In »PESERA-DESMICE: Land management evaluation model it is described 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. The original PESERA model was extended to capture the role of grazing, fire and wind erosion more effectively, and enhance pedotransfer functions on the basis of dialogue and data within each study area; this work is described in »PESERA: improved biophysical descriptions. In this section we describe how, in order to generate model output for each DESIRE study site, PESERA was adapted to reflect indicators and land degradation drivers identified in Phase A of DESIRE (»Study site contexts & goals: local desertification extent and impact and »Assessment with land degradation indicators) as closely as possible. The modified model was used to look at the biophysical effects of different remediation options that have been trialled in study areas.

The strategy to do this is by comparison of baseline to modified conditions:

1. The PESERA baseline is an assessment of a series of biophysical descriptors at an equilibrium state driven by mean climate, land use, soil and topography. These descriptors are an estimate of monthly estimates of biomass (productivity), runoff and erosion. The PESERA baseline assessment was achieved with best understanding and interpretation of current land management practice and technologies, and constitutes the "without" case in technology assessment.
2. The adapted PESERA assessment is a representation of the same biophysical descriptors, but now evaluated as the simulated effects of a specific desertification remediation option. Adapted assessments were achieved with best understanding of the functioning of technologies. It hence forms the "with" case of technology application.

Both baseline assessments and adapted PESERA assessments are input for DESMICE.

Climatic regimes and appropriate technologies

The DESIRE study sites represent a very wide range of climatic conditions, and the climate exerts perhaps the strongest constraint on what are appropriate remedial technologies. Figure 1 illustrates this climatic range. Months represented as points above and to the left of the Rf=PE have insufficient water for unrestrained growth, so that water is a limiting resource.  If rainfall is less than about 60% of the potential evapotranspiration during the growing season, then rain-fed agriculture is severely limited, and only some specialised crops, such as olives or agave, can survive without irrigation.  However, the high temperatures provide good conditions for rapid growth, and often for several crops per year where irrigation water can be provided economically.

The various climatic regimes provide different constraints to sustainable land use, and these are summarised in Figure 2, drawn with the same axes and scales as Figure 1.  Under appropriate conditions, the greatest constraints may be through wind or water erosion, water scarcity, wildfires or frost damage.

Figure 1: The climatic environment of study sites. Loops show mean monthly precipitations and temperatures for representative DESIRE study site areas.	Figure 2: Factors that constrain sustainable land use.

PESERA adaptation for DESIRE technologies
Many of the DESIRE technologies consider rainfed cereal agriculture, where natural soil erosion rates are increased by an order of magnitude with long term on- and off-site impacts. Other technologies focus on biomass protection but more commonly on a combination of more than one technology (Table 1).

Table 1: Mitigation measures accommodated within the adapted PESERA modules

In rainfed cereal agricultural protection from erosion is generally most effective through measures that increase infiltration rates and so reduce the amount of overland flow runoff and soil loss.  The most reliable measure is generally to increase ground cover.  In areas at greatest risk, this may require the maintenance of a natural vegetation cover (without excessive grazing), but a number of conservation measures can reduce erosion within cropland.  Inter-cropping ensures ground cover throughout the rainy season. Strip cropping reduces the distance over which runoff can build up before flowing back into a vegetated strip. Terracing reduces the overall gradient, and so the erosive power of runoff, but must be combined with measures to protect the over-steepened terrace risers, by strengthening them with stone or perennial vegetation and/or by diverting runoff away from them.  Over time, terraces generally accumulate deeper soils along their lower margins, often at the expense of the upper part of the terrace, and the deeper soils may help to retain more water for the growing crops. Table 2 shows typical change in PESERA parameters and variables used to simulate mitigation options and associated changes in cultivation management.

 
Table 2: Typical change in PESERA parameters and variables used to simulate mitigation options

Prescriptive and preventative management is adopted in areas prone to wildfire (Esteves et al., 2012). Wildfire occurs wherever there is a substantial accumulation of dry above-ground biomass. This combination is usually associated with forest or shrub vegetation rather than with cropland. Fires are generally ignited either by lightning strikes, which are generally more frequent in the tropics, or through human intervention, either deliberate or accidental, related to the number of people using or visiting the forests and so substantial in Europe with its high densities of roads and population. Fires, once started, are most severe when the biomass loading is high, but they spread most quickly when the biomass is less and wind speeds are high, so that the fire moves through the canopy and burns the soil less severely. Under severely water-scarce conditions, biomass is dry, but too sparse to support large fires. Under humid conditions, there is a high biomass but it rarely dries out enough to support a fire. Intermediate conditions provide the conditions of greatest fire risk, with sufficient moisture to provide good growth and a dry season to reduce the moisture content of the canopy.  Figure 3 shows how the greatest risk is associated with these intermediate areas.

