Using indicators to assess the environmental impacts of wine growing activity: The INDIGO® method

Europ. J. Agronomy 62 (2015) 13–25

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European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja

Using indicators to assess the environmental impacts of wine growing activity: The INDIGO® method Marie Thiollet-Scholtus a,∗ , Christian Bockstaller b,c a b c

INRA, UE1117 Vigne et Vin, UMT Vinitera, F-49071 Beaucouzé, France INRA, UMR 1121, BP 20507, 68021 Colmar, France Université de Lorraine, UMR 1121, BP 20507, 68021 Colmar, France

a r t i c l e

i n f o

Article history: Received 4 June 2013 Received in revised form 26 August 2014 Accepted 9 September 2014 Keywords: Assessment Sustainability Practice Decision aid tool Vineyard

a b s t r a c t Environmental assessment methods are needed by agronomists working on the enhancement of cropping systems to meet the demand for more sustainable farming practices. A growing number of operational methods based on a set of indicators have been designed, more for arable crops and livestock than for perennial crops like viticulture. Among them, the INDIGO® method, originally developed for arable crops, offers a compromise between feasibility and predictive quality. Here we present a modified and expanded version of INDIGO® for viticulture. The development of new indicators specific to viticulture and the adaptation of existing ones followed a five step approach: (i) preliminary definition of the objectives and identification of the end-users, (ii) construction of the indicator, (iii) selection of a reference value, (iv) sensitivity analysis and (v) validation. Stakeholders from professional institutions and winegrower organizations were closely associated with step (i) to define the framework and step (ii) to supply technical databases. We designed INDIGO® indicators with all available scientific and expert knowledge which was aggregated into expert systems associating fuzzy subsets or, when possible, quantitative equations. Four indicators; pesticides, nitrogen, energy and soil organic matter, were directly adapted from the initial INDIGO® method, whereas soil cover and frost protection management were new indicators. Potentialities of their use are highlighted by examples of implementation on different scales and for various purposes. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Wine consumers associate more and more wine quality with the geographical origin and with the sustainability of the production, encompassing the agro-ecological conditions and the reduction of environmental impacts in viticulture (Warner, 2007). Like in other monocultures (e.g. orchards), the disease and pest pressure is high in viticulture and forces winegrowers to an intensive use of pesticides (Butault et al., 2010). This spraying of pesticides has an impact on water quality due to pesticide leaching to groundwater and transfer by runoff and erosion to surface water (Fiener et al., 2005; Gregoire et al., 2010; Holvoet et al., 2008; Louchard et al., 2001). The contamination of surface water by pesticides has acute

∗ Corresponding author. Tel.: +33 0 3 89 22 49 20; +33 6 86 71 25 96 (mobile); fax: +33 0 3 89 22 49 21. E-mail addresses: [email protected] (M. Thiollet-Scholtus), [email protected] (C. Bockstaller). http://dx.doi.org/10.1016/j.eja.2014.09.001 1161-0301/© 2014 Elsevier B.V. All rights reserved.

or chronic effects on aquatic living organisms (Bony et al., 2008; Schulz, 2004). Runoff may cause damage downstream in watershed (Auzet et al., 1993) and erosion also impacts soil quality. The risk of such event is important in vineyards (Battany and Grismer, 2000), because vine is grown in many areas on slopes to provide grape with a good sun exposition and to secure the quality of berries. Soil cover is the main factor to control erosion in vineyard (Klik, 1994). Limiting the decline of organic matter stock in vineyard soil reduces runoff and erosion risk as well as soil compaction risk by engines and tractors, and contributes to the nutrient supply of vine (Bartoli and Dousset, 2011; Ramos and Martinez-Casasnovas, 2010; Ruiz-Colmenero et al., 2011). Water quality is also deteriorated by nitrate leaching although vineyard areas are less fertilized than arable cropping areas (Barlow et al., 2009; Steenwerth and Belina, 2010; Thiebeau et al., 2005). Gaseous emissions of nitrogen in form of nitrous oxide may also be a concern in some situations (Steenwerth and Belina, 2010). Last, in high quality vineyards in the North of France, in Ontario, and North of the United States, frost protection equipment is used because frost damage may be very

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M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

important during spring at budburst and flowering stages resulting in significant economic losses (Smyth and Skates, 2009). Different frost protection systems based either on irrigation or heating systems are supposed to impact environment by water or fossil energy use, and greenhouse gases emissions. In response to the increasing societal concern for such environmental impacts, a growing number of studies have been published since the 90s, which go in two complementary directions. On one hand, crop scientists, in collaboration with other disciplines, have worked on reducing environmental impacts of agriculture with a growing degree of innovation, from improving the efficiency to the redesign of the system (Hill et al., 1999). On the other hand, assuming that any work on the enhancement of cropping management requires the assessment of the system at different step of the process (Meynard et al., 2002; Vereijken, 1997), more and more crops scientists have published on operational assessment methods based on indicators (Bockstaller et al., 2009), leading to an indicator explosion (Riley, 2001). Such sets of indicators or structured in framework may be aimed to different use (Bockstaller et al., 2008a): diagnosis of the current situation, monitoring the evolution of impacts in an ex post way as well as assessing innovative solution in an ex ante way (Sadok et al., 2008). Rosnoblet et al. (2006) showed in an exhaustive inventory of assessment methods in agriculture, that arable farming and livestock were covered by many methods but few were explicitly designed for perennial crop like viticulture. Some authors carried out life cycle analysis to viticulture (Gazulla et al., 2010; Mila-i-Canals, 2003). This global method is tackling the whole production chain, yielding results going beyond the direct concerns of winegrowers and their advisers. Those are interested in methods addressing the direct impacts at farm or field levels. Among such methods, the French IDEA method covering the three dimension of sustainability at farm level (Zahm et al., 2008) was adapted to viticulture. However IDEA is based on simple environmental indicators that address only management variable but not soil and climate data, so that they integrate poorly processes (Bockstaller et al., 2008a). Some methods were restricted to one environmental issue like the development of a connectivity index addressing pesticide transfer on wine growing water catchment (Payraudeau and Grégoire, 2011; Wohlfahrt et al., 2010). Others like the EIOVI method were explicitly developed for one type of viticulture, the organic viticulture (Fragoulis et al., 2009). The INDIGO® method developed initially in arable farming belongs to this group of methods and presents several interesting features. The first strength of INDIGO® indicators is the use of predictive indicator based on operational models assessing the effect of farm practices in interaction with soil and climate and using a restricted number of available input data (Bockstaller et al., 1997, 2008a). Another advantage of INDIGO® indicators is that they are adaptable to available scientific knowledge and to all agricultural productions like viticulture. INDIGO® indicators also offer a good compromise between measured indicators which allow a “closer” assessment of environmental effects than simple indicators which are easy to calculate but are individually of low predictive quality. Furthermore, as predictive indicators based on operational models, they can also be used for ex ante assessment of innovative systems (Pelzer et al., 2012; Sadok et al., 2009). All these considerations led us to adapt the INDIGO® method to viticulture. We followed the five steps identified by (Bockstaller et al., 2008a; Girardin et al., 1999) to design indicators: (i) preliminary definition of the objectives and identification of the end-users, (ii) construction of the indicator, (iii) selection of a reference value, (iv) sensitivity analysis and (v) validation. To ensure chances of the adoption by the end-users (Jakku and Thorburn, 2012), stakeholders from professional institutions and winegrowers organizations were closely associated with steps (i) to define the framework and (ii) to supply technical databases.

