93 5 Integrated Assessment of Agroecosystems and Multi- Criteria Analysis: Basic Definitions and Challenges This chapter addresses the specific challenges faced by scientists willing to contribute to a process of integrated assessment. Integrated assessment, when applied to the issue of sustainability, has to be associated with a multi-criteria analysis (MCA) of performance, which, by definition, is controversial. This in turn requires (1) a preliminary institutional and conflict analysis (to define what are the relevant social actors and agents whose perceptions and values should be considered in the analysis, and what are the power relations among them); (2) the development of appropriate procedures able to be involved in the discussion about indicators, options and scenarios on the largest number of relevant social actors; and (3) the development of fair and effective mechanisms of decision making. The continuous switching of causes and effects among the activities related to both the descriptive and normative dimensions makes this discussion extremely delicate. Scientists describe what is considered relevant by social actors, and social actors consider relevant what is described by scientists. The two decisions—(1) who are the social actors included in this process and (2) what should be considered relevant when facing legitimate but contrasting views among the social actors—are key issues that have to be seriously considered by the scientists in charge of generating the descriptions used for the integrated assessment. This is why, in this chapter, I decided to provide an overview of terms and problems related to this relatively new field. 5.1 Sustainability of Agriculture and the Inherent Ambiguity of the Term Agroecology The two terms included in the title of this chapter— integrated assessment and agroecosystems —are terms about which it is almost impossible to find definitions that will generate consensus. In fact, integrated assessment is a neologism that is becoming more and more popular in the scientific literature dealing with sustainability. An international journal (http://www.szp.swets.nl/szp/ frameset.htm?url=%2Fszp%2Fjoumals%2Fia.htm) and a scientific society bear this name, to which one should add a fast-growing pile of papers and books dedicated to the subject. This term, however, is mainly gaining popularity outside the field of scientific analysis of agricultural production. Very little use of the term can be found in journals dealing with the sustainability in agriculture. The other term, agroecosystems, is derived from the concept of agroecology, which is another neologism that was introduced in the 1980s. Unlike the first term, this one is very popular in the literature of sustainable agriculture. At this point in the book, it is possible to make an attempt to justify the abundant use of neologisms so far. Nobody likes using a lot of neologisms or, even worse, “buzzwords” in scientific work. A simple look at the two definitions of neologism found in the Merriam-Webster Dictionary explains why: Neologism —(1) a new word, usage, or expression; (2) a meaningless word coined by a psychotic. Introducing a lot of neologisms without being able to share their meaning with the reader tends to classify the user or proponent of these neologisms in the category of psychotic. On the other hand, when an old scientific paradigm is no longer able to handle the challenge (and I hope that at this point the reader is convinced that this is the case with integrated analyses of sustainability), it is necessary to introduce new concepts and words to explore and build new epistemological tools. Moreover, a lot of © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems94 new words and concepts are already used in the fields of integrated assessment and multi-criteria analysis (and this author has nothing to do with this impressive flow of neologisms), so I find it important to share with the reader the meaning of these new terms. In particular, what is relevant here is the application of the concept of integrated assessment to the concept of agroecosystems. Before getting into this discussion, let us start with the definition of the term agroecosystem, which implies dealing with the concept of agroecology. The term agroecology was proposed in a seminal book by Altieri (1987). This was an attempt to put forward a new catchword pointing to the need to introduce a paradigm shift in the world of agricultural research when taking seriously the issue of sustainability. In that book, Altieri focuses on the unavoidable existence of conflicts linked to the concept of sustainability in the field of agriculture. His main point is that if we define the performance of agricultural production only in economic terms, then other dimensions such as the ecological, health and social dimensions will be the big losers of any technical development in this field. When mentioning conflicts here, we do not refer only to conflicts between social actors, but also to conflicts between optimizing principles derived by the adoption of different scientific analyses of agriculture (when getting into the normative side by using different definitions of costs and benefits). For example, an anthropologist, a neoclassical economist and an ecologist tend to provide very different views of the performance of the very same system of shifting cultivation in Papua New Guinea. Two main lines of action were suggested by Altieri: 1. The concept of agroecology has to be associated with a total rethinking of the terms of reference of agriculture. (What should be considered an improvement in the techniques of production? Improvement for whom? In relation to which criterion? Which time horizon should be adopted to assess improvements?) 