha 178 40–410 60 4–121 Grassland area ha 224 19–1000
Forage area ha 40 9–67
Farm intensity
Annual crop production q.li/ha 20 3–37 26 15–56 Annual livestock
productiona
q.li/yr 56 0–150
Herd size Number of heads 293 90–520 Financial capital .000 EUR 96 22–318 173 16–307 Subsidies .000 EUR 70 14–126 30 4–44 Labor Average Work Unit 1 0,1–2 2 1–2
a
The production of one of the mixed farms was excluded by the calculation because it is the only case that produce sheep meat.
For the life cycle impact assessment (LCIA) the endpoint ReCiPe method (Goedkoop et al., 2012) was used, which integrates the ‘prob-lem oriented approach’ of CML-IA (Guinée et al., 2002) and the‘damage
oriented approach’ of Eco-indicator 99 (Goedkoop and Spriensma,
2001). Both these approaches have strengths and weaknesses related to: (i) the level of uncertainty and (ii) the interpretability of the results. The ReCiPe methods implement both strategies and have both midpoint (problem oriented) and endpoint (damage oriented) impact categories. In order to consider the high importance of“Alta Murgia” in terms of vas-cular plants and animals biodiversity (Perrino et al., 2006; Cotecchia, 2010) and its role of the main water resource for the entire Apulia Region (Canora et al., 2008), the impact categories used for this study (see
Section 2.4) were able to capture disturbances occurring in the major drivers of plant and animal diversity, soils (Wagg et al., 2014), water-use and land-water-use changes (Chapin et al., 2000). The ReCiPe normalization factors were mainly based on the whole economic system at both the European and global level, whereas policy makers often were interested in using lower geographical level as reference systems (Sleeswijk et al., 2008). In this study the selected impact categories were normalized based on the rate of yearly species lost for the Mediterranean basin in the year 2000 as explained byBrooks et al. (2002).
Taking into account the“conceptual and data limitations” existing for the inclusion of biodiversity and ecosystems quality into the LCA frame-work (Tuomisto et al., 2012; Curran et al., 2010; Schmidt, 2008, see also
Section 2) the selected impact categories were considered good proxy to assess the damages produced by farms activities to the quality of eco-systems, landscapes and wildlife habitats. The others impact categories associate with the human health and resources areas of protection (seeGoedkoop et al., 2012) were exclude form the study. The as-sumption for this choice was that the Park Authority was more in-terested in understanding how agriculture activities affected biodiversity and ecosystems' quality at the local level, which can provide a more direct link to political goals (Sleeswijk et al., 2008). Land occupation (agricultural and urban) impact categories are usu-ally estimated using as baseline the species richness on the type of land ignoring human distortion (De Schryver et al., 2010). There-fore, these impact categories are also excluded from the study to avoid damages overestimation.
3.3. The Sustainable Value Approach (SVA)
The SVA methodology assumes that afirm contributes to sustainable development whenever it uses its resources more efficiently than other
companies, reducing or unchanging the overall resource used (Van
Passel and Meul, 2012). The methodological steps to calculate the
sustainable value of afirm was:
• First, define the aims of the analysis and detect the addressed stakeholders.
• Second, the relevant resources with regard to sustainability
perfor-mance of thefirms or the economic sector need to be determined
and collected.
• Third, the benchmark values need to be determined. The bench-mark determines the costs of the resource that afirm (or economic sector) has to exceed in order to produce sustainable value. • Fourth, the productivity level of a company resource was compared
with the benchmark's one while maintaining the overall resource use constant. When the productivity of the company exceeds the opportu-nity cost, the company contributes to a sustainable use of the consid-ered resource.
The opportunity cost of a resource form is the cost of the most valuable alternative and can be calculated as:
opportunity cost¼ value addedbenchmark=resourcebenchmark: ð1Þ
Afirm creates sustainable value when it uses resources more efficient-ly than the benchmark, accordingefficient-ly, by subtracting the opportunity cost from the efficiency of resource use for the company (2).
value speadi¼ value addedi=capitali
−value addedbenchmark=resourcebenchmark
ð2Þ
Therefore, the sustainable value of the companyiis assessed by summing up the value contribution for every form of resource
Table 2
Life cycle inventory of crop and mixed farms (yearly based).