Figure 3: Climatic component of wildfire risk for Europe under natural vegetation.

Scenario analysis with DESMICE model

The DESMICE model is developed as a series of ARCGIS Modelbuilder modules with subroutines programmed in Python. As explained in »PESERA-DESMICE: Land management evaluation model the socio-economic model (DESMICE) informs PESERA where remediation technologies can be implemented, and PESERA provides the biophysical output on which DESMICE will subsequently elaborate to calculate economic feasibility. Relative to the original model description, some simplifications were implemented. In some cases, this reflects the fact that data was limited. However, this also stems from a separation of model steps and scenario analysis, reducing the number of model steps from 12 to 6. The 6 model steps are shown below:

1. First it is necessary to define where each technology can, on biophysical grounds, in principle be applied. This is an important step in that it rules out the area where technologies cannot be applied e.g. terraces on steep slopes with shallow soils. Factors considered include: soil depth, slope, land use, climate and distance to streams.
2. The PESERA model is run, taking into account each technology’s potential applicability area, and compared to a case where no technology is applied. In practice, applicability limitations can also be clipped out later to reduce coordination effort.
3. WOCAT technology questionnaires currently show only one cost estimate; in reality this will depend on location. DESMICE can consider two different aspects: environmental conditions (e.g. terrace spacing and hence cost depends on slope) and distance to market. The latter functionality was not implemented in the analyses for this report.
4. The technologies that are being assessed may have different economic lifetimes. Therefore, shorter-lived technologies are assessed over several cycles of re-investment (over the length of time that the longest lived technology is likely to last for). Years of (re-)investment are filled first; maintenance costs are subsequently added for years in between investment. Production costs need also to be considered because application of technologies may lead to a change of land use or use of input (e.g. more labour because of larger harvest).
5. To value effects of a remediation strategy, the following will be assessed on a yearly basis for the lifetime of the technology (or multiple lifetimes): A. Evolution of production output (yield x value) over time B. Evolution of costs of implementing the technology and land use associated with it C. Evolution of production output (yield x value) as it would develop were the mitigation strategy not applied D. Evolution of the costs associated with the land use in this ‘without’ case For each year, the net result can then be calculated as [A-B-C+D] (note that benefits and costs may vary both in space and time).
6. The annual cash-flows of step 5 are subsequently used in a Financial Cost-Benefit Analysis (FCBA). An important issue in FCBA is discounting, i.e. introducing an interest rate that depreciates costs or benefits occurring in the future relative to those felt now. Summing discounted cash-flows gives the Net Present Value (NPV) for each technology. For each grid cell, one of the following three possible outcomes will apply: •    The technology with highest NPV will be selected (when positive) (the adoption grid shows a possible configuration of technology A, B and C) •    No technology will be selected if all NPVs are negative (i.e. white pixels in potential adoption grid) •    No technology will be selected if no technology is applicable in the area (blue cells in adoption grid)

Model input data primarily comes from the WOCAT database. Additional data requests were made using two information sheets (for study sites and technologies respectively). Furthermore, data from field trials were used in parameterizing the DESMICE model.  

Different types of scenarios were developed to simulate the effects of proposed remediation strategies as well as of policies. These were:

1. Baseline scenario, the PESERA baseline run, see above.
2. Technology scenario, assessing the effects and financial viability of mitigation options for those areas where they are applicable.
3. Policy scenario, assessing the effectiveness of financial incentive (and alternative) mechanisms to stimulate adoption of technologies if they are not economically attractive. Local policies have in some cases been considered based on information from WB1 and study sites.
4. Adoption scenario, considering the simulated technologies (if more than one) in conjunction and assumes that the most profitable option has the highest potential for uptake by land users. In order to make the net present value of different options comparable, the same time horizon is applied to the analysis.
5. Global scenario; two types were defined, the food production and minimizing land degradation scenarios. The food production scenario selects the technology with the highest agricultural productivity (biomass) for each cell where a higher productivity than in the baseline scenario is achieved. The minimizing land degradation scenario selects the technology with the highest mitigating effect on land degradation or none if the baseline situation demonstrates the lowest rate of land degradation.

