Ecological Indicators 45 (2014) 677–686
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Environmental sustainability indicators for cash-crop farms in Quebec, Canada: A participatory approach Marie-Noëlle Thivierge a , Diane Parent a , Valérie Bélanger a , Denis A. Angers b , Guy Allard a , Doris Pellerin a , Anne Vanasse a,∗ a b
Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec, QC, Canada G1V 0A6 Soils and Crops Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Québec, QC, Canada G1V 2J3
a r t i c l e
i n f o
a b s t r a c t
Article history: Received 17 June 2013 Received in revised form 22 April 2014 Accepted 11 May 2014
On-farm environmental assessment, with consideration to the specificity of the farming system and the geographic zone, can enable farmers to include the environmental aspect in their management decisions. In the province of Quebec, Canada, 45% of the cultivated land is dedicated to grain production and among the 13,800 farms that sell grains, 3975 are specialized in this production. Cereal-based systems have their own constraints and realities and could benefit from a specific tool to assess their environmental sustainability. The objective of this research was to adapt and further develop a set of indicators of environmental sustainability at the farm level for cash-crop farms of the province of Quebec, in order to provide a self-assessment and decision-aid tool to farmers. Using a methodology based on focus groups of experts (researchers, stakeholders, and farmers), several indicators developed for dairy farms were adapted to cash-crop farms. Then the set of indicators was tested on cash-crop farms across the province through interviews with 31 farmers. The indicators were weighted according to their contribution to four sub-objectives of environmental sustainability (soil, water, air, and biodiversity conservation). A new type of chart was designed to help farmers understand and interpret the scores obtained from the set of indicators. Finally, a questionnaire was sent to the 31 farmers for end-use validation. A total of 16 indicators emerged from this research. The weighting reveals that, out of a total of 177 points, the indicators that contribute the most to environmental sustainability of cash-crop farms are “integrated pest management” (21 points), “crop diversity” (19 points), “riparian buffer strip” (18 points), and “incorporation of manure into the soil” (16 points). In comparison with a radar chart and a conventional bar chart, a new bar chart revealed to be a better decision aid tool, allowing the majority of farmers to identify the sustainability weaknesses of a fictive farm. However, the graphic design of this chart could be improved for easier understanding. The end-use validation confirmed the interest of farmers in this decision-aid tool. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Sustainable agriculture On-farm assessment Decision-aid tool Cropping system Bar chart End-use validation
1. Introduction Environmental sustainability can tenance of natural capital, which providing sink and source functions 1995; Van Cauwenbergh et al., 2007).
be defined as the maincomprises the resources in ecosystems (Goodland, Many attempts to address
∗ Corresponding author. Tel.: +1 418 656 2131x12262; fax: +1 418 656 7856. E-mail addresses:
[email protected] (M.-N. Thivierge),
[email protected] (D. Parent),
[email protected] (V. Bélanger),
[email protected] (D.A. Angers),
[email protected] (G. Allard),
[email protected] (D. Pellerin),
[email protected] (A. Vanasse). http://dx.doi.org/10.1016/j.ecolind.2014.05.024 1470-160X/© 2014 Elsevier Ltd. All rights reserved.
sustainability have been made since the Rio Earth Summit of 1992, through efforts from several countries to establish indicators for measuring progress (Rigby et al., 2001). Indicators are variables that provide information on other variables that are less available (Gras et al., 1989). They simplify the information (Andersen et al., 2013; Girardin et al., 1999; Mitchell et al., 1995; Rigby et al., 2001; Singh et al., 2012) and serve as a benchmark to make a decision (Gras et al., 1989) or to quantify the degree of compliance with environmental objectives (Van der Werf et al., 2007). In agriculture, on-farm assessment is essential to guide farmers with their management decisions (Häni et al., 2003; Pacini et al., 2003; Van Cauwenbergh et al., 2007). The use of a set of indicators constitutes a holistic approach that takes into account all agricultural practices within the system (Bockstaller et al., 1997).
