PESERA-DESMICE: Land management evaluation model

Table of Contents

1. Authors
2. Summary
3. Acronyms
4. Introduction
5. Coarse-scale model description
   1. The PESERA model: summary
   2. Alternative approaches to assessing soil erosion
   3. Process representation in PESERA
   4. Extensions of PESERA
   5. Adapting PESERA
   6. Assessment at catchment scales
6. Cost-effectiveness modelling
   1. Step 1
   2. Step 2
   3. Step 3
   4. Step 4
   5. Step 5
   6. Step 6
   7. Step 7
   8. Step 8
   9. Step 9
   10. Step 10
   11. Step 11
   12. Step 12
7. Agent-Based modelling in Spain
   1. Phase 1
   2. Phase 2
   3. Phase 3
   4. Phase 4
   5. Phase 5
   6. Phase 6
8. Input-Output modelling in Spain
   1. Model construction
   2. Scenario analyses
9. References

Introduction

The aim of Deliverable 5.1.1 is to "develop a model for the main bio-physical and socio-economic processes interacting within an agroecosystem, building on existing experience in combination with results generated within WBs 1-4". The inter-linked models described in this report are designed to evaluate the likely biophysical and socio-economic effects of applying remediation strategies selected by stakeholders in WB3 at a regional scale, by scaling up results from field trials and secondary data. These models will be applied in all study areas for which there is sufficient data. Additional socio-economic models have been developed for application in a single study site to further explore factors influencing the adoption of remediation strategies by land managers and the wider effects of adoption on the regional economy.

Increasingly sophisticated models are being used to represent land degradation processes in highly complex environmental, economic and social systems. Modelling has primarily been used by natural scientists as a means of capturing and predicting aspects of these systems, usually within disciplinary boundaries (e.g. hydrology, soil or atmospheric models). However, many of these models bear little or no relation to physical reality (Prell et al., 2007). In contrast, the (relatively recent) development of 'theoretical' models, which possess some physical basis, allows the real possibility of application over a wide range of conditions and locations, as well as aiding our understanding of natural processes and systems (Anderson and Burt, 1985). Economists also have a fairly long tradition of modelling components of socio-ecological systems, especially human-environment interactions (e.g. Ciriacy-Wantrup, 1952; Clark, 1976; Bergh and Straaten, 1997). However, many of these models also bear little or no relation to physical reality, often being based on stringent assumptions such as perfect information, optimal behaviour, and rational choice (e.g. Simon, 1955). Partly in response to these limitations, models are now increasingly being informed by inputs from stakeholders. The importance of participatory modelling, especially in land degradation and rehabilitation, derives from the awareness of the inadequacy of traditional, engineering approaches to dealing with 'complex and ill-structured problems' (Giordano et al., 2007). It has become increasingly obvious that traditional modelling approaches based on optimization have to be combined with inputs from stakeholders, if their outputs are to feed effectively into policy design and implementation (Giordano et al., 2007; Funtowicz and Ravetz, 1990, 1994; Funtowicz et al., 1998).

Although there are now approaches that can incorporate inputs from stakeholders into model development, many limitations remain. Firstly, stakeholder knowledge tends to be restricted to local contexts, so input to models with regional or global coverage is difficult (Wohling, 2009). Second, there are many (often competing) stakeholder interests in land degradation and rehabilitation, with different knowledges and priorities over the processes and potential solutions that should be modelled (Raymond et al., under review). Finally, although there have been many separate attempts to incorporate stakeholder inputs into models of biophysical systems, human behaviour and the local or regional economy, there have been no attempts to do this for combined social, economic and/or environmental systems (Prell et al., 2007; Hubacek and Reed, 2009). In response to these challenges, the modelling approach described in this report incorporates various inputs from stakeholders to enhance both the realism and relevance of outputs for application in policy and practice:

• The DESIRE project collaborates with stakeholders to define the most important land degradation processes (WB1) and potential solutions to model in WB5. Stakeholder analysis is used to ensure a cross-section of stakeholders with different knowledge are represented and decision support tools are used to negotiate differing stakeholder priorities (WB3);
• Information collected from stakeholders in WB3 provides the basis for assessing the cost-effectiveness of remediation options across environmental and socio-economic gradients;
• Environmental effects of selected remediation options are evaluated using the PESERA model;
• The resulting linked models have the potential to be applied around the world through the case study approach of the DESIRE project, whilst retaining and building on inputs based on local knowledge;
• In one study site, this is expanded by incorporating stakeholder inputs into (Agent Based) models of human behaviour using data from structured questionnaires and combining this with a (Input-Output) regional economic model.

