Part 3 Complex Systems Thinking in Action: Multi-Scale Integrated Analysis of Agroecosystems © 2004 by CRC Press LLC 279 Introduction to Part 3 What Is the Beef That Has Been Served in the First Two Parts of This Book? After this long excursion through different issues and innovative concepts that has led us through very old philosophical debates and innovative scientific developments, it is time to get back to the original goal of this book. Why and how is the material presented and discussed so far in this book relevant for those willing to study the sustainability of agroecosystems? Part 3 provides examples of applications aimed at convincing the reader that the content of Parts 1 and 2 is relevant indeed to an analysis of the sustainability of agroecosystems. Before getting into such a presentation, however, it could be useful to have a quick wrap-up of the main points made so far: 1. Science deals not with the reality but with the representation of an agreed-upon perception of the reality. Any formalization provided by hard science starts from a given narrative about the reality. That is, any formalization requires a set of preanalytical choices about what should be considered relevant and on what time horizon. These preanalytical choices are value loaded and entail an unavoidable level of arbitrariness in the consequent representation. Substantive models of the sustainability of real systems do not exist. 2. To make things more difficult, science dealing with sustainability must address the process of becoming of both the observed system and the observer. This implies dealing with an unavoidable load of uncertainty and genuine ignorance, which is associated with the existence of legitimate nonequivalent perspectives found among interacting agents. 3. The process of generation of useful knowledge is therefore a continuous process of creative destruction. In his book The Science of Culture, White starts the first chapter, entitled “Science Is Sciencing,” by saying: “Science in not merely a collection of facts and formulas. It is preeminently a way of dealing with experience. The word may be appropriately used as a verb: one sciences, i.e., deals with experience according to certain assumptions and with certain techniques” (1949, p. 3). Especially when dealing with science used for governance, it is easy to appreciate a sort of Yin-Yang tension in the process used by humans for dealing with their experience. The description of this tension by White says it all. There are two basic ways for dealing with the need to update our knowledge: one is science the other is art. The purpose of science and art is one: to render experience intelligible, i.e., to assist man to adjust himself to his environment in order that he may live. But although working toward the same goal, science and art approach it from opposite directions. Science deals with particulars in terms of universals: Uncle Tom disappears in the mass of Negro slaves. Art deals with universals in terms of particulars: the whole gamut of Negro slavery confronts us in the person of Uncle Tom. Art and science thus grasp a common experience of reality, by opposite but inseparable poles. (White, 1949, p. 3) We have at this point developed a new vocabulary to express this concept. To handle the growing mass of data associated with experience, humans must: a. Compress the requirement of computational capability needed to handle more sophisticated models and larger data sets. To do that they need science that uses types to describe equivalence classes of natural entities. b. Expand the information space used to make sense about the reality. This can only be done by adding new types and new categories about which it is possible to obtain a shared understanding. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems280 This is where art enters into play. Art is needed to find out the existence of new relevant aspects of the reality, about which it is important to dedicate a new entry in our language or a new narrative about the meaning of reality. This leads to the idea that when dealing with science for governance, science cannot be taken from the shelf, as a repertoire of useful data and protocols. On the contrary, it is important to imagine science for governance as a set of procedures that can be used to do “sciencing.” 4. There are already several attempts to develop procedures aimed at implementing the concept of sciencing. In Chapter 5 an example was given in relation to the soft system methodology proposed by Checkland. However, several other similar efforts in this direction can be found in the literature. The basic rationale is always the same. When dealing with a given perception of the existence of a problem, one has to start, necessarily, with a narrative. However, such a narrative should not be used directly, as such, to get into a scientific characterization. Rather, it is important to explore as many alternative narratives as possible to expand the possible useful perspectives, detectors, indicators and models to be used, later on, in the scientific problem structuring. Obviously, in the final choice of a given scientific problem structuring, the number of narratives, indicators