The combined PESERA-DESMICE model was run for all study sites with data and degradation processes for which the model can be applied to simulate both the bio-physical and socio-economic consequences of these scenarios. The field data collected in Phase C of DESIRE »Implementing & monitoring field trials part of allowed performing a calibration check to get biophysical effects in the right order of magnitude. Model output was discussed in final stakeholder workshops to allow further broad-based qualitative evaluation of integrated model results. The evaluation is discussed in »Evaluation of remediation recommendations.

The PESERA-DESIMCE model output for each study site is presented in a generic format which is explained here, together with any assumptions made in preparing the results. 

Study site details

The front page for each study site starts with a short facts section and overview map of the study site. The facts include a one-sentence description of the study site, the coordinates (latitude, longitude) of either the boundaries or center point of the study site and size of the area. Together with the overview map, these facts help the reader to locate the area. Note that none of the subsequent maps of model results offer location or coordinates – hence the importance of this front page section.

Further details include data on altitude, precipitation levels, temperature range, land use, population and perceived most important degradation processes and drivers. This information is mostly summarized from »Compilation & synthesis of study site descriptions. Note that for model analyses, DEMs and spatial data sets of climate, soil and land use were used – the details provided here are just to give a snapshot view of study site characteristics. The number of inhabitants here reported is used in calculations such as per capita food production.
    
Overview of scenarios

The front page also lists the scenarios run for the particular study site. Their number varies, e.g. depending on the number of technologies tested or for which there was sufficient data, or the existence of policy options.

Where scenarios relate to technologies included in the DESIRE WOCAT database the corresponding reference number is given (e.g. CPV01). 

Baseline scenario

The PESERA baseline run shows model results for the study site under current conditions (see »PESERA-DESMICE model for technical details). Usually, only one set of output maps is shown here. However, sometimes, such as in this Chilean case, there is a lack of clarity over current study site conditions – in this case the level of compaction. Hence two baseline output maps are shown, one for uncompacted and one for compacted conditions. The first map (top right) shows a landforms map for the study area which is produced by a DESMICE submodule from the study site DEM. Not only is the landforms map frequently used to determine applicability limitations (as per WOCAT records), it is shown here as it gives a good overview of the topographic conditions of the study site.

Soil erosion maps are presented with fixed classification of soil loss, so that one can compare the severity of land degradation across study sites. To note that PESERA soil loss estimates are field-based, i.e. there is no routing of sediments through stream networks. In the Portuguese study sites, instead of soil erosion, maps of fire severity index (FSI) are depicted, as the local degradation problem is susceptibility to and occurrence of wildfires.

Biomass production maps do not have a fixed legend, as variations between study sites are too large for a single classification to be relevant. Within a study site, the map can show nuances in productivity caused by environmental gradients as well as the sometimes large variation between different land uses – e.g. arable land versus forests. The units of biomass production are kg/ha or ton/ha and include whole-plant biomass, not just yields. A harvest index is required to calculate the latter.
     

 
Technology scenario

A technology scenario presents the model output for a specific remediation option. They form the core of the scenario simulations, as policy, adoption and global scenarios are based on them. The description starts with a number of facts or assumptions that were used in the simulations. These may pertain to the situation under the current conditions ('without' case), the situation after implementation of the technology, or both. Costs and prices are given in local currency and Euros to facilitate cross-comparison between sites. All numbers are either based on reports by study sites (WOCAT database), secondary sources or in some cases derived from other study sites with comparable conditions. Applicability limitations show the share of the study area where the technology can, in biophysical terms, be implemented.

 Soil erosion maps compare annual soil erosion in the with and without situation. For the Portuguese study areas, where wildfires are the main degradation problem, erosion maps are replaced with fire severity index (FSI) maps. The impact of the technology on biomass is here considered especially as a degradation mitigation outcome and hence focuses on total biomass rather than yields (one can multiply values with a harvest index if given to arrive at yield levels; or refer to the global food production scenario). Shown are a map of the percentage of biomass increase relative to current conditions and total biomass after implementation of the technology.