Crop farms Mixed farms Reference
Farm 1 Farm 2 Farm 3 Farm 4a Farm 5 Farm 6a Farm 7a Farm 8 Farm 9b Farm 10 Farm 11 Farm 12 Farm 13 Farm 14
Land size (ha of UAA) 75 165 250 400 145 40 240 90 121 130 101 155 146 195
Herd size (n° of heads) 520 262 90 160 384 410
Inputs— agriculture Gasoil (l) 7150 6750 30,500 51,780 58,000 4233 11,076 8906 13,195 4768 7715 7900 7884 9805 Lubrificant oil (l) 179 152 763 1376 1528 103 277 344 330 112 193 198 325 245 Seeds (q) 169 348 835 1758 68 298 153 235 385 160 42 117 209 Fertilizers (q) 1260 Herbicide (l) 2 60 Pesticide (l) 120 Manure (q) 14,820 100 1815 160 4518 Inputs— livestock
Animal feed (q.li) 106 20 62 12 806
Electricity (kWh) 3508 2425 300 1496 3508 2588 4139
Gasoil (l) 269 31 200 300 1660
Lubrificant oil (l) 7 1 5 8 42
Emissions— agriculture
Carbon dioxide (q) 210.92 184.06 324.03 5933.02 746.28 12,043.53 233.46 102.51 330.58 96.78 239.64 258.29 170.86 119.51 Ecoinvent 2.2
Carbon monoxide (kg) 55.68 33.17 56.83 1457.08 121.34 3722.36 48.10 18.35 42.04 12.48 38.66 37.67 30.94 16.11 Ecoinvent 2.2
Methane (kg) 0.88 0.77 4.84 24.66 3.10 50.45 0.97 0.76 1.37 0.42 1.00 1.07 0.71 0.50 Ecoinvent 2.2
Sulfur dioxide (kg) 6.83 5.98 10.57 191.93 24.22 391.48 7.59 3.59 10.71 3.14 7.78 8.37 6.31 3.87 Ecoinvent 2.2
Nitrogen oxides (q) 2.79 2.72 4.80 78.16 10.86 198.72 3.52 1.38 4.67 1.32 3.50 3.67 2.51 1.70 Ecoinvent 2.2
Ammonia (q) 0.001 0.001 1.60 552.90 37.13 5.69 114.88 0.03 0.002 1.03 0.002 0.05 1.65 91.83 Econvent 2.2, IPCC Tier 2
Nitrate (q) 534.38 124.48 12.02 111.04 12.16 3.81 0.18 6.64 514.25 Econvent 2.2, IPCC Tier 2
Phosphorus (q) 672.43 21.95 6.86 14.28 21.95 222.29 Econvent 2.2, IPCC Tier 2
Fenoxaprop-p-ethyl ester (q) 0.03 PestLCI model
Emissions— livestock
Methane (kg) 24.53 19.66 18.23 19.63 23.44 18.23 27.77 Econvent 2.2, IPCC Tier 2
Nitrous oxide (kg) 548.84 5.36 262.04 501.92 1604.20 2139.51 3083.67 Econvent 2.2, IPCC Tier 2
a
Are managed using conventional agricultural practices.
b
Livestock uses only indoor rearing techniques.
15 0 M .Mo re tti et a l. / A gr ic ul tu ra l Sy st em s 14 4 (20 1 6 ) 1 44 – 155
(3) that has been estimated by multiplying the value spreadifor a certain form of resource by the amount of resource used by companyi.
sustainable valuei¼ 1 n X s¼1 value spread s i capital s i ð3Þ According toVan Passel et al. (2007), dividing for the number of resource forms considered (n) allowed to correct for the overestimation of value created, avoiding double counting (Figge and Hahn, 2005).
In order to account for the company size, the Return to Cost Ratio (RTC) of farms was calculated (Van Passel et al., 2009) according to Eq.(4).
Return to Costs ratioi
¼ value addedi= value addedð i−sustainable valueiÞ ð4Þ
A return to RTC higher than the unit means that the company is overall more efficient in resource allocation than the benchmark. The most criticized aspect of this method was the definition of the bench-mark (Mondelaers et al., 2011). This because the method itself was not able to capture if the overall resources use ensure a sustainable out-come (Figge and Hahn, 2004a); and so the benchmark could be defined in such a way that it does not describe a sustainable resource use (Ang et al., 2011). Although, the choice of the benchmark strongly affects the explanatory power of the analysis (Figge and Hahn, 2005),Van Passel et al. (2007)showed in an application on Flemish dairy farms that the ranking of the companies does not differ between several types of benchmarks. An interesting alternative approach is the construction of a sustainability benchmark using appropriate agro-environmental farm models (Merante et al., 2015). Unfortunately, these models were not available to assess agricultural systems in the studied protected area. For the abovementioned reasons, the average for each form of resources has been used as a benchmark in this study. To test the ro-bustness of the sustainable value calculations, the rank correlation (Spearman's rho) of RTC using different benchmarks is calculated (Table 3). The correlations are high and significant.
The different forms of capital considered were: (i) labor, (ii) farm capital, (iii) used land (ha), (iv) ecosystems quality damage (species lost∗ yr). For each farm, labor was measured in Annual Working Unit (AWU); farm capital (assets) was calculated as the total capital minus the value of land to avoid overlapping; while the ecosystems quality damage index was calculated by summing up the outcomes of the con-sidered environmental impact indicators of the LCA analysis. Therefore, in this study the Sustainable Value was expressed as function of farm capital, used land, labor and ecosystems quality damage (Eq.(5)). sustainable valueif ð farm capitali; used landi; labori; ecosystem
quality damageiÞ
ð5Þ This highly relevant selection of resource forms is ignored by previous studies. This is especially a concern for natural resources for which the choice was merely data driven without a sound selection method (seeAng et al., 2011; Van Passel et al., 2007, 2009). Only
Merante et al. (2015)andPacini et al. (2015)used agro-environmental models to outline environmental thresholds to be used as the reference farm sustainability benchmark.
4. Results
There is large within-group variability in indicators scores between specialized crop farms and mixed farms. The ecosystem quality damage scores for the overall farms in our case study range between 3.60E−05 and 3.89E−02 species lost ∗ yr as shown inTable 4. Specialized crop farms have less impact on the environment in terms of cumulative eco-systems quality damages, accounting for almost the 30% of the total es-timated damages to ecosystems (Table 4).