The purposes of this article are (i) to present calculation method of the new indicators and those adapted from the initial method for arable farming and (ii) examples of implementation to highlight the potentialities of the method. We divided it into four sections: the first, the material and methods presents the different steps necessary to design the indicators of the INDIGO® method according to the methodology summarized by Bockstaller et al. (2008a). The second details the algorithm calculation and shows results of the sensitivity test: (i) for newly designed indicators i.e. soil cover management (Isoilcover) and frost protection management (Ifrost), (ii) indicators adapted from the initial INDIGO® method i.e. pesticide management (I-Phy), energy consumption (Ien), nitrogen fertilization (IN) and organic matter management (IOM). The third section presents three examples of the implementation of the INDIGO® method in viticulture. Finally, the interest of the INDIGO® method for viticulture is discussed.

2. Material and method 2.1. Indicator development 2.1.1. Preliminary choice: definition of the objectives and identification of the end-users Like the initial INDIGO® method developed for arable systems, the INDIGO® method is here aimed at being used by advisers and agronomists working on sustainable wine growing systems. To identify the issues of concern by these targeted end-users, we implied extension agents (IFV, chambres d’agriculture), agricultural teachers (viticulture schools of Avize, Beaune, Davayé, Montmorion and Rouffach,) and winegrowers’ regional groups, the CIVA for the Alsace region, the BIVB for the Bourgogne region, the CIVC for the Champagne region, and the CIVJ for the Franche-Comté region. A group of 14 stakeholders and experts were associated to the first phase of the project to clarify preliminary choices and assumptions regarding issue of concern, scales, viticulture practices to assess, etc. (Bockstaller et al., 2008a). Among them, eight belong to technical services of wine inter-branch organizations, respectively three in Champagne, two in Burgundy, two in Alsace and one in Franche-Comté. The rest of the group was experts from INRA. Their fields of expertise were viticulture and/or environmental impacts. First, stakeholders were involved in formalizing the choices and assumption in form of a double-entry evaluation matrix that crosses environmental issues and viticulture practices during a first meeting (Girardin et al., 1999). Then, stakeholders were invited, separately, to fulfill the matrix according to the most important environmental impacts of viticulture practices on each environmental compartment. During a second meeting, the group of stakeholders and experts was asked to finalize and validate the double-entry matrix. The first author of this paper conducted meetings and discussion with a senior scientist (retired). This first step of discussion with the end-users led to the selection of following environmental issues to be addressed by indicators in viticulture: surface water quality, groundwater quality, air quality, non-renewable energies, soil erosion and beneficial abundance (Table 1). The group of end-users also selected six groups of vine growing practices considered as having important impact on the selected environmental compartments: pesticide protection, soil cover, frost protection, energy consumption, organic and mineral fertilizations. Next, we put in relation each vine-growing practice with the concerned environmental issues to set the list of basic indicators to develop or to adapt from the initial INDIGO® method in arable farming. In a last step, an aggregation procedure was designed for basic indicators addressing different environmental impacts associated to one practice (Table 1).

M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

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Table 1 General features of the indicators for the INDIGO® method applied to winegrowing systems. Indicator

Practices covered

Environmental issues

Method of design

Development status

References

I-Phy

Pesticide applications

Groundwater quality Surface water quality Air quality Beneficial

Fuzzy expert system

Adaptation of sub-indicator “air”New sub-indicator “beneficial”

Ifrost

Use of frost protection system Soil cover management

Surface water Consumption Energy Water erosion

Fuzzy expert system

New

Thiollet and Girardin (2003); Thiollet-Scholtus (2004) and Payraudeau and Grégoire (2011) Thiollet (2003)

Operational quantitative model

New

Energy consumption by machinery, pesticides and fertilizers Nitrogen fertilization (mineral and organic; soil and foliar) Organic fertilization

Fossil energy

Operational quantitative model

Adaptation

Groundwater quality Gas emissions

Operational quantitative model

Adaptation

Bockstaller et al. (2009)

Soil fertility

Operational quantitative model

Adaptation

Bockstaller et al. (1997)

Isoilcover

Ien

IN

IOM

Indicators are calculated each agricultural year at field scale according to the INDIGO® method. To get a value of the indicator at farm level, field results are averaged with help of a weighted mean by field size (Girardin et al., 1999). 2.1.2. Design of the indicator The indicators of the INDIGO® method were based on an operational model, i.e. predictive functions linking input variables of practices to a variable expressing an environmental impact. The design of this model depends on the available knowledge about the relations between vine-growing practices and its environmental impacts. To keep the model operational, we limited the number of variable and selected only available input variables to end-users. If possible, a quantitative model was developed (Table 1). We designed decision trees based with fuzzy subsets when quantitative data were not available or when the environmental issue was too complex, as for pesticide management (see Section 2.2). 2.1.3. Output of indicator and reference value In a next step, each indicator was expressed on scale of environmental performance between 0 (worst environmental impact) and 10 (no environmental impact) in order to obtain an indicator readable by end-users. A reference value of 7 expressing a maximal acceptable environmental effect was defined according to available data (Bockstaller et al., 2008a). It was based on (i) regulation guidelines (e.g. water quality for NO3 leaching) (ii) published thresholds (e.g. critical loads linked to NH3 emissions), or (iii) to an indicator value corresponding to an acceptable effect by experts (e.g. energy consumption (Pervanchon et al., 2002)), (Appendix A). 2.1.4. Sensitivity analysis The two last steps consisted in testing respectively the sensitivity and the validity of the indicator after it had been designed. Sensitivity analysis enabled to test whether the indicator was able to distinguish two vineyard managements which are supposed to be different by experts, to assess the behaviour of the indicator when input variables varied and the weight of these (Bockstaller et al., 2008a). As described for example in Pervanchon et al. (2002), each input variable was varied over a transition interval while the others input variables were kept fixed. These