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One of the first sets of indicators at the farm level was the Farmer sustainability index of Taylor et al. (1993), with 33 weighted indicators designed for cabbage farmers in Malaysia. Their methodology included a panel of experts as well as interviews with farmers. Bockstaller et al. (1997) and Girardin et al. (2000) went one step further by linking their indicators with sustainability sub-objectives or components. Their AGRO*ECO method for cash-crop farms was validated in France and Germany, and the results were presented to farmers using a radar chart. Since its first edition in 2000, the IDEA method (Vilain et al., 2008) moved the focus to the educational aspect of assessing the sustainability at the farm level. The EVAD method (Rey-Valette et al., 2008), inspired by IDEA’s principles, improved and documented the methodology to help farmer groups to construct their own set of indicators using a participatory methodology. The MOTIFS method (Meul et al., 2008) contributed to the user-friendliness of this kind of tool with an improved version of the radar chart. Other indicators and methods for on-farm sustainability assessment were developed within the European Union, as the Common Agricultural Policy (CAP) reform under Agenda 2000 made of sustainable development a priority (Commission of the European Communities, 1999). Recently, in the province of Quebec, Canada, Bélanger et al. (2012) developed agri-environmental indicators to specifically assess the sustainability of dairy farms. Indicators at the local production level have to reflect sitespecific characteristics (Sattler et al., 2010), including the climatic and natural conditions of the site (Commission of the European Communities, 1999), and the particularities of the farming system under study (Meul et al., 2008). The climatic and natural conditions prevailing in Quebec differ from those of Europe, mostly regarding the length of the growing season, the water regime, and the nature of arable soil. As those factors have a strong influence on crop production, it appears relevant to offer farmers a tool adapted to their specific conditions. Moreover, in Quebec, grain production has increased by 25% between 1998 and 2007 (BPR, 2008), and 47% of the cultivated land is now dedicated to this production (ISQ and MAPAQ, 2013). Cash crops in Quebec mostly include grain maize (corn), wheat, oats, barley, canola (colza), and soybeans. Among the 13,800 farms that sold grains in 2010, there were 3975 for which it accounted for more than half of the farm income (ISQ and MAPAQ, 2013; Statistics Canada, 2012). Therefore, this specific farming system deserves some attention. The objective of this research was to adapt and further develop a set of farm-level indicators of environmental sustainability for Quebec cash-crop farms, in order to provide a self-assessment and decision-aid tool to farmers. Complementary objectives were to improve the methodology to allocate weights to such indicators, and to design a new type of chart leading to a better interpretation of the scores resulting from the sustainability assessment. 2. Methodology The conceptual framework of the methodology is illustrated in Fig. 1 and will be detailed in Sections 2.1–2.5. The steps in the construction of indicators are interactive: the results from one step could lead to some modifications in previous ones (Rey-Valette et al., 2008). Those feedbacks are illustrated by the arrows in Fig. 1. Furthermore, this methodology can be described as adaptive and iterative (Meul et al., 2009; Rey-Valette et al., 2008). 2.1. Adaptation of indicators from dairy farms to cash-crop farms The original set of indicators from Bélanger et al. (2012) had been developed using the Delphi method (Delbecq et al., 1975) to inquire 25 experts through anonymous individual questionnaires,
Fig. 1. Conceptual framework of the methodology developed to adapt a set of indicators of environmental sustainability to cash-crop farms of the province of Quebec. The arrows illustrate the many feedbacks, making it an adaptive and iterative process.
for several rounds of questions. Thereafter, 12 experts (researchers, stakeholders, and farmers) were gathered to discuss the results in a panel, also referred to as a focus group. See Bélanger et al. (2012) for the detailed methodology regarding the Delphi method and the focus group. This participatory approach is named co-construction of indicators (Rey-Valette et al., 2008) or bottom-up approach (Fraser et al., 2006; King et al., 2000; Singh et al., 2012). According to Rey-Valette et al. (2008), it is important to bring together different stakeholders, including farmers, in the process of indicator construction. The inputs of farmers, often neglected in such processes, increase the likelihood of the indicators being accepted by the users (Dalal et al., 1999; Fraser et al., 2006; King et al., 2000). Thus, to adapt the dairy farm indicators from Bélanger et al. (2012) to the reality and constraints of cash-crop farms, the same type of methodology based on the consultation with experts was chosen, though with a smaller panel of eight experts (researchers, stakeholders, and farmers). The evaluation criteria described by Bélanger et al. (2012) were being sought during the adaptation process and must be seen as guidelines. Thereby, selected indicators should aim at being: (1) easy to implement, (2) immediately understandable, (3) reproducible, (4) sensitive to variations, (5) adapted to the objectives, and (6) relevant for users (see Bélanger et al., 2012, for a detailed description of these evaluation criteria). The discussions among experts were recorded for future references. 2.2. Testing of the indicators on cash-crop farms After a first focus group with the panel of experts, the selected indicators were tested on 31 cash-crop farms across eight areas of the province of Quebec (Table 1). A cash-crop farm can be defined as a farm where cash crops production accounts for 50% or more of its income (Statistics Canada, 2012). The objectives of these tests were to validate the calculations for each indicator, verify if the indicators fulfilled some of the criteria (criteria 1, 3, and 4 of Section 2.1), establish their suitability for all cropping systems, and determine whether the questions were understandable to all farmers. The farms were recruited with the help from several AgriEnvironmental Advisory Clubs across the province. For each farm, a one-to-one interview with the farmer was conducted. During this 2-h interview, a questionnaire was filled with the farmer. The agri-environmental fertilization plan of the farm (AOR, 2002) was also used as a data source. To be easy to implement, on-farm indicators must take advantage of the information already available that is credible (Bockstaller et al., 1997; Halberg, 1999; Meul et al., 2009; Mitchell et al., 1995; Rigby et al., 2001). Feedbacks from farmers were collected to improve the indicators.
