New methodologies for a new transition towards sustainable food systems and agroecological landscapes

As I stated from the beginning, this thesis has mainly been focused on developing novel methodologies. Some part of what we learnt about the socioecological transition, has been the result of novel methodologies that we have proposed throughout this thesis. However, these methods can also be useful to enlarge the available toolbox of the Social Metabolism, and to connect it with other disciplines. In this section I will summarize them and the transdisciplinary connections set in the same sequence they have been presented in the thesis, and by linking those to new forthcoming paths which I consider that might be opened.

1.1 A consistent methodology for energy balances of farm systems

First of all, and thanks to the collective research undertaken by our SFS project, a common methodological framework of reference for accounting energy balances of farm systems has been consolidated. This multi-EROI accounting allows analysing the functioning of agricultural systems, and their multidimensional energy efficiency, in order to study their evolution along socioecological transitions in a comparable way in space and time.

The adoption and implementation of the criterion of a ‘forced local fund sustainability assumption’ has allowed us to reduce the uncertainties in these comparisons, especially in historical studies, by considering what the maximum values of energy efficiency achievable would have been if the agroecosystem considered had been managed ensuring the sustainability of their funds. This was done by taking into account animal feed balances, soil nutrient balances, and a sustainable exploitation of forest resources. This triple balance establishes a sustainability test of those three fund elements, and in chapter 4 has been complemented with a balance of nutritional and fuel requirements for the maintenance of the farming community and society. In this way, a full reproductive analysis of the material social metabolism has been carried out taking into account the closure of all socio-metabolic cycles considered.

In the same vein, I have introduced waste flows when the biophysical relationship between society and nature is accounted. This allows fine-tuning socio-metabolic analysis by introducing a possible third path. This waste path became previously hidden when all possible directions were restricted to whether flows exited or were left within the agroecosystem boundaries. Yet there can be a third type of flow that appears mainly in industrial societies: the ones that become resources out of place. This means bringing to light the environmental impacts of wasted resources in an economy that does not consider negative or positive externalities of

Chapter 8. Conclusions

market decisions. True, it is not easy to characterize quantitatively which part of a flow actually performs a potential benefit or not for the agroecosystem—as it always happens in balance sheets of everything, by the way. However, to consider the opportunity cost that this type of flows might entail in a certain position (a waste) or another (a resource) may help to clarify the issue—at least as a first approximation.

We adopted an accounting model where the process of calculating energy balances of farm systems does not fall into reductionism, an energy dogma. In each case, we account for the flows using the units that are significant for the specific fund element to which they are going (metabolizable energy, nutrients, gross calorific value). Subsequently, at an aggregate scale, we use energy analysis as a proxy that allows us to make a series of calculations of both multidimensional and joint energy efficiencies. In doing so, we are aware of the simplification that adding flows of different energy qualities and power levels involves. We warn that the qualitative differences lost this way of accounting must always be kept in mind when it comes to interpreting the meaning of the different energy efficiency indicators obtained.

At the same time, the impact that inequality in access to natural resources and the decision making power has on energy balances has to be taken into account. If these social conditions are not accounted for at farm scale, as was done in Chapter 6, we cannot analyse the specific biophysical limits set forth by the immaterial sides of social metabolism—such as property rights, inheritance systems, institutional settings, prevailing social values, etc. In the context of a class society, it is ludicrous to consider that all biophysical flows could circulate freely among all fund elements that actually belonged to different owners. The influence of the immaterial metabolism on material metabolism is a pending task for socio-metabolic quantitative analysis. However, I consider that the methodological developments of this thesis are a useful contribution to cope with this challenge.

1.2 The study of cultural landscapes as a footprint of social metabolism

The Energy-Landscape Integrated Analysis (ELIA) studies the relationship between social metabolism and landscape structure for the first time in an integrated and quantitative manner. We do this task by rethinking the agroecosystem pattern of flows from a cyclical conception that allows closing the whole biophysical turnover, and working with an accounting methodology that allows making spatial-explicit all the values accounted for.

The cyclic structure of flows drawn through graph modelling starts with the photosynthetic capacity (the current Net Primary Production that takes place in the system boundaries). Then, following the graph model, we can count the fraction of each energy flow that reaches a node and then is split into two, either to go outside through Final Produce or loop inside the agroecosytem to connect with another node of its energy network (except when there is a third waste path). The graph also includes the energy entries coming from outside that become interlinked with the rest of flows. In this way we avoid adding flows; the entire energy turnover can be closed without incurring in double counting the same flow; the energy temporarily stored within the agroecosystem is separated from the one dissipated outside; the pattern complexity can be assessed as information content; and all values can be counted in a spatial explicit manner.