Thiollet and Girardin (2002) and Thiollet-Scholtus (2010) Pervanchon and Thiollet (2004)

were set according to three scenarios at a median, favourable or unfavourable level. 2.1.5. Validation The last step consisted in assessing the validation of indicators, which covered the scientific quality including the predictive quality, as well as the utility to end-users. Bockstaller and Girardin (2003) developed a methodological framework based on three steps: (i) validation of the indicator design to improve the scientific soundness of the indicator design, (ii) validation of outputs indicator to assess reliability of the indicator and (iii) the end-use validation to check the usefulness of the indicator to the end-users, here the winegrowers and the extension agents. 2.2. Design of decision trees with fuzzy subsets Expert systems in form of decision tree associating fuzzy subsets have been implemented in environmental assessment since end of the 90s (Enea and Salemi, 2001; Fragoulis et al., 2009; Silvert, 2000; Tixier et al., 2007; van der Werf and Zimmer, 1998). We implemented the method to design some indicators. It consists in linguistic rules in form of “if then” rules which are easy to understand for a non-specialist (Phillis and Andriantiatsaholiniaina, 2001). The introduction of fuzzy subset allows avoiding the effect of knife-edge limit of qualitative classes normally associated with such decision trees. In the INDIGO® method, the I-Phy indicator (van der Werf and Zimmer, 1998) and the newly Ifrost indicator were based on this approach. Fig. 1 inspired from Ricou et al. (2014) illustrates the approach through a simplified example of a decision tree with two input variables (V1 and V2). In this example, we assumed that V1is continuous, and V2 is discrete for the sake of genericity. Each criterion was also structured into two classes: favourable (F) and unfavorable (U), as shown in Fig. 1. 2.3. Network of vineyard farms To test the feasibility and the robustness of the indicator calculation we set up a network of a vineyard farms with a maximum range of variation for the (i) farming systems, (ii) farm structure and size, (iii) site-specific conditions and (iv) pressure of fungal pathogen depending of climate rainfall area or vintage determining the use of fungicides, main treatment in viticulture (Butault et al., 2010).

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Step 1: Design of a decision tree (input variables V1, V2, output Y) with fuzzy subsets (favourable F and unfavourable U) with YFF: output when V1 and V2 are favourable (F), YFU: output when V1 is favourable (F) and V2 unfavourable (U) U

F

V1 V2

F

Y

yFF

U

F

U

yFU

yUF

yUU

Step 2: Fuzzification: definition of fuzzy subsets and membership function calculating the degree of membership of V1 and V2 (taking value V1 and V2) to the fuzzy subsets (favourable F and unfavourable U). A value of 1 expresses a total membership to one fuzzy subset (implying that the membership to the other subset is 0)

1

F

U mFV2

mFV1 mUV1

0

1

F

U

mUV2 V1 Fuzzy subset

V1

0

V2 Fuzzy subset

V2

Step 3: Use of the function minimum (MIN) for each rule calculation of a membership degree e.g. if V1 is F and V2 is U then mFU = MIN (mFV1 , mUV2) Step 4: Defuzzification: Calculation of a unique indicator value (I) by the barycentre of the output values (y) weighted by the membership degree of each rule: (yFF. MIN (mFV1; mFV2)+yFU. MIN (mFV1; mUV2)+yUF. MIN (mUV1; mFV2)+yUU. MIN (mUV1; mUV2)

1

I= (MIN (mFV1; mFV2)+ MIN (mFV1; mUV2)+MIN (mUV1; mUV2)+MIN (mUV1; mUV2) Fig. 1. Simplified example of a decision tree using fuzzy logic. The membership function in step 2 can take another shape (e.g. sinusoidal) and be either continuous or discrete. The MIN function can be replaced by another operator (for an example, see Phillis and Andriantiatsaholiniaina, 2001).

The network was located in five French Northern vineyards: Alsace, Burgundy, Champagne, Franche-Comté and Loire valley belonging to the three main French climatic area according to the typology of Blenkinsop et al. (2008): (i) Alsace, Burgundy and Franche-Comté under ‘North Mediterranean climate’ summarized as warm and with moderate precipitation, (ii) Champagne under ‘temperate maritime climate’ summarized as moderate precipitations with less extremes and (iii) Loire valley under ‘moderated temperate maritime climate’ summarized as warm and wet but with rather relatively few wet days in the spring. Farms were chosen according to following criteria:

- The distribution of farms between the five areas in Northern France should be balanced. - Winegrowers had to be volunteers to take part to a research program and to discuss on their practices. The refusal rate was respectively 0% in Franche-Comté, 13% in Alsace, 13% in Burgundy, 33% in Champagne and 58% in Loire valley. - Farms should represent the diversity of French vineyard farms regarding (i) the farming systems; conventional, integrated or organic viticulture, (ii) the Winemaking and maturing processes: winegrowers making their own wine and members of a cooperative and (iii) the farm size: small (under 5 ha) and big (up to 30 ha) farms. Table 2 shows an overview of the selected farms.

Data of the farms shown in Table 2 were collected by only one person, the first author of this paper, what avoided problem of data quality like in case of multiple interviewers. Farm interviews were performed each year during the winter period.

3. Results: calculation method of the Indicators of the INDIGO® method 3.1. The soil cover indicator (Isoilcover) 3.1.1. Design of the new indicator, Isoilcover Isoilcover indicator is based on the universal soil loss equation (USLE) (Wischmeier, 1975, 1976) to vineyard soil loss risk. The universal soil loss equation (USLE) assesses the average annual soil loss (tones/acres) by multiplying six erosion factors, which are functions of numerous secondary variables. One factor is a measure of the erosive forces of rainfall and runoff. The next factor concerns soil erodibility and assess the erosion of soil type for standardized conditions of slope, slope length and soil cover. The four last factors are dimensionless and express the effects of (i) length, (ii) percentage and shape of the field slope, (iii) soil cover and (iv) protecting practices against soil loss. The same factors controlling erosion were identified in vineyard (Battany and Grismer, 2000; Louw and Bennie, 1991; Zuzel and Pikul, 1993). Assuming that data on climate effect and soil erodibility are not available at local scale and that soil cover is the main protecting practice in vineyard, we simplified the USLE equation: Isoilcover = P · LgP ·

n 

(Scoveri · Priski )

i=1

Eq. (1): Isoilcover indicator calculation. Where Isoilcover; the indicator calculated on a dimensionless scale between 0 (maximum risk of runoff and soil erosion impact) and 10 (no risk of runoff and soil erosion impact); P: coefficient depending on the field slope (%), in four classes <1%, 1–5%, 5–15%

M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

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Table 2 Description of the farm network used for indicator calculation. Geographic location

Number of farm

Vintages

Alsace Burgundy Champagne Franche-Comté Loire valley Total

13 12 10 10 15 61

2000, 2001, 2002 2000, 2001, 2002 2000, 2001, 2002 2002, 2003 2008

Winegrower system

Size (ha)

Wine process

Organic Integrated Conventional Farm surface average Field surface average Winemaking Cooperation 3 2 0 4 3 12