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Table 1 Characteristics and geographical distribution of the 31 cash-crop farms used for the testing of the indicators. Geographic areas
Montérégie-Ouest Montérégie-Est Centre du Québec Estrie Chaudière-Appalaches Bas-St-Laurent Mauricie Saguenay/Lac St-Jean
Number of farms
6 6 2 4 2 5 2 4 31
Average land area (ha/farm)
275.9 183.6 348.2 317.3 41.0 266.6 196.0 164.3 231.8
Type of production
Type of soil tillage
Organic production
Conventional production
Conservation tillage only
Conventional tillage only
Mix of both types
2 2 0 2 0 0 1 1 8
4 4 2 2 2 5 1 3 23
2 4 1 0 1 3 0 2 13
0 0 0 2 1 0 0 2 5
4 2 1 2 0 2 2 0 13
2.3. Weighting of the indicators The weight is the measure of the relative importance of an item in an ensemble (Morris, 1992). Here, the importance of an item is seen in regard of environmental sustainability, which can be defined as the maintenance of natural resources that are provided by the ecosystem (Goodland, 1995; Van Cauwenbergh et al., 2007). To facilitate a structured discussion about weighting, the concept of environmental sustainability was subdivided into four subobjectives. These sub-objectives were based on the four challenges ensuring a sustainable agriculture (Lefebvre et al., 2005; Michaud et al., 2006) and are: (1) soil quality conservation (comprising physical, chemical, and biological aspects), (2) water quality conservation (regarding pollution by fertilizers, pesticides, suspended solids, and pathogens), (3) air quality conservation (regarding greenhouse gases, ammonia, and pesticides), and (4) aboveground biodiversity conservation. The aim of these subdivisions is not to oversimplify the relationships existing in this ecosystem, but to structure the discussions among experts. According to Lefebvre et al. (2005), biodiversity comprises the indigenous species which, if reduced, will disrupt the ecosystem functions. To avoid doubleweighting of some indicators, it was decided that belowground biodiversity (microorganisms, fungi, etc.) would be comprised in the biological aspect of soil quality, rather than as part of biodiversity. Although farmers were consulted for the adaptation (Section 2.1), testing (Section 2.2), and validation of the indicators (Sections 2.4 and 2.5), they were not convened for the weighting. This second panel gathered seven experts (researchers and stakeholders). As reported by King et al. (2000), the bottom-up approach, which favors the creation of indicators by the users (farmers), is often criticized because it implies that scientific knowledge has less value than that of farmers. We argue that the weighting of indicators should be based on scientific references in order to reflect the contribution of each indicator to environmental sustainability. The indicators were weighted according to their contribution to each of the sub-objectives. A scale was built from 0, for a nil contribution of the indicator to a sub-objective, to 5 points, for a major contribution of the indicator to a sub-objective. The points awarded to indicators act as units of sustainability. Care was taken to be consistent both horizontally (between sub-objectives for the same indicator) and vertically (between indicators for the same sub-objective), which required several adjustments (Table 2). The points of sustainability of an indicator do not constitute an absolute value, but rather an indication of its relative importance (Rigby et al., 2001) compared to others in the achievement of environmental sustainability as defined in this study. The total contribution of an indicator to the overall environmental sustainability was taken as the sum of the individual contributions of this indicator to the conservation of soil, water, air, and biodiversity (Table 2). As an
implicit premise behind this methodology, the four sub-objectives (soil, water, air, and biodiversity conservation) were assumed of equal importance in environmental sustainability. 2.4. Global expert validation According to Girardin et al. (1999), to validate indicators is to verify if they meet the objectives for which they were created. This corresponds to the “accuracy evaluation” described by Meul et al. (2009). As an indicator is by definition a variable that provides information on other less accessible variables (Gras et al., 1989), it is often impossible to validate it by comparing with real data (Bockstaller and Girardin, 2003; Girardin et al., 1999; Rigby et al., 2001; Vilain et al., 2008). Furthermore, there is rarely a linear relationship between an indicator and a given measure (Bockstaller and Girardin, 2003; Girardin et al., 1999). The participation of experts and the constant reference to scientific literature were considered to be an a priori validation of the set of indicators, called design validation (Bockstaller and Girardin, 2003; Meul et al., 2009). Moreover, after the weighting of the indicators by the panel of experts, the final product was sent to all the experts involved at this point for a global expert validation or output validation (Bockstaller and Girardin, 2003; Rigby et al., 2001; Vilain et al., 2008). All the experts (researchers, stakeholders, and farmers) had the opportunity to provide comments about the calculation or the weighting, which were used to make small adjustments to the set of indicators. We consider that this validation is still in progress: publishing the results in peer-reviewed scientific journals is part of it, as independent experts will express their comments (Meul et al., 2009; Vilain et al., 2008). Furthermore, as this set of indicators will be implemented on farms, more feedbacks and comments from farmers and advisors will be taken into consideration. 