Importantly, all this relies on the previous results obtained by our energy balances of farm systems that for the first time bring to light the internal loops that remain within the agroecosystem, and increase its complexity. Without the novel methodology of energy balances summarized in the previous section all ELIA advances would have not been possible, or would be lacking a fund-flow reproductive vision of agroecosystems.

Another analytical step forward made thanks to the systemic approach of the agricultural metabolism adopted has been making energy balances spatially explicit, by linking the activity of the farming community with livestock metabolism and farmland-uses according to the services provided through the integration between the fund elements in the landscape. As a result, we can

Chapter 8. Conclusions

observe for the first time the joint effect of the agroecological functioning driven by farm social metabolism—i.e. its ability to emulate the natural processes of energy storage through the interrelations set among the various funds, and the resulting landscape functional structure allowed by these interrelations, which can be analysed through their patterns.

This allows a first approach to what the material conditions for farm-associated biodiversity are. We do this by means of a spatial explicit analysis of the internal accumulation of energy available for all agroecological food chains (food and feed for all living funds), and the equidiversity of land covers (habitat differentiation) in each cell of a grid in the landscape. In this way we can compare situations of different periods and territories and, by difference, to highlight in which cases better conditions occur. ELIA becomes a tool for comparative analysis. While the actual impact of these material conditions on farm-associated biodiversity remained as an hypothesis in the ELIA presented in this thesis, further researches have demonstrated its usefulness in order to assess it.

Therefore, ELIA is a methodology that connects for the first time the disciplines of social metabolism and landscape ecology. It becomes a fundamental step towards the landscape modelling of agroecosystems that can take into account the effect of agricultural activity on farm-associated biodiversity, with the objective of evaluating the progress towards new agroecological horizons.

1.3 Modelling agroecosystems as socio-ecological systems

Finally, I consider that the most relevant methodological contribution made in this thesis is the Sustainable Farm Reproductive Analysis (SFRA) model. This approach allows making the leap from the analysis of agricultural systems carried out so far, to its programming modelling. I consider it a first step towards a prospective-deliberative Social Metabolism, beyond an analytical one.

By using linear and non-linear optimization of flows and fund elements, this methodology allows identifying not only what should be the configuration of the uses that respond to social needs, but the entire fund-flow pattern that would allow having an organic farming with a sustainable metabolism. It can be applied to foresee how agroecosystems would perform either at plot, farm or landscape scales, allowing the definition and testing of feasible, technically viable, and desirable farm systems. Obviously it does so by simplifying things, as in any model. Yet it maintains a biophysically realistic reproductive approach by choosing the different units which are relevant to keep each fund alive. In addition, the possibility of incorporating non-linear programming in the second case study has allowed increasing the degrees of freedom of the model to address aspects such as landscape patterns. This opens a door to increase the complexity of these programming models, as long as they continue responding to coherent biophysical problems from an agroecological standpoint.

To develop this SFRA it has been necessary to link various disciplines: recovering the role of reproductive studies in the agrarian economy, connecting them with current research on landscape ecology and land-use planning, and incorporating them to a novel approach to programming modelling based on social metabolism.

Its usefulness has been proven as a historical tool for counterfactual analysis, as well as a prospective tool for land-use planning to generate agroecological scenarios from which deliberative processes can be established. In the first case, we have considered the study at farm level as a basis to compare the optimum situation with different desirable aims, i.e., minimizing land-use, minimizing labour requirements, or maximizing cash flow. This has allowed us to understand better the reasons behind land-use intensification in advanced organic farming, and to highlight its social and biophysical limits.

Chapter 8. Conclusions

In the second case study we applied the SFRA model to a current situation to compare how strategies oriented to plan new agroecological landscapes can allow the recovery of organic metabolism in agricultural systems. I consider that this modelling quantitatively solves the leap of agroecological scale from plot level to the landscape one. This can be a key tool to guarantee:

i) the closure of metabolic cycles; ii) the recovery of certain biocultural legacies of farm management; iii) an improvement in the material conditions for farm-associated biodiversity; and, iv) the facilitation of deliberative processes for a new socio-ecological transition towards more sustainable farm systems.

These are the first examples of what this methodological approach can give us. In spite of limitations I believe that through forthcoming improvements, and the consolidation of this tool by controlling the sources of uncertainty in the programming model, it can become robust enough to facilitate new social and political processes of transition to new agro-food regimes; while, at the same time, also help to deepen historical analysis of agrarian systems from a comparative perspective.