2. The concept of agroecology requires expanding the universe of possible options (technical solutions, technical coefficients, socioeconomic regulations) for agricultural development. This can be obtained in two ways: a. By exploring new alternative techniques of production (changing the existing set of available technical coefficients) b. Studying and preserving the cultural diversity of agricultural knowledge already existent in the world (preserving techniques guaranteeing technical coefficients, which could be useful when adopting different optimizing functions) It should be noted that the majority of groups using the term agroecology, especially in the developed world, endorse basically the second line, without fully addressing the implications of the first. The basic idea of this position can be characterized as follows: The sustainability predicament and the existing difficulties experienced by agriculture in both developed and developing countries are just because humans are not using the most appropriate technologies and not relying on a given set of sound principles. Put another way, this second historical interpretation of agroecology assumes a substantive definition of it. The vast majority of the people using this interpretation tend to associate agroecology with concepts like organic farming, low-external-input agriculture, “small is beautiful,” and empowerment of family farms. They are assuming that the way out of the current lack of sustainability in agriculture can be found by relying on sound principles and by studying how to produce more profit with (1) less environmental impact and (2) happier farmers. The problem with this position is that it does not address (1) the unavoidable existence of conflicts implicit in the concept of sustainable development and (2) the unavoidable existence of uncertainty and ignorance about our knowledge of future scenarios. Put another way, the very concept of sustainability entails an unavoidable dialectic between actors and strategies. When discussing the development of agricultural systems, there is no single set of most appropriate technologies. At each point in space and time, the objectives (goals, targets), constraints (resources, laws, taboos), the available sets of options and of acceptable compromises among which to choose must first be explicitly defined for the scientists. Only at this point does it become possible for them to identify a set of appropriate technologies based on either politically defined priorities among the different objectives or a negotiated © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi-Criteria Analysis 95 consensus on a compromise solution that realizes all the various goals (as expressed by relevant social actors) to some extent. This is why, in the last two decades, the first direction of research suggested by Altieri, “totally rethinking the terms of reference of agriculture,” has also been gaining attention. This radical position seems to be supported by those working on scenarios about the future of agriculture (e.g., within the U.S. to avoid the Blank hypothesis (Blank, 1998)). It is also shared by those working on ex post evaluation of agricultural policies (e.g., the massive failure of development programs of UN agencies in developing countries and that of agricultural policies in the EU). In fact, a complete recasting is at the moment the official position of the European Commission for the future of European agriculture (e.g., http://www.newscientist.com/news/news.jsp?id=ns99991854). In the face of this mounting pressure, the forces for business as usual (economic and political lobbies, academic institutions) are trying to develop a strategy of damage control. Many within the agricultural establishment say that a total rethinking is not really needed. They suggest that a few technical adjustments and a little more talking with the farmers will suffice. They also recommend a few new regulations to internalize some of the externalities that have until now escaped market mechanisms. This position has important ideological implications. It accepts the notion that technical development of agriculture should be driven, by default, by the maximization of productivity and profit (bounded by a set of constraints to take care of the environment and the social dimension). I have no intention of getting into an ideological discussion of this type. This chapter and book are written assuming that the emerging paradigm that perceives the development of rural areas in terms of integrated resource management carried out by multifunctional land use systems is valid. In this paradigm, flexibility in the management strategy and participatory techniques for defining what should be the desirable characteristics of the system are assumed to be necessary steps to achieve such a goal. Therefore, in the rest of this chapter, I will not deal with the question, “Why should we do things in a different way when perceiving and representing the performance of agriculture?” but rather with the question, “How can we do things in a different way?” In fact, acknowledging the need for a total rethinking of agriculture is just the first step. To act, we must first reach an agreement as to how things should be done differently. This can be achieved only by answering some tough