2.5. End-use validation The end-use validation, or usefulness test, is a feedback process between users (the farmers to whom the indicators are dedicated) and designers of the indicators (Bockstaller and Girardin, 2003; Bockstaller et al., 1997; Girardin et al., 1999; Mitchell et al., 1995). It corresponds to the “credibility evaluation” described by Meul et al. (2009). It can be performed through a survey among users (Girardin et al., 1999) to verify their satisfaction with the proposed tool (Vilain et al., 2008), their understanding of the results (Bockstaller and Girardin, 2003), and the usefulness of the indicators as a decisionaid tool (Meul et al., 2008). Thereby, a questionnaire was sent to the 31 farmers who participated in the interviews, and it was filled and returned by 16 of them. The questionnaire was designed to inquire about: (i) the perceptions of farmers about the proposed approach, (ii) their opinion about the scores of their farm, (iii) their willingness to use this set
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Table 2 Example of the weighting of three indicators according to their contribution to environmental sustainability sub-objectives. Scale 0 (nil contribution) to 5 points of sustainability (major contribution). Indicator
Soil organic matter content (#1) Soil phosphorus saturation (#2) Deep and surface drainage (#3)
Contribution to environmental sustainability sub-objectives
Contribution to sustainability
Soil quality
Water quality
Air quality
Biodiversity
Maximum weight of the indicator
5 1 4
3 4 2
2 0 4
2 3 0
12 8 10
of indicators, and (iv) the most effective chart to illustrate their scores. It allowed the authors to verify if the indicators fulfilled some of the selection criteria (criteria 2, 5 and 6 of Section 2.1). The questionnaire, which could be filled out in 20–30 min, included multiple-choice questions and spaces for feedbacks.
3. Results and discussion 3.1. Selected indicators The adaptation of the dairy farm indicators of Bélanger et al. (2012) by the panel of experts led to 16 indicators for cash-crop farms, grouped into four components (Table 3). As mentioned in the methodology, the original indicators from Bélanger et al. (2012) had been developed using the Delphi method and focus groups. Despite its high value to structure group communication when dealing with complex problems, the Delphi method has some limitations that must be recognized (Linstone and Turoff, 2002). In the literature, most of the common pitfalls reported are associated with unclear objectives (too specific or too vague), a lack of criteria in the selection of experts, bias introduced by the main researcher’s influential position, the desire for excessive simplicity that leads to a reductionist approach of the studied system, the loss of interest from the participants over time, the creation of an artificial consensus by ignoring some dissident opinions, and finally, the risk to conclude that consensus always means righteousness (Hasson and Keeney, 2011; Keeney et al., 2001; Linstone and Turoff, 2002; Vernon, 2009). Many of these pitfalls are not unique to the Delphi method, but apply to many consensus techniques in action research. Taking appropriate precautions and documenting explicitly the communication process, as the authors endeavored to do, can reduce the vulnerability of the method to criticism (Linstone and Turoff, 2002; Vernon, 2009). As stated by Vernon (2009), the Delphi method “will only be as robust as the researchers’ justification for their protocol.” Indicators #4 to #16 (Table 3) are means-based indicators, also called action variables or management indicators (Payraudeau and Van der Werf, 2005; Von Wirén-Lehr, 2001). These indicators are generally based on methods or means of practicing agriculture (Bockstaller et al., 2008), which arise from decisions taken by the farmer (e.g. to incorporate or not the manure into the soil). It is therefore an indirect assessment, or a prediction, of the environmental impact (Bockstaller et al., 2008; Rigby et al., 2001). Indicators #1 to #3 are state indicators, or effect-based indicators (Payraudeau and Van der Werf, 2005; Von Wirén-Lehr, 2001), calculated from measures taken directly into the field (e.g. soil phosphorus saturation). They identify the state of the environment at a given time (Gras et al., 1989) but without identifying the cause–effect relationship (Bockstaller et al., 2008). This set of indicators being intended as an educational tool, it was considered relevant to combine means-based and state indicators. Moreover, Quebec’s regulation (AOR, 2002) provides requirements covering both agricultural practices and state of the environment. According to Heink and Kowarik (2010) and Rey-Valette et al. (2008), it