Specialized crop farms score better for impacts on freshwater and terrestrial ecotoxicity; while they have higher impacts for, terrestrial acidification, and transformation of natural land (Fig. 4).
Those results are due to the higher intensity in terms of human-made resources used in crop farms management, especially gasoil and seeds, but also fertilizers and pesticides. Usually, mixed farms produce only the forage needed for feeding the livestock and give greater reli-ance on natural pastures for grazing animals. Therefore, they have less cultivated land devoted to crop production that means a decreasing number of soil tillage operations and a less intensive use of chemicals. Moreover, seed recycling is more widely practiced in mixed farms gen-erating lower impacts on soil, natural land transformation and climate changes. The higher impacts of mixed farms on freshwater (ecotoxicity and eutrophication) and terrestrial ecotoxicity is determined by fresh-water nitrogen and phosphorus leaching as a result of animals grazing and manure management.
Table 3
Correlation between the Return-to-cost ratio using different benchmarks.
Return-to-cost Benchmark 1 Benchmark 2 Benchmark 3 Benchmark 1a
1 0.9428*** 0.6131**
Benchmark 2 1 0.6440**
Benchmark 3 1
* Significant at 10% ** significant at 5% ***significant at 1%.
a
Benchmark base using the average for each form of resources.
Table 4
Characterization of the environmental impacts of crop and mixed farms (species lost*yr.). Terrestrial acidification Freshwater eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Natural land transformation Ecosystem quality damage index Farm 1 CF 1.88E−06 2.84E−07 −3.89E−08 4.43E−08 3.38E−05 3.60E−05 Farm 2 CF 1.44E−05 3.47E−07 6.17E−06 1.79E−07 6.43E−05 8.54E−05 Farm 3 CF −6.73E−06 4.72E−07 −1.48E−05 −1.87E−08 1.75E−04 1.54E−04 Farm 4 CF 4.01E−03 3.99E−05 1.21E−04 5.31E−06 3.42E−03 7.60E−03 Farm 5 CF 1.80E−04 2.14E−03 1.11E−02 1.38E−03 2.00E−04 1.50E−02 Farm 6 CF 9.64E−05 2.27E−05 2.75E−05 2.46E−06 3.53E−04 5.02E−04 Farm 7 CF 1.03E−03 8.34E−06 1.98E−05 1.01E−06 4.82E−04 1.54E−03 Farm 1 MF −6.94E−05 2.82E−03 1.46E−02 1.82E−03 8.38E−05 1.92E−02 Farm 2 MF 6.62E−06 2.21E−06 1.94E−05 3.76E−07 3.22E−04 3.51E−04 Farm 3 MF 7.54E−05 7.46E−04 3.86E−03 4.80E−04 7.86E−05 5.24E−03 Farm 4 MF −3.79E−06 1.32E−08 1.36E−07 −4.28E−08 4.97E−05 4.60E−05 Farm 5 MF 2.39E−05 7.92E−04 4.09E−03 5.09E−04 5.38E−04 5.95E−03 Farm 6 MF −3.93E−05 1.91E−04 9.72E−04 1.23E−04 1.26E−04 1.37E−03 Farm 7 MF 6.25E−04 5.58E−03 2.89E−02 3.59E−03 1.40E−04 3.89E−02
SD 0.001 0.002 0.008 0.001 0.001 0.01
The performance of farms within the two groups clearly differs (Fig. 5). Overall, most of the specialized crop are sustainable showing a RTC higher than 1, while most of the mixed farms are unsustainable showing RTC lower than 1. However, both the two groups of farms ex-hibit frontrunners whit a RTC higher than 1 (Table 5).
The possible determinants in our data set that can explain the differ-ence in farms performances are explained by the differdiffer-ences in average capitals productivity and eco-efficiency between crops and mixed farms (Table 6).
On average, the highest sustainable farm maximizes the productivity of capital, labor and land while minimizes the one of the ecosystem quality damage index. Mixed farms perform well in terms of land pro-ductivity, while specialized crop farms display better results in terms of labor and capital productivity and have a lower impact on ecosys-tems' quality. From these sustainable value calculations, it can be ad-vised that a clear focus on the reduction of ecosystems' quality damages of mixed farms and on the increase of land productivity of
crop farms are important to strengthen the sustainability performance of agricultural activities within the Park.
5. Discussions
The study develops an integrated sustainability assessment tool, combining an environmental biophysical approach (the LCA) with a monetary-oriented method (the SVA). The RTC indicator used in this research is an aggregated indicator taken into account different aspects of sustainability.
In our sample, by using the average farm as benchmark, the special-ized crop and mixed farms contributions to economic and environmen-tal sustainability are addressed accounting for their sustainable value creation. Different benchmarking approaches were used byVan Passel et al. (2009)andMerante et al. (2015)which estimated, respectively, the production frontier indicating the maximum feasible production possibilities and the Environmentally Sustainable Farm (ESF), indicating
Fig. 4. Characterization of the environmental impacts of the sampled crop and mixed farms.
Fig. 5. Sustainable value efficiency using the average benchmarks. 152 M. Moretti et al. / Agricultural Systems 144 (2016) 144–155
the farm which comply with a set of environmental thresholds (pesticides use, plant diversity, nitrogen emissions and soil erosion). The frontier analysis has the advantage that the particular situation of each company is taken into account when assessing sustainability (Van Passel et al., 2009); while the ESF could be used to obtain more effective and outcome-oriented sustainability policies measures (Merante et al., 2015; Pacini et al., 2015).