and models used has to be compressed again. In a finite time, scientists can handle only a finite and limited information space. But exactly because of this, it is important to work on a semantic check of the validity of the narratives chosen as the basis for the analytical part. 5. If one agrees with the statements made in the previous four points, one is forced to conclude that when dealing with science for governance, there are two distinct tasks, which require a different type of expertise and a different approach. These two tasks, which imply facing a formidable epistemological challenge, should not be confused—as is done, unfortunately, by reductionist scientists. Task 1 is related to the ability to provide a useful and sound input on the descriptive side. This implies the ability to tailor the development of models, the selection of indicators and the gathering of data according to the specificity of the situation. Task 2 is related to the ability to handle the unavoidable existence of legitimate but contrasting values, fears and aspirations. This unavoidable existence of conflicts in terms of values will be reflected in the impossibility to determine in a substantive way (1) what should be considered the best problem structuring, (2) what should be considered the best set of alternatives to be evaluated, (2) what should be considered the best set of scenarios, (4) what should be considered the best alternative among those considered and (5) what is the best way for handling the unavoidable presence of uncertainty and ignorance in the problem structuring used in the process of decision making. Using the vocabulary adopted in Chapter 5, we can say that: • Task 1 scientists should be able to provide a flexible input consisting of a multi-scale integrated analysis (generating a coherent but heterogeneous information space able to represent changes and dynamics at different hierarchical levels and in relation to different forms of scientific disciplinary knowledge). • Task 2 has to be based on a process. That is, the issue of incommensurability and incomparability can only be handled in terms of societal multi-criteria evaluation. This concept implies forgetting about the approach proposed by reductionism. Different indicators should not be aggregated into one single aggregate function (e.g., as done in cost-benefit analysis). In this way, one loses track of the behavior of the individual indicators, meaning that their policy usefulness is very limited. The assumption of complete compensability should not be adopted, i.e., the possibility that a good score on one indicator can always compensate a very bad score on another indicator (money cannot compensate the loss of everything else). Any process of analysis and decision making has to be as transparent as possible to the general public. From this perspective, we can define a reductionist approach as an approach based on the use of just one measurable indicator (e.g., a monetary output or a biophysical indicator of efficiency), one dimension (e.g., economic or biophysical definition of tasks), one scale of analysis (e.g., the farm or the country), one objective (e.g., the maximization of economic © 2004 by CRC Press LLC Introduction to Part 3 281 efficiency, the minimization of nitrogen leakage in the water table) and one time horizon (e.g., 1 year). Reductionist analyses also imply a hidden claim about their ability to handle uncertainties and ignorance when they claim that a particular option (e.g., technique of production) is better than another one. This is the reason why in multi-criteria evaluation it is claimed that what is really important is the decision process and not the final solution. 6. The set of innovative concepts presented in Part 2 can be used to organize a multi-scale integrated analysis of agroecosystems. These tools are required to organize conventional scientific analyses in a way that make explicit and transparent the chain of preanalytical choices made by the analyst. Actually, these decisions become an explicit object of discussion, since they are listed as required input to impredicative loop analysis. In conclusion, what is presented in Part 3 is not an analytical approach aimed at finding the best course of action or indicating to the rest of society the right way to go to improve the sustainability of our agroecosystems. The text of Part 3 is just a series of examples of how the insight derived from complex systems theory can be used to organize scientific information to generate informed discussions about sustainability. To do that, the proposed approach generates useful information spaces made up of nonequivalent descriptive domains (integrated packages of nonreducible models) that can be tailored on the specific characteristics of relevant agents. The ultimate goal is that of structuring available data sets and models according to a selected set of narratives that have been defined as relevant for a given situation. What Is the Beef That Is Served in Part 3? If we do a quick overview of the literature dealing with sustainable agriculture, we will find a huge number of papers dealing with assessments and comparisons of either different farming