4 8 6 4 9 31

6 3 4 2 3 18

and >15%, taken from Schwertmann equation (Schwertmann et al., 1987); LgP: coefficient depending on slope length of the field (m) taken from Schwertmann equation (Schwertmann et al., 1987); Scoveri : soil cover crop of the field for decade i; Priski : climate risk period coefficient expressed on a scale between 0 (no risk for runoff) and 1 (high risk for runoff). Scoveri results from the calculation of an average soil cover weighted by the proportion of soil cover on each inter-row and the proportion of soil cover on the row according to the growth curve of plants (Rühling et al., 2002; von Rieder and Uhl, 1987). Scoveri is weighted by a climatic risk factor that mitigates the effect of decades with unfavourable soilcover for climatic conditions presenting a low risk. The product of Scoveri and Priski for each decade is summed up for the whole year. 3.1.2. Reference value The reference value is 7 and corresponds to a low risk of erosion and runoff. It can be obtained from the combination of a soil cover by natural grass of the field, a semi-continental climate, and a field slope belonging to the class between 1% and 5% with a maximal length of 100 m (Appendix A). 3.1.3. Sensitivity analysis For the Isoilcover indicator we set the range of variation between −50% and +50% of a median value of P, Lg P, Prisk and Scoveri , respectively for a variation between 5% and 15% for the slope percentage, between 50 and 400 m for the slope length, for oceanic to continental climate conditions for Prisk variable and, between 0% and 80% for soil cover (Fig. 2). Favourable scenario of Priski was based on data from Franche-Comté, medium scenario from Burgundy and unfavourable scenario from Champagne. Fig. 2 shows that Scoveri has the greatest weight followed by Lg P and then by P. Scoveri range is 7.7, 6.6 and 4.3 points of the indicator result, Lg P range is 4.3, 3.6 and 0.5 points, P range is 2.3, 2.1 and 0.9 points and finally Prisk range is 0.7, 1.5 and 0.2 points, respectively when other variables are set respectively at favourable, medium and unfavourable levels.

12.5 14.3 10.4 10.9 34.0 82.1

0.55 0.49 0.28 0.40 1.10 2.82

13 12 8 10 10 54

0 0 2 0 5 7

Table 3 Characteristics of Ifrost indicator variables: Practice, Day and Area. Parameters taken into account

Unit

Limits of fuzzy class

Practice Day Area

Dimensionless Day Hectare

0 0.5 0.5

10 6 50

3.2.2. Description of the input variables of Ifrost (i) Variable practice From the different protection systems identified, we listed the potential impacts. Five environmental components may be impacted: water quality, soil quality, landscape and non-renewable energies and wastes management. Systems were ranked according to their non-renewable energies consumption by means of technical data provided by one partner, the CIVC (not published). For the other environmental impacts, four experts were asked to rank the systems. The ranks for the five environmental impacts were recalibrated on a scale between 0 and 10 and averaged, providing an impact score for each system giving the value of the variable Practice. This yielded a range of variation between 4.6 for “chaufferette” and 9.2 for Heathen metal wires. (ii) Variable area Area variable is the area (ha) of the field covered by a frost protection system. We set the whole range of variation of field size in the fuzzy subset (Table 3). (iii) Variable day Day variable is the length of implementation of a given frost protection system. According to proposals of local technical experts we defined the fuzzy subset between 0.5 and 6 days, the latter being considered as a high number of days requiring a frost protection system.

3.2.3. Reference value The reference value 7 corresponds to an acceptable risk for environmental compounds which can be obtained for example by the use of heathen metal wires system during two days a year, on an area smaller than 20 ha (Appendix A).

3.2. Design of the second new indicator, Ifrost 3.2.1. Overview of the indicator We decomposed the frost protection impact on environment in three variables: (i) the frost protection system expressing the potential impact (Practice), and two variables to calculate the extend of the implementation of this system: (ii) area of the field where the frost protection system is settled (Area) and (iii) the duration expressed in number of days during the year when the frost protection system is switched on (Day). Due to lack of quantitative knowledge on the relations between the three variables and their dissimilarity, we decided to aggregate them with a fuzzy expert system in form of a decision tree (Fig. 3). Information on the three variables is given in Table 3.

3.2.4. Sensitivity analysis Like for the Isoilcover indicator, we set the variation range between −50% and +50% of a median value for the Practice, Area, Day variable, this for a favourable, medium and unfavourable scenario (Fig. 4). For all scenarios, the most influent variable is Area that varies for more than 4 points. In the unfavourable scenario the Day variable has the same weight than Area. This can be explained by the decision rules in Fig. 3. For the variable Practice, we expected a greater weight than for the two other variables when considering the decision tree. However the range of variation obtained with the available frost protection systems varies between 4.6 and 9.2 and hence does not cover the whole range of the fuzzy set.

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M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

3.3. Adaptation of the pesticide indicator I-Phy

10

Indicator value

8 6 4 2 0 -50%

-25%

25%

50%

Soil cover Slope Lengh of slope Prisk 10

Indicator value

8 6 4 2 0 -50%

-25%

25%

50%

Soil cover Slope Lengh of slope Prisk

- To recalibrate the equation aggregating the values of I-Phy per a.i. for a spraying program (see Section 3.3.1). - To adapt the parameterization of the pesticide interception by plant cover for the ESO and ESU sub-indicators, we took not only the interception of the grape but also the soil cover under the rows and between rows, variables which are also used for Isoilcover indicator. - To keep the aggregation of ESO, ESU, AIR with RATE as an “environment” sub-indicator (ENV). - To add a sub-indicator addressing the impact on beneficial (BEN), (see Section 3.3.2). - To aggregate the sub-indicators ENV and BEN to assess the global risk per a.i. (Section 3.3.3). - To improve the AIR sub-indicator by adding decision rules addressing the drift to air due to sprayer which can be very important in viticulture (Flari et al., 2007; Garreyn et al., 2007; Landers and Farooq, 2004), the AIR addressing only volatilization of a.i. in the version for arable crops (see Section 3.3.4). 3.3.1. Recalibration of the aggregation equation for a spraying program Like for arable crops, we proposed a value for the whole spraying program to make possible comparison between fields. The principle is that the value of the spraying program should be equal or smaller than the worst value for one a.i. This difference between the former and the latter should increase the lower the values of the other a.i. are. We developed a scoring system, which was calibrated to take into account the high number of treatments in perennial crops (Butault et al., 2010) so that comparisons between arable crops, vineyard and orchard systems are possible (Eq. (2)).