The crop farms, in our sample, have on average a higher sustainabil-ity performance than mixed farms. Thus, they create more sustainable value using their economic and environmental resources. These results show that mixed farms need to improve their resource use efficiencies. The relatively good performance of crop farms in labor productivity and ecosystems quality damage, out-weights the low performance of using land compared to mixed farms. Land productivity is a relevant measure of farms' economic performance, and in our study mixed farms achieve better results in terms of land productivity. The outcomes of this ex-planatory study suggest that policy makers of the Park should support specialized crop farms that, on average, perform better in terms of systems quality preservation. Therefore, the trade-off between eco-systems quality, eco-efficiency and land productivity needs special awareness from the Park Authorities.
This study suggests that policy instruments should be focused on specialized crop farms that switch to local varieties of grains and le-gumes, improving land productivity and the sustainability of agricultur-al activities within the Park. Improving ecosystems' quagricultur-ality by using local germplasm was reported to increase agro-ecosystems resilience (Altieri et al., 2015). Hence, designing agro-environmental policies and subsidies for: (i) promoting local germplasm; (ii) supporting agro-marketing of these products, e.g. by creating a Park's quality trade-marks for local food productions; and in this manner, (iii) fostering local community role in ecosystems' preservation actions emphasizing the role of local agro-ecological knowledge (Iniesta-Arandia et al., 2015; Dougill et al., 2006) are policy options to increase the sustainability of farming systems within the Park. Moreover, this outcome reveals the important issue of agro-ecosystems expansion and the related challenge of reducing the pressure and intensity of land-use. Thus, to have a more appropriate development of agricultural policies, it is interesting to compare the return to cost ratio results with other
measures, such as partial productivity and eco-efficiency measures. Additionally, the effectiveness of policy measures should be in-creased by the evaluation of farm environmental and economic performance.Pacini et al. (2015)showed that sustainable value cal-culations combined with modeling results can be used to design more effective agro-environmental payment schemes. Integrating environmental and economic aspects to assess agricultural systems is highly important to boost the development of agro-ecological sys-tems and to connect farming activities with ecosyssys-tems quality preservation challenges.
6. Conclusions
In this paper, we explored the possibilities to integrate biophysical and monetary sustainability assessment tools, through combining the impacts of agriculture activities on ecosystems with the concept of nat-ural capital; in such a way that a relevant selection of resource forms can be performed. To achieve this goal, we performed a case study where Life Cycle Assessment and Sustainable Value Approach were simulta-neously used to assess the sustainability of agricultural systems within the Park. The methodology presented in this study allowed an integrat-ed assessment of the economic and environmental dimensions of sus-tainability, providing decision-makers an overview of the effects of agriculture activities on local sustainable development. Moreover, the use of a benchmark to measure farms overall performances and their relative efficiency can be useful to highlight opportunities of im-provement both at farm level and higher spatial levels. It was our aim
to develop afirst framework for combining biophysical and monetary
oriented tools to assess sustainability of agricultural systems. However, considering the large variability in farm accountancy data and agricul-ture management practices, a higher numbers of farms need to be sam-pled, in order to avoid the inference on outcomes of frontrunners and laggards. Further research is needed to improve benchmarking such as e.g. efficiency frontiers which require more data availability in order to secure its robustness. Although further improvement is desirable, the methodology developed in this research to measure farm sustainability proofs to be promising.
Table 5
Sustainable Value creation and return to cost ratio of the sampled crop and mixed farms. Value added (€K) Financial capital (€K) Labor (Annual Work Unit) Ecosystem quality damage (species lost∗ yr) Land (ha) Value spread financial capital Value spread labor Value spread ecosystem quality damage Value spread land Sustainable value Return to cost ratio Crop farms Farm 1 17.56 21.97 0.18 3.60E−05 65 0.20 27,141.74 476,123,984.96 −322.82 1350.54 1.08 Farm 2 88.83 47.04 1.52 8.54E−05 90 1.29 −10,560.00 1,028,237,530.44 394.05 41,930.97 1.89 Farm 3 50.49 146.16 0.83 1.54E−04 214 −0.26 −8337.85 316,719,407.69 −356.98 −18,075.47 0.74 Farm 4 124.43 318.30 1.25 7.60E−03 410 −0.21 30,749.29 4,550,124.42 −289.42 −28,325.59 0.81 Farm 5 255.30 34.07 0.82 1.50E−02 284 6.89 240,955.00 5,251,940.55 306.04 149,648.10 2.42 Farm 6 16.91 32.93 0.09 5.02E−04 40 −0.09 118,228.74 21,904,897.19 −170.05 2,977.59 1.21 Farm 7 76.86 68.85 0.46 1.54E−03 160 0.51 99,580.72 38,110,917.92 −112.56 30,337.44 1.65 Mixed farms Farm 8 52.14 307.20 1.53 1.92E−02 103 −0.43 −35,156.09 −9,107900.88 −86.73 −92,763.42 0.36 Farm 9 24.26 211.78 1.64 3.51E−04 121 −0.49 −54,359.53 57,290,823.79 −392.38 −55,033.84 0.31 Farm 10 151.67 283.72 1.48 5.24E−03 55 −0.07 33,317.12 17,114,366.00 2,164.80 59,666.87 1.65 Farm 11 46.32 16.00 1.58 4.60E−05 83 2.29 −39,838.91 994,461,755.32 −34.79 4,138.60 1.10 Farm 12 9.31 232.10 1.47 5.95E−03 40 −0.56 −62,810.16 −10,256,634.61 −360.24 −74,661.10 0.11 Farm 13 159.50 61.00 1.92 1.37E−03 146 2.01 13,947.85 104,305,518.03 499.58 91,425.29 2.34 Farm 14 60.06 98.60 1.62 3.89E−02 101 0.01 −32,087.49 −10,275,031.03 1.70 −112,615.97 0.35 Average benchmark 80.97 134.27 1.17 6.85E−03 136.57
Benchmark 2 255.30 318.30 1.92 3.89E−02 410 Benchmark 3 255.30 34.07 0.82 1.50E−02 284
Table 6
Average resources productivities and eco-efficiency of crop and mixed farms.