Economic viability maps come in two flavours:

1. For agronomic measures that need to be repeated annually as part of the production cycle, the maps present the outcome of a partial budget analysis of the difference of costs and benefits in the with and without situation.
2. For technologies requiring investment (also if only in kind) and where benefits accrue only after a certain period, cost-benefit analysis (CBA) is applied and includes the use of a discount factor. The map in this case presents the Net Present Value (NPV) of the investment.  

 
Policy scenario

Policy scenarios are presented for any incentive or strategy that could help to improve the viability and/or extend the adoption of a technology with the final goal of enhanced mitigation of land degradation. Most frequently, policy scenarios assess the cost-effectiveness of subsidies to reduce investment costs to implement a technology for land users (e.g. an incentive in the form of a 50% reduction is often presented). The policy scenario starts with a description of the issue and the type of incentive/strategy to be evaluated. Subsequently, the profitability of the technology with and without the policy is compared. In the Góis example used here, estimates for profitability are given for the entire area and are not spatially-explicit. Hence, the comparison is made in a table format and instead, the changes in the area subjected to prescribed fire are depicted in maps.

Finally, cost-effectiveness indicators are presented to assess the cost of the policy measure (from a public, or governance perspective) in relation to the environmental benefit obtained. Cost-effectiveness can be expressed in monetary units per ton of soil loss prevented, or per hectare of land saved from burning.    

Adoption scenario

Adoption scenarios are presented where multiple technologies with partially overlapping applicability areas are being assessed. The purpose of the adoption scenario is to provide a summary overall view of the spatial arrangement of the possible mitigation options, and the adoption patterns if it is assumed that in each cell, the most profitable technology (i.e. the one with the highest NPV) is selected. This assessment is made for all technology scenarios (‘without policies’) and all policy scenarios combined (‘with policies’). For many study sites, only a single technology scenario was run, or different technologies had mutually exclusive applicability areas. In such cases, there would be no added value in presenting an adoption scenario, which is hence not elaborated.

Global scenario

The final type of scenario takes a reverse approach to the policy scenario. Instead of asking the question what the effectiveness of a policy is, it considers the technical capabilities of the remediation option(s) in creating impact across the study area, and then provides an investment requirement. The objective of this analysis is not so much a local analysis, but to provide a global comparison of potential impact – hence the name ‘global scenario’.

Two types of global scenarios are presented: Scope for increased food production, assessing how much more food could be produced in an area if desertification remediation strategies were adopted to the maximum extent (insofar as they enhance crop production); and Scope for minimizing land degradation, assessing by how much soil erosion could be curbed if effective remediation strategies were fully implemented.

In both cases, the absolute and percentage improvements relative to current conditions are presented. Note that for food production, yield increases are reported rather than biomass increases. For erosion reduction, negative rather than positive numbers are effective and colour coding for soil erosion reduction classes have been inverted to illustrate this fact.   

Biophysical impact and economic indicators are subsequently provided. These are also used to calculate the main indicators presented in the top-right corner: yield increase per hectare and per capita for food production scenarios, and erosion reduction per hectare and cost per ton of soil prevented from eroding for land degradation minimization scenarios.

Concluding remarks

The final page of each study site report recaptures the main points of the analyses, and provides a narrative for the specific study site context and processes. Where possible, reference is made to other DESIRE results such as:

The expert mapping of land degradation in »Mapping desertification extent is compared to the PESERA baseline run; Technology scenarios are assessed against experimental results »Local field experiment results and conclusions; Stakeholder opinion about technologies and its evolution in time between »Selecting strategies for field testing and evaluation of experimental results by stakeholders is discussed in relation to model results; Local policies (»Drivers and policy context) and stakeholder opinion about how to promote sustainable land management are revisited when discussing results of policy scenarios   Adoption and global scenario results are presented with a view of supporting recommendations for extension and policy. Finally, an overall conclusion is given which refers to the general context of environmental change and the feasibility of the remediation options considered to build resilience, as well as any remaining research before such recommendations can be made.

1 Botswana (Boteti) 2 Cape Verde (Ribeira Seca) 3 Chile (Seccano Interior) 4 China (Yan River Basin) 5 Greece (West-Crete) 6 Mexico (Cointzio) 7 Morocco (Sehoul) 8 Portugal (Góis) 9 Portugal (Mação) 10 Spain (Guadalentín) 11 Tunisia (Zeuss-Koutine) 12 Turkey (Eskişehir) 13 Turkey (Karapinar)
Figure 1: Locations of DESIRE study sites for which PESERA-DESMICE was run.