By linking these models of human behaviour to models that describe the wider regional economic and biophysical implications of people's actions, it may be possible to better understand how people are likely to respond to environmental change, and how their responses in turn are likely to influence their environment. Such models may offer us the opportunity to explore how land managers might react to different future policy options and provide ways to make refinements to policy design that can more effectively achieve stated goals.

Site-selection modelling for optimisation of conservation efforts is a well established research area on biodiversity conservation (e.g. Camm et al., 1996; Crossman et al., 2007), but has so far not been applied to the mitigation of land degradation. This research will enable landscape-scale assessments of the most economically optimal ways to attain environmental targets. Furthermore, although Cost-Benefit Analysis is an established method in evaluating soil and water conservation measures, from individual measures (de Graaff, 1996; Ludi, 2004; Posthumus and de Graaff, 2005; Fleskens et al. 2005, 2007) to projects (de Graaff, 1996; Ninan and Lakshmikanthamma, 2001) to continental and global scales (Pimentel et al., 2005; Kuhlman et al., in press), so far the spatial variability of the profitability of SWC measures has received little attention. The model described in this report offers a method which considers the perspective of both individual land users and policy makers, and can scale up results from the field to the region and beyond.

Linking environmental and socio-economic models not only facilitates a spatially explicit evaluation of mitigation strategies, but vice-versa, the biophysical effects simulated by the environmental model can be attributed real meaning as the spatial configuration of the adoption of mitigation strategies by individual land users is based on economic analysis of available alternative options. The coupled models can be used to model environmental (e.g. climate change) as well as socio-economic (e.g. policy) scenarios. The fact that this is done for multiple study areas based on data gathered by a collective effort between researchers and local stakeholders makes the approach truly unique. Cross-site scaling-up of the model will for the first time be able to provide estimates of global impact of land degradation mitigation, built on local realities.

Another innovative aspect of the approach is that it considers the effect of land degradation mitigation on a regional economy. Regional economic modelling using input/output modelling is a long-established discipline (Miller and Blair, 1985) that has to our knowledge never been used to consider the effects of desertification. Although environmental effects have been considered in such models (e.g. water use, Duarte et al., 2002; Guan and Hubacek, 2008), this is one of the very first models that consider soil erosion.

Section 2 shows how the biophysical model proposed for the DESIRE project builds on and extends the PESERA model (Kirkby et al., 2008), originally developed for Pan-European Soil Erosion Risk Assessment within a dedicated EU (FP5) project (section 2.1). It also describes a database that is used to assess the effects of soil and water conservation measures on runoff, soil loss and sediment yield at catchment scales (section 2.2). In order to determine which remediation strategies should be implemented where to achieve desertification policy targets at least cost, and to make an investment analysis of these strategies for both land users and societies, section 3 links PESERA outputs to the implementation costs of remediation strategies in each study area (identified in WB3) to produce a cross-site cost-effectiveness analysis, and financial and economic cost-benefit analyses. Finally, section 4 describes models that have been developed to investigate the regional economic effects of adopting different remediation strategies (using input-output analysis for regional economic modeling), and determine what factors influence land managers to adopt different remediation strategies and change land use under different future scenarios (using Agent-Based Modeling), which will be applied in Spain. Figure 1.1 shows how the different models are interrelated.

This report is the first of a series of deliverable reports from WB5. The PESERA model described in this report is being extended to capture the role of grazing, fire and wind erosion more effectively, and enhance pedotransfer functions (to be reported in Deliverable 5.2.1). The model is being adapted to each study area to reflect indicators and land degradation drivers identified in WBs 1 & 2 as closely as possible. We will use this model to look at the biophysical effects of different remediation options that we have trialed in study areas at a regional or perhaps national scale (Deliverable 5.3.1). These results will be integrated with field trial results in all study areas, and will form the basis of a final stakeholder workshop, in which we will discuss recommendations for policy-makers and extension services (Deliverable 5.4.1). Locally calibrated application of the fine-scale PESERA and/or alternative models (WP 5.2 below) will then be used to extend the results of pilot area studies to a larger hinterland, in order to evaluate the impact of recommended conservation measures (from WB 3) for the surrounding area. The extent of this wider hinterland will be constrained by broad similarities of environment (guided by WB 2) and the availability of coarse (1km) resolution data, although reference data is already available at this scale for much of Europe.

Figure 1.1: Schematic overview of model interrelations within WB5