Capital productivity (€/€) Labor productivity (M€/AWU) Land productivity (€/ha) Eco-efficiency (€/species lost ∗ yr)
Crop farms 1.79 1.40 514.09 2.82E + 08
Acknowledgments
We would like to thank the editor and the anonymous referees for their helpful suggestions and insightful comments that have signi
ficant-ly improved the paper. The authors want to thank the“Alta Murgia”
Park Authority for the support in this study and all farmers that collab-orated with the authors providing data. Moreover, the authors want to thank Dr. Sylvestre Njakou Djomo for the useful discussion and sugges-tion to build the LCA model.
References
Altieri, M.A., Nicholls, C.I., Henao, A., Lana, M.A., 2015.Agroecology and the design of climate change-resilient farming systems. Agron. Sustain. Dev. 1-22.
Ang, F., Van Passel, S., Mathijs, E., 2011.An aggregate resource efficiency perspective on sustainability: a sustainable value application to the EU-15 countries. Ecol. Econ. 71, 99–110.
Atkinson, G., 2000.Measuring corporate sustainability. J. Environ. Plan. Manag. 43 (2), 235–252.
Azeez, J.O., Van Averbeke, W., 2010.Nitrogen mineralization potential of three animal manures applied on a sandy clay loam soil. Bioresour. Technol. 101 (14), 5645–5651.
Belcher, K.W., Boehm, M.M., Fulton, M.E., 2004.Agroecosystem sustainability: a system simulation model approach. Agric. Syst. 79 (2), 225–241.
Brentrup, F., Küsters, J., Lammel, J., Kuhlmann, H., 2000.Methods to estimate on-field nitrogen emissions from crop production as an input to LCA studies in the agricultural sector. Int. J. Life Cycle Assess. 5 (6), 349–357.
Brooks, T.M., Mittermeier, R.A., Mittermeier, C.G., Da Fonseca, G.A., Rylands, A.B., Konstant, W.R., ... Hilton‐Taylor, C., 2002.Habitat loss and extinction in the hotspots of biodiversity. Conserv. Biol. 16 (4), 909–923.
Canora, F., Fidelibus, M.D., Sciortino, A., Spilotro, G., 2008.Variation of infiltration rate through karstic surfaces due to land use changes: a case study in Murgia (SE-Italy). Eng. Geol. 99 (3), 210–227.
Chapin III, F.S., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., ... Díaz, S., 2000.Consequences of changing biodiversity. Nature 405 (6783), 234–242.
Commission Regulation (EC) No 1242/2008 of 8 December 2008 establishing a Commu-nity typology for agricultural holdings, OJ L 335 of 13.12.2008, 2010.
Conway, G.R., 1985.Agroecosystem analysis. Agric. Adm. 20 (1), 31–55.
Costanza, R., 2000.The dynamics of the ecological footprint concept. Ecol. Econ. 32 (3), 341–345.
Cotecchia, I.V., 2010. Quadro Conoscitivo ed Interpretativo.http://www.parcoaltamurgia. gov.it/relazioni/Quadro_conoscitivo_REV4.pdf.
Curran, M., de Baan, L., De Schryver, A.M., van Zelm, R., Hellweg, S., Koellner, T., ... Huijbregts, M.A., 2010.Toward meaningful end points of biodiversity in life cycle assessment†. Environ. Sci. Technol. 45 (1), 70–79.
Dale, V.H., Polasky, S., 2007.Measures of the effects of agricultural practices on ecosystem services. Ecol. Econ. 64 (2), 286–296.
Dantsis, T., Douma, C., Giourga, C., Loumou, A., Polychronaki, E.A., 2010.A methodological approach to assess and compare the sustainability level of agricultural plant production systems. Ecol. Indic. 10 (2), 256–263.
de Baan, L., Mutel, C.L., Curran, M., Hellweg, S., Koellner, T., 2013.Land use in life cycle assessment: global characterization factors based on regional and global potential species extinction. Environ. Sci. Technol. 47 (16), 9281–9290.
De Schryver, A.M., Goedkoop, M.J., Leuven, R.S., Huijbregts, M.A., 2010.Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment. Int. J. Life Cycle Assess. 15 (7), 682–691.
Dijkman, T.J., Birkved, M., Hauschild, M.Z., 2012.PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA. Int. J. Life Cycle Assess. 17 (8), 973–986.
Dougill, A.J., Fraser, E.D.G., Holden, J., Hubacek, K., Prell, C., Reed, M.S., ... Stringer, L.C., 2006.Learning from doing participatory rural research: lessons from the Peak District National Park. J. Agric. Econ. 57 (2), 259–275.