The remaining study sites have not been included in this report for a variety of reasons. In the Rendina basin (Italy) shallow landslides are the main land degradation problem for which PESERA was extended (PESERA-L ; Borselli et al, 2011). The temporal and spatial dimensions at which shallow landslides occur are not readily translatable in land use management options for which to conduct a cost-benefit analysis, and therefore the DESMICE model could not be applied. However, the results of PESERA-L are described in DESIRE report 82 (Borselli et al, 2011). The Nestos River Delta site (Greece) and two Russian study sites (Novy and Dzhanibek) feature salinization and water logging problems for which PESERA is not applicable. In principle, it would be possible to couple the DESMICE model with alternative models that are more suitable for these problems than PESERA. The biophysical model results for the Russian sites are presented in the individual study site sections. 

PESERA Baseline runs
Baseline assessments of soil erosion under current conditions were made for a range of study sites (Figure 2). Comparing these assessments, it becomes apparent that there are large differences between sites. One very remarkable result is the low degradation problem in Karapinar (Turkey). In this site, wind erosion rather than water erosion is the main degradation problem. Either lower soil loss rates are already alarming or wind erosion processes were not adequately modelled, e.g. because of a lack of good wind speed data. PESERA results put the Seccano Interior (Chile) in first place regarding the severity of soil erosion, while Yan River Basin (China) and Eskişehir (Turkey) also rank high. West-Crete (Greece), Cointzio (Mexico) and Sehoul (Morocco) show a more mixed picture, with both pockets of unaffected and severely affected land. According to these results, the Guadalentín (Spain) and Zeuss-Koutine (Tunisia) areas are only moderately affected by soil erosion.

Figure 2: Overview of PESERA baseline run erosion rates for selected study sites	Figure 3: Degradation degree and extent in study sites according to WOCAT mapping. Source: Van Lynden et al., 2011

It is interesting to compare model assessment of soil erosion with land degradation mapping using expert knowledge (Figures 2 and 3). The latter was done in WB1 using the WOCAT mapping method (Van Lynden et al., 2011). When comparing Figure 2 with Figure 3 (taking care that not all sites feature in both charts), one can note:

• China – that the proportion of the area affected by serious land degradation is roughly similar; experts are more optimistic in classifying the remaining land as little affected than model results suggest;
• Mexico – little agreement between model results and expert opinion, with the latter assessing the situation much less degraded;
• Morocco – both model and experts sketch a mixed picture of land degradation, with a striking level of agreement;
• Spain – although both methods emphasize intermediate classes of land degradation, the model is on this account more optimistic than the experts;
• Tunisia – experts consider over 70% as severely degraded, whereas the model assesses 70% as very little degraded;
• Turkey (Eskişehir) – again a striking agreement between model and expert opinion, and a severely degraded site;
• Turkey (Karapinar) – little agreement, with experts noting severe land degradation and the model missing any degradation problem (as is briefly discussed above).

The Tunisian site is the most arid, followed by the Spanish and Turkish sites, which overall seem to have more severe land degradation in expert opinion than model assessment. It could be that low levels of vegetation typical for those more arid conditions influence the experts, or that PESERA is too sensitive to slope angle in comparison to plant cover.

Technology scenarios
The effectiveness and financial viability of a total of 22 technologies were simulated in the combined study sites. As Table 1 shows, structural measures (n=8) were the most common, followed by agronomic measures (7), management measures (5) and vegetative measures (2). In order to include technologies, availability of experimental data (»Local field experiment results and conclusions) was in many cases a requirement to understand the functioning and effectiveness of the technology and to calibrate PESERA to local site conditions.  

Table 1: Overview of technologies in each study site for which PESERA-DESMICE simulations were run and their classification according to main WOCAT categories: agronomic, management, structural & vegetative.

When classifying the simulated technologies according to the type of measure, a gradient of increasing cost of investment can be observed going from Agronomic < Management < Structural measures ≈ Vegetative (Figure 4A). Agronomic measures were very cheap and in one case actually presented a cost saving (range  -€30 - €79 per ha); they can be incorporated in the annual crop production cycle and are confined to application on arable land. Management measures are more versatile and included a variety of technologies ranging from biogas to prescribed fire for fire prevention and controlling access to fields or rangelands. They typically command an investment analysis as benefits tend to accrue in the medium to long term. The same holds for structural measures. Variability in investment costs was high in this category due to the inclusion of some expensive structures (e.g. checkdams for land - China). Vegetative measures were surprisingly the most expensive category. Although only consisting of a non-representative sample size of two technologies, one could generalize and say that due to their implementation in restoration activities, large investments were required and in order to enable seedlings to survive additional management and structural measures are also used.   