techniques or different farming systems operating in different areas of the world. The vast majority of these papers are affected by a clear paradox: 1. Analyses of farming systems and assessments of the sustainability of agricultural techniques generally start with an introduction that makes an explicit or implicit reference to the following, quite obvious, two statements: a. What can be produced and what is produced in a farming system depends on the set of boundary conditions in which the farming system is operating (the characteristics of both the ecological and the socioeconomic interface of the farm). After conditioning what to produce, these characteristics also influence how to produce it (the choice of techniques of production and the choice of related technologies). b. Any assessment of the agricultural process obtained by considering only a particular perspective of farming (e.g., agronomic performance, economic return, social and cultural effects, ecological impact) necessarily misses other important information referring to other perspectives of the same process. To be meaningful, any evaluation of agricultural techniques should consider a plurality of perspectives through a holistic description of farming processes. So far, so good; the main message about the need for integrated analysis for complex systems seems to be clear to the majority of authors, at least when reading the introductory paragraphs. However, such wisdom tends to disappear in the rest of the paper. 2. Before entering into a discussion of case studies, comparisons of techniques of production or, more in general, analyses of sustainability of farming systems, authors omit providing in an explicit form all three pieces of information listed below: a. Characterization of boundary conditions with which the farming system is dealing: • According to the set of constraints coming from the socioeconomic side, how fast must be the throughput in the farming system? For example, what is the minimum level of © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems282 productivity per hour of labor that is acceptable for farmers and the minimum level of productivity per hectare forced by demographic pressure, where applicable? • According to the set of constraints coming from the ecological side—type of ecosy stem exploited and intensity of withdrawal on primary productivity—what is the current level of environmental loading and what do we know about the eco-compatibility of such a throughput? That is, what room is left for intensification? b. Characterization of the basic strategy affecting a farmer’s choice: • What is the optimizing strategy under which farmers are making decisions? For example, are they minimizing risk (farming system must be resilient since it is on its own in case of troubles), or are they maximizing return (the farming system is protected against risks such as crop failure by the rest of the society to which it belongs, as in developed countries)? Are there location-specific strategies affecting their choices? • Are farmers sustaining the development of the rest of society (are farmers net tax payers), or are they subsidized by the rest of society (are farmers supported by subsidies)? c. A critical appraisal about the limits of validity of the particular type of analysis performed on the farming system: • Out of the many possible perspectives under which farming activities can be represented and assessed, any choice of a particular window of observation and a particular set of attributes to define the performance of farming (i.e., the one that was adopted in the study) implies missing other important views of the process. What consequences does it carry for the validity of the conclusions? For example, checking the agronomic performance and the ecological compatibility of different techniques does not say anything about the sustainability of these techniques. To discuss sustainability, we also need a parallel check on economic viability and on the compatibility of these techniques with cultural identity and aspirations of farmers that are supposed to adopt them. • How possible is it to generalize the validity of the conclusions of this paper that are related to a location-specific analysis? The three chapters of Part 3 have the goal of showing that it is possible to develop a tool kit for multi- scale integrated analysis of agroecosystems that makes it possible to: 1. Link the economic and biophysical reading of farming in relation to structural changes occurring in the larger socioeconomic system to which the farming system belongs during the process of development. This makes it possible to use an integrated set of indicators of development, able to represent the effects of changes on different hierarchical levels (from the country level to the household level)—Chapter 9. 2. Establish a bridge, which can be used to explain how changes occurring in the socioeconomic side are reflected in changes in the level of environmental impact associated with agriculture. The biophysical reading of these changes at the farm level makes it possible to explain the existing trends of increased environmental impact of agriculture to the existing trends of technical progress of agriculture—Chapter 10. 