questions such as: Who is supposed to rethink the terms of reference of agriculture? How might we change the shape of the plane on which we are flying? What do we do if different social actors have different views on how to make changes? An acute problem in this regard is that both colleges of agriculture and reputable scholars, in general, are less than fully willing to engage in this debate, perhaps because they view totally rethinking the terms of reference of agriculture as a threat to their present agenda. This is, however, not reasonable: If we acknowledge that changes on the societal side resulted in a shift in the priorities among objectives and, in some cases, led to the formulation of completely new objectives in agriculture, then we are forced to accept the following conclusions: (1) We have to do things differently in agriculture, and to do that (2) we have to perceive and represent things differently in the scientific disciplines dealing with the description of agricultural performance. As soon as one tries to draw this logical consequence, however, one crashes against one of the mechanisms generating the lock-in on business as usual. Much funding of colleges of agriculture is channeled through private companies with a clear agenda (maximizing profit through maximization of productivity). Even public funding is heavily affected by lobbies that are operating within the conventional paradigm. These lobbies perceive agriculture as just an economic sector producing commodities and added value. To the best of my knowledge, the only big agricultural university that is working hard on a radical and dramatic restructuring of its courses (to reflect a total rethinking of the terms of reference for agriculture) is Wageningen University in the Netherlands. Actually, the restructuring started with its very name. It used to be the glorious WAU (Wageningen Agricultural University) until 2 years ago, and then they dropped the A. A very quick summary of relevant events leading to this restructuring is that, in the early 1990s, the big departments resisted any friendly attempts at change from the inside. Actually, they reacted to signals of crisis by continuing to do more of the same thing. The concept of “ancient regime syndrome,” © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems96 proposed by Funtowicz and Ravetz (when facing a crisis, do more of the same, even though it is not working) , discussed in Chapter 4 should be recalled here. The fatal response of agricultural departments was better and more complicated, optimizing models to get additional economies of scale and increases in efficiency. At the very moment when the basic assumptions of agriculture as an economic sector just producing commodities were under revision, the credibility of these assumptions was stretched even further. The catastrophe came when the rest of society (e.g., consumers, farmers, politicians) imposed a new research agenda in a quite radical way. They were told, “No more money for models that optimize the ratio of milk produced per unit of nitrogen and phosphorus in the water table.” And the edict was given almost overnight. Central to any discussion about a different way to perceive and represent the performance of agricultural systems is the idea that agricultural production is not the full universe of discourse for any of the relevant agents operating at different levels (households, local communities, counties, states, countries, international bodies). Then it becomes obvious that analytical approaches aimed at optimizing production techniques do not represent the right way to go. When we analyze the livelihood of households, local communities, counties, states, countries and international bodies, a sound representation of the performance of agricultural activities (how to invest a mix of production factors to alter ecosystems to produce food and fibers) is just a part of the story. That is, (1) the mix of relevant activities considered in the analysis has to include more than just the production of crops and animal products and (2) the list of consequences considered in the analysis has to include more than the economic and biophysical productivity of agricultural techniques (e.g., additional relevant indicators should address social, health and ecological impacts and quality of life). Performing this integrated analysis does not require the introduction of new revolutionary analytical tools, but rather the ability to provide new packages for existing tools. In engineering, for example, it is possible to have a rigorous treatment of decision support analysis for design. The terms used there are multi-objective decision making and multi-attribute decision making (e.g., http://design.me.uic.edu/~mjscott/papers/95f.pdf). The great advantage of industrial design is that all the relevant information for defining the performance of the designed system is supposed to be available to the designer. The same approach is explored in other fields dealing with the issue of sustainability (e.g., ecological economics, science for governance (participatory integrated assessment), evaluation of sustainability, natural resources management). The application of these concepts is generally indicated under a family of names like integrated assessment, sustainability impact assessment, strategic environmental assessment and extended cost-benefit analysis (CBA). However, when applying these tools to self-organizing systems, especially when dealing with reflexive systems (humans), a multi-criteria evaluation