	Figure 4: Investment costs (a), applicability limitations (b) and financial viability (c) of different types of measures.

Next, we verified that for technologies modelled (under widely variable circumstances), most frequently about half of the study site can be treated due to applicability limitations. However, in some cases this is considerably less (checkdams for land – China: 9%; gully control by planting atriplex – Morocco: 10%) or more (terraces with pigeon peas – Cape Verde: 76%; rangeland resting – Tunisia: 69%). When aggregating per type of measures, management measures seem to have the widest range of applicability, followed by structural and agronomic measures (Figure 4B). It is suggested that vegetative measures typically demand more specific conditions and are consequently not as widely applicable.

Within applicable areas, many technologies are not profitable in about 70% of the area. Figure 4C shows aggregated financial feasibility of the technologies considered. This figure needs to be interpreted with caution as many factors come into play. For agronomic measures, effectiveness is an important factor. Yields may not respond or even be negatively affected, rendering the technology uneconomic despite low cost. For management measures, their versatile nature makes that although they are widely applicable, they are not universally financially sustainable. Together with structural measures, another factor with large influence is the time horizon after which the technology is evaluated. Some examples are included of measures that are not profitable after 10 years, but very profitable after 20 years. For structural measures, another factor that contributes to mixed financial performance is their sometimes very high investment cost. For the two vegetative measures, which are shown to be attractive in 100% of their applicability area, one should not forget that this is on a limited area – i.e. they may be highly specialized measures.  More importantly however, the without case is unproductive in these cases, and the fact that plants need to grow to maturity means that the right time to evaluate the measure may be more easily determined.

Policy scenarios
A total of 11 policy scenarios were run for 8 different sites, of which this section provides a brief overview. The first question we can ask is whether policies contributed to the aim to facilitate upscaling of desertification remediation options. Figure 5A shows a large spread in feasibility of technologies under situations with and without policy interventions. The 1:1 line is the no-effect line and usually one expects only the area above the line to be populated; the larger the distance to this line the more effective a policy is. The chart shows that in a few instances, policies do not result in increased feasibility. On two occasions, there are slight improvements of an already quite high feasibility, e.g. from 81 to 93%. In the remaining cases, an unprofitable technology is raised to being feasible in between 33 and 94% of the applicable area.     

Comparing the per area unit costs of technologies with their effectiveness in reducing soil erosion, from a sample of policy scenarios for which cost data was available (n=5), a general trend of increasing effectiveness with increasing cost can be observed (Figure 5B).  A much better correlation was found between total cost of a policy and its effectiveness in reducing soil erosion (Figure 8C). The difference between the two charts is that in the first instance, the area aspect relates to the cost of (subsidies towards implementation of) technologies on a per hectare basis, whereas in the second case the total cost of a policy can be high because of a large applicability area.

	Figure 5: a) Effectiveness of policy scenarios on feasibility of technologies; b) per unit cost-efficiency of policy measures assessed; and c) total cost-efficiency of policy measures assessed.

Global scenarios
Figures 6 and 7 respectively show results of cross-site analyses of opportunities for increased food production and reduced soil erosion. Turning first to the food production scenario, average potential yield increase ranges from less than 50 kg/ha to more than 3000 kg/ha (Figure 6A). However, in three quarters of the study sites, productivity can increase by more than 500 kg/ha. In half of the cases where increased food production is possible, improvements can cover the lion share of the applicability area (Figure 6B). In all sites, yield increases can be obtained in more than 20% of the applicable area. The investment costs required to achieve this are substantial when looking at the first year (Figure 6C, n=12, average cost €567/ton when one case with ‘cost’ below zero is excluded), but are reduced when aggregating over the economic life of technologies (Figure 6D, n=9, average cost €145/ton).