10 8 Indicator value

The I-Phy indicator developed by van der Werf and Zimmer (1998) for arable systems was based on an indicator calculated for each active ingredient (a.i.) and consisting in a fuzzy decision tree aggregating three sub-indicators expressing the risk respectively for groundwater (ESO), surface water (ESU) and air (AIR) with a fourth one based on the amount of a.i. (RATE). The risk subindicators are structured on the same way in fuzzy decision tree aggregating variable of pesticide properties, pesticide spraying and field sensitivity to pesticide transfer (Bockstaller et al., 2008b; van der Werf and Zimmer, 1998). All the sub-indicators are expressed on the same scale between 0 (unacceptable risk) and 10 (no risk), the fuzzy subset ranging between those two values. Based on recommendation from experts in viticulture and in agreement with the partners of the project we decided:

6 4 2 0 -50%

-25%

25%

50%

I-Phy = MIN(Iphysai ) −

n  j=1

Soil cover Slope Lengh of slope Prisk Fig. 2. Sensitivity test on the Isoilcover indicator. Influence of the variation of the percentage of the percentage of the soil cover (Scoveri ) the slope (P), the lengh of the slope (Lg P) and Prisk on the soil-cover indicator (Isoilcover). Each variable varies from the lower to the higher value, the others variables being constant to an unfavourable, median and favourable value corresponding respectively to an unfavourable, median and favourable scenario.

 kj ·

10 − Iphysaj



10

Eq. (2): I-Phy calculation. Where: Iphysai or j: indicator value between 0 and 10 for respectively a.i, i and j, i concerning all the a.i. of a spraying program whereas j concerns all excepted this with the lowest value (Min (I-Physai)). IPhysa is weighted by the percentage of field sprayed. kj Weighting coefficient dimensionless (with kj = −1.7175.e (−0.2913. Iphysaj ) depending on the value of the indicator for each a.i. (I-Physaj ). The factor kj (10 − I-Physaj )/10) is a penalty subtracted to the lowest value of I-Physa. The lower Iphysaj is the higher the penalty is. For example if Iphysaj are respectively equal to 7, 5 and 2, then Min (Iphysai ) = 2 and the penalties are −0.07, −0.20 so that I-Phy = 2–0.07–0.2 = 1.73; if Iphysaj are respectively equal to 7, 3

M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

Practice

Practice

Area

Day

Area

Day

19

Day

Area

Area

Day

Day

7

6

Day

Day

Day Unfavourable class

10

8

8

3

4

0

Favourable class

Fig. 3. Summary of decision rules. The effect of the input variables, Practice: impact of the frost protection system, Area; the area covered by the system, Day: duration of implementation of the system on the value of the conclusions of the decision rules of the frost protection management indicator (Ifrost) according to their membership to the fuzzy sets favourable (non-shaded boxes) and unfavourable (shaded boxes). For details see text.

and 2, then Min (Iphysai ) = 2 and the penalties are −0.07, −0.50 so that I-Phy = 2–0.07–0.5 = 1.43 I-Phy-farm is the field-size-weighted sum of I-Phy of each field. 3.3.2. Design of the “beneficial” sub-indicator BEN The sub-indicator BEN assesses the toxicity of a.i. of pesticide on a common beneficial organism in vineyard, the Phytoseiiae family (i.e. Typhlodromus pyri and Kampimodromus aberrans) playing an important role in pest control (Barbar et al., 2006; Kreiter et al., 2002). For those beneficials, data on the impact of pesticides are available for a large number of a.i. This was not the case for other beneficials (e.g. Chrysoperla carnea) (Sentenac, 2011) so that we restricted BEN to the Phytoseiiae family. For each a.i., we used toxicity thresholds for the amount applied, based on values taken from field toxicity studies when available, otherwise data from laboratory tests. The data were provided by a partner of the project (Sentenac personal com.) and from Agroscope Changins (Genini, 2000). This led to a characterization of a.i.in five groups: 1: “no toxic”, 2: “no toxic” or “moderately toxic”, 3: “moderately toxic” or “toxic”, 4: “toxic” and 5 “no toxic” or “moderately toxic” or “toxic”, group 2 and 3 having one threshold for the amount of a.i. applied, and group 5 having 2 thresholds. 3.3.3. Aggregation of the “environment” and “beneficial” sub-indicators We aggregated the two sub-indicators “environment” (ENV) which was the former indicator for arable crop (van der Werf and Zimmer, 1998) and the newly developed sub-indicator “beneficial” BEN according to the rules shown in Fig. 5. All the stakeholders agreed to add the “beneficial” BEN sub-indicator to the I-Phy indicator due to the importance of beneficial in reducing insecticide use, but with lower weight than the others environmental subindicators ESO, ESU, AIR and RATE. As a consequence, we decided: - to give a value lower than 7 to I-Phy when BEN was totally defavourable (=0), - not to improve I-Phy by a favourable value of BEN when the ENV was defavourable, so that ENV had a greater weight. 3.3.4. Adaptation of the AIR sub-indicator We introduced in the existing AIR sub-indicator (van der Werf and Zimmer, 1998), a drift component based on the sprayer used in vineyard. This variable Dair was scored between 0 (maximum drift, e.g. spraying by helicopter) and 10 (low drift e.g. compressed air sprayer with collection-retrieval panels), the classical sprayer used for spraying grape (air blast sprayer) having a value of 5. The

decision tree shown in Fig. 6 associating transfer by volatilization (already in the initial version) and aerial drift has the same structure than the ESU or ESO decision rules trees (van der Werf and Zimmer, 1998). 3.3.5. Reference value We applied the precautionary principle to the calculation of IPhy for a spraying program as shown by Eq. (2) which is based on the worst value of the indicator for each a.i. Eq. (2) was determined across production systems, arable crops, vineyard, and orchard systems. In all cases, a spraying program that does not have any a.i. yielding a I-Phy value under 7 and less than five a.i. with I-Phy = 7 remains close to 7 (Appendix A). 3.3.6. Sensitivity analysis The weight of each input variable was tested for the environment sub-indicator by van der Werf and Zimmer (1998). We tested the effect of the newly introduced Dair for three scenarios. A variation of ±50% of Dair yielded a variation of about 1 point of the final I-Phy for the favourable and medium scenario and less for the unfavourable scenario. The weight of the BEN and the ENV subindicators are easy to understand regarding the structure of the tree in Fig. 5. 3.4. Adaptation of others indicators 3.4.1. Ien adaptation Energy indicator for vineyard is an adaptation of the energy indicator (Ien) for arable crops (Pervanchon et al., 2002). Ien assesses fossil energy consumption of farming systems, contributing to the depletion of non-renewable resources (oil, gas, etc.). First, we adapted the structure of Ien indicator by removing the irrigation sub-indicator because irrigation is not allowed in protected designation of origin (PDO wine) areas for normal production systems. We kept the three other sub-indicators of the arable version of Ien, addressing respectively direct energy consumption due to use of machines at field and indirect consumption due to production of fertilizers and pesticides. Second, we recalibrated the equation transforming energy consumption into scores between 0 and 10. This adaptation was necessary because of the higher number of practices in vineyard than in arable systems: between 10 and 25 machinery uses (soil management between rows, canopy management and spraying), and between, 10 and 20 fungicide applications (Butault et al., 2010). Like for arable systems, we based the 0 value of Ien on a very intensive management system (e.g. 28 machinery uses, treatment