has to deal with three very large systemic problems: • It is not possible to formalize a procedure to define in a substantive way (outside of a specific and local context of reference) what is the right set of relevant criteria of performance that should be considered for a sound analysis. • It is unavoidable to find legitimate contrasting views on what should be considered an improvement or what should be the best alternative to select. Social agents will always have divergent opinions. For example, it is unavoidable to find different opinions on whether it is good or bad to have nuclear weapons or use genetically modified organisms. • It is not possible to get rid of uncertainty and ignorance in the various scientific analyses that are required. This implies that not all the data, indicators and models required to consider different dimensions of analysis (the views of different agents at different levels) have the same degree of reliability and accuracy. Because of these three major problems, there is a general convergence in the field of integrated assessment and multiple-criteria analysis that it is not possible to achieve the right problem structuring of a sustainability problem without the integrated and iterative use of two types of tool kits: 1. Discussion support systems (term introduced by H.van Keulen) © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi-Criteria Analysis 97 In this activity scientists are the main actors and social actors are the consultants; the goal is the development of integrated packages of analytical tools required to do a good job on the descriptive side. The resulting information space used in the decision-making process has to represent the system of interest, in scientific terms, on different scales and dimensions of analysis. This information space has to be constructed according to the external input received from the social actors of what is relevant and what is good and bad. The social actors, as consultants, have to provide a package of questions to be answered. But the scientists are those in charge of processing such an input according to the best available knowledge of the issue. This is a new academic activity, which implies a strong scientific challenge: keeping coherence in an information space made up of nonequivalent descriptive domains (different scales and different models). This requires an ability to make a team of scientists coming from different disciplines interact on a given problem structuring provided by society. This is what we will introduce later on under the label of multiple-scale integrated analysis (MSIA). 2. Decision support systems In this activity, social actors are the main actors and scientists the consultants; the goal is the development of an integrated package of procedures required to do a good job on the normative side. The resulting process should make it possible to decide, through negotiations: a. What is relevant and what should be considered good and bad in the decision process b. What is an acceptable quality in the process generating the information produced by the scientists (e.g., definition of quality criteria—relevance, fairness in respecting legitimate contrasting views, no cheating with the collection of data or choice of models) c. Deciding on an alternative (or a policy to be implemented) This process requires an external input (given by scientists) consisting of a qualitative and quantitative evaluation of the situation on different scales and dimensions. In their input, scientists also have to include information about expected effects of changes induced by the decision under analysis (discussion of scenarios and reliability of them), but the social actors are those in charge of processing such an input. This is what we will introduce later as social multi-criteria evaluation (SMCE), following the name proposed by Munda (2003). Since the scientific process associated with the operation of tool kit 1 affects the social process associated with the operation of the tool kit 2 and vice versa, the only reasonable option for handling this situation is to establish some form of iteration between the two. In doing this, however, it must be clear that process 1 is a scientific activity (which requires an input from social actors) and process 2 is a social activity (which requires input from scientists). Each, however, depends on the other. This is where the need of a new type of expertise enters into play. To have such an iterative process, it is necessary to implement an adequate procedure. The rest of this chapter is divided into three sections. Section 5.2 discusses the systemic problems faced when considering agriculture in terms of multifunctional land use. Any analysis based on indicators reflecting legitimate but contrasting views and referring to events described at different scales implies facing serious procedural problems. This section makes the point that, when dealing with the sustainability of agriculture, we do face a postnormal science situation. Section 5.3 provides an overview of concepts and tools available for dealing with such a challenge (e.g., integrated assessment, multi-criteria evaluation, and a first view at multi-objective multi-scale integrated analysis), as well as practical examples of problems associated with their use. Section 5.4 briefly describes existing attempts to establish procedures able to generate the parallel development of discussion support systems and decision support systems, and then an iteration between the two (e.g., the soft systems methodology proposed by Checkland, 1981, Checkland and Scholes, 1990)), Section 5.5 provides a practical example (the current making of farm bills) in which we can appreciate the need of developing these procedures as soon as possible. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems98 5.2 Dealing with Multiple Perspectives and Nonequivalent Observers In this section I elaborate on the two points discussed in the introduction: 1. It is unavoidable to find legitimate contrasting views on what should be considered an improvement or what should be considered the best alternative to select (Section 5.2.1). 2. It is not possible to formalize a procedure to define in a substantive way what is the right set of relevant criteria that should be considered to perform a sound analysis (Section 5.2.2). 5.2.1 The Unavoidable Occurrence of Nonequivalent Observers The lady shown in Figure 5.1 is performing a very old traditional technique of Chinese farming. She is applying “night soil” (human excrement) to her garden, making sure that as little as possible of this valuable resource gets lost in the recycling. This is why she carefully pours only small amounts of the organic fluid on each plant. There are plenty of such pictures of this woman, since the colleagues (i.e., ecologists and experts of organic agriculture) who were working with me on a project there were delighted by this image. They took about 50 pictures of her in different moments of her daily routine. For Westerners, this picture is a vivid metaphor of the ultimate ecological wisdom of ancient agriculture—the closure of the cycle of nutrients between humans and nature. The unexplained mystery associated with such a vivid metaphor, though, is that this image is disappearing from this planet pretty quickly. Later on, when talking to that woman, I asked about the explanations for the abandonment of this and other ecologically friendly activities (such as digging silt out of channels) so valuable for the preservation of Chinese agroecological landscapes. She replied abruptly, “Have you been in Paris?” “Of course I have been in Paris” was my immediate (and careless) answer. At that point she could go for it: “I have never been in Paris. None of those living in this village have ever been in Paris. None of my daughters will ever go to Paris. You want to know why? Because we have been digging channels and carefully pouring night soil to preserve this agroecosystem instead. Personally, I don’t want to do that anymore. If things will not change during my lifetime, I want that at least my great-grandchildren will FIGURE 5.1 Nonequivalent observers of agroecosystems. (Photo by M.G Paoletti.) © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi-Criteria Analysis 99 have the option to go to Paris. If this agroecosystem is going to hell, I am happy about that, the sooner the better.” The three relevant points about this story are: 1. A clear disagreement about basic goals and strategies among different actors. Our team of scientists was in China with the goal of preserving that agroecosystem, whereas the lady had the goal of getting rid of it (she was forced to keep recycling night soil, but for her this was only a temporary solution needed for feeding her family). 2. The parallel use of different and logically independent indicators of performance for a given agroecosystem. The agroecologists in our project were happy about her recycling according to the indications given by bioindicators (earthworms) assessing changes in the health of the soil. The lady was unhappy about night soil in relation to her impossibility to go to Paris, used as indicator of the performance of agronomic activities. 3. The tremendous speed at which human systems can redefine what is desirable and acceptable. Our local students told us, to explain her reaction, that a TV set had just arrived in the village, and this generated a communal daily watching. The soap opera in fashion at that moment featured two Chinese yuppies living in Paris and drinking champagne from cold flutes. This was enough for the villagers watching the show to update their representation of what should be considered a desirable and acceptable socioeconomic performance of agricultural activities. The picture that the woman pouring night soil had in mind for the future of her great-granddaughter was more related to what is shown in Figure 5.2. 5.2.2 Nonreducible Indicators and Nonequivalent Perspectives in Agriculture When dealing with sustainable agriculture, we have to expect a representation of performance that is based on different criteria (reflecting the different values and goals) and different hierarchical levels (requiring a mix of nonequivalent descriptive domains). Without using a multi-level analysis, it is very easy to get models that simply suggest shifting a particular problem between different descriptive domains. Put another way, optimizing models based on a simplification of real systems within a single descriptive domain just tends to externalize the analyzed problem out of their own boundaries. For FIGURE 5.2 Models presented at Beijing’s fashion week 2002. (Photo by Wilson Chu, Reuters. With permission.) © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems100 example, economic profit can be boosted by increasing ecological or social stress. In the same way, ecological impact can be reduced by reducing economic profit, and so on. That is, conventional scientific analyses in general provide policy suggestions that are based on the detection of some