Figure 6A-D: Results for cross-site comparison of food production scenario

Opportunities to reduce land degradation exist universally across applicability areas: at minimum, soil can be conserved by the technologies assessed on 70% of the applicable area. The rate by which soil loss can be reduced is either very high (80-100%) or moderate (0-40% reduction). In some cases, there are no additional costs involved to reduce soil loss, in others substantial investments (>€1000/ton) need to be made if analyses are done on a single year of erosion reduction. When spread out over the lifetime of technologies, erosion reduction becomes much more affordable, at rates often below €250/ton and in a considerable number of cases below €100/ton.

Figure 7A-D: Results for cross-site comparison of minimizing land degradation scenario.

1.     Spatial variability of investment costs is poorly known;
2.     Timing of biophysical effects is not explicit;

The effects of these bottlenecks are explored in two case studies in the subsections below.  

Effect of spatial variability of investment costs

Taking as an example the application of bench terraces with loess soil walls in the Yanhe River basin in the Loess Plateau of China, spatial variability of investment costs was defined as follows:

INVs=US$1,823 *S/30            [1]
         
where INVs is the investment cost per hectare for slope gradient S (in percent) and US$1,823 is the investment cost reported for a standard slope of 30%.

Calculating the average investment cost per hectare across the area where the technology is applicable (3,732 km²) with Equation 1 gives US$1,591 ± 717. To assess the effect of different levels of variation of investment costs with slope gradient, the mean was subtracted from the INVs data layer and the resulting raster multiplied with factors 0.75, 0.5, 0.25 and 0 before adding the mean investment cost again. This approach resulted in a number of rasters with the same average investment cost but different standard deviation and ranges (Table 1), which were subsequently used to assess the financial viability of the technology following the steps of the PESERA-DESMICE framework.

Table 1: Levels of spatial cost variability and resulting range of investment costs for bench terraces in Yan River Basin, China.

The case study of bench terraces in the Yan River Basin in China shows an important influence of variable investment costs (Figure 1A). When no spatial variability is taken into account, terraces are financially attractive in 13% of the area where they can technically be implemented. This proportion rises to 50% if costs are taken proportionate to the reference slope (Equation 1). Figure 1A clearly demonstrates that the effect of spatial cost variability is not linear; not considering or underestimating the level of variability in costs may hence considerably underestimate potential profitability of bench terracing, whereas overestimating the level of variability of the required investment may rapidly lead to exaggerated viability estimates. Not only does the percentage of the area where the technology can be economically implemented change, but also the locations (results not shown). In absence of slope-related spatial variability, slope does not exert any influence and viability is in this case primarily responding to climatic variation. As the slope dimension is phased in, more and more less sloping land in areas with suboptimal climatic conditions replaces rugged areas with highly suitable climate.

Figure 1: A. Financial viability of bench terraces in Yanhe river basin under different levels of spatial investment cost variability;	B. Financial viability of gully control with atriplex in Sehoul as a function of time to reach maximum productivity.

Effect of timing of biophysical effects
The technologies assessed in the DESIRE project included agronomic (e.g. minimum tillage) as well as structural, vegetative and management SLM measures. All measures, but especially the second group, impact on slow soil ecological processes and will gradually improve soil structure and fertility, and hence system productivity. The PESERA model simulates the equilibrium conditions in the with and without technology case. One of the sites where PESERA predicted a particularly large improvement in productivity was in the Sehoul area close to Rabat, Morocco – for gully control by plantation of atriplex (Atriplex halimus). In the standard calculation, it was assumed that production would increase linearly until reaching its maximum value after 20 years -  i.e. time to maturity TTM = 20. By employing Equation 2, net present value was calculated for time productivity series with different TTM values (15, 18, 25, 27, 30 and 33 years):  

    [2]
    
Where NPV_TTM refers to the net present value of the cashflow series over 20 years for the case with implementation of gully control only; j and t are measured in years and NPV in currency. After calculating NPV_TTM values, investment costs and total discounted production in the without case (which remain the same under different TTM values) need to be subtracted. Finally, for evaluation of the effect of TTM, the percentage of cells in the applicability area of the technology is calculated.

Gully control with atriplex in Sehoul, Morocco is not very sensitive to small changes around the assumed 20 years it takes to reach maximum productivity (Figure 11B). However, this is a rough assumption, so we should look further than the short range between 18 and 25 years where the viability of the technology is not affected. When approximating a TTM of 15 years, the viability of atriplex planting rapidly reaches 100% of the applicable area, up from 82% on the stable area from 18-25 year. Even more dramatic is the drop between a TTM of 25 and 30 years, when the technology seizes to viable in more than 60% of the applicable area. The negative slope of the relation flattens of after 30 years, but gully control with atriplex by then remains profitable in only 13% of the area. From this example, it is clear that one would need to be confident of the interval 18-25 years it would take vegetation to reach maximum productivity, outside of which the system becomes very sensitive to the issue of timing.  