3. Represent agroecosystems in terms of holarchic systems. This makes it possible to study the reciprocal influence of the decisions of agents operating at different levels in the holarchy. In this case, indicators related to economic, social and ecological impacts can be integrated across levels to indicators of environmental impact based on changes in land use—Chapter 11. Reference White, L.A., (1949), The Science of Culture, Grove Press, Inc., New York, 444 pp. © 2004 by CRC Press LLC 283 9 Multi-Scale Integrated Analysis of Agroecosystems: Bridging Disciplinary Gaps and Hierarchical Levels This chapter has the goal of illustrating examples of multi-scale integrated analysis of societal metabolism that are relevant for the analysis of the sustainability of agroecosystems. In particular, Section 9.1 illustrates the application of impredicative loop analysis (ILA) at the level of the whole country using in parallel different typologies of variables. In this way, one can visualize the existence of a set of reciprocal constraints affecting the dynamic equilibrium of societal metabolism. That is, feasible solutions for the dynamic budget represented using a four-angle figure can only be obtained by coordinated changes of the characteristics of parts in relation to the characteristics of the whole, and changes in the characteristics of the whole in relation to the characteristics of the parts. Section 9.2 provides the results of an empirical validation based on a data set covering more than 100 countries (including more than 90% of the world population) of this idea. In particular, such an analysis shows that an integrated set of indicators derived from ILA makes it possible to (1) establish a bridge between economic and biophysical readings of technical progress and (2) represent the effect of development in parallel on different hierarchical levels and scales. Section 9.3 deals with the link between changes occurring at the level of the whole country (society) and changes in the definition of feasibility for the agricultural sector. That is, socioeconomic entities in charge of agricultural production must be compatible with their socioeconomic context. This implies the existence of a set of biophysical constraints on the intensity of the flow of produced output. Finally, Section 9.4 deals with trend analysis of technical changes in agriculture. Changes in the socioeconomic structure of a society translate into pressure for boosting the intensity of agricultural output in relation to both land (demographic pressure=increase in the output per hectare of land in production) and labor (bioeconomic pressure=increase in the output per hour of labor in agriculture). Indices assessing these two types of pressures can be used as benchmarks to frame an analysis of agroecosystems. 9.1 Applying ILA to the Study of the Feasibility of Societal Metabolism at Different Levels and in Relation to Different Dimensions of Sustainability 9.1.1 The Application of the Basic Rationale of ILA to Societal Metabolism The general rationale of impredicative loop analysis, illustrated in Chapter 7, is applied here to the analysis of societal metabolism. The level considered as the level n is the level of the whole society (country). This requires: 1. A characterization of total requirement at the level n —this is a consumed flow assessed in relation to the whole. This is done by using an intensive variable 3 (IV3) mapping the level of dissipation (consumption of extensive variable 2) per unit of size of the whole (measured in terms of extensive variable 1). 2. A characterization of internal supply at the level n -1—this is a produced flow assessed in relation to a part of the whole. This is done by using an intensive variable 3 mapping the flow of supply (measured using extensive variable 2) per unit of size of the part (measured in terms of extensive variable 1). © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems284 3. An analysis of the congruence over the loop of the reciprocal definition of identities of (1) the whole, (2) parts, (3) subparts and inputs and outputs of parts, and (4) the weak identity assigned to the environment (reflecting its admissibility). The applications discussed below are based on the use of: • Two extensive variables 1 used to assess the size of the system, providing a common matrix representing its hierarchical structure. These two EVl are human activity and land area. • Three extensive variables 2 used to assess the intensity of a flow, which can be associated with a certain level of production or consumption. These three EV2 are exosomatic energy dissipated, added value related to market transactions, and food. The definition of the size of parts (lower-level compartments), in terms of EVl, has to be done in a way that guarantees the closure of the assessments of the size of the whole across levels. The same applies to the distinction between the direct compartment generating the internal supply and the rest of society. 