Ente Parco Nazionale dell'Alta Murgia, 2010. Redazione del Piano per il Parco e del Regolamento per il Parco Nazionale dell'Alta Murgia. Quadro conoscitivo. Available on-line athttp://www.parcoaltamurgia.gov.it/index.php?option=com_content&view= article&id=270&Itemid=324.
Farrell, A., Hart, M., 1998.What does sustainability really mean?: The search for useful in-dicators. Environ. Sci. Policy Sustain. Dev. 40 (9), 4–31.
Figge, F., Hahn, T., 2004a.Sustainable value added—measuring corporate contributions to sustainability beyond eco-efficiency. Ecol. Econ. 48 (2), 173–187.
Figge, F., Hahn, T., 2004b.Value‐oriented impact assessment: the economics of a new approach to impact assessment. J. Environ. Plan. Manag. 47 (6), 921–941.
Figge, F., Hahn, T., 2005.The cost of sustainability capital and the creation of sustainable value by companies. J. Ind. Ecol. 9 (4), 47–58.
Garabet, S., Ryan, J., Wood, M., 1998.Nitrogen and water effects on wheat yield in a Mediterranean-type climate. II. Fertilizer-use efficiency with labelled nitrogen. Field Crop Res. 58 (3), 213–221.
Gasparatos, A., Scolobig, A., 2012.Choosing the most appropriate sustainability assessment tool. Ecol. Econ. 80, 1–7.
Gasparatos, A., El-Haram, M., Horner, M., 2008.A critical review of reductionist ap-proaches for assessing the progress towards sustainability. Environ. Impact Assess. Rev. 28 (4), 286–311.
Gasparatos, A., El-Haram, M., Horner, M., 2009.The argument against a reductionist ap-proach for measuring sustainable development performance and the need for method-ological pluralism. Accounting Forum vol. 33, No. 3. Elsevier, pp. 245–256 (September).
Goedkoop, M., Spriensma, R., 2001.The Eco-indicator99: a Damage Oriented Method for Life Cycle Impact Assessment: Methodology Report.
Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., van Zelm, R., 2009.
ReCiPe 2008.
Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., Van Zelm, R., 2012.
ReCiPe 2008. A Life Cycle Impact Assessment Method Which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level— Report I: Characterisa-tion (Updated 13 July 2012).
Gómez-Limón, J.A., Sanchez-Fernandez, G., 2010.Empirical evaluation of agricultural sustainability using composite indicators. Ecol. Econ. 69 (5), 1062–1075.
Guinée, J.B., Gorrée, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., de Bruijn, H., van Duin, R., Huijbregts, M.A.J., 2002.Handbook on Life Cycle Assessment. Operational Guide to the ISO Standards. I: LCA in Perspective. IIa: Guide. IIb: Operational Annex. III: Scien-tific Background. Kluwer Academic Publishers, Dordrecht 1-4020-0228-9 (692 pp.).
Hahn, T., Figge, F., Barkemeyer, R., 2007.Sustainable Value creation among companies in the manufacturing sector. Int. J. Environ. Technol. Manag. 7 (5), 496–512.
Hoogmartens, R., Van Passel, S., Van Acker, K., Dubois, M., 2014.Bridging the gap between LCA, LCC and CBA as sustainability assessment tools. Environ. Impact Assess. Rev. 48, 27–33.
Hou, L., Hoag, D., Keske, C.M., Lu, C., 2014.Sustainable value of degraded soils in China's Loess Plateau: An updated approach. Ecol. Econ. 97, 20–27.
Huysveld, S., De Meester, S., Peiren, N., Muylle, H., Lauwers, L., Dewulf, J., 2015.Resource use assessment of an agricultural system from a life cycle perspective—a dairy farm as case study. Agric. Syst. 135, 77–89.
Iniesta-Arandia, I., del Amo, D.G., García-Nieto, A.P., Piñeiro, C., Montes, C., Martín-López, B., 2015.Factors influencing local ecological knowledge maintenance in Mediterranean watersheds: insights for environmental policies. Ambio 44 (4), 285–296.
IPCC, 2006.4: Agriculture, Forestry and Other Land Uses (AFOLU): 2006 IPCC/Guidelines for National Greenhouse Gas Inventories. IPCC/IGES, Hayama, Japan.
ISTAT, 2010.6° Censimento generale dell'agricoltura 2010 (Rome, Italy).
Janssen, S., van Ittersum, M.K., 2007.Assessing farm innovations and responses to policies: a review of bio-economic farm models. Agric. Syst. 94 (3), 622–636.
Jeanneret, P., Baumgartner, D.U., Knuchel, R.F., Koch, B., Gaillard, G., 2014.An expert system for integrating biodiversity into agricultural life-cycle assessment. Ecol. Indic. 46, 224–231.
Jeswani, H.K., Azapagic, A., Schepelmann, P., Ritthoff, M., 2010.Options for broadening and deepening the LCA approaches. J. Clean. Prod. 18 (2), 120–127.
Kloepffer, W., 2008.Life cycle sustainability assessment of products. Int. J. Life Cycle Assess. 13 (2), 89–95.
Koellner, T., Scholz, R.W., 2008.Assessment of land use impacts on the natural environment. Int. J. Life Cycle Assess. 13 (1), 32–48.