Discussion of scale issues
In studies of adoption of SLM technologies, plot location is often found to be of importance (e.g. Staal et al, 2002; Noltze et al., 2012). The spatial variation in investment costs of SLM technologies and distance to markets are likely to play a key role, although explicit studies of variations in costs are scarce (e.g. Shively, 1999; Tenge et al., 2005). As Heidkamp (2008), it in a more general context, puts it: “the environment has been largely ignored beyond its treatment as a more or less passive location condition or resource factor input”. Although the illustration of cost differentiation with slope for bench terraces in China provides an example of the susceptibility of outcomes to this factor, the finding that taking variability in investment cost into account leads to a larger viability is specific. In other cases, for example where data is gathered from a relatively cheap experiment in optimal conditions, considering spatial variability factors might lead to reduced levels of predicted viability. Much data on spatial variability of different types of SLM technologies probably exists in design manuals, project documents, and other grey literature. A review of those materials is recommended to define some generic relations that can be used to improve model assessments of SLM.

The timing of biophysical effects has potentially significant influence on viability of technologies. The point version of PESERA allows simulation in time series mode after equilibrium conditions have been established. The grid version of the model, which was used here, lacks this facility. Still, model validation, specifically of timing of effects, is difficult due to interactions and the paucity of long-term field trials which are intensively monitored. Although the illustrative case study had a long term restoration goal, the cumulative effects of annually repeated SLM technologies may also be significant (see e.g. Hobbs et al., 2008). The importance of the temporal dimension in evaluating technologies is clear from the inclusion of a discount factor in CBA. This can work two ways: in the case of technology application, it is important for land users to start reaping benefits as early as possible; but in the without case, ongoing degradation can further affect yield levels (Lal, 1995).       

Quality and quantity of input data

• DESMICE primarily relies on economic data reported in the WB3 WOCAT database. It further makes use of additional information requested in information sheets from study sites. Variation of investment costs of technology has proved to be difficult to obtain, while, as shown in »Scale issues and uncertainty analysis, this can have important implications for the analysis. A review of international published and grey literature is therefore recommended as follow up work. Where price information was not available additional secondary data was collected. Input map material to a large extent coincides with PESERA input data. A digital elevation model is by default taken from the publicly available SRTM90 dataset. Price conversions of local currencies to Euro were done using oanda.com. Taking into consideration this need for secondary data, PESERA-DESMICE can be run but shortcomings should be kept in mind.

Findings

• (Simple) technological options exist that can minimize land degradation and increase food production. Many technologies are however only profitable in the long run (e.g 20 years) which means that high investment costs are a bottleneck for adoption.
• Low (zero) cost agronomic measures and other options that deliver important benefits in the short term are the preferred technologies. Stakeholder evaluation and model output mostly concur.
• There are important design and opportunity cost considerations which influence the analysis. For larger (more expensive) technologies feasibility studies will need to be done on a case by case basis. Model can be used for first approximation.

Novelties

• The PESERA-DESMICE modelling approach overcomes a number of challenges to incorporate inputs from multiple stakeholders in very different contexts into the modelling process, in order to enhance both the realism and relevance of outputs for policy and practice.
• Site-selection modelling is being applied to land degradation mitigation to enable landscape-scale assessments of the most economically optimal way to attain environmental targets.
• Use of Cost-Benefit Analysis to investigate the spatial variability of the profitability of SWC measures, which may have important implications for the adoption of measures across landscapes and their consequent environmental effects.

Shortcomings

• It appeared to be difficult for study sites to estimate spatial variation in investment costs of technologies (a review of data to produce estimates for different types of technologies to fill this gap is recommended that could serve to define default parameters in the DESMICE model).
• The temporal dimension of changes in productivity is crucial for land users. Biophysical models (e.g. PESERA) should be able to separate immediate and gradual aspects. Ongoing degradation in the without case is not yet implicitly considered. Analysis of robustness to climatic variability and prices is also essential.
• Factors such as attitude towards conservation and risk are likely to be very important in decision-making and could further limit adoption of technologies.