9.1.1.1 Step 1: Discussing Typologies—Two possible choices considered here for extensive variable 1 are useful for addressing two main dimensions of sustainability: (1) Human time—when used as extensive variable 1—is useful for checking the compatibility of a given solution within the socioeconomic dimension. (2) Land area—when used as extensive variable 1—is useful for checking the ecological dimension of compatibility. The first thing to do is therefore an analysis of possible types that can be used to establish an ILA according to the general scheme presented in Figure 9.1. When applying the scheme of Figure 9.1 to the analysis of the dynamic equilibrium of societal metabolism of a whole society using human activity as extensive variable 1, we are in a case that has been discussed on two occasions so far. There are different sets of types on different quadrants. The profile of distribution of individuals over the set of types will determine the value taken by the angle. For example, starting with the upper-left angle, we find that the level of physiological overhead on disposable human activity (DHA) can be expressed as generated by a set of types and a profile of distribution over it. This has been discussed in Figure 6.9 (profile of distribution of individuals over age classes) and Figure 6.10 (profile of distribution of kilograms FIGURE 9.1 ILA: general relation among types in societal metabolism. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems 285 of body mass over age classes). The effect of changes (either in the set of types—e.g., longer life span— or in the profile of distribution over the types), which can affect the physiological overhead, has been discussed in relation to Figure 7.2 and Figure 7.3 (when illustrating a simplified analysis of the dynamic budget of the societal metabolism—using food as extensive variable 2—for a hypothetical society of 100 people on a remote island). After subtracting from total human activity the physiological overhead, we obtain the amount of disposable human activity for the society—left side of Figure 9.2. This amount of disposable human activity is then invested in a set of possible activities. The various categories of human activities can be divided between work and leisure. Making this distinction always implies a certain degree of arbitrariness. This is why it is important to have (1) the constraint of closure across levels and (2) the possibility of making in parallel various ILAs based on a different selection of extensive variable 2. This is particularly important for the decision about the definition of the direct compartment, the compartment providing the internal supply, which is characterized in the lower-right quadrant. For example, we can decide to include the service sector among those lower-level parts making up the indirect compartment when studying the dynamic budget of exosomatic energy. That is, when making a four-angle figure with exosomatic energy as EV2, we can assume that the service sector does not produce either a direct supply of exosomatic energy or machines for using exosomatic energy. But when making a four-angle figure with added value as EV2, we have to include the service sector in the direct compartment. In fact, when considering the dynamic budget of added value, the service sector is among those sectors producing added value. Obviously, the choice of the set of typologies used to obtain closure on disposable human activity is necessarily open. In this regard we can recall the crucial role of the category “other” to obtain closure (Figure 6.1). In this example, the difference between DHA and the sum of the various investments on working activities can be considered in this system of accounting as leisure. With this choice we can end up including into leisure investments of human activity typologies of work not included in the list of typologies. The scheme of Figure 9.1 can also be applied to an analysis of the dynamic budget of societal metabolism, which uses land area as extensive variable 1. In this case, we start with a level of total available land defined as the area associated with the entity considered as the whole socioeconomic system (e.g., the border for a country or the area needed to stabilize a given flow). Also in this case, this scheme can be used to have a preliminary discussion of the standard typologies to be used for the analysis of land use. In general, a first list of land typologies is found when looking at data (e.g., desert, FIGURE 9.2 Choosing how to define and aggregate typologies over the ILA EV1: human activity. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems286 too hilly, permanent ice, swamps, arable land, forest). The categories found in published data are not necessarily useful for a particular ILA. As soon as the analysts manage to obtain a set of useful typologies for the analysis, the profile of distribution of individuals hectares (unit used to assess the size according to extensive variable 1) over the set will define the level of biophysical overhead (reduction I) determining the colonized appropriated land (CAL) (see Figure 9.3). To indicate the process of permanent alteration of the identity of terrestrial ecosystems due to human interference on biological and ecological mechanisms of control, the group of the IFF of Vienna (Institute of Interdisciplinary Studies of Austrian Universities, see for example, Fischer-Kowalski and Haberl, 1993; Haberl and Schandl, 1999) suggests the term colonization. By adopting their suggestion we use the acronym CAL (colonized appropriated land). At this point, we need a set of possible typologies of land use covering the entire colonized appropriate land to classify investments of human activity within this compartment. This is illustrated on the left in Figure 9.3. This is a very generic example, and depending on the type of problem considered, it requires an additional splitting of these coarse typologies into a more refined classification. 