is possible to use different types of indicators within the same set, provided that the method of construction and calculation of each indicator is specified. In an effort of transparency, the three state indicators were then identified as such in the component state of the soil resource (Table 3). Among the many differences from the dairy farm indicators of Bélanger et al. (2012) is the addition of an indicator referring to energy consumption. On cash-crop farms of Quebec, 52% of the energy consumption can be attributed to the use of diesel, mainly for motorized vehicles (AGECO, 2006). The diesel consumption is difficult to quantify at the farm level because diesel is used for personal purposes as well as for farm work (Bélanger et al., 2012). Furthermore, the intensity of soil tillage, which is linked to the fuel consumption, is already assessed with indicator #4 (Table 3). Hence, diesel consumption was not retained as a potential indicator. As second in order, the propane gas accounts for 23% of the total energy consumption of cash-crop farms and is mainly used to dry maize grains (AGECO, 2006; La Financière agricole du Québec, 2010). For this reason, the drying of maize was considered relevant as an energy means-based indicator (#8, Table 3). The other differences from Bélanger et al. (2012) include the addition of indicators about split nitrogen applications (#11, Table 3), presence of annual legume crops in the rotation (#6C), seed treatment (#7C) and implementation of refuges along with insect-resistant transgenic crops (#7D), and the removal of the indicator about manure storage. Also, major changes have been made in the calculation of indicators #1, 3, 5, 6A, 6B, 12, and 13. Most of the indicators (Table 3) are expressed with interval classes (0–20–40–60–80–100%) rather than a continuous scale (0–100%). This allows some consistency between quantitative and qualitative indicators and avoids putting too much emphasis on the accuracy of the measurements but rather on the diversity of the selected indicators (Rey-Valette et al., 2008).
3.2. Modifications following on-farm testing The testing on 31 farms led to improvements for some indicators in regard with their understanding by farmers. For erosion in slopping fields (indicator #13), farmers were first asked to identify areas with erosion issues by coloring their farm map. Large differences were observed in the way farmers responded. Many of them did not have the expertise to diagnose soil erosion problems. Gomez et al. (1996) faced a similar problem and used cover crops as an indirect assessment of the risk of soil erosion. The objective behind indicator #13 is not to evaluate the farmer’s ability to identify problems of erosion, but rather the susceptibility of farmlands to erosion (Joel Aubin, 2009, pers. comm.). In such a case, one solution is to ask a succession of yes or no questions to the farmer regarding concrete and objective key factors that play a role in erosion (e.g. slopes, plowing, cover crops, and riparian buffer strip). By aggregating the answers to these questions, it is possible to estimate more objectively the susceptibility of farmlands to erosion. It was decided to present these successive yes or no questions in the form of a decision tree (Fig. 2),
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Table 3 Definition of the 16 indicators for the assessment of environmental sustainability on cash-crop farms in the province of Quebec. Component
Indicator (number)
State of soil resource
Soil organic matter content (#1) Soil phosphorus saturation (#2) Deep and surface drainage (#3) Conservation tillage (#4)
Cropping practices
Sub-indicator
Proportion of the cultivated area with soil organic matter content greater or equal to 3%
Cover crops (#5)
Crop diversity (#6)
Sequence of crops (6A) Perennial crops (6B)
Integrated pest management (IPM) (#7)
Annual legume crops (Fabaceae) (6C) Herbicide use (7A) Insecticide and fungicide use (7B) Seed treatment (7C) Refuges along with insect-resistant transgenic crops (7D)
Drying of maize (#8) Fertilization management
Phosphorus balance (#9) Nitrogen balance (#10) Split nitrogen applications (#11) Incorporation of manure into the soil (#12)
Erosion in sloping fields (#13) Riparian buffer strip (#14)
Windbreaks (#15) On-farm woodlot (#16)
Proportion of the cultivated area with soil phosphorus saturation under the maximum level allowed according to the province of Quebec regulation (80 m ha−1 = score of 100%; 60–80 m ha−1 = score of 75%; 40–60 m ha−1 = score of 50%; 20–40 m ha−1 = score of 25%;