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M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

10

ENV

ENV

8

Indicator value

BEN

BEN

6

Unfavourable class 10

6.6

0

Favourable class

4 2 0 -50%

-25%

25%

50%

Practice (good scenario) Day (good scenario) Area (good scenario)

Fig. 5. Summary of decision rules. The effect of the input variables, ENV: Environment sub-indicator, which is the aggregation of surface water quality, groundwater quality, air quality and quantity of a.i. pesticide applied in the field and BEN: beneficial organisms sub-indicator on the value of the conclusions of the decision rules for I-Phy, the pesticide risk indicator, according to their membership to the fuzzy sets favourable (non-shaded boxes) and unfavourable (shaded boxes). For details see text.

Third, we added specific data on pesticides and fertilizers to viticulture to the database. For machinery use, we took data from Rühling et al. (2002) and von Rieder and Uhl (1987).

10

Indicator value

8 3.4.2. IN adaptation Nitrogen indicator is based on an operational model nitrogen assessing NO3 leaching and, NO2 and NH3 emissions to the atmosphere in a quantitative way (Bockstaller et al., 2008a; Pervanchon et al., 2005). For this indicator, we modified the parameterization of the models to adapt it to viticulture. This led us to take into account for example value of mineralization for non-exported pruned shoots and leaves, as well as for incorporated soil cover.

6 4 2 0 -50%

-25%

25%

50%

Practice (medium scenario) Day (medium scenario) Area (medium scenario)

3.4.3. IOM adaptation As for IN, we adapted the indicator developed for arable systems (Bockstaller et al., 1997) by parameterizing and populating the database with values about pruned shoots, leaves, different cover crops implemented in viticulture as well as organic fertilizers.

10 3.5. Indicators validation

Indicator value

8 Bockstaller and Girardin (2003) proposed a three steps validation.

6 4 2 0 -50%

-25%

25%

50%

Practice (bad scenario) Day (bad scenario) Area (bad scenario) Fig. 4. Illustration of the sensitivity test on the Ifrost indicator. Influence of the variation of Practice: frost protection system, Day: the number of days and Area the surface of the field covered by the frost protection system, on the frost indicator (Ifrost). Each variable varies from the lower to the higher value, the others variables being constant to an unfavourable, median and favourable value corresponding respectively to an unfavourable, median and favourable scenario.

frequency index at 12, N fertilization: 28 kg N ha−1 ), the 7 value on a integrated system (e.g. 19 machinery uses, treatment frequency index at 12, N fertilization: 30 kg N ha−1 ), and the 10 value on a lowinput system (e.g. 10 machinery uses, treatment frequency index at 9, non fertilization).

3.5.1. Design validation The first step of the validation methodology proposed by Bockstaller and Girardin (2003) consists in assessing the scientific validity of the design by submitting it to a panel of experts and published in a peer-review journal. This was the case of most INDIGO® indicators like: I-Phy (Bockstaller and Girardin, 2003; Bockstaller et al., 2008b; van der Werf, 1996; van der Werf and Zimmer, 1998; Wohlfahrt et al., 2010), Ien (Pervanchon et al., 2002), IN (Bockstaller et al., 2008a; Pervanchon et al., 2005) and IOM, (Bockstaller et al., 1997, 2008b). This remains to be done for the new indicators Isoilcover and Ifrost, as well as for the adapted indicators to viticulture. 3.5.2. Output validation This step aims at assessing the predictive quality of an indicator. As pointed out by Bockstaller and Girardin (2003), there is a need of a set of measurements of environmental impacts. Until now, the only comparison work in viticulture was performed for the surface water sub-indicator of I-Phy (Bockstaller et al., 2008a). The predictive quality was assessed by a probability test, which yielded a percentage of comparisons of indicator output with pesticide concentration in water, being considered as acceptable regarding the design of the indicator. This percentage was between 68% and 85% for a comparison on plots of a watershed in Champagne.

M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

Dair

KH

Dair

KH

Position

Dair

KH

KH

21

Dair

Position

DT50

DT50

ADI

ADI

ADI

ADI

Unfavourable class 10

9

8

4

4

Favourable class

0

Fig. 6. Summary of decision rules. The effect of the input variables, KH: Henry constant, Dair: air drift to sprayer Position: pesticide application location (in or on the soil), DT50: field half-life of the a.i. ADI: Admissible daily ingestion of the a.i. on the value of the conclusions of the decision rules for sub-indicator AIR (risk of air contamination) of I-Phy, the pesticide risk indicator, according to their membership to the fuzzy sets favourable (non-shaded boxes) and unfavourable (shaded boxes), adapted from van der Werf and Zimmer (1998). For details see text.

3.5.3. End-users validation The last consists in verifying whether an indicator is implemented by end-users and meets their demands. Besides examples shown in the next section implemented by our team, we registered 13 implementations mainly only I-Phy although we did not implemented a real dissemination strategy neither developed a calculation software meeting standards for a broad dissemination. Another example is the implementation at a research station (Forget et al., 2009) calculated INDIGO® indicators for environmental assessment of pesticide protection strategy for the Bordeaux vineyard and concluded that even organic vineyard can be optimized to reduce vineyard impact on environmental non-renewable resources like soil of water. A last, but not least, example is the creation in 2005 of the Envilys start-up which aimed at using INDIGO® method in viticulture (Envilys, 2012). Since 2005, the Envilys Company assessed 105 wineries shared out on 3738 hectares. 4. Implementation of indicators at farm and field scales 4.1. Assessment of implemented winegrowers’ strategies Implemented field and farms strategies can be assessed in an ex post way with help of a set of indicators: this was the original idea of the INDIGO® method (Bockstaller et al., 1997). Using spider diagram is very useful to present simultaneously indicators results for non-equivalent issues like pesticides, nitrogen, etc, and to compare different agricultural systems. Fig. 7 shows an example of INDIGO® indicators calculations for four different vineyards management in Franche-Comté: one organic (ORG), two integrated (INTEG1 and INTEG2) and one conventional system (CONV). Spider diagram also shows immediately to the winegrower strong and weak points of their systems. CONV yielded best results for Ien due to low tillage applications and for IN due to moderate fertilization rate and low results for the other indicators. ORG yielded values close to the best value of 10 for IN and IOM. However ORG showed a value below 7 for I-Phy because of high rate of copper and sulphur but was better than the other systems, and a low value for Isoilcover due to mechanical weeding on the row and one interrow. I-Phy results for INTEG2 and ORG fields are better thanks to a lower number of pesticide applications and a lower environmental toxicity of applied