9.1.1.2 Step 2: Defining the Critical Elements of the Dynamic Budget—Depending on the EV2 that is chosen for the impredicative loop analysis and the specificity of the questions posed, it is necessary at this point to interpret the metaphorical message associated with Figure 9.2 and Figure 9.3. This requires that the analyst discuss how to formalize this rationale in relation to a specific situation, in terms of numerical assessments based on an available data set. 9.1.1.2.1 Example 1: Human Activity as EV1 and Food as EV2 Let us start with the example of an impredicative loop analysis referring to human activity (as extensive variable 1) and food (as extensive variable 2). This is a case that has already been discussed in the example of the 100 people on the remote island. 9.1.1.2.1.1 Assessing Total Requirement at the Level of the Whole: Level n —This is an assessment of total consumption associated with the metabolism of a given human system. In the case of food this flow can be written at the level n as FIGURE 9.3 Choosing how to define and aggregate typologies over the ILA EV1: land area. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems 287 Population×Consumption p.c.=Total Food Requirement (EV2) (9.1) In Equation 9.1, total food requirement is expressed as a combination of the extensive variable 1 (size of the system—mapped here in terms of population) and the intensive variable 3 (consumption per capita (p.c.), which means a given level of dissipation per unit of size). Equation 9.1 can be easily transformed into THA×FMR AS =Total Food Requirement (EV2) (9.2) when considering that THA=population×8760=total amount of hours of human activity per year,and that consumption per capita represents an assessment of a given flow (e.g., megajoules of food orkilograms of food per year) that can be transformed into FMR AS (food metabolic rate assessed as averageof society) by dividing the relative value of consumption per capita (flowing in a year) by 8760. Thisprovides the amount of flow of food consumed per hour of human activity. With this change we can write FMR AS =(Consumption p.c./8760)=IV3 n (9.3) 9.1.1.2.1.2 Assessing Internal Supply: Level n-1 —This is an assessment of the internal supply of input provided to the black box because of the activities performed within the direct compartment (HA AG ). This internal supply requires the conversion of energy input into useful energy able to fulfill the tasks. When mapping the effect of agricultural activities against human activity at the level n- 1 we can write (HA AG ×BPL AG )=Internal Food Supply (EV2) (9.4) The total supply assessed at the level n—1 is expressed as a combination of extensive variable 1 (size of the lower-level compartment—HA AG =human activity invested in the agricultural sector—the one labeled “direct” in the upper part of Figure 7.8) and intensive variable 3 (BPL AG —biophysical productivity of labor in agriculture—which assesses the return of human activity invested in the set of tasks performed in the compartment labeled “direct”). BPL AG measures the input of food taken from the land and delivered to the black box per unit of human activity invested in the direct compartment. This is the lower-level compartment in charge with the direct interaction with the context to get an adequate supply of input (see upper part of Figure 7.8). BPL AG =Biophysical Productivity of Labor in Agriculture (IV3) (9.5) 9.1.1.2.1.3 Checking the Congruence of the Required and Supplied Flows —At this point, by combining Equation 9.2 and Equation 9.4 we can look for the congruence among the two flows: THA×FMR AS =HA AG ×BPL AG (9.6) As noted before, these two flows do not necessarily have to coincide in either the short term (periods of accumulation and depletion of stocks) or long term (a society can be dependent on import for its metabolism or can be a regular exporter of food commodities). Additional information can be added to the congruence check expressed by Equation 9.6. For example, recall the discussion given in Chapter 6 about the characterization of endosomatic flow in Spain across different levels (Figure 6.8). The characterization of the total food requirement can be expanded to include information referring to different hierarchical levels by substituting the term FMR AS with three terms—in parentheses—as done in the following relation: THA×(ABM×MF×QDM&PHL)=HA AG ×BPL AG (9.7) In Equation 9.7 the total requirement of endosomatic energy, assessed at the level n, is expressed as a combination of extensive variable 1 (size of the system, mapped here in terms of total human activity, linked directly to the variable population) and three variables: (1) ABM (average body mass); (2) MF (metabolic flow), endosomatic metabolic rate per kilograms of human mass and unit of time; and (3) QDM&PHL, a factor accounting for quality of diet multiplier and postharvest losses. QDM&PHL accounts for the difference between the energy harvested in the form of produced food at the food system level (recall the assessment of embodied kilograms of grain vs. kilograms of grain consumed © 2004 by CRC Press LLC [...]