benefits by a given model referring to a certain descriptive domain and by the neglecting of other costs ignored by the model, since they are detectable only on different descriptive domains (when adopting a different selection of variables). This epistemological cheating can be avoided only by adopting a set of different descriptive domains able to see those costs externalized (put under the carpet) by a given mechanism of accounting. By using an integrated set of indicators, we can observe that problems externalized by the conclusions suggested by one model (based on an optimizing variable defined on a given scale— e.g., when describing things in economic terms over a 10-year horizon) reappear amplified in one of the parallel models (based on a different optimizing variable defined on a different scale—e.g., when describing the same change in biophysical terms on a 1000-year horizon). As discussed in Chapters 2 and 3, the ability of any model to see and encode some qualities of the natural world implies that the same model cannot see other qualities detectable only on different descriptive domains. To provide an example of nonequivalent indicators that can be used to characterize historical changes in a farming system, Figure 5.3 provides examples of four numerical assessments that characterize the dramatic developments of farming systems in rural China. 5.2.2.1 Land Requirements for Inputs—The first indicator used in Figure 5.3a is related to the profile of land use. In particular, the numerical assessment indicates the percentage of cropland invested by farmers with the aim of guaranteeing nutrient supply to crop production. In the 1940s, about 30% of cropland was allocated to green manure cultivation, and hence, this land was unavailable for subsistence or cash crop production. The intensification of crop production, driven by population growth and socioeconomic pressure, led to a progressive abandonment of the use of green manure (too expensive in terms of land and labor demand) and general switching to synthetic fertilizer use. This resulted in a sensible increase in multiple-cropping practices and, consequently, in a dramatic improvement of agronomic indices of crop production (e.g., yields per hectare), that is, a dramatic increase in crop production for self-sufficiency and freeing land for cultivation of cash crops (Li et al., 1999). However, according to current trends, a further increase in demographic and economic pressure can lead to further intensification of agricultural throughputs (Giampietro, 1997a, b). In this case, depending on the ratio of sales price of crops and cost of fertilizer, as well as technical coefficients, we could easily return—in the first decade of the third millennium—to the 30% mark, the same as it was in the 1940s. FIGURE 5.3 Different indicators that can be used to characterize historical trends in rice farming in China. © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi-Criteria Analysis 101 That is, about 30% of the land invested in cash crops will be used just to pay for technical inputs. Put another way, when considering the criterion “land requirement for stabilizing agricultural production” (resource eaten by an internal loop within the system of production), the two solutions requiring a 30% investment of the total budget of available land to make available the required production inputs are equal for the farmer. According to farmers’ perception, the same fraction of land is lost whether it is to green manure production or to crop production to purchase chemical fertilizer. The characterization (mapping of system qualities) given in Figure 5.3a is not able to catch the difference implied by these two solutions. Other criteria (and therefore indicators) are needed if we want to obtain a richer characterization (a better explanation) of such a trend. 5.2.2.2 Household’s Perspective—When considering the parameter “productivity of labor” as an indicator of performance (Figure 5.3b), we see that the solution of chemical fertilizer implies a much higher labor productivity than the green manure solution. Higher labor productivity in this case translates into a higher economic return of labor. Depending on the budget of working time available to the household, it is possible to reduce, in this way, the fraction of working time allocated to self- sufficiency and, as a consequence, to increase the fraction of working time allocated to cash flow generation (either on or off the farm) and leisure. Thus, even if 30% of the available budget of land is lost to fertilization, according to the new criterion “labor productivity,” farmers will prefer the solution of chemical fertilizer because it enables a better allocation of their time budget. 