Köpke, U., Nemecek, T., 2010.Ecological services of faba bean. Field Crop Res. 115 (3), 217–233.
Maes, D., Van Passel, S., 2014.Advantages and limitations of exergy indicators to assess sustainability of bioenergy and biobased materials. Environ. Impact Assess. Rev. 45, 19–29.
Merante, P., Van Passel, S., Pacini, C., 2015.Using agro-environmental models to design a sustainable benchmark for the sustainable value method. Agric. Syst. 136, 1–13.
Meul, M., Van Passel, S., Nevens, F., Dessein, J., Rogge, E., Mulier, A., Van Hauwermeiren, A., 2008.MOTIFS: a monitoring tool for integrated farm sustainability. Agron. Sustain. Dev. 28 (2), 321–332.
Meul, M., Van Middelaar, C.E., de Boer, I.J., Van Passel, S., Fremaut, D., Haesaert, G., 2014.
Potential of life cycle assessment to support environmental decision making at com-mercial dairy farms. Agric. Syst. 131, 105–115.
Mohamad, R.S., Verrastro, V., Cardone, G., Bteich, M.R., Favia, M., Moretti, M., Roma, R., 2014.Optimization of organic and conventional olive agricultural practices from a life cycle assessment and life cycle costing perspectives. J. Clean. Prod. 70, 78–89.
Mondelaers, K., Van Huylenbroeck, G., Lauwers, L., 2011.Sustainable value analysis: sustainability in a new light results of the EU SVAPPAS Project analyse de la valeur durable: Le projet SVAPPAS de l'Union européenne éclaire la durabilité sous un jour nouveau Sustainable‐Value‐Analyse: Nachhaltigkeit in einem neuen Licht als Ergebnis des SVAPPAS‐Projekts der EU. EuroChoices 10 (2), 9–15.
Moretti, M., De Boni, A., Roma, R., 2014.Economic and environmental sustainability of forestry measures in Apulia Region Rural Development Plan: an application of life cycle approach. Land Use Policy 41, 284–289.
Nemecek, T., Dubois, D., Huguenin-Elie, O., Gaillard, G., 2011.Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agric. Syst. 104 (3), 217–232.
Nest, T.V., Vandecasteele, B., Ruysschaert, G., Cougnon, M., Merckx, R., Reheul, D., 2014.Effect of organic and mineral fertilizers on soil P and C levels, crop yield and P leaching in a long term trial on a silt loam soil. Agric. Ecosyst. Environ. 197, 309–317.
Neumayer, E., 2003.Weak Versus Strong Sustainability: Exploring the Limits of Two Opposing Paradigms. Edward Elgar Publishing.
North, L.A., Van Beynen, P.E., Parise, M., 2009.Interregional comparison of karst disturbance: West-central Florida and southeast Italy. J. Environ. Manag. 90 (5), 1770–1781.
Pacini, G.C., Lazzerini, G., Vazzana, C., 2011.AESIS: a support tool for the evaluation of sus-tainability of agroecosystems. Example of applications to organic and integrated farming systems in Tuscany, Italy. Ital. J. Agron. 6 (1), e3.
Pacini, G.C., Merante, P., Lazzerini, G., Van Passel, S., 2015.Increasing the cost-effectiveness of EU agri-environment policy measures through evaluation of farm andfield-level environmental and economic performance. Agric. Syst. 136, 70–78.
Paracchini, M.L., Bulgheroni, C., Borreani, G., Tabacco, E., Banterle, A., Bertoni, D., Rossi, G., Parolo, G., Origgi, R., De Paola, C., 2015.A diagnostic system to assess sustainability at a farm level: The SOSTARE model. Agric. Syst. 133, 35–53.
Payraudeau, S., van der Werf, H.M., 2005.Environmental impact assessment for a farming region: a review of methods. Agric. Ecosyst. Environ. 107 (1), 1–19.
Perfecto, I., Vandermeer, J., 2002.Quality of agroecological matrix in a tropical montane landscape: ants in coffee plantations in southern Mexico. Conserv. Biol. 16 (1), 174–182.
Perrino, P., Laghetti, G., Terzi, M., 2006.Modern concepts for the sustainable use of Plant Genetic Resources in the Mediterranean natural protected areas: the case study of the Alta Murgia Park (Italy). Genet. Resour. Crop. Evol. 53 (4), 695–710.
Plevin, R.J., Delucchi, M.A., Creutzig, F., 2014.Using attributional life cycle assessment to estimate climate‐change mitigation benefits misleads policy makers. J. Ind. Ecol. 18 (1), 73–83.
Pluimers, J.C., Kroeze, C., Bakker, E.J., Challa, H., Hordijk, L., 2000.Quantifying the environ-mental impact of production in agriculture and horticulture in The Netherlands: which emissions do we need to consider? Agric. Syst. 66 (3), 167–189.
Pope, J., Annandale, D., Morrison-Saunders, A., 2004.Conceptualising sustainability assessment. Environ. Impact Assess. Rev. 24 (6), 595–616.
Pretty, J., 2008.Agricultural sustainability: concepts, principles and evidence. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 363 (1491), 447–465.
Reap, J., Roman, F., Duncan, S., Bras, B., 2008.A survey of unresolved problems in life cycle assessment. Int. J. Life Cycle Assess. 13 (5), 374–388.