... internal supply of food, we obtain that the size of the compartment rest of society is Rest of society=Red I (71.0% THA)+Red II (28.8% THA) =99 .8% THA © 2004 by CRC Press LLC (9. 8) Multi- Scale Integrated Analysis of Agroecosystems 2 89 FIGURE 9. 4 Profile of consumption×end uses of investments of human activity in the U.S., a developed country (Giampietro, M and Mayumi, K (2000), Multiple -scale integrated. .. example of the parallel assessment of the metabolism of the human body and of its parts (Chapter 7), it is actually possible to associate the identity of a particular lower-level element © 2004 by CRC Press LLC 294 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 9. 7 Example 4: Representation of ILA based on EVl of human activity and an EV2 of added value different from that given in Figure 9. 5... to 0.87)—and the lower part of Figure 9. 8 for a graphic representation All health indicators (Figure 9. 9, upper part) and social development indicators (Figure 9. 9, lower part)(—average value of r=0.76 (ranging from 0.44 to 0. 89) See the gray column of Table 9. 2 © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems 303 FIGURE 9. 9 Conventional indicators of development vs BEP (Pastore,... left) of Figure 9. 6.The value of EMRPS can be related to lower-level charac- © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems 297 teristics, the level of capitalization of the various subsectors making up the PS sector: [EMRPS=Sxi (EMRi)].This analysis can be done by using the same approach discussed in Chapter 6 (dividing sector PS in lower-level compartments in terms of investments... Variables: The Correlation of BEP with the Chosen Set of Indicators of Development The analysis of Pastore et al (2000) indicates that BEP is strongly correlated with: © 2004 by CRC Press LLC 302 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 9. 8 Conventional indicators of development vs BEP (Pastore, G., Giampietro, M and Mayumi, K (2000), Societal metabolism and multiple-scales integrated assessment:... 2004 by CRC Press LLC 306 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 9. 11 Conventional indicators vs ABM × MF and THA/HAPS (Pastore, G., Giampietro, M and Mayumi, K (2000), Societal metabolism and multiple-scales integrated assessment: Empirical validation and examples of application Popul Environ 22 (2): 211–254.) 9. 3 9. 3.1 Applications to the Analysis of the Role of Agricultural Systems... ratio 23= Illiteracy rate 24= Access to safe water (percent of population) All data on these 24 indicators come from the FAO ( 199 5), United Nations ( 199 5) and World Bank ( 199 5a), and each one of refers to the latest available year between 199 1 and 199 3 Data on prevalence of malnutrition in children come from ACC/SCN ( 199 3) 9. 2.2 The Representation of Development According to Economic Variables Can Be Linked... In fact, the assessment of ELPPW refers to the combined effect of labor, capital, know-how and the availability and quality of natural resources used by a particular economy, sector, subsector, typology of activity or firm/farm (9. 11) © 2004 by CRC Press LLC 291 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 9. 5 Example 2: ILA with an EVl of human activity and an EV2 of added value (Giampietro,... far in Figure 9. 2 and Figure 9. 5 When applying the rationale implied by Equation 9. 14, we obtain a four-angle loop figure that has been already illustrated in Chapter 7 (Figure 7.5) As promised then, we can now go into a detailed discussion about the selection of the set of parameters used over the loop © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems 293 FIGURE 9. 6 Example 3:... indicator © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems 3 299 of development valid at the socioeconomic hierarchical level (reflecting short-term efficiency (Giampietro, 199 7a)).This ratio can be easily calculated by using available data on consumption of commercial energy of a country (assessing the exosomatic flow) and the assessment of endosomatic flow (food energy flow) . LLC 283 9 Multi- Scale Integrated Analysis of Agroecosystems: Bridging Disciplinary Gaps and Hierarchical Levels This chapter has the goal of illustrating examples of multi- scale integrated analysis of. (lower left) of Figure 9. 6. The value of EMR PS can be related to lower-level charac- © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems 297 teristics, the level of capitalization. supply of food, we obtain that the size of the compartment rest of society is Rest of society=Red. I (71.0% THA)+Red. II (28.8% THA) =99 .8% THA (9. 8) © 2004 by CRC Press LLC Multi- Scale Integrated Analysis