5.2.2.3 Country’s Perspective—When considering the parameter “productivity of food of cropped land” as the indicator of performance (Figure 5.3c), we see that the solution of chemical fertilizer implies a much higher land productivity than the green manure solution. In fact, the land used to produce crops for the market to pay for chemical fertilizer (perceived as lost by farmers), when considered at the national level, is seen as land that produces food for the urban population. On the contrary, green manure production is seen by the national government as a use of cropping area that does not generate food. Indeed, the goal of the central government of China to boost food surplus in rural areas, making it possible to feed the growing urban population, can actually lead to a promotion of policies of intensification of agricultural production by boosting the use of technical inputs. Given this goal, an excessive fraction of farmers’ land budget eaten by the cost of purchasing chemical fertilizer would discourage farmers from intensive use of technical inputs. Therefore, the central government can decide to subsidize the use of these inputs. As seen from the farmers’ perspective, a lower cost of fertilizer reduces the fraction of their land that has to be invested in procuring fertilizer and therefore induces an intensification of agricultural production. Note, however, that the reduction of land lost to buy chemical fertilizer (as detected by the farmers’ perception) and an increase in cropland productivity (as detected by the central government) obtained by subsidization of fertilizer, in turn increase another relevant indicator—the economic cost of internal food production (yet another relevant criterion for the Chinese government when deciding about policies of agricultural development). That is, the advantage given by the use of subsidies to fertilizer—characterized by the indicator “cropland productivity”— induces a side effect that can be detected only by using an additional criterion (and relative indicator) referring to the country level: the economic burden of subsidizing technical inputs (note that this is a relevant indicator that is not given in Figure 5.3). 5.2.2.4 Ecological Perspective—When considering the ecological perspective, we find a totally different picture of the consequences of the two “30% of land budget allocation to fertilizer” solutions. The use of green manure in the 1940s was certainly benign to the environment because the flow of nutrients in the cropping system was kept within a range of values of intensity close to those typical of natural flows. Put another way, the acceleration of nutrient throughputs induced by the use of synthetic fertilizers dramatically increased the environmental stress on the agroecosystems. When biophysical indicators of environmental stress are considered to characterize the trend, we obtain an assessment of performance that is totally unrelated (logically independent) to assessments based on the use of economic variables. For example (Figure 5.3d), 800 kg of synthetic fertilizer applied per hectare per year (due to © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems102 the high multiple-cropping index) is too much fertilizer for healthy soil, no matter how the economic cost of fertilizer compares with its economic return. A couple of points can be driven home from this example: (1) The same criterion (land demand per output) can require different indicators, when reflecting the perspective of performance related to different hierarchical levels. The indicators in Figure 5.3a and c are giving contrasting indications about the solution of green manure vs. that of synthetic fertilizer in relation to use of land. Farmers see no difference between the two solutions; the government of the country sees the two solutions as dramatically different. (2) Criteria and indicators referring to different descriptive domains (Figure 5.3b and d) (environmental loading assessed in kilograms of fertilizer per hectare vs. labor productivity expressed either in added value per hour or kilograms of crop per hour) reflect not only incommensurable qualities, but also the existence of unrelated (logically independent) systems of control. As a consequence, when dealing with trade-offs defined on different descriptive domains, we cannot expect to work out simple protocols of optimization able to compare and maximize relative costs and benefits. Recalling the examples provided in Chapter 3, we can say that the existence of multiple relevant hierarchical levels, nonequivalent descriptive domains, can imply a nonreducibility of models on the descriptive side. This leads to a problem that Munda (2003) calls technical incommensurability (the impossibility of establishing a clear link between nonequivalent definitions of costs and benefits obtainable only on nonreducible descriptive domains). A difference in the perception about priorities (the two different views about the future of agriculture shown in Figures 5.1 and 5.2) found in social actors carrying conflicting goals and values should be associated with social incommensurability (Munda, 2003). There will be more on this in the following section. 5.3 Basic Concepts Referring to Integrated Analysis and Multi-Criteria Evaluation In this section I provide an overview of concepts and definitions that is an attempt to frame the big picture within which the various pieces of the puzzle belong. A more detailed discussion about how to build an analytical tool kit for integrated analysis of agroecosystems is provided in Part 3. 5.3.1 Definition of Terms and Basic Concepts 5.3.1.1 Problem Structuring Required for Multi-Criteria Evaluation—This refers to the identification of relevant qualities of the system under investigation that have to be characterized, modeled and assessed in relation to the specified set of goals expressed by relevant social actors. This integrated appraisal leads to the individuation of a set of relevant issues to be considered in the formal problem structuring in terms of a list of options, criteria, and indicators and measurement scheme that will be used to decide about the action. 