a.i. than for CONV. Like ORG, INTEG 1 and INTEG 2 yielded good results for IN and IOM but low results for the other indicators. 4.2. Calculation and simulation of farmers’ advices Another example goes a step further and combines an ex post environmental assessment of practices of the winegrower with an ex ante assessment of advised practices to enhance their environmental performances. This was tested through the following example based on the calculation of I-Phy in 2002 for 15 fields in Franche-Comté. For most of the fields studied, surface water quality and beneficial organisms viability were the more impacted environmental compartments (results not shown). Due to the predictive nature of the INDIGO® indicators, it was possible to calculate I-Phy for scenarios based on advice to protect beneficial organisms or, quality of ground- and surface water (e.g. respectively fields 8 4, 7 11 and 2 4 in Table 4). The highest enhancement of I-Phy resulted in an increase of 3 points of indicator for the combination of reducing the number of pesticides, substitute toxic a.i. on beneficial organisms and replacing herbicides by tillage or soil cover

CONV INTEG1 INTEG2 ORG Advised Best

Isoilcover 10 8 6 4

Ien

I-Phy

2 0

IOM

IN

Fig. 7. Spider diagram of environmental assessment for four vineyards management in Franche-Comté for 2002: one organic (ORG), two integrated (INTEG1 and INTEG2) and one conventional system (CONV).

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M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

Table 4 Jura fields I-Phy indicator results and simulations for 2002 data and advices to go forward more ecological-friendly vineyard practices. BEN: beneficial organisms sub-indicator; AIR: air sub-indicator; ESO: groundwater sub-indicator; OM: fertilization of the soil; a.i.: active ingredient. Fieldcode

8 1 1 1 6 4 5 3 7 2 2 10 10 2 2

4 8 4 1 11 14 11 6 11 4 3 2 3 1 2

I-Phy calculation

Advices (* concerned; not concerned)

Real

Simulation

Improved Environmental compartment

Reduce number of pesticides

0.8 1.9 2.5 2.5 3.1 3.2 3.3 4.3 3.4 5.9 6.4 6.6 6.7 8.3 8.8

2.5 4.6 5.2 4.8 4.7 4.8 4.1 6.7 4.1 6.6 7 7.3 7.3 8.5 9

BEN BEN BEN BEN BEN BEN BEN BEN AIR and ESO ESU ESU ESU ESU ESU ESU

* * * * * * * * * * *

* *

crop (fields 1 4 and 1 8). Such work was possible by the analysis of intermediate results to which end-users has access in the software. In this way, end-users can derive advises to design environmental friendly pesticide program.

4.3. Calculation for regional of time evolutions of environmental assessment Another use of INDIGO® indicators consisted in assessing the variability of I-Phy, Isoilcover and Ien indicators at a larger scale than the field, such as a region. This is illustrated in Fig. 8 with Isoilcover results for five French vineyards between 2001 and 2008. Results show better values for the Alsace vineyard in comparison to the Bourgogne, Champagne and Franche-Comté between 2001 and 2002. The higher value of Loire valley could not be compared to the other ones because of the difference of calculation period and a lower field number, (Tonus, 2009). Isoilcover results show a very large diversity within average values and large amplitude inside vineyards and between vineyards, which highlight the diversity of soil cover management in vineyards combined with storms risk frequency.

10

I-soil-cover

8

6

4

2

0 Alsace-2001 (374)

Burgundy-2001 (376)

Champagne-2001 Franche-Comté-2002 Loire valley-2008 (207) (274) (15)

Vineyards Fig. 8. Average and standard deviation of Isoilcover for fields from five French vineyard regions for different years (X-axis code: Region-vintage (number of plots); Y-axis: Isoilcover indicator value).

Reduce rate of pesticides

* * *

* *

Remove toxic a.i. for beneficial

Increase soil cover between rows

* * * * * * * *

* * * * * *

OM manure

*

* * * * * * * *

5. Discussion The present work aimed at developing an environmental assessment method based on indicators for agronomists involved in the enhancement of wine growing systems to reduce their environmental impacts. We adapted the INDIGO® method, initially developed for arable systems by introducing two new indicators and keeping four existing indicators addressing issues relevant in viticulture. Those indicators were modified to different extent. We changed the structure of the pesticide and energy indicators by adding or removing sub-indicators. For nitrogen and organic matter, the parameter tables were adapted to viticulture and the spatial heterogeneity of vineyard (row and interrows) was introduced in the calculation. The type of indicators used in the INDIGO® method differs totally from those of another French assessment method adapted to viticulture: the IDEA method (Zahm et al., 2008). Indicators in the INDIGO® method were predictive based on an operational model whereas those of IDEA are simple, i.e. scores linked to practices (Bockstaller et al., 2008a; Ricou et al., 2014). For the pesticides issue where IDEA calculates the treatment frequency index (TFI) indicator based on a ratio of the pesticide rate by the recommended rate (Butault et al., 2010) and the INDIGO® method calculates a more elaborated pesticide risk indicator which was evaluated for its scientific quality (Bockstaller et al., 2008b; Reus et al., 2002). The INDIGO® method presents similarity to the EIOVI method (Fragoulis et al., 2009) regarding the aggregation method based on decision trees using fuzzy subsets. For the soil organic issue, EIOVI uses the IMO indicator of the INDIGO® method and transforms IMO outputs by a fuzzy expert system. For pesticides, both indicators yielded correlated results in spite of their totally different calculation method (Reus et al., 2002) and similar predictive quality in a study with data sets from three vineyard locations (Girardin et al., 2007). Participation of end-users in the development of a tool is very useful to guarantee the future use of indicators and to avoid failure like it was the case in the implementation of crop model (van Ittersum and Donatelli, 2003) and decision aid model in crop protection (Cox, 1996). Technical representative from winegrowers extension service took part to the development of the INDIGO® method, but no stakeholder from the administration or policy. The former asked scientists to develop an indicator on the risks linked to the implementation of frost protection