Reytar, K., et al., 2014. Indicators of sustainable agriculture: a scoping analysis. Working Paper, Installment 6 of Creating a Sustainable Food Future. World Resources Institute, Washington, DC (Available online athttp://www.worldresourcesreport.org). Ripoll-Bosch, R., De Boer, I.J.M., Bernués, A., Vellinga, T.V., 2013.Accounting for
multi-functionality of sheep farming in the carbon footprint of lamb: a comparison of three contrasting Mediterranean systems. Agric. Syst. 116, 60–68.
Rosenbaum, R.K., Bachmann, T.M., Gold, L.S., Huijbregts, M.A., Jolliet, O., Juraske, R., ... Hauschild, M.Z., 2008.USEtox—the UNEP-SETAC toxicity model: recommended char-acterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int. J. Life Cycle Assess. 13 (7), 532–546.
Ryden, J.C., Ball, P.R., Garwood, E.A., 1984a.Nitrate leaching from grassland. Nature 311, 50–53.
Ryden, J.C., Ball, P.R., Garwood, E.A., 1984b. Nitrate leaching from grassland. Nature 311, 50–53.http://dx.doi.org/10.1038/311050a0(06 September 1984).
Schmidt, J.H., 2008.Development of LCIA characterization factors for land use impacts on biodiversity. J. Clean. Prod. 16 (18), 1929–1942.
Shields, D.J.,Šolar, S.V., Martin, W.E., 2002.The role of values and objectives in communi-cating indicators of sustainability. Ecol. Indic. 2 (1), 149–160.
Sleeswijk, A.W., van Oers, L.F., Guinée, J.B., Struijs, J., Huijbregts, M.A., 2008.Normalisation in product life cycle assessment: an LCA of the global and European economic systems in the year 2000. Sci. Total Environ. 390 (1), 227–240.
Stoorvogel, J.J., Antle, J.M., Crissman, C.C., Bowen, W., 2004.The tradeoff analysis model: integrated bio-physical and economic modeling of agricultural production systems. Agric. Syst. 80 (1), 43–66.
Tendall, D.M., Gaillard, G., 2015.Environmental consequences of adaptation to climate change in Swiss agriculture: an analysis at farm level. Agric. Syst. 132, 40–51.
Thabrew, L., Wiek, A., Ries, R., 2009.Environmental decision making in multi-stakeholder contexts: applicability of life cycle thinking in development planning and implemen-tation. J. Clean. Prod. 17 (1), 67–76.
Thomassen, M.A., Van Calker, K.J., Smits, M.C.J., Iepema, G.L., De Boer, I.J.M., 2008.Life cycle assessment of conventional and organic milk production in the Netherlands. Agric. Syst. 96 (1), 95–107.
Tropeano, M., Sabato, L., Pieri, P., 2002.Filling and cannibalization of a foredeep: the Bradanic Trough (Southern Italy). In: Jones, S.J., Frostick, L.E. (Eds.), Sediment Flux to Basins: Causes, Controls and Consequences. Geol. Soc. Spec. Publ vol. 191, pp. 55–79.
Tuomisto, H.L., Hodge, I.D., Riordan, P., Macdonald, D.W., 2012.Does organic farming re-duce environmental impacts?—A meta-analysis of European research. J. Environ. Manag. 112, 309–320.
Van Cauwenbergh, N., Biala, K., Bielders, C., Brouckaert, V., Franchois, L., Garcia Cidad, V., ... Peeters, A., 2007.SAFE—a hierarchical framework for assessing the sustainability of agricultural systems. Agric. Ecosyst. Environ. 120 (2), 229–242.
Van der Werf, H.M., Petit, J., 2002.Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agric. Ecosyst. Environ. 93 (1), 131–145.
van der Werf, H.M., Kanyarushoki, C., Corson, M.S., 2009.An operational method for the evaluation of resource use and environmental impacts of dairy farms by life cycle assessment. J. Environ. Manag. 90 (11), 3643–3652.
Van Passel, S., Meul, M., 2012.Multilevel and multi-user sustainability assessment of farming systems. Environ. Impact Assess. Rev. 32 (1), 170–180.
Van Passel, S., Nevens, F., Mathijs, E., Van Huylenbroeck, G., 2007.Measuring farm sustain-ability and explaining differences in sustainable efficiency. Ecol. Econ. 62 (1), 149–161.
Van Passel, S., Van Huylenbroeck, G., Lauwers, L., Mathijs, E., 2009.Sustainable value assessment of farms using frontier efficiency benchmarks. J. Environ. Manag. 90 (10), 3057–3069.
Wagg, C., Bender, S.F., Widmer, F., van der Heijden, M.G., 2014.Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl. Acad. Sci. 111 (14), 5266–5270.
WCED, 1987.Our Common Future vol. 383. Oxford University Press, Oxford.
Wei, Y., Davidson, B., Chen, D., White, R., 2009.Balancing the economic, social and environmental dimensions of agro-ecosystems: an integrated modeling approach. Agric. Ecosyst. Environ. 131 (3), 263–273.
Zhang, Y., Singh, S., Bakshi, B.R., 2010a.Accounting for ecosystem services in life cycle assessment, Part I: a critical review. Environ. Sci. Technol. 44 (7), 2232–2242.
Zhang, Y.I., Baral, A., Bakshi, B.R., 2010b.Accounting for ecosystem services in life cycle assessment, part II: toward an ecologically based LCA. Environ. Sci. Technol. 44 (7), 2624–2631.