5.3.1.2 Multi-Scale Integrated Analysis (Multiple Set of Meaningful Perceptions/ Representations)—This is the simultaneous consideration of a set of system qualities (judged relevant for the goals of the study in the first step of problem structuring) that must be observable and can be encoded into variables used in the set of selected models. Depending on the set of relevant criteria, MSIA might require the parallel use of indicators referring to different scales and dimensions of analysis, e.g., gross national product (GNP) in U.S. dollars, life expectancy, megajoules of fossil energy, level of food intake, fractal dimension of cropfields, Gini index for equity, efficiency indices and nitrogen concentration in the water table. 5.3.1.3 Challenge Associated with the Descriptive Side (How to Do a MSIA)—This is the study of nonequivalent typologies of (1) performance indicators and (2) mechanisms generating relevant constraints, in relation to a given problem structuring. © 2004 by CRC Press LLC [...]... sustainability tradeoffs (on different scales) in relation to the set of legitimate views considered relevant (multi- objective) and to the set of nonequivalent dimensions of viability considered relevant (multidimensional).This is why such an input is called multi- scale integrated analysis (multi- objective and multidimensional) Scientists © 2004 by CRC Press LLC 120 Multi- Scale Integrated Analysis of Agroecosystems. .. 106 Multi- Scale Integrated Analysis of Agroecosystems 5. 3.2.2 A Graphical View of The Impact Matrix: Multi- Objective Integrated Representation— The graph shown in Figure 5. 5 (a different representation of the information presented in the impact matrix given in Figure 5. 4) is an example of a multi- objective integrated representation (MOIR) (a set of different indicators reflecting different criteria of. .. in step 6 of Figure 5. 8, when an overall agreement has to be reached on the validity of the existing problem structuring as the agreed-upon scientific input to be adopted in the process of social multi- criteria evaluation © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi- Criteria Analysis 123 In conclusion, the scientific aspects of multi- scale integrated analysis, which is the... such a problem in terms of either stochastic uncertainty (thoroughly studied in probability theory and statistics) or fuzzy uncertainty (focusing on the ambiguity of the © 2004 by CRC Press LLC Integrated Assessment of Agroecosystems and Multi- Criteria Analysis 1 05 FIGURE 5. 5 Multi- objective integrated representation of car performance description of the event itself) (Munda, 19 95) However, one should... software can be very useful in a process of decision making based on the adoption of multi- criteria methodologies Rather, © 2004 by CRC Press LLC 112 Multi- Scale Integrated Analysis of Agroecosystems this is a warning against the application of these formal protocols without an adequate quality control on the relative semantic Coming to the representation of different matrices in Figures 5. 4 and 5. 7... simplification of the reality represented according to a single dimension at a time (ceteris paribus) That is, © 2004 by CRC Press LLC 122 5 6 Multi- Scale Integrated Analysis of Agroecosystems conventional reductionist analyses providing the picture of the position of the system on a multi- objective performance space (as the radar diagrams shown in Figure 5. 5) have to be complemented by analyses of (1) evolutionary... first step of the process? As noted in Chapters 2 and 3, depending on the various perceptions of the physical structure of the system of interest, we should expect to find different identities for the same system These identities will change depending on the scale or the points of view adopted © 2004 by CRC Press LLC 116 Multi- Scale Integrated Analysis of Agroecosystems About the existence of multiple... Scholes, J., (1990), Soft-Systems Methodology in Action, John Wiley, Chicester, U.K © 2004 by CRC Press LLC 126 Multi- Scale Integrated Analysis of Agroecosystems Dodgson, J., Spackman, M., Pearman, A and Phillips, L (2000) Multi- Criteria Analysis: A Manual Great Britain Department of the Environment,Transport and the Regions Office of the Deputy Prime Minister DETR, London Georgescu-Roegen, N (1971),... types of pollution) A great advantage of multi- criteria evaluation is the possibility of taking these different factors into account 5. 3.2.1 Formalization of a Problem Structuring through a Multi- Criteria Impact Matrix—A very familiar example of an impact matrix related to the structuring of a decision process is provided in Figure 5. 4 This is a typical multi- criteria problem (with a discrete number of. .. generating better programs of extension, but rather looking at a two-way direction of information flows.The ivory tower so useful in the past to © 2004 by CRC Press LLC 124 Multi- Scale Integrated Analysis of Agroecosystems preserve the special status of academic research has to become more open To make this point clear, consider the example of the making of a farm bill 5. 6.2 The Case of the U.S Farm Bill . 5. 5 Multi- objective integrated representation of car performance. © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems1 06 5. 3.2.2 A Graphical View of The Impact Matrix: Multi- Objective. 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems1 04 require the integration of a broad set of various and conflicting points of view and the parallel use of nonequivalent representations overview of concepts and tools available for dealing with such a challenge (e.g., integrated assessment, multi- criteria evaluation, and a first view at multi- objective multi- scale integrated analysis) , as