M. Thiollet-Scholtus, C. Bockstaller / Europ. J. Agronomy 62 (2015) 13–25

management. Thus, participatory approach was limited to the first step of preliminary choices (Prost et al., 2012; van Meensel et al., 2012). This can be easily understood because the design step of the indicators requires scientific knowledge. However data from technical experiments can be used for the parameterization (e.g. non intentional effect of pesticide on beneficial). In Section 3.5.3, we provided data on the implementation of INDIGO® in France to show first implementations in diverse pedoclimatic contexts. However, we did not test the interest of end-users for the method as recommended by Bockstaller and Girardin (2003) and carried out for the EIOVI method (Fragoulis et al., 2009). The results shown in Section 3 are example and do not have a representative value of environmental performances of French winegrowers. They highlighted some potentialities of using the INDIGO® method. The first presented results show an example multicriteria assessment of different systems. Such assessment can be carried out at field or at farm level with the weighted average values by field size. The spider diagram points out strong and weak points of each system but does not permit to compare or to rank them. In this case, a composite aggregated indicator would be needed. To achieve this, Fragoulis et al. (2009) used the approach of fuzzy decision tree to aggregate the indicators of the EIOVI approach. Another approach is the MASC model developed for arable cropping systems by Sadok et al. (2009) and using the DEXi software based on qualitative “if then” rules. Upscaling of the indicator at higher scale (e.g. watershed or region) is necessary to address some environmental impact. For surface water the upscaling of the I-Phy indicator led to integrate hydrological processes (Wohlfahrt et al. (2010). For other indicators like the groundwater sub-indicator, an aggregation based on the calculation of weighted mean by field size could be acceptable. Non-sprayed area should be included in the calculation of the groundwater sub-indicator at higher scale due to their compensation effect, as well as for their impacts on beneficials, for which semi-natural area are very important (Bianchi et al., 2006). So far, biodiversity is not covered by INDIGO® method. Recent initiatives having developed a predictive indicator for biodiversity open new perspectives {Bockstaller, 2011 #495;Ricou, 2014 #536}.

6. Conclusion We adapted an environmental assessment method initially developed for arable systems to apply it to viticulture. The aim of this new needed tool for viticulture is to support the enhancement of wine-growing systems by agronomists. We modified four of eight indicators from the initial method and designed two new indicators: soil cover dealing with soil protection against erosion and frost indicator specific to viticulture (and orchard systems). The selection was carried out with the participation of technical staff from winegrowers’ organizations and stakeholders. The feasibility of the INDIGO® method was tested for different French vineyards and several applications were highlighted. The INDIGO® method enables assessing existing wine growing systems (ex post) as well as potential ones (ex ante) due to the predictive nature of the indicators based on operational models. In this way, advisors could assess the impact of their advice and to which extent they can enhance the environmental performances of winegrowers’ practices. First applications confirm the potentialities of the method. Calculations can also be carried out at regional scale for a sample of fields. However at this stage of the development, processes outside fields (e.g. hydrological ones) are not taken into account in the indicators when they are upscaled. Other issues like biodiversity may be added to the INDIGO® method. A step further would be to

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develop an aggregated composite indicator to assess sustainability of winegrowers systems with INDIGO® environmental indicators associated to economic and social indicators, as it was done for arable cropping systems through the MASC model (Sadok et al., 2009). Acknowledgements The authors thank Région Alsace, Région Bourgogne, Région Champagne-Ardennes, Région Franche-Comté, and Région Pays de Loire, France-Agrimer and INRA (French National Institute for Agricultural Research) for funding. We are grateful to Frédéric Carnec, Thierry Coulon, Arnaud Descôtes, Philippe Kuntzmann, Emile Meyer, Dominique Moncomble, and Guillaume Morvan, Yolande Nöel, Anne Poutarraud, Alexis Tonus, Adrienne Trillaud, and Envylis company for their helpful discussions. The authors thank Philippe Girardin, retired searcher in agronomy, head of the project and Marc Spiller from Wageningen University, for English language review. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.eja.2014.09.001. References Auzet, A.-V., Boiffin, J., Papy, F., Ludwig, B., Maucorps, J., 1993. Rill erosion as a function of the characteristics of cultivated catchments in the North of France. CATENA 20, 41–62. Barbar, Z., Tixier, M.-S., Cheval, B., Kreiter, S., 2006. Effects of agroforestry on phytoseiid mite communities (Acari: Phytoseiidae) in vineyards in the South of France. Exp. Appl. Acarol. 40, 175–188. Barlow, K., Bond, W., Holzapfel, B., Smith, J., Hutton, R., 2009. Nitrogen concentrations in soil solution and surface run-off on irrigated vineyards in Australia. Aust. J. Grape Wine Res. 15, 131–143. Bartoli, F., Dousset, S., 2011. Impact of organic inputs on wettability characteristics and structural stability in silty vineyard topsoil. Eur. J. Soil Sci. 62, 183–194. Battany, M.C., Grismer, M.E., 2000. Rainfall runoff and erosion in Napa Valley vineyards: effects of slope, cover and surface roughness. Hydrol. Process. 14, 1289–1304. Bianchi, F., Booij, C., Tscharntke, T., 2006. Sustainable pest regulation in agricultural landscapes: a review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B: Biol. Sci. 273, 1715–1727. Blenkinsop, S., Fowler, H.J., Dubus, I.G., Nolan, B.T., Hollis, J.M., 2008. Developing climatic scenatios for pesticide fate modelling in Europe. Environ. Pollut. 154, 219–231. Bockstaller, C., Girardin, P., 2003. How to validate environmental indicators. Agric. Syst. 76, 639–653. Bockstaller, C., Girardin, P., Van Der Werf, H.M.G., 1997. Use of agroecological indicators for the evaluation of farming systems. Eur. J. Agron. 7, 261–271. Bockstaller, C., Guichard, L., Keichinger, O., Girardin, P., Galan, M.-B., Gaillard, G., 2009. Comparison of methods to assess the sustainability of agricultural systems. A review. Agron. Sustain. Dev. 29, 223–235. Bockstaller, C., Guichard, L., Makowski, D., Aveline, A., Girardin, P., Plantureux, S., 2008a. Agri-environmental indicators to assess cropping and farming systems. A review. Agron. Sustain. Dev. 28, 139–149. Bockstaller, C., Wohlfahrt, J., Hubert, A., Hennebert, P., Zahm, F., Vernier, F., Mazzela, N., Keichinger, O., Girardin, P., 2008b. Les indicateurs de risque de transfert de produits phytosanitaires et leur validation: exemple de l’indicateur I-Phy. Sci. Territ., 103–114. Bony, S., Gillet, C., Bouchez, A., Margoum, C., Devaux, A., 2008. Genotoxic pressure of vineyard pesticides in fish: field and mesocosm surveys. Aquat. Toxicol. 89, 197–203. Butault, J.-P., Dedryver, C.-A., Gary, C., Guichard, L., Jacquet, F., Meynard, J.-M., Nicot, P., Pitrat, M., Reau, R., Sauphanor, B., Savini, I., Volay, T., 2010. Ecophyto R&D. In: Quelles voies pour réduire l’usage des pesticides. Synthèse du rapport d’étude. INRA, France. Cox, P.G., 1996. Some issues in the design of agricultural decision support systems. Agric. Syst. 5, 355–381. Enea, M., Salemi, G., 2001. Fuzzy approach to the environmental impact evaluation. Ecol. Model. 136, 131–147. Envilys, 2012. Envilys, Creative Solutions